The World Bank Economic Review

Volume 32 • Number 1 2018 ISSN 0258-6770 (PRINT) ISSN 1564-698X (ONLINE)

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Volume 32 • 2018 • Number 1 THE WORLD BANK ECONOMIC REVIEW

Public Disclosure Authorized Inventor Diasporas and the Internationalization of Technology Ernest Miguélez The Impact of Rural Pensions in China on Labor Migration Karen Eggleston, Ang Sun, and Zhaoguo Zhan When the Cat’s Away … The Effects of Spousal Migration on Investments on Children Lucia Rizzica Can Parental Migration Reduce Petty Corruption in Education? Lisa Sofie Höckel, Manuel Santos Silva, and Tobias Stöhr

Symposium of papers from the 8TH AFD-World Bank Migration and Development Conference Guest editors: David McKenzie, Caglar Ozden, and Hillel Rapoport

The Long-Term Impacts of International Migration: Evidence from a Lottery

Public Disclosure Authorized John Gibson, David McKenzie, Halahingano Rohorua, and Steven Stillman Migration and Cross-Border Financial Flows Maurice Kugler, Oren Levintal, and Hillel Rapoport Heterogeneous Technology Diffusion and Ricardian Trade Patterns William R. Kerr Pages 1–220 How and Why Does Immigration Affect Crime? Evidence from Malaysia Caglar Ozden, Mauro Testaverde, and Mathis Wagner Migrant Remittances and Information Flows: Evidence from a Field Experiment Catia Batista and Gaia Narciso ISBN 978-0-19-880922-7

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Volume 32 r 2018 r Number 1 Downloaded from https://academic.oup.com/wber/issue/32/1 by Joint Bank-Fund Library user on 09 August 2019 Migrant Labor Markets and the Welfare of Rural Households in the Developing World: Evidence from China 1 Alan de Brauw and John Giles The Impact of the Syrian Refugee Crisis on Firm Entry and Performance in Turkey 19 Yusuf Emre Akgündüz, Marcel van den Berg, and Wolter Hassink Inventor Diasporas and the Internationalization of Technology 41 Ernest Miguélez The Impact of Rural Pensions in China on Labor Migration 64 Karen Eggleston, Ang Sun, and Zhaoguo Zhan When the Cat’s Away ...The Effects of Spousal Migration on Investments on Children 85 Lucia Rizzica Can Parental Migration Reduce Petty Corruption in Education? 109 Lisa Sofie Höckel, Manuel Santos Silva, and Tobias Stöhr

Symposium of papers from the 8TH AFD-World Bank Migration and Development Conference

Guest editors: David McKenzie, Caglar Ozden, and Hillel Rapoport

The Long-Term Impacts of International Migration: Evidence from a Lottery 127 John Gibson, David McKenzie, Halahingano Rohorua, and Steven Stillman Migration and Cross-Border Financial Flows 148 Maurice Kugler, Oren Levintal, and Hillel Rapoport Heterogeneous Technology Diffusion and Ricardian Trade Patterns 163 William R. Kerr How and Why Does Immigration Affect Crime? Evidence from Malaysia 183 Caglar Ozden, Mauro Testaverde, and Mathis Wagner Migrant Remittances and Information Flows: Evidence from a Field Experiment 203 Catia Batista and Gaia Narciso Erratum 220 The World Bank Economic Review, 32(1), 2018, 1–18 doi: 10.1093/wber/lhx023 Article

Migrant Labor Markets and the Welfare of Rural Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Households in the Developing World: Evidence from China Alan de Brauw and John Giles

Abstract Increased ability to migrate from China’s rural villages contributed to significant increases in the consumption per capita of both non-durable and durable goods, and these effects were larger in magnitude for households that were relatively poor before the easing of restrictions to migration. With increased out-migration, poorer households invested more in housing and durable goods than rich households, while richer households invested significantly more in non-agricultural production assets. As migration became easier, increased participation in migrant employment was greater among poorer households on both the extensive and intensive margins, and poorer households reduced labor days in agriculture.

JEL classification: O12, O15, J22, J24

Keywords: migration, migrant networks, consumption growth, inequality

In developing countries, barriers to the movement of labor are a common institutional feature that may contribute to geographic poverty traps. Whether maintained by formal institutions, by cultural or linguistic differences across regions, or simply by high transaction costs associated with finding migrant employment, constraints on the movement of labor within developing countries may reinforce an inef- ficient allocation of resources across regions and influence investment levels in poor areas, potentially hindering growth (Jalan and Ravallion 2002). When barriers to cross-regional labor mobility are removed, the resulting improved efficiency of resource allocation may have important consequences for rural living standards (Yang 2008). Remittances to household or family members remaining in rural areas may supplement income earned locally and directly reduce exposure to poverty. Migration may also have indirect effects on household and individual welfare within their home communities, either in the form of increased wages with the depletion of the local labor force, or through remittances from migrant employment that are invested in local production (e.g., Woodruff and Zenteño 2007).

Alan de Brauw is a Senior Research Fellow at the International Food Policy Research Institute (IFPRI); his e-mail address is [email protected]. John Giles (corresponding author) is a Lead Economist in the Development Research Group at the World Bank and a Research Fellow at IZA; his e-mail address is [email protected]. The research for this article was supported by the Knowledge for Change Program at the World Bank and the CGIAR research program on Policies, Institutions, and Markets. The authors are grateful to three anonymous referees and a host of participants in seminars and conferences where earlier versions of the paper were presented. A supplementary online appendix for this article can be found at The World Bank Economic Review website, and at https://sites.google.com/site/decrgjohngiles/publications one may find a “director’s cut” version.

A growing body of research examines the impact of both international and internal migration on investment, growth, and other outcomes. Relevant for thinking about the identification strategy employed in this paper, considerable research has focused on the role of networks in facilitating migration (e.g., Munshi 2003) and influencing migration decisionsMunshi ( 2011; Munshi and Rosenzweig 2016), and

the role that risk and liquidity constraints may play in shaping migration decisions (Bryan et al. 2014). Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Within the China literature, recent research has focused on the relationship between migration and poverty (Du, Park, and Wang 2005); migration, remittances, and household income (Taylor,Rozelle, and de Brauw 2003); migration and risk-coping (Giles 2006; Giles and Yoo 2007); and the prospects of migration and life satisfaction (Frijters, Liu, and Meng 2012), but identification of migration in each of these papers requires uncomfortable assumptions. To date, the published literature on the effects of out-migration has considered neither the distributional effects of migration within villages, nor the effects of out-migration on the sectoral distribution of household activities across the within-village wealth distribution. This paper addresses these gaps in the literature and makes four important contributions to our understanding of the impact of rural-urban migration on both consumption and sectoral choice in rural China. The main specification first highlights the positive effect of migration on annual changes in log consumption between 1988 and 2002: out-migration explains between 2 and 2.9 percent of the annual increase in consumption per capita, or between 65 and 93 percent of the annual consumption growth recorded in the sample villages between 1988 and 2002. As earlier work suggests that income from work outside the village (by both migrants and commuters) contributes to reducing inequality (Benjamin, Brandt, and Giles 2005), this paper exploits the opportunity to use matching village surveys to examine the local general equilibrium effects of migration on the per capita consumption of households across the initial consumption distribution. Whether households actually participate in migration or not, consumption grows more rapidly among households in the poor and middle terciles of the initial average consumption distribution. Maintaining a focus on within-village differences across the initial consumption distribution, the paper examines possible mechanisms through which migration influenced earnings, labor supply,and investment decisions. Where others have found that migration contributed to increased labor supply of residents remaining behind (e.g., Mu and van de Walle 2011), this paper finds that out-migration from the village leads to labor reallocation across activities, and labor allocation changes differ by wealth tercile. Agricul- tural labor days decline among poorer households, who also provide a relatively larger increase in days spent working outside the home township. More affluent households, by contrast, increase labor supply to local non-agricultural activities, potentially reflecting general equilibrium effects of increased local opportunity as migrants remit earnings. In terms of investment decisions, poorer households invest more in housing and durable goods, while more affluent households invest more in non-agricultural productive assets. If focusing on the period of rapid expansion in migration alone, from 1995 to 2002, the effects of migration on both labor supply and investment decisions are even more pronounced. A fourth contribution is the relatively unique instrumental variables (IV) strategy used to identify village-level effects of migration on household consumption and other outcomes. The approach takes advantage of an early reform to China’s residential registration (hukou) system making it easier for rural migrants with national identification cards (IDs) to legally reside in cities after 1988. National IDs,first available to urban residents in 1984, were not available in all rural counties as of 1988. While allowing that the timing of ID distribution may be related to fixed unobserved characteristics of villages, the annual change in the share of the village population working as migrants outside the village is shown to be a nonlinear function of the time since residents of a county received IDs. As migrant networks in cities take time to build up, this non-linearity should not be surprising. After controlling for household fixed effects, village-specific trends, and province-wide macroeconomic shocks, the change in the cost of migratingis identified by exploiting differences in the timing of access to IDs and the non-linearity in the relationship between the annual change in the village migrant share and the time since IDs were distributed. As IDs were not randomly distributed, the paper demonstrates that the timing of ID distribution was unrelated to other policy and economic shocks that might be correlated with both migration and well-being. The World Bank Economic Review 3

1. Background Over the period covered by the primary data source (1986 to 2002), rapid growth in the number of rural migrants moving to urban areas signaled a dramatic change in the nature of China’s labor market. Estimates using the one percent sample from the 1990 and 2000 rounds of the Population Census and the 1995 one percent population survey suggest that the inter-county migrant population grew from just Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 over 20 million in 1990 to 45 million in 1995 and 79 million by 2000 (Liang and Ma 2004). Surveys conducted by the National Bureau of Statistics (NBS) and the Ministry of Agriculture include more detailed retrospective information on past short-term migration, and suggest even higher levels of labor migration (Cai, Park, and Zhao 2008).

Rural-Urban Migration in China, Migrant Networks, and Well-Being The use of migrant networks and employment referral in urban areas are important dimensions of China’s rural-urban migration experience. Rozelle et al. (1999) emphasize that villages with more migrants in 1988 experienced more rapid migration growth by 1995. Zhao (2003) shows that the number of early migrants from a village is correlated with the probability that an individual with no prior migration experience will choose to migrate. Meng (2000) further suggests that variation in the size of migrant flows to different destinations can be partially explained by the size of the existing migrant population in potential destinations. Finally, appendix table A.1 of de Brauw and Giles (2017) notes that a 2001 survey of migrants in five large Chinese cities shows that more than 90 percent of migrants knew an acquaintance from their home village before migrating. Before labor mobility restrictions were relaxed, households in remote regions of rural China faced low returns to local economic activity, reinforcing geographic poverty traps (Jalan and Ravallion 2002). Over the period of study, household income per capita grew in both urban and rural areas, but at significantly higher rates in urban areas. After spatially deflating for cost-of-living differences, the urban-rural differential in per capita income increased from 38 percent to 71 percent by 2001. Thus, as documented by a considerable body of descriptive evidence, the growth of migration in China raises the possibility that migrant opportunity may be an important mechanism for poverty reduction in rural areas. Recent research in China suggests that migration is associated with higher incomes (Taylor, Rozelle, and de Brauw 2003; Du, Park, and Wang 2005), facilitates risk-coping and risk management (Giles 2006; Giles and Yoo 2007), is associated with higher levels of local investment in productive activities (Zhao 2002), and may alter household labor allocation decisions (Mu and Van de Walle 2011). Earlier research on rural-urban migration in China has suggested large impacts on the earnings of households with migrants, but some of the identification strategies employed likely produced estimates of migration impacts that suffer from significant bias. For example, Taylor, Rozelle, and de Brauw (2003) find that per capita incomes of households with migrant family members were 22, 26, and 29percent higher at the 25th,50th, and 75th percentiles of the sample (not within villages). However, the authors used a retrospective report of the village migration network size from seven years earlier as an instrument for household migration, and therefore cannot control for village fixed effects, such as proximity to cities (and markets) and other factors affecting the local economy,which will lead to an upward bias to estimates of the effect of migration on earnings. Another study examines both migrant participation and earnings across the earnings distribution (Du, Park, and Wang 2005), finding that poor households are unlikely to have migrants, but the main barrierto migrant employment is lack of capable laborers rather than other obstacles to working in urban areas. The authors estimate models in first differences, and identify migration through the one-year lag of the village migrant network, finding that households with migrants have per capita incomes 8.5 to 13.1 percent higher than those without migrants. A concern with this approach, however, is that shocks to the village economy, which affect migration decisions, have persistent effects over time. Both current migration and a 4 de Brauw and Giles household’s position within the earnings distribution are likely to be affected by shocks reflected in prior migration from the village.

The RCRE Household and Village Surveys

The primary data sources used for analysis are the village and household surveys conducted by the Re- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 search Center for Rural Economy (RCRE) at China’s Ministry of Agriculture from 1986 through 2002. The paper uses data from 88 villages in eight provinces (Anhui, Jilin, Jiangsu, Henan, Hunan, Shanxi, Sichuan, and Zhejiang) surveyed over the 16-year period, with an average of 6,305 households surveyed per year. Depending on village size, between 40 and 120 households were randomly surveyed in each village. Each village in the sample is in a different county, so county-level policies affect each village in this sample differently. The RCRE household survey collected detailed household-level information on incomes and expenditures, education, labor supply, asset ownership, land holdings, savings, formal and informal access to credit, and remittances. In common with the National Bureau of Statistics (NBS) Rural Household Sur- vey, respondent households keep daily diaries of income and expenditures, and a resident administrator living in the county seat collects the diaries monthly. As in the NBS Rural Household Survey, estimates of the income and expenditures of household members currently living and working as migrants outside the household were included among household earnings and expenditures. This unusual feature of the survey is beneficial because household size does not change with out-migration. Details on the calculation of consumption from expenditure values and information on housing and durable goods are provided in supplementary online appendix S1, and in the analysis below, both income and consumption are deflated to 1986 values. The paper also makes use of an annual village survey enumerated from meetings with village accoun- tants and other key informants that provides important village-level information on the local economy and on the land, production, and the work locations (migrant, local, and in the village) and occupations of registered residents. Time-varying information from this survey is important for characterizing connection to migrant networks, and when controlling for other time-varying village demographic and economic factors. The village-level survey includes all information on current registered residents who are working outside the village, and does not miss entire families who move (which was a very rare occurrence in rural China during this period). Finally, the identification strategy makes use of retrospective information, specifically the timing of ID availability, from a Village Governance Survey conducted by the authorsin 2004.

Trends in Migration, Consumption Growth, and Poverty The accompanying village survey includes questions asked annually of village leaders about the number of registered village residents working and living outside the village. For the analysis in this paper, all registered village residents who work outside the home county are considered migrants. Both the tremendous increase in migration from 1987 onward and heterogeneity across villages are evident in fig.. 1 In 1987, an average of 3 percent of working-age laborers in RCRE villages worked outside their home counties, and this share rose steadily to 23 percent by 2003. Moreover, the share of working-age laborers working as migrants varies considerably by village. Whereas for some villages, only a small share of legal residents are employed as migrants, from other villages more than 50 percent of working-age adults are employed outside their home county by 2003. The relationship between migration and consumption is of central concern for the analysis. The linear fit of the relationship between annual changes in share of the village workforce employed as migrants (village migrant share) and growth in village average consumption suggest a positive relationship (fig.). 2 The local polynomial fit, however, suggests the presence of non-linearities. If out-migration is driven by negative shocks or return migration by positive shocks, and both are correlated with The World Bank Economic Review 5

Figure 1. Share of Village Labor Force Employed as Migrants, by Year Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019

Source: RCRE Village Surveys 1986 to 2002. Note: The figure shows the share of registered village residents living and working outside the village and homecounty.

Figure 2. Village Average Consumption Growth and Change in Migrant Share of Village Population

Source: RCRE Village and Household Surveys, 1986–2003. Note: Figure 2 shows the linear and local polynomial fits of the relationship between annual village average consumption growth and annual changes in the share of migrants from the village. Migrants are registered residents of the village who live and work outside the village and home county. movements in consumption, one should be concerned that migration and consumption are endogenous. Even if consumption grows with an increase in the number of residents employed as migrants, it is of particular policy interest to understand which residents within villages are experiencing increases in consumption.

2. Empirical Methodology Behind the estimation approach lies a simple model highlighting the direct and indirect mechanisms through which expanded migrant employment opportunities may affect household consumption (see de Brauw and Giles 2008). The migrant network, which facilitates job referral in distant destinations, may directly and indirectly affect permanent household income and thus also consumption. First, a larger 6 de Brauw and Giles village migrant network may facilitate higher family income from earnings in migrant destinations. The wealth effect eases credit constraints associated with accumulating assets for productive activities (both agricultural and non-agricultural) and “non-productive” uses (e.g., investments in housing and durable goods). Household consumption may also increase by relaxing a credit constraint that led households to

consume less and save more in each period as a precaution against potential future production shocks. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Second, an increase in the village migrant network size may affect the shadow price of household labor time. If leisure is a normal good, the net effect on family labor supply is indeterminate. A substitution effect will lead families to supply more labor to productive activities, and perhaps shift from farming into other higher-return activities, and an income effect may lead to a reduction in family labor supply. To flesh out the productive mechanisms through which increases in migration may lead to higher consumption, the impacts of migration on household labor supply are also examined, and disaggregated into three broad categories: agricultural, local non-agricultural, and non-agricultural self-employed work.

Estimating the Effect of Migration on Consumption An empirical model consistent with the conceptual framework outlined above suggests the following linearized specification for household consumption, cijt (in logs): cijt = βM jt + X ijtα + Z jt γ + ui + v j + t j + εijt (1) The logarithm of per capita consumption for household i in village j during period t is a function of the share of the registered working-age village residents working outside the home county as migrants, M jt . Household characteristics, X ijt, influence consumption through endowments, such as human capital, which affect household permanent income, and through demographic characteristics that influence consumption preferences. Time-varying village variables account for heterogeneity across villages in policies and economic conditions, Z jt , that may influence consumption through productivity. Village-specific trends, t j, account for underlying endowments and initial conditions in the village that may contribute to differences in consumption growth across villages. Finally, village- and household-level unobservables, v j and ui, respectively, related to location (village), to consumption preferences (household), and to the ease of household participation in the migrant labor market (household).1 Equation (1) is initially first-differenced to control for fixed effects at the household and village level: cit = βM jt + X ijtα + Z jt γ + d j + εijt (2)

Differencing the village-specific trend leaves a vector of village dummy variables, d j, that control for differences in consumption growth trends across villages, which may be related to differences in proximity markets for labor or goods in townships or cities. Finally, province-wide macroeconomic shocks, driven by policy or market effects, might affect the relationship between household consumption and employment opportunities. Province-year interactions, p ⊗ t, are added in (3) to control for the presence of these shocks: cit = βM jt + X it α + Z jt γ + d j + p ⊗ t + εijt (3) In equation (3), the coefficient of primary interest, β, measures the effect of the migrant labor market on consumption. Given descriptive evidence from the literature suggesting that the effect of migration may differ across the within-village wealth distribution (e.g., Benjamin, Brandt, and Giles 2005), the sample is next split into low, middle, and upper initial wealth terciles within each village. The tercile rank (low, middle, and high) is assigned to households based on average household consumption per capita for each household

1 Supplementary online appendix table S1.1 provides summary statistics for selected years on key outcomes, and household- and village-level control variables. The World Bank Economic Review 7 over the 1987–1989 period. Estimating models by this initial 1987–1989 consumption tercile facilitates examining the different impacts of migration across the initial wealth distribution.

Endogeneity Concerns In equation (3), M jt suffers from a well-known endogeneity problem. The village migrant share reflects Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 factors affecting both changes in demand for migrant labor and changes in the labor supply decisions of migrants and potential migrants. Local shocks, for example, decrease household consumption per capita while increasing the relative return to migrant employment in more distant locations, potentially leading to an observed negative relationship between increases in migration and consumption growth. To identify the effect of migration on consumption, it is necessary to find an instrument correlated with the share of village residents working as migrants, but otherwise unrelated to factors affecting growth or negative shocks experienced by the village. The identification strategy employed makes use of two policy changes that, working together, affect the strength of migrant networks outside home counties but are plausibly unrelated to average village consumption growth. First, a new national ID card (shenfen zheng) was introduced in 1984. While urban residents received IDs in 1984, residents of most rural counties did not receive them immediately. In 1988, a reform of the residential registration system made it easier for migrants to gain legal temporary residence for work in cities under a “guest worker” system, but a national ID card was necessary to obtain a temporary residence permit (zanzu zheng)(Mallee 1995). While some counties made national IDs available to rural residents as early as 1984, others distributed them in 1988, and still others did not issue IDs until several years later. In a follow-up survey conducted with RCRE in 2004, local officials were asked when IDs had actually been issued to rural residents of the county. In the analysis sample, 41 of the 88 counties issued ID cards in 1988, but cards were issued as early as 1984 in three counties and as late as 1997 in one county. It is important to note that IDs were not necessary for migration, and large numbers of migrants live in cities without legal temporary residence cards. With temporary residence cards, however, migrants were under a “guest worker” system providing a more secure position in the destination community, and thus held better jobs and made up part of a longer-term migrant network in migrant destinations. Thus, ID distribution had two effects after the 1988 hukou reform. First, the costs of migrating to a city should fall after IDs become available. Second, as the quality of the potential migrant network improves with the years since IDs are available, the costs of finding migrant employment should continue to fall over time. The relative size of the migrant network should therefore be a function of both whether or not cards have been issued and the time since cards have been available to village residents. As we use the share of the village workforce working as migrants to proxy for the migrant network, the size of the potential network has an upper bound. Thus, we expect years-since-IDs-issued to have a non-linear relationship with the share of the village labor force working as migrants, as growth in the migrant network should decline after initially increasing with distribution of IDs. In fig., 3 a polynomial smoother shows the relationship between years since IDs were distributed and the share of working-age village residents working as migrants in year t. Within a couple of years after IDs are distributed, the share of the village labor force working as migrants grows sharply, and then slows after seven years. This pattern suggests non- linearity in the relationship between ID distribution and new participants in the village migrant labor force. Therefore, the primary instrument is specified as a dummy variable indicating that IDs had been issued, interacted with quartic functions of years since IDs were issued.2

2 de Brauw and Giles (2017) review various ways of using the years since ID distribution to identify the village migrant share, and fig. 5 of that paper shows that the quartic function predicts the same pattern observed from a fully non-parametric set of 19 dummy variables indicating years since ID distribution. As using the quartic requires fewer regressors than a fully non-parametric specification, the F-statistic on the first stage is higher, yielding reduced weak instrument bias. 8 de Brauw and Giles

Figure 3. Share of Village Out-Migrants, versus Years Since IDs Were Distributed Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019

Source: RCRE Village Surveys, 1986–2003, and Supplementary Village Governance Survey (2004). Note: Figure 3 shows the relationship between change in annual share of migrants from the village and number of years since ID cards became available in the county.

As IDs were not randomly distributed, it is important to consider the plausible exogeneity of the identification strategy. More detail on the distribution of IDs and the conditional exogeneity of thetiming is provided in supplementary online appendix S2, and additional discussion based on a four-province sample can be found in de Brauw and Giles (2017). A second, mechanical source of endogeneity that is well known in the panel data literature may arise, as the regressors included in X it and Z jt might not be strictly exogenous. For example, income shocks that affect household consumption decisions may also have an impact on household composition, land characteristics, or village policy. To examine the robustness of our result, initial estimates of equation (3) exclude X it and Z jt . If the years-since-IDs quartic is a valid instrument, meaning that it is uncorrelated with the changes in household and village characteristics that are now part of the unobservable, then this model will be identified and potentially endogenous regressors are not a concern. The simplified model is included for estimates over both the full sample and the models estimated by initial consumption tercile. When models include the village and household regressors, X it and Z jt , both likely to explain some variation in consumption, they are treated in successive models as exogenous and then as predetermined but not strictly exogenous. For models in which regressors are treated as predetermined, first-differenced predetermined variables are instrumented with their t − 2 lagged levels [X it−2, Zit−2], a standard panel data approach. X it−2 and Zit−2 will be valid instruments if they are correlated with X it and Z jt , but uncorrelated with any time-varying household unobservables remaining in the differenced error term, εit . Most importantly, these lagged values, X it−2 and Zit−2, are uncorrelated with shocks that affected household demographic composition or the education composition of the household, and thus reduce one potential source of endogeneity. Use of lagged values does not resolve all endogeneity problems related to forward-looking household behavior. For example, a husband and wife making plans to migrate in the distant future may choose to co-reside with parents and in-laws who could farm land and watch children. In this sense, household structure is endogenous to future migration plans. These unobserved plans are considered a fixed preference that are differenced out in estimation. Further, precise unbiased estimates of the coefficients on these variables are not our central focus. Rather, these controls are included toallow for a more precise estimate of the effects of migration on household consumption, and later on labor supply and investment decisions. The World Bank Economic Review 9

3. Results The First Stage Before estimating equation (3), it is important to first establish that the instruments, period t − 2 values of a polynomial function of the years since ID cards were issued, are significantly related to the change in the share of working-age village residents working as migrants between period t − 1andt. First-stage Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 models were initially specified with years since IDs were issued as a quadratic, cubic, and quartic function, along with village and province-year dummies (table 1, columns 1 through 3). The quartic is preferred to the quadratic and the cubic due to the added flexibility in the functional form for the effects of ID card distribution on the migrant network. To anticipate models controlling for endogenous changes in village or household variables, two-period lags of village and household controls are added in columns 4 and 5, respectively. At the village level, the size of the village labor force is included to control for local returns to labor, the cultivable share of village land, total village land, and the share of land planted in orchards, which control for village land endowment and specialization in high-value crops, and the share of village assets held by collectives, which controls for the returns to capital outside agriculture as well as local government involvement in the economy. To control for household-level human and physical capital endowments, the number of working-age members of the household, the share of household members that are working-age males and females, respectively, land per capita, and the average education level of household adults are included. In both columns, the relationship between the migrant network variable and instruments for migration remains strong, and the F-statistic suggests that the complete set of instruments continues to have sufficient power to ease concerns over weak instrument bias after adding the full set ofcon- trols. While sufficiently strong, one might be concerned that the timing of ID distribution is alsocor- related with changes in a range of different village policies. These possibilities are examined in supplementary online appendix S3, and again there is no direct evidence that ID distribution is systematically related to policy changes that may affect consumption through channels other than migration from the village.

Ta b l e 1. Developing the First Stage: Timing of ID Card Distribution and Change in Share of Migrants in Village Population

(1) (2) (3) (4) (5)

(Years since IDs issued)t-2 −0.000 0.029*** 0.044*** 0.027*** 0.026*** (0.001) (0.002) (0.004) (0.004) (0.004) 2 [(Years since IDs issued)t-2] /10 −0.007*** −0.065*** −0.121*** −0.098*** −0.095*** (0.001) (0.004) (0.012) (0.013) (0.013) 3 [(Years since IDs issued)t-2] /100 0.029*** 0.092*** 0.073*** 0.069*** (0.002) (0.013) (0.014) (0.014) 4 [(Years since IDs issued)t-2] /1000 −0.022*** −0.017*** −0.015*** (0.005) (0.005) (0.005) Two-period lag village controls included? No No No Yes Yes Two-period lag household controls included? No No No No Yes Number of observations 69,878 69,878 69,878 67,529 66,487 F-statistic, instruments 43.7 83.2 74.0 75.0 50.6

Source: RCRE Household and Village Surveys, 1986–2002, and the RCRE 2004 Supplementary Village Governance Survey. Note: (1) All models include jointly significant controls for village and province* year effects, as well as other included instruments. (2) Dependentiable var is change in share of working-age residents from the village living and working outside their home county between year t-1 and t. (3) Robust standard errors are cluster-corrected at the village, and there are 88 village clusters. (4) Two-period lag village controls include: total number of working-age laborers in registered village labor force, total village land, share of land in village in orchards, and share of total assets owned by the village collective. (5) Two-period lag household controls include: number of working-age laborers in the household, male working-age laborer share of household population, female working-age laborer share of household population, household land per capita, value of household productive assets, and average years of education of working-age laborers. ***indicates significance at the 1 percent level. 10 de Brauw and Giles

The Effect of Migration on Household Consumption To begin the examination of the effects of migration on consumption, OLS models of the effects of migration on consumption in both levels and first differences are estimated across all three terciles ofthe initial consumption distribution. As one might expect if unobserved local shocks are an important factor

driving initial migration decisions, the coefficient on migration is positive and insignificant intheOLS Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 levels model, whether or not household- and village-level covariates are included (supplementary online appendix table S3.2). Similarly, when estimated by OLS in first differences, the coefficients on the change in migration are small and not statistically significant from zero. Next, the analysis uses IV-GMM models, which control for simultaneity bias and other unobservables potentially related to the migration measure. The weighting matrix used in the GMM estimator accounts for arbitrary heteroskedasticity and intra-cluster correlation, and it is asymptotically efficient in the presence of heteroskedasticity (Wooldridge 2010). Three first-differenced models are estimated (table 2). In column 1, estimates exclude village and household controls. Village and household controls are then added, treated first as exogenous (column 2) and then as predetermined (column 3),using

Ta b l e 2 . Migration and Household Consumption in Migrant-Sending Villages (All Models in First Differences)

Ln(consumption per capita) Ln(non-durable consumption per capita) (1) (2) (3) (4) (5) (6)

Share of migrants in village population 3.563*** 3.602*** 2.507** 3.285*** 3.434*** 2.308** (1.188) (1.103) (1.132) (1.268) (1.184) (1.131) Village-level control variables Village labor force −0.001 0.003 −0.003 0.000 (0.005) (0.014) (0.005) (0.015) Cultivable share of village land 0.269*** 0.819** 0.270*** 0.796** (0.092) (0.382) (0.093) (0.380) Total village land 0.016* 0.127*** 0.015 0.129*** (0.009) (0.046) (0.010) (0.047) Share of assets owned by village collective −0.014 −0.157 −0.015 −0.169 (0.028) (0.165) (0.032) (0.175) Share of village land in orchards −0.163 0.420 −0.100 0.443 (0.180) (0.777) (0.181) (0.780) Household-level control variables Working-age male share of household population −0.070** 0.427*** −0.064* 0.479*** (0.032) (0.127) (0.036) (0.151) Working-age female share of household population −0.076** 0.131 −0.066* 0.152 (0.031) (0.127) (0.035) (0.149) Number of working-age laborers in the household −0.038*** −0.003 −0.026*** 0.023** (0.004) (0.009) (0.005) (0.010) Cultivable land per capita 0.085*** 0.114*** 0.085*** 0.125*** (0.008) (0.021) (0.009) (0.023) Household average years of education 0.004** −0.012** 0.005*** −0.013** (0.002) (0.005) (0.002) (0.005) Village, HH controls predetermined? No Yes No Yes Regression statistics Hansen J-statistic 1.860 2.235 4.130 1.244 1.564 4.569 p-value, J-statistic 0.602 0.525 0.248 0.742 0.668 0.206 Cragg-Donald F-statistic 87.23 95.66 32.68 87.56 95.76 32.66 Number of clusters 88 88 88 88 88 88 Number of observations 69,878 68,110 65,607 69,834 68,072 65,570

Source: RCRE Household and Village Surveys, 1986–2002, and the RCRE 2004 Supplementary Village Governance Survey. Note: All models are run in first differences and include jointly significant village fixed effects to control for village-specific trends, reffectstocontroland province-yea for province-wide macroeconomic shocks. Standard errors clustered at the village. *indicates significance at the 10 percent level; **indicatesnificance sig at the 5 percent level; ***indicates significance at the 1 percent level. The World Bank Economic Review 11 t − 2 levels of the household and village controls as instruments. The Cragg-Donald F-statistic indicates that the bias in the IV coefficient is less than 5 percent of the OLS bias. Evidence from all three modelssug- gests that the growth of the village migrant share in the labor force has a positive, statistically significant effect on consumption among all households.

In all three specifications, the coefficients on the village migrant share are relatively close to one another. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Coefficients in columns 1 and 2 suggest that a one-percentage-point increase in migration at thevillage level is associated with a 3.6 percent increase in household consumption per capita, while column 3 (controlling for the dynamic endogeneity of control variables) indicates a 2.5 percent increase. Average village migration increased from 0.012 to 0.126 of the village workforce from 1988 to 2002, so a one- percentage-point rise is slightly higher than the average annual increase in the village migrant share, which is 0.8 percent. At the average migrant share, these estimates imply that migration is associated with a 2 percent annual increase in household per capita consumption in those models treating controls as predetermined, or 2.9 percent when treated as exogenous. With an average of 3.1 percent consumption growth per annum in the RCRE villages, increased ability to migrate thus explains from 65 to 93 percent of the average per capita consumption growth in this sample. Recall that the consumption measure includes the flow of services from housing and durable goods. Given that the value of this flow may be imputed incorrectly for migrant family members living outside the household, the effect of migration on non-durable consumption growth is shown in columns 4 through 6, with education expenses and the flow from housing and durable goods excluded.3 The coefficient on the village migrant share is slightly smaller (suggesting a 2.3 to 3.4 percent increase), but remains statistically significant at the 5 percent level or better. An additional concern might bethatthe results are driven by comparisons between villages with and without migration. At the beginning of the study period, some villages had no migrants, though by 1996 all 88 villages had some migrants. This possibility is explored by dropping the early years of villages without migrants (supplementary online appendix table S3.3, columns 1 through 3), and there is little difference from the main specifications shown in table 2. In any long panel, one should also be concerned over whether attrition might be biasing results. The average annual household attrition rate is 5.8 percent; by 2002, the average probability that original households are still in the sample is 62 percent. In this paper, as in two others using different samples from this data source (Benjamin, Brandt, and Giles 2011; Giles 2006), there is no evidence of a statistically significant relationship between the village migrant share (or changes in the village migrant share)and attrition in the current or following year, nor are consumption per capita and attrition correlated. To check further for potential attrition bias, we weight each observation by the inverse of the cumulative probability of survival to the current year, with probabilities for survival in each year calculated using household and village characteristics and a province fixed effectWooldridge ( 2010, chapter 19). After controlling for attrition, there is little change in the coefficients estimated for the three main models (columns 4 through 6 of supplementary online appendix table S3.3), suggesting that the main results do not suffer from attrition bias.

Who Benefits from Migration? By measuring effects of new migration from the village on household consumption, and with consumption of migrants included in the measure of household expenditures, both made possible through interesting

3 During the period studied, it was rare for rural residents to be able to legally purchase housing or have the funds to do so, so the value of housing imputed from any rental payments is not problematic. A larger problem is imputing the consumption value of employer-provided housing, as it is certain that this dimension of consumption was not considered by enumerators. Education expenses are also excluded, as that consumption may only increase because migrants face higher education expenses for their children. 12 de Brauw and Giles features of the data source, we capture the general equilibrium effects of increasing out-migration. Start- ing in 1995, the household-level questionnaires began to enumerate whether or not a household has a migrant, so direct household-level participation in migration can be examined, also by terciles of the initial distribution of consumption per capita. For the period beginning in 1995, fig.A 4 shows the share

of households with at least one household member working and living outside their home township. For Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019

Figure 4. A. Share of Households with Member Working Outside the Township, by Initial Consumption Tercile, 1995–2002 B. Average Days Employed Outside the Township

(A)

(B)

Source: RCRE Household Surveys, 1995–2002. The World Bank Economic Review 13 each year through 2001, households in the lowest tercile were roughly five percentage points more likely than upper-tercile households to have a registered member living and working outside the village. Further, the lower tercile averages about 12 more days of migrant employment per capita than the upper tercile (fig.B). 4 These differences are statistically significant, and remain so after controlling for village fixed

effects (supplementary online appendix table S4.2). Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Splitting the sample by the initial consumption terciles, equation (3) is re-estimated by tercile, with consumption and non-durable consumption as dependent variables (table 3).4 Coefficients are estimated by tercile for the full 1988–2002 period and for a shorter 1995–2002 period, after migration had begun to grow more rapidly. Households that were initially in the lowest within-village tercile experience more rapid growth in consumption per capita and non-durable consumption per capita than households in the upper tercile. In fact, estimates for the upper tercile for the entire 1988–2002 period show that neither total nor non-durable consumption per capita grows significantly as a result of village-level out-migration. For the lowest and middle within-village tercile, however, a one-percentage-point increase in migration over the 1988–2002 period is associated with a 3 to 4 percent increase in both consumption per capita and non-durable consumption per capita. Over the period when out-migration was growing rapidly (1995– 2002), coefficient estimates suggest more rapid consumption growth for both the lowest and themiddle

Ta b l e 3 . Village-Level Migration and Consumption across the Initial Consumption Distribution (All Models in First Differences)

Dependent variable With controls? Lowest tercile Middle tercile Upper tercile

Log (consumption per capita) No 3.979** 3.936** 1.985 1988–2002 (1.420) (1.447) (1.471) Yes 3.484*** 3.718** 2.536** (1.260) (1.350) (1.353) Pre-det. 2.783* 1.541 1.305 (1.450) (1.344) (1.252) Log (consumption per capita) No 10.262*** 7.703*** 3.856** 1995–2002 (2.630) (2.037) (1.898) Yes 8.742*** 7.242*** 4.476** (2.815) (2.156) (2.184) Pre-det. 9.069** 5.776* 3.459 (4.393) (3.323) (2.451) Log (non-durable consumption per capita) No 3.732** 3.413** 1.516 1988–2002 (1.536) (1.467) (1.554) Yes 3.318** 3.330** 1.999 (1.385) (1.390) (1.412) Pre-det. 2.646 1.022 0.936 (1.557) (1.444) (1.354) Log (non-durable consumption per capita) No 9.253*** 7.694*** 2.864 1995–2002 (2.535) (2.123) (1.789) Yes 7.99*** 7.459*** 3.371 (2.774) (2.270) (2.012) Pre-det. 8.452* 5.991* 1.864 (4.421) (3.469) (2.244)

Source: RCRE Household and Village Surveys, 1986–2002, and the RCRE 2004 Supplementary Village Governance Survey. Note: Standard errors in parentheses clustered at the village level. * indicates significance at the 10 percent level; ** indicates significant atent the5perc level; *** indicates significance at the 1 percent level. Each cell represents a separate regression estimated in first differences with IV-GMM,ntly andincludesjoi significant village fixed effects and province-year effects to control for province-wide macroeconomic shocks. The share of migrants from the village is treatedasendogenous, as are control variables, which follow the specification in columns 3 and 6 of table 2.

4 In supplementary online appendix S4, similar results are estimated and discussed for income per capita. 14 de Brauw and Giles tercile (10 percent and 7 percent, respectively, with a one-percentage-point increase in migration), and also suggest positive consumption growth for the upper tercile (4 percent), though the latter increase appears attributable to the accumulation of durable goods and housing. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019

Migrant Networks, Investment, and Specialization Taken together, the results in tables 2 and 3 demonstrate that increases in household consumption per capita are associated with increasing out-migration, and that these effects are stronger among the poor. However, they do not shed light on how migration may have affected the investments of village residents, and the extent to which out-migration may contribute to productive activity within the local economy.The migrant labor market may relax local credit constraints through remittances, resulting in higher productive investment either in agriculture or non-agricultural self-employment, which contributes to increased earnings. Alternatively, households may respond to the relaxation of credit constraints by investing proceeds from migration in housing or consumer durables. Third, households may shift their labor to more productive activities, either directly as employees in migrant destinations, or through local employment as out-migration reduces the local labor supply. Understanding these mechanisms may have significant implications for rural policy in China. For example, if labor market policies relaxing restrictions on living in urban areas increase agricultural investment, policymakers charged with designing agricultural policy should take these increases into account. Alter- natively, if loosening labor market restrictions does not affect agricultural investment, and agricultural policymakers have reason to believe there are still important credit market failures leading to low investment in high-return activities, then these failures should be approached more directly. To shed light on these mechanisms, we next examine the relationship between migration and both investment and labor supply.

Investment To observe whether credit constraints are relaxed by migration, dependent variables measuring productive investment, or investment in housing and durables, are estimated using the following specification:

Kit = βM jt + d j + p ⊗ t + εijt (4)

In alternate models, Kit is the change in log value of productive assets, the change in ln(1+value of productive assets related to agriculture), the change in ln(1+value of productive assets for non-agricultural activities), and the change in log of the imputed value of housing and durable goods. The coefficient β measures how each type of investment changes with change in the share of the village labor force employed as migrants; and these models are also estimated by the initial per capita consumption tercile. Results from this exercise make it clear that all groups of village residents increase investment in durable goods and housing with out-migration; and that such investment growth is larger among poorer households (table 4, panel A). Upgrades to housing with out-migration are consistent with other work; Mu and de Brauw (2015) find suggestive evidence that investment in housing, with improved accessto tap water, may have contributed to improved nutritional status of children with migrant parents. With the exception of the model for non-agricultural productive assets among households in the upper tercile, coefficients on change in village migrant share are not significant at better than the 5 percent level.This association suggests that remitted earnings from migrants contribute sufficiently to the local economy to increase returns to local activity, perhaps related to home renovation or construction or other activities. An indirect benefit of out-migration may be expansion of non-agricultural productive activities among households in the upper tercile of the initial consumption distribution. The World Bank Economic Review 15

Ta b l e 4 . The Impact of Migration from the Village on Household Investment and Labor Allocation (All Models in First Differences)

Dependent variable Lowest tercile Middle tercile Upper tercile

A. Investment in physical assets, housing, and consumer durables Log (productive assets per capita) 2.768 2.630 4.689* Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 (3.655) (3.268) (3.289) Log (agricultural assets per capita + 1) 3.296 1.239 2.302 (4.346) (3.186) (2.967) Log (non-agricultural assets per capita + 1) 4.626 4.794 11.067** (3.377) (3.882) (5.018) Log (durables plus housing per capita + 1) 6.76** 5.493** 3.421* (2.383) (2.184) (1.882)

B. Share of migrants from village and household labor allocation Total labor days per capita −1.152 −1.086 −0.173 (1.716) (1.998) (2.055) Agricultural (days per capita) −7.407* −2.790 0.471 (3.630) (2.761) (3.019) Local off-farm (days per capita) 4.043 17.299** 16.506** (6.847) (7.726) (7.967) Self-employment (days per capita) −1.439 −10.042 −7.592 (6.341) (8.314) (8.073)

Source: RCRE Household and Village Surveys, 1986–2002, and the RCRE 2004 Supplementary Village Governance Survey. Note: Standard errors in parentheses clustered at the village level. * indicates significance at the 10 percent level; ** indicates significant atent the5perc level; *** indicates significance at the 1 percent level. Each cell represents a separate regression estimated in first differences with IV-GMM,ntly andincludesjoi significant village fixed effects and province-year effects to control for province-wide macroeconomic shocks. The share of migrants from the village is treatedasendogenous, but control variables are treated as exogenous, following the specification in columns 2 and 5 of table 2.

Labor Supply Increases in the ability to earn income from the migrant labor market may have negative effects on household labor supply if the wealth effect dominates the substitution effect. Households may have initially faced constraints in their ability to supply labor to the market, and if so, the expansion of migrant opportunity may allow them to increase income through expanded employment. Direct effects on labor supply through work in the migrant labor market may be complemented by indirect effects through depletion of the local labor force or demand for labor in the local construction and service sectors. To investigate this hypothesis, we modify equation (5) and use four measures of the logarithm of the number of labor days supplied (+1) as the dependent variable (table 4, panel B). These measures include the total labor days per capita, and the days per capita worked in agriculture, local wage labor, and non- agricultural self-employment. For the total labor days worked, no significant coefficients are found for any of the three terciles. Based on these results, one might assume that changes in village-level migration did not affect rural household labor supply. The absence of an increase in overall labor supply masks changes in labor allocation across activities, which demonstrate some interesting changes. Specifically, among the lowest tercile, the number of agricultural days worked per capita decline with increased migration; the estimated coefficient is significant at the 10 percent level and suggests a 7.4 percent decline with a one-percentage-point increase in migrant share at the sample mean. Meanwhile, the number of days allocated to the local off-farm market increases among the middle and upper terciles; these coefficients are fairly large and significant at the 5 percent level, and suggest that initially better-off households expand work in the local labor market, most likely as a result of more local activity with investment of remittances in productive activities. 16 de Brauw and Giles

Summary The main results suggest that consumption per capita increases with migration, and impacts are stronger among poorer households than among households in the upper tercile of the initial consumption distribution. Later in the study period (after 1995), incomes also increase among all households, though

the increase is faster among households that were initially poorer. Increases in consumption and income Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 among poorer households appear to come directly through migration with reduced labor allocation to farming, whereas increases among households in the upper tercile appear to come more from stronger investments in non-agricultural business and increased labor allocation to non-agricultural activities.

4. Conclusion This paper shows the positive effect that internal migration in China has had on the consumption per capita of households remaining in migrant-sending communities, and also demonstrates that these effects are stronger for poorer households within villages. In common with McKenzie and Rapoport (2007) for Mexico, the increased ease of migration from villages of rural China is associated with decreasing inequality within communities. Further, increases in migration from rural China are associated with increased accumulation of housing wealth and consumer durables, with more pronounced growth among poorer households within rural communities. Consistent with both research on return migration in China and the international literature (Woodruff and Zenteño 2007; Yang 2008), this paper shows that migration is associated with more investment in non-agricultural production activities, but these investments are concentrated among more affluent households within villages. From the perspective of raising consumption, shifting labor allocation of the poor, and promoting non-agricultural investment, allowing the expansion of migration played a significant, positive role as a development strategy for China’s rural areas. As this research was conducted using data from the period when rural-urban migration in China first accelerated, it is worth thinking about the relevance of these findings for China’s ongoing process of urban- ization over the past fifteen years. First, while there are announced plans to eliminate hukou, or household registration, these reforms have not been enacted to date. The hukou system does not prevent mobility, but does prevent full integration of rural migrants into China’s urban economy. Although recent joint work conducted by the World Bank and China’s Development Research Center has concluded that more complete integration of rural migrants into the urban economy will be important for sustaining growth, most migrants do not plan on remaining in cities (Meng 2012). In addition to discrimination in access to housing and education, migrants also found themselves without any employment protection in the face of sharp dislocations, as witnessed in the wake of the Global Financial Crisis (Huang et al. 2011). Indeed, Kong, Meng, and Zhang (2009) report evidence suggesting strong reluctance among those migrants laid off in the wake of the crisis to return to urban centers once the demand for migrant labor picked up again. In the face of continued discrimination in cities, the advent of e-commerce in China may offer a means for more productive investments of migrant earnings in home townships or counties. The e-commerce giant, Alibaba, which operates Taobao (a Chinese equivalent of eBay and Amazon), is explicitly attempting to expand its services to rural communities. There is also explicit interest in promoting the use of Taobao for rural residents to sell both processed agricultural outputs and non-agricultural goods such as handicrafts. Indeed, some observers have raised the question of whether Taobao might spur a “rural revolution” (e.g., Feng 2016). While the opening of markets through Taobao is unlikely to draw the vast majority of migrants back to rural areas, it does raise the prospect that the return to investments in small-scale activities in migrant-sending communities may rise over the next decade.

References Benjamin, D., L. Brandt, and J. Giles. 2005. “The Evolution of Income Inequality in Rural China.” Economic Devel- opment and Cultural Change 53 (4): 769–824. The World Bank Economic Review 17

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Wooldridge, J. M. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. Cambridge, MA: MIT Press. Yang, D. 2008. “International Migration, Remittances and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks.” Economic Journal 118 (March): 1–40. Zhao, Y. 2002. “Causes and Consequences of Return Migration: Recent Evidence from China.” Journal of Compar- ative Economics 30 (2): 376–94. Zhao, Y. 2003. “The Role of Migrant Networks in Labor Migration: The Case of China.” Contemporary Economic Downloaded from https://academic.oup.com/wber/article-abstract/32/1/1/4835580 by Joint Bank/Fund Library user on 08 August 2019 Policy 21 (4): 500–511. The World Bank Economic Review, 32(1), 2018, 19–40 doi: 10.1093/wber/lhx021 Article

The Impact of the Syrian Refugee Crisis on Firm Entry Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 and Performance in Turkey Yusuf Emre Akgündüz, Marcel van den Berg, and Wolter Hassink

Abstract We analyze how the Syrian refugee inflows into Turkey affected firm entry and performance. To estimate the causal effects, we use instrumental variables, difference-in-differences, and synthetic control methodologies. The results suggest that hosting refugees is favorable for firms. Total firm entry does not seem to be significantly affected. However, there is a substantial increase in the number of new foreign-owned firms. In line with the increase in new foreign-owned firms, there is some indication of growth in gross profits and netsales. JEL classification: F22, J61, R23

Keywords: refugees, firm entry, firm performance

The Syrian Civil War that began in 2011 has had an enormous human cost. The United Nations puts the number of people who have fled their homes at 9 million. While 6.5 million of these refugees are internally displaced in Syria, 2.5 million have become refugees in Syria’s neighboring countries: Turkey, Lebanon, Jordan, and Iraq. At first, the number of refugees arriving in Turkey was relatively small, at about 7,600 in November 2011 according to United Nations High Commission for Refugees (UNHCR) statistics. The situation had reached crisis proportions by the end of 2012, when the total number of registered refugees in Turkey was approximately 135,000. The flood of refugees continued and reached more than amillion and a half by the end of 2014. The refugee crisis has already led to research on the economic impact of hosting refugees in Turkey. Two recent papers analyze the impact of the Syrian refugee crisis in Turkey on native employment: Ceritoglu et al. (2017) and Del Carpio and Wagner (2015).1 The main difference between the two is methodological. While the former uses a difference-in-differences (DD) methodology, the latter employs an instrumental variables (IV) strategy. Both papers find a negative effect on informal employment, but Del Carpio and Wagner (2015) also find positive effects for native high-skill employment. In this paper, we complement

Wolter Hassink (corresponding author) is a professor at Utrecht University, Utrecht, the Netherlands; his email address is [email protected]. Yusuf Emre Akgündüz is an economist at the Structural Economic Research Department of the Central Bank of the Republic of Turkey; his e-mail address is [email protected]. Marcel van den Berg is a senior researcher at Statistics Netherlands; his email address is [email protected]. The content of this publication does not refect the official opinion of Statistics Netherlands or the Central Bank of the Republic of Turkey. Responsibility forthe information and views expressed in the paper lies entirely with the authors. 1 In a companion paper, Akgündüz, Van den Berg, and Hassink (2015), we found no effects of the refugee influx on formal employment. However, unlike that of Del Carpio and Wagner (2015) and Ceritoglu et al. (2017), our data are structured at the aggregate level. Also studying the Syrian refugee inflows in Turkey, Balkan and Tumen (2016) find negative effects on prices from hosting refugees. In a related study, Fakih and Ibrahim (2016) find no correlation between labor market outcomes and the arrival of Syrian refugees in Jordan.

© The Author 2018. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please e-mail: [email protected] 20 Akgündüz, van den Berg, and Hassink the findings of these papers by analyzing the effects of refugee inflows on firms rather than employment. More specifically, we analyze the effects of refugee inflows on entry of new firms, new foreign firms,gross profits, and total sales in hosting regions. Up to 2014, the Syrian refugees’ destination in Turkey was limited geographically to the location of the refugee camps in the border region. The size of the inflow is large enough to allow for the estimation Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 of the effects on the outcomes using a DD model, which is the strategy employed by Ceritoglu et al. (2017). Potentially worrying, however, is that the treated regions are close to the Syrian border and might be affected in other ways than the refugee crisis, such as trade, which would lead to biased estimates. Furthermore, the DD approach might be less appropriate after 2013 since refugees began spreading to the rest of Turkey from 2014 onward and started moving to Europe in late 2014 and 2015 (Tumen 2016). Therefore, we employ the IV strategy used by Del Carpio and Wagner (2015) to estimate the effects of the number of refugees in 2014 on firm entry while controlling for the proximity to the closest border crossing with Syria. We also test the results from the IV estimations with the DD model using data from 2012 and 2013 when the refugee populations were concentrated much closer to the border. A second problem in the DD framework is that the provinces receiving refugees are located in the southeast of Turkey, which is less developed than the western portion. While we limit the control region to eastern Turkey, which is more similar economically to the treatment region, we further test the results from DD models using the synthetic control method of Abadie, Diamond, and Hainmueller (2010) to give higher weights to control group provinces that have pretreatment outcomes closer to those of hosting regions. The strongest finding of our analysis is that the refugee influx results in an increase in thenumberof foreign firm entries. While we find positive effects in some estimations, the effect on total firmentryis not statistically signficant in the main IV estimations. We find suggestive evidence pointing towardan increase in the size of agricultural and transportation sectors. There is indication of an increase in total profits and sales in provinces hosting refugees. However, the placebo tests suggest differential trendsin the sales and profits in treatment and control regions. Our results complement the results of Del Carpio and Wagner (2015), who find occupational upgrading among formal employees in sectors that benefit from low-skilled informal labor. Refugee labor appears to be a complement to formal labor and capital and a substitute for native informal employment. Our primary contribution to the broader literature is the empirical evidence on the effect of the refugee inflows on local firm performance and entry, a potential channel discussed by Maystadt and Verwimp (2014) that has received limited empirical attention in the literature.2 The findings can also help explain the relatively small effects on wages and formal employment in the migration literature. While the influx of refugees reduces the demand for low-skilled labor of the natives, the growth of the number of firms can mitigate this decline. The emerging picture fits well with conventional economic theory; an increase in the supply of low-skilled employees appears to be met with more capital investment. Firms may benefit from the increased availability of unskilled labor and increased demand, if these are what refugees indeed provide. Specifically, we might expect lower production costs for firms in sectors that utilize unskilled labor and increased demand for goods they consume.

1. Syrian Refugees in Turkey In November 2011, responding to the civil war reaching the northern areas of Syria, approximately 7,000 refugees crossed the Turkish border. By November 2014, Turkey was hosting more than a million and a half refugees. Officially, the Turkish government did not recognize the Syrian refugees as asylum seekers.

2 The literature on the impact of refugee crises on the native population has analyzed outcomes ranging from child health to prices in Tanzania and Darfur (Alix-Garcia and Saah 2010; Baez 2011; Alix-Garcia, Bartlett, and Saah 2012; Maystadt and Verwimp 2014; Alix-Garcia and Bartlett 2015). The World Bank Economic Review 21

In technical terms, the refugees were being treated as guests (Ozden 2013). This has two important implications. First, they cannot apply for asylum in a third country. This limits the opportunities of migrating to other countries. Second, unlike refugee status, guest status implies that refugees can be relocated by the Turkish government without any legal process. To alleviate the conditions of the Syrian refugees and to limit uncertainty, the government enacted a temporary protection policy that ensures an open border Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 between Turkey and Syria and that promises no forced exit. The guest status of the Syrian refugees leads to their informal employment in the labor market; Syrian refugees’ guest status does not provide them with a work permit to find formal employment. We found no official numbers (nor reliable estimates) on how many refugees have entered the local labormarket, but Syrians could reportedly find jobs since employers can pay themDinçer less( et al. 2013). Syrian employment will most likely be limited to low-skilled jobs because the language barrier and the requirement for formal employment is likely to limit refugees’ employment opportunities in high-skilled jobs. However, the guest status does not seem to be an impediment for investing in or registering new firms. The ORSAM (2015) report notes that there was a large inflow of Syrian capital along with the refugee influx. Gobat and Kostial (2016) report that a small amount of financial capital was brought into Syria after the beginning of the civil war, while a great deal of capital fled through informal channels. At least one of the destinations appears to be Turkey, considering the rapid rise in deposits made by Syrians in Turkish banks from 311 million Turkish Liras in 2012 to 1.2 billion in 2015 (Sagrioglu 2004). Figure 1 confirms that Syrian capital as a share of total foreign capital in new firms increased substantially once the refugee inflows took off in 2012. Financial capital movements are generally less controversial than movements in the labor market. Syrian investors can also have Turkish partners when founding firms to make the transition easier, which might also explain the rapid increase seen in fig.. 1 While most refugees were located in the southeastern part of Turkey in 2012 and 2013, where the camps were located, the number of people located outside that region increased considerably in 2014. The distribution of refugees in 2014 is shown in fig.. 2 The map is based on the figures of Erdogan˘ (2014),who

Figure 1. Syrian Share of Total Foreign Investment in New Firms

Source: Turkish Chamber of Commerce. Note: The numbers on the vertical axis refer to the Syrian share in total foreign investment in firms. 22 Akgündüz, van den Berg, and Hassink Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 . ˘ gan (2014) Syrian Refugees in Turkey in 2014 Erdo : : The gray scale corresponds to the number of refugees in the province. Figure 2. Source Note The World Bank Economic Review 23 provides the estimated numbers for all 81 Turkish provinces, which correspond to the NUTS-3 regions of Turkey. His estimates are similar to those of other sources (ORSAM 2015). The UNHCR report of September 2014 only concerns the southeastern provinces of Turkey and therefore only accounts for about half the number of refugees in Turkey as a whole. While the refugee concentration is still highest in the southeast, there are also substantial numbers of refugees located in big cities particularly, such as Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Istanbul and Ankara. Only about 13 provinces are reported as having no refugees at all. As a result, it becomes difficult to argue that there is limited selection in the locations of the Syrian refugees in2014.By late 2014 and 2015, refugees appear to not only select their region within Turkey, but also start moving to Europe (Tumen 2016).

2. Theoretical Framework The study of Borjas and Monras (2016) is based on a Marshallian factor-demand theory of a competitive labor market, by which they investigated various episodes of exogenous flows of refugees on the labor market in the receiving countries. They find a negative effect on the labor market position of competing natives, whereas there they obtain a positive effect on the position of complementary workers. Ozden and Wagner (2014) further add that immigrants entering the labor market can have a scale effect that raises output and reduces costs. Other studies suggested that the arrival of refugees has a much broader effect on the economy of the receiving countries. In particular, it has consequences for the demand for goods. Bozzoli, Brück, and Wald (2013) find a positive effect on self-employment from the influx of the internally displaced in receiving regions of Colombia, and they expect that the effect is due to self-employment in the informal sector by the displaced or rising demand for goods. Furthermore, while their empirical analysis is about consumption by specific subgroups of Rwandese and Burundi refugees in Tanzania, Maystadt and Verwimp (2014) argue that refugee inflows may have an effect on local economies through anumber of channels—prices, employment, wages, business, infrastructure, health and sanitation services, crime, and unrest. The empirical results of studies so far suggest that the general conclusion of Borjas and Monras can be extended to the Turkish labor market in the Syrian refugee crisis. In the receiving provinces, there was a reduction of Turkish informal labor (Tumen 2016), who may be considered the “competing natives” in Borjas and Monras’s terminology. Furthermore, it was found that the influx increased the demand for formal labor in sectors that use informal labor, who may be interpreted as complementary workers (Del Carpio and Wagner 2015). There was a reduced internal migration to the Turkish provinces that located refugees, which may have mitigated the supply of native labor (Akgündüz, Van den Berg, and Hassink 2015; Del Carpio and Wagner 2015). We make use of the arguments of the studies of Bozzoli, Brück, and Wald (2013) and Maystadt and Verwimp (2014) to claim that Syrian refugees may have a broader effect on the Turkish economy, in particular for the product market. We argue that there may have been an increase in the number of firms, which has partially offset the adverse effect of the refugee flows on Turkish native employment. We hypothesize the existence of three channels in the product market. First, the arrival of refugees will raise the demand for goods because of the higher number of consumers as well as the differences between refugees and natives in terms of tastes and preferences. We hypothesize that the refugee crisis resulted in higher sales and profits. Second, the additional demand for goods by refugees will create opportunities for the entry of new firmsFoster, ( Haltiwanger, and Syverson 2008). It leads to the hypothesis that there was in increase in the number of new firms, in particular in the sectors that depend on informal labor. Third, because the refugees have better information about the refugees’ needs for products, some of them may exploit their entrepreneurial abilities to set up new firms. While there is no easy way to measure 24 Akgündüz, van den Berg, and Hassink to what extent refugees are able to transfer their financial capital from Syria to Turkey, it seems safe to assume that their more liquid capital arrives with them. Indeed, their networks and social capital certainly do. It leads to the hypothesis that the refugee crisis has increased the number of foreign-owned firms. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019

3. Methodology

The primary equation of interest is given by equation (1), where Yit is the province-level outcome, Rit is the number of refugees in the province, Pi are province fixed effects, and Tt are year fixed effects. Subscript i indicates individual provinces and t years. The standard criticism to the ordinary least squares (OLS) estimator is that the resulting estimate of ρ in equation (1) is likely to be biased if refugees choose areas with better prospects (Dustmann, Glitz, and Frattini 2008).

Yit = a + ρRit + Pi + Tt + eit (1)

We deal with the potential endogeneity problem using the two methods previously introduced in analyzing the impact of Syrian refugees on the labor market. The first method is to follow Del Carpio and Wagner (2015) and exploit the presence of six border crossings between Syria and Turkey to construct a weighted distance variable to instrument the number of refugees in Turkey at the province level using data compiled by Erdogan˘ (2014) for the year 2014. The second method is to assume that the region nearest the Syrian border where the initial refugee camps were located is the treatment region, which allows for an estimation using the difference-in-differences method as in Ceritoglu et al. (2017). The instrumental-variables method to identify the effects of refugees is preferable over the DD method for three reasons that are noted by Del Carpio and Wagner (2015). First, regions bordering Syria are more likely to be affected economically by the war in Syria through trade channels. Second, these regions tend to have lower income than the northern and western regions of Turkey, and any ongoing income convergence between regions may influence the results of the DD methodology. Finally, recent reforms— particularly the raising of compulsory education ages from eight to 12 years—are more likely to affect poorer regions near the Syrian border. However, we control simultaneously for the distance to the closest border crossing of each province, which limits the variation exploited by the instrument to the potential use of different border crossings by refugees coming into Turkey. The instrument is therefore likely to be exogenous as long as the control for the distance to the closest border crossing captures effects from proximity to Syria. 1 IV = π R (2) it T s t s si

We instrument the number of refugees in a province by a weighted average of distances between 13 Syrian governorates and 81 Turkish provinces. Google Maps was used to calculate the distances between each province and border crossing as well as the distance between each Syrian governorate’s capital and border crossing. We use the same formula as Del Carpio and Wagner (2015) to construct the instrument, which is given by equation (2). The main difference is that we use data at the level of 81 provinces corresponding to the NUTS-3 level while Del Carpio and Wagner (2015) use data at the level of 26 3 NUTS-2 regions. In equation (2), Tsi is the shortest travel distance between each Syrian governorate and

3 While the instrument is statistically significant in all estimations, the first-stage F-test seems be a smaller valuewhen 81 provinces are used compared to the case of 26 NUTS-2 regions. The instrument becomes even weaker if we use refugee-to-population ratios rather than the number of refugees; therefore, we use the absolute number of refugees throughout. The World Bank Economic Review 25

Turkish province in kilometers, π s is the fraction of the Syrian population living in a governorate prior to the civil war in 2010, and Rt is the total number of refugees in Turkey in 2014, the only year for which we have complete information on the number of Syrian refugees in all provinces. Since the closest border crossing from a Turkish province is not necessarily the border crossing on the shortest routes between all Syrian governerates and the province, the IV approach allows us to simultaneously control for the Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 distance to the closest border crossing in the estimations. The specification estimated by 2SLS is presented by equation (3), where Rit is instrumented by the instrument defined in equation (2). Since we use all provinces in the main IV estimations, i is defined as i = (1, … , 81) while t is defined as t = (2011, 2014). Following Del Carpio and Wagner (2015), we use the year-specific natural logarithm of the distance to the closest border crossing, LDit, as a control variable. The year-specific distance control is defined as LDi2011 = 0andLDi2014 = LDi.

Yit = a + ρRit + Pi + Tt + βLDit + eit (3)

Our second strategy is to estimate a linear difference-in-differences model with province-level fixed effects for all outcomes, which is the method used by Ceritoglu et al. (2017). Their approach defines the years 2012 and 2013 as treatment years and the previous years as pretreatment. We do exclude 2014 in the DD model since Syrian refugees have been spreading across Turkey from 2014 onward, while they were more concentrated near the border areas that we define as the treatment region in 2012 4and 2013. In effect, the DD estimates use treatment years that are completely excluded from the IV model: 2012 and 2013. A second issue in the DD specification is the definition of the control area. We follow Ceritoglu et al. (2017) and define the control region as the northeastern part of Turkey, which neighbors the treatment region. Ceritoglu et al. (2017) argue that the common trend assumption is more likely to hold in this region, which is more similar to the treatment region than western Turkey. The basic DD specification is presented in equation (4). Yit is the outcome variable, (Treatment)indicates regions hosting refugees, and (post2012) indicates the period after refugees arrive. The main parameter of interest is ρ, which gives the DD estimate for the effect of hosting refugees. The controls for (Treatment)and(post2012) are dropped once we include year (Tt) and province fixed effectsP ( i). The analysis is performed on data from 29 provinces since we limit the control group provinces to eastern Turkey, following Ceritoglu et al. (2017). Subscript i still stands for individual provinces but is now defined as i = (1, … , 29). Since refugees spread out across Turkey in 2014, we only exploit the refugee shock in 2012 and 2013, when most of the refugees were located close to the border (Tumen 2016). Pre- treatment years include all years for which we have data, and subscript t is therefore defined as t = (2009, … , 2013). Two alternative treatment variables can be introduced. The binary treatment variable equals 1 when a province is reported as having refugees by the UNHCR reports from late 2012 and 2013. The continuous treatment variable is simply the number of refugees reported. The latter is more likely to give stronger effects since the binary treatment is relatively imprecise when province-level data are analyzed. Provinces like Malatya and Adiyaman have very few refugees compared to the other provinces hosting refugees, but still have a value of 1 for the treatment dummy variable.

Yit = a + b(Treatmenti ) + c(Post 2012t ) + ρ(Treatmenti × Post 2012t ) + eit (4)

One of the issues with the DD model is that the regions hosting refugees in southeastern Turkey are much less developed than the western portion of Turkey, so that any choice with regard to the control group will be arbitrary. We complement the standard DD results by using the synthetic control method of

4 Including 2014 data in the DD estimations generally results in more statistically significant and larger coefficients but does not change the direction of the results. 26 Akgündüz, van den Berg, and Hassink

Abadie, Diamond, and Hainmueller (2010), which explicitly recognizes uncertainty regarding the validity of the control group. We can therefore include all provinces outside the treatment area in synthetic control estimations. The method then constructs a synthetic control unit from a combination of the provinces in the control group that most resemble the treatment provinces in the pretreatment period. Rather than giving equal weights to all provinces as in the standard DD model, the synthetic control method gives more Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 weight to provinces that have outcomes more similar to the treatment provinces. The basic estimation procedure is presented in equation (5), where Y is the mean outcome in treated provinces. Since we cannot differentiate between provinces that first received refugees in 2012 or 2013, we define all provinces that have refugees in either year as the treatment group. The 71 provinces from the control group are weighted 5 using the vector W =w2,…,w1+71 where w2 + … + w1+71 = 1.

1+71 ρ = Y1 − wiYi (5) i=2

The weight given to each province in constructing the synthetic control is based on pretreatment outcomes. We use the pretreatment average of the outcome dimension, Y, the unemployment rate, employment rate, and the import and export per capita of the province to determine the degree of similarity between the control group provinces and treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance, while trade values are added to control for the degree of “openness” of the province.6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013.

4. Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014, while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR’s official weekly statements in December. Data on the number of refugees in 2014 are from Erdogan˘ (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis are provided at the level of 81 provinces. There were in total 1.6 million refugees by November 2014. Data on the number of new firms and their ownership characteristics and value are provided by the Turkish Chamber of Commerce.7 Data on total sales and gross profits are obtained from the Turkish Ministry of Science, Industry, and Technology. Other economic indicator variables, such as population and unemployment rates, are obtained from Turkish Statistics. Since Syrians usually have guest status rather than resident status during the period of analysis, they are not counted in official statistics such as province population and unemployment rates. Turkey is officially divided into 81 provinces, and that is the level of our analysis and variables throughout. The Chamber of Commerce provides data on the number of new firms and the number of new foreign- owned firms at the provincial level. Enterprises defined as firms do not include self-proprietorships (the self-employed). We further exclude business cooperatives from the analysis. New cooperatives are founded

5 The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie, Diamond, and Hainmueller (2011). 6 Tunceli’s 2011 export and import values are missing for 2011; therefore, only 2009 and 2010 values could be used in calculating Tunceli’s average. 7 The Chamber of Commerce also provides information on the number of firms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no significant effects in both the IV and DD estimates on the number of firms that shut down. The World Bank Economic Review 27 usually in construction and are relatively few in number (less than 2 percent of the number of new firms in 2014). Furthermore, no information is given on whether cooperatives are foreign owned. The available data encompassed the period between 2009 and 2014. The number of new firms reported by the Chamber of Commerce (57,710) seems to match the World Bank data on new business registrations

(57,760) for Turkey in 2014. The World Bank data places Turkey below even the lower-middle-income Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 country average on a per capita new business density scale, implying considerable room to improve entrepreneurship and firm entry (World Bank 2014). The data on foreign-owned firms begin one year later, in 2010. While the data provided are relatively basic, they allow us to make the distinction between new firms and new foreign firms. We take the natural logarithm of the firm entry variablesfor a more parsimonious estimate and easier interpretation.8 Without the log transformation of the outcome variables, the estimated effects remain positive but have larger standard errors, especially in the DD models. The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Indus- try, and Technology are compiled from administrative taxation data and were provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses, including self-proprietorships.9 Data were provided for the years between 2010 and 2014 and are reported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition, and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets, which include sales and profit figures.10 We scale the variables according to province size by dividing sales and profits by the population of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results in the main IV andDD regressions. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we treat 2011 as the prerefugee year in our IV analysis. Since 2014 is the first year for which we have complete information on the numberof refugees in each province, it is set as the treatment year. The summary statistics for the outcome variables used in the IV analysis are presented in table 1. To estimate the DD model, we use data from the years 2009 to 2013. Only data on firm entry are available in 2009; the rest of the outcome data are available from 2010 onward. Table 2 shows the descriptive statistics for the treatment and control group provinces before and after 2012, along with the averages for the rest of Turkey. While the rest of Turkey has generally higher outcome averages than the treatment region, the control region has smaller averages. The region definitions are shown in fig. 3 and are constructed by following the definitions used by Ceritoglu et al. (2017). Including the treatment provinces, there are in total 29 provinces in this limited sample. Data on the number of refugees in southeastern Turkey in 2012 and 2013 are drawn from the 2012 and 2013 December reports of the UNHCR on Syrian refugees in Turkey (UNHCR 2014). The number of Syrian refugees in Turkey in 2012 only concerns refugees in camps, while the 2013 numbers also include refugees in urban areas. Nonetheless, the number of refugees outside camps was still relatively low in 2012—40,000 to 60,000 people—so this is not likely to affect our results. Data are provided

8 Since the number of new foreign firms is 0 in several observations, we add 1 to the value. As an alternative, weusedthe hyperbolic inverse sine transformation, which does not have the same problem with zeros as log transformation, and found similar results (Burbidge, Magee, and Robb 1988). 9 Publicly available data from the Ministry of Science, Industry, and Technology indicate that less than 10 percent of total revenue is from micro-establishments. Most of the net sales and gross profits reported stem from larger firms that should be included in the Chamber of Commerce data. 10 All firms exceeding 200,000 Turkish Liras (ca. $85,000) in sales are obligated to report detailed balance sheets. Smaller firms may still report their balance sheets but would be doing so on a voluntary basis. 28 Akgündüz, van den Berg, and Hassink

Ta b l e 1. IV Estimation Annual Summary Statistics: 2011 and 2014

Province average 2011 2014

New firms 659.37 712.47 New foreign firms 44.14 58.47 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Share of foreign firms× ( 100) 2.56 3.48 Gross profits per capita (TL) 1,969.59 2,740.89 Net sales per capita (TL) 16,561.75 23,381.10 Population 922,522 959,209 N 81 81

Source: Summarized data from the sources described in the text. Note: Averages at the level of 81 provinces presented for each variable. “New firms” and “New foreign firms” are the number of firms. “Share of foreign firms”isthe average of the ratio of “New firms” and “New foreign firms.” “Gross profits” and “Net sales per capita” are the province totals divided by thelation provincepopu and are provided in nominal Turkish Liras. *p < .1 **p < .05 ***p < .01

Ta b l e 2 . Difference-in-Differences Estimation Annual Summary Statistics: 2009 to 2013

Pre-2012 Post-2012

Province average by region Control Southeast Rest Control Southeast Rest

New firms 202.00 465.00 796.38 147.05 396.50 715.36 New foreign firms 3.87 7.85 55.29 6.53 19.20 66.79 Share of foreign firms× ( 100) 1.33 1.74 2.51 2.05 5.05 4.01 Net sales per capita (TL) 6,795.29 12,336.84 18,365.23 10,115.69 18,680.56 25,549.34 Gross profits per capita (TL) 700.79 1,122.56 2,323.96 985.28 1,501.67 2,932.98 Population 585,092 1,074,304 1,027,701 517,817 1,106,135 1,062,457 N 10 19 52 10 19 52

Source: Summarized data from the sources described in the text. Note: Averages at the level of the province groups shown in Figure 3 presented for each variable. “New firms” and “New foreign firms” are the number offirms. “Share of foreign firms” is the average of the ratio of “New firms” and “New foreign firms.” “Gross profits” and “Net sales per capita” sare theprovincetotal divided by the province population and are provided in nominal Turkish Liras. *p < .1 **p < .05 ****p < .01 at the provincial level for the southeastern provinces, allowing us to vary the treatment by the number of refugees sheltered in a specific province for the treatment region. The total number of refugees in 2012 amounts to about 200,000, while in 2013 this number increases to about 560,000. Three of the provinces—Adana, Malatya, Adıyaman—first received refugees in 2013. The numbers of refugees staying in provincial southeastern Turkey for each year are presented in table 3. A visual inspection of the figures shown in appendix A1 of the outcome means over time appears to support the parallel trend assumption, but we test the assumptions more formally using placebo tests in the Results section. The number of new firms shows a relatively cyclical behavior, which generally follows the business cycle in Turkey. After a rapid growth of more than 8 percent in GDP 2011, the growth rate dropped to around 2 percent in 2012, which is clearly visible also in the sharp drop in the number of new firms. We have less information about the economic sectors that new firms in host regions are entering. Turk- ish Statistics (TUIK) reports the number of enterprises by sector in each province, but the data are limited to 2013–2014. Similar to the data on sales and profits, TUIK data reports the number of all enterprises, including firms and other types of entrepreneurial activity such as sole proprietorships. Sectoral dataare therefore aggregated at a higher level than the firm-entry numbers reported by the Chamber of Commerce. Nevertheless, the dataset is useful in determining which sectors are the largest in host regions and what the change in sector size was between 2013 and 2014. The World Bank Economic Review 29 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Treatment and Control Regions : Authors’ own construction based on UNHCR data. : Dark-shaded areas are the treatment and lightly-shaded areas the control group in the difference-in-differences estimations. Figure 3. Source Note 30 Akgündüz, van den Berg, and Hassink

Ta b l e 3 . Number of Refugees in 2012 and 2013 in Southeastern Provinces

2012 2013

Adana 0 16,666 Kahramanmara¸s 16,830 28,882 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Malatya 0 7,205 Mardin 0 40,965 Osmaniye 7,914 18,046 Adıyaman 8,880 10,053 Hatay 12,776 85,642 Kilis 13,510 63,292 Gaziantep 25,512 145,905 Sanliurfa¸ 58,558 134,357

Source: Data from UNHCR. Note: Data for 2012 only include refugees in camps while 2013 data include refugees in and outside of camps. December values are used for both years. *p < .1 **p < .05 ***p < .01

5. Results IV Results Table 4 presents the results of IV estimates in three panels. Panel A presents our main results from the IV estimations, which uses all provinces in Turkey. We find statistically significant and positive effects onall outcomes except for the number of new firms. The number of new firms with foreign capital shares risesby 15 percent per 10,000 refugees. The share of new firms with foreign capital also rises by approximately one percentage point. For total firm entry, the effects are not statistically significant. Since thenumber of foreign firms is much smaller than the total number of firms, the result does not necessarily indicatea crowding out of Turkish firms.11 Both gross profits and net sales also appear to be positively affected, with the effect size around 2 to 3 percent of the province means in 2014. The ratio of the increase in profits and sales is only marginally larger than the ratio of the means of the two variables, indicating that there is no dramatic increase in the profit margin. The effect on business activity may therefore be interpreted asthe result of increased demand leading to more sales. Table 4 also presents the corresponding OLS estimates in panel B. The direction of the effects is generally similar to the main IV estimates. The main difference is that the effect on the number of new firms is statistically significant. The threat to the validity of the IV results stems from a potential correlation between province-specific trends in outcomes and our instrument. To minimize differences in trends, we estimate the IV regressions in a limited sample of provinces that are in control and treatment regions shown in fig., 3 which excludes the western provinces of Turkey. The results from the limited sample are presented in panel C. The effects on profits and sales appear to be reduced once we limit the sample. We can test for the existence ofbias due to different trends in the pretreatment period by using data from the years 2010 and 2011. Panel A of table 5 presents the results of a placebo exercise where we assume that the refugees in 2014 had been there in 2011 and use 2010 as the pretreatment control year to estimate the IV regressions. Unfortunately, data are not available to test for long-term trends. There is evidence of different trends for net sales and profits when the full sample is used, as shown in table 5. However, once we limit the sample to eastern Turkey, the placebo treatment only remains statistically significant for net sales. In the case of net sales, the real treatment has a considerably larger coefficient than the placebo treatment, suggesting that itis upward biased, but the true effect is still positive. Nevertheless, we remain cautious about interpreting the results from net sales given the difficulty of finding a region where the common trend assumption seems to hold.

11 We tested a potential crowding out of Turkish firms by estimating the effects on the number of new firms after subtracting the number of new foreign firms. The full-sample IV result is negative but insignificant and close to 0,whiletheDD estimates are positive. Overall, there does not seem to be a clear effect on Turkish firms’ entry. The World Bank Economic Review 31

Ta b l e 4 . IV Estimates

New firms New foreign Share of Gross profits Net sales firms foreign firms per capita per capita

Panel A: Instrumental variables estimates Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Refugees (×10,000) 0.0098 0.1482*** 0.0107*** 77.7970*** 790.0787*** (0.0143) (0.0402) (0.0032) (16.9487) (131.7215) Panel B: Ordinary Least Squares Refugees (×10,000) 0.0096*** 0.0446*** 0.0020** 93.8231*** 901.8617*** (0.0022) (0.0144) (0.0008) (33.2922) (198.0958) Observations 162 162 162 162 162 Panel C: Instrumental variables regional Refugees (×10,000) −0.0237 0.1506*** 0.0115*** 79.7056** 575.1354*** (0.0210) (0.0530) (0.0042) (34.1529) (187.6326) Observations 58 58 58 58 58

Source: Authors’ own analysis results from the data described in the text. Note: All models include a year-specific log distance control, province, and year fixed effects. Robust standard errors are shown in parentheses. Dataarefrom 2011 and 2014 for 81 provinces in panels A and B and 29 provinces in panel C. The refugee variable is the number of refugees in the province in 10,000s. The first-stage F-test for the instrument in IV regressions is 12.44. For the regional sample, the F-test is 6.45. *p < .1 **p < .05 ***p < .01

Ta b l e 5 . IV Placebo Estimates

New firms New foreign Share of Gross profits Net sales firms foreign firms per capita per capita

Panel A: Full Sample Refugees (×10,000) 0.0112* −0.0244 0.0002 43.3222*** 589.5904*** (0.0063) (0.0176) (0.0007) (14.0182) (111.4231) Observations 162 162 162 162 162 Panel B: Regional Refugees (×10,000) 0.0122 −0.0337 0.0002 25.0731 438.8429*** (0.0095) (0.0280) (0.0010) (18.8053) (132.5547) Observations 58 58 58 58 58

Source: Authors’ own analysis results from the data described in the text. Note: All models include a year-specific log distance control, province and year fixed effects. Robust standard errors are shown in parentheses. Dataarefrom 2010 and 2011 for 81 provinces in panel A and 29 provinces in panel B. The refugee variable is the number of refugees in the province in 10,000s. The first-stage F-test for the instrument in IV regressions is 12.44. For the regional sample, the F-test is 6.45. *p < .1 **p < .05 ***p < .01

DD Results While the IV methodology provides our preferred estimates, we also estimate the effects using the DD method by defining southeastern provinces of Turkey as the treatment group and the unaffected northeastern provinces as the control group. Since refugees begin moving out to the rest of Turkey in 2014, we define the years 2009 to 2011 as the pretreatment years and the years 2012 and 2013 as thetreatment years. While closely related, the IV and DD methods are not estimating the same effect. In 2012 and 2013, most refugees were in camps and are therefore less likely to be economically active. In addition, the DD estimate will be the effect of all border-related shocks caused by the refugee crisis, which includes the effect of camp construction, aid, and declines in trade with Syria. It might therefore be more correct to interpret the DD estimates as the effects of the Syrian Civil War across the border and the resulting refugee crisis, including refugee camps and associated funding, rather than just hosting refugees. Table 6 shows the results of the DD estimates. While we used the number of refugees as the treatment variable in the IV analysis, we define the treatment variable as either a continuous variable indicating the number of refugees or a binary variable indicating treatment status in the DD analysis. Panel A reports 32 Akgündüz, van den Berg, and Hassink

Ta b l e 6 . Difference-in-Differences Estimates

New firms New foreign Share of Gross profits Net sales firms foreign firms per capita per capita

Panel A: Binary Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 Treatment (1/0) 0.1025 0.4090 0.0266 94.4093 2166.0120 Robust SE (0.0673) (0.2122)* (0.0129)** (83.8947) (938.6023)** Clustered SE (0.1010) (0.3254) (0.0213) (111.3936) (1531.0429) Panel B: Continous Refugees (×10,000) 0.0273 0.1153 0.0046 38.6701 545.0321 Robust SE (0.0063)*** (0.0160)*** (0.0015)*** (20.9750)* (235.7229)** Clustered SE (0.0079)*** (0.0228)*** (0.0023)* (29.0360) (335.0298) Observations 145 116 116 116 116 Panel C: Placebo Refugees (×10,000) 0.0017 0.0191 0.0004 15.5967 273.6829 Robust SE (0.0056) (0.0138) (0.0008) (14.3532) (140.9966)* Clustered SE (0.0067) (0.0197) (0.0011) (20.4790) (201.1717) Observations 87 58 58 58 58

Source: Authors’ own analysis results from the data described in the text. Note: All models include province and year fixed effects. Robust standard errors are shown in parentheses. Data are from 2009 to 2013 for 29 provincesls inpane A and B; 2009 data are only available for the new firms variable. The placebo treatments in panel C are estimated by assuming that the number013 ofrefugeesin2 had arrived in 2011 and use data from 2009 to 2011 for 29 provinces. *p < .1 **p < .05 ***p < .01 the results of using a binary treatment variable for refugee presence, while panel B reports the coefficient estimates when we use the number of refugees in the southeastern provinces reported by UNHCR as the treatment variable. Since there are multiple years of data in the DD analysis, we report both robust and clustered standard errors to deal with potential serial correlation. However, it is worth noting that the number of clusters is low, at 29. The effect sizes estimated with the continuous treatment are similar in direction to the IV estimates but are slightly smaller and less statistically significant for gross profits and net sales. While the coefficients are positive, the binary treatment generally results in less precise estimates, which may be explained by the large difference in the number of refugees in provinces shown in table 3. In panel C, we perform a placebo test by assuming that the refugees in 2013 had arrived in 2011. As in the IV estimates, the common trend assumption seems to hold for all outcomes except for net sales, which has a weakly significant placebo estimate. The choice for the control group in the DD estimation is made to satisfy the common trend assumption by limiting the control group to eastern provinces. Nevertheless, the choice is arbitrary. In figs. 4 and 5, we present the results from the synthetic control method. We construct the synthetic control for the log of the number of new firms and the number of new firms with foreign owners and define thetreatment area as all 10 provinces that received refugees in 2012 and 2013. The difference is that the control group is composed now of all provinces in Turkey, including western provinces, which are weighted according to their pretreatment similarity to the treatment region. The findings appear to confirm the IV andDD estimates. The positive effect on the number of new firms with foreign owners is visible, as the gap between the synthetic and treatment group lines widens after 2012. The total number of new firms in the treatment region appears to be largely unaffected, though the gap between the treatment and synthetic control lines widens and closes slightly in 2012 and 2013, respectively. We find positive effects for gross profits and net sales, and the effect appears stronger for gross profits. In all outcomes, the positive effects are limited or absent in 2012. We might assume that the reason is that the regions near the border hosting the refugees were initially negatively affected by the civil war in Syria, but the effect turned positive in 2013 with increasing supply of unskilled labor and demand. A weaker effect in 2012 would also explain the The World Bank Economic Review 33

Figure 4. Synthetic Control Estimates for Firm Entry Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019

Source: Authors’ analysis based on the data and methodology described in text. Note: The numbers on the vertical axis refer to the log of the number of new firms and the number of new foreign firms. 34 Akgündüz, van den Berg, and Hassink

Figure 5. Synthetic Control Estimates for Sales and Profits Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019

Source: Authors’ analysis based on the data and methodology described in the text. Note: The numbers on the vertical axis refer to the gross profits and net sales per capita in Turkish Liras. The World Bank Economic Review 35 stronger positive effects we find in the IV model for sales and profits where the treatment year is2014 compared to the DD model, where the treatment years are 2012 and 2013.

Sectoral-Level Effects Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 The empirical results suggest that the number of new firms increases when refugees are hosted ina province, but they do not clarify the mechanism behind the increase. Furthermore, the increase in the number of new firms is not explained by new foreign firms in all estimations, which we might attributemore easily to entrepreneurial activity by the refugees themselves. The economic explanation would be that the increase in the supply of low-skilled labor increases the value of capital found by Del Carpio and Wagner (2015), which leads to more firms entering the market. We can informally test this hypothesis by checking which sectors were most influenced by new firm entries. If low-skill employment is the main driver ofnew firm entries, we would expect sectors that utilize low-skill employment to be most strongly affected. The data from the Chamber of Commerce and the Ministry of Science, Industry, and Technology are insufficient to determine which sectors are most affected by the inflow of new firms. We usedatafrom 2013–2014 reported by Turkish Statistics on the number of enterprises in each sector at the province level to show the relative change in business activity by sector in host provinces. We estimate IV regressions for each sector where 2013 is defined as the control year, where we assume the number of refugees tobe 0, and 2014 is set as the treatment year. Since we do not know the exact number of refugees in 2013 who were in each province but do know that they were present, the estimates will be biased downward by definition. Nevertheless, since the total number of refugees doubled between 2013 and 2014, the results may give an idea about the most affected sectors. The dependent variable in each regression is the log of the number of enterprises operating in a given sector. The results of the sectoral-level IV regressions are shown in table 7. Column 1 of the table shows the average number of enterprises per province in the specified sector in 2013. The IV estimates

Ta b l e 7. Sectoral-Level Changes Between 2013 and 2014

IV estimates

Average Coeff. SE

Agriculture 375.2 0.0181*** (0.0059) Mining 90.4 −0.0128* (0.0056) Manufacturing 5269.0 −0.0005 (0.0012) Electricity 115.4 0.0366 (0.0180) Construction 3208.8 0.0010 (0.0015) Retail 101.1 0.0010 (0.0006) Transportation 6927.3 0.0054** (0.0027) Hotel 3771.9 0.0038* (0.0021) Information 503.6 0.0072 (0.0050) Financial 320.5 −0.0095*** (0.0027) Real estate 634.6 0.0035 (0.0028) Professional 2381.4 −0.0001 (0.0006) Administrative 602.8 0.0019 (0.0021) Education 355.8 −0.0004 (0.0021) Health 528.9 −0.0144*** (0.0038) Culture 425.4 −0.0129*** (0.0050) Total 43574.6 0.0029*** (0.0011)

Source: Authors’ own analysis results from the data described in the text. Note: All models include province and year fixed effects and a year-specific control for distance to the closest border crossing with Syria. Data arefromthe years 2013 and 2014 for 81 provinces. The dependent variables are the log of the number of firms in the specified sector. Each OLS and IV coefficient is from aseparateregression with 162 observations. *p < .1 **p < .05 ***p < .01 36 Akgündüz, van den Berg, and Hassink presented in columns 2 and 3 suggest that provinces with refugees experienced an increase in the total number of enterprises between 2013 and 2014. At the sectoral level, the strongest increase is found in the agricultural sector. There also appear to be declines in the number of enterprises in the health, culture, financial, and mining sectors. With the exception of mining, the declining sectors appeartobe service-based sectors that require at least some skilled labor. The sectoral-level developments appear Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 to fit with the interpretation that there was an increase in unskilled labor due to the refugee inflows. However, the difficult-to-explain effects found for some sectors indicate that the estimates are likelyto be biased.

6. Conclusions Due to the unfortunate rise in the number of refugee crises, the effects on host economies are being studied from multiple angles. The Syrian refugee crisis in Turkey represents one of the larger refugee crises in recent decades, with more than a million displaced people crossing the border in just three years. The present study focused on how the refugees affected firm entry and business performance since hosting regions. The most robust effect we find is the large increase in the number of foreign firms in hosting regions.We also find a weaker effect on the total number of new firms in regions hosting refugees, which islikely to be driven by the new foreign firms. The results also suggest that businesses benefit in regions hosting refugees. The findings with regard to increases in gross profits and net sales reported by firms inregions hosting refugees support the hypothesis that business may benefit from refugees. In line with the finding that unskilled labor increased in regions with refugees, we also find some limited evidence showing that the increase in the number of firms is driven by sectors that are most likely to benefit from low-skill employment. Our findings on firm entry and performance complement the previous studies on the economic impact of Syrian refugees in Turkey. Taken together, the findings are quite in line with economic theory. Both Del Carpio and Wagner (2015) and Ceritoglu et al. (2017) find negative effects on the labor market outcomes of unskilled natives who have to compete with Syrian refugees. Del Carpio and Wagner (2015) further finds that men who have completed high school benefit from occupational upgrading, which may be due to the complementarity between skilled Turks and Syrian refugees. Our study adds that businesses appear to have benefited from hosting Syrian refugees who appear to have themselves opened firms in Turkey. There is a clear increase in the entry of new foreign firms that may bedriven by refugees’ enterpreneurship. Cheaper low-skill labor may have helped all businesses cut costs. Balkan and Tumen (2016) also find a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In the case of the Syrian refugee crisis in Turkey, the business activity in hosting regions appears to have benefited. For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health, and longer-term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, production, and investments to refugee inflows. The World Bank Economic Review 37

Appendix A1: Means of Outcome Variables over Time

Figure A1.1 Annual Firm Entry Data over Time. A1.1a: Number of firms opened. A1.1b: Number of firms with foreign capital Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019

Source: Turkish Chamber of Commerce. Note: The numbers on the vertical axis refer to the number of new firms and the number of new foreign firms. “Treatment,” “control,” and “rest” regions are shown in fig. 3. 38 Akgündüz, van den Berg, and Hassink

Figure A1.2 Annual Profit and Sales Data over Time. A1.2a: Net sales. A1.2b: Gross profits Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019

Source: Turkish Ministry of Science, Industry, and Technology. Note: The numbers on the vertical axis refer to gross profits and net sales per capita in Turkish Liras. “Treatment,” “control,” and “rest” regions are shown in fig. 3. The World Bank Economic Review 39

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Tumen, S. 2016. “The Economic Impact of Syrian Refugees on Host Countries: Quasi-Experimental Evidence from Turkey.” American Economic Review 106 (5): 456–60. UNHCR. 2014. “UNHCR Turkey Syrian Refugee Daily Sitreps Dec-2012 and Dec-2013.” United Nations High Com- missioner for Refugees, Geneva, Switzerland. World Bank. 2017. World Development Indicators: Private Sector in the Economy. Washington, DC: World Bank. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/19/4835084 by Joint Bank/Fund Library user on 08 August 2019 The World Bank Economic Review, 32(1), 2018, 41–63 doi: 10.1093/wber/lhw013 Article Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Inventor Diasporas and the Internationalization of Technology Ernest Migue´ lez

Abstract This paper documents the influence of diaspora networks of highly-skilled individuals—that is, inventors—on international technological collaborations. Using gravity models, it studies the determinants of the internationalization of inventive activity between a group of industrialized countries and a sample of developing and emerging economies. The paper examines the influence exerted by skilled diasporas in fostering cross-country co-inventorship as well as R&D offshoring. The study finds a strong and robust relationship between inventor diasporas and different forms of international co-patenting. However, the effect decreases with the level of for- mality of the interactions. Interestingly, some of the most successful diasporas recently documented—namely, Chinese and Indian ones—do not govern the results. JEL classification: C8, J61, O31, O33

International innovation networks are critical stepping stones for accessing frontier knowledge from the most industrialized economies, both in the form of formal information exchanges as well as through knowledge spillovers (Hall 2011). International spillovers are essential for developing countries to catch up with advanced economies, and this subject matter ranks high in the development policy agenda (WorldBank2010). However, geographical and cultural barriers seriously hinder the formation of innovation networks across countries, thereby remaining primarily a national phenomenon—contrary to other features such as trade or Foreign Direct Investment (FDI) (Patel and Pavitt 1991). Guellec and van Pottelsberghe de la Potterie (2001) report that only 4.7% of EPO patents and 6.2% of USPTO patents in 1995 have at least one foreign co-inventor. Picci (2010) estimates this figure to be around 8% for European patents in 2005. Cultural and language differences seriously undermine the internationalization of inventive

Ernest Migue´lez is a junior researcher in the CNRS, affiliated to the GREThA UMR CNRS 5113 research group, Universite´ de Bordeaux. He is also visiting fellow at the AQR-IREA, University of Barcelona, and research affiliate at CReAM, UCL. His email address is [email protected]. The research for this article was financed by the Regional Council of Aquitaine (Chaire d’Accueil en Economie de l’Innovation, to Francesco Lissoni) and the Regional Council of Aquitaine (PROXIMO project, to Christophe Carrincazeaux). The author thanks Michel Beine, Gaetan de Rassenfosse, Stuart Graham, William Kerr, Francesco Lissoni, Catalina Martinez, C¸aglar Ozgen, Hillel Rapoport, Massimo Riccaboni, Valerio Sterzi, seminar participants at GREThA-Bordeaux University (2013), GATE-LSE Saint Etienne (2014), Temporary Migration Cluster UC Davis (2015), the workshop “The Output of R&D and Innovative Activities: Harnessing the Power of Patent Data” (JRC-IPTS Seville, 2013), the 7th MEIDE conference (Santiago de Chile, 2013), the PSDM 2013 conference (Rio de Janeiro, 2013), the 54th ERSA conference (Saint Petersburg, Russia, 2014), and the 2nd Geography of Innovation Conference (Utrecht, The Netherlands, 2014) for valuable comments; Julio Raffo for helpful discussions on previous versions of this paper; as well as the contribution of three anonymous referees. A supplemental appendix to this article is available at https://academic.oup.com/wber.

VC The Author 2016. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please e-mail: [email protected]. 42 Migue´ lez activity. Other features inherent to geographical separation are also relevant, such as difficulties in screening potential partners, managing and administration of common projects across borders, and differences in legal frameworks and the rule of law—especially regarding the issue of intellectual property rights (Montobbio and Sterzi 2013; Foray 1995). This paper examines how high-skilled migration and the diasporic networks it creates may overcome Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 cross-country barriers and foster the internationalization of inventive activity. Diaspora networks have been largely studied in the context of trade and FDI. More recent evidence has looked at the international diffusion of ideas (Agrawal et al. 2011; Kerr 2008; Oettl and Agrawal 2008) and firms’ internationalization strategy (Foley and Kerr 2013; Saxenian et al. 2002). In parallel, numerous papers have investigated the internationalization of R&D activities (Guellec and van Pottelsberghe de la Potterie 2001; Patel and Vega 1999; Picci 2010). This paper builds on and extends these two streams of literature by looking at the role of migrant inventors in fostering the internationalization of innovation activities. Specifically, the paper looks at inventor diasporas and transnational inventive activities between developed and developing countries, which is gaining momentum from a development perspective (Clemens et al. 2014; Montobbio and Sterzi 2013). It also aims to see whether differences emerge across the type of technological internationalization—co-inventorship vs. R&D offshoring. Finally, the extent to which countries’ characteristics govern these potential relations is also investigated—that is, whether the least similar countries, for which informal barriers are more acute, have the greatest potential to benefit from diaspora networks. The study extends the existing literature in a critical way. Contrary to most migration research, which retrieves migration data from decennial censuses, it exploits inventors’ information listed in patent applications. Inventors make up a specific class of workers at the upper end of the skills distribution.1 Differently from former approaches to inventor migration that use ethnic name recognition algorithms to infer their migratory background (Agrawal et al. 2011; Breschi et al. 2014; Breschi et al 2015; Kerr 2008), direct nationality information of the inventors is used, allowing for the introduction of a richer range of origin and destination countries. Admittedly, the use of nationality data does not come without its limitations, the most important being the loss of all naturalized inventors who have acquired their host country citizenship. Appendix S3 (in the supplemental material, available at https://academic.oup. com/wber) compares nationality data with ethnic name recognition algorithms (see Breschi et al. 2015), which has the advantage of taking into account all the naturalized immigrants who have acquired their host country nationality. Although some differences emerge—large coefficients when name recognition algorithms are used—the main conclusions of this paper remain unaltered. To anticipate the results to come, I find a robust effect of highly-skilled diasporas on the internationalization of inventive activity between developed, receiving countries and developing, sending economies: a 10% increase in the inventor diaspora abroad is associated with a 2.0–2.2% increase in international patent collaborations. The evidence found survives the inclusion of a large number of controls, fixed-effects (FE), robustness checks, and identification issues. Moreover, the effect is stronger for inventor-to-inventor collaborations—co-inventorship—than for applicant-to-inventor co-patents— R&D offshoring—suggesting that diaspora effects specifically mediate interpersonal relations between co-workers.2

1 In this respect, the PatVal survey shows that 76.9% of European inventors underwent tertiary education (26% with PhD) (Giuri et al. 2007). Similarly, the RIETI-Georgia Tech inventor survey shows that 94% of US inventors have college degrees (46% with PhD), with 88% of the cases for Japanese inventors (13% with PhD) (Walsh and Nagaoka 2009). 2 This paper uses the term “applicant” to describe the owner of the patent, unless otherwise stated, which is normally a firm or research institution for which the inventors usually work. I am aware that in some patent jurisdictions the owner is termed “assignee,” although this term is not used here. The World Bank Economic Review 43

The outline of the paper is as follows: Section I reviews previous theoretical and empirical contributions on the relationship between migration and other international economic interactions. Section II presents the novel dataset on inventor migration flows and develops the methodological setting, including all the econometric concerns. Section III presents the results, and section V concludes. The reader may also find the appendix useful, giving more details on the novel data used and robustness results Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 (available as supplemental electronic material at https://academic.oup.com/wber).

I. Related Literature and Theoretical Background Much of the public debate and academic research on the role of skilled diasporas for development tries to answer a question fraught with political and economic significance: how will growing diasporas—and in particular skilled diasporas—contribute to their home country development? In the migration literature, diasporas have been defined as “part of a people, dispersed in one or more countries other than its homeland, that maintains a feeling of transnational community among a people and its homeland” (Chander 2001). Potential benefits can be realized exploiting this feeling to the advantage of the home countries, through the individuals’ embedded knowledge as well as through their accessible resources—such as capital or the expatriates’ network of colleagues and acquaintances. Traditionally linked to diasporas’ role on financial remittances and capital formation, the migration and development literature has also moved to investigating their role in favoring other economic transactions, such as trade (Gould 1994), FDI (Javorcik et al. 2011; Kugler and Rapoport 2007), international diffusion of ideas (Agrawal et al. 2011; Kerr 2008), and firms’ internationalization strategies (Saxenian et al. 2002; Foley and Kerr 2013). Diasporas affect their home countries both directly and indirectly (Kapur and McHale 2005). The direct effect is linked to the diaspora members’ willingness to interact individually with their home countries, in the form of remittances, investments, or sharing ideas and information. This eventually includes the role of returnees’ direct contribution. Highly-skilled migrants may decide to move back or set up entrepreneurial activities in their homelands, while keeping in touch with the destination countries (Wadhwa, Rissing, et al. 2007; Wadhwa, Saxenian, et al. 2007). The indirect effects refer to the role of diaspora members in leveraging their home countries’ reputation in international business networks; facilitating searching and matching between partners, customers-suppliers or in the labor market; and in ensuring the contract fulfillments of the two parties involved (Kapur and McHale 2005b34). Because of their familiarity with local market needs, diasporas provide information about business opportunities in their home countries, and thus are critical in providing access to relevant information otherwise inaccessible because of cultural, language, institutional, administrative, or geographical barriers. Thus, migrant networks lower transaction costs associated with problems of incomplete information. This is particularly the case when informational difficulties are large, when involving countries with very different social and cultural backgrounds. They also lower the transaction costs associated with the existence of asymmetric information. As Rauch (2003; 2001) posits, social networks operating across national borders build up or substitute for trust when contract enforcement is weak or nonexistent. Indeed, diasporas create trust by establishing a kind of “moral community,” which is used to transmit information about past opportunistic behavior in international business relations. The enforcement mechanism is particularly important in the absence of effective protection of contractual/property rights and should therefore be critical in the relationships of developed-developing countries. In the trade context, Gould (1994) finds that the stock of migrants in the United States (US) from 47 US trading partners increases US trade with these countries—a pioneering contribution to the topic of migrant networks and trade is due to Greif (1989). This is confirmed by Rauch and Trindade (2002) and Head and Ries (1998), who find that a 10% increase in the number of immigrants increases exports by one percent and imports by three percent (see also Aleksynska and Peri 2014; Felbermayr and Toubal 44 Migue´ lez

2010, 2012). Similar conclusions emerge in the case of FDI. Javorcik et al. (2011) investigate the link between the presence of migrants in the United States and US FDI to the migrants’ countries of origin. They find that US FDI to sending countries is positively correlated with the diaspora of that country in the United States—especially migrants with college degree qualifications (see also Kugler and Rapoport 2007). Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Less evidence is available on diaspora externalities and international technology cooperation. Saxenian (1999) argues that skilled immigrants in the United States are playing a growing role in linking domestic technology businesses to their countries of origin. Her study on Chinese and Indian immigrant engineers in Silicon Valley shows that these immigrants are uniquely placed to locate foreign partners quickly and manage complex business networks across cultural, institutional, and linguistic boundaries, which is especially relevant in high-tech industries. Systematic evidence for the case of migrant scientists is provided by Scellato et al. (2012) for a group of surveyed scientists from 16 countries. Their study finds that around 40% of foreign-born researchers in these countries maintain research links with their homeland colleagues. For the specific case of inventors, Agrawal et al. (2011) study knowledge flows between India and the Indian diaspora in the United States, identified through inventors of USPTO patents. Kerr (2008) extends this analysis to nine foreign ethnicities in the United States. By means of citation analysis, he confirms that knowledge diffuses internationally through ethnic networks—especially with regards to the Chinese diaspora, which also has sizeable effects on home country output. Foley and Kerr (2013) find significant effects of US firms’ ethnic inventors in promoting linkages between these firms and their R&D staff home countries, in the form of knowledge flows or R&D alliances. As these authors argue, ethnic inventors in host countries are particularly apposite for helping firms to capitalize on foreign opportunities and overcome barriers to the internationalization of inventive activity. Ethnic inventors usually have the expertise essential for developing products crucial for that particular ethnicity, giving privileged access to foreign markets and business opportunities. Obviously, they possess the language skills and cultural sensitivity necessary to promote international collaborations in their host countries, while at the same time knowing how to conduct business with their homeland colleagues. They also belong to those networks that foster trust and convey information about past opportunistic behavior across national boundaries. However, this latter evidence might not be conclusive, in the sense that it is limited to the largest receiving country, the United States, and its top providers of foreign scientists and engineers (Breschi et al. 2014). This leads some authors to argue that highly-skilled emigrants may not systematically engage in business networks and knowledge transfers with their homelands but rather that the Indian and Chinese diasporas are so famous in being the exception rather than the rule (Gibson and McKenzie 2012). The present paper sheds some light into this issue too.

II. Research Methods Empirical Approach The gravity model to be estimated takes the following form:

b0 b1 cn si sj dt COPATijt ¼ e DIASPORAijt Zijt e e e eijt (1) where COPATijt stands for the number of collaborations between i’s developing country (out of 67) 3 and j’s developed country (out of 20) for year t. b1 is the parameter of interest in this work, while DIASPORAijt is the focal variable and is computed as the number of inventor nationals of country

3 Appendix S1 lists the countries used in this study. Note that some high income countries are included among the list of developing economies (e.g., Luxemburg). Removing them from this group does not alter the results. The World Bank Economic Review 45

i residing in country j for annually repeated 5-year time-windows. Zijt is a set of bilateral and attribute control variables, and si, sj, and dt are, respectively, developing, developed, and time FE. eijt denotes the error term. Log-linearizing equation (1) and using OLS techniques would be a straightforward estimation method. However, cross-country co-patents are rare phenomena, which translate into a dependent varia- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 ble with a very large proportion of zeros, making the logarithmic transformation of these observations impossible. Dropping these zero observations or adding an arbitrary constant to enable logarithmic transformation would be clearly misleading (Burger et al. 2009). In addition, Santos Silva and Tenreyro

(2006) show that log-linearizing equation (1) requires eijt, and therefore lneijt, to be statistically independent of the regressors, otherwise the condition for consistency of OLS would be violated. As the authors show, there is “overwhelming evidence that the error terms in the usual log linear specification are heteroskedastic” (Op. Cit., 642), making the expected value of the error term depend on one or more explanatory variables, leading to inconsistent OLS estimates. Note importantly that this kind of heteroscedasticity cannot be corrected using standard techniques, such as applying a robust covariance estimator, since it does not only affect the estimation of the standard errors, but also the coefficients. Because of the presence of this heteroscedasticity in the original nonlinear gravity specification, the authors suggest estimating the multiplicative form of the model using nonlinear models, such as the Poisson pseudo-maximum likelihood (PPML), which also provides a natural way of dealing with zero co-patenting and the extreme skewness of the dependent variable, intrinsically heteroscedastic with variance increasing with the mean (Cameron and Trivedi 1998).4 Thus, equation (1) is estimated by means of PPML using the fact that the conditional expectation of COPATijt in (1) can be written as the following exponential function:

EðCOPATijtjXijtÞ¼exp½b0 þ b1lnDIASPORAijt þ cnlnZijt þ si þ sj þ dtþeijt (2)

Data

Inventors’ International Migration A large part of the migration literature surveyed above has made use of census-based migration datasets becoming available during the last 15 years, broken down by skills—primary, secondary, and tertiary level of education (for a recent data contribution, see Ozden€ et al. 2011).In contrast, the present analysis is based on a dataset of inventors with migratory backgrounds applying for PCT patent applications between 1990 and 2010. The use of inventor data for migration analysis comes with two main advantages. First, patent data (together with inventor information) are registered and so can be organized on a yearly basis—contrary to census data, which are collected only every 10 years. Second, the level of education attained may still differ markedly among tertiary educated workers. Tertiary education can include nonuniversity tertiary degrees, undergraduate university degrees, and postgraduate and doctor- ate degrees, which may not be fully comparable across different countries. Inventors on the other hand constitute a specific class of highly-skilled workers that is more homogeneous than the tertiary-educated workers as a whole. They are behind the production of new knowledge and innovation that encourage economic growth and well-being. The seminal contribution using inventor data for migration research is due to Kerr (2007) and his successive papers, which analyze immigrants’ contribution to US invention. Kerr takes inventors’ names from USPTO applications and assigns them an ethnic affiliation using a commercial repository of names

4 Moreover, Santos Silva and Tenreyro (2010, 2011) use simulation techniques to show that the PPML estimator is generally well-behaved in the presence of a large proportion of zeros. As a robustness check, appendix S8 replicates the main estimations using alternative count data methods for zero-inflated models. No important differences with respect to the main results and conclusions emerge. 46 Migue´ lez and surnames of US residents classified by likely country of origin5—for a more recent contribution along the same lines, see Breschi et al. (2014, 2015). Albeit extremely valuable, Kerr’s contribution comes with some limitations: first, it is focused entirely on US immigration, while migration is a multi- faceted phenomenon including numerous receiving countries. Second, it comprises a limited number of countries of origin—though it suffices for his analysis on skilled immigration in the United States. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Finally, ethnic methods cannot distinguish between first and second generation immigrants—for example, African immigrants in Europe—or across countries belonging to the same linguistic group—for example, inventors from Australia, Canada, the United Kingdom, and the United States.6 Recently, the World Intellectual Property Organization (WIPO) released a new dataset on inventors of PCT applications containing not only their current country of residence but also their nationality, representing a promising data source for migration research (Miguelez and Fink 2013). The PCT is an international treaty administered by WIPO offering an advantageous route for seeking for patent protection in more than one jurisdiction (in its 148 contracting states). In general, patent rights only apply in the jurisdiction granting them, be it national (e.g., USPTO) or regional (e.g., the Africa Regional Intellectual Property Organization). To seek for patent protection in multiple countries, applicants need to apply for patents in multiple offices. One simplifying route for doing this is offered by the PCT treaty. In short, by choosing the PCT route, applicants gain additional time—typically 18 months—to decide whether to pursue patent protection internationally and, if so, in which jurisdictions. An international patent right, as such, does not exist, so the applicants still have to apply for patent protection in all countries in which they eventually seek protection. However, the additional time gained by choosing the PCT route is valuable for applicants, as witnessed by the increasing share of international patent protection going through this system (about 54% of multi-jurisdictional applications in 2010). For the purpose of migration analysis, inventor information retrieved from PCT applications presents several advantages, such as being associated with the most valuable inventions (van Zeebroeck and van Pottelsberghe de la Potterie 2011) or the fact that the system applies a set of procedural rules common to all participant countries, hence eliminating the “home bias effect” introduced when working with patent data from one single office. Appendix S2 enumerates the advantages of using PCT data for economic analysis, discusses how countries make use of this system, and details the extent of overlap with other patent data sources, such as the USPTO and the EPO. More importantly for the sake of the present analysis, PCT patent applications are the only ones recording the nationality of the inventors. The reason for that is as follows: because not all countries are PCT contracting states, only national or resident applicants of a PCT contracting state can file PCT applications. In order to verify that applicants meet at least one of the two eligibility criteria, the PCT application form asks for both nationality and residence. In parallel to this, US laws bind the applicant also to be the inventor; US laws also request the applicant to be an individual, not a firm. Thus, if a given PCT application includes the United States as a country in which the applicant has considered pursuing a patent—a so-called designated state in the application—all inventors are listed as “applicants/ inventors,” and their residence and nationality information are, in principle, available. All in all, between 1990 and 2010, the share of inventors’ records for which we can retrieve nationality and residence information is pretty high, around 80% of the cases.7 Admittedly, this coverage is unevenly distributed over time—around 60–70% during the 1990s and 70–95% during the 2000s—as well as across countries—the United States (66%), Canada (81%), the Netherlands (74%), Germany

5 In particular, nine broad ethnic affiliations are identified: English, European, Russian, Hispanic-Filipino, Chinese, Indian, Japanese, Korean, and Vietnamese. 6 See appendix S3 for a discussion of advantages and disadvantages of name recognition systems to identify the migratory background of inventors, alongside a number of robustness checks carried out using this alternative approach. 7 The use of the word “record” here signifies the unique combination of “inventor name” and “application number.” The World Bank Economic Review 47

(95%), the United Kingdom (92%), France (94%), Switzerland (93%), China (92%), and India (90%), among others.8 Patent data do not provide unique identifiers for inventors appearing in more than one application. Although this is a drawback, I follow Kerr (2008) and treat each record as if it were a different individual, and compute the migration variables aggregating by country pairs and moving time-windows of five years.9 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Out of all records with complete information, about 5 million, around 9–10% have a migratory background, that is, residence different from their nationality. Fig. 1 depicts the evolution of the share of inventors with migratory background (solid line), alongside the same figures broken down by a number of selected receiving countries/areas. As can be observed, the share of worldwide migrant inventors has steadily increased over time.10 Among the most receiving countries of the world, Canada, Australia, and, notably, the United States, stand out as being the primary receiving countries, when compared to their resident stock of inventors. On the other hand, Japan is, and has been over the years, one of the developed countries with a smaller share of inventor immigrant population.

Figure 1. Share of Immigrant Inventors over Time, 1990–2010, by Selected Countries

Source: Authors’ analysis based on Migue´ lez and Fink (2013).

Meanwhile, technology-leading European countries, such as Germany or France, lag way behind compared to the United States (fig. 2). The exceptional performance of the United States in attracting

8 Compared to other highly patenting countries, the United States, Canada, and the Netherlands present relatively low coverage rates. This is because numerous applications with inventors from these three countries were applied to the USPTO before being extended internationally through the PCT system. In consequence, the United States was not mentioned in the applications as designated state and the inventors were only inventors and not applicants at the same time, which exonerates them from providing nationality information. In principle, there is no reason to believe that this less than complete coverage in these countries may bias the analysis one way or another. However, in order to address this inconsistent coverage of migration information, I repeated the analysis splitting the sample into shorter time windows and excluding some countries. No important differences arise regarding the main conclusions of the study. 9 The priority date of applications is used to allocate individuals in time. By “priority date” I mean the first year the patent was applied worldwide. 10 In order to make these figures comparable, it is worth looking at differences with other migration datasets. While 8.62% of inventors of PCT patents have a migratory background in 2000, data compiled by Beine et al. (2007) show that general migration rates in 2000 for populations aged 25 years and over were estimated around 1.8%, including 1.1% of immigrants among the unskilled population, 1.8% among populations with secondary education, and 5.4% among populations with tertiary education. 48 Migue´ lez

Figure 2. Immigration Rates of Iventors, 2001–2010, Receiving Countries Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019

Source: Authors’ analysis based on Migue´ lez and Fink (2013). talent is even more notable when considering only immigrant inventors coming from low- and middle- income economies (see also fig. 2). Appendix S4 shows the top 20 most populated inventor migration corridors, where again, the United States stands out as the most typical choice for destination country.

Dependent Variable International co-patent data are retrieved from PCT applications (WIPO IPSTATS databases). The first focus is on co-patenting at inventor level—co-inventorship. All the co-inventions between inventors residing in country i and inventors residing in country j are added up by year. To be precise, it includes 67 developing/emerging/transition countries, on the one hand, that co-invent with 20 developed countries, on the other, where diasporas from the former countries reside. If inventors from more than two countries participate in the patent, an international co-inventor for each country-pair is counted, irrespective of the total number of countries involved in that particular invention.11 Next, I measure R&D offshoring using patent applications in which at least one applicant is a resident from country j (developed) and simultaneously at least one inventor is a resident from country i (developing/emerging/transition) —similar R&D offshoring measures in the context of internationalization of inventive activity are used in Guellec and van Pottelsberghe de la Potterie (2001), Harhoff et al. (2013), and Thomson et al. (2013). Again, when inventors come from various countries, a single co- patent for each bilateral i-j pair is computed. It is worth mentioning that previous studies on the determinants of international co-patenting use information from single patent offices only, with few exceptions (Martınez and Rama 2012; Picci 2010). This practice is likely to deliver biased estimates due to the “home bias effect,” which may emerge when using patent data from one single office for cross-country analysis. Since patents at the USPTO, EPO, or

11 Results with alternative ways of computing the dependent variable, where the number of inventors per patent is taken into account, are presented in appendix S5, with no remarkable differences with respect to the results presented in the main text. In contrast, the ranking of inventors listed in patents is not taken on board, due to the difficulties of determining whether the ordering of names bears any similarity with sorting of authors in scientific publications. However, understanding the relationship between immigrant status and ranking in a patent is an interesting avenue of research, which goes beyond the scope of the present paper. The World Bank Economic Review 49

JPO, for instance, protect innovation within their respective geographical areas, they are preferred by domestic firms and thus their innovative capability is overestimated with respect to foreign firms. Using data from the PCT mitigates this effect.12 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Control Variables Control variables include geographical, linguistic, cultural, and historical barriers to cross-country collaborations. In particular, the great circle distance between the most populated cities of countries (measured in km) is included, as well as a dummy variable indicating whether two countries share a common border, a dummy variable valued 1 if the same language is spoken in both countries, and a dummy variable valued 1 when the two countries share the same colonial past—these variables come from the CEPII distance database (Mayer and Zignago 2011). Two additional variables, suggested by Melitz and Toubal (2014), help in controlling for cultural similarities between country pairs: first, an index of language similarity. People whose languages share common roots will likely share similar cultural backgrounds. To compute this index, one single language is assigned to every country, and using information on the classification of languages provided by the Ethnologue Project, a language similarity index based on the distance between branches in this classification is computed.13 Then the number of branches that coincide between each pair of languages are added together and the result is divided by the sum of branches of each of the two languages (in order to take into account the fact that the granularity of branches may not be the same across languages). As a result, an index between 0 and 1 is obtained, where 0 means complete dissimilarity and 1 means that these two languages are almost the same in linguistic terms.14 Second, the religious heritage of countries is a critical element of their culture and identity (Guiso et al 2009). Countries with similar religious roots, culturally closer, are likely to interact more (for an application in the trade literature, see, among others, Melitz and Toubal 2014). Religion similarity is proxied with an index built as follows: for each country, data on the percentage of population adher- ing to one of eight major religions is retrieved (data from the CIA World Factbook dataset)—these eight religion groups differ slightly from Melitz and Toubal (2014). Then the following formula for each country pair is computed, which results in a variable ranging from 0 (no believers in common) to 1:

Religion Sim:ij ¼ð%muslimi %muslimjÞþð%catholici %catholicjÞ

þð%orthodoxi %orthodoxjÞþð%protestanti %protestantjÞ (3) þð%hinduismi %hinduismjÞþð%buddhisti %buddhistjÞ

þð%easterni %easternjÞþð%judaismi %judaismjÞ I also control for the intensity of economic linkages between countries using the share of bilateral trade (exports plus imports, EXPþIMP) between a given pair over their total trade (COMTRADE data).15 Trade is a conduit of information that may foster technological partnerships too, while it might

12 Other biases inherent to the existence of multiple jurisdictions and patent offices are discussed in de Rassenfosse et al. (2014)—such as the nonrandom choice of patent office. Again, the use of PCT applications should mitigate these biases. 13 For example, the linguistic classification of Portuguese, Swedish, and Danish, from the largest, most inclusive grouping to the smallest, is: Indo-European, Italic/Romance, Italo-Western, Western, Gallo-Iberian, Ibero-Romance, West Iberian, Portuguese-Galician (Portuguese); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Swedish (Swedish); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Danish-Riksmal, Danish (Danish). www.ethnologue.com, accessed May 20, 2014. 14 I arbitrarily set to 0 this variable when the countries share exactly the same language, in order to avoid collinearity with the variable “same language.” 15 Changing this variable to trade in absolute numbers does not alter the main results and conclusions, though it makes the variable EXPþIMP non-significant—results provided upon request from the author. 50 Migue´ lez be linked to the presence of migrants at the same time. I also account for the common technological specialization of country-pairs introducing an index of technological distance measured as P fihfjh Tech:distanceij ¼ 1 P P ; (4) 2 2 1=2 ð fih fjhÞ Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 where fih stands for the share of patents of one technological class h according to the IPC classification of country i and fjh for the share of patents of one technological class h of country j. Values of the index close to the unity indicate that a given pair of countries are technologically different, and values close to zero indicate that they are technologically similar (Jaffe 1986). Again, PCT patents are used to compute this index. Finally, two additional attribute variables of individual countries are used. In particular, the number of PCT patents per country, for five-year annually repeated time-windows. This variable controls for the size of country innovation systems, which clearly determines a country’s capacity to collaborate with foreigners, as well as its capacity to attract inventors from abroad or send them to other locations. In addition, it includes GDP per capita, from the World Development Indicators (World Bank), expressed in US$ 2005 at PPP, in order to capture the market potential of countries as well as their capacity to innovate. Table 1 contains summary statistics of the variables included in the models, and appendix S6 the correlation matrix.

Table 1. Summary Statistics

Observations Mean St. Dev Min. Max.

Collab. inv_i- inv_j 26,160 1.26 10.55 0 678 Collab. app_i- inv_j 26,160 1.73 14.87 0 708 Diaspora size 26,160 22.29 447.18 0 26,661 Distance 26,160 6,889.27 4,539.38 59.62 19,629.50 Contiguity 26,160 0.01 0.10 0 1 Common language 26,160 0.09 0.29 0 1 Lang. similarity 26,160 0.16 0.18 0 0.89 Colonial links 26,160 0.04 0.18 0 1 Religion similarity 26,160 0.13 0.19 0 0.90 EXPþIMP 26,160 0.01 0.03 0 0.41 Tech.distance 26,160 0.48 0.27 0.02 1 # patents_i 26,160 816.86 3,501.12 0 64,990 # patents_j 26,160 43,314.25 99,659.75 52 692,364 GDP p.c._i 26,160 9,258.23 9,566.77 432.05 74,113.90 GDP p.c._j 26,160 29,423.23 6,171.54 11,382.60 48,799.70

Notes: “_i” and “_j” stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYRof Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia and Eritrea only from 1993. In consequence, the ﬁnal sample consists of 26,160 observations, instead of 26,800. Source: Authors’ analysis based on data described in the text.

Note that all time-variant explanatory variables are lagged one period in order to lessen potential biases caused by system feedbacks. Notwithstanding this common practice, other sources of endogeneity and biased estimates are likely to arise. Hence, I discuss alternative solutions in the results section.

III. Results Baseline Estimations Table 2 presents the results of baseline PPML estimations, with robust, country-pair clustered standard errors. Column (1) regresses international co-inventorship against the focal variable, plus The World Bank Economic Review 51

Table 2. Baseline Speciﬁcations without and with Time-Varying Multilateral Resistance

(1) (2) (3) (4) PPML PPML PPML PPML Co-inventorship R&D offshoring Co-inventorship R&D offshoring Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 ln(Diaspora) 0.200*** 0.0929** 0.243*** 0.111*** (0.0229) (0.0407) (0.0233) (0.0423) ln(Distance) 0.267*** 0.105 0.0468 0.173** (0.0523) (0.0721) (0.0552) (0.0760) Contiguity 0.210* 0.0996 0.116 0.271 (0.127) (0.211) (0.122) (0.191) Common language 0.625*** 0.931*** 0.434*** 0.703*** (0.132) (0.220) (0.108) (0.187) Lang. similarity 0.523** 0.822** 0.405** 0.694** (0.225) (0.386) (0.202) (0.352) Colonial links 0.0654 0.299* 0.0315 0.291** (0.124) (0.172) (0.103) (0.143) Religion similarity 0.706*** 0.219 0.612*** 0.185 (0.256) (0.446) (0.235) (0.419) ln(EXPþIMP) 0.0457** 0.0710*** 0.228*** 0.329*** (0.0197) (0.0249) (0.0335) (0.0495) ln(Tech.distance) 0.0637 0.245*** 0.136*** 0.287*** (0.0469) (0.0598) (0.0506) (0.0689) ln(# patents_i) 0.334*** 0.354*** (0.0530) (0.0693) ln(# patents_j) 0.0568 0.333 (0.115) (0.205) ln(GDP p.c. _i) 1.098*** 1.807*** (0.233) (0.318) ln(GDP p.c. _j) 0.0776 1.107 (0.552) (0.823) Constant 6.072 4.780 0.410 1.726** (6.006) (8.651) (0.652) (0.791) Observations 26,160 26,160 19,676 20,574 Pseudo R2 0.958 0.918 0.978 0.956 Sending FE Yes Yes No No Receiving FE Yes Yes No No Year FE Yes Yes No No Sending FE*Time FE No No Yes Yes Receiving FE*Time FE No No Yes Yes Log Lik 18492.28 23746.92 16226.61 20463.96

Notes: Country-pair clustered robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. The lower number of observations between columns (1), (2) and (3), (4) is due to the inclusion of fixed effects in pseudo-maximum likelihood estimations: the PPML method automatically drops the country-specific fixed-effects (and their corresponding observations) for which the country has zero recorded inventors’ flows to every other country in the sample in order to achieve convergence. Results are comparable to other count data methods without removing these observations (see Santos Silva and Tenreyro 2010 for further details). Source: Authors’ analysis based on data described in the text. 52 Migue´ lez individual-country and time FE, as well as the list of controls.16 The effect of the variable is positive and statistically significant. In particular, column (1) shows an elasticity of 0.20. That is to say, a 10% increase in the size of the inventor diaspora abroad is associated with a two-percent increase in international patent collaborations, which is also economically meaningful. This result is of the same order of magnitude as estimates for the case of diasporas and trade (Felbermayr and Toubal 2010, 2012; Head Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 and Ries 1998; Rauch and Trindade 2002, although slightly larger than in Aubry et al. 2014) and diasporas and FDI (Aubry et al. 2014). The results for the remaining explanatory variables are interesting in themselves. As expected, physical distance between the most populated cities exerts a negative influence on the likelihood for cooperation across national boundaries, although sharing a common border barely affects co-inventorship. Common language has a strong positive estimated effect on collaborations between inventors of different countries. Other proxies for cultural similarity, such as language and religion proximity, exert a strong positive effect too. However, historical links between country pairs expressed by their colonial past are not significant. As expected, bilateral trade is positive and significant, while technological distance between countries, that is, how distant countries are in their technological specialization, exerts a negative influence on bilateral co-patents. Finally, both attribute variables—total number of patents and GDP per capita—are significant for the case of origin countries but not for destinations. Thus, it appears that differences across industrialized economies in terms of technological and economic development are relatively minor and are picked up by their country FE. Column (2) looks at R&D offshoring—co-patents between applicants in developed countries and inventors in developing economies. Comparing the estimates with those of column (1), interesting results emerge. First and foremost, the estimated elasticity of inventor diaspora size is notably reduced in these latter estimations—less than a half. That is, diaspora networks particularly mediate interpersonal relations between co-workers. Meanwhile, they have a more nuanced effect on transnational employer- employee linkages. Second, geography per se does not play a significant role in explaining R&D offshoring. The diaspora and geography results put together seem to suggest that personal face-to-face relations and trust building are critical in explaining co-inventorship—where contracts are usually more tacit and contract enforcement is difficult—but less important in explaining more formal and hierarchical relationships, such as those represented by offshoring relationships, where probably explicit, written contracts are the rule. Other remarkable differences are worth reporting. For instance, the coefficient associated with a colonial past increases its point estimate and now becomes significant. That is, historical ties between the former metropolis and its formal colonies seem to have left an enduring effect over time that, still today, influences innovation networks across national borders. Finally, the common specialization of countries seems to play a greater role too when looking at applicant-to-inventor co-patents, as compared to inventor-to-inventor collaborations. Furthermore, these specifications (columns [1] and [2]) are used to include the focal variable interacted with time dummies, in order to explore the effects of skilled diasporas over time. Figs. 3 and 4 present the estimated coefficients of the interaction variables, and show quite interesting, although expected, results. As can be seen, diaspora effects over time on co-inventorship and R&D offshoring present a marked decreasing trend. Quite likely, the use of information and communication technologies

16 In unreported results it is included bilateral (country-country) fixed-effects—results provided from the authors upon request. In a nutshell, in those estimations all time-variant variables dramatically decrease their coefficients, becoming statistically nonsignificant. However, regressions with only country FE and time FE are preferred because: (1) including bilateral FE removes 35% of the observations of the original sample; and (2) both diaspora networks as well as international co-patenting move slowly over time, which makes it difficult to identify any effect coming from within- pair variation—therefore identification relies on cross-country-pair differences only. The World Bank Economic Review 53

Figure 3. Yearly Estimated Coefﬁcients of Diaspora on Co-inventorship, 1991–2010 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019

Source: Authors’ analysis based on data described in the text.

Figure 4. Yearly Estimated Coefﬁcients of Diaspora on R&D Offshoring, 1991–2010

Source: Authors’ analysis based on data described in the text.

(ICT) has made the utilization of international ethnic ties less relevant now than 20 years ago. Similarly, the economic and institutional development of emerging economies has also contributed to mitigate the role of skilled diasporas in reducing the costs of asymmetric information—for example, think of the role of intellectual property in these emerging countries in the aftermath of their subscription to TRIPs, the Trade Related Intellectual Property Agreements that comes with membership of the World Trade Organization (WTO). Columns (3) and (4) of table 2 mimic estimations (1) and (2) but control for time-variant multilateral resistance. While country FE controls for average multilateral resistance to collaborating over time (Feenstra 2004), some elements of this multilateral resistance are likely to be time-variant and may not be picked up by the attribute variables included (Adam and Cobham 2007).17 Consequently, these columns include country-specific time dummies, and repeat the main estimations, focusing attention only on bilateral variables. Some nameable differences with respect to the baseline regressions emerge, like a reduced role of distance in explaining inventor-to-inventor collaborations. However, the focal variable

17 For an application to the migration literature, see Bertoli and Fernandez-Huertas Moraga 2013. 54 Migue´ lez remains positive and strongly significant, and it presents coefficients that are slightly larger than previously.

Identification: Cultural Proximity and Instrumental Variables Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 Table 3 adds interaction terms between the inventor diaspora variable and different dimensions of cultural proximity between countries—common language, language similarity, common colonial past, and religion similarity. Given that transnational migrant networks mitigate the costs of incomplete information beyond country boundaries, one would expect their impact to be stronger for country pairs exhibit- ing larger informational frictions. Hence, negative and significant interaction terms will provide evidence on the least similar countries relying more on diaspora externalities than pairs of countries that are culturally closer. Results (table 3) partially confirm this extreme: the interaction with colonial ties is negative and significant—also the interaction with language similarity in the last specification.18 As in Kugler et al. (2013), I interpret these negative coefficients as evidence of a causal link between inventor diasporas and international co-inventive activity. If unobserved confounding factors drive both migration and co-patenting at the same time, they should work in such a way that they are capable of explaining not only the main results—the diaspora-co-patenting relation—but also the differentiated effect of diaspora networks across different cultural dimensions, which is unlikely. More importantly, instrumental variable estimates are also provided. Potential and available candidates for such a role are (i) the size of the bilateral diaspora between countries i and j in 1960 (data from Ozden€ et al. 2011) and its square, and (ii) the size of the unskilled diaspora (migrants with only primary education) originally from country i residing in country j in 1990 (data from Docquier et al. 2009) and its square. First, the stocks of migrants by country of origin in the 1960 censuses (therefore immigrants arrived between the end of the Second World War and 1960) are likely to affect the current stocks of highly-skilled migrants through network effects favoring further migration flows over the long run. Note that these figures include foreign-born people counts in dates closer to the age of mass migration than to the technological revolution of the 1990s and the 2000s. Quite probably, they are uncorrelated with current levels of cross-country collaborations, apart from influence through current skilled diasporas. Similarly, the current stocks of migrants with primary or lower levels of education correlate with current stocks of highly-skilled diasporas. The relation between existing diasporas and existing migration flows not only operates at a labor market level, but also among ethnic communities operating across different skills groups. Large stocks of unskilled immigrants in a given country will mean the existence of attractive factors—for example, amenities—which are also attractive to highly-skilled immigrants (Hunt and Gauthier-Loiselle 2008). On the other hand, uneducated migrants should play a non-existent role in boosting co-inventorship or R&D offshoring with their homelands—justifying their exclusion from the main equations, apart from their effects through inventor diasporas. Moreover, unskilled diaspora data come from the 1990 census—which accounts for the unskilled migrant flows of the 1980s—so as to be more confident that they are unaffected by unobserved factors influencing co-patenting patterns between 1990 and 2010. Table 4 presents GMM estimations of the PPML—see Windmeijer and Silva (1997). Column (1) presents the first-stage results. Note that the value of the F-test statistic of the first stage, 344.58, is well above 10, which is usually considered a good threshold, and so the instruments cannot be judged as weak. Moreover, Hansen J statistics for mutual consistency of available instruments are provided at the bottom of columns (2) and (3), and they do not reject the null hypothesis that the excluded instruments are valid and uncorrelated with the error term, so there are no over-identification problems. Column (2)

18 The same estimation procedure using R&D offshoring as the dependent variable delivers not significant coefficients. This is further evidence of the critical role of diasporas for worker-to-worker collaborations and their more nuanced effects for the case of more hierarchical, R&D offshoring relations. The World Bank Economic Review 55

Table 3. Inventor Diaspora Effect between Culturally Closer/More Distant Countries

(1) (2) (3) (4) (5)

PPML PPML PPML PPML PPML

Co-inventorship Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 ln(Diaspora) 0.204*** 0.199*** 0.198*** 0.201*** 0.206*** (0.0247) (0.0229) (0.0230) (0.0230) (0.0249) ln(Distance) 0.263*** 0.270*** 0.267*** 0.275*** 0.262*** (0.0535) (0.0517) (0.0520) (0.0560) (0.0557) Contiguity 0.213* 0.217* 0.233* 0.216* 0.250** (0.127) (0.128) (0.127) (0.127) (0.127) Common language 0.690*** 0.613*** 0.630*** 0.628*** 0.776*** (0.193) (0.132) (0.131) (0.131) (0.198) Lang. similarity 0.508** 0.781*** 0.528** 0.532** 0.858*** (0.227) (0.273) (0.224) (0.224) (0.285) Colonial links 0.0502 0.0774 0.473* 0.0724 0.468* (0.131) (0.124) (0.248) (0.126) (0.253) Religion similarity 0.702*** 0.676*** 0.692*** 0.782*** 0.639** (0.254) (0.262) (0.256) (0.267) (0.287) ln(Dia.)*Com. language 0.0122 0.0302 (0.0223) (0.0241) ln(Dia.)*Language sim. 0.0829 0.117* (0.0522) (0.0606) ln(Diaspora)*Colonial 0.106** 0.110** (0.0498) (0.0494) ln(Diaspora)*Religion 0.0526 0.00141 (0.0708) (0.0758) Controls Yes Yes Yes Yes Yes Constant 6.102 5.847 6.941 5.701 6.745 (6.019) (6.000) (5.985) (6.080) (6.044) Observations 26,160 26,160 26,160 26,160 26,160 Pseudo R2 0.958 0.958 0.957 0.957 0.958 Sending FE Yes Yes Yes Yes Yes Receiving FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Log Lik 18490.83 18482.05 18478.32 18490.28 18459.73

Table 4. GMM Estimates with Instrumented Diaspora

(1) (2) (3) 1st stage results GMM GMM Co-inventorship R&D offshoring Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 ln(Diaspora) 0.223*** 0.120 (0.0774) (0.170) ln(Distance) 0.268*** 0.247*** 0.132 (0.0315) (0.0649) (0.0985) ln(diaspora 1960s) 0.0128 (0.0102) ln(diaspora 1960s)2 0.00534*** (0.00163) ln(low-skilled diaspora) 0.0549*** (0.00971) ln(low-skilled diaspora)2 0.0137*** (0.00195) Controls Yes Yes Yes Constant 14.86*** 6.866 0.0369 (1.967) (5.998) (8.833) Observations 26,160 26,160 26,160 Controls Yes Yes Yes Sending FE Yes Yes Yes Receiving FE Yes Yes Yes Year FE Yes Yes Yes F-test 344.58 p-value 0.0000 Hansen’s J chi2 3.00468 6.13152 p-value 0.3909 0.1054

Table 5. Baseline Regressions with Bilateral Highly-Skilled (HS) Migration Stocks

(1) (2) (3) (4)

Co-inventorship R&D offshoring Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 ln(Inventor diaspora) 0.200*** 0.189*** 0.0929** 0.0639 (0.0229) (0.0236) (0.0407) (0.0408) ln(HS migration) 0.0409 0.103*** (0.0252) (0.0362) Controls Yes Yes Yes Yes Observations 26,160 26,160 26,160 26,160 Sending FE Yes Yes Yes Yes Receiving FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes

Note: Country-pair clustered robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Source: Authors’ analysis based on data described in the text. tertiary educated stocks of migrants becomes positive and strongly significant. Although not conclusive, I interpret this result as evidence that other types of skilled migrants may play an important role in boosting R&D offshoring networks, beyond what inventors (most of them scientists and engineers) do. Managers, executives, CEOs, trade agents, venture capitalists, and in general, migrant entrepreneurs, armed with their knowledge of technology markets and their global social capital, might be better placed to favor international R&D businesses and R&D offshoring back home. Unfortunately, these migrant entrepreneurs do not necessarily apply for patents and therefore may remain hidden to our proxy for highly-skilled diasporas. However, confirming this extreme would require further empirical examination that goes beyond the scope of this paper.

Are China and India Different after All? Next, I look at the robustness of the results once the main players are removed from the analysis. This is motivated by the observation that the majority of studies look at the case of the largest receiving country, that is, the United States, and its main providers of skilled talent—that is, China, India, and other Asian economies (Kerr 2008; Agrawal et al. 2011; Breschi et al. 2015; Saxenian 1999, 2006). In the light of this, some scholars argue that lessons from case studies on China and India cannot be extrapolated to other migrant communities—that is, it is difficult to say whether highly-skilled emigrants systematically engage in business networks and knowledge transfers with their homelands or rather that the Indian and Chinese diasporas are so famous for being an exception rather than the rule (Gibson and McKenzie 2012). In order to explore this issue, table 6 repeats co-inventorship estimations—with and without country- specific time dummies—but removing from the sample either the BRICS countries (Brazil, Russia, India, China, and South Africa) or the United States or both. Contrary to the arguments posited by Gibson and McKenzie (2012), among others, the coefficient accompanying the diaspora variable remains strongly significant and economically meaningful in all models and barely lower when compared to previous estimates.

Further Robustness Analysis To further check the robustness of the results, table 7 runs the baseline specification using different estimation methods and dependent variables—for the case of co-inventorship only. In particular, it includes OLS and Negative Binomial models. For both cases the estimated coefficients are slightly larger compared to table 2, but they are fairly comparable. Column (3) takes on board the potential heterogeneity 58 Migue´ lez

Table 6. Are China and India the Exception Rather Than the Rule?

