Relevant studies utilizing quasi-experimental methods

There are several channels through which female education and fertility may be connected. The opportunity cost of child-bearing and rearing may be related to female education. For women with more years of schooling this cost may be higher than those with less years of schooling, which acts to reduce fertility (Becker, 1991). Child health and mortality may also be associated with mother’s years of schooling. If more years of schooling lead to improvements in child health and mortality then preferred family size may be reached with fewer births (Schultz, 1994). Another channel between female education and fertility may be established via knowledge and use of family planning methods. Thus, female education may cause a change in reproduction technology and this may have an impact on fertility (Rosenzweig & Schultz, 1985). Lastly, women’s status and bargaining power within the household may be related to years of schooling and this may affect fertility decisions (Eswaran, 2002).

The number of studies investigating causal links between female education and fertility is indeed very limited. There are only three studies for developed countries (Black, Devereux, & Salvanes, 2008; McCrary & Royer, 2006; Monstad, Propper, & Salvanes, 2008) and five studies for

developing/low income countries (Breierova & Duflo, 2004; Gulesci & Meyersson, 2012; Kırdar, Dayıoğlu, & Koç, 2009; Kırdar, Dayıoğlu Tayfur, & Koç, 2011; Osili & Long, 2008).

For developed countries, the available empirical evidence comes solely from the US and Norway. McCrary and Royer (2006) use age-at-school entry policies to identify the effect of mother’s schooling on the probability of motherhood and age at birth in Texas and California between 1989–2002. Their regression discontinuity approach highlights clearly that school entry policies have large effects on schooling: one-fourth of young Texan mothers and one-seventh of young California mothers born after the school entry date have a year less education than they otherwise would. Then they combine school entry laws with the mother’s exact date of birth to form an instrument for schooling and their two-stage least squares (TSLS) analysis shows that these individuals are equally likely to become mothers and their age at birth are statistically not different.

Black, Devereux and Salvanes (2008) investigate the relationship between female education and the probability of teenage motherhood. They make use of changes in compulsory schooling laws in the US and Norway and they find evidence that increasing mandatory education reduces the probability of teenage motherhood and the size of the effect is similar in the US and Norway. However they only estimate reduced form equations. Thus their findings establish a link between changes in compulsory schooling laws and teenage motherhood but not years of schooling and teenage motherhood.

Monstad, Propper and Salvanes (2008) extends Black, Devereux and Salvanes (2008)’s study by solely focusing on Norway but also investigating the causal effect of education on the timing of the fertility of women born between 1947 and 1958 in Norway within an two-stage least squares framework. The source of exogenous variation in mothers’ education is the change in

compulsory years of schooling from seven to nine which was implemented over a 12-year period from 1960 to 1972 in different municipalities at different times. Their results indicate that an

additional year of schooling leads to reduction in the probability of giving birth when women are between 15 and 20 years old.

For developing countries, the empirical evidence comes from three different countries, i.e. Indonesia, Nigeria and Turkey. Breierova and Duflo (2004) study the impact of primary school construction programs in Indonesia between 1973–1974 and 1978–1979 on years of schooling and fertility. In this time period, over 60 thousand new schools were constructed and the

allocation of these new schools varied considerably across the country’s 281 districts. They use the product of year of birth and number of new schools built by district as an instrument for schooling to estimate the effects of mother’s and father’s education in fertility functions. They find that average years of schooling of parents have a negative impact on number of children born before age 15; however estimated standard errors are too large to draw the same conclusion for number of children born before age 25 and number of children ever born.

Osili and Long (2008) capitalize on a nationwide program in Nigeria that provided tuition-free primary education and which increased the number of primary school classrooms at varying rates across the 19 states of Nigeria in 1976 to investigate the connection between female education and fertility. The conception of their identification strategy is identical to Breierova and Duflo (2004)’s identification strategy. They utilize the product of year of birth and per capita amount of federal funds given to each state for classroom construction as an instrument for mother’s

schooling and estimate the number of births before age 25 in the 1999 Nigerian Demographic Health Survey. Two-stage least squares estimates of the effect of mother’s schooling on the number of births before age 25 are negative and statistically significant.

For Turkey, three are three studies which examine the association between female education and timing of fertility and marriage mainly exploiting the variation in years of schooling triggered by the Compulsory Education Law which raised compulsory years of schooling from five to eight in 1997. Kırdar, Dayıoğlu and Koç (2009; 2011) investigated the impact of the Compulsory

Schooling Law on the probabilities of getting married at 16 and giving birth at 17 in two

consecutive studies. They primarily utilized duration analysis and compared cohorts which were born before and after 1986 in the cross sections of Turkey Demographic and Health Surveys (TDHS) of 2003 and 2008. Their analysis with TDHS 2003 shows that women born after 1986 were 34 percent less likely to get married at age 16 and also 44 percent less likely to give birth at age 17 (Kırdar, et al., 2009). Their analysis with TDHS 2008 shows that women born after 1986 were 44 percent less likely to get married at age 16 and 34 percent less likely to give birth at age 17 (Kırdar, et al., 2011).

Gulesci and Meyersson (2012) analyze the ever-married female subsample of TDHS 2008 to examine the same research questions with a regression discontinuity approach claiming that those who were born after September 1986 were under treatment and those who were born before September 1986 constitute the control group. The first stage estimation clearly highlights a significant difference between treatment and control groups. The treatment group had

approximately one more year of schooling when compared to the control group. However, reduced form estimations and regression discontinuity-instrumental variable estimations do not provide any evidence indicating a difference in age at first marriage and age at first birth. Based on RD and RD-IV estimations they also present findings showing that women in treatment group were more likely to marry after age 16 and give birth after 17; however it is not clear whether it is appropriate to study these outcome variables with the ever-married female subsample. In

discussing their results on age at first marriage and age at first birth, Gulesci and Meyersson (2012) suggest that schooling may increase a woman's opportunities in the marriage market, which might lead to finding a husband earlier.

An important caveat concerning the examinations by Kırdar, Dayıoğlu and Koç (2009; 2011) and Gulesci and Meyersson (2012) is that these researchers do not exploit any measure of treatment intensity in their analyses and their results depend solely on variation over time. In essence, they compare cohorts in terms of timing of marriage and fertility before and after the initiation of the Compulsory Education Law. This study, on the other hand, makes use of a treatment intensity measure and capitalizes not only on variation in time but as well as variation within cohorts.