in the treatment group. The evaluation found that actual and perceived tenure security increased in the treatment group. The clarification of property rights increased the likelihood of households having purchased land, and a decrease in renting and sharecropping led households to move from merely using the land through rental contracts to ownership through land purchases. Labor use on plots declined and was reallocated to non-farm activities with higher returns. In particular, women were 10 percentage points more likely to be engaged in off-farm work as a result of the intervention (Agyei-Holmes et al., 2020). In this study, we focus on the data collected in the first and second survey waves in 2010 and 2011, one year and two years after the beginning of the intervention in 2009.

Our last dataset is from the phase-in randomization of the national land tenure regularization (LTR) program launched in 2010 by the government of Rwanda. An evaluation of the pilot had already shown a positive impact: improved land access for married women, better records of inheritance rights, a doubling in soil conservation investment and a reduction in land market activity (Ali et al., 2014). Based on these encouraging results, a randomized impact evaluation was designed for the scale-up of the LTR program nationwide. Out of 100 eligible ‘sectors’ spread across the country, 50 were randomly selected to receive the program. The other half of the sectors were to receive the program at the very end of the IE. 3,600 rural households across 300 villages evenly split across the 50 sectors were selected for the evaluation. Whereas the land titling interventions in Benin and Ghana merely formalized existing land rights, the intervention in Rwanda actively increased women’s perceived land rights, as it considered all landholdings by married men as marital property and included wives’ names on the land certificates. Married women in treated areas were also more likely to be able to lease, mortgage, sell or bequeath land (Ali et al., 2015). We use the baseline data collected in early 2011 before the beginning of the program and the first follow-up collected in 2012.

# **3.2 Sample Characteristics**

For each of the six datasets, we restrict our sample to female potential beneficiaries of reproductive age (18-50) at baseline.<sup>10</sup> Since across our datasets, less than 5% of women who report being single have children, we further restrict our sample to women that are married or in a relationship at baseline.<sup>11</sup> Table 1 shows descriptive statistics for the resulting sample of entrepreneurs, while Table 2 depicts our land titling sampling frame. The statistics are computed using baseline data for each sample, or the first follow-up if no baseline was conducted (this was the case in Benin and Ghana). The women in our dataset are on average in their thirties, with the youngest average age in Ethiopia DOT (31.6) and the oldest average age in the Togo sample (38.3).<sup>12</sup> Education levels vary substantially across datasets.

> 10While our main analysis restricts our sample to female potential beneficiaries, we do use the Togo study’s broader sample (including potential male beneficiaries) as a robustness check to analyze whether there are effects on fertility when the woman is not the direct beneficiary of the treatment.

> 11The one exception is for Ethiopia DOT, where we keep the entire sample because 61.8% of single women report having children at baseline.

> 12In our four study countries, women bear approximately 22% of their children at age 35 or above. This figure is calculated using the most recently available round of DHS data for each of the four countries, accessed via USAID’s statcompiler.com. We performed the following calculation: sum(age-specific fertility rates for 3549)/sum(age-specific fertility rates 15-49)*100.

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