Natural Resource Shocks and Fertility in Indonesia

Drill, Baby, Drill: Natural Resource Shocks and Fertility in Indonesia

Margaret E. Brehm∗1 and Paul A. Brehm*1

1Oberlin College

August 11, 2021

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Abstract We find that positive natural resource shocks lead to increased fertility in Indonesia by exploiting temporal variation in world oil prices and cross-sectional variation in oil endowments across regencies. The fertility results are driven by women of all ages, by both first and higher order births, and we find no evidence of changes in birth spacing. Altogether, this indicates an increase in completed fertility. We present empirical evidence and cite prior literature demonstrating corresponding increases in households’ economic outcomes, consistent with positive income effects on fertility in a developing economy. We also find evidence of an increase in the likelihood of a male birth among older women, suggesting that some of the increase in total fertility among older women is driven by averted fetal losses.

JEL Classiﬁcation Codes: J13, J24, Q31, Q33, R2

Keywords: Natural Resource Shocks, Income Eﬀects, Fertility, Sex Ratios

*Department of Economics, Oberlin College and Conservatory, 233 Rice Hall, Oberlin, OH 44074; email [email protected] (corresponding author) and [email protected]. We thank Traviss Cassidy for data assistance, Martin Saavedra for title assistance, and Natsumi Osborn, Anna Slebonick, Sun Moon, Juliet Flam-Ross, and Joe Schermer for excellent research assistance. We also thank two anonymous referees for helpful comments and sug- gestions. Some data used in this project was made available through the Minnesota Population Center. The authors wish to acknowledge the statistical oﬃce, BPS (Statistics Indonesia), that provided the underlying data making this research possible. We also acknowledge funding from Oberlin College for data from Rystad Energy. All errors are our own.

1 1 Introduction

We use the natural experiment of local resource booms and busts in Indonesia as an opportunity to study the response of family formation outcomes to economic conditions. Our analysis is motivated by several studies documenting the eﬀects of resource booms on wage and employment growth (Black et al., 2005; Jacobsen and Parker, 2016; Marchand, 2012; Michaels, 2010), and the long literature studying the relationship between income and wealth and the demand for children

(Heckman and Walker, 1990; Clark, 2005; Galor, 2005; Amialchuk, 2013; Bar and Leukhina, 2010;

Black et al., 2013; Lindo, 2010; Kearney and Wilson, 2018).

In this paper, we estimate the eﬀect of economic shocks through oil booms and busts on fertility using Indonesian Census data from 1970 to 2008. Our analysis compares women’s fertility across regions experiencing a more or less severe boom or bust, depending on the local economy’s oil intensity. Our identiﬁcation strategy exploits the interaction between exogenous time series variation in global oil prices and regional variation in resource abundance. For the same change in the world price of oil, regions with more oil experience larger shocks than regions with less oil. Our proxy for resource abundance is the size of preexisting oil endowment in a regency. We argue the economic shocks are independent of unobserved factors correlated with the fertility decision because oil endowments depend on geology and the oil price is set on the world market and exogenous to individuals’ fertility decisions.

We find that an increase in oil endowment value per capita increases the probability of having a child. The effect is driven by an increase in fertility among women of all ages and by both first and higher parity births. We find no evidence that oil endowment value per capita affects birth spacing.

Taken together, these results suggest the overall increase in fertility is not only driven by women having desired children sooner, but also reflects an increase in completed fertility. Because male fetuses are at greater risk of death before birth, we provide indirect evidence of whether the increase in fertility is driven by improvements in fetal health by estimating whether the oil endowment value per capita affects the likelihood of male births.1 We find an increase in the likelihood of a male birth among older women. However, we conclude that the majority of the overall increase in fertility among older women is due to an increase in total fertility, with about one third of the effect among

1Male fetuses have higher miscarriage rates and the sex ratio falls from conception to birth from about 120 male to 100 female conceptions to roughly 105/100 (Kraemer, 2000).

2 older women explained by averted fetal male losses.2

We also explore several mechanisms through which oil shocks may affect fertility. We use supplemental data from Indonesia’s labor force surveys, socioeconomic household surveys, and village census to estimate effects of oil endowment value per capita on labor market outcomes, government expenditures, and proxies for infrastructure, economic development, and healthcare access. We find evidence that an increase in the oil endowment value per capita has a positive effect on households’ economic outcomes, indicating that a positive income effect is an important mechanism through which fertility responds to oil shocks.

Prior work has found both positive and negative relationships between measures of economic prosperity and fertility. In an unpublished manuscript, Cotet Grecu and Tsui (2009) use worldwide discoveries and production of oil to estimate the effect of oil wealth on development, and find some evidence oil abundance positively affects fertility rates. There is also empirical evidence of a negative relationship between income and fertility using time-series data; several papers have documented the link between higher incomes as a result of the industrial revolution and significant declines in fertility (the “Fertility Transition” or “Demographic Transition”) (Clark, 2005; Galor,

2005; Jones and Tertilt, 2008; Bar and Leukhina, 2010). Heckman and Walker (1990) and Schultz

(1985) provide evidence of a negative relationship between incomes and fertility due to higher women’s wages and a substitution eﬀect. Finally, a general ﬁnding using cross-country data is a clear negative relationship between income and fertility (e.g., Dasgupta, 1995).

More recent work has sought to identify causal eﬀects using exogenous shocks to male income created by the 1970’s West Virginia coal boom, local-area fracking production, or job displacement to estimate the income eﬀect on fertility in the United States (Black et al., 2013; Kearney and

Wilson, 2018; Lindo, 2010; Amialchuk, 2013). Lovenheim and Mumford (2013) and Dettling and

Kearney (2014) use wealth shocks driven by housing market variation in the United States to estimate the eﬀect of family resources on fertility. Schaller (2016) and Autor et al. (2018) show shocks to mens’ labor market demand are associated with changes in fertility in the United States.

These papers find positive income effects on births and that changes in male labor market conditions and fertility move in tandem. Our results are consistent with these findings.

We contribute to the existing literature by demonstrating positive natural resource shocks

2Point estimates suggest it is between roughly 21 percent and 43 percent.

3 lead to increased fertility and providing additional evidence that our findings are consistent with positive income effects and children being normal goods. Our findings are interesting in light of declining fertility rates during demographic transitions and the negative cross-country income-fertility relationship. Our paper builds on related prior work in the US in several key ways. Most importantly, Indonesia is a very different context from the United States; local economies in Indonesia depend to a larger extent on natural resources and the United States is considerably wealthier.

We also provide evidence that the overall increase in fertility is explained mostly by an increase in total fertility, with no changes in birth spacing and with some of the effect among older women explained by averted miscarriages. This latter finding provides indirect evidence of the role of improved maternal and fetal health in estimates of increases in fertility rates and is consistent with prior work showing external stressors, including job loss and economic contraction, are related to the proportion of male births (Sanders and Stoecker, 2015; Halla and Zweimüller,2014; Catalano et al., 2010; Mathews et al., 2008; Catalano, 2003). The existing literature on economic shocks and fertility has not yet explored the role of improved fetal health in estimated increases in fertility rates.

The setting and data availability also confer two uncommon advantages onto our analysis.

Our long sample period (1970-2008) allows us to consider the eﬀects of multiple booms and busts, and whether there are symmetric responses. Additionally, our data directly identiﬁes women who did and did not migrate, allowing us to rule out bias in our estimates from selective migration.

There is also a large literature studying the eﬀects of resource booms and supply-side shocks on a variety of outcomes, including wages, income, and employment (Allcott and Keniston, 2017;

Black et al., 2005; Feyrer et al., 2017; Munasib and Rickman, 2015; Bartik, Currie, Greenstone, and Knittel, Bartik et al.; Maniloﬀ and Mastromonaco, 2017; Weber, 2014; Paredes et al., 2015;

Cust et al., 2019), education (Løken, 2010; Cascio and Narayan, 2015; Marchand et al., 2015), housing values (Muehlenbachs et al., 2015), spending (Acemoglu et al., 2013), crime (James and

Smith, 2017), and civil conﬂict and political violence (Angrist and Kugler, 2008; Dube and Vargas,

2013; Cotet and Tsui, 2013a) and growth (Cotet and Tsui, 2013b). There is some work on resource booms specifically in the Indonesian context. Cassidy (2020) studies district fiscal responses to shared revenue from local oil and gas production in Indonesia, exploiting a policy reform and variation in resource endowments. Cust et al. (2019) find positive effects of oil and gas “windfalls”

4 in Indonesia on employment, wages, and labor productivity.

Our paper also relates to research on family formation decisions and macroeconomic conditions. A long literature has documented that fertility is procyclical, with decreases in births during economic downturns, measured through changes in GDP, consumer conﬁdence, or unemployment

(e.g. Yule, 1906; Galbraith and Thomas, 1941; Silver, 1965; Ben-Porath, 1973; Sobotka et al., 2011;

Currie and Schwandt, 2014; Chatterjee and Vogl, 2018; Buckles et al., 2018).

The paper proceeds as follows. Section 2 provides background on oil production and fertility in Indonesia. Section 3 discusses the possible eﬀects of local economic shocks on fertility and mechanisms. Section 4 details the data and Section 5 outlines our empirical strategy. Section 6 presents our main results. Section 7 then empirically explores many of the mechanisms discussed in Section 3. Finally, Section 8 places our ﬁndings in context with prior work and concludes.

2 Institutional Background

Indonesia is currently the fourth most populous country in the world and has experienced both strong economic growth and rapid fertility decline over the last several decades.

Figure 1 shows real world oil prices from 1970 to 2008. Oil prices were quite volatile, increasing over ﬁve-fold from 1970 to 1980. Prices then decreased, reaching a low in 1997, then quintupled again over the next nine years.

Figure 1 also illustrates total oil production in Indonesia over the same period. During the mid-1970’s, crude oil production rose at an average annual rate of over 17 percent between 1968 and

1973 (Arndt, 1983). Subsequently, a rapid increase in the world oil price in the 1970’s substantially increased oil export revenues, which doubled in 1973 and again doubled in 1974. In the 1980’s,

Indonesia experienced a slowdown in economic growth due to the sharp reductions in oil prices. By then, Indonesia had become quite dependent on oil; the oil sector accounted for 78 percent of total exports and oil company taxes contributed 70 percent of domestic government revenues (Arndt,

1983). Oil production steadily declined over the 1980’s. Oil production increased again starting in the mid 1980’s and was then steady until about 2000, after which production has again steadily declined. The decline is attributed to a lack of investment in the sector and decreasing exploration

(Roach and Dunstan, 2018).

5 Figure 1: Total Oil Production and Real Oil Prices, 1970-2008 600 100 80 500 60 400 40 Real oil price ($) price oil Real 300 20 Total of barrels) (millions production oil 0 200 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year

Oil production Oil price

Oil production come from UCube database of Rystad Energy. Oil prices come from FRED, Federal Reserve Bank of St. Louis. Oil prices are inﬂated to 2010 dollars using the CPI-U.

Figure 2 provides a map of known oil endowments by asset across Indonesia in 1970. In many of these regions, oil production plays an important role in the local economy. In these areas, changes in the oil price would be expected to have a large economic impact. With large variation across Indonesia in the importance of oil to the local economy, diﬀerent areas experienced shocks of varying size due to changes in oil prices.

Figure 2: Oil Endowments by Asset, 1970

Oil endowment data are from Rystad Energy’s UCube database.

During the oil boom in the 1970’s, localities with and without oil production experienced an

6 increase in resources for economic and social development. This was reﬂected in increased salaries of civil servants and school teachers, subsidies to provincial governments, and expenditures on social welfare programs (Arndt, 1983). Revenues increased by a factor of six between 1972 and

1975, with most oil tax revenue being collected by the central government. Spillover effects to non oil-producing regions is due to the distribution of central government oil revenues to provincial, regency, and village-level governments, governed largely by the INPRES (presidential instructions) program (Bank, 1990). The INPRES program was established in 1973, coinciding with the first oil price shock and start of an oil boom. INPRES grants provide financing for projects related to regional development and are directed towards specific types of infrastructure (Bank, 1990).

The largest grant provided funding for the construction of a school in every village in the country

(Booth, 2011; Duﬂo, 2001). Another provided funding for primary care health centers (Booth,

2011). Indonesia also focused on rural development, investing oil revenues in agriculture and transportation (Agrawal, 1999).

Following government decentralization in 2001, a new intergovernmental revenue sharing ar- rangement was implemented. The sharing rule redistributes net oil revenues from an oil-producing regency, with 84.5 percent going to the central government, 3.1 percent going to the provincial government, 6.2 percent to the producing regency, and 6.2 percent divided equally among other regencies in the same province (Agustina et al., 2012).3

Over the 1970’s, Indonesia experienced strong economic growth, with gross domestic product increasing on average 8 percent per year (Arndt, 1983). Classiﬁed by the World Bank as one of the world’s poorest countries at the start of 1970’s, Indonesia was listed as a middle-income country by 1981 (Arndt, 1983). Subsequently, with the decline in oil prices (and oil revenues) during the

1980’s, the government made expenditure cuts (Booth, 2011).

