Microfinance Can Raise Incomes: Evidence from a Randomized Control Trial in China*
Shu Cai, Albert Park, Sangui Wang†
November, 2019
Abstract
This study evaluates the impact of a randomized control trial (RCT) in China that introduced externally funded village credit funds in poor, rural villages. In contrast to recent RCT-based studies that have failed to find evidence of significant increases in income from microfinance interventions, we find that the Chinese program significantly raises household income and reduces poverty. We explore possible explanations as to why the estimated impacts may be greater in China: lump-sum repayments, lower interest rates, less access to formal credit before the program, and greater potential returns from off-farm employment opportunities that are credit-constrained.
Keywords: microfinance, program evaluations, randomized control trial
JEL Codes: D12, D22, G21, I32, O16
* We thank Richard Freeman, Maitreesh Ghatak, John Gibson, Bob Gregory, Li Han, Guojun He, Joe Kaboski, Chris Udry, and Sujata Visaria for their helpful comments and suggestions, as well as comments from seminar or conference participants at ABFER, CCER summer institute, HKUST, Korea University, Peking University, SWUFE, U Bristol, UCSD, University of Delhi, U Michigan, and Wuhan University. The financial support of the National Natural Science Foundation of China (71703058) is gratefully acknowledged. All errors remain our own. † Cai: Institute for Economic and Social Research, Jinan University ([email protected]); Park: Department of Economics, HKUST ([email protected]); Wang: Renmin University of China ([email protected]). 1
1. Introduction
Microcredit has been regarded as highly successful in fighting poverty (Morduch, 2005).
However, recent studies based on randomized control trials find no transformative impacts of microfinance on increasing income (Angelucci et al., 2015; Attanasio et al., 2015; Augsburg et al., 2015; Banerjee et al., 2015; Crepon et al., 2015; Karlan and Zinman, 2011; Tarozzi et al.,
2015). This finding casts doubt on the role of microfinance in reducing poverty and raise discussions among researchers and policy makers. In this study, we evaluate the world’s largest microcredit program – a village banking program in China. We undertake a randomized control trial (RCT) similar to those conducted by the aforementioned studies, and find substantial increases in income from both self-employment activities and wage employment activities in areas where the program has been introduced.
In 2006, China’s national government initiated the village banking program to reduce poverty. The main component of the program is that each recognized poor village would receive 150,000 yuan (US$24,000) from the national government as the initial capital of a self- organized village bank. Households in the program village can borrow from the village bank to finance their income-generating activities. The program began among trail villages, and then expanded gradually to other poor villages across the country. By the end of 2013, the program had covered 19,400 villages in 1,407 counties of China’s 28 provinces.
To evaluate the impact of the village banking program, we conducted an RCT among 10 counties in five provinces in 2010, which is authorized by the State Poverty Alleviation Office in charge of implementing the program. In each county, we randomly selected three out of five candidate villages to participate in the experiment. A baseline survey prior to the intervention
2 collected information regarding the demographics, income, consumption, and production investment and revenue from 30 households randomly selected in both treatment and control villages. Two years later, the same households were interviewed again in a follow-up survey.
Our evaluation suggests that the village banking program increases the likelihood of borrowing for production by 15 percentage points. Households from treatment villages plant more cash crops and invest more in inputs of feedstuffs in animal husbandry than those in the control villages. Revenues and profits from their agricultural activities have increased in response to the program. Different from Crepon et al. (2015), we find the program also increases the employment income of households in the treatment villages. Further analyses suggest that households allocate more of their labor force to employment activities outside their home township, particularly employment activities beyond their home province, while they allocate less labor to employment activities within their home township. This leads to significant increases in employment income, since the wage rate of employment in foreign provinces (usually coastal areas) is much higher than that in rural villages or nearby townships.
Eventually, the village banking program raises households’ total income and reduces the rate of poverty significantly. The expenses on consumption increase as well, particularly for the consumption of durable goods and housing services. The improvement in living standards is confirmed by subjective assessment of quality of life and life satisfaction.
In our main specification, we drop one county due to late implementation in treatment villages and trim 1 percent of the observation to reduce the influence of possibly spurious outliers on the estimates. However, the results are robust to either of the two restrictions on the estimation sample. We also examine the robustness of the inference to address potential
3 problems of multiple tests and the small number of clusters. For most outcomes, our judgement on the statistical significance is robust based on p-values that adjust for multiple hypotheses tests or randomization inference.
To understand why microcredit may raise income in China, we first test the hypotheses that the estimated impact of microcredit on income depends on factors relating to loan terms and site context. More specifically, it decreases with repayment frequency, interest rate, saturation of financial market, and ratio of time spent on employment activity (an indicator of labor market imperfection) using meta-analyses to compare results across countries. After justifying these hypotheses, we assess each of the factors to ascertain the substantial impacts on income of microcredit found in China. We conclude that lump-sum repayment, lower interest rate, less access to formal credit before the program, and greater potential returns from off-farm employment opportunities that are credit-constrained are important factors for a microcredit program to be effective in raising income.
We also examine the heterogeneity in treatment impacts across different counties of the same Chinese population. Given that the implementation of the village banking program follows the guidelines of the national government, there is not much variation in loan terms, such as repayment frequency and interest rate. But we can examine heterogeneity along contextual factors of the study sites, including the saturation of the financial market and the imperfection of the labor market. The results suggest that the impact of microcredit access on income decreases with the ratio of time spent on employment activities and the availability of formal loans before the intervention. These echo the findings from meta-analyses, and confirm that whether microcredit can raise income depends on market conditions of the sites.
4
This paper contributes to the literature in several ways. First, we find that the extension of microcredit encourages households to seek employment farther away, which leads to an increase in employment income, a fact that has not been documented in studies of programs in other countries. Crepon et al. (2015) found that access to microfinance boosts businesses profits, but leads to a decline in employment income. Overall, income does not change significantly.
In a companion study, Cai (2018) shows that the microcredit program increases migration by household members in treatment villages, especially in villages with a low level of assets and high migration costs. The abundant off-farm employment opportunities and liquidity constraints on migration contribute significantly to the increase in employment income in
China. Second, the paper complements previous studies by implementing an RCT to evaluate a microcredit program in a country that has not yet been studied in the literature, and the scale of the program makes it a significant case study for research on microcredit. Third, the paper contributes to recent discussion about the external validity of an intervention. By using meta- analyses across countries and examining heterogeneity in the impacts across subpopulations within China, we show that the role of microcredit in raising income relies on the characteristics of loan terms and the market conditions of study sites. These may have implications for policies on the design and targeting of programs.
The paper is organized as follows. In Section 2, we introduce the village banking program, the experimental design, the context of the surveyed villages before the program, and the data employed. Section 3 presents the empirical strategy for identification. Section 4 reports the estimated average intention-to-treat effects on credit, investment, income, consumption, etc. In
Section 5, we conduct a meta-analysis by comparing our study with other studies that have
5 evaluated microcredit programs. In Section 6, we examine the heterogeneous treatment impacts by exploring contextual variation within our study. Section 7 concludes.
2. The Program and Experimental Design
Village Banking Program
Credit markets were underdeveloped in rural China, particularly in poor areas. Since 1990, the Chinese government has made many attempts to expand access to credit for the poor by learning experiences from other developing countries, including Grameen Bank in Bangladesh
(Yunus, 2006). Nonetheless, most of the efforts proved to be unsuccessful. To alleviate credit constraints and reduce proverty, the Chinese government (the State Council Leading Group
Office of Poverty Alleviation and Development and the Ministry of Finance) initiated a village banking program in 2006. Unlike previous anti-poverty programs implemented in the country, the village banking program targeted households instead of the villages. Meanwhile, the program aims to build up self-managed organizations to provide sustainable credit services.
Members of village banks elect managers, formulate bylaws, conduct bookkeeping, and manage funds on their own1, under the supervision of the county Poverty Alleviation Office.
Every program village received 150,000 yuan (US$24,000) from the central government.
Membership fees paid by the households comprise an additional portion of the village bank funds, which average around 200,000 yuan (US$32,000). The program firstly started among
1 Each village bank consists a general meeting, an administrative council, and a supervisory board. As the highest authority of the village banks, the general meeting elects and dismisses members in the administrative council and the supervisory board. The administrative council is made up of three to five person, which include a president, accountant, and treasurer, etc. The administrative council is mainly in charge of the disbursement and recovery of loans. The supervisory board usually consists three person who are responsible for supervising the operation of funds and the work of the administrative council. It is required that at least one member of the supervisory board should be the one from the poor households. 6
100 trial villages in 14 provinces in 2006, then extended to another 270 villages in 2007.
According to the Yearbook of China’s Poverty Alleviation and Development (2014), it had covered 19,400 villages in 1,407 counties of 28 provinces in China by the end of 2013. This study conducts the first systematic evaluation of possibly the world’s largest village banking program by using an RCT.2
Experimental Design
To evaluate the program at its early stage, the State Poverty Alleviation Office appointed a research team from Renmin University of China. The team was led by one of the program’s authors, Sangui Wang, to conduct a randomized intervention of village banking programs in poor villages in five provinces in China. The provinces were selected for the purpose of regional representativeness. Among the five provinces, Shandong province is in Eastern China;
Henan and Hunan provinces are in Central China; and Sichuan and Gansu provinces are in
Western China. In each province, provincial officers recommended the inclusion of two counties that had not previously implemented the village banking program. Furthermore, five officially recognized poor villages in each county were recommended as candidates for the program. To ensure random selection, the research team randomly chose three villages as treatment subjects and left the other two villages untreated. Thirty households were randomly selected in each of the treatment and control villages.3 Figure 1 illustrates the geographic
2 Early efforts to evaluate microfinance programs in China include works by Park, Ren, and Wang (2001) and Park et al. (2003), in which they compared the efficacy of various microfinance programs and found nongovernmental programs perform better than governmental programs in terms of targeting, sustainability, and program benefits. 3 The research team used the stratified systematic sampling method to obtain a random sample of households in the village. Specifically, the investigators asked the village officials to provide a list of all natural villages (a subunit of administrative villages that exists spontaneously) ranked by per capita income. The investigators then randomly selected one natural village from the list. In case there were less than 50 households in the selected natural village, the investigators would select another natural village to make sure there were adequate number of households. Given the randomly selected natural village(s), the 7 location of the selected provinces and counties, of which all are geographically separated.
Moreover, treatment villages were also separated from control villages in each county by an average distance of 14.5 kilometers (not shown in Figure 1). This may alleviate the concern for possible spillover effects between the treatment and control villages. In total, 1,500 households in 50 villages, comprising 10 counties, were surveyed at baseline in August 2010, before the initiation of the village banking program. The household survey collected detailed information on demographic characteristics and employment profiles of each household member4, as well as income (including income from crop farming, animal husbandry, small business, and employment), consumption expenses, assets, and any loans taken out from January 2009 to
August 2010.5 The survey also collected detailed information about the villages via village questionnaires, such as population, area of arable land, and amounts of public spending from superior governments on various village projects. After the baseline survey, the village banking program was gradually implemented in the treatment villages.6 Two years later, in July 2012, the research team conducted the second wave of the survey by following up with the same households in both the treatment and control villages. In addition to questions asked at baseline, the researchers asked questions about the village banking program in both household and village questionnaires.7 A total of 1,351 households were successfully re-interviewed. The attrition rate is 9.9 percent, which was due mainly to household migration or temporary absence
investigators asked the village officials to provide a complete list of households in that natural village(s) ranked by per capita income. The investigators then selected 30 households from the household list by systematic sampling. 4 The survey collected information on days of employment, work place, and earnings of each household member in the year before the survey. 5 For each loan, the survey collected information on the timing, amount, source, interest rate, duration, purpose, collateral, and guarantees. 6 The village banking programs were not anticipated by villagers. In most counties, the village banking program started to deliver loans during the first half of 2010. The only exception was one county in Hunan province, in which all of the three treatment villages did not start to deliver loans until one month before the second wave of the survey. 7 The second wave of the survey recorded all the loans taken out between August 2010 and July 2012 for every household in the survey. 8 of all household members for reasons such as visiting relatives. Later, we examined the balance of attrition between the treatment and control villages.
Program Implementation and Loan Characteristics
The county Poverty Alleviation Office was in charge of promoting the village banking program in the treatment villages at its early stage. They organized a meeting of all cadres in the village to introduce the program, including its objections, rules, management, etc. The information was passed on to the villagers by the village cadres via separate meetings in each nature village. Then, a general meeting of all members in the administrative village was organized to explain the program, collect feedback, and elect members of a preparatory team.