(1) (2) (3) (4) PPML PPML PPML PPML

Co-inventorship Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019

No BRICS No US No BRICS, no US No BRICS, no US ln(Diaspora) 0.192*** 0.204*** 0.200*** 0.190*** (0.0384) (0.0358) (0.0419) (0.0465) ln(Distance) 0.447*** 0.325*** 0.515*** 0.269*** (0.0631) (0.0582) (0.0654) (0.0759) Bilateral controls Yes Yes Yes Yes Attribute controls Yes Yes Yes No Constant 5.626 7.494 5.900 1.753*** (7.610) (5.929) (7.425) (0.667) Observations 24,180 24,852 22,971 14,752 Pseudo R2 0.900 0.828 0.658 0.725 Sending FE Yes Yes Yes No Receiving FE Yes Yes Yes No Year FE Yes Yes Yes No Sending FE*Time FE No No No Yes Receiving FE*Time FE No No No Yes Controls Yes Yes Yes Yes Log Lik 14236.62 15541.05 11836.79 10463.32

Table 7. Robustness Checks. Co-inventorship

(1) (2) (3) (4) (5) (6)

OLS NegBin Citation weighted USPTO EPO TPF co-inv.þ1

Co-inventorship ln(Diaspora) 0.232*** 0.221*** 0.165*** 0.229*** 0.174*** 0.219*** (0.0136) (0.0267) (0.0268) (0.0230) (0.0234) (0.0263) ln(Distance) 0.0136 0.453*** 0.291*** 0.213*** 0.243*** 0.207*** (0.0156) (0.0486) (0.0621) (0.0546) (0.0626) (0.0663) Controls Yes Yes Yes Yes Yes Yes Constant 1.089 6.659 0.0461 10.27 2.879 1.395 (0.835) (5.554) (7.122) (6.523) (5.891) (6.584) Obs. 26,160 26,160 26,160 26,160 25,760 24,700 R2 0.571 0.354 0.903 0.980 0.937 0.883 Sending FE Yes Yes Yes Yes Yes Yes Receiv. FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Notes: Country-pair clustered robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the ﬁnal sample consists of 26,160 observations, instead of 26,800. Source: Authors’ analysis based on data described in the text. The World Bank Economic Review 59 of international co-patents in terms of quality and weights them according to the number of forward citations received within five years after their priority year. As can be seen, results are robust for this different approach to computing the dependent variable—although further research, possibly at the micro level, should shed more light on this issue. Finally, in order to be sure that the choice of PCT applications to build the co-inventorship variable does not bias the results, the baseline regression is replicated using Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 alternative patent data sources, such as the USPTO, the EPO, and “Triadic Patent Families” (TPF) (OECD Triadic Patent Families database, January 2014). TPF consist of a set of patents filed at the EPO, the Japan Patent Office (JPO), and granted by the USPTO that share one or more priority applications. If anything (columns [4] through [6]), it seems that using alternative sources of patent applications may overestimate the relationship between high-skilled migration and international co-patenting. Appendix S7 replicates the baseline regressions but splits the count of co-patents and foreign inventors into five broad technology fields (Schmoch 2008). Appendix S8 uses alternative count data methods intended to zero-inflated dependent variables, with no important differences with respect to the main results.

IV. Conclusion This paper examines the impact of highly-skilled migrant networks in high income countries on the internationalization of inventive activity between high income and developing economies—measured as cross-country PCT co-patenting (co-inventorship and R&D offshoring). In order to study this relationship, it makes use of a unique dataset on inventors with a migratory background. To my knowledge, there have been few previous attempts to measure the mentioned links, and therefore this constitutes the main contribution of the paper. The results show a strong and positive association between highly-skilled diasporas and the internationalization of inventive activity between developed and developing countries. The effect is statistically and economically significant: a 10% increase in the inventor diaspora abroad is associated with a 2.0– 2.2% increase in international patent collaborations at the level of inventors. The effect found is robust for the large list of controls and robustness checks performed—such as alternative ways of measuring the dependent and the focal explanatory variables. Regressions also include country FE and country- specific time dummies, in order to control for any time-invariant and time-variant characteristics of the country of origin or the country of destination of the migrants that may affect the relationship between inventor diasporas and international co-patenting. Hence, any source of bias should come from country- pair (omitted) variables, for which an IV approach is used in order to avoid the possibility of the focal regressors picking up any confounding effect that might bias their point estimates. These findings do not suffice to conclude that a “brain gain” exists that makes up for the loss of highly-skilled human capital of sending economies, although they are undeniably necessary elements. Note, however, that boosting international co-inventorship and team formation is only one of the multiple brain gain effects of emi- grant inventors, which may eventually include the international diffusion of knowledge (Kerr 2008; Breschi et al. 2015) or the accumulation of human capital in sending economies (Beine et al. 2008)— higher expected returns to the inventor activity. In developing/emerging countries, accessing foreign technologies via international collaborations is a hot political issue (Sterzi and Montobbio 2013). From a development policy perspective, these findings support the idea that exploiting highly-skilled diaspora networks in technology frontier economies might be an instrumental way of engaging in international innovation networks. This subject matter ranks high among policymakers in these countries, as witnessed by the recent visit of the Indian Prime Minister to Silicon Valley.19

19 See: http://www.theguardian.com/world/2015/sep/23/narendra-modi-aims-to-bolster-indias-tech-credentials-on-us-visit (accessed 28th September 2015). 60 Migue´ lez

Interestingly enough, the effect, although relatively diminished, does not depend on the remarkable performance of particular diasporas abroad, such as Chinese or Indian inventors. Equally, results are not particularly driven by the country both attracting the largest number of migrant inventors and concen- trating a significant proportion of North-South international collaborations, that is, the United States. It seems therefore that inventor diaspora effects are exhausted at relatively low levels of highly-skilled dia- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/41/2897378 by Joint Bank/Fund Library user on 08 August 2019 sporas (for similar results for the trade-migration relationship, see Egger et al. 2012). The IV results suggest that highly-skilled diaspora effects weaken dramatically in the case of R&D offshoring—collaborations between applicants in developed countries and inventors in developing ones. These results seem to suggest that personal relations and trust building are critical to explain co-inventorship—where contracts are usually more tacit, contract enforcement is difficult, and diaspora networks may play a referral role—but are less important in explaining more formal and hierarchical relationships—where probably explicit, written contracts are the rule. Technical highly-skilled workers, such as scientists and engineers, are possibly not the best placed to boost R&D offshoring networks to their countries of origin, but other skilled workers, such as executives, managers, trade agents, or entrepreneurs, may play a more critical role. Although some preliminary evidence seems to give support to this hypothesis, further research, possibly at the firm and inventor levels, will shed more light on these particular issues. Of course, using patent data for migration research does not come without limitations. One important caveat is that inventors are only observed when they seek patents. However, not all inventions are patented; indeed, the propensity to patent for each dollar invested in research and development differs considerably across industries. In addition, studies have documented a skewed distribution of patent values, with relatively few patents yielding high economic returns. Another limitation of the dataset used in this study is that it misses inventors with a migratory background that have become nationals of their host country. To the extent that it is easier to gain citizenship in some countries than in others, this introduces a bias in the data. A related bias stems from the possibility that migrants of some origins may be more inclined to adopt the host country’s nationality than migrants from other origins. Unfortunately, the data do not allow the severity of these biases to be assessed. Notwithstanding these caveats, we believe that this database meaningfully captures a phenomenon of growing importance.

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Abstract We study the impact of China’s new rural pension program on promoting migration of labor by applying a regression discontinuity analysis to this new pension program. The results reveal a perceptible difference in labor migration among adult children whose parents are just above and below the age of pension eligibility: The adult children with a parent just attaining the pension-eligible age are more likely to be labor migrants compared with those with a parent just below the pension-eligible age. We also find that with a pension- eligible parent, the adult children are more likely to have off-farm jobs. These abrupt changes in household behavior at the cutoff suggest that these households are credit constrained. In addition, we find that the pension’s effect on migration is greater among adult children with a parent in poor health; pension-eligible elderly report that they are more likely to use inpatient services when needed and less likely to rely on adult children for care when they are ill. These results suggest that (expectations regarding) providing care for elderly parents has constrained labor migration from China’s rural areas to some extent, and that the new rural pension program has helped to relax this constraint. JEL classification: H23, H31, H55, I38, J22, O15

“While your parents are alive, do not journey afar.” — Confucius

The social costs of inadequate mobility of rural labor in the developing world, despite the large wage gap between urban and rural areas, have attracted increasing awareness and scrutiny among researchers and policy makers. This issue is especially salient in China, where city dwellers often earn more than three times as much as farmers do (National Bureau of Statistics of China). At the same time, as China’s population rapidly ages, the factory labor shortage (Yong Gong Huang) highlighted in recent reports may intensify unless China finds ways to relax the constraints on full utilization of China’s vast albeit shrinking working-age

Karen Eggleston is a senior fellow at Shorenstein Asia-Pacific Research Center, Freeman Spogli Institute, Stanford University; her email address is [email protected]. Ang Sun (corresponding author) is an assistant professor at school of economics, Central University of Finance and Economics, China; her email address is [email protected]. Zhaoguo Zhan is an assistant professor at Tsinghua University, China and Kennesaw State University; his email address is zhanzhg@ sem.tsinghua.edu.cn. The research for this article was financed by the Shorenstein Asia-Pacific Research Center, Freeman Spogli Institute for International Studies, Stanford University. The authors thank the editor and three anonymous referees for helpful comments and suggestions, the efforts of the Xi’an Jiaotong University research team who paid to field the survey, the Laiwu respondents for their willingness to answer our survey questions, and the constructive comments of participants in the conference on “Economic Aspects of Population Aging in China and India” held at Stanford in March 2013, especially co-organizer David Bloom and our discussant Alfonso Sousa-Poza. A supplemental appendix to this article is available at https://academic.oup.com/wber.

VC The Author 2016. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please e-mail: [email protected] The World Bank Economic Review 65 population (Meng 2012). To facilitate economic development, it is critical to understand the barriers that prevent people living in rural areas from migrating to find better jobs. In this paper, we use regression discontinuity analysis of the novel pension program in rural China to estimate the effect of the “windfall” income on rural migration. Since households can anticipate pension receipt, our findings of abrupt changes in labor migration and off-farm employment of adult children with parent(s) become pension-eligible, suggest Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 that credit constraints are likely to be one factor preventing individuals from migrating in rural China. This paper also contributes to our general knowledge about public pension programs in developing countries. China’s rural pension system is only a few years old and not well studied yet. In comparison, the old age pension system in South Africa, which rapidly expanded in the early 1990s, has been extensively scrutinized. The pensions in South Africa were very generous—twice the median income per capita in rural areas and therefore were plausibly expected to play a significant role in household behaviors.1 By contrast, the new rural pension program in China provides an opportunity to examine the margins of household behavior when the pension payment is not as generous. The pension is about 10% of the average income in Laiwu county, Shandong province, where our survey is fielded. Will such pension income have a perceptible impact on the well-being of the elderly or impact household decision-making in observable ways? If so, which household behaviors are most responsive? The answers to these questions hold important policy implications as well as general lessons for household theory.2 The pension program in Laiwu, launched in 2007, provides 55 RMB yuan per month to every resident age 60 or older, with funds supplied by the central, provincial, and county governments. The age- based eligibility criterion of the pension program allows for a regression discontinuity design. To evaluate the effect of the pension program, we draw on a detailed survey targeting rural elderly near the pension-eligible age of 60.3 For each individual between 55 and 70 years old, in addition to basic demographics and socioeconomic information, we asked the respondent detailed questions about the attitudes and plans for support in old age (anticipated sources of long-term care, living arrangements, etc.). We also asked detailed questions about each of their children, including migration history, occupational choices, and living arrangements, regardless of whether the adult child was counted as a household member or was living under the same roof with the respondent at the time of the survey (July 2012). Comparing the households with an elderly parent just attaining the pension eligible age to households with an elderly parent just below the eligible age, our results reveal several significant patterns. First, even though the pension represents only 10% of average income and households could anticipate receipt, regression discontinuity analysis reveals a perceptible difference in household behavior around the age of pension eligibility, suggesting that these households are likely to be credit constrained. In particular, our results show that after the older parent receives a pension, the adult children are more likely to be a labor migrant and take an off-farm job.4 Second, we find that the pension’s effect on migration is

1 Researchers find evidence consistent with the hypothesis that pension income also benefits prime-aged adults (promoting migration for some and support within the household for others) as well as the pensioner’s grandchildren (Ardington, Case, and Hosegood 2009). 2 For example, there had been concern that the pension income may not substantially improve outcomes for pensioners because children would reduce their financial support in lock step with the increase in social support. Our results suggest that adult children do not significantly reduce financial transfers to their elderly parents when the parents became pension-eligible. Instead, our findings suggest that the new rural pension income benefits multiple generations in the household, in the sense that pensioners report better security and more labor migration among their adult children, a margin of occupational choice that had been previously constrained. 3 Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center, FSI, at Stanford University. 4 For clarity, we use “elderly” or “older parent” to refer to the older adults in our sample, age 55 to 70, to distinguish them from their adult children (typically in prime work ages of 20–40 and mostly parents themselves). 66 Eggleston, Sun, and Zhan greater among adult children with a parent in poor health. These results are consistent with the previous findings that migration decisions of adult children are influenced by the well-being of elderly parents.5 Third, we find that pensioners expect that they will be more likely to use hired services and slightly less likely to rely on care from an adult child when ill. There is no change in self-assessed health or health insurance coverage after the pension-eligible age threshold. These results are consistent with the interpreta- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 tion that the increase in income relaxes the household credit constraint, making medical services more affordable and enabling pensioners to substitute purchased services for instrumental support directly provided by the adult children. This substitution presumably provides the adult children with greater flexibility to participate in the nation-wide labor market. In addition to exploiting the pension program to scrutinize the household credit constraint, this paper has two key advantages over the prior research evaluating the impact of old-age pensions. The first advantage lies in our data for studying the effect of a financial windfall on migration. Prior evidence suggests living arrangements are endogenous and directly affected by a financial windfall. Therefore, the characteristics of household members in households receiving a pension may be systematically different from those of nonpension households (Klasen and Woolard 2000).6 Selective attrition will complicate identifying impacts if a household survey only includes the intact family or cannot track individuals who move away from the pensioner’s residence or those who migrate out of the demographic surveillance area. For example, when estimating the effect of pensions on adult labor supply in South Africa, researchers found opposite patterns when the sample only included residents (Bertrand et al. 2003) compared to when the sample was extended to include nonresident household members (Posel, Fairburn, and Lund 2004) within the demographic surveillance area.7 In this paper, we ask the respondent detailed questions about each of their children, regardless of whether the child is counted as a household member or is living under the same roof. Thus we generate data on the work, migration, and living arrangements of all adult children of pensioners and nonpensioners, without the complication of sample attrition caused by endogenous household formation. The second advantage of our study is the regression discontinuity design. Life-cycle patterns and cohort heterogeneity profoundly shape the migration decisions of household members. Researchers examining the linkage between pension eligibility and migration have difficulty separating the effect of pension income from that of age or cohort heterogeneity. In this paper, we apply regression discontinuity analysis to address this problem. Residents in Laiwu are eligible for rural pensions beginning at age 60 for both men and women, similar to most other rural areas of China. A basic pension is available to all residents financed by the collective fund, and individuals can purchase additional coverage by choosing from a limited menu of options for contributory pensions. A regression discontinuity design has several advantages over other methods of identification (Edmonds, Mammen, and Miller 2005) and has been applied to many different settings (e.g., Card, Dobkin, and Maestas 2009; Miller, Pinto, and Vera- Hernandez 2013). We recognize that Laiwu is not nationally representative, but we believe this study has methodological advantages and results that make it of broader interest. In particular, the novelty of the survey

5 Giles and Mu (2007) find a significantly lower probability that a son will work as a migrant when a parent is seriously ill. 6 For example, the presence of a pension attracts household members who are significantly less educated, more likely to be unemployed and to report being sick or injured (Ardington, Case, and Hosegood 2009). Moreover, there is evidence of a direct causal effect of pension income on household composition through channels such as labor specialization within the extended household (Edmonds, Mammen, and Millier 2005). 7 Ardington, Case, and Hosegood (2009) use panel data with individual fixed effects to partially address the issue of different characteristics of individuals living with pensioners and those living without a pensioner, but they admit that they cannot follow changes for individuals who exited the demographic surveillance area between the waves of the survey because of migration. The World Bank Economic Review 67 enables us to shed light on intrahousehold, intergenerational allocation when facing credit constraints. In addition to our survey data, we also use the nationally representative China Health and Retirement Longitudinal Study (CHARLS) baseline data to test the first stage of our fuzzy regression discontinuity design: the impact of age eligibility on pension receipt. However, since CHARLS only has the information on adult children who co reside with the elderly parents, replicating our results in a nationally repre- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 sentative sample is not feasible.8 The rest of the paper is organized as follows. Section I introduces the related literature and setting of our study. Section II describes the data and our identification strategy. Section III presents the main results and discusses interpretations. Section IV examines other outcomes to compare with prior research (e.g., on the case of South Africa). Section V offers concluding remarks. Supplemental appendix S1 develops a conceptual framework about migration and contains other supplementary results.

I. Credit Constraints and Pension in Rural China Low incomes and limited wealth, combined with incomplete credit markets, make it difficult for households to finance investments even when the returns exceed the costs, and constrain ability to smooth consumption. Certainly it is clear that the rural elderly represent one of the lowest income groups in China, with more than one in four falling below the poverty line. According to the CHARLS baseline study in 2011–12, financial support from children and other household members plays a key role in reducing consumption poverty: the poverty rate based only on the income of the rural elderly and their spouses (including pensions) is 65.1%, but falls to 28.9% when including intrahousehold transfers and transfers from nonresident adult children, as well as public transfers and savings (CHARLS Team 2013). That lack of access to credit further contributes to constraining rural household’s choice sets is also evident, despite rapid improvement alongside economic growth. Much prior research has found that Chinese rural households suffer from a lack of access to credit (International Fund for Agricultural Development 2001; Luo 2004; Dong and Featherstone 2006; Yu 2008). Rural credit markets in China can be separated into formal and informal markets. Formal credit is mostly used for the financing of agricultural production (Feder et al. 1990). The formal financial institutions currently serving rural China include the Agricultural Bank of China, Agricultural Development Bank of China, Rural Credit Cooperatives, and Rural Postal Savings. Formal financial institutions have strict requirements for rural loans and limited lending (Dong, Lu, and Featherstone 2010). Informal rural credit can take various forms. Interpersonal lending, which includes loans extended among friends, relatives, neighbors, or colleagues, is among the most basic strategies that farmers use to deal with liquidity requirements. The informal rural credit markets are highly segmented, with participants limited to only those with personal relationships. Moreover, informal loans are small and short- term. Other forms include lending from money lenders, pawnshops, and private money houses, some of which are illegal (Tsai 2004). These forms require collateral. In our sample, 40.23% of the elderly individuals report 0 savings, suggesting the limited availability of both formal and informal loans. Although China’s overall savings rate is extraordinarily high, the older generation largely missed the opportunity to invest and save except through investing in their children, especially sons who are expected to support them in their old age.

8 Moreover, China’s large regional diversity allows for testing theories of household behavior under different circumstances regarding local social norms, labor market conditions, and generosity of the social safety net. Thus localized information can provide insight, highlighting dimensions of heterogeneity that may be less evident when the initial analyses aggregate to the national level. Indeed, the strong statistical significance of locality (such as indicator variables for county or province) has been noted by previous researchers on China’s economic demography (e.g., Smith et al. 2013). 68 Eggleston, Sun, and Zhan

China is not unique in this regard. Throughout the developing world, having children is a way to allocate household resources intertemporally. As Banerjee and Duflo note in Poor Economics, “for many parents, children are their economic futures: an insurance policy, a savings product, and some lottery tickets, all rolled into a convenient pint-size package” (118). Both theory and empirical evidence suggest that households often allocate tasks among the extended family members according to comparative ad- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 vantage as shaped by social norms, with some adult children providing financial support to elderly parents while other children mainly invest their time to assist with the parents’ daily activities and provide care when parents are sick or in need of long-term care. In this case, the credit constraint of the overall household can be relaxed by remittances from migrant children. However, small families cannot necessarily use such a strategy. In our sample, 23.4% of the respondents only have one child. Moreover, social norms still dictate that sons carry more responsibility to provide financial and instrumental help to parents than daughters do, and over 50% of the respondents we surveyed only have one son. China’s relatively low fertility, largely attributable to China’s family planning policies that began during the 1970s and are only recently being relaxed, implies that for a great portion of households in rural China, including in our sample, it is not feasible to use the remittance of one child to enable other children to migrate. Recognizing that China’s demographic change was squeezing families and imperiling the traditional modes of elderly support—as well as the need for a social protection system to undergird a “harmonious society” and help address burgeoning urban-rural disparities—China’s leadership introduced the New Rural Pension System (NRPS). Beginning with 320 pilot counties, NRPS covered 838 counties by the end of 2010 and virtually all of rural China by the end of 2012.9 The program represents a financial “windfall” for the current elderly—those who were already, or approaching, 60 years old when the program started. The pension consists of three main parts: an individual premium (ge ren), a local government subsidy (ji ti), and a central government subsidy (zheng fu). For those who voluntarily enroll, the individual premium comprises five categories: 100, 200, 300, 400, and 500 RMB per person per year, with correspondingly increasing benefits that are also supposed to be adjusted according to the increase in China’s per capita income. The local government subsidy is required to be no less than 30 RMB per person per year. The central government subsidy, also called the basic pension, starts at 55 RMB per month per person. Current elderly receive the basic pension without ever having paid contributions. Shandong Province implemented the national standard pension policies in virtually all respects. Laiwu County, located in central Shandong province, has income per capita close to the average for China as a whole. In sum, 60% of Laiwu’s total population, and 70% of the residents above age 60, reside in Laiwu’s rural areas. Laiwu began the pilot of NRPS in 2007. The pension provided 55 RMB yuan per month to every resident age 60 or older, with funds supplied by the central, provincial, and county governments. In 2012, this pension was raised to 60 RMB yuan per month. Each district and village can offer additional pension options to their residents (such as contributory pensions on top of the guaranteed state-financed minimum), according to local conditions. In our sample, 68.4% of the elderly received only the basic pension—60 RMB. For pension recipients, the pension payments constitute 60.2% of their total income, defined as the sum of labor income and pension income. Like most rural areas of China, Laiwu has limited options for institutional care; most elderly reside in their own homes, with or without coresidence of (one or more of) their adult children. Over the past several years, a few villages have set up community care centers from village collective funds, providing no- rent dormitory-like housing and cafeteria meals to all elderly village residents. These changes overlap in

9 In 2009, the State Council of China released “the State Council Guidance Regarding the Development of New Rural Pension Pilot” (China [2009] No. 32). Based on a number of pilot studies in selected rural areas, government officials indicated that they expected the scheme to cover (i.e., be available for voluntary enrollment) 10% of rural regions by the end of 2009, 50% by 2012, and 100% by 2020. These expectations have been more than exceeded, at least in terms of coverage by the basic benefit program (not voluntary enrollment). The World Bank Economic Review 69 time with the roll out of the pension scheme. In this article, we focus on the effect of the pension financial “windfall” rather than the community care centers’ impact on living arrangements, for two primary reasons. First, institutionalized care, even in the modest form of these community care centers, is not well accepted according to traditional social norms, especially in rural China. In our sample, over 50% of the elderly believe living with children is the most ideal arrangement. Second, most individuals in our Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 sample, especially those around the eligibility threshold of 60 years old, still work in the fields and do not need institutional settings for long-term care.

II. Data and Fuzzy Regression Discontinuity Data This study compares the migration status of adult children who have a pension-eligible parent to the migration status of adult children whose parent(s) are nearly eligible. Data for the study come from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. Our survey is among the most recent and detailed available for studying the impact of the new rural pension system on household decisions. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University. We first categorize all villages into three strati according to GDP per capita in 2010. In the first stage, within each stratus, four villages and one community care center are randomly selected and the village heads are interviewed to assess basic village characteristics and the implementation of the pension program.10 Village heads also provide a residents’ roster with basic demographic information of each individual. In the second stage, from each village or community care center a random sample of residents who fall in the age range from 55 to 70 are interviewed with detailed questions about household composition, living arrangements, work-related migration of adult children, and the health and health care utilization of the elderly. In particular, the correspondents report the demographic information and work- related migration for each of their children above age 16. Our stratified random sample of 12 villages and three community care centers in rural Laiwu includes data on 676 older adults with 1,441 children and 1,553 grandchildren. Table 1 presents summary statistics for the outcome measures used in this study. Panels A and B report the outcomes for adult children and elderly parents, respectively. The first column reports the mean and standard deviations for the full sample, that is, households with respondents age 55 to 70. Columns (2) and (3) report means and standard deviations when confining the sample to households with a member between ages 55 and 65—5 years around the age cutoff. Column (4) reports the result of a t -test comparing the difference between the mean of those below and above the pension eligibility threshold. Note that these descriptive statistics could be different from the true causal effect of pension receipt because the means and t -tests capture not only the pension effect, but also any trends regarding aging and cohort heterogeneity. The potential difference underscores the importance of using a rigorous study design such as regression discontinuity to distinguish the effect of pension income from the life cycle pattern of extended household formation and other outcomes.

Fuzzy Regression Discontinuity Our empirical approach is driven by the age discontinuity in the benefit structure of the social pension program. Among the elderly in Laiwu, Shandong, social pension eligibility depends almost exclusively

10 We over-sampled elderly living in these dormitory-style community care centers (“yanglao yuan,” rarely providing comprehensive nursing services). According to the 2005 National Aged Population Survey only 1.2% of China’s rural elderly lived in a nursing home (Chen, Eggleston, and Li 2012). 70 Eggleston, Sun, and Zhan

Table 1. Sample Summary Statistics of the Main Outcome Variables

Dependent variable [55,70] [55,60) [60,65] Diff (3)-(2)

(1) (2) (3) (4)

Panel A. Adult Children Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 N 1,441 396 355 Migrant laborer (1¼Yes, 0¼No) 0.192 0.260 0.200 0.060* (0.394) (0.439) (0.401) (0.031) Off-farm job (1¼Yes, 0¼No) 0.560 0.659 0.634 0.025 (0.497) (0.475) (0.482) (0.035) Co-reside with elderly parents (1¼Yes, 0¼No) 0.060 0.141 0.031 0.110*** (0.238) (0.349) (0.174) (0.020) Panel B. Elderly Parents N 676 254 172 Expect to be admitted to hospital when 0.893 0.925 0.924 0.001 recommended (1¼ Yes, 0¼No) (0.309) (0.264) (0.265) (0.026) Expect to use family care when ill 0.700 0.642 0.686 0.044 (1¼Yes, 0¼No) (0.459) (0.480) (0.465) (0.047)

Notes: Panel A reports the summary statistics of the main outcome variables of adult children of the survey respondents; panel B reports the summary statistics of the main outcome variables for the elderly respondents. Column (1) reports the mean and standard deviation (in parentheses) for all respondents. In column (2) and (3), we confine the sample to respondents who fall in the age range of [55, 60) and [60, 65], respectively. The entries in column (4) are the mean and standard errors when conducting the t-test comparing the mean in (2) and (3). ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

on age—one becomes eligible for the pension at age 60. Thus pension income does not depend on cumulative work history or the circumstances of the extended family. To identify the effect of the pension program, we adopt the nonparametric Fuzzy Regression Discontinuity (FRD) design. This design allows the jump in the probability of assignment to the treatment at the threshold to be less than 1 (as indicated by fig. 1). In our survey, we collect information on the implementation of the pension program at the village level. We found pension receipt is largely determined by age eligibility and the deviations are mostly driven by the variation in implementation at the village level. Wealthier villages grant pensions earlier, whereas in the poorest village in our sample, only 35% of the eligible elderly received pension income. The validity of the regression discontinuity design crucially depends on the continuity of the running variable, that is, age measured in days in our FRD design. In this paper, we adopt McCrary’s (2008) density test to formally examine whether the density of age is continuous at the cutoff point 60. The outcome of this test is presented in supplemental appendix fig. S3.1 (available at https://academic.oup.com/ wber), and the null of continuity is not rejected. Graphically, supplemental appendix fig. S3.1 shows that there is no immediate jump after age 60, so manipulation of age in order to be eligible for a pension is unlikely to exist in our study. In this design, the ratio of the jump in the regression of the outcome (denoted by yi in (1)) on age to the jump in the regression of the treatment indicator (denoted by pensioni in (1)) on age is interpreted as an average causal effect of the treatment (Imbens and Lemieux 2008). Formally, the estimand is

lima#60Eðyijagei ¼ aÞlima"60Eðyijagei ¼ aÞ sFRD ¼ (1) lima#60Eðpensionijagei ¼ aÞlima"60Eðpensionijagei ¼ aÞ The identification assumption is that the individuals near pension eligibility (i.e., in their late 50s) and those who just became eligible (in their early 60s) are comparable except for pension eligibility. The World Bank Economic Review 71

Figure 1. Pension Receipt According to Normalized Age (0 ¼ eligibility threshold, age 60) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Notes: This figure uses the sample of the respondents in the age range of (55, 65). The pension eligible age is 60. This figure shows the relationship between the relative age (horizontal axis) and pension receipt (vertical axis). The dots show the sample mean of the indicator variable for receiving a pension (1 if receiving pension, 0 otherwise) in each quarter of (55, 65), that is, by three-month increments of age. The line represents the linear regression of the dummy indicator of receiving pension income on the relative age. The thin, curves show the 90% confidence interval. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

Specifically, two conditions must hold. First, the outcome for an individual depends on resources and characteristics of the household and all of its potential members, and these (unobserved) characteristics must have “continuous” effects on the outcome variables. Second, the characteristics of the household and all its potential members should have continuous distributions around the age threshold. We provide suggestive evidence to show that the unobservable respondents’ characteristics are likely balanced within a small neighborhood around the age threshold to the extent that the observable predetermined covariates do not show any significant jump at the threshold. Such graphical evidence is shown in supplemental appendix fig. S3.2.

We use local linear regression to calculate lima#60Eðyijagei ¼ aÞ , lima"60Eðyijagei ¼ aÞ, lima#60Eðpensionijagei ¼ aÞ and lima"60Eðpensionijagei ¼ aÞ, respectively. For the implementation of local linear regression, we choose the bandwidth by the method proposed by Imbens and Lemieux (2008)11 and use the rectangular kernel. For each main outcome variable yi , we conduct RD estimation with local linear regression described above. Both the reduced form estimand lima#60Eðyijagei ¼ aÞlima"60Eðyijagei ¼ aÞ and the FRD estimand are reported, together with the bandwidth values. To interpret our result, we need to impose the assumption of monotonicity, that is, older individuals are more likely to receive a pension than younger ones. Specifically, the effect of interest is how pension

11 The optimal bandwidth is chosen by the criteria of minimizing squared bias and variance. 72 Eggleston, Sun, and Zhan income impacts subsequent household outcomes for “compliers,” defined as individuals who do not receive a pension if not eligible (below age 60) and do receive a pension if eligible (age 60 or older). Then,

lima#60Eðyijagei ¼ aÞlima"60Eðyijagei ¼ aÞ sFRD ¼ lima#60Eðpensionijagei ¼ aÞlima"60Eðpensionijagei ¼ aÞ

(2) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

¼ Eðyið1Þyið0Þjindividual i is a complier and agei ¼ aÞ where yið1Þ denotes the outcome when the respondent i is “treated” by the pension program (pension receipt ¼ 1) and yið0Þ denotes the outcome when the respondent is “not treated” by the pension program (pension receipt ¼ 0). In addition, we interpret Eðyið1Þyið0Þjindividual i is a complier and agei ¼ aÞ as the lower bound of the pension program’s average effect at the threshold age a for the compliers because more households will respond to the income windfall given a longer period of time. To check the sensitivity of the reduced-form nonparametric estimation, we also employ an alternative parametric method using all observations, by modeling the outcome yi as

yi ¼ a þ s 1ðagei 60Þþb f ðagei 60Þþd 1ðagei 60Þf ðagei 60Þþi (3)

12 where f ðagei 60Þ is a polynomial function of the normalized age, agei 60. To test the robustness of the nonparametric FRD estimands, we employ the 2SLS estimator s in (4), using Zi ¼ 1ðagei 60Þ as the IV for pensioni

yi ¼ a þ s pensioni þ b f ðagei 60Þþd 1ðagei 60Þf ðagei 60Þþi (4) Both the reduced-form estimand s in equation (3) and the IV estimand s from (4) are reported. Standard errors of estimates reported in the paper are clustered at the village level.13 More detailed discussion on clustered standard errors is contained in the supplemental appendix S3.2. During the survey, we asked the respondents about the main outcomes prior to the survey, when the pension program was not yet launched. As a placebo test, we redo the reduced form estimations using this preprogram information and show no effect prior to the pension policy.

III. Results and Interpretations The First Stage: Pension Receipt Fig. 1 plots the probability that a respondent reports receiving a social pension, according to the respondent’s normalized age (¼0 if age is 60). The 20 dots on each side of the cutoff are the 3-month average of the dummy indicator of receiving a pension. In addition, the figure shows the regression line and the 90% confidence interval, where y is the dummy indictor of receiving pension (1 if receiving pension, 0 otherwise). We see a sharp jump of the fitted value of pension receipt at the age cutoff of 60. Fig. 1 shows a strong first stage of our FRD estimation. Table 2 reports the result of RD estimation. In panel A, when using our Laiwu county data, the two estimation methods yield very similar results: at the eligibility cutoff point, approximately 59.4% of the elderly receive pension payments. This jump is highly significant. To confirm that our Laiwu data are not misleadingly different from the national situation in rural China, we show in panel B that using the nationally representative CHARLS data yield smaller but equally significant OLS and RD estimands.

12 We report the outcome of OLS and IV based on second order polynomials for consistency, since quantitative criteria suggest various choices of the order, and produce qualitatively similar results for most of outcome variables we study. 13 They are computed by the routine STATA cluster option. Alternatively (as suggested by one referee), we also consider the [Moulton 1986 random] correction for standard errors, which is reported in supplemental appendix (table S3.2–S3.7). The World Bank Economic Review 73

Table 2. First Stage: RD Estimands of Pension Receipt

Dependent variable N Local linear regression OLS with 2nd-order polynomial

(1) (2) (3)

Panel A. Laiwu Survey Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 Indicator of Pension Receipt 670 0.594*** 0.589*** (1¼Yes; 0¼No) (0.080) (0.084) Bandwidth 7.508 Panel B. CHARLS Sample Indicator of Pension Receipt 3,671 0.252*** 0.444*** (1¼Yes; 0¼No) (0.036) (0.032) Bandwidth 4.180

Notes: Panel A uses the data from our Laiwu Survey and panel B uses the China Health and Retirement Longitudinal Study (CHARLS) sample. In each panel, column (1) is the number of observations; column (2) reports the nonparametric RD estimand. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008); the results in column (3) report the parametric RD estimand with 2nd-order polynomials of the running variables, using all observations. ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

To have a better understanding of the first stage, we also examine the discontinuity of living in community care centers at the eligible age cutoff because together with the pension program, some villages in Laiwu County also provide subsidized community care centers. Supplemental appendix fig. S3.3 does not reveal a significant jump of the propensity of living in institutions at the pension-eligible age. Neither nonparametric nor parametric RD regression shows a significant change at the age cutoff. Therefore, in this paper we focus on the income effect of the pension program.

Migration and Occupation of Adult Children In our survey, we asked the respondent about whether each adult child was a migrant worker. If not, we asked the timing of her last work-related migration if she migrated before. We also solicited information about each adult child’s current job, her last job, and the timing of the last change of jobs. Therefore, we can examine migration status and off-farm employment in 2012 (after the pension program was fully in effect) as the main results and compare to results in 2006 (one year before pensions were introduced) as a placebo test.14 Fig. 2 plots fitted values from a linear regression of the dummy indicator for work-related migration on relative age, as well as the 90% confidence band. Fig. 2 panel A for treatment in 2012 shows the jump of the dummy indicator for adult children migrating for work at the age when the elderly parent becomes pension-eligible. By contrast, we do not see the same pattern in panel B using the migration information one year before implementation of the pension program. Table 3 panel A reports the RD estimands using the sample composed of all adult children of the respondents. Columns (2) and (3) show the nonparametric RD and FRD estimands using local linear regressions in the postprogram period are statistically significant at the 5% level. Columns (4) and (5) show that the RD and FRD estimands from fitting OLS regressions with second-order control functions yield similar results. The coefficients of pension eligibility are significant at the 10% level. The magnitudes of the nonparametric FRD and 2SLS estimands are 31.1 and 25.4 percentage points, respectively. The large magnitude could be in part because of our small sample size, which leads to large confidence intervals but also because the pension, although modest, constitutes a substantial increase in the income of the elderly. In our sample, most elderly rely upon support from their adult children. For pension recipients, the pension payments constitute 60.2% of their total income, defined as the sum of labor income and pension income.

14 Most of the adult children only changed jobs once during the period between 2006 and 2012. 74 Eggleston, Sun, and Zhan

Figure 2. Work-Related Migration Status of Adult Children, for Treatment (2012) and Placebo (2006) Years Panel A. Treatment 2012 Panel B. Placebo 2006 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Notes: This figure uses the sample composed of the adult children of respondents in the age range of (55, 65). The pension eligible age is 60. This figure shows the relationship between the elderly parent’s relative age and work-related migration of adult children. The dots show the sample mean of the dummy indicator for work-related migration (1 if migrate for work, 0 otherwise) in each quarter of (55, 65). The line represents the linear regression of the dummy indicator of work-related migration on the relative age. The thin, curves show the 90% confidence interval. Panel A shows the result for the treatment year 2012, while panel B shows a placebo test of the same relationship in the year 2006 prior to the first implementation of the pension program in Laiwu. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

In table 3 columns (6) and (7), we report the nonparametric and parametric reduced-form RD estimands using migration status in the placebo year and do not find any discrete change at the age cutoff, which lends credibility to the linkage between the discrete change and pension receipt in our benchmark analysis. The World Bank Economic Review 75

Table 3. RD Estimation on Labor Migration for Adult Children

After implementation (2012) Before implementation (2006)

Dependent variable Local linear OLS with 2nd-order Local linear OLS with 2nd-order

regression polynomial regression polynomial Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Migrant laborer¼1; 0 otherwise N Reduced form FRD Reduced FRD Reduced Reduced RD form RD form RD form RD (1) (2) (3) (4) (5) (6) (7)

Panel A. All adult children of the respondents 1,441 0.154** 0.311** 0.138* 0.254* 0.020 0.001 (0.074) (0.147) (0.073) (0.139) (0.041) (0.039) Bandwidth 3.800 3.827 11.765 Divide the sample by elderly parents’ health Panel B. Adult children with a parent in fair or poor health 885 0.193** 0.383** 0.175* 0.353* (0.084) (0.166) (0.086) (0.199) Bandwidth 4.790 4.818 Panel C. Adult children with a parent in good health 556 0.099 0.208 0.113* 0.173** (0.099) (0.193) (0.058) (0.077) Bandwidth 3.362 3.391

Notes: Panel A uses the data from the whole Laiwu sample; panel B uses the sample confined to adult children with a parent in fair or poor health; and panel C uses the sample confined to adult children with a parent in good or excellent health. In each panel, column (1) is the number of observations; using the nonparametric method, column (2) and column (3) report the reduced form RD estimands and the FRD estimands, respectively. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008). As robustness checks, for both columns (4) and (5), we use all observations to fit an OLS regression with 2nd-order polynomials on both sides of the age cutoff. In column (4), the coefficient of the dummy indicator of age eligibility is interpreted as the reduced form RD estimand, and in column (5) the coefficient of the dummy indicator of pension receipt instrumented by age eligibility is interpreted as the FRD estimand. Column (6) and column (7) report the reduced form RD estimand using nonparametric and parametric methods, respectively, as a placebo test using the data one year prior to the implementation of the pension program. ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

We run similar analyses for the dummy indicator of off-farm employment of adult children. Fig. 3 panel A shows a slight insignificant increase in the likelihood of off-farm employment, when bandwidth is 5 years of age. Table 4 panel A column (3) shows that the FRD estimand on the likelihood of off-farm employment increases significantly at the margin of pension eligibility when bandwidth is confined to around 3.57 years of age. Fig. 3 panel B and table 4 panel A columns (6) and (7) show that the increase in off-farm employment disappears when examining the placebo before-pension period. It is worthy to note that without the pension program, the signs of the RD estimands are negative, which is consistent with lower off-farm employment among older cohorts. We study heterogeneity in the pension effect to shed light on the mechanism(s) through which pension income affected household decisions. As discussed in section I, similar to other countries in the developing world, elderly residents in rural China suffer a high risk of poverty and lack social safety net. In this circumstance, instrumental help from adult children is essential. In particular, family care provided by adult children is most common when an elderly family member falls ill. In the supplemental appendix S1, we sketch a simple model to illustrate how the commitment of adult children to invest their time in instrumental support of their elderly parent constrains them from migration to improve their earnings in the next period. This framework implies that adult children with parent(s) in poor health are more constrained from migrating than those with parent(s) in better health. (Of course, a parent’s health status is far from random; for example, richer households are both more likely to have healthy parents and be 76 Eggleston, Sun, and Zhan

Figure 3. Off-Farm Employment Status of Adult Children, for Treatment (2012) and Placebo (2006) Years Panel A. Treatment 2012 Panel B. Placebo 2006 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Notes: This figure uses the sample composed of the adult children of respondents in the age range of (55, 65). The pension eligible age is 60. This figure shows the relationship between the elderly parent’s relative age and off-farm employment of adult children. The dots show the sample mean of the dummy indicator for off-farm employment (1 if employed off-farm, 0 otherwise) in each quarter of (55, 65). The line represents the linear regression of the dummy indicator of off-farm employment on the relative age. The thin, curves show the 90% confidence interval. Panel A shows the result for the treatment year 2012, while panel B shows a placebo test of the same relationship in the year 2006 prior to the first implementation of the pension program in Laiwu. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University. The World Bank Economic Review 77

Table 4. RD Estimation on Off-Farm Employment for Adult Children

After implementation (2012) Before implementation (2006)

Dependent variable Local linear OLS with 2nd-order Local Linear OLS with 2nd-order

regression polynomial Regression polynomial Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Off-farm job¼1; 0 otherwise N Reduced FRD Reduced FRD Reduced Reduced Form RD Form RD Form RD Form RD (1) (2) (3) (4) (5) (6) (7)

Panel A. Whole Sample 1,441 0.118 0.231* 0.200** 0.365*** 0.094 0.069 (0.075) (0.137) (0.069) (0.116) (0.188) (0.103) Bandwidth 3.564 3.570 2.597 Divide the sample by elderly parents’ health Panel B. Adult children with a parent in fair or poor health 885 0.155 0.374** 0.287*** 0.621*** (0.108) (0.190) (0.085) (0.198) Bandwidth 4.225 4.201 Panel C. Adult children with a parent in good health 556 0.071 0.081 0.106 0.128 (0.131) (0.219) (0.138) (0.193) Bandwidth 3.607 3.656

Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China and the Shorenstein Asia-Pacific Research Center at Stanford University. Notes: Panel A uses the data from the whole Laiwu sample; panel B uses the sample confined to adult children with a parent in fair or poor health; and panel C uses the sample confined to adult children with a parent in good or excellent health. In each panel, column (1) is the number of observations; using the nonparametric method, column (2) and column (3) report the reduced form RD estimands and the FRD estimands, respectively. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008). As robustness checks, for both columns (4) and (5), we use all observations to fit an OLS regression with 2nd-order polynomial control functions on both sides of the age cutoff. In column (4), the coefficient of the dummy indicator of age eligibility is interpreted as the reduced form RD estimand, and in column (5) the coefficient of the dummy indicator of pension receipt instrumented by age eligibility is interpreted as the FRD estimand. Column (6) and column (7) report the reduced form RD estimand using nonparametric and parametric methods, respectively, asa placebo test using the data one year prior to the implementation of the pension program. ***Significant at 1%. **Significant at 5%. *Significant at 10%. free from credit constraints.15) This framework is also consistent with Giles and Mu’s (2007) empirical findings of a significantly lower probability that a Chinese son will work as a migrant when a parent is seriously ill. In this setting, pension income makes medical services more affordable, therefore, enables pensioners to substitute purchased services for instrumental support directly provided by the adult children. This substitution presumably provides the adult children with greater flexibility in participation in the nation- wide labor market. Such flexibility matters more for the most constrained households, that is, the households with a parent in poor health. This heterogeneity could also reflect the lack of a complete insurance market because respondents in poor health could face higher interest rates for a loan and/or caring for an elder in poor health is likely to lower households’ overall income. We categorize the sample by respondents’ self-reported health status. We find that the RD estimands for migration and off-farm employment are consistently of larger magnitude for those adult children whose parent(s) report fair or poor health (in panel B of table 3 and table 4), compared to the estimands using the whole sample (in panel A of table 3 and table 4) or the estimands using the sample composed of households with a parent in good health (in panel C of table 3 and table 4).16

15 If the elderly respondents’ health reflects the extent to which a household is credit constrained, it is still interesting to document the heterogeneity of the pension effect. 16 For the subsample composed of households with a parent in good health, the magnitude of the jump for both results are of much smaller magnitude, indicating the statistical insignificance is unlikely to be caused by smaller sample size. 78 Eggleston, Sun, and Zhan

Conducting RD estimation using an even smaller sample could limit precision of the estimation, which may partly explain the larger magnitude and confidence intervals of the RD estimands, but comparison between the results in panels B and C (both with smaller subsamples) clearly illustrates that the households with a parent in fair or poor health are mostly affected by the pension program. As a robustness check, we run a placebo test of the RD analysis to investigate the indicator of pension Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 receipt and the potentially interesting outcomes at the placebo “assumed age cutoff” of 58. Under our premise of credit constraint, household behavior should not change at age 58 because the “windfall” has not yet been received. As shown in the supplemental appendix table S3.1, none of the RD coefficients are statistically significant, consistent with our premise.

Respondents’ Expectations on the Form of Care They Will Receive When Ill If adult children are constrained from leaving their parents because purchasing substitute instrumental support is unaffordable and if such a constraint is relaxed by pension income, then we should be able to observe the substitution of hired services for instrumental support from adult children, especially in household plans for intensive care of ill parents. To test this prediction, we examine respondents’ expectations regarding the kind of care that they expect to receive if they become ill. We ask the respondents “If you get ill, will your adult children provide you care?” We draw upon the question “If you get ill and are recommended by a doctor to be hospital- ized, will you stay in hospital?” to shed light on whether more income boosts the respondents’ expectations of access to nonfamilial care when ill.17 Consistent with this hypothesis, we find that elderly parents have lower expectations of receiving care provided by children, as shown in table 5 panel A, although the nonparametric estimand for expectation of receiving care from adult children is not significant when bandwidth is four years of age. The OLS estimation with second-order polynomials yields significant negative RD estimand upon the pension eligible age. The insignificant nonparametric estimand could be consistent with the roughness of our survey indicator and China’s tradition of filial piety. Children are regarded as responsible to provide care to parents when sick, and depending on the severity of illness, adult children are almost inevitably involved in some form of family care. Thus, the indicator may not be able to capture the change in expected intensity of involvement of adult children in future family care. In addition, table 5 panel A demonstrates that after they become pension-eligible, respondents report being more likely to use inpatient services when recommended by a doctor. The results are consistently significant at the 5% level. We address the skepticism that such increase could be caused by more affordable medical services or worsening of health. Rural residents of all ages are covered by the subsidized New Cooperative Medical Scheme (NCMS) health insurance. There is no evidence in our data of different coverage between residents below and above age 60. Table 5 panel B shows no discrete change in NCMS enrollment at age 60 in either the nonparametric or OLS regression. In addition, we examine the health status of individuals and find no discontinuity of the self-reported health status at age 60, suggesting that the abrupt change in expectation of using inpatient services is unlikely to be attributable to sud- den worsening of health. The magnitudes of the RD coefficients in panel B are very small, indicating that the chance of a discrete change in these variables is very low.

17 Although inpatient medical care (e.g., for a surgical procedure) is different from in-home care by children when sick (e.g., cooking, preparing an herbal remedy, feeding, bathing), at least in rural China there are reasons to suspect a degree of substitutability. Even in high-income Japan, there is a large controversy about “social hospitalization” for elderly with long-term care needs, as manifest by hospital lengths of stay far exceeding most other OECD countries. The World Bank Economic Review 79

Table 5. Expectation on Services Will Receive When Ill by Elderly Parents

Dependent Variable Local linear regression OLS with 2nd-order polynomial

N Reduced form RD FRD Reduced form RD FRD

(1) (2) (3) (4) (5) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Panel A. Suggestive evidence on consumption Expect when ill to receive family care provided 676 0.066 0.125 0.193** 0.321*** by adult children (1¼Yes, 0¼No) (0.082) (0.143) (0.076) (0.107) Bandwidth 3.993 3.978 Expect to use inpatient services when 676 0.130** 0.232** 0.173** 0.277** recommended (1¼Yes, 0¼No) (0.059) (0.107) (0.077) (0.123) Bandwidth 3.696 3.682 Panel B. Test continuity of health and take-up rate of social health insurance NCMS enrollment (China’s subsidized health insurance 676 0.013 0.009 for rural residents)(1¼Yes, 0¼No) (0.019) (0.019) Bandwidth 4.980 Self-reported health status (1-5) 676 0.002 0.035 (0.100) (0.112) Bandwidth 6.438

Notes: The results use the data of correspondents in the Laiwu Survey. In panel A, column (1) is the number of observations; using the nonparametric method, column (2) and column (3) report the reduced form RD estimands and the FRD estimands, respectively. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008). As robustness checks, for both columns (4) and (5), we use all observations to fit an OLS regression with 2nd-order polynomials on both sides of the age cutoff. In column (4), the coefficient of the dummy indicator of age eligibility is interpreted as the reduced form RD estimand, and in column (5) the coefficient of the dummy indicator of pension receipt instrumented by age eligibility is interpreted as the FRD estimand. In panel B, we check the continuity of respondents’ self-reported health and the dummy indicator of taking up the New Cooperative Medical System, which is the subsidized voluntary health insurance program for rural residents in China. Columns (2) and (4) report the RD estimands using nonparametric and parametric methods, respectively. ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

IV. Other Outcomes In addition to labor migration, the effect of pensions on other outcomes, such as intergenerational transfers and living arrangements, are of economic interest and have been studied in prior research on South Africa’s pension program (Ardington, Case, and Hosegood 2009; Duflo 2003; Edmonds, Mammen, and Miller 2005). To compare the pension program in rural China with that in South Africa, we provide supplementary results on how these outcomes respond to pension receipt in our Laiwu data.

Living Arrangements The migration of adult children could directly lead to a change in living arrangements, with the adult children moving away from their natal home either temporarily or permanently. However, labor-related migration of adult children does not necessarily result in a significantly smaller family size or lower likelihood of the pensioners living together with their adult children because the low proportion of initial coresidence may have been too small to detect much impact on that margin and because of competing forces changing other margins of living arrangements upon pension receipt. For example, Edmonds, Mammen, and Miller (2005) find that prime working age women depart, but the presence of children under five and young women of child-bearing age increases. They interpret the finding as suggestive evidence about a setting where prime age women have a comparative advantage in work away from extended family relative to younger women. This subsection studies the setting of rural China to explore the effect of the pension program on extended family living arrangements. 80 Eggleston, Sun, and Zhan

In rural China, extended families have traditionally been composed of parents, their unmarried children, and one or more married sons with wives and children, while daughters leave their natal family behind and become part of their husbands’ families at the time of marriage (Lang 1946). For extended families with multiple adult sons, one or more married sons usually move away from the parents’ house to live independently. Economic factors are major forces in determining when adult children leave their Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 parents’ house (Cohen 1992). Therefore, to investigate the potential effect of pension receipt on family living rearrangements, we confine the sample to adult sons and focus on the outcome of coresidence with elderly parents. Table 6 reports the results regarding living arrangements. Panel A uses the sample of adult sons. In the postprogram period, for adult sons, although there is no significant effect on coresidence, the coefficients in column (2)–(5) are consistently negative. The insignificant estimands could partially be because the portion of coresidence in our sample is low on average and therefore not very responsive to migration. In table 6 columns (6) and (7), we document the living arrangements of adult children in the placebo year 2006.18 Without the implementation of pension program, the RD coefficient on coresidence is not significant. Moreover, the RD coefficients are positive, indicating the average likelihood of coresidence is greater among the older cohorts. This is consistent with the declining time trend of coresidence across generations in rural China (Peng 2011). We examine the outcome of coresidence with respondents using the sample of grandchildren from adult sons’ families. The results are shown in table 6 panel B. The coefficients of pension eligibility in columns (2)–(5) are consistently negative but not significant. The results show no evidence of family division or respondents bringing in grandchildren upon pension receipt. In table 6 panel C, we report the results estimating the RD coefficient of family size for respondents with adult sons (i.e., excluding the childless and those who have only daughters). The results are similar to those in panel A: the coefficients of pension eligibility in panel C columns (2)–(5) are consistently negative but not significant, and columns (6) and (7) show that the results do not have stable negative signs using the data of 2006. The results in panels B and C show no evidence that pension receipt was associated with significant effects on extended family living arrangements.

Intergenerational Transfers This paper emphasizes the mechanism that pension income enables elderly pensioners to be less reliant on instrumental support from their adult children, by for example substituting use of hired or inpatient services. Prior research focuses on other channels behind the effect of pension income on prime-age household members’ migration, such as using the pension income to cover migration costs of adult children or enabling the elderly parents to take care of grandchildren left behind by their migrating parents. The channel of “staking” the migrant has been discussed by Ardington, Case, and Hosegood (2009) in the case of South Africa.19 Duflo (2003) also shows in-kind transfers from grandmothers to granddaughters when receiving the pension income. Our simple model discusses the relationship between the two channels. The model is presented in supplemental appendix S1. To compare this study with prior research by Ardington, Case, and Hosegood (2009) in the case of South Africa, we examine whether the pension payment impacts intrahousehold, intergenerational

18 In our survey, we asked the respondents “in what year did the current living arrangement start?” and “with whom did you live before this change?” For the latter question, the respondents choose from among “living alone,” “living with spouse,” “living with (other) children,” “living with (other) relatives,” and “living in a nursing home.” The respondents are also asked to provide the demographic information of current household members, from which we construct the variables describing current living arrangements. 19 Note that these mechanisms are consistent with independent living being a normal good (i.e., that an income windfall would allow greater privacy to each generation, while still providing companionship and support; see Costa 1997, 1999 and Salcedo, Schoellman, and Tertilt 2012). The World Bank Economic Review 81

Table 6. RD Estimation on Living Arrangements

After implementation (2012) Before implementation (2006)

Dependent variable Local linear OLS with 2nd-order Local linear OLS with 2nd-order

regression polynomial regression polynomial Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

N Reduced FRD Reduced FRD Reduced Reduced form RD form RD form RD form RD (1) (2) (3) (4) (5) (6) (7)

Panel A. Sample of adult sons Co-reside with the respondent 768 0.058 0.088 0.063 0.084 0.164 0.179 (1¼Yes, 0¼No) (0.088) (0.148) (0.094) (0.132) (0.154) (0.146) Bandwidth 4.551 4.542 7.760

Panel B. Sample of the grandchildren from the sons’ families Co-reside with the respondent 849 0.114 0.168 0.098 0.148 (1¼Yes, 0¼No) (0.090) (0.166) (0.100) (0.154) Bandwidth 4.351 4.333 Panel C. Sample of the elderly parents with adult sons Numbers of co-residing adult 533 0.123 0.206 0.093 0.126 0.299 0.042 sons (0.108) (0.185) (0.117) (0.162) (0.251) (0.068) Bandwidth 5.680 5.675 6.852

Notes: Panel A uses the data of adult children from the Laiwu Sample; panel B uses the sample of grandchildren from the sons’ families; and panel C uses the sample of elderly parents with at least one adult son. In each panel, column (1) is the number of observations; using the nonparametric method, column (2) and column (3) report the reduced form RD estimands and the FRD estimands, respectively. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008). As robustness checks, for both columns (4) and (5), we use all observations to fit an OLS regression with 2nd-order polynomial control functions on both sides of the age cutoff. In column (4), the coefficient of the dummy indicator of age eligibility is interpreted as the reduced form RD estimand, and in column (5) the coefficient of the dummy indicator of pension receipt instrumented by age eligibility is interpreted as the FRD estimand. Column (6) and column (7) report the reduced form RD estimand using nonparametric and parametric methods, respectively, as a placebo test using the data one year prior to the implementation of the pension program. ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University. transfers. Our survey asks parents to recall the value of all monetary transfers and service exchange between them and their adult children and grandchildren in the previous 12 months. To examine the transfers during the 12-month period after and before pension eligibility, we redefine the cutoff age as 61. The results are reported in table 7. Regarding pecuniary transfers, we find no effects on transfers to the elderly from all adult children or on transfers in the opposition direction. It is not surprising that respondents do not transfer more to adult children, given that the pension income only amounts to around 10% of the average income in the locality. This is also consistent with the interpretation of decreasing instrumental help from children and the elderly parents spending more money on purchased services. The lack of effect of a pension on transfers from children to elderly parents has multiple aspects. On the one hand, the adult children may earn more income after migrating, enabling them to send remittances to their parents. On the other hand, migration costs could exhaust the premium wage they earn, at least for the first months or year. The competing forces render net transfers to parents undetermined. To study intergenerational exchange of services (i.e., nonpecuniary transfers), we estimate the pension effect on a dummy indicator for the respondents providing day-care services to their grandchildren. The RD coefficient is not significant. On the one hand, upon parental migration, if a child is left behind, then she is likely to be looked after by grandparents. On the other hand, the child might migrate to live in a new location with their parents, which decreases the likelihood of receiving care from grandparents. 82 Eggleston, Sun, and Zhan

Table 7. Transfers between Elderly Parents and Adult Children

Dependent variable Local linear regression OLS with 2nd-order polynomial

N Reduced form RD FRD Reduced form RD FRD

(1) (2) (3) (4) (5) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019

Panel A. Adult children Range of money transfers from the elderly 1,437 0.050 0.139 0.287 0.492 to all adult children (0.385) (0.665) (0.320) (0.525) Bandwidth 6.431 6.689 Range of money transfers to the elderly 1,437 0.343 0.329 0.209 0.323 from all adult children (0.365) (0.767) (0.414) (0.710) Bandwidth 3.719 3.887 Panel B. Grandchildren from adult sons’ family Receive presents or money from the elderly 594 0.088 0.091 0.113 0.154 (1¼yes, 0¼no) (0.150) (0.260) (0.108) (0.196) Bandwidth 3.898 3.950 Care by the elderly (1¼yes, 0¼no) 594 0.007 0.032 0.042 0.093 (0.129) (0.241) (0.096) (0.179) Bandwidth 4.844 4.868

Notes: In panel A, the money transfers received from each child are aggregated to a range: 0 for no transfer, 1 for below 50 RMB yuan, 2 for 5099RMB yuan, 3 for 100199RMB yuan, 4 for 200499RMB yuan, 5 for 500999RMB yuan, 6 for 10002999RMB yuan, 7 for 30004999RMB yuan, 8 for 50009999RMB yuan, 9 for more than 10000RMB yuan. Panel A uses the data of adult children from the Laiwu sample. We collect the data of intergenerational transfers by asking about such transfers during the past 12 months in our survey. We aim to investigate the transfers during the 12 months following pension receipt. Therefore, we define the cutoff age as 61 when running the RD modules. In each panel, column (1) is the number of observations; using the nonparametric method, column (2) and column (3) report the reduced form RD estimands and the FRD estimands, respectively. We use local linear regression with the uniform kernel and choose the bandwidth by the method proposed by Imbens and Lemieux (2008). As robustness checks, for both columns (4) and (5), we use all observations to fit an OLS regression with 2nd-order polynomials on both sides of the age cutoff. In column (4), the coefficient of the dummy indicator of age eligibility is interpreted as the reduced form RD estimand, and in column (5) the coefficient of the dummy indicator of pension receipt instrumented by age eligibility is interpreted as the FRD estimand. Panel B uses the sample of grandchildren from adult sons’ family. ***Significant at 1%. **Significant at 5%. *Significant at 10%. Source: Data for the study comes from the July 2012 Survey of senior citizens in Laiwu, Shandong, China. The survey was conducted by Xi’an Jiaotong University, China, and the Shorenstein Asia-Pacific Research Center at Stanford University.

In sum, in contrast with the case of South Africa, we do not find significant transfers between pensioners and other household members. This difference is not surprising, given that the pension income in rural China is not as sizable as that in South Africa, which leaves the direction of transfers unclear.

V. Conclusions Estimating the effect of a novel rural pension program on household outcomes in rural China, we find significant impact of the windfall income on the migration of adult children. The likelihoods of labor-related migration and off-farm employment increased by over twenty percentage points. The magnitude of the estimated pension effects seems particularly striking. The large magnitude could be because our sample is small and therefore, confidence intervals are large. However, the magnitude may not be too surprising in this setting: although the current pension is modest (only about 10% of average income in Laiwu), it represents a large increase in the income of the elderly (who often rely upon support from their adult children). The empirical results are consistent with our hypothesis that the monetary support upon pension eligibility enables other family members to reduce the time commitment to instrumental support of aging parents or at least to defer those commitments to when the elderly parent might need them more (in their 70s or 80s).20 These findings suggest that a market for in-home medical and long-term care services is likely to develop in

20 In a somewhat similar way, Antman (2012) finds that adult children substitute for their siblings’ time contributions with their own financial contributions to support elderly parents. The World Bank Economic Review 83 rural areas alongside the consolidation and strengthening of the pension program so that such services will become increasingly available for purchase. An interesting further question for research is how this service market develops and who participates as suppliers. Labor migration to fulfill the long-term care needs of the urban elderly may ironically constrain the availability of labor for long-term care in rural areas. Encouragingly, we do not find evidence that adult children significantly reduce transfers to their el- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/64/2669656 by Joint Bank/Fund Library user on 08 August 2019 derly parents when the parents became pension-eligible, as some analysts and policymakers had feared might happen. Instead, the public expenditures contribute to multigeneration improvements, with better security for the elderly and more freedom of occupational choice and urban migration for the adult children.

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In a context of global increase of international migration of workers, a more recent phenomenon is represented by female independent migration, sometimes referred to as feminization of migration. The most significant flows of female migrants are those of women from less developed countries who leave their families behind and migrate on their own to richer countries where they are usually employed as domestic workers and where they remain for a few years before going back to their country of origin to rejoin their families. Scholars have suggestively labeled this phenomenon as “the servants of globalisation” (Parrenas 2001) or “the global nanny chain” (Lan 2006) or “the globalisation of household production” (Kremer and Watt 2006). In the light of the rising importance of this phenomenon, it becomes imperative to understand its consequences for the household left behind and, in particular, for the children. The present work contributes to answering this question by analyzing the differences in the effects of parental migration on investments on children depending on whether it is the mother of the child that leaves or the father. From a policy perspective, understanding whether and how migration of the father or of the mother differently affects the children left behind can have important implications: for instance it can help

Lucia Rizzica is an economist at the Directorate General for Economics, Statistics and Research of the Bank of Italy, via Nazionale 91, 00184, Rome, Italy; her email is [email protected]. The opinions expressed in this paper are those of the author alone and do not necessarily reflect those of the Bank of Italy. The author thanks the editor Andrew Foster, two anonymous referees, Francesca Carta, Riccardo De Bonis, Andrea Ichino, Italo Lopez Garcia, Steve Machin, Imran Rasul, and Marcos Vera-Hernandez as well as audiences at seminars at IFS, UCL, Royal Holloway, the International Conference on Migration and Development, the Cream-Northface Conference and the European Economic Association annual meeting for all their helpful comments and suggestions. A supplemental appendix to this article is available at https://academic.oup.com/wber.

VC The Author 2016. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For Permissions, please e-mail: [email protected] 86 Rizzica governments as well as nongovernmental organizations decide about how to target financial and nonfi- nancial support to the families of migrants,1 or provide useful insights for the regulation of migration both in sending and receiving countries; indeed, while receiving countries increasingly adopt policies that allow the immigration of female domestic workers from developing countries to face the aging of their population and to encourage the labor force participation of local women,2 sending countries are Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 starting to perceive the dangers entailed by the massive outflows of women and react by putting legal limits to emigration.3 This paper also provides an original contribution to the existing literature on migration: while numerous studies have analyzed the impact of migrant inflows on the destination countries’ labor markets (Card 1990; Altonji and Card 1991; Borjas 1994, 1999; Kremer and Watt 2006), considerably fewer have considered the effects on the sending country and on the households of origin (Cox-Edwards and Ureta 2003; Yang 2008). In this context, some authors have provided estimates of the impact of parental migration on the well-being of children left behind (Hanson and Woodruff 2003; McKenzie and Hildebrandt 2005; Mansuri 2006; Chen 2013), but these typically considered cases of paternal migration. Extending the results found for fathers to mothers is nontrivial for several reasons: first, because the migration experience of men and women is generally different, for example, women tend to migrate for shorter periods of time and to maintain stronger links with the household of origin (de la Brie´re et al. 2002); second, because mothers typically provide more care to the children, so that when the mother is away children may suffer a more severe emotional loss (Cortes 2015); finally, because men and women have different preferences over investments on children (Thomas 1990; Duflo 2003; Qian 2008), so that the change in the structure of the household induced by migration of one of the spouses is likely to have different effects depending on who migrates and who stays.4 The present paper moves in the latter direction and complements the existing literature by investigating the allocation of resources within the household in order to understand whether spousal migration generates diversions of the household budget that affect children. Answering this question requires understanding what are the determinants of female migration. The few existing papers that investigated this aspect seem to suggest that female migration would be a means to provide the family left behind with a more stable and reliable source of income than what would be in case of male migration, this would happen because the jobs chosen by migrant women are typically less risky than those chosen by men (Lauby and Stark 1988) and because women are intrinsically more attached to the family left behind and thus tend to send more remittances (de la Brie´re et al. 2002). Building on this literature, I propose a sequential model in which first the two spouses cooperatively decide who should migrate so as to maximize the expected returns from migration while minimizing the associated risk, second, the migrant spouse chooses how much to send back home through remittances, and finally the spouse left behind chooses how to allocate the available household budget across different commodities. The resulting subgame perfect Nash equilibrium will be one in which diversions of the budget off of children-related expenditures are offset by a strategic change in the amount of remittances that the migrant sends back, so that the share of budget devoted to children-related expenditures is the same no matter whether it is the mother or the father that migrates, even if the two have different preferences over investments on children. The empirical analysis, based on Indonesian data, shows indeed that, when the mother migrates and the husband is left behind with the children, the share of income spent for exclusively adult goods

1 UNICEF, for example, promotes policy research on migration and children left behind with a special focus on gender issues. 2 Similar policies are for instance in place in Hong Kong and Singapore, as analyzed in Kremer and Watt (2006). 3 In 2008, the government of Sri Lanka, passed a law that banned the international migration of mothers of children under the age of five. 4 A similar reasoning has been applied to the case in which one of the spouses passes away by Gertler et al. (2004). The World Bank Economic Review 87 increases but that devoted to children-related expenditures does not change significantly. The results are obtained through a two-stage least squares estimation procedure that exploits a set of instrumental variables derived from the theoretical setup, that is, the expected value and volatility of previous migrants’ earnings. Note that these results do not necessarily imply that children are no worse off when left behind with the father instead of the mother because other mechanisms, different from that of the allocation of Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 the budget may play a crucial role; to shed light on this, I further estimate the effects of maternal versus paternal migration on children’s health and education indicators and find no difference in health measures but a significant decrease in the amount of time spent at school for children left with their fathers mirrored by an increase in the amount of time devoted to housework. This type of disruptive effect is likely to explain the result found by Cortes (2015) that children of migrant mothers are more likely to lag behind at school than children of migrant fathers. The remainder of the paper is structured as follows: Section I describes the model of migration choice and of intra-household allocation of resources of the household; section II introduces the data; section III is dedicated to the identification and estimation strategy; section IV shows the estimation results; section V provides some robustness checks; and section VI concludes.

I. Theoretical Framework I propose a three-stage decision process in which first the spouses cooperatively5 decide whether and who should migrate so as to insure the household against income shocks; second, the spouse who migrates decides how much of her income to remit back to the household of origin; finally, the spouse left behind allocates all the available budget, made of her own income plus the remittances received, among different categories of goods.

General Setup Consider a rational, utility optimizing, risk averse household faced with a risky source of income. Following Levhari and Stark (1982) and Lucas and Stark (1985), the household decides to reduce this risk by diversifying its income sources and specifically by placing one member of the household to a different location so that her income streams are less correlated with those of the household left behind. In a household composed of parents and children the choice becomes who to send away between the two spouses. We can call this a portfolio choice (Markowitz 1952) where “woman migrates” and “man migrates” are two risky assets that can be combined with other two risky assets that are “man stays” and “woman stays,” so that either both spouses stay or one of the two migrates and the other stays.6 I refer to the latter type as mixed migration portfolios. I assume that the household decides whether someone should migrate and who migrates but does not decide where the migrant will go, just men and women may migrate to different predetermined destinations. This assumption is supported by the sociological and economic literature that has widely documented that migrants tend to show very little variation in the choice of their destinations, following instead quite stable patterns of migration from one place to the other (Bartel 1989; Altonji and Card 1991).

