Bayesian Models and Methods in Public Policy and Government Settings

TL;DR

This paper advocates for the increased adoption of Bayesian methods in public policy and government, demonstrating their effectiveness through diverse real-world examples and arguing against the historical skepticism.

Contribution

It provides a compelling argument and practical examples supporting Bayesian approaches as standard tools in public policy analysis, emphasizing formal and informal prior assessments.

Findings

Bayesian methods are effective in census and small area estimation.

Bayesian approaches improve election forecasting accuracy.

Bayesian analysis aids in climate change and health studies.

Abstract

Starting with the neo-Bayesian revival of the 1950s, many statisticians argued that it was inappropriate to use Bayesian methods, and in particular subjective Bayesian methods in governmental and public policy settings because of their reliance upon prior distributions. But the Bayesian framework often provides the primary way to respond to questions raised in these settings and the numbers and diversity of Bayesian applications have grown dramatically in recent years. Through a series of examples, both historical and recent, we argue that Bayesian approaches with formal and informal assessments of priors AND likelihood functions are well accepted and should become the norm in public settings. Our examples include census-taking and small area estimation, US election night forecasting, studies reported to the US Food and Drug Administration, assessing global climate change, and measuring potential declines in disability among the elderly.