Nonparametric Approaches to Empirical Welfare Analysis
2024 Bhattacharya, Debopam
Nonparametric Approaches to Empirical Welfare Analysis - Journal of Economic Literature 2024 - 554-593
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 subsidies, and informs the current debate on a universal basic income. This paper provides a survey of existing empirical methods, based on cross-sectional microdata, for calculating welfare effects and deadweight loss resulting from realized or hypothetical policy change. We briefly outline classical parametric methods that are computationally tractable, then discuss recent nonparametric approaches that avoid making 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 distribution 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 demand estimation. We conclude by suggesting areas for future research.
0022-0515
Semiparametric and Nonparametric Methods
Consumer Economics
Discrete Regressors
Welfare Economics
Nonparametric Approaches to Empirical Welfare Analysis - Journal of Economic Literature 2024 - 554-593
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 subsidies, and informs the current debate on a universal basic income. This paper provides a survey of existing empirical methods, based on cross-sectional microdata, for calculating welfare effects and deadweight loss resulting from realized or hypothetical policy change. We briefly outline classical parametric methods that are computationally tractable, then discuss recent nonparametric approaches that avoid making 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 distribution 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 demand estimation. We conclude by suggesting areas for future research.
0022-0515
Semiparametric and Nonparametric Methods
Consumer Economics
Discrete Regressors
Welfare Economics