Essays on social networks in development economics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-210).This thesis examines the role that social networks play in developing economies. The first two chapters analyze econometric issues that arise when researchers work with sampled network data. The final two chapters study how the embedding of agents in a network affects a group's ability to overcome weak contracting institutions and what models of social learning are important in describing the diffusion of information. These chapters make use of experiments that I conducted in rural Karnataka, India. The first chapter (co-authored with Randall Lewis) examines the econometric difficulties that applied researchers face when using partially observed network data. In applied work, researchers generally construct networks from data collected from a partial sample of nodes. Treating this sampled network as the true network of interest, the researcher constructs statistics to describe the network or specific nodes and employs these statistics in regression or GMM analysis. This chapter shows that even if nodes are selected randomly, partial sampling leads to non-classical measurement error and therefore bias in estimates of the regression coefficients or GMM parameters. We provide analytical and numerical examples to illustrate the severity of the biases in common applications and discuss possible solutions. Our analysis of the sampling problem as well as the proposed solutions are applied to rich network data of Banerjee et al. (2012) from 43 villages in Karnataka, India. In the second chapter, 1 develop an econometric method to cope with sampled network data. I develop a method, graphical reconstruction, by which a researcher can consistently estimate the economic parameters of interest. Graphical reconstruction uses the available (partial) network data to predict the missing links and uses these predictions to mitigate the biases. As each network may be generated by a different network formation model, the asymptotic theory allows for heterogeneity in the network formation process across graphs. The third chapter (co-authored with Cynthia Kinnan and Horacio Larreguy) analyzes how social networks affect the provision of informal insurance. Social networks are understood to play an important role in smoothing consumption risk, particularly in developing countries where formal contracts are limited and financial development is low. Yet understanding why social networks matter is confounded by endogeneity of risk-sharing partners. This chapter, first, examines the causal effect of close social ties between individuals on their ability to informally insure one another. Second, we examine how the interaction of social proximity and access to savings affects consumption smoothing. Theoretically, they could be complements or substitutes. Savings access may crowd out insurance unless social proximity is high, in which case it benefits the highly connected. Or savings may crowd out risk sharing among the highly connected while helping the less connected smooth risk intertemporally. By conducting a framed field experiment in Karnataka, India, we study the relationships between inability to commit to insurance, ability to save, and social proximity. We find that limited commitment reduces risk sharing, but social proximity(cont.) substitutes for commitment. On net, savings allows individuals to smooth risk that cannot be shared interpersonally, with the largest benefits for those who are weakly connected in the network. The final chapter (co-authored with my classmates Horacio Larreguy and Juan Pablo Xandri) attempts to determine which models of social learning on networks best describe empirical behavior. Theory has focused on two leading models of social learning on networks: Bayesian and DeGroot rules of thumb learning. These models can yield greatly divergent behavior; individuals employing rules of thumb often double-count information and may not exhibit convergent behavior in the long run. By conducting a unique lab experiment in rural Karnataka, India, set up to exactly differentiate between these two models, we test which model best describes social learning processes on networks. We study experiments in which seven individuals are placed into a network, each with full knowledge of its structure. The participants attempt to learn the underlying (binary) state of the world. Individuals receive independent, identically distributed signals about the state in the first period only; thereafter, individuals make guesses about the underlying state of the world and these guesses are transmitted to their neighbors at the beginning of the following round. We consider various environments including incomplete information Bayesian models and provide evidence that individuals are best described by DeGroot models wherein they either take simple majority of opinions in their neighborhood.by Arun Gautham Chandrasekhar.Ph.D

Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

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

Minnesota COVID-19 Testing

In the United States, recent statistics show that African American and Latinx communities bear a disproportionate burden from COVID-19. Reaching vulnerable and underserved populations is therefore crucial to combating the disease. However, most public messaging campaigns are not targeted toward underserved communities and don't address fears of social stigma, mistrust in the healthcare system, or concerns about immigration status. The goal of this project is to help the state of Minnesota understand why individuals are not getting tested and potentially identify trusted individuals or organizations that could be used in follow-up work to send messages. To do so, we are deploying flyers through 10 Twin City area food shelves and potentially through public housing units with information on how to answer an online questionnaire. This provides us with an opportunity to study who answers surveys and why - and what questions are particularly sensitive. This is of general interest to academicians and policymakers alike. According to Meyer, Mok, and Sullivan (2015) the quality of household surveys is in decline, for three main reasons. First, households have become increasingly less likely to answer surveys at all (unit nonresponse). Second, those that respond are less likely to answer certain questions (item nonresponse). Third, when households do provide answers, they are less likely to be accurate (measurement error). This is important since household surveys help to estimate the employment rate, healthcare needs and of course the census determines resources/representation. We focus on the first two issues of unit and item nonresponse, which is not random across the population and thus could lead to nonresponse bias. Griffin (2002) found that census tracts with predominantly Hispanic or Black residents had significantly lower response rates to the American Community Survey as compared to the response rates in predominantly white tracts. Similarly, Maitland et al. (2017) found that response rates to the Health Information National Trends Survey (HINTS) were lower in areas with higher levels of Hispanic and minority residents. We hypothesize that financial incentives may encourage unit response; conversely, a close association with the government may discourage response. To test these hypotheses, we plan to cross-randomize the incentive amount offered and the emphasis placed on government involvement in the study on flyers advertising the baseline survey. Individuals will see either a) a 10 dollar incentive, or b) a 20 dollar incentive; and either a) messaging that emphasizes government involvement in the study, or b) messaging that emphasizes the involvement of academic researchers. Flyers will be randomized at the foodshelf-day level. To test what affects item non-response on potentially sensitive questions, such as questions which ask for health information, we hypothesize that ethical framing may encourage individuals to answer questions. This takes two forms --- the deontological (or duty based) frame, and the consequential (or cost-benefit) frame. Moreover, knowing others feel the same way (regarding the obligation or benefits of providing health information) may amplify motivation. Finally, there is the possibility that emphasizing the importance of ethnic and racial disadvantage associated with COVID-19 outcomes may be important for improving item non-response on sensitive questions. Upon completion of the demographic module of the survey but prior to starting several potentially sensitive survey modules, individuals will see a message that either a) emphasizes the public health benefits of answering the survey questions (cost-benefit frame); b) emphasizes an individual's responsibility to their community (duty frame); c) emphasizes the disproportionate impact of COVID-19 on certain ethnic and racial groups; or d) provides no messaging. Messaging content will be randomized at the individual level. AEA Registry Number: AEARCTR-000627