This paper studies a class of information transmission processes called rumors. The distinctive features of these processes are that the information transmission takes place in such a way that the recipient does not quite know whether or not to believe the information and that the probability that someone receives the information depends on how many people already have it. Counter-intuitive comparative statics results are obtained. For example, more information and higher productivity may reduce welfare, while changing the speed with which the rumor spreads has no welfare effect.

This paper studies the effect of community identity on investment behaviour in the knitted garment industry in the South Indian town of Tirupur. We document very large and systematic differences in both levels of capital stock and the capital intensity of production in firms owned by people from two different community groups. We argue that the differences in investment cannot be explained by productivity differences alone. We suggest that the most likely explanation is that the two communities differ in their access to capital.

This paper develops the stochastic theory of distribution with a dynamic model which focuses on the role of incomplete insurance in generating inequality. Unlike previous work, our approach takes explicit account of the reason for market incompleteness in modeling agents' behaviour; in particular, the amount of risk borne is endogenous. Using a model of growth with altruism in which agents are risk-averse and there is moral hazard, we show that lineage wealth follows a Markov process which converges globally to an ergodic distribution; this also represents the long-run population distribution of wealth. We discuss the role of particular assumptions, such as availability of production loans and unboundedness of utility, in yielding the qualitative properties of the distribution of wealth, the choice of “occupation” and the prevention of poverty traps.

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.

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 show that the entry of formal financial institutions can have far-reaching and long-lasting impacts on informal lending and social networks more generally. We first study the introduction of microfinance in 75 villages in Karnataka, India, 43 of which were exposed to microfinance. Using difference-in-differences, we show that networks shrank more in exposed villages. Moreover, links between households that were both unlikely to borrow from microfinance were at least as likely to disappear as links involving likely borrowers. We replicate these surprising findings in the context of a randomised controlled trial (RCT) in Hyderabad, where a microfinance institution randomly selected 52 of 104 neighbourhoods to enter first. Four years after all neighbourhoods were treated, households in early-entry neighbourhoods had credit access longer and had larger loans. We again find fewer social relationships between households in these neighbourhoods, even among those ex-ante unlikely to borrow. Because the results suggest global spillovers, atypical in usual models of network formation, we develop a new dynamic model of network formation that emphasizes chance meetings, where efforts to socialize generate a global network-level externality. Finally, we analyse informal borrowing and the sensitivity of consumption to income fluctuations. Households unlikely to take up microcredit suffer the greatest loss of informal borrowing and risk sharing, underscoring the global nature of the externality.