Social norms have the potential to alter the functioning of economic markets. We test whether norms shape the aggregate labor supply curve by preventing workers from supplying labor at wage cuts -- leading decentralized individuals to implicitly behave as a cartel to maintain wage floors in their local labor markets. We partner with 183 existing employers, who offer jobs to 502 workers in informal spot labor markets in India. Unemployed workers are privately willing to accept jobs below the prevailing wage, but rarely do so when this choice is observable to other workers. In contrast, social observability does not affect labor supply at the prevailing wage. Workers give up 49% of average weekly earnings to avoid being seen as breaking the social norm. In addition, workers pay to punish anonymous laborers who have accepted wage cuts -- indicating that cartel behavior is reinforced through the threat of social sanctions. Punishment occurs for workers in one's own labor market and for those in distant regions, suggesting the internalization of norms in moral terms. Finally, consistent with the idea that norms could have aggregate implications, measures of social cohesion correlate with downward wage rigidity and business cycle volatility across India.

How do inexperienced consumers learn to use a new financial technology? We present results from a field experiment that introduced payroll accounts in a population of largely unbanked factory workers in Bangladesh. In the experiment, workers in a treatment group received monthly wage payments into a bank or mobile money account while workers in a control group continued to receive wages in cash, with a subset also receiving an account without automatic wage payments. We find that exposure to payroll accounts leads to increased account use and consumer learning. Those receiving accounts with automatic wage payments learn to use the account without assistance, begin to use a wider set of account features, and learn to avoid illicit fees, which are common in emerging markets for consumer finance. The treatments have real effects, leading to increased savings and improvements in the ability to cope with unanticipated economic shocks. We conduct an additional audit study and find suggestive evidence of market externalities from consumer learning: mobile money agents are less likely to overcharge inexperienced customers in areas with higher levels of payroll account adoption. This suggests potentially important equilibrium effects of introducing accounts at scale.

This thesis is a collection of three chapters in empirical development economics. The first chapter investigates the impact of the dramatic growth of the fresh-cut flower industry in Colombia on different forms of violence. My empirical strategy exploits variation in the geo-climatic suitability for flowers to understand how export shocks affect violence at the municipality level. I show that flower shocks lead to a differential reduction in unorganized violent crime (homicide rates) in the suitable municipalities, but not to any changes in participation in guerrilla warfare. In contrast, increases in the coffee price are associated with a decrease in civil conflict (as in Dube and Vargas, 2013) but, as I find in this paper, an increase in homicide. I propose a household model where households both participate in and indirectly consume criminal activities (organized and unorganized) and women have different preferences than men, which can explain these asymmetric results. The second chapter studies the relationship between the arrival of employment opportunities in the fresh-cut flower industry and investments in human capital in Colombia. I study how schooling completion and grade enrollment respond to local employment shocks. I show that blooming periods for the flower industry are associated with a differential increase in the probability that a student will graduate from secondary schooling. I do not find evidence of an asymmetrical impact by gender. My results remain robust to different forms of shock aggregation, and accounting for differential trends by municipality characteristics. The third and final chapter uses the fresh-cut flower industry to understand the impact that the access to the export jobs had on the lives of Colombian women. My goal is to understand how flower shocks affect the timing of fertility and marriage decisions for women exposed to them during their adolescence. I find that girls exposed to the flower shocks are more likely to have initiated sexual activity, to be pregnant and married at younger ages. The results remain robust to different forms of shock aggregation, differential trends by municipality characteristics, accounting for migration, and geographically restricting the sample to the departments that concentrate flower production.

The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naive learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent's social influence in this generalized DeGroot model is essentially proportional to the number of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. We then simulate the modeled learning process on a set of real world networks; for these networks there is on average 21.6% information loss. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real world network data show that with clustered seeding, information loss climbs to 35%.

