January 2003

Microfinance institutions have started to bundle their basic loans with other financial services, such as health insurance. Using a randomized control trial in Karnataka, India, we evaluate the impact on loan renewal from mandating the purchase of actuarially-fair health insurance covering hospitalization and maternity expenses. Bundling loans with insurance led to a 16 percentage points (23 percent) increase in drop-out from microfinance, as many clients preferred to give up microfinance than pay higher interest rates and receive insurance. In a Pyrrhic victory, the total absence of demand for health insurance led to there being no adverse selection in insurance enrollment.

We examine the interactions between different institutional arrangements in a general equilibrium model of a modernizing economy. There is a modern sector, where productivity is high but information asymmetries are large, and a traditional sector where productivity is low but information asymmetries are small. Consequently, agency costs in the modern sector make consumption lending difficult, while such lending is readily done in the traditional sector. The resulting trade-off between credit availability and productivity implies that not everyone will move to the modern sector. In fact, the laissez-faire level of modernization may fail to maximize net social surplus. This situation may also hold in the long run: in a dynamic version of the model, a “trickle-down” effect links the process of modernization with reduction in modern sector agency costs. This effect may be too weak and the economy may get stuck in a trap and never fully modernize. The two-sector structure also yields a natural theoretical testing ground for the Kuznets inverted-U hypothesis: we show that even within the “sectoral shifting” class of models, this phenomenon is not robust to small changes in model specification.

We analyze the colonial land revenue institutions set up by the British in India, and show that differences in historical property rights institutions lead to sustained differences in economic outcomes. Areas in which proprietary rights in land were historically given to landlords have significantly lower agricultural investments and productivity in the post-independence period than areas in which these rights were given to the cultivators. These areas also have significantly lower investments in health and education. These differences are not driven by omitted variables or endogeneity problems; they probably arise because differences in historical institutions lead to very different policy choices.

This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective.

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people’s knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions.

In disseminating information, policymakers face a choice between broadcasting to everyone and informing a small number of “seeds” who then spread the message. While broadcasting maximises the initial reach of messages, we offer theoretical and experimental evidence that it need not be the best strategy. In a field experiment during the 2016 Indian demonetisation, we delivered policy information, varying three dimensions of the delivery method at the village level: initial reach (broadcasting versus seeding); whether or not we induced common knowledge of who was initially informed; and number of facts delivered. We measured three outcomes: the volume of conversations about demonetisation, knowledge of demonetisation rules, and choice quality in a strongly incentivised policy-dependent decision. On all three outcomes, under common knowledge, seeding dominates broadcasting; moreover, adding common knowledge makes seeding more effective but broadcasting less so. We interpret our results via a model of image concerns deterring engagement in social learning, and we support this interpretation with evidence on differential behaviour across ability categories.

We compare how in-kind food assistance and an electronic voucher-based program affect the delivery of aid in practice. The Government of Indonesia randomized across 105 districts the transition from in-kind rice to approximately equivalent electronic vouchers redeemable for rice and eggs at a network of private agents. Targeted households received 46 percent more assistance in voucher areas. For the bottom 15 percent of households at baseline, poverty fell 20 percent. Voucher recipients received higher-quality rice, and increased consumption of eggs. The results suggest moving from a manual in-kind to electronic voucher-based program reduced poverty through increased adherence to program design.

This paper reports an experiment in 640 Indonesian villages on three approaches to target the poor: proxy-means tests (PMT), where assets are used to predict consumption; community targeting, where villagers rank everyone from richest to poorest; and a hybrid. Defining poverty based on PPP$2 per-capita consumption, community targeting and the hybrid perform somewhat worse in identifying the poor than PMT, though not by enough to significantly affect poverty outcomes for a typical program. Elite capture does not explain these results. Instead, communities appear to apply a different concept of poverty. Consistent with this finding, community targeting results in higher satisfaction.

We use unique data from over 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting.