Dynamic Partisanship: How and Why Voter Loyalties Change 9780226762531

Citation preview

Dynamic Partisanship

Dynamic Partisanship How and Why Voter Loyalties Change

ken kollman and john e. jackson

the university of chicago press

chicago and london

The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London © 2021 by The University of Chicago All rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637. Published 2021 Printed in the United States of America 30 29 28 27 26 25 24 23 22 21   1 2 3 4 5 isbn-­13: 978-­0-­226-­76222-­7 (cloth) isbn-­13: 978-­0-­226-­76236-­4 (paper) isbn-­13: 978-­0-­226-­76253-­1 (e-­book) doi: https://doi.org/10.7208/chicago/9780226762531.001.0001 Library of Congress Cataloging-in-Publication Data Names: Kollman, Ken, 1966 – author. | Jackson, John E. (John Edgar), 1942 – author. Title: Dynamic partisanship : how and why voter loyalties change / Ken Kollman and John E. Jackson. Description: Chicago ; London : The University of Chicago Press, 2021. | Includes bibliographical references and index. Identifiers: lccn 2021003490 | isbn 9780226762227 (cloth) | isbn 9780226762364 (paperback) | isbn 9780226762531 (ebook) Subjects: lcsh: Party affiliation. | Comparative government. | Party affiliation— United States. | Party affiliation— Great Britain. | Party affiliation— Canada. | Party affiliation—Australia. Classification: lcc jf2071 .k65 2021 | ddc 324.2 — dc23 LC record available at https://lccn.loc.gov/2021003490 ♾ This paper meets the requirements of ansi/niso z39.48-­1992 (Permanence of Paper).

to colleen and the kids, including the new member of the family —­k en to my family—­g retchen, michael, carrie, and charles, who all contributed significantly —­j ohn

Contents Preface ix chapter 1. Introduction: Why Study Dynamic Partisanship?  1 chapter 2. Partisanship: Meaning and Measurement  24 chapter 3. Consistent Partisanship Models  50 chapter 4. The United States  70 chapter 5. A  ustralia, Canada, and the United Kingdom: The Setup  111 chapter 6. A  ustralia, Canada, and the United Kingdom: Results 131 chapter 7. Explaining Partisanship Dynamics  173 chapter 8. Parties and Partisanship  204 References 219 Index 233

Preface

T

he purpose of this book is to improve explanations for the dynamics of partisanship in mass publics. Research findings presented throughout the book draw from a novel conceptual and methodological approach, allowing us to weigh different factors that might cause partisanship to change for groups in the population. Central to our enterprise are models that indicate our view of how different factors relate to each other. The chapters explore the relationships between individual and mass partisanship and these other factors: •

public opinion on policy issues and ideologies,

•

public perceptions of political party positions on issues and ideology,

•

party strategies for winning votes,

•

current events such as economic outcomes, immigration, and international crises and war.

The project fits in the genre of work on nonlinear dynamic systems. Such a class of models is characterized by a focus on the interactions among sets of variables that do not conform to the static equilibrium models common in formal political theory or the linear and additive models common in much empirical work. The models we propose have a relatively small number of variables, but their interactions follow a complex pattern that varies considerably over time and in different circumstances. Parameters can be endogenous to the model, which in our context is another way of saying that the model can exhibit feedback and nonlinear dynamics. The kinds of structural modeling and theoretical development in this book are not as common in political science and in several other social sciences as in the past. We applaud the advances in recent years in the

x

preface

study of causal processes and causal inference, yet something can be lost when researchers are discouraged from proposing theories driven by a desire to understand how complicated social systems work and how a mix of factors relate to each other in systematic ways. Bivariate findings, however solidly they point to causal impacts of one factor on another, can be arid without a sense of the overall structure of social systems. We are of course not alone in the view that structural theories should continue to have a place in the social sciences. In fact, we go so far as to say that the modeling and empirical evidence in this book are examples of what can be accomplished with a structural framework. Our models are intended to piece together complicated and interrelated elements of the political world and lead to empirical tests. Modeling the evolution of partisanship adequately, we believe, requires a complex nonlinear dynamic setup. The data are time series of aggregate partisanship and party utilities for subgroups of voters in four countries. These observational data methods have the limitations and required assumptions that have fueled the current emphasis in social science methodology on causal inference. Unfortunately, as powerful as causal, bivariate methods are, they are essentially based on conceptual frames that do not capture the nonlinear and dynamic properties in our proposed model. Our choice is to use the data and methods appropriate for the model while acknowledging their limitations. Collaboration on this project began many years ago at an informal seminar on path dependence in the Center for Political Studies at the University of Michigan. Participants included Robert Mickey, Scott Page, Jenna Bednar, Arthur Lupia, Jim Morrow, Rob Franzese, and Burt Monroe. Several people at that seminar, including the two of us, wrote and published papers on path dependence. Following the seminar, we became motivated to revisit the study of partisanship by the belief that the distribution of partisan loyalties in the electorate is an interesting case of possible path dependence. We do not in this book emphasize the potential path-­dependent properties of partisanship, but those potential elements are there and worth continuing exploration in the future. Collaborating with colleagues you respect is one of the joys of being in research. This book is the product of a remarkably productive and thoroughly enjoyable collaboration producing work that neither of us could have done on his own. The strongest disagreement was about the ordering of the authors’ names. The younger partner preferred alphabetical order. The older and wiser partner insisted on and got his way. Despite this

preface

xi

disagreement, both wish for lunches to continue where the conversation ranges from three stage least squares to survey research design to the Canadian New Democratic Party. Elizabeth Levesque collaborated on much of the research on American partisanship and is a coauthor of chapter 4. We are grateful for her terrific work and for her patience through many rounds of journal submissions and revisions. Several research assistants have helped with this project: Su-­Hyun Lee and Erica Mirabitur deserve our high praise for putting together much of the data sets on the four countries. Megan Bilbao helped at crucial points in the data-­collection process. We have benefited from the advice, encouragement, and constructive feedback of colleagues and fellow researchers in conducting this research and preparing this book. We thank participants at several annual meetings of the Society for Political Methodology. Participants in the weekly seminar of the Center for Political Studies gave feedback, as did participants at Canada’s Electoral Future in Comparative Perspective workshop at the University of British Columbia (held in honor of Richard Johnston). We benefited from the ideas and insights of Carl Simon, Stuart Soroka, Richard Johnston, Elizabeth Levesque, and Chuck Shipan. Two anonymous reviewers for the University of Chicago Press gave excellent suggestions. None of these helpful people, nor any of the terrific colleagues mentioned in this preface, bears responsibility for any errors or omissions. Chuck Myers, as always, has been the consummate professional and a patient editor. George Roupe and Christine Schwab at University of Chicago Press were meticulous and helpful. We gratefully acknowledge the funding for the project that came from the Center for Political Studies and the Institute for Social Research at the University of Michigan; the College of Literature, Science, and Arts at the University of Michigan; and the Center for Advanced Study in the Social and Behavioral Sciences at Stanford.

chapter one

Introduction Why Study Dynamic Partisanship?

E

lectoral politics are in tremendous flux all over the world. Antiestablishment, often right-­wing parties are making gains against center-­left and center-­right parties. Nontraditional candidates are capturing candidacies for president within establishment parties, and upstart parties are leading or becoming part of governments in parliamentary democracies. New movements are roiling formerly dominant coalitions within parties. Loyalties to traditional governing political parties are breaking down in many sectors of societies, with long-­standing loyal groups changing their partisanship or voting against their traditional partisanship to support alternatives. It can be tempting to conclude that parties themselves as institutions are becoming less important, less vital to democracy. Perhaps politicians and the mass public are using partisan cues less frequently and relying on other kinds of markers—­ideologies, specific candidates, antiestablishment stances—­as rallying points for coordinated political behavior.1 The evidence, however, appears to belie this interpretation. If anything, partisanship among the public continues to drive vote choices at least as strongly as in the past. And politicians group themselves into tightly knit parties with recognizable ideologies that carry through to government policy.2 The strong role of partisanship as a motivator of political behavior has hardly diminished. Donald Trump, for instance, benefited tremen­ dously from Republican voters who stayed with their partisanship in the 1. This was a prominent theme in the political science literature on parties in the 1980s and continues somewhat. See Wattenberg (1998). Dalton (2000, 2013), and Dalton and Wattenberg (2000). 2. See Bafumi and Shapiro (2009), Bartels (2000), Hetherington (2001), Huddy, Mason, and Aaroe (2015), and Smidt (2017).

chapter one

2

presidential election of 2016 even though many had deep misgivings about their candidate. Controversies over Brexit in the United Kingdom, and over environmental policies, immigration, and religion in Australia and Canada, have been shaped by partisan cleavages. Partisanship systematically affects vote choice, the news sources people rely on, and even personal in­­ teractions and friendships. It structures democratic politics and clarifies, and likely deepens, social and political divisions. Stating that partisanship remains highly relevant, however, says little about individual or aggregate change. More specifically, it says nothing about whether people maintain their partisanship over time. If political scientists thought they knew anything sixty years ago about partisanship, it was the stability of it. Most people most of the time keep their partisanship throughout their adult lives and vote accordingly.3 It continues to show up in high-­ quality surveys as a highly stable political attitude for people in many advanced democracies. What is happening now? Have times changed? Was our understanding flawed? Likewise, parties themselves continue to be critical elements of democratic societies. While traditional partisan categories may be fraying and large numbers of people (both politicians and voters) may be becoming unmoored from their previous party labels and adopting new labels, this does not imply that parties are any less important. The rise of new parties and the shifting of party loyalties among people and population groups instead imply a complex dynamic about party systems and about partisanship that is not well understood. This book offers a new, comprehensive, and consistent theoretical approach to the dynamics of partisanship. Theoretical and empirical models of partisanship to this point have flaws that hinder our understanding of the current moment and the past as well as our anticipation of future trends. Previous models are valuable and important, but to understand what has happened and what is currently happening, we need a new way to think about partisanship dynamics that can accommodate both stasis and change. Using our approach, analysts of electoral politics will be better positioned to understand why partisanship changes and when it is likely or unlikely to change among population groups or the whole electorate. It can help party strategists with their own goals within their parties. With this understanding, reformers can improve our democratic institutions to connect voters better to political parties and the governments that those parties operate. 3. See, for instance, Converse (1969) and Goren (2005).

why study dynamic partisanship?

3

Moreover, with careful modeling and good data, using our approach one can estimate the duration and depth of partisanship change in specific populations under alternative scenarios. Take the contemporary American parties. Both Democratic and Republican party leaders faced fundamental decisions about strategies for contesting the 2020 national elections. The Democrats especially were pulled by different factions in centrist and more leftist directions. Republicans faced complicated tensions between Trumpists and establishment factions in contesting congressional elections. We show evidence in the final chapter of this book that, should either party pull toward its extremes while the other stays more centrist, the party moving toward the extremes will lose large proportions of partisans. For the Democrats, this includes both white and black partisans. We show throughout the book with ample evidence that our models are good representations of how partisanship changes in response to party changes, and using the same models we can predict long-­term electoral disaster for either party should its more extreme flanks win out in moving the party reputation toward the wings. This conclusion is based on patterns of partisanship and estimated models using data back to the 1950s, with hypothetical projections for where the Democrats or Republicans might lean ideologically through and following 2020. How long would such changes last and what are their electoral consequences? In chapter 2 we estimate the links between levels of partisanship in the population and congressional election returns. Previewing conclusions that we can draw from analyses in this book, declines for instance in Democratic partisanship in key populations resulting from moving too far to the left in 2020 and 2022 will likely lead to substantial and consequential declines in votes for Democrats in national elections for years thereafter. Similarly for Republicans under the opposite scenario (they deepen their Trumpist reputation and the Democrats signal that they are centrist). These scenarios are discussed in the final chapter.

1.1 Why Study Partisanship? A statement made about partisanship (or party identification) nearly two decades ago still stands: “Few subjects in political science have received as much attention.”4 For good reason. Partisanship is important for social 4. Green, Palmquist, and Schickler (2002), 5.

4

chapter one

scientific and civic reasons.5 Its social scientific importance derives from the research showing that partisanship drives the vote more than any other psychological predisposition in mature democracies. It also affects how people receive, respond to, and organize information about politicians and current events.6 Macropartisanship for the entire electorate and electoral outcomes are strongly associated, as seen in figure 1.1. The plots show the total vote for the major left party in congressional/parliamentary elections and the nationally aggregated strength of major-­party partisanship. Partisanship is measured in slightly different ways in these figures (discussed in the next chapter), but the overall patterns are unmistakable. For each of the four countries shown, election outcomes and partisanship exhibit similar patterns. The correlations range from about 0.6 and 0.7 for Australia after 1985 and the United States to 0.9 for the United Kingdom and Canada.7 One cannot tell from the figures whether changes in partisanship precede or lag changes in election outcomes, whether they are simultaneously related or correlated because of a common third factor. These possibilities are discussed in the next chapter. The type and quality of partisanship have important civic and normative implications. While partisanship perhaps has drawbacks and can be seen as hardening societal divisions, it has also been tied to the health of modern democracies. There is largely a consensus that partisanship is a

5. For convenience we will use the terms partisanship and party identification interchangeably unless the subtle distinction between the two is relevant to our discussion. Their respective use can signal differences in conceptualization, with the latter more often used by political psychologists and the former more broadly among electoral politics scholars and comparativists. 6. This reflects a near consensus in voting research; see Abramson and Ostrom (1991); Aldrich and Griffin (2018); Bafumi and Shapiro (2009); Bartels (2000); Belanger and Stephenson (2010); Blais et al. (2001); Brader and Tucker (2012); Butler and Stokes (1969); Campbell et al. (1960); Converse (1966); Dalton (1996); Green, Palmquist, and Schickler (2002); Gerber and Jackson (1993); Hetherington (2001); Highton (2010); Highton and Kam (2011); Jacobson (2019); Jennings and Niemi (1981); Jerit and Barabas (2012); Johnston (2006); Klar (2014); Levendusky (2009a, 2009b); Lewis-­Beck et al. (2008); MacKuen, Erikson, and Stimson (1989); Miller (1991, 1992); Miller and Shanks (1996); Page and Jones (1979); Shafer (1998); Smidt (2017); Sniderman, Forbes, and Melzer (1974); Stewart and Clarke (1998); Weinschenk (2010); and Zuckerman, Dasovic, and Fitzgerald (2007). 7. The correlation for Australia is computed for 1987 to 2013 because election surveys are missing for many previous years. See Jacobson (2019, 142) for a similar analysis of the US data.

0

-1.5

-1

.2

-.5

.4

0

.5

.6

% Labor Vote

Labor Partisanship

1

.8

1.5

2

1

Australia

1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

Year

Labor Vote figure 1.1.  Vote shares and partisanship in four countries

Labor PID

figure 1.1.  (continued )

why study dynamic partisanship?

7

key component of democratic politics linking voters to leaders.8 Partisanship anchors people’s expectations about the actions of actual and potential leaders and also anchors leaders’ and candidates’ expectations about the voting behavior of citizens. In other words, it serves as a key coordination device, allowing ordinary people and leaders to have reasonable expectations about others’ behavior.9 A hallmark of stable democracy is relatively durable partisanship among groups in the population directed toward a relatively small number of political parties for a given electoral arena.10 Scholars have generally assumed that the decline of partisanship is bad (e.g., Beck 1979; Dalton 2000) and that new democracies need stable partisanship to survive as democracies (e.g., Brader and Tucker 2001; Lupu 2013). Strong and stable parties enhance the connections between the public and the government in a number of ways. An important one is the adoption of an appropriate balancing of short-­term versus long-­term benefits. Individual politicians have incentives to focus on the next election, giving disproportionate weight to short-­term costs and benefits and less weight to long-­term consequences. Strong parties, however, if they are to remain strong, must consider these longer-­term effects. Younger party members know they will have to campaign in future elections based on their party’s performance over the longer term and will evaluate and consider these longer-­term effects. Alesina and Spear (1988) refer to an overlapping-­ generations model, arguing that these younger members’ incentives and be­­ havior will force a longer time horizon on the party’s decisions. Simmons (2016) provides empirical evidence connecting stronger parties with an increased likelihood of longer-­term as opposed to short-­term economic policies. Two additional concepts further clarify the importance of partisanship: contract and principal agent. The connection between the public and the government, operating through the parties and partisanship, is an implicit contract. Members of the public “exchange” their long-­term support for, or identification with, a party for the expectation that the party leaders will pursue policies that reflect the members’ political view and interests. This exchange is then expected to constrain the actions of the party elites

8. Aldrich (2011); Campbell et al. (1960); Green, Palmquist, and Schickler (2002); Miller and Shanks (1996); and Niemi and Jennings (1991). 9. Aldrich (2011), Cox (1987, 1997). 10. Brader and Tucker (2001, 2012).

chapter one

8

while in or competing for office. The elites must be mindful of this implicit contract as they compete for votes, execute their duties if elected, and pursue their own policy goals. There is also a related element of the classic principal-­agent situation in partisanship, especially with regard to the attention paid by the public to party leaders. The public’s partisanship, if it is essentially a fixed entity acquired early in one’s political life and never changes, will not constrain the behavior of party leaders. If partisanship is never updated, what incentives do party leaders have to pay attention to voters’ preferences to try to win or hold onto that loyalty among voters? In contrast, even if updating is rare, the fact that it might occur among voters can give incentives for party leaders to pay attention to their principals (voters).

1.2 Partisanship in Four Countries To motivate what follows in the remainder of this chapter and the rest of the book and to challenge thoughts that partisanship might be mostly a fixed entity (especially in the United States), consider this quote from The American Voter: “The South, despite its occasional desertions of the Democratic presidential candidate, is still the citadel of the Democratic Party, with the Republican Party offering hardly more than token opposition in large parts of the area.”11 It goes without saying that things have changed. In fact, they have turned upside down in half a century. The regional, partisan pattern from this quote is exactly the opposite of the red and blue patterns today. Our attention will be on the politics of the last several decades in four countries—­Australia, Canada, Great Britain, and the United States. All these countries have experienced large partisan shifts, though rarely if ever as dramatic as in the American South. The figures that follow plot partisanship for selected population subgroups in these four countries. Subsequent chapters present more detail about the definition and measurement of partisanship and the subgroups selected for each country. The figures show that partisanship, at least when aggregated among subpopulations, is far from an unchanging entity. If fixed partisanship and unmoving, loyal voters (in theory) give too much leeway to elected officials, then empirically in these four countries, mass partisanship meets at 11. Campbell et al. (1960), 152.

why study dynamic partisanship?

9

least a minimal condition to act as a constraint on party behavior because it can and does change. Subsequent chapters examine in detail whether other conditions are met, such as whether these changes are responsive to party actions. If so, this bolsters the views just described that partisanship can be an implicit contract and that there are meaningful principal-­agent relationships between voters and parties. 1.2.1 United States Our deepest explorations in this book will be in the American case because the data are of the highest quality, are the most consistent over time, and go back the longest in history. Figure 1.2 plots partisanship among three groups in the US electorate—­northern whites, southern whites, and African Americans—­for 1956 to 2016.12 There are distinct patterns in these graphs. Northern white partisanship comes closest to the traditional view of an unchanging party identification. The only ways that northern white partisanship differs from being twenty-­six random draws from a distribution with the same mean is a Democratic shift in 1964 and a slight Republican shift in 1984. For the other periods the actual series are nearly indistinguishable from draws from distributions with single means. This is close to what the traditional view of party identification as an unchanging personal attribute would predict, at least if a stable aggregate mean were somewhat reflective of individual-­level stability.13 Southern white and African American partisanship deviate substantially from a prediction of stability but in quite different ways. Both these series for southern whites and for African Americans demonstrate substantial and important partisan shifts from the mid-­1950s to the mid-­2010s.14 African American partisanship depicts what is traditionally seen as a realignment triggered by a critical election. In 1964 and 1968, African American partisanship becomes over one point more Democratic, which 12. We will use African American and black interchangeably throughout this book in reference to the American case. Surveys over time used a variety of terms, including Negro in early instantiations, but the most common term in surveys is black. 13. The aggregate trends cannot say much at all about individual-­level stability without further analysis, which we provide in later chapters. 14. Many scholars, including Carmines and Stimson (1989), say these partisan shifts are the defining features of US politics since the 1960s. See Mickey (2015) for an important study of southern transformation. See Valentino and Sears (2005); Osborne, Sears, and Valentino (2011); Lublin (2004); Kuziemko and Washington (2015); Hood, Kidd, and Morris (2012); Aldrich and Griffin (2018); and Shafer and Johnston (2006) on partisanship in the South.

10

chapter one

figure 1.2.  US partisanship

in this context is a large change.15 From 1964 through 2016 African American partisanship is best described as twenty-­three random draws from a single distribution with the same mean, which is 0.8 more Democratic than the pre-­1964 mean.16 Carmines and Stimson (1989) would define this as a punctuated equilibrium. Southern white partisanship is best described as a time series with a strong trend.17 The deviations from this trend are consistent with the conclusion that southern white partisanship is best described as a sixty-­one-­ year shift from being moderately Democratic (mean partisanship close to 1.4) to weakly Republican (mean partisanship less than −0.7) with no evi­ dence this trend is flattening, let alone reversing. 15. There were no American National Election Studies in 1962 and 1966, so we can only compare presidential election years in the 1960s. 16. In a simple regression for the period 1964 to 2016 the trend coefficient is −0.001 with a standard error of 0.003. Seventy percent of the draws are within one standard deviation, and 96 percent are within two standard deviations of the period mean, all of which are within sampling variation for a single normal distribution. 17. The trend coefficient for 1956 to 2016 is −0.036 with a standard error less than 0.002. Sixty-­nine percent of the deviations from this trend line are within one standard error, and all are within two standard deviations of the trend line. The R2 for this regression is 0.94.

why study dynamic partisanship?

