Arun G. Chandrasekhar
Stanford University
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Publication
Featured researches published by Arun G. Chandrasekhar.
National Bureau of Economic Research | 2016
Abhijit V. Banerjee; Arun G. Chandrasekhar; Esther Duflo; Matthew O. Jackson
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.
National Bureau of Economic Research | 2015
Attila Ambrus; Arun G. Chandrasekhar; Matthew Elliott
This paper studies costly network formation in the context of risk sharing. Neighboring agents negotiate agreements as in Stole and Zwiebel (1996), which results in the social surplus being allocated according to the Myerson value. We uncover two types of inefficiency: overinvestment in social relationships within group (e.g., caste, ethnicity), but underinvestment across group. We find a novel tradeoff between efficiency and equality. Both within and across groups, inefficiencies are minimized by increasing social inequality, which results in financial inequality and increasing the centrality of the most central agents. Evidence from 75 Indian village networks is congruent with our model.
arXiv: Physics and Society | 2017
Abhijit V. Banerjee; Arun G. Chandrasekhar; Esther Duflo; Matthew O. Jackson
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.
Archive | 2018
Arun G. Chandrasekhar; Robert Townsend; Juan Pablo Xandri
We study an endowment economy in which agents face income risk, as if uncertain returns on a portfolio, and agents can only make transfers in states when they are actively participating in the market. Besides income risk, agents also have limited and stochastic market access, with a probability distribution governed by an underlying social network. While network connections may serve to dissipate shocks, they may also provide obstacles to the sharing of risk, as when participation frictions are generated through the network. We identify and quantify the value of key players in terms of whether they are likely to be able to smooth the resulting market participation risk and how valuable that smoothing would be when they are there. We define financial centrality in economic terms, given the model, as the ex ante marginal social value of injecting an infinitesimal amount of liquidity to the agent. We show that the most financially central agents are not only those who trade often – as in standard network models – but are more likely to trade when there are few traders, when income risk is high, when income shocks are positively correlated, when attitudes toward risk are more sensitive in the aggregate, when there are distressed institutions, and when there are tail risks. We extend our framework to allow for endogenous market participation. Observational evidence from village risk sharing network data is consistent with our model.
Archive | 2018
Abhijit V. Banerjee; Emily Breza; Arun G. Chandrasekhar; Benjamin Golub
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.
Archive | 2017
Abhijit V. Banerjee; Arun G. Chandrasekhar; Esther Duflo; Matthew O. Jackson
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.
The American Economic Review | 2016
Vivi Alatas; Abhijit V. Banerjee; Arun G. Chandrasekhar; Rema Hanna; Benjamin A. Olken
National Bureau of Economic Research | 2012
Arun G. Chandrasekhar; Matthew O. Jackson
Archive | 2006
Joshy Joseph; Anand C. Ramanathan; Arun G. Chandrasekhar
National Bureau of Economic Research | 2012
Abhijit V. Banerjee; Arun G. Chandrasekhar; Esther Duflo; Matthew O. Jackson