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Nonparametric Welfare Analysis

Author

Listed:
  • Jerry A. Hausman

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • Whitney K. Newey

    (Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

Abstract

Exact consumer's surplus and deadweight loss are the most widely used welfare and economic efficiency measures. These measures can be computed from demand functions in straightforward ways. Nonparametric estimation can be used to estimate the welfare measures. In doing so, it seems important to account correctly for unobserved heterogeneity, given the high degree of unexplained demand variation often found in applications. This review surveys work on nonparametric welfare analysis, focusing on work that allows for general heterogeneity in demand, such as that of Hausman & Newey (2016).

Suggested Citation

  • Jerry A. Hausman & Whitney K. Newey, 2017. "Nonparametric Welfare Analysis," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 521-546, September.
  • Handle: RePEc:anr:reveco:v:9:y:2017:p:521-546
    DOI: 10.1146/annurev-economics-080315-015107
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    Citations

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    Cited by:

    1. Hidehiko Ichimura & Whitney K. Newey, 2022. "The influence function of semiparametric estimators," Quantitative Economics, Econometric Society, vol. 13(1), pages 29-61, January.
    2. Richard Blundell & Joel Horowitz & Matthias Parey, 2022. "Estimation of a Heterogeneous Demand Function with Berkson Errors," The Review of Economics and Statistics, MIT Press, vol. 104(5), pages 877-889, December.
    3. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers 60/17, Institute for Fiscal Studies.
    4. 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).
    5. Cattaneo, Matias D & Ma, Xinwei & Masatlioglu, Yusufcan & Suleymanov, Elchin, 2020. "A Random Attention Model," University of California at San Diego, Economics Working Paper Series qt34m788c3, Department of Economics, UC San Diego.
    6. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    7. Adusumilli, Karun & Otsu, Taisuke & Qiu, Chen, 2023. "Reweighted nonparametric likelihood inference for linear functionals," LSE Research Online Documents on Economics 120198, London School of Economics and Political Science, LSE Library.
    8. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    9. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2020. "A Random Attention Model," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2796-2836.
    10. Richard Blundell & Joel L. Horowitz & Matthias Parey, 2018. "Estimation of a nonseparable heterogenous demand function with shape restrictions and Berkson errors," CeMMAP working papers CWP67/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Sarantis Tsiaplias, 2021. "The Welfare Implications of Unobserved Heterogeneity," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(4), pages 1029-1051, December.
    12. Karun Adusumilli & Taisuke Otsu & Chen Qiu, 2020. "Reweighted nonparametric likelihood inference for linear functionals," STICERD - Econometrics Paper Series 614, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    More about this item

    Keywords

    consumer surplus; deadweight loss; identification; quantiles;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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