11

1.2.2 Australia

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Labor Partisanship

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Australia, among the other countries in this study, most resembles the United States in having close to a two-­party system, at least when taking the long view across decades since the 1960s. The proportion of survey respondents choosing a minor party is consistently less than 10 percent, though it has increased to that level among the middle class in the 2000s with the rise of a Green Party. Figure 1.3 plots Australian partisanship for the 1967 to 2013 period by self-­identified class. The partisanship measure follows the format used in the United States, with minus three being very strong Liberal, zero being no party, and plus three being very strong Labor. The important trends are twofold: members of the working class becoming decidedly less Labor beginning in the late 1970s continuing right through to 2013 with only a small and short-­lived recovery in 2007, and middle-­class partisanship following just the opposite path (though timed somewhat differently), with far weaker Liberal support by 2013. The opposing shifts over the entire time period are about equal in magnitude. The Australian patterns are different from those in the United States, as there is no evidence of a rapid shift as among African Americans or

1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

Year

Middle figure 1.3.  Australian partisanship by class

Working

Other, None

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chapter one

a long-­term steady movement to another party, as with southern whites. There is, however, clear evidence that Australian partisanship is not a steady, relatively unchanging entity that fluctuates randomly about a fixed mean. 1.2.3 United Kingdom British partisanship offers another example where partisanship changes over time but in yet another pattern. Partisanship strength for now is measured in our standard way for the two major parties, with the scale ranging from minus three for very strong Conservative supporters to zero for no support for either of the two major parties to plus three for strong Labour supporters. Figure 1.4 plots this partisanship measure for those identifying as working class, the black line, and those identifying as middle class, the gray line, for the period 1963 to 2015. The years that Margaret Thatcher led the Conservative Party are denoted with long dashes and the period of Tony Blair’s Labour leadership are shown with the short dashes. Later analysis in this book will say more about the dynamics of those periods.

figure 1.4.  UK partisanship by class

why study dynamic partisanship?

13

The patterns depicted in figure 1.4 exhibit definite trend lines, with both groups becoming far less partisan over time, at least until the 2000s. The plots also suggest the importance of the party leaders. Both classes show a decided Conservative shift during the early years of the Thatcher administration and a decided shift toward Labour during the Blair regime. The weak exceptions are that the middle class showed some defection from the Conservatives toward the end of the Thatcher era and similarly the working class Labour support wanes in the last years of the Blair government.18 1.2.4 Canada Canada presents yet a different pattern and analytical challenge. An immediate issue is the presence of three major parties in most of Canada. Beginning in 1997 there are four in Quebec. Figure 1.5 plots each party’s strength in each of four regions. Party strength is shown separately, as there is no obvious major-­party pair from which to create a partisanship scale as in the other countries. Several features are quickly evident. Canadian party support seems to be the least stable of the four countries examined. This is most evident with the Conservatives experiencing periods of decline in certain regions (such as the Maritimes), short-­term success (such as 1984 in all regions), and dramatic recovery since 2004 (particularly in the West). The Liberals, particularly in Quebec and the Maritimes, have been largely a party in long-­term decline, despite winning government in recent years. The New Democratic Party (NDP) is overall a minor party except in the West, where it competes successfully with the Liberals for second place. 1.2.5 Patterns of Partisanship Different patterns of partisanship are exhibited in the four countries shown in figures 1.2 through 1.5. These range from long periods of stable partisanship in two of the US cases to long-­term trends observed among southern whites in the United States and the Liberals in parts of Canada to dramatic rapid change exhibited by African Americans in the United States to fluctuations that describe British partisanship in the successive 18. Simple regressions with a trend variable and dummies for the Thatcher and Blair governments have significant coefficients for all terms except the dummy for the Thatcher gov­ ernment in the middle-­class equation, which still has the expected sign.

figure 1.5.  Canadian partisanship by region

figure 1.5.  (continued )

chapter one

16

Thatcher and Blair governments. Stable, statistically unchanging partisanship for key demographic or regional groups over extended time periods is a rarity. The patterns succinctly pose the challenge for this book. Is there a common conceptual structure, and is there a set of factors that can “explain” these patterns and that can provide consistent accounts of their occurrences? We propose that the answer is yes.

1.3 Parties and Partisanship Consider three general arguments about dynamic partisanship. They are as follows, in answer to the question of what moves partisanship: 1. People change: changes within voters (their preferences and attitudes) relative to the major parties. One argument is that people (potential voters) change. It could be that voters change their attitudes toward political issues, toward other groups, or toward the government (including specific politicians or types of politicians) and that those changes drive changing partisanship. 2. Circumstances change: evaluations of performance (e.g., retrospective voting). Another argument is that policy consequences matter. Leaders make decisions that have consequences, and voters evaluate those decisions and the competence of politicians and choose partisan loyalties subsequently. The well-­known retrospective voting model assumes that voters look to past performance of the government and then vote accordingly; this notion can be extended to party identification.19 People are more likely to identify with a party the more that party is associated with satisfying policies—­typically the focus is on economic policies and economic outcomes. An incumbent party overseeing good economic times will be rewarded with more partisans among the electorate. 3. Parties change: changes by political parties (their positions on issues) relative to voters’ preferences. Or the parties change. It might be less about performance of incumbents in office and more that parties are changing their positions on issues. Voters,

19. Fiorina (1981).

why study dynamic partisanship?

17

whose attitudes are relatively fixed, then reevaluate their existing partisanship and update their partisanship accordingly.

Undoubtedly, all three of these things matter to some degree. Disentangling which matters most is challenging because all three factors are often moving and changing at the same time. The main substantive arguments that our model and our data lead us to make are as follows: •

that what matters in moving partisanship varies across countries and contexts

•

and that in general, the third factor (party changes) is what mostly matters, and sometimes the first factor (people change) and the second factor (circumstance) matter. The second factor (circumstance) plays a significant role in specific instances and countries, but its impact overall is the least among the three factors, at least in terms of moving long-­term partisanship.

These conclusions are suggested, for instance, in a deeper look at the foregoing figures. Figure 1.4 provides suggestive hints. Both working-­and middle-­class respondents exhibit significant shifts toward the Conservatives during Thatcher’s government and then toward Labour during Blair’s tenure. The conditional terms “suggestive” and “hints” are used because these plots are far from an explicit analysis of British partisanship, and there may be other explanations that need to be explored. Yet the plots are certainly fingerprints of either party effects or preference effects that we will examine carefully. The partisanship of US southern whites in figure 1.2 provides a stronger yet still incomplete picture of the association between party activity and partisanship. Figure 1.6 reproduces the plot of southern white partisanship from the previous graph. Also included in this new plot are three new series. The first, labeled “Preference,” is a plot of southern white responses to questions asking for opinions about federal government activity to ensure equal treatment of blacks. The second and third are southern whites’ placements of the Democratic and Republican parties on these same questions.20 The foregoing discussion noted the almost monotonic, continuing shift in southern white partisanship from Democratic to Republican over this 20. The full set of questions and response codings for preferences and party positions is dis­ cussed in chapter 4.

chapter one

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1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016

Year

Partisanship

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Rep Position

figure 1.6.  US parties, preferences, and southern white partisanship

entire period. The striking plots in figure 1.6 are the complete reversal in the parties’ positions on civil rights issues. Prior to 1960, and notably in 1958 following the Little Rock, Arkansas, school integration conflict, southern whites associated pro– ­civil rights positions with the Republican Party by a significant margin.21 These associations began to change in 1960 with Nixon’s southern strategy and Kennedy’s support for Rev. Martin Luther King Jr. late in the campaign. The parties’ reversals were completed in 1964 with Lyndon Johnson’s successful efforts to pass and Republican candidate Goldwater’s opposition to the Civil Rights Act of 1964. With the exception of an increase in perceived Republican support in the 1970s the parties’ trajectories continued to move in opposite directions up to and including 2016. Equally notable though not as striking is the almost complete absence of any comparable trend in southern white preferences opposing federal civil rights policies. Chapter 4 analyzes and discusses this

21. Recall that Little Rock is remembered for Republican president Eisenhower sending federal troops to integrate the public schools forcibly, implementing a Supreme Court decision written by a Republican chief justice, while Democratic governor Faubus sought to prevent black students from entering.

why study dynamic partisanship?

19

case with additional data and far more appropriate methods. The point here is that these plots strongly suggest, along with but even more cogently than the British figures, that it is the parties’ deliberate choices in the policies they adopt and pursue that are responsible for changes in partisanship. In all four countries the policy positions voters associate with the competing parties have a central role in the model of partisanship. The partisanship model developed throughout this book gives parties, and their behavior, a central role. This is a relatively underexplored contributing factor to partisan dynamics but one that is critically important.22 Moreover, the results presented throughout this book challenge prominent arguments common in the literature on parties and elections in advanced democracies, including the countries in this study, that voters’ issue preferences are ephemeral and do not drive partisan loyalty and voting decisions (e.g., Achen and Bartels 2016) and that the main story about partisanship in recent decades is the decline in the importance or stability of partisanship (e.g., Dalton and Wattenberg 2000; Dalton 2013). Chapter 8 addresses several of these specific arguments.

1.4 A Comprehensive, Consistent Model of Partisanship The work presented in this book advances research on this important topic by presenting and examining empirically a model that is more comprehensive and consistent in important ways than previous models and methods. Our theoretical approach synthesizes and broadens previous theories.23

22. Important exceptions are Carmines and Stimson (1989); Schofield and Miller (2003, 2007); and Schofield, Miller, and Martin (2003). The work in this book has some connections to thermostatic theories of partisan support (Wlezien 1995; Ura and Ellis 2012; Soroka and Wlezien 2010). In thermostatic theories, public opinion about parties, and by extension partisanship, responds to policy overshooting by incumbent parties with shifts toward the other party or parties. Thermostatic theories can be interpreted to imply an equilibrium partisanship, and the focus is typically on party reputations and policy positions set with policy programs while in office. We put more emphasis on campaign strategies and do not assume equilibrium partisanship. Also, we are focused empirically and theoretically on long-­term shifts in partisanship (over decades). 23. There are many theories of partisanship, discussed in chapter 2. See Blais et al. (2004) and Blais et al. (2001) as examples of approaches with the intent of accommodating different party systems.

20

chapter one

Using the model we can examine systematically the interaction of and weight given to different factors likely related to partisanship change across a wide range of levels, time periods, and countries. Variations in any of the relevant factors—­voters’ policy preferences, party messages about changes in ideology and policy goals, parties’ performance when in office— ­can alter partisanship. The questions are what role each of these plays, how they interact in likely complex ways to produce change, and what is the relative importance of each across different contexts. Addressing these questions requires a model that specifically addresses the dynamics of partisanship in different kinds of party systems. Despite all the attention to studying partisanship, and likely because of it, in the current literature there are incommensurate models at the micro and macro levels, for different time periods, and for different countries. Our understanding of partisanship as reflected in the voluminous literature consists of disparate models that do not form a consistent “whole,” a common conceptualization needed for the accumulation of knowledge. Researchers disagree over how to depict changing partisanship and its role in broader changes in political parties and party systems. “Controversies over the dynamics of party identification are protracted and unresolved,” wrote Clarke and McCutcheon (2009, 704) over a decade ago. Little has changed. As part of the contribution to the substantive questions surrounding partisan dynamics we offer some resolution to methodological controversies by articulating and statistically estimating models that are consistent, or comprehensive, across different levels of aggregation, different time intervals, and different party systems. Our conceptual tools enable us to make substantive claims based on decades of data from numerous surveys and other sources of information. Specifically, we can test for why partisanship changed over time for different groups in the electorate in four different countries. 1.4.1 Normal Partisanship The first defining feature of our approach is that partisanship can be decomposed into two distinct components, normal partisanship and short-­ term forces. Normal partisanship is derived from preferences reflecting relatively stable social cleavages, such as in the US case over civil rights, income distribution, religion, and/or views about the role of government in regulating the economy, and the parties’ positions on policies related

why study dynamic partisanship?

21

to these cleavages. Short-­term forces refer to events that potentially shift partisanship but with no lasting effects. These can include economic cycles, scandals, and/or candidate characteristics. This decomposition parallels Converse’s (1966) discussion of a normal vote and short-­term forces and Friedman’s (1957) separation of income into its permanent and transitory components. The normal partisanship concept is crucial to understanding our model and how it creates consistency over different time periods. Normal partisanship is what produces temporal stability to partisanship, as any change in normal partisanship in one period is expected to affect partisanship in future periods. Short-­term forces alter partisanship in the current period but with no effect in any future period. We formalize these concepts in the next chapter. Let us return to figure 1.6 to compare both components. Southern whites’ measured, aggregated position on the issue of the federal government’s role in guaranteeing equal treatment of African Americans in education, jobs, and housing changed little over the sixty-­year period covered in the figure. Indeed, some of the observed fluctuations are likely due to sampling variations, question wording and format changes, and other episodic factors. Other groups, such as blacks and possibly some northern whites, have opposite but likely equally stable preferences. Party positions changed dramatically in the 1960s and then became stable or even more polarized in the subsequent fifty years. Normal partisanship, as we define it, is a function of these preferences and party positions. Subsequent work summarized later in this book extends measurement of normal partisanship to additional issues, but the concept remains the same (more details in the next chapter). Partisanship, as measured by the conventional questions and plotted in the figure and as distinct from normal partisanship, is the sum of this normal partisanship and short-­term forces present during any election. The slight Democratic movement in 2008, for example, may well be the consequence of the economic collapse in the Great Recession (2008 –­9) and of candidate qualities. 1.4.2 Level Consistency A second feature of our approach continues and expands our work (Jackson and Kollman 2011) to develop consistent micro and macro models. Although our primary empirical interest is in aggregate partisanship among the electorate and a few of its subgroups, the model’s development begins with a micro model of individual partisanship containing the

22

chapter one

normal and short-­term components. This model is then estimated and tested with US and British panel data (summarized later in the book with details in an appendix). A theoretically justifiable micro foundation and explicit aggregation process are critical to a credible aggregate, or macro, model—­what Achen and Shively (1995, 25) call “macro-­consistency”: “Without that constraint (macro-­consistency), macrolevel research too easily slips into studies of the interrelationships of meaningless statistical aggregates. Only when both macrotheoretical propositions and statistical assumptions are rigorously inferred from the microlevel can we have faith in macrolevel studies.” Our paper (Jackson and Kollman 2011) began but did not complete the process of creating and testing a level consistent model. We finish that task by directly linking micro and macro models of partisanship. 1.4.3 Consistency over Party Systems As already indicated, our focus is on politics over long spans of time in four countries. We have developed novel data sets from surveys in each of these countries going back many decades and offer analyses from a long sweep of contemporary political history. These countries differ in the number of effective parties, though all rely on plurality, first-­past-­the-­post de­­ cision rules (with Australia using a variant). The model in this book is applied successfully to explain political change, shown in figures 1.2 through 1.5, within those countries. Small alterations are required to accommodate variations in the numbers and prominence of the parties in each country, but the central structure and relationships among the factors listed above remain the same. Then using these results we make substantive claims about these diverse party systems and what likely motivates partisanship change. 1.4.4 Temporal Consistency The final comprehensive aspect is that the model encompasses periods of relative stability and instability in party systems. Most other theories of partisanship focus on stable situations, or as with theories of critical elections only on brief, and usually rare, episodes of rapid and dramatic change. Figures 1.2 through 1.5 show periods of relative stability, such as among blacks after 1964, and many different patterns of change: rapid movements in one direction, slow drifts, and fluctuations. Even, by default, stasis can

why study dynamic partisanship?

23

be explained using the model. We can accommodate all of those patterns without resorting to external “fixes” or explanations. 1.4.5 Qualification A scope condition on this research is that the ideas apply mainly to long-­ standing or mature democracies where partisanship has taken hold, or has the potential to take hold, in the mass public, and the main questions have to do with how partisan loyalties are distributed and evolve in the population. We do not address questions of how partisanship first becomes established in a new democracy, nor do our ideas apply to systems where parties do not stand much for specific policies in relation to other parties. As newer democracies evolve to have party systems with widespread partisanship and genuine policy battles among ambitious, partisan-­labeled, office-­seeking politicians, these ideas will apply.

1.5 What’s Next In the next chapter we devote attention to the concept of partisanship and then describe our data on four countries. Thereafter, chapter 3 proposes our basic framework and model. Chapters 4 – ­6 apply variations of the model to different party systems. Chapter 7 presents results from analyses of counterfactual simulations in three of our four countries (the Australian data do not permit analysis) to evaluate different explanations for partisanship change. Chapter 8 concludes, and among other topics, considers the consequences in the United States of different patterns of party competition on partisanship into the future.

chapter two

Partisanship Meaning and Measurement

W

e began, in the opening chapter, describing why parties and mass partisanship are important, showing that common measures of partisanship exhibit different temporal patterns, and claiming that the actions of parties as organizations play an important role in these patterns. The four countries of interest, with different party systems, all share some common electoral rules. The end of the chapter posed the challenge, “Is there a common conceptual structure and is there a set of factors that can ‘explain’ these patterns and that can provide consistent accounts of their occurrences?” This chapter begins to address that challenge by presenting a definition of partisanship, discussing how we intend to measure some of its major components in these four countries, and using these measures to locate partisanship at the center of the electoral process.

2.1 What Is Partisanship? A debate that has animated the literature on partisanship is whether, among ordinary people, partisanship is affective or cognitive. A common viewpoint is that it is affective, a psychological predisposition that biases perception and guides behavior, and that it ought to be considered an identity marker like race or religion.1 Another viewpoint is that it is a cognitive shortcut for 1. The most influential statement of this view was, of course, Campbell et al. (1960). Green, Palmquist, and Schickler (2002) offer a full, widely cited account of this view. See also Huddy, Mason, and Aaroe (2015) and Klar (2014).

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assessments by rational people who sensibly incorporate information about the parties’ positions and past behavior before the vote choice.2 There are nuanced versions of these two broad conceptualizations, which are sometimes linked to disciplines other than political science—­psychology (affective) or economics (cognitive, rational choice). Our approach can encompass either viewpoint, and we see no reason to take a firm position on this debate given our purposes.3 Recent scholarship reorients that debate slightly by interpreting partisanship as being akin to notions of brand loyalty in theories of consumer behavior.4 Partisanship as brand loyalty can accommodate either the affective or cognitive viewpoints and often is intended to be a bridge (i.e., consistent with the broad thrust of behavioral economics). Social scientists can agree that people have affective attachments but also use cognition to reevaluate. It is uncontroversial to conclude, based on data across time and across countries, that people are habitual, or “sticky,” in their voting, contributing, and participatory decisions in politics. They develop an attachment to a party and then continue to support that party unless there is evidence to challenge that loyalty. In this way partisanship is a decision-­making shortcut. Consumer research and ideas from microeconomics also make substantively useful contributions to the study of partisanship as brand loyalty.5 In particular, the literature on brand loyalty was developed with the strategic incentives of consumer product firms in mind; researchers were (and are) motivated to understand consumers’ brand loyalty in relation to firms’ strategic positioning against competitors, and firms try to find a mix of product attributes to appeal to consumers, much like parties or candidates try to find a mix of policies to appeal to voters. Brand loyalty is typically conceived as a dynamic rather than a static concept. Firms and consumers are engaged in coevolving relationships based on firms’ actions in designing and marketing their products and consumers’ experiences with these products.

2. See Achen (1992), Gerber and Green (1998), Grynaviski (2010), Highton and Kam (2011), and Page and Jones (1979), for different approaches using this viewpoint. 3. See Bartels (2002); Bullock (2009); Carsey and Layman (2006); Huber, Kernell, and Leoni (2005), and Jerit and Barabas (2012) for innovative viewpoints. 4. See Lupu (2013). 5. See, for instance, Bloemer and Kasper (1995); Dick and Basu (1994); Odin, Odin, and Valette-­Florence (2001); and Punniyamoorthy and Raj (2007).

26

chapter two

Additionally, conceptualizations of brand loyalty incorporate consumers’ experiences with a given brand. Referred to as “experiential utility,” the level of loyalty functions as an updating mechanism for consumers (or voters) when considering their decisions’ behavioral biases. For partisanship, voters evaluate parties and their politicians (“experience them”) and update their partisanship. Partisanship, however, is certainly more than brand loyalty. Even if one finds it useful to adopt a mostly economic conceptualization, it is clearly the case that partisanship has aspects in common with other psychological predispositions that bias decision making. For instance, partisanship can lead to cascading evaluations and behavior, where being a partisan leads to biased evaluations of the summary of perceived benefits from a given party winning (or possibly winning) representation in government. This is similar to what is now often referred to as confirmation bias, where the evaluation of evidence confirms what one already believes and even hardens those beliefs, leading to more confirmation bias. Moreover, partisanship can be consistent with brand loyalty but moves beyond it to include loyalty to ideas about how society should operate, and those ideas are understood to be in opposition to ideas held by others. Partisanship involves a sense of belonging to a group with common interests and that the group has opposition from other groups, especially those with different partisanship. It is more than brand loyalty because it is political, with both values-­based and group-­based components. Partisans have identities linked to a sense of “us and them,” and this kind of identity is absent in what we often think of as brand loyalty among consumers.6 Put another way, brand loyalty and partisanship differ with regard to the degree to which loyalists (partisans) care about long-­term reputations and long-­term normative goals of the organizations providing the relevant goods. Consumer brand loyalty, at least as traditionally understood, does not make an important contribution to economic welfare because consumers are not assumed to be making decisions based on collective goods. The traditional debates over partisanship can be boiled down to whether there is any purchase in assuming that people, as individuals with individual interests, use their cognition to evaluate candidates and parties actively.7 Certainly, it is not difficult to find evidence, often interpreted as 6. E.g., Green, Palmquist, and Schickler (2002) and Huddy, Mason, and Aaroe (2015). 7. See Krause (1997, 2000).

