a total of 1,245 households. There were two types of ranking questions: RMG priority ranking and ranking based on specific criteria:

# _“RMG Priority Ranking”:_

Please rank who you would prioritize to join the RMG if it were solely up to you and not determined by eligibility and a lottery. Start with the highest priority to the least priority. For example, if I were in the list and you really thought I should receive the RMG job, you would rank me number 1. If you think I am the last person in the list you would give the job to, rank me (total number in the list).<sup>12</sup>

# _Criteria Rankings:_

- Rank who owns the least to most square meters of land, agricultural and residential land combined.

- Rank the likelihood that, in the last 12 months, the person or someone in her household was hungry but did not eat because there was not enough rice or food at home, and not enough money or other tradeable goods to purchase rice or food. Start with the person or household most likely to have had been hungry but did not eat.

- Rank who needs the most to least help right now.

The variety of data collected, including actual data on selection, respondent self-reports, and data collected on hypothetical ranking by village heads, combined with an intention sampling design that includes both selected and non-selected among eligible and a random sample of villagers, enable us to explore both the actual targeting by village heads, the proximate inputs into the targeting choices, and the potential information constraints that may limit the ability of village heads to target the poorest households in their villages.

4. Do village heads effectively select poorer households for participation in RMGs? Given the extensive baseline surveys administered to the RMG-eligible and the RS, we can compare them on a wide range of dimensions, including income, assets, land holding, etc. Notably, the baseline questionnaires did not include a comprehensive consumption module, which elsewhere is commonly used for assessing poverty status. Table A1 in the Appendix shows 127 variables describing the participants’ basic demographic characteristics, household composition, income, possession of durables and livestock, housing conditions, nutrition, and experience of economic shocks, available in our survey.

To reduce dimensionality and address the likelihood of Type I errors in our numerous possible comparisons between the RMG-eligible and RS groups, we follow Katz, Kling and Liebman (2007) and Andersen (2008) by constructing composite z-scores and adjusting the p-values for multiple hypothesis testing. The z-scores of the individual variables are calculated and then averaged by category to construct composite z-scores. The z-scores of variables assumed to be negatively correlated with wealth are inverted. Altogether, we construct 7 composite z-scores to encompass broad dimensions of welfare: land and buildings ownership, household composition, durables ownership, livestock ownership, housing conditions, nutrition, and shocks. We also test for differences in household per capita total income. We apply Bonferroni correction to adjust p-values for the differences in means tests, to further reduce the

> 12 Sixteen villages did not have enough RMG, WL, and RS members to rank 15 individuals because of either smaller RMG-eligible memberships or participant absence at time of survey. In these cases, the maximum number of households available for ranking was specified in the instructions. Eight villages had 14 households ranked, 5 villages had 13, 2 villages had 12, and 1 village had 9.

8