Welfare Analysis Meets Causal Inference
We describe a framework for empirical welfare analysis that uses the causal estimates of a policy’s impact on net government spending. This framework provides guidance for which causal effects are (and are not) needed for empirical welfare analysis of public policies.The key ingredient is the construction of each policy’s marginal value of public funds (MVPF). The MVPF is the ratio of beneficiaries’ willingness to pay for the policy to the net cost to the government. We discuss how the MVPF relates to “traditional” welfare analysis tools such as the marginal excess burden and marginal cost of public funds. We show how the MVPF can be used in practice by applying it to several canonical empirical applications from public finance, labor, development, trade, and industrial organization.
We are grateful to Alan Auerbach, Dave Donaldson, Xavier Jaravel, Amy Kim, Henrik Kleven, Enrico Moretti, Matthew Notowidigdo, Ben Olken, Ben Sprung-Keyser, and Sammy Young for helpful comments; to the JEP Editors (and especially Timothy Taylor) for extensive and helpful comments and edits; and to the numerous students and seminar audiences whose (understandable) questions and confusions prompted us to write this essay. Hendren acknowledges support from the Gates Foundation and Chan Zuckerberg Initiative. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Amy Finkelstein & Nathaniel Hendren, 2020. "Welfare Analysis Meets Causal Inference," Journal of Economic Perspectives, vol 34(4), pages 146-167.