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Nonparametric Welfare Analysis for Discrete Choice

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  • Debopam Bhattacharya

Abstract

We consider empirical measurement of exact equivalent/compensating variation resulting from price-change of a discrete good, using individual-level data. We show that for binary and multinomial choice, the marginal distributions of EV/CV are nonparametrically point-identified solely from the conditional choice-probabilities, under extremely general preference-distributions. These results hold even when the distribution/dimension of unobserved heterogeneity are neither specified, nor identified and utilities are neither quasi-linear nor parametrically specified. Welfare-distributions can be expressed as closed-form functionals of observable individual choice-probabilities, thus enabling easy computation in applications. Average EV for price-rise equals the change in average consumer-surplus and is smaller than average CV for a normal good. Point-identification fails for ordered choice if the unit-price is identical for all alternatives, thereby providing a connection to Hausman-Newey's (2013) partial identification results for the limiting case of continuous choice.

Suggested Citation

  • Debopam Bhattacharya, 2013. "Nonparametric Welfare Analysis for Discrete Choice," Economics Series Working Papers 669, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:669
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    Cited by:

    1. Romuald Meango, 2023. "Identification of Ex Ante Returns Using Elicited Choice Probabilities," Papers 2303.03009, arXiv.org.
    2. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    3. Sam Cosaert & Thomas Demuynck, 2018. "Nonparametric Welfare and Demand Analysis with Unobserved Individual Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 349-361, May.
    4. Rachel Griffith & Lars Nesheim & Martin O'Connell, 2018. "Income effects and the welfare consequences of tax in differentiated product oligopoly," Quantitative Economics, Econometric Society, vol. 9(1), pages 305-341, March.
    5. Su Thet Hninn & Keisuke Kawata & Shinji Kaneko & Yuichiro Yoshida, 2016. "A nonparametric welfare analysis on water quality improvement of the floating people on Inlay Lake via a randomized conjoint field experiment," IDEC DP2 Series 6-2, Hiroshima University, Graduate School for International Development and Cooperation (IDEC).
    6. Debopam Bhattacharya & Tatiana Komarova, 2021. "Incorporating Social Welfare in Program-Evaluation and Treatment Choice," Papers 2105.08689, arXiv.org, revised Nov 2022.
    7. Kory Kroft & Yao Luo & Magne Mogstad & Bradley Setzler, 2020. "Imperfect Competition and Rents in Labor and Product Markets: The Case of the Construction Industry," Working Papers tecipa-666, University of Toronto, Department of Economics.
    8. Debopam Bhattacharya, 2021. "The Empirical Content of Binary Choice Models," Econometrica, Econometric Society, vol. 89(1), pages 457-474, January.
    9. Bhattacharya, D. & Dupas, P. & Kanaya, S., 2018. "Demand and Welfare Analysis in Discrete Choice Models under Social Interactions," Cambridge Working Papers in Economics 1885, Faculty of Economics, University of Cambridge.
    10. Thiptaiya Sydavong & Daisaku Goto & Keisuke Kawata & Shinji Kaneko & Masaru Ichihashi, 2019. "Potential demand for voluntary community-based health insurance improvement in rural Lao People’s Democratic Republic: A randomized conjoint experiment," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-21, January.
    11. Thomas Demuynck, 2018. "Testing the homogeneous marginal utility of income assumption," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1120-1136, November.
    12. Lee, Ying-Ying & Bhattacharya, Debopam, 2019. "Applied welfare analysis for discrete choice with interval-data on income," Journal of Econometrics, Elsevier, vol. 211(2), pages 361-387.
    13. Jerry A. Hausman & Whitney K. Newey, 2016. "Individual Heterogeneity and Average Welfare," Econometrica, Econometric Society, vol. 84, pages 1225-1248, May.
    14. Bart Cap'eau & Liebrecht De Sadeleer & Sebastiaan Maes, 2023. "Identifying the Distribution of Welfare from Discrete Choice," Papers 2303.02645, arXiv.org.
    15. Dupas, Pascaline & Bhattacharya, Debopam & ,, 2019. "Demand and Welfare Analysis in Discrete Choice Models with Social Interactions," CEPR Discussion Papers 13707, C.E.P.R. Discussion Papers.
    16. Haikady N Nagaraja & Shane Sanders, 2020. "The aggregation paradox for statistical rankings and nonparametric tests," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    17. Romauld Méango, 2023. "Identification of ex ante returns using elicited choice probabilities," Economics Series Working Papers 1007, University of Oxford, Department of Economics.
    18. Paolo Delle Site & André de Palma & Karim Kilani, 2021. "Consumers’ welfare and compensating variation: survey and mode choice application," THEMA Working Papers 2021-11, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    19. Bart Capéau & Liebrecht De Sadeleer & Sebastiaan Maes & André Decoster, 2020. "Nonparametric welfare analysis for discrete choice: levels and differences of individual and social welfare," Working Papers of Department of Economics, Leuven 674666, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    20. KANEKO Shinji & KAWATA Keisuke & YIN Ting, 2019. "Estimating Family Preference for Home Elderly-care Services: Large-scale Conjoint Survey Experiment in Japan," Discussion papers 19092, Research Institute of Economy, Trade and Industry (RIETI).
    21. Sebastiaan Maes & Raghav Malhotra, 2023. "Robust Hicksian Welfare Analysis under Individual Heterogeneity," Papers 2303.01231, arXiv.org, revised Nov 2023.
    22. John K. Dagsvik, 2020. "Marginal compensated effects and the slutsky equation for discrete choice models," Discussion Papers 930, Statistics Norway, Research Department.
    23. Allen, Roy & Rehbeck, John, 2022. "Latent complementarity in bundles models," Journal of Econometrics, Elsevier, vol. 228(2), pages 322-341.
    24. Maes, Sebastiaan & Malhotra, Raghav, 2024. "Robust Hicksian Welfare Analysis under Individual Heterogeneity," CRETA Online Discussion Paper Series 84, Centre for Research in Economic Theory and its Applications CRETA.
    25. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

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    More about this item

    Keywords

    Multinimial choice; Compensating and equivalent variation; unobserved heterogeneity; unrestricted heterogeneity; deadweight loss;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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