# **E Additional treatment effects analysis**

## **E.1 Robustness of treatment**

Our robustness tests focus on the five main summary outcomes. First, in Table E.1, we show robustness to alternative ways of constructing the indexes and pooling or averaging of endlines. Columns 2–4 report results from the main paper for comparison. Recall that in this main specification we averaged endline surveys (at 2 and 5 weeks, and 11 and 13 months), took an index of composite measures rather than individual survey questions and used equal weights. In columns 5–7, we do the same except use randomization inference to assess statistical significance. In columns 8–10, we pool our composite measures from both endline surveys and cluster our standard errors by individual. In columns 11–13, we do the same except weight each survey question equally. In columns 14–16, we use covariance-weighted indexes from Anderson (2008) and average both endlines.<sup>16</sup> The conclusions from these three specifications are quantitatively similar to those from the main specification. Exceptions are as follows:

- The impact of cash and therapy on the covariance-weighted antisocial behaviors index is not significant in the long term at conventional levels. This is because half of this index’s weights come from domestic violence and number of arrests, two components that were unaffected by treatment. If we exclude domestic violence from the index and recalculate covariance weights, cash and therapy lead to a .26 standard deviation decline in antisocial behaviors in the long run (columns 19, significant at 99% level).

- Cash increases antisocial behaviors in the long term in some specifications. In Column 15 we see that after a year the men who report cash only increased their antisocial behaviors by 0.17 standard deviations. In the other specifications, the coefficients are positive as well but smaller and not statistically significant. One possibility is that receiving a cash grant and failing, or having the money stolen, reinforces men’s participation in crime. This is largely speculative, however.

Next, we check for robustness to alternative attrition scenarios by bounding treatment effects. We impute outcome values for unfound individuals at different points of the observed outcome distribution. The most extreme bound, from Manski (1990), imputes the minimum value for unfound treated members and the maximum for unfound controls. Following Karlan et al. (2015), we also calculate less extreme bounds by imputing relatively high values of the dependent variables for missing control group members, and relatively low values for missing treatment group members.<sup>17</sup> Specifically, we impute missing dependent variables for the treatment (control) group as the found treatment (control) mean minus (plus) 0.10, 0.25, or 1 SD of the found treatment (control) distribution. Note these imply large and systematic differences between missing treatment and control members—Columns 8 – 10 assume unfound control group member outcomes are roughly 2 SD greater than unfound treatment group member outcomes.

> 16For this index, each component is weighted by the inverse of the covariance matrix of all index components. Outcomes that are highly correlated with each other receive less weight while outcomes that are uncorrelated receive more weight as they represent new information. We cannot covariance weight the pooled endlines, since they are unbalanced in the sense that some outcome measures appear in only one endline while others appear in both.

> 17This assumes the dependent variable points in the positive direction. If treatment leads to a decrease in the outcome variable, as is the case for antisocial behaviors and antiviolent and anticriminal values, we impute in the opposite direction (i.e smaller values for control, larger values for treatment).

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