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27

opposing the economic version, that people’s partisanship derives from their conceptualization of the political world as made up of groups conflicting with each other, and party is an identity based on group affiliations. Yet there is also evidence that people update and revise their partisanship occasionally, often as groups of people. And they do so correlated with other evaluative acts, like indicating that they believe parties have moved “away from them” or their group in ideological or issue space, and thus they change their partisanship.8 In fact, we will show evidence of this kind of adaptation in people’s survey responses. This leads us to the following definition, which suffices for our purposes: “Partisanship: a group-­based, shared identity that leads to habitual and durable yet malleable patterns of political participation and shapes how people understand and evaluate candidates and political parties.” The definition allows us to use our modeling approach as a means of understanding what moves partisanship. We assume that partisanship is a function of, among other things, party utility, a subjective evaluation matching either individual or group interest to the promises party leaders make to people in campaigns and the actions of those leaders if they happen to be in office. Party utility substantially comprises what determines partisanship but is conceptually distinct from it. The utility people get from the competing parties is similar to consumer choice based on comparing the utilities associated with different brands. For most of the analysis in this book, we consider it to be the value derived from the policies espoused by a given party.9 In the tradition of spatial models, a person i’s utility for party K is

UtiliK = f (|Xi − K|), where Xi is the person’s ideology or policy preference and K is the policy position of the party. Note that for a given party K the larger the value of this measure, the worse off people are. It can sometimes make sense to refer to it as “disutility,” though for ease of presentation we often will refer to utility. The expectation is that this disutility will be negatively related 8. E.g., Franklin (1992). 9. In later chapters we will broaden the notion to include the possibility that economic performance evaluations figure into party utility as well.

chapter two

28

to support for party K. If there are J relevant policies, or dimensions, the expression calculating party utilities is (2.1)

Util iK = Σ j = 1 γji ƒ(|Xj i − Kj |) , J

where γji is the weight person i gives to issue j. Spatial, or proximity, models have been used extensively in a wide array of political models. Such models nonetheless have their critics. A recent critique by Achen and Bartels (2016), based on research about how far short voters fall from the idealized version of the rational individual and how poorly the models predict candidate behavior in the United States, leads to the conclusion that spatial models deserve to be retired because they have never worked well as predictive theory. In assessing spatial models it is important to distinguish between the predictions by some that parties locked in a two-­party competition will converge to the median voter (most researchers and careful observers believe that such parties generally do not) and proximity as an assumed heuristic used to predict vote choices and partisanship. The latter use finds support in a methodologically diverse set of empirical studies. (See the discussion on this point in section 8.2.2.) An important feature of the definition for party utilities in equation 2.1 is that it explicitly introduces the behavior of the parties into the model of partisanship. Parties, facing competition and assumed to be strategic, may modify their platforms for various reasons: to try to attract voters or subgroups of voters (Schofield and Miller 2003; Schofield, Miller, and Martin 2003), to gain resources from activists (Schofield and Miller 2007), or to express the policy views of party members (Aldrich 1983a, 1983b; Wittman 1983). Changes in party positions, by this logic, will contribute to partisanship change by inducing some voters to increase and some to de­ crease their support for different parties. In a system with two major parties, say Republicans and Democrats in the United States, we define relative party utility as (2.2)

Utili = ∑ jJ=1 γji [ ƒ(| Xi − Ri |) − ƒ (| Xi − Di |)].

Larger positive values mean the Democrats are closer to the individual, while negative values mean the opposite. In this case of the two American parties we expect Utili to be positively related to Democratic partisanship.

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29

Party utilities are central to our model and to the concept of normal partisanship, as they provide the continuity to partisanship. Equation 2.1 provides the preferred specification for party utilities, as it includes both individual preferences and party positions. In subsequent empirical work described in later chapters utilities are not limited to this spatial definition. In some years for certain countries this is because information about party positions is not available, so only preference information can be included. In other cases even preference data are not available, so observable proxies for both opinions and party positions must be used (especially in the Australian data). Lastly, the nature of the relevant issue may not lend itself to a spatial interpretation. For example, if economic performance has an impact on partisanship beyond the current period, distinguishing it from a short-­term force (an empirical question), there is no spatial measure.10 Examples of these situations are found in chapters 4 through 8.

2.2 Measurement and Data For the analysis in the remainder of this book, we will mostly rely on a unique compilation of data sets constructed from surveys in the four countries. Each of the four countries has a long tradition of election surveys. The United States has had the American National Election Studies (ANES) since the mid-­1950s; we also use data from national election surveys in Australia since 1967, in Canada since 1965, and in the United Kingdom since 1963. We will use both micro and macro data, but mostly the latter. Some of the data analyses in later chapters describe and test models using micro-­ level survey data in the United States and the United Kingdom, with a case being an individual respondent to a survey. The bulk of our analysis will be on aggregate measures of entire electorates or groups in the population. As discussed in chapter 1, a key contribution of our approach is that the variables and measurements for the micro-­level and macro-­level analyses are analogous. The aggregate measures begin with measures at the individual level from surveys, which were then aggregated into measures of collectives. 10. It might be what is called a valence issue. See Clarke and Stewart (1985) and Clarke and McCutcheon (2009).

chapter two

30

Here we provide an overview and summary of the measures we use so that it is possible to understand the graphs, charts, and tables going forward in this chapter and the remaining chapters. The details of all our var­ iables and the data organization are provided in an online appendix.11 2.2.1 Measuring Partisanship To be comparable to and build on past research, scales of partisanship are based on answers by individuals to an ordered set of survey questions. These questions are similar but not identical across countries and often not identical over time within countries. In some cases we have had to make compromises to complete a time series of a given measure. In fact, changes in question wording for many of our measures presented challenges throughout this study, and we often had to “splice” time series data and work around knotty measurement issues. We tested our results for robustness where possible using different methods of measurement. Interested readers are directed to the online appendix. Following convention, initial answers by respondents about partisanship are weighted by the strength of that partisanship as expressed by the respondent rather than by a simple dichotomy. There are important substantive reasons for approximating interval measures of partisanship, in addition to the estimation advantages in models with such variables as compared to models with dichotomous variables. Strong partisans are more likely to participate in politics in a variety of ways, including turning out to vote. They are less likely to defect in their vote choices.12 There is also evidence that strength of partisanship relates to the ability of party elites to influence the policy preferences of the electorate.13 In the US case, the famous question and its follow-­ups are “Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what?” [If independent] “Do you think of yourself as closer to the Republican or Democratic Party?” 11. The online appendix can be found on the professional website of Ken Kollman, which at the time of publication is https://cps.isr.umich.edu /people/kkollman/. 12. This is a widespread finding in the literature. Campbell et al. (1960) make such a claim a key part of their arguments. 13. Gerber and Jackson (1993), Keith et al. (1992), Miller and Shanks (1996).

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table 2.1  American partisanship scale Survey responses Strong Republican Weak Republican Ind. leaning Republican Independent (not leaning) Ind. leaning Democrat Weak Democrat Strong Democrat

Coding −3 −2 −1 0 1 2 3

[If Republican or Democrat] “Would you call yourself a strong [Rep/Dem] or not very strong [Rep/Dem]?”

Much has been published on the appropriate creation of a seven-­point scale from strong Democrat to strong Republican. We follow convention on this and scale American respondents in the order shown in table 2.1.14 For the United Kingdom, something similar has been used most of the time, with slight differences from the US version: “Generally speaking, do you think of yourself as Labour, Conservative, Liberal Democrat, (Scottish National / Plaid Cymru) [in Scotland/ Wales] or what?” with this follow up question: “Would you call yourself very strong [party], fairly strong, or not very strong?”

For Canada and Australia we used questions similar to those for the United Kingdom. Coding data in the non-­US cases leads to four-­point scales. An ordinal scale for a single party (using the UK Labour Party as the example) is shown in table 2.2. In some instances we will examine a seven-­point scale similar to the US scale based on subtracting one of the major-­party four-­point scales from the other. This gives a scale that ranges from minus three for very strong

14. In work not shown, we compare responses to thermometer questions asking respondents’ feelings about each party on a zero to one-­hundred scale to this coding of the responses to the party identification. The ordering and distances on the thermometer-­based measure, which is designed to be an interval scale, closely match the distances and ordering of the party ID variable.

chapter two

32 table 2.2  UK single-­party partisanship scale Survey responses Independent Not strong Labour Fairly strong Labour Very strong Labour

Coding 0 1 2 3

supporters of one party to zero for those who do not support either major party to plus three for strong supporters of the other party. 2.2.2 Measuring Party Utilities and Related Concepts Party utility, defined in equation 2.1, contains two elements: individuals’ positions on issues or ideology and party positions on the same item. The most common measurement method for utilities relies on a seven-­or an eleven-­point scale. As an example, the ANES asked a question (referenced in chapter 1) about the federal government’s role in providing support for minorities. Respondents are first read short sentences describing two policy options that are posed as alternatives; for instance: “Some people feel that the government in Washington should make every effort to improve the social and economic position of Blacks. Others feel that the government should not make any special effort to help Blacks because they should help themselves.” They are then shown, or read, the following scale: 1 Government should help Blacks. 2 3 4 5 6 7 Blacks should help themselves.

Respondents are then asked to place themselves and each party on this scale. Denote an individual’s self-­placement as Xi and Democratic Party placement as Di. The Democratic Party utility measure for this person on the aid to blacks issue is Utili = (| Xi − Di |). A challenge is that there are variations on this structure, including question wording changes over

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33

time for ostensibly the same issue. There are even greater disparities in the surveys across the four countries. (The online appendix contains the details for each case, including information on how responses with different question wording are spliced to create as uniform a time series as possible.) We paid special attention in our efforts to obtain as uniform and comparable measures as possible. An appendix to this chapter shows that these measures of party placements based on individuals’ perceptions (e.g., the Di’s) are remarkably consistent with party locations on scales derived from the Manifesto Research on Political Representation Project (MARPOR).15 This consistency provides important external validity for our party placements and is evidence that our measurements do not bias our results because of misperceptions and rationalizations by voters. 2.2.3 Short-­Term Forces The measurement of partisanship, as described in chapter 1, includes both normal partisanship and short-­term forces. Short-­term forces fluctuate over time and move partisanship and voting outcomes in the current period, but they do not produce changes in partisanship that persist into any future periods. It is an empirical question what actually counts as a short-­term force in different contexts. We often think of economic outcomes, candidate characteristics, or foreign policy crises that flair up. Economic outcomes, such as real income changes or unemployment levels or changes, are commonly considered short-­term forces. Measures of these are readily available for all countries, which makes such measures substantively valuable in a comparative study but also convenient for research purposes. Yet, as we will show in chapter 6, empirical results may suggest that economic outcomes in some countries (e.g., the United Kingdom) may not be so “short term” and are best treated as integral to the party util­ ity term.

2.3 Population Subgroups It is important to emphasize again that these measures of partisanship and party utility are initially measured at the individual level. We then 15. MARPOR (2019).

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chapter two

aggregate these measures to give average values for a given group. One such group, of course, is the entire electorate. So, for instance, the macropartisanship measure for the entire electorate, as has been done with previous research, can be measured monthly, quarterly, or biennially, depending upon the data and movements in partisanship of interest.16 We will typically use aggregate measures for a given subset of respondents, say, the mean for a regional group such as all respondents from Quebec, a religious group such as all respondents reporting to be Catholic, or a class group such as all respondents reporting to be working class. We also cross some of the group categories; for instance, working-­class Catholics or southern US whites. We build from individual data to aggregates because individual-­level data alone cannot fully address our fundamental research questions. Our focus is on long-­term, sometimes gradual shifts in partisanship. Those shifts can last decades. Panel data sets, studies where individuals are interviewed at multiple times over years, typically span four years. This makes panel studies of individual respondents too short to capture our phenomena of interest. Moreover, we need variation over time on key variables of interest such as party changes, and sometimes party changes take longer than the individual-­level panel studies. Since we do not have the lengthy panel data, we instead aggregate to groups that are more homogeneous than the entire electorate. Groups, especially sizable ones with a shared identity of relevance to politics, are important entities to study. If mass partisanship for a given group shifts in a direction—­say working-­class voters increase their support for the rightist party—­all things being equal we predict that the group’s vote choice will shift in that same direction. Working-­class voters will choose the rightist party more than previously. This is intuitive, of course, and reflects some of the most solid and consistent research findings from political science. And indeed, at least using reported votes by working-­class people from surveys, that is indeed what scholars have found.17 Within each country the groups are selected to give a sizable and substantively meaningful amount of partisan change over the period for

16. MacKuen, Erikson, and Stimson (1989) is widely credited as the original work; see also Abramson and Ostrom (1991); Erikson, MacKuen, and Stimson (1998, 2002); Green, Palmquist, and Schickler (1998); Jackson and Kollman (2011); and Meffert, Norpoth, and Ruhil (2001). 17. E.g., Johnston (2017); Zuckerman, Dasovic, and Fitzgerald (2007); Webb (2000).

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table 2.3  Subgroup definitions by country Country

# Groups

Australia

9

United Kingdom

6

Canada

United States

12

5

Group 1

by

Religion (3) Catholics Non-­Catholic Christian Other Region (3) London & Southeast Scotland Other Region (4) Quebec Maritimes Ontario West Region (3) South Northeasta a Other

Group 2 Class (3) Working Upper & middle Other Class (2) Working Middle Religion (3) Catholic Non-­Catholic Christian Other Race (2) White Blacka

a 

Blacks combine northeast and other to define nonsouthern black category.

which there are survey data. The contrast between blacks and southern whites in the United States plots in chapter 1 provides a good example. We further disaggregate blacks into southern and northern subgroups and nonsouthern whites into northeastern and other. Here is a continuation of the quote from Campbell et al. (1960, 152) cited in chapter 1: “The Northeast, including New England and the Middle Atlantic States, which was the center of abolitionist sentiment, is now the strongest Republican area of the country.” The current political map looks quite different for this group as well as for southern whites. The same strategy is followed in the other three countries, though the definition of the subgroup varies. Table 2.3 shows the group definitions for each country along with the total number of groups for each country analyzed in later chapters. Understanding the changing partisanship of these specific groups has intrinsic value. The partisanship of Catholics in Canada, for instance, and specifically their varying levels of support for the Liberals, has been a long-­standing point of research for scholars of Canadian politics and has enormous significance for electoral outcomes.18 Likewise, the declining 18. See Stephenson (2010) for a summary of the extensive research on this topic.

chapter two

36

support (self-­identified or otherwise) of working-­class people for leftist parties is a story of utmost significance.19 These groups are determined at least in part from subjective survey responses. Thus working-­class respondents in the United Kingdom prior to 2000 enter that category because they answered “working class” on the question of their class status. We acknowledge a potential analytical problem. Fewer (or potentially more) people will answer “working class” on that question over a span of time. We cannot track partisanship of exactly the same people over time in the United Kingdom when we have measures for partisanship and party utilities over a span of even a few years, let alone over decades. This is not primarily because they drop out of the population of interest (death or some other reason for not being available) and are replaced by new voters. It is more importantly and consequentially because of changes over time in the meaning and appeal of certain labels. We recognize that the terminology of class, for instance, has changed over time. Even given the changes over time in the subjective meaning of the labels and the size of the groups, changes in the partisanship of these groups we have identified and measured have deep and lasting consequences for electoral politics in these four countries. While our measures are aggregates, we show evidence in later chapters that our results are consistent with the conclusion that individuals change their partisanship in response to the dynamics of the party system.

2.4 Partisanship and the Vote Having described our main concepts and measures, we now return to an issue left unaddressed in the preceding chapter and stated without evidence in the preceding section. It is the claim that an important reason to study partisanship is because it is a central factor in the electoral process and that a group’s voting patterns will typically follow their partisanship. Figure 1.1 in the preceding chapter shows strong associations between aggregate partisanship, measured as described above, and major-­party vote shares in all four countries. Discussion of this figure posed several different processes that might explain these high correlations— ­changes in partisanship may precede or lag changes in election outcomes, they may be 19. See Dunleavy (1987) and Evans and Tilley (2012).

partisanship: meaning and measurement

37

simultaneously related, or they may be correlated because of a common third factor. These possibilities can be addressed by estimating the following cross-­ lagged model of partisanship and the vote: (2.3)

Pt = β01 + ρ1Pt−1 + α1Vt−1 + β1Utilt + u1t,

(2.4)

Vt = β02 + ρ2Vt−1 + α2Pt−1 + β2Utilt + u2t.

One interpretation of this model is that α1 measures the effect of votes on partisanship and α2 estimates the effect of partisanship on votes. These interpretations are predicated on the assumption of no contemporaneous relationships between partisanship and votes. An alternative interpretation of this cross-­lagged model is that it is the reduced-­form version of a structural model relating current values of partisanship and votes, with the lagged values of these variables being the instruments. Denote by γ1 the expected difference in current partisanship, Pt, for a unit difference in current vote proportions, Vt, and by γ2 the expected difference in current vote proportions for a unit difference in current partisanship. The reduced-­form interpretation of equations 2.3 and 2.4 gives the following estimates for these coefficients: γˆ 1 = α1 ρ2 and γˆ 2 = α 2 ρ1. The different propositions make different predictions about α1 and α2, or γ1 and γ2, depending upon which interpretation one prefers. 1. Vote leads partisanship: In this version partisanship is derived from the vote choice, which implies α1 > 0 and α2 = 0, or by implication that γ2 = 0. 2. Partisanship leads vote: In this version vote choices follow from partisanship, which is essentially the argument of the American Voter authors. Such a version implies that α1 = 0 and α2 > 0, or by implication that γ1 = 0. 3. Joint determination: In this version partisanship and vote choice mutually reinforce and drive each other in the classic simultaneous equation form. This version predicts that α1 > 0 and α2 > 0. 4. Separate events: In this version partisanship and vote choice evolve separately, implying that α1 = α2 = 0 or that γ1 = γ2 = 0. They are likely to be correlated, maybe highly so, but it is because of their relationship with utilities. If β1 and β2 are both large and positive there will be a high correlation between partisanship and votes, even with α1 = α2 = 0, meaning no direct association between partisanship and votes.

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chapter two

Equations 2.3 and 2.4 are estimated for both the United States and the United Kingdom. 2.4.1 US Votes and Partisanship The US model is estimated using the data on the five American subgroups from table 2.3, with group-­level measures of their partisanship and how people reported voting in each election. If we were to use the national-­ level data shown in figure 1.1, it would contain only twenty-­four observations when the years with no ANES data are excluded, which is far too few to be able to estimate these equations reliably. Using the data for the five US groups provides enough information, though it requires that vote shares be estimated for each group based on their reported votes in ANES surveys because actual vote shares are not available by subgroup. Utility is measured by responses to a question asking if the federal government should guarantee that everyone has a job and a good standard of living. This is one of the most consistently asked and worded questions in the ANES surveys. Estimation is done with a seemingly unrelated regression estimator, which allows for covariance between each group’s stochastic terms and between the party and vote equations.20 Figure 2.1 shows the estimated coefficients and 95 percent confidence intervals. The coefficients and standard errors are shown in table 2.4. The first three lines are for the cross-­lagged party identification equation, and the next three lines are the cross-­lagged vote equation. The bottom two lines are the implied contemporaneous structural equations relating party identification (PID) to current vote and vote to current PID, respectively. Party utility is strongly and statistically related to both partisanship and voting, with the latter association being larger than the former, implying that differences in utility are associated with larger differences in voting than in partisanship. Thus one explanation for the association between partisanship and voting is the presence of a common factor, in this case utilities. Both series show evidence of being autoregressive, with values for ρ of 0.51 and 0.28 for partisanship and voting, respectively. Not surprisingly,

20. There are actually ten separate equations: a partisanship and a vote equation for each of the five groups. Separate intercepts are estimated for each group, corresponding to group fixed effects. Other than the intercepts the respective coefficients are constrained to be equal for each group.

PID_PID(t-1) PID_Vote(t-1) PID_Utility(t) Vote_PID(t-1) Vote_Vote(t-1) Vote_Utility(t) PID_Vote(t) Vote_PID(t) -.1

0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1

Coefficient Value

1.1 1.2 1.3 1.4 1.5 1.6

figure 2.1.  Coefficients in US PID and vote equations

table 2.4  Estimated cross-­lagged US model Outcome equationa Variable Reduced form Pt−1 Vt−1 Utilt Structural model Vt − γ1

Pt

Vt

0.51 (0.10) 0.08 (0.05) 0.25 (0.11)

0.42 (0.18) 0.28 (0.09) 0.43 (0.15)

0.29 (0.27)

Pt − γ2

0.82 (0.37)

Note: Coefficient standard errors in parentheses. a  Includes group-­specific constants.

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chapter two

the value of ρ is greater in the partisanship equation, indicating that partisanship is more stable than voting. For our purposes the important results are that the relationship between current party and lagged vote is substantively and statistically not different from zero, but conversely, the lagged effect of partisanship on current vote is large and quite different from zero. The classic interpretation of this cross-­lagged model is that partisanship influences voting but not the reverse. The interpretation of the cross-­lagged model as the reduced form of a structural equation model with possibly asymmetric associations between partisanship and voting leads to virtually the same conclusion. The structural coefficients implied by the coefficients are γˆ 1 = 0.29 and γˆ 2 = 0.82 γˆ 1 = 0.29 and γˆ 2 = 0.82. The distributions of these coefficients are examined with a Monte Carlo simulation.21 The bottom two bars in figure 2.1 show the means and confidence intervals for the γ estimates from the Monte Carlo analysis. The magnitude of the coefficient relating vote to partisanship, γˆ2 = 0.82, implies a substantively important relationship, with a two-­ standard-­ deviation difference in partisanship being associated with just over a standard-­deviation difference in vote share. The coefficient relating partisanship to voting is small, γˆ1 = 0.29, and the 95 percent confidence interval includes zero. In sum, the evidence with both the cross-­lagged and the structural coefficients supports the proposition that partisanship drives voting and not the reverse. The evidence is also strong that party utilities have a direct association with both partisanship and voting.22 The dynamics implied by the model in equations 2.3 and 2.4 and the results in figure 2.1 are shown in the plots in figure 2.2. These graphs depict simulations of what happens to partisanship and vote share if there are unit-­sized exogenous shocks to any of the three variables (utilities, 21. The simulation draws random values for ρ1, α1, ρ2, and α2 from a multivariate normal distribution with the variances and covariances estimated for these coefficients. For each draw the values for γ1 and γ2 are computed and stored. This process is iterated 11,000 times, and the distribution of these values is used to estimate the distribution of values for the γs. The simulated standard errors are 0.27 and 0.37, respectively. 22. When the model in equations 2.3 and 2.4 is estimated with the twenty-­seven observations in the nationally aggregated data, the autoregressive and utility coefficients are virtually unchanged from the group-­based analysis reported here. Both cross-­lagged coefficients are negative and far from being statistically significant. These results raise the possibility that party identification does not directly affect vote decisions, meaning that it could be the only explanation of the high correlation between partisanship and vote is the common utility variable.