5 Individual migration choices are similar to individual labor supply choices, for which collective models have been proposed (Chiappori 1992). 6 Note that the model would potentially allow for the definition of a fourth portfolio, that is, a full migration portfolio in which both spouses migrate. Yet this portfolio will be excluded from the analysis in that it would not be feasible for a household with children because migrants cannot typically bring their children with them, given the types of jobs and accommodation they get upon migration, and cannot leave their children alone either. 88 Rizzica

In this setting the expected returns associated with each of the three possible portfolios will be the sum of the expected wages of the two spouses, specifically:

h h EðR0Þ¼EðwmÞþEðwf Þ d h EðRMÞ¼EðwmÞþEðwf Þ (1) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 d h EðRFÞ¼Eðwf ÞþEðwmÞ where EðR0Þ; EðRMÞ and EðRFÞ are, respectively, the expected returns to the household when none of the d d spouses migrates, when the man only migrates and when the woman only migrates; wm and wf are the h h wages of men and women upon migration (at destination d)andwm and wf the wages of men and women if they do not migrate (in the home village h). The risk associated with each of the three portfolios is instead:

2 h h h h r0 ¼ Varðwf ÞþVarðwmÞþ2 Covðwm; wf Þ 2 h d d h rM ¼ Varðwf ÞþVarðwmÞþ2 Covðwm; wf Þ (2) 2 h d d h rF ¼ VarðwmÞþVarðwf Þþ2 Covðwf ; wmÞ If the household’s utility is increasing in the expected returns of the portfolio chosen and decreasing in the associated risk, the migration choice will also be affected by the household’s degree of risk aversion (RAh) so that more risk averse households will choose the migration portfolio with lower risk, even if the expected returns are lower.

The Allocation of Resources within the Household

Each of the two risk averse spouses, i, obtains utility from the consumption of some private good Xi and from that of a common good Z that yields utility to both of them. The vector of common goods Z will contain all children-related expenditure, that is, both parents care about investment on children. Assuming Cobb-Douglas preferences, I express the preferences of men and women in the following way:

Um ¼ a log Xm þð1 aÞ log Z

Uf ¼ b log Xf þð1 bÞ log Z where ð1 aÞ and ð1 bÞ, respectively, indicate the man’s and the woman’s willingness to contribute to the common good, and thus can be interpreted as measures of their generosity toward children.7 No migration portfolio. In the case in which none of the spouses migrates, they cooperatively choose how to allocate the available resources under income pooling so as to maximize the sum of their private utilities with equal weights assigned to each of the two:

max a log Xm þð1 aÞ log Z þ blog Xf þð1 bÞ log Z Xf ;Xm;Z

s:t: : Xm þ Xf þ Z ¼ EðR0Þ The household utility maximization problem yields the following optimal allocations:8 a X0 ¼ EðR Þ m 2 0

0 b Xf ¼ EðR0Þ (3) 2 a þ b Z0 ¼ 1 EðR Þ 2 0

7 A large and well established literature, among which Thomas (1990), Duflo (2003), Qian (2008), documented that women have stronger preferences for investing on children than men, this paper will provide an indirect test of this hypothesis. 8 These are Pareto efficient (Chiappori and Donni 2009). The World Bank Economic Review 89

Mixed migration portfolios. Suppose now that the household prefers a mixed migration portfolio, for illustration, that in which the wife migrates and the husband stays with the children. In this case, the wife chooses how much remittances to send back home and the husband decides how to spend the available resources. The absence of the spouse induces the spouse left behind to deviate from the Pareto efficient equilibrium that would arise if they were together and divert resources toward his own private Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 good. This situation can thus be seen as a noncooperative sequential game in which the subgame perfect Nash equilibrium (SPNE) can be obtained by backward induction: the husband decides how to allocate the budget available to him according to his own preferences; the wife anticipates this allocation and incorporates the husband’s choice in her decision problem of how much of her income to send back in remittances (R). Thus at the final stage of this game the husband left behind solves:

max a log Xm þð1 aÞ log Z Xm;Z

h s:t: : Xm þ Z ¼ EðwmÞþR which yields:

h Z ¼ð1 aÞðEðwmÞþRÞ h Xm ¼ aðEðwmÞþRÞ In the previous stage of the game, instead, the migrant wife anticipates the husband’s choice and decides how much to send back through remittances by solving:

max b log Xf þð1 bÞ log Z R d s:t: : Xf ¼ Eðwf ÞR 0 h Z ¼ Z ¼ð1 aÞðEðwmÞþRÞ The wife’s problem allows for only one possible internal solution.9 In this case, the migrant will choose to send remittances:

d R ¼ Eðwf ÞbEðRFÞ and the resulting allocations will be:

Xm ¼ að1 bÞ EðRFÞ Xf ¼ b EðRFÞ (4) Z ¼ð1 aÞð1 bÞ EðRFÞ Symmetrically, when it is the husband that migrates and sends back remittances to the wife, the resulting equilibrium allocations will be:

Xm ¼ a EðRMÞ Xf ¼ b ð1 aÞ EðRMÞ (5) Z ¼ð1 aÞð1 bÞ EðRMÞ with remittances sent by the husband:

d R ¼ EðwmÞaEðRMÞ

9 A corner solution arises instead when the migrant’s expected income is too low to send remittances; this case is discussed in the online appendix. 90 Rizzica

Clearly, these allocations are second best compared to the allocations that would be chosen if the spouses decided together how to spend the (higher) available budget. The first best allocation would be analogous to those in 3.

The Migration Choice Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 The subgame perfect Nash equilibrium (SPNE) of the three-stage migration game is found by comparing the level of utility that the household obtains from the three portfolios. Foreseeing that the final allocations will be 3, 4, and 5, the two spouses cooperatively decide, at the first stage, which migration portfolio maximizes the joint expected utility. ( ) X X X 0 0 max EUiðXi ; Z Þ; EUiðXi ; Z Þ; EUiðXi ; Z Þ i¼f ;m i¼f ;m i¼f ;m Pairwise comparison of the expected values of indirect utility of the three portfolios delivers the following:

Proposition 1. When the spouses cooperatively choose the migration portfolio that ex-ante maximizes their joint expected utility,

1. Female migration is preferred to male migration if: 2 b EðRFÞ ð1 aÞ a (6) EðRMÞ ð1 bÞ 2. Female migration is preferred to no migration if: 2 2ab EðRFÞ ð2 a bÞ 2b 2ab (7) EðR0Þ 4ð1 bÞ ð1 aÞ 3. Male migration is preferred to no migration if: 2 2ab EðRMÞ ð2 a bÞ 2a 2ab (8) EðR0Þ 4ð1 aÞ ð1 bÞ

These conditions highlight that the main driver of the choice is the expected financial gain from migration. Indeed, if the expected returns from migration do not exceed those of no migration, the spouses will prefer to stay in their village of origin. Similarly, if the expected gains from female migration significantly exceed those from male migration, households will prefer the former. The comparisons above neglected the risk dimension of the three portfolios, which is implicit in the structure of individual and household preferences. Proposition 1 is indeed based on the comparison between levels of utility of the expected returns of each portfolio. But because individuals are risk averse the difference between utility of the expected returns may differ from that between levels of expected utility. For example, a riskier portfolio, that is, one with more volatile returns, may give lower expected utility than a safer one even if the associated expected returns are higher. Therefore, the final migration portfolio choice will crucially depend on relative expected risk too. Finally, the spouses’ individual preferences may still play a key role in determining the optimal migration portfolio. Figure 1 shows how the optimal choice varies depending on a (horizontal axis) and b (vertical axis) when the expected returns from male and female migration are the same and are 50% higher than in the case in which no one migrates. It turns out that when both spouses are very selfish (high values of a and b), it will be optimal not to migrate. This happens because the spouse left behind would allocate very little resources to the common goods and the migrant spouse would not be willing to compensate for this by sending The World Bank Economic Review 91

Figure 1. Optimal Migration Portfolios, by Parental Preferences Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

Notes:ERf ¼ ERm ¼ 1.5 ERo. Shaded area is where male migration is preferred to no migration. Area with horizontal solid lines is where female migration is preferred to no migration. Dotted area is where female migration is preferred to male migration. more remittances. This relation is not linear though: when the spouse left behind is very selfish, no migration will be preferred also in the case in which the migrant is very generous, because in this situation, the utility of the latter will be too low compared to the case of no migration. Finally, condition (6) also implies that when the expected returns from male and female migration are the same, the spouses will prefer the more generous spouse to leave as she would extract less private gains from her first mover advantage.

Comparative Statics Having derived the optimal allocations in each scenario and the conditions under which the corresponding portfolios are SPNE, I can examine some comparative statics to understand how much of the (maxi- mized) household income is spent on which types of goods and who benefits the most from the increase in the available budget that results from migration. Indeed, let small letters denote the share of household income devoted to each type of consumption goods. The model delivers the following prediction:

Proposition 2. The share of total household income spent on expenditure on children will be the same no matter which of the parents migrates:

z ¼ z (9) where z ¼ Z and z ¼ Z . EðRFÞ EðRMÞ 92 Rizzica

Proposition 2 results from the ability of the migrant to anticipate the allocation that will be chosen by the spouse left behind and offset the implied shift of resources away from the children by sending back more remittances. Note that, because these are shares of different amounts of income, proposition 2 does not imply that the amount spent for children is the same in the two scenarios but only that their share, relative to the available income, is.10 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 On the other hand, when comparing the shares of income spent for the private consumption of the adults I obtain the following:

Proposition 3. The share of total household income spent for adult private goods is higher when the wife migrates and the husband stays than when the husband migrates and the wife stays if and only if women have stronger preferences for expenditure for children than men ða bÞ:

( 0ifa b xm xf ¼ að1 bÞbð1 aÞ¼a b ¼ (10) < 0 otherwise

This result derives from the fact that the share of income available for private consumption of the spouse left behind depends on the “generosity” of the migrant so that if women are more generous than men, the latter can enjoy more private consumption out of the wife’s remittances. Symmetrically, the implied loss of private good consumption of the migrant will be larger if she is more generous than the spouse left behind. Finally, I can compare the allocations resulting from the mixed migration portfolios with those chosen when no migration takes place. It turns out that:

Proposition 4. The migrant spouse consumes a larger share of the available income with respect to the situation in which no one migrates:

0 xf xf > 0 (11) The spouse left behind gains from migration of the spouse, in terms of shares of income spent on his own private consumption, only when the migrant spouse is “generous enough”

1 x x0 0ifb (12) m m 2 Children always receive a larger share of income when none of their parents migrates, because either of them has incentives to shift resources onto own private consumption when alone.

z ¼ z < z0 (13)

These results should not be interpreted as suggesting that, for example, children are always worse off when either of their parents migrates, or that the spouse left behind would prefer no migration to take place, because the allocations compared are in terms of shares of the total household budget available, which is generally larger than the budget available in the case of no migration, as suggested in proposition 1. Yet they highlight how, in the absence of a moral hazard problem, children of migrant parents would instead receive a larger share of the household income.

10 This result depends on the assumption that preferences have either unitary elasticity of substitution, that is, they are Cobb-Douglas, or null elasticity of substitution, that is, they are Leontief. The World Bank Economic Review 93

II. Data The empirical analysis is based on data from the Indonesia Family Life Survey (IFLS), an ongoing longitudinal survey of Indonesian households, which started in 1993, was repeated in 1997, 2000, and 2007, and contains a sample that is representative of about 83% of the total Indonesian population, including Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 over 30,000 individuals living in 13 of the 27 provinces of the country.11 By tracking individuals over time, these data allow detecting migration. Indeed, for all individuals who appeared in the first wave of the survey the IFLS roster provides information on where they currently are (if they are not in the household anymore), why and when they left and how much they earned in the past twelve months. I define migrants as those adult people who have left the household and are reported to having done so for work reasons or explicitly to help the family. Table 1 shows that from the time of the first interview in 1993, fourteen years later more than one household out of four had at least one member that had migrated and not come back while, among individuals, migrants represented about 9%.

Table 1. Migration in the IFLS

Wave Individuals Migrants % Households Migrant Households %

1993 33,081 – – 7,224 – – 1997 39,714 1,701 4.28 7,699 1,304 16.94 2000 54,991 2,835 5.16 10,435 2,022 19.38 2007 73,016 6,352 8.70 13,536 3,787 27.98

Table 2 shows the gender composition of these migration flows: almost two thirds of those recorded as migrants in 2007 are men, but women are more than twice as likely as men to migrate internationally, and this is particularly true for mothers who leave their family behind. On the other hand women, especially those who leave their children behind, tend to stay away for a period of time that is significantly shorter than that of men.12

Table 2. Migrants by Gender. IFLS 2007

Number % of total % Migration migrants international spell (months)

Migrants 6,352 – 11.04 62.89 Men 4,046 63.70 7.74 64.83 without children 3,255 51.24 7.74 64.10 with children along 493 7.76 2.44 78.23 with children left behind 298 4.69 16.55 51.23 Women 2,306 36.30 17.00 59.48 without children 1,892 29.79 13.27 59.76 with children along 222 3.49 10.36 76.43 with children left behind 192 3.02 60.32 37.48

11 For a more detailed description of the dataset see Thomas et al. (2012). 12 Table S.1 in the supplemental appendix reports descriptive statistics of the characteristics of migrants by gender, comparing those who have no children with those who migrate with their families and those who leave their family behind. While there are sizable differences between migrants who have children and those who don’t, parents who bring their children along when they migrate do not appear to be significantly different from those who leave them behind, except for the fact that those who bring their children along are generally more educated. 94 Rizzica

To identify households who have chosen the different migration portfolios described in section I, I check, for every child under the age of 18, whether his father or mother migrated and the other parent is in the household. Table 3 shows the partition of households with children in the 2007 IFLS sample: the vast majority of them, over 95% chose a no migration portfolio, while 1.6% chose the female migration portfolio and 2.7% the male migration portfolio. A negligible share, about 0.4% chose a portfolio in Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 which both parents migrated leaving their children behind with other relatives.

Table 3. Migration Portfolio Choices of Households with Children. IFLS 2007

Mother Mother migrates stays

Father migrates 40 258 Father stays 152 9186

Table 4 provides a comparison between households that have chosen different migration portfolios: column (1) reports the characteristics of the households in which none of the parents migrated, column (2) refers to households in which either the mother or the father migrated, while columns (3) and (4) split this sample between male and female migration. It appears that the differences between nonmigrant households (column 5) and migrant households are very stark: households with no migrants are more likely than migrant households to reside in urban areas, they generally have less children and are richer and more educated. The comparison between households in which the mother migrated and the father stayed and households in which the father migrated and the mother stayed (column 6) further reveals that these differences are amplified for households with migrant mothers; these are indeed more likely to live in rural areas, to be less educated and poorer, and to have more children, though these are on average older than those of migrant fathers. The last row of table 4 finally shows differences in terms of

Table 4. Descriptive Statistics, Households with Children. IFLS 2007

(1) (2) (3) (4) (5) (6) Nonmigrant Migrant Migrant Migrant Difference Difference households households father mother (1)-(2) (3)-(4)

Size of household 4.623 4.590 4.570 4.558 0.033 0.012 (1.797) (1.933) (2.014) (1.790) Rural 0.545 0.643 0.587 0.711 0.098*** 0.124* (0.498) (0.480) (0.493) (0.455) Number of children 1.728 2.066 2.050 2.079 0.338*** 0.029*** (1.009) (1.114) (1.135) (1.077) Age of children 8.456 10.05 9.680 10.39 1.594*** 0.71* (4.701) (3.502) (3.706) (3.299) Share of female children 0.494 0.474 0.462 0.499 0.02 0.037 (0.403) (0.363) (0.363) (0.360) Mother’s years of education 8.301 7.250 7.325 6.994 1.051*** 0.331* (4.333) (3.364) (3.593) (2.789) log total expenditure 15.22 15.08 15.15 14.93 0.14*** 0.22** (0.937) (0.849) (0.905) (0.659) Risk aversion of head 3.693 3.757 3.738 3.784 0.064* 0.046 (0.794) (0.706) (0.732) (0.666) N 9186 450 258 152

Notes: Standard deviations reported in parenthesis. Differences computed with standard erorrs clustered at village level. ***p < 0.01, **p < 0.05, *p < 0.1. The World Bank Economic Review 95 degree of risk aversion of the head of household.13 This measure is a 1–4 score derived from the answers to a sequence of questions in which respondents are faced with hypothetical lotteries with high stakes.14 It appears that migrant households are on average more risk averse than nonmigrant ones, while there is no significant difference between households who choose female migration and those who choose male migration. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

III. Empirical Strategy The equation of interest is one in which I estimate the shifts in the shares of total household expenditure from one category of consumption goods to the other when the parent that migrates is the mother rather than the father. This is:

wih ¼ aih þ bln nh þ cFh þ dXh þ uih (14) where on the left-hand side is the share of total household income allocated to expenditure for commodity i by household h, and on the right-hand side is the (log) number of members in the household nh together with several household’s observable characteristics Xh and a term Fh equal to one if the household is one in which the mother of the children has migrated, while the father did not, and zero if instead it was the father who is away and the mother remained with the children. The coefficient of interest is c, associated with the term Fh; this provides us with a measure of the difference between the budget allocation of households with migrant mothers and households with migrant fathers. Equation (14) provides an empirical test of propositions 2 and 3. We will consider that there are some types of goods that are exclusively consumed by adults (Xm and Xf), while all other goods are, to different extents, consumed by both adults and children (Z). Specifically, expenditure for food, health, education, and durables will be proxies of Z, where education is a purely child-related expenditure, while food, health, and durables also partly pertain to adults; on the other hand, alcohol, tea, coffee, and tobacco will proxy for adults’ exclusive private consumption, following Deaton (1989).15 Estimating the effects of migration and how they differ depending on the gender of the migrant spouse entails problems of endogenous selection into treatment of two types, as suggested in table 4: first there is a problem of selection into migration as households that decide to send some member out for migration will likely differ from the others on both observable and unobservable characteristics; second, there is a problem of selection into female migration because households from which it is the mother that migrates are likely to differ from those from which the father migrates in a number of unobservable factors that might as well influence the outcomes of interest. The focus of the paper will be addressing the second type of selection so as to be able to compare households with migrant mothers to households with migrant fathers. On the other hand, the results of proposition 1 will provide a guide for interpreting the direction of the bias that may arise from conditioning on selection of households into migration.

13 Or the spouse when no measure for the head is available. Indeed the data show that the head’s and her spouse’s degree of risk aversion are significantly positively correlated, q ¼ 0:34. 14 For example the first question asks: “Suppose you are offered two ways to earn income. With option 1, you are guaranteed an income of Rp 4 million per month. With option 2, you have an equal chance of earning either the same income, Rp 4 million per month, or, if you are unlucky, Rp 2 million per month, which is less. Which option will you choose?” 15 Note that these adult goods are typically consumed more by men than women. In female headed households, the share of total income spent on these items is about 2% for each adult member, while in male headed households over 4%. This difference is larger if one compares households with no adult men with households with no women, in the former the share of total income spent on alcohol, tea, coffee, and tobacco is about 2.6% per adult, while in all men households it exceeds 9%. These figures, therefore, confirm that men have stronger preferences than women for this type of goods but also show that women, too, consume positive amounts of them. 96 Rizzica

I thus build on the intuition of the theoretical model introduced in section I to derive a set of instrumental variables that may influence the decision of migrant households about which of the spouses to send out for migration but will not have any direct effect on the outcome variables of equation (14). The model of section I is translated to the data by first assigning “counterfactual” gender specific destinations to each household. To this purpose I identify, for each household, the year in which the migration deci- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 sion has been taken as that in which the migrant has departed; I then consider the destinations chosen by previous migrants from the same village and take the destination that was most common among female migrants as counterfactual destination for women and the one that was most common among male migrants as counterfactual destination for men. Counterfactual destinations will vary by gender, time of departure, and village. The choice of assigning the most common destination of migrants from the same village to perspective migrants is supported by the evidence reported in table 5, which shows that over 60% of migrants from the same village had chosen the same destinations. This is in line with the literature that has documented the formation of networks of migrants (Bartel 1989; Altonji and Card 1991; Lafortune and Tessada 2012; Patel and Vella 2013) but can also, in this particular context, be explained by the widespread use in South Asia of recruiting agencies that are connected to other agencies in a foreign country and, therefore, typically send all of the people of the village they visit to the same destination (Parrenas 2001; Suradji 2004). Table 6 shows the gender specific destinations assigned to each household for the whole IFLS sample and for the restricted sample of households with a migrant parent. The only significant difference between the two samples is that migrant mothers are much more likely to be assigned Saudi Arabia as counterfactual destinations than the full sample of women, this is in line with the evidence in table 2 that women who leave children behind are much more likely to migrate internationally than any other type of migrant.

Table 5. Migrants per Village. IFLS 2007

Men Women

Adults per village 55.31 58.70 (40.99) (42.29) Migrants per village 15.64 9.39 (14.002) (9.185) % Migrants at same destination .617 .622 (0.231) (0.234)

Notes: Standard deviations reported in parenthesis.

Once I have assigned gender specific destinations to each household, I exploit again the information about previous migrants and generate, for every destination and year of migration decision, a measure of expected returns and risk by taking the mean and standard deviation of the incomes of all male and female migrants that previously migrated to that destination, and combining them as in equations (1) and (2). Note that these instrumental variables will take different values depending on the village of origin and time of migration, so that they will eventually be household specific, in that, once households from the same village have been ordered on the basis of the time of departure, the values of wages of previous migrants will be different from one another. For what concerns the covariance between income at home and income at destination, instead, I exploit the longitudinal dimension of the data, and compute the covariance for every village-destination pair across waves. Table 7 shows that, on average, the expected wages of men upon migration are higher than those of women, but also more volatile. The difference between men’s and women’s expected wages is even larger if they remain in the home village, while the difference in variance remains similar. In terms of covariance between migrant’s and spouse’s wages, men’s migration is associated with higher risk. Finally, when combining the expected wages and the associated The World Bank Economic Review 97

Table 6. Households’ Counterfactual Gender Speciﬁc Destinations, 2007

Counterfactual destinations Men Women

Full sample Restricted sample Full sample Restricted sample Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 West Java 24.19 34.99 19.49 21.53 East Java 17.2 8.68 19.74 9.53 Central Java 13.43 14.66 14.41 14.48 Jakarta 12.02 15.69 12.49 15.08 North Sumatra 6.16 2.87 5.3 6.7 Lampung 4.44 5.76 2.93 2.73 South Sumatra 3.93 2.99 3.06 2.58 South Sulawesi 3 2.09 2.94 2.75 West Nusa Tenggara 2.29 3.32 1.96 3.10 Bali 2.16 1.07 1.86 1.08 South Kalimantan 2.08 0.52 2.06 0.53 West Sumatra 1.97 0.95 2.28 3.7 Yogyakarta 1.86 0.45 1.89 0.43 Riau 0.74 0.30 0.39 0.33 Banten 0.35 0.1 Southeast Sulawesi 0.24 0.50 East Kalimantan 0.18 0.37 0.49 0.50 Central Kalimantan 0.09 N Aceh Darussalem 0.03 0.07 Irian Jaya 0.02 Sumatra 0.01 Bengkulu 0.01 West Sulawesi 0.01 Central Sulawesi 0.28 North Sulawesi 0.11 Malaysia 3.43 4.74 1.98 3.83 Singapore 0.03 Taiwan 0.02 Saudi Arabia 0.01 5.65 18.60 Timor Leste 0.03 United Arab Emirates 0.56 0.43 N 13273 335 13134 336 risk as in expressions (1) and (2), the difference between the two portfolios becomes much less clear with male migration being slightly less remunerative and riskier. The first stage specification for selection into female migration will include relative expected returns of the two migration portfolios as suggested in proposition 1, but also relative risk of the two portfolios and risk aversion of the household. The latter will be included as a control variable on its own and then as an instrumental variable when interacted with the level of risk: ERf rf rf Fh ¼ ah þ b1 þ b2 þ b3RAh þ c1RAh þ c2ln nh þ c3Xh þ vh (15) ERm h rm h rm h

The coefficients of the instrumental variables are b1, b2 and b3, while the other variables will not be excluded from regression (14). In terms of validity of these instruments, I imagine that households will make their migration decision based on their information about the possibilities they might have at destination; thus, it is reasonable to believe that the experience of previous migrants best represents the information set available to potential migrants. The implicit assumption is that migrants’ wages are not determined by the individual 98 Rizzica

Table 7. Expected Returns and Risk from Migration Portfolios (Log)

Men Women

(1) (2) (3) (4) (5) (6)

Full Restricted Full Restricted Difference Difference Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 sample sample sample sample (1)-(3) (2)-(4)

d Eðwi Þ 16.218 16.205 15.945 16.067 0.273*** 0.138*** (0.516) (0.403) (0.361) (0.353) h Eðwi Þ 16.056 15.964 15.641 15.647 0.415*** 0.317*** (0.494) (0.490) (0.614) (0.604) d Varðwi Þ 16.390 16.345 15.912 15.934 0.478*** 0.411 *** (0.881) (0.766) (0.556) (0.529) d Varðwi Þ 16.133 16.007 15.723 15.779 0.41*** 0.228*** (0.731) (0.734) (0.766) (0.834) d h Covðwi ; wiÞ 32.051 32.094 32.030 31.928 0.021*** 0.166*** (1.235) (1.110) (0.903) (0.947)

EðRiÞ 16.722 16.719 16.739 16.755 0.017*** 0.036* (0.408) (0.365) (0.347) (0.336) ri 16.870 16.905 16.799 16.744 0.071*** 0.161*** (0.788) (0.728) (0.567) (0.562) N 13507 337 13457 337

Notes: Standard deviations reported in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1. characteristics of the migrant but are somehow exogenously set so that nonmigrants would be faced with the same wages if they migrated. This assumption is supported by the data reported by the Indonesian Statistical Office (Badan Pusat Statistik 2007) about jobs of international migrants. These show that there is strikingly little variation in the types of jobs Indonesian migrants get at destination: 53% of migrants who were abroad in 2007 worked as domestic helpers, while 42% as either construction, factory, or plantation workers. Although this information is not available by gender, one can see a clear difference between typical female jobs and typical male jobs: domestic workers are very likely to be the female migrants (whose total percentage is in fact around 55% of total international migrants in the IFLS), while the construction, factory, and plantation workers are likely to be the men. As these are all very low skilled jobs, it becomes reasonable to assume that wages are fixed and exogenous. Moreover, at least for women, there is vast anecdotal evidence that they are hired to go work abroad as domestic workers under standard contracts that specify the same wage and duration of employment for all (Lan 2006; Parrenas 2001; Suradji 2004).

IV. Results The outcomes of interest are the shares of total household income spent for a given type of commodity. To compute the denominator, I follow Dai et al. (2011) who have estimated the distribution of household income using the same data from the fourth wave of the IFLS. Income is thus obtained summing up five components: labor income; income from agricultural business; income from nonagricultural business; household nonlabor income (scholarships, pensions, other transfers); household assets income. Moreover, to correct for attrition, a two step Heckman procedure is applied, in which the probability of response is predicted by dummy variable for whether the respondent is the head of the household. Levels of (log) income and predicted income are reported in table 8 suggesting, as in table 4 that migrant households are on average poorer than nonmigrant households and households with migrant mothers poorer than households with migrant fathers. The World Bank Economic Review 99

Table 8. Household Income Levels and Shares

(1) (2) (3) (4) (5) (6) Nonmigrant Migrant Migrant Migrant Difference Difference households households father mother (1)-(2) (3)-(4) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

Household income (log) 16.56 16.26 16.25 16.26 0.3*** 0.01 (0.711) (0.704) (0.717) (0.621)

Predicted household 16.47 16.36 16.42 16.25 0.11*** 0.17** income (log) (0.711) (0.644) (0.687) (0.500)

Shares of income spent on:

Food 0.634 0.711 0.764 0.582 0.077 0.182** (4.717) (0.800) (0.858) (0.668)

Education 0.153 0.161 0.171 0.137 0.008 0.034 (1.460) (0.255) (0.304) (0.158)

Health 0.0513 0.0481 0.058 0.027 0.003 0.031 (0.586) (0.228) (0.292) (0.094)

Durables 0.0139 0.130 0.0202 0.001 0.116 0.019 (0.528) (2.188) (0.377) (0.007)

Adult goods 0.117 0.080 0.060 0.108 0.037*** 0.048*** (0.826) (0.135) (0.133) (0.133)

Notes: Standard deviations reported in parenthesis. Differences computed with standard erorrs clustered at village level. ***p < 0.01, **p < 0.05, *p < 0.1.

Table 8 further shows descriptive statistics for the shares of income allocated to the various types of commodities, distinguishing, in particular, between common goods, that is, food, education, health, and durables and exclusively adult goods, which include alcohol, tobacco, coffee, and tea.16 It appears that generally migrant and nonmigrant households devote similar shares of household income to the various commodities, with the exception of adult goods, whose share is significantly larger in nonmigrant households. This is somehow suggestive of the fact that both parents consume alcohol, tobacco, coffee, and tea, so that when there is only one of them in the household, the share of income spent on these items falls. Yet this drop is larger in the case in which the husband migrates, because men generally have stronger preferences for these goods. Indeed, the comparison between households with migrant mothers and households with migrant fathers reveals that the latter devote a significantly larger share of income to food expenditure and less to adult goods, reflecting differences in tastes between men and women. Equation (14) is first estimated through OLS; the results are reported in table 9, controlling only for household size in the first panel, and adding an indicator for rural households, the number of years of education of the mother and the (log of) total household expenditure in the second panel. To correct for correlation of errors within villages, standard errors are heteroskedastic robust and clustered at village level in all specifications. The OLS results confirm that households in which mothers have migrated spend significantly more on adult goods, the shift being about 6 percentage points, and less on common

16 Note that these shares do not add up to one for two reasons: the first is that the denominator is a measure of predicted income and not total household expenditure; this is done to better replicate the theoretical predictions. Second, there is a residual, unobserved category of expenditure, which is that for private consumption of the migrant. 100 Rizzica

Table 9. OLS Estimation. Household Level

Shares of household income spent on:

(1) (2) (3) (4) (5)

Food Education Health Durables Adult Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

A. No controls Migrant mother 0.206** 0.036 0.034 0.024 0.057*** (0.096) (0.029) (0.023) (0.018) (0.017) Size of household 0.402*** 0.078*** 0.105* 0.004 0.019 (0.104) (0.023) (0.062) (0.005) (0.020)

Observations 337 337 337 337 337 R2-squared 0.053 0.019 0.033 0.001 0.045

B. Controls included Migrant mother 0.104 0.012 0.019 0.024 0.062*** (0.079) (0.022) (0.019) (0.019) (0.017) Size of household 0.032 0.036 0.080 0.000 0.000 (0.099) (0.031) (0.052) (0.006) (0.019)

Observations 337 337 337 337 337 R2-squared 0.376 0.097 0.064 0.003 0.078

Notes: Controls are rural household, years of education of mother, log total household expenditure. Standard errors robust to village level clustering in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1. goods, although these coefficients are not statistically significant once I control for those characteristics that most differ between the two groups of households. Indeed, the inclusion of control variables seems to amplify the difference in adult goods consumption revealed in table 8 and to reduce the difference in terms of food consumption. If households from which mothers migrate are on average poorer, more rural and less educated than households from which fathers migrate (table 4), the OLS coefficient associated with common goods will likely be downward biased. For this reason, in order to control for the possibility that households from which mothers migrate differ from those from which it is the father who leaves, I proceed to estimate equation (14) by two-stage least squares (TSLS). Table 10 shows the results of the first stage regression: columns (1)–(3) use the measures of expected returns and risk of migration portfolios constructed according to equations (1) and (2). The signs confirm the existence of a trade off between expected returns and expected risk and acknowledge the ampli- fying effect of risk aversion. Moreover, more risk averse households tend to prefer female migration thus confirming the idea, suggested in Lauby and Stark (1988) and de la Brie´re et al. (2002),thatfemalemigra- tion is perceived as a “safer” investment than male migration. Columns (4)–(6) then use only the destination side of the migration portfolios; this is done to reduce the number of instruments and their potential collinearity and hence increase the power of the first stage predictions. These regressions confirm that female migration is more likely when it is associated with higher expected returns and lower variance relative to male migration. For all specifications, the last three rows of the table report the values of the F statistic of excluded instruments. A first look at these values convinces us that the most efficient specification is that of column (7), which displays the highest level of the F statistic and includes the control variables. Table 11 shows the results of the TSLS estimation of equation (14). Comparing these with those of the OLS, we observe that the increase in adult goods expenditure is larger than it was in the OLS, while the effect on all types of common goods is not statistically different from zero. These findings confirm The World Bank Economic Review 101

Table 10. First Stage Regression

Migrant mother

(1) (2) (3) (4) (5) (6) (7) (8) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 EðRf Þ=EðRmÞ 0.059 0.241 0.362** 0.379** (0.069) (0.185) (0.180) (0.174) rf =rm 0.089 0.113 0.000 (0.081) (0.078) (0.127)

RA rf =rm 0.029 (0.031) d d Eðwf Þ=EðwmÞ 0.149*** 0.297*** 0.316*** 0.308*** (0.057) (0.101) (0.100) (0.100) d d Varðwf Þ=VarðwmÞ0.092* 0.090* 0.039 (0.048) (0.047) (0.106) d d RA Varðwf Þ=VarðwmÞ 0.032 (0.026) RA 0.032 0.013 (0.040) (0.043)

Controls n n y y n n y y Observations 377 369 334 318 379 372 337 321 R2-squared 0.002 0.010 0.053 0.067 0.026 0.046 0.081 0.090 F statistic 0.746 1.919 3.941 3.232 10.03 8.840 9.374 6.297

d Notes: EðRiÞ are the expected returns from migration of individual I, rf is the associated risk, RA is the degree of risk aversion, Eðwi Þ are the expected wages of mi- d grant i at destination, Varðwf Þ is their variance. Controls are: size of household, rural household, years of education of mother, log total household expenditure. Standard errors robust to village level clustering in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1. the predictions of the model in section I for which the difference in the share of household income devoted to investment on children, here expenditure on food, health, education, and durables, is the same in households from which the father migrated and households from which it was the mother that migrated, z ¼ z. This would be due to the fact that the migrant spouse can use remittances as a way to offset the shifts in the allocation of the household budget that would be made by the spouse left behind. On the other hand, proposition 3 was predicting that the difference in the share of household income devoted to adult’s private consumption between the case in which the mother migrated and that in which the father migrated, xm xf , was equal to a b, positive if women have a stronger preference for investment on children with respect to men. The estimated coefficient thus suggests that men’s preferences are such that they would like to spend around 15% more than women on private consumption rather than on expenditure on common goods. These results also implicitly indicate that the migrant mother suffers a loss of private consumption that is larger than that suffered from a migrant father.17

17 An alternative mechanism underlying these results could be that women and men, when migrating, change their preferences over investment on children. Specifically, if women experienced very low returns to education when migrating, relative to what they observed before migrating, it may be that they decide to invest less on children’s education. This would explain the result that investments on children’s education are not affected by female versus male migration. To rule out this mechanism, I computed returns to education at each counterfactual destination and compared them to returns to education in the migrant’s home village. Results are reported in table S.4 in the supplemental appendix and show that there is no significant difference between returns to education observed in the home village and those observed by the migrant at destination. It is thus unlikely that the pattern of results observed can be explained by changes in preferences over investment on children’s human capital generated by the different migration experience of men and women. 102 Rizzica

Table 11. Two-Stage Least-Squares Estimation. Household Level

Shares of household income spent on:

(1) (2) (3) (4) (5)

Food Education Health Durables Adult Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

A. No controls Migrant mother 0.026 0.082 0.089 0.007 0.165** (0.335) (0.112) (0.150) (0.019) (0.083) Size of household 0.419*** 0.074*** 0.114 0.005 0.027 (0.102) (0.023) (0.070) (0.005) (0.022) Observations 337 337 337 337 337 Uncentered R2 0.443 0.278 0.0163 0.00285 0.162 F statistic 8.604 8.604 8.604 8.604 8.604 Hansen J statistic 0.847 1.025 1.018 2.127 0.812 p-value 0.357 0.311 0.313 0.145 0.367

B. Controls included Migrant mother 0.293 0.082 0.082 0.017 0.149** (0.337) (0.102) (0.135) (0.020) (0.074) Size of household 0.027 0.034 0.083 0.000 0.002 (0.102) (0.030) (0.054) (0.005) (0.021) Observations 337 337 337 337 337 Uncentered R2 0.633 0.329 0.0652 0.00462 0.230 F statistic 9.374 9.374 9.374 9.374 9.374 Hansen J statistic 0.723 0.775 0.771 1.938 0.637 p-value 0.395 0.379 0.380 0.164 0.425

The estimates may be biased by selection into migration if households that choose to send one of the spouses away for migration systematically differ from nonmigrant households in terms of spouses’ preferences.18 Indeed, figure 1 suggested that, if the returns to migration are not high enough, a mixed migration portfolio may not be optimal if either of the spouses was excessively selfish, that is, if either a or b were particularly high. In this sense, the estimated difference would be a lower bound of the actual difference between men’s and women’s generosity toward children.19 That women have stronger preferences for consumption on common goods than men has been highlighted by many studies: Thomas (1990), Lundberg et al. (1997), Duflo (2003), Qian (2008), and Ashraf (2009) all show that income accruing to women generates larger benefits for children than that accruing

18 Another source of selection bias may be due to differences between migrants who bring their children along and those who leave them behind. Table S.1 in the supplemental appendix shows that these are very similar, the only significant difference being the level of education, which is controlled for in all specifications. 19 Tables S.2 and S.3 in the supplemental appendix report the results of two different empirical specifications which are meant to account for selection into migration. The first is panel fixed effect estimation in which I distinguish the effect of having one spouse away from that of having the mother instead of the father. The estimates show that households with migrant mothers still spend significantly more on adult goods. The difference (þ6%) is smaller but not statistically different from that of table 11. Table S.3 in the supplemental appendix, instead, reports the estimates of a three-stage estimation (Wooldridge 2010) in which risk aversion is used in a first stage as instrument for selection into migration, and then, in a second stage, the usual instruments are used to correct for selection into female migration and the Inverse Mills Ratio from the first stage is included. Unfortunately, the inclusion of the IMR, weakens the instruments so that the final coefficients are very imprecisely estimated. The coefficient for adult goods yet is now considerably larger in magnitude. The World Bank Economic Review 103 to men. Unfortunately, it is generally difficult to compare the magnitude of these estimates with those found in this paper because in most cases both the outcome and the explanatory variables are defined differently. Yet there are at least three papers that contain comparable estimates as they use as outcome variables the shifts in the shares of household expenditure. Aggregating their shares in a way that is comparable to the one used in this paper, Hoddinott and Haddad (1995) show that the shares of the house- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 hold budget allocated to adult goods are between 3.2 and 6.6 percentage points lower in the case in which the woman earns the whole household budget with respect to the case in which it is the man. Similarly, Attanasio and Lechene (2002) find that a 100% increase of the woman’s household income share generates a decrease in the share of expenditure allocated to alcohol and tobacco between 19 and 40 percentage points. The two papers just cited do not provide an exact test of the model introduced in this paper because the presence of the spouse, even when she does not contribute to the household’s income at all, is likely to affect the choice of how to allocate it and because in the case of migration the migrant spouse does no typically earn the full household income although generally becomes the main income earner. Finally, the paper by Ashraf (2009) offers an interesting comparison: she uses an experimental setting in the Philippines to test whether husbands and wives have different preferences over the allocation of the household budget and how information and communication affect their choices. Interestingly, she shows that in situations in which one of the spouses receives a temporary shock to income and the other spouse is not able to control how she spends this extra budget (in the setting of her experiment this is the “Private” treatment), 60.4% of men versus 52.1% of women choose to deposit that money on their own private account rather than converting it into food vouchers. This is a rough test of the difference in the “generosity” parameters included in the parents’ utility functions as described in section I and suggests that a b ¼ 8:3%, a number lower but still close enough to the estimates of table 11.

V. Robustness Checks Table 12 reports the results obtained under several alternative empirical specifications together with the main OLS and TSLS estimates. The first alternative specification is a method proposed by Wooldridge (2010) to improve efficiency of TSLS estimates. This consists in estimating first a binary response model

PðFh ¼ 1jx; zÞ¼Gðx; z; cÞ by maximum likelihood (typically probit), obtaining the fitted probabilities ^ ^ Fh and then estimating the outcome equation by IV using as instrument Fh . The estimates obtained through this method are very similar in magnitude to the TSLS ones but more precise. The main concern, though, relates to the possibility that the instruments employed may be weak and thus the TSLS estimates would be biased toward the corresponding OLS estimates. Indeed, the F statistic of the TSLS only exceeds the critical value corresponding to 20% size of test as of Stock and Yogo (2002). To test the robustness of the TSLS results to instruments’ weakness I first estimate equation (14) d d using only the most powerful instrument I have, namely Eðwf Þ=EðwmÞ. As suggested by the higher value of the corresponding F statistic, these estimates are less biased toward the OLS. Indeed, the coefficient for adult goods is larger in magnitude than the TSLS one but not statistically different from it. Second, in the case of over identified models, the limited information maximum likelihood estimator (LIML) and the Fuller-k estimator (with a ¼ 1) are less subject to weak instruments bias than TSLS, as showed by Stock et al. (2002). Indeed, the LIML estimates reported in table 12 turn out to be associated with the lowest critical values of the F statistic and are thus the most unbiased as the corresponding value of the F statistic exceeds the 10% critical value. As predicted by Blomquist and Dahlberg (1999), the absolute magnitude of the coefficients estimated through LIML is slightly larger than the TSLS estimates, as are the standard errors, but the fact that the difference between the coefficients estimated with the two methods is negligible reinforces the hypothesis that the instruments have enough predictive power. Indeed, if they were weak, the TSLS estimates would have been much closer to the OLS ones than to the LIML 104 Rizzica

Table 12. Robustness Checks: Weak Instruments

Shares of household income spent on Stock-Yogo weak ID

(1) (2) (3) (4) (5) F statistic test critical values Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 Food Education Health Durables Adult 10% 20% 30%

OLS 0.104 0.012 0.019 0.024 0.062*** (0.079) (0.022) (0.019) (0.019) (0.017) TSLS 0.293 0.082 0.082 0.017 0.149** 9.374 19.93 8.75 7.25 (0.337) (0.102) (0.135) (0.020) (0.074) TSLS Wooldridge 0.096 0.079 0.074 0.028 0.143** 18.61 16.38 6.66 5.53 (0.251) (0.094) (0.131) (0.020) (0.071) IV 0.153 0.017 0.150 0.053 0.186** 14.03 16.38 6.66 5.53 (0.342) (0.126) (0.158) (0.041) (0.087) LIML 0.299 0.086 0.088 0.017 0.154** 9.374 8.68 4.42 3.92 (0.348) (0.107) (0.143) (0.020) (0.078) Fuller (a ¼ 1) 0.288 0.081 0.082 0.018 0.149** 9.374 10.89 9 7.49 (0.329) (0.101) (0.135) (0.019) (0.073) Fuller (a ¼ 4) 0.262 0.071 0.067 0.019 0.136** 9.374 10.89 9 7.49 (0.282) (0.087) (0.116) (0.016) (0.062)

Notes: Controls are size of household, rural household, education of mother, log total household expenditure. Standard errors robust to village level clustering in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1. ones. Also, the Fuller specifications20 with a ¼ 1 and a ¼ 4 produce lower threshold values for the F statistic and thus are less biased than the TSLS coefficients, and their magnitude is almost identical to the TSLS. In terms of exogeneity of the instruments, as those I am employing are time (of migration) and village specific, one may be concerned that some villages have unobserved characteristics that simultaneously affect the share of household income devoted to a certain commodity and the destination, or the wages at destination, of the migrants. For example, suppose a village has a very good nursing school, women there will mainly be nurses and thus obtain, when they migrate, a certain specific wage. Yet the fact that the village hosts a good nursing school will presumably be correlated to a higher taste for expenditure on health. To rule out similar possibilities, I regress a number of village specific characteristics on the instruments to check that there is no correlation between the two. The results are reported in table 13 and show no clear pattern of correlations that give enough confidence about the exogeneity of the instruments. As a last check I performed some Montecarlo simulations to assess the robustness of the estimates. I have drawn 1,000 random samples, adequately calibrated to reproduce the correlation between endogenous and outcome variable of the real sample, and I estimated the TSLS and the LIML coefficients for each drawn. Table 14 reports the averages and standard deviations of the coefficients estimated, together with the corresponding F statistic, the p-value of the test of over identifying restrictions and the confidence interval corresponding to the test of equality between the coefficient estimated from the simulated sample and the ones estimated from the real sample and reported in table 12. The coefficients estimated are very similar to those of table 11, I do not reject that they are equal in more than 95% of the cases (coverage). Moreover, the F statistic is now systematically larger than 10, which is above the 20% level of the Stock and Yogo (2002) critical values for the TSLS, and above the 10% one for the LIML

20 When errors are normally distributed and instruments are fixed, the Fuller-k with a ¼ 1 is best unbiased to second order (Rothenberg 1984). While the critical values for the F statistic are not significantly lower than those for TSLS, this estimator has been proved to yield more precise estimates than both TSLS and LIML when instruments are weak. The World Bank Economic Review 105

Table 13. Robustness Checks: Exogenous Instruments

(1) (2) (3) (4) (5) (6) (7) % of Households Elementary Junior High Number of Health Distance to Distance to with electricity schools schools midwives posts coach station post office Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 d d Eðwf Þ=EðwmÞ5.901 0.599 0.316 0.115 0.950 23.984 0.950 (4.430) (0.499) (0.283) (0.106) (1.233) (21.131) (1.940) d d Varðwf Þ=VarðwmÞ 3.782** 0.293 0.336** 0.015 0.264 11.480 1.190 (1.815) (0.226) (0.163) (0.048) (0.540) (8.781) (0.872) Observations 12,217 12,766 12,686 8,796 12,190 9,229 10,183 R2 0.0188 0.0046 0.0217 0.0056 0.0155 0.0105 0.0299

Notes: Unit of observation is household. Standard errors robust to village level clustering in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1.

Table 14. Robustness Checks: Montecarlo Simulations

Shares of household income spent on:

(1) (2) (3) (4) (5) Food Education Health Durables Adult

OLS 0.104 0.011 0.020 0.032 0.062 (0.079) (0.033) (0.028) (0.066) (0.016) TSLS 0.113 0.084 0.068 0.042 0.14 (0.348) (0.141) (0.124) (0.283) (0.075) Coverage [0.969] [0.964] [0.964] [0.967] [0.951] F statistic 10.669 10.723 10.719 10.654 10.624 Hansen J Statistic– p value 0.502 0.503 0.502 0.993 0.504 LIML 0.113 0.092 0.074 0.043 0.146 (0.393) (0.164) (0.139) (0.319) (0.084) Coverage [0.968] [0.965] [0.968] [0.966] [0.956] F statistic 10.669 10.723 10.719 10.654 10.624 Hansen J Statistic– p value 0.504 0.506 0.505 0.957 0.508

Notes: Standard deviations in parenthesis. Number of iterations ¼ 1,000. Coverage is the frequency with which the hypothesis of equality between coefﬁcient estimated from actual sample and coefﬁcient estimated from simulated sample has not been rejected. Controls included. Standard errors robust to village level clustering estimator. Finally, the Hansen J test of overidentifying restrictions leaves us little doubt about the possibility that the instruments are not exogenous.

VI. Concluding Remarks This paper explores the effects of parental migration on investments on children left behind. The main concern is that migration of one of the spouses may be associated with a shift of resources away from the children due to a moral hazard problem: as the migrant loses the ability to observe the behavior of the spouse left behind, this creates for the latter incentives to shift resources away from the common goods, which include investments on children, onto the private ones. I showed that in a model in which first the two spouses cooperatively choose whether and who should migrate, then the migrant decides how much to send back home in the form of remittances and, finally, the spouse left behind chooses how to allocate the available budget within the household, the subgame perfect Nash equilibrium is one in which the share of total income devoted to children is the same no matter which of the parents migrates. This is because remittances act as a device in the hands of the migrant to offset the decisions of the spouse left behind. 106 Rizzica

Table 15. Effects on Children. IFLS 2007

(1) (2) (3) (4) (5) (6) (7) (8) Weight Height Weight Hours at Cognitive Hours work Hours work Hours work for height for age for age school test for wage family business household

z-score z-score z-score weekly score weekly weekly weekly Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019

OLS Migrant mother 0.008 0.057 0.017 3.699** 0.014 0.063 0.382 0.144 (0.034) (0.113) (0.108) (1.468) (0.023) (0.437) (0.655) (0.730) Age 0.083*** 0.018* 0.020** 1.260*** 0.022*** 0.142 0.222*** 0.462*** (0.003) (0.010) (0.009) (0.252) (0.003) (0.094) (0.069) (0.108) Female 0.035 0.307*** 0.038 0.939 0.032* 0.222 0.640 1.616*** (0.032) (0.098) (0.087) (1.225) (0.019) (0.433) (0.662) (0.543) Controls y y y y y y y y Observations 610 611 610 348 439 404 404 404 R-squared 0.546 0.042 0.033 0.110 0.173 0.013 0.049 0.082

TSLS Migrant mother 0.139 0.511 0.070 15.505** 0.010 0.605 0.203 6.406** (0.106) (0.373) (0.370) (6.409) (0.078) (1.199) (1.932) (3.177) Age 0.084*** 0.019* 0.014 1.174*** 0.022*** 0.151 0.226*** 0.474*** (0.003) (0.012) (0.010) (0.279) (0.003) (0.100) (0.072) (0.121) Female 0.027 0.327*** 0.079 1.789 0.036* 0.183 0.713 1.367** (0.034) (0.104) (0.087) (1.370) (0.020) (0.517) (0.649) (0.589) Controls y y y y y y y y Observations 566 567 566 324 404 378 378 378 R-squared 0.537 0.009 0.027 0.081 0.166 0.009 0.049 0.202 Uncentered R2 0.769 0.0334 0.0595 0.710 0.923 0.0200 0.0741 0.00585 F statistic 22.57 22.59 22.57 10.62 21.23 11.49 11.49 11.49

Notes: Controls are size of household, rural household, mother’s years of education, log total household expenditure. Standard errors robust to household level clustering in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1.

I tested the predictions of this model on data from Indonesia, where female migration is particularly widespread. To account for endogenous selection into migration, I exploited information about previous migrants’ earnings and built measures of expected returns and risks of, respectively, male and female migration to use as instrumental variables. I thus showed that households prefer to send away the member who is expected to earn more upon migration and whose earnings will be less volatile. The estimation results revealed that the share of total income devoted to children-related expenditure does not change significantly between the case in which the father migrates and that in which it is the mother that leaves. On the other hand, the difference in the share of total household income devoted to private adult consumption between the case in which the mother migrates and that in which it is the father is positive and reflects the difference in tastes for investment on children. This would be about 15 percentage points. The findings of this paper indicate that, at least through the channel of household expenditure, mother’s migration has no detrimental effects on children compared to father’s migration as long as the migrant mother has the possibility of compensating the shift of resources away from children by sending more remittances back home. As this comes at the expenses of the mother’s own private consumption, it may be desirable to improve the control of migrants over the money they send back home, for example by allowing them to directly pay for children’s schools and cares. A similar policy would reduce the moral hazard problem that arises when one of the spouses migrates and generate an allocation of the household budget closer to the first best one. Moreover, if migrants were better able to control the allocation of the remittances they send back home, more people, especially women, would be willing to migrate to increase the household budget and, through this, investment on children. The World Bank Economic Review 107

Clearly, parental migration may well affect children’s well being through channels that go beyond the financial resources spent on their health or education. While quantifying these “non-monetary” effects of parents’ migration on children is beyond the aim of this paper, in table 15 I estimated the direct effects of mother’s versus father’s migration on several indicators of children welfare, which I split into health, education, and work measures. These estimates reveal no differences in the effects on conventional anthropometric Downloaded from https://academic.oup.com/wber/article-abstract/32/1/85/2669778 by Joint Bank/Fund Library user on 08 August 2019 measures, nor on cognitive test scores. On the other hand, there appears to be a significant drop in the amount of time children spend at school, mirrored by an increase in the time they spend on house work when the children’s mother migrates. This disruption effect may have long lasting consequences on human capital accumulation that are not observable in the short term (as, for example an increase in grade repetition as documented by Cortes [2015]). It may therefore be desirable to provide more assistance to households of migrant mothers in terms of housework aid and school support for children. Further research should investigate the long term effects on children’s human capital so as to provide a broader assessment of the effects of the feminization of migration. This would also require some analysis of the behaviors of female migrants upon return to their country of origin and to their household. The experience acquired by these women during their migration spell may, indeed, induce significant changes in the household’s decision making process and thus on children’s welfare.

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Abstract The income generated from parental migration can increase funds available for children’s education. In countries where informal payments to teachers are common migration could therefore increase petty corruption in education. To test this hypothesis, we investigate the effect of migration on educational inputs. We use an instrumental variables approach on survey data and matched administrative records from the World Bank’s Open Budget Initiative (BOOST) from Moldova, one of the countries with the highest emigration rates. Contrary to the positive income effect, we ﬁnd that the strongest migration-related response in private education expenditure is a substantial decrease in informal payments to public school teachers. Any positive income effect due to migration must hence be overcompensated by some payment-reducing effects. We discuss a number of potential explanations at the family level, school level or community level. We furthermore rule out several of these explanations and highlight possible interpretations for future research. JEL-Classiﬁcation: F22, I22, H52, D13 Keywords: migration, emigration, corruption, education spending, social remittances

Emigration has long been considered detrimental to origin countries’ human capital due to the loss of skilled workers. However, positive effects are possible either through the brain gain mechanism (Mountford 1997) or due to a positive income effect increasing households’ inputs in education.1 That positive income effect could in theory also increase spending on a particularly corrosive education input–informal payments to teachers. Such payments are common in many developing countries and have also become widespread in post-Soviet countries after the collapse of the USSR as real wages for

Lisa Sofie Ho¨ ckel is a doctoral researcher at the RWI - Leibniz Institute for Economic Research and a Ph.D. Student at the Ruhr-University Bochum; her email address is [email protected]. Manuel Santos Silva is a Ph.D. Student at the University of Goettingen his email address is [email protected]. Tobias Sto¨hr (corresponding author) is a researcher at the Kiel Institute for the World Economy (IfW); his email address is tobias. [email protected]. He acknowledges funding from EuropeAid project DCI-MIGR/210/229-604. The authors are very grateful to the editors, three anonymous referees, Inga Afanasieva, Toman Barsbai, Julia Bredtmann, Elena Denisova-Schmidt, Iulian Gramatki, Artjoms Ivlevs, Stephan Klasen, Miquel Pellicer, Rainer Thiele, some unnamed experts as well as participants at seminars at the University of Go¨ ttingen, the IOS Regensburg, the 2015 PEGNet conference, the 2016 AEL conference and the NOVAFRICA Ph.D. Workshop for valuable comments. The usual disclaimer applies. A supplemental appendix to this article is available at http://wber.oxfordjournals.org/. 1 For example, McKenzie and Rapoport (2010), Antman (2011, 2012), Caleroet al. (2009), Yang (2008), Bansak and Chezum (2009), Cortes (2015).

VC The Author 2017. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For Permissions, please email: [email protected] 110 THE WORLD BANK ECONOMIC EEVIEW teachers declined abruptly. This paper shows that the positive income effect can be overcompensated by other effects leading to an overall decrease in informal payments to teachers due to parental migration. These informal payments are problematic for two main reasons. First, they impose a “tax” on education that may reduce the incentives to human capital accumulation. Second, they distort performance Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 incentives for teachers, parents, and students, for example, by motivating teachers to provide exam results to students instead of teaching them in line with the curriculum. Thus, informal payments are understood to contribute to a less functional and less egalitarian public education system.2 Often, they are raised by informal parental committees on a per capita basis and tend to be regressive. Parts of the raised funds are spent on maintenance of the school and a large part will supplement wages of teachers. These payments have many organizational similarities to weakly enforced per capita taxes, a fact that can help tailor responses to them. The second and even more problematic form of payments to teachers is competition for higher grades or better treatment of individual students. Here, migrants can be expected to spend more money per child due to an income effect. These bribes are especially common in higher education (ESP/NEPC 2010). We study the effect of migration on informal payments and other forms of private educational expenditure and control for self-selection into migration by employing an instrumental variable approach. Our instrument is a network-based pull-effect at the local level, which is constructed using past migrant shares and destination-specific economic growth over time. The identifying assumption is that this network-growth interaction provides exogenous variation in the ex-ante costs and returns to migration but does not otherwise affect the household’s educational investment decision. Our paper is, to our knowledge, the first to document a negative causal effect of parental migration on such informal payments to teachers. We show that the reduction in petty corruption occurs even though migrant households are, on average, wealthier than their non-migrant counterparts. This suggests that the income effect is overcompensated by other channels. School-level variation indicates strong spillovers within schools, that could partly be due to social remittances (i.e., migrants affecting the opinions of those left behind) and partly due to migrant families’ behavior leading to a breakdown of the social norm of taking part in petty corruption. The results are neither explained by differences in public school funding nor by differences in the share of migrant children across schools. The money saved on informal payments to teachers does not translate into higher spending on out-of-school tutoring (hence- forth: tutoring), which is an alternative way of teachers to make up for lost informal wage supplements. Rather, we find some evidence that main caregivers allocate more time to educational and school-related activities in migrant households. The reduction in informal payments might be explained by access to information or value change due to migration–a literature which finds that the migration experience can alter migrants’ and their left behind families’ political values, social norms, and behavior in general.3 Since the underlying preferences and beliefs about the spread of corruption are unobserved, this remains a tentative hypothesis. We are, however, able to rule out several alternative explanations: income- effects, the valuation of education, non-parental caregivers, and several supply side factors, which we measure using matched school budget data, community level data as well as additional parts of the survey. The remainder of the paper proceeds as follows. Section I anchors the paper in the literature. Section II provides information on Moldova and corruption in education. Section III describes the data used, and section IV presents our empirical strategy. The main results are presented in section V. Section VI tests alternative explanations and the robustness of the main results before section VII concludes.

2 For example, ESP/NEPC (2010), Heyneman et al. (2008), Osipian (2009). 3 For example, see the contributions of Cameron et al. (2015), Beine et al. (2013), Barsbai et al. (forthcoming), Spilimbergo (2009), Batista and Vicente (2011), Ivlevs and King (2014). Ho¨ ckel, Santos Silva, and Sto¨hr 111

I. RELATED LITERATURE Especially in developing countries, individual migration can be beneficial for children’s education by raising and diversifying overall household income and alleviating credit constraints (Adams and Page

2005, Calero et al. 2009). However, parental migration can prove detrimental to children’s educational Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 achievement. First, parental absence can cause emotional distress jeopardizing school outcomes of children, especially if mothers or both parents are absent (e.g., Zhang et al. 2014, Cortes 2015). Second, children could substitute for the absent migrant in household chores or even paid work (McKenzie and Rapoport 2010, Antman 2011). Third, parental migration might drastically reduce the educational input of the migrant’s time. Crucially, parents could try to make up for such negative effects by paying teachers informally to give their children extra attention or even bribe them for better grades. In addition, we expect caregivers’ time allocation to adjust when family members migrate. The income effect could also decrease time allocated to children by remaining adults. However, parents often cite improving the lives of their children as the most important motive for migration. Therefore, we expect them to treat time spent with their child for educational activities as a normal or even a luxury good. Thus, parents would invest more time if remittances allow them to work less. Hence, instead of consuming more leisure we expect the remaining caregiver to increase education inputs. In addition to affecting the budget constraint, migration can affect households’ educational investment more fundamentally. The preferences and views of immigrants are known to change through accul- turization, personal experience, and the exposure to new ideas, knowledge, and institutions (Berry 1997, Careja and Emmenegger 2012). For example, the values of immigrants living in Western societies are found to converge with those of the host population over time. Such changed values can have a lasting effect when migrants return to their country of origin (Spilimbergo 2009, Batista and Vicente 2011).4 These effects are not confined to return migration but can also be transmitted through communication with family or friends. Chauvet and Mercier (2014) find spillover effects from the migrant to the non- migrant population in terms of participation and electoral competitiveness. Barsbai et al. (forthcoming) provide evidence that emigration from Moldova, the country studied in this paper, changed political attitudes and may have lost the incumbent Communist government in the 2009 elections. As the authors discuss, Moldova had very little exposure to the outside world before migration took off. In such settings where information is scarce, diffusion processes are likely to be influential. As petty corruption is often found to be dependent on the societal belief that it is widespread (Corbacho et al. 2016, Dong et al. 2012), migration might broaden the horizon and thus decrease its likelihood by showing migrants that school systems can work without informal payments. In particular, payment schemes that depend on public-good-style contributions may dissolve if a few individuals cease contributing (Fehr and Gachter€ 2000).

II. MOLDOVA AND CORRUPTION IN EDUCATION Moldova is the poorest country in Europe with an estimated GDP per capita (PPP-adjusted) of $4,521 (World Bank 2014).5 The potential effects of migration and societal spillovers are therefore particularly visible in a country like Moldova because it is the country with the third highest remittance to GDP ratio (24.9%), only surpassed by the Kyrgyz Republic and Nepal (World Bank 2014). In comparison, other commonly studied economies like Mexico (remittances to GDP ratio of 2%) or the Philippines (9.8%) are considerably less dependent on remittances. Another advantage is that migration has been a relatively recent phenomenon. After the dissolution of the Soviet Union in 1991, some Moldovans continued

4 See Docquier and Rapoport (2012) for an excellent discussion of the literature. 5 In 2013, countries with a comparable per capita GDP (in 2011 $-PPP) were, for example, Pakistan ($4,454), Nicaragua ($4,493), and Laos ($4,667). 112 THE WORLD BANK ECONOMIC EEVIEW working in what is now Ukraine and Russia and were thus suddenly called international migrants. Mass migration, however, only started when the Russian financial crisis of 1998 hit and increased unemployment and poverty considerably in Moldova. In 2011, emigrants made up 17% of the total population (MPC 2013), which means that 30–40% of children, depending on the sample, are affected by emigra- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 tion of at least one parent.6 As a former member of the Soviet Union, Moldova’s public educational system has good coverage (even in rural areas) with enrollment rates of nearly 100% for primary and lower secondary schooling and 87% for upper secondary schooling (table S.1 in the supplemental appendix, available at http:// wber.oxfordjournals.org/). Attendance is formally free of charge from first grade up to high school com- 7 pletion, and below tertiary education there are few private schools. There is a steep socioeconomic gradient in educational achievement (Walker 2011), which some worry might increase due to migration, not least due to widespread informal (and often illegal) payments to schoolteachers and other officials. The institutional causes of these are twofold: teachers’ wages are low and often delayed, and, socially, there is public tolerance of corruption and insufficient critical input of mass media. According to the 2013 Global Corruption Barometer, 37% of households in Moldova that came into contact with education authorities paid bribes in the 12 months before the survey and 58% of respondents perceived the education system to be corrupt or highly corrupt (Transparency International 2013). Similarly, in the 2011 Citizen Report Card study, corruption is cited to be the most common difficulty when requiring services from public educational institutions, and paying bribes is the second most common way of solving problems after insistence, joint with using personal contacts. Another form of corruption in the education system is the acquisition of unnecessary tutoring from a child’s teacher (Carasciuc 2001). This means that tutoring is often in a gray area between a productive investment in students’ cognitive achievement and paying teachers informally. Besides seeking individual gains for one’s own child, there is an important social component to making illicit payments to teachers resulting from the interaction of parents, teachers, and school principals (ESP/NEPC 2010).8 The less frowned upon kind of these payments are monetary transfers or in-kind “gifts” that are often collected by informal parental committees. Typically, they either supplement teachers’ wages or finance maintenance spending in schools. These expenditures face some of the organizational issues of public goods, including committees dissolving and payments stopping once the number of parents who are willing to contribute declines. There are only relatively blunt mechanisms to enforce payment (e.g., parents being excluded from the committee and teachers ignoring children in class). While payments can be seen as necessary to motivate teachers, there are widespread detrimental consequences such as especially motivated teachers providing solutions to (standardized) exams, a practice that clearly undermines the education system.9 Furthermore, monetary transfers that are imposed on a per capita basis might also affect poor households disproportionately since they have to pay a higher share of their income (Emran et al. 2013).

6 The most common emigration destination for circular migrants is Russia. While migration to Russia is usually characterized by short-term stays and manual labor, emigration to the West is more permanent, service-sector oriented, and feminized (60% women). Italy and Romania are particularly important destination countries due to linguistic proximity. 7 Moldova has compulsory schooling until the end of lower secondary schooling (roughly age 15). 8 ESP/NEPC (2010) describes results from in-depth interviews on informal payments in seven ex-communist countries. In that study, a majority of Moldovan parents report being pressured by both teachers and other parents to comply with informal payments. 9 This problem was so widespread that, sometime after our survey took place, the education minister introduced video surveillance during the final high school exam, a move that lead to a spike in failure rates. Something similar has recently been studied in Romania; see Borcan et al. (2017). Ho¨ ckel, Santos Silva, and Sto¨hr 113

The form of corruption in schools that is locally perceived as most problematic is direct bribing with the purpose of increasing the attention or grades a teacher gives to an individual student at the expense of others. Furthermore, bribes can be necessary to gain access to the best public high schools and to universities.10 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 In sum, while payments to teachers are in part motivated by grade-buying or seeking better treatment for the child, a larger share seems to operate as a per capita tax. In the latter case, the extent and magnitude of informal payments is more likely to be determined by local norms, the preferences and the bar- gaining power of teachers, parents, and school officials, and less by the pursuit of inflated grades or preferential treatment for the child. Both kinds of petty corruption, however, can be expected to affect incentives negatively, increase the socioeconomic gradient in educational outcomes, and contribute to a social climate where corruption is an everyday experience.

III. DATA AND DESCRIPTIVES In this section, we discuss the data and present key descriptive statistics of our sample.

Data We use data from a nationally representative household survey conducted in Moldova in 2011–12 (hence- forth abbreviated CELB 2012) which was specifically designed to investigate the effects of migration on children and elderly left behind. The survey includes 3,568 households with 12,333 individuals, of which 2,501 are children of age 6–18.11 In addition to socioeconomic characteristics of household members, detailed information on private financial and non-financial inputs into children’s education was collected by identifying and interviewing each child’s main caregiver.12 Financial expenditures include payments and other “gifts” to schoolteachers, tutoring expenditures, and transportation expenditures that we will use as different dependent variables in the analysis.13 Non-financial inputs include how often the main caregiver helps the child with homework and other school activities in the month prior to the survey interview on a six-point scale ranging from “never” to “every day.” In addition to the household survey, community questionnaires were filled out by local officials, typically in the mayoral office. Finally, we match data from the World Bank’s open budget initiative (BOOST) to provide school-level data on public education expenses in the respective communities and schools (see appendix S.1 for more details). Our baseline sample consists of 2,148 children from 1,448 households.

Descriptive Statistics A migrant household is defined by the existence of at least one adult who, in the 12 months prior to the survey, has spent a minimum of three months living abroad. In our sample, 29% of children live in a migrant household (table 1).14 The average student from migrant households is 12.6 years old, five

10 Heyneman et al. (2008), for example, discuss survey data that indicate that about 80% of university students in Moldova, Bulgaria, and Serbia were aware of payments of illegal bribes in university admission. 11 The response rate was above 80%. For detailed information on the survey, see Bo¨ hme and Sto¨ hr (2014) and Bo¨ hme et al. (2015). 12 The main caregiver is the person responsible for nutrition, health, and schooling of a child at the time of the survey. 13 In addition, there is a residual category of “other expenditure” for which we find statistically insignificant effects. 14 Our dataset does not allow us to compare the differences between migrant households with and without children. Comparisons to other representative data (details on request) reveal that, in households with children, female migration is on average less common. The education level and gender composition do not differ markedly. 114 THE WORLD BANK ECONOMIC EEVIEW

Table 1. Selected Summary Statistics

Non-migrant Households Migrant Households Mean equality (t-test) N Mean (SD) N Mean (SD) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 Child characteristics Age 1783 12.28 (3.73) 718 12.68 (3.79) ** Male 1783 0.51 718 0.51 Grade Point Average (GPA) (0–10) 1355 8.04 (1.07) 555 8.10 (0.93) * Serious illness (past year) 1783 0.29 718 0.26 Distance to school (min) 1659 20.76 (18.39) 668 19.92 (17.53) Household characteristics Total income 1783 33819.11 (36592.44) 718 48901.40 (49005.71) *** Household size 1783 4.70 (1.39) 718 5.13 (1.75) *** Mean years education 1782 10.74 (2.40) 718 10.68 (1.93) Urban 1783 0.24 718 0.15 *** Older siblings 1783 0.59 718 0.58 Parents divorced 1783 0.12 718 0.10 Private inputs to child’s education Caregiver time 1565 3.78 (1.94) 640 3.62 (1.97) * Payments to teachers 1552 89.09 (275.56) 635 65.62 (163.85) ** Out-of-school tutoring 1572 192.70 (1179.89) 642 86.57 (376.58) *** Transportation expenditures 1565 202.85 (775.61) 644 209.29 (902.19)

Network-Growth Instrument Number of Mean (SD) Min Max communities 129 277.66 (140.22) 2.33 691.70

Source: Authors’ calculations based on CELB 2012. Notes: All monetary values are expressed in Moldovan Lei. *, **, and *** indicate p < 0:10, p < 0:05, and p < 0:01, respectively. months more than her non-migrant peer. Before accounting for selection into migration, the average grade (GPA) is 0.06 points higher for children in migrant households. Migrant families are slightly larger on average and more likely to come from rural areas. Despite this, their average total income and average per capita income are significantly higher than those of non-migrants.15 Figure 1 also reflects the underlying effect of migration, showing no difference in assets in 1999 but significantly higher assets for migrant families in 2011.16 Households in our sample report positive payments to teachers for about 37% of all school-age children.17 Payments to teachers typically vary from 5–40 USD per child per year, which is substantial given that public expenditure for teaching materials per pupil is about 30 USD per year, and wage bills per

15 In reality, the difference could be even wider since migrant households systematically underreport their received remittances and other sources of income (Akee and Kapur 2012). 16 The asset indexes were constructed by a weighted-sum of the following items: (i.) number of cars, motorcycles, bicycles, washing machines, refrigerators, radios, TVs, computers, and cell phones; (ii.) existence of working phone landline and internet access; and (iii.) number of rooms in the house. For 1999, the last three items were excluded due to a large number of missing values. The weights for the index were obtained from a principal component analysis of the asset list. Dividing the divisible assets by the squared root of household size as an equivalent scaling rule does not change figure 1 in any qualitative way. 17 This figure is remarkably similar to the one reported in the 2013 Global Corruption Barometer: 37% of households in Moldova that came into contact with education authorities paid bribes in the 12 months before the survey (Transparency International 2013). We focus on the likelihood of paying informal fees rather than the values paid since we assume the decision to participate in the informal fee scheme to be the most affected by a change in preferences. Note that we added one LCU to each private expenditure to ensure that the log exists. Ho¨ ckel, Santos Silva, and Sto¨hr 115

Figure 1. Kernel Density Plots of the Household Asset Index in 1999 and 2011

1999 2011 1 .8 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 .8 .6 .6 .4 density .4 .2 .2 0 0 −2 −1 0 1 2 −1 0 1 2 x x

migrant non−migrant migrant

Source: Authors’ calculations based on CELB 2012

pupil are about 300 USD per year (cf. appendix S.1 and table S.2). In contrast, households only report tutoring expenses for approximately 10% of children (cf. figure S.1). Despite higher income, both per child informal payments to teachers and tutoring expenses are significantly lower in migrant households compared to non-migrant ones. For transportation expenditure there is no such difference. The differences in informal payments and tutoring are mostly driven by more migrant households reporting zero payments (not refusals or “don’t know” answers) rather than by smaller positive expenses. This is not only evident at the individual level but also results in a strong negative correlation at the community-level between the share of migrant households and the share of respondents reporting payments to teachers (table 2: panel A, column 1).18 The slope of the regression line is approximately 0.4, a very high value that is statistically and economically significant. Note, though, that our data are designed to be representative at the national but not at the community level. The negative correlation also holds at the individual level (table 2: panel A, column 2–5).