In addition to experiencing strong economic growth over the sample period, Indonesia has also seen rapid fertility declines. Total fertility rates declined 22 percent from 1970 to 1980, then another 25 percent in the next nine years, with fertility rates continuing to fall through the 1990’s

(Gertler and Molyneaux, 1994; Molyneaux and Gertler, 2000). The government has played a role, implementing the National Family Planning Coordinating Board (BKKBN).

3There are special autonomy laws for Nanggroe Aceh Darussalam and Papu. These regions provide 30 percent of revenue earned from oil resources to the center and keep the remaining 70 percent (Resosudarmo, 2005).

7 The BKKBN promotes two-child families by advocating delayed marriage and contraceptive use. The program began in Java and Bali in 1969, then expanded to ten additional provinces in 1974, reaching all provinces by 1980 (Hull et al., 1977; Gertler and Molyneaux, 1994). The BKKBN’s family planning activities were not uniform across the country, but were instead designed speciﬁcally to address local needs and conditions, and the family planning campaign was also dispersed through radio and television (Gertler and Molyneaux, 1994). Gertler and Molyneaux (1994) estimate that the family planning program explains only 4 to 8 percent of the fertility decline. Instead, the vast majority of the decline has been attributed to improvements in women’s education and labor market opportunities, rather than the family planning programs (Gertler and Molyneaux, 1994;

Angeles et al., 2005; Hull and Hatmadji, 1990; Hull et al., 1977; McNicoll and Singarimbun, 1983;

Pitt et al., 1993).

3 Possible Eﬀects on Fertility and Mechanisms

There are several channels through which local economic shocks may aﬀect fertility decisions.

In this section, we discuss mechanisms for shifts in demand for children in both the short and long-run, and propose alternate explanations for changes in fertility rates that are not due to shifts in demand.

3.1 Short-Run Shifts in Demand

First, there may be an income effect. Households may experience increased income either through changes on the extensive margin, with increases in employment, or through changes on the intensive margin, with increases in wages. An increase in household prosperity would result in higher fertility if children are “normal goods.” The empirical evidence on the income effect and fertility is mixed. In the United States, Jones and Tertilt (2008) find a strong negative relationship between income and fertility from the 1820’s to 1960’s. Several papers have also documented the link between higher incomes as a result of the industrial revolution and significant declines in fertility

(Clark, 2005; Galor, 2005; Bar and Leukhina, 2010). More recent work, however, has focused on causal relationships between income and fertility and documented a positive income eﬀect (Kearney and Wilson, 2018; Black et al., 2013; Dettling and Kearney, 2014; Lovenheim and Mumford, 2013;

8 Lindo, 2010; Amialchuk, 2013; Schaller, 2016; Autor et al., 2018). Also investigating fertility eﬀects of natural resource shocks, Black et al. (2013) shows the 1970’s West Virginia coal boom had a large positive shock on male incomes and increased fertility and Kearney and Wilson (2018) shows shocks to male income generated by the fracking boom in the 2000’s also increased fertility.4

Local economic shocks may also have short-run effects on women’s wages that result in an opposite-signed substitution effect. That is, an increase in women’s wages increases the opportunity cost of not working, and could mitigate any positive income effects (Becker, 1960). Several papers provide evidence that increased women’s wages, which increase the value of time working, result in a substitution effect that reduces fertility. Schultz (1985) estimates the effect of women’s relative wages on fertility in Sweden using the relative increase in the price of butter, which improved women’s wages. He finds the relative increase in the value of women’s time explains a quarter of the decline in fertility from 1860 to 1910. Galor et al. (1996) models the relationship between higher relative wages for women and fertility, showing higher relative wages for women lead to a reduction in fertility because the price of children increases by more than higher wages raise household income.

It is possible that income and/or substitution eﬀects occur within the broader population and are experienced by households whose family members work in a range of sectors not directly related to oil production. Cust et al. (2019) provide empirical evidence of such “spillover eﬀects,”

ﬁnding oil and gas shocks in Indonesia increase wages and employment in a range of sectors. We explore heterogeneous fertility eﬀects of oil shocks by sector below.

Oil shocks may aﬀect the indirect costs of raising children through changes in children’s labor market participation and their wages. Over two-thirds of working children ages 10-15 in Indonesia work in agriculture (Kis-Katos and Sparrow, 2011). If increases in the return to children are driving our results, we would expect the largest eﬀects to be in the agricultural sector.

It is also possible oil shocks aﬀect demand for children through channels other than higher wages. Oil shocks may aﬀect government revenues and subsequent expenditures and the provision of public goods in the short-run. While changes in government expenditures might be more likely

4Black et al. (2013) estimate the eﬀect of lagged earnings on marital birthrates, instrumenting for lagged earnings with the lagged value of coal reserves. Kearney and Wilson (2018) provides reduced-form estimates of the eﬀect of lagged simulated new fracking production on birthrates, and also provide estimates from an IV regression in which they instrument for lagged earnings with lagged simulated fracking production.

9 to aﬀect fertility in the long-run, we do not preemptively rule out the possibility of short-run effects. Following the oil boom in the early 1970’s, government expenditures in Indonesia increased

(Arndt, 1983). Additionally, El-Katiri et al. (2011) find that oil revenues in Kuwait increase public employment and transfers and subsidies for healthcare, education, and goods and services, including electricity, water, food, and housing. Economic development, improvements in infrastructure, healthcare quality and access, and the provision of public services and social programs could affect fertility rates.5 In particular, government expenditures on campaigns that increase incomes or reduce the cost of raising a child could increase the demand for children, while government programs that increase contraceptive access could have an opposite effect.

In Indonesia speciﬁcally, government provision of the BKKBN family planning programs likely inﬂuenced fertility rates. The implementation and maintenance of BKKBN programs may have been in response to local macroeconomic conditions such that regions experiencing oil booms saw more or less intense family planning programs. Increased BKKBN activities could reduce fertility.

Due to a lack of data, we are unable to control for the introduction and maintenance of these programs. However, Gertler and Molyneaux (1994) and Molyneaux and Gertler (2000) ﬁnd that these programs were only responsible for a very small portion of the overall fertility decline. Even the larger estimated eﬀects of family planning programs on fertility in Molyneaux and Gertler

(2000) show that contraceptive subsidies reduce fertility by 3 percent, and are smaller than the eﬀects of expanding secondary education for women.6

Because we estimate short-run fertility responses to oil shocks, we empirically test whether oil shocks operate through some of the short-run mechanisms proposed here by looking at changes in labor market outcomes, government expenditures and provision of public goods, and healthcare quality and access.

5The private market could also be responsible for improvements in healthcare quality and access. 6Both papers look to causally estimate the effect of the family planning programs on fertility. While Gertler and Molyneaux (1994) uses fixed effects to account for endogenous placement, Molyneaux and Gertler (2000) use an instrumental variables strategy, with variables that shift contraceptive demand in competing districts as instruments. Both papers measure family planning program inputs using data from Indonesia’s Family Planning Board (BKKBN). Unfortunately, these data are no longer available.

10 3.2 Long-Run Shifts in Demand

Though we do not estimate long-run fertility responses or related mechanisms, some examples of how oil shocks may shift demand for children in the long-run include a decline in infant and child mortality, an increase life expectancy, and investments in human capital (Galor, 2005).7 In the long-run, reduced infant and child mortality and increased life expectancy could reduce demand for children, with fewer births resulting in the same number of surviving children (Schultz, 1997;

Galor, 2005). Oil shocks can also affect fertility in the long-run through changed investments in human capital. An income effect or increased government expenditures on education could increase education levels. Particularly for women, increased education raises the opportunity cost of having children, reducing fertility. Additionally, increased provision of high-quality public education for children can lead to parents investing in the human capital of their children and increase the return to children or shift preferences toward child quality rather than child quantity (Galor, 2005). As discussed above, government expenditures and the provision of public goods may also have long- run effects on fertility. Thus, there are several ways long-run economic growth in response to oil shocks could affect fertility. Notably, the literature on the relationship between resource wealth and long-run economic growth is mixed (e.g. Cotet and Tsui, 2013b; Frankel, 2012; Brunnschweiler and

Bulte, 2008; Van der Ploeg and Poelhekke, 2010). These longer-run mechanisms could reconcile the positive short-run income eﬀects that we ﬁnd with the observed negative cross-country relationship between income and fertility.

3.3 Other Explanations for Changes in Fertility

Oil shocks may also affect fertility in the short-run in ways that do not reflect shifts in demand for children. First, there is evidence that changes in fertility may reflect changes in the timing of conception. Heckman and Walker (1990) find that higher male earnings are associated with shorter times to conception but that rising female wages delay times to conception. Lindo (2010) finds negative shocks to husbands’ earnings accelerate the timing of fertility.

Second, it is possible that oil shocks lead to improvements in maternal and fetal health that inﬂuence fertility through reduced infant mortality and averted fetal losses. Sanders and Stoecker

7We do estimate how changes in infant and child mortality could aﬀect short-run estimates of changes in fertility.

11 (2015) use the sex ratio in live births as a measure of changes in fetal and maternal health because male fetuses are on average more sensitive than females to negative health shocks. Thus, an increase in male births suggests an increase in fetal and maternal health. However, a similar ratio of male and female births is consistent with changes in fertility driven by fertility choices, reﬂecting a real change in the number of pregnancies rather than changes in fetal health. Prior work has found evidence of eﬀects of external stressors, including job loss and economic contraction, on the fetal sex ratio and that maternal nutritional intake is related to the proportion of male births (Sanders and

Stoecker, 2015; Halla and Zweim¨uller,2014; Catalano et al., 2010; Mathews et al., 2008; Catalano,

2003).

While we cannot estimate effects on health outcomes directly, we do examine the effect of shocks on the probability of a male birth, where an increase in the sex ratio provides indirect evidence of improvements in fetal health. Changes in the sex ratio have not yet been considered in the existing literature on economic shocks and fertility. We also estimate the effect of oil shocks on proxies for healthcare quality and access as another indirect way of measuring whether changes to fertility may be explained by changes in maternal and fetal health.

Finally, we observe that there is the potential for an asymmetric response in fertility to booms versus busts. Existing literature has not separated the causal effect of positive versus negative economic shocks on fertility. Prior work shows investors react more to negative shocks compared to positive shocks of the same absolute magnitude in financial markets (Hong et al., 2007; Ang and Chen, 2002). Related to fertility, work on the effect of housing wealth and fertility finds some evidence effects of housing booms and busts are not symmetric, with larger fertility responses to housing losses than for equivalent gains (Iwata and Naoi, 2017; Lovenheim and Mumford, 2013).

A large literature has explored potential asymmetries in how oil prices aﬀect the economy, with recent work ﬁnding symmetric responses of industrial production to oil price increases and decreases

(Kilian and Vigfusson, 2011; Hatemi-j, 2012; Herrera et al., 2015; Hamilton, 2011).

4 Data

We use microdata from the Indonesian Population Census and Intercensus Population Surveys to examine changes in fertility patterns. These data are made available as part of the Integrated

12 Public Use Microdata Series (IPUMS) International by the Minnesota Population Center. We use surveys from 1971, 1976, 1980, 1985, 1990, 1995, 2000, 2005, and 2010; they are cross-sectional nationwide census data.8 They report birth histories for all children living with interviewed women at the time of the survey.9 The data do not report birth histories for deceased children or children no longer living in the household.10 The data also do not include information on miscarriages or terminated pregnancies. The survey provides the regency of residence at the time of the survey.

Regencies are the most detailed unit of geography provided. While boundaries for regencies have changed over time, the variable identifying the regency in the IPUMS data has been constructed to reﬂect consistent boundaries from 1971 through 2010.11

We combine each of the cross-sections and construct an unbalanced retrospective woman- year panel of women between ages 14-45, inclusive. The age range ensures we include only women considered to be of reproductive age, consistent with the deﬁnition used by the Demographic Health

Surveys. Our measure of fertility is an indicator variable equal to 1 if a woman conceives in that year, constructed as a lagged indicator for giving birth. Women enter the panel ﬁve years prior to the survey year, or at age 14, and exit in the year of survey, or age 45. The panel structure of the data also allows us to identify eﬀects on the timing of fertility, where we use as an outcome variable the number of years between births. The sample period is from 1970 to 2008.12

We restrict the retrospective birth histories to five years prior to the survey to minimize measurement error in the dependent variable and prevent double-counting, while still providing woman-year observations for every year of the sample period. There are two sources of potential measurement error in the fertility variable. First, the fertility variable will not reflect births of children deceased prior to the survey date. Similarly, the fertility variable will not reflect births of

8The surveys use different sample fractions of the population (ranging from 0.22 percent to 10 percent), resulting in large differences in the number of observations from each survey. To use a similar number of observations from each survey, we use a random sample of 5 percent from the 2000 and 2010 surveys, 15 percent from the 1980 survey, and the full sample from all other surveys. In the analyses below, we use weights to correct for this sampling. 9We use familial relationship variables in the IPUMS data to identify mother-child relationships. Familial relationship variables are not comprehensive, however. Most importantly, in multi-generational households, matching mothers to their children is fraught - there is no data specifying to which child of the head of household a grandchild belongs. We are able to match roughly 60 percent of women of childbearing age with likely children. 10We provide analyses to determine the extent to which child mortality and (likely older) children living away affect our fertility estimates. 11Indonesia has undergone extensive decentralization during our time period. One result of this is that the number of regencies has approximately doubled during our time period, from just over 250 to just over 500. 12Surveys are administered in the middle of the year. Hence, with 2009 as the last full year of births in the sample, 2008 is the last full year of conceptions.