Members of the preparatory team were in charge of drafting charters for the village bank, approving applications for membership of the village bank, and organizing the first general meeting of all member households to elect an administrative council and supervisory board.
After that, the village bank was registered at the county Civil Affairs Bureau, and the management of the village bank was handed over from the preparatory team to the administrative council.
After registration, each treatment village received 150,000 yuan (US$24,000) from the central government, and the village bank started to deliver loans. The maximum size of a loan was typically 5,000 yuan, equivalent to about 45 percent of the average annual household income at baseline. The duration was normally one year, with repayment of one instalment.
The average annual nominal interest rate of the program loans was 9.4%, while that was 10.8% of loans borrowed from the Rural Credit Cooperatives with the same duration. To remove the
9 need for collateral, the village banks usually adopted group lending, whereby groups of between five and seven people share joint liability. 8 Unlike many other group-lending practices, there was no requirement of regular meetings for group members.9 In principle, loans should be used for income-generating activities.
Only one member of a household can become a member of a village bank. To qualify for the program, the representative of a household should be a regular resident in the village, above
18 years old, and with work capacity. They also need to submit an application and pay a lump- sum membership fee if the application is approved. The membership fee is usually 200 yuan
(US$32), which can be deducted or waived for poor households. The members can retrieve their membership fee (without interest) when they withdraw from the village bank if they have no default loans. Village bank members apply for loans from the administrative council. For group lending, the application needs the agreement of all other members in the same group (co- signers). The administrative councils make the final decision on the application according to the stated purpose, loan size, and duration. Applications from poor households and female members are given priority. When an application is approved, the applicants sign a contract with the council before they get the loan. The village bank does not provide a savings service.
By the time of the second survey, 57 percent of the households in the treatment village had joined the program and 28 percent of the households had borrowed from the village banks. At the same time, 70 percent of the village bank funds were lent out, and the repayment rate was
8 If any member defaults, the other group members are obliged to repay the loan, and are not allowed to borrow from the village bank until the loan is repaid. Exceptions are allowed for reasons such as disaster. The application of such default is reviewed by the administrative council and discussed at a general meeting of all members of the village bank. 9 As documented later, the villages usually are geographically small, and the relationship between village members is usually based on kinship. Although group members are not required to meet formally, they can easily be aware of the activities of other members through informal interaction. 10
98%.10,11
Contexts of the Villages
The villages in our survey are relatively poor. As shown in Table 1, in 2009, the average annual income per capita was 3,865 yuan, while that was 5,153 yuan in rural areas nationwide
(National Bureau of Statistics of China).12 About 45 percent of the households in our sample would be classified as poor households at baseline according to the national poverty line.13
Regarding production activities, almost all households conducted crop farming at baseline (98 percent), and 79 percent of the households conducted animal husbandry. Whereas agricultural production was popular in these villages, small businesses and employment activity were much less frequent. Only 14 percent of households engaged in small business at baseline, and 51 percent of the households were involved in some form of employment activity. In particular, merely 23 percent of the households had some member engaged in employment in foreign provinces at baseline (not reported in the Table). In regard to the income portfolio, the average annual earnings from self-employment (including income from crop farming, husbandry farming, and small business) and employment activities of these households are 4,126 yuan and 4,847 yuan, respectively, accompanied by additional income from other sources (including public and private transfers) with an average value of 3,048 yuan.
10 The repayment rate is defined as the percentage of the amount of loans due that are repaid on time. Besides the cross- guarantees, the high repayment rate may be partly because the loans are used productively, as shown below. 11 The State Poverty Alleviation Office and the Ministry of Finance published a booklet to guide the operation of village banking programs. They also organized a group training among representatives from all program villages throughout the country in October 2010. However, in practice, there are considerable differences in implementation across regions. 12 Hereafter, all monetary values are measured in 2009 RMB by adjusting provincial level rural CPI. The results are robust if we national-level rural CPI for the adjustment, and are available upon request. 13 The national poverty line was set based on annual income per capita, which was 2,300 yuan in 2011 (in 2010 price), or $1.57 per person per day at 2005 PPP by using 3.46 yuan to a dollar (Chen and Ravallion, 2010). We adjust this value by rural CPI, and define a household as poor household if its annual income per capita in 2009 was equal to, or less than, 2,220 yuan. 11
Access to production loans among these households was quite limited. Only three percent of households borrowed production loans from formal financial institutions during the period between January 2009 and August 2010, primarily from the Rural Credit Cooperative.14 Some
16 percent borrowed from informal sources without interest. In total, less than one fifth of the households had borrowed production loans from any sources over the period. Panel B shows the median amount of loans among panel households that have borrowed each type of loan from January 2009 to August 2010. The result shows that the median amount of loans from formal sources is much greater than interest-free informal loans, even though the latter are more popular. Within the informal interest-free loans, the median amount of production loans is also smaller than consumption loans. These results indicate that informal financial networks play a critical role only in consumption credit, but not in production credit. With focus to expand credit access for income generation, the village banking program would potentially be effective to address the market failure and improve welfare of these households.
Among the control villages, Table 2 reveals that 56 percent are in mountainous areas, 33 percent are in hilly areas, and only 11 percent are in plains areas. The average size of the village population is around 1,000. The average diameter of the villages is 2.74 kilometers. Some 70 percent of the households has a surname the same as one of the top three surnames in the village.
The relatively small geographic size of the villages and close kinship within each village indicate that people who live in these villages are usually familiar with one another. In terms of resources, the average area of arable land in the villages is 1,490 mu (around one square
14 The organization of Rural Credit Cooperatives is analogous to a bank. They are usually located in the administrative center of the township. 12 kilometer).15 About half of the villages’ population are labor force. The educational level of this labor force is very low. Only 13 percent have attained an educational level of senior high school or above. Another 31 percent have an educational level of junior high school. The rest have education lower than junior high school. Some 40 percent of the labor force are migrants, having worked outside their home town more than three months during the previous year.
Among the migrants, about half worked in foreign provinces, mostly in coastal cities such as
Shanghai and Guangzhou. The average daily wage of skilled laborers in the villages was 68 yuan, while the average daily wage of unskilled laborers in the villages was 46 yuan. The outreach of credit from formal financial institutions was very limited before the village banking program. Only 14 percent of households borrowed from the Rural Credit Cooperatives. The average amount of these loans is about 2,440 yuan. Infrastructure in these villages is poor. Only
77 percent of the households are accessible by telephone or mobile phone connections. Some
56 percent of the nature villages have paved roads. Living conditions are bad as well. Only 56 percent of the households have access to clean drinking water, and only 16 percent are equipped with a sanitation toilet. Most of the nearest Rural Credit Cooperatives or commercial banks are located in the administrative center of local township governments, with an average distance of 4.8 kilometers away from the village. The primary target of public spending financed by the superior government is education, while public spending on other aspects is relatively smaller.
Besides village characteristics, we present summary statistics of household characteristics at baseline in Panel B of Table 2. Among households in the control villages, most of the household heads are male (94%). Their average age is about 52 years old. The average household size is
15 1 mu is equal to 666.7 square meters. 13
4.28. The school attendance rate for children of 17 to 19 years old, who would go to senior high school after finishing compulsory education, is only 65%.
The RCT is designed to randomly place the treatment village within selected counties. In columns 3 and 4 of Table 2, we examine the randomness of village placement by comparing the baseline characteristics of the villages and households between the treatment and control groups. We do not find significant differences among almost all of the characteristics, except that the ratio of “creditworthy households” in the treatment villages is smaller (p-value 0.060), and households in the treatment villages have less access to electricity than households in the control villages (p-value 0.071). Households in the treatment villages are similar to those in the control villages in terms of household heads’ demographic characteristics and household composition as well. Of the 60 variables tested, only two variables are differences between treatment and control villages at the significant level of 10%.16 This is very much within the range we would expect. To summarize, there are no statistically significant differences between the treatment and control villages, which confirms the randomness of the treatment assignment.
Attrition
One concern regarding analysis based on panel data is the sample attrition. This is particularly important in program evaluation, since attrition may affect the internal validity of the estimated impacts (Karlan and Zinman, 2010). We thus examine whether attrition is independent of village treatment status.
The attrition rate in the control villages is 9.5%, compared to 10.2% in the treatment
16 We also check the balance among the sample which drop the aforementioned county that delivered loans very late. The results are robust and available upon request. 14 villages. However, the difference is not statistically significant (the p-value is 0.660). Table A1 reports the estimation results of a probit model of household attrition status in the follow-up survey. In all of the regressions, we cluster the standard errors by village. The result in the first column confirms that the attrition rates in the treatment and control groups are not statistically different. In columns (2) to (4), we further control for characteristics of the head of household, household, and villages. Overall, we do not find significant difference in attrition between households in the treatment and those in the control villages. The results indicate that some of the covariates are correlated with the likelihood of attrition (not reported in Table A1). For instance, households in villages located in mountainous or hilly areas are more likely to be subject to attrition than those from the plains areas. The household attrition rate increases with log arable land and decreases with log labor force in the villages. The probability of attrition is also lower among households in which the heads are older, married, or less educated. The correlation between the probability of attrition and having outstanding loans at the time of the baseline survey is not significant. However, households with fewer assets are more likely to experience attrition. Statistics on testing the joint significance of all the explanatory variables, excluding the village treatment dummy, suggests these characteristics are jointly significant in predicting attrition of the household.17
We further check the balance of household characteristics among the panel sample
(dropping or without dropping the aforementioned county). All the household level variables listed in Panel B of Table 2 are not statistically different between the treatment and control groups of the panel households at the significant level of 10%. Results are available upon
17 The results reported in Table A1 are robust if we drop the aforementioned county from the analysis sample. 15 request.
3. Methods
Empirical Strategy
We employ the difference-in-difference (DID) method to estimate the intention-to-treat effects of access to the village banking program by utilizing the following regression equation
∆ = + + + ∆ , (1) where indicates household, and indicates village. ∆ is the change in outcome variables of interest for household i in village j between the end-line and baseline surveys; is a dummy of the program treatment status of village j ( = 1 if village j is in the treatment group, and 0 otherwise); is the control variables of village j at the baseline used to take account of village-specific trends, including type of geographic features, population, area of arable land, and public spending financed on various village projects by superior governments; and ∆ is the error term, clustered by village. is the parameter of interest, which reflects the average intention-to-treat effect of the village banking program on outcome y. Since is independent of the error term, the parameter can be identified by ordinary least squares (OLS) estimation in the above first-difference equation. This compares the difference in changes in outcomes between households in the treatment and control villages.18
18 We examine an alternative specification, namely, analysis of covariance (ANOCA), in which we further control for the lagged dependent variable in equation (1). The results are robust in sign for all outcomes in Table 3 to Table 8 (reported later), except for the variables of access to interest-free informal loans in Table 3, total sown area in Table 5, and expenses on nondurable items and total consumption expense in Table 8. The sign of these variables turns to the opposite, although none of the coefficients is statistically significant. The following variables turn out to be insignificant in the alternative specification: access to formal loans, amount of any loans in Table 3, household employment income, and pre-transfer income in Table 4, sown area in cash crops in Table 5, employment working days in the home village, and employment working days in the foreign province in Table 7, and total income and expense on durable services in Table 8. Impacts on some other outcomes turn out to be significant, including expenses on irrigation, herbicide etc. in Table 5, and profit from animal husbandry in Table 6. The results are reported in Table A8. 16
Inference
Since we examine many outcome variables in the study and some of them belong to the same group or family (such as credit access), the type I error will increase with the number of individual hypotheses testing. We thus implemented multiple hypotheses testing by controlling the family-wise error rate (FWER) for each individual hypothesis. More specifically, we treat variables in the same table reported below as an outcome family and use the free step-down resampling methodology of Westfall and Young (1993) to adjust the p-values for testing each individual outcome.19,20 Furthermore, we examine outcomes by following the template used in the aforementioned six studies to avoid selection on significant results. Generally, the significance of individual tests do not change by correcting multiple inference.
To address the concern of the small number of clusters that may distort the inference estimated in the clustered regressions (Donald and Lang, 2007), we also report the p-values using the randomized inference method (Young, 2017) and wild-bootstrap method (Cameron et al., 2008).
