We then tested statistically whether _p_pam_ is indeed an exogenous variable for the extreme poverty measures and labor supply variables of interest using a Wooldrige’s robust regression based test.<sup>16</sup>

# 3.6 Results

## **3.6.1 Intention-to-treat Effects (ITT)**

Results tables below show the three main variables: the interaction term of the treatment group dummy variable and the time dummy variable, which captures the treatment effect of the intervention (i.e. ITT); the treatment group dummy variable, which captures the potential differences between treatment and control groups prior to the expansion of the program; and the time dummy variable, which captures the aggregate factors or secular changes that would have happened even in the absence of the expansion of the program.

Results estimated using the LPM with the extreme poverty headcount index as the outcome variable, suggest that the expansion of the program reduced the probability of a person in the eligible-fortreatment group being poor by 5.4 percentage points. The difference-in-differences estimates for the effects of the expansion of the program on the poverty headcount index are similar in magnitude and consistent when calculated using the three methods: LPM, AME of the Probit model, and AME of the logit model, all with and without covariates to control for differences across treatment and control groups. Table 3 shows the estimates of the treatment effects of the expansion of PAM on extreme monetary poverty.

Results suggest that the income shock from the expansion of the program also reduced the poverty gap and the poverty severity indexes on the eligible-for-treatment group. The estimated effect of the expansion of PAM on the poverty gap index using the LPM is a reduction of 3.2 percentage points without covariates and of 3.5 percentage points with covariates. These results are statistically significant at the 5% level. Similarly, the estimated effect on the poverty severity index is a reduction of 2.5 percentage points without covariates and 2.6 percentage points with covariates. Table 4 shows the estimates of the treatment effects of the expansion of PAM on the extreme monetary poverty gap and the extreme poverty severity indexes.

We estimated the effects of the expansion of the program on the official multi-dimensional extreme poverty measure and found that the expansion of PAM also had a statistically significant reduction on this wellbeing indicator. The estimates obtained using the LPM and the AMEs of the Probit and Logit models with covariates, show that the expansion of PAM reduces de probability of multidimensional extreme poverty in 3.8 to 5.8 percentage points. Table 5 shows the estimates of the treatment effects of the expansion of PAM on the multidimensional extreme poverty index.

Results are consistent when we included all members of the household in both the treatment and control groups, showing that these findings are generalizable to all members of the treated household (Appendix E).

We also estimated the attributable effects of the expansion of PAM on social rights deprivation

> 16After a 2SLS estimation with robust clustered standard errors, a regression-based test can be implemented under the null-hypothesis that the variable under consideration is exogenous (Wooldridge, 1995). If the null-hypothesis cannot be rejected, then the OLS estimator

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