IV. EMPIRICAL STRATEGY To analyze whether this strong negative correlation between migration and petty corruption at the community-level as well as the individual level is indeed closely tied to migration, we estimate the styl- ized model: 0 yihcs ¼ a þ dMighc þ Xihcsb þ ihcs (1) where yihcs are private inputs to the education of child i in household h from community c and school s. We consider three financial inputs (informal payments to teachers, tutoring, and transport expenditures) and two non-financial inputs if the child is enrolled in school and the frequency with which the caregiver spends time supporting the child in educational activities. The main explanatory variable of interest,

Mighc is a household-level dummy variable taking the value one if the child lives in a migrant household and zero otherwise; Xihcs is a vector of child- and household-level control variables; and ihcs is the error term.

18 See figure S.2 for an illustration. 116

Table 2. The Effect of Migration on Private Education Inputs

PANEL A Basic OLS results Payment to teachers

Community Individual level level Age group (1) (2) (3) (4) (5) all all 10þ 15þ 18þ

Migration 0.415*** 0.046 0.055* 0.069* 0.131** (0.119) (0.028) (0.031) (0.040) (0.058)

N 129 2287 1764 898 330

PANEL B Reduced form estimates (OLS) School Payments Out-of-school Transportation Caregiver enrollment to teachers tutoring expenditure time

(1) (2) (3) (4) (5) (6) (7) (8) log DðY > 0Þ log DðY > 0Þ log DðY > 0Þ

Network-Growth 0.000** 0.005** 0.001** 0.002*** 0.000*** 0.000 0.000 0.003*** (0.000) (0.002) (0.000) (0.001) (0.000) (0.002) (0.000) (0.001) Child characteristics Yes Yes Yes Yes Yes Yes Yes Yes Household characteristics Yes Yes Yes Yes Yes Yes Yes Yes

Main migration destinations Yes Yes Yes Yes Yes Yes Yes Yes T HE

N 2223 2148 2148 2170 2170 2168 2168 2162 W R2 0.064 0.068 0.042 0.095 0.084 0.173 0.168 0.265 ORLD

F-stat 5.72 7.85 5.86 4.11 4.43 13.3 14.0 40.5 B ANK E CONOMIC

EVIEW Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 August 08 on user Publications Bank World by https://academic.oup.com/wber/article-abstract/32/1/109/3105865 from Downloaded Table 2 (continued) Ho

PANEL C Sto and Silva, Santos ckel, ¨ First stage IV regressions Migration

(1) (2) (3) (4) (5) (6) (7) (8)

Instrument Network-Growth 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Child characteristics Yes Yes Yes Yes Yes Yes Yes Yes Household characteristics Yes Yes Yes Yes Yes Yes Yes Yes

Main migration destinations Yes Yes Yes Yes Yes Yes Yes Yes ¨hr

N 2223 2148 2148 2170 2170 2168 2168 2162 F Statistic 9.6 10.6 10.6 11.0 11.0 10.7 10.7 10.6

Second stage IV regressions School Payments Out-of-school Transportation Caregiver enrollment to teachers tutoring expenditure time

(1) (2) (3) (4) (5) (6) (7) (8) log DðY > 0Þ log DðY > 0Þ log DðY > 0Þ

Migration 0.129 4.430*** 0.829** 1.987** 0.274** 0.247 0.091 2.667* (0.097) (1.717) (0.332) (0.869) (0.126) (1.905) (0.304) (1.449) Child characteristics Yes Yes Yes Yes Yes Yes Yes Yes Household characteristics Yes Yes Yes Yes Yes Yes Yes Yes Main migration destinations Yes Yes Yes Yes Yes Yes Yes Yes

N 2223 2148 2148 2170 2170 2168 2168 2162 K-P weakid 9.6 10.6 10.6 11 11 10.7 10.7 10.6

95% CLR conﬁdence set [0.4, 0.06] [9.25, 1.88] [1.76, 0.32] [4.99, 0.01] [0.71, 0.02] [2.34, 2.82] [0.28, 0.49] [0.99, 5.57] CLR test p-value 0.17 0.00 0.00 0.05 0.07 0.83 0.60 0.00 Cluster-robust 95% AR conf. set [0.51, 0.02] [9.12,0.83] [1.68,0.08] [5.12, 0.65] [0.72,0.07] [3.45, 5.91] [0.47, 1.02] [0.66, 8.35] Cluster-robust AR p-value 0.11 0.03 0.04 0.01 0.01 0.90 0.76 0.01

Source: Authors’ calculations based on CELB 2012. Notes: Standard errors in parentheses. Panel A uses heteroskedasticity-robust standard errors throughout. Panels B and C use heteroskedasticity-robust standard errors that cluster at the community level. All models include a constant. Child characteristics: age, gender, serious illness in the past 12 months (dummy variable), and the (log) distance to school in minutes. Household characteristics: mean years of education of adult members, older siblings (dummy variable), household size, parents divorced, and urban/rural residence status. Main migration destinations: 2004 share of the community’s population that is a migrant to Italy, Ukraine, Romania, and Russia (four variables). *, **, and *** indicate p < 0:10, p < 0:05, and p < 0:01, respectively. In panel A, column 1: migration indicates the share of migrant households in the community; the dependent variable is the community’s share of respondents reporting positive informal payments to schoolteachers. Interpreting panel A column 1, please note that the survey was not designed to be representative at the community level. Panel B reports the reduced form where the outcome of the second stage is regressed on the instrument (Network Growth) and the endogenous variable (migration) is excluded. Note that interpreting the size of the instrumental variable is not easy, because it is a sum of Network- Growth Interactions. Differences in missing values for the dependent variables explain the different number of observations across columns. Panel C shows the first and second stage regressions. Migration is instrumented using a network-growth interaction IV. K-P weakid is the Kleibergen-Paap weak identification statistic. The CLR test refers to confidence region and the test statistic using the “condivreg” package by Mikusheva and Poi (2006). The cluster- 117 robust AR 95% confidence set is calculated using the “rivtest” package by Finlay and Magnusson (2009). Coefficients for all the control variables shown in supplemental appendix: reduced form (panel B) in table S.3, first stage

(panel C) in table S.4, column 1, and second stage (panel C) in table S.5. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 August 08 on user Publications Bank World by https://academic.oup.com/wber/article-abstract/32/1/109/3105865 from Downloaded 118 THE WORLD BANK ECONOMIC EEVIEW

Clearly, migrants are not a random population group but rather self-select into migration. Thus, it can be expected that they systematically exhibit distinct unobservable characteristics relative to non- migrants that might bias OLS estimates of equation (1). To overcome this problem, we estimate an instrumental variable approach by two-stage least squares (2SLS).19 Our instrument for migration status is the interaction between pre-existing migration networks at the local-level and destination-specific eco- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 nomic conditions. Formally, we use the growth rate of per capita GDP for each destination country between 2004–2010 and weight it with the share of migrants that, by 2004, had migrated from the community to that destination.20 The data for the migrant-destination share at the community level are derived from the 2004 Moldovan Census.21 The variable has already been employed as an instrument for migration in other studies of the Moldovan context (e.g., Lu¨ cke et al. 2012, Bo¨ hme et al. 2015). The rationale behind the use of Network-Growth is twofold. First, migrant networks are known to be very important in facilitating current migration. The network can provide ex ante information and assistance and ex post support for the migrant upon arrival (e.g., short-term accommodation, job-searching expertise, paperwork). Thus, pre-existent migrant networks effectively reduce the costs of migration (e.g., McKenzie and Rapoport 2010). Secondly, the growth of GDP per capita at the destination is a proxy for the country’s economic performance, and, more importantly, employment conditions that are exogenous to potential migrants in Moldova. An expanding job market is highly attractive for potential migrants and hence a pull factor to this destination (e.g., Antman 2011).22 As a whole, our instrument captures the exogenous variation of migrant networks at the community level,which lowers migration costs, and economic conditions at the destination country, which increase the expected returns of migration. Exploiting variation at the community level, our instrument does not allow exogenizing household-level choices regarding migration such as the identity of the migrant or the duration of the stay abroad. We can only successfully predict the probability of at least one household member becoming a migrant and, therefore, use the household’s migration status as the main variable of interest in our analysis. Therefore, our results should be interpreted as the average effects across all migrants and migratory spells. The validity of the instrument depends on the exclusion restriction that Network-Growth must only affect the provision of private educational inputs through migration status. This seems self-evident for the growth of GDP per capita at the destination. It is hard to conceive of a different relationship (i.e., other than migration) through which the changes in per capita growth rates in a set of foreign countries would affect the education investment decisions of a Moldovan household differentially between

19 The most common approach in the literature is instrumental variable strategies exploiting exogenous aggregate factors at the origin or destination: past migration rates (McKenzie and Rapoport 2010, Antman 2011, Zhang et al. 2014), financial infrastructure (Calero et al. 2009), and political unrest (Bansak and Chezum 2009) at the origin-level; employment conditions (Antman 2011, Cortes 2015) and exchange rate crises (Yang 2008) at the destination-level. 20 Analytically: ! XJ XT migrants GDP GDP Network-Growth ¼ c;j;2004 j;tþ1 j;t c population GDP j¼1 c;2004 t¼1 j;t

where c is the Moldovan community; j ¼ 1; 2; 3; :::; J is the migration destination countries and t ¼ 2004, 2005,..., 2010. 21 An advantage of our setting is that migration has been a relatively recent phenomenon in Moldova, and, thus, there is little scope for the non-migrant population to be influenced over time due to spillovers and long-term confounding developments that might have arisen over time. As a robustness check, we exclude for the analysis the migrant households which already had a migrant in 2004 or before as they might be included in the Census migration rates. The main results do not change qualitatively (available upon request). 22 To better capture the individual gains from migration, rather than the rise in opportunities, we alternatively use the change in GDP per capita. This results in comparable results in magnitude and significance. The Kleibergen-Paap weak identification statistic is however smaller than when using GDP growth for the IV. Ho¨ ckel, Santos Silva, and Sto¨hr 119 communities. For the migration network, we assume that past migration rates are predictors of current migration rates only via network effects and, otherwise, have no influence on the household’s education spending. Accordingly, we include the 2004 share of the community’s population who is a migrant to Italy, Romania, Russia, and Ukraine as additional controls in the 2SLS setup to account for proximity to Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 the border and any systematic differences in development that may have arisen because of migration to any of these important destinations between the take-off of migration, in 1999, and 2004, as in Bo¨ hme et al. (2015).23 The IV is not systematically correlated with school expenditures, local economic conditions as proxied by night lights (Henderson et al. 2012), local infrastructure or public goods as reported in the community questionnaire. Further, communities with IV values above and below median values are distributed evenly across the country (figure S.3). Summary statistics for the IV variable can be found at the bottom of table 1.

V. MAIN RESULTS The dependent variables of our empirical analysis are the child’s school enrollment status, the three categories of private education spending–payments to teachers, tutoring expenses, transportation expenses– and the time spent by the caregiver. The reduced form estimates are reported in table 2, panel B. The lack of a selection correction results in a statistically significant correlation between the instrument and 24 school enrollment, which indicates better migration options for those who leave school after the end of compulsory schooling. Correlations between the instrument and payments to teachers, as well as tutoring expenses, are negative and statistically significant.25 The first stage IV estimates are reported in panel C of table 2. The Network-Growth-IV is a positive and highly significant predictor of the household’s migration status. The instrument’s estimated coefficient implies that a one standard deviation increase in Network-Growth increases the likelihood of (at least one) household adult member migrating by approximately 14 percentage points. The Kleibergen- Paap rank test rejects underidentification at least at the 5% significance level in all the 2SLS regressions. The second stage indicates no statistically significant effect of migration on the enrollment probability (column 1) as a result of parental migration. Instead, the results indicate a strong reduction in the likelihood to pay teachers conditional on individual characteristics that is even more pronounced than the negative correlation in panel A (column 3). For tutoring, we see a similar negative effect whereas transport expenditure remains unchanged (columns 5 and 7). Interestingly, the determinants of tutoring are similar to those of paying bribes, supporting the view that tutoring offers a “cleaner” way of making informal payments to teachers. There is some evidence of caregivers more frequently spending time on the education of their children (column 8). In order to account for potentially inflated point estimates due to weak IVs, we provide the conditional likelihood ratio (CLR) confidence region and cluster robust confidence sets for the respective migration effect at the bottom of the table (Moreira 2009, Mikusheva and Poi 2006, Finlay and Magnusson 2009). Both methods show that the effect of migration on informal payments is bounded away from zero even when accounting for weak IVs.26 The results point to a statistically, as well as economically, significant negative effect of migration on informal payments.27

23 Alternatively using only one control for all migrant shares does not yield different results, but we prefer keeping to the more conservative ability to control also for different border effects as in that earlier paper. 24 A one standard deviation increase in the instrument implies a 2.5 percentage point reduction in enrollment. 25 See tables S.3, S.4, and S.5 for the point estimates of the control variables for panels B and C. 26 In addition, alternative estimates obtained from an IV probit estimation can be found in table S.6 for comparison. 27 Table S.7 presents OLS estimates for the same set of covariates. Due to the inclusion of a selection correction, covariates such as household size that are predictive of migration but not of informal payments pick up the correlation between migration and informal payments to teachers. The lack of a selection correction also results in 120 THE WORLD BANK ECONOMIC EEVIEW

Table 3. The Effect of Migration on Private Education Inputs: Controlling for Household Assets

Second stage IV regressions D(Payments to teachers) > 0 Caregiver time

(1) (2) (3) (4) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 Migration 0.964** 0.636** 3.887* 2.665*** (0.434) (0.259) (1.996) (1.003) Household asset index (log) 0.212*** 0.417 (0.080) (0.364) Household asset index 1999 (log) 0.031 0.151 (0.037) (0.098) Child characteristics Yes Yes Yes Yes Household characteristics Yes Yes Yes Yes Main migration destinations Yes Yes Yes Yes N 2186 1708 2354 1824 K-P weakid 6.601 16.857 6.935 22.776

Source: Authors’ calculations based on CELB 2012. Notes: Heteroskedasticity robust standard errors that cluster at the community level in parentheses. See footnote 16 for a list of assets included in the asset index. Child characteristics: age, gender, serious illness in the past 12 months (dummy variable), and the (log) distance to school in minutes. Household characteristics: mean years of education of adult members, older siblings (dummy variable), household size, parents divorced, and urban/rural residence status. Main migration destinations: 2004 share of the community’s population that is a migrant to Italy, Ukraine, Romania, and Russia (4 variables). *, **, and *** indicate p < 0:10, p < 0:05, and p < 0:01, respectively. K-P weakid refers to the Kleibergen-Paap weak identiﬁcation statistic. Migration is instrumented using a network-growth interaction IV.

The very strong negative correlation, even after rigorously accounting for self-selection, cannot be the consequence of a mere income effect. At the same time, children’s or parents’ socioeconomic characteristics do not predict petty corruption at the extensive margin very well. While there is more reporting of payments for older students, girls, and by more educated parents–one of the core predictors of income– the other controls are statistically insignificant. Additional analyses yield no evidence of heterogeneous treatment effects by age, yet this is partly due to imprecise estimates in small subsamples (table S.8). Our main results are not explained by differences in household wealth (proxied by a household asset index, table 3). Contemporaneous assets are endogenous to migration and, in fact, constitute one of the main expected transmission channels of migration on education inputs (column 1 and 3). Pre-migration differences in wealth across households (columns 2 and 4) should not and do not have any impact on the second stage migration coefficient. To sum up, our finding on bribes can neither be explained by wealth differences across migrant and non-migrant households nor by the income effect of remittances. Regardless of this, the income effect of migration matters by improving families’ ability to keep children in school. Whereas over 50% of non-migrant parents report barriers that will prevent the child from achieving the caregiver’s desired level of education this is the case only for 35% of migrant parents (table S.9: panel A). The modal reason, a lack of finances, is cited by over 80% of caregivers in either group. Migration reduces barriers in general and financial barriers in particular (table S.9: panel B). The income effect in education is thus strong, in stark contrast with its effect on petty corruption. As a supporting ad hoc assessment of the mechanism, log remittances received by the household can be used in place of the migration dummy as the endogenous variable (results available on request). In this case, no more significant correlation between the endogenous variable and informal payment is found in the second stage, which may be taken as tentative evidence that variation from the instrument does not affect bribe-paying through the remittance channel. Even though one has to be careful interpreting such evidence because it is no longer a valid IV approach, this may be interpreted as suggesting that, instead of remittances, other aspects of migration are likely to be the source of the bribe-reducing effect.

statistically significant positive effects on transport expenditure, which are explained by higher available income as additional results show (available upon request). Ho¨ ckel, Santos Silva, and Sto¨hr 121

In line with other research, one might hypothesize that the negative coefficient of migration is explained by a lower willingness to bribe officials in the education system. This could be due to former migrants’ own likelihood of bribing teachers or through social remittances (cf. Ivlevs and King 2014, Barsbai et al. forthcoming). Irrespectively of whether it is the migrants themselves or their families who decrease Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 bribe-paying, our finding is promising from a normative point of view. From an economic standpoint, the money not given to teachers as informal “service fees” or “presents” (i.e., for rent-seeking) could be used more productively on other household expenses and would stop distorting incentives for teachers and students. The emerging picture is thus a reduction in bribes and a simultaneous increase in the frequency of parental involvement in children’s education due to migration. In the next section, possible transmission channels will be discussed in more detail.

VI. TRANSMISSION CHANNELS AND ROBUSTNESS According to the community leaders interviewed in the survey, the most widely perceived constraint to school quality is not a scarcity of staff but of other inputs, such as teaching materials or utilities (table S. 10). Parental education inputs could be affected by the public funding situation of local schools, causing omitted variable bias.28 Thus, we match our household data with administrative school-level expenditure data from an open budget initiative of the World Bank (BOOST) to ensure that the instrument is not picking up community-level variation in the supply of public education. Matching both datasets is imperfect because the availability of the budget data was not anticipated at the time of the household survey (see appendix S.1 for a detailed description of the data and matching procedure). We include the school-level executed budget in several expenditure categories as additional explanatory variables.29 The strong negative effect on bribes remains even after adding the additional controls, which approximately halves the sample size.30 Schools’ wage bills, which closely correspond to the schoolteachers-per-pupil ratio (cf. figure S.4), teaching material and schools’ maintenance funds are not significantly correlated with household educational expenditures (table S.11: columns 1–6). By contrast, schools’ expenditures on utilities and transports, where community leaders often report lacking funds, exhibit signs of substitutability of private and public expenditure. There is also some tentative evidence of substitution between the parental investment of time and the time teachers could allocate to individual children (column 7).31 The strong correlation between migration and informal payments to teachers is also robust when controlling for an index of infrastructural quality of the school (table 4: column 1). We furthermore tested whether the migration-induced reduction in informal payments is lower in worse-funded schools where informal payments may be less controversial but did not find any robust differences (results available upon request). Sending students to schools with funding for school buses and attending a more distant school, both of which proxy secondary and advanced secondary schools that cover larger areas, correlate positively (although statistically insignificantly) with informal payments. Using school fixed-effect regressions to compare students within schools, we find that better off parents pay more to teachers and buy more tutoring from their children’s teachers (table 4: columns 3 and 4).32 This underscores the importance of the income effect. The migration coefficient is negative but insignificant, suggesting that

28 Private educational spending responds to public funding, see for example Houtenville and Conway (2008). 29 We do not find evidence that they are systematically correlated with migration. 30 The first-stage estimates are reported in column 2 of table S.4. 31 Table S.12 provides OLS results when the sample is split by migration status. The negative coefficient on the teacher-pupil ratio (proxied by wages per pupil) is similar for both migrant and non-migrant households, although statistically insignificant for the former. 32 Note that migration as a major source of income inequality is not exogenized here due to a lack of a valid within-community IV. 122 THE WORLD BANK ECONOMIC EEVIEW

Table 4. Detailed School Funding, Infrastructure Controls and School Fixed Effects

(1) (2) (3) (4) log(Payments log(Out-of-school log(Payments log(Out-of-school to teachers) tutoring) to teachers) tutoring) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 Estimator OLS OLS FE FE

Migration 0.449** 0.062 0.099 0.074 (0.210) (0.170) (0.214) (0.195) Log(household income) 0.107 0.186*** 0.142 0.123** (0.087) (0.055) (0.086) (0.052) School infrastructure and budget School asset index 0.000 0.000 (0.000) (0.000) Wages (log) 0.111 0.019 (0.457) (0.332) Teaching materials (log) 0.062 0.145 (0.257) (0.201) Utilities (log) 0.244 0.106 (0.176) (0.118) Transports (log) 0.138 0.041 (0.101) (0.056) Maintenance (log) 0.034 0.031 (0.079) (0.067) Number of school ﬁxed effects 148 148 Child characteristics Yes Yes Yes Yes Household characteristics Yes Yes Yes Yes N 873 880 875 882 R2/R2 within 0.116 0.141 0.026 0.027

Source: Authors’ calculations based on CELB 2012. Notes: Heteroskedasticity robust standard errors that cluster at the school level in parentheses. The school asset index is based on dummies for working water supply, working hot water, working sewage, working heating capacity, the existence of a library and of a medical office, the number of classrooms, auditorium seats, and the gym and sports field size. This information as well as the school budgets come from BOOST data. School budget variables are per student amounts. School fixed effects estimates in columns 3 and 4. Sample size decreased due to inability to match all schools (c.f. table S.17 in the online appendix. Child characteristics: age, gender, serious illness in the past 12 months (dummy variable), and the (log) distance to school in minutes. Household characteristics: mean years of education of adult members, older siblings (dummy variable), household size, parents divorced, and urban/rural residence status. Main migration destinations: 2004 share of the community’s population that is a migrant to Italy, Ukraine, Romania, and Russia (four variables). *, **, and *** indicate p < 0:10, p < 0:05, and p < 0:01, respectively. much of the variation associated with migration occurs at the school level. This fits well our discussions with Moldovan experts, who stated that the payments to teachers that are collected by informal parental committees can quickly stop completely once a few parents refuse to pay them–an effect that often occurs in public-good settings if punishment is weak (cf. Fehr and Gachter€ 2000). In line with our expert discussions, we thus interpret the migration effects as quickly spilling over within schools.33 To ensure that our results are not driven by local heterogeneity across communities rather than migration, we add a within-community dimension to the original IV’s community-level variation. We interact the network-growth IV with the household’s mean years of education since more educated households can be expected to be better able to respond to the growth-pull mechanism approximated by our IV. The new variable is positively related with migration and statistically significant at the 1% level. The estimated effect of migration on payments to teachers is almost identical to our main estimates (table S.13).

33 There is no statistically significant correlation between any school budget variable and the migration share of pupils in the household survey. Also, if migrant parents were planning to send their children abroad and therefore stopped paying local teachers, there should be strong differences within schools. Ho¨ ckel, Santos Silva, and Sto¨hr 123

Two motivations for ceasing bribe-paying are plausible: (i) migrant parents being generally less toler- ant of corruption due to their experience abroad; and (ii) migrant parents demanding actual cognitive achievement instead of good grades because they have witnessed the unimportance of Moldovan certifi- cates relative to actual skills for success abroad. A full 96% of caregivers replied that education was Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 important to be successful abroad. Yet, there is no significant reduction in the perceived quality of children’s individual schools (table S.14).34 This gives us confidence that our results are not driven by differences in the cost-benefit analysis of the Moldovan school system between non-migrant and migrant households. In order to provide some evidence of robustness as well as external validity of our study, we draw on another, less detailed, dataset to show that a similar negative correlation of migration and bribe paying exists also in data independent of ours. The so called Barometer of Public Opinion of Moldova’s Institute for Public Policy, a well-regarded biannual survey which collects individuals’ opinions on a wide range of topics regarding politics, values, and related issues in Moldova, covered informal payments to authorities and migration status in the April 2013 survey.35 Those individuals with migration experience to the West were more likely to have had contact with the justice system and were more likely to have been asked for bribes for the solution of their problem. Conditional on reporting not paying a bribe, people with any experience of migrating and especially the typically more wealthy migrants to the West were more likely to have been asked to pay informal fees than those without migration experience (odds ratio: 3.6 times). Individuals with migration experience thus seem to be less likely to pay bribes under a given level of pressure to do so.36 Finally, our results could be driven by the migration-induced change in the identity of the child’s caregiver, for example, reflecting that non-parental caregivers (e.g., grandparents, siblings, aunts, or uncles) have less involvement in (or knowledge of) the education system and are, therefore, less likely to bribe teachers. They may also have lower opportunity costs of time and may therefore spend more time on the child’s education. To rule out this mechanism we re-estimate the main results while excluding all children with caregivers who are not one of their biological parents (table S.15). The slightly, but not significantly, larger coefficients of migration provide strong evidence that our results are not driven by caregiver change. Our results are furthermore robust to alternative but similar definitions of the migration dummy (e.g., who migrates or how long migration spells have to be). We also find no evidence that our effect is driven by caregivers who are return migrants.37 More generally, including a dummy variable for return migrant households (i.e., those households with at least one return migrant but no current migrants) does not affect the migration coefficient in our educational input IV regressions. Return migration itself has a negative coefficient which is smaller in absolute magnitude than the (current) migration estimate, but statistically insignificant (available upon request). Note that correcting for self-selection into return migration lies beyond the scope of this paper. Despite controlling for households’ mean years of education in all regressions, it could still be possible that households were sorted on unobserved ability within Moldova. In that case, the size of the 2004

34 Alternatively, the main effects of migration on the provision of educational inputs remain unchanged after including the perceived school quality variable as an additional control (available upon request). 35 The sample contains 1100 individuals from 76 communities and is nationally representative of the adult population. All results are available upon request. 36 Our instrumental variable strategy does not allow us to identify destination specific effects. Therefore, our results are the average migration effect across all destinations, not just Western countries. If the effect is entirely driven by migration to the West, where corruption is far less common than in Moldova, then our 2SLS estimates are a lower bound for the true Western migration effect. 37 We define a return migrant as an adult that spent more than three months abroad in one single spell since 1999 but is no longer a migrant at the time of the survey. 124 THE WORLD BANK ECONOMIC EEVIEW network could be correlated with families’ unobservable skills. In the Moldovan context, this hypothesis is very unlikely. In Soviet times, internal migration was highly restricted and centralized. Highly skilled individuals were not only concentrated in the main cities, where tertiary education was available, but were often deployed as state bureaucrats to agricultural or industrial projects all over the country, espe- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 cially the countryside. After the collapse of the Soviet Union, there has not been much internal migration. To corroborate our arguments, we re-run our main specifications excluding children living in the two cities, Chis¸inau and Balt ¸i, that exert the main pull effect internally. Our results remain fully robust throughout (available upon request). As seen above, our main results are robust to a host of alternative explanations. If not paying bribes, however, had dire consequences for the children’s educational performance, lower corruption might not be in their best interest. We therefore estimate the effects of migration on students’ grade point average (GPA) (table S.16). Throughout the different specifications, payments to teachers remain insignificant. In addition, and in line with the literature, we find a negative correlation between migration and the GPA that is partly compensated by household wealth. This underlines that most of the informal payments may not be directly meant to improve grades relative to classmates but rather operate as illicit user fees or per capita taxes. If their payment ceases, students on average do not suffer worse grades. However, students who, relative to their classmates, receive extra attention from teachers due to tutoring (which are partly mere bribes), do better gradewise. Also, many Moldovans suggest that bribing of teachers for grades is not effective anyway because students study less hard if they expect to receive higher scores. Another possibility is that deviating from the common situation of paying bribes has no adverse effects, especially as standardized tests are increasingly used in the most important exams with the deliberate aim of fighting corruption in education.

VII. CONCLUSION In this paper, we analyze the effect of emigration on petty corruption in education, in particular on informal payments to teachers. Such payments are typically understood to have a dual motivation: fundrais- ing for maintenance of schools as well as supplementing teachers’ wages to increase their motivation and/or to focus their attention on individual children. We use the interaction between migrant networks and economic growth at the destination as an instrumental variable for the household’s migration status in order to control for selection into migration. Using this IV approach, we document a reduction in informal payments to teachers. This aggregate migration effect consists, among others, of a non-negative income effect that is counteracted by other factors. By excluding alternative explanations, and in line with an emergent literature, we speculate that the widening of migrants’ horizon (i.e., additional information or value change) may be the main driver of the reduction in petty corruption. Incorporating school-level budget data in our analysis, we show that there is no strong correlation between public school funding and petty corruption. Thus, the most socially accepted justification for informal payments to teachers—the need for school maintenance and wage supplements for motivation—is not a good predictor of differences between schools. Within schools, additional analysis suggests that reductions in payments to teachers quickly spill over to non-migrants. This is in line with qualitative evidence according to which per capita payments to teachers cease once a few parents in a class refuse to pay due to only weak enforcement devices in the hands of teachers or other paying parents. Our results fit with novel research that shows how participation in corruption often depends on people perceiving it as widespread. This is a prevalent phenomenon in low- and middle-income countries. In such a setting, simply increasing teachers’ salaries and school resources might decrease the perceived legitimacy of informal payments. If budget constraints made this impossible and these payments continued to exist, structures such as teacher-parent committees should formalize them as donations. The available funds should then be focused on making teachers wages sufficient while stepping up enforcement of laws Ho¨ ckel, Santos Silva, and Sto¨hr 125 against individual corruption. This way, transparency and accountability would be improved while providing solutions for underfunding that do not distort incentives. Both the opportunity to siphon off part of the payments for private use and the necessity to do so would thus decrease. For bribes which are used to get one’s own child ahead of the competition, other measures are likely to be more effective. In a Downloaded from https://academic.oup.com/wber/article-abstract/32/1/109/3105865 by World Bank Publications user on 08 August 2019 bold move, the Moldovan government recently introduced videotaping of the most important high school exam to put an end to teachers, motivated by informal payments, telling answers to their classes or, worse, individual students. As such laudable reforms reduce the scope for corruption they may also make it easier for both migrants and non-migrants to resist corruption. Focusing reform efforts on increasing awareness that petty corruption in education is a problem, stoking demand for educational achievement rather than for good grades, and creating incentives to deviate from the social norm of par- ticipating in petty corruption hold promise. This paper thus provides evidence of a petty corruption channel through which the all too often forgotten positive effects of emigration on origin countries can arise. Future work should seek to more clearly disentangle how such effects occur and what role the institutional and social contexts play.

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Abstract We examine the long-term impacts of international migration by comparing immigrants who had successful ballot entries in a migration lottery program, and first moved almost a decade ago, with people who had unsuccessful entries into those same ballots. The long-term gain in income is found to be similar in magnitude to the gain in the first year despite migrants upgrading their education and changing their locations and occupations. This results in large sustained benefits to their immediate family who have substantially higher consumption, durable asset ownership, savings, and dietary diversity. In contrast we find no measureable impact on extended family. JEL classification: F22, O15 Key words: International Migration, Natural experiment, Assimilation, Household Wellbeing

Income differences between developed and developing countries are vast: in 2014, GDP per capita in high-income OECD countries was nine times that in middle-income countries and 68 times the per capita income in low-income countries.1 These differences hold even within very narrowly defined occupations, with Ashenfelter (2012) showing wages of workers at McDonald’s differ by as much as a factor of ten across countries. How much of this difference can someone migrating from a poorer to a richer country hope to gain?

John Gibson (corresponding author) is a professor at the University of Waikato, New Zealand; his email address is [email protected]. David McKenzie is lead economist in the Development Research Group at the World Bank; his email address is [email protected]. Halahingano Rohorua is a research fellow at the University of Waikato; her email address is [email protected]. Steven Stillman is a professor at the Free University of Bozen-Bolzano, Italy; his email address is [email protected]. We thank the Government of the Kingdom of Tonga for permission to conduct the survey, the New Zealand Ministry of Business, Innovation and Employment for the sampling frame, Geua Boe-Gibson, Paul Merwood, Fred Rohorua, and the interview assistants, and most especially the study participants. Three anonymous referees, Taryn Dinkelman, and seminar participants at the AFD/CGD/World Bank conference on migration and development provided helpful comments. Financial support from the World Bank, the University of Otago, and Marsden Fund grant UOW1101, and ethical approval from the Human Ethics Committee of the Waikato Management School are acknowledged. The views expressed here are those of the authors alone and do not necessarily reflect the opinions of the World Bank, the New Zealand Ministry of Business, Innovation and Employment or the Government of Tonga.

1 Source: World Development Indicators online query September 23, 2015: high-income OECD countries had a percapita GDP of US$43,619, middle-income countries US$4,694, and low-income countries US$639.

VC The Author 2017. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please email: [email protected] 128 Gibson, McKenzie, Rohorua, and Stillman

The answer depends on the sources of these income differentials. If better institutions, higher quality capital, and other factors raise the productivity of all workers, then the same worker will be immediately vastly more productive working in a rich country than in a poorer one and so should earn more as soon as they move. In contrast, if benefiting from these factors requires language skills, country-specific knowledge, Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 and other attributes that are embedded in native workers, immigrants may be initially no more productive abroad than they were at home, and their short-term income gains may be limited. In the longer-term, any income gains will then only occur if immigrants can assimilate and gain these country-specific skills. Determining either short-term or long-term gains from migration is complicated by the fact that migrants are self-selected, differing in skills, ambition, and other attributes from those who do not migrate. One approach is to try to control for as many observable differences as possible between migrants and non-migrants and then argue that the self-selection on unobserved factors needed to over- turn the measured gains is too extreme to be likely. Using such an approach, Clemens et al. (2009) document that a typical individual from the average developing country should expect to earn 2.5 to 3 times their income upon moving to the United States. Another way to deal with selection issues is to use evidence from migration lotteries. In this paper, we use a ten-year follow-up survey of Tongans who had applied to the migration lottery conducted under New Zealand’s Pacific Access Category (PAC), measuring long-term impacts of migration by comparing outcomes for successful and unsuccessful applicants. In previous work, McKenzie et al. (2010) and Stillman et al. (2015), we examine the short-run impacts of migration on income (and in the second paper, also other forms of wellbeing). However, this paper is the first we know of that examines both short-term and long- term impacts of migration in a modern setting where it is possible to get unbiased estimates of both.2 Our previous work shows that migrants from Tonga to New Zealand are positively self-selected, have larger returns to unobservable attributes in New Zealand than in Tonga, and that migration leads to a 263 percent gain in income in the first year after migration. While this is a large impact, it is just half the gap in percapita incomes between the two countries. A key question, for which there is little evidence to date, is whether migrants are able to gain more of this gap over time. One hypothesis is that the income gains increase over time as migrants assimilate and gain new skills. But a competing hypothesis is that the gains may weaken over time if migrants have given up occupations at home with rising income trajectories to work in occupations abroad that offer higher immediate incomes but less prospect of career growth. For example, migrants working as teachers, public servants, or doctors in their home countries may face wage structures determined strongly by seniority, and earn more abroad as cleaners, shop assistants, and agricultural workers but with less scope for wage growth. It is complex and costly to track immigrants and a comparison group of non-migrant lottery applicants over a decade and this is unlikely to be feasible elsewhere. It would be much easier if one could just take an unbiased estimate of the short-run impact of migration and add an assimilation profile from either the same migrant group or a more general group of migrants in the destination country. This approach has two problems. First, as noted by Borjas (1985) and Abramitzky and Boustan (2016), if the quality of each immigrant cohort differs, the experience of previous cohorts will give a biased picture of the true assimilation path for current migrants. Second, even if longitudinal data for an immigrant group are available, without a counterfactual group of non-migrants in the origin country, one must use destination country natives to estimate the extent to which any changes in income over time reflect actual

2 Abramitzky et al. (2014) examine short-run and long-run impacts of migration for individuals who moved to the United States at the beginning of the twentieth century. Other studies of migration lotteries include Clemens (2010), who examines Indian IT workers selected to migrate to the United States under the H1B visa lottery, and two studies of impacts on those left behind but not on the migrants themselves: Mergo (2011) for Ethiopian households with a family member migrating through the U.S. Diversity Visa lottery and Gibson et al. (2013) for Samoan households with a family member migrating through the Samoan Quota lottery. The World Bank Economic Review 129 assimilation versus returns to work experience, life-cycle effects, or general business cycle effects. Thus, one must assume that these effects are the same for migrants and natives. It is thus unsurprising that Abramitzky et al. (2014) find that changes over time for natives provide a poor counterfactual for estimating assimilation among immigrants. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 Our first contribution is to show, in section II, the assumptions needed if one uses evidence from assimilation studies that compare migrants with destination country natives as a proxy control group for estimating long-term migration impacts. This indirect approach requires origin and destination countries to experience similar income growth rates and to have similar lifecycle patterns in the labor market. While the first condition holds for the specific example of Tonga and New Zealand, the second condition does not. More generally, these conditions would often be violated for a developing country origin and a developed country destination, so indirect estimates of long-term migration impacts based on the assimilation literature are unlikely to equal direct estimates that use origin country comparison groups. Moreover, while native-to-migrant comparisons can inform about inequality in destination countries, they cannot inform about the sources of cross-country income differences in the way that migrant to origin country non-migrant comparisons can. Given the above considerations, in order to study long-term migration impacts one needs a sample of migrants and a comparison group from the origin country. We discuss how we get such samples in section III and how we deal with issues that arise when migration occurred up to a decade earlier, including individuals who on-migrate to other countries, individuals who had not moved at the time of short-term follow-ups but have now moved (“slow compliers” rather than non-compliers) and ballot losers who migrate via substitute pathways. We also consider locational changes within the destination country that were less prevalent when the migrants were first studied, along with (associated) occupational changes. A further important contribution of our paper is that we have a long-run follow-up survey of the extended families of both the migrant households and the unsuccessful applicants, allowing us to examine the long-term impacts on extended family members. We find that the long-term impact on income of Tongans migrating to New Zealand is similar to that found in the short-term. The economic payoff to this migration appears to come immediately and then hardly grow, notwithstanding various investments by the immigrants in qualifications, internal mobility, and occupational change.3 There are a number of possible explanations for this finding. Unlike many immigrants in other settings, Tongans speak the local language (English) prior to migration, many have previously been to New Zealand, and most have a family network in New Zealand. Hence, this group of individuals likely had a high level of New Zealand-specific knowledge prior to migrating. Furthermore, many of these individuals worked in public sector jobs in Tonga that have high returns to seniority and, hence, perhaps traded-off some future increased earnings growth for large immediate gains in income. In previous work, Tongan migrants to New Zealand are seen to be quite similar in many dimensions to Mexican migrants to the US (McKenzie et al. 2010). This is also true among the dimensions discussed above. For example, Mexican migrants speak a language that is widely used in the destination country, many have previously traveled to the US, and most have an extended network already there. Furthermore, many worked in similar jobs as the Tongan migrants prior to migration (for example, teachers and nurses). Previous work using traditional methods has found limited assimilation for Mexican migrants to the US, which is typically viewed negatively (Borjas 2015). Seen in the light of our findings, perhaps Mexican migrants are also trading-off large short-run wage gains for lower future earnings growth and the reason that ‘assimilation’ does not occur is not lack of US specific human capital. We also examine the impact of migration on a range of other outcomes. Since the initial gains in income are so large, migrants derive very large lasting benefits. They earn almost 300 percent more than

3 Stillman and Mare´ (2009), using traditional methods, also find no evidence of improvement over time in economic outcomes for a general group of Pacific Island migrants to New Zealand in a slightly earlier time period. 130 Gibson, McKenzie, Rohorua, and Stillman non-migrants, have better mental health, live in households with more than 250 percent higher expenditure, own more vehicles, and have more durable assets. We estimate a conservative lifetime gain to the adult principal migrants of NZ$315,000 in net present value terms (approximately US$237,000). These gains seem to accrue mostly to the migrant and their immediate family who accompany them with little Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 significant measureable impact of this migration on extended family remaining in Tonga.

I. Direct and Indirect Migration Counterfactuals The development literature and our own previous work (McKenzie et al. 2010) identifies the immediate gain from migration by comparing outcomes for migrants with those for similar non-migrants. In the case of a migration lottery, in which these two groups would have identical outcomes prior to migration and would exhibit the same trend in the absence of migration, this short-term gain is given by the difference in outcomes between migrants and non-migrants at time t ¼ 1. This is indicated by the term B in figure 1. In contrast, studies on assimilation in the immigration literature compare the change over time in income or occupation for migrants and for natives in the destination country. In figure 1, this is given by the difference between the D term at t ¼ 1 and the C term at t ¼ 10. In the example shown, this difference is positive, with migrants catching up to the native workers over time.

Figure 1. Direct and Indirect Estimates of the Long-term Gain from Migration

Outcome

Naves C D Migrants A

B Non-migrants

t = -1 t = 1 t = 10 Time since migraon

An indirect estimate of the long-term gain from migration would add together these two effects, giving B þ (D-C), combining the short-term income gain with the assimilation effect. From figure 1 it is clear that this only gives an accurate estimate of the true, directly estimated, long-term gain (A) if the outcome trajectories for the native workers and for origin country non-migrants are parallel between time t ¼ 1and t ¼ 10. For this to be the case, the home and destination economies must experience similar income growth rates over the decade and the labor market life-cycle dynamics must be similar in the two countries. In many cases, we might expect this assumption to be violated so indirect estimates will be inaccurate. As a first example, consider migrants from fast-growing economies like China and India who move to the US. Then we might expect the income profile for Chinese or Indian non-migrants to exhibit much steeper wage growth over a decade than would be the case for US native workers, in which case the indirect estimate would overestimate the long-term income gain from migrating. In contrast, consider migrants fleeing impending civil war in Syria or Iraq and moving to Germany. In those cases, we might The World Bank Economic Review 131 expect the origin country non-migrant income profile to exhibit negative growth over the subsequent decade compared to positive growth for native workers in Germany. In this case, the indirect estimate would underestimate the long-term income gain from migrating. Even when the origin and destination countries experience similar income growth rates over the Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 period, the indirect estimate may not deliver an accurate estimate of the long-term gain if the two countries differ in lifecycle profiles. For example, if the origin country largely pays people based on job tenure rather than on productivity, the age-earnings profile may be steeper there than in the destination country. Similarly, if people retire or change occupations at different points in the lifecycle in the two countries, combining the short-term impact estimate (B) with the assimilation profile (D-C) will give a biased estimate of the impacts of migration on occupational mobility. For the specific example of migration from Tonga to New Zealand, the income growth rates are similar but the labor market lifecycle dynamics are not. In recent years, both countries have experienced similarly faster rates of economic growth than their historic norms. Data from the World Development Indicators show an average growth of GNI per capita (in US dollars) of 6.8% for NZ and 6.7% for Tonga over 2005 to 2014. As a result, at the macroeconomic level, the assumption of parallel trends needed by the indirect estimator may be reasonable.4 However, the lifecycle effects are different, especially in terms of occupational mobility, with workers in Tonga leaving professional jobs, typically in the public sector, at a much earlier age than in New Zealand and shifting to occupations such as farmers and fishermen. Thus, part of the gains from migration in not having this enforced early exit from the primary labor market would be missed by using the indirect approach to form a counterfactual for the migrants.

II. Context to Our Surveys The Kingdom of Tonga is an archipelago of islands in the Pacific approximately three hours north of New Zealand by airplane. The resident population is just over 100,000, and the gross national income (GNI) per capita is $4,150 (in 2005 PPP $), which is similar to Indonesia and ranks Tonga 121st out of 190 countries.5 Average incomes in New Zealand are about six times as high with a per capita GNI of $24,400 in 2012. Tonga and New Zealand both rank 26 places higher in the Human Development Index than they do in terms of GNI, making New Zealand the largest positive outlier of very high human development countries and Tonga the second largest outlier of countries with medium levels of human development (UNDP 2013). Hence, while the countries differ a great deal in terms of average incomes, higher incomes in New Zealand are not compensating for lower levels of other aspects of human development. Emigration out of Tonga is high with 30,000 Tongan-born living abroad, mainly in New Zealand, Australia, and the United States. Despite an earlier history of employment migration to New Zealand, family reunification was the main channel of access in the 1990s following New Zealand’s implementation of a points-based immigration system that favours highly skilled migrants.6 In 2002, New Zealand introduced a new migration program, the Pacific Access Category (PAC), that lets a quota of 250 Tongans permanently

4 However, New Zealand has also seen net long-term immigration rise to annual totals equivalent to 1.4 percent of the resident population with these inward flows comprised especially of returning New Zealanders, skill-selected workers, and international students. Strong growth in labor supply may create a wedge between macroeconomic trends and labor market trends; more generally, macroeconomic aggregates tend to over-estimate income gains from migration (McKenzie et al. 2010). 5 All statistics in this section are from the 2013 Human Development Report (UNDP 2013). 6 Family reunification migration to New Zealand is restricted to parents and spouses, with adult children and siblings having to seek entry through other residence channels such as the skilled migrant category. In 2007 and, again in 2012, rules to sponsor parents were tightened with income and wealth thresholds that exclude most Tongans and a rule that all children of the sponsored parent had to be in a different country than the parent with more than half the total children in New Zealand. Bedford and Liu (2013) show that these policy changes had a larger negative effect on Tongans than on 132 Gibson, McKenzie, Rohorua, and Stillman migrate each year. Any Tongan citizens aged 18 to 45 who meet certain English, health, and character requirements may register. A random ballot selects amongst applicants (known as “principal applicants”) with odds of about 1-in-13 (7.8%) during the 2002–05 ballot years that our sample is drawn from. If their ballot is selected, applicants have six months to obtain a full-time job offer in New Zealand Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 that meets an income threshold similar to the minimum wage. This ensures self-reliance since Tongans are not eligible for most forms of welfare until they reside in New Zealand for two years. After a job offer is filed along with a residence application, it typically takes from three to nine months to receive residence approval, and immigration to New Zealand must then occur within 12 months. Spouses and any unmarried children up to age 24 are also eligible to immigrate with the ballot winner (and are part of the annual quota of 250 people). The typical applicant is age 33 at the time of application with 11 to 12 years of schooling and two- thirds of them married (McKenzie et al. 2010). Just over half of the applicants were living in households in which all members would be eligible to migrate if they won while the rest were living in households that contained extended family such as their parents, siblings, nephews, and nieces (Gibson et al. 2011). The context is thus one in which migration results in a nuclear household moving permanently abroad and potentially leaving behind extended family members who they had been living with at the time, but would not necessarily continue living with, as they got older even without migration.

Sampling Design Our population of interest consists of entrants to the 2002 to 2005 PAC migration lotteries. There were a total of 4,696 principal applicants of whom 367 were randomly selected as ballot winners (figure 2). Official records provided by the New Zealand immigration authorities in late 2012 show that 307 of these winners (84%) had residency applications approved and had ever migrated to New Zealand. The remaining 60 ballot winners did not migrate and are thus non-compliers to the treatment of migration. Our prior studies measuring the short-term impacts of migration examined outcomes for a random one-third subset of the ballot winners who eventually moved to New Zealand. The main reason for using a

Figure 2. Sampling Frame

Ever Migrate to New Zealand (307) - Long survey: 133 Short survey: 61 Ballot winners (367) Don't migrate (60) Ballot Applicants in 2002-05 - Long survey 6 (4,696) Ballot Losers (4329) Short survey 3 - Long survey 143 Short survey 39

subset was that the New Zealand government’s longitudinal survey of immigrants had randomly selected a large pool of the ballot winners as potential individuals to interview in their survey. To avoid respondent burden from being in two surveys, their details were not released to us at the time of our initial surveys. In late 2012, we received permission from the immigration authorities to view the names of all PAC ballot winners from 2002–05, including those earlier reserved for the official government survey.

any other immigrant group in New Zealand; the number of residence approvals for Tongans under the parents category fell by 70% after the 2007 policy changes. The World Bank Economic Review 133

In order to have a larger sample to examine long-term impacts, we decided that, instead of just trying to recontact the sample used previously, we would track all migrants from this full list. Given that the only address details were from about ten years earlier, when the individuals had applied for residency and in many cases were for relatives, we undertook simultaneous fieldwork in both New Zealand and Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 Tonga since people in their home village were often the best source of updated contact information.7 These tracking and surveying operations were costly and are unlikely to be possible in many other contexts when the countries and populations involved are larger. Our main survey involved an extensive face-to-face interview, which also collected anthropometrics, blood pressure, peak lung flow, and included lab-in-the-field games. Of the 307 principal applicants ever migrating to New Zealand, 133 completed the full survey between late 2013 and the end of 2014. In order to bolster our sample size, in early 2015 we fielded a shortened survey that did not include health measurements or the lab-in-field games. This was mainly done as a telephone interview and was designed to reach those who had on-migrated beyond New Zealand or were located in parts of New Zealand that were impractical for face- to-face interviewing, although we also learned, through snowball effects, of more migrants in our face-to-face survey area and gave them the short survey as well. Overall, 61 additional ballot winners who had ever migrated to New Zealand were given the short survey, including 11 who had now on-migrated to Australia (ten) and the UK (one). In total, we were able to survey 194 households with principal applicants who ever migrated to New Zealand after winning the ballot (figure 2), which is 64 percent contact rate. We had even less information available for the ballot losers and non-compliers since these individuals had not filled out residency applications. We therefore used the same surveying approach for these groups as we had in our previous survey, which was to sample from the same villages in Tonga from which our migrants originated. Out of 4329 ballot losers, 143 were administered the long form survey and 39 the short survey (of which nine had subsequently moved to New Zealand through alternative pathways, including by winning a later round of the PAC lottery). Finances limited us to this relatively small sample, but, based on our previous research, we judged that it would give us enough power to measure economically significant impacts. An advantage of surveying from the same origin villages is that we can implicitly control for any unobserved characteristics that vary spatially in Tonga. Finally, we have a small sample of nine non-compliers; six who received the long survey and three the short survey. This is out of a population of 60 non-compliers, which hence made it difficult to find many individuals in this group. Figure 2 shows these groups. In our main results, we weight the ballot winner sample to reflect the population proportions of ever migrating to New Zealand versus non-compliers. This is necessary because we effectively have a choice- based sample, although our previous research suggests that there is no selection among the non-compliers, and, hence, they can effectively be excluded from the sample (McKenzie et al. 2010). We also examine robustness, using two other weighting schemes. The first uses a snapshot of data from late 2012 on cross- border movements coming from passport scans, which revealed that 265 of the 307 ever-migrating ballot winners were in New Zealand at that point in time.8 This set of weights allows for the possibility that we found it harder to track individuals who had left New Zealand and so puts more weight on the ballot winners in our survey who were found outside New Zealand. The second alternative weighting scheme allows for the possibility that on-movement among the ballot losers is higher than our sample suggests. Our survey collects data on the principal applicants and the household members living with them, which is the focus of the majority of our analysis. As noted, principal applicants are typically adults in their early thirties who brought both a spouse and children to New Zealand. We also linked these applicant households

7 Only one-third of the migrants surveyed used fixed line telephones with the remainder relying on mobiles, and very few were listed in any type of telephone directory. Moreover, a large fraction of the migrants had moved out of the main destination city of Auckland, which made it harder to track them through churches and other local social networks. 8 This overstates the importance of on-migration since the PAC migrants who reside in New Zealand but were abroad on the day we received the record of arrivals and departures are not counted in the 265. 134 Gibson, McKenzie, Rohorua, and Stillman to a “partner” household in Tonga containing either a parent or elder sibling of the principal applicant (with priority on parents over siblings and females over males).9 In about 15% of cases, the partner defined by these rules was in the same dwelling in Tonga as the unsuccessful PAC applicant, but most are not co- residents and, in this regard, differ from the left-behind family studied in Gibson et al. (2011). The advantage Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 of this approach is that we are not conditioning on changes in household structure that occurred postmigration when drawing the sample; in other words, we always look for the same “partner” regardless of whether and how many times they have subsequently changed households. We collect data on 258 such households, which we use to measure the impact of migration on a specific type of extended family.

Assessing Balance The use of a random lottery ensures that the ballot winners and losers have similar characteristics in the population of ballot applicants. We only have a sample of this population, so we check the extent to which this sample is balanced on observable, pre-determined characteristics. Since the ballots were at least a decade ago, the only such characteristics available in our survey are age, gender, and birthplace (a dummy for being born off the main island of Tongatapu). Table 1 compares these characteristics for the samples of ballot winners and losers. Our samples are balanced on gender and island but not completely balanced on age. Nevertheless, a joint test of orthogonality cannot reject that these three characteristics are jointly balanced. This suggests that our treatment (ballot winner) and control (ballot loser) samples are comparable, and we can use the random assignment provided by the lottery to assess causal impacts. Nevertheless, we condition on age, gender, and island of birth to improve power and control for any effect of this slight difference in age, and we consider robustness to alternative weighting schemes that allow for different response rates among subgroups. Table 1 shows that the average applicant is 41 years old at the time of the follow-up survey. A 10–90 percentile range for age is 32 to 51. Two-thirds of the principal applicants are male.

Table 1. Pre-determined Characteristics

Ballot Winners Ballot Losers P-value

Age 40.11 41.80 0.045 Male 0.66 0.67 0.814 Born in Outer Islands 0.23 0.24 0.973 Sample Size 203 182 Joint orthogonality test p-value 0.306

Source: Author’s calculation from survey data described in text.

Survey Variables for Measuring Impacts In both the long and short survey, the PAC principal applicant reported pre-tax earnings in the previous week and total weekly earnings of all household members. These earnings, and other monetary values, are converted to New Zealand dollars (NZD) at the average market exchange rate for the month of the interview. The New Zealand CPI rose just 0.4 percent from December 2013 to March 2015, covering the span of the survey, and so nominal values can be treated as real values.10 For robustness, we will also

9 The sampling was based on these rules because we had the principal applicants and the partners play lab-in-field games, and we wanted to restrict the dyads for the games to specified relationships. 10 Over the same period, the Pa’anga exchange rate averaged 0.65 NZD and had a range of just three NZ cents. To allow comparison with results from other countries, the NZD-USD exchange rate averaged 1.33 over this period. The World Bank Economic Review 135 show impacts using PPP exchange rates to convert the income gains, using prices we collected in both countries. We also calculate annual income for households that responded to the long survey. This includes: earnings; net returns from sales of food crops, fish, livestock, tapa cloth and mats (from household Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 reports on an average month); income from investments, pensions, and rentals (from reports for the previous two weeks); imputed values for own-produced or own-captured food consumed by the household (from reports for the previous week); and, remittance receipts (from a household-level annual recall). Household expenditure is measured with a 20-category recall module with reference periods ranging from one week to six months, depending on the source of expenditure. The imputed value of items consumed from own-production is added to the cash expenditure variable to measure total expenditures on consumption. Impacts on income and expenditure are estimated for total values and in per capita terms since household size can change with migration. One category of expenditure collected separately is remittance spending over the previous year, which is captured also for households in Tonga from questions about transfers given to other Tongan households. The other monetary measures for households in the long survey are the total value of savings and of net worth while ownership of various durable goods, financial access, and dietary diversity is also collected at the household level. In addition to measuring monetary welfare, the subjective wellbeing of the principal applicant was elicited. Respondents were asked to imagine a ten-step ladder, where on the bottom step were the poorest people and the top step the richest people, and to state which step of the ladder they were on today. Ravallion and Lokshin (2001) refer to this as an economic ladder question and note that it leaves it up to the individual to define what constitutes “poor” or not and captures subjective economic welfare. Prior research shows PAC migrants perceive climbing the welfare ladder in a retrospective comparison with their premigration life but not when compared to unsuccessful PAC applicants (Stillman et al. 2015).11 Other questions answered at an individual level are for education, mental health, and occupations. Mental health was measured using the MHI-5 index of Veit and Ware (1983). Respondents reported occupational changes in the past five years and what the prior and current occupations were. We code these responses using the New Zealand Socioeconomic Index of Occupational Status (NZSEI) of Davis et al. (1997), which gives a continuous measure ranging from ten (e.g., non-ordained religious associate professionals) to 90 (e.g., health professionals). Scores reflect characteristics of occupations that either translate into observable lifestyle factors such as incomes or reflect valued socioeconomic inputs such as educational levels. An occupational status index for New Zealand should give a better basis for cross- country comparisons with occupations in Tonga than would an explicit international index such as the ISEI developed by Ganzeboom and Treiman (1996), since an international index is unlikely to have a country as small as Tonga in mind. Moreover, given the role of New Zealand as the major migration outlet and one of the main suppliers of higher education to Tonga, it is reasonable to believe that the same occupations have similar status in both countries.

III. Methods Our focus is on estimating the long-term impact of migration on both the immigrants themselves and on their extended families. To deal with self-selection bias, we rely on the PAC lottery randomly choosing a subset of individuals who become eligible to migrate from a larger pool of individuals who wanted to

11 In contrast, Tongan seasonal migrants show significant rises in their ladder position compared to respondents in households without seasonal workers (Gibson and McKenzie 2014b). These divergent results may be due to frame of reference effects; settlement migrants may use destination country standards to re-evaluate their former life in Tonga while seasonal migrants continue to evaluate using Tonga as a reference standard since they are not attempting to integrate into the destination country. 136 Gibson, McKenzie, Rohorua, and Stillman migrate. Given this mechanism, in the absence of non-compliance, a direct comparison of outcomes for lottery winners and losers would give us an unbiased estimate of the impact of migration. However, in practice, some lottery winners do not migrate and some lottery losers migrate to New Zealand via other pathways, including winning the PAC lottery in later rounds. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 Therefore, we use two approaches to measure impacts. First, we estimate the intention-to-treat (ITT) effect of being offered the opportunity to migrate through the Pacific Access Category irrespective of whether the individual does migrate. This is done by simply comparing outcomes for ballot winner to those of losers:

Outcomei ¼ a þ bBallotWini þ cXi þ ei (1) where BallotWin takes value one if the principal applicant was chosen in the 2002 to 2005 Pacific Access Category ballots and zero otherwise, and X is a vector of controls for the pre-determined characteristics (age, gender, and island of birth), for survey type, and for each of the different PAC ballot years that the individual entered. The median and modal applicant in our sample entered only one ballot between 2002 and 2005. However, since those who entered multiple times had a higher probability of being chosen, we correct for this by conditioning on which lotteries were entered (Abdulkadiroglu et al. 2011).12 The second approach is to use assignment to the treatment (winning the PAC lottery) as an instrumental variable (IV) for the actual treatment of migrating, in regressions like:

Outcomei ¼ l þMigratei þ dXiþxi (2) where Migratei is a dummy variable that equals one if person i ever migrated to New Zealand and is zero otherwise. This includes any individuals who thereafter on-migrated to other countries, since we want to estimate the impact of having ever migrated, and, in practice, on-migrants are a selected group and hence examining them separately would require another valid instrument. Estimating k by instrumental variables gives us the local average treatment effect (IV-LATE) of ever migrating to New Zealand, which can be interpreted in this case as the impact on people who would migrate only if they won the PAC lottery and not otherwise.

IV. Results We first examine impacts on income earned by the principal applicant and then their other individual level outcomes. We then turn to measuring household level outcomes, first for the household that lives with the principal applicant and then for the specific type of extended family in Tonga defined by our partner household rules.

Impacts of Migration on Income of the Migrant Table 2 presents the estimated gain in weekly income (in current New Zealand dollars) under a variety of different specifications. Columns 1 to 3 show ITT impacts and columns 4 to 6 the LATE impacts. Columns 1 and 4 in panel A are our base specifications. Here, the market exchange rate is used to convert Tongan pa’anga into New Zealand dollars, and the sample is weighted to reflect the population proportions of ballot winners ever migrating to New Zealand versus those who are non-compliers. We estimate that winning the ballot leads to a NZ$247 increase in weekly earnings while ever migrating

12 This is a specific case of the more general idea of conditioning on randomization strata; conditional on which ballots a person entered it is random whether or not they would be chosen. The World Bank Economic Review 137

Table 2. Impact on Income Gain

ITT ITT ITT LATE LATE LATE

Panel A: Impact on Income Gain in New Zealand Dollars

Ballot winner 246.9*** 240.0*** 220.9*** Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 (32.8) (35.8) (36.7) Migrate 340.0*** 334.1*** 328.1*** (24.5) (31.0) (33.9) Weighting used: w1 w2 w3 w1 w2 w3 Sample Size 385 385 385 385 385 385 Control Mean 125.6 125.6 125.6 125.6 125.6 125.6

Panel B: Impact on Income Gain in PPP-adjusted New Zealand Dollars Ballot winner 260.5*** 252.8*** 233.3*** (34.1) (37.0) (37.8) Migrate 358.7*** 351.9*** 346.5*** (24.8) (31.2) (34.0) Weighting used: w1 w2 w3 w1 w2 w3 Sample Size 385 385 385 385 385 385 Control Mean 126.1 126.1 126.1 126.1 126.1 126.1

Notes: Robust standard errors in parentheses, *, **, *** indicate signiﬁcance at the ten, ﬁve, and one percent levels, respectively. All regressions include controls for age, gender, island of birth, ballot years entered, and survey form (long or short). Weights: w1 weights for population proportions of compliers versus non-compliers, w2 also weights migrants for observed movements out of New Zealand, w3 additionally allows ballot losers who migrate to have twice the weight of those who remain in Tonga. Source: Author’s calculation from survey data described in text. leads to a NZ$340 increase in weekly earnings. Average earnings for ballot losers in Tonga are NZ$126 per week, so we estimate that migration leads to a 271 percent increase in weekly earnings.13 Incolumns2and5,weuseanalternativeweightingschemebasedontheinformationprovidedtous from cross-border passport scans, giving a higher weight to on-migrants than to ballot winners in New Zealand.14 In columns 3 and 6, we further allow for migration to New Zealand through other channels by giving twice the weight to the migrants among the ballot loser sample as to the non-migrants. Our estimated impact of migration on earnings is qualitatively unaffected by these different weighting choices.15 In panel B, we examine whether the results are robust to adjustments for cost of living differences between Tonga and New Zealand and between different regions in New Zealand. To examine differences in the cost of living between New Zealand and Tonga, we collected prices for a common basket of goods in both countries and calculated a PPP exchange rate. When we did this in 2005 (McKenzie et al. 2010), we found the exchange rate and PPP exchange rate coincided. Redoing these calculations in 2015, the PPP exchange rate was 1.62 pa’anga per New Zealand dollar while, over the course of the survey, the market exchange rate had varied from 1.43 to 1.54 pa’anga per New Zealand dollar. As a result, using the market exchange rate would slightly undervalue the gains to migration.

13 Migrant ballot winner earnings are distributed more like a normal than a log-normal since people with very high earning potential would likely have migrated through skill immigration categories. If log earnings are used, the sample size falls to n ¼ 310, and the LATE coefficient is 1.121 (implying a 208 percent increase due to migration). 14 This assumes that, while our sample of on-migrants is proportionally too small compared with those still in New Zealand, this sample is representative of all on-migrants. 15 In other tests of robustness we estimated without weights, we adjusted non-complier incomes to have the same mean as ballot losers in Tonga, and we dropped all non-compliers and estimated equation (2) with OLS. The estimates of migration impacts were $342, $345, and $347, compared to the $340 in table 2. The lack of fragility in the estimates to different ways of dealing with non-compliers supports the finding of McKenzie et al. (2010) that there is little selection among non-compliers. 138 Gibson, McKenzie, Rohorua, and Stillman

The second adjustment was to put everything into Auckland prices based on a spatial cost of living index reflecting rental cost differences and a housing budget share of 44 percent. The majority of PAC migrants start by living in the Auckland metro area, but, over the ten years of our sample period, many moved to locations with cheaper living costs (see figure 3). The proportion of our sample located outside Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 Auckland rose from 14 percent in the first survey in 2005–06 to 39 percent in 2013–14. Interestingly, the majority of migrants who left Auckland moved to regions that are the main locations for Tongan seasonal workers. It was not the case that PAC migrants had adopted an itinerant lifestyle and moved to the regions in search of seasonal work. Instead, in qualitative interviews, most said that they only became

Figure 3. Movement of Migrants Out of Auckland over the Decade The World Bank Economic Review 139 aware of opportunities in these regions through the experience of the seasonal workers often mediated by extended family in Tonga. Some of the PAC migrants who moved to the regions also noted that they had worked in elementary occupations in Auckland, such as factory workers and cleaners, and did not expect to get back to the same occupations they had in Tonga (most commonly this was teaching, and also police Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 and clerical work). Therefore, if they were going to do work of similar elementary status, they may as well get the benefits of a lower cost of living and less crowded accommodation by moving out to the regions. These price adjustments increase the gain to migrating by five to six percent, which is only a small fraction of the overall gain of between 275 and 285 percent. By comparison, the estimated income gain approximately one year after migration was NZ$308 (Stillman et al. 2015) in June 2006 currency (equivalent to NZ$366 in current dollars), which was a 263 percent income gain.16 These results suggest that, almost a decade after migration, the impacts on earnings are much the same as they were in the first year. In other words, the economic payoff to migrating from a poor country to a richer one seems to come immediately and not grow substantially thereafter. Given that the gain in migration appears to be a level effect of approximately NZ$340 per week and that we do not observe return migration over the decade, we can estimate the lifetime income gain from winning the PAC ballot. The typical PAC migrant moves to New Zealand aged around 32. If we assume they work for 33 years thereafter, then the lifetime earnings gain is NZ$583,440. This assumes all income is consumed, so that there is no investment return on this gain. In addition, they would then be eligible for New Zealand Superannuation (retirement benefit), which currently has an after-tax value of NZ$288 per week for each spouse in a married couple. In contrast, Tonga’s retirement benefits fund is a defined contribution fund that will offer much less.17 If we assume a retirement of 15 years, this is an additional benefit of NZ$188,760, for a total lifetime benefit of NZ$772,200. Using a five percent dis- count rate, this has a net present value of NZ$315,000. This is conservative as it allows for no asset earnings and yet still represents a 48-fold increase.

Impacts of Migration on Other Individual Outcomes for the Migrant Table 3 examines the impact of migration on other individual level outcomes. We examine changes in employment, occupation, subjective welfare, mental health, and education. Since these are outcomes in several different domains, it does not make sense to aggregate them into a single index. Instead, we allow for multiple hypothesis testing by noting whether the result is still statistically significant when using the Holm (1979) sequentially rejective Bonferroni method. Column 1 shows a large impact on the likelihood of being employed, which survives corrections for multiple hypothesis testing. The LATE impact is a 30 percentage point increase in the likelihood of paid employment.18 Column 2 shows that migrants are more likely to have changed occupation in the past five years, but this impact is only significant at the ten percent level and does not survive correcting for multiple testing. Migrants who change occupation appear to maintain a relatively constant occupational status with an average value of 33 for the old jobs and 34 for the new jobs on the 10–90 point NZSEI scale. One-third of the occupational changes recorded amongst the migrants were to working on farms, in orchards, or in pack houses.