Outlier
19 There are other methods to control FWER. For instance, the Bonferroni method simply multiplies the unadjusted p-values by the number of outcomes in the “family.” The Holm (1979) method multiplies the original p-value by its rank in the family (in decreasing order; thus a lower p-value has a higher rank) and rejects the individual hypotheses in a step-down sequence, while Hochberg (1988) modified the procedure and rejected the hypotheses in a step-up sequence. A caveat of the above methods is that the power for rejecting the null hypotheses decreases with the numbers outcomes in the family. In addition, these methods are based on the assumption that the individual tests are independent of each other, which is likely violated in our study. The algorithm of Westfall and Young (1993) takes account of the dependence structure of outcomes and is more powerful than the algorithm of Bonferroni and its modifications, especially when outcomes are highly correlated (see Anderson 2008 for details). In practice, we use the Stata command “wyoung” (Jones et al., 2018) and use 10,000 bootstraps to adjust p- values for multiple hypotheses tests. 20 An alternative approach to address the multiple inference problem is to construct a summary index among the same outcome family and then implement a global hypothesis test on this index (Kling, Liebman, and Katz, 2007). This method ignores impacts on individual outcomes, and the estimated impacts on the index are hard to interpret. Meanwhile, most outcomes we examined are of interest on their own and we want to compare them with other studies. 17
Since the treatment villages in one county of Hunan province began to extend loans very late, we dropped this county from the analysis. We check robustness of the estimates by dropping sample from the county.
To reduce the influence of possibly spurious outliers on the estimated coefficients and standard errors, we use truncation to trim 1 percent of the observation by following the procedure used in Crepon et al. (2015).21, 22
Eventually, the analysis sample consisted of 1,222 households in 45 villages, 9 counties, and 5 provinces. Among them, 487 are in control villages, and 735 are in treatment villages.
Table A7 shows that the results are quantitatively and qualitatively robust to both truncation of the extreme observations and exclusion of households in the aforementioned county.
4. Results
In this section, we present estimation results of the average intention-to-treat effects of the microcredit program in China. We start from the first-order effect on access to credit from various sources as a result of the introduction of the program. We then examine program impacts on total income and income by components. To detect reasons for change in income, we examine more details on income-generating activities, including self-employment activities
21 Firstly, for each of the main continuous outcome variables (i.e., amount of loans from any sources, self-employment income, employment income, total pre-transfer income, self-employment income per capita, employment income per capita, total pre- transfer income per capita, profit in crop farming, profit in animal husbandry, total income, consumption, and savings), we calculate the ratio between the value of the observation and the values of the ninetieth percentile (which is positive for all variables) if the value of the observation is positive, or the ratio between the value of the observation and the values of the tenth percentile (which is negative for all variables) if the value of the observation is negative. We obtained a maximum value of the ratios over the aforementioned variables for each observation and then trimmed 1 percent of the observations with the highest maximum value of the ratios. One advantage of the method is that the estimations use the same sample for every outcome variable. 22 Alternatively, we censored the observation with a 99 percent winsorization for each of the outcome variables. That is, we replaced data below the 0.5th percentile by the value of the 0.5th percentile and data above the 99.5th percentile by the value of the 99.5th percentile variable-by-variable. The results are robust and available upon request. 18
(crop farming and husbandry farming) and employment activities. Following this, we investigate program impacts on poverty rate, expenses on consumption, and subjective well- being.
Borrowing
We begin by examining the program impacts on access to credit, because if the program has any impact on investment or income, it should first affect the credit access of the households. Since the duration of the village bank loans is usually one year, we focus on the impacts on the short-term credit market, namely credit with a duration of no more than one year. 23 Panel A in Table 3 shows that 23 percent of the households in treatment villages borrowed from the village bank for production purposes.24 Exposure to the village bank crowded out production loans from other sources to some extent, especially for those who borrowed from formal financial institutions.25 This may be because the interest rates of loans from village banks are generally lower than rates of formal financial institutions, or borrowing from the village bank does not require collateral and is much easier. The last row shows that the village banking program significantly increases total credit access among households in treatment villages. The share of households with production loans increased by 15 percentage
23 Over the period from January 2009 to August 2010, only about 2.6 percent of the new credit taken out had a duration of more than one year (mostly two or three years). Over the period from September 2010 to July 2012, the share was also very low, rarely 1.8 percent. 24 In principle, the village bank offers loans for production purposes. But in practice, the rule is not strictly enforced. There are five percent of the households borrow from the village bank for consumption. We find that, access to the program reduces the average incidence of borrowing for consumption, particularly for informal interest-free loans. Overall, we found no impact on the likelihood of any loans for either production or consumption. Further analysis suggests the incidence of any loan increases among program borrowers in the treatment villages, while the incidence of consumption loans decreases for both borrowers and non-borrowers from the program in the treatment villages. These results suggest that, while the village banks increase overall credit access for borrowers, it reduce total credit for non-borrowers. The results related to the program impacts on consumption loans and loans for any purposes are available upon request. 25 Park et al. (2003) find a similar crowd-out effect on formal financial institutions by increased competition in the financial markets. 19 points, a sizeable impact compared with the average 19 percent of control households measured at baseline.
Turning to the amount of credit, households in the treatment villages borrowed 967 yuan from the village bank, on average. Considering that only 23 percent of households in the treatment villages borrowed program loans for production, this indicates that the average size of program loans was 4,204 yuan. The programs crowd out interest-free informal loans by 531 yuan. They also crowd in informal loans with a positive interest rate by an amount of 244 yuan, although this is statistically insignificant. Overall, the total credit amount of households in the treatment villages increased by 731 yuan, with significance at the 10 percent level. The positive impacts on total credit amount indicates that, in total, the injection of village banking loans does not simply crowd out loans from other sources.
Columns (1) and (2) in Table A2 in the appendix examine the impact of the village banking program on the interest rate, by regressing the annual real interest rate of loans on dummies that indicate village treatment status, after the time of the first survey, and their interaction term among short-term production loans. While the sample in the first column includes loans from all sources, the sample in Column (2) excludes program loans. Coefficients of the interaction term suggest the interest rates of production loans increase with the injection of village bank loans, although this is statistically insignificant. The results are robust by using household sample, where the outcome variable is defined as household average interest rate, as done by
Kaboski and Townsend (2012). These results confirm that the positive program impacts on total credit are not a result of a possible decrease in interest rate, but rather evidence of credit constrained among the households in financing production before the program (Banerjee and
20
Duflo, 2014).
Income
Table 4 presents the estimates of program’s impacts on income, including self-employment income and employment income. Columns (1) to (3) of the table report the estimated program impacts on household income. On average, the introduction of village banking program increased self-employment income by 2,430 yuan, and increased employment income by 1,720 yuan among households in the treatment villages. Total pre-transfer income increased by 4,151 yuan, which is about 46 percent of the average income among households in the control villages at baseline. Columns (4) to (6) report the estimated impacts on income per capita, which also increased substantially among households in the treatment villages. Access to the program raises the per capita income from self-employment activity and employment activity by 679 yuan and 759 yuan, respectively. In total, per capita pre-transfer income increased by 1,438 yuan for households in the treatment villages. This is about 55 percent of the control mean at baseline. Unlike the studies of Crepon et al. (2015) and Banerjee et al. (2015), we found that employment income increased substantially as a result of the program. We discuss this in more detail in Section 5.
Aside from the pre-transfer income, Table A4 in the Appendix examines the program’s impacts on other income, including public transfers, private transfers, sales of assets, insurance claims, and others. The results show that there were no significant impacts on public transfers, suggesting that estimated impacts did not crowd-in or crowd-out public transfers to the households. In addition, there was no significant impact on private transfers. For average
21 households in the treatment villages, income from selling assets decreased as a result of the program, indicating they relied less on “buffers” for financial needs. In total, we find no significant impacts on other income.
Self-Employment Activity
To assess how the village banking program has affected self-employment activity in the treatment villages, we start by investigating the impacts on investments in crop farming and animal husbandry, and then examine whether these investments are profitable.
Table 5 shows that the sown area of cash crops of households in the treatment villages increased by 0.49 mu, on average. The sown area of grain crops of these households decreased, and the total sown area increased. However, neither of the change is statistically significant.
These results suggest that households in the treatment villages tend to switch their planting from grain crops to cash crops. This could be because cash crops are riskier than traditional grain planting. The microcredit program may play a role as a form of insurance (Bryan et al.,
2014). Columns (4) to (7) show that inputs in crop farming increased by 183 yuan as a result of the program, which is about 14 percent of the mean value in the control group at baseline.
Among the inputs, the increase of inputs in irrigation and herbicides is the highest, although not statistically significant. Are the investments in crop farming profitable? Columns (8) and
(9) examine the program impacts on revenue and profit for crop farming. The results show that the average revenues from crop farming increased by 1,170 yuan as a result of the program.
The village banking program eventually led to a net profit of 988 yuan from crop farming, which is about 49 percent of the average profit in the control villages at baseline.
22
Table 6 shows the estimated impacts on animal husbandry. On average, investments in animal husbandry in the treatment villages increased by 700 yuan, which was driven primarily by increased inputs in feedstuffs. The impact on expenditures for hired labors was nearly 0.
Meanwhile, we do not find significant changes in investments in the purchase of new animals.
This suggests that the increased investment in animal husbandry is primarily on the intensive margin (increases in investments in the working capital of existing business), rather than the extensive margin (expanded business size). Revenue from animal husbandry also increased significantly by 802 yuan for households in the treatment villages. The effect represents about
41 percent of the revenue in the control villages at baseline. In terms of profits from animal husbandry, the impact is positive. But the standard error of the estimate is high, suggesting that the profitability from animal husbandry varies over the sample.
Besides crop farming and animal husbandry, we examine the impacts on small business.
The results are reported in Table A3 in the Appendix. We do not detect any significant impacts on the establishment of small businesses. The average profit of businesses outside home villages increased by 1,131 yuan for households in the treatment groups as a result of exposure to the program, but it is statistically insignificant. Overall, among households in the treatment villages, profit from small businesses increased by 1,340 yuan. As stated, the program increased profits from crop farming and animal husbandry by 988 yuan and 102 yuan, respectively. These add up to a total increase in profits from self-employment income of 2,430 yuan (59 percent of the mean of the control groups at baseline).
As shown in Tables 5 and 6, the introduction of the microcredit program increased the average expenses on investments in crop farming and animal husbandry among households in
23 the treatment villages by 183 yuan and 700 yuan, respectively. These add up to a total increase in agricultural investment of 883 yuan. By taking account of possible increases in investments in small business and financing migration, the magnitude of the estimated impacts on investment seems greater than the estimated impacts on production loans from the program,
731 yuan, as shown in Table 3. This may imply some co-funding for investments from savings.
Labor Employment
The impact of expanding access to credit on employment activity is ambiguous from a theoretical point of view. On the one hand, increased investments in self-employment activity as a result of financial inclusion, such as feedstuffs and fertilizer, may increase the demand for labor in self-employment activity, thus decreasing labor related to employment. On the other hand, access to extra credit may increase employment when initiation of employment is costly and requires a lump-sum investment before receiving the returns (Evans, 1989; Nelson, 2011;
Angelucci, 2015). Therefore, how the microcredit program affects labor employment is an empirical question. We examine the impact on employment in Table 7. Since we only have information on the months of working on self-employment activities (including farm and non- farm activities) from the follow-up survey, in columns (1) to (3), we replace the difference in outcomes in equation (1) by the outcomes in the follow-up wave.26 As shown, the average number of employed working days for households in the treatment villages increased by 8 days
(about 6 percent of the average employed working days in the control groups at baseline). Self-
26 We conduct the same exercise for all the other outcomes in Table 3 to Table 8. Only eight of them are of the opposite sign among the total 46 outcome variables, comparing with results using the specification in equation (1). This makes us more confident about the results reported in columns (1) to (3) in Table 7 and columns (1) and (2) in Table 9 (discussed later), for which we lack information at baseline. 24 employed working days decreased by 4 days. As a result, total working days increased by about
4 days for households in the treatment villages.
To get a sense of whether the estimated impacts on employment income are reasonable, we decompose the impacts on labor employment according to work location and account for wage differences. Columns (4) to (8) in Table 7 report impacts on labor employment of households by work location, which we define as ∆ . On average, the program increased employment in foreign provinces by 24 days for households in the treatment villages. It also increased employment in the home county and home province by 5 days and 8 days, respectively, although the impacts are not statistically significant. At the same time, the program decreased employment in home villages and home townships by 8 days and 4 days
(not significant), respectively. The bottom of Table 7 shows the average wage by work location, which we define as . The data demonstrates that wages differ by destination. Generally speaking, wages increase according to the distance from the home village. More specifically, the monthly wage at the home village averages 1,078 yuan at baseline. The average monthly wage in the home township, home county, and home province is similar and of about 1,200 yuan. The monthly wage in foreign provinces averages 1,510 yuan. A back-of-the-envelope calculation suggests that the impact on earnings from employment of a household in a treatment village is ∑ ∆ ∙ , namely, 1,349 yuan, which is comparable to the estimated impact of exposure to the program on employment income, i.e., 1,720 yuan (as shown in Table 4). The impact is about 35 percent (=1720/4847) of its mean value at baseline. To summarize, these results suggest that households in the treatment villages tend to allocate more of their labor force to remoter work locations, where they can earn a higher wage. This is consistent with the
25 findings of increased income from employment for households in the treatment villages.
