The Production Function of the Regulatory State

Abstract

How much will our budget be cut be this year? This question has loomed ominously over regulatory agencies for over three decades. After the 2016 presidential election, it now stands front and center in federal policy, with the Trump administration pledging over $50 billion in cuts. Yet very little is known about the fundamental relationship between regulatory agencies’ budgets and the social welfare outcomes they are charged to produce. Indeed, the question is scarcely studied in scholarship from law, economics, or political science.

This article lays the groundwork for a new field of theoretical and empirical research, using what we call the “regulatory production function,” to understand the marginal effects of changes in regulatory agency budgets (both reductions and increases) on the levels of benefits they produce. Our proposed theoretical framework and empirical findings have important implications across the regulatory state on the relationship between agency funding and outcomes for public health, safety, and welfare agencies. This model of the regulatory state informs agency-scale decisions regarding institutional design and instrument choice as well as the broader set of decisions regarding the balance of federalism and reliance on private governance as a supplement to public authority.

Part I describes relevant scholarship on the broad topic of regulatory agency resources and outcomes, showing a paucity of theoretical and empirical analysis of the question. Using the Environmental Protection Agency (EPA) and environmental quality as a case study, Part II develops a conceptual model of a regulatory production function for thinking more clearly about linkages between agency funding and regulatory outcomes. Using this model, Part III turns to generating hypotheses that could explain why EPA funding levels may or may not have a strong effect on environmental quality. Part IV uses regression analyses to test whether there is a statistically significant relationship between agency funding and air pollution. In the face of significant data and modeling constraints, we found none. Part V then explores the important research questions that emerge from the study and proposes a research agenda going forward. Much as the tools of cost-benefit analysis and risk assessment have transformed the study of policy choice and legal design, development of the regulatory production function model and the data needed to study it will allow scholars to examine fundamental questions of the regulatory state.