13 children no longer living in the household (e.g., an older child who may have moved out). A shorter time frame minimizes measurement error in these two types of lost birth histories. Finally, surveys usually occur every ﬁve years and the ﬁve-year restriction ensures against including one woman’s birth histories more than once in the case she was interviewed in consecutive surveys.

Using a shorter time frame also limits measurement error in the regency of residence at the time of a conception due to migration. That is, children may not have been conceived in the same regency as where the woman is living while she is surveyed. However, the IPUMS data contain information on migration in the five years prior to the survey date. Thus, we are able to provide evidence on whether selective migration is a concern by providing results using subsamples of women who did and did not move over the previous five years. Ninety-seven percent of women live in the same province as they did five years prior, where a province is one level of geography above a regency. The data identify movements at the more detailed regency level in all surveys but the 1980 and 1990 censuses. Among those with this variable, 94 percent have lived in the same regency over the previous five years.

Our identiﬁcation strategy requires time series data on oil prices and cross-sectional data on oil endowments by regency in order to measure the magnitude of local oil booms or busts. Data on the West Texas Intermediate spot crude oil price is from FRED, at the Federal Reserve Bank of St. Louis. Data on oil endowments is from the Rystad Energy’s proprietary UCube database.13

We use oil endowments per capita at the start of our sample period in 1970.14 It is plausible that predetermined regency-level oil endowments per capita in 1970 and oil prices are both exogenous to subsequent changes in fertility rates, conditional on regency-level fixed effects. We construct the 1970 per-capita value of regency-level oil endowments using population data from the IPUMS census files.15

We drop women in the top two oil-producing regions, Kutai and Siak, due to the uniquely massive amount of oil in these areas.16 The 1970 oil endowment is 9,434 million barrels in Siak

13For more information, see https://www.rystadenergy.com/products/EnP-Solutions/ucube/. 14We do not have a 1970 estimate of oil endowments. The endowment variable reﬂects oil in the ground within assets discovered prior to 1970. We provide a robustness check of the estimates below using an alternative endowment measure that includes oil discoveries on all known assets as of 2015 (additional reserves on the extensive margin). These results inform the direction of the estimates were we to use an endowment measure that excludes post-1970 oil discoveries on assets discovered prior to 1970. 15We extrapolate 1970 populations using the 1971 and 1976 surveys. 16These two regions as coded in the data represent the present-day regencies of Siak, Bengkalis, Rokan Hilir, Kepulauan, Kutai Barat, Kutai Kartanegara and Kutai Timur. Results including these regencies are qualitatively

14 and 1,070 million barrels in Kutai, compared to an average of 145 million barrels in the remaining oil-producing regions. The value of the 1970 oil endowment value per capita in Siak averages nearly

$1,300,000 across the sample period. Siak is home to several exceptionally large oil fields, including the Minas field, which is the largest known oil field in Southeast Asia. At one time, this field alone produced more than half of Indonesia’s crude supply.

Table 1: Summary Statistics

Non-Oil-Producing Full Sample Oil-Producing Regions Regions Mean S.E. Mean S.E. Mean S.E. [1] [2] [3]

Number of women-year observations 5,469,549 580,369 4,889,180 Conception dummy 0.12 0.32 0.12 0.33 0.12 0.32 Years since previous birth 3.82 2.18 3.76 2.15 3.83 2.18 Male conception dummy 0.06 0.24 0.06 0.24 0.06 0.24 Parity 1.94 1.60 1.92 1.60 1.94 1.60 Woman's age 29.94 8.24 29.29 8.20 30.01 8.24 Urban 0.36 0.48 0.28 0.45 0.37 0.48 No partner present 0.06 0.24 0.06 0.23 0.06 0.24 Woman's education Some primary schooling or less 0.39 0.49 0.43 0.50 0.39 0.49 Primary (6 years) completed 0.33 0.47 0.33 0.47 0.33 0.47 Lower secondary completed 0.12 0.32 0.12 0.32 0.12 0.32 Secondary completed 0.12 0.33 0.10 0.31 0.12 0.33 More than secondary 0.03 0.18 0.02 0.15 0.04 0.19 Partner's education Some primary schooling or less 0.31 0.46 0.33 0.47 0.31 0.46 Primary (6 years) completed 0.31 0.46 0.31 0.46 0.31 0.46 Lower secondary completed 0.11 0.32 0.12 0.32 0.11 0.32 Secondary completed 0.15 0.35 0.14 0.35 0.15 0.35 More than secondary 0.05 0.21 0.03 0.18 0.05 0.21 Oil Endowment Value Per Capita (Hundred thousands 2010 USD) 0.02 0.10 0.17 0.26 0.00 0.00 Notes: Indonesian Population and Census Population Surveys, 1971-2010. Sample comprises women ages 14 to 45. Excludes regencies of Siak and Kutai. Sample means provided with standard errors in adjacent columns. The urban and schooling variables are measured at the time of survey. Figures weighted using survey population weights.

With this exclusion, there are 266 regencies with a total of 1,193,806 women comprising

5,469,549 women-year observations, with 640,357 children born over the sample period. Table 1 contains summary statistics for the full sample and separately for women in regions with and without oil. Observable characteristics are similar for women in regions with and without oil. In both sets of regions, the mean probability of conception is 12 percent. Women average about 30 similar and available in Table A5.

15 Figure 3: Fertility Rate and Value of 1970 Oil Endowment Per Capita, 1970-2008

Conception rates are calculated as the annual number of births per 1,000 women ages 14-45 lagged by one year from the Indonesian Population Census and Intercensus Population Surveys from 1976 to 2010. The real value of the 1970 oil endowment per capita is averaged across regencies. 1970 oil endowments are from the UCube database of Rystad Energy and oil prices come from FRED, Federal Reserve Bank of St. Louis. Oil prices are inﬂated to 2010 dollars using the CPI-U. years old. Education levels for women and their partners are low; overall, seventy-three percent of women, and sixty-two percent of their partners, have 6 or fewer years of education completed.

Women in non-oil-producing regions are more likely to live in an urban setting (37 percent compared to 28 percent in oil-producing regions).

On average, the oil endowment value is about $1,700 per capita. Importantly, a very large majority of regencies have no oil production; 92 percent of the 266 regencies have zero oil endowment in 1970. The mean 1970 oil endowment value per capita is $17,000 among those living in regencies with oil production.

Figure 3 illustrates the correlation between fertility rates and the value of the 1970 oil endowment per capita, plotting fertility rates separately for regions with and without oil endowments.

In both types of regions, there is a notable downward trend in fertility, with the conception rate dropping by roughly half. There is also substantial variation in the average oil endowment value

16 per capita over this period. While the two groups’ fertility rates follow a similar overall trend, some diﬀerences suggest fertility is responding to oil shocks. For example, during the 1970’s boom, fertility rates remained high in oil regions, while trending down in non-oil regions. There also appears to be a faster fertility decline in oil regions during the subsequent bust. We now turn to a careful regression analysis that leverages spatial variation across regencies in the size of oil endowments and temporal variation in oil prices.

5 Estimation Strategy

Our empirical analysis uses individual-level panel data to measure diﬀerences in fertility among those living in higher versus lower endowment regencies through the boom and bust cycles between 1970 and 2008. Formally, we estimate linear probability models in the following form:

birthirt+1 = β0 + β1OilEndowmentV alueP erCapitart + γXit + θr + φt + ρr × t + irt (1)

where i indexes women, r indexes regency, and t indexes year. The dependent variable is a binary variable for whether or not the woman gave birth the subsequent year, in order to capture the fertility decision in year t. The variable OilEndowmentV alueP erCapita is the per capita quantity of oil endowment in regency r in 1970 multiplied by the world oil price in year t. We measure the value of the oil endowment per capita in hundreds of thousands of 2010 USD. Xit is the set of individual-level observable characteristics, including a set of indicator variables for age, an indicator for living in an urban area, an indicator for not having a partner, and an indicator

17,18 for having previously given birth. θr represents regency fixed effects, which control for cross- regency selection correlated with unobserved fertility preferences of households. φt represents survey year fixed effects, while ρr × t represents regency-specific linear time trends that address concerns

17We can also include controls for a woman’s and their partner’s education level. We exclude these in the main results due to concerns that education is endogenous. Online Appendix Table A1 shows weak evidence that education increases with increasing oil endowment value. The results are very similar when including education controls (see Online Appendix Table A2). 18With the reshaped data, this specification is similar to a discrete time hazard model. “Duration” may be thought of as measured in years from age 14, when women are first “at risk” of giving birth. The baseline hazard is then given by the coefficients on the age dummies, and the coefficients on the additional covariates provide estimated shifts in the baseline hazard. Online Appendix Table A3 provides the main results estimated using a logit, a more common choice of functional form for a discrete time hazard model. Results are similar.

17 about omitted time-varying factors.19 We also provide results with a cubic time spline, and results replacing the survey year indicators and time spline with calendar year fixed effects. This regression does not explicitly model mechanisms through which endowment value influences fertility, though we explore potential mechanisms below.20 We cluster standard errors at the regency level because of the common regency component to endowment value variation. We estimate weighted regressions, using the survey weights provided in the IPUMS data.21

In this reduced-form regression, the coeﬃcient of interest is β1, which shows how the likelihood of having a child is associated with oil shocks.22 Our empirical strategy leverages variation in the size of oil endowments across regions so that the price shocks have diﬀerent weights across districts.

That is, for the same change in the world price of oil, regions with more oil experienced larger shocks than regions with less (or no) oil. Identiﬁcation of β1 arises from variation in the oil endowment value within regions endowed with at least some oil. There is more within-region variation in

OilEndowmentV alueP erCapita the larger the initial oil endowment.

The identification assumption is that the oil endowment value (the interaction of 1970 oil endowment and the world price of oil) is exogenous to the fertility decision, conditional on included controls. We argue this measure captures plausible exogenous variation in oil shocks because the level of the region’s 1970 oil endowment depends on geology and, conditional on controls, is not influenced by regency-specific factors correlated with fertility rates. Further, the oil price is set on the world market and not influenced by individuals in Indonesia. Under these assumptions, women in regencies with no oil endowment, or endowed with less oil, provide counterfactual fertility trajec- tories for women in regencies that are heavily oil-endowed. This identification strategy follows that

19The rollout of the government family planning program across provinces from 1969 to 1980 is also a potential source of bias. However, we have no reason to believe the initial year of the family planning program in a regency is correlated with endowment value per capita. While we were unable to obtain data on the rollout years by province, the robustness of the results to the inclusion of individual fixed effects and constant effect across the sample period suggest the family planning program is not a large concern. 20We provide estimates using a complementary difference-in-differences approach with interactions between indicators for whether a regency has oil reserves and indicators for whether the oil price is relatively high or low. Online Appendix B provides more details on the specification and presents results. The conclusions from this exercise are similar. 21We sum person weights by region and year, linearly interpolating within each region to obtain weights for non-survey years. 22We estimate the reduced form relationship between oil shocks and fertility. We do not follow Black et al. (2013) and Kearney and Wilson (2018) in estimating an IV specification; Black et al. (2013) instruments for county earnings using the value of coal reserves in the county, and Kearney and Wilson (2018) instruments for earnings using oil endowment value per capita. As discussed by Kearney and Wilson (2018), the required exclusion restriction (that the only channel through which natural resource shocks affect fertility are through earnings) may not hold. These authors note that estimates from their IV analysis should be interpreted with caution.

18 of Cust et al. (2019), Dube and Vargas (2013), and Cotet and Tsui (2013a), which combine world market prices and region-speciﬁc measures of resource intensity to generate exogenous variation in resource shocks.23

It is critical for identification that the 1970 oil endowments per capita, which weight price shocks across regencies, are exogenous conditional on included controls. Using initial endowments rather than annual oil production is important, as oil production may be endogenous to a regency’s economic conditions, which are likely correlated with a regency’s fertility rates. Indeed, there is the concern that the initial known oil endowment in a regency may be determined by regency- specific factors that drive the amount of exploration, such as the political institutions, leadership, or financial resources of district governments, or local economic factors, which are also related to fertility rates (Cust and Harding, 2020). However, as explained by Cassidy (2020), incentives to explore for oil were uniform across the country prior to fiscal decentralization in 2001 because only the central government negotiated with oil companies. Thus, it is plausible that the oil endowment per capita in each regency in 1970, conditional on included controls, and in particular, regency fixed effects, is uncorrelated with unobserved regency-specific factors that may also covary with fertility rates (Cust et al., 2019). Nevertheless, to address that unobserved regency-level characteristics could be a source of bias, we also provide results including individual fixed effects to exploit within-woman variation over time. This helps control for time-invariant characteristics at both the aggregate regency level (e.g., geography, local culture) and the individual level (e.g., family background).

