A Unified Welfare Analysis of Government Policies

Datos Bibliográficos

ID23344442
AutoresNathaniel Hendren (0000-0002-6385-6506, Harvard University), Ben Sprung-Keyser (Harvard University)
Año2020
Volumen135
Número3
Páginas1209-1318
Fecha de publicación2020-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaThe Quarterly Journal of Economics (JOURNAL)
Identificadores de la revistaISSN: 0033-5533 • E-ISSN: 1531-4650
EditorialOxford University Press (OUP) (PUBLISHER)
DOI10.1093/qje/qjaa006
IdiomaEN
Citas recibidas54
Referencias citadas119

We conduct a comparative welfare analysis of 133 historical policy changes over the past half-century in the United States, focusing on policies in social insurance, education and job training, taxes and cash transfers, and in-kind transfers. For each policy, we use existing causal estimates to calculate the benefit that each policy provides its recipients (measured as their willingness to pay) and the policy’s net cost, inclusive of long-term effects on the government’s budget. We divide the willingness to pay by the net cost to the government to form each policy’s Marginal Value of Public Funds, or its ``MVPF''. Comparing MVPFs across policies provides a unified method of assessing their effect on social welfare. Our results suggest that direct investments in low-income children’s health and education have historically had the highest MVPFs, on average exceeding 5. Many such policies have paid for themselves as the government recouped the cost of their initial expenditures through additional taxes collected and reduced transfers. We find large MVPFs for education and health policies among children of all ages, rather than observing diminishing marginal returns throughout childhood. We find smaller MVPFs for policies targeting adults, generally between 0.5 and 2. Expenditures on adults have exceeded this MVPF range in particular if they induced large spillovers on children. We relate our estimates to existing theories of optimal government policy, and we discuss how the MVPF provides lessons for the design of future research.