Credit information affects the allocation of consumer credit, but its effects on other markets that are relevant for academic and policy analysis are unknown. This paper measures the effect of negative credit information on the employment and earnings of Swedish individuals at the margins of the formal credit and labor markets. We exploit a policy change that generates quasi-exogenous variation in the retention time of past delinquencies on credit reports and estimate that one additional year of negative credit information causes a reduction in wage earnings of $1,000. In comparison, the decrease in credit is only one-fourth as large. Negative credit information also causes an increase in self-employment and a decrease in mobility. We exploit differences in the information available to employers and banks to show suggestive evidence that this cost of default is borne inefficiently by the relatively more creditworthy individuals among previous defaulters.

Social norms have the potential to alter the functioning of economic markets. We test whether norms shape the aggregate labor supply curve by leading decentralized individuals to maintain wage floors in their local labor markets. We partner with existing employers who create new jobs for workers in informal spot labor markets. Unemployed workers would like to find work, and prefer to do so even at wages below the prevailing wage rather than remain unemployed. However, they rarely do so when this choice is observable to other workers. In contrast, social observability does not affect labor supply at the prevailing wage. Consistent with the idea that norms could have aggregate implications, measures of social cohesion correlate with downward wage rigidity and business cycle volatility across India.

Formal financial institutions can have far-reaching and long-lasting impacts on informal lending and information networks. We first study 75 villages in Karnataka, 43 of which were exposed to microfinance after we first collected detailed network data. Networks shrink more in exposed villages. Links between households that were unlikely to ever borrow from microfinance are at least as likely to disappear as links involving likely borrowers. We replicate these surprising findings in the context of a randomized controlled trial in Hyderabad, where a microfinance institution randomly selected neighborhoods to enter first. Four years after all neighborhoods were treated, households in early-entry neighborhoods had credit access longer and had larger loans. We again find fewer social relationships between households in early-entry neighborhoods, even among those ex-ante unlikely to borrow. Because the results suggest global spillovers, which are inconsistent with standard 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 analyze 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.

Formal financial institutions can have far-reaching and long-lasting impacts on informal lending and information networks. We first study 75 villages in Karnataka, 43 of which were exposed to microfinance after we first collected detailed network data. Networks shrink more in exposed villages. Links between households that were unlikely to ever borrow from microfinance are at least as likely to disappear as links involving likely borrowers. We replicate these surprising findings in the context of a randomized controlled trial in Hyderabad, where a microfinance institution randomly selected neighborhoods to enter first. Four years after all neighborhoods were treated, households in early-entry neighborhoods had credit access longer and had larger loans. We again find fewer social relationships between households in early-entry neighborhoods, even among those ex-ante unlikely to borrow. Because the results suggest global spillovers, which are inconsistent with standard 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 analyze 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.

How should policymakers disseminate information: by broadcasting it widely (e.g., via mass media), or letting word spread from a small number of initially informed "seed" individuals? While conventional wisdom suggests delivering information more widely is better, we show theoretically and experimentally that this may not hold when people need to ask questions to fully comprehend the information they were given. In a field experiment during the chaotic 2016 Indian demonetization, we varied how information about demonetization's official rules was delivered to villages on two dimensions: how many were initially informed (broadcasting versus seeding) and whether the identity of the initially informed was publicly disclosed (common knowledge). The quality of information aggregation is measured in three ways: the volume of conversations about demonetization, the level of knowledge about demonetization rules, and choice quality in a strongly incentivized decision dependent on understanding the rules. Our results are consistent with four predictions of a model in which people need others' help to make the best use of announced information, but worry about signaling inability or unwillingness to correctly process the information they have access to. First, if who is informed is not publicized, broadcasting improves all three outcomes relative to seeding. Second, under seeding, publicizing who is informed improves all three outcomes. Third, when broadcasting, publicizing who is informed hurts along all three dimensions. Finally, when who is informed is made public, telling more individuals (broadcasting relative to seeding) is worse along all three dimensions.