Consumption and Poverty Reduction
One aim of the village banking program is to alleviate poverty. In Table 8, we examine impacts of the village banking program on income, poverty reduction, consumption, and saving.
As shown, among households in the treatment villages, income (the sum of pre-transfer income and other income, which is primarily income from transfers) increased by 4,395 yuan, which is about 37 percent of the income of households in the control villages at baseline. To calculate the poverty line in 2009 RMB, we adjusted the official poverty line by rural CPI and arrived at the adjusted poverty line, which is 2,220 yuan in 2009 RMB. A household is then defined as being under the poverty line if its per capita income is below the adjusted poverty line. The estimate in column (2) suggests 18 percentage points more households in the treatment villages crossed the poverty line than those in the control villages.27 These results suggest that the village banking program has significantly reduced poverty among households in the treatment villages.
Columns (3) to (6) examine the program impacts on consumption expenses. To calculate the flow of consumption emanating from expenses on durable or housing, we multiply the current value of durable goods or housing by ⁄(1 − ), and assume the service life of durable goods and housing is seven years and 20 years, respectively, where is the depreciation rate and equals to the inverse of years of service life. The estimates suggest that expenses increase
27 According to the old criteria of China’s poverty line, namely 1,067 yuan in 2008 price, the adjusted poverty line in 2009 was 1196 yuan. Using this old poverty line, the poverty rate at baseline in the control group was 24 percentage points, and the program reduced the poverty rate by 0.15 (with a standard error of 0.05). 26 for non-durable items, durable services, and housing services among households in the treatment villages, although the impact on non-durable items is insignificant. Total yearly expenditures and savings increase for households in the treatment villages, but neither is statistically significant. Table A5 in the Appendix shows details of the impacts on expenditures for non-durable items. Among households in the treatment villages, expenditures on medical needs and daily necessities increase substantially, but none of the effects is statistically significant.
To summarize, among households in the treatment villages, income increases and the poverty rate falls. Expenditures on non-durable items increase, but the increase is insignificant, while expenditures on durable goods and housing services increase significantly. The results are consistent with the findings of Ravallion and Chen (2005) that development projects in
China result in income gains, although the impact on consumption is not as strong.
Subjective Well-Being
Apart from the objective measures of living standards, we evaluate program impacts on subjective quality of life in Table 9. The measurements of subjective well-being examined include self-assessed quality of life, life satisfaction, and village officials’ assessments of the living standards of households. In the 2012 survey, the respondents of households were asked,
“How about the current quality of life of your household compared with two years ago?” and
“Are you satisfied with your life currently?” The answers to these questions were increasingly coded with results demonstrating better quality of life or life satisfaction. In addition, the survey asked the village officials, “How about the economic condition of this household in 2006?”
27
Similar retrospective questions were posed regarding the economic conditions of the household from 2007 to 2011. We calculate the average score for the assessment of economic conditions before 2011 and compare it with the score in the year after the program. The estimates in columns (1) and (2) suggest that self-assessed quality of life and satisfaction with life were significantly higher among households in the treatment villages than their counterparts in the control villages after the intervention.
One concern about the self-assessed quality of life or life satisfaction is that people may be unhappy about not receiving loans. On the one hand, this may cause an underestimation of the intention-to-treat effect if non-borrowers in the treatment village are aware they are not receiving loans. On the other hand, if those in the control villages were also aware of their non- treatment, it could overestimate the intention-to-treat effect. To address the concern, we examine the program impacts on households’ living standards through the assessments of village officials. While column (3) suggests no significant difference in living standards between households in the treatment and control villages before the program, column (4) shows that living standards among households in the treatment villages was significantly higher than that in the control villages after the intervention, according to the assessments of village officials. The difference-in-difference estimate in column (5) confirms the village officials’ assessment on the living standards of households in the treatment villages significantly increases as a result of the program.28
28 One may speculate that village officials in treatment village may have an incentive to under-report the quality of life of the households before the program and over-report it after the program. The results of insignificant difference in villager officials’ assessments on quality of life between the treatment and control groups suggests there is no clear under-reporting on quality of life. Moreover, we examine the correlation coefficients between expenses on durable services, which we considered as an objective measure of quality of life, and village officials' subjective assessment. The correlation coefficients are 0.1294 and 0.2785 in the two waves of the survey, respectively. These are high, especially for the correlation in the second year (remember the retrospective property may lead to more error in measurement of village officials’ assessment on baseline economic conditions). For reference, the autocorrelation of expenditure on durable services among the two waves is 0.2552. 28
5. Comparison of Microcredit Programs
In this section, we compare the results of the present study with those of the other six RCT- based studies published in the American Economic Journal: Applied Economics (2015), a similar RCT-based study by Karlan and Zinman (2011), and a study by Kaboski and Townsend
(2012). We summarize the estimated impacts on incomes and their components from the nine studies in Table 10.
For each outcome variable, we report the intention-to-treat (ITT) estimates, scaled by the mean of the variable in the control group, as a scale-free measure of the impacts. Panel A of
Table 10 shows that when all nine studies find positive impacts on self-employment income
(although some are economically insignificant), the impacts on employment income are quite different. Studies have shown that the employment income decreases in some countries, such as India, Mexico, Mongolia, Morocco, and the Philippines as a result of expanded access to exogenous credit, but increases in other countries, including China, Bosnia and Herzegovina,
Ethiopia, and Thailand. Particularly for China and Thailand, the increase in employment income is substantial. While some studies have found negative ITT impacts on total income in
India, Mongolia, and the Philippines, other studies have shown positive impacts, particularly in China and Thailand. The magnitude of ITT impacts on income is remarkable in Ethiopia as well, albeit statistically insignificant.29
29 The results are generally the same for the impact on labor supply. Expanding access to credit increases labor supply in self- employment income activities in all countries where data is available. While access to microcredit reduces labor supply in employment activities in countries such as Bosnia and Herzegovina, India, Mongolia, and Morocco, it increases labor supply in employment in China. There is no significant change in consumption in all countries except for China and Thailand. The results are available upon request. 29
Several potential explanations for lacking income effects of microcredit programs have been discussed in the literature, including low take-up rates or small differentials in take-up rates between the treatment and control groups; small interventions in terms of loan amounts; features of loan products, such as frequent repayments or high interest rates, which reduce risk- taking; lack of demand for credit given the availability of credit from other lenders; and the extent of labor market imperfection (Banerjee, 2013; Banerjee, Karlan, and Zinman, 2015;
Buerea et al., 2016; Cai, 2018; Cull and Morduch, 2017; Field et al., 2016). We first examine differences in the take-up rate and size of the program loan and provide alternative measures on income impacts to take account of these differences. Then, we empirically test the hypotheses that the impacts on income of a microcredit program are more substantial in places where program loans have lower interest rates, or less repayment frequency, or in areas with less available formal credit prior to intervention, or more serious constraints on engaging in employment activities. Lastly, we assess the possible explanations as to why China’s microcredit program can raise incomes and why there are no transformative impacts on income in some other countries.
Panel B of Table 10 shows that all of the RCT studies are based upon randomization on a regional level, except for Augsburg et al. (2015) and Karlan and Zinman (2011), in which randomly assigned marginal clients received a loan.30 Individual-level randomizations among marginal clients usually achieve a higher take-up rate differentials between treatment and control groups than regional-level randomization (Kalan and Zinman, 2010; Kalan and Zinman,
30 The information on the take-up rate, the randomization process, sampling frame, loan size as a proportion of (annual household) income, repayment frequency, and interest rate for studies (2) through (7) reported in Table 10 are adapted from Tables 1 and 2 in Banerjee, Karlan, and Zinman (2015). The other information in the table is summarized by ourselves from the corresponding studies. See the note of Table 10 for details. 30
2011).31 As shown in Table 10, the differences in take-up rate between the treatment and control groups in Bosnia and Herzegovina and the Philippines is 100% and 70%, respectively, which are much greater than that in the other studies. The take-up rates in programs using the same randomization method could still be different if they employ different sampling strategies.
As shown in Table 10, the take-up rate in the treatment villages of Mongolia is 57%, a rate much higher than other regional-level randomization. One reason is that the respondents in
Mongolia’s study were marginal clients, who are very likely to take loans from the programs.
However, sampling on potential borrowers may not necessarily increase the differences in the take-up rates of loans between treatment and control groups. Angelucci et al. (2015) used the same sampling strategy by focusing on “high potential” borrowers in their study in Mexico.
The difference in take-up rates between the treatment and control groups in their study is only
13.1%. The study in Morocco by Crepon et al. (2015) is another example, which focused on a sample of households deemed likely borrowers and turned out to obtain a modest take-up rate differential (17%).
Small intention-to-treat effect does not mean the effect of the treatment-on-the-treated
(TOT) is small as well. A big effect of the TOT could be diluted by a low take-up rate. Given such differences in the randomization method and sampling strategy, we thus divide the ITT estimates by the differential in take-up rates between the treatment and control groups to measure the TOT effects, assuming no spillovers or general equilibrium effects within the treatment villages. We then scale it by the mean in the control group at baseline to make it
31 Ideally, the estimates from individual-level randomization identify the effect of treatment-on-the-treated. But, it could be biased as a result of possible spillover effects on the control group, especially when treatment and control clients live in the same areas (Augsburg et al., 2015). Randomization on a regional level solves the endogeneity problem by internalizing any spillover or general equilibrium effects within community (Banerjee, Karlan, and Zinman, 2015). The identification is valid if we are only interested in the intention-to-treat effects. 31 comparable across studies.32
Table 10 shows that the size of program loans as a proportion of household annual income varies substantially across setting. It is substantial in some studies (such as in Ethiopia), whereas quite small in the others (such as Philippines and Mexico). To control for the differences in loan size, we normalize the ITT effects on income impacts by the average ITT impacts on program loans (as shown in Panel A of Table 10). It also takes account of the differences in take-up rates across studies. By examining this measurement, we can focus on the other characteristics of the loan terms and context of the study sites that determine the impacts of microcredit on income.
Table 11 tests the hypotheses we mentioned earlier by regressing the estimated program impacts (scaled by the average ITT on the amount of program loans) on the loan characteristics
(repayment frequency and interest rate) and the context of study sites (saturation of financial market and labor market friction). Panel A reports the results, using all the countries in Table
10 where data are available. The results in Columns (1) to (4) show that the program impacts on income are negatively correlated with repayment frequency, interest rate, share of households that borrowed from banks at baseline, and ratio of time on employment and self- employment activity (an indicator of labor market friction), which is consistent with our hypotheses. By looking at the correlation with income components in columns (8) and (12), we find the negative correlation between impacts on income and labor market friction is driven
32 A more fundamental question is why the take-up rate in treatment groups is so low in most of the studies. Theoretically, take-up of credit depends on the interest rate charged, the potential average return to capital, risk of return, and preference. Interest rates of the microcredit product are usually similar to, or cheaper than, market interest rates. Empirical studies verify that the return to capital in these less developed areas is typically high (De Mel et al., 2008; Urdy and Anagol, 2006). But the high average rate of return is usually associated with a large variance. Angelucci et al. (2015) found the distribution of profit from business is more dispersed in treatment groups than in control groups. Risk in production and incomplete insurance play an important role in hindering loan take-up and investment (Kalan et al., 2013). 32 mainly by the impacts on employment income. In Panel B, we drop the sample of China. The results are quite similar to the results in Panel A, suggesting the relationships we found in Panel
A are not driven by China. Figure 3 illustrates the scatters and linear predictions of the impacts on income by ratios of time spending on employment and self-employment activities across studies. The scatters justify the linear specification of regressions in Table 11, and the predicted slopes confirm that the imperfection of labor market affects the impact on income of microcredit access predominantly through earnings from employment activities.
To answer the question why estimated impacts of microcredit on income may be greater in China, we use an eyeball test to assess the importance of the aforementioned factors. The comparison in Table 10 shows that China has the greatest effects of TOT on employment income. The effect of TOT on self-employment income is also higher than many other countries.
