stage _F_ -statistic ranges from 5.93 to 37.62.<sup>7</sup> For all three samples, the intensity measure predicts a strong negative relationship between exposure to the FPE reform and number of children born. In column 3, reduced-form estimates show that each additional year of free schooling made available reduces the number of births by between 0.057 and 0.064. Estimates across all three samples yield values that are qualitatively and quantitatively similar, providing evidence that the associations found in table 2 are not reliant on any one source of data.

The results from the first stage demonstrate a broad strength in the intensity measure’s ability to identify the increase in schooling generated by FPE in Ethiopia. Estimating the second stage of the 2SLS model focuses on the relationship between the predicted level of schooling and birth rates, as described by equation (9). The results in column 4 of table 2 demonstrate that the exogenous increase in schooling generated by the reform led to a reduction in fertility of 0.437 births for each additional year of schooling when using the combined sample. The estimate is larger when using the more recent data from the DHS, but remains similar across all three data combinations. Each estimate is statistically significant at the 99 percent confidence level.<sup>8</sup> Consistent with the findings in table 2, 2SLS estimates obtained by Osili and Long (2008) for Nigeria, Keats (2018) for Uganda, and Fort et al. (2016) for the United Kingdom are significantly larger in magnitude than the negative OLS relationship, and Zenebe Gebre (2018) found similar evidence in Malawi linking schooling to reductions in fertility.

# **6.2 Mechanisms**

The results in table 2 provide evidence that additional schooling generated by the removal of school fees led to a reduction in fertility for Ethiopian women. This subsection explores in greater detail the decisions and changes in behavior that may be driving the relationship between schooling and fertility.

Once married, there are three broad, but not mutually exclusive, avenues through which the household fertility decision is made. First, the increase in schooling could increase a woman’s opportunity cost of time, impacting her desired number of children. Second, increased schooling could potentially lead to a change in relative bargaining power over the joint fertility decision, and this is likely to lower fertility rates because women generally desire fewer children than their husbands.<sup>9</sup> A change in the use of contraception, especially of forms not visible to the spouse, could be one way in which a change in the bargaining position might be observable in the data. Finally, higher levels of schooling could lead to different outcomes in the marriage

> 7The first stage _F_ -statistics for the DHS-only data are less than 10; therefore, Anderson and Rubin (1949) confidence sets are given in the supplementary online appendix for all DHS-only 2SLS estimates from the main body of the article.

> 8Across all three samples, the upper bound of the Anderson-Rubin weak IV robust confidence sets is never more positive than _−_ 0.265 (Appendix Table B.1).

> 9More than one in three women from pre-reform cohorts report their husband wanting more children than they do, while only nine percent report that they would like to have a larger family than their husband.

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