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UNIVERSITY of CALIFORNIA Los Angeles the Effect of Party Networks on Congressional Primaries a Dissertation Submitted in Partial

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UNIVERSITY of CALIFORNIA Los Angeles the Effect of Party Networks on Congressional Primaries a Dissertation Submitted in Partial UNIVERSITY OF CALIFORNIA Los Angeles The Effect of Party Networks on Congressional Primaries A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Political Science by Shawn Thomas Patterson, Jr. 2018 c Copyright by Shawn Thomas Patterson, Jr. 2018 ABSTRACT OF THE DISSERTATION The Effect of Party Networks on Congressional Primaries by Shawn Thomas Patterson, Jr. Doctor of Philosophy in Political Science University of California, Los Angeles, 2018 Professor Kathleen Bawn, Chair The scholarship on political parties has largely focused on their declining influence. Specif- ically, many claim that through the widespread adoption of the partisan primary, control over the nomination of candidates has been largely relegated to the ambitions and talents of the office-seekers themselves. I challenge this perspective, arguing that networks of partisan interests still play a major role in determining a party's nominee. To support this claim, I combine field interviews, journalistic accounts, election results, and campaign finance disclo- sures to demonstrate the systematic effect of political networks on the electoral prospects of primary candidates. I provide a series of case studies to show the impact of party networks and to demonstrate the underlying mechanism { the diverse campaign resources that these networks are able to marshal on behalf of their candidates. To generalize these findings, I use campaign finance data for candidates between 1980 and 2014 to construct a novel measure of group support { existing network density { derived from the degree of coordination present among a candidate's campaign contributors. I find that greater network support provides a significant benefit to candidates seeking consequential open-seat nominations for the House of Representatives. These effects remain over time and across parties after controlling for measures of candidate viability, such as fundraising and previous elected experience. This suggests that while the party organizations may have fewer formal powers over the selection of candidates for office, the constellation of organized interests constituting these political parties have lost little of their clout in the electoral process. ii The dissertation of Shawn Thomas Patterson, Jr. is approved. John R Zaller Lynn Vavreck Lewis Seth Masket Kathleen Bawn, Committee Chair University of California, Los Angeles 2018 iii To my grandparents Anne, Edward, Bill, and Nancy iv TABLE OF CONTENTS 1 Introduction :::::::::::::::::::::::::::::::::::::: 1 1.1 Requiem for the Smoke-Filled Rooms . .1 1.2 Overview . .7 2 Primary Elections and Political Parties ::::::::::::::::::::: 11 2.1 Overview . 11 2.2 A Textbook Theory of Party Nominations . 12 2.3 A Group-Centered Alternative . 13 2.4 Coordination and Competition in the Extended Network . 19 2.5 A Toy Example . 21 2.6 The Importance of Consequential Open-Seat Nominations . 28 3 Networks on the Ground :::::::::::::::::::::::::::::: 31 3.1 Parties on the Ground . 31 3.2 Groups within the Extended Party Network . 34 3.3 The Party Sometimes Decides . 37 3.3.1 Party Conventions . 38 3.3.2 `Firehouse' Primaries . 40 3.3.3 The County Line . 44 3.3.4 The Party's Primary . 47 3.3.5 Party Capture and the Formal Powers of Local Organizations . 50 3.4 Candidate-Centered Informal Party Networks . 56 3.4.1 Bill Schuette's `Midland Team' . 56 3.4.2 Charlotte's Empty Bench . 60 v 3.5 National Interest Groups . 66 3.5.1 EMILY's List . 66 3.5.2 The Chamber of Commerce . 71 3.6 Local Interest Groups . 74 3.6.1 IBEW Local 98 . 74 3.6.2 Cajun Industries . 79 3.7 Activist Networks . 86 3.7.1 Meddling Marcel . 86 3.8 Networks At Work . 90 4 Measuring Group Support ::::::::::::::::::::::::::::: 96 4.1 It's Hard to See into a Smoke-Filled Room . 96 4.2 Network Resources . 98 4.2.1 Recruitment, Dissuasion, and Field Shaping . 99 4.2.2 Training and Campaign Management . 