_T_<sup>ˆ</sup> ( _h_ ) = _m_ − ( _h_ ) − _m_ + ( _h_ ) = _x_ (Equation 2) ii XX where m-(h) and m+(h) denote the intercepts at zero of weighted linear regressions for only treated and only control units, respectively, while h is the chosen bandwidth. To program the estimation strategy, we follow Calonico et al. (2014b) and use the rdrobust command in Stata.<sup>20</sup> In the preferred specification, we estimate the treatment effect with a vector of demeaned control variables, which includes variables on respondent’s age, gender, religion, and ethnicity.

As discussed in Section 4.1, we examine the effect of the cash transfers on _multiple_ GBV outcomes, based on type of violence inflicted and the relationship to the perpetrator. To adjust for multiple hypothesis testing and address the issue of over-rejection of the null hypothesis due to multiple inferences, we use summary index tests and false discovery rate adjusted p-values or FDR q-values (Anderson 2008).

# VI. Results

## 6.1 Primary Findings

Estimates from the preferred specification are presented in Tables 3-6. Our key findings suggest no significant change in lifetime incidence and frequency of exposure to reported IPV among women who were assigned 4P beneficiary status compared to women who were assigned non-beneficiary status (please see Tables 3 and 4). Similarly, we do not find any change in incidence of reported non-domestic violence (refer to Table 6). To put these findings in perspective, it is worth noting that reported rates of GBV in the Philippines (14%) is lower than several of its counterparts in Africa (33%), South Asia (35%), and Latin America and the Caribbean (25%); thereby, making it more difficult to capture comparable impacts as the event of violence is relatively rare.<sup>21</sup> At the same time, we find a measurable decline in reported emotional non-partner domestic violence, i.e., any form of humiliation or insult perpetrated by victim’s family, her in-law’s family, or other relatives (shown in Table 5). Notably, the effect size is substantial largely due to methodological reasons. The effect of treatment on emotional NPDV is presented graphically in Figure 3. In Figure 3, linear approximations for both treatment and control observations show that emotional NPDV is negatively correlated with income – as income falls, emotional NPDV tends to increase. Counterfactual for the treated group is captured by the dashed red line, i.e., we assume that without treatment the linear trend observed for control observations would continue to increase with falls in income. Considering that the effect of the program is captured by the difference in intercepts of local regressions for beneficiaries and nonbeneficiaries as shown in Equation 2 (m-(h)-m+(h)), we find strong discontinuity at the threshold with a significant drop in reported emotional NPDV.

On investigating further, we find that the estimated reduction in NPDV is mainly confined to decline in violence perpetrated by sibling or sibling-in-law and not parents/parents-in-law or other relatives (shown in Table 5a). Interestingly, we find that household structure and co-location among household members

> 20 Refer to Calonico et al. 2014a for a description of the command.

> 21 GBV rates refer to lifetimes experience of intimate partner violence. Source: Global Database on Prevalence of Violence Against Women, The World Health Organization.

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