These results suggest that the bigger ITT effect on employment and self-employment income found in China is driven primarily by the greater effects of TOT on income, particularly of employment income. The loan size in China is about half of household income, which is relatively high in all the studies. This may partially explain the bigger ITT impacts on income in China than in other countries. As shown in Panel B of Table 10, loans in China are paid annually, while in other countries loans are paid either monthly or weekly. The relatively longer periods for repaying loans allow borrowers to invest in more profitable projects that require more time to realize returns, such as planting cash crops or migrating to foreign provinces. The interest rate of the program loans in China is among the lowest in all of the nine studies (as shown in Panel B). The management of China’s microcredit program is based on a self- organized committee, which significantly reduces implementation costs. The program thus can
33 be sustainable under a relatively low interest rate. This makes the program loans more attractive for poor households and leads to higher net return. As shown in Table 10, some 13 percent of the households had borrowed from formal financial institutions at baseline in the sample of
China. This is relatively smaller than those in Mexico (28.8%), Mongolia (47.7%), and Bosnia and Herzegovina (30%). As mentioned previously, credit service from a formal financial institution is rarely accessible for households in poor rural China. Microfinance organizations are heavily regulated in China, rendering credit rationing much more severe. However, the availability of loans from formal financial institutions in China is not the lowest among all the nine countries. The share of households with loans from formal financial institutions is much lower in Ethiopia (2.6%), India (3.6%), and Morocco (6%) than in China. This suggests that the rare availability of formal credit may not be the primary reason for the success of the microcredit program in China. Table 10 also shows that the relative wages in employment and self-employment activity at baseline in China are higher than all the other countries except for
Mongolia and India. Meanwhile, the ratio of time spent on employment activities and self- employment activities in China is relatively lower than all the other countries except for
Ethiopia. These results suggest that the surveyed households in China are likely being constrained from engaging in employment activities, although the return to off-farm employment is high, which is verified in Cai (2018). Overall, the eyeball test suggests that a less frequent repayment schedule, lower interest rate, and higher return from credit-constrained off-farm employment are relatively important for China to realize strong impacts from the microcredit program on raising income and reducing poverty.
The reasons for no transformative impacts of microcredit on income in other countries
34 could be different from one another. For Bosnia and Herzegovina, the program loan size is relative small - only about 9 percent of income. The bank loans were already very popular at the study sites in Bosnia and Herzegovina before the program. Over half of the respondents received bank loans at baseline. The relative return to off-farm employment is also smaller than in China. For Ethiopia, the relative return to off-farm employment is smaller, and the loans are paid more frequently than in China. In India, the differences in take-up rate between the treatment and control groups may be too small to identify any ITT effects on income. The weekly repayment may also restrict efficient investment. In addition, an abundant share of labor had engaged in employment activities, indicating less friction in the labor market. Mexico has a relatively low take-up rate, small loan size, high repayment frequency, and high interest rate.
In Mongolia, nearly half of the respondents were accessible to formal loans before intervention.
Employment activities were already quite popular in Mongolia as well before intervention, although the relative wage in employment and self-employment activities were also high.
Morocco has a low take-up rate and high repayment frequency. In the Philippines, the loan size is quite small - only 3 percent of income. Weekly repayment and a high interest rate (60%) may weaken the program’s impacts. Meanwhile, the initial financial markets seem already saturated.
Some 40 percent of the respondents had borrowed from a bank before the intervention. In sum, different contextual factors may matter in different settings.
6. Heterogeneity of Treatment Impacts
We evaluate the heterogeneity of treatment impacts on the income of microcredit programs by exploring the variation in site context with our data in China. We estimate the following
35 specification:
∆ = + + + + + ∆ , (2) where indexes household, indexes village, indexes county, ∆ is the change in income and its components, and have the same definition as in equation (1). is the characteristics of the randomized strata (i.e., county) at baseline, including labor market imperfection or the saturation of the financial market. 33 Labor market imperfection is measured by the ratio of average time spent on employment activity and self-employment activity of sampled households in a county. The saturation of the financial market of a county is measured by the average share of households that borrowed from RCC in the sampled villages. The error term is clustered by village.
The parameter quantifies the heterogeneity of treatment impacts on income with respect to the characteristics of the context. As discussed earlier, the hypotheses predict that the program impacts decrease with the ratio of time spent on employment and self-employment activities and the saturation of the financial market. That is, < 0. We use OLS regression to estimate equation (2). To benchmark the program impacts, we normalized the level of variable by its median. Thus, the estimates of parameter indicates the median treatment effects. To address the concern of the small number of clusters, we also report the p-values of the randomization inference tests by performing 2,000 random permutations.
Table 12 reports the estimation results. As shown in Panel A, for households in a county with a median of the ratio of time spent on employment and self-employment activities, the
33 We are incapable to examine the heterogeneity of income impacts along repayment frequency or interest rate by using our data in China, since the programs are implemented under the guideline of the central government and there is not so much variation in these loan terms across counties. 36 program impacts on their self-employment income, employment income, and total income are significantly positive. The inferences are robust by adjusting for the degree of freedom. These results confirm our previous estimates of positive average treatment impacts on income. The negative coefficient of the interaction term in regression on total income indicates that the program impacts decrease with the ratio of time spent on employment and self-employment activities. The coefficient are significant at the 5% level if adjusted for the degree of freedom.
These are consistent with results in across-country comparisons, which suggest that the impact of the microcredit access on raising income is more substantial in areas with more serious friction in the labor market. Columns (2) and (3) suggest the heterogeneous impacts on income along labor market friction are driven essentially by the heterogeneity of impacts on employment income.
Panel B examines the heterogeneity of income impacts across financial markets with various degrees of saturation. The results confirm that the impacts of access to microfinance on income decrease with the initial saturation of the financial market, as found in cross-country analyses. These coincide with the conjecture that microcredit programs are less effective in increasing income if a place is already well accessible to loans from formal financial institutions. Given that loans from formal financial institutions are rarely used for employment activities, particularly for cross-province migration, this may explain that the heterogeneity of income impacts along the saturation of financial markets are driven mainly by impacts on self- employment income rather than employment income.
Overall, the analyses on the heterogeneity of the impacts of microcredit programs in China across various sites show results that are quite consistent with our meta-analyses by comparing
37 program impacts across studies. That is, the effectiveness of microcredit programs on raising income depends on the context of the sites. More specifically, the income impacts of the microcredit program is greater in a place where financial and/or labor markets are less perfect.
7. Conclusion
Using a randomized control trial, we conduct a systematic evaluation of the largest village banking program in the world in this study. We examine the average intention-to-treat effects on all households in the treatment villages. Randomized access to microcredit significantly increases the likelihood of borrowing among households in the treatment villages, as well as the amounts of loans, indicating that households are credit constrained, on average. Their investments in crop farming and animal husbandry substantially increase, while they also plant more cash crops. The increased investment leads to significant boosts in revenue and profit.
Unlike the study by Crepon et al. (2015), we find that increases in self-employment income are not offset by any decreases in employment income. The village banking program increases income for both self-employment and employment activities in China. As a result, total income increases, as well as expenses on consumption, particularly for durable goods and housing services. The improvement in quality of life is confirmed by strong positive impacts on subjective well-being.
38
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Figure 1: The Location of Sampled Provinces and Counties
Note: The figure shows the sampling of five provinces (Gansu and Sichuan in Western China; Henan and Hunan in Middle China; Shandong in
Eastern China), ten counties (two in each province).
47
Figure 2: Timeline of the Experiment and Survey
Source: Cai (2018).
48
Figure 3: Ratio of Time Allocated to Employment and Self-employment Activities
and Income Impacts
Panel A: self-employment income Panel B: employment income 4 4 India China Morocco 2 2 China Ethiopia
B&H 0 B&H Mongolia 0 Ethiopia Mongolia Morocco -2 -2 -4 -4 India Impact normalized by loan amount 0 .5 1 1.5 2 0 .5 1 1.5 2 Panel C: total income China 4 2 Ethiopia Morocco B&H 0 Mongolia India -2 -4
Impact normalized by loan amount 0 .5 1 1.5 2 Ratio of time allocated to employment and self-employment activities
Notes: The figure illustrates the scatters and linear prediction of program impacts on income by the ratio of time allocated to employment and self-employment activities in the studies shown in Table 10, where data are available. See Table 10 for details of the data and definition of the variables.
49
Table 1: Summary Statistics and Balance Tests of Household Baseline Characteristics
Control Control Mean Normalized t-test Baseline characteristics mean S.D. difference difference (T=C) (1) (2) (3) (4) (5) Observation unit: Household (# control hh=487, # treatment hh=735) Household composition Number of member 3.41 1.43 -0.16 -0.08 0.422 Number of adult male 1.31 0.61 -0.04 -0.05 0.472 Number of adult female 1.29 0.58 0.01 0.01 0.913 Number of children (age<18) 0.82 0.92 -0.12 -0.10 0.305 Male head 0.94 0.23 0.01 0.04 0.519 Head's age 51.96 11.18 0.87 0.05 0.539 Head's education: illiterate 0.22 0.42 0.01 0.01 0.894 Head's education: primary school 0.39 0.49 0.06 0.09 0.148 Head's education: junior high school 0.31 0.46 -0.05 -0.08 0.214 Head's education: senior high school or above 0.08 0.27 -0.02 -0.05 0.345 Average standardized difference (p-value) 0.684 Credit for production Access to formal loans 0.03 0.17 0.03 0.10 0.081 Access to informal loans with positive interest 0.01 0.11 0.01 0.05 0.535 Access to interest-free informal loans 0.16 0.37 0.02 0.03 0.711 Access to any loans 0.19 0.40 0.04 0.08 0.332 Amount of formal loans 650 5748 -155 -0.02 0.671 Amount of informal loans with positive interest 69 876 143 0.05 0.201 Amount of interest-free informal loans 1052 6290 -171 -0.02 0.694 Amount of any loans 1772 9133 -184 -0.02 0.767 Average standardized difference (p-value) 0.274 Income-generating activities Conduct crop farming 0.98 0.16 -0.02 -0.07 0.184 Conduct animal husbandry 0.79 0.41 -0.02 -0.03 0.783 Run a business 0.14 0.35 -0.02 -0.03 0.607 Engage in employment 0.51 0.50 -0.05 -0.07 0.289 Employment at home village (labor-days) 11.20 51.65 6.10 0.07 0.090 Employment at home township (labor-days) 16.86 62.51 -5.13 -0.06 0.286 Employment at home county (labor-days) 19.41 69.97 4.27 0.04 0.514 Employment at home province (labor-days) 24.00 79.19 -3.08 -0.03 0.703 Employment at foreign province (labor-days) 44.84 125.87 -15.51 -0.10 0.123 Average standardized difference (p-value) 0.174 Income profile Self-employment income 4126 9855 1 0.00 0.999 Employment income 4847 8426 -956 -0.09 0.217 Other income 3048 8770 -606 -0.05 0.487 Total income 12021 15252 -1561 -0.08 0.293 Income per capita 3865 5797 -319 -0.04 0.495 Poverty (dummy) 0.45 0.50 0.02 0.03 0.683 Average standardized difference (p-value) 0.236 Consumption Expenses on non-durable goods 11460 11755 -922 -0.06 0.466 Expenses on durable service 642 962 -51 -0.04 0.597 Expenses on housing service 1374 2039 -58 -0.01 0.871 Total expenses on consumption 13476 12498 -1031 -0.06 0.485 Average standardized difference (p-value) 0.548
Note: The observation unit is household. All monetary values are in 2009 RMB. Columns (1) 50 and (2) report the mean and standard deviation in control group at baseline, respectively.
Column (3) reports the difference in means between the treatment and control groups. Column
(4) reports the normalized difference computed as the difference in means between the treatment and control groups divided by the square root the sum of the variances in both groups
(Imbens and Wooldridge, 2008). The p-values in the last column are estimated by regressing the variable on a dummy of treated village, with standard errors clustered by village.
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Table 2: Impact on Credit for Production
Informal loans Difference between end-line with positive Interest-free and baseline Village bank Formal interest informal loans Total (1) (2) (3) (4) (5) Panel A: Credit access Treated village 0.23*** -0.03** -0.02 -0.02 0.15*** (0.04) (0.01) (0.02) (0.04) (0.05) p -values correcting for MHT 0.000 0.023 0.110 0.366 0.002 p -values correcting for RI 0.001 0.070 0.412 0.620 0.009 p -values wild cluster 0.001 0.091 0.327 0.612 0.009 Observations 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 0.00 0.02 0.01 0.07 0.10 Panel B: Loan amount Treated village 967*** 244 51 -531** 731* (191) (376) (138) (239) (405) p -values correcting for MHT 0.000 0.780 0.780 0.093 0.167 p -values correcting for RI 0.001 0.617 0.835 0.080 0.183 p -values wild cluster 0.000 0.543 0.726 0.047 0.108 Observations 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 0 648 123 885 1,657
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The monetary values are in 2009 RMB. The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling
52 methodology of Westfall and Young (1993) and treating columns (1) through (6) in each panel of this table separately as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al. (2008) with 2,000 replications.
