IDEAS home Printed from https://ideas.repec.org/a/anr/reveco/v9y2017p521-546.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1146/annurev-economics-080315-015107
    Download Restriction: Full text downloads are only available to subscribers. Visit the abstract page for more information.
    File URL: https://libkey.io/10.1146/annurev-economics-080315-015107?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers CWP60/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. 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.
    7. Hidehiko Ichimura & Whitney K. Newey, 2022. "The influence function of semiparametric estimators," Quantitative Economics, Econometric Society, vol. 13(1), pages 29-61, January.
    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. 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.
    10. 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.
    11. 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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anr:reveco:v:9:y:2017:p:521-546. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: http://www.annualreviews.org (email available below). General contact details of provider: http://www.annualreviews.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.