Table 4: Impact on number of children at follow-up

||_Ethiopia DOT_|_Ethiopia WALN_|_Togo_|_Benin_|_Ghana_|_Rwanda_|
|---|---|---|---|---|---|---|
|||Business training|||Land titling||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment follow-up 1|0.112**|-0.160|0.391***|0.202**|0.262**|0.008|
||(0.055)|(0.261)|(0.122)|(0.098)|(0.099)|(0.016)|
|Treatment follow-up 2|0.084|-0.191||0.078|0.078||
||(0.060)|(0.278)||(0.087)|(0.134)||
|Observations|1,380|178|391|3,784|1,576|1,971|
|Control group mean|2.326|3.588|0.569|4.021|3.474|3.390|
|p-val|0.646|0.934||0.253|0.044||
|Controls|YES|YES|YES|YES|YES|YES|
|Clustered SE|YES|NO|NO|YES|YES|YES|
|Strata fxed effects|NO|YES|YES|YES|YES|NO|
|Time lag b/w interven-<br>tion and follow-up 1|12 months|12 months|18 months|24 months|12 months|12 months|
|Time lag b/w interven-<br>tion and follow-up 2|24 months|36 months||72 months|24 months||

Notes: This table describes the average treatment effect of each intervention on the women’s number of children. Standard errors are clustered at the randomization level. The outcome of column (3) corresponds to the number of children under 5 in the household. The controls include: number of children at baseline, marital status, highest level of school completed, age, age square, a dummy equal to one if the woman is household head, and household size. If there is no baseline (as in Benin and Ghana), the number of children at baseline corresponds to the number of children born before the beginning of the treatment built from the midline household roster. We include stratification fixed effects when required. We control for time when there are two periods. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

tervention (statistically significant at the 1% level).<sup>19</sup> Compared to Ethiopia DOT, in Togo the outcome was measured six months later (18 months after the start of the intervention, instead of 12 months) and take-up rates were substantially higher at 84%. Women in the WALN sample—who were selected because they run well-established businesses<sup>20</sup> —do not increase their fertility. This is consistent with women business owners within WALN being located on the demand curve beyond the revenue threshold after which demand for children is income inelastic; women in the WALN sample’s control group have on average 1.3 more children than women in the DOT sample’s control group.

Turning to the land interventions, we detect a positive impact on women’s fertility in Benin and Ghana but not in Rwanda. Women randomized into the treatment group in Benin have 0.2 more children two years after the start of land demarcation, a 5% impact over the control mean (statistically significant at the 5% level). In Ghana, women in the treatment group have on average 0.26 more children than women in the control group 12 months after the beginning of demarcation, (also statistically significant at the 5% level). This effect

> 19This dataset does not contain information on the number of biological children of the beneficiary woman, so we compute the outcome as the number of children born post-intervention in the household. Given that women in our sample are married to or in a relationship with the head of the household, we assume that most of those newborns are the children of the women under study. To verify that our estimated impact is not driven by an increase in fostered children as women improved their financial status, we show that women older than reproductive age do not see any impact on their fertility (Appendix Table A8).

20While women in the Ethiopia DOT sample have 1.4 employees on average, this number is 3.6 in the WALN sample. Moreover, the monthly revenue in the DOT sample is 40,479 birr, but 72,000 birr in the WALN sample.

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