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41

figure 2.2.  US partisanship-­vote dynamics

partisanship, and the vote) using the coefficients from the estimated models in table 2.4. In the leftmost graph, a utility shock raises both partisanship and vote share, with the latter rising almost 0.2 higher. Within four time periods both are less than 0.1 units away from their long-­term equilibrium. A unit shock to partisanship (middle graph) raises vote shares by 0.8 units. The effects of the shock persist for between five and six periods. Lastly, a shock-­to-­vote share (rightmost graph) increases partisanship by less than 0.3, and partisanship is back to 0.1 by the third period. The plots illustrate three things based on parameterized models from the US data: that in the United States at least, utilities drive both the vote and partisanship, partisanship drives the vote, and the vote does not drive partisanship. In short, number 2 in our list above is most supported by these data, with a crucial role for utilities. Partisanship among American voters is fundamentally important in explaining election outcomes. 2.4.2 UK Votes and Partisanship Equations 2.3 and 2.4 are estimated with the UK data on the six subgroups from table 2.3, three regions by two classes. Measures of voting and

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partisanship are aggregated for each group from the British Election Surveys during election years from 1963 to 2015. As with the United States there are too few elections (thirteen) for which data on partisanship are available to enable an analysis of national data. Utility is measured by party proximities computed from self-­and party placements on a scale asking about taxes and spending on social services. Partisanship is measured with the single variable Labour strength minus Conservative strength. As shown in later chapters, this is the best way to represent the central feature of partisanship in the United Kingdom. Commensurate with this measure of partisanship, utility is defined as respondents’ utility for Labour on the taxes/spending question minus their utility for the Conservatives on this issue, Util = −(| Self − Labour | − | Self − Conservative |), where Self, Labour, and Conservative denote the placements of the respondent, Labour and the Conservatives, respectively.23 The vote variable is the Labour share of the two major-­party votes.24 Equations 2.3 and 2.4 are estimated as a seemingly unrelated regression system.25 Figure 2.3 is a coefficient plot with 95 percent confidence intervals with all the estimated coefficients shown in table 2.5. The first six lines in the figure and the first three lines in the table are the coefficients in the cross-­ lagged partisanship and vote equations, respectively. The last two lines in both the figure and the table are the estimated structural coefficients relating current partisanship to current votes and current votes to current partisanship, respectively. Party utility is strongly and statistically related to both partisanship and votes, supporting the proposition that a common third variable could be explaining the association between votes and partisanship. Partisanship is strongly autocorrelated with a value of ρ = 0.68. This is not the case for votes, as the value for ρ is only a statistically insignificant 0.18. The important coefficients for the current discussion show that partisanship is unrelated to lagged votes, with a coefficient of only 0.02, while votes are strongly and significantly related to lagged partisanship, with a coefficient of 0.84. These and the autoregressive coefficient results imply that lagged partisanship is strongly related to both current partisanship 23. This definition for utilities transposes that used in the rest of the book to create a positive coefficient. 24. Models with the Labour share of the three-­party vote are also estimated, with virtually identical results. 25. An F-­test for the presence of group fixed effects had a p-­value of 0.61, leading to their omission for the sake of parsimony given the limited number of observations per group.

PID_PID(t-1) PID_Vote(t-1) PID_Util Vote_Vote(t-1)

Vote_PID(t-1) Vote_Util

PID_Vote(t) Vote_PID(t) -1.2

-1

-.8

-.6

-.4

-.2

0

.2

.4

.6

.8

1

figure 2.3.  Coefficients in UK PID and vote equations

table 2.5  Estimated cross-­lagged UK model Outcome equation Variable

Pt

Vt

0.68 (0.09)

0.84 (0.30)

Vt−1

0.02 (0.05)

0.18 (0.16)

Utilt

0.30 (0.04)

0.66 (0.14)

Reduced form Pt−1

Structural model Vt − γ1

0.11 (0.58)

Pt − γ2 Note: Coefficient standard errors in parentheses.

1.22 (0.33)

1.2

1.4

1.6

1.8

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chapter two

and votes, but lagged votes are not significantly or substantively related to either current votes or partisanship. A second interpretation of these results, as discussed with the US analysis, is that they are reduced-­form expressions of a structural system in which current partisanship and current vote are asymmetrically related to each other, to utilities, and to their lagged values. The expected values of these structural coefficients and their standard errors are computed with a Monte Carlo simulation based on the results in table 2.5 and as described in note 21.26 The estimates for γ2 giving the relationship of vote to variations in partisanship is statistically different from zero, substantively important, and stable to changes in the trimmed distribution. The estimates for γ1, the relationship of partisanship to votes, however, is not statistically or substantively significant even with the trimmed distribution. The ratio of the mean value for γˆ 1 to its standard deviation is only 0.19.27 The results show an asymmetric relationship between current partisanship and votes, with votes associated with partisanship but no association connecting partisanship to votes. The conclusion from these results is the same as for the United States, that utilities are central to both partisanship and voting and that partisanship motivates vote choice controlling for utilities. Secondly, lagged votes are essentially irrelevant for current votes and partisanship.28 These small and insignificant coefficients lead to the conclusion that partisanship is not motivated by vote choice. Figure 2.4 shows the dynamics resulting from shocks to utilities, partisanship, and votes using the coefficients in table 2.5. These plots closely 26. A potential difficulty is that the estimate for the autoregressive coefficient in the vote equation is small with a large standard error. A consequence of this uncertainty is that the estimate for γ1 is very unstable, as it depends on dividing the value for α1 by the value for ρ2 drawn from the random distribution, which may be near zero. To accommodate this problem the means and standard deviations from trimmed distributions where the extreme 1 percent of values in both tails are dropped are shown in the bottom two lines in figure 2.3. The untrimmed γˆ 1 mean is 0.06 with a standard deviation of 10.15, and the γˆ 2 mean is 1.22 with a standard deviation of 0.36. 27. Based on its skewness and kurtosis the distribution of the trimmed values closely approximates a t-­distribution with five degrees of freedom. This distribution implies that the p-­value for the test of the null hypothesis that γ1 = 0 is 0.86. 28. An F-­test of the null hypothesis that both coefficients on lagged vote has a p-­value of 0.41, making it difficult to reject the proposition that the lagged votes are irrelevant to current outcomes.

2

3

4

5

6

7

8

9

10

figure 2.4.  UK partisanship-­vote dynamics

Period

0

0.25

0.25

1

0.5

0.5

0

0.75

0.75

1.25

1

0

Utility Shock

1

1.25

0

1

2

PID

3

4

Vote

Period

5

PID Shock

6

Utility

7

8

9

10

0

0.2

0.4

0.6

0.8

1

1.2

0

1

2

3

4

Period

5

Vote Shock

6

7

8

9

10

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chapter two

parallel the dynamics shown for the United States in figure 2.2. A unit shock to utilities has a large impact on votes, which takes six or seven periods to dissipate. The utility shock impact on partisanship is much smaller and attenuates more quickly. A unit shock to partisanship, which is an extremely large difference, has a very large, even implausible, impact on vote shares. A reasonable interpretation is that this plot suggests the implausible shift in macropartisanship from being very strongly Conservative (or Labour) to very strongly Labour (or Conservative) would produce a vote share shift of 100 percent. If such shifts did occur, they would persist for over ten periods (with four or five years between elections this amounts to forty or fifty years). Conversely a unit shift in vote shares, also very unlikely except possibly in very small subgroups, shifts partisanship by about 0.1, a trivial amount. Though these shocks are not in the realm of possibility, they are useful to show the vast difference in the impact of a shift in partisanship versus a shift in vote shares. Taken together the US and UK results are consistent with the original proposition that partisanship is a central factor in political behavior. Further, the UK results imply that this notion is not merely an American phenomenon. The UK and US party systems are sufficiently different that these consistent results lend additional credence to the already well-­established claim of the centrality of partisanship and why it is worth studying.

2.5 Conclusion This chapter has ended by offering concrete evidence for the central motivating claim for our modeling approach—­understanding the dynamics of partisanship is important. One could make this claim for no other reason than that partisanship drives voting and thus election outcomes. But chapter 1 gave additional reasons that partisanship is important: democratic stability, linking citizens to government, and converging expectations of governed and government; we do not need to repeat those other arguments here. The analysis of the cross-­lagged models also provides a foretaste of what is to come in later chapters. We will analyze rigorous empirical models on data from population groups with special attention to interpreting what the coefficients imply for dynamic relationships between variables, and more broadly for what the models can tell us about the political his-

partisanship: meaning and measurement

47

tory of our four countries. The next chapter describes the general form of the modeling approach.

2.A Appendix: Robustness Check on Party Movements This appendix attends to a potential criticism of the measures of party positions. These measures are central to our models and their results. Our measurements of party positions throughout this book rely on respondents’ subjective placements of parties on spatially defined scales. The exact issues vary across countries. In the US case, the survey questions ask about these placements on aid to minorities and guaranteed jobs and social spending; in the United Kingdom they ask about taxes and social services; and in Canada the questions are about a general left-­right ideology scale.29 Respondents across all the countries were first asked where they placed themselves on a scale and then were asked their views on where the major parties were on the same ideological or issue scales. These party placements and how they change over time are central to the measures of party utility and to how much partisan updating might take place. A common concern with using subjective measurements is the effect of projection, as that term is used in psychological research. Respondents project on parties they like positions close to themselves and on parties they do not like positions far away from themselves. If in using subjective evaluations the goal is to approximate an objective measure of where the parties are located in ideological or issue space, and if the population at a given moment in time dislikes a party, then this could bias results because that party could be further away from the population according to subjective evaluations than the party actually is objectively. It is hard to know the degree to which this problem affects our results, except that we can show some evidence here to mitigate the concern. The evidence presented here favors the view that these subjective evaluations by survey respondents are substantially related to independent and presumably more objective measures obtained from MARPOR. For decades the project has coded party manifestos, or what in the United States are called platforms, for content. Party manifestos give imperfect measures of 29. In Australia they generally were left-­right numerical scales. We do not analyze Australian data in this appendix due to data limitations, namely gaps in the survey data and the inconsistency of the issue question format over time.

chapter two

48

how the parties are perceived by the mass public, but they are systematically produced from published and publicly available documents. Along different dimensions, coders count the number and proportion of words, phrases, and paragraphs devoted to certain topics, such as promoting social welfare programs, multiculturalism, or the well-­being of vulnerable minorities. On some dimensions, such as left-­right, they code the content to give a score on a dimension. Left-­right, the main ideological dimension animating democratic politics since World War II, is based on policies and emphases typical for the major political parties in North America, Europe, and Australia since the advent of the welfare state in the early twentieth century. Our assessment here of the party placement variables depends on their relationship with the MARPOR measure of each party’s left-­right ideology. Let Yjt be the mean value of all respondents’ survey placement of party j’s position in year t on the main left-­right issue measure where j varies over all possible party and country combinations. For our US data this is the numerical scale on the guaranteed jobs question from ANES, which is the most consistently asked question and a good ideological proxy. For the United Kingdom it is the placement on the taxes versus services question, and for Canada it is the broader left-­right ideological question. We only use election-­year surveys for these measures to match the MARPOR data. Then let Xijt be the MARPOR left-­right measure for party j at election year t. We assess party placement measures, the Yjt’s, based on the following regression model: (2.5)

Yjt = αj + βj Xjt + εjt.

The tests of the consistency of the party placements and the MARPOR measures are based on the values of βj and its statistical significance. Two different forms of the equation are estimated. The first uses the raw mea­ sures with the US party placements rescaled from their original 0 – ­6 scale to a 0 –­10 scale used in the United Kingdom and Canada. The second estimation standardizes both Yj and Xj by their sample standard deviations to adjust for any unobserved scale differences (i.e., Yj* = Yj σyj and Xj* = Xj σxj ). * Yj = Yj σyj and Xj* = Xj σxj ). The data are pooled for each country and equation 2.5 estimated with separate intercept, αj, and slope, βj, coefficients for each party-­country sample. To accommodate likely heteroskedasticity a feasible generalized least squares (FGLS) estimator is used where an ordinary least squares

partisanship: meaning and measurement

49

table 2A.1  Party placement—­manifesto coefficients Dataa

Manifesto, Right − Left (β) St. error p-­value a

Raw

Standardized

0.021 0.005 0.000

0.411 0.108 0.000

Outcome variable is party placements.

regression gives residuals for each country-­party sample from which estimates for each sample’s stochastic term variance are estimated. These variance estimates are then used in a weighted regression, the FGLS.30 The first test is the consistency of the values for βj across all groups. The greater this similarity, the greater the confidence that the party placement variables are measuring a common concept. For both forms of the equation the F-­test of no difference in the slope coefficients has p-­values of 0.35 and 0.32 for the raw and standardized data, respectively. Based on these results the model is reestimated with single slope coefficient using the same FGLS weights. Table 2A.1 shows the estimated values for β, its standard errors, and the associated p-­values assuming normally distributed errors and a t-­distribution for the coefficient estimates. The estimated values for β with each sample are statistically significant and show a strong relationship between our party placement variables and the MARPOR measures of party ideology. With the standardized data a one-­standard-­deviation difference in the manifesto variable is associated with a 0.4-­standard-­deviation difference in the party placement variable. These results show that this association is consistent across all three countries and their parties. Table 2A.1 shows that public perceptions of party positions, as measured by the surveys in our study, are strongly associated with the independently collected MARPOR data. These results lend important external validity to our measures of party placements and party utilities. They give us confidence that public perceptions of positions taken by each country’s major parties over time reflect reality in direction and magnitude. The survey-­based measures correspond to evidence in party manifestos, arguably a more objective set of measures. 30. The model with the equivalent of panel-­corrected standard errors was also estimated, which led to similar results.

chapter three

Consistent Partisanship Models

W

e now describe our model of partisanship in general form. Some specific elements and versions tailored to the four countries will be discussed in later chapters. As described in chapter 1, our model as fully developed in this chapter is more comprehensive than existing models in the literature because ours is consistent across many domains. Previous models from the literature rely on separate and at times contradictory models. The prominent areas of consistency are as follows:

a. Different patterns of partisan change can be accommodated, including periods of stability, fluctuations, and both rapid and slowly evolving realignments, as seen in the figures in chapter 1. b. Different levels of partisanship are coherently and consistently linked, extending Jackson and Kollman (2011) to construct a macro model from a theoretically and empirically defensible micro foundation. c. Data measured and organized with different time units can be analyzed, from quarterly to biennial. d. Different party systems can be analyzed, from two-­party to multiparty.

That all of these elements—­stasis and change, micro and macro, quarterly and biennial, and two and multiple parties—­exist in one modeling framework distinguishes us from what has come before. A final distinguishing feature is the prominent role given to party behavior in a model of partisanship.

3.1 The Core Partisanship Model The core model consists of two components, normal partisanship and short-­term forces, as discussed in chapter 1. Normal partisanship derives

consistent partisanship models

51

from the utility individuals associate with parties’ performance and positions on relatively long-­standing societal divisions and gives partisanship its continuity between and across elections. References to an equilibrium partisanship (as in Achen 1992) are in essence references to normal partisanship. Short-­term forces are factors that alter partisanship for the period in which they occur but have no lasting consequences. Likely examples are economic fluctuations related to the business cycle, scandals, foreign policy crises, or candidate personalities. The model for partisanship is Pit = Pit* + Xit β + uit ,

(3.1)

where Pit* denotes person i’s normal partisanship at time t and Xitβ are the short-­term forces. For instance, Xit may be retrospective economic evaluations, changes in real disposable income, or variables documenting the presence and duration of a scandal. It is important to be clear that Pit is individuals’ “real” partisanship at time t. This is the strength of their party support and as such influences their voting decisions, their response to political information, and the many other things that partisanship does. It is what is attempted to be measured with the various questions and formats in credible surveys. The remainder of this section provides more detail on these components and how they work together to create our model of partisanship. This model then undergoes slight modifications to fit the different party systems found in the four countries. 3.1.1 Normal Partisanship Our model for normal partisanship is based on a framework developed by Achen (1992). Achen proposes a Bayesian model, one that has spurred other research since and has proved to be a valuable starting point for many others. Following Achen, we begin modeling normal partisanship as follows: (3.2)

Pit* = ρit Pi,t* −1 + (1− ρit) Util it + εit .

Current normal partisanship for person i at time t is a function of that person’s past normal partisanship weighted by an autoregressive term, ρ, and current party utility weighted by (1 − ρ). In Achen’s Bayesian framework ρit increases with the certainty that previous normal partisanship and the

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chapter three

uncertainty that current utility are accurate predictors of current normal partisanship. This proposition means that larger values of ρ imply increasing stability to normal partisanship, while smaller values imply that normal partisanship is likely to exhibit substantial changes from one period to the next depending upon the values of Util. Achen (1992, 199) says his “model describes a stable period between realignments.” By this he means a period where Utilit is stable, implying that partisanship is as well. The only dynamic aspect of the model is that people acquire new information about their true (i.e., normal) partisanship each election. The more elections individuals have experienced, the more information they have acquired, and the better the last election’s normal partisanship is as a predictor of current normal partisanship, which according to equation 3.1 is the major component of partisanship. This makes ρit a function of the number of elections the person has experienced. The model does not address situations where party utilities are changing because the ideological or issue placements of the parties or voters’ preferences have changed. Achen was not intending to capture the dynamics of partisanship as we mean it in this book. He does, however, offer an appropriate foundation for estimating how previous normal partisanship might be updated on the basis of changes in party positions and/or voter preferences. Simply put, the greater the magnitude of either of these changes, the less reliable past normal partisanship and the more reliable current utilities are as predictors of current normal partisanship. This statement implies that ρ can and likely will vary among individuals but for more reasons than Achen proposes and over time as individuals and parties evolve. 3.1.2 An Analogy from Consumer Economics It is worth pausing for a moment to clarify the distinction between the original Achen model with a fixed state of the world and a model that opens the door to partisanship dynamics based on changes in the state of the world. An analogy will help, and it refers to the analogy between brand loyalty and partisanship in chapter 2, ignoring for the moment the limitations of the analogy. Suppose one were a loyal Ford customer and generally considered other brands like Chevrolet and Toyota inferior. As in Achen’s model every Ford purchase updates the consumer on the value of Ford automobiles. The logic is that Ford as a brand has a fixed quality or type, and the consumer learns that type over time through experience.

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In an unchanging world this predicts a higher probability of buying a Ford with each successive purchase. But suppose that the state of the world changes. Ford’s quality might deteriorate, leading to one or two bad experiences. The recent unsatisfying purchases allow the consumer to learn the truth more accurately, leading our consumer to decide, “Ford is no longer the company it once was. I think I will consider a different car maker.” This type of thinking describes a purely retrospective consumer. The consumer’s utility for Ford is based solely on historical experience with Fords. Only if the consumer is disappointed in the last one or few Fords does she or he consider revising the evaluation of a Ford’s utility and of switching the next car purchase. There are other scenarios for how the state of the world might change. Ford’s competition may come out with a new model that offers exciting features not available on a Ford. The purely retrospective buyer may ignore this information and stay with Ford assuming no bad Ford experiences. There are certainly other consumers who will observe the new features and say, “The automobile world has changed, and I must evaluate these changes.” This assessment likely leads to efforts to collect additional information from road tests in journals such as Consumer Reports or Car and Driver or newspapers or even visits to dealers for a test drive. The new information may convince the potential buyer that her or his previous Ford ownership experience is not a good predictor of future car satisfaction. There is yet a third way the state of the world might change. The consumer might decide that because of changes in income, family circumstances, or other aspects of lifestyle a Ford is no longer the most desired option: “I used to like Fords, but I have changed and now I might like another company’s automobiles better.” This buyer may engage in the same information search as the consumer previously described. The net effect is that the consumer has experienced a preference change that has increased a competitor’s utility relative to a Ford’s utility. The knowledge that preferences have changed reduces the value of past Ford purchases as a predictor of future satisfaction with a Ford. All three scenarios imply previous Ford experiences now provide a poorer forecast of current and future satisfaction with a Ford. The second and third scenarios especially imply that the utility associated with other brands likely increased based on the information collected. In the model’s terms ρ has decreased, implying that (1 − ρ) has increased, meaning that less weight will be given to past purchases and more weight will be given to the different brands’ current relative utilities when making a purchase.

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So it is with some voters, who may use the occasion of an election to reassess their subjective party utilities in the process of deciding on their partisanship and vote. Models of partisanship that permit dynamics in the external environment, including poor performance by one’s traditional party, changes in the ideological and policy positions of the parties, or changes in the preferences of the potential voters, are akin to these several ways of reasoning by our automobile consumer. (And in fact, our models can incorporate all modes of reasoning exemplified by the consumers’ hypothetical quotes.) 3.1.3 Variations in Updating A critical component of the model of normal partisanship from above and its ability to represent the patterns of partisan change pictured in chap­­ter 1 is ρ, and more specifically how it varies over time and across subgroups. Fixed values for ρ predict, at best, a small number of simple temporal patterns, not the wide variety seen there. A few previous studies include heterogeneity in ρ. In virtually all of these studies variations are related to individual characteristics, such as age and education.1 Box-­Steffensmeier and Smith (1998) report heterogeneity in ρi but do not relate it to any individual characteristics. Unfortunately most of the studies do not include a utility term, so it is hard to see the results’ implications for our model.2

1. See Franklin and Jackson (1983) and Bartels et al. (2011), though Green and Yoon (2002) report no such variation. See also Box-­Steffensmeier, De Boef, and Lin (2004). 2. One exception is Franklin and Jackson (1983). In Jackson and Kollman (2011) we estimate a macropartisanship model similar to the one proposed here and estimated in chapter 4 with ANES biennial time series data. We report that ρit varies with a spline function of education, with the knot at twelve years of schooling and the number of elections in which individuals could have participated, though the latter coefficient is borderline insignificant. As summarized in an appendix to chapter 4 we (along with Elizabeth Mann Levesque) estimate a comparable model with individual ANES panel data for the early 1990s and find no association between ρit and either education or number of elections. Individual data should provide more reliable estimates for individual heterogeneity than time series data because these individual specific variables have far more variation among a cross section of individuals than among a time series of aggregate data. We explain the absence of a relationship with age in contrast to Franklin and Jackson (1983), who also use individual data, by noting the different time periods. The Franklin and Jackson data are from the 1950s and early 1960s, a period that fits Achen’s (1992) description of a stable period between realignments, while our individual-­level data are from 1992 to 1996, which hardly fits the definition of a stable political period (especially with the switch in party control of the presidency and Congress, but in different directions).

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Temporal variations in ρ are far more important to our model than are individual variations. These variations allow the model to fit the different patterns of partisan change seen in our four countries. The model continues the specification in Jackson and Kollman (2011) that ρjt, the updating term for party j, varies with squared changes in party positions, (3.3)

2

2

jt = j e  ( P1t +  P2 t +

…) ,

where ∆Pj2t is the squared change in party j’s positions between period t − 1 and period t.3 It is assumed that αj ≤ 1. The expectation is that δ < 0 so the value of ρt decreases as the amount of change in party positions increases. There are small variations to this specification to accommodate individual countries’ electoral systems. These are detailed in later chapters discussing each country. This specification of ρ is elemental to our adopted Bayesian approach, as it allows us to test for the key intuition that the larger the change in party positions, the less reliable is past normal partisanship in predicting current normal partisanship. The more reliable current utilities are as predictors of current normal partisanship, the less weight that should be given to lagged normal partisanship. When parties change their positions dramatically, ρjt is small and people are more apt to change their current normal partisanship. With only small changes in party positions ρjt is closer to one, and people are inclined to keep their normal partisanship. Formally, if there are no changes in party positions, ∆Pj2t = 0 and ρjt = αjt. If * αjt = 1 and ρjt = 1 then Pijt* = Pij,t −1 + εijt , indicating that normal partisanship is stable except for random fluctuations. Specifying and estimating this adjustment parameter and making it a function of party behavior provide ways to analyze long-­term dynamics. The trajectory of party positions determines the sequence of values of ρ, which affects an individual’s (or group’s) partisan trajectory. This offers the flexibility to cover different temporal and geographic contexts. Depending upon this sequence, the partisan trajectory may follow any pattern from stable to rapid or evolutionary change from one equilibrium to another.