16 The four year impact reported in Stillman et al. (2015) is NZ$367 with a 95 percent confidence interval of $305 to $428. 17 It is a defined contribution fund, with the worker and government each paying 5–7.5% of the worker’s pay into an account with the worker then getting this benefit plus any earnings upon retirement. Taking 7.5% of the average income of ballot losers of NZ$125/week, and assuming 2% annual income growth and 5% annual return, this benefit is equivalent to NZ$35,807 at retirement, or NZ$46/week for 15 years. 18 If we break the overall income gain into changes in intensive and extensive margins, 84% of the gain is due to higher earnings conditional on working, although this calculation assumes there is no selection as to who works. 140 Gibson, McKenzie, Rohorua, and Stillman

Table 3. Impact on Outcomes for the Migrant

Currently Changed Change New Old Has Subjective Mental Employed Occupation in occupation occupation occupation Tertiary Currently Welfare Health in last 5 years score score score Education Studying Ladder MHI-5 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019

Panel A: ITT Impact Ballot Winner 0.217*** 0.093* 17.100*** 11.935** 5.164 0.146** 0.078** 0.011 0.687*** (0.070) (0.052) (5.486) (4.604) (3.541) (0.066) (0.031) (0.084) (0.259) Panel B: LATE Impact Migrate 0.299*** 0.128* 21.908*** 15.292*** 6.616 0.201** 0.103*** 0.016 0.871** (0.085) (0.066) (6.655) (5.293) (4.687) (0.091) (0.038) (0.115) (0.371) Sample Size 385 385 70 70 70 385 282 385 279 Control Mean 0.659 0.121 16.952 23.143 40.095 0.181 0.000 5.852 17.475

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the ten, five, and one percent levels respectively. All regressions include controls for age, gender, island of birth, ballot years entered, and survey form (long or short). Regressions weighted to account for the population proportions of compliers and non-compliers. New and old occupation scores are only available for those who changed occupation in the last five years. Source: Author’s calculation from survey data described in text.

Figure 4 provides a summary of the occupational history of the PAC migrants from their premigration occupations in Tonga, their first job in New Zealand, and their occupations at the time of the first two surveys, which were roughly one and four years after they moved. The figure also includes the previous and current occupation at the time of the current (third) survey, but this is available only for those who had changed occupations in the last five years. Since the samples are changing over time, and there is a larger than five-year gap between the second and third surveys, this figure does not give a longitudinal account but should still approximate the pattern experienced by the average migrant.

Figure 4. The “Reverse J-Shaped” Pattern of Occupational Decline for Immigrants Occupaonal Status Scores: NZSEI 55

30 Pre-migraon 1st NZ job Job at Job at Prior job* Job at (retrospecve) (retrospecve) 1st survey 2nd survey 3rd survey*

*measured only for those changing main occupaon in the last ﬁve years

There is a reverse-J pattern in figure 4, where occupational status falls with migration and does not recover. This contrasts with the U-shaped pattern suggested in the literature (e.g., Chiswick et al. 2005, The World Bank Economic Review 141

Akresh 2008), where the first job in the destination is of lower status than the last job in the home country but the migrant then regains some occupational status over time. However, this idea of a U-shaped pattern is also under challenge from other studies that note that the literature may be overly optimistic about immigrant convergence with natives. For example, Abramitzky and Boustan (2016) report that, Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 both in the past and the present, long-term immigrants to the United States experienced occupational growth at about the same pace as natives, so those who held lower-paid occupations upon arrival did not catch up to natives over a single generation. More generally, the prior studies of occupational assimilation have used non-experimental methods and, hence, may be subject to the same critique as the wage assimilation literature, specifically that assimilation is overstated because of changes in cohort quality over time. If this is the case as well for occupational assimilation, this may explain why we find a reverse-J as opposed to a U-shaped pattern. To provide some qualitative evidence on the dynamics of occupational change, we contacted PAC migrants included in the first survey of McKenzie et al. (2010) who had been teachers in Tonga, which was the most common prior occupation in that sample. All of these migrants had applied for jobs as teachers in New Zealand, but just half were successful; at the time of the third survey, a decade after migrating, the others were working as a social worker, a recruitment officer, farm supervisors (n ¼ 2), a bus driver, and a farm worker. The ones who failed to regain their occupation from Tonga had lower premigration qualifications, holding a teaching diploma rather than a bachelor degree, fewer years of teaching experience in Tonga, and were less likely to have undertaken postmigration training in New Zealand. Specifically, all six who became teachers and the two whose occupational status did not fall so far (the social worker and the recruitment officer) invested in postmigration training. Since a teaching diploma is a higher level of education than is needed for a bus driver or farm worker, there appears to be some degree of ‘brain waste’ in this occupational downgrading, which may reflect poorly transferrable skills (Mattoo et al. 2008). Despite largely static occupational status for the PAC migrants, the estimated impacts in columns 3 and 4 show a strong and significant impact of migration on occupational status for those who do change occupations. Migration leads to a 22-point increase in occupational status on the 80-point scale, and this impact remains significant after correcting for multiple hypothesis testing. The effect is driven by declining occupational status among ballot losers, where those changing occupations dropped from an average occupational score of 40 down to just 23. Some of this reflects people leaving professional careers to farm their ancestral lands and may be due to a relatively early retirement age of 55 in Tonga. Since many of the PAC migrants were public sector workers in Tonga and some are now in their early 50s, the correct counterfactual for what their occupations in Tonga would have been is unlikely to be a continuation of the premigration occupation. Therefore, a potential benefit of migration in this case is the movement to a labor market with fewer restrictions on older workers, where those restrictions in turn reflect a lack of employment opportunities for qualified young people. The apparent lack of growth in postmigration wage impacts and in occupational status is despite a significant migration impact on human capital. Migrants are more likely to be currently studying (only observed in the long survey), and this is significant even after correcting for multiple hypothesis testing. They are 20 percentage points more likely to have attained tertiary education (which is typically from subdegree tertiary providers rather than from universities).19

19 This strong response of tertiary study for working-age adults may reflect some features of tertiary funding in New Zealand. Student loans are widely available, are nominal interest free as long as the borrower stays in New Zealand, and repayments are income-contingent. Specialized tertiary providers (called Wananga) have responded to these market conditions by providing tertiary study options for Maori, Pacific, and new settler groups who may not have the usual preparation for university study and now rival the largest university in terms of enrollments. 142 Gibson, McKenzie, Rohorua, and Stillman

Columns 8 and 9 show two other welfare measures—the economic ladder and mental health (only in the long survey). Consistent with the short-term results in Stillman et al. (2009), there is an improvement in mental health with the ITT impact remaining significant after multiple testing corrections, although the magnitude of the long-term impact (0.87) is just under half of the one-year impact (1.94). In contrast, Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 the economic ladder question shows no difference in subjective welfare between the migrants and those staying in Tonga, although these comparisons use unanchored scales and so may reflect changes in the frame of reference as discussed in Stillman et al. (2015).20

Household-Level Impacts The typical migrant lives in a nuclear household in New Zealand with 84 percent of the migrants living with a spouse and 80 percent having a child in the house. Conditional on having at least one child, the mean (median) oldest child in the household is 11.1 (12) years old. Household outcomes for the migrant therefore reflect the gain from migration on this family unit that is eligible to move with the migrant under the Pacific Access Category. Table 4 presents the estimated impacts of migration on household outcomes. Incomes and expenditures are transformed into logarithmic terms, since they are always non-zero whereas weekly earnings may be zero; thus, proportionate changes for these measures may be estimated as: [exp (bj) – 1]. We consider both total and per-capita impacts, although we do not find evidence of an impact of migration on household size. Migration leads to a 185 to 215 percent increase in household income and a 258 to 295 percent increase in household expenditure, with both effects statistically significant. In terms of annual household labor earnings, migration leads to a gain of NZ$27,000, which is almost four times as large as labor earnings in Tonga. This is greater than the gain in total income, reflecting that households in New Zealand have little own production while this is an important source of income for households in Tonga. Migrant households also have substantially more durable goods: they have 0.8 more vehicles, are 58 percentage points more likely to own a computer, 24 percentage points more likely to own a DVD player, almost all have microwaves and are 58 percentage points more likely to own a washing machine. The only durable asset where ownership does not increase with migration is cellphones. We see no significant impact on financial access as measured by having a bank account or ATM card with there being little room for improvement here due to 99 percent of ballot-loser households in Tonga having these. We also estimate migrant households to have 18 times the level of savings as ballot-loser households and to be consuming a more diverse diet. All these impacts are significant at the one percent level and survive corrections for multiple hypothesis testing. Overall, households of migrants are much better off. A key reason given for migration is often to improve the welfare of one’s children by providing them with access to better schooling opportunities. Schooling in Tonga is near universal through secondary education, and 100 percent of the 14 to 17 year olds in ballot- loser households in Tonga are currently attending school. The effect is therefore likely to occur in terms of tertiary education, where opportunities are much more limited in Tonga. However, most children are too young for this age range, so we cannot yet detect an effect.21

20 Anchoring of these scales using vignettes would be a useful topic for future research. Stillman et al. (2015) find that migrants report significant improvements in their subjective welfare when making retrospective comparisons with their pre-migration life but place themselves at the same point on an unanchored scale as do the non-migrants, and this dis- crepancy may reflect a changing frame of reference as they adapt to the destination country. 21 Only 28 of the 282 households in the long survey have a child in the 18 to 22 age range. The World Bank Economic Review 143

Table 4. Impact on Household Level Outcomes

Sample Control ITT LATE Outcome Size Mean Impact Impact

Log Household Income 282 9.488 0.791*** 1.047*** Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 (0.134) (0.134) Log Household Income per capita 282 7.987 0.866*** 1.146*** (0.152) (0.129) Log Household Expenditure 282 9.185 0.962*** 1.274*** (0.122) (0.115) Log Expenditure per capita 282 7.684 1.037*** 1.373*** (0.142) (0.104) Household Size 385 5.264 0.229 0.385 (0.241) (0.390) Household Earned Income 385 6829 16182*** 27131*** (2178) (2216) Remittances sent to Tonga 385 118 911*** 1528*** (137) (180) Number of Vehicles 385 0.797 0.462*** 0.775*** (0.074) (0.122) Household has a Cellphone 385 0.511 0.114* 0.192* (0.065) (0.107) Household has a Computer 385 0.181 0.348*** 0.584*** (0.068) (0.118) Household has a DVD player 385 0.104 0.140*** 0.235*** (0.037) (0.064) Household has a Microwave 385 0.082 0.563*** 0.944*** (0.067) (0.075) Household has a Washing Machine 385 0.385 0.344*** 0.576*** (0.073) (0.120) Household has a Bank Account 282 0.986 0.015 0.020 (0.011) (0.015) Household has an ATM card 282 0.986 0.015 0.020 (0.011) (0.015) Total Savings 282 428 5944*** 7869*** (1100) (1102) Number of Foods in Diet 282 7.385 1.571*** 2.080*** (0.239) (0.284)

Notes: Robust standard errors in parentheses, *, **, *** indicate signiﬁcance at the ten, ﬁve, and one percent levels respectively. All regressions include controls for age, gender, island of birth, ballot years entered, and survey form (long or short). Regressions weighted to account for population proportions of compliers and non-compliers. Source: Author’s calculation from survey data described in text.

Impacts on Extended Family Finally, we consider the impacts of migration on one particular type of extended family, consisting of a household containing a parent or elder sibling of the PAC applicant. By defining the partner household in terms of a fixed relationship rule, it prevents incorrect inferences from conditioning on changes in household structure that occurred postmigration. The impacts of having a child or younger sibling migrate through the PAC on these extended households are reported in table 5. The only significant differences found are that families of PAC migrants receive more remittances and are slightly more likely to have bank accounts and an ATM card. However, the magnitude of additional remittances received is relatively low: NZ$215 annually in additional remittances, which is less than one week of the income gain to migrants and only 1.4 percent of 144 Gibson, McKenzie, Rohorua, and Stillman

Table 5. Impact on Partner Household Level Outcomes

Sample Control ITT LATE Outcome Size Mean Impact Impact

Log Household Income 282 9.447 0.077 0.109 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 (0.097) (0.140) Log Household Income per capita 282 8.213 0.030 0.043 (0.101) (0.144) Log Household Expenditure 282 9.126 0.018 0.025 (0.097) (0.137) Log Expenditure per capita 282 7.892 0.090 0.126 (0.115) (0.165) Household Size 282 3.720 0.295 0.415 (0.234) (0.354) Household Earned Income 282 7809 155 218 (1098) (1542) Remittances received 282 466 153* 215** (78) (97) Number of Vehicles 282 0.678 0.052 0.073 (0.082) (0.119) Household has a Cellphone 282 0.364 0.053 0.075 (0.085) (0.119) Household has a Computer 282 0.119 0.032 0.045 (0.062) (0.088) Household has a DVD player 282 0.028 0.005 0.007 (0.015) (0.021) Household has a Microwave 282 0.063 0.020 0.029 (0.024) (0.035) Household has a Washing Machine 282 0.287 0.076 0.107 (0.081) (0.118) Household has a Bank Account 282 0.951 0.060** 0.084** (0.026) (0.036) Household has an ATM card 282 0.951 0.062** 0.087** (0.027) (0.038) Total Savings 282 545 194 273 (201) (286) Number of Foods in Diet 282 7.308 0.269 0.378 (0.194) (0.282)

Notes: Robust standard errors in parentheses, *, **, *** indicate signiﬁcance at the ten, ﬁve, and one percent levels respectively. All regressions include controls for age, gender, island of birth, ballot years entered, type of relative of the PAC applicant, and for whether the partner co-resided with the applicant. Regressions weighted to account for the population proportions of compliers and non-compliers. Source: Author’s calculation from survey data described in text. annual total household income for ballot- loser households. Given this small difference in remittances, it is unsurprising that we do not see impacts on other household outcomes for extended family. This limited impact is in a context where the households that applicants lived in at the time of applying for the migration lottery would naturally be dissolving as adults leave their parental homes and set up households of their own. Even if these individuals had stayed in Tonga, most would be living in a separate dwelling from this extended family. As such, these extended households will have had to adjust to the absence of the applicant regardless of their migration outcome. One caveat that we should note is that it is possible that a key benefit of having a migrant abroad is as an insurance policy, someone that can provide help in the case of major shocks, something we are unable to measure with our data. The World Bank Economic Review 145

V. Conclusions and Discussion A long-term follow-up survey of Tongans who applied to migrate to New Zealand through a visa lottery enables us to measure the impact of international migration after a decade. We find that the long-term

income gain for migrants is similar to the short-term estimates. That the income gain occurs immediately Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019 upon migrating to a richer country suggests that the same labor and skills can be vastly more productive when used with the institutions and complementary physical and human capital present in a developed country rather than in a developing country. However, the gain in income is less than the percapita income gap between countries and does not appear to grow further despite various postmigration investments made by the immigrants in qualifications, occupational change, and internal mobility. Even if the income gain is reasonably static, it is so large that the lifetime benefit from migrating for the migrant and his or her accompanying spouse and children is enormous. The effort and cost of tracking applicants down a decade after applying for migration, coupled with the migration lottery, make this study unique in its ability to provide estimates of the long-term causal impacts of migration. A question that then arises in any evaluation is the extent to which its findings may apply beyond the specific setting of the study. The findings here are most likely to generalize to the likely longer-term impacts of migration on people from small, island countries. Ozden€ et al. (2011) note the salience of such places: “the origin countries most affected by international migration are small, typically island states, mostly in the Pacific or the Caribbean. The destination countries most affected by migration are the countries of the New World (the United States, Canada, Australia, and New Zealand)....” In addition, two other factors make Tonga-New Zealand migration of potential interest beyond the methodological value provided by the lottery-determined migration flow. First, it may provide some insight into on-migration because the immigrants have unrestricted entry to Australia under the Trans-Tasman Travel agreement once they obtain New Zealand permanent residence. The easy access to an economy that is over five times larger sees New Zealand normally lose about one percent of its residents per year to Australia (although this flow has recently reversed), which it then replaces with immigrants (in both countries, one-quarter of the resident population are foreign born). There are many other examples of on-migration, such as the de facto free mobility for third- country nationals once within the borders of the Schengen area (Koikkalainen 2011). This on- migration provides one reason why short-term and long-term gains from migration may differ. Moreover, on-migration reduces the value of using destination country natives as a comparison group for studying labor market trajectories of migrants, since the migrant experience may span several destination countries. The second factor of potential interest is that New Zealand opened a much larger seasonal work option in 2007 for up to 9000 migrants per year in horticulture, with preference for Pacific Islanders. About 1500 Tongans come to New Zealand each year under this scheme (Gibson and McKenzie 2014a); more than six times the number coming via PAC settlement migration. There is no formal interaction between these two migration pathways (seasonal workers are told they have no option to settle), and they select from different parts of the skills distribution in Tonga with the PAC applicant pool positively selected (McKenzie et al. 2010) and the seasonal workers negatively selected by a focus on people not in formal employment (Gibson and McKenzie 2014b). Moreover, the seasonal workers go to regional New Zealand while Auckland is the first destination for most PAC migrants. Yet our surveys and follow-up qualitative interviews uncovered at least two linkages; since many Tongans come to regional areas on a rolling basis throughout the year for seasonal work, it creates a derived local demand for settlement migrant Tongans. Second, information about opportunities in regional centers appears to flow from seasonal workers to settlement migrants indirectly through family and other contacts in Tonga; this stickiness of information corroborates findings of McKenzie et al. (2013) that migrants may 146 Gibson, McKenzie, Rohorua, and Stillman not be well informed about opportunities in destination countries. Other countries are also expanding seasonal and temporary migration, so these interactions with settlement migration may be of interest, especially because they provide another lens for comparing short-term and long-term gains from migration. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/127/3105864 by World Bank Publications user on 08 August 2019

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Abstract Migration facilitates the flow of information between countries, thereby reducing informational frictions that potentially hamper cross-country financial flows. Using a gravity model, migration is found to be highly correlated with financial flows from the migrant’s host country to her home country. The correlation is strongest where information problems are more acute (e.g., between culturally more distant countries), for asset types that are more informational sensitive, and for the type of migrants that are most able to enhance the flow of information on their home countries, namely, skilled migrants. These differential effects are interpreted as evidence for the role of migration in reducing information frictions between countries. JEL classification: F21, F22 Key words: Migration, international financial flows, international loans, gravity models, information asymmetries

Financial flows across countries have increased dramatically over the past decades (Bank of International Settlements 2010). Nevertheless, the global financial market is still far from being a fully integrated market due to pervasive frictions that hamper the flow of finance (Lane and Milesi-Ferretti 2008). The literature assigns a key role to informational frictions. In particular, gravity variables that affect the extent of informational frictions (e.g., lower distance, common language, common legal origin, etc.) have consistently been shown to increase bilateral financial flows and to mitigate home bias (Coeurdacier and Rey 2013). Portes and Rey (2005) and Lane and Milesi-Ferretti (2008) show this for cross-border equity flows. Aviat and Coeurdacier (2007) extend the analysis to bank loans, equity flows and bond holdings.1

Oren Levintal (corresponding author) is an assistant professor at the Interdisciplinary Center (IDC), Herzliya, Israel; his email address is [email protected]. Maurice Kugler is a principal research scientist and managing director at IMPAQ International, Washington DC, U.S.A.; his email address is [email protected]. Hillel Rapoport is a professor at the Paris School of Economics, University Paris 1 Panthe´on-Sorbonne, Paris, France, and a scientific ad- visor at CEPII, Paris, France; his email address is [email protected]. We are grateful to Dany Bahar, Jeffrey Frankel, Ricardo Hausman, three anonymous referees and to participants at the 8th AFD-World Bank Migration and Development Conference, World Bank, June 2015 and the LACEA Conference, 2015. This paper was initiated as part of a project on “Migration, International Capital Flows and Economic Development” based at the Harvard Center for International Development and funded by the MacArthur Foundation’s Initiative on Global Migration and Human Mobility. See Kugler and Rapoport (2011) for an early version focusing on equity portfolio flows. Hillel Rapoport acknowledges support from Cepremap and from the “Investissements d’Avenir” program (ANR- 10-LABX-93). A supplemental appendix to this article is available at https://academic.oup.com/wber.

1 Historical perspectives are provided by Chitu, Eichengreen, and Mehl (2013) and Flandreau (2006).

This paper posits that the cross-border movement of people reduces informational frictions across countries and stimulates bilateral financial flows. The reason is that migration from country i to country j has the potential to reveal information on country i which is valuable for investors in country j that seek to invest in country i (e.g., information on the characteristics of the home country’s financial and Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 political institutions). In addition, migrants create or integrate into international business and financial networks, thereby enhancing financial transactions between their home and host countries. Anecdotal evidence suggests that this channel is indeed operative. For instance, the Bank of China owns a subsidiary in the US. The core business of the US subsidiary is lending to Chinese firms. About 90% of the employees working at the New York branch are locals and most of them are ethnically Chinese (Wall Street Journal 2016). Similarly, the Turkish bank Akbank owns a subsidiary in Germany, which hosts a large community of migrants from Turkey. The CEO of the German subsidiary in Frankfurt was born in Turkey in 1963, and she moved to Germany as a young child (Frankfurter Rundschau 2015). The German subsidiary, named Akbank AG, lends mainly to “multinationals based in Turkey” (Akbank annual report 2015: 62). Akbank AG receives finance from international banking institutions (Global Trade Review 2015), which indicates that the bank funnels funds from a wide range of global investors into Turkey. This evidence is consistent with the view that migrants working in financial institutions at their host countries help to facilitate the transfer of financial flows to their home countries. In this paper, we present empirical results that support this argument. In doing so, we follow a growing literature demonstrating the role of migration in facilitating trade (Gould 1994, Head and Ries 1998, Rauch and Trindade 2002, Rauch and Casella 2003, Combes et al. 2005, Iranzo and Peri 2009, Parsons and Vezina 2017, Felbermayr and Toubal 2012, Felbermayr, Jung and Toubal 2010), FDI (Kugler and Rapoport 2007, Leblang 2010, Javorcik et al. 2011, Aubry et al. 2016), and the diffusion of knowledge (Kerr 2008, Agrawal et al. 2011, Bahar and Rapoport 2017).2 The paper most closely related to ours is Leblang (2010) who proposes a comparative analysis of the effect of migration on FDI and portfolio asset flows. Regarding the latter, Leblang (2010) focuses on the main effect, which we believe is hard to properly identify, while we focus on differential effects by degree of information imperfection as discussed below. We test the hypothesis that migration affects international financial flows. We proceed as follows. First, we introduce migration into an otherwise standard gravity model of financial flows, following Martin and Rey (2004) and Aviat and Coeurdacier (2007). As a general proposition, we expect the effect of migration on financial flows to be larger where informational imperfections are more pervasive. This is supposedly the case for country pairs that differ in terms of cultural proximity, or for investment in developing countries compared to developed countries. The effect of migration should also depend on migrants’ skills. In particular, highly educated migrants are more likely to be part of international business and financial networks, and, hence, likely more able to transfer information and reduce bilateral transaction costs. Hence, we expect their influence on financial flows to be larger. Our empirical strategy relies on the estimation of differential effects along a number of dimensions: a skill dimension, a cultural dimension, an asset-type dimension, and an income status dimension. We start by showing that migration has a significant positive impact on international bank loans. When we distinguish between skilled and unskilled migrants, we find consistently a larger coefficient for skilled migration. For example, when we introduce skilled and unskilled migrants jointly into a gravity model of international bank loans, we find a significant positive elasticity of skilled migrants of about 0.2 while no significant effect is found for unskilled migrants. We take this result as initial supportive evidence of our conjectures.

2 See Docquier and Rapoport (2012) and Rapoport (2016) for recent surveys of the literature on migration networks, globalization, and development. 150 Kugler, Levintal, and Rapoport

Next, we allow for the effect of migration to vary with the degree of cultural proximity. We find that the effect of migration is nearly zero for country pairs that share common language, colonial history, or legal origin, and positive otherwise. These results are consistent with the view that the potential for migration to alleviate informational frictions is higher for culturally distant countries. In addition, we Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 expect the effect of migration to be larger where information imperfections are more pervasive, that is, in developing countries. And indeed, when we interact our migration variables with a dummy for developing country status, we find a magnified effect coming mostly from the extensive margin. Furthermore, when we distinguish between investments in long- versus short-term bonds, we find a stronger impact on investment in long-term bonds issued by developing countries, which is arguably a riskier type of investment. Taken together, these differential effects are in line with the theoretical arguments outlined above and provide supportive evidence that migration contributes to lower informational frictions across countries. The main concern in this analysis is the possibility of omitted variables governing the joint pattern of emigration and reverse financial flows. However, to be able to explain the entire set of results, a possible confounder should explain not just the main effect but also the differential effects for skilled versus unskilled migrants, culturally close versus distant country-pairs, developed versus developing countries, and long- versus short-term bonds, as well as the time structure of our results. We find the existence of such a variable unlikely.

I. Methodology Our main assumption is that financial investments are informational sensitive. In other words, investors tend to invest more in places they know better. The well-known implication of this assumption is the home bias in financial flows, which has been found repeatedly in the literature (Coeurdacier and Rey 2013). Given these information frictions, the presence of migrants is expected to stimulate bilateral financial flows. Specifically, migrants facilitate the flow of information from their home countries to their host countries. We build on the gravity model developed by Okawa and van Wincoop (2012). In particular, we assume that investors in foreign markets perceive higher volatility of asset returns compared to domestic markets. This assumption gives rise to a gravity equation where the log of bilateral financial flows depends on the log of information frictions. As shown by Okawa and van Wincoop (2012), this specification depends on the assumptions that there are no borrowing constraints and that asset returns are uncorrelated across countries, except for global or regional shocks, against which investors can fully hedge their portfolios. To the extent that these assumptions do not fully hold in our context, we view the gravity model as an approximation of the true model. Importantly, our identification strategy, as explained in more detail below, builds on differential effects rather than main effects. In this regard, we assume that deviations from the conditions of Okawa and van Wincoop (2012) that bias the main coefficients do not affect the difference between the coefficients (i.e., the differential effect). Information frictions depend on a set of bilateral variables such as distance, common language, etc. We test the hypothesis that the information frictions between country j and country i are alleviated by migration from country i to j. In particular, we posit that migrants from country i that live in country j convey important information about country i to investors in country j. This information reduces investment risk and enhances financial flows from country j to country i. A simple gravity model is likely to suffer from an omitted variable bias. Hence, we focus our analysis on differential effects across a number of dimensions. The first dimension is the general level of education of the migrants. To become involved in the financial sector, migrants need to have high cognitive skills. First, they are expected to have deep knowledge about their home country economy and be able to gain new knowledge constantly. Second, they need to be able to exchange this knowledge with the The World Bank Economic Review 151 financial sector of their host country in a credible way. This task requires high communication skills. Third, skilled migrants are more likely to serve as liaison agents, or reference, and allow for reaching out to the business and financial community in their home country. Hence, the migration effect is likely to be stronger when migrants are more skilled. We test this differential effect by the following regression: Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019

log ðLoansjiÞ¼a1log ðSkilled MigrantsijÞþa2log ðUnskilled MigrantsijÞ (1) þa3log ðDistanceijÞþa4Xij þ ci þ cj þ ij:

Loansji denotes the stock of international bank loans from country j to country i as of 2000. Skilled Migrantsij is the stock of skilled migrants (i.e., with college education) from origin country i living in destination country j, and Unskilled Migrantsij refers to unskilled migrants (i.e., with below-college education). Hence, our first empirical test is to estimate the differential effect of skilled-unskilled migration from country i to country j on financial flows in the reverse direction, namely, from country j to country i. The regression controls for the distance between the two countries as well as for other variables that are associated with cultural and economic proximity, denoted collectively by Xij. In addition, we control for origin and destination-country fixed effects, which are denoted by ci and cj. Identifying differential effects is a common strategy to deal with endogeneity problems (e.g., Rajan and Zingales 1998). The identification assumption in this case is that the estimate of the differential 3 effect a1 – a2 is unbiased, while the estimates of a1 and a2 separately might be biased. Indeed, this assumption implies that the bias of a1 and a2 is in the same direction and of the same magnitude. Moreover, if the bias is upward, we assume that it is not too large, so that the true effects are not negative. We deal with these limitations by exploring various dimensions of the data, as explained below. In most cases, we find that migration is associated with stronger financial flows between countries for which information is most needed. We find it unlikely that the overall set of results is produced by omitted variables. The second dimension that we exploit is the cultural proximity between the two countries. Since migrants alleviate informational frictions, their impact on financial flows should be strongest for country pairs that exhibit high informational frictions. For instance, migrants are likely to have a stronger impact on financial flows between the US and Egypt than between the US and Canada, simply because informational frictions in the latter case are very weak. We test this dimension by the following regression:

log ðLoansjiÞ¼a1log ðMigrationijÞðCultural ProximityijÞ

þa2log ðMigrationijÞþa3log ðCultural ProximityijÞ (2)

þa4log ðDistanceijÞþa5Xij þ ci þ cj þ ij: Here, Cultural Proximity is an indicator for the cultural proximity of countries i and j. It is measured by three alternative variables: common language, colonizer, and legal origin.4 Our hypothesis is that migration should have a stronger effect on financial flows between countries that are culturally more distant. We test this hypothesis in section III. As explained, we also expect stronger effects when the migrants’ homecountry is a developing country, and, in a last set of regressions, reported in section IV, we interact our variable of interest with a non-OECD dummy. Finally, we also look at differences in the underlying investment risk. Specifically, we examine investment in developing versus developed countries and find that the former is more sensitive to migration than the latter. We also examine investment in long- versus short-term bonds, assuming that the former is riskier than the latter. We find that the migration coefficient is most significant for investment in long- term bonds issued by developing countries.

3 We thank an anonymous referee for pointing out this issue. 4 That is, in our paper, Cultural Proximity is not a composite index. 152 Kugler, Levintal, and Rapoport

Our empirical analysis focuses on the effects of migrants on financial flows from their host country to their home country. However, a similar effect may work in the opposite direction. Namely, a migrant coming from the US to Egypt may encourage US investors to invest in Egypt for the exact same reasons (i.e., by reducing the informational frictions between the two countries). Our sample is less suitable for Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 testing this direction, because we have data on financial flows from the small set of OECD countries to the rest of the world, whereas the main migration flows are in the opposite direction. Indeed, when we reduce our sample to country pairs where migration and financial investments flow in the same direction, the sample size drops by around 75%. Therefore, in this study, we concentrate on the effects of migration on financial flows to their home countries. To summarize, we estimate differential effects along four dimensions: (i) skilled versus unskilled migrants; (ii) cultural proximity; (iii) long- versus short-term bonds; and (iv) developed versus developing countries. These dimensions form the basis of our empirical strategy.

II. Data The migration data come from Artuc, Docquier, Ozden€ , and Parsons (2015) data set, the last extension of the Docquier and Marfouk (2006) dataset, which includes bilateral data on migration by country of birth,5 skill category (skilled versus unskilled, the former having college education) and gender for 195 sending/receiving countries in 1990 and 2000. The main additional novelty is that the dataset now captures South-South migration based mainly on observations (taken from Ozden€ et al. 2011) and occasion- ally on estimated data points (for the skill structure).6 Our financial data come from two commonly used sources. Data on international bank loans are from the Consolidated Banking Statistics published by the Bank of International Settlements (BIS). These data capture the worldwide consolidated positions of internationally active banking groups headquar- tered in the reporting countries. The data include the claims of reporting banks’ foreign affiliates but exclude intragroup positions. Nationality of the borrower is determined by the immediate borrower with whom the bank contracts to lend (see Bank of International Settlements [2015] for more details). Our main specification refers to the year 2000, for which we have 17 lending countries, 175 borrowing countries, and a total of 1,628 country pairs (observations) with positive loan values, given missing data. In addition, we take data on investment in short-term bonds and long-term bonds from the International Monetary Fund (IMF) in its Coordinated Portfolio Investment Survey (CPIS). Further details on the sample and summary statistics are reported in the supplemental appendix, available at https://academic.oup.com/wber (tables S.1–S.4). Our migration data include two years, 1990 and 2000. Ideally, we would like to estimate a panel equation, but since we only have two time periods, this is not feasible. Alternatively, we could run a difference equation. However, this would introduce a serious endogeneity problem. Specifically, the sample period is characterized by the formation of many new countries, mainly in Eastern Europe. These countries experienced strong outflows of migrants, and strong inflows of foreign investments, to build the new economies. This phenomenon introduces endogeneity to the sample, which is difficult to control for. In addition, both our main dependent variable, loans, and main variable of interest, migration, suffer from a measurement error problem, which is exacerbated when moving to first differences. Since our data do not allow a proper panel data analysis or a difference regression, we choose to focus on the cross-section dimension of our data. As a result, the analysis concentrates on differential effects rather than on the main effects.

5 For some countries for which information on immigrants’ country of birth is missing, immigrants are defined by their citizenship. 6 See http://perso.uclouvain.be/frederic.docquier/filePDF/DMOP-ERF.pdf for further details. The World Bank Economic Review 153

We construct a cross section that minimizes as much as possible endogeneity concerns. First, we take a lag of ten years between the migration data and the financial data. Hence, we regress financial flows in 2000 on migration data in 1990. This way we reduce short-term endogeneity, such as new countries that were established in the 1990s and experienced an outflow of migrants and an inflow of foreign Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 investments. Second, we choose as our benchmark results the regression of financial investments in 2000 on migration in 1990, although we also ran similar regressions for 2010 financial data on 2000 migration data. The reason we focus on the earlier period is that our dataset indicates that many country pairs had no financial investments in the 1980s and started their investment connections only in the 1990s. For instance, if we restrict our sample to 16 countries that were active in foreign lending in 1985, we obtain around 1,400 lending-borrowing country pairs between 1985 and 1992. The number of country pairs increased significantly during the 1990s and reached 1,877 by the end of the decade. By contrast, in the following decade, there was no significant change in the number of country pairs.7 Part of the increase in lending-borrowing country pairs in the 1990s is due to new countries that were formed in the 1990s, and part is due to a rise in the volume of international loans. In this respect, the migration stock in 1990 can be regarded as a predetermined variable for country pairs that had no lending activity in the 1980s and started to lend in the 1990s.8 We complete our dataset with gravity variables taken from Aviat and Coeurdacier (2007) and CEPII (CHELEM dataset). Trade data are taken from Feenstra et al. (2005).

III. Results We build on the specification of Aviat and Coeurdacier (2007), who studied a similar regression without the migration variable. We include in the regression source and destination fixed-effects as advocated by Okawa and van Wincoop (2012). Table 1 presents the first set of results. Column (1) reports a standard gravity equation without migration, and column (2) introduces the migration variable. Country fixed- effects are included in columns (3) and (4). The migration variable is highly significant, implying a strong correlation of migration with international loans.9 Following Okawa and van Wincoop (2012), column (5) drops the fiscal variables and stock market correlation. This does not affect much the results. On the other hand, we gain a much larger sample with 17 lending countries, 175 borrowing countries and a total of 1,628 observations (country pairs), given missing data and zeros, which is twice the original sample size. The migration coefficient remains highly significant in the Poisson regression proposed by Santos Silva and Tenreyro (2006), which is reported in column (6).10 These results are robust to different sets of fixed-effects and further control variables, i.e., remittances and political institutions (appendix table S.5). We now turn to heterogeneous effects along a number of dimensions.

7 In 2010, the number of country pairs reached 1,908. 8 However, our results are fully robust to the use of data for the 2000–2010 period. Note that measurement errors might be larger for the earlier period due to the appearance of new countries in the 1990s. 9 The coefficients of the gravity variables change to some extent (compare column 1 with column 2), because the migration variable is correlated with the gravity variables (e.g., see Artuc et al. [2015]). In fact, Artuc et al. (2015), which is our source of migration data, use gravity regressions to interpolate missing data. 10 The Poisson estimator increases the sample size by adding observations with zero loans. However, the rise in the sample size is moderate for two reasons. First, we include only country pairs that have positive migration flows. Second, countries that do not borrow from at least two lending countries are dropped due to the fixed effects. Hence, conditional on having positive migration and at least two lending countries per each borrowing country, the share of country pairs with zero loans is roughly 11 percent of the sample. In addition, the Poisson estimator imposes adding-up constraints and gives a larger weight to larger observations (Fally 2015). 154 Kugler, Levintal, and Rapoport

Table 1. International bank lending and migration

Dependent Variable: log ðLoansjiÞ Loansji (1) (2) (3) (4) (5) (6)

*** ** *** *** Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 log Migrationij 0.185 0.128 0.150 0.177 (0.038) (0.051) (0.038) (0.031) *** ** *** *** *** *** log Distanceij 0.353 0.229 0.790 0.676 0.790 0.246 (0.088) (0.092) (0.139) (0.137) (0.115) (0.052) *** *** *** *** *** ðColonial LinkÞij 0.899 0.780 1.335 1.164 1.335 0.181 (0.238) (0.238) (0.267) (0.271) (0.204) (0.164) * ðLanguageÞij 0.291 0.041 0.195 0.117 0.294 0.212 (0.206) (0.225) (0.182) (0.184) (0.160) (0.245) *** *** *** *** *** ðLegal originÞij 0.689 0.574 0.501 0.425 0.156 0.449 (0.108) (0.116) (0.124) (0.116) (0.111) (0.120) * ðBorderÞij 0.157 0.181 0.430 0.453 0.551 0.042 (0.212) (0.211) (0.292) (0.290) (0.305) (0.154) * ðFiscal TreatyÞij 0.010 0.007 0.000 0.002 (0.005) (0.005) (0.004) (0.004) * ** ðDividend TaxÞij 0.012 0.015 0.022 0.024 (0.011) (0.011) (0.011) (0.011)

ðInterest TaxÞij 0.014 0.014 0.014 0.014 (0.012) (0.012) (0.011) (0.010) *** *** ðCorrelationÞij 2.227 1.922 0.319 0.375 (0.471) (0.509) (0.530) (0.542) Country ﬁxed effects No No Yes Yes Yes Yes N 824 824 824 824 1,628 1,827 L 161616161717 B 62 62 62 62 175 178 Estimator OLS OLS OLS OLS OLS Poisson

Notes: Analysis is based on data described in section II. Columns (1)–(5) are estimated by OLS. Column (6) is estimated by the Poisson estimator. Standard errors are clustered at the borrowing country level. N, L, and B denote number of observations, number of lending countries, and number of borrowing countries. Columns (1) and (2) include, in addition, the following country speciﬁc variables that are not reported: the log of GDP of countries i and j, the average stock return of country i, a dummy if country i is a tax haven and corruption dummies for countries i and j. These variables are dropped out in columns (3)–(6), which include country ﬁxed effects for countries i and j.

Skilled Versus Unskilled Migration The significance of migration in the gravity regressions of foreign loans suggests that migrants alleviate informational frictions between their host and home countries. Arguably, to be able to perform such a role, migrants should have the required skills and connections that allow them to communicate effi- ciently with the financial markets in their host country and connect them with their home countries. Hence, one would expect the migration effect to work mainly through skilled migrants. We test this hypothesis by comparing the coefficients of skilled migrants versus unskilled migrants in the gravity model. Table 2 re-estimates the gravity model by distinguishing between skilled and unskilled migrants. Skilled migrants are defined as migrants with at least college education. The results strongly indicate that the migration effect is driven primarily by skilled migrants, because only skilled migration is significant. This holds for the OLS (column 1) as well as for the Poisson (column 2) regressions. The hypothesis that these coefficients are identical is rejected at significance levels of 5% for the OLS and 15% for the Poisson estimators. Note that the correlation between skilled migration and unskilled migration is 0.93, which is extremely high. Consequently, the standard errors of the estimated coefficients are large, which explains the relatively high P-values. The World Bank Economic Review 155

Table 2. Skilled versus unskilled migration - OLS and Poisson

Dependent Variable: log(Loans) Loans log(Loans) Loans log(1þLoans) (1) (2) (3) (4) (5) log(Skilled) 0.204*** 0.234** 0.108 0.088 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 (0.066) (0.093) (0.072) (0.102) log(1þSkilled) 0.182** (0.078) log(Unskilled) 0.022 0.002 0.035 0.135 (0.054) (0.076) (0.060) (0.085) log(1þUnskilled) 0.010 (0.058) log(Distance) 0.832*** 0.253*** 0.846*** 0.318*** 0.665*** (0.120) (0.053) (0.157) (0.084) (0.213) Fixed effects i and jiand jigand jigand jigand j P-value 0.0470 0.1508 0.5574 0.7926 0.1273 N 1,424 1,546 1,303 1,406 1,833 L 1717171717 B 155 157 153 156 158 Estimator OLS Poisson OLS Poisson OLS

Notes: Columns (1) and (2) include country fixed effects for countries i and j. Columns (3)–(5) include fixed effects for lending country j and for the interaction ig, where ig implies that country i is the borrowing country and the lending country belongs to group g. We distinguish between three groups of lending countries indexed by g: Anglo-Saxon countries, continental Western European countries, and other. Standard errors are clustered at the borrowing country level. P-value refers to the test that the coefficients of log(Skilled) and log(Unskilled) are identical. N, L, and B denote number of observations, number of lending countries, and number of borrowing countries, respectively. All columns include colonial link, language, legal origin, and common border as additional controls. The data is described in section II.

Columns (3)–(5) report the robustness of the results to interactions of the borrowing country dummy i with a dummy for groups of lending countries g. We consider three groups of lending countries: Anglo- Saxon countries, continental Western European countries, and the rest of the world. To do so, we introduce a fixed-effect specific to i and g in addition to the fixed effect of the lending country j. This is a highly demanding test, because it ignores all variations in loans to country i between lending countries from different groups. Namely, country i must borrow from at least two different countries that belong to the same group in order to contribute to the regression results. As a result, the size of the sample falls by about 10%. The small sample size and limited within-group variation, together with the high correlation between skilled and unskilled migration, eliminate the skill differential effect obtained in columns (1) and (2). To resolve this problem, we extend the sample size as much as possible, by using log ð1 þ xÞ instead of log x on both sides of the regression. This transformation introduces zero values for skilled and unskilled migration, which are omitted from the previous specifications. Consequently, the sample size increases from 1,303 to 1,833, as reported in column (5). In that extended sample, we are able to replicate our initial result that the skilled coefficient is positive, significant, and higher than the unskilled one. One may be concerned that the effect of migration on financial flows captured in our regressions may, in fact, be mediated through trade. Since migrants stimulate trade and trade affects financial flows, a correlation between migration and financial flows is expected even if there is no causal effect. However, to explain our differential effects for skilled versus unskilled migrants, it should be the case that skilled migration has a stronger effect on trade than unskilled migration. We tested this hypothesis by running the same regressions on export (instead of loans) and found no difference between skilled and unskilled migration (appendix table S.6).11 The skill dimension is explored further below.

11 Aubry et al. (2016) found a similar result on a much larger sample. 156 Kugler, Levintal, and Rapoport

Cultural Proximity We next examine the migration effect on countries that are culturally close to each other compared to countries that are culturally distant. Our conjecture is that migration should have a stronger effect on financial flows between countries that are culturally more distant. We therefore add interaction terms Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 between migration and cultural proximity variables, such as common language, common colonizer, and common legal origin. We expect a negative coefficient on these interaction terms. We also include interactions of migration with distance, because distance is also a proxy for information frictions. These interactions should have positive coefficients, as migration should be more important for distant countries. Table 3 presents the results for the Poisson regressions (similar results are obtained with OLS, appendix table S.7). The results show clearly that the migration effect is smaller for countries that are culturally or physically closer to one another. Hence, the presence of migrants is mostly critical for financial flows between countries that are culturally/physically more distant. Interestingly, the interaction

Table 3. Comparison between culturally closer/more distant countries (Poisson results)

(1) (2) (3) (4)

Dependent Variable: Loans ** *** *** *** log Migrationij log Distanceij 0.056 0.079 0.081 0.058 (0.023) (0.019) (0.017) (0.022) *** ** log Migrationij Languageij 0.301 0.242 (0.083) (0.102) *** log Migrationij Colonizerij 0.252 0.128 (0.097) (0.112) * log Migrationij Legalij 0.103 0.008 (0.053) (0.053) *** *** log Migrationij 0.293 0.458 0.467 0.291 (0.206) (0.168) (0.151) (0.191) *** *** *** *** log Distanceij 0.837 1.027 1.084 0.829 (0.228) (0.207) (0.184) (0.221) Dependent Variable: Export *** *** *** *** log Migrationij log Distanceij 0.082 0.072 0.075 0.081 (0.016) (0.015) (0.015) (0.015) ** * log Migrationij Languageij 0.099 0.122 (0.047) (0.066) log Migrationij Colonizerij 0.007 0.057 (0.056) (0.058) log Migrationij Legalij 0.021 0.003 (0.029) (0.031) *** *** *** *** log Migrationij 0.490 0.413 0.441 0.476 (0.137) (0.130) (0.127) (0.114) *** *** *** *** log Distanceij 1.421 1.332 1.361 1.403 (0.159) (0.160) (0.157) (0.144) N 1,588 1,588 1,588 1,588 L 17171717 B 158 158 158 158 Estimator Poisson Poisson Poisson Poisson

Notes: This table reports the Poisson estimates of interaction terms of migration with the following measures of cultural proximity: (1) common language; (2) common colonizer; and (3) common legal origin. Standard errors are clustered at the borrowing country level. N, L, and B denote number of observations, number of lending countries, and number of borrowing countries, respectively. All columns include country ﬁxed effects, colonial link, language, legal origin, and common border as additional controls. Samples for the Loans and Export regressions are the same. The data is described in section II. The World Bank Economic Review 157 with language turns out the most significant and robust among the cultural interactions and remains significant when all the interactions are included together in column (4).

Financial Flows Versus Trade Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 The lower panel of table 3 presents similar regressions for exports (instead of loans). Overall, the interaction of migration with physical distance remains positive and significant in the export regressions as in the loan regressions. However, the interactions with the cultural variables are either insignificant or have the wrong sign. For instance, the interaction with language in the export regression (column 1) is positive rather than negative. This stands in contrast to the loan regressions where the interaction with language is significantly negative. This test indicates that the differential effect of migration on financial flows across different levels of cultural proximity reflects informational frictions that are stronger for financial flows than for exports.

Skill-Proximity Interaction In this section, we ask whether the stronger effect of skilled migrants is magnified when home and host countries are more distant physically and culturally; that is, we look for the interaction between skill and distance (Grogger and Hanson 2011 and Beine et al. 2011).12 Since informational frictions increase with (physical and cultural) distance, we expect that the presence of skilled migrants will be even more important when countries are more distant. We therefore introduce an interaction between the stocks of skilled and unskilled migrants, rather than the total stock of migrants, with our measures of physical and cultural distance. The results are shown in table 4. The coefficient of the interaction for skilled migration and distance is larger than for unskilled migration across all specifications (see columns [1]–[4]). Namely, the impact of skilled migration on financial flows is rising with distance faster than the impact of unskilled migration. Similarly, interactions between skilled/unskilled migration and common language/colonizer/legal are in general more negative for skilled migrants (except for one case in column [4]). These results suggest that skilled migration is more effective than unskilled migration between more distant countries. Similar findings are obtained in OLS regressions (appendix table S.8).

Short-Term and Long-Term Bonds Table 5 replicates the baseline migration regression of table 1 (column 5) using different financial data. The dependent variable is cross-country investment in short-term bonds and long-term bonds instead of international bank loans. The migration coefficient is positive and significant in most specifications, which shows the robustness of the results to the use of various financial data. The results for short-term bonds are somewhat weaker, though not statistically different from the long-term bonds. We explore this point further in section IV, where we distinguish between developed and developing countries.

IV. DEVELOPED VERSUS DEVELOPING COUNTRIES The migration effect is likely to be more pronounced for developing countries, where information imperfections are more pervasive. Migrants who move from these countries to other countries convey with them information on their origin countries that is otherwise hard to obtain. We test this hypothesis by adding interactions of the migration variable with a dummy variable for developing country status.13 Table 6 re-estimates the benchmark regression by allowing the migration coefficient to vary across developed and developing countries. Here we see a difference between the OLS estimator and the Poisson estimator. While the OLS estimator shows positive though not statistically significant effects for

12 We thank one of the anonymous referees for this suggestion. 13 Developing countries are defined as non-OECD countries. 158 Kugler, Levintal, and Rapoport

Table 4. Skill-proximity interactions (Poisson)

Dependent Variable: Loansji

(1) (2) (3) (4) Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 ** * log Skilledij Distanceij 0.039 0.085 0.051 0.068 (0.034) (0.038) (0.031) (0.035) log Unskilledij Distanceij 0.019 0.001 0.027 0.002 (0.036) (0.036) (0.032) (0.036) log Skilledij Languageij 0.200 0.108 (0.178) (0.175) log Unskilledij Languageij 0.096 0.233 (0.188) (0.168) *** *** log Skilledij Colonizerij 0.624 0.512 (0.162) (0.150) * * log Unskilledij Colonizerij 0.372 0.365 (0.193) (0.192) *** ** log Skilledij Legalij 0.310 0.207 (0.100) (0.082) * * log Unskilledij Legalij 0.173 0.144 (0.103) (0.082) * log Skilledij 0.160 0.573 0.191 0.382 (0.278) (0.295) (0.289) (0.287) log Unskilledij 0.131 0.027 0.263 0.036 (0.297) (0.277) (0.282) (0.303) *** *** *** *** log Distanceij 0.816 1.004 1.032 0.894 (0.234) (0.203) (0.185) (0.218) N 1,546 1,546 1,546 1,546 L 17171717 B 157 157 157 157 Estimator Poisson Poisson Poisson Poisson

Notes: Standard errors are clustered at the borrowing country level. N, L,andB denote number of observations, number of lending countries, and number of borrowing countries. All columns include country ﬁxed effects, colonial link, language, legal origin, and common border as additional controls. The data is described in section II. the interaction, the Poisson estimator is positive and highly significant. Recall that the OLS estimator omits country pairs with zero value for the dependent variable (loans, in this case). By contrast, the Poisson estimator accounts for the zero values. This suggests that the significance of the interaction in the Poisson regression is driven mostly by the extensive margin. This is apparent from the composition of the sample. In the OLS regression there are 427 developed borrowing countries. This number hardly changes in the Poisson regression. However, the number of developing borrowing countries increases from 1,024 in the OLS sample to 1,157 in the Poisson sample. Namely, the main change between the two samples is the introduction of new country pairs, where the borrowing country is a developing country that does not borrow from the lending country (hence the value of loans is zero for that pair). A similar result is obtained when we re-estimate the bond regressions by adding an interaction with a developing country dummy. The results are reported in table 7. Again, the OLS estimator does not reveal any pattern across developed/developing countries (see columns [1] and [2]). However, the Poisson estimator, presented in columns (3) and (4), shows clearly that investment in long-term bonds in a developing country is larger if the investing country hosts migrants from the developing country. This effect is mute when we look at short-term bonds, which we judge as less information sensitive than long-term bonds. Note that the Poisson regression introduces 728 new country pairs with developing borrowing The World Bank Economic Review 159

Table 5. Long-term versus short-term bonds - OLS and Poisson

Dependent variables: log(Long bonds) log(Short bonds) Long bonds Short bonds

(1) (2) (3) (4) log(Migration) 0.169** 0.071 0.161*** 0.143** Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 (0.076) (0.100) (0.029) (0.071) log(Distance) 0.471*** 0.800*** 0.329*** 0.145 (0.107) (0.156) (0.083) (0.094) N 541 541 1,517 1,517 L31313232 B85859292 Estimator OLS OLS Poisson Poisson

Notes: In this table the dependent variable is investment in long-term bonds and short-term bonds. N, L, and B denote number of observations, number of lending (investing) countries, and number of borrowing (issuing) countries, respectively. Regressions are estimated by OLS and Poisson. Standard errors are clustered at the borrowing country level. All columns include country ﬁxed effects, colonial link, language, legal origin, and common border as additional controls. The data is described in section II.

Table 6. Comparison between Developed and Developing countries - OLS and Poisson

Dependent variables: log(Loans) Loans (1) (2)

** *** log Migrationij 0.112 0.126 (0.049) (0.041) *** log Migrationij ðDeveloping CountryÞj 0.045 0.145 (0.044) (0.039) *** *** log Distanceij 0.837 0.266 (0.121) (0.052) Obs. 1,451 1,588 No. of Lending Countries 17 17 No. of Borrowing Countries 158 158 Obs. 1,451 1,588 Obs with Developed Borrowing Countries 427 431 Obs with Developing Borrowing Countries 1,024 1,157 No. of Lending Countries 17 17 No. of Borrowing Countries 158 158 Estimator OLS Poisson

Notes: This table adds an interaction of migration with developing countries. N, L, and B denote number of observations, number of lending (investing) countries, and number of borrowing (issuing) countries, respectively. Regressions are estimated by OLS and Poisson. Standard errors are clustered at the borrowing country level. All columns include country ﬁxed effects, colonial link, language, legal origin, and common border as additional controls. The data is described in section II. countries, compared to the OLS sample, and only 248 country pairs with developed borrowing countries. Hence, here again the main effect comes from the extensive margin. Overall, these results are consistent with our hypothesis that migrants enhance the flow of information across countries, which facilitates cross-border financial flows.14

14 When we interact the dummy variable of developing countries with skilled and unskilled migration separately, the main result is preserved, mainly in the Poisson regressions. Namely, migration is more important a determinant of financial flows for developing countries. However, we cannot identify robust differences between skilled and unskilled migrants. These results are available upon request. 160 Kugler, Levintal, and Rapoport

Table 7. Long versus Short Bonds, Developed versus Developing countries - OLS and Poisson

Dependent variables: log(Long bonds) log(Short bonds) Long bonds Short bonds (1) (2) (3) (4)

* *** ** Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 log Migrationij 0.152 0.050 0.134 0.142 (0.078) (0.109) (0.028) (0.072) *** log Migrationij ðDeveloping CountryÞj 0.047 0.057 0.177 0.010 (0.072) (0.124) (0.033) (0.054) *** *** *** log Distanceij 0.463 0.790 0.332 0.144 (0.110) (0.153) (0.087) (0.095) Obs. 541 541 1,517 1,517 Obs. with Developed Borrowing Countries 395 395 643 643 Obs. with Developing Borrowing Countries 146 146 874 874 No. of Lending Countries 31 31 32 32 No. of Borrowing Countries 85 85 92 92 Estimator OLS OLS Poisson Poisson

Notes: This table estimates the effect of migration on investment in long-term bonds and short-term bonds, with interaction with developing countries. N, L, and B denote number of observations, number of lending (investing) countries, and number of borrowing (issuing) countries, respectively. Regressions are estimated by OLS and Poisson. Standard errors are clustered at the borrowing country level. All columns include country ﬁxed effects, colonial link, language, legal origin, and common border as additional controls. The data is described in section II.

V. Conclusion This paper investigates the role of migration as a determinant of international financial flows. We introduce migration into a standard gravity model and find a strong correlation with financial flows. Our results are consistent with the view that migration affects bilateral financial flows through the information channel. Indeed, we find that the effect of migration on financial flows is strongest where informational problems are more acute. This is the case when we compare country pairs characterized by different levels of cultural proximity (e.g., countries sharing versus not sharing a common language), migrants’ source countries characterized by more information imperfections (i.e., developing countries), asset types that differ in terms of informational sensitivity (e.g., short-term versus long-term bonds issued by developing countries) and types of migrants that differ in their ability to disseminate information across borders (e.g., high-skill versus low-skill migrants). Overall, our results suggest that migration contributes to reduce home bias and information frictions across countries.

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Rauch, J.E., and A. Casella. 2003. “Overcoming Informational Barriers to International Resource Allocation: Prices Downloaded from https://academic.oup.com/wber/article-abstract/32/1/148/3797271 by World Bank Publications user on 08 August 2019 and Ties.” The Economic Journal 113: 21–42. Rauch, J.E., and V. Trindade. 2002. “Ethnic Chinese Networks in International Trade.” Review of Economics and Statistics 84: 116–30. Santos, S.J.M.C., and S. Tenreyro. 2006. “The Log of Gravity.” The Review of Economics and Statistics 88: 641–58. Wall Street Journal. 2016. “The Bank of China Takes Manhattan.” C. Cui and D. Huang. The World Bank Economic Review, 32(1), 2018, 163–182 doi: 10.1093/wber/lhx002 Article Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 Heterogeneous Technology Diffusion and Ricardian Trade Patterns William R. Kerr

Abstract Migration and trade are often linked through ethnic networks boosting bilateral trade. This study uses migration to quantify the importance of Ricardian technology differences for international trade. The framework provides the first panel estimates connecting country-industry productivity and exports, and the study exploits heterogeneous technology diffusion from immigrant communities in the United States for identification. The latter instruments are developed by combining panel variation on the development of new technologies across US cities with historical settlement patterns for migrants from countries. The instrumented elasticity of export growth on the intensive margin with respect to the exporter’s productivity growth is between 1.6 and 2.4, depending upon weighting. This provides an important contribution to the trade literature of Ricardian advantages, and it establishes a connection of migration to home country exports beyond bilateral networks. JEL classification: F11, F14, F15, F22, J44, J61, L14, O31, O33, O57 Key words: Trade, Exports, Comparative Advantage, Technological Transfer, Patents, Innovation, Research and Development, Immigration, Networks

Trade among countries due to technology differences is a core principle in international economics. Countries with heterogeneous technologies focus on producing goods in which they have comparative advantages; subsequent exchanges afford higher standards of living than are possible in isolation. This Ricardian finding is the first lesson in most undergraduate courses on trade, and it undergirds many

William Kerr (corresponding author) is Dimitri V. D’Arbeloff—MBA Class of 1955 Professor of Business Administration, Harvard Business School, Boston, Massachusetts, and Faculty Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts; his email address is [email protected]. I am grateful to Daron Acemoglu, Pol Antras, David Autor, Dany Bahar, Nick Bloom, Ricardo Caballero, Arnaud Costinot, Julian Di Giovanni, Robert Feenstra, Fritz Foley, Richard Freeman, Gordon Hansen, Sam Kortum, Ashley Lester, Matt Mitchell, Peter Morrow, Ramana Nanda, Giovanni Peri, Hillel Rapoport, Ariell Reshef, Tim Simcoe, Antonio Spilimbergo, Scott Stern, Sarah Turner, and John Van Reneen for advice on this project and to seminar participants at the eighth AFD-World Bank Migration and Development Conference, American Economic Association meetings, Clemson University, Columbia University, European Regional Science Association meetings, Georgetown University, Harvard University, International Monetary Fund, London School of Economics, MIT Economics, MIT Sloan, NBER High Skilled Immigration Conference, NBER Productivity, Queens University, University of California Davis, University of Helsinki, University of Toronto Rotman, World Bank, and Yale University for helpful comments. This paper is a substantial revision of chapter 2 of my Ph.D. dissertation (Kerr 2005). This research is supported by the National Science Foundation, MIT George Schultz Fund, HBS Research, and the Innovation Policy and Economy Group. A supplemental appendix to this article is available at https://academic.oup.com/wber.

VC The Author, 2017. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please email: [email protected] 164 Kerr modeling frameworks on which recent theoretical advances build (e.g., Dornbusch et al. 1977, Eaton and Kortum 2002, Costinot et al. 2012). In response to Stanislaw Ulam’s challenge to name a true and nontrivial theory in social sciences, Paul Samuelson chose this principle of comparative advantage due to technology differences. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 While empirical tests date back to David Ricardo (1817), quantifying technology differences across countries and industries is extremely difficult. Even when observable proxies for latent technology differences are developed (e.g., labor productivity, industrial specialization), cross-sectional analyses risk confounding heterogeneous technologies with other country-industry determinants of trade. Panel data models can further remove time-invariant characteristics (e.g., distances, colonial histories) and afford explicit controls of time- varying determinants (e.g., factor accumulation, economic development, trading blocs). Quantifying the dynamics of uneven technology advancement across countries is an even more challenging task, however, and whether identified relationships represent causal linkages remains a concern. These limitations are particularly acute for developing and emerging economies. This is unfortunate as non-OECD economies have experienced some of the more dramatic changes in technology sets and manufacturing trade over the last thirty years, providing a useful laboratory for quantifying Ricardian effects. This study contributes to the empirical trade literature on Ricardian advantages in three ways. First, it utilizes a panel dataset that includes many countries at various development stages (e.g., Bolivia, France, South Africa), a large group of focused manufacturing industries, and an extended time frame. The 1975–2000 World Trade Flows (WTF) database provides export data for each bilateral route (exporter-importer-industry-year), and data from the United Nations Industrial Development Organization (UNIDO) provide labor productivity estimates. The developed data platform includes substantially more variation in trade and productivity differences across countries than previously feasible. The second contribution is to provide panel estimates of the elasticity of export growth with respect to productivity development. Following the theoretical work of Costinot et al. (2012) that is discussed below, estimations include fixed effects for importer-industry-year and exporter-importer-year. The importer- industry-year fixed effects control, for example, for trade barriers in each importing country by industry seg- ment while the exporter-importer-year fixed effects control for the overall levels of trade between countries (e.g., the gravity model), labor cost structures in the exporter, and similar. While these controls account for overall trade and technology levels by country, permanent differences in the levels of these variables across industries within a country are used for identification in most applications of this approach. This paper is the first to quantify Ricardian elasticities when further modeling cross-sectional fixed effects for exporter- importer-industry observations. This panel approach only exploits variation within industry-level bilateral trading routes, providing a substantially stronger empirical test of the theory. The third and most important contribution is to provide instruments for the labor productivity development in exporting countries. Instruments are essential in this setting due to typical concerns: omitted variable biases for the labor productivity measure, reverse causality, and the potential for significant measurement error regarding the productivity differences across countries. The instruments exploit heterogeneous technology diffusion from past migrant communities in the United States for identification. These instruments are developed by combining panel variation on the development of new technologies across US cities during the 1975–2000 period with historical settlement patterns for migrants and their ancestors from countries that are recorded in the 1980 Census of Populations. The foundation for these instruments is the modeling of Ricardian advantages through differences across countries in their access to the US technology frontier. Recent research emphasizes the importance of immigrants in frontier economies for the diffusion of technologies to their home countries (e.g., Saxenian 2002, 2006, Kerr 2008). These global connections and networks facilitate the transfer of both codified and tacit details of new innovations, and Kerr (2008) finds foreign countries realize THE WORLD BANK ECONOMIC REVIEW 165 manufacturing gains from stronger scientific integration, especially with respect to computer-oriented technologies. Multiple studies document specific channels sitting behind this heterogeneous diffusion.1 As invention is disproportionately concentrated in the United States, these ethnic networks significantly influence technology opportunity sets in the short-run for following economies. This study uses Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 heterogeneous technology diffusion from the United States to better quantify the importance of technology differences across countries in explaining trade patterns. Trade between the United States and foreign countries is excluded throughout this study due to network effects operating alongside technology transfers. Attention is instead placed on how differential technology transfer from the United States— particularly its industry-level variation by country—influences exports from the foreign country to other nations. Said differently, the study quantifies the extent to which India’s exports, for example, grow faster in industries where technology transfer from the United States to India is particularly strong. This provides an important complement in the migration literature to the typical focus on how ethnic networks boost bilateral trade. The instrumented elasticity of export growth on the intensive margin with respect to the exporter’s productivity growth is 2.4 in unweighted estimations. The elasticity is 1.6 when using sample weights that interact worldwide trade volumes for exporters and importers in the focal industry. Thus, the study estimates that a 10% increase in the labor productivity of an exporter for an industry leads to about a 20% expansion in export volumes within that industry compared to other industries for the exporter. This instrumented elasticity is weaker than Costinot et al.’s (2012) preferred estimate of 6.5 derived through producer price data for OECD countries in 1997, but it is quite similar to their 2.7 elasticity with labor productivity data that are most comparable to this study. The two analyses are also qualitatively similar in terms of their relationships to uninstrumented elasticities. This study does not find evidence of substantial adjustments in the extensive margin of the group of countries to which the exporter trades. These results are robust to sample composition adjustments and variations on estimation techniques. Extensions quantify the extent to which heterogeneous technology transfer can be distinguished from a Rybczynski effect operating within manufacturing, evaluate differences in education levels or time in the United States for past migrants in instrument design, and test the robustness to controlling for direct ethnic patenting growth by industry in the United States. This study concludes that comparative advantages are an important determinant of trade; moreover, Ricardian differences are relevant for explaining changes in trade patterns over time. These panel exercises are closest in spirit to the industrial specialization work of Harrigan (1997) and the structural Ricardian model of Costinot et al. (2012). Other tests of the Ricardian model are MacDougall (1951, 1952), Stern (1962), Golub and Hsieh (2000), Chor (2010), Morrow (2010), Fieler (2011), Bombardini et al. (2012), Costinot and Donaldson (2012), Shikher (2012), Levchenko and Zhang (2014), Simonovska and Waugh (2014a,b), and Caliendo and Parro (2015). Recent related work on the industry dimension of trade includes Autor et al. (2013), Kovak (2013), and Hakobyan and McLaren (2016). Costinot and Rodriguez-Clare (2014) review empirical aspects and challenges of this literature. The comparative advantages of this work are in its substantial attention to non-OECD economies, the stricter panel assessment using heterogeneous technology diffusion, and the instruments built off of differential access to the US frontier. Work on migration-trade linkages dates back to Gould (1994), Head and Reis (1998), and Rauch and Trindade (2002), with Bo and Jacks (2012), di Giovanni et al. (2015), Bahar and Rapoport (2016), and Cohen et al. (2016) being recent contributions that provide references

1 Channels for this technology transfer include communications among scientists and engineers (e.g., Saxenian 2002, Kerr 2008, Agrawal et al. 2011), trade flows (e.g., Rauch 2001, Rauch and Trindade 2002), and foreign direct investment (e.g., Kugler and Rapoport 2007, Foley and Kerr 2013). The online supplement (available at https://academic.oup.com/wber) provides further references to the role of international labor mobility and other sources of heterogeneous technology frontiers (e.g., Eaton and Kortum 1999, Keller 2002). 166 Kerr to the lengthy subsequent literature. This paper differs from these studies in its focus on technology transfer’s role for export promotion as an independent mechanism from migrant networks. In addition to contributing to the trade literature, the study documents for emerging economies an economic consequence of emigration to frontier economies like the United States.2 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019

I. Estimating Framework This section extends the basic estimating equation from Costinot et al. (2012) to a panel data setting. A simple application builds ethnic networks and heterogeneous technology diffusion into this theory. The boundaries of the framework and the statistical properties of the estimating equation are discussed.3

Estimating Equation Costinot et al. (2012) develop a multi-country and multi-industry Ricardian model that has been widely studied and utilized in the trade literature. This framework builds off the model of Eaton and Kortum (2002) to articulate appropriate estimation of Ricardian advantages with industry-level data. The supplemental appendix shows how this model provides a microfoundation for studying Ricardian trade through an econometric specification of the form

k k k k ln ðx~ijÞ¼dij þ dj þ h ln ðz~i Þþeij; (1) where i indexes exporters, j indexes importers, and k indexes goods. Each good k has an infinite number of subvarieties that are being bought and sold with observed trade flows being an aggregation of the sub- k varieties. In the estimating equation, x~ij represents trade flows from exporter i to importer j for good k k that adjust for country openness, and z~i represents observed labor productivity in exporter i for good k. As described in the supplemental appendix, the theory framework requires including fixed effects for k bilateral trade routes (dij) and importer-industry fixed effects (dj ) to account for unmodeled factors like consumer preferences, country sizes, and delivery costs. Finally, the estimated coefficient h has a specific interpretation related to the Fre´chet distribution that underlies this model and Eaton and Kortum (2002). Specifically, a low h suggests a large scope for intraindustry comparative advantage, while a high h (corresponding to large observed adjustments in exports with industry-level productivity shifts) suggests a limited scope for intraindustry comparative advantage. Estimates of h in the trade literature have been derived with cross-sectional regressions using equation (1). This study seeks identification of the h parameter within the Costinot et al. (2012) setting via first differencing and instrumental variables.4 The first step is to extend equation (1) to include time t,

k k k k ln ðx~ijtÞ¼dijt þ djt þ h ln ðz~itÞþeijt: (2) It is important to note that this extension is being applied to the fixed effect terms. Thus, the exporter- importer fixed effects in the cross-sectional format become exporter-importer-year fixed effects in a panel format. It is assumed that h does not vary by period, although stacked versions of the Costinot et al. (2012) model could allow for this. The empirical work below estimates equation (2) for reference, but most of the specifications instead examine a first-differenced form,

2 Davis and Weinstein (2002) consider immigration to the United States, technology, and Ricardian-based trade. Their concern, however, is with the calculation of welfare consequences for US natives as a consequence of immigration due to shifts in trade patterns. 3 Dornbusch et al. (1977), Wilson (1980), Baxter (1992), Alvarez and Lucas (2007), Costinot (2009), and Costinot and Vogel (2015) provide further theoretical underpinnings for comparative advantage. 4 Daruich et al. (2016) estimate this framework encompasses about 20% of the variation in trade flows. Other studies seek to jointly model Ricardian advantages with other determinants of trade (e.g., Davis and Weinstein 2001, Morrow 2010). THE WORLD BANK ECONOMIC REVIEW 167

k k k k Dln ðx~ijtÞ¼dijt þ djt þ hDln ðz~itÞþeijt; (3) where the fixed effects and error term are appropriately adjusted. The motivation for first differencing is stronger empirical isolation of the h parameter. By themselves, exporter-importer-year and importer-industry-year fixed effects in equation (2) allow identification of Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 k k the h parameter in two ways: (i) longitudinal changes in z~it over time; and (ii) long-term differences in z~it across industries for the exporter. In a cross-sectional estimation of equation (1), it is not feasible to distinguish between these forms. This second effect persists when extending the equation (2) to a panel setting because the exporter-importer-year fixed effects dijt only account for the aggregate technology changes for exporters. First differencing best isolates the particular role of longitudinal changes in pro- k ductivity z~it over time. Whether estimating the h parameter through both forms of variation is appropriate depends upon model assumptions, beliefs about unmeasured factors, and measurement error. It is helpful to illustrate by considering the exports of Germany in automobiles. The study examines trade over the 1980–1999 period. Throughout this period, Germany held strong technological advantages and labor productivity for manufacturing automobiles relative to the rest of the world. Over the course of the period, this productivity also changed in relative terms. If one can feasibly isolate these productivity variables, then having both forms of variation is an advantage. A second and related issue is that first differencing the data exacerbates the downward bias that measurement error causes for estimates of the h parameter. There are plenty of reasons to suspect non-trivial measurement error in industry-level labor productivity estimates developed from the UNIDO database. On the other hand, removing time-invariant differences to identify the h parameter can be an advantage. The basic identification constraint for the econometric analysis is that technology levels of exporters cannot be distinguished from other unobservable factors that also vary by exporter-industry or exporter- industry-year for the long-term technology levels and their longitudinal changes, respectively. The first is particularly worrisome given its general nature. First differencing is not foolproof against omitted factors, but it does require that the changes in these factors correlate with the changes in the focal produc- k tivity level in the exporters of z~it. This latter approach of panel estimation, while very common in microeconomic analyses, has yet to be extended to the Ricardian literature.5 Beyond this discussion, a few other notes about the estimation of (3) are warranted. The dependent variable is bilateral manufacturing exports by exporter-importer-industry-year. The lack of trade for a large number of bilateral routes at the industry level creates econometric challenges with a log specification. These zero-valued exports are predicted by the model as an exporter is rarely the lowest-cost producer for all countries in an industry. This study approaches this problem by separately testing the intensive and extensive margins of trade. Most of the focus is on the intensive margin of trade expansion, k where the dependent variable is the log growth in the value of bilateral exports Dln ðx~ijtÞ. The intensive margin of exports captures both quantities effects and price effects (e.g., Acemoglu and Ventura 2002, Hummels and Klenow 2005). In tests of extensive margin of trade expansion—that is, commencing exports to new import destinations—the dependent variable becomes a dichotomous indicator variable for whether measurable exports exist. Differences in the sample construction for these two tests are discussed when describing the trade dataset.

