Time Counts: Quantitative Analysis for Historical Social Science

by Gregory Wawro, Ira Katznelson

Overview

How to study the past using data

Quantitative Analysis for Historical Social Science advances historical research in the social sciences by bridging the divide between qualitative and quantitative analysis. Gregory Wawro and Ira Katznelson argue for an expansion of the standard quantitative methodological toolkit with a set of innovative approaches that better capture nuances missed by more commonly used statistical methods. Demonstrating how to employ such promising tools, Wawro and Katznelson address the criticisms made by prominent historians and historically oriented social scientists regarding the shortcomings of mainstream quantitative approaches for studying the past.

Traditional statistical methods have been inadequate in addressing temporality, periodicity, specificity, and context—features central to good historical analysis. To address these shortcomings, Wawro and Katznelson argue for the application of alternative approaches that are particularly well-suited to incorporating these features in empirical investigations. The authors demonstrate the advantages of these techniques with replications of research that locate structural breaks and uncover temporal evolution. They develop new practices for testing claims about path dependence in time-series data, and they discuss the promise and perils of using historical approaches to enhance causal inference.

Opening a dialogue among traditional qualitative scholars and applied quantitative social scientists focusing on history, Quantitative Analysis for Historical Social Science illustrates powerful ways to move historical social science research forward.

Product Details

ISBN-13:	9780691155050
Publisher:	Princeton University Press
Publication date:	05/03/2022
Pages:	264
Product dimensions:	6.12(w) x 9.25(h) x (d)

About the Author

Gregory J. Wawro is professor of political science at Columbia University. His books include Filibuster: Obstruction and Lawmaking in the U.S. Senate. Ira Katznelson is the Ruggles Professor of Political Science and History at Columbia University. His books include Fear Itself: The New Deal and the Origins of Our Time.

Table of Contents

List of Figures ix

List of Tables xi

Preface and Acknowledgments xiii

List of Abbreviations xvii

1 Designing Historical Inquiry 1

1.1 Conundrums 2

1.2 History and Political Science: A Century of Divergence 4

1.3 Post-War Divisions between History, Economics, Political Science, and Sociology 7

1.4 Possibilities 12

1.5 Ways Ahead 25

2 Quantitative Pathways for Qualitative Purposes 27

2.1 Orientations 31

2.2 Analytical History: Two Modes 37

2.3 Identifying a Middle Range 40

3 Methods 42

3.1 Methodological Issues 43

3.2 Semiparametric Methods 45

3.3 Change Point Models 55

3.4 Important Concerns 59

3.4.1 Adjudicating between simple and more complex models 59

3.4.2 Imposing less structure is not atheoretical 60

3.5 Conclusion 61

4 Congressional Demonstrations 64

4.1 Coalition Sizes, Agenda Change, and Supermajority Rules in the U.S. Senate 64

4.2 Party Power in the U.S. House of Representatives 68

4.3 Realignment, the 17th Amendment, and Split Party Delegations in the Senate 74

4.4 The 17th Amendment and Representation 79

4.5 Sectionalism and Labor Policy in the New Deal and Fair Deal Periods 83

4.6 Conclusion 94

5 Path Dependence 96

5.1 Path Dependence in Economics 97

5.2 Contingency and Deterministic Patterns 100

5.3 Positive Feedbacks 101

5.4 Stability through Change 102

5.5 Sequence, Externalities, and Path Dependence 103

5.6 Empirical Modeling of Path Dependence 105

5.7 Alternative Approaches to Modeling Path Dependence 109

5.8 Critical Junctures and Initial Conditions 111

5.9 Markov Switching Models with Time-Varying Transition Probabilities 116

5.9.1 A representative MSM-TVTP model 119

5.9.2 Stylized examples of MSM-TVTP indicating path dependence 121

5.10 Replication of Path Dependence and Macropartisanship 129

5.11 Path Dependence and Partisan Polarization 133

5.12 Conclusion 139

6 Natural Experiments, Causality, and Historical Analysis 141

6.1 Randomness, Counterfactuals, and Comparisons for Causal Inference 143

6.2 Opportunities and Challenges 147

6.3 Historical Events and Causal Leverage 148

6.3.1 Extreme weather events and economic development 153

6.4 Discontinuities 155

6.5 Instrumental Variables 158

6.6 Persistence 162

6.6.1 Potential problems with standard errors 168

6.6.2 Multilevel concerns 169

6.6.3 Path dependence and causal analysis 170

6.6.4 A closer look at two studies featuring historical IV estimation 171

6.7 Discussion 177

7 Conclusion 182

Notes 189

Bibliography 203

Index 229

What People are Saying About This

From the Publisher

“This accessible book bridges history and other social science disciplines, where methods of statistical analysis can be leveraged to enhance the work of historians and historical social scientists. Meeting readers where they are, Wawro and Katznelson take those who are the most comfortable with statistics and move them to qualitative realms and vice versa. The content is perfectly organized.”—Janet Box-Steffensmeier, coauthor of Times Series Analysis for the Social Sciences

Editorial Reviews

"[A] major new book…all political scientists stand to learn from this balanced and nonpolemical effort to reorient quantitative analysis for historical social science."—-James Mahoney, Perspectives on Politics

From the Publisher