As the world price of oil is the second component of our measure of oil shocks, we argue the world price of oil is exogenous to Indonesians’ fertility decisions. It is unlikely there is reverse causality, with fertility shocks in Indonesia inﬂuencing oil prices. Using world oil prices for time variation in natural resource shocks follows Cust et al. (2019), Dube and Vargas (2013), Bazzi and

Blattman (2014), and Cotet and Tsui (2013a).

23Cust et al. (2019) looks at the effect of oil and gas “windfalls” in Indonesia, with windfalls defined as the interaction between the world oil price and the number of nondry wells over total wells in a district, on employment, wages, and labor productivity. Dube and Vargas (2013) uses price variation in world oil and coffee markets and regional variation in production of these resources in Columbia to estimate the effect of economic shocks on civil conflict. Finally, using a country-level analysis, Cotet and Tsui (2013a) estimates the effect of oil wealth on political violence, where oil wealth is defined as the interaction between the world oil price and a country’s level of oil reserves. This strategy is also similar to that used by Allcott and Keniston (2017), where instead of using prices, they interact oil and gas employment with oil and gas endowment to identify effects of natural resources shocks.

19 For causal identiﬁcation, we must assume there are no confounders that are correlated with both the oil endowment per capita and the oil price. One threat to this assumption is non-random government spending on the BKKBN family planning programs. We provide evidence that this is not a large concern in Section 7, where we investigate the relationship between the oil endowment value and proxies for government investment in family planning.

A second concern is that non-oil-endowed regencies may be aﬀected by aggregate gains and losses from the oil booms and busts. This may occur, for example, through the INPRES grants and revenue sharing or through cross-regency economic spillovers. This is expected to introduce downward bias, such that we obtain estimates that underestimate the true size of the fertility eﬀect.

There is also concern that measurement error from unobserved births in our measure of fertility may result in biased estimates of β1. If the unobserved fertility is due to child mortality, and child mortality is lower when endowment values are higher, then our estimates will be biased upward. Unobserved fertility due to surviving children living away from the mother could also bias estimates in an unclear direction.24 To evaluate these potential biases, we provide estimates of the relationship between the endowment value measure and: (i) the number of children ever born to a woman no longer living at the time of the survey, and (ii) the number of surviving children who are no longer living with their mother at the time of the survey.

Another threat is migration and the selection of households across regencies. If women who are planning to have children live in regencies that are most likely to experience large increases in endowment value, our estimates will be biased upward. Conditional on regency fixed effects, however, the endowment value measure allows only within regency variation in the endowment value over time. Thus, our estimates of β1 will be biased upward only if there is selective migration such that families who are more likely to have a child in the near future begin moving disproportionately into regencies where the endowment value with be highest. To rule out this concern, we provide robustness checks using samples of women who did not move over different time horizons.

There is concern the 1970 endowment measure contains some within-asset discoveries made after 1970. The Rystad endowment data reﬂects all oil, within an asset, that is known at the time

24The bias could be downwards if, e.g., children are more likely to be sent away to make money during to a resource boom. Alternatively, it could be upwards if, e.g., children are more likely to be sent away to work when family incomes are low (due to a resource bust). Note that older children are more likely to live away from home and therefore this is less likely to be a source of bias than child mortality (which is most common in the ﬁrst few years of life).

20 the data was downloaded. For example, if an asset was initially discovered in 1950 and new oil within the asset is discovered in 1980, the new oil will be included in the 1970 endowment measure.

To sign the bias in our estimates from including oil along this intensive margin, we provide a robustness check using an alternative endowment measure that also reﬂects oil discoveries made on the extensive margin – new assets discovered after 1970. The results imply the estimates would increase were we to have a 1970 estimate of oil endowments, implying our estimates using equation 1 underestimate the fertility response to changes in the oil endowment value.

In addition to estimating the effect on the probability of conception, we look at the effect of endowment value per capita on time in between births, measured as the number of years between birth events. Estimated effects on the fertility rate may reflect changes in the timing of births, rather than changes in the number of conceptions. For example, an increase in fertility could reflect a decrease in the average time since a prior birth. This analysis is possible only for women who have had at least one child. Thus, we estimate a version of equation 1 using as the dependent variable the number of years between birth events among the sample of women with at least one child.

Finally, while we cannot estimate the effect on miscarriages directly, we look at the probability of a male birth, conditional on having a birth, as a reflection of changes in fetal health and miscarriage rates (Sanders and Stoecker, 2015; Halla and Zweimüller,2014). We estimate equation 1 using as the dependent variable a binary indicator for whether or not the woman gave birth to a male in the subsequent year. Our sample is the set of woman-year observations in which a woman gives birth. Because male fetuses are more likely to miscarry, an increase in the probability of male births may indicate that estimated increases in the fertility rate are due to improvements in fetal health and a decrease in miscarriages, as opposed to an increase in conceptions.

6 Results

Table 2 shows that the probability of conception increases with the value of oil endowment per capita. Across a range of specifications, the estimated coefficient on the interaction term between oil price and endowment per capita is positive and statistically significant.25 Column (1) estimates

25Our results are similar across women’s education and urbanicity levels, as well as their partners’ education levels. Please see Online Appendix Table A4.

21 a baseline model with only survey-year ﬁxed eﬀects. Column (2) adds individual-level covariates;

Column (3) adds a set of regency indicators; Column (4) replaces survey-year indicators with year indicators; Column (5) replaces year indicators with the survey year indicators and a time spline; and Column (6) adds regency-specific linear time trends. Finally, Columns (7) and (8) include individual fixed effects. In all columns, the estimate is statistically significant and stable, even in the most demanding specification. We note that the estimates from regressions including individual

fixed effects are derived from variation in the dependent variable within women. Thus, marginal effects are identified off of women who conceived at least one child (but not in all years). Because women who conceive are a non-random subsample, the estimates in columns (7) and (8) are not directly comparable with estimates excluding individual fixed effects.

According to the estimate in Column (6), an increase of $100,000 in the oil endowment value per capita increases the probability of a woman conceiving by 2.17 percentage points. As the probability of conceiving in any given year is 12 percent, this represents an increase in the probability of conceiving of 19 percent. To contextualize an increase of $100,000 in the oil endowment value per capita, we note that during our sample this value ranged between $45,000 and $238,000 in the most intensive oil-producing area.26 As an alternative interpretation, a one standard deviation increase in the oil endowment value increases the probability of conceiving by 1.8 percent.

The overall increase in the likelihood of conception with an increase in oil endowment value per capita could reﬂect an increase in overall fertility over the life cycle, changes in the timing of fertility, or improvements to fetal health and averted fetal losses. To determine the role of changes in the timing of births, we examine heterogeneity by women’s age and birth parity, and estimate eﬀects on the interval since a previous birth. An increase in births to older women and an increase in higher order births are consistent with an increase in completed childbearing, rather than women having desired children sooner. Table 3 provides results from estimating equation 1 including interaction terms between oil endowment value per capita and age group indicators. In

Column (1), we group women by ages 22 through 32, 33 through 27, and 38 through 45, so that the coefficients on the interaction terms provide the differential effect relative to women ages 21

26Range provided for Pelalawan. Table A5 provides results including the excluded regions, as well as results excluding Pelalawan (the third most-endowed region). The estimates using the full sample are much smaller in magnitude, indicating there are non-linearities in the fertility response. This is reasonable given biological constraints to large fertility responses.

22 Table 2: The Eﬀect of Oil Endowment Value on Conception Probability

[1] [2] [3] [4] [5] [6] [7] [8]

Oil Endowment Value 0.0268*** 0.0147** 0.0192** 0.0149** 0.0161** 0.0217** 0.0237** 0.0189** Per Capita (0.0073) (0.0073) (0.0089) (0.0075) (0.0081) (0.0086) (0.0100) (0.0078)

Survey Year FE Y Y Y Y Y Woman's age indicators Y Y Y Y Y Y Y Individual-level covariates Y Y Y Y Y Regency FE Y Y Y Y Year FE Y Y Time spline Y Y Individual FE Y Y Regency-specific trends Y Mean birthrate 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 Women 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 Observations 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 Clusters 266 266 266 266 266 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the woman conceived in a given year. Covariates include an indicator variable for no partner, an urban indicator variable, and an indicator for having previously given birth. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

and younger. The interaction term among women ages 22 through 32 is positive, suggesting a larger total effect among this age group. However, it is not statistically different from zero. The two interaction terms among older women are negative, though small in magnitude and also not statistically different from zero. In Column (2), we use larger age bins, including an interaction term between oil endowment value per capita and an indicator for ages 33 and above. Again, the estimate on the interaction term is small in magnitude and not statistically different from zero.

Overall, these results indicate that women in all age groups respond positively to changes in the oil endowment value per capita, suggesting that shocks to the endowment value per capita increase total fertility rather just its timing.

The third column of Table 3 provides results investigating heterogeneity by birth parity.

To see whether there are differential effects between women with no children and women who already have children, we interact oil endowment value per capita with an indicator equal to one if the woman already has one or more children. The estimate on the interaction term is positive, indicating a larger response among women who already have at least one child. However, it is not statistically different from zero, suggesting the increase in fertility is driven by both increases in

ﬁrst and higher order births. Taken together, the increase in fertility among women of all ages and

23 Table 3: Heterogeneity by Age and Parity

Heterogeneity Heterogeneity by age by parity [1] [2] [3] Oil Endowment Value Per Capita 0.0192* 0.0244*** 0.0166** (0.0113) (0.0072) (0.0081) Oil Endowment Value Per Capita x 0.0074 Ages 22-32 (0.0085) Oil Endowment Value Per Capita x -0.0021 Ages 33-37 (0.0062) Oil Endowment Value Per Capita x -0.0026 Ages 38-45 (0.0062) Oil Endowment Value Per Capita x -0.0076 Ages 33-45 (0.0076) Oil Endowment Value Per Capita x 0.0065 1 or more children (0.0100)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Estimates from equation (1) with included specified interaction terms. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Each column is a separate regression. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

among women with and without children is suggestive that the positive fertility response reﬂects an increase in completed fertility rather than simply changes in birth timing.

As a more direct test of whether changes in the oil endowment value per capita aﬀect the timing of births, we next study the duration between births. This is possible only for women who have given birth before, thus, the sample is limited to women who have at least one child.27

The dependent variable is the number of years passed since the previous birth. Table 4 reports results. The estimate in Column 1 is negative, though not statistically different from zero. The point estimate suggests a $100,000 increase in the oil endowment value per capita decreases the time between births by 0.094 years, or about 1 month. The subsequent two columns allow for heterogeneous effects by women’s age. While all the estimates are negative, none are statistically significant. Further, the magnitudes of the estimates are small, translating into small decreases in the time between births. These results do not provide robust evidence that oil endowment per capita shortens time between births and are consistent with the above findings of similarly sized

27We also limit the sample to women with 10 or fewer years between births. This helps to eliminate data errors and reduce inaccuracies due to data omissions from children dying or moving away.

24 Table 4: The Eﬀect of Oil Endowment Value on Conception Timing

Dep. Var.: Number of years since previous birth All Heterogeneity by age [1] [2] [3] Oil Endowment Value Per Capita -0.094 -0.089 -0.019 (0.063) (0.064) (0.097) Oil Endowment Value Per Capita x -0.022 Ages 22-32 (0.049) Oil Endowment Value Per Capita x 0.088 Ages 33-37 (0.100) Oil Endowment Value Per Capita x 0.028 Ages 38-45 (0.145) Oil Endowment Value Per Capita x -0.088 Ages 33-45 (0.119)

Observations 432,318 432,318 432,318

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Estimates from equation (1) where the dependent variable is equal to the number of years since the previous birth. Each column is a separate regression. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

eﬀects across the age distribution and among ﬁrst and higher order births.28

We next explore whether improved fetal health and decreases in fetal losses plays a role in explaining the estimated increase in fertility. We provide indirect evidence of changes in fetal health by using a version of equation 1 to estimate whether changes to the oil endowment value aﬀect the sex ratio. We use a binary dependent variable equal to one if the conception results in a live male birth using a sample of observations in which a child was born the following year. The results in Column 1 of Table 5 imply that a $100,000 increase in the oil endowment per capita increases the probability of a male birth by 1.5 percentage points. However, the estimate is not statistically signiﬁcant.

The speciﬁcation in the second and third columns provide estimates using interactions with

28In Online Appendix Table A6, we present results estimating equation 1 including up to three lags of the oil endowment per capita measure to allow for a dynamic response over time. We do not find evidence that the initial increase in conception probability is offset by a decrease in the conception probability two or three years later. These results provide additional evidence that the fertility response we find is not driven by changes in timing.

