Constituents often fail to hold their representatives accountable for federal spending decisions—even though those very choices have a pervasive influence on American life. Why does this happen? Breaking new ground in the study of representation, The Impression of Influence demonstrates how legislators skillfully inform constituents with strategic communication and how this facilitates or undermines accountability. Using a massive collection of Congressional texts and innovative experiments and methods, the book shows how legislators create an impression of influence through credit claiming messages.
Anticipating constituents’ reactions, legislators claim credit for programs that elicit a positive response, making constituents believe their legislator is effectively representing their district. This spurs legislators to create and defend projects popular with their constituents. Yet legislators claim credit for much more—they announce projects long before they begin, deceptively imply they deserve credit for expenditures they had little role in securing, and boast about minuscule projects. Unfortunately, legislators get away with seeking credit broadly because constituents evaluate the actions that are reported, rather than the size of the expenditures.
The Impression of Influence raises critical questions about how citizens hold their political representatives accountable and when deception is allowable in a democracy.
Justin Grimmer is associate professor of political science at Stanford University. He is the author of Representational Style. Sean J. Westwood is a postdoctoral researcher at the Center for the Study of Democratic Politics at Princeton University. Solomon Messing is a research scientist with Facebook’s Data Science Team.
"This important book provides novel insights into the strategic interactions among legislators, citizens, and the bureaucracy that shape federal spending. Grimmer, Westwood, and Messing deploy new observational and experimental analyses to understand which legislators are most likely to seek government spending in their districts, how they advertise this information, and what effect it has on their electoral fortunes."--Gregory A. Huber, Yale University
"Drawing from a cognitive model of voter attention and learning, The Impression of Influence generates a system of credit claiming that far surpasses prior models. Providing a compelling view of what drives voter attention and congressional credit claiming, this book will interest political psychologists and congressional scholars for generations to come."--Mathew D. McCubbins, Duke University
"This book, which could not have been written just a few years ago, is a must-read for students of American politics and all who want to know where social science is headed. The authors use the latest text as data methods and modern online platforms to conduct experiments, including on the world's largest social network. While their techniques are cutting edge, the substantive questions they illuminate are as old as our republic."--Jasjeet S. Sekhon, University of California, Berkeley
"The Impression of Influence throws new light on the credit claiming behavior of members of the U.S. House. The topical book makes a strong case for the idea that politicians need to generate the impression that they are cooking up benefits for those back home. This is the most convincing and interesting work about the credit claiming front that has appeared in a long time."--David Mayhew, Yale University
Table of Contents:
List of Illustrations ix
List of Tables xi
1 Representation, Spending, and the Personal Vote 1
2 Solving the Representative’s Problem and Creating the Representative’s Opportunity 15
3 How Legislators Create an Impression of Influence 32
4 Creating an Impression, Not Just Increasing Name Recognition 64
5 Cultivating an Impression of Influence with Actions and Small Expenditures 81
6 Credit, Deception, and Institutional Design 121
7 Criticism and Credit: How Deficit Implications Undermine Credit Allocation 148
8 Representation and the Impression of Influence 174
9 Text as Data: Methods Appendix 186