5 Estimations of the Costinot et al. (2012) model rely on fixed effects to handle delivery costs and other aspects of trade that are not due to the productivity of exporters. Thus, a cross-sectional estimation (1) requires unmodeled delivery costs k k be only comprised of a bilateral component and an importer-industry component (dij ¼ dij dj ). A panel estimation (3) k k k allows this proportionate structure to be extended to dijt ¼ dijt djt dij, where the third term represents the long-term delivery costs for the exporter to the importer by industry. 168 Kerr

Beyond the model’s background, the exporter-importer-year fixed effects perform several functions. They intuitively require that Germany’s technology expansion for auto manufacturing exceed its technology expansion for chemicals manufacturing if export growth is stronger in autos than chemicals. Thus, these fixed effects remove aggregate trade growth by exporter-importer pairs common across Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 industries. These uniform expansions could descend from factors specific to one country of the pair (e.g., economic growth and business cycles, factor accumulations, terms of trade and price levels) or be specific to the bilateral trading pair (e.g., trade agreements, preferences6). This framework is thus a powerful check against omitted variables biases, helping to isolate the Ricardian impetus for trade from relative factor scarcities and other determinants of trade. The fixed effects also control for the gravity covariates commonly used in empirical trade studies. National changes in factor endowments may still influence industries differentially due to the Rybczynski effect, which is explicitly tested for below. The importer-industry-year fixed effects control for tariffs imposed upon an industry in the importing country. More broadly, they also control for the aggregate growth in worldwide trade in each industry, relative price changes, and the potential for trade due to increasing returns to scale (e.g., Helpman and Krugman 1985, Antweiler and Trefler 2002). More subtly, a key difference between multicountry Ricardian frameworks and the classic two- country model of Dornbusch et al. (1977) is worth emphasizing. This difference influences how the com- k parative static of increasing a single country-industry technology parameter z~it, ceteris paribus,is k viewed. The multicountry theoretical framework allows for increases in z~it to reduce exports on some bilateral routes for the exporter-industry. This effect is due to general equilibrium pressures on input costs and extreme value distributions—while the productivity growth makes the exporter more competitive, the rising wage rates in the country may make it less competitive for a particular importer. This more nuanced pattern is different from the stark prediction of a two-country model where productivity growth in an industry for a country would never lead to declines in exports to the other country in that industry. The proper treatment effect for productivity growth is measured across all export destinations, as in the empirical work of this paper, and thus captures the general Ricardian pattern embedded in the model. This treatment effect is a net effect that may include reduction of exports on some routes.7

Heterogeneous Technology Diffusion and Ricardian Trade While the Ricardian framework assigns a causal relationship of export growth to technology development, in practice the empirical estimation of specification (3) can be confounded by reverse causality or omitted variables operating by exporter-industry-year even after first differencing. Reverse causality may arise if engagement in exporting leads to greater technology adoption, perhaps through learning-by- doing or for compliance with an importer’s standards and regulations. An example of an exporter- industry-year omitted factor is a change in government policies to promote a specific industry, perhaps leading to large technology investments and the adoption of policies that favor the chosen industry’s exports relative to other manufacturing industries. This would lead to an upward bias in the estimated h parameter.8 Heterogeneous technology transfer from the United States provides an empirical foothold against these complications. Consider a leader-follower model where the technology state in exporter i and industry k is

6 Hunter and Markusen (1988) and Hunter (1991) find these stimulants account for up to 20% of world trade. 7 Costinot et al. (2012) provide a more detailed discussion, including the extent to which the industry ordering of the two- country model is found in the relative ordering of exports for countries. 8 More specifically, the innovation in industrial policy support must be non-proportional across manufacturing industries. Long-term policies to support certain industries more than others are accounted for by the first differencing. Uniform changes in support across industries are also jointly accounted for by panel fixed effects. THE WORLD BANK ECONOMIC REVIEW 169

k k;US k k z~it ¼ z~t !i !it Mit: (4) k;US z~t is the exogenously determined US technology frontier for each industry and year. Two general shifters k govern the extent to which foreign nations access this frontier. First, !i models time-invariant differences in the access to or importance of US technologies to exporter i and industry k, potentially arising due to geo- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 graphic separation (e.g., Keller 2002), heterogeneous production techniques (e.g., Davis and Weinstein 2001, Acemoglu and Zilibotti 2001), or similar factors. The shifter !it models longitudinal changes in the utilization of US technologies common to all industries within exporter i, such as changes due to declines in communication and transportation costs, greater general scientific or business integration, and so on. In what follows, both of these shifters could further be made specific to an exporter-importer pair. By themselves, these first three terms of model (4) describe the realities of technology diffusion but k;US are not useful for identification when estimating specification (3). The technology frontier z~t is cap- k tured by the importer-industry-year fixed effects, the bilateral !i shifter is removed in the first differencing, and the longitudinal !it shifter is captured in the exporter-industry-year fixed effects. The final term k Mit, however, describes differential access that the migrants to the United States from exporter i provide to the technologies used in industry k. This term models the recent empirical literature that finds that overseas diaspora and ethnic communities aid technology transfer from frontier countries to their home countries. If there is sufficient industry variation in this technology transfer, after removing the many fixed effects embedded into specification (3), then this transfer may provide an exogenous instrument to k the exporter productivity parameter z~it in a way that allows very powerful identification for the role of Ricardian advantages in trade. The design of this instrument combines spatial variation in historical settlement patterns in the United States of migrant groups from countries with spatial variation in where new technologies emerged over the period of the study. The instrument takes the form "# X Techk;AS Mk M% c;t ; (5) it ¼ i;c;1980 k;AS c2C Techc;1980 where c indexes US cities. M%i;c;1980 is the share of individuals tracing their ancestry to country i— defined in more detail below and including first-generation immigrants—that are located in city c in 1980. These shares sum to 100% across US cities. The bracketed fraction is a technology ratio defined for an industry k. The ratio measures for each city how much patenting grew in industry k relative to its initial level in 1980. The fraction exceeds one when a city’s level of invention for industry k grows from the base period, and it falls below one if the city’s invention for an industry weakens. The instrument thus interacts the spatial distribution across US cities of migrants from exporter i with the city-by-city degree to which technological development for industry k grew in locations. By summing across cities, equation (5) develops a total metric for exporter i and industry k that can be first differ- k k enced in a log format, Dln ðMitÞ, to instrument for Dln ðz~itÞ in equation (3). A subtle but important point is that the instrument can only work in a first-differenced format (or equivalent panel data model with bilateral route fixed effects). This restriction is because the expression (5) does not have a meaningful k cross-sectional level to it—for all countries and industries, the value of Mit is equal to one in 1980 by def- k k inition. As such, Mit cannot predict the cross-section of trade in 1980. However, Mit does provide insight about changes in technology opportunity sets over time that can be used for identification in estimations that consider changes in technology and trade over time. Three other points about the instrument’s design are important to bring out as they specifically relate to potential concerns about the instrument. First, the technology trend modeled in equation (5) is at the city-industry level, not at the city level. This is vital because the instrument interacts the US spatial ancestry distribution for a country with these city-industry patenting trends. As the estimations include exporter-importer-year fixed effects, any variable or instrument that would interact a city-level trend 170 Kerr

(e.g., population growth, housing prices, local public expenditures, etc.) with the city spatial distribution will be completely absorbed by the exporter-importer-year fixed effects (specifically, the exporter-year part of these fixed effects). In other words, general city-level factors like the total patenting of a location are anticipated to impact industries for a country in a proportionate way, and the estimations only use Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 disproportionate variation over industries to identify the empirical effects. Second, one concern would be that migrants from exporter i select cities specifically to acquire technologies useful for their home country’s exports. This seems less worrisome perhaps for individual migrants, but it is quite plausible when contemplating a German automobile manufacturer opening a new facility in the United States (e.g., Alcacer and Chung 2007). The instrument seeks to rule out this concern by fixing the city distribution of migrants from exporter i at their city locations in 1980. This approach eliminates endogenous resorting, and the results below are also shown to be robust to focusing on second-generation and earlier migrants. Additional analyses also consider dropping industries for each country where the concerns could be most pronounced. A third concern is one of reverse causality. The United States relies extensively on immigrants for its science and engineering labor force, with first-generation immigrants accounting for about a quarter of the bachelor’s educated workforce and half of those with PhDs. Moreover, immigrants account for the majority of the recent growth in the US science and engineering workforce. The spatial patterns of new high-skilled immigrants frequently build upon ethnic enclaves and impact the innovation levels in those locations (e.g., Kerr and Lincoln 2010, Hunt and Gauthier-Loiselle 2010, Peri et al. 2015). Thus, a worry could be that the technology growth for cities in model (5) is endogenous. The concern would be that Germany is rapidly developing innovations and new technologies for the automobile industry, and this expansion is simultaneously leading to greater exports from Germany and the migration of German scientists that are patenting automobile technologies to the United States. This concern is addressed in several ways throughout this study, including sample decomposition exercises, lag structure tests, and similar exercises. The most straightforward safeguard, however, is already built into model (5). The patenting data, as described below, allow us to separate the probable ethnicities of inventors in the United States. By focusing on inventors of Anglo-Saxon ethnic heritage, one can remove much of this reverse causality concern. The Anglo-Saxon group accounts for about 70% of US inventors during the time period studied, and so this group reflects the bulk and direction of US technological development. Extensions will further consider settings where patent citation records suggest that the Anglo- Saxon inventors are mainly drawing on other Anglo-Saxon inventors in their research.9 Addressing these concerns also provides the approach (5) with a conceptual advantage with respect to the fixed effect estimation strategy. The first differencing in specification (3) controls for the initial distri- k butions M%i;c;1980, and the importer-industry-year fixed effects djt control for the technology growth ratio for industry k. This separation is not perfect due to the summation over cities, but it is closely mim- icked. Thus, the identification in these estimations comes off these particular interactions. This provides a strong lever against concerns of omitted factors or reverse causality, and the well-measured US data can provide instruments that overcome the downward bias in coefficients due to measurement error.

II. Data Preparation This section summarizes the key data employed in this study and their preparation, with the online supplement providing further details. Table S1a describes the 88 exporting countries included, and table S1b provides similar statistics for the 26 industries, aggregating over countries.

9 Very strong crowding-in or crowding-out of natives by immigrant scientists and engineers would create a bias in the Anglo-Saxon trend itself. Kerr and Lincoln (2010) find very limited evidence of either effect at the city level for the United States during this time period and for the time horizons considered here (i.e., first differencing over five-year periods). THE WORLD BANK ECONOMIC REVIEW 171

Labor Productivity Data k Productivity measures z~it are taken from the Industrial Statistics Database of the United Nations Industrial Development Organization (UNIDO). The UNIDO data provide an unbalanced panel over countries, industries, and time periods, and the availability of these data are the key determinant of this Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 study’s sample design. Estimations consider manufacturing industries at the three-digit level of the International Standard Industrial Classification system (ISIC3). Data construction starts by calculating the annual labor productivity in available industries and countries during the 1980–1999 period. These annual measures are then collapsed into the mean labor productivity level for each five-year period from 1980–1984 to 1995–1999. This aggregation into five-year time periods affords a more balanced panel by abstracting away from the occasional years when an otherwise reported country-industry is not observed. The higher aggregation is also computationally necessary below due to the tremendous number of fixed effects considered. These labor productivity measures are first differenced in log format for inclusion in equation (3). Thus, an exporter i and industry k is included if it is observed in the UNIDO database in two adjacent periods. Sample inclusion also requires that the country-industry be reported in two observations at least five years apart (e.g., to prevent an included observation only being present in 1989 and 1991). The main estimations consider the three change periods of 1980–1984 !1985–1989, 1985–1989 !1990– 1994, and 1990–1994 !1995–1999.

Export Volumes k Bilateral exports x~ijt are taken from the 1975–2000 World Trade Flows Database (WTF) developed by Feenstra et al. (2005). This rich data source documents product-level values of bilateral trade for most countries from 1980–1999. Similar to the development of the labor productivity variables, these product flows are aggregated into five-year periods from 1980–1984 to 1995–1999 and then first differenced in log format. Each productivity growth observation available with the UNIDO dataset is paired with industry-level bilateral export observations from that country. All exporting countries other than the United States are included. The majority of export volumes for bilateral routes are zero-valued, which creates challenges for the estimation of equation (3). It is also the case that the minimum threshold of trade that can be consistently measured across countries and industries is US $100,000 in the WTF database. While Feenstra et al. (2005) are able to incorporate smaller trading levels for some countries, these values are ignored to maintain a consistent threshold across observations. To accommodate these conditions, the empirical approach separately studies the extensive and intensive margins of export expansion. Mean export volumes are taken across exporter-importer-industry observations for five-year time periods. For the extensive margin, entry into exports along an exporter-importer-industry route is defined as exports greater than US $100,000.

US Historical Settlement Patterns The first building block for the instrument is the historical settlement patterns of migrants from each country M%i;c;1980. These data are taken from the 1980 Census of Populations, which is the earliest US census to collect the detailed ancestry of respondents (as distinguished from immigration status or place of birth). The detailed ancestry codes include 392 categories with positive responses, and this study maps these categories to the UNIDO records. Respondents are asked primary and secondary ancestries, but the classifications only focus on the primary field given the many missing values in the secondary field. There are multiple ancestry groups that map to the same country, but the mapping procedure limits each ancestry group to map to just one UNIDO country. Categories not linked to a specific UNIDO country are dropped (e.g., Western Europe not elsewhere classified, Ossetian). In total, 89% of the US population in 1980 is mapped. 172 Kerr

Metropolitan statistical areas, which will be referred to as cities for expositional ease, are identified using the 1% Metro Sample. This dataset is a 1-in-100 random sample of the US population in 1980 and is designed to provide accurate portraits of cities. The set C over which M%i;c;1980 is calculated includes 210 cities from the 1980 census files that are linked to the US patent data described next. The primary Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 measures of M%i;c;1980 include all individuals regardless of age or education level to form M%i;c;1980, only dropping those in group quarters (e.g., military barracks) or not living in an urban area. Extensions test variations on these themes.

US Patenting Data k;AS The second building block for the instrument is the trend in patenting for each city Techc;t . These series are quantified through individual records of all patents granted by the United States Patent and Trademark Office (USPTO) from January 1975 to May 2009. Each patent record provides information about the invention (e.g., technology classification, citations of patents on which the current invention builds) and inventors submitting the application (e.g., name, city). USPTO patents must list at least one inventor, and multiple inventors are allowed. Approximately 7.8 million inventors are associated with 4.5 million granted patents during this period. The online supplement documents how the patent data are augmented in terms of city and industry definitions. Only patents with all inventors living in the United States at the time of their patent application are included, and multiple inventors are discounted so that each patent receives the same weight when measuring inventor populations. Concordances link USPTO technology classes to ISIC3 industries in which new inventions are manufactured or used. The main estimations focus on industry-of-use, affording a composite view of the technological opportunity developed for an industry. The probable ethnicities of inventors are estimated through the names listed on patents (e.g., Kerr 2007). This procedure exploits the fact that individuals with surnames Gupta or Desai are likely to be Indian, Wang or Ming are likely to be Chinese, and Martinez or Rodriguez are likely to be Hispanic. The name matching work exploits two commercial databases of ethnic first names and surnames, and the procedures have been extensively customized for the USPTO data. The match rate is 98% for US domestic inventors, and the process affords the distinction of nine ethnicities: Anglo-Saxon, Chinese, European, Hispanic, Indian, Japanese, Korean, Russian, and Vietnamese. Most of the estimations in this paper only use whether inventors are of Anglo-Saxon origin, as a means for reducing the potential of reverse causality as discussed above. The Anglo-Saxon share of US domestic patenting declines from 73% in 1980–1984 to 66% in 1995–1999 (table S2). This group accounts for a majority of patents in each of the six major technology categories developed by Hall et al. (2001). As with the productivity and trade data, the patenting series are aggregated into five-year blocks by city and industry. These intervals start in 1975–1979 and extend through 1995–1999, and the series are normalized by the patenting level of each city-industry in 1980–1984. These series are then united with the spatial distribution of each country’s ancestry group using model (5) to form an aggregate for each country-industry, and the log growth rate is then calculated across these five-year intervals. The lag of this growth rate is used as the instrument for the productivity growth rate in an exporter-industry. That is, the estimated growth in technology flows from Brazil’s chemical industry during 1975–1979 !1980–1984 is used as the instrument for the growth in Brazil’s labor productivity in chemicals for the 1980–1984 !1985–1989 period. This lag structure follows the emphasis in Kerr (2008) on the strength of ethnic networks for technology diffusion during the first 3–6 years after a US invention is developed, and the comparison to contemporaneous flows is shown in robustness checks. The online supplement provides additional notes on the instrument design and its connection to patent data. THE WORLD BANK ECONOMIC REVIEW 173

III. Empirical Results This combined dataset is a unique laboratory for evaluating Ricardian technology differences in international trade. This section commences with ordinary least squares (OLS) estimations using the UNIDO

and WTF data. The instrumental variable (IV) results are then presented. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019

Base OLS Specifications Table 1 provides the basic OLS estimations. Column 1 presents the “between” estimates from specification (2) before first differencing the data; the dependent variable is the log mean nominal value of bilateral exports for the five-year period. These estimates identify the h parameter through variation within bilateral trading routes and variation across industries of an exporter. This framework parallels most Ricardian empirical studies. Column 2 presents the “within” estimate from specification (3) that utilizes first differencing to isolate productivity and trade growth within exporter-importer-industry cells. Estimations in panel A weight bilateral routes by an interaction of total exporter and importer trade in the industry. For example, the weight given to Germany’s exports of automobiles to Nepal is the total export volume of Germany in the auto industry interacted with the total imports of Nepal in the auto industry, using averages for each component across the sample period. These weights focus attention on routes that are likely to be more important and give a sense of the overall treatment effect from

Table 1. OLS estimations of labor productivity and exports

Between estimation FD estimation (1) (2)

Panel A: Weighting bilateral routes by the interaction of exporter and importer trade in industry (summed across all bilateral routes) DV: Log bilateral exports DV: D Log bilateral exports Log country-industry labor 0.640*** productivity (0.242) D Log country-industry labor 0.573*** productivity (0.185) Observations 149,547 103,839 Importer-Industry-Yr FE Yes Yes Exporter-Importer-Yr FE Yes Yes Panel B: Excluding sample weights DV: Log bilateral exports DV: D Log bilateral exports Log country-industry labor 0.361*** productivity (0.091) D Log country-industry labor 0.210*** productivity (0.041) Observations 149,547 103,839 Importer-Industry-Yr FE Yes Yes Exporter-Importer-Yr FE Yes Yes

Notes: Panel estimations consider manufacturing exports taken from the WTF database. Data are organized by exporter-importer-industry-year. Industries are defined at the three-digit level of the ISIC Revision 2 system. Annual data are collapsed into five-year groupings beginning with 1980–1984 and extending to 1995– 1999. The dependent variable in Column 1 is the log mean nominal value (US$) of bilateral exports for the five years; the dependent variable in Column 2 is the change in log exports from the prior period. The intensive margin sample is restricted to exporter-importer-industry groupings with exports exceeding $100 k in every year. The $100 k threshold is chosen due to WTF data collection procedures discussed in the text. Labor productivity from the UNIDO database measures comparative advantages. Column 1 estimates Ricardian elasticities using both within-panel variation and variation between industries of a country. Column 2 estimates Ricardian elasticities using only variation within panels. Estimations in Panel A weight bilateral routes by the interaction of total exporter and importer trade in industry; estimations in Panel B are unweighted. Estimations cluster standard errors by exporter-industry. Importer-Industry-Yr FE are defined at the two-digit level of the ISIC system. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Source: Author’s analysis based on data described in text. 174 Kerr

Ricardian advantages. The weights, however, explicitly do not build upon the actual trade volume for a route to avoid an endogenous emphasis on where trade is occurring. Estimates in panel B are unweighted. This study reports results with both strategies to provide a range of estimates. Estimations cluster standard errors by exporter-industry. This reflects the repeated application of Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 exporter-industry technology levels to each route and the serial correlations concerns of panel models. Other variants are reported below, too. Finally, the combination of 88 countries, 26 industries, and three time intervals creates an enormous number of exporter-importer-year and importer-industry-year fixed effects. The number of import destinations is in fact larger than the 88 exporters, as a UNIDO data match is not required for import destinations. With such a large dataset, it is computationally difficult to include exporter-importer-year and importer-industry-year fixed effects, especially when considering IV estimations. By necessity, manual demeaning is employed to remove the exporter-importer-year fixed effects, and this procedure is applied over the importer-industry-year fixed effects. The baseline estimates also use an aggregated version of the importer-industry-year fixed effects where the industry level used for the groups is at the two-digit level of the ISIC system rather than the three-digit level (reducing this dimension from 26 industries to eight higher-level industry groups). Robustness checks on these simplifi- cations are reported below. Interestingly, the “between” and “within” elasticities estimated in panel A are both around 0.6 on the intensive margin. These coefficients suggest that a 10% growth in labor productivity for an exporter- industry is associated with a 6% growth in exports. The estimates in panel B are lower at 0.2–0.4, but they remain economically and statistically important. These elasticities are somewhat lower than the unit elasticity often found in this literature with OLS estimation techniques and cross-sectional data. There are many empirical reasons why this might be true, with greater measurement error for productivity estimates outside of OECD sources certainly being among them. An elasticity greater than or equal to one is also the baseline for the Ricardian theory presented earlier. The IV estimates reported below are greater than one and have a comparable level on some dimensions to those estimated with OECD countries. The next subsection continues with extensions for these OLS estimates to provide a foundation for the IV results.

Extended OLS Results Table 2 provides robustness checks on the first-differenced estimates, which are the focus of the remainder of this study. The first column repeats the core results from column 2 of table 1. The next two columns show robustness to dropping Brazil and China. Brazil, of all included countries, displays the most outlier behavior with respect to its productivity growth rates, likely due to definitional changes, but Brazil’s exclusion does not affect the results. The results are also similar when excluding China, which experienced substantial growth during the sample period. It is generally worth noting that the 1980–1999 period predates the very rapid take-off of Chinese manufacturing exports after 2000 (Autor et al. 2013). Unreported tests consider other candidates like Mexico, Germany, and Japan, and these tests, too, find the results very stable to the sample composition, reflective in large part of the underlying exporter-importer-year fixed effects. Column 4 shows the results when excluding industry 383 (Machinery, electrical). The coefficient estimates are reduced in size by about 30% from column 1, but they remain quite strong and well-measured overall. The exclusion of industry 383 has the largest impact on the results of the 26 industries in the sample, which is why it is reported. This importance is not very surprising given the very rapid development of technology in this sector, its substantial diffusion around the world, and its associated trade. On this dimension, the industry-year portion of the importer-industry-year fixed effects play a very stabiliz- ing role. Quantitatively similar results are also found when excluding the Tobacco and Petroleum sectors (314, 353, 354). Column 5 shows that winsorizing the sample at the 2%/98% level delivers similar results, indicative that outliers are not overly influencing the measured elasticities. T HE W ORLD B ANK E CONOMIC

Table 2. Robustness checks on OLS speciﬁcations in Table 1 R EVIEW

Base estimation Excluding Excluding Excluding Using a Using ISIC Kerr (2008) Kerr (2008) Using (Column 2, exports exports electrical 2%/98% 2-digit level sample sample using exporter-level Table 1) from Brazil from China machinery winsorized industry using Imp-ISIC3-Year clustering sample groups Imp-ISIC2-Year fixed effects fixed effects (1) (2) (3) (4) (5) (6) (7) (8) (9)

The dependent variable is D log bilateral exports on the intensive margin by exporter-importer-industry Panel A: Weighting bilateral routes by the interaction of exporter and importer trade in industry D Log country-industry 0.573*** 0.573*** 0.472*** 0.390*** 0.493*** 0.266** 0.287** 0.281** 0.573*** labor productivity (0.185) (0.185) (0.097) (0.121) (0.133) (0.112) (0.113) (0.138) (0.086) Observations 103,839 103,010 101,221 97,326 103,839 51,483 23,345 23,345 103,839 Importer-Industry-Yr FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Exporter-Importer-Yr FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel B: Excluding sample weights D Log country-industry 0.210*** 0.210*** 0.221*** 0.154*** 0.264*** 0.097*** 0.248*** 0.184*** 0.210*** labor productivity (0.041) (0.041) (0.041) (0.039) (0.043) (0.037) (0.066) (0.069) (0.048) Observations 103,839 103,010 101,221 97,326 103,839 51,483 23,345 23,345 103,839 Importer-Industry-Yr FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Exporter-Importer-Yr FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Notes: See Table 1. Source: Author’s analysis based on data described in text.

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It was earlier noted that computational demands require that the main estimations employ the ISIC2- level industry groups when preparing importer-industry-year fixed effects. Columns 6–8 test this choice in several ways. First, column 6 shows that the results hold when estimating the full model with ISIC2- based cells, so that the importer-industry-year fixed effects exactly match the cell construction. The Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 weaker variation reduces the coefficient estimates by half, but the results remain statistically and economically important. Columns 7 and 8 alternatively estimate the model using the sample from Kerr (2008) that focuses on a subset of the UNIDO data in the 1985–1997 period. The Kerr (2008) sample is substantially smaller in size than the present one, and so there is greater flexibility with respect to these fixed effect choices. The choice of industry aggregation for the importer-industry-year fixed effects does not make a material difference in this sample.10 Finally, column 9 shows the results with exporter-level clustering. The labor productivity and export development of industries within countries may be correlated with each other due to the presence of general-purpose technologies, learning-by-doing (e.g., Irwin and Klenow 1994), and similar factors, and Feenstra and Rose (2000) show how the export ranges of countries can change over time in systematic ways across industries. Clustering at the exporter level allows for greater covariance across industries in this regard, returning lower standard errors. Most papers in this literature use robust standard errors on cross-sectional data, which would translate most closely to bilateral-route clustering in a panel model. Unreported estimates consider bilateral-route clustering and alternatively bootstrapped standard errors, and these standard errors are smaller than those reported in column 9. Further extensions are contained in tables S3 and S4. Interacting our core regressor with the GDP/ capita level of the exporter suggests that the OLS link between productivity and exports is mainly through lower-income countries, suggestive of higher trade due to varieties among developed economies. It is similar to the conclusion of Fieler (2011) that trade among advanced economies links to product dif- ferentiation and variety (low h), while trade among emerging economies links more closely to fundamental productivity levels (higher h). By contrast, there is limited heterogeneity by country size or geographic distances, excepting the fact that the growth in exports is not simply happening to bordering countries. Additional tests further confirm that the observed role for technology within manufacturing is not due to specialized factor accumulations and a Rybczynski effect. Under the Rybczynski effect, the accumulation of skilled workers in country i shifts country i’s specialization toward manufacturing industries that employ skilled labor more intensively than other factors. When incorporating country-specific time trends for subgroups of manufacturing industries according to their capital-labor ratio, mean wage rate, and non-production worker share as evident in the United States, technology’s importance is confirmed. Finally, there is limited adjustment on the extensive margin of trade routes compared to the intensive margin adjustments.

Base IV Results k Table 3 presents the core IV results. The first column reports the first-stage estimates of how Dln ðMitÞ k predicts Dln ðz~itÞ. The first-stage elasticity in panel A is 0.6, suggesting a 10% increase in the technology flow metric from the United States predicts a 6% increase in labor productivity abroad at the exporter- industry level. The unweighted estimates in panel B suggest a smaller 3% increase. While the second elasticity is lower, the instrument generally performs better in the unweighted specifications due to its more precise measurement. The F statistics in panels A and B are 4.7 and 11.6, respectively. The sample weights in panel A place greater emphasis on larger and more advanced countries that have large export volumes (e.g., Germany, Japan). While this framework finds a substantial response, the weighted

10 This extra check also has the advantage of linking the two studies closer together since the Kerr (2008) paper focuses extensively on productivity growth due to technology transfer. Stability to the somewhat different data preparation steps in Kerr (2008) is comforting. THE WORLD BANK ECONOMIC REVIEW 177

Table 3. IV estimations of labor productivity and exports

First-stage estimation Reduced-form estimation IV estimation (1) (2) (3)

Panel A: Weighting bilateral routes by the interaction of exporter Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 and importer trade in industry

DV: D Log country-industry DV: D Log DV: D Log labor productivity bilateral exports bilateral exports D Log estimator for technology 0.589** 0.938*** ﬂows from the United States (0.272) (0.298) D Log country-industry labor 1.592** productivity (0.637) Observations 103,839 103,839 103,839 Importer-Industry-Yr FE Yes Yes Yes Exporter-Importer-Yr FE Yes Yes Yes

Panel B: Excluding sample weights

DV: D Log country-industry DV: D Log DV: D Log labor productivity bilateral exports bilateral exports D Log estimator for technology 0.267*** 0.648*** ﬂows from the United States (0.078) (0.112) D Log country-industry labor 2.429*** productivity (0.791) Observations 103,839 103,839 103,839 Importer-Industry-Yr FE Yes Yes Yes Exporter-Importer-Yr FE Yes Yes Yes

Notes: See Table 1. The instrument combines panel variation on the development of new technologies across US cities during the 1975–2000 period with historical settlement patterns for migrants and their ancestors from countries that are recorded in the 1980 Census of Populations. The F statistics in Panels A and B are 4.7 and 11.6, respectively. Source: Author’s analysis based on data described in text. dependency of this group on heterogeneous technology transfer from the United States is noisier than in the unweighted estimations that emphasize more developing and emerging countries. k k The second column presents the reduced-form estimates where Dln ðMitÞ predicts Dln ðx~ijt) using a format similar to equation (3). In both panels, there is substantial reduced-form link of technology flows to export volumes. The interquartile range in the reduced form, conditional on the fixed effects, can account for around 6% of the interquartile range of export growth using a 0.8 coefficient estimate that sits in between panels A and B. k The third column provides the second-stage estimates from equation (3) having used Dln ðMitÞ to pre- k dict Dln ðz~itÞ. In panel A’s estimation, the weighted elasticity is 1.6, suggesting a 16% increase in export volumes for every 10% increase in labor productivity. In panel B’s unweighted estimation, the 10% increase in labor productivity is linked to a 24% increase in export volumes. The second-stage elasticity in panel B is larger than in panel A, as the IV estimates provide the reduced-form scaled up by the first- stage effects. Thus, even though the unweighted reduced-form estimate in column 2 is smaller than the weighted reduced-form estimate, this ordering reverses once scaled-up by the first stages. This study does not overly favor one set of estimates. The weighted and unweighted approaches both have merits and liabilities. Instead, the conclusion from this work is that the instrumented elasticity is in the neighborhood of two. While it is impossible to differentiate among the various reasons as to why the IV estimates are larger than the OLS estimates, a very likely candidate is that OLS suffers from a substantial downward bias due to measurement error in the labor productivity estimates, especially with the substantial differencing embedded in equation (3). While it is likely that omitted factors or reverse 178 Kerr causality influenced the OLS estimations as well, these appear to have been second-order to the measurement issues.11 This instrumented h elasticity is at the lower end of the estimates provided in the literature. Costinot et al. (2012) is the closest comparison given their use of industry-level regressions of productivity data Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 and trade. Using a cross-sectional analysis of producer price data for OECD countries in 1997, they derive their preferred estimate of 6.5, which is substantially larger than this study’s estimate of about two. On the other hand, Costinot et al. (2012) derive a quite similar elasticity of 2.7 when they consider labor productivity metrics, the metric considered here. They too have IV estimates that are considerably larger than OLS estimates. More broadly, Eaton and Kortum (2002) provide larger initial estimates of the h elasticity, with a preferred estimate in the range of eight. Simonovska and Waugh (2014a) revisit these results with a new estimator and come to a preferred estimate of about four. Overall, this study’s estimates are again lower than this connected work. The robustness checks described below find h estimates that continue in this ballpark, never exceeding four. Thus, this empirical approach consistently derives h estimates that are among the lowest in the literature, with some part of this difference due to methodology but an important part being substantive in interpretation. It is important to identify the dual meaning of the higher IV results compared to OLS with respect to the h parameter. In the Ricardian model, a higher h parameter corresponds to a reduced scope for intraindustry trade due to comparative advantages across varieties. IV estimations thus suggest that OLS specifications overestimate the scope for intraindustry trade because they understate the link between country-industry productivity improvements and their associated export volumes. Both impetuses can be connected to Ricardian theories of comparative advantage for trade, but the role of the structural h parameter needs to be carefully delineated. This partitioning can also have important consequences for views of development and export success. Compared to OLS, the IV results shift more emphasis toward fundamental country-industry productivity improvements rather than intraindustry varieties; yet, on the whole, the overall work in this paper with emerging economies provides more support for intraindustry varieties than typically found in the Ricardian literature that has focused mostly on OECD trade flows (as evidenced by the lower h estimates compared to prior studies). These estimates are significant in terms of their potential economic importance and explanatory power. Using an elasticity of two, the interquartile range of country-level labor productivity growth, conditional on fixed effects, can explain up to 35% and 39% of the interquartile range in conditional export growth levels using unweighted and weighted specifications, respectively.

Extended IV Results The online supplement provides many robustness checks on these IV estimations in tables S5-S8b. The results are robust to the various specification checks conducted in table 2. Moreover, dynamic estimations find lagged productivity growth estimators consistently stronger than contemporaneous productivity growth estimators, providing comfort in the estimation design and the proposed causal direction of the results. The IV is further analyzed when: (i) considering variations on the city-industry technology k trend terms and cross-sectional distributions used to weight US cities for the development of Mit; (ii) sample composition exercises that aggressively test for data quality and reverse causality concerns; and (iii) direct inclusion of ethnic patenting in the United States as a control. The online supplement also

11 Costinot et al. (2012) adjust export volumes for trade openness using the import penetration ratio for a country- industry (to link observed productivity to “fundamental productivity”). The estimates are very similar when undertak- ing this approach, being 1.163 (0.457) and 2.513 (1.138) for weighted and unweighted specifications, respectively. The unadjusted and adjusted first differences have a 0.93 correlation. This approach is not adopted for the main estimations due to worries about mismeasurement in the import penetration ratio when combining UNIDO and WTF data. THE WORLD BANK ECONOMIC REVIEW 179 contains an extended discussion of the identification achieved in this study and its limitations. In the end, the paper is able to make substantial progress toward causal identification in a Ricardian model, beginning with the panel estimation approach and extending through many IV approaches. This effort remains incomplete, however, and it is hoped that future work both identifies natural experiment set- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/163/3105863 by World Bank Publications user on 08 August 2019 tings to test these arguments and also identifies other forms/impetuses for heterogeneous technology transfer that can provide identification in a setting that focuses on large country-industry samples like this one.

IV. Conclusions While the principle of Ricardian technology differences as a source of trade is well established in the theory of international economics, empirical evaluations of its importance are relatively rare due to the difficulty of quantifying and isolating technology differences. This study exploits heterogeneous technology diffusion from the United States through ethnic migrant networks to make additional headway. Estimations find bilateral manufacturing exports respond positively to growth in observable measures of comparative advantages. Ricardian technology differences are an important determinant of trade in longitudinal changes in addition to their cross-sectional role discussed earlier. Leamer and Levinsohn (1995) argue that trade models should be taken with a grain of salt and applied in contexts for which they are appropriate. This is certainly true when interpreting these results. The estimating frameworks have specifically sought to remove trade resulting from factor endowments, increasing returns, consumer preferences, and so on, rather than test against them. Moreover, manufacturing exports are likely more sensitive to patentable technology improvements than the average sector, and the empirical reach of the constructed dataset to include emerging economies like China and India heightens this sensitivity. Further research is needed to generalize technology’s role to a broader set of industrial sectors and environments. Beyond quantifying the link between technology and trade for manufacturing, this paper also serves as input into research regarding the benefits and costs of emigration to the United States for the migrants’ home countries (i.e., the “brain drain” or “brain gain” debate). While focusing on the Ricardian model and its parameters, the paper establishes that the technology transfers from overseas migrants are strong enough to meaningfully promote exports. Care should be taken to not overly interpret these findings as strong evidence of a big gain from migration. The paper does not seek to establish a clear counterfactual in the context of immigration from the source countries’ point of view (e.g., Agrawal et al. 2011). As such, the positive export elasticities due to US heterogeneous technology diffusion do not constitute welfare statements relative to other scenarios. Future research needs to examine these welfare implications further.

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Abstract The perception that immigration fuels crime is an important source of anti-immigrant sentiment. Using Malaysian data for 2003-10, this paper provides estimates of the overall impact of economic immigration on crime, and evidence on different socio-economic mechanisms underpinning this relationship. The IV estimates suggest that immigration decreases crime rates, with an elasticity of around 0.97 for property and -1.8 violent crimes. Three-quarters of the negative causal relationship between immigration and property crime rates can be explained by the impact of immigration on the underlying economic environment faced by natives. The reduction in violent crime rates is less readily explained by these factors. JEL classiﬁcation: F22, K42 Key words: crime, immigration, labor markets

Increased crime is among the main fears voiced in public opinion surveys on immigration.1 Crime even surpasses economic concerns such as ”immigrants take jobs away from natives” as the main reason for public demands for more restrictive immigration policies in many destination countries (Mayda 2006; Bianchi, Pinotti, and Buonanno 2012). Despite its prominence in the public narrative, the academic literature on the linkages between immigration and crime is still sparse and often inconclusive (see Bell and Machin 2013 for a survey of the literature). This paper makes three main contributions to this literature. First, it provides causal estimates of the overall impact of immigration on different types of crime. Second, the paper presents evidence on the mechanisms that underlie this impact. Third, while previous work has focused almost entirely on high-income OECD destination countries, the paper provides analysis for Malaysia, a major middle-income destination where there is considerable public concern about the impact of immigration on crime.

Caglar Ozden is Lead Economist in the Development Research Group at the World Bank; his email is cozden@worldban- k.org. Mauro Testaverde is Economist in the Social Protection and Labor Practice at the World Bank; his email address is [email protected]. Mathis Wagner (corresponding author) is a professor at Boston College, Boston, MA, USA; his email address is [email protected]. The authors thank Michel Beine, David McKenzie, Chris Parsons, Giovanni Peri, and participants at the Migration and Development Conference for comments and discussion. They are grateful to Ximena Del Carpio who led the technical advisory project that forms the basis for this paper and provided invaluable guidance. The findings, conclusions, and views expressed are entirely those of the authors and should not be attributed to the World Bank, its executive directors, and the countries they represent. A supplemental appendix to this article is available at https://academic.oup.com/wber.

1 See, for example, Duffy and Frere-Smith (2013).

The paper uses variation in crime rates and immigrant stocks across Malaysian states for the period 2003–10 to identify the impact of immigration on crime.2 The instrumental variable estimates show that immigration results in a significant reduction in crime rates. The IV estimates of the elasticity of the crime rate with respect to immigration are large and statistically significant, 0.97 for property crime rates and Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 1.8 for violent crime rates. The implication is that an increase in immigrants in a state’s population from 10 to 11 percent decreases the property crime rate from 0.83 to 0.75 percent, and the violent crime rate from 0.19 to 0.16 percent, calculated at the average immigration and crime levels for 2010. The empirical approach adopted in this paper is most closely related to recent area panel studies on Italy (Bianchi, Pinotti, and Buonanno 2012), the United States (Spenkuch 2014; Chalfin 2014, 2015) and the United Kingdom (Bell, Fasani, and Machin 2013).3 The evidence for the United States and Italy is, on the whole, inconclusive on whether there is a causal relationship between immigration and crime (property or violent). Among the papers that find significant effects, Chalfin (2015) shows there is a negative impact of Mexican immigrants in the United States on property crime. For the United Kingdom, Bell, Fasani, and Machin (2013) find that economic migration, from the new member states entering the European Union in 2004, caused a decrease in property crimes. In contrast, asylum seekers, mostly without access to the formal labor market, caused an increase. The vast majority of immigration to Malaysia is based on economic motivations. Hence, the estimates presented on the overall impact of immigration on crime rates are consistent with the evidence from economically motivated Eastern European migration to the United Kingdom and Mexican migration to the United States. There are numerous plausible channels through which immigration affects crime rates, but evidence on their relative importance is scarce. First, there is a direct impact. Immigrants may have a different propensity than natives to commit or be victimized by crime due to their different economic, social, and cultural profiles. A change in their share in the population will lead to a direct change in crime rates. Second, the arrival of immigrants may change the economic outcomes and prospects, such as employment and wages, of the existing native population. This, in turn, changes the relative attractiveness of criminal activities for natives. Third, immigration might induce a change in the size and composition of the local population in a region. Natives with different propensities to commit crimes may be differentially affected by immigration and, maybe, induced to move to or from other regions of the country. As a result, each region will face inflows and outflows of different types of natives, further affecting the crime rates. The empirical strategy in the paper is designed with these different channels in mind. After identifying the causal relationship between immigration and crime, the empirical approach uses the decomposition of Gelbach (2016) to determine the relative importance of the various channels that underlie this relationship. The empirical literature on the economics of crime suggests that the most important (and robust) variables explaining crime rates are related to the number and economic prospects of young (ages 15–29) males in a region’s population.4 Fully consistent with this evidence, the main results in the paper show that around 50 percent of the estimated impact of immigration on property crime can be accounted for by the immigration-induced change in the fraction of young males in a state. Also, the earnings potential

2 The identifying variation of the instrument comes from changes in the population and age structure of the main migrant source countries and the differential historic propensity of these groups to migrate to particular regions in Malaysia. The instrument combines the demographic variation used in, for example, Hanson and McIntosh (2010) with the typical Altonji-Card instrument. The major advantage of this instrument is that it provides both exogenous time-series and cross-sectional variation. 3 Further evidence comes from Alonso, Garoupa, Perera, and Vazquez (2008) for Spain and Butcher and Piehl (1998) for the US. Other approaches include individual-level studies of criminal behavior (Papadopoulos 2011, Nunziata 2015), and evidence from imprisonment rates (Butcher and Piehl 2007). 4 See, for example, Gottfredson and Hirschi (1983), Farrington (1986), Levitt (1997), Grogger (1998), Freeman (1999), Gould, Mustard, and Weinberg (2002), Machin and Meghir (2004), and Dills, Miron and, Summers (2010). The World Bank Economic Review 185 of young males, as measured by the wages of the 25th percentile in the earnings distribution, and their employment rates are negatively correlated with property crime rates, though they are uncorrelated with violent crime rates. Changes in the number of police in a state are uncorrelated with both property and violent crimes. The fraction of working poor among the employed, those below 50 percent of the median Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 earnings in a state and year, is negatively correlated with property crimes rates and uncorrelated with violent crime rates. The implication is that immigration improves labor market conditions for people who are at high-risk of unemployment which, in turn, reduces their incentives to engage in property crimes. Controlling for these covariates decreases the magnitude of the estimated causal impact of immigration on property crimes by three-quarters, from an elasticity of 0.97 to an elasticity of 0.25. It is also no longer statistically significant. In contrast, the inclusion of covariates decreases the estimated magnitude of the impact of immigration on violent crime by only 20 percent, and the estimated elasticity of - 1.5 remains statistically significant. These results suggest that immigration decreases property crime rates primarily because it changes various economic conditions for natives. It is less clear though why immigration also decreases violent crime rates.5 Malaysia is an important setting in which to explore the relationship between low-skilled immigration and crime. It is a major destination country with over ten percent of the population composed of foreign-born people according to the official numbers.6 As in most non-OECD countries, these migrants are primarily from countries in the same region, with neighboring Indonesia as the largest source country. However, like in OECD destinations, immigration to Malaysia is primarily economically motivated and should have similar labor market effects (Docquier, Ozden, and Peri 2014). As a consequence, economic migrants are expected to have a negative impact on crime rates, adding to the evidence from Bell, Fasani, and Machin (2010). In another contrast to OECD countries, however, immigrants in Malaysia are overwhelmingly low skilled. Furthermore, there are almost no pathways to permanent residency or citizenship for such immigrants. Most migrant workers in Malaysia are on fixed-term employment visas or have overstayed them.7 As the demand for and the share of migrant workers rose rapidly in Malaysia over the last two decades, a widespread public belief emerged on the role of migrants in explaining rising incidence of violent crimes during the same period.8 The remainder of the paper proceeds as follows: Section I provides a description of the data. Section II describes the empirical strategy and instrument, and section III presents the results. Section IV concludes.

I. Data and Descriptive Statistics The analysis relies on two main data sources. The crime data for the years 2003 to 2010, by type and state, come from the Department of Statistics publication titled Social Statistics Bulletin and are based on the Royal Malaysian Police crime database. There are 14 states and federal territories in the analysis as Putrajaya and Labuan are included in Kuala Lumpur and Sabah, respectively. Data on economic and

5 A possibility is that migrants are less likely to both commit crimes and report being victimized. A lower propensity of immigrants to be involved in violent crimes cannot, however, fully explain the estimated reduction in violent crimes. 6 The true number may be over 20 percent of the workforce according to some estimates. 7 The role of legal status of migrants and the permanency of migration on the propensity to commit crime is explored in recent work by Baker (2015) and Mastrobuoni and Pinotti (2015). 8 Free Malaysia Today, “Foreign migrants pushing up crime rates” published on November 20, 2014. Malaysian concerns about immigration and crime have made it as far as the New York Times, “Malaysia’s Immigrant Worker Debate” published March 28, 2016. See also New Strait Times, “Illegal workers a threat to security” published on February 16, 2016; The Star, “Deputy IGP: Locals, not migrant workers, are major perpetrators of crime” published February 19, 2016; and The Straits Times, “In Malaysia: ‘No’ to having more foreign workers” published February 20, 2016. 186 Ozden, Testaverde, and Wagner demographic variables and the number of immigrants come from the annual Labour Force Survey (LFS) of Malaysia, which is available for the period 1990 to 2010.9 Information on wages, salaries, and overtime pay, as well as days and hours worked, is collected as part of a supplement to the main LFS since 2007. The main survey samples, on average, around one percent of the population. The variables for this paper are con- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 structed from the underlying micro-level data, 300-400 thousand observations per year. The micro-level data are aggregated to the state-year level since the data on crime is only available by state and year.

Crime in Malaysia The main crime measures are based on total crime and its components: property and violent crime. Property crimes include house break-ins and theft, vehicle thefts, snatch theft, and other thefts. Violent crimes include murder, rape, robbery (with and without a firearm), and offenses causing bodily injuries. Figure 1 presents a time-series graph of both property and violent crime together with the immigrant stock. Table 1 reports these numbers and their more detailed breakdown for the years 2003 and 2010.10 Property crimes are far more prevalent than violent crimes, with respectively 827 and 186 incidences per 100,000 (working-age population of 15-64 year olds) in 2010. Vehicle thefts make up nearly half of all property crimes, followed by house break-ins (around 20 percent). The large majority of violent crimes are robberies (around two-thirds), though there has been a marked increase in (reported) rapes and

Figure 1. Immigrant Stock and Crime Trends, Malaysia, 2003-10

Note: Data from the Malaysian Labor Force Survey and the Malaysian Social Statistics Bulletin.

9 The exceptions are 1991 and 1994 when the survey was not conducted and 2008 for which the survey weights are not available. 10 Note that Malaysia has quite low violent crime rates by international standards. The homicide rate per 100,000 inhabitants is 2.3, compared to 4.8 in the United States. Other South East Asian countries have much higher homicide rates, with 8.1 in Indonesia, 8.8 in the Philippines and 5.0 in Thailand. Statistics are from the United Nations Office on Drugs and Crime for the latest year available. See https://data.unodc.org. The World Bank Economic Review 187

Table 1. Property and Violent Crime Statistics for Malaysia

Property Crimes Violent Crimes

2003 2010 2003 2010 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 Total Number 133,525 152,029 22,713 34,133 Rate 0.85% 0.83% 0.14% 0.19% Breakdown: Breakdown: House Breaking 19% 23% Murder 2% 2% Vehicles Theft 48% 49% Rape 6% 11% Snatch 12% 4% Robbery 72% 64% Other 21% 24% Body Injuries 19% 24%

Note: Data from the Malaysian Social Statistics Bulletin (based on Royal Malaysian Police crime data). bodily injuries. Violent crime rates have increased by almost 30 percent between 2003 and 2010, from 145 to 186 per 100,000 people. Annual property crime rates in contrast have fallen by about two percent from 851 to 827 incidences per 100,000 people. There are large variations in property and violent crime rates across states (Supplemental appendix figure S.1 presents property and violent crimes rates per 100,000, inhabitants by state in 2010, available at https://academic.oup.com/wber). For example, crime is particularly high in the main city Kuala Lumpur (2,555 total offenses per 100,000 inhabitants) and particularly low in Sabah in northern Borneo (351 offenses per 100,000 inhabitants). Violent and property crime rates are highly correlated, with a correlation coefficient of 0.93. However, there is still considerable variation in the relative prevalence of property and violent crimes across states. The fraction of all offenses that are violent crimes ranges from 25 percent in Negeri Sembilan, in Peninsular Malaysia south of Kuala Lumpur, to 12 percent in Sarawak in Northern Borneo.

Immigrants in Malaysia The share of immigrants in the Malaysian population has increased from 3.6 to 10.6 percent between 1990 and 2010, according to the Malaysian LFS. In the sample period, about 55 percent of all immigrants come from Indonesia, 20 percent from the Philippines and the remainder from other Asian countries such as Bangladesh, Cambodia, India, Laos, Myanmar, Sri Lanka, Thailand, and Vietnam. Supplemental appendix figure S.2 presents the immigrant share in the working-age population in each state in 2010. This share varies from 1.8 percent in Perlis, in the northwest of Peninsular Malaysia bordering Thailand, to 26 percent in Sabah and eight percent in Kuala Lumpur.11 Migrants come to Malaysia overwhelmingly for economic reasons.12 Table 2 provides basic descriptive statistics for the native and immigrant populations in 2010. Immigrant labor force participation is 79 percent, compared to 61 percent for natives, and the unemployment rate only 1.8 percent, compared to 3.5 percent for natives. Immigrants are primarily in Malaysia for work and are highly integrated in the labor market.13 Immigrants are very low-skilled with 66 percent of the employed immigrants having at most completed primary school and only 4.3 percent are educated beyond high school. The same

11 Sabah is a clear outlier in terms of the share of immigrants in the working-age population due to its proximity to Indonesia and the high demand for migrant workers in plantations. All of the results are robust to dropping Sabah from the analysis. 12 UNHCR data shows that in 2003 there were less than 10,000 refugees in Malaysia; by 2010 that number had increased to around 80,000 primarily due to refugees from Myanmar. 13 The other main reason for immigration is for marriage reasons with primarily southern Filipino women marrying Malaysian men. 188 Ozden, Testaverde, and Wagner

Table 2. Native and Immigrant Characteristics (2010)

Natives Immigrants

Labor Force Participation rate (%) 61.5 78.9

Unemployment Rate (%) 3.5 1.8 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 Among Employed (in %) Fraction Female in Labor Force 36.6 31.7

At most primary 20.5 66.0 Lower secondary 13.8 15.1 Upper secondary 43.3 14.6 Diploma/Certiﬁcate 11.6 1.2 Degree 10.7 3.1

Ages 15–19 3.2 5.1 Ages 20–29 30.7 19.3 Ages 30–39 27.0 39.5 Ages 40þ 39.1 36.0

Agriculture and Mining 11.9 32.5 Manufacturing 17.1 17.9 Construction 8.6 14.0 Services 42.5 33.6 Public Admin, Health, Education 19.8 2.0 Mean Monthly Wages by Educational Attainment (in US Dollars, Purchasing Power Parity) At most primary 763 550 Lower secondary 862 620 Upper secondary 1044 828 Diploma/Certiﬁcate 1758 2250 Degree 2845 5253

Number of Observations 242,276 16,224 Total 16,980,672 1,394,177

Notes: Data is from the Labour Force Survey of Malaysia. 1 US dollar is around 1.44 Malaysian Ringgit (RM) in purchasing power parity. numbers are 20 and 22 percent, respectively, for the Malaysian citizens. Immigrants are over-represented in the 30-39 age group and underrepresented in the 20-29 age group. Nearly one-third of all immigrants work in agriculture and mining as compared to only 12 percent of Malaysians. At the low-end of the educational distribution, Malaysians earn significantly more than immigrants. For example, among those with at most primary school education, average monthly earnings for natives are $763 and for immigrants $550, a 28 percent difference. The native wage premium diminishes at higher levels of educational attainment, and immigrants with more than high school completion actually earn more than natives on average. There are two types of formally registered immigrants in Malaysia: expatriates and foreign workers. Expatriates are highly-skilled or educated professionals and make up only two percent of the total immigrant stock in 2010. The remaining 98 percent of the immigrant population enter Malaysia with temporary work permits, which are generally valid for a year and renewable for at most five years. These workers are not allowed to bring any dependents and are required to exit Malaysia upon termination of their contract. It is almost impossible for this group to obtain permanent residency or citizenship. Formal employment of foreign workers is regulated by quotas assigned to specific sectors, which are adjusted annually if there are extraordinary changes in underlying demand conditions in the labor markets. There are a substantial number of irregular or undocumented foreign workers in the labor force due to the restrictions on formal employment. Many of the undocumented migrants have entered Malaysia legally but overstayed their visas. Precise estimates are not available, but a 1996/97 regularization program resulted The World Bank Economic Review 189 in almost one million unregistered migrants being legalized. Another program, labelled the 6P, implemented in 2011, also registered over one million undocumented foreign workers. This evidence suggests that as many as half of migrant workers might be employed without proper documentation. In principle, the Malaysian LFS attempts to survey these undocumented immigrants as well. It is, for example, reassuring that Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 there is no discontinuous increase in the estimated number of immigrants during the 1996/97 regularization. The LFS is, of course, unlikely to obtain a fully representative sample of undocumented migrants in practice.14 This creates measurement errors and general undercounting of immigrants in the data. The instrumental variable strategy, as discussed below, aims to ensure that this measurement error does not bias the estimates.

Variable Descriptions There are a number of demographic and economic variables identified in the crime and economics literature as important determinants of crime rates. The main specifications include seven variables that arguably proxy for most of the major explanations for crime: demographic composition, legal earning opportunities for potential criminals, general economic conditions, poverty, the economic benefits of crime, deterrence (police), and population density. All are year- and state-specific. The main demographic variable is the number of males aged 15–29, since this group is the most prone to engaging in criminal activities.15 Consistent with economic models of crime (Becker 1968; Ehrlich 1973), several economic variables that proxy the expected costs and benefits of criminal activities are included. First variable is the 25th percentile of young, male earnings to proxy the returns to legal income opportunities available to the key demographic group likely to commit crimes.16 Second variable is the total number of employed people (ages 15–64), as a general proxy for employment opportunities in a region. 17 Third is a poverty measure. The connection between poverty and crime is a heatedly debated topic, and the linkages are far from clear (see, for example, Heller, Jacob, and Ludwig 2011). It is not possible to access regional household poverty rates, so instead, a measure of the working poor at the state level is used. Specifically, the main metric is the number of employed people with earnings below the 50 percent of the median monthly earnings by state and year.18 Note that there are two main ways how this measure may change over time. People might become poorer, and poor people might obtain jobs. Hence, it is unclear how the poverty measure will be related to crime. Fourth is a measure of the opportunities for criminal activity, such as theft. For this, the 75th percentile of earnings in a state and year is used as a proxy.

14 Note that, for example, the LFS does not survey those in communal housing. There are estimates that place the true immigrant to population rate at over 20 percent. 15 See Gottfredson and Hirschi (1983); Farrington (1986); Levitt (1997); Grogger (1998); Freeman (1999); and Dills, Miron, and Summers (2010). The results are robust to varying definitions of who counts as young, specifically using ages 15–24 or ages 15–34. 16 See Grogger (1998); Gould, Mustard, and Weinberg (2002); and Machin and Meghir (2004) on the connection between wages and crime. The monthly earnings measure reflects both the hourly wage and the hours worked per month. It is a broader measure of earnings than simply the hourly wage. Wage and salary information is available for all private and public sector employees starting in 2007. 17 More commonly, the literature uses the unemployment rate to proxy for employment opportunities. See, for example, Raphael and Winter-Ember (2001); Fougere, Kramarz, and Pouget (2009); Bianchi, Pinotti, and Buonanno (2012); Gronqvist (2013); and Spenkuch (2014). However, Malaysia has a particularly low unemployment rate—only 2.2 percent of the native working-age population—which, given the relatively small size of the LFS, is subject to a lot of measurement error. Hence, instead the number of employed in a given state in a given year is used, which is far more precisely measured. 18 In 2014 only 0.6 percent of Malaysians fell below the official national poverty line (RM930 in the peninsula, RM1,170 in Sabah and Labuan and RM990 in Sarawak). Given the small incidence of absolute poverty in Malaysia, a standard, relative poverty measure for the working poor using the Malaysian LFS is constructed. 190 Ozden, Testaverde, and Wagner

The role of deterrence on criminal behavior is another critical issue in the literature. The number of police employed by year in each state is used in this paper as a potential measure of deterrence.19 Finally, all of the specifications include the log of population (ages 15-64) as a covariate. This variable implicitly controls for population density, another key determinant of the level of criminal activity (Glaeser and Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 Sacerdote 1999), since all of the estimating equations include state fixed effects. There are numerous alternative measures that could be included in these specifications. Most importantly, there is extensive work on the impact of education on crime.20 The results are robust to using low educated men and their earnings as explanatory variables instead of young males.21 Table 3 presents summary statistics for the main explanatory variables used in the analysis. The data are for 2003 and 2010, except for variables based on earnings that are for 2007 and 2010, since the LFS started collecting wage and salary information only at this date. The number of immigrants increased by 18 percent over this period. The fraction of young males in the working-age population is stable at 22 percent and equally split between men and women. The fraction employed fell from 61 to 59 percent, while unemployment remained stable at slightly above two percent. The number of police increased by 42 percent between 2003 and 2010. The number of working poor also increased rapidly, from ten to 14 percent. The 25th percentile of monthly earnings for young males increased from 714 to 764 Malaysian Ringgit. The 75th percentile of the overall earnings distribution increased more rapidly from 1908 to 2296 Malaysian Ringgit.22

Table 3. Descriptive Statistics for Working-Age Population (15–64)

2003 2010

Native Population (ages 15–64) 14,514,910 16,979,767 Immigrant Population (ages 15–64) 1,182,995 1,395,003

Demographics of Malaysians Fraction Aged 15–29 21.6% 21.7% Fraction Employed 61.3% 58.9% Fraction Unemployed 2.5% 2.2%

Number Police 71,896 101,898 2007 2010 Earnings Indicators Working Poor 10.3% 13.6% Earnings Male, Ages 15–29, 25th pctl. RM 714 RM 764 Earnings, 75th pctl. RM 1908 RM 2296 LFS Observations 279,224 397,467

Notes: Data is from the Malaysian LFS, earnings information available beginning in 2007. Monthly earnings have been adjusted for changes in the consumer price index and are in 2010 Malaysian Ringgit (RM). 1 US dollar is around 1.44 RM in purchasing power parity.

19 For evidence that police reduces crime, see Levitt (1997) or Draca, Machin, and Witt (2011). Kessler and Levitt (1999) consider whether longer mandated sentence lengths reduce crime. Similarly, Langan and Farrington (1998), building on a large body of cross-national studies, find substantial negative correlations between the likelihood of conviction and crime rates. The number of police is calculated from the Malaysian LFS using occupation codes (MASCO 5142). These are not official police numbers. 20 There is evidence showing a causal crime-reducing impact of education. For the US, see Lochner and Moretti (2004), and for England and Wales see Machin, Marie, and Vujic (2011). 21 When including both sets of variables, the estimates become a lot less precise and only those based on age remain significant. The likely reason is that, in Malaysia, educational attainment has been increasing rapidly, and thus age and education are highly correlated. This makes it hard to disentangle the impact of education from that of age. 22 Monthly earnings have been adjusted for changes in the consumer price index and are in 2010 Malaysian Ringgit. One US dollar is around 1.44 Malaysian Ringgit (RM) in purchasing power parity. The World Bank Economic Review 191

III. Empirical Strategy The Impact of Immigration on Crime The first step of the empirical strategy aims to identify the total effect of immigration on crime. The base-

line specification takes the following standard form: Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019

B ln Crt ¼ b ln Mrt þ a1 ln poprt þ dr þ dt þ ert; (1) where ln Crt is the natural log of the number of crimes, ln Mrt is the log of the number of immigrants, and 23 ln poprt is the log of the total population of state r in a particular year t. All specifications include state dr and year fixed effects dt. Hence, the identifying variation comes from state-level deviations of changes in immigration and crime rates over time from the national average. The inclusion of the natural logarithm of population implies that the main coefficient of interest, bB, is the elasticity of the crime rate with respect to the immigrant share in a given state and year. If bB > 0 then the total impact of immigration is to increase the crime rate in a Malaysian region. In contrast, if bB < 0, then immigration decreases the crime rate. In equation (1) all variables are aggregated at the state by year level. That is also the level of variation of the instrumented immigration numbers. Remaining concerns pertain to the possible heteroscedasticity of standard errors and serial correlation within a state (see Bertrand, Duflo, and Mullainathan 2004). All reported standard errors are robust to heteroscedasticity. The results are also robust to clustering standard errors by state. However, there are only 14 states of very different sizes, which is likely too few to make clustering advisable with unbalanced clusters (Angrist and Pischke 2009; Cameron and Miller 2015; MacKinnon and Webb 2017).

Channels and Decomposition The next step in the analysis aims to identify the channels through which immigration affects crime. In order to do so, a full regression is specified, denoted by superscript “F,” with a full set of covariates:

F ln Crt ¼ b ln Mrt þ a1 ln poprt þ a2 ln youngrt þ a3 ln earnings25thrt þ a4 ln emprt þ a5 ln workingpoorrt þ a6 ln earnings75thrt þ a7 ln policert þ dr þ dt þ vrt; (2) where all variables are specific to a state r and year t.lnyoung is the number of men between the ages of 15 and 29; ln earnings25th is the 25th percentile of the log earnings of young men; ln emp is the log of the number of employed people; ln workingpoor is the log number of employed with earnings below the 50 percent of the median monthly earnings; ln earnings75th is the 75th percentile of the log earnings among all employed people, and ln police is the log number of police. Section 1, above, provides a discussion of the economic justification for the inclusion of each of these covariates. A common strategy for identifying how much of the relationship between the outcome and an explanatory variable of interest, such as between crime and immigration, can be attributed to various factors is to sequentially add covariates to a baseline specification. The change in the estimated coefficient of the immigration variable ln M is then interpreted as being due to the most recently added set of variables. In the literature on crime, for example, this is the strategy pursued by Donohue and Levitt (2001) and Lee and McCrary (2005). Sequential covariate expansion exercises are not, however, necessarily very informative about the true impact of a covariate, as Gelbach (2016) argues. This is because their interpretation often crucially

23 The log-log specification is common in this literature; see, for example, Bianchi, Pinotti, and Buonanno (2012) and Spenkuch (2014). Bell, Fasani, and Machin (2013) estimate the effect of immigration ratios on crime rates in levels. Chalfin (2014, 2015) regresses the log crime rate on the immigrant share. Given the log-log specification, the results are identical if the dependent variable is the log of the crime rate or the log of the number of crimes. 192 Ozden, Testaverde, and Wagner depends on the sequence in which covariates are added to the regression. To put it differently, altering the order of the covariate expansion changes the difference in the estimated coefficients that will be attributed to each new covariate. As long as covariates are correlated with each other, only the full specification is truly informative about the impact of these covariates. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 In order to overcome this issue, Gelbach (2016) develops a different decomposition that relies on the following insight: Equation (2) is the complete model whereas equation (1) is a model with the variables lnyoung,lnearnings25th,lnemp,lnworkingpoor,lnearnings75th, and lnpolice omitted. If equation (1) is interpreted in this way, standard omitted variable bias formula can be applied.

Consider an auxiliary model with six regressions where the dependent variable in each regression, Xj, is one of the covariates of the full specification in equation (2), and lnM is the only explanatory variable.24 Then the relationship between the coefficient on immigration in the baseline and full model is given by the following expression:

X7 B F b ¼ b þ hj aj; (3) j¼2 where hj is the coefficient on ln M in the regression j of the auxiliary model with Xj as the dependent variable. Note that hjaj is the contribution of covariate j in explaining the immigration-crime relationship. It should be added that the decomposition relies on correctly identifying bB and bF as defined above. Potential endogeneity concerns require immigration to be instrumented using two-stage least squares. However, it is not necessary that the causal impact of additional covariates is correctly identified, and thus these variables do not need to be instrumented (see Gelbach 2016 for a more detailed discussion).

Instrument The central challenge in estimating equations (1) and (2) is the possible endogeneity of immigrant location decisions, a problem common to almost all migration-related papers. Migrants may choose to locate in states that experience unobserved (positive or negative) shocks to factors that also affect the crime rate. The likelihood of biased OLS estimates makes it important to instrument for the stock of immigrants in a state, the main explanatory variable. A valid instrument for immigration patterns across states needs to be uncorrelated with shocks that may deter or encourage crime. These shocks may be caused by changes in demographics, labor market opportunities, or policing. In order to construct such an instrument, changes in the population and age structure of immigrant source countries over time are used. The main source countries are Indonesia, the Philippines, Bangladesh, Cambodia, India, Laos, Myanmar, Sri Lanka, Thailand, and Vietnam. Using the data from the United Nations Population Division, the number of individuals in each of seven age- groups in each of these source countries in every year during 2003-2010 are calculated.25 These populations form the potential pool of immigrants to Malaysia by the likelihood of migration varies by age ac group, country of origin, and year. This is the measure of the supply of immigrants St to Malaysia from source country c in age-group a and year t. Since the Malaysian LFS consistently categorizes immigrants’ nationality only as Indonesians, Filipinos, and the rest of the world, the measure of the supply of immigrants for all other countries are aggregated into a single category. Hence, there are effectively three source countries: Indonesia, the Philippines, and Other.26

24 These covariates are ln young,lnearnings25th,lnemp,lnworkingpoor,lnearnings75th, and ln police. 25 The age groups are 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45 and above. 26 The population numbers of each source country are multiplied by the average propensity of people from that country to migrate to Malaysia. These propensities are calculated from data provided by the Ministry of Home Affairs of Malaysia and are: Bangladesh 1.96%, Cambodia 1.03%, India 0.11%, Laos 0.01%, Myanmar 2.18%, Sri Lanka 0.16%, Thailand 0.22%, Vietnam 0.78%, Indonesia 5.56%, and Philippines 0.38%. The World Bank Economic Review 193

What remains to be determined are the states within Malaysia in which the immigrants choose to live. In order to construct this variable, the LFS for the years 1990 to 1993, at least ten years before the start of the analysis are used, to calculate the probability that individuals from a source country and age group to be employed in a certain state: Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019

1993P 1 ac T Mrt cac ¼ t¼1990 ; (4) r 1993P 1 ac T Mt t¼1990

ac ac where Mrt is the number of immigrants from a source country in an age group, state and year. Mt is the total number of immigrants in Malaysia from a source country and in a given age group. The source country and age-group specific instrument for the immigration flows in a certain region and year is then given by:

ac ac ac IVrt ¼ cr St (5) The main instrument is constructed by summing over the age-specific instruments by source country and over source countries (and take the natural logarithm). The instrument varies by state and year: X X ac ac lnIVrt ¼ ln cr St : c a

c P The results are also robust to allowing for three separate instruments by source country: lnIVrt ¼ ln ac ac a cr St for Indonesians, Filipinos, and all other nationalities. ac ac The identifying variation comes from the interaction of cr and St ; and is conditional on the included fixed effects. The variation in the instrument generated by the differential propensity of immigrant groups (defined by nationality and age) to work in different states in Malaysia is similar to the commonly used Altonji-Card instrument (Altonji and Card 1991, Card 2001). The variation induced by the demographic changes in source countries is similar to the instrument constructed by Hanson and McIntosh (2010, 2012). The instrument takes advantage of both exogenous time-series and cross-sectional variation.27 ac The supply of potential migrants to Malaysia from different source countries (St ) is determined by the demographic factors that were relevant in those countries several decades earlier. These are most likely to be exogenous with respect to contemporaneous labor market shocks in Malaysia. The average ac propensity of an immigrant from a source country to be employed in a certain state (cr ) in this earlier pre-period depends on permanent differences in the levels of labor demand across local labor markets. This is why state-specific fixed effects are included in all of the regression specifications. It is, of course, independent of any transitory shocks that may affect demand for natives and immigrants in a particular year. However, the concern is that persistent demand shocks (i.e., long periods of decline or growth in certain states) would result in a correlation between the average distribution of immigrants in the pre- period (1990-93) and current period (2003-10) demand shocks. By allowing for a ten-year lag between ac 28 these two periods to construct (cr ), that concern is minimized.