53
Table 3: Impact on Pre-transfer Income
Household income Income per capita Self- Self- Difference between end-line employment Employment employment Employment and baseline income income Total income income Total (1) (2) (3) (4) (5) (6)
Treated village 2,430** 1,720* 4,151*** 679* 759*** 1,438*** (1,049) (1,018) (1,461) (381) (243) (439) p -values correcting for MHT 0.002 0.023 0.000 0.023 0.000 0.000 p -values correcting for RI 0.056 0.043 0.013 0.115 0.001 0.007 p -values wild cluster 0.063 0.146 0.023 0.130 0.003 0.009 Observations 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 5,166 7,773 12,940 1,728 1,946 3,674
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. Self- employment income is the sum of income from crop farming, animal husbandry, and small business. Employment income represents the earnings from the employment activities of all household members. The monetary values are in 2009 RMB. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through
(6) of this table as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for
54 degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al. (2008) with 2,000 replications.
55
Table 4: Impact on Crop Farming
Input expenses Sown area Sown area: Sown area: Difference between end-line Revenue Profit grain crops cash crops Chemical Hired labor, Irrigation, and baseline fertilizer rent capital herbicide etc. Total (1) (2) (3) (4) (5) (6) (7) (8) (9)
Treated village 0.27 -0.22 0.49* 54 -27 156 183 1,170* 988* (0.53) (0.39) (0.25) (83) (45) (108) (190) (613) (531) p -values correcting for MHT 0.664 0.664 0.012 0.664 0.664 0.080 0.352 0.012 0.014 p -values correcting for RI 0.641 0.635 0.047 0.575 0.659 0.152 0.409 0.040 0.070 p -values wild cluster 0.638 0.624 0.077 0.558 0.600 0.180 0.363 0.084 0.095 Observations 1,222 1,222 1,222 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 7.04 4.51 2.53 804 157 742 1,703 4,958 3,255
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. The monetary values are in 2009 RMB. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through (9) of this table as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al.
(2008) with 2,000 replications.
56
Table 5: Impact on Animal Husbandry
Input expenses Difference between end-line Medical Wages for Purchasing Revenue Profit and baseline Feedstuffs expenses hired labor new animals Other costs Total (1) (2) (3) (4) (5) (6) (7) (8)
Treated village 502** 19 2 101 76 700** 802*** 102 (217) (22) (3) (98) (86) (294) (293) (248) p -values correcting for MHT 0.444 0.790 0.790 0.775 0.790 0.439 0.412 0.790 p -values correcting for RI 0.032 0.538 0.768 0.480 0.860 0.060 0.032 0.764 p -values wild cluster 0.071 0.400 0.580 0.383 0.521 0.050 0.016 0.777 Observations 1,222 1,222 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 1,100 46 0 294 17 1,457 1,782 325
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. The monetary values are in 2009 RMB. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through (8) of this table as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al.
(2008) with 2,000 replications.
57
Table 6: Impact on Small Business
Any business Profit of business Any Profit of Difference between end-line in home outside in home outside business business and baseline village home village village home village (1) (2) (3) (4) (5) (6)
Treated village 0.02 -0.01 0.03 1,340 210 1,131 (0.03) (0.02) (0.02) (844) (328) (736) p -values correcting for MHT 0.781 0.781 0.298 0.267 0.781 0.283 p -values correcting for RI 0.574 0.740 0.212 0.209 0.557 0.241 p -values wild cluster 0.623 0.746 0.229 0.190 0.587 0.217 Observations 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 0.12 0.04 0.08 1,587 381 1,207
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. All monetary values are in 2009 RMB. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through (6) of this table as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al.
(2008) with 2,000 replications.
58
Table 7: Impact on Labor Supply
Working days at end-line Difference of employment working days between end-line and baseline by destination (∆L d ) Self- Home Home Foreign employment Employment Total Home village township Home county province province Total (1) (2) (3) (4) (5) (6) (7) (8) (9)
Treated village -3.69 8.07 4.38 -7.95** -3.56 5.34 7.94 24.33* 26.11 (18.54) (16.84) (18.93) (3.11) (4.65) (7.01) (5.51) (13.55) (16.04) p -values correcting for MHT 0.890 0.713 0.890 0.014 0.538 0.538 0.201 0.098 0.142 p -values correcting for RI 0.831 0.600 0.813 0.048 0.541 0.501 0.294 0.051 0.101 p -values wild cluster 0.872 0.682 0.86 0.036 0.529 0.563000005 0.210000008 0.135000011 0.187999991 Observations 1,222 1,222 1,222 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 260.31 129.95 390.26 11.79 20.56 22.52 26.54 48.54 129.95
Monthly wage at baseline (w d ) 1078 1222 1240 1265 1510
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. All of the variables of labor supply are measured at the household level. Self-employment working days represent the sum of self-employment days worked on farm and non-farm activities. Self-employment days worked on non-farm activities are constructed by subtracting employment working days on non-farm activities from the total days worked on non-farm activities. Both the self-employment time on farm activities and total time on non-farm activities were measured in months in the questionnaire. They were converted into days by assuming people work 20 days per month.
Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05,
59
* p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young
(1993) and treating columns (1) through (3) and columns (4) through (9) of this table separately as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild bootstrap method of Cameron et al.
(2008) with 2,000 replications. The monthly wage at each geographic location is predicted from the regression of monthly wages on the dummies of gender, age categories, education categories, geographic location, and village fixed effect.
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Table 8: Impact on Welfare
Total Poverty Consumption expenses Saving Difference between end-line income (dummy) Non- Durable Housing and baseline duable service service Total (1) (2) (3) (4) (5) (6) (7)
Treated village 4,395** -0.18*** 589 153** 456* 1,199 3,196 (1,874) (0.05) (1,097) (72) (230) (1,207) (2,194) p -values correcting for MHT 0.006 0.000 0.438 0.013 0.013 0.176 0.061 p -values correcting for RI 0.027 0.005 0.649 0.063 0.066 0.391 0.205 p -values wild cluster 0.062 0.010 0.640 0.068 0.096 0.369 0.215 Observations 1,222 1,222 1,222 1,222 1,222 1,222 1,222 Control mean at follow-up 16,659 0.32 12,404 563 1248 14,215 2,445
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. All monetary values are in 2009 RMB. The official poverty line is 2,300 yuan at 2010 prices. To calculate the poverty line in 2009 RMB, we adjusted the official line by rural CPI and arrived at the adjusted poverty line, which is
2,220 yuan in 2009 RMB. Expenses on non-durables include expenditures on food, daily necessities, repairs to the house, education, health insurance, taxes, and medical expenses.
Durable services and housing services are constructed by multiplying the current value of assets by d/(1-d), where d is the depreciation rate. We assume that the lifespan of durable goods is seven years, and the lifespan of housing services is 20 years. Thus, the depreciation rates for durable goods and housing services are 1/7 and 1/20, respectively. Saving is measured by the difference between income and consumption expenses. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through (7) of this table 61 as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild boostrap method of Cameron et al. (2008) with 2,000 replications.
62
Table 9: Impact on Subjective Well-Being
Self-assessment Village officials' assessment of household quality of life End-line data Standard of Satisfaction living with life Before 2011 2011 Difference (1) (2) (3) (4) (5)
Treated village 0.27*** 0.29*** 0.16 0.28* 0.12** (0.05) (0.07) (0.15) (0.14) (0.06) p -values correcting for MHT 0.000 0.000 0.065 0.004 0.004 p -values correcting for RI 0.000 0.001 0.340 0.097 0.012 p -values wild cluster 0.000 0.000 0.339 0.087 0.063 Observations 1,185 1,185 1,178 1,171 1,170 Mean in control 0.56 0.50 2.28 2.31 0.03
Notes: The table presents the coefficient of a treatment dummy in the regression of change in outcomes between the end-line and baseline. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values correcting for multiple-hypothesis testing (MHT) use the free step-down resampling methodology of Westfall and Young (1993) and treating columns (1) through (5) of this table as an outcome family. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations. The p-values of wild cluster are based on clustered standard errors computed by wild boostrap method of Cameron et al. (2008) with 2,000 replications.
63
Table 10: Comparison of Microfinance Programs
Bosnia and f Studiesa China Herzegovina Ethiopia India Mexico Mongolia Morocco Philippines Thailand (1) (2) (3) (4) (5) (6) (7) (8) (9) b Panel A: Impact on income Impact on self-employment income effect/control mean 0.47 0.01 0.68 0.48 0.07 0.18 0.22 0.15 0.17 (effect/control mean)/take-up rate 1.68 0.01 2.72 3.66 0.53 0.35 1.30 0.21 0.31 effect/impact on loan amount 2.51 0.05 1.39 3.18 1.11 -0.01 2.52 1.15 1.23 Impact on employment income effect/control mean 0.22 0.05 0.17 -0.18 -0.01 -0.61 -0.07 -0.13 0.36 (effect/control mean)/take-up rate 0.79 0.05 0.67 -1.35 -0.05 -1.20 -0.39 -0.18 0.67 effect/impact on loan amount 1.78 0.20 0.13 -4.73 -0.57 -0.69 -1.32 -2.81 1.26 Impact on pre-transfer income effect/control mean 0.32 0.03 0.54 -0.05 0.01 -0.56 0.04 -0.06 0.23 (effect/control mean)/take-up rate 1.15 0.03 2.14 -0.35 0.04 -1.10 0.23 -0.08 0.43 effect/impact on loan amount 4.29 0.25 1.53 -1.55 0.53 -0.70 1.20 -1.66 2.49 Panel B: Loan terms and site context T: 28% T: 100% T: 31% T: 18% T: 18.9% T: 57% T: 17% T: 72% h c 54% Take-up rate C: 0% C: 0% C: 6% C: 5% (SR) C: 5.8% C: 6% C: 0% C: 2%g
Randomization processc Villages Individuals Villages Clusters Clusters Clusters Clusters Individuals N.A. Random Marginal Random Constructed Potential Eligible women Households Marginally Random sample clients sample representaive borrowers willing to deemed likely creditworthy sample c Sampling frame in village in village sample in bastis participate borrowers applicants in village
Loan size as proportion of incomec 45% Aveg. 9% 118% 22% 6% 43% 21% 3% 15% Yearly Monthly Regularly Weekly Weekly Monthly Weekly to Weekly i Repayment frequencyb Yearly (not fixed) monthly Interest rate (annual percentage rate)c 9.4% 22% 12% 24% 110% 26.8% 14.5% 60% 7% Share of households with bank loans at baselined 13% 51.4% 2.6% 3.6% 28.8% 47.7% 6% 40.8% 26.1% Ratio of time allocated to employment and self- 0.50 1.02 0.26 1.69 N.A. 1.57 0.72 N.A. 0.27 employment activitiese Relative wage in employment and self-employment 2.35 1.10 1.50 2.38 N.A. 4.19 2.41 N.A. 1.78 activitiese
Notes:
64 a. (1) our study; (2) Augsburg et al. (2015); (3) Tarozzi et al. (2015); (4) Banerjee et al. (2015); (5) Angelucci et al. (2015); (6) Attanasio et al.
(2015); (7) Crepon et al. (2015); (8) Karlan & Zinman (2011); (9) Kaboski & Townsend (2012). For study (8), we also refer to Karlan & Zinman
(2009) – the working paper version – to obtain some of the numbers. See notes b, d, and e for details. b. For studies (1), (2), (3), (6), (7), and (9), income is measured as annual household income. For study (6), we use the estimates for impact of joint-liability lending (rather than individual lending), which is also their focus. In studies (4) and (5), income is measured as monthly household income. We multiply them by 12 to obtain annual household income. For study (4), we use the estimates of impacts by End-line 1. For study (8), self-employment income refers to profit in all household businesses in the month before the survey. Total income refers to household income in the last month. Employment income is computed as the difference between total income and employment income. The impacts on self-employment income are from their Table 5, and the impacts on employment income are inferred from their Tables 5 and 10. The loan amount is measured as the average impact on loan amount from the treating lender, reported in their Table 4. For study (9), the impact on self-employment income is measured by adding up impacts on business profits, rice farming, other crops, and livestock, as shown in Table 7 in Kaboski & Townsend (2012).
For easy comparison, we normalize the estimated effects on income by the control mean reported in the same table. We use the difference in take- up rates between the treatment and control groups to compute the normalized treatment-on-the treated effects. For both income components and their aggregation, we also report the estimated effects normalized by the average impact on the amount of program loans.
