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Demand and Welfare Analysis in Discrete Choice Models with Social Interactions

Author

Listed:
  • Debopam Bhattacharya

    (University of Cambridge)

  • Pascaline Dupas

    (Stanford University)

  • Shin Kanaya

    (University of Aarhus and CREATES)

Abstract
Many real-life settings of consumer-choice involve social interactions, causing targeted policies to have spillover-effects. This paper develops novel empirical tools for analyzing demand and welfare-effects of policy-interventions in binary choice settings with social interactions. Examples include subsidies for healthproduct adoption and vouchers for attending a high-achieving school. We establish the connection between econometrics of large games and Brock-Durlauf-type interaction models, under both I.I.D. and spatially correlated unobservables. We develop new convergence results for associated beliefs and estimates of preference-parameters under increasing-domain spatial asymptotics. Next, we show that even with fully parametric specifications and unique equilibrium, choice data, that are sufficient for counterfactual demand - prediction under interactions, are insufficient for welfare-calculations. This is because distinct underlying mechanisms producing the same interaction coefficient can imply different welfare-effects and deadweightloss from a policy-intervention. Standard index-restrictions imply distribution-free bounds on welfare. We illustrate our results using experimental data on mosquito-net adoption in rural Kenya.

Suggested Citation

  • Debopam Bhattacharya & Pascaline Dupas & Shin Kanaya, 2019. "Demand and Welfare Analysis in Discrete Choice Models with Social Interactions," CREATES Research Papers 2019-09, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2019-09
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    References listed on IDEAS

    as
    1. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    2. Pascaline Dupas, 2014. "Short‐Run Subsidies and Long‐Run Adoption of New Health Products: Evidence From a Field Experiment," Econometrica, Econometric Society, vol. 82(1), pages 197-228, January.
    3. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    4. Brock, William A. & Durlauf, Steven N., 2001. "Interactions-based models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 54, pages 3297-3380, Elsevier.
    5. Brock, William A. & Durlauf, Steven N., 2007. "Identification of binary choice models with social interactions," Journal of Econometrics, Elsevier, vol. 140(1), pages 52-75, September.
    6. Debopam Bhattacharya, 2018. "Empirical welfare analysis for discrete choice: Some general results," Quantitative Economics, Econometric Society, vol. 9(2), pages 571-615, July.
    7. Debopam Bhattacharya, 2015. "Nonparametric Welfare Analysis for Discrete Choice," Econometrica, Econometric Society, vol. 83, pages 617-649, March.
    8. Konrad Menzel, 2016. "Inference for Games with Many Players," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(1), pages 306-337.
    9. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers 52/15, Institute for Fiscal Studies.
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    11. Jerry A. Hausman & Whitney K. Newey, 2016. "Individual Heterogeneity and Average Welfare," Econometrica, Econometric Society, vol. 84, pages 1225-1248, May.
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    17. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LP, vol. 8(2), pages 190-220, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Wilfried Youmbi, 2024. "Nonparametric Analysis of Random Utility Models Robust to Nontransitive Preferences," Papers 2406.13969, arXiv.org.
    2. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    3. Michael P. Leung, 2020. "Equilibrium computation in discrete network games," Quantitative Economics, Econometric Society, vol. 11(4), pages 1325-1347, November.
    4. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.

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    More about this item

    Keywords

    Policy targeting; welfare analysis; social interaction; spillover; externality; convergence of Bayesian-Nash equilibria; spatial dependence; Kenya;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • H4 - Public Economics - - Publicly Provided Goods
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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