27 Other work that adds a plausibly exogenous time-series dimension to the Altonji-Card shift-share instrument includes Chalfin (2015) and Chalfin and Levy (2015), who follow a similar idea to develop an IV strategy based on fertility shocks in source areas of Mexico. Other related papers are Pugatch and Yang (2012) and Chalfin (2014), who use rainfall shocks in source areas as exogenous determinants of migrant outflows. The instrumenting strategy is very similar to that in Del Carpio et al. (2015) and Ozden and Wagner (2016). 28 Using the same data but a longer time period, Del Carpio et al. (2015) and Ozden and Wagner (2016) are able to show that, at the level of Malaysian states, there do not seem to be persistent underlying labor market trends that are correlated with immigration. 194 Ozden, Testaverde, and Wagner

An additional important advantage of an instrumental variable approach is that it helps to address classical measurement problems. The Malaysian LFS attempts to survey undocumented immigrants in Malaysia, yet the survey is unlikely to obtain a fully representative sample. This creates measurement errors and general undercounting of immigrants in the data. All of the specifications include year fixed ef- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 fects, which will control for general undercounting of immigrants over time. State fixed effects are also included, which will control for state-specific differences in the propensity of immigrants to be surveyed by the Malaysian LFS. These will be fully absorbed by the fixed effects in the (log-log) specifications as long as the measurement error is proportional to the true number of immigrants (where that proportion can vary by state and year).29 In addition, there is likely to be idiosyncratic measurement errors (e.g., sampling error that will attenuate the OLS estimates but not the IV estimates). If there is an even more complicated form of measurement error, then the OLS estimates will be biased but not necessarily attenuated, even though proportional measurement error seems reasonable to assume. However, for the IV estimates to be biased that residual measurement error (controlling for the fixed effects) would have to be somehow correlated with the instrument. The identifying variation in the instrument relies on the interaction between the demographic transition in migrant source countries and the 1990-93 distribution of immigrants. It is unlikely for the instrument to be systematically correlated with measurement error in the period 2003–10. Figure 2 plots the actual and predicted log number of immigrants for all 14 Malaysian states in 2010. Clearly, there is a close fit between instrumented and actual immigration numbers, suggesting the instrument is highly predictive of actual immigrant numbers.

Figure 2. Actual and Predicted Log Immigrant Numbers in 2010

Notes: Predictions are based solely on the instrument, having controlled for a full set of covariates and state and region ﬁxed effects. Data are from the Malaysian LFS and based on authors’ estimates.

29 The model of measurement error we have in mind is that the observed number of immigrants MObs is equal to the actual number of immigrants MTrue scaled by a state-specific factor sr and a year-specific factor st, such that Obs True Mrt ¼ srstMrt The World Bank Economic Review 195

III. Results The Impact of Immigration on Crime Table 4 reports the results for the baseline equation (1), with (log) total, property and violent crimes as the

dependent variable in each column, respectively. OLS estimates and IV estimates are presented in panel A Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 and panel B, respectively. The crime rate is negatively correlated with the number of immigrants in a state’s population. The OLS point estimates imply an elasticity of around 0.47 for all types of crime.

Table 4. Impact of Immigration on Crime, OLS and IV, Baseline Speciﬁcation

Crime Property Violent

Panel A. OLS Log Immigrants 0.479** 0.470** 0.465*** (0.211) (0.224) (0.168) Panel B. IV Log Immigrants 0.988*** 0.970** 1.810*** (0.372) (0.398) (0.599) First-stage T-stat 2.5 2.5 2.5 Observations 98 98 98

Notes: The baseline specification includes log total population as a covariate and state and year fixed effects. Standard errors are robust to heteroscedasticity. *, **, *** denote significance at the ten, five, one percent significance level.

The IV estimates suggest that this negative relationship between immigration and crime is causal. The point estimates show that immigration causes a decrease in crime rates, with an elasticity slightly below -1 for both total and property crimes.30 The implication is that an increase in the fraction of immigrants in a state’s population from ten to 11 percent decreases the property crime rate from 0.83 to 0.75 percent (calculations are for average immigration and crime levels in 2010). The impact on violent crime is even greater, with an elasticity of 1.8. Since the standard errors are also larger for violent crime, the difference between the estimates for property and violent crimes are not statistically significant. The instrument is statistically significant in the first-stage, with a coefficient of 5.65 and t-statistic of 2.5.31 The fact that the IV estimates are more negative than the OLS estimates suggests that immigrants are more likely to migrate to states that, for other reasons, are experiencing a decrease in crime rates. It may also, of course, be that the OLS estimates are simply attenuated due to measurement error in the number of immigrants. Finally, Bianchi, Pinotti, and Buonanno (2012), Spenkuch (2014), and Chalfin (2014) do not find conclusive evidence on the causal relationship between immigration and crime. Their estimates are mostly not significantly different from zero, and the standard errors of their IV estimates are sufficiently large that they cannot rule out an effect. Consistent with these findings, Bell, Fasani and Machin (2013) and Chalfin (2015) find a negative impact on property crime of immigrants coming to the United Kingdom for work from Eastern European accession countries and for Mexican immigrants to the United States, though no effect on violent crime.32

30 Recall that over 80 percent of crimes in Malaysia are property crimes; hence, the estimates for total and property crimes are always very similar. 31 Since the two-stage least squares estimates are just-identified (a single instrument and endogenous variable) they are “approximately unbiased” (Angrist and Pischke 2009). Hence, the key issue is only whether the instrument is statistically significant in the first-stage. Thus the first-stage t-statistic and not the F-statistic is reported as would be the case if there were multiple instruments. 32 Appendix table S.3 presents estimates of the causal impact of immigration on further disaggregated crime rates. Specifically, property crimes are disaggregated into vehicle thefts and other property crime, and violent crimes into robberies and other violent crimes. 196 Ozden, Testaverde, and Wagner

Table 5 shows that the causal relationship between immigration and crime rates is highly robust to several different specifications. Panel A presents the results with the same point estimates but with standard errors that are clustered by state to account for serial correlation. All of the estimates remain statistically significant despite only having 14 clusters. Panel B shows estimates when Sabah, which is an outlier with im- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 migrants accounting for around one-quarter of the working-age population, is dropped from the sample. The point estimates and standard errors are almost identical to those in table 4. Analysis in panel C uses three instruments based on nationality, (for Indonesians, Filipinos, and Other, please see section 3.3 for details on the construction of the instruments). The point estimates somewhat increase and remain statistically significant.33 In panel D, the independent variable is the number of employed immigrants, as opposed to all immigrants. The point estimates are almost identical to the main estimates in table 4 and the standard errors are slightly larger. All estimates remain statistically significant. This finding reinforces the idea that the estimates reflect the impact of economically active migrants as opposed to all migrants. They also likely reflect some combination of impacts from both documented and undocumented immigrants. Panel E presents results using male migrants as the independent variable of interest. The point estimates and the standard errors increase slightly when compared to the main estimates, though all estimates remain statistically significant.

Table 5. Impact of Immigration, Robustness Checks, IV Estimates, Baseline Speciﬁcation

Crime Property Violent

Panel A. Standard Errors Clustered by State Log Immigrants 0.988** 0.970* 1.811*** (0.480) (0.528) (0.555) First-stage T-statistic 2.33 2.33 2.33 Panel B. Excluding Sabah Log Immigrants 1.002*** 0.994** 1.759*** (0.381) (0.407) (0.592) First-stage T-statistic 2.41 2.41 2.41 Panel C. Three Instruments by Nationality Log Immigrants 1.266*** 1.257*** 2.521*** (0.338) (0.359) (0.949) First-stage T-statistic 1.60 1.60 1.60 Panel D. Employed Migrants Log Immigrants 1.074** 1.055** 1.968*** (0.434) (0.460) (0.681) First-stage T-statistic 2.64 2.64 2.64 Panel E. Male Migrants Log Immigrants 1.223** 1.201** 2.241** (0.572) (0.594) (0.906) First-stage T-statistic 2.18 2.18 2.18 Observations 98 98 98

Notes: Estimates are for the baseline specification including log total population as a covariate and state and year fixed effects. Standard errors are robust to heteroscedasticity. *, **, *** denote significance at the ten, five, one percent significance level.

Determinants of Crime Rates Section I presented a detailed discussion of various demographic and economic variables that the literature identifies as likely factors to influence crime rates. These are included in the full specification given by equation (2), and table 6 reports these estimates. Panel A presents OLS estimates, and panel B

33 The t-statistic for the instrument in the first-stage is only 1.6 though. The World Bank Economic Review 197

Table 6. The Impact of Immigration and Covariates on Crime, OLS, and IV Estimates

Panel A: OLS Estimates Panel B: IV Estimates

Crime Property Violent Crime Property Violent Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 Log Immigrants 0.342** 0.343** 0.292* 0.347 0.253 1.464*** (0.148) (0.164) (0.152) (0.294) (0.327) (0.499) Log Total Population 0.476 0.300 0.991 0.471 0.397 0.279 (1.220) (1.290) (1.360) (1.130) (1.220) (1.500) Log Young-Male Pop. 0.814** 0.815** 0.970** 0.815** 0.780** 1.195* (0.379) (0.406) (0.426) (0.332) (0.367) (0.620) Log Young-Male 25th Income Pctl. 0.227* 0.289* 0.068 0.227** 0.296** 0.024 (0.128) (0.147) (0.150) (0.115) (0.132) (0.140) Log Total Employed 0.779 0.526 1.716* 0.770 0.695 0.504 (0.892) (0.949) (1.020) (1.010) (1.090) (1.230) Log Working Poor 0.085** 0.099** 0.018 0.085** 0.101*** 0.020 (0.038) (0.041) (0.043) (0.034) (0.036) (0.042) Log 75th Income Pctl. 0.569** 0.644** 0.233 0.568*** 0.651*** 0.134 (0.230) (0.254) (0.238) (0.199) (0.220) (0.231) Log Police 0.010 0.009 0.026 0.010 0.009 0.025 (0.041) (0.044) (0.057) (0.035) (0.037) (0.061)

Observations 98 98 98 98 98 98

Notes: All specifications state and year fixed effects. In Panel B the first-stage t-statistic for the instrument is 3.2. Standard errors are robust to heteroscedasticity. *, **, *** denote significance at the ten, five, one percent significance level. presents the IV estimates.34 Clearly, the negative correlation between immigration and crime rates is robust to the inclusion of these additional covariates. The instrument continues to be statistically significant in the first-stage, with a coefficient of 5.73 and t-statistic of 3.2 when the covariates are added. The most important change due to the inclusion of the covariates is the decrease in the magnitude of the estimated impact on property crimes. Specifically, the coefficient declines by three-quarters, from an elasticity of 0.97 to 0.25 (in the IV specification), and it is no longer statistically significant. In contrast, the inclusion of covariates changes the estimated magnitude of the impact of immigration on violent crimes by only 20 percent, from 1.81 to 1.46. The estimated elasticity also remains statistically significant. The included covariates clearly have a far greater role in explaining the relationship between immigration and property crime than between immigration and violent crime. More importantly, the results suggest that immigration decreases property crime rates mainly because it changes conditions for natives. It is less clear, though, why immigration also decreases violent crime rates. A plausible answer is that immigrants commit (or report) fewer violent crimes.35 However, the magnitude of the estimated impact is too large to be simply explained by immigrants’ lower propensity for committing violent crime. The estimated impact of the covariates on crime rates are very similar in panels A and B. Interestingly, the population variable, reflecting the correlation between population density and crime, is not statistically significant in explaining crime rates.36 Instead, consistent with the literature, the most important and robust variable explaining changes in crime rates is the fraction of young men (ages 15–

34 Appendix table S.1 reports standard errors clustered by state. For the OLS estimates these are near identical. For the IV estimates they are somewhat larger, but not sufficiently so as to invalidate any of the conclusions from the baseline results. 35 “Parliament: Only 1% of crimes are committed by foreigners, says Wan Junaidi,” The Star, published on Tuesday, July 9, 2013. 36 Glaeser and Sacerdote (1999) find a positive correlation between city density and crime. These results suggest that this correlation may be the result of the demographics of cities rather than their density per se. 198 Ozden, Testaverde, and Wagner

29) in the population. The impact is large with an elasticity of around 0.8 for property crimes and 1.2 for violent crimes. Consistent with economic theories of crime, the earnings potential of young males, as reflected by the 25th percentile of their (log) earnings, is negatively correlated with property crime rates. The elasticity is around -0.3 and statistically significant. In contrast, this variable is uncorrelated with vi- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 olent crime rates. Similarly, the earnings potential from illegal activities, as proxied by the 75th percentile of the earnings distribution in a state, is positively correlated with property crimes, with an elasticity of around 0.65. This variable is also uncorrelated with violent crime rates. The employment rate is consistently negatively correlated with crime rates, as expected, but the impact is never statistically significant. This finding mirrors the literature’s general difficulties in identifying a relationship between employment opportunities, typically proxied by the unemployment rate, and crime (see, for example, Freeman 1999; Gould, Mustard, and Weinberg 2002). Perhaps most surprisingly, the fraction of working poor among the employed, those below 50 percent of the median earnings in a state and year, is negatively correlated with property crimes rates and uncorrelated with violent crime. This result suggests that the variable is actually picking up labor market conditions for people who are at a high-risk of being unemployed. Improved labor market conditions for these people reduces their incentives to engage in property crimes and also increases the measured number of working poor. Finally, changes in the number of police in a state are uncorrelated with both property and violent crimes. This is, of course, not a causal relationship. In sum, variables reflecting the costs and benefits of engaging in illegal activity play an important role in explaining property crimes but not violent crimes. The fraction of young males in the population explains both property and violent crimes.

Decomposing the Immigration-Crime Relationship The remaining question is how each of the covariates in the full specification in equation (2) explain the relationship between immigration and crime identified in the baseline equation (1). Table 7 reports the results of the Gelbach (2016) decomposition for the IV estimates for all property and violent crimes.37 For convenience, the estimates reported in tables 4 and 6 in the rows ‘immigrant - baseline’ and ‘immigration full’, respectively, are presented again. The total change in the coefficient (bB - bF) is statistically

Table 7. Decomposition of the Impact of Immigration on Crime, IV Estimates

Dependent crime variable: Crime Property Violent

Immigrants - Baseline 0.988*** 0.970** 1.810*** (0.372) (0.398) (0.599) Immigrants - Full 0.347 0.253 1.464*** (0.294) (0.327) (0.499) Total Change in Coefﬁcient 0.642** 0.717** 0.346 (0.287) (0.320) (0.224)

Fraction of Change in Immigrant Coefﬁcient (Baseline - Full) Explained by: Young-Male Population 56% 49% 151% Young-Male 25th Income Pctl. 8% 6% 10% Total Employed 32% 34% 14% Number Working Poor 43% 51% 9% 75th Income Pctl. 39% 40% 17% Number Police 0% 0% 2%

Notes: The Baseline specifications is identical to that in table 4, Panel B. The Full specification is identical to that in table 6, Panel B. Standard errors are robust to heteroscedasticity. *, **, *** denote significance at the ten, five, one percent significance level.

37 Since the inclusion of covariates has, as discussed, very little impact on the crime-immigration correlation in the OLS estimates, a further decomposition is not informative. The World Bank Economic Review 199 significant for total and property crimes but not for violent crime. In sum, additional covariates explain two-thirds of the causal relationship between immigration and total crime rates and three-quarters of the causal relationship with property crime rates.38 Table 7 also reports, in percentage terms, how important each covariate is in explaining the Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 immigration-crime relationship. The immigration-induced changes in the fraction of young males and the number of working poor explain around 50 percent of the overall immigration-property crime relationship. The impact on total employment explains around one-third of the relationship. In contrast, the induced change in the 75th percentile of the earnings distribution goes against explaining the observed relationship (-40 percent). Changes in the earnings of young males and the number of police have no explanatory power. In contrast, these covariates do not significantly explain the causal impact of immigration on violent crime. The only variable that has any explanatory power is the fraction of young males in the population of a state. The decomposition presented in table 7 reflects two distinct relationships. First, immigration causes changes in each of the covariates. Second, the change in each covariate then affects crime rates. The decomposition only reports the total impact, but using tables 6 and 7, the underlying causal relationships can be inferred. The implication of these estimates is that immigration decreases the fraction of young males in a state and thereby decreases property and violent crimes. This is consistent with the pattern of population movements in response to immigration in Malaysia identified by Del Carpio et al. (2015). Immigration also increases the 25th earnings percentile of young males, the fraction of people employed, and the fraction of working poor thereby further decreasing property crime rates. In addition, it increases the 75th earnings percentile in the general population, however, that results in higher crime rates. The implication that immigration has a positive impact on the earnings and employment of Malaysians in a state is consistent with the findings in Del Carpio et al. (2015) and Ozden and Wagner (2016). Those papers find that all except the very least educated (without primary school education) Malaysians benefit from immigration. As a result of immigration, most native wages increase and states with an inflow of immigrants also experience an inflow of natives. Supplemental appendix TABLES S.4 AND S.5 show that the results are robust to alternative choices of covariates. In panel A of each table, all covariates are defined for the Malaysian population as a whole, such that none of the variables are specific to males. In panel B, all variables are defined exclusively for male Malaysians.39 While there are some differences in the salience of particular covariates, the estimated impact of immigration on crime is near identical to that in the baseline results.40

IV. Conclusion The perception that immigration fuels crime is an important source of anti-immigrant sentiment in almost every destination country. This paper makes three important contributions to this debate. First, it finds a sizable and statistically significant negative causal impact of economic immigration on property and violent crime rates. Second, it decomposes the impact into those attributable to immigration induced socioeconomic changes among the native population. The results suggest that immigration decreases

38 Appendix table S.2 reports standard errors clustered by state. This increases standard errors sufficiently such that the change in the immigration coefficient due to the inclusion of covariates is no longer statistically significant. 39 The data does not allow distinguishing between crimes by the gender of the perpetrator or victim. Consequently, the dependent variable in these regressions is the same as in the main regressions. 40 The decompositions presented in appendix table S.5 are also very similar regardless of how the covariates are constructed. In addition, the decompositions are robust to the previous specifications of excluding Sabah from the analysis, using three instruments by nationality and using employed immigrants or male immigrants as the independent variable of interest. Finally, appendix table S.3 presents the Gelbach (2016) decomposition for further disaggregated types of crime. 200 Ozden, Testaverde, and Wagner property crime rates primarily because it changes economic conditions for natives, while the decrease in violent crimes is harder to explain. Third, it provides some of the first evidence for non-OECD destinations, where around half of all migrants in the world live. An important question raised by the findings in this paper is why immigrants in Malaysia seem to Downloaded from https://academic.oup.com/wber/article-abstract/32/1/183/3866886 by World Bank Publications user on 08 August 2019 commit fewer crimes than natives. A potential explanation is that immigrants in Malaysia are mainly economic migrants and a considerable fraction is undocumented. Such economic migrants are distinct from other migrants, notably refugees, on a number of dimensions. First, their access to the labor market reduces the benefits and increases the costs of engaging in criminal activities. Second, they self-select into migrating for work and their underlying propensity to commit crimes is likely to be quite different from that of non-migrants. The fact that a large fraction of foreign workers in Malaysia are undocumented may be salient as well. Undocumented workers may be more likely to be victimized by crime but also less likely to report crime due to their fear of deportation. If immigration results in a drop in the reporting of crimes, the findings in this paper may overstate the degree to which the actual number of crimes, specifically targeting foreign workers, declines with immigration. Undocumented workers’ wariness of the police and the judicial system may also act as a deterrent to committing crimes, not just reporting them. As such, the availability of more detailed data on undocumented immigrants would be crucial to better understand the impact of immigration on crime in Malaysia. Conflict of interest: none declared.

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Migrant Remittances and Information Flows: Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 Evidence from a Field Experiment Catia Batista and Gaia Narciso

Abstract Do information flows matter for remittance behavior? We design and implement a randomized control trial to quantitatively assess the role of communication between migrants and their international network on the extent and value of remittance flows. In the experiment, a random sample of 1,500 migrants residing in Ireland was offered the possibility of contacting their networks outside the host country for free over a varying number of months. We find a sizeable positive impact of our intervention on the value of migrant remittances sent. Larger remittance responses are associated with individuals who are employed and earn higher incomes. This evidence is consistent with the idea that the observed increase in remittances is not a consequence of relaxed budget constraints due to subsidized communication costs but rather a likely result of improved information, perhaps due to better migrant control over remittance use, enhanced trust in remittance channels due to experience sharing, or increased remittance recipients’ social pressure on migrants. JEL classification: F22, J61, O15 Key words: migrant remittances, information flows, international migration, migrant networks, randomized control trial

Migrant remittances have grown substantially over the past decades, while showing remarkable resil- ience in the face of recent economic crises around the world. The financial flows generated by international migrants are surpassing the national public budget resources of several developing countries, as well as the Foreign Direct Investment and Official Development Aid flows these countries receive. It is therefore of great interest to learn more about the determinants and consequences of such important international financial flows.1

Catia Batista (corresponding author) is Associate Professor at Nova School of Business and Economics, Universidade Nova de Lisboa; her email address is: [email protected]. Gaia Narciso is Assistant Professor at Trinity College Dublin; her email address is: [email protected]. The authors thank the guest editor (David McKenzie) and three anonymous referees. They also thank Janis Umblijs for excellent research assistance with all aspects of fieldwork and Marta Zieba, Vasco Botelho, Tomas Sousa, Antonio Bernardo, and Michael O’Grady for additional superb research assistance. Useful comments were received by a number of participants mainly at several NORFACE conferences over the course of the project, 2014 NEUDC conference, 2015 AEA meetings, and 2015 World Bank Conference on Migration and Development. The authors gratefully acknowledge financial support from the NORFACE Research Program “Migration in Europe - Social, Economic, Cultural and Policy Dynamics,” as well as from the Department of Economics and the Arts and Social Sciences Benefactions Fund at Trinity College Dublin, and from Nova Forum at Universidade Nova de Lisboa. A supplemental appendix to this article is available at https://academic.oup.com/wber.

1 See Yang (2011) for a literature review on this topic.

One area of study crucial to understanding the determinants of migrant remittances concerns the relationship between migrants and their transnational networks and how it affects migrant decisions to remit. Often, migrants are part of a transnational household that was separated by considerable geographic distance at the time of migration. Distance between migrants and their networks is likely to affect this relationship in a variety of ways. For instance, this separation creates asymmetric information Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 in the sense that neither the migrant nor the network can accurately observe each other’s actions. In particular, at most times, the network outside the immigration country cannot accurately know the migrant’s occupation, earnings, or standard of living, while migrants cannot perfectly observe their networks’ true needs and uses of any financial transfers received. In this context, it becomes most relevant to examine the role of information flows between migrants and their network outside the country of immigration in determining migrant remittance behavior. The impact of these information flows on migrant transfers is eminently an empirical question. Indeed, one can conjecture about several possible mechanisms that could affect remittances in different directions. First, communication flows should contribute to an increase in the information available within transnational households, thereby mitigating asymmetric information problems, which could increase or decrease migrant remittances depending on the direction of earlier informational deficiencies. Second, additional contact between migrants and their networks may stimulate the demand for remittances on the recipients’ side, which would cause upward pressure on remittances. Third, the increased communication flows may lower the remittance costs and enhance trust in remittance channels due to experience sharing, which would likely increase remittance flows. A fourth mechanism could be that improved communication between migrants and their networks could actually substitute for remittances in the sense that contacts by migrants may be interpreted as a form of attention and caring, a role that could alternatively be performed by remittances. In this instance, improved informational flows would have a negative impact on transfers sent by migrants. In this paper, we examine the role of information flows between migrants and their networks abroad in determining remittance behavior. To do so we design a randomized control trial under which we vary the magnitude of information flows between migrants and their transnational networks by distributing international calling credit to a randomly selected treatment group. This field experiment is conducted on a random sample of 1,500 immigrants residing in the greater Dublin area in Ireland. The high incidence of phone use to contact the transnational network in our sample provides us with a clear indication of the potential impact of the calling credit, which could be used either on a mobile phone or on a landline phone. In particular, we provide evidence of a sizeable, statistically significant impact of the treatment on the extent of the communication flows in terms of the number of individuals contacted abroad, number of calls made, and conversation topics the migrant discussed with his/her transnational network in the month prior to the interview. Our results show that the increased information flows that we generate experimentally have a significant and substantial role in raising the value of remittances sent to existing recipients. However, we find only modest support for the hypothesis that increased contact with non-remittance recipients positively affects the decision to remit to those individuals. Migrants are mobile by definition and, due to the length of the intervention, this project experienced high levels of attrition. Our analysis is particularly careful in examining the impact of potential selective attrition in the estimation of the treatment effects of our intervention. Even though we find no evidence that the attrition in our sample was selective, we use the Lee (2009) bounds estimator accounting for potential selective attrition and obtain that our uncorrected estimates are all within the confidence intervals estimated in this way. The role of information flows on remittance behavior has been previously examined in the existing migration literature. McKenzie, Gibson, and Stillman (2013) describe survey evidence according to which migrants underreport their earnings when contacting their family members in the country of origin in order to moderate their remittance requests and limit new immigrant arrivals. This finding is The World Bank Economic Review 205 consistent with ours, but we further show using experimental evidence that increasing information exchanges between migrants and their transnational networks increases the amount of remittance flows. There are several recent papers on remittance-related strategic behavior by both migrants and their networks when their relationship is characterized by asymmetric information. Ashraf, Aycinena, Martinez, and

Yang (2015) find, in a randomized field experiment, that savings in migrant-origin households in El Salvador Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 rise when migrants (in the United States) are given new financial products that improve migrant control of savings in remittance-recipient households. Consistent with this finding, Batista, Silverman, and Yang (2015) use a lab-in-the-field experiment to show that urban individuals in Mozambique prefer to remit in kind (as opposed to in cash) in ways that express their preference to control recipient use of their transfers. Ambler (2015) conducts a lab-in-the-field experiment confirming that remittance recipients use resources differently when migrants can monitor this use. Chen (2013) also finds evidence of non-cooperative behavior related to the use of household resources in migrant households. Ambler, Aycinena, and Yang (2015), however, find that migrants offered a channel through which they could channel funds toward the education of a student of their choice choose not to use this service unless the use of this service is subsidized. Finally, Seshan and Zubrickas (2016) describe evidence of existing asymmetric information within transnational households and of its impact on remittance flows in the context of an adapted model of costly state verification. All of this work is consistent with our finding that improving information flows, and hence diminishing asymmetric information problems, can increase remittance flows. An additional strand of related literature emphasizes the importance of transaction costs and trust in the remittance channel as determinants of remittance flows. Aycinena, Martinez, and Yang (2010) conducted a Randomized Control Trial (RCT) among Salvadorian migrants in the Washington DC area, showing that lower remittance costs increased both the magnitude and frequency of remittance flows, while Batista and Vicente (2013, 2016) also present experimental evidence for migrants in Mozambique, indicating that not only lower remittance costs but also the availability of a more trustworthy mobile banking remittance channel increase the magnitude and frequency of remittance flows. These results are also consistent with our findings in the sense that increased communication flows may lower remittance costs and enhance trust in remittance channels due to the sharing of experience between migrants and their network. Finally, the positive role of information flows on remittance behavior can also be related to better integration of migrants in their networks at the origin country. Chort, Gubert, and Senne (2012) and Batista and Umblijs (2016) emphasize how remittances are used as a reciprocation or insurance mechanism from which migrants hope to benefit upon return to their home country. This idea is consistent with our findings in the sense that improved contact between migrants and their networks at origin is likely to deepen migrants’ integration in these networks, a mechanism that is complementary to remittances in this framework.2 In the remainder of the paper, Section I describes our experimental design and the identification strategy. Section II presents the data collection procedure, summary statistics, and a discussion of balance at baseline. Section III discusses the econometric model and the empirical results. Section IV concludes.

I. Experimental Design and Identification Strategy In order to quantitatively assess the role of communication flows in determining the extent and value of remittance flows between migrants and their contacts abroad, we implement a randomized field experiment, which consists of distributing international calling credit to a randomly selected treatment group.

2 A related branch of literature examines the role of networks and information on migration behavior. Notable recent examples of this line of work are McKenzie and Rapoport (2007), Beine et al. (2011), Aker, Clemens, and Ksoll (2012), Umblijs (2012), Farre and Fasani (2013), Bryan, Chowdhury, and Mobarak (2014), Elsner, Narciso, and Thijssen (2014), and Beam, McKenzie, and Yang (2016). 206 Batista and Narciso

Respondents in the treatment groups received a letter at the end of the baseline survey with the information on how to redeem the calling credit.3 The international calling credit could be used to contact any number outside of Ireland, either landline or mobile, with the objective of increasing the communication flows between immigrants in Ireland and their family and friends outside of Ireland. The total amount of calling credit was 90 minutes irrespective of the destination country to be called. The cost of the interna- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 tional calling credit was about e 0.12 per minute to the researchers, and it was not disclosed to the participants.4 However, the actual value of the calling credit to the respondent could vary, depending on the destination country. For example, a phone call from Ireland to South Africa could cost between e1.12 and e1.26 per minute with the main Irish landline operator (Eircom) while the cost of a call to Poland was about e0.39 per minute.5 Participants in the experiment were randomly assigned to one of three groups. Respondents in Treatment Group 1 received 90 minutes of free international calling credit every month for five months. Migrants in Treatment Group 2 received 90 minutes of free international calling credit for three months (every other month). Finally, one third of the participants were assigned to the Control Group.6 Differences in the remittance behavior between the treated and control groups will allow identification of the intention-to-treat (ITT) effects of our intervention. Differences between the two treatment groups would arise as a result of the treatment frequency. Upon completion of the baseline survey, participants were contacted by Computer-Assisted Telephone Interviewing (CATI) every month for a period of five months. The aim of the short (about 15 minutes in duration) monthly surveys was to gather information about remittance behavior, contacts with family and friends outside of Ireland, and the main topics of conversation. The calling credit accounts were topped up by the calling card provider on a monthly basis. The top-up was provided independently of the actual usage in the previous month. The respondents were informed about the top up at the end of the monthly survey. About six to nine months after the fifth monthly survey, the final round of the survey was conducted and all participants were contacted again by CATI to elicit information about remittance behavior.7 Figure 1 outlines the timeline adopted for the various surveys and the intervention.8 All participants were informed of the timeline of the initial and follow-up surveys before the baseline interview could be initiated. Respondents in the treatment groups were made aware of the calling credit at the end of the baseline interview, and they were also informed at that stage about the timing of future top-ups.

II. Data Collection and Summary Statistics The data used in our analysis consist of a representative household sample of 1,500 immigrants,9 aged 18 years or older, residing in the greater Dublin area, who arrived in Ireland between the year 2000 and

3 The letter provided the account details, that is, the number to call to activate the calling credit, the account number, and the PIN number. Participants were given the option to change the PIN number and to save the account information. 4 The international calling credit was provided by Swiftcall/Ninetel. 5 http://www.eircom.ie/bveircom/pdf/Part2.1.pdf. 6 Due to funding constraints, it was not possible to distribute the equivalent amount of the calling credit to the control group. 7 To guarantee that the person being interviewed was the initial respondent, the CATI agent would ask some basic questions to confirm the identity of the migrant. 8 McKenzie (2012) discusses the advantages of conducting multiple follow-ups, which increase statistical power in the case of outcomes with low autocorrelation. 9 Immigrants in our sample are defined as not being Irish or British citizens. British citizens were excluded due to the close historical ties between Ireland and Great Britain, which still entitle British citizens to vote at parliamentary elections, for instance. The World Bank Economic Review 207

Figure 1. Timeline

t t 2 t 3 t t 5 t 6 t 1 4 7 Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019

Baseline Final interview interview Monthly Distribution interviews of calling Top-up of credit to international treatment calling credit to group treatment group

six months prior to the interview date. The baseline sample was collected between February 2010 and December 2011. Survey activities were conducted by Amarach Research, a reputable survey company with experience conducting research surveys in Ireland, under the close supervision of the authors and their research team. Eligibility requirements for survey respondents were set to maximize the probability that migrants still kept contacts outside of Ireland (hence the 2000 initial arrival threshold) but were already minimally established in Ireland (for at least six months) so that contacts with their networks abroad could provide useful information. Due to missing relevant information about eligibility for nine respondents, the final sample size is 1,491. Random sampling was performed in the following way. First, 100 Enumeration Areas (EAs) were randomly selected out of the 323 Electoral Districts in the greater Dublin area. This selection was performed according to probability-proportional-to-size sampling, in which size is defined as the total number of non-Irish and non-British individuals residing in Ireland, according to the 2006 Census of Ireland. Second, 15 households were selected within each EA using a random route approach.10 Finally, in the presence of more than one eligible respondent in the household, the individual respondent was randomly selected based on a next-birthday rule. In the absence of the designated respondent, an appointment was set up for a later date. The random route approach consisted of the following procedure: each enumerator was given a map of the assigned EA and a pre-selected random starting address within the allocated EA. After a successful interview, enumerators were instructed to exit the house, turn left, count five houses down and approach this new address.11 In the case of an absent household, interviewers were requested to call back to the address for a maximum of five times at different times of the day and different days of the week. Each call-back was recorded on the interviewer’s report. When an address was exhausted after five call-backs, deemed ineligible, or in the case of a refusal, the interviewer followed predefined instructions in order to get the next address, namely the address next door to the left when exiting the house. All enumerators were initially trained by the research team and were subsequently supervised by the survey company and, randomly, by members of the research team. Each enumerator had to complete an

10 The 15 households are drawn from the non-Irish/non-British population. 11 A set of standard rules were given in the case of crossroads, apartment buildings, and culs de sac. 208 Batista and Narciso enumeration report, listing each address approached, the number of call-backs, and the outcome of each visit. The enumeration reports were closely inspected and verified by the research team. If the randomization instructions were not followed, interviews had to be replaced. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 Descriptive Statistics Most immigrants included in our sample are of Nigerian nationality (19%), followed by Polish (11%), Indian (6%) and South African (5%). In total, the sample covers 101 nationalities.12 Table 1 presents the summary statistics for a set of basic demographic characteristics of migrants for both treatment and control groups at baseline. The average participant age is 32 and a slight majority of respondents is female. About 42% of the respondents are married and the average length of stay in Ireland is five years. A large majority of respondents have parents living in the country of origin. Survey participants report a high degree of education with about 70% having a post-secondary degree or higher and 28% having a secondary school degree. About 75% of the respondents in our sample are employed, compared to 51.4% of the overall population in Ireland in 2011 (ILO). The net monthly income earned by surveyed individuals is around e1,200 per month, with an average of 23 working hours per week. About half of the respondents planned to return to their home country in five years or less at the moment of arrival. However, when asked about their current intentions to move away from Ireland, less than 40% of the respondents intended to leave the host country in the following five years.

Table 1. Demographics of Respondents at Baseline vs. Census 2011

Variable Own Survey

Treatment Control Difference Sample Size Census 2011 Mean Mean T-C (S.E.)

Age 32.80 32.20 0.59 (0.44) 1491 32.6 Female 0.55 0.52 0.03 (0.03) 1491 0.50 Married 0.42 0.42 0.00 (0.03) 1491 0.49 Years in IRL 5.38 5.29 0.09 (0.16) 1489 – College or Secondary Education 0.69 0.72 0.02 (0.02) 1483 0.70 Secondary Education 0.28 0.27 0.01 (0.02) 1483 0.31 Employed 0.75 0.76 0.02 (0.02) 1491 0.58 Number of children 0.96 0.88 0.08 (0.07) 1491 – Parents living in country of origin 0.84 0.83 0.01 (0.02) 1491 – Net Monthly Income (in Euro) 1,165 1,193 28 (63.94) 1356 – Number of working hours per week 22.95 24.32 1.38 (0.96) 1375 – Intended to return in 5 years or less at arrival 0.51 0.52 0.01 (0.03) 1389 – Currently intends to return in 5 years or less 0.39 0.36 0.03 (0.03) 1370 – Average monthly communication costs (in Euro) 20.04 18.26 1.78 (1.17) 1458 – Remitted in previous year (binary variable) 0.36 0.32 0.04 (0.03) 1458 – Value of monthly remittances sent in previous year (in Euro) 47.79 47.62 0.17 (7.68) 1458 –

Notes: ***p < 0.01, **p < 0.05, *p < 0.1.

12 Table S.1 in the online appendix (available at https://academic.oup.com/wber) presents the distribution of the top nationalities in our sample. The distribution of the top nationalities is balanced between treatment and control group. The comparison between our survey and the census (2011) distribution of the main immigrant nationalities in the greater Dublin area shows that our survey over-represents the proportion of African immigrants in our sample, while under-representing immigrants of Eastern European nationality. The proportion of immigrants from Asia and Latin America is similar in our survey and in the census (2011). The World Bank Economic Review 209

A great fraction of the individuals in our sample moved to Ireland for work reasons (40%), although acquiring education and the presence of an existing migration network are also cited as motives to migrate to Ireland (15% and 16% respectively). Language seems to matter as 9% of respondents chose Ireland because it is an English-speaking country. About 6% of respondents picked Ireland for religious motives, and a similar percentage moved to Ireland due to its immigration policies and visa Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 requirements.13 The baseline survey also provides extensive information regarding the transnational network of migrants, namely the size of this network, the cost of keeping in contact with it, whether remittances are sent and, if so, the amount remitted. On average respondents are in contact with two people living outside of Ireland and the average monthly cost of contacting a network contact abroad is around e20.14 As shown at the bottom of table 1, about one third of the participants in our sample send remittances with a monthly amount of remittances sent averaging around e47 (and over e125 when restricting to positive amounts only). We do not find any evidence of statistically significant differences between control and treatment groups for any of the described variables at baseline. The last column of table 1 presents the relevant demographics from the Irish Census (2011) and compares them to the ones in our sample. Overall, our sample captures the majority of features the migrant population according to the Census (2011).

Follow-up Surveys and Attrition Migrants are mobile by definition, and, given the length of the project,15 selective attrition could be a cause of concern. Respondents in the treatment group received an international calling credit at the end of the baseline survey and upon completion of short phone surveys. We therefore anticipated a higher dropout rate in the control group relative to the treatment group.16 A higher dropout rate in the control group is indeed confirmed by the attrition analysis presented in table 2. Initially, about 35% of the respondents in the treatment group dropped out compared to 44% of the control group. These attrition rates worsened after each round of the survey, ending up at 84% and 89% for the treatment and control groups, respectively. The difference in the dropout rates between the treatment group and the control group is statistically significant for each round of the survey.

Table 2. Attrition

Control Treatment Difference (S.E.) Mean Mean

Dropout – 2 rounds 44% 35% 0.08 (0.03)*** Dropout – 3 rounds 56% 51% 0.05 (0.03)* Dropout – 4 rounds 67% 62% 0.05 (0.03)** Dropout – 5 rounds 74% 68% 0.06 (0.02)** Dropout – 6 rounds 78% 72% 0.06 (0.02)** Dropout – 7 rounds 89% 84% 0.06 (0.02)***

Notes: ***p < 0.01, **p < 0.05, *p < 0.1.

13 See table S.2 in the online appendix for further details. 14 Participants mainly contact their parents (35%), siblings (31%), and friends (23%). See table S.3 in the online appendix for more information about the relationship between participants and their transnational network. 15 More than one year went by between the first baseline and last follow-up interviews. 16 In order to counter dropout rates, we provided incentives to all participants in the project by giving away five lottery prizes with a e100 value and a final lottery prize of e500. The prizes were highly advertised by the enumerators. 210 Batista and Narciso

To exclude the possibility of selective attrition, we evaluate the difference between treatment and control dropouts relative to the set of baseline observable variables presented in the descriptive statistics. We focus on the participants who dropped out after the first round of the survey at each of the following survey rounds. The results of this analysis are presented in table S4 of the online appendix. We find no systematic evidence of selective attrition as differences between characteristics in the control group and Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 in the treatment group are nearly always not statistically significant. These results are reassuring in terms of the validity of the analysis. We nevertheless address the impact of potential selective attrition in the estimation of treatment effects by following Lee (2009) to estimate bounds on our coefficients of interest. These estimation results are presented in section V.

III. Estimation Strategy In order to estimate the effect on remittance behavior of increased information flows between migrants and their network outside of the host country, we focus on two main dependent variables: the probability of remitting (extensive margin) and the value of monthly remittances (intensive margin). The design of the RCT and multiple-round survey we conducted allows us to estimate the effect of the treatment in two ways. First, we adopt a single difference approach by analyzing the post-intervention data (rounds two to seven of the survey) and we estimate the following specification:

0 Yit ¼ b0 þ b3Ti þ X id þ #t þ it (1) where Yit is either an indicator variable taking the value 1 if the migrant remits and 0 otherwise, or the amount of monthly remittances sent by respondent i at time t, where t is the time of the intervention period (round two to round seven of the survey). Xi is a vector of individual baseline characteristics: age, employment status, marital status, gender, number of individuals regularly contacted abroad, average monthly cost of calling network abroad, post-secondary education, whether the parents of the respondent are alive and live outside of Ireland, number of years in Ireland, continent of origin, and enumeration area fixed effects. Finally, #t represents survey round fixed effects. Given the availability of pre-intervention data on outcome variables from the baseline survey, we also use a difference-in-differences approach and estimate the following specification:

0 Yit ¼ b0 þ b1Ti þ b2postt þ b3Ti postt þ X id þ #t þ it (2) where postt is an indicator variable that takes the value 1 for post-intervention period (rounds two to seven) and 0 for the pre-intervention period (round one). Yit, Xi and #t are defined as before. As a further robustness check, we estimate a difference-in-differences specification with individual fixed effects (#t):

Yit ¼ b2postt þ b3Ti postt þ di þ #t þ it (3) where the impact of increased communication flows is captured by the b3 coefficient. In both specifications, we are interested in identifying the intention-to-treat effect, that is, the impact of the treatment Ti on remittance behavior variable Yit, which is given by the coefficient b3. Regular least squares estimates are used to estimate b3. Standard errors are clustered at the individual and time level, following Cameron et al. (2011).

IV. Main Empirical Results We begin the empirical analysis by showing that the experimental intervention effectively increased communication flows between migrants and their network abroad. According to the baseline survey, mobile The World Bank Economic Review 211 phones, landline phones, and international calling cards make the primary mode of contacting people abroad for 75% of our participants.17 The high incidence of international phone use in our data provided us with a first indication of the potential usage of the calling credit, which is similar to an international calling card and could be used either on a mobile or landline phone. This suggestive evidence is strengthened by the estimation results Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 reported in table 3, according to which there was a sizeable, statistically significant impact of the treatment on the extent of the communication flows. The monthly CATI interviews reported information about the number of individuals contacted abroad, number of calls made, and conversation topics the migrant discussed with his/her transnational network in the month prior to the interview.18 On average, respondents in the treatment group contact more people, make a greater number of calls, and talk about a larger number of topics regarding both Ireland and the country of residence of the contact person. Overall, it seems that the international calling credit was effective in increasing the information flows between migrants and their network abroad. These findings hold also when we include the set of demographic controls (column 2), time-fixed effects (column 3), enumeration area-fixed effects (column 4), and continent-fixed effects (column 5).

Effect On Remittances Having established the effectiveness of our intervention in terms of its take-up, we now turn to examining the impact of the intervention in terms of our outcome of interest: migrant remittances. The lower panel of table 3 reports the results of the single difference estimation of specification (1) for the extensive margin, that is, the probability of remitting using a linear probability model. The dependent variable in this specification is an indicator variable that takes the value 1 if the respondent sends monthly remittances and 0 otherwise. We find that the treatment has a positive and statistically significant impact on the probability of remitting. Treated migrants are 5.3 percentage points more likely to remit than respondents in the control group, an effect that is robust to the inclusion of demographic and communication controls as well as survey round-fixed effects. The estimated coefficient is still statistically significant when we introduce enumeration area-fixed effects (column 4) and continent of origin-fixed effects (column 5). The strongest results in our analysis arise when we analyze the impact of the increased communication flows on the value of monthly remittances.19 The last row of table 3 presents the effect that providing additional free calling credit to individuals in the treatment group has on the value of monthly remittances. This impact is positive and highly statistically significant. Treated migrants increase the amount of monthly remittances sent to their transnational network by about e40. Adding demographic and communication controls in column (2) slightly increases the magnitude of the treatment impact without changing its statistical significance. In columns (3)–(5) we progressively add time-fixed effects (column 3), enumeration area-fixed effects (column 4), and continent of origin-fixed effects (column 5). Treated migrants are still found to remit more than respondents in the control group. The average treatment effect in the specification with all controls and fixed effects included is about e45, as shown in column (5). Overall, we conclude that the increased communication flows triggered by the treatment (upper panel of table 3) produce a strong, significant increase in the amount of remittances sent (intensive margin) and also a smaller increase in the probability of remitting (extensive margin).

17 Please see table S.5 for further details. 18 These conversation topics include the level of wages, opportunities to find a job, cost of living, regulation for foreign migrants, unemployment benefits and other social benefits, health care system, education system, and taxes both in Ireland and in the country of residence of the contact person. 19 Our analysis is based on the unconditional value of gross remittances sent, including zeros. 212

Table 3. Intention-to-Treat Effects of Intervention on Communication and Remittance Outcomes

Variables (1) (2) (3) (4) (5) (6) (7)

Number of individuals Coefficient 0.383*** 0.341*** 0.319*** 0.227*** 0.214*** contacted in previous month Standard Error [0.087] [0.075] [0.074] [0.069] [0.067] Sample Size 2,764 2,702 2,702 2,702 2,702 Number of calls made Coefficient 2.498*** 2.287*** 2.382*** 1.773** 1.671** in previous month Standard Error [0.860] [0.858] [0.857] [0.853] [0.823] Sample Size 2,605 2,544 2,544 2,544 2,544 Number of host country related Coefficient 1.015*** 0.991*** 1.003*** 0.956*** 0.918*** topics talked about in previous month Standard Error [0.223] [0.221] [0.223] [0.208] [0.197] Sample Size 2,622 2,560 2,560 2,560 2,560 Number of origin country related Coefficient 0.971*** 0.967*** 0.978*** 0.901*** 0.870*** topics talked about in previous month Standard Error [0.191] [0.178] [0.180] [0.166] [0.156] Sample Size 2,622 2,560 2,560 2,560 2,560 Remittances sent (indicator variable) Coefficient 0.053** 0.055*** 0.052** 0.048** 0.043** 0.016 0.029 Standard Error [0.021] [0.020] [0.020] [0.023] [0.020] [0.018] [0.023] Sample Size 2,702 2,639 2,639 2,639 2,639 4,089 4,160 Remittances sent (monthly value in EUR) Coefficient 38.082*** 40.759*** 42.048*** 45.389*** 44.562*** 42.522*** 38.541*** Standard Error [9.501] [9.915] [9.672] [11.365] [11.306] [10.225] [12.094] Sample Size 2,702 2,639 2,639 2,639 2,639 4,089 4,160 Specification Single Diff Single Diff Single Diff Single Diff Single Diff Diff-in-Diff Diff-in-Diff Controls No Yes Yes Yes Yes Yes No Time FE No No Yes Yes Yes Yes Yes EA FE No No No Yes Yes Yes No Continent FE No No No No Yes Yes No Individual FE No No No No No No Yes

Notes: ***p < 0.01, **p < 0.05, *p < 0.1. Controls include employment status, age, post-secondary degree or college dummy, whether parents are alive and live abroad, gender, number of contacts abroad, average monthly cost of calling, and length of stay in Ireland.

Standard errors are two-way clustered at the level of the individual and time. Narciso and Batista Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 August 08 on user IMF and Bank World by https://academic.oup.com/wber/article-abstract/32/1/203/2420645 from Downloaded The World Bank Economic Review 213

Difference-in-Differences Estimation The analysis presented so far made use of the post-intervention data, that is, survey rounds two to seven. Using the baseline survey allows us to also adopt a difference-in-differences estimation strategy. Column (6) of table 3 reports the estimation results for the specification detailed in equation (2). The estimated ITT effect

(the coefficient on the interaction between the treatment and the post-intervention indicator) takes a positive Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 and statistically significant value. Treated migrants send e43 more remittances than the control group. Column (7) presents the specification outlined in equation (3), that is, a difference-in-differences specification with individual fixed effects in addition to the survey round-fixed effects already included in column (6). The estimated coefficient of interest keeps a similar positive magnitude with statistical significance at the 5% significance level. Columns (6) and (7) replicate the analysis for the extensive margin as well. We do not find any statistically significant impact of the treatment on the probability of remitting. We summarize by stating that the treatment had a strong effect on the intensive margin while its impact on the extensive margin appears less robust. In section V, we analyze some of the possible mechanisms at play.

Two Treatments As described in section I, the two treatment groups in the experimental intervention differ only in the frequency of the calling credit top-up. Migrants in treatment group 1 received a monthly calling credit top-up for a total of five months. Respondents in treatment group 2 received a calling credit top-up every other month for a total of three times. Table S.6 in the online appendix reports the results of the estimation of equation (1) differentiating between the two treatments. Both treatments have a statistically significant impact on the amount of remittances with an estimated average treatment effect of e42 for treatment 1 and e35 for treatment 2, according to the difference-in-differences specification using individual fixed effects. The two treatments increase the probability of sending remittances, although the effect is only statistically significant for the most frequent treatment, which increases the probability of remitting by five percentage points according to the difference-in-differences specification with individual fixed effects. The test of equality of the coefficients of the two treatments cannot reject the null hypothesis that the two coefficients are of the same magnitude in any of the specifications for either the intensive or the extensive margins. This is only close to happening with a 0.15 p-value in the case of the extensive margin where the more frequent treatment seems to trigger substantially stronger treatment effects. Interpreting this result precisely would require further experimentation: the evidence at hand does not allow us to distinguish whether this result is due to the fact that a single episode of improved communication is capable of breaking asymmetries in information in a way that increases remittances or whether some other frequency of change in communication patterns is necessary to achieve that result. Since there is no statistically significant difference between the two treatments we proceed by evaluating the joint impact of the two treatments in the remainder of the analysis.

V. Robustness Checks Given the extent of attrition in our sample and the fact that we cannot a priori predict whether attrition could generate an upward or downward bias in our treatment effect estimates, we estimate lower and upper bounds to our estimates following the methodology put forward by Lee (2009).20

20 The Lee (2009) bounds estimator relies on two main assumptions: random assignment of the treatment, which we already verified in our balance tests, and monotonicity. Monotonicity implies that the assignment of the treatment might affect attrition in one way only. This appears to be the case in our study, as attrition is higher in the control than in the treatment group for each of the survey rounds as shown in table S.4. 214 Batista and Narciso

According to our estimates (displayed in table S.7 in the online appendix), both the lower and upper Lee bounds are of the same sign and close magnitude to our main point estimate of the impact of our intervention on the value of remittances—the comparable point estimate is 38 (see column (1) in table 3) whereas our lower bound estimate is 37, and the upper bound estimate is 50. In addition, all our bound estimates are statistically significant at the 1% level. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 Similar results apply to our Lee (2009) bound estimates of the impact of our intervention on the probability of remitting. The comparable point estimate is 0.05 (see column (1) in table 3) whereas our lower bound estimate is 0.05, and the upper bound estimate is 0.06. Again, both bound estimates are statistically significant at the 1% level. These bound estimates are supportive in that, despite the high levels of attrition experienced over the course of this project, potential differential attrition does not seem to have been a cause of bias in our estimates.

Is It Just A Fungibility Effect? One possible concern is that treated migrants are simply using the savings from the decreased costs of calling their international networks to increase the remittances they send. In order to tackle this potential alternative explanation of our findings, one should first of all find a good proxy for the value of the savings provided by the calling credit given to treated individuals. The most expensive official Eircom rates (which would place the value of the calling credit between e35.1 and e113.40, depending on the country called as discussed in the first paragraph of section 2 of the paper) provide an upper bound to the value of the savings provided by the calling credit that was offered to the treated migrants. This is, however, an unlikely upper bound to be generally achieved since the baseline survey responses show that only 10% of the respondents use landline phones to communicate with their network abroad, and these are not necessarily all using the most expensive Eircom international calling rates. An alternative could be to consider the e10.80 that were paid monthly by the research team for the calling credit of each treated migrant in our sample. This amount provides a reasonable average of the value of the calling credit since some individual migrants are likely to be knowl- edgeable of country-specific saving forms of communication whereas others may be less savvy or interested in this type of saving. This would seem like a good average approximation to the value of the calling credit and should definitely be closer to a lower bound than the official landline Eircom rates. A conservative alternative assumption is to consider the individual baseline average monthly calling cost of the migrants in our sample as a good proxy for the value of the calling credit that was offered to treated individuals in our sample. This is a conservative assumption in the sense that it assumes that all the migrant’s monthly communication costs were paid by the research team, that is, we are assuming that the migrants were at the baseline not talking more than 90 minutes per month to their networks abroad. Under this assumption, the value of the calling credit varies between e0 and e350 and averages e19.4. It is an intermediate assumption between the two scenarios discussed above. Under this assumption that the savings provided by the calling credit can be well approximated by the migrant’s average monthly communication costs, we performed a simple accounting exercise to evaluate the impact of the savings provided by the intervention on the value of remittances—assuming perfect substitutability between saved communication costs and remittances, a somewhat strong conservative assumption. To perform this accounting exercise, we subtracted the average baseline communication cost from the remittance value sent by each treated individual after the intervention. This adjusted remittance value is now on average e19 higher in the treatment group relative to the remittance value sent by control individuals. Using this adjusted remittance value as the dependent variable (in single difference and difference-in-differences regressions with individual controls) yields significant intention-to-treat coefficients as displayed in table S.8. These estimates yield point estimates a little in excess of e20 with the The World Bank Economic Review 215 confidence intervals ranging between e3.20 and e48.94 in additional remittance flows after accounting for potential fungibility of the calling credit provided to migrants in the treatment group. Overall, there seems to be a significant positive effect of the intervention on the value of remittance flows even when accounting for a relatively large substitution effect. However, as could be expected, this reduces the magnitude and economic significance of the estimated treatment effect. Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 An additional test that allows us to refute the fungibility effect concern over our results is provided in table 4. This table reports the results from a difference-in-differences specification (with and without individual fixed effects), which now also includes an interaction term between the treatment indicator and the monthly average calling cost.21 As shown in columns (1) and (2), treated migrants are found to remit between e66 and e61 more than migrants in the control group after the intervention, and the estimated coefficient is statistically significant at the 1% level. In addition to the positive impact of the treatment on the value of monthly remittances, the triple interaction term between the treatment, the average communication costs, and the after intervention indicator is negative and statistically significant also at the 1% level. This means that the greater the communication costs between migrants and their network abroad, the lower the impact of the treatment on the value of monthly remittances. Columns (3) and (4) of table 4 show that a similar pattern emerges in the analysis of the impact of the intervention on the extensive margin of remittances. Treated migrants are about eight percentage points more likely to remit once we control for the interaction between the treatment and the average cost of

Table 4. Interaction with Calling Costs

Variables (1) (2) (3) (4)

Value of monthly remittances Monthly remittances - dummy

Treatment 5.415 0.011 [7.452] [0.017] Treatment* Post 66.454*** 61.473*** 0.088*** 0.086*** [11.473] [15.464] [0.019] [0.030] Treatment*Avg. cost 1.221*** 1.112*** 0.004*** 0.003*** of Calling*Post [0.136] [0.305] [0.000] [0.001] Treatment* 0.115 0.001 Avg. cost of Calling [0.384] [0.001] Avg. cost of calling 0.558* 0.003*** [0.286] [0.001] Individual Controls Yes No Yes No Round FE Yes Yes Yes Yes EA FE Yes No Yes No Continent FE Yes No Yes No Individual FE No Yes No Yes Speciﬁcation Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Sample Size 4089 4108 4089 4108 Number of individuals 1458 1458 R-squared 0.012 0.003 0.024 0.005

Notes: ***p < 0.01, **p < 0.05, *p < 0.1. Controls include employment status, age, post-secondary degree or college dummy, whether parents are alive and live abroad, gender, number of contacts abroad, length of stay in Ireland. Standard errors are two-way clustered at the level of the individual and time.

21 The monthly average of the cost of calling is included in the list of communication controls used in all regression specifications. 216

Table 5. Interaction with Employment (Indicator) and Income (in Thousands of Euro) Variables

(1) (2) (3) (4) (5) (6) (7) (8)

Variables Value of monthly remittances Monthly remittances - Indicator Value of monthly remittances Monthly remittances - Indicator

Treatment*Employed/ Income 54.413*** 57.785*** 0.034* 0.037 0.035*** 0.036*** 0.000 0.000 *Post [10.342] [13.601] [0.021] [0.031] [0.012] [0.011] [0.000] [0.000] Treatment*Post 2.357 4.147 0.042* 0.056* 1.807 8.499 0.015 0.024 [7.970] [8.304] [0.023] [0.031] [10.204] [9.077] [0.024] [0.029] Treatment*Employed/ Income 13.736 0.004 0.001 0.000 [16.255] [0.024] [0.007] [0.000] Treatment 7.872 0.023 3.847 0.021 [12.760] [0.018] [8.866] [0.015] Employed/Income 14.772 0.075*** 0.003 0.000 [13.371] [0.022] [0.007] [0.000] Speciﬁcation Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Diff-in-Diff Controls Yes No Yes No Yes No Yes No Round FE Yes Yes Yes Yes Yes Yes Yes Yes EA FE Yes No Yes No Yes No Yes No Continent FE Yes No Yes No Yes No Yes No Individual FE No Yes No Yes No Yes No Yes Sample Size 4089 4160 4089 4160 3771 3829 3771 3829 Number of individuals 1473 1473 1343 1343 R-squared 0.063 0.006 0.125 0.044 0.067 0.007 0.139 0.053

Notes: ***p < 0.01, **p < 0.05, *p < 0.1. Controls include employment status, age, post-secondary degree or college dummy, whether parents are alive and live abroad, gender, number of contacts abroad, and length of stay in Ireland. Standard errors are two-way clustered at the level of the individual and time.

ait n Narciso and Batista Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 August 08 on user IMF and Bank World by https://academic.oup.com/wber/article-abstract/32/1/203/2420645 from Downloaded The World Bank Economic Review 217 calling, as can be seen in column (3). The estimated coefficient is positive and statistically significant at the 1% level. This result also holds when we consider the difference-in-differences controlling for individual fixed effects as shown in column (4). The coefficient on the triple interaction between treatment, post intervention, and calling costs is again negative and statistically significant at the 1% level.

Were our results driven by fungibility of the calling credit provided, then we would have expected to Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 see the opposite relationship between the cost of calling and remittance behavior for treated participants, that is, we would have observed a greater positive impact of the treatment on remittance behavior for those with higher cost of calling. In fact, the results reported in table 4 present the opposite effect: the impact of the treatment is larger for the participants with lower communication costs. It may be argued that higher communication costs at baseline may capture a higher disposable income. We explore the relationship between income and remittance behavior in the next section and provide further evidence to refute a fungibility or substitution effect between the decreased costs of communication elicited by the experiment and remittance behavior.22

Interpretation of the Findings The increased communication flows might improve migrant’s control over remittance use and enhance trust in remittance channels due to experience-sharing. If this is the case, we can expect treated migrants who are regularly employed and who have higher income to send more remittances, the assumption being that these individuals are more likely to have the financial liquidity to send more remittances should they wish to do so. We test this hypothesis by focusing on the interaction between the employment status dummy and the treatment indicator (table 5, columns 1–4) and, as a further robustness check, the interaction between income and the treatment indicator (table 5, columns 5–8). The estimation results confirm the hypothesis: treated migrants who are employed tend to remit more while no clear effect is found on the probability of remitting. A similar result emerges when we consider the interaction with the income variable (columns 5 and 6). The greater the earned income the greater the increase in the amount of money remitted by treated migrants. No effect is found on the probability of remitting (columns 7 and 8). Table 5 provides further support to the idea that the observed increase in remittances is not due to relaxed budget constraints thanks to subsidized communication costs but rather a result of improved information. In this sense, these findings offer further evidence to contradict the substitution effect discussed previously.

VI. Conclusions Our results show that improving communication flows between migrants and their networks abroad may promote more migrant remittances. In particular, we identify a significant positive increase in the value of remittances sent (which nearly doubles relative to baseline) as a result of experimentally subsi- dizing communication between migrants and their networks outside of the immigration country. We find, however, only a relatively small (about 25% relative to baseline) increase in the probability of migrants in our sample sending remittances to a larger number of individuals in their network.

22 Communication costs may also be correlated with transfer costs, that is, the cost of sending remittances. While migrants could use the savings from the calling credit to transfer money to their friends and family members, they might also have to pay higher remittance fees. To this end, we use data on remittance costs at baseline and include this information in our specification. The impact of the treatment is robust to the inclusion of remittance costs in the regression (table S.9 in the online appendix). 218 Batista and Narciso

In our analysis, we devote particular attention to the high levels of attrition experienced in the project participation, which could potentially affect our estimation results. We find that the main findings are robust even when adopting the Lee bounds estimator that takes into account selective attrition. Even though our research design did not explicitly test for the mechanisms underlying this finding, our analysis shows that we can confidently exclude that the remittance effect we identify is a simple sub- Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 stitution or fungibility effect whereby those with higher subsidized communication costs increase their remittance flows by more. To exclude this substitution effect, we start from noting the necessary condition that the effects of the intervention on various indicators of communication between migrants and their international networks are economically meaningful and statistically significant at the 1% level. In addition, we perform an accounting exercise where we test for treatment effects on an adjusted measure of remittances that excludes the value of the subsidized communication costs. Our main results still hold even though the magnitude of the effects on the value of remittances is decreased, a mechanical result of this exercise. We also find that the impact of the treatment is significantly larger for the participants with lower communication costs, which is the opposite of what we could expect to happen if our results were driven by a positive income effect of the intervention. Similar results and reasoning hold when controlling for the cost of sending remittances. Finally, we obtain that larger remittance responses are associated with individuals who are employed and earn higher incomes. This evidence is consistent with the idea that the observed increase in remittances is not a consequence of relaxed budget constraints due to subsidized communication costs, but rather a likely result of improved information perhaps due to better migrant control over remittance use, enhanced trust in remittance channels due to experience-sharing, or increased remittance recipients’ social pressure on migrants. While additional research is necessary to distinguish the different mechanisms potentially at play, we believe this paper achieves an important first step in showing in a rigorous experimental way that information flows do play a role in determining migrant behavior. The findings of our work highlight the importance of investment in technology that increases the reach and efficiency of communication flows. In addition to other beneficial effects already documented in the literature, such an investment may be valuable to developing countries with substantial emigration stocks as there may be increased remittances flowing back to these migration countries of origin.

References Aker, J. C., M. A. Clemens, and C. Ksoll. 2012. “Mobiles and Mobility: The Effect of Mobile Phones on Migration.” Mimeo, Tufts University. Ambler, C. 2015. “Don’t Tell on Me: Experimental Evidence of Asymmetric Information in Transnational Households.” Journal of Development Economics 113: 52–69. Ambler, C., D. Aycinena, and D. Yang. 2015. “Channeling Remittances to Education: A Field Experiment among Migrants from El Salvador.” American Economic Journal: Applied Economics 7 (2): 1–27. Ashraf, N., D. Aycinena, C. Martinez, and D. Yang. 2015. “Savings in Transnational Households: A Field Experiment Among Migrants from El Salvador.” Review of Economics and Statistics 97 (2): 332–51. Aycinena, D., C. Martinez, and D. Yang. 2010. “The Impact of Remittance Fees on Remittance Flows: Evidence from a Field Experiment Among Salvadoran Migrants.” Mimeo, University of Michigan. Batista, C. and J. Umblijs. 2016. “Do Migrants Send Remittances as a Way of Insurance?” Oxford Economic Papers 68 (1): 108–130. Batista, C., and P. C. Vicente. 2013. “Introducing Mobile Money in Rural Mozambique: Evidence from a Field Experiment.” NOVAFRICAWorking Paper 1301. ———. 2016. “Promoting Migrant Remittances using Mobile Money: Evidence from a Field Experiment.” Mimeo, Universidade Nova de Lisboa. Batista, C., D. Silverman, and D. Yang. 2015. “Directed Giving: Evidence from an Inter-Household Transfer Experiment in Mozambique.” Journal of Economic Behavior and Organization 118 (C): 2–21. The World Bank Economic Review 219

Beam, E., D. McKenzie, and D. Yang. 2016. “Unilateral Facilitation Does Not Raise International Labor Migration from the Philippines.” Economic Development and Cultural Change 64 (2): 323–68. Beine, M., F. Docquier, and C. Ozden. 2011. “Dissecting Network Externalities in International Migration.” UCL IRES Working Paper Series 2011–22. Bryan, G., S. Chowdhury, and M. Mobarak. 2014. “Underinvestment In A Proﬁtable Technology: The Case Of Downloaded from https://academic.oup.com/wber/article-abstract/32/1/203/2420645 by World Bank and IMF user on 08 August 2019 Seasonal Migration In Bangladesh.” Econometrica 82 (5): 1671–1748. Cameron, C., J. Gelbach, and D. L. Miller. 2011. “Robust Inference with Multi-way Clustering.” Journal of Business and Economic Statistics 29 (2): 238–49. Chen, J. 2013. “Identifying Non-Cooperative Behavior Among Spouses: Child Outcomes in Migrant-Sending Households.” Journal of Development Economics 100 (1): 1–18. Chort, I., F. Gubert, and J. Senne. 2012. “Migrant networks as a basis for social control: Remittance incentives among Senegalese in France and Italy.” Regional Science and Urban Economics 42 (5): 858–74. Elsner, B., G. Narciso and J. Thijssen. 2014. “Migrant Networks and the Spread of Misinformation.” CReAM Discussion Paper Series 1403. Farre, L., and F. Fasani. 2013. “Media Exposure and Internal Migration: Evidence from Indonesia.” Journal of Development Economics 102: 48–61. Lee, D. 2009. “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies 76: 1071–1102. McKenzie, D. 2012. “Beyond baseline and follow-up: The case for more T in experiments.” Journal of Development Economics 99 (2): 210–21. McKenzie, D., J. Gibson, and S. Stillman. 2013. “A Land of Milk and Honey with Streets Paved with Gold: Do Emigrants have Over-optimistic Expectations about Incomes Abroad?” Journal of Development Economics 102: 116–27. McKenzie, D. and H. Rapoport. 2007. “Network effects and the dynamics of migration and inequality: Theory and evidence from Mexico.” Journal of Development Economics 84 (1): 1–24. Seshan, G., and R. Zubrickas. 2016. “Asymmetric Information about Migrant Earnings and Remittance Flows.” World Bank Economic Review, Forthcoming. Umblijs, J. 2012. “The Effect of Networks and Risk Attitudes on the Dynamics of Migration.” Oxford IMI Working Papers 54/2012. Yang, D. 2011. “Migrant Remittances.” Journal of Economic Perspectives 25 (3): 129–52. Volume 32 • Number 1 2018 ISSN 0258-6770 (PRINT) ISSN 1564-698X (ONLINE)

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Volume 32 • 2018 • Number 1 THE WORLD BANK ECONOMIC REVIEW

Migrant Labor Markets and the Welfare of Rural Households in the Developing World: Evidence from China Alan de Brauw and John Giles The Impact of the Syrian Refugee Crisis on Firm Entry and Performance in Turkey Yusuf Emre Akgündüz, Marcel van den Berg, and Wolter Hassink Inventor Diasporas and the Internationalization of Technology Ernest Miguélez The Impact of Rural Pensions in China on Labor Migration Karen Eggleston, Ang Sun, and Zhaoguo Zhan When the Cat’s Away … The Effects of Spousal Migration on Investments on Children Lucia Rizzica Can Parental Migration Reduce Petty Corruption in Education? Lisa Sofie Höckel, Manuel Santos Silva, and Tobias Stöhr

Symposium of papers from the 8TH AFD-World Bank Migration and Development Conference Guest editors: David McKenzie, Caglar Ozden, and Hillel Rapoport

The Long-Term Impacts of International Migration: Evidence from a Lottery John Gibson, David McKenzie, Halahingano Rohorua, and Steven Stillman Migration and Cross-Border Financial Flows Maurice Kugler, Oren Levintal, and Hillel Rapoport Heterogeneous Technology Diffusion and Ricardian Trade Patterns William R. Kerr Pages 1–220 How and Why Does Immigration Affect Crime? Evidence from Malaysia Caglar Ozden, Mauro Testaverde, and Mathis Wagner Migrant Remittances and Information Flows: Evidence from a Field Experiment Catia Batista and Gaia Narciso ISBN 978-0-19-880922-7

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