during the experiment, their valuation of fuel vouchers obtained via a post-experiment BDM auction, as well as basic demographic, socioeconomic, and attitudinal information on each participant collected via a short written survey administered after the experiment. Finally, we collected a rich qualitative dataset from structured in-depth focus groups that were conducted by facilitators after all quantitative data was collected.

# 3 Empirical model

We estimate by ordinary least squares (OLS) a set of treatment-effects models of the following form: Yi = α + δTi + βXi + ei (1) where: Yi is the an outcome variable (mean giving rate in models using information from all the proposals, or the binary decision to accept or reject a specific proposal in models using information from a specific proposal) for individual i ; Ti is an indicator variable equal to one if the individual was assigned to the treatment group and 0 otherwise; Xi is the vector of baseline characteristics; and ei is the error term. The parameter of interest δ is the average treatment effect. Estimates are computed with a linear regression model even for binary dependent variables, such as decisions on specific proposals, as the coeffi cients are nearly identical to the marginal effects of a probit model (as discussed in Miguel, Satyanath, and Sergenti 2004). The advantage of using OLS is the availability of an established procedure to compute clustered wild bootstrap-t standard errors, which are more suitable for estimations with a small number of clusters. The standard errors are clustered at the PSU level, which accounts for the design effect of our PSU level treatment and for heteroscedasticity inherent in the linear probability model.<sup>17</sup> We estimate results of equation (1) for six outcomes, two aggregating individual i’s decisions across the four proposals (as described below) and four using decisions on each proposal at a time. “Mean giving rate”is the share of accepted proposals to give up the fuel voucher (out of possible four); “Frequent giver”is an indicator variable equal to one if individual i’s mean giving rate is greater than 0.5, i.e. if the individual gave up the voucher in the majority of the proposals. The other four outcomes are binary indicator variables equal to one if individual i indicated that he or she would give up his/her voucher for that specific proposal (unconditional cash transfer, unconditional food transfer, unconditional cash transfer and school, or cash transfer conditional on training).

> 17 Clustering at the PSU level was used to account for the first stage of the sampling strategy, which picked PSUs from the census sampling frame. Clustering thus adjusts the standard errors to take into account intra-cluster correlation, which could be relatively high for outcomes related to redistribution preferences. Given that Huber-White heteroskedastic-standard errors (commonly known as “cluster-robust”) are potentially underestimated when the number of clusters is small (as discussed in Bertrand, Duflo, and Mullainathan 2004; Cameron, Gelbach, and Miller 2008; Cameron and Miller 2015), in this paper we recomputed all standard errors with wild cluster bootstrap-t statistics following the procedure by Cameron, Gelbach, and Miller (2008), which avoids standard error underestimation in the presence of few clusters.

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