3. This definition is sufficient for now though it is modified slightly to accommodate the particulars of each country, such as presidential election years in the United States.

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3.1.4 Illustrating the Dynamics of Normal Partisanship The model’s dynamics are built on either changes in party positions or in preferences. Movements in either (or both) change utilities and the amount of normal partisanship updating. We can now illustrate these dynamics with a generic, simplified version of the model in equations 3.2 and 3.3 and data from two of the US groups—­southern whites and African Americans. The objective here is not to claim this model “fits” these groups’ partisanship. A better-­fitted model is analyzed in later chapters. Instead, we use real data on party placements to show the dynamics. A simplified version of the model with only one issue and the two major American parties is estimated. The estimated model used in the simulations is4 (3.4)

(3.5)

Pt* = ρt Pt*− 1 + (1 − ρt )[0.3 + 1.2(| Xt − Rept | − | X t − Dem t |)],

ρt = 1* e

− 0.8(∆ Dem t2 + ∆Dem 2t − 1 + ∆Rep 2t + ∆Rep 2t − 1)

.

For purposes of illustration, policy preferences, X, are fixed so the only “moving” parts are party positions. All changes in partisanship are consequences of these party placement changes. The first set is blacks’ placements of each party on the civil rights issue. Preferences are set at the group’s mean for the 1956 to 2016 period, which is +1.0. The second set is southern whites’ party placements on a question the ANES introduced in 1980 asking about the trade-­off between lower taxes and more spending on public services such as education and health care. The mean preference is −0.15. The first relationship shown is that between changes in party positions and ρt. Figure 3.1A for blacks is a scatterplot of the values for ρt and the absolute values of the changes in party positions. The distinguishing feature of these data is the two elections with very large changes in party positions, which are associated with values for ρt that equal zero. There are only six other periods with ρt < 0.9, five between 1970 and 1980 and then 2008. This pattern is expected to produce one election with a large change in utilities and normal partisanship, what is commonly called a critical election. This is precisely what we observe in figure 3.1B, which 4. The specification for ρt includes a second lag to the squared term. This is discussed in chapter 4.

-1.5

-1

-.5

0

.5

1

1.5

2

2.5

3

A

1956

1960

1964

1968

1972

1976

1980

1984

Year

1988

Democratic Placement Rho(t) B figure 3.1.  US partisan dynamics: punctuated equilibrium

1992

1996

2000

2004

Republican Placement Normal Partisanship

2008

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plots the party positions, the values for ρt, and the resulting measured partisanship. The parties actually switched positions between 1960 and 1964, and by very large amounts. In 1960 the Republicans were perceived as the more pro– ­civil rights party, by just over 0.5 points. In 1964 the Democrats were the more pro– ­civil rights party by 2.75 points. The party shifts are associated with just over a 3.0 point shift in party utilities, favoring the Democrats. As shown in figure 3.1A, the value of ρt falls to zero, which implies complete updating to normal partisanship, which will equal the value of party utilities. In this simulation normal partisanship, and thus estimated partisanship, moves in a Democratic direction by 2.0 points. All these changes occur in 1964, the year of the large party changes. These movements, other than the utilities, are shown in figure 3.1B. After 1964 and particularly from 1978 to 2008 there are only small changes in party positions, with accompanying high values for ρt, as noted above. The changes in party positions in the 1970s are effectively the parties searching for and finding their new equilibrium positions. These small movements produce a small decline in normal partisanship of about 0.28 points to 2.42 by 1974, and the simulated normal partisanship never falls below 2.33 for the next thirty-­four years. The whole system of party positions and normal partisanship constitutes a measured stable equilibrium for the thirty-­plus years until 2008. This thirty-­four-­year period is the second part of the Carmines and Stimson punctuated equilibrium, which is a rapid and large change from one equilibrium to another followed by a long period of stability. It is important to note that the model is able to describe both periods—­the critical election driven by party shifts and the long period of stability created by stable party positions. The second pattern depicted, for southern whites, is an extended, rather than rapid, shift in partisanship. The important features here are continuing party changes. These produce both continual changes in utilities and values for ρt that are much lower than one. Figure 3.2A again plots the values for ρt and the absolute value of the changes in party positions. There are only fourteen observations in this simulation because the spending on services question was not introduced until 1980. Of these fourteen elections the value of ρt is 0.85 or less in five of them, but there is not a single election with a small value for ρt (compared with the previous simulations for blacks). We expect this pattern of party position changes to produce gradual but consistent changes in utilities and in normal partisanship. Figure 3.2B plots the party positions, ρt, and normal partisanship for these data. Party positions are not stable, with the Democrats perceived

1 .9 .8 .7 .6 .5

Rho(t)

.4 .3 .2 .1 0

0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1

Absolute Change in Party Positions over Two Elections

1.1

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-1 -.75 -.5 -.25

0

.25

.5

.75

1

1.25 1.5

A

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Year

Democratic Placement Rho(t) B figure 3.2.  US partisan dynamics: long-­term change

Republican Placement Normal Partisanship

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as becoming more prospending, particularly after 1990. The Republicans are always perceived as far more opposed to social spending than the Democrats, though their positions exhibit considerable fluctuation, with increasing support from 1984 to 1990 and again from 1996 to 2008 and decreasing support from 1980 to 1984 and from 1990 to 1996. The positions of the two parties combined with the weak preference for lower taxes rather than more spending (Xt = −.15 for all t) produce utilities that are quite Republican. The continued changes in party positions produce values for ρt that are consistently less than one, as seen in both panels of figure 3.2. The combined effect of two patterns—­increasingly Republican utilities and values of ρt less than one and as low as 0.8 —­is a continual movement in normal partisanship to the Republicans, but one that extends for the whole period and not abrupt over one or two elections.5 All of the changes in utilities and partisanship are consequences of party changes, as preferences are constant for the entire period (by assumption in this exercise). It is important to remember that these are simulations to illustrate the different patterns of partisan change produced by different patterns of party changes. These are not intended to track actual partisanship. This will be done with a more complete specification and estimation in chapter 4. Two important conclusions about the model arise from these simulations. First, the model can produce quite different patterns of partisan change, from long-­term stability to rapid change during a “critical” election to protracted periods of evolution. And second, what parties do plays a key role in the evolution of normal partisanship in our model. Every movement shown in these simulations derives from changes in party positions. Changes in these positions change party utilities to favor one or the other party. They also alter the rate at which normal partisanship updates as these utilities change. The larger the change in party positions, the faster normal partisanship updates to bring it in line with utilities. 3.1.5 Short-­Term Forces The final component of the partisanship model is what we refer to as short-­term forces, denoted by Xitβ or just Xtβ at the aggregate level.6 5. The initial partisanship is 0.5, which is the partisanship of southern whites in 1980 when the simulations begin. 6. See Clarke and Stewart (1985) for an early study using the same concept.

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Across the various models in the literature on partisanship dynamics, a key difference is in how researchers treat factors such as economic and foreign policy shocks, scandals, and candidate characteristics. Any and all of these factors affect individuals’ and groups’ partisanship. Our approach includes measures of short-­term forces such as economic shocks in the full models of partisanship estimated in later chapters. In the literature, many use retrospective economic evaluations, usually measured by responses to questions about whether the individual and/ or the country is better or worse off financially than at some previous point such as a year ago. A second subjective measure used in much of the macropartisanship literature is the Michigan Index of Consumer Sentiment. More objective measures are levels or changes in real income or GDP, unemployment, or inflation. Personal assessments of an incumbent administration’s scandals or handling of a foreign policy crisis have been proxied by the variable measuring presidential approval. A more objective measure of the effect of the Vietnam War was the accumulated casualty count. Lastly, dummy variables representing individual administrations and their duration in office have been used to represent the implicit popularity of different presidents. The models estimated here will use a measure of the annual change in real personal income in the year prior to the election or the level of unemployment in the election year. These are chosen because they are objective and not subject to being influenced, at least directly, by partisanship.7 Variations on this specification will be discussed in the context of each country. 3.1.6 An Estimable Model of Partisanship The different pieces can be put together to get a model with only observable variables. Combining equations 3.1 and 3.2 gives the following model for current partisanship: Pit = Pit* + Xit  + uit = it Pi,t* −1 + (1 − it)Utilit + Xit  + uit + it it . 7. In contrast, presidential approval can be a proxy for partisanship in the United States. See Jacobson (2019). Otherwise identical Republicans and Democrats would not give a sitting president identical approval ratings. We address the potential endogeneity of presidential approval later in our models for the United States, sometimes using instruments for approval measures.

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Equation 3.1 for the previous period can be rearranged to obtain an expression for lagged normal partisanship, Pi,t* −1 = Pi,t −1 − Xi,t −1β − ui,t −1, which is then substituted into the previous equation for Pit to give (3.6) Pit = ρit Pi,t − 1 + (1 − ρit )Util it + X itβ − ρit X i, t −1β + uit + ρit (εit − ui, t −1 ). Equation 3.6 contains only observable variables, which makes it estima­ble. A critical part of this equation is the −ρitXi,t−1β term, as its presence specifies that normal partisanship rather than partisanship is being updated.8 This basic equation will be altered to accommodate different party systems with the intention of eventual estimation using data. Note that equation 3.6 is the fundamental empirical equation we will estimate for each country. Normal partisanship, an unmeasured concept undergirding our theoretical and modeling approach, is implicit in this equation through the presence in the updating term of ρt (Pt−1 − Xt−1β).9 We will thus be referring to partisanship and not normal partisanship in most of what follows—­including in later chapters—­because we are moving toward empirical analysis requiring measurable variables. Yet even as estimation proceeds, normal partisanship is an integral, albeit implicit, part of the model that is being updated each election conditional on utilities, while measured partisanship is also affected by short-­term forces.

3.2 Different Party Systems Most of the world’s democracies have more than two parties with loyal supporters. Our general model of partisanship allows for multiple parties where individuals compare and make choices among all alternatives in choosing a partisanship. This section proceeds by laying out the multi­ party version and its empirical implications. We then show how this reduces to the two-­party model conventionally used in the voluminous

8. To see this, note that the above equation substitutes (Pt−1 − Xt−1β) for Pt*−1 . That introduces the −ρitXi,t−1β term. Without that term the equation is Pt = t Pt*−1 + (1 − t )Util t + Xt , which is the model for updating partisanship rather than normal partisanship. 9. Without the ρtXt−1β equation 3.6 is updating partisanship, Pt−1 = f(Utilt−1 + Xt−1β), rather than normal partisanship, which is only a function of Utilt without short-­term forces.

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studies of US partisanship. Lastly we introduce a synthesis of the two-­and three-­party models, which we dub the two-­plus-­or 2.5-­party model. 3.2.1 Multiparty Model Presentation of the multiparty model begins with some notation and definitional material. Assume there are J parties, indexed by the counter j, j = 1, . . . , J, meaning there are J measures of partisanship for an individual i at time t, Pijt. Each of these measures the strength of person i’s support for party j. For example, as described in chapter 2 we could think about placing everyone on the zero (no support) to three (very strong support) party support scale shown in table 2.2. Following equation 3.6 a person’s strength of support for any party is

(3.7)

Pijt = ρijtPij,t−1 + (1 − ρijt)(γ1jUtili1t + … + γjjUtilijt + … + γJjUtiliJt) + Xitβj − ρitXi,t−1β + uijt,

where, as before, Utilijt is the person’s utility for party j at time t, and Xitβ are the short-­term forces relevant to person i at time t. The multiparty model makes specific predictions about the values for the γs. Because the utility terms measure increasing distance of a party from the individual’s preferences, the coefficient on party j’s utility term in party j’s support equation, γjj, should be negative. The further the party’s positions are from the individual’s preferences, the weaker the person’s support for that party. By the same logic the other γ’s should be positive, because the support for party j increases as other parties’ positions are moving further from the person’s preferences. A possible weakening of these predictions may occur if the parties can be easily arrayed along a single dimension, say from most right to most left. Assume three parties, A, B, and C, arrayed in that order along the ideological dimension. There may be only a small impact of increasing disutility for party A on support for party C and vice versa. The most likely consequence of increasing disutility for parties A or C is increased support for party B. This conjecture, if correct, implies that γ13 and γ31 will be relatively small and that γ12 and γ32 will be large. These predictions about the values for γ in a multiparty system constitute a test of the appropriateness of the model in equation 3.7 as a description of that country’s electoral system. If these predictions are not met, we need to consider an alternative to the pure multiparty model.

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3.2.2 Pure Two-­Party System While equation 3.7 can apply to any system with two or more parties, for only two parties it is more efficient to transform this equation. In chapter 4 we discuss how close the United States comes to fitting the pure two-­ party case, but suffice it to say it is close enough to warrant developing the two-­party version. A pure two-­party system, under the standard models in the literature, implies that party strength can be arrayed along a single dimension with increased support for one party implying commensurate decreased support for the other. This aspect of party competition is captured by a single measure of partisanship that is the difference in party strengths, Pit = Pi2t − Pi1t. As discussed in the previous chapter, the common way to measure partisanship in such a system is simply to have individuals locate themselves on a single dimension with zero being indifference and either extreme being the strongest partisans for one or the other party. Chapter 2 gives the American example of such a scale from −3 to +3 constructed with the ANES party identification questions, with −3 being a strong Republican, 0 being a true Independent, and +3 being a strong Democrat. The partisanship model for this two-­party system is derived by subtracting the equation for party 1 from that of party 2 and assuming that ρit is the same for both parties: (3.8)

Pit = ρitPi,t−1 + (1 − ρit)(Utili2t − Utili1t) + Xitβ − ρitXi,t−1β + Ut.

An important implication of this model is that any shifts in disutilities move partisanship toward the party with the lower disutility and away from the competing party, in equal amounts. If party 1 were to become closer to a majority of the voters’ preferred policies, either because preferences or party positions shift, the disutility for party 1 relative to party 2 decreases and party 1’s strength would increase, party 2’s strength would decrease, and measured partisanship would shift in party 1’s direction. Short-­term forces are appropriately polarized so that positive forces, such as income increases, aid the incumbent party. Equation 3.8 is the basis for virtually all the studies of US partisanship (including normal partisanship). 3.2.3 Hybrid Party Systems Some countries, such as the United Kingdom, might not be well described by either a multiparty or a two-­party categorization. It is likely that neither

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the full multiparty model nor the two-­party model captures how voters choose their partisanship and their votes. This alternative, what we will call a two-­plus-­or 2.5-­party model, fits when there are two major parties and at least one minor party. A good illustration is the British case with its two major parties, Labour and Conservative, and the minor Liberal Party. Voters’ primary consideration is between these two major parties as in a two-­party system because it is almost certain that one of these parties will choose the government (or at least dominate the government). This will be particularly true in a first-­past-­the-­post system where strategic voting considerations lead to a focus on the two major parties. The two-­plus model includes equations for the likelihood of major-­party support and for possible support for one or more minor parties. Similar to the two-­party model, this version measures major-­party strength on a single dimension that is measured on a scale of −3 for very strong Conservative to +3 for very strong Labour with zero indicating no support for either major party. This is equivalent to partisanship in the pure two-­party case, which would be Labour partisanship minus Conservative partisanship, Pit = Plabit − Pconit. Those who choose a third party, the Liberals, or a minor / other / no party are coded as zero. Again, as in the two-­party model individuals’ location on this party scale is related to the con relative disutility for these two parties, Util it = (Util lab it − Util it ).. The smaller the perceived difference between the two parties, the closer to zero will be the placement on this scale. Two additional factors are incorporated here. The first assesses individ­uals’ alienation from the major parties, defined as Alienit = min(Util itlab ,Util con it ). The higher the disutility for the favorite (or least disliked) of the two major parties, the greater the alienation. The more alienated respondents are from both parties, the closer to zero will be their major-­party partisanship, indicating no affiliation with either party. We expect in an empirical estimation that the coefficient on Utilit in the major-­party equation decreases as alienation increases, which requires an interaction term between alienation and utility, Alienit*Utilit. The second additional term is a variable measuring indifference to the major parties, Indifft = |Util itlab − Util itcon |. Indifference increases the likelihood the major-­party scale equals zero and is associated with higher levels of support for minor-­or no-­party support. Indifference is transformed by subtracting the absolute value of the difference in major-­party utilities from its maximum value so that increasing values indicate increasing indifference. The expression for major-­party utility in the two-­plus system is a modification of the model for two-­party partisanship:

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(3.9)



Pit = ρitPi,t−1 + (1 − ρit)(γ11Utilt + γ21 * Alien * Utilit) + Xitβ1 − ρitXi,t−1β + Ui,1t.

The expectations are that γ11 < 0 and γ21 > 0. The higher the level of alienation, the smaller the effective coefficient on disutility. This means that with high levels of alienation Pit will be close to zero even with a large disutility between the two parties. The expectation that those who are indifferent to the major parties will be coded as zero on the major partisanship variable is implicit in this specification. The value for Utilit equals zero with perfect indifference, which means the expected value for Pit is also zero. The further respondents move away from absolute indifference, the higher the level of support for the more favored party. With the exception of the alienation interaction term, this equation resembles that for the pure two-­party model, though it is possible that alienation might lead to no support for either party in the two-­party model. Alienation and indifference are explicit elements in the support equation for third and/or minor parties. In the British case it is expected that indifference to or alienation from the major parties is associated with greater support for Liberals or a minor or no party. The choice here will depend upon the disutility of the third, or Liberal, party. The models for third-­and minor-­party support include the alienation and indifference var­­ iables and a measure of disutility for the third, or Liberal, party. (3.10)

(3.11)

Liberal: Plibit = ρit 2 Plibi,t −1 + (1 − ρit 2 )( γ 12 Indiff it + γ 22 Alien it + γ 32 Util itlib ) + Xit β2 − ρit 2 Xi,t −1β2 + u2it . Other: POthit = ρit 3 POth i,t −1 + (1 − ρi t 3 )( γ13 Indiff it + γ23 Alien it + γ 33 Util itlib ) + Xit β3 − ρit 3 Xi,t −1β3 + u3it .

With the transformation of the Indiff variable described above the expectation is that γ12, γ22, γ13, and γ23 > 0. Further, we expect γ32 < 0 and γ33 > 0, so that for given levels of indifference and alienation, higher levels of disutility for the Liberals is associated with lower Liberal and higher other-­party support. The two-­plus model collapses to the two-­party model if γ21 = 0 in the major-­party equation and γ12 = γ22 = γ13 = γ23 = 0 in the third-­and other-­ party equations. The more interesting and important comparison is between the multiparty model in equation 3.7 and the hybrid or 2.5-­party

t ) + Xkt βj

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model in equations 3.9 through 3.11. As noted above, the multiparty has explicit predictions for the γjj′ coefficients, namely that γjj < 0 and γjj′ ≥ 0, j ≠ j′. If these conditions are met for a given country we conclude that country has a full multiparty system. If one or more of these conditions fail the two-­plus-­party model is estimated with the expectations laid out in the preceding paragraph. If these conditions also fail, then we must conclude that the country has a party system that is not accommodated by our model.10

3.3 Level Consistency: Micro to Macro In Jackson and Kollman (2011) we show that the core model of partisanship developed here can be expressed as an individual model, as above, and then aggregated to a macropartisanship model. How the aggregation occurs depends on assumptions about individual and temporal heterogeneity and about short-­term forces.11 Of particular relevance here is the analysis (in chapter 4) of the individual ANES 1990s panel data that show no consequential individual heterogeneity. Given that result the following macro model with no individual heterogeneity can be derived for the multiparty case for population subgroup k:

(3.12)

Pjkt = jkt Pjk,t −1 + (1 − jkt )(1 Util 1kt + … + j Util jkt + … + γJ Util Jkt ) + Xkt βj − ρjkt Xk,t −1βj + ujkt ,j = 1, . . . , J, and k = 1, . . . , K ,

− ρjkt Xk,t −1βj + ujkt ,j = 1, . . . , J, and k = 1, . . . , K , where the items with overlines are the means of the respective variables computed for the members of group k, for example, k Pjkt = (1 Ni ) ∑ i =1 Pijkt and Util jkt = (1 N k)∑ i =k1 Util ijkt ,

N

N

and it is assumed that ρjkt is constant for all individuals in group k. It is straightforward to apply the same logic and aggregation method to the two-­party and hybrid 2.5-­party cases. The coefficients in the micro 10. As one might expect and we are pleased to report in later chapters, all four of our coun­­ tries fit into one of the categories: two, two-­plus, or multiparty. 11. In that paper we describe the different possibilities in detail.

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and macro equations in all versions are consistent; that is, they can be interpreted similarly at both levels. The macro model, which averages these effects over a defined group, can be for the entire electorate or for a group selected for specific study, such as southern blacks in the United States or Catholic residents in Quebec. For ease of exposition we will suppress indications of means (overlines) in most of what follows, and it should be clear from the context whether we are referencing a micro or macro model. Having consistent models for micro and macro is an advance over the previous literature on partisanship, as we discussed in chapter 1. Overall, that literature consists of research findings based on models that focus on either micro or macro patterns. But consistency can be elusive. In their seminal paper on macropartisanship, MacKuen, Erikson, and Stimson (1989, 1129) state that “[their] findings will neither support nor undermine particular models of individual partisanship.” In later work, Erikson, MacKuen, and Stimson (1998) estimate a macropartisanship model and link it descriptively to a micro model but do not present the algebra connecting the two models.12 Thus we believe that Jackson and Kollman (2011) and the resulting work for this book are the only studies that explicitly achieve level consistency.

3.4 Next: Estimation To summarize, we build on decades of previous research and offer a new, comprehensive framework that incorporates and encompasses much that has come before but extends it in important directions. The models are flexible to include different types of party systems, levels of analysis, and assumptions about how partisanship works among individuals and groups and with different party systems. Models are logical arguments summarizing theoretical assumptions and conclusions, and good models help us make sense of patterns that are difficult to understand otherwise. Models focus attention on what is important in a recurring social process, capturing the essence of a situation and abstracting away from what is less important. They also describe precisely how those important elements work together (interact) to produce 12. See also Erikson, MacKuen, and Stimson (2002). We tried to derive their macro model from the equations for individual partisanship but do not believe it aggregates to their macro model without additional assumptions that are not explicitly stated in their article.

