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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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    References listed on IDEAS

    as
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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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