65 c. The information on take-up rate, randomization process, sampling frame, loan size as a proportion of (annual household) income, repayment frequency, and interest rate for studies (2) – (7) are from Tables 1 and 2 in Banerjee, Karlan, and Zinman (2015). We adopt the take-up rate in control groups (C) from each study based on the table that examines the impact on credit, and change some of the descriptions on the sampling frame for brevity. d. The numbers are from the table on baseline summary statistics in each study in columns (1) to (8). For study (1), access to formal credit is measured as the share of households that had ever borrowed from formal financial institutions over about one-and-a-half years prior to the baseline survey. For studies (2), (3), (4), and (6), it refers to the fraction of households that had at least one outstanding loan from a bank at the time of the baseline survey. For study (5), the variable refers to the percent of households that had ever borrowed a loan from a bank over the last two years before the baseline interview. For study (7), the variable refers to the share of households had an outstanding loan over the 12 months prior to the baseline survey. For study (8), the number is from Table 4 in Karlan & Zinman (2009), which refers to the fraction of respondents that have at least one outstanding loan from the formal sector in the month before the baseline survey. For study (9), the numbers is computed by us, the authors, from the data published by Kaboski & Townsend (2012). It refers to the percentage of households that had ever borrowed from any formal institutions (BAAC, agricultural coop credit, and commercial banks) in a year, averaged over a period of five years prior to the introduction of the program.
66 e. For studies (1), (2), (3), (4), (5) and (7), the ratios are calculated based on the control mean reported in tables that examine the impacts on income and labor supply in each study, and the wage ratio between employment and self-employment activities are imputed by dividing the ratio of income from employment and self-employment activities by the ratio of time spent on the two activities. For study (6), income is measured as the average profit of the respondent's enterprise and wage income in the control groups at baseline, reported in Table 1 of Attanasio et al. (2015), while the time ratio is calculated based on the control mean of hours worked on respondent's business and outside activities, in Table 5 of Attanasio et al.
(2015). For study (8), the ratio of income from employment and self-employment is calculated according to the numbers reported in Tables 5 and
10 in Karlan & Zinman (2009), where they show the average profit in all household business in the last month was 17,075 pesos and the household income in the month before the survey was 64,447 pesos. f. The information reported here is of joint-liability lending (rather than individual-liability lending) in Attanasio et al. (2015), which is also their focus. g. These numbers are calculated based on the text on page 1280 in Karlan & Zinman (2011), i.e., “there were 351 applications assigned out of the
1272 assigned to treatment that did not ultimately result in a loan. Conversely, there were five applications assigned to the control (329).” h. Every village in the sample of Kaboski & Townsend (2012) was treated by the program. On average, some 54% of the households received loans from the village fund in the two years after the starting of program (see their Table 1).
67 i. We obtain the information from page 16 in Buera et al. (2016), where they state a “single repayment at the end of the loan.”
68
Table 11: Across-country Regressions on Income Impacts
Impact standardized by loan amount Total income Self-employment income Employment income (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Panel A: sample includes China Repayment frequency -0.06** 0.01 -0.08** (0.03) (0.02) (0.02) Interest rate -9.20** -1.61 -7.58 (3.26) (2.72) (4.17) Share of households with bank loans -3.49 -4.93*** 1.45 at baseline (3.57) (1.09) (3.94) Ratio of time allocated to employment -2.92** -0.25 -2.67 and self-employment activities (1.00) (1.17) (1.36) Constant 2.25** 2.75** 1.56 3.64** 1.15 1.86** 2.67*** 1.85 1.10 0.89 -1.11 1.79 (0.85) (0.88) (1.09) (1.10) (0.68) (0.74) (0.33) (1.28) (0.73) (1.13) (1.20) (1.49) Observations 8 8 9 6 8 8 9 6 8 8 9 6 R-squared 0.513 0.571 0.120 0.681 0.058 0.055 0.745 0.011 0.669 0.356 0.019 0.491 Panel B: sample excludes China Repayment frequency -0.04 0.03 -0.07** (0.02) (0.02) (0.03) Interest rate -7.32** -0.98 -6.33 (2.46) (2.98) (4.40) Share of households with bank loans -2.02 -4.71*** 2.70 at baseline (2.92) (1.14) (3.73) Ratio of time allocated to employment -2.10*** 0.06 -2.15 and self-employment activities (0.30) (1.39) (1.53) Constant 1.36 1.96** 0.78 2.36*** 0.58 1.59 2.55*** 1.36 0.78 0.37 -1.76 0.98 (0.83) (0.71) (0.93) (0.36) (0.75) (0.86) (0.36) (1.64) (0.91) (1.27) (1.19) (1.80) Observations 7 7 8 5 7 7 8 5 7 7 8 5 R-squared 0.417 0.639 0.074 0.941 0.225 0.021 0.740 0.001 0.592 0.292 0.080 0.399 Notes: The unit of observation is country contained in Table 10. Panel A uses a sample including China, while Panel B uses a sample excluding
China. In columns (2), (6), and (10), we drop the observation of Mexico to reduce influence of its extreme value in interest rate. If we don’t exclude this observation, the estimates are -2.26 (SD=2.02, R2=0.152), -0.71(SD=1.21, R2=0.047), and -1.54 (SD=2.21, R2=0.064) for Panel A, and -1.33
69
(SD=1.67, R2=0.095), -0.44(SD=1.28, R2=0.019), and -0.88 (SD=2.22, R2=0.025) for Panel B. The reports the results of OLS estimates by regressing income impacts (scaled by the average ITT on the amount of program loans) on each of the following factor independently: repayment frequency, interest rate, s hare of households with bank loans at baseline, ratio of time allocated to employment and self-employment activities.
See Table 10 for details of the measurement of dependent and independent variables. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The numbers of observations vary across regressions because of data availability.
70
Table 12: Heterogeneity of Impacts on Income
Total pre- Self-employment Employment Difference between end-line and baseline transfer income income income (1) (2) (3) Panel A: heterogeneity by labor market friction Treated village 3,831*** 2,306** 1,525** (1,322) (1,053) (699) Randomization inference p-values 0.025 0.081 0.060
(County level ratio of employment to self-employment time - median value) * Treated village -3,194 4,254 -7,448*** (4,606) (3,647) (2,238) Randomization inference p-values 0.049 0.934 0.055
Observations 1,222 1,222 1,222
Panel B: heterogeneity by saturation of financial market Treated village 5,027*** 3,045** 1,983** (1,535) (1,164) (892) Randomization inference p-values 0.003 0.019 0.016
(County level share of households borrowed from RCC - median value) * Treated village -11,185 -11,983* 798 (7,822) (6,573) (4,224) Randomization inference p-values 0.000 0.001 0.999
Observations 1,222 1,222 1,222
Notes: The table presents the coefficient of a treatment dummy and its interaction with the ratio of average employment time to self-employment time of sampled households in a county subtracted from its sample median (Panel A) or with the share of households that borrowed from Rural
71
Credit Cooperatives subtracted from its sample median (Panel B) in the regressions of change in outcome between the end-line and baseline. Other control variables include village characteristics at baseline (type of geographic feature, population, area of arable land, and public spending on various village projects financed by superior governments). The monetary values are in 2009 RMB. The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The p-values of the randomization inference (RI) tests are based on clustered standard errors adjusted for degrees of freedom (Young, 2016) by performing 2,000 random permutations.
72
APPENDIX
Figure A1: Repayment Frequency and Income Impacts
Panel A: self-employment income Panel B: employment income 4 4 India China Morocco 2 2 China Thailand MexicoPhilippines Thailand B&H 0 MongoliaB&H 0 Mongolia Mexico Morocco -2 -2 Philippines -4 -4 India Impact normalized by loan amount 0 12 24 36 48 60 0 12 24 36 48 60
Panel C: total income China 4
Thailand 2 Morocco B&H Mexico 0 Mongolia PhilippinesIndia -2 -4
Impact normalized by loan amount 0 12 24 36 48 60 Repayment frequency
Notes: The figure illustrates the scatters and linear prediction of program impacts on income by the repayment frequency in the studies shown in Table 10, where data are available. See
Table 10 for details of the data and definition of the variables.
73
Figure A2: Interest Rate and Income Impacts
Panel A: self-employment income Panel B: employment income 4 4
India
2 ChinaMorocco 2 China Ethiopia Thailand Philippines Mexico Thailand 0 0 B&HMongolia EthiopiaB&H Mongolia Mexico Morocco -2 -2
Philippines -4 -4
Impact normalized by loan amount India 0 .25 .5 .75 1 0 .25 .5 .75 1
Panel C: total income
4 China
2 Thailand Ethiopia Morocco 0 B&H Mexico Mongolia
-2 India Philippines -4
Impact normalized by loan amount 0 .25 .5 .75 1 Interest rate
Notes: The figure illustrates the scatters and linear prediction of program impacts on income by the interest rate in the studies shown in Table 10, where data are available. See Table 10 for details of the data and definition of the variables.
74
Figure A3: Share of Households with Bank Loans at baseline and Income Impacts
Panel A: self-employment income Panel B: employment income 4 4 India MoroccoChina 2 2 China Ethiopia ThailandMexico Philippines Thailand B&H 0 MongoliaB&H 0 Ethiopia Mexico Mongolia Morocco -2 -2 Philippines -4 -4 India Impact normalized by loan amount 0 .2 .4 .6 0 .2 .4 .6
Panel C: total income China 4
Thailand 2 Ethiopia Morocco Mexico B&H 0 Mongolia India Philippines -2 -4
Impact normalized by loan amount 0 .2 .4 .6 Share of households with bank loans at baseline
Notes: The figure illustrates the scatters and linear prediction of program impacts on income by the share of households with bank loans at baseline in the studies shown in Table 10, where data are available. See Table 10 for details of the data and definition of the variables.
75
Table A1: Comparing Characteristics in the Study Sample and Universe
Panel A: County in study sample versus other poor counties in the selected provinces Other poor county in the Study sample Difference N N selected province p-value in mean (RCT) (universe) mean s.d. mean s.d. Number of villages 459 154 414 279 45 0.616 10 142 Number of poor villages 102 57 153 112 -51 0.158 10 142 Number of households 117932 38436 118458 84880 -526 0.985 10 142 Number of population 438311 111778 467104 311218 -28793 0.772 10 142 Area of arable land 583623 439808 630356 442586 -46733 0.747 10 142 Area of cultivated land 577751 442026 607735 434247 -29984 0.833 10 142
Panel B: Village in study sample versus other poor villages in the selected counties Other poor village in the Study sample Difference N N selected county p-value in mean (RCT) (universe) mean s.d. mean s.d. Number of households 269 130 239 118 29 0.089 50 1000 Number of population 1030 526 930 452 100 0.131 50 1000 Area of arable land 1625 2023 1483 1475 141 0.517 50 1000 Area of cultivated land 1583 2022 1474 1477 109 0.617 50 1000
Notes: Panel A compares the mean of the ten counties in the study and the other nationally-designated poor counties in the five provinces (Shandong,
Henan, Hunan, Sichuan, and Gansu), including the total number of villages, total number of poor villages, total number of households, total number of population, total area of arable land (mu), and total area of cultivated land (mu). Panel B compares the mean of the fifty villages in the study and the other poor villages in the ten counties in this study, including the number of households, number of population, area of arable land, and
76 area of cultivated land. The statistics are computed from the China Agriculture Census in 2007.