25 Table 5: The Eﬀect of Oil Endowment Value on the Probability of a Male Birth

Dep. Var.: Probability of male birth All Heterogeneity by age [1] [2] [3] Oil Endowment Value Per Capita 0.015 0.012 0.013 (0.011) (0.015) (0.011) Oil Endowment Value Per Capita x 0.001 Ages 22-32 (0.008) Oil Endowment Value Per Capita x 0.013 Ages 33-37 (0.013) Oil Endowment Value Per Capita x 0.034 Ages 38-45 (0.022) Oil Endowment Value Per Capita x 0.019** Ages 33-45 (0.008)

Observations 640,357 640,357 640,357

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived a male in a given year. Each column is a separate regression. Regressions in columns 2 and 3 include interaction terms with age bins. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

age group indicators. The estimates are all positive and increase in magnitude with age. Column 3 uses larger age bins and we ﬁnd that a male birth is 1.9 percentage points more likely among women ages 33 through 45 relative to younger women. This estimate is statistically diﬀerent from zero.

The magnitude of this estimate is large, and implies a $100,000 increase in the oil endowment per capita increases the likelihood of a male birth among women ages 33 through 45 by 3.2 percentage points.29 Alternatively, a one standard deviation increase in the oil endowment value increases the likelihood of a male birth among older women by 0.31 percentage points.

This result is consistent with a decrease in miscarriages of males among older women.30 With

29The total effect of 3.2 percentage points (s.e. 0.0092) is statistically different from zero at the 1 percent level. We provide robustness checks of these results, including a set of indicators for partner’s age and individual fixed effects, in Table C1. While this result is robust to the inclusion of paternal age, it fades with the inclusion of individual fixed effects and when we use the complementary specification in Online Appendix B. The latter specifications ask more of the data and the standard errors are larger in both cases. 30There may be other explanations for changes in the sex ratio besides changes in fetal health. Four other common determinants of the sex ratio at birth are selective abortion, paternal age, maternal age, and birth order (Chahnazarian, 1988). Online Appendix Section C discusses why we find each of the other four common determinants to be less likely to be driving estimated changes in the sex ratio among older women.

26 a 3.2 percentage point increase in the likelihood of male births among 33 through 45 year olds, we would expect to see a 6.9 percent increase in the number of live births, increasing the share of male births in the sample from 51.2 to 54.3 percent.31 However, we estimate an increase in fertility of

32 percent among women ages 33 through 45, implying about 21 percent of the overall increase in fertility among older women is explained by averted fetal losses and the remaining 79 percent is due to an increase in total fertility.32 Notably, this calculation assumes female fetuses were entirely unaﬀected and thus provides a lower bound on the role of averted fetal loss in explaining the overall increase in fertility among older women. A reasonable upper bound could be if 75 percent of averted fetal losses are male. In this case, we would expect about 43 percent of the fertility increase to be from averted fetal loss.33

A concern with the above conclusions is that mothers may have moved across regencies prior to the survey year, falsely assigning changes in oil endowment value per capita for their regency of current residence to prior different regencies of residence in each year prior to the survey. We provide several robustness checks to address this concern. We estimate equation 1 using subsamples comprising those living in the same geographical units over the last five years. Results using subsamples of women who did not move are very similar to those using the full sample, suggesting migration does not pose a threat. The first column of Table 6 replicates results from our preferred specification in Table 2. The second and third columns provide results using samples of women living in the same province as five years prior, comprising about 97 percent of the full sample. The difference between the two columns is in using two different variables identifying migration patterns across provinces.

The fourth column uses a subsample of women living in the same regency, as women may have migrated across regencies within a province. This requires dropping observations from two survey years when the regency of residence ﬁve years prior was not collected. Movement across regencies remains low. About ninety-four percent of women lived in their current regency ﬁve years

31The 6.9 percent increase is calculated as 3.15/(1-0.543), where 3.15 is the estimated percentage point increase in the likelihood of a male birth and 0.543 represents the higher share of births that are male from the baseline 51.2 percent (.512 + 0.032). 32The average fertility rate among women ages 33 through 45 is 5.3 percent, so a total effect of a 1.7 percentage point increase (the sum of coefficients on oil endowment value per capita and the interaction term in Column (3) of Table 3) represents a 32 percent increase relative to the mean. 33This three to one fetal loss ratio is more conservative than the five to one ratio found in Sanders and Stoecker (2015).

27 Table 6: Migration Robustness Checks

Women living in Women living in Women living in Women living in the the same province the same province the same regency same province in Base as 5 years prior as 5 years prior as 5 years prior which they were born [1] [2] [3] [4] [5] Oil Endowment Value 0.0217** 0.0231*** 0.0236*** 0.0234*** 0.0212* Per Capita (0.0086) (0.0088) (0.0090) (0.0080) (0.0114)

Mean birthrate 0.12 0.12 0.12 0.11 0.12 Women 1,193,806 1,161,320 1,146,695 815,177 1,038,811 Observations 5,469,549 5,317,512 5,247,860 3,850,464 4,747,954 Clusters 266 266 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Estimates from equation (1) using the indicated sample. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Each column is a separate regression. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

prior.34 Finally, in column 5, we provide estimates using a sample to women who were born in the same province where they report currently living. Eighty-seven percent of women live in the same province in which they were born. The estimate is similar in magnitude, though less precise.35

We also address the concern that the 1970 oil endowment measure contains some within-asset discoveries made after 1970. Online Appendix Table A8 compares the main result (from Column 6 in Table 2) with that using an alternative endowment measure that includes oil discoveries made within-assets after 1970 as well as oil discoveries in new assets discovered after 1970. The estimate is much larger using the endowment measure that excludes oil discoveries along the extensive margin. This suggests that the estimates would increase if we used an endowment measure that additionally excludes oil discoveries along the intensive margin, implying our results are downward- biased, underestimating the fertility response to changes in the oil endowment value.

Finally, we investigate whether there is a symmetric relationship between increases and decreases in the value of the oil endowment per capita and fertility. We estimate equation 1 including interactions between the oil endowment value per capita and indicators for the time periods of booms and busts. We deﬁne 1973 through 1980 as a boom period; 1981 through 1986 as a bust

34Author’s tabulations; calculation for living in current regency excludes 1980 and 1990 survey year data. 35We address the concern that measurement error from unobserved births may result in biased estimates in Table A7. The results imply that our main ﬁndings are not likely to be explained by changes in child mortality and downward biased if oil shocks cause younger children to move outside the home.

28 Table 7: Booms versus Busts

Heterogeneity by time period [1] Oil Endowment Value Per Capita 0.0518*** (0.015) Oil Endowment Value Per Capita x -0.002 1973 - 1980 (0.015) Oil Endowment Value Per Capita x -0.004 1981 - 1986 (0.003) Oil Endowment Value Per Capita x -0.033** 2003 - 2008 (0.014)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses. Estimates from equation (1) with interaction terms for specified time period. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Regressions are weighted using survey population weights.

period; and 2003 through 2008 as a second boom period. Thus, the estimates on the interaction terms provide the diﬀerential eﬀect on the probability of conception relative to the left out years,

1970 through 1972 and 1987 through 2002. The results in Table 7 indicate a symmetric response over the boom period from 1973 to 1980 and the bust period from 1981 to 1986, with small and statistically insignificant estimates on the interaction terms. The estimate on the interaction term for the period 2003 through 2008 is negative and statistically significant. This lower total effect on fertility during the latter boom may be due to the aforementioned relatively recent decline in investment and exploration (Roach and Dunstan, 2018). It is also possible there are larger substitution effects in later time periods due to higher women’s wages and better employment opportunities.

Note that the total eﬀect during this boom period is still positive, implying a $100,000 increase in the oil endowment per capita increases the probability of conception by 1.9 percentage points.36

While the total eﬀect in this second boom period is smaller in magnitude than during the other time periods, due to falling fertility rates over time, the percent changes relative to the mean fertility

36Online Appendix Table A9 provides results using 1970-1972 and 1987-2002 separately as the base period, rather than combined. Again, we ﬁnd similarly sized eﬀects over the separate boom and bust periods, with the exception of the most recent boom.

29 rate are more comparable: the total eﬀects represent a 22 percent increase in 2003-2008, compared to a 33 percent increase in the boom period 1973-1980, and a 38 percent increase in the subsequent

1981-1986 bust period.

7 Mechanism Exploration

Local economic shocks may aﬀect fertility decisions through several diﬀerent channels. We now examine the relationship between oil endowment values and short-run changes in labor markets, government expenditures, development levels, and healthcare access as discussed in Section 3.1.

We use labor market data at the individual-level from 1976 through 2010 IPUMS survey years.

Regency-level data on 2007-2010 labor market indicators are from the National Labor Force Survey

(SAKERNAS); regency-level data on 1996-2010 economic development and infrastructure are from the annual Indonesian national socioeconomic household survey (SUSENAS); regency-level data on accessibility of health care are from the village census (PODES) for 1996, 2000, 2003, 2006, and

2008; and regency-level data on 2001-2010 total expenditures are from the Ministry of Finance. The regency-level data are reported in the World Bank’s Indonesia Database for Policy and Economic

Research (INDO-DAPOER). Unfortunately, with the exception of the IPUMS data, all data are from the tail-end of our analytic sample and miss much of the rich variation available during the

1970’s and 1980’s.37 However, our ﬁnding of positive fertility eﬀects is present even in these later years. Thus, while data limitations prevent a comprehensive look at which mechanisms might be in play over the entire time period, we expect the results to be meaningful.

We estimate the relationship between outcomes and the oil endowment value per capita at the regency-year level:

yrt = β0 + β1OilEndowmentV alueP erCapitart + θr + φt + rt (2)

where we again include a set of regency indicators and year indicators. There are two primary diﬀerences from our main speciﬁcation. First, these data are at the regency-level, with one observation per regency-year. Second, we estimate contemporaneous changes. Standard errors remain

37The three datasets, SAKERNAS, SUSENAS, and PODES, are maintained by Statistics Indonesia. While each of the three surveys was initiated prior to the years used in our analyses, earlier data is not available.

30 clustered at the regency level. When using the individual-level labor market indicators reported in the IPUMS data, we estimate equation 2 at the individual-level in survey years.

7.1 Income Eﬀects

We ﬁrst investigate whether the increase in fertility may be due to positive income eﬀects.

Following Grimm et al. (2015), we use data in the SUSENAS surveys on household per capita expenditures as a proxy for household income.38 We estimate a $100,000 increase in the oil endowment per capita increases household per capita expenditures by 76,320 IDR per month (Column 1 in Table 8), or about $5 USD. This is robust to using the log of household per capita expenditures as the dependent variable, with an estimated increase in expenditures of about 9 percent (Column

2 in Table 8).

Table 8: Eﬀect on Household Expenditures, Infrastructure, and Socio-Demographic Indicators

Household Health Household Household Share of births expenditures Log (household expenditures Electricity access to access to safe attended by skilled (IDR) expenditures) (IDR) coverage safe water sanitation health worker [1] [2] [3] [4] [5] [6] [7] Oil Endowment Value 76,320.4*** 0.0900* -981.6 10.66** -3.023 -0.689 -4.645 Per Capita (27080.1) (0.0523) (1669.2) (4.172) (9.409) (4.772) (3.865)

Survey Year FE Y Y Y Y Y Y Y Regency FE Y Y Y Y Y Y Y Observations 3658 3658 3658 3671 3937 3937 3924 Clusters 266 266 266 266 266 266 266

Notes: SUSENAS years are 1996 through 2010. Monthly household expenditure data are not available in 2008 and electricity coverage is not available in 2005. Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses.

Previous research has also established that oil and gas shocks in Indonesia aﬀect wages and employment; Cust et al. (2019) ﬁnds a 10 percent increase in oil and gas “windfalls” causes a

1.8 percent increase in wages and a 0.2 to 0.9 percent increase in employment. They use data on

ﬁrm-level wages and employment from the Indonesian Manufacturing Census from 1990-2008 and a similar identiﬁcation strategy as in this paper, measuring windfalls through the interaction between

38Expenditures are converted to real 2015 IDR using the Indonesian Consumer Price Index from the Organization for Economic Co-operation and Development, retrieved from FRED, Federal Reserve Bank of St. Louis. Unfortu- nately, wage and income data are not available in the SUSENAS surveys and the IPUMS Census collected wage and salary information in only two surveys, in 1976 and 2010.

31 the share of drilled wells in a regency that are non-dry and the world oil price. Importantly, their

ﬁndings establish oil and gas windfalls increase wages and employment in 16 out of 23 sectors, most of which are unrelated to oil production (e.g., machinery and equipment; tobacco products; paper and paper products; rubber and plastic products; food products and beverages).39

We now provide evidence that positive fertility effects are broad-based and prevalent among workers in a range of fields, consistent with the broad-based findings in Cust et al. (2019). We use reported data on the industry of employment in the IPUMS census data to estimate equation 1, and include interaction terms between the oil endowment measure and indicators for sector of employment.40 As the left-out sector is the agricultural, fishing, and forest industry, the results in

Table 9 show a positive and statistically significant effect among workers in the agricultural sector, with an additional positive and statistically significant effect among those working in the service- providing industries of trade, transportation, and leisure and hospitality. These results indicate the fertility effects were experienced by workers across the economy. Additionally, because we do not find the largest fertility effects in the agricultural sector, we do not find evidence supporting increases in the return to children as the primary driver of our results.