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specific, potentially different outcomes. Lastly, they provide the basis for systematic empirical estimation. The ultimate evaluation of the soundness of modeling of the type we have done here is how well the models fit data and whether the estimated parameters make sense in the models’ contexts. In the next chapters we move on to examining specific countries using carefully constructed data describing the historical evolution of each country’s parties.

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T

he United States offers extensive and rich data with which to begin estimating and evaluating the model from chapter 3. Data are plentiful to measure and analyze short-­term quarterly and longer-­term biennial changes in partisanship at the micro and macro levels. The primary focus in this chapter, as throughout the book, is on macropartisanship among electoral groups. Estimation of the US macro models for both short and long terms builds on micro results summarized toward the end of this chapter and reported in more detail in appendix 4C. We begin this chapter with a testing of whether the United States is best represented as a two-­party or hybrid 2.5-­party system, concluding that the United States is best represented as a two-­party system. This is not surprising, but for two reasons the analysis moves this study forward. First, the analysis serves as a baseline for analyzing the other countries in later chapters, and second, it helps solidify our understanding of the role of independent voters in American politics. We then take two crucial steps in analyzing the fit of our model. The primary analysis involves estimating the model for the updating of normal partisanship during election periods. Then we examine short-­term partisan change between elections to bolster our claims of the distinction be­­ tween normal partisanship and measured partisanship that is conditional on utilities and short-­term forces.

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4.1 The United States: Two-­versus 2.5-­Party Models The United States is often described as the epitome of the two-­party system. The number, strength, and stability (or lack thereof) of alternatives to the two major parties provide important context for our expectations about the two-­and two-­plus-­party models. Political scientists have provided ample data and decades of analysis, giving us a good understanding of why the United States has had such weak third or minor parties, and we do not dispute the main arguments, which mostly center on electoral institutions and the nature of presidential elections.1 Like the other three countries, the United States mostly has single-­member districts, and it uses plurality rules for the most part in its legislative elections. Among the four, however, it is the only one with an independently elected president with substantial powers. This feature, including the existence of an electoral college that by custom uses plurality and winner-­take-­all rules at the state level, has been proposed as leading the major parties to absorb nascent third parties.2 ANES data provide a picture of the proportion of the electorate choosing independence or no party, and a summary is shown in figure 4.1. Unfortunately because of question wording, only beginning in 1972 do the data reveal the true proportion saying none or other.3 Prior to 1972 the

1. Riker (1982), Cox (1997), Lijphart (1994), Gaines (1999), Chhibber and Kollman (2004). 2. While minor parties received substantial votes earlier in American history, and while there have been visible and consequential third-­party candidates for president, such as Theodore Roosevelt (1912), Ross Perot, John Anderson, and George Wallace, these campaigns have never been converted into viable political parties that can outlive the candidate past one or two elections. All of these electoral institutions coexist with statutes at the federal level and especially in the states that make it challenging for third parties to survive and thrive. “Sore loser” laws in some states prohibit candidates for Congress or for governor losing in a party primary from running in the general election under a different label or as an independent. Likewise, onerous signature requirements for parties in many states make it difficult for any organization other than Democrats or Republicans to get on the ballot. 3. Prior to 1972 respondents selecting none or other were then asked if they leaned toward the Republicans, the Democrats, or neither. Only those who answered neither to the follow-­up question were reported as none or other. In other words, data are lost for the proportions who answered the first question none or other. Starting in 1972 the proportion choosing none or other in the first question were recorded and preserved before being treated as potentially leaning partisans.

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figure 4.1.  Percent none (includes other) and independent—­US

proportion none/other reaches around 5 percent. Most of the none and other through 1966, though, is accounted for by southern blacks, shown with the very short dashed line in the figure, where about 30 percent report none until 1964, and then in 1966, where it drops to 10 percent and then to about 5 percent in 1968. This period, not surprisingly, coincides with the years where blacks, particularly in the South, were systematically and strenuously prevented from participating in elections. Registration drives in the early 1960s and ultimately the Voting Rights Act of 1965 dramatically altered ballot access and partisanship. The southern black proportion responding none in ANES begins to match that of the entire population by the early 1970s. Even with the full reporting of none and other beginning in 1972, there is still only a relatively small proportion among the total population choosing those options. The average is about 9 percent and exceeds 10 percent in only five of the nineteen elections. It is worth noting that the respondents explicitly choosing other rather than none are such a small fraction as to be meaningless. This proportion was consistently in the fractions of a percent, averaging 0.7 percent between 1972 and 2012; hence the combining of the two categories.

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Figure 4.1 also plots on the solid line the proportion of respondents choosing to say they are independents. The independent category is peculiar to the ANES format and can be considered to be a mixture of three potentially different sets of respondents. The first set has a taste for saying independent but with definite and consistent policy and party preferences that tilt them toward one of the major parties. A long tradition in American politics, dating back to even before the Progressive Era, when political parties deserved their pejorative connotation due to corruption and local monopolies (e.g., party machines), voters have been known to state that they are “independent” and claim that they vote for the candidate, not the party.4 This group does not want to associate with either party initially and chooses independent rather than none. Yet while these respondents may be alienated from parties, they are not alienated from the parties’ policy positions or from voting for a party. In fact, over two-­thirds of independents, when they answer the first ANES question, readily respond that they “lean” toward either the Republicans or the Democrats. This set (i.e., the leaners) are excluded from the independent category in figure 4.1. A second set are those with no reason to prefer one party or the other on policy grounds—­the true indifferents. These respondents are logically coded as zero on the partisanship scale. The third set, which is what we really wish to estimate in size with the hybrid 2.5-­party model, are those who choose independent as an alternative to the major parties because they are alienated from or indifferent to both parties’ positions and see independent or none as an alternative because of their attitudes toward the main parties. This group is crucial to the proposed two-­plus-­party model. If a sizable proportion of the respondents choose none/other or independent, then the hybrid 2.5-­party model should be a decent description of the US case. Referring back to chapter 3, the two critical coefficients in this test are γ21 in equation 3.9 and γ23 in equation 3.11. The former estimates how much people become alienated from the major parties and the degree to which they give less weight to party utilities and thus are more likely to be coded as zero on the partisanship scale. The second estimates the degree that alienation from the major parties is associated with a greater likelihood of choosing other/none or independent. We apply the two-­party and the hybrid 2.5-­party model to the United States, examining both the individual and aggregate data in the models 4. See Wattenberg (1998), Keith et al. (1992), and Smidt (2017).

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figure 4.2.  Two-­ vs. hybrid-­party model—­US

described in chapter 3. The aggregate test uses measures of the groups over the decades, as described in chapter 2: southern blacks, nonsouthern blacks, southern whites, northeastern whites, and other whites. Analysis of the individual data, though from only one four-­year panel in the early 1990s, constitutes the stronger test. Alienation, which is the key distinguishing feature of the two-­plus model, is an individual-­level phenomena, so the propositions related to its role and importance are better measured and estimated with the micro data. Figure 4.2 is a combined plot, showing the coefficients and 95 percent confidence intervals for the alienation and indifferent coefficients from both the individual-­level and aggregate models. For the individual-­level model the coefficients on the alienation-­utility interaction term in the major-­party equation and on alienation in the independent equation are substantively and statistically zero and in one case has the wrong sign.5 In the aggregate model the coefficient on the alienation-­utility interaction term again has the wrong sign and is statistically insignificant though it is 5. There is no equation for none, as there were far too few respondents choosing none to support a model.

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large. The alienation coefficients in both the independent and none equations have the correct signs and are statistically significant, though not substantively large.6 The results taken collectively, particularly the result that increased alienation is not associated with less weight being given to party utilities in the major-­party equations, imply that we can be confident in the following: from the way voters think about being an independent, the United States is a two-­rather than a 2.5-­party system.7 There is one additional important result from the individual model estimations, although it is not part of the distinction between the two-­and 2.5-­party models. From our analysis of the results (not shown), there is no evidence of individual heterogeneity in the values for ρit.8 Recall the importance of ρt in our models as the parameter for the stickiness of partisanship over time. On the basis of these results individual variations in ρit are omitted, which simplifies the aggregation process and focuses attention on how ρt varies over time with changes in party positions.

4.2 Election Periods and Normal Partisanship We now focus on estimating the parameters in our main model to learn what contributes to changes in normal partisanship, including the role of party change. To do so, the model needs to be tweaked to accommodate the specificities of the American case, including features of the ANES data and also incorporating quarterly data between elections analyzed later in the chapter. 6. A one-­standard-­deviation difference in alienation is associated with a 0.012 increase in the proportion independent and a 0.010 increase in the proportion none, corresponding to 0.34 and 0.20 standard deviations, respectively. 7. Put more formally in statistical terms, these coefficients imply that there is a small chance of making an error in accepting the proposition that the United States is a two-­rather than a hybrid-­party system. 8. Tests of the proposition that the amount of updating varied with number of elections, as proposed by Achen (1992), and with education, both as reported in Jackson and Kollman (2011), strongly point in the direction of no such associations. The p-­values testing whether the respective coefficients are different from zero varies from 0.4 to 0.8, depending upon the model and sample. It is important to note that Achen (1992) says his model and the prediction about how the amount of updating varies with number of elections applies to a stable partisan period, which hardly describes the decade prior to the 1990s ANES panel. Further, the association with number of elections reported by Jackson and Kollman (2011) is substantively and statistically weak. See Dinas (2014) on this question as well.

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4.2.1 Modeling Normal Partisanship in Election Periods To model the distinction between election and intervening periods throughout this chapter we need additional notation. First the counter t refers to election periods, which with quarterly data occur every eight periods, and s counts the periods between elections. The generic reference is partisanship at period t + s, Pi,t+s. For example, Pi,t+1 is the first period following an election and with quarterly data Pi,t+7 is the period prior to the next election, meaning s = 0, . . . , 7. Equation 3.8 gives the estimable model for individual partisanship in the two-­party system. Aggregating the individual partisanship values for the members of group g as in section 3.3 gives a following estimable expression for macropartisanship for group g in election period t: (4.1)

Pgt = ρgt Pgt −1 + (1 − ρgt )Util gt + Xgt β − ρgt X gt − 1 β + εgt − ρg t εgt −1 .

Again the absence of an i subscript denotes an aggregate measure, for Ng Pgit . Utilt and ρt are defined in equations 2.2 and example, Pgt = (1 Ng )  i =1 3.3. Based on the results with the individual model Pgt does not contain any individual terms but does vary across groups and over time, depending on the changes of perceived party positions among members of each group in each election. In the US case equation 3.3 is expanded to treat midterm and presidential elections differently. Squared changes in party positions are also lagged a second time to allow more time for updating to occur. These changes give

(4.2)

ρt = (a1Cong t + a2 Prest ) ∗ [Cong t δ1 ( ∆PP1t2 + ∆PP1t2−1)+Prest δ 2 ( ∆PP1t2 + ∆PP12t −1 + ∆PP2t2 + ∆PP2 2t −2 )],

e

where Congt and Prest are dummy variables denoting midterm and presidential elections.9 The presence of the term − gt Xgt −1 in equation 4.1 provides a direct test of the proposition that normal partisanship is distinct from partisan-

J

J

9. Formally, PP12t =  j =1 [ (Djt − Dj t −1)2 + (Rj t − Rj t −1 )2 ] , PP12t −1 =  j =1 [ (Dj t −1 − Djt −2 )2 + (Rj Djt −2 )2 + (Rj 2

J

t−1

t−1

− Rj t − 2 ) 2 ] , 

2 J − Rj t − 2 ) 2 ] , PP2 2t = Pres t   Jj =1 [ (D j t − Dj t − 2 )2 + (R j t − Rj t −2 )2 ] , and  PP2t − 2 = Pres t   j = 1 [ (Dj t −2 − Dj

 PP2t − 2 = Pres t   j = 1 [ (Dj t −2 − Dj t − 4)2 + (Rjt − 2 − R jt − 4 )2 ] where D and R denote the posi­­tions of the Democratic and the Republican parties on issue j at election t with the appropriate lag.

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ship, which includes both normal partisanship and short-­term forces, and that it is normal partisanship that is being updated each election. The de­­ rivation of the estimable form in equation 3.6 depends upon the substitu­ tion Pi,t* −1 = Pi,t −1 − Xi,t −1β − εi,t −1, which distinguishes normal partisanship from partisanship. Further, if it is partisanship and not normal partisanship that is being updated, then the term − gt Xgt −1 should be excluded from equation 4.1. This proposition is testable by multiplying this term by the constant λ, for example, − gt Xgt −1. The model with normal partisanship implies that λ = 1, while the model without normal partisanship implies that λ = 0. 4.2.2 Data and Estimation We begin with election years. The data used to estimate the model come from five mutually exclusive groups of respondents in ANES, as previously mentioned—­northeastern whites, southern whites and other whites (neither southern nor northeastern), and southern and nonsouthern blacks. These groups’ partisanship evolved in different ways in the second half of the twentieth century, providing much better evidence about the proposed relationships than nationally aggregated data where offsetting movements, such as with African Americans and southern whites, disguise aggregate partisan change. Partisanship is measured in the manner described in chapter 2, from −3 to +3. Party utilities and changes in party positions are measured by responses to three questions: civil rights, whether the federal government should guarantee jobs and a standard of living, and from 1980 on whether spending on social services should be increased. The truncated series for the spending issue requires a small modification to the utility expression, which is detailed in the online appendix. These are the same utility measures used in the individual model. Carmines and Stimson (1989) argue that the parties’ evolving positions on the civil rights issue after the 1950s are strongly related to partisanship and a major factor restructuring US politics during and after the 1960s. Niemi and Jennings (1991, 978) present evidence that both the civil rights and jobs issues are more strongly related to partisanship changes in young adults in 1973 and in 1982 than attitudes about school prayer and Vietnam, controlling for parents’ partisanship. The civil rights series for 1956 to 1968 is constructed from responses to questions about respondents’ and party positions on the federal government’s role in ending school segregation and for 1970 to 2016 on the aid to minorities question. (Details are provided in the online appendix.)

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Short-­term forces are measured by changes in real disposable income in the quarter prior to the election. An incumbency interaction is used so the incumbent party benefits from positive income changes and loses support with negative growth. Presidential approval is omitted, for reasons that will become clearer below having to do with endogeneity.10 Appendix 4C presents a model using presidential approval, which does not vary by group, and an alternative measure for rating presidents contained in the ANES studies but only beginning in 1968. The data are a pooled time series cross section, and several features of the estimation accommodate this structure. Equations are estimated for each group with separate intercepts, which is equivalent to including group fixed effects.11 4.2.3 Empirical Results Results are shown in figure 4.3 with the full results in appendix table 4A.1.12 First, the coefficients strongly support the hypothesized relationships between changes in normal partisanship and party position changes. The values for δ1 and δ2 are statistically different from zero and imply a significant decrease in ρt with increased changes in party positions. The values for δ1 and δ2 cannot be compared because the squared change in party positions variable for presidential elections includes changes for midterm election periods. For that reason and because of the greater stability in party positions during midterm elections, the values for squared party change will be larger during presidential elections. The average squared party change for midterm elections is 0.70 with a standard deviation of 0.28, while for presidential elections the mean is 2.24 with a standard deviation of 1.43, indicating much more stability to party positions in midterm elections. The 10. Jacobson’s (2019) recent study of the relationship between presidential approval and partisan attitudes bolsters our claims of endogeneity. 11. Because the model is nonlinear in the parameters the equations are estimated using nonlinear seemingly unrelated regressions (NLSUR). Likely correlations among the groups’ stochastic terms in a given year are included by using the iterated feasible generalized NLSUR (with the iterated feasible generalized nonlinear least squares option). If the stochastic terms are normally distributed, this method provides maximum likelihood estimates for the model. 12. As a technical fix and to recenter the data, the model is estimated with the αj coeffi­ cients in equation 3.3 constrained to one. In the initial model the estimates for both α1 and α2 exceed one, which is not permissible, as they produce values for ρt that exceed one in periods of stable party positions. With αj = 1 partisan updating only occurs when there are changes in party positions.

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ADJUSTMENT ̗Prty_Pos_Midterm ̗Prty_Pos_Pres UTILITIES Civil_Rights Civil_Rights_South Jobs Services SHORT_TERM Inc_Change -3

-2

-1

0

1

figure 4.3.  Normal partisanship—­US

values for δ1 and δ2 and their impact on ρt indicate that party decisions in adopting positions are a significant contributor to the amount and rate of partisan change in both midterm and presidential elections. Second, and more generally, results show that party utilities play a critical role in the dynamics of partisanship. The associations between normal partisanship and utilities are statistically and substantively significant. Averaged across the five groups, a one-­standard-­deviation difference in total utility is associated with a three-­quarter-­standard-­deviation difference in normal partisanship. The inference is that the closer a party’s positions are to individuals’ policy preferences, the stronger the individuals’ normal identification with that party. Further, more stable partisan utilities, which arise from stable preferences and stable party positions, result in more stable aggregate partisanship. But shifts in party positions change these utilities and decrease the weight given to previous partisanship, which substantially changes normal partisanship, which in turn leads to changes in partisanship. This sequence of events supports the proposition that parties’ decisions about issue positions contribute in important ways to partisan change. To analyze this relationship further we separate out different parts of the expression for utilities in equation 2.2. The analysis shows, not

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surprisingly, that southern whites give significantly more weight to civil rights utilities than do the other groups. The difference is 0.40 with a standard deviation of 0.15. Moreover, the coefficient on jobs utilities starting in 1980, coinciding with the introduction of the social services utility variable, is effectively zero.13 This result could mean the salience of the jobs issue decreased by the 1980s, consistent with Reagan’s and the Republicans’ agenda, or that social service utilities are a better measure of cleavages on domestic policy issues. The estimations reported in figure 4.3 and ta­ ble 4A.1 treat this coefficient as zero. Third, in another set of tests we studied whether it is normal partisanship, as opposed to partisanship, that is being updated. This test is based on the value for λ, which is zero if partisanship is being updated each election and one if it is normal partisanship that is being updated. The estimated value for this coefficient is 1.11 with a standard error of 0.05. This result clearly rejects the hypothesis of zero and is completely consistent with the proposition about normal partisanship being updated. The conclusion is that normal partisanship is a distinct and important component of partisanship. This evidence strongly shows that normal partisanship is stable between elections and updated in election periods if there are changes in party positions. Quarterly income changes are positively and significantly associated with partisanship. The magnitude of the association is modest. A one-­ standard-­deviation difference in income change, which is also the average income change, is associated with only a sixth of a standard-­deviation difference in incumbent party partisanship. Quarterly income changes have been treated as a short-­term force separate from normal partisanship up to this point. A test of this distinction is presented in column 3 in ta­­ ble 4A.1. The variable measuring change in income interacted with in­­ cumbency is included in the party utility component that models normal partisanship. A large and significant coefficient would indicate that these economic conditions are part of normal partisanship updating and thus more than a short-­term force. The estimated coefficient is −0.04 with a standard error of 0.06. This coefficient has the wrong sign and is not statistically different from zero. The coefficient on income change as a short-­term force is hardly changed, actually increasing from 0.08 to 0.10.14 Income changes, as short-­term forces, do not affect normal partisanship. 13. The coefficient is 0.06 with a standard error of 0.09. 14. Looking ahead, the results for both the quarterly model for nonelection periods in figure 4.5 and table 4.2, and this model for election periods show a substantively small

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4.2.4 Robustness Check: Out-­of-­Sample Test We will now bolster these claims by showing that the model forecasts out-­ of-­sample observations well. The 1958 to 2000 elections were used to estimate the model above.15 ANES and quarterly income change data for the 2002, 2004, 2008, 2012, and 2016 elections constitute new observations to enable a test of the model’s predictive ability. If the results reported in figure 4.3 overfit and/or are specific to the 1958 –­2000 elections, the forecast errors for the 2002 to 2016 elections will be large relative to the residuals in the fitted models and relative to an alternative simpler model, casting doubt on the reliability of the results just discussed.16 In addition to comparing the forecast errors to the residuals in the estimation model, it is useful to compare them to the errors in an alternative, simpler model. The comparison model is a basic autoregressive equation with both party utilities and short-­term forces but with a constant ρ. This makes a good comparison because of its simplicity and because it is the basis for many models of individual partisanship estimated with survey panel data. The model for group g is (4.3)

Pgt = ρPgt −1 + (1 − ρ)Util gt + X gt β + εgt .

Column 4 in table 4A.1 shows the estimated coefficients and fit for this model. All the coefficients match expectations, but the model has a worse fit to the sample data than the model with separate normal partisanship, temporal variations in ρ, and short-­term forces. Figure 4.4 shows the models’ root mean square errors (RMSEs) for the in-­sample estimation, for the twenty forecast observations for 2002 –­2012 and for the fifteen out-­of-­sample observations during only presidential

but marginally statistically significant effect of income change on partisanship. This effect, though, is a short-­term force that does not alter, and is separate from, normal partisanship. 15. The ANES did not conduct midterm election studies in 2006, 2010, or 2014, and thus we could not adequately test the claims about normal partisanship or the distinctions between midterm and presidential elections. 16. The 2002 election study omitted the respondent and party placement issue questions though it did ask about partisanship. Retaining a midterm election is important in evaluating the model’s forecasting ability, so 2002 party positions and aggregate utilities for each group are interpolated from the 2000 and 2004 values.

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Root Mean Square Error

0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

1956 - 2000

2002 - 2012

Sample

Normal Partisanship

2004 - 2012 Forecast

Autoregressive

figure 4.4.  Sample and forecast errors—­US

elections after 2000.17 The forecast RMSEs are all less than those from the estimation sample, indicating the estimated model nicely fits the out-­ of-­sample observations. The low out-­of-­sample RMSEs also imply the estimated models are not overfitting and are not just specific to the sample data. It is also noteworthy that the equations with the normal partisanship both fit and forecast better than the autoregressive model by 6 percent for the sample data and by about 12 to 25 percent for the out-­of-­sample data. The out-­of-­sample performance and the performance relative to an often-­ 17. The in-­sample RMSEs include a correction for the number of parameters in the equation.

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used autoregressive model lend important credibility to the model with separate normal partisanship and short-­term force components.