77
Table A2: Summary Statistics of Village Baseline Characteristics
Control Control Mean t-test Baseline characteristics mean S.D. difference (T=C) (1) (2) (3) (4) Observation unit: Village (# control village=18, # treatment village=27) Basic features Geographic features Mountainous (yes=1) 0.56 0.51 -0.04 0.813 Hilly (yes=1) 0.33 0.49 0.04 0.805 Plain (yes=1) 0.11 0.32 0.00 1.000 Population 1028 482 188 0.277 Number of households 270 119 39 0.340 Diameter distance of the village (unit: km ) 2.74 2.36 1.24 0.276 Share of households belonging to top three surnames 0.70 0.21 0.00 0.978 Ethnic minorities (yes=1) 0.11 0.32 0.00 1.000 Area of arable land (unit: mu ) 1490 1669 712 0.269 Labor market Share of labor force among the village population 0.47 0.09 0.02 0.590 Quality of labor force (share among the labor force) with senior high school education 0.13 0.10 -0.01 0.641 with junior high school education 0.31 0.17 0.07 0.234 Share of migrants among the labor force 0.40 0.21 -0.04 0.567 Geographic distribution of migrants (share among total migrants) within home county 0.28 0.24 0.04 0.575 within home province and outside home county 0.29 0.21 -0.05 0.473 outside home province 0.43 0.28 0.00 0.972 Daily wage of unskilled labor in the village (yuan ) 45.58 9.96 -0.36 0.903 Daily wage of skilled labor in the village (yuan ) 68.37 16.16 -3.31 0.526 Finacial market "Creditworthy village" rated by Rural Credit Cooperatives (dummy) 0.56 0.51 -0.04 0.813 Share of "creditworthy households" 0.37 0.30 -0.17 0.060 Share of households borrowed from Rural Credit Cooperatives 0.14 0.11 0.05 0.422 Loans per household from Rural Credit Cooperatives or banks (thousand yuan ) 2.44 1.55 -0.28 0.708 Share of households had deposit in Rural Credit Cooperatives or banks 0.47 0.34 -0.06 0.626 Infrastructure in village Households with electricity connection (%) 100.0 0.0 -0.5 0.071 Households with telephone connection (%) 77.1 21.4 -6.5 0.328 Households with TV set (%) 84.2 11.7 -3.8 0.414 Households with clean drinking water (%) 55.9 39.1 0.2 0.986 Households with sanitation toilet (%) 16.4 18.2 0.4 0.949 Households using straw/wood burning as main heating energy (%) 73.6 34.6 0.2 0.987 Natural villages with paved road (%) 56.4 44.9 0.8 0.953 Access to public service Distance to the home township government (unit: km ) 4.9 4.3 0.5 0.685 Distance to the nearest township government (unit: km ) 10.4 6.3 -0.3 0.889 Distance to the home county government (unit: km ) 28.1 11.5 -1.3 0.749 Distance to the nearest primary school (unit: km ) 2.6 4.1 -1.0 0.277 Distance to the nearest junior high school (unit: km ) 4.7 4.0 0.6 0.663 Distance to the nearest senior high school (unit: km ) 21.4 14.5 1.6 0.736 Distance to the home township hospital (unit: km) 4.8 4.0 1.0 0.446 Distance to the nearest Rural Credit Cooperatives or banks (unit: km) 4.8 4.0 0.4 0.749 Public spending financed by superior governments (thousand yuan) Telephone connection, audio broadcasting, cable television 21.2 70.0 -13.3 0.349 Energy (electricity, gas, etc.) 47.0 88.6 -14.2 0.518 Drinking water 56.9 145.3 -20.6 0.556 Irrigation and water conservancy 5.4 22.8 108.0 0.243 Land improvement 10.5 33.1 49.5 0.375 Enviroment improvement 0.0 0.0 26.6 0.392 Education 133.5 422.9 -124.9 0.131 Hospital and clean toilet 14.2 27.5 2.5 0.778 Others 4.2 18.0 4.3 0.489
78
Note: The observation unit is village. All monetary values are in 2009 RMB. 1 mu is equal to
667 square meters. Columns (1) and (2) report the mean and standard deviation in control group at baseline, respectively. Column (3) reports the difference in means between the treatment and control groups. The last column reports the p-value on testing the equality of means between the treatment group and the control group.
79
Table A3: Probit Regression on Attrition
Attrited (yes=1) Dependent Variable (1) (2) (3) (4)
Treatment 0.040 0.120 0.091 0.104 (0.146) (0.127) (0.112) (0.115) Characteristics of village N Y Y Y Characteristics of household head N N Y Y Characteristics of household N N N Y Observations 1,500 1,500 1,500 1,500
Notes: The dependent variable is a dummy, equal to 1 if households attrited between baseline and the second wave of the survey. The variables of village characteristics include type of geographic features, log of population, and log of arable land. The variables of household head include age and dummies indicating gender, minority, married, and education categories. The variables of household characteristics include household size, log of assets, a dummy indicating any outstanding loans, and a dummy indicating any employed member in the household at baseline. The standard errors in parentheses are clustered by village. *** p<0.01, ** p<0.05, * p<0.1.
80
Table A4: Robustness of Outlier
Baseline results Without truncation Without droping one county Outcome variables coef. p -values coef. p -values coef. p -values (1) (2) (3) (4) (5) (6) Number of observation 1,222 1,234 1,338 A. Credit (Table 3) Access to village bank loans 0.23 0.000 0.23 0.000 0.21 0.000 Access to formal loans -0.03 0.048 -0.03 0.050 -0.02 0.072 Access to informal loans with positive interest -0.02 0.243 -0.01 0.404 -0.01 0.314 Access to interest-free informal loans -0.02 0.603 -0.02 0.670 -0.02 0.564 Access to any loans 0.15 0.005 0.16 0.003 0.13 0.004 Amount of village bank loans 967 0.000 961 0.000 898 0.000 Amount of formal loans 244 0.521 -29 0.951 298 0.391 Amount of informal loans with positive interest 51 0.716 505 0.132 73 0.563 Amount of interest-free informal loans -531 0.032 -414 0.130 -364 0.137 Amount of any loans 731 0.078 1022 0.070 905 0.035 B. Pre-transfer income (Table 4) Self-employment income 2430 0.025 2414 0.160 2252 0.044 Employment income 1720 0.098 1749 0.087 1217 0.237 Total pre-transfer income 4151 0.007 4163 0.045 3469 0.018 Self-employment income per capita 679 0.082 564 0.183 740 0.055 Employment income per capita 759 0.003 759 0.003 634 0.015 Pre-transfer income per capita 1438 0.002 1323 0.009 1375 0.002 C. Crop farming (Table 5) Sown area 0.27 0.616 0.26 0.637 0.22 0.656 Sown area: grain crops -0.22 0.569 -0.23 0.551 -0.20 0.575 Sown area: cash crops 0.49 0.060 0.49 0.065 0.42 0.082 Expenses on chemical fertilizer 54 0.523 42 0.629 24 0.747 Expenses on hired labor, rent capital -27 0.549 -27 0.551 -23 0.565 Expenses on irrigation, herbicide etc. 156 0.155 149 0.176 114 0.262 Total expenses 183 0.341 165 0.408 115 0.516 Revenue 1170 0.063 1199 0.055 980 0.127 Profit 988 0.069 1035 0.057 865 0.114 D. Animal Husbandry (Table 6) Expenses on feedstuffs 502 0.026 1049 0.014 243 0.303 Expenses on medical expenses 19 0.391 31 0.401 9 0.615 Expenses on wages for hired labor 2 0.518 2 0.511 1 0.824 Expenses on purchasing new animals 101 0.305 8 0.943 28 0.817 Expenses on other inputs 76 0.379 75 0.381 68 0.385 Total expenses 700 0.021 1166 0.011 349 0.242 Revenue 802 0.009 2127 0.043 499 0.156 Profit 102 0.684 962 0.306 150 0.604 E. Labor supply in labor days (Table 7) Self-employment (endline) -3.69 0.843 -3.16 0.867 -8.12 0.625 Employment (endline) 8.07 0.634 8.75 0.599 3.21 0.841 Total labor supply (endline) 4.38 0.818 5.59 0.770 -4.92 0.780 Employment at home village -7.95 0.014 -8.60 0.009 -7.71 0.022 Employment at home township -3.56 0.448 -3.37 0.471 -3.29 0.480 Employment at home county 5.34 0.450 5.19 0.462 6.66 0.289 Employment at home province 7.94 0.156 7.44 0.194 4.77 0.367 Employment at foreign province 24.33 0.079 25.45 0.066 18.39 0.193 Total employment 26.11 0.111 26.11 0.106 18.82 0.296 F. Welfare (Table 8) Total income 4395 0.024 4457 0.055 3362 0.072 Poverty (dummy) -0.18 0.002 -0.18 0.002 -0.16 0.006 Expenses on non-durable goods 589 0.594 239 0.838 430 0.664 Expenses on durable service 153 0.039 127 0.093 96 0.232 Expenses on housing service 456 0.053 402 0.105 194 0.500 Total expenses on consumption 1199 0.326 768 0.560 719 0.530 Savings 3196 0.152 3689 0.172 2643 0.196 G. Subjective well-being (Table 9) Self-assessment on quality of life 0.27 0.000 0.27 0.000 0.23 0.000 Self-assessment on satisfaction with life 0.29 0.000 0.28 0.000 0.26 0.000 Village officials' assessment of quality of life 0.12 0.048 0.11 0.053 0.10 0.058
Notes: The p-values are based on clustered standard errors. 81
Table A5: ANOCA Analyses
Westfall-Young randomization p -values based adjusted inference - on clustered p Outcome variables coef. s.e. p -values values standard errors as in the text (1) (2) (3) (4) (5) A. Credit (Table 3) Access to village bank loans 0.23 0.04 0.000 0.000 0.001 Access to formal loans 0.00 0.01 0.804 0.871 0.814 Access to informal loans with positive interest 0.00 0.01 0.691 0.871 0.671 Access to interest-free informal loans 0.02 0.01 0.227 0.388 0.361 Access to any loans 0.23 0.04 0.000 0.000 0.000 Amount of village bank loans 967 191 0.000 0.033 0.001 Amount of formal loans -13 286 0.964 0.965 0.975 Amount of informal loans with positive interest 181 156 0.254 0.442 0.437 Amount of interest-free informal loans -395 242 0.110 0.365 0.167 Amount of any loans 739 455 0.111 0.365 0.161 B. Pre-transfer income (Table 4) Self-employment income 1789 898 0.052 0.009 0.139 Employment income 999 1071 0.356 0.156 0.259 Total pre-transfer income 2649 1450 0.074 0.016 0.067 Self-employment income per capita 489 319 0.133 0.044 0.210 Employment income per capita 584 287 0.047 0.009 0.027 Pre-transfer income per capita 1021 429 0.022 0.002 0.028 C. Crop farming (Table 5) Sown area 0.00 0.44 0.992 0.987 0.993 Sown area: grain crops -0.26 0.34 0.455 0.512 0.504 Sown area: cash crops 0.18 0.19 0.351 0.436 0.411 Expenses on chemical fertilizer 118 64 0.072 0.031 0.071 Expenses on hired labor, rent capital -6 37 0.876 0.966 0.892 Expenses on irrigation, herbicide etc. 193 106 0.076 0.031 0.050 Total expenses 289 162 0.081 0.033 0.076 Revenue 1430 629 0.028 0.004 0.024 Profit 1137 532 0.038 0.008 0.045 D. Animal Husbandry (Table 6) Expenses on feedstuffs 336 207 0.112 0.452 0.334 Expenses on medical expenses -10 16 0.544 0.823 0.569 Expenses on wages for hired labor 0 2 0.913 0.955 0.935 Expenses on purchasing new animals 19 94 0.841 0.955 0.821 Expenses on other inputs 75 86 0.386 0.726 0.869 Total expenses 472 337 0.169 0.470 0.394 Revenue 704 502 0.168 0.470 0.291 Profit 344 219 0.123 0.456 0.133 E. Labor supply in labor days (Table 7) Self-employment (endline) -3.69 18.54 0.843 0.890 0.831 Employment (endline) 8.07 16.84 0.634 0.713 0.600 Total labor supply (endline) 4.38 18.93 0.818 0.890 0.813 Employment at home village -2.43 3.11 0.439 0.511 0.485 Employment at home township -2.43 3.11 0.439 0.511 0.420 Employment at home county -6.14 4.81 0.208 0.255 0.301 Employment at home province 7.90 5.91 0.188 0.255 0.169 Employment at foreign province 10.72 6.21 0.092 0.112 0.954 Total employment 0.60 12.41 0.962 0.944 0.272 F. Welfare (Table 8) Total income 1731 1660 0.303 0.155 0.494 Poverty (dummy) -0.09 0.05 0.069 0.012 0.147 Expenses on non-durable goods -461 1174 0.696 0.543 0.717 Expenses on durable service 129 95 0.182 0.077 0.160 Expenses on housing service 309 212 0.151 0.055 0.159 Total expenses on consumption -14 1266 0.991 0.984 0.993 Savings 2154 1926 0.269 0.138 0.294 G. Subjective well-being (Table 9) Self-assessment on quality of life 0.27 0.05 0.000 0.000 0.000 Self-assessment on satisfaction with life 0.29 0.07 0.000 0.000 0.001 Village officials' assessment of quality of life 0.13 0.06 0.031 0.001 0.008
Notes: Columns (1) and (2) report the coefficients and standard errors (clustered by village) from ANOCA analyses. Column (3) reports the p-values based on clustered standard errors as 82 in the text. Column (4) reports the p-values using the free step-down resampling methodology of Westfall and Young (1993). Column (5) reports the p-values based on clustered standard errors adjusted for degrees of freedom (Young, 2016).
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