We are unable to estimate whether oil shocks are associated with changes in wages, which would aﬀect income along the intensive margin. However, we can use labor market data to see whether changes in employment could explain changes in income along the extensive margin.41 We

ﬁnd no evidence of an increase in labor force participation or employment, among either women or their partners, using the IPUMS individual-level data from various survey years from 1976 through

2010 (Table 10). However, using data from SAKERNAS from 2007 through 2010, we estimate a $100,000 increase in the oil endowment value per capita increases the employment rate by 1.76 percentage points, or about 4 percent relative to the mean (Table 11 ). This estimate is statistically significant at the 1 percent level. We also estimate a statistically significant and similarly sized decrease in the unemployment rate. There is no effect on the labor force participation rate.42

39Unfortunately, we are unable to provide similar estimates using our identification strategy because these data, maintained by Statistics Indonesia, are no longer available to researchers. 40We use the partner’s industry instead of the woman’s industry because more than 60 percent of women in the sample report not being employed. We do caution that both employment and the sector of employment are likely endogenous. 41With the lack of available data on wages, we are also unable to test whether there is a substitution effect, with increases in women’s wages increasing the opportunity cost of children. 42Indonesia was relatively unaffected by the Great Recession, with GDP growth of 4.6% in 2009 and roughly 6.2% in 2007, 2008, and 2010. Real oil prices were $76, $101, $63, and $79/barrel over the four years.

32 Table 9: Heterogeneous Fertility Eﬀects by Occupational Sector

Heterogeneity by industry [1] Oil Endowment Value Per Capita 0.0246*** (0.0090) Oil Endowment Value Per Capita x Goods-producing industries 0.0005 (0.0041) Oil Endowment Value Per Capita x Trade, transportation, and leisure and 0.0144*** hospitality service-providing industries (0.0041) Oil Endowment Value Per Capita x Professional, business, education, and health 0.0010 service-providing industries (0.0049) Observations 4,025,315

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses. Estimates from equation (1) with interaction terms with specified industry. Left-out sector is "Agriculture, fishing and forestry," the industry of 47 percent of the sample. Goods-producing industries includes mining and extraction; manufacturing; electricity, gas, water, and waste management; and construction. Second industry group includes wholesale and retail trade; transportation, storage and communication; and hotels and restaurants. Third industry group includes financial services and insurance; public administration and defense; business services and real estate; education; health and social work; other services; and private household services. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, regency-specific linear time trends, and industry indicators. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Regressions are weighted using survey population weights.

Table 10: Eﬀect on Labor Market Indicators: IPUMS individual-level data

Woman Partner In the labor In the labor Employed Unemployed force Employed Unemployed force [1] [2] [3] [4] [5] [6] Oil Endowment Value -0.0600 0.0076 -0.0524 -0.0079 -0.0031 -0.0113 Per Capita (0.0764) (0.0097) (0.0733) (0.0062) (0.0083) (0.0088)

Survey Year FE Y Y Y Y Y Y Regency FE Y Y Y Y Y Y Observations 702,716 702,716 702,716 651,585 651,585 651,585 Clusters 266 266 266 266 266 266

Notes: IPUMS survey years are 1976, 1980, 1985, 1990, 1995, 2000, 2005, and 2010. Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses.

33 Table 11: Eﬀect on Labor Market Indicators: SAKERNAS regency-level data

Employment Unemployment Labor force rate rate participation rate [1] [2] [3] Oil Endowment Value 0.0176*** -0.0181** 0.0100 Per Capita (0.00536) (0.00716) (0.007)

Survey Year FE Y Y Y Regency FE Y Y Y Observations 1064 1064 1064 Clusters 266 266 266

Notes: SAKERNAS years are 2007, 2008, 2009, and 2010. Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses.

Taken together, our estimated increase in household expenditures and prior literature finding that natural resources shocks increase wages and employment across a variety of industries, provide evidence that an increase in the oil endowment value per capita has a positive effect on households’ economic outcomes. Along with the estimates showing positive fertility effects among workers in several different kinds of sectors, we conclude these findings are consistent with oil shocks causing income changes experienced by a broad base of households. Mixed evidence on employment rates suggests that the effects of oil shocks on income may operate through the intensive margin, rather than the extensive margin. Overall, the evidence is consistent with a positive income effect being an important mechanism for the fertility effects we estimate – and with children being “normal goods.”

7.2 Government Expenditures and Provision of Public Goods

We next explore the eﬀect of oil shocks on channels other than earnings in the short-run. We consider government expenditures and the provision of public goods using indicators for economic development, infrastructure, and healthcare quality and access. While such mechanisms could also be in play in the long-run, we do not provide empirical tests for this possibility, as our estimation approach does not identify long-run fertility responses.

First, we test whether oil shocks affect local government expenditures. We do not find a statistically significant or economically meaningful effect of the oil endowment measure on annual total expenditures, and only weak evidence of an increase in annual personnel and education ex-

34 penditures per capita (Table 12).43 These ﬁndings are consistent with Cassidy (2020), which ﬁnds little response in public service delivery to oil and gas grants in Indonesia.

Table 12: Eﬀect on Public Expenditures

Goods and Total Health Personnel Services Education Expenditures Expenditures Expenditures Expenditures Expenditures Per Capita Per Capita Per Capita Per Capita Per Capita [1] [2] [3] [4] [5] Oil Endowment Value -0.1183 0.0834 0.2881* -0.0247 0.1702* Per Capita (0.9032) (0.0723) (0.1675) (0.1808) (0.1009)

Survey Year FE Y Y Y Y Y Regency FE Y Y Y Y Y Observations 2,495 2,495 2,471 2,463 2,494 Clusters 261 261 261 261 261

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Ministry of Finance, SIKD, 2001- 2010. Each column is a separate regression. Standard errors are clustered at the regency level and shown in parentheses.

Second, we use several proxies to test for changes in the provision of public goods. Using regency-level data from the SUSENAS surveys, we find a $100,000 increase in oil endowment per capita increases electricity coverage by nearly 11 percentage points. However, there is no effect on household access to safe water or safe sanitation (Column 4 in Table 8). As Grimm et al. (2015) note, the dependent variable is a measure of potential electrification access, where electrification in a village is defined as at least one village household reporting using electricity as the main source of lighting. Thus, the estimated increase in electrification provides some evidence that economic development may be a mechanism for short-run fertility effects.

We use several healthcare measures to test eﬀects on healthcare quality and access. We do not

ﬁnd evidence of any change in average monthly health expenditures per capita (column 3 in Table 8).

We ﬁnd no eﬀect on the share of births that are attended by a skilled health worker (column 7 in in

Table 8), but we do ﬁnd an increase in the number of midwives per capita (column 1 in Table 13).

We estimate that a $100,000 increase in oil endowment per capita leads to 0.77 additional midwives per 10,000 population (statistically diﬀerent from zero at the 10 percent level). This represents a

43The estimates imply that a one standard deviation increase in the oil endowment measure is associated with a decrease in total expenditures of less than 1 percent, and about a 4 percent increase in both personnel and education expenditures per capita.

35 Table 13: Eﬀect on Health Care Access

Midwives Doctors Hospitals Polyclinics Puskesmas per capita per capita per capita per capita per capita [1] [2] [3] [4] [5] Oil Endowment Value 0.773* 0.0758 0.0148 -0.107 -0.0882 Per Capita (0.446) (0.206) (0.0284) (0.281) (0.322)

Survey Year FE Y Y Y Y Y Regency FE Y Y Y Y Y Mean of dep. var. 5.02 1.84 0.09 1.86 1.98 Observations 1043 1043 1024 1176 1030 Clusters 266 266 266 262 266

Notes: PODES years are 1996, 2000, 2003, 2005, and 2008. Data for the numbers of doctors and midwives are not available in 2000 and data for the numbers of hospitals and puskesmas are not available in 2008. The dependent variable in each column is measured as the number, e.g. of midwives, per 10,000 population. Regressions are weighted by the regency population. Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses.

15 percent increase relative to the mean. We interpret the ﬁnding of an increase in midwives as very limited evidence that access to prenatal and obstetric care has increased, though it is not clear whether additional midwives improve maternal care, prenatal care, or decrease child mortality. It is also not clear whether additional midwives are due to increased prosperity or a response to an increase in demand for birth delivery services due to the increase in fertility. Estimates with respect to other forms of healthcare (doctors, hospitals, clinics, and community health centers (puskesmas) per capita) are mixed, though the magnitudes are small and not statistically diﬀerent from zero

(columns 2-5 in Table 8). We do not find evidence that government provision of healthcare has increased in the short-run in response to oil shocks. Therefore, we do not find evidence that this potential mechanism contributes to our findings of increased fertility.

We also consider these healthcare measures, particularly the number of midwives, polyclinics, and puskesmas per capita, as proxies for government provision of family planning programs.

Midwives in Indonesia receive formal training (currently a three-year program) and are central to Indonesia’s goal of providing family planning services (Heywood et al., 2010). Puskesmas are government-funded community health clinics that provide family planning services (in addition to other types of health services). If Indonesia’s government used revenues from increased oil endowment values to provide family planning services more or less intensively in oil regions, we would

36 expect to see a correlation between the oil endowment value and these proxies for family planning services.

We would be concerned if there were a negative relationship between the oil endowment value and the health measures. This could have been caused by disproportionate family planning services investments in regions without oil endowments. In this case, an estimated increase in fertility could reflect decreased fertility in non-oil-endowed regions (from the increase in family planning services), rather than an increase in demand for children from price shocks in oil-endowed regions. Thus, while we are unable to directly estimate the effect of oil shocks on BKKBN services due to lack of data, and cannot definitively rule out non-random (from an oil endowment perspective) investment in family planning, we do not have any evidence supporting this theory. Furthermore, even if there is non-random allocation of family planning programs in response to the oil shocks, the small estimated effect of the family planning programs on reducing fertility suggest any bias in our fertility estimates is small (Gertler and Molyneaux, 1994; Molyneaux and Gertler, 2000).

Overall, we find limited empirical evidence that oil shocks result in short-term increases in government expenditures and the provision of public services that might be mechanisms for increased fertility. We do, however, present evidence consistent with oil shocks affecting fertility through positive income effects.

8 Discussion and Conclusion

This paper explores whether local economic shocks aﬀect fertility decisions in Indonesia. We use a natural experiment in the form of oil booms and busts from 1970 to 2008. Using Indonesian

Census data, we estimate reduced form regressions comparing the fertility of women in regions experiencing varied boom and bust strengths, depending on the relative importance of oil to the local economy. Our identification strategy uses time series variation in global oil prices and regional variation in resource abundance. We find an increase in oil endowment value increases the probability of having a child, with stable estimates across a variety of specifications and samples. We

find evidence that the effect is driven by an increase in fertility among women of all ages and by both first and higher parity births. We find little evidence that there is an effect on birth spacing, consistent with the increase in fertility reflecting an increase in lifetime fertility rather than short-

37 ened time between births. However, we ﬁnd an increase in the likelihood of a male birth among older women, suggesting some of the increase in total fertility among older women is driven by improvements in fetal health.

Our estimates provide a lower bound of the fertility response, as we note two potential sources of downward bias. The first is that non-oil-endowed regencies may be affected by aggregate gains and losses from oil booms and busts. With positive spillover effects on fertility in areas without oil, this implies we underestimate the true size of the effect. The second is because our 1970 oil endowment measure contains some within-asset discoveries that occurred after 1970. By using an alternate endowment measure that includes oil discoveries on new assets discovered after 1970, we are able to sign the bias that results from including oil discoveries along the intensive margin.

Finally, we rule out bias in our estimates from selective migration using a sub-sample of women who did not move.

We also provide analyses probing mechanisms through which oil shocks may aﬀect fertility.

Using supplemental data from Indonesia’s labor force surveys, socioeconomic household surveys, and village census, we estimate the effects of oil endowment values on labor market outcomes, government expenditures, and proxies for infrastructure, economic development, and healthcare access. We find evidence that increases in the oil endowment value increase proxies for household income, suggesting a positive income effect is an important mechanism.

Our ﬁndings are consistent with the literature documenting positive income eﬀects on fertility.

Our study’s developing country context complements recent related work in the United States and the results are important in light of declining fertility rates during demographic transitions and the negative cross-country income-fertility relationship. Our estimates imply an increase in the probability of conceiving of 19 percent at the peak of the oil boom in the most intensive oil- producing areas.

