IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/4002.html
   My bibliography  Save this paper

Robust Bounds for Welfare Analysis

Author

Listed:
  • Kang, Zi Yang

    (Stanford U)

  • Vasserman, Shoshana

    (Stanford U)

Abstract

Economists routinely make functional form assumptions about consumer demand to obtain welfare estimates—often for convenience, tractability, or both. How sensitive are welfare estimates to these assumptions? In this paper, we answer this question by providing bounds on welfare that hold for families of demand curves commonly considered in different literatures. We show that typical functional forms—such as linear, exponential and CES demand—are extremal in different families: they yield either the highest or lowest welfare estimate among all demand curves in those families. To illustrate the flexibility of our approach, we apply our results to the welfare analysis of trade tariffs, income taxation, and energy subsidies.

Suggested Citation

  • Kang, Zi Yang & Vasserman, Shoshana, 2021. "Robust Bounds for Welfare Analysis," Research Papers 4002, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:4002
    as

    Download full text from publisher

    File URL: https://www.gsb.stanford.edu/faculty-research/working-papers/robust-bounds-welfare-analysis
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Juan Pablo Atal & José Ignacio Cuesta & Felipe González & Cristóbal Otero, 2024. "The Economics of the Public Option: Evidence from Local Pharmaceutical Markets," American Economic Review, American Economic Association, vol. 114(3), pages 615-644, March.
    2. Jäger, Philipp, 2023. "Can pensions save lives? Evidence from the introduction of old-age assistance in the UK," Ruhr Economic Papers 995, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Aaron Bodoh-Creed & Brent Hickman & John List & Ian Muir & Gregory Sun, 2023. "Stress Testing Structural Models of Unobserved Heterogeneity: Robust Inference on Optimal Nonlinear Pricing," Natural Field Experiments 00776, The Field Experiments Website.
    4. Sebastiaan Maes & Raghav Malhotra, 2023. "Robust Hicksian Welfare Analysis under Individual Heterogeneity," Papers 2303.01231, arXiv.org, revised Nov 2023.

    More about this item

    JEL classification:

    • 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
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ecl:stabus:4002. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.html .

    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.