Person:

Hanna, Rema

We use a unique data-set from Indonesia on what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed, and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. To understand what the theory predicts for cross-village patterns, we estimate a simple diffusion model using within-village variation, simulate network-level diffusion under this model for the over 600 different networks in our data, and use this simulated data to gauge what the simple diffusion model predicts for the cross-village relationship between information diffusion and network characteristics (e.g. clustering, density). The coefficients in these simulated regressions are generally consistent with relationships suggested in previous theoretical work, even though in our setting formal analytical predictions have not been derived. We then show that the qualitative predictions from the simulated model largely match the actual data in the sense that we obtain similar results both when the dependent variable is an empirical measure of the accuracy of a village’s aggregate information and when it is the simulation outcome. Finally, we consider a real-world application to community based targeting, where villagers chose which households should receive an anti-poverty program, and show that networks with better diffusive properties (as predicted by our model) differentially benefit from community based targeting policies.

We use a unique data-set from Indonesia on what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. To understand what the theory predicts for cross-village patterns, we estimate a simple diffusion model using within-village variation, simulate network-level diffusion under this model for the over 600 different networks in our data, and use this simulated data to gauge what the simple diffusion model predicts for the cross-village relationship between information diffusion and network characteristics (e.g. clustering, density). The coefficients in these simulated regressions are generally consistent with relationships suggested in previous theoretical work, even though in our setting formal analytical predictions have not been derived. We then show that the qualitative predictions from the simulated model largely match the actual data in the sense that we obtain similar results both when the dependent variable is an empirical measure of the accuracy of a village’s aggregate information and when it is the simulation outcome. Finally, we consider a real-world application to community based targeting, where villagers chose which households should receive an anti-poverty program, and show that networks with better diffusive properties (as predicted by our model) differentially benefit from community based targeting policies.

This paper investigates the impact of elite capture on the allocation of targeted government welfare programs in Indonesia, using both a high-stakes field experiment that varied the extent of elite influence and non-experimental data on a variety of existing government transfer programs. Conditional on their consumption level, there is little evidence that village elites and their relatives are more likely to receive aid programs than non-elites. Looking more closely, however, we find that this overall result masks a difference between different types of elites: those holding formal leadership positions are more likely to receive benefits, while informal leaders are actually less likely to. We show that capture by formal elites occurs during the distribution of benefits under the programs, and not during the processes when the beneficiary lists are determined by the central government. However, while elite capture exists, the welfare losses it creates appear quite small: since formal elites and their relatives are only 9 percent richer than non-elites, are at most about 8 percentage points more likely to receive benefits than non-elites, and represent at most 15 percent of the population, eliminating elite capture entirely would improve the welfare gains from these programs by less than one percent.

This paper explores whether ordeal mechanisms can improve the targeting of aid programs to the poor ("self-targeting"). We first show that theoretically the impact of increasing ordeals is ambiguous: for example, time spent applying imposes a higher monetary cost on the rich, but may impose a higher utility cost on the poor. We examine these issues by conducting a 400-village field experiment with Indonesia’s Conditional Cash Transfer program (PKH), where eligibility is determined through an asset test. Specifically, we compare targeting outcomes from self-targeting, where villagers came to a central site to apply and take the asset test, against the status quo, an automatic enrollment system among a pool of potential candidates that the village pre-identified. Within self-targeting villages, we find that the poor are more likely to apply, even conditional on whether they would pass the asset test. Exploiting the experimental variation, we find that self-targeting leads to a much poorer group of beneficiaries than the status quo. Self-targeting also outperforms a universal asset-based automatic enrollment system that we construct using our survey data. However, while experimentally increasing the distance to the application site reduces the number of applicants, it does not differentially improve targeting. Estimating our model structurally, we show that there are large unobserved shocks in the decision to apply, and therefore increasing waiting times to 9 hours or more would be required to induce detectable additional selection. In short, ordeal mechanisms can induce self-selection, but marginally increasing the ordeal can impose additional costs on applicants without necessarily improving targeting.