To draw comparisons with prior work relating changes in income to changes in fertility, we can assume that oil shocks aﬀect fertility only through changes in income and take at face value our estimates of the eﬀect of oil shocks on household expenditures as a proxy for income. The estimates imply that a 10 percent increase in expenditures is associated with an 11 to 21 percent increase in fertility.44 This is moderately higher than prior work in the United States; Kearney and

44Calculations based on estimates in columns 1 and 2 of Table 8; the estimate in column 1 implies an 18 percent

38 Wilson (2018) finds that a 10 percent increase in earnings is associated with a 12.5 percent increase in birth rates and Black et al. (2013) finds a 10 percent increase in earnings increases the birthrate by 7 percent. However, data limitations suggest that this comparison should be viewed with an abundance of caution. Our fertility findings also reflect an increase in the likelihood of a male birth among older women, suggesting that a portion of our fertility result reflects improvements in fetal health. Additionally, we estimate the effect of oil shocks on expenditures, rather than income, and over a different time period than that used for our fertility estimates. If percent increases in income exceed percent increases in expenditures, due to a marginal propensity to save that exceeds average savings rates, then this calculation overstates the fertility response to changes in income.

Our findings consider the role of changes in the timing of births and provide indirect evidence of the role of improvement in fetal health in explaining the estimated increase in fertility rates. The included analysis estimating the effect of shocks on the probability of a male birth brings together the literature on economic shocks and fertility and the literature on external stressors and the fetal sex ratio. Finally, using a long sample period, we are able to demonstrate similar positive effects on fertility across both booms and busts.

increase in household expenditures relative to the mean.

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47 Online Appendices

A Robustness Checks

Inclusion of Additional Controls

In Table A1 we estimate equation 1 using right-hand side control variables and education variables as dependent variables. This allows us to investigate if they are endogenous. Because there is evidence that an increase in the oil endowment value raises the probability of the woman having more than a secondary education and of their partner having secondary education, we exclude education covariates from the main results. Table A2 presents results using the full set of covariates. Results are very similar to those in Table 2.

Table A1: The Eﬀect of Oil Endowment Value on Education Levels

Woman's education Woman's Less than Lower More than Urban age primary Primary Secondary Secondary secondary [1] [2] [3] [4] [5] [6] [7] Oil Endowment Value -0.0002 -0.237 -0.020 -0.005 0.015* 0.010 0.001 Per Capita (0.026) (0.157) (0.022) (0.018) (0.008) (0.008) (0.003)

Partner's education Less than Lower More than No partner primary Primary Secondary Secondary secondary [8] [10] [11] [12] [13] [14] Oil Endowment Value -0.005 -0.009 -0.009 0.015 0.017** -0.002 Per Capita (0.003) (0.020) (0.014) (0.013) (0.007) (0.003)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. All specifications include survey year indicators and regency indicators. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights. Regressions use 1,085,668 observations from Census years 1976, 1980, 1985, 1990, 1995, 2000, 2005, and 2010.

Alternative Functional Form

Table A3 presents the main results estimating equation 1 using a logit, a more common choice of functional form for a discrete time hazard model than ordinary least squares. Average partial eﬀects are in brackets and are similar in magnitude, if modestly smaller, and similar in statistical signiﬁcance to the OLS estimates in Table 2.

48 Table A2: The Eﬀect of Oil Endowment Value on Conception Probability - Including Education Controls

[1] [2] [3] [4] [5] [6] [7] [8]

Oil Endowment Value 0.0268*** 0.0142* 0.0192** 0.0149** 0.0160** 0.0217** 0.0237** 0.0189** Per Capita (0.0073) (0.0073) (0.0089) (0.0075) (0.0081) (0.0086) (0.0100) (0.0078)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the woman conceived in a given year. Covariates include an indicator variable for no partner, an urban indicator variable, an indicator for having previously given birth, and controls for education levels. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

Additional Heterogeneity Analyses

We investigate heterogeneity by education levels of women and their partners, and by urbanicity. While data on wages and income are not available in the IPUMS data, education level and urbanicity provide proxies for differences in opportunity costs and resource constraints across the population. We estimate equation 1 including interactions between the oil endowment value per capita and indicators for two higher level education bins. The coefficients on the interaction terms provide the differential effect relative to women with less than a primary school education.

We estimate two separate regressions including first a set of interaction terms using the women’s education levels and second the partner’s education levels. Results in Table A4 show there are no differential effects by either women’s (column 1) or their partner’s (column 2) education levels.

In each regression, the estimates on the interaction terms are small in magnitude and statistically insigniﬁcant. We caution about inferring causal heterogeneous eﬀects from this analysis due to the possibility that education levels respond to oil shocks (see Table A1). In column (3), we interact oil endowment value per capita with an indicator equal to one for an urban area. We also do not

find differences in the effect by urbanicity, with a small and statistically insignificant estimate on the interaction term.

49 Table A3: The Eﬀect of Oil Endowment Value on Conception Probability - Logit Estimates

[1] [2] [3] [4] [5] [6]

Oil Endowment Value 0.2509*** 0.1450** 0.1711** 0.1290* 0.1406* 0.1795** Per Capita (0.0623) (0.0652) (0.0856) (0.0728) (0.0785) (0.0843) [0.0255] [0.0141] [0.0166] [0.0125] [0.0136] [0.0174]

Survey Year FE Y Y Y Y Y Woman's age indicators Y Y Y Y Y Individual-level covariates Y Y Y Y Y Regency FE Y Y Y Y Year FE Y Time spline Y Individual FE Regency-specific trends Y Mean birthrate 0.12 0.12 0.12 0.12 0.12 0.12 Women 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 1,193,806 Observations 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 5,469,549 Clusters 266 266 266 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the woman conceived in a given year. Covariates include an indicator variable for no partner, an urban indicator variable, and an indicator for having previously given birth. Estimates are logit coefficients with average partial effects shown in brackets. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

Robustness to Regency Sample

We next check the sensitivity of our analysis to the inclusion of key oil producing regions.

Table A5 presents results analogous to those in Table 2, column (6). Column (1) uses the same speciﬁcation, but includes Kutai and Siak – two provinces with extremely large oil endowments.

Column (2) drops Siak (the regency with the largest endowment), and then column (3) additionally drops Siak and Pelalawan (the regencies with the second and third largest endowments). The coefficient of interest generally increases as regencies are dropped, suggesting that there may be nonlinearities in the fertility response in regencies with very large oil endowments. This is plausible as, e.g., a woman would likely find it difficult to respond to an enormous boom by having two additional children in a given year.

50 Table A4: Heterogeneity by Education and Urbanicity

Heterogeneity by Heterogeneity by Heterogeneity by Women's Education Partner's Education Urbanicity [1] [2] [3] Oil Endowment Value Per Capita 0.0241* 0.0251*** 0.0216** (0.0133) (0.0095) (0.0087) Oil Endowment Value Per Capita x -0.0024 Primary (0.0065) Oil Endowment Value Per Capita x -0.0043 Lower secondary plus (0.0091) Oil Endowment Value Per Capita x -0.0036 Primary (Partner) (0.0040) Oil Endowment Value Per Capita x -0.0051 Lower secondary plus (Partner) (0.0047) Oil Endowment Value Per Capita x -0.0052 Missing education (Partner) (0.0086) Oil Endowment Value Per Capita x 0.0007 Urban (0.0057)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Estimates from equation (1) with included specified interaction terms. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Each column is a separate regression. All specifications include age indicators, individual-level covariates, including indicators for women's and partner's education, survey year indicators, regency indicators, and regency-specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

Lagged Eﬀects on Conception Probability

Table A6 estimates versions of equation 1 including up to three lags of the oil endowment value per capita variable. While statistical significance for individual coefficients varies, all coefficients are positive and the cumulative effects are very consistent. We find statistically significant cumulative effects, except in column 4 where the standard error is much larger. We do not find evidence that the immediate increase in the conception probability is offset by a negative conception probability two or three years later. This provides additional evidence that the shocks to the endowment value per capita increase total fertility rather than just its timing.

Eﬀect on Child Mortality and the Number of Children Living Away from Home

In Table A7, we estimate whether oil shocks cause changes to the number of children ever born to a woman who were no longer living at the time of the survey or the number of surviving children living away from their mothers. Either of these eﬀects could suggest bias in our main

51 Table A5: The Eﬀect of Oil Endowment Value on Conception Probability - Sample Checks

Excluding Siak, Excluding Siak Kutai, and Full Sample Only Pelalawan [1] [2] [3] Oil Endowment Value 0.0017*** 0.0092* 0.0428*** Per Capita (0.00024) (0.00521) (0.0099)

Mean Oil Endowment Value Per Capita 0.089 0.025 0.013 Mean birthrate 0.12 0.12 0.12 Women 1,210,482 1,202,566 1,187,974 Observations 5,548,089 5,510,707 5,442,078 Clusters 268 267 265

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Weighted regressions are weighted using survey population weights.

52 Table A6: The Eﬀect of Oil Endowment Value on Conception Probability - Lagged Eﬀects

[1] [2] [3] [4]

Oil Endowment Value Per Capita (t) 0.0217** 0.0054 0.0070 0.0076 (0.0086) (0.0089) (0.0087) (0.0096)

Oil Endowment Value Per Capita (t-1) 0.0194*** 0.0118 0.0117 (0.0062) (0.0115) (0.0121)

Oil Endowment Value Per Capita (t-2) 0.0056 0.0009 (0.0115) (0.0166)

Oil Endowment Value Per Capita (t-3) 0.0034 (0.0150)

Cumulative effect 0.0217** 0.0247*** 0.0244*** 0.0227 (0.0086) (0.0079) (0.0060) (0.0171) Observations 5,469,549 5,428,173 5,386,547 5,345,628

results due to incomplete birth histories. The two variables are reported in the 1976, 1980, 1990,

1995, and 2010 surveys. We estimate equation 1 using one observation per woman, in the year of the survey. We provide both OLS and Poisson regression results in Table A7. The estimates do not suggest an increase in the oil endowment value aﬀects child mortality. In columns 1 and 2, both the OLS and Poisson estimates are positive, but small in magnitude and not statistically diﬀerent from zero.

There is evidence, however, that an increase in the oil endowment value increases the number of children living away from the mother. The OLS and Poisson coeﬃcients are both statistically diﬀerent from zero at least at the 5 percent level, and are similar in magnitude. The Poisson estimate in column 3 implies a $100,000 increase in the oil endowment value increases the number of children not living with the mother by about 14 percent. This is a potential source of bias if increases in the oil endowment value lead to an increase in younger children living away from home.

Because we use only a ﬁve-year retrospective panel for each woman, missing birth histories for older children (age 5 and older) do not aﬀect our estimates. In the event we are missing birth histories

53 Table A7: Eﬀect on Child Mortality and the Number of Children Living Away from Home

Number of surviving Number of surviving Number of children Number of children children not living children not living dead - OLS dead - Poisson with mother - OLS with mother - Poisson [1] [2] [3] [4] Oil Endowment Value 0.0538 0.0783 0.0562** 0.1416* Per Capita (0.0345) (0.0956) (0.0237) (0.0724) [0.0319] [0.0489]

Mean dep. var. 0.40 0.40 0.34 0.34 Observations 565,631 565,631 612,484 612,484 Clusters 266 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. In columns 1 and 2, the dependent variable is the number of children ever born to a woman who were no longer living at the time of the census. The dependent variable in columns 3 and 4 is the number of suriviving biological children not living with their mother (the respondent) at the time of the census. All specifications include age indicators, individual-level covariates, survey year indicators, and regency indicators. Standard errors are clustered at the regency level and shown in parentheses. Average marginal effect in columns 2 and 4 shown in brackets. Regressions are weighted using survey population weights. Observations from Census years 1976, 1980, 1990, 1995, and 2010.

for younger children moving away from mothers, these results imply our estimates of the eﬀect of oil shocks on fertility are downward-biased. Our interpretation is that any bias is likely to be small.

These results imply that our main ﬁndings are less likely to be explained by changes in child mortality, and downward biased if oil shocks cause younger children to live outside the home.

Sensitivity of Results to Post-1970 Oil Discoveries

We check the sensitivity of our results to including post-1970 oil discoveries along the extensive margin (new assets). This is instructive because our data includes some post-1970 oil discoveries along the intensive margin (within an existing asset). While extensive margin discoveries are more frequent and larger, one could still be concerned about residual bias from intensive margin discoveries. Column 2 of Table A8 demonstrates that when we include discoveries along the extensive margin, our results become smaller and (marginally) less statistically significant than our original estimates (replicated in column 1). This is suggestive that if intensive margin discoveries were removed, we would find larger and more statistically significant results.

54 Table A8: The Eﬀect of Oil Endowment Value on Conception Probability Endowments Including All Oil Known byThe 2010 effect of oil endowment value on conception probability in pooled Indonesia census data

Estimates with post- 1970 oil discoveries Estimates with post-1970 included along the oil discoveries included intensive margin along both the intensive (original estimates) and extensive margin [1] [2] Oil Endowment Value 0.0217** 0.0069** Per Capita (0.0086) (0.0028)

Mean birthrate 0.12 0.12 Women Observations 5,469,549 5,469,549 Clusters 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. All specifications include age indicators, individual- level covariates, survey year indicators, regency indicators, and regency- specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Weighted regressions are weighted using survey population weights.

