Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment

Breza, Emily; Chandrasekhar, Arun G.

doi:10.3386/w21169

Cited by 41 publications

(66 citation statements)

References 33 publications

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“…The first method is random assignment, either of some intervention provided to a subset of nodes in the network, or of links in the network. Random assignments of interventions have been used to study a wide range of questions, including the diffusion of innovations in social networks (Aral and Walker, ; Oster and Thornton, ; Cai et al ., ; among others), social learning (Godlonton and Thornton, ), sharing of resources and savings (Comola and Prina, ; Angelucci et al ., forthcoming), peer effects in exercise (Babcock et al ., ), peer effects in education (Angelucci et al ., ; Babcock and Hartman, ) and peer monitoring (Breza and Chandrasekhar, ).…”

Section: Dealing With Endogeneity Of Network Formationmentioning

confidence: 99%

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Advani

Malde

2018

Journal of Economic Surveys

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Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.CREDIBLY IDENTIFYING SOCIAL EFFECTS 1017 unobserved dimensions could influence within-group interactions, and through this the actual outcome. Ignoring variation in interactions within such groups can lead to misleading conclusions and policy design, as shown in recent work by Carrell et al. (2013).More recently, a growing body of research within empirical economics uses data which directly measure interactions between pairs of agents (network data hereon) to sidestep these issues. This growth has been spurred by the increasing availability of such data, as well as the development of methods to identify and estimate social effects with such data. Starting with Bramoullé et al. (2009) andDe Giorgi et al. (2010), methods have been developed to overcome the reflection problem. They show how information on network structure can be used to break the simultaneity, and obtain the necessary exclusion restrictions for parameter identification. These methods, reviewed in detail by Advani and Malde (2014), Topa and Zenou (2015) and Boucher and Fortin (2015), impose strong restrictions on the network formation process and the quality of the data.In particular, the network is assumed to be exogenous conditional on observed agent-and network-level characteristics, and to be fully and perfectly observed by the researcher. Both assumptions are unlikely to hold in practice. In a schooling context, for example, personality traits which are rarely observed by a researcher might influence both a child's choice of friends and her schooling performance. Estimates of the influence of a child's friends' outcomes on her outcomes will be biased if her choice of friends is not accounted for. Similarly, accurately collecting fine-grained information on all connections is very costly and logistically challenging, making it rare to observe a complete, perfectly measured network. This has important implications for identification of social effects using restrictions based on the network struct...

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Section: Dealing With Endogeneity Of Network Formationmentioning

confidence: 99%

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Advani

Malde

2018

Journal of Economic Surveys

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“…We discuss savings balances in the villages with endogenous monitor assignment in Section , and Section is a conclusion. Robustness exercises and extensions can be found in the Supplemental Material ( Breza and Chandrasekhar ()) and the Auxiliary Online Appendix (available on the authors' webpages at https://stanford.edu/~arungc/BC_aux.pdf).…”

Section: Introductionmentioning

confidence: 99%

Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment

Breza¹,

Chandrasekhar²

2019

Econometrica

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We conduct an experiment to study whether individuals save more when information about the progress toward their self-set savings goal is shared with another village member (a "monitor"). We develop a reputational framework to explore how a monitor's effectiveness depends on her network position. Savers who care about whether others perceive them as responsible should save more with central monitors, who more widely disseminate information, and proximate monitors, who pass information to individuals with whom the saver interacts frequently. We randomly assign monitors to savers and find that monitors on average increase savings by 36%. Consistent with the framework, more central and proximate monitors lead to larger increases in savings. Moreover, information flows through the network, with 63% of monitors telling others about the saver's progress. Fifteen months after the conclusion of the experiment, other villagers have updated their beliefs about the saver's responsibility in response to the intervention.

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“…A growing literature suggests that people may fail to save because doing so is not salient (Taubinsky 2014;Karlan, McConnell, Mullainathan, Zinman, 2016); and, correspondingly, they might be induced to do save by reminders. In-person visits may also generate peer pressure (Kast, Meier, and Pomeranz, 2012;Chandrasekhar and Breza, 2015). Second, the collection service has a commitment feature: we reduce the cost of making deposits, but not withdrawals.…”

Section: Why Do Deposit Collections Increase Savings and Labor Effort?mentioning

confidence: 99%

Enabling Micro-Savings Through Mobile Banking in Sri Lanka

McIntosh¹,

Sellman²,

Welch³

et al. 2016

AEA Randomized Controlled Trials

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We thank the Consortium on Financial Systems and Poverty at the University of Chicago, and the International Growth Centre for financial support for the project. Kandy Consulting Group provided excellent service in carrying out the baseline and follow-up surveys and assisting in carrying out the interventions on which the project is based. Jiyoung Kim provided excellent research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment

Cited by 41 publications

References 33 publications

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment

Enabling Micro-Savings Through Mobile Banking in Sri Lanka

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