4.3 Macropartisanship in Nonelection Periods We now conduct an additional robustness check, especially on the claims about the differences between election and nonelection periods. This analysis also has the virtue of tying the themes and model from this book to previous research on dynamic partisanship at the aggregate level. We will focus on short-­term partisanship of the entire electorate, what has traditionally been referred to as macropartisanship. We follow that literature using the common data from Erikson, MacKuen, and Stimson (1998) and Green, Palmquist, and Schickler (1998). The short-­term model proposed and tested here permits us to examine carefully the possible role of normal partisanship in the periods between elections and specifically to see whether partisanship changes in response to short-­term forces. This constitutes one important test of the proposition that partisanship is composed of two separate components. 4.3.1 Modeling Short-­Run Macropartisanship Keep in mind that we are testing the proposition that updating of normal partisanship occurs at election periods because that is when updating of party positions happens for voters, and that short-­term forces might make measured partisanship bounce around but that these forces do not affect normal partisanship, the crucial component for the long term. The model for macropartisanship in a nonelection period t + s begins with equation 3.1. Using the above notation and the assumption (to be tested) that normal partisanship is only updated in election periods the short-­term model is Pi,t + s = Pit* + Xi,t + sβ + ui,t + s . We add an additional wrinkle to the model to compare our approach to the common approach for macropartisanship. The common approach uses an error correction mechanism to test for persistence over time. We alter the model by adding a term corresponding to the correction term in the error correction model (ecm), the most common specification for

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macropartisanship models. This adjustment term is based on how the previous period’s partisanship differs from its systematic component, which is normal partisanship plus short-­term forces: (4.4)

Pi,t + s = Pit* + X i,t + s β + φ[ Pi,t+s −1 − (Pit* + X i,t + s β* )] + εi,t + s .

There are alternative interpretations of the adjustment term depending upon the value of ϕ. We do not need to dwell on the details for our purposes.18 This quarterly macropartisanship model is estimated in an aggregated form (i.e., aggregating equation 4.4) establishing the model’s micro-­and macroconsistency: (4.5)

Pt + s =

( N1 ) ¦ iN=1 Pi,t+ s = Pt+s *

= Pt* + X t + s E + I[ Pt+s −1 − (Pt* + X t + s −1E*)] + Ht + s  where the absence of the i subscript denotes an aggregate variable. This expression is then first differenced for periods two to seven, which eliminates the unobserved normal partisanship term:19 (4.6)

Pt +s = t0+s+=X0t + X + t+s( Pt+ −t +Xst−1 *t +) s−−1* ) − P + s + s−1(P + s− −1X

s =t +2,...,7. Pt +s =  0 +  Xt +s  +  (Pt + s −1 − Xt + s −11*(P ) t−+ s−2 −t +Xs t−2 t*+)s+−2 *)t ++s,  1(P +s− −2X s , s = 2,...,7. 1(Pt + s −2 − Xt +s −2We *) add + tadditional variables to what is an encompassed model, in the + s , s = 2,...,7. sense of incorporating the ecm directly for comparison. The ecm, as men­­ tioned, has been used in the landmark studies of macropartisanship (Erik­­ son, MacKuen, and Stimson 1998, 2002; Green, Palmquist, and Schickler 1998, 2002; MacKuen, Erikson, and Stimson 1989). The ecm proposes that changes in partisanship are a function of a constant term, changes in short-­term forces, and a correction for deviations of partisanship from its long-­run equilibrium in the previous period. Given that these macropar­­ 18. If ϕ = 0 there is no adjustment. If ϕ < 0 then this is a correction term where values for Pi,t+s−1 that are “too high” given Pit* and Xi,t+s−1 lead to lower values of Pi,t+s, and vice versa if Pi,t+s−1 is “too low.” If ϕ > 0 it indicates persistence where any positive or negative shock to partisanship at t + s − 1 continues in period t + s so that partisanship in the current period is also too high, or too low, given the values for Pit* and Xi,t+s. This adjustment term is not included in the individual model because for most likely values of ϕ the effects being adjusted will dissipate in the periods between surveys. 19. Election periods are denoted as period zero.

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tisanship models only include variables that we refer to as short-­term forces and do not include party utilities or normal partisanship, this long-­ run equilibrium is given by Xs+t−1β*. Equation 4.6 with ϕ1 = 0 is the ecm, which makes it easy to distinguish from the model proposed here, with separate normal partisanship and short-­term forces terms. This latter model implies that β0 = 0 and that ϕ = ϕ1, which are alternative testable predictions. The values for the coefficients on the short-­term forces variables in the adjustment term, β*, have important implications for the basis for these adjustments. If β* = β then adjustments are being made to deviations from the expected value of partisanship in the previous period. If β* = 0 then adjustments are being made to deviations from normal partisanship, which gives normal partisanship an even more central place in the model. It is important to see that even though the estimable equation for periods two through seven derived from equation 4.5 closely resembles the ecm, its content and implication are very different from the ecm. The model in equation 4.5 is not a first-­order autoregressive process, unlike the model motivating the ecm. Successive values of partisanship may be highly correlated, but that depends upon the importance and variance of the short-­ term forces, not because they are generated by an autoregressive process. 4.3.2 Data and Estimation The data come directly from Erikson, MacKuen, and Stimson (1998) and Green, Palmquist and Schickler (1998)20 but extend through 2000. The third quarter is considered the election quarter, meaning quarters two through seven in equation 4.6 span the first quarter in the year following an election to the second quarter in the next election year.21 These are the quarters used to estimate the nonelection-­year model in equation 4.6. Partisanship in this section is measured as in the previous macropartisanship studies, which is the proportion of Democrats divided by the proportion Democrats plus proportion Republicans in each Gallup survey.22 20. https://sites.google.com/site/donaldpgreen/mp-­ascii-­data. 21. The model was estimated treating the fourth quarter of each election year as the election quarter. The fit was much worse, as measured by the model’s RMSE. Further, the mean squared residual for quarter three in election years was significantly larger than the mean squared residual for the other quarters, implying the model does not fit what we have chosen as the election quarter, as would be expected. D D R 22. Ps  t  Ps  t ( Ps  t  Ps  t ).

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We use this measure because it is what the Gallup data allow and also to compare to previous research. The transitory forces include presidential approval (PA) and economic conditions as measured by the percent change in per capita real disposable personal income from the previous quarter (Dinc) (Federal Reserve Bank of St. Louis 2015).23 Both variables are multiplied by an incumbency variable, I, that is plus one during a Democratic administration and minus one during Republican presidencies. These in­teraction terms, denoted as (Dinc*I) and (PA*I), ensure that positive changes in the economy or in presidential approval benefit the incumbent party.24 Including measured presidential approval is problematic because it is surely endogenous to partisanship.25 The estimation strategy, described in appendix 4C, is to create instruments for Δ(PA*I)s+t and (PA*I)s+t−1 and estimate equation 4.6 as part of a hierarchical structure with the equations for Δ(PA*I)s+t and (PA*I)s+t−1 being the first equations and equation 4.6 for ΔPs+t being the last.26 23. Our use of presidential approval as a transitory force (or short-­term force) contrasts with how Jacobson (2019) considers presidential approval. An analysis of data since the early 1970s leads Jacobson to conclude that presidential approval is a fundamental force shaping partisanship and other partisan attitudes. The influence goes in both directions for Jacobson, as partisanship also shapes presidential approval, though his emphasis is mostly on how presidential approval shapes partisanship. Jacobson does not cite Brandt and Freeman (2009), who show with sophisticated analysis that presidential approval has no independent effect on dynamic macropartisanship. In using instruments in this analysis for presidential approval in a model of dynamic partisanship, we come down on the side of Brandt and Freeman mostly, treating presidential approval as a function of partisanship more than the other way around. 24. A dummy variable for a Democratic administration, Dem, centers the relationships with these variables. These specifications give the following expressions for the short-­term forces: (4.7) X t + s = 1  Demt  s +  2  (PA  I )t + s +  3 (Dinc  I )t + s , (4.8) X t + s − 1 * = *1 Demt + s − 1 + *2 (PA  I )t + s − 1 + *3 (Dinc  I )t + s − 1. 25. Note that Brandt and Freeman (2009) treat presidential approval as endogenous as opposed to most other studies (including Jacobson’s, which occurred after Brandt and Freeman) that treat it as exogenous. This is possibly why Brandt and Freeman find that it has no association with partisanship. 26. The instruments include variables measuring the presence and duration of crises such as Watergate, the Iranian hostage crisis, and the Vietnam War and a time trend. De­ note these instruments as Zs+ t and Zs+ t − 1 plus a constant term. The instrumental variables equation for (PA  I )st + s is  (PA  I )t + s = Zt + s + u1 t+s and that for (PA * I )t + s − 1 is (PA * I )t + s − 1 =  0 + Zt + s − 1  + u 2 t+s−1 . These expressions are then used to estimate the equation for ΔPs+t. The discussion in the appendix indicates that these instruments meet the

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figure 4.5.  Short-­run partisanship between elections—­US

The estimated equation is nonlinear in the parameters, necessitating a nonlinear least squares estimator (Davidson and MacKinnon 1993, 2004).27 4.3.3 Empirical Results The important results are plotted in figure 4.5. Table 4A.2 at the end of the chapter shows the full results. The empirical evidence consists mostly of a series of negative results that altogether strongly support the propo­sition criteria for good instruments. The instruments are strongly related to the respective approval variables with F-­statistics well above ten. The Sargan test for the independence of the instruments and the stochastic term has a χ2 statistic of 4.30, which with six degrees of freedom has a p-­value of 0.64. It is important to note that if the independence assumption is not correct, the expectation is that because the instruments are related to presidential approval in the expected way, the results would overstate the relationship between partisanship and presidential approval, contrary to what we find. 27. The estimator is the NLSUR, so all equations are estimated simultaneously. The esti­­mation used is iterated feasible generalized nonlinear least squares.

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that partisanship between elections consists of random fluctuations around normal partisanship and that small adjustments are made to correct for these deviations from normal partisanship. The coefficients that distinguish between the model with normal partisanship and the ecm autoregressive model all reject the ecm. The top two bars in figure 4.5 show that the β* coefficients on changes in disposable per capita income and presidential approval in the adjustment term in equation 4.5 are not statistically different from zero. Zero values imply that equilibrium partisanship is unrelated to these short-­ term forces and that adjustments are made to deviations from normal partisanship.28 The third and fourth plots are tests that distinguish between the normal partisanship and ecm models. The ecm implies that β0 ≠ 0 and that ϕ1 = 0. The third line shows that β0 is not substantively or statistically different from zero. The estimated value for ϕ1 equals −0.28 with a t-­statistic of 3.5.29 Both results further indicate that the model with normal partisanship is more consistent with the data than is the ecm.30 Accepting the null hypotheses that β0 = β* = 0 and that ϕ = ϕ1 ≠ 0 gives the following model for quarterly partisanship: (4.9)

Pt + s = Pt* + X t + s β + φ(Pt + s − 1 − Pt*) + εst + s ,

indicating that partisanship equals normal partisanship plus short-­term forces plus an adjustment based on the deviation of the previous period’s partisanship from normal partisanship. The short-­term forces relate weakly, at best, to quarterly changes in disposable real income per capita but not to presidential approval. The fifth and sixth plots in figure 4.5 show the values and confidence intervals for the coefficients on changes in real income per capita and presidential approval. The coefficient on the economic variable has a t-­statistic of 1.6, which has a p-­value of 0.099, and its substantive significance is small. A two-­standard-­deviation difference in real income per capita is associated with a 0.35 point difference in the 28. A Wald test of the null hypothesis that these two coefficients are zero is 1.22, which has a p-­value of 0.54. 29. Wald tests of the joint hypotheses that (ϕ − ϕ1) = β0 = 0 have a p-­value of 0.51. 30. A relevant side note is that with ϕ = ϕ1 and β* = 0 the expression involving ϕ reduces to the moving average, or the MA(1) term (Ps+t−1 − Ps+t−2). The estimate of ϕ = −0.25 corresponds to the coefficients Green, Palmquist, and Schickler (1998, 898, table A-­2) report for an MA(1) term. The difference is that they attribute the result to measurement error, while here it has a substantive interpretation related to corrections for shocks in the previous period.

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incumbent party’s partisanship, which is less than 0.2 standard deviations of partisanship. The coefficients on presidential approval are 0.04 but have a t-­test value of about one for the null hypothesis that the true coefficient is zero, which have p-­values about 0.3. These results suggest small and possibly insignificant associations between partisanship and short-­ term forces, consistent with Brandt and Freeman (2009).31 The implication is that short-­term changes in partisanship are not related to these two commonly used terms and remain random in our model. Lastly, partisanship in one quarter responds to deviations of observed partisanship from normal partisanship in the previous quarter. The bottom plot in figure 4.5 shows the value and confidence interval for the estimates for ϕ, the adjustment coefficient. Deviations could be the result of a shift in disposable real income, ΔXt+s, or more likely a shock to the stochastic term εs+t. The coefficient of −0.25 indicates a correction of 0.25 times the deviation. For example, if partisanship in period (t + s) exceeds normal partisanship by one unit, partisanship in period (t + s +1) is 0.25 units less than normal partisanship, (−0.25)2 = 0.06 units greater than normal partisanship in period (t + s + 2), and (−.25)4 < 0.004 points different from normal partisanship by the next year. This simple example indicates that any shock that moves partisanship away from equilibrium partisanship dissipates quickly. The further implication is that these short-­run adjustments can be ignored when partisanship is observed only annually, as in the first three waves of the individual panel examined in appendix 4C, and certainly when it is observed only every two years, as with election-­ year studies.32 This evidence from the quarterly macropartisanship model is consistent with the results of the individual model in appendix 4C. Partisanship between election periods fluctuates around normal partisanship with only at best small responses to economic conditions. Shocks to partisanship in the form of random factors, the ε term, or very large changes in economic 31. Note that changes in disposable real income per capita are treated exogenously here, while they are endogenous in Brandt and Freeman (2009). 32. These results overall have a bearing on thermostatic theories of party support (Wlezien 1995; Ura and Ellis 2012; Soroka and Wlezien 2010). Our analysis is not a direct test of those theories, but we see in these results evidence that policy preferences do provide an anchor around which party reputations can get pulled away from extreme positions if they stray too far from public opinion. But short-­term forces and unexpected party shifts should not be treated the same. The effects of the former dissipate relatively quickly, while the latter can have lasting effects on partisanship.

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conditions, are associated with a small adjustment in the opposite direction in the next period, but the responses to these shocks barely last into the second period. We concur with Brandt and Freeman’s conclusion that macropartisanship is its own series subject to its own shocks. 4.3.4 Summary of Robustness Tests The empirical results with the separation of normal partisanship and short-­term forces show that the latter, at least as measured by quarterly changes in real disposable income, have only small associations with partisanship, and these small effects dissipate quickly.33 Normal partisanship is only updated during elections, with the amount of updating based on the amount of change in party positions. The former feature imparts considerable stability to partisanship between elections, while the latter means that partisanship can also be stable, or not, from election to election depending upon the parties’ actions. Any changes in normal partisanship during an election constitute the shock to partisanship to which Brandt and Freeman (2009) refer. The partisanship model with the two separate components and its accompanying implications are consistent with individual and aggregate data and with quarterly Gallup and biannual ANES election-­year data.

4.4 Tracking Party Behavior and Partisan Change This last section demonstrates another aspect of the model’s performance by extending two of its implications and properties. The first is the central role of parties and their strategically and ideologically motivated actions in generating partisan change. The second is the ability of the model with only this information about parties and individual policy preferences to track different patterns of US partisanship over extended time periods. The policy positions of the competing political parties and changes in these positions from election to election play a critical role in the development and evolution of partisanship. Party positions on policy issues, such as civil rights and social spending, including changes in these positions, 33. The quarterly and election-­year models were also estimated including unemployment as a short-­term force. The coefficients were consistently insignificant, with t-­values of one or less.

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enter the model in two ways. First, party positions relative to citizens’ preferences on these issues affect the party utilities, which in turn play a major role in individual and aggregate partisanship. Any changes in party positions alter these relative utilities, which in turn contributes to change in partisanship. Second, the magnitude of the change in party positions over recent election periods then affects the speed with which partisanship adjusts to changes in utilities. Incorporating party activity in the model of partisanship enables it to be consistent with quite different patterns of partisanship and partisan change. The most obvious pattern is that with temporally stable party positions the model predicts there will be stable normal partisanship. Partisanship will exhibit short-­run fluctuations in response to short-­term forces but will not show any trend over such a period. One antithesis of this stable period would be an election featuring large shifts in party positions, which the model predicts will be associated with equally large and dramatic changes in partisanship. Such an event is what many have called a critical election.34 A third possible pattern is a slow, steady shift in party positions over an extended time period, which the model predicts will lead to an evolution in partisan realignment over this period without any critical election. The dramatic and continuing transformation of southern white partisanship is an example of such a change.35 We illustrate each of these patterns with the macropartisanship model and data and demonstrate how well the model fits each pattern. Figure 4.6 plots observed and predicted partisanship for southern blacks and southern whites for 1956 to 2012. The figures also show the distance between the two parties averaged over the issues used to construct the utility measures. Given the mean preferences of each group, larger party differences will produce larger Democratic utilities and partisanship for southern blacks and larger Republican utilities and partisanship for southern whites. Each group, either for the whole period or for distinct subperiods, illustrates each of the previously described patterns, which are discussed in turn. Before beginning the discussion it is

34. This treatment of partisan change is attributed to Key (1955) and is most associated with Burnham (1970). Mayhew (2002) presents extensive evidence refuting the claim that there have been critical elections, but the metaphor is still credible for our purposes. See Shafer (1991); Nardulli (1995); Meffert, Norpoth, and Ruhil (2001); and Rosenof (2003). 35. See Carmines and Stimson (1989); Green, Palmquist, and Schickler (2002); Kuziemko and Washington (2015); Miller (1991, 1992); and Osborne, Sears, and Valentino (2011).

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Partisanship

2.5 2.25 2 1.75 1.5 1.25 1 0.75 0.5 0.25 0 -0.25 -0.5 -0.75 -1

1956

1960

1968

1972

1976

1980

1984

1988

1992

1996

2000

2004

2012

1996

2000

2004

2012

Year PID

Predicted

Party Diff

Southern White 3 2.75 2.5 2.25

Partisanship

2 1.75 1.5 1.25 1 0.75 0.5 0.25 0 -0.25 -0.5 -0.75 -1

1956

1960

1968

1972

1976

1980

1984

1988

1992

Year PID

Predicted

Party Diff

Pred w/Gay Attitudes

figure 4.6.  Patterns of partisan change—­US

important to describe how the predicted values are obtained. These predicted values are based solely on the observed utilities and changes in party positions. Using equation 4.1 with Xt  now being the average of the short-­term forces for the whole period the prediction equation is (4.10)

Pˆt = ρt Pˆt − 1 + (1− ρt)Utilt + X β − ρt X β.

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The predictions here are based on normal partisanship, as there are no variations in short-­term forces. This is a hard prediction test because the only observed partisanship value used in the whole process is that for 1956, the initial year. That value is used to predict partisanship in 1958, the predicted 1958 value is used in the prediction for 1960, and so on.36 4.4.1 Southern Blacks Southern black partisanship exhibits two of the patterns described above. The first is a classic depiction of a critical election arising from a large shock to party positions in the 1964 election. Party differences, which had averaged about 0.2 from 1956 to 1960, expanded substantially to over 2.6 in 1964. This shift in party positions is associated with a large shift in partisanship. Southern blacks went from being marginally Democratic in 1956 to 1960 with an average partisanship of about 0.6 to strongly Democratic in 1964 with a partisanship of 2.02. These levels of party difference and partisanship remained in 1968 and 1970, with party differences equal to 2.75 and 2.22 and partisanship values of 2.45 and 2.26, respectively. The model tracks these large and rapid changes in partisanship quite well. The root mean square error for the period 1964 to 1970 is 0.17, compared to 0.24 for the whole series, indicating the model fits the periods around the critical election as well as it does the other periods. The thirty-­four years following the critical election, from 1972 to 2004, exhibit a period of stability.37 There is no trend away from the stable partisanship between 1972 and 2004. Mean partisanship is 1.62 for the first three elections after 1968 and 1.63 for the three elections from 2000 to 2004. Further, in fourteen of the seventeen elections between 1972 and 2004 southern black partisanship is within one standard deviation of the mean. That exceeds what would be expected with seventeen random draws from a normal distribution. The year when partisanship exceeds this range is instructive and reinforces the proposition of a very stable period. The largest deviation, accounting for 35 percent of the total sum of square errors, is in 1982, the midterm during Reagan’s first term in the

36. The distinction here with the fitted values from the estimated equations is that the predict =  P tions are based on the observed value of lagged partisanship, P t t − 1 + (1− t )Utilt + X t  − t Xt − 1 . + (1− t )Utilt + X t  − t Xt − 1 . This keeps the fitted values from diverging too far from the observed values. 37. Just for this illustration of stability, the 2008 to 2012 elections are omitted because they show a significant increase in Democratic partisanship. This Democratic gain could be a short-­term force associated with the Obama presidency or possibly a shift in normal partisanship associated with increased party differences, which increased noticeably after 2008.

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midst of a serious recession, making it plausible that the bump in Democratic partisanship is the result of short-­term forces. The model tracks this stable period well. There is no statistic for measuring how well a model fits essentially a random series, but the plots in figure 4.6 show that the predicted values closely match the partisanship series with virtually identical mean values for both actual and predicted partisanship. It is worth noting that most scholars subscribing to the idea of critical elections expect the stable period between critical elections to be about thirty-­plus years, the same as the period described here. These two periods when seen in sequence constitute what Carmines and Stimson (1989) call a punctuated equilibrium. The model makes it clear that this punctuated equilibrium is strongly associated with large shifts in party positions in a very short period, such as a single election, followed by an extended period of stable positions. A consequence of the new party positions is new equilibrium utilities that in turn produce a new equilibrium partisanship, what we refer to as normal partisanship. This new equilibrium then remains in place for as long as the parties maintain their new positions. 4.4.2 Southern Whites Southern white partisanship, shown in the bottom panel of figure 4.6, follows a third pattern, that of a major realignment extending over a long time period without a dramatic election. Party differences increase markedly in 1964 and 1968, drop in 1970, and then rise steadily for the remainder of the elections.38 The notable exception to the steady trend after 1970 is increased party distance from 1980 to 1984, the beginning of the Reagan era. Changes in southern white partisanship correspond to this long-­term change in party positions. They are moderately Democratic in the 1950s, with an average partisanship of 1.34. There is a steady decline afterward, becoming moderately Republican by the 2000s, with an average partisanship of −0.5.39

38. A simple time trend explains 68 percent of the variance in the party difference variable for the elections from 1970 to 2016 with an expected increase of 0.06 per election. 39. The year 1964 is an anomaly, with increased Democratic partisanship despite the large increase in party differences. This anomaly may be due partially to sampling error. There were no ANES studies in 1962 and 1966, but the party identification questions were added to other surveys providing estimates for southern white partisanship in those years.