Booms versus Busts: Base Period Checks

Table A9 presents results similar to those in Table 7, though using diﬀerent base periods.

Column 1 is similar to the specification in Table 7, but sets the base period to 1970-1972 instead of 1970-1972 and 1987-2002, combined. Column 2 provides analogous results with the base period set to 1987-2002. Column 3 provides the total effect on fertility over the specified time period.1

The estimated eﬀect for the period 1970-1972 is negative and large, though it is not statistically diﬀerent from zero.

The total eﬀects column demonstrates that there is a similar sized positive fertility response across most time periods. There are two notable results. First, while neither 1970-1972 nor 1987-

2002 have sustained booms or busts, the results show a statistically diﬀerent eﬀect between the two periods. This is possibly being driven by the fact that while the latter had higher price years and lower price years, 1970-1972 prices are stable at a low level and are at the tail-end of a period

1The composite estimated effect for each period is identical for each of the specifications in Columns 1 and 2, though the coefficients look different because of the negative estimated effect in 1970-1972.

55 where the world oil price gradually fell for 15 years. Therefore, provinces with large oil endowments could actually be economically depressed relative to those without. Second, the most recent boom

(2003-2008) still has smaller eﬀects. As discussed in the main text, this could be because of a decline in exploration and production or because of a larger substitution eﬀect in more recent years

(with higher women’s wages and better employment opportunities).

Table A9: Booms versus Busts: Base Period Checks

Total effect Mean fertility Percent change over rate over the relative to Base period: Base period: specified specified time mean fertility 1970-1972 1987-2002 time period period rate [1] [2] [3] [4] [5] Oil Endowment Value Per Capita -0.0961 0.051*** (0.064) (0.015) Oil Endowment Value Per Capita x -0.147** -0.0961 0.18 -54% 1970 - 1972 (0.060) (0.064) Oil Endowment Value Per Capita x 0.134** -0.014 0.038 0.16 24% 1973 - 1980 (0.052) (0.012) (0.024) Oil Endowment Value Per Capita x 0.136** -0.011** 0.040*** 0.14 29% 1981 - 1986 (0.059) (0.005) (0.013) Oil Endowment Value Per Capita x 0.147** 0.051*** 0.10 50% 1987 - 2002 (0.060) (0.015) Oil Endowment Value Per Capita x 0.120* -0.028** 0.023*** 0.08 28% 2003 - 2008 (0.063) (0.013) (0.004)

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Standard errors are clustered at the regency level and shown in parentheses. Estimates from equation (1) with interaction terms for specified time period. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Regressions are weighted using survey population weights.

56 B Complementary Difference-in-Differences Specification

We provide estimates using an alternative difference-in-differences specification that leverages time-series variation across regencies with and without oil:

high low high low birthr,(t+1) = a + bOILr + cTt + dTt + eOILr × Tt + fOILr × Tt + irt

As in our main speciﬁcation, the dependent variable is a binary variable for whether or not the woman gave birth the subsequent year. OILr is an indicator for whether a regency has oil

high low reserves, Tt is an indicator if oil prices are in the top tercile in year t, and Tt is an indicator if oil prices are in the lowest tercile. If an increase in the oil endowment value causes an increase in

high fertility, then we expect the coeﬃcient on the interaction between OILr and Tt to be positive, such that higher oil prices cause higher fertility in oil-endowed regencies. Conversely, we expect

low the coefficient on the interaction between OILr and Tt to be negative. We find that there are differential effects of price shocks across regions with and without oil, consistent with the conclusions from our estimates in Table 2. The results in column 1 of Table B1 show that for our full sample, the coefficient on the high price interaction term is positive and the coefficient on the low price interaction term is negative. The estimates on the interaction terms indicate that when the oil price increases from the middle to the top tercile, in an oil-endowed regency, the probability of conceiving increases by 0.3 percentage points on average, and falls by about 0.38 percentage points on average when the oil price decreases from the middle to the bottom tercile. As shown by the p-value in the bottom of the table, these two estimates are statistically different from each other at the 1 percent level.

We estimate the above equation separately among younger (ages 14-32; column 2) and older

(ages 33-45; column 3) women and separately by women who have not previously given birth

(column 4) and women who have given birth (column 5) to consider heterogeneous effects by age and birth parity, analogous to the results in Table 3. Among both younger and older women, and among women who have previously given birth, the estimates on the interaction terms are statistically different from each other, providing evidence of differential effects of price shocks across regions with and without oil among these groups. The estimates are not statistically different from

57 Table B1: Eﬀect of higher oil prices on fertility in oil-endowed regencies

Younger women Older women Higher order All (ages 14 - 32) (ages 33 - 45) First birth births [1] [2] [3] [4] [5] Oil 0.0076 0.0040 0.0038 0.0018 0.0091 (0.0051) (0.0058) (0.0035) (0.0050) (0.0055)

Thigh 0.0012* 0.0042*** 0.0001 0.0059*** 0.0027*** (0.0007) (0.0009) (0.0007) (0.0014) (0.0008)

Tlow -0.00004 0.00067 0.00010 0.0157*** -0.0023*** (0.0007) (0.0010) (0.0007) (0.0016) (0.0008)

Oil x Thigh 0.0031* 0.0007 0.0042* 0.0014 0.0033* (0.0017) (0.0027) (0.0022) (0.0041) (0.0019)

Oil x Tlow -0.0038*** -0.0038* -0.0016 0.0006 -0.0054*** (0.0014) (0.0023) (0.0016) (0.0037) (0.0015)

high low H 0: Oil x T = Oil x T p-value 0.0001 0.0657 0.0277 0.8133 0.0000 Observations 5,469,549 3,296,819 2,172,730 1,266,993 4,202,556 Clusters 266 266 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Each column shows results from a separate regression. Standard errors are clustered at the regency level and shown in parentheses. Covariates include indicators for the suvey year. Regressions are weighted using survey population weights.

each other among first births. Using this specification, the results appear to be driven by higher order births. While the estimates in Table 3 show similar effects by parity, both sets of estimates are consistent with the conclusion that there is an increase in completed fertility, as opposed to changes in timing.

We also use the above specification for our other two primary outcomes: birth spacing and the probability of a male birth. We provide estimates among the full sample, as well as separately among younger and older women. Consistent with the conclusions from our main specification, we do not find evidence of differential effects of price shocks across regions with and without oil on the time between births (Table B2). We also do not find statistically significant evidence that price shocks differentially affect the probability of a male birth in oil-endowed regions (Table B3). Among older women, the coefficient on the high price interaction term is positive, and the coefficient on the low price interaction term is negative. Both of these are the expected direction. However, in

58 contrast to the estimates in Table 5, there is a loss of statistical precision and the estimates are not individually statistically signiﬁcant nor statistically diﬀerent from each other.

Table B2: Eﬀect of higher oil prices on birth spacing in oil-endowed regencies

Dep. var.: Number of years since Younger women Older women previous birth All (ages 14 - 32) (ages 33 - 45) [1] [2] [3] Oil -0.0799 -0.0760 -0.0372 (0.1017) (0.1041) (0.1051)

Thigh 0.0150 -0.0063 0.0967 (0.0170) (0.0192) (0.0413)

Tlow 0.0571*** 0.0578 0.0611* (0.0146) (0.0160) (0.0314)

Oil x Thigh -0.0683 -0.0576 -0.1293* (0.0518) (0.0568) 0.0680

Oil x Tlow -0.0568 -0.0634 -0.0575 (0.0390) (0.0424) (0.0712)

high low H 0: Oil x T = Oil x T p-value 0.7665 0.8842 0.345 Observations 432,318 335,421 96,897 Clusters 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived in a given year. Covariates include survey year indicators. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

59 Table B3: Eﬀect of higher oil prices on the probability of a male birth in oil-endowed regencies

Younger women Older women Dep. Var.: Probability of male birth All (ages 14 - 32) (ages 33 - 45) [1] [2] [3] Oil 0.0045 0.0041 0.0072 (0.0048) (0.0039) (0.0072)

Thigh -0.0010 0.0000 -0.0048 (0.0030) (0.0033) (0.0077)

Tlow 0.0026 0.0047 -0.0068 (0.0029) (0.0035) (0.0070)

Oil x Thigh 0.0003 0.0000 0.0011 (0.0087) (0.0090) (0.0188)

Oil x Tlow -0.0044 -0.0031 -0.0117 (0.0072) (0.0031) (0.0242)

high low H 0: Oil x T = Oil x T p-value 0.5771 0.7442 0.3732 Observations 640,357 525,518 114,839 Clusters 266 266 266

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. The dependent variable is an indicator variable equal to one if the mother conceived a male in a given year. Covariates include indicators for the suvey year. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

60 C Additional Sex Ratio Analysis and Discussion

Alternative Explanations

We discuss four alternative explanations for changes in the sex ratio: changes to sex-selective abortions, maternal age, paternal age, or higher order births. These are presented in Chahnazarian

(1988) as primary drivers of the sex ratio and the most likely alternative explanations for our ﬁnding of an increased probability of male births among older women.

Sex-selective abortions are an alternative explanation for the increased sex ratio. Oil intensive regions have a sex ratio of 106.5 males/100 females, above the 105.1/100 ratio in non-oil regions and consistent with sex-selective abortion.2 However, Kevane and Levine (2000) ﬁnds no evidence of “missing daughters” in Indonesia and we are unaware of work suggesting otherwise. Abang Ali and Arabsheibani (2020) builds on this work and ﬁnds evidence that son preference in Indonesia manifests itself by driving future births rather than through sex-selective abortion.

Further, if sex-selective abortion is driving the sex ratio diﬀerence, increases in the practice would need to be concentrated among older women where we ﬁnd larger changes in the sex ratio.

We find it more plausible that this pattern is explained by the fact that miscarriage rates are much higher among older women (e.g. Magnus et al., 2019) and this age group is therefore where we might expect the largest effects from, e.g., improved nutrition due to higher incomes. Therefore, we find it unlikely on balance that sex-selective abortions are driving this change. We also note that because our primary result is that fertility increases, to the extent that sex-selective abortion increases, our primary results would underestimate changes in conceptions.

Because we directly control for maternal age, this is unlikely to be driving changes in the sex ratio. Additionally, we have added a speciﬁcation with “paternal age” included as a control. The

first three columns of Online Appendix Table C1 (below) include a set of indicators for the woman’s partner’s age. Because the estimated effects are robust to this inclusion, we rule out paternal age as a primary driver. Finally, because we find that higher order births (who are more likely to be female) increase, we find it unlikely that changes to the birth order such as, e.g., more first births are driving this change.

2Sex ratios range across countries with, e.g., Belgium having a ratio of 104.0/100 and Portugal having a ratio of 107.3/100 in 2001. For most of our time period, the ratio in the United States was about 105/100. (Mathews et al., 2005).

61 Alternative Speciﬁcations

Table C1 also provides specification checks adding individual fixed effects. Here, we lose much of our statistical power and no longer find that older women see their probability of a male birth increase. However, the point estimate among all women in Column 4 suggests that the effect is larger than estimated in our preferred specification. Because the individual fixed effects absorb much of our identifying variation, coefficients are identified off of women who have had multiple births with at least one child of each sex during the five years preceding the survey.3 We interpret this result as due to the limitations of our data, as opposed to evidence that the effect is not real.

Table C1: The Eﬀect of Oil Endowment Value on the Probability of a Male Birth: Speciﬁcation Checks

Dep. Var.: Probability of male birth All Heterogeneity by age All Heterogeneity by age [1] [2] [3] [4] [5] [6] Oil Endowment Value Per Capita 0.015 0.012 0.013 0.039 0.024 0.045 (0.011) (0.015) (0.011) (0.090) (0.106) (0.088) Oil Endowment Value Per Capita x 0.001 0.028 Ages 22-32 (0.008) (0.042) Oil Endowment Value Per Capita x 0.013 -0.026 Ages 33-37 (0.014) (0.130) Oil Endowment Value Per Capita x 0.034 -0.017 Ages 38-45 (0.022) (0.256) Oil Endowment Value Per Capita x 0.019** -0.052 Ages 33-45 (0.008) (0.106)

Paternal age indicators Y Y Y Individual FE Y Y Y Observations 640,357 640,357 640,357 640,357 640,357 640,357

Notes: Significance levels are indicated as *0.10, **0.05, and ***0.01. Estimates from equation (1) where the dependent variable is an indicator variable equal to one if the mother conceived a male in a given year. Each column is a separate regression. All specifications include age indicators, individual-level covariates, survey year indicators, regency indicators, and regency-specific linear time trends. Standard errors are clustered at the regency level and shown in parentheses. Regressions are weighted using survey population weights.

3A woman with births of only one sex will have their ﬁxed eﬀect perfectly identify the probability of a male birth.