Making Policies Work
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Making Policies Work

First- and Second-order Mechanisms in Policy Design

Edited by Giliberto Capano, Michael Howlett, M Ramesh and Altaf Virani

Policy design efforts are hampered by inadequate understanding of how policy tools and actions promote effective policies. The objective of this book is to address this gap in understanding by proposing a causal theory of the linkages between policy actions and policy effects. Adopting a mechanistic perspective, the book identifies the causal processes that activate effects and help achieve goals. It thus offers a powerful analytical tool to both scholars and practitioners of public policy seeking to design effective policies.
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Chapter 10: Reverse engineering and policy design

R. Kent Weaver

Abstract

“Reverse engineering” in public policy analysis and design involves ascertaining the causal mechanisms at work in social phenomena – both those resulting from intended program effects and from other factors – and using those findings to alter policy design and implementation in order to improve the fit between policy goals and policy outcomes. The first section of the chapter briefly introduces the concept of reverse engineering. The second section illustrates the complexity of causal mechanisms in social policy, using the example of mechanisms that constrain household savings for retirement in advanced industrial countries. The third section examines alternative government policies to improve household retirement savings, and the ways that governments have tried to use knowledge of causal mechanisms to “reverse engineer” improve policy improvements. The fourth section uses the retirement savings policy example to highlight several constraints on using reverse engineering to improve program design and implementation, including incomplete knowledge of causal mechanisms, conflicting policy objectives, path dependence and its political manifestations that constrain policy revision, heterogeneity of target populations, complexity of behaviors among target populations, and hard-to-anticipate unintended consequences of policy revisions.

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