Nonparametric Approaches to Empirical Welfare Analysis

Abstract

Welfare analysis of policy interventions is ubiquitous in economic research. It plays an important role in merger analysis and antitrust litigation, design of tax and subsidy programs and informs the current debate on a universal basic income. In this paper, we provide a survey of existing empirical methods, based on cross-sectional micro-data, for calculating welfare-effects and deadweight loss resulting from realized or hypothetical policy change. We briefly outline classical parametric methods that are computationally tractable, and then discuss recently developed non-parametric approaches in greater detail. The latter avoid imposing statistical and functional-form restrictions on individual preferences. This makes the welfare estimates theoretically more credible, and clarifies exactly what welfare-relevant information is contained in demand distributions in various choice settings. However, these methods also demand greater in-sample variation in the data for practical implementation than classical parametric approaches. We then cover settings with externalities. The above results are theoretical, and take the demand function as known; therefore, we briefly discuss empirical problems around the estimation of demand itself and of welfare based on it. We conclude by suggesting areas for future research.

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Nonparametric Approaches to Empirical Welfare Analysis

Abstract

Forthcoming Article Downloads