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generational change? testing an alternative explanation. It could be that changes in southern white partisanship were due not to changes in partisanship within individuals but simply because of cohorts moving into and out of the electorate. Indeed, a frequent explanation for this group’s increasingly Republican partisanship is generational replacement (Green, Palmquist, and Schickler 2002; Miller 1991, 1992; Osborne, Sears, and Valentino 2011) where individuals do not alter their partisanship but successive cohorts entering the electorate are more Republican. Green, Palmquist and Schickler (2002, 148, 151, tables 6.3, 6.4) analyze a series of ANES southern white cohorts for the 1952 to 1998 period. Their evidence, however, shows that even within cohorts partisanship declines by about 0.02 per year. Appendix 4B extends their analysis to 2012, adding additional cohorts. The analysis in the appendix shows that conversion continued after 1998 at the same rate. Over the 1952 to 2012 period with two exceptions each southern white cohort was more Republican than its predecessor, as claimed, but that within cohorts partisanship declined by close to 0.03 per year.40 Further, this decline is statistically identical for all eleven cohorts. Averaging over cohorts the analysis shows that each cohort is about 0.12 more Republican than its predecessor. Cohorts span ten or eleven years, which means that in the years the next cohort is becoming 0.12 more Republican the current cohort becomes 0.30 to 0.33 more Republican or over twice the rate of generational change. partisan change after 1992.  The bottom panel in figure 4.6 shows that the model tracks southern white partisan change very well from 1956 to 1992. For this period the root mean square error is 0.17, and the correlation between actual and predicted partisanship is 0.82.41 The model’s performance is quite different after 1992, as is evident in the figure. The root mean square prediction error increases to 0.39 and the correlation drops to 0.57. Clearly something changes after 1992 that leads to southern whites becoming even more Republican than the model predicts. In all studies from 1960 to 1968 except 1964 southern white partisanship is consistently 0.9, making 1964 an outlier. 40. The exceptions are the cohort entering between 1909 and 1919, which is more Republican than adjacent cohorts, and the cohort entering in 1994, which is more Democratic than its predecessors.  , do not incorporate any 41. It is important to remember that these predicted values, P information from the partisanship variables but are derived solely from utilities, changes in party positions, and the coefficients from the core analysis in this chapter.

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There are several possible explanations for the change, which are explored in secondary analyses. The most credible substantively and statistically is the increased importance of social issues, particularly among southern whites, in contributing to the regional partisan divisions.42 Our suggestion, supported by secondary analysis using limited data, is that positions on the issue of legal protections for LGBT individuals, as an example of these social issues, accounts for the pro-­Republican shift evident in figure 4.6 that is not explained by the model. When a measure of the relative support for antidiscrimination legislation is included in the partisanship model, it is strongly related to southern white partisanship beginning in 1988, when the ANES first asked the question. The dotted line in figure 4.6 plots the predicted values for southern white partisanship for the period 1988 to 2012 with LGBT attitudes included. This inclusion in the tracking equation reduces the root mean square error for the period 1994 to 2012 to 0.18, which nearly equals the root mean square for the model for the 1968 to 1992 period and raises the correlation for that entire period to 0.93. The inference from this additional analysis is that the core model explains this evolutionary pattern of partisan change quite well once the partisan cleavage associated with social issues since the 1990s is included in the model. 4.4.3 Summarizing the Tracking of Partisanship In addition to a single model that can encompass different levels of aggregation, from the individual to demographic groups to the entire electorate, and different levels of temporal spacing, from quarterly to biannual elections, the model can explain different patterns of partisan change. These patterns vary from long-­term stability to extended evolutionary change to rapid realignments, similar to what some call critical elections. The key to the model’s ability to track all of these patterns is the central role played by the political parties and the positions they choose to advocate. These “choices” may be strategic (i.e., deliberate by party elites) as parties compete for votes or the consequence of policy-­motivated activists determined to have the party reflect their preferences, or some varying combination of the two. (Section 8.2.1 discusses possible explanations for party positions.) Whatever the explanation, we find in this analysis of the 42. One of the explored but rejected propositions is that presidential popularity, espe­ cially its opposite in the case of Bill Clinton (unpopularity), explains the increasing Repub­ lican partisanship of this group.

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two demographic groups from the South that party issue positions and how they changed combined to drive partisanship dynamics throughout the period from the mid-­1950s into the 2000s.

4.5 Conclusion The broader conclusion from the results presented in this chapter is that our model allows for analysis of different kinds of data on partisanship, micro-­level panel data (see appendix 4C), quarterly macro, and annual or biannual macro data, including entire electorates and distinct population groups. It also allows for estimating the effects of party changes relative to short-­term forces in moving partisanship for individuals and groups. The model encompasses previous models of dynamic partisanship and adds flexibility so that researchers can explore different patterns of partisanship: abrupt changes, gradual changes, and stasis. The next two chapters show that the model can be adapted to multiparty systems and fit data from those countries well.

4.A Appendix: Tables of Empirical Results table 4A.1  Normal macropartisanship

Variablesa

Base

Normal partisanship updating ρt − Midterm election α1

1b

(ΔPartyPos1)2: δ1 ρt − Presidential election α2 (ΔPartyPos2)2: δ2

−2.11 (0.67) 1b

Civil rights, so. whites: γ12

1b −1.98 (0.58) 1b

1b

Forecast model

0.47c (0.06)

−2.01 (0.59) 1b

−0.53 (0.13)

−0.14 (0.03) 1.11 (0.05)

−0.59 (0.14)

0.26 (0.06) 0.67 (0.15)

0.23 (0.11) 0.50 (0.16)

0.27 (0.06) 0.65 (0.16)

λ Utility Civil rights: γ1

Tests of normal partisanship

0.47c (0.06)

0.24 (0.09) 0.98 (0.15) (continued)

table 4A.1 (continued)

Variablesa Jobs, 1956 –­1978: γ2 Jobs, 1980 –­2000 Soc. services: γ3 D80 – ­00

Base

Tests of normal partisanship

0.25 (0.09) 0.00d 0.44 (0.10) −0.10 (0.07)

0.39 (0.13) 0.00d 0.53 (0.13) −0.10 (0.12)

0.23 (0.09) 0.00c 0.43 (0.10) −0.10 (0.07) −0.04 (0.06)

0.37 (0.14) 0.00c 0.64 (0.14) −0.19 (0.09)

0.08 (0.02) 48.68 3.887

0.07 (0.03) 51.21 4.024

0.11 (0.04) 48.85 3.943

0.06 (0.03) 47.67 4.326

Full model

β* = 0

β* = β0 = ϕ − ϕ1 = 0

−2.72 (5.18) 0.04 (0.04) 0.17 (0.14)

−2.59 (4.36) 0.04 (0.04) 0.20 (0.12)

−2.70 (4.45) 0.04 (0.04) 0.20 (0.12)

−0.32 (0.08) −0.28 (0.08) −10.86 (11.47) 0.11 (0.10) −0.17 (0.57) 2.40 (2.07)

−0.26 (0.08) −0.23 (0.08)

−0.25a (0.08) −0.25a (0.08)

Dinc*Inct: γ4 Short-­term forces Dinc*Inct: β1 Log-­likelihood Res. sum squares

Forecast model

Note: Coefficient standard errors in parentheses. a Includes group-­specific fixed effects. b Coefficient constrained to equal one. c Coefficients constrained to be equal. d Coefficient constrained to equal zero.

table 4A.2  Quarterly macropartisanship Variable Short-­term ΔDems+t − β1 ΔPA*Is+t − β2 ΔDinc*Is+t − β3 Adjustment ϕ ϕ1 Dems + t −1 − 1* PA*Is + t −1 − *2 Dinc  Is + t − 1 − *3 β0 Wald testb p-­value t-­testc

1.22 0.54 1.16

1.55 (1.73)

0.87

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table 4A.2 (continued) Variable p-­value Wald testd p-­value Wald teste p-­value

Full model

β* = 0

β* = β0 = ϕ − ϕ1 = 0

0.25 1.35 0.51 2.16 0.34

0.39 0.93 0.63 3.74 0.15

3.67 0.16

Note: Coefficient standard errors in parentheses. a Constrained to be equal. b Test of the null hypothesis that *2 = *3 = 0. c Test of the null hypothesis that ϕ − ϕ1 = 0. d Test of the null hypothesis that β0 = ϕ − ϕ1 = 0. e Test of the null hypothesis that β2 = β3 = 0.

4.B Appendix: Generational Change This appendix reports the results, extending, both by time period and model detail, the Green, Palmquist, and Schickler (2002) analysis of the generational change explanation for the increasing southern white Republican partisanship. Green, Palmquist, and Schickler (2002, 148, 151, tables 6.3, 6.4) pool ANES studies from 1952 to 1998 and show that aggregate partisanship among native southern whites changes by 0.02 per year even controlling for ten different cohorts.43 They conclude by saying, “The pace of partisan conversion appears to have slowed” (Green, Palmquist, and Schickler 2002, 163). The previous plots of southern white partisanship indicate that shifts definitely continued after 1998. We repeat the Green, Palmquist, and Schickler analysis of ANES data extending the years to 2012 and adding two new cohorts to accommodate the longer series.44 The full estimated equation for the partisanship of person i at election t is (4.11)

Pit = Cijαj + tβ + (t * Cij)γj + D96δ + Uit,

43. Our estimate for the time trend coefficient is −0.024 for the 1952 to 1998 period using the cohorts they define in their table 6.4. The cohorts in their table 6.3 are not identified. 44. After the 1994 survey it is not possible to distinguish native from nonnative southern whites, as the necessary question was dropped. Thus, for 1996 to 2012 the sample includes all white southern respondents. A dummy variable for the post-­1994 period is included to accommodate any shift in macropartisanship associated with this omission.

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where Cij is a dummy variable that equals one if individual i is in age cohort j. The αj coefficients indicate the partisanship of each cohort, so decreasing values indicate how much more Republican each cohort becomes. The coefficient β indicates the conversion rate, meaning that southern whites become β units more Republican each year controlling for respondents’ cohort. The important implication is that this coefficient should be zero if the southern white realignment is all replacement, as measured by the αj coefficients. The (t*Cjt) term denotes a set of interaction terms between the time variable and the cohorts. The coefficients γj indicate if there is any difference in the conversion rate among cohorts. The strong conversion rate reported by Green, Palmquist, and Schickler (2002) for 1952 to 1998 shows up in the data after 1998 as well, indicating no slowing of the realignment among southern whites. Further, an F-­test of the null hypothesis that γj = 0 has a value of 1.15, which with ten degrees of freedom has a p-­value of 0.32. Consequently there is no reason to reject the null hypothesis that the conversion rate varies by cohort or that conversion is specific to a small number of cohorts. The evidence suggests it can be accepted, meaning the individual conversion rate is equal across all cohorts. The model is reestimated with the time by cohort terms omitted. Table 4B.1 reports the results, showing the birth year and first election year range for the cohorts Green, Palmquist, and Schickler (2002) define. The important result is that the coefficient on time, which assesses the annual conversion rate within cohorts, actually increases to −0.026 (from −0.024) with the addition of the 2000 to 2012 elections.45 The coefficients on the cohort dummies in table 4B.1 show that successive cohorts are becoming increasingly Republican as the replacement hypothesis contends with the exception of the 1909 –­19 and 1994 –­2004 cohorts. It should be noted that the generational decline in Democratic partisanship began well before the Goldwater candidacy in 1964. The largest shift is in the cohort that entered the electorate between 1953 and 1963, the Eisenhower era. The model in table 4B.1 is reestimated with a linear trend for cohort partisanship,

45. The model was reestimated with a dummy variable for every possible birth year, effectively creating 125 cohorts, as a check on whether the Green, Palmquist, and Schickler defined cohorts might be affecting the time coefficient. The time coefficient in this model is −0.026 with a standard error of 0.002, exactly the same as with the Green, Palmquist, and Schickler–­defined cohorts.

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table 4B.1  Southern white partisanship: replacement and conversion Variable

Cohort

Birth year

Cohort first election

1986

2006

Year > 1994 Time

Coeff.

St. error

1.71 1.44 1.72 1.64 1.53 1.04 0.92 0.83 0.58 1.27 0.84 −0.16 −0.026

(0.17) (0.10) (0.08) (0.07) (0.07) (0.08) (0.08) (0.09) (0.10) (0.13) (0.25) (0.08) (0.002)

table 4B.2  Cohort and time conversion rates

(4.12)

Variable

Coeff.

St. error

Cohort Time Year > 1994 Constant

−0.115 −0.031 −0.074 1.996

(0.012) (0.002) (0.075) (0.063)

Pit = β0 + β1Cohorti + β2t + D96δ + Uit,

where Cohort ranges from zero to ten with the value identifying each cohort. The purpose of this model is to compare by how much each cohort’s partisanship changes, the value for β1, with the expected yearly partisan change within cohorts, the value for β2. Table 4B.2 shows the estimated values for each coefficient. By these estimates each cohort is 0.12 more Republican than its predecessor, as predicted by the generational change explanation, and the members of each cohort are becoming 0.03 more Republican each year, or 0.25 more Republican each cycle of an eight-­year, two-­term presidency. These results show there are certainly replacement effects with successive cohorts of southern whites being increasingly Republican. There is, however, strong evidence of a realignment going on within each cohort.

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The estimate is that within cohorts southern whites are becoming more Republican at a rate between 0.026 and 0.031 points per year, or 0.26 to 0.31 points per decade.

4.C Appendix: Micro Foundations for US Macropartisanship (with Elizabeth Mann Levesque) We return to the Achen and Shively (1995, 25) argument for macroconsistency, meaning that models relating aggregate-­level variables, such as macropartisanship, should be derivable from theoretically justifiable micro-­level models and an explicit aggregation process: “Without that constraint, macrolevel research too easily slips into studies of the interrelationships of meaningless statistical aggregates. Only when both macrotheoretical propositions and statistical assumptions are rigorously inferred from the microlevel can we have faith in macrolevel studies.” The formal development of the macro model demonstrated this consistency. This appendix shows that the US macro model is also empirically consistent with the micro model. This is done in this appendix for the United States and in an appendix to chapter six for the United Kingdom, which are two countries with the necessary panel data for this analysis. 4.C.1 Model We first estimate the US model with individual-­level panel data from the ANES 1992 –­96 panel study. The estimated model is the micro version of equation 4.1 prior to aggregation, (4.13)

Pit = ρitPi,t−1 + (1 − ­ρit)Utilit + Xitβ − ρitXi,t−1β + εit − ­ρitεi,t−1.

This equation is estimated for the years 1993, 1994, and 1996, which include one nonelection year, one midterm election, and one presidential election. The 1992 wave provides the necessary lagged variables. If there is no updating to normal partisanship in nonelection years, then ρi,93 = 1, which is a testable proposition. There are three important outcomes for this micro model’s estimation, all of which are better addressed with individual data. The first, as stated above, is to test the empirical consistency of the theoretically derived macro model and its micro foundation. It is one thing to state a micro

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model and derive its aggregate form. It remains important to examine the empirical validity of that micro model and its macro form. A second outcome is to estimate the amount of individual heterogeneity in ρit. Achen (1992) proposes that ρ varies with the person’s number of elections, which he derived for a period of political stability. In Jackson and Kollman (2011), we show evidence that ρ also varies with education. Lastly, the individual data provide an important test of the two-­versus two-­plus-­party model. Alienation and its relationship with both major and minor party support is a key feature distinguishing the two models. These latter two propositions are much better addressed at the individual level, as they are individual phenomena, as opposed to changes in party positions, so this is where these factors show the greatest variation. These variables at the aggregate level change slowly and only within a small range, making associations with partisanship difficult to estimate. The estimates with the individual model address these propositions, and the results have important implications for the specification of the aggregate model as noted in earlier chapters. 4.C.2 Data The 1992 –­96 ANES panel study surveys 512 respondents who participated in all four waves. (A second estimation is done using all respondents who participated in at least two waves, which produces a sample of 698.) Partisanship is the same measure, ranging from minus three for strong Republicans to zero for pure independents and plus three for strong Democrats. The same three policy questions are used to model utilities and changes in party positions—­aid to minorities, good jobs and standard of living, and social spending. All are seven-­point scales with information on the respondent’s placement and, except for the minorities question in 1992 and 1996, party placements as needed for the utility measure in equation 2.2. Respondents’ party placements on the minority aid question in 1992 are measured by their placements in the 1994 study, which implies the respondents associate the parties with the same position in both elections. The 1996 study asked for placements of the parties’ presidential candidates, which are substituted for party placements. Unfortunately, the ANES did not ask the policy questions or party placement questions in 1993. These utilities are estimated as an average of the 1992 and 1994 utilities. The 1993 survey did ask if the federal deficit should be reduced even if it means spending less for health and education,

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shown as Servi, and half the respondents were asked if there should be preferential hiring and promotion of blacks, shown as Aidi. Responses range from agree strongly to disagree strongly. These variables along with a variable that identifies those asked the aid to minorities question, denoted as Opaidi, are included in the 1993 partisanship equation (i.e., π0 + π1Opaidi + π2Aidi + π3Servi) along with 1992 partisanship and the interpolated party utility variables. The expectation is that if the interpolated utility variables for 1993 accurately assess these utilities and respondents are updating their partisanship based on 1993 opinions, there will be a positive association between these opinions and partisanship. If there is no updating to normal partisanship in nonelection years, then π0 = π1 = π2 = π3 = 0 in addition to ρi,93 = 1.46 The party positions used to calculate the squared changes in party positions in the expression for ρit are computed separately for each of five subgroups that are the basis for the macro model. The party placements are the mean placement on the ANES seven-­point scales for the members of each group. Party placements for 1988 and 1990 are calculated for these same groups using the ANES data from those election years. This method gives different values for the squared changes in party positions, and thus for ρit, for each group. Short-­term forces are measured with two variables, one designed to reflect changes in collective economic performance and the other to capture changes in personal economic conditions. The collective measure is the percent annual change in per capita personal income in a respondents’ region. (There are eight regions as defined by the Bureau of Economic Analysis.) The individual variable is the change in the log of respondents’ income. 4.C.3 Estimation Estimating equation 4.13 with individual panel data presents a number of challenges. The first is that utilities and possibly lagged partisanship are likely not exogenous and must be treated as endogenous variables. A second is that partisanship and utilities are likely observed with measurement

46. The proposition that ρi,93 = 1 is tested by including a term, denoted by π4, that measures by how much ρi,93 = 1 differs from one, with the expectation that this coefficient equals zero.

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error.47 Both problems are addressed with the use of instrumental variables for utilities and lagged partisanship. The instruments used here are lagged utilities, for each contemporaneous utility and for lagged partisanship, and a small set of individual variables: age, age2, income, gender, union membership, and white Protestant. The assumption that the demographic variables can be excluded from the partisanship equation and thus can function as instruments for utilities and lagged partisanship is supported by Achen’s (1992, 209) discussion of the Bayesian partisanship model: “In this new framework, demographic variables do not appear. They may be useful in various econometric procedures as instrumental variables.” See Reed (2015) on the legitimate use of lagged endogenous variables as instruments. The estimation method is a nonlinear seemingly unrelated regression model (Greene 2012, 305). This creates a hierarchical system with the first three equations modeling the likely error-­containing and endogenous measures for aid to minorities, guaranteed jobs, and spending utilities. The fourth equation is a model for lagged partisanship. The fifth equation is the nonlinear expression for current partisanship. The data for 1993, 1994, and 1996 are pooled by “stacking” the data to create a data set with 1,525 observations for the complete panel and 2,072 for the full data set.48 Nonindependence in the stochastic terms from the same respondent is accommodated by using cluster robust standard errors with each individual defining a cluster. 4.C.4 Results In order to constrain and center the model and make it consistent with the logic of dynamic systems, it was estimated imposing the constraints α1 = α2 = 1, which produced ranges for ρit that are well within sampling variations of zero and one. The model is decomposed into five separate parts to simplify discussion of the results: 47. Green and Palmquist (1990) and Schickler and Green (1993) make the case that the ANES partisanship variable contains significant measurement error. Prior to that Achen (1975), Erikson (1979), and Jackson (1983) provide evidence of the measurement error in issue questions. This work predates the current seven-­point issue measures, but it is unlikely that these measures eliminated the unreliability in measures of policy preferences and party locations. 48. Eleven and twenty-­two cases in the respective data sets are dropped because the respondents did not report a value for partisanship.

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1. the γ coefficients relating partisanship to the policy utilities, 2. the β coefficients estimating the role of short-­term forces, 3. the δ coefficient relating ρit to changing party positions,49 4. the α coefficients testing the individual heterogeneity of ρis, and 5. the π coefficients testing whether normal partisanship is updated in nonelection years.

We first test a series of initial hypotheses. Different possible outcomes for the estimation would imply that one or more of these sets of coefficients is zero. For example, the model with no normal partisanship updating during nonelection years implies that π = 0. Column 1 in table 4C.1 reports the results of Wald tests that the coefficients in each set are zero. (The tests of the utility coefficients are not shown as the p-­values for these coefficients are well below 0.01 for all model specifications and samples.) Subsequent columns show the tests with sets of coefficients set to zero; π = 0 is the model without partisan updating in 1993 and α = 0 is the model with no individual heterogeneity in the amount of updating. The Wald tests that the coefficients in each component are zero have p-­values ranging from 0.38 to 0.85 in the full sample and from 0.14 to 0.79 in the panel sample. These results imply there is no association between these components and the evolving partisanship. Based on these results we conclude there is no evidence of updating to partisanship in nonelection years, that there is no individual heterogeneity to the updating parameter ρ, and that partisanship is unrelated to the short-­term forces examined here, which include both individual and collective terms. These results lead us to a simple model of partisanship, which is presented in equation 4.13 for election years and in equation 3.1 in nonelection years, but with β = 0. Both of these are derived from the model that treats partisanship as being composed of normal and short-­term forces components with updating to normal partisanship occurring in election years. The model for election years, equation 4.13, without the short-­term effects and no individual heterogeneity in ρ, is estimated with both samples and the results reported in table 4C.2. 49. To economize on the number of estimated parameters given the limited number of elections, the temporal heterogeneity coefficients are constrained so that δ1 = δ2 in equation 4.2. Also, the squared changes in party positions shown in note 9 are lagged only one election.

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table 4C.1  Wald tests of the model’s components (1) Full model Component Full sample STF: β Indiv. heterogeneity: α Temporal heterogeneity: δ 1993 updating: π Complete panel sample STF: β Indiv. heterogeneity: α Temporal heterogeneity: δ 1993 updating: π

(2) with π = 0

(3) with α = 0

χ2

p-­value

χ2

p-­value

χ2

1.22 3.02 7.49

(0.54) (0.39) (