100 4.2.3 Ground Game and GOTV Efforts . 101 4.2.4 Endorsements and Voting Cues . 103 4.2.5 Formal Powers . 104 4.2.6 Financial Support . 104 4.3 Difficulties Measuring Group Support . 105 4.4 Groups in the Extended Party Network . 108 4.5 Measuring Support through Donor Networks . 111 4.5.1 Network Density . 112 4.5.2 Existing Network Density . 114 4.5.3 Limitations and Alternatives . 117 vi 4.6 Dealing with Endogeneity . 119 4.7 Summary . 122 5 Clearing the Field :::::::::::::::::::::::::::::::::: 124 5.1 The Political Influence of Candidate Dissuasion . 124 5.2 Research Design . 127 5.2.1 Data . 127 5.2.2 Hypotheses . 130 5.2.3 Model Specification . 131 5.3 Results . 133 5.4 Discussion . 139 6 Winning the Nomination :::::::::::::::::::::::::::::: 144 6.1 The Influence of Political Networks in Primary Elections . 144 6.2 Research Design . 146 6.2.1 Data . 146 6.2.2 Hypotheses . 146 6.2.3 Model Specification . 147 6.3 Networks on the Ground Revisited . 149 6.4 Network Density and Primary Outcomes . 154 6.5 Bandwagons or Gatekeepers? Issues of Endogeneity . 164 6.6 Discussion . 169 7 Conclusion ::::::::::::::::::::::::::::::::::::::: 171 7.1 Party Networks and Primary Elections . 171 7.2 Implications . 174 vii 7.2.1 Nominations and Representation . 174 7.2.2 Contributing to the Divide . 176 7.3 Next Steps . 182 7.3.1 Champions in the Arena . 182 7.3.2 Beyond Density . 183 7.3.3 ENDless Possibilities . 184 7.4 Summary . 185 viii LIST OF FIGURES 1.1 Campaign Contributions to Elizabeth Fletcher and Laura Moser . .5 2.1 An Example Network Strategy with Competitive General Election . 25 2.2 Expected Utility of Group Participation in Nominations . 26 2.3 Competition in Consequential Open-Seat Primaries . 27 2.4 Open Seat Congressional Races Since 2006 . 30 3.1 Political Parties as Enduring Multi-Layered Coalitions . 35 3.2 Canvass Locations in Virginia's 10th District Republican Primary . 43 3.3 Sample Ballots from New Jersey's 12th District Democratic Primary . 45 3.4 Overlap in Donors between Ken Buck, Corey Garnder, and the RNC . 48 3.5 North Carolina's 12th District Democrat Primary Results . 65 3.6 Michigan's 14th District Democratic Primary Results . 68 3.7 U.S. Chamber's Advertisement Against Woody White . 72 3.8 Pennsylvania's 13th District Democratic Primary Results . 78 3.9 A Conservative Diagram of Lane Grigsby's Network in Support of Graves . 83 3.10 Campaign Contributors to Louisiana's 6th District Republicans . 84 3.11 Campaign Contributions to Pennsylvania's 6th District Democrats . 89 4.1 An Illustration of Network Density . 114 4.2 Calculating A Candidate's Existing Network Density . 115 4.3 Distribution of Existing Network Density (END) Scores . 116 4.4 Network Structure and Density . 118 5.1 Predicted Probabilities of Candidate Drop-out by Candidate Quality . 137 ix 5.2 Candidate Drop-out Over Time by Party . 138 6.1 Network Density and Campaign Finance in Pennsylvania's 13th District . 150 6.2 Network Density for Boyle and Arkoosh . 153 6.3 Network Density and Likelihood of Winning Primary . 157 6.4 Network Density and Likelihood of Winning Primary by Candidate Quality . 158 6.5 ROC Diagnostics for Model 6.2.4 . 159 6.6 Network Density and Likelihood of Winning Primary by Political Party . 161 6.7 Network Density and Primary Outcomes Over Time . 163 7.1 Partisan Divide in Individual Contributors . 179 7.2 Partisan Divide in PAC Contributors . 180 7.3 Modularity of State Primary Donor Networks . 181 x LIST OF TABLES 2.1 A Toy Example of Group Preferences . 22 3.1 County Results in New Jersey's 12th District Democratic Primary . 46 3.2 County Results in Michigan's 14th District Democratic Primary . 70 4.1 Granger Causality Tests of Fundraising Share and Party Support . 121 5.1 Existing Network Density's Relationship with Primary Drop-out . 134 5.2 Existing Network Density's Effect on Primary Drop-out . 136 5.3 Primary Drop-out by Fundraising Levels . 139 6.1 END Scores for Case Study Winners and Runners-Up . 150 6.2 Existing Network Density's Effect on Likelihood of Winning a Primary . 156 6.3 Existing Network Density's Effect on Primary Vote Share . 162 6.4 Networks Beating the Odds . 166 6.5 Granger Causality Tests of Fundraising Share.
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