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UNIVERSITY of CALIFORNIA, SAN DIEGO Essays UNIVERSITY OF CALIFORNIA, SAN DIEGO Essays in American Political Behavior A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Political Science by Robert Bond Committee in charge: Professor James Fowler, Chair Professor Charles Elkan Professor David Huber Professor Thad Kousser Professor Gary Jacobson 2013 Copyright Robert Bond, 2013 All rights reserved. The dissertation of Robert Bond is approved, and it is accept- able in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2013 iii DEDICATION My grandparents – Harry Bycroft, Betty Bycroft, Ronald Bond, and Lucy Stockton – did not live to see the completion of this dissertation. It is dedicated to their lives and their memory. iv TABLE OF CONTENTS Signature Page . iii Dedication . iv Table of Contents . v List of Figures . vii List of Tables . x Acknowledgements . xii Vita and Publications . xiii Abstract of the Dissertation . xiv Chapter 1 Social Information and Participation . 1 1.1 Introduction . 2 1.2 Social norms and voting behavior . 4 1.3 Experimental Process and Results . 6 1.4 Discussion . 18 Chapter 2 The Dynamic Spread of Voting . 21 2.1 Introduction . 22 2.2 Peer effects and voting . 23 2.3 Data and methods . 24 2.4 Matching . 26 2.5 Calculation of Treatment Effect . 28 2.6 Results . 28 2.7 Discussion . 32 2.8 Conclusion . 33 Chapter 3 Estimating Ideology using Facebook’s ‘Like’ Data . 36 3.1 Introduction . 37 3.2 Facebook ‘Like’ Data . 39 3.3 Using Facebook data to scale ideological positions . 42 3.3.1 Model of liking . 43 3.3.2 Estimation of ideology from liking . 45 3.4 Validation of the measure . 51 3.5 Age and Ideology . 54 3.6 Ideology in Social Space . 58 3.7 Effect of Friend Ideology on Turnout . 63 v 3.8 Discussion . 65 Appendix A Supplementary information for “Social Information and Participa- tion” . 68 Appendix B Supplementary information for “The Dynamic Spread of Voting” . 73 B.1 Regression results by decile . 73 B.2 Matching to Voting Records . 73 B.3 Determination of “Close” Friends . 75 B.4 Alternate method of analysis . 76 Appendix C Supplementary information for “Estimating Ideology using Face- book’s ‘Like’ Data” . 79 C.1 Variable coding . 79 C.2 Matching to Voting Records . 80 C.3 Determination of “Close” Friends . 81 C.4 Expertise and estimation of ideology . 82 C.5 Ideology Scores . 84 Bibliography . 109 vi LIST OF FIGURES Figure 1.1: Examples of the treatment messages. (a) Example of the social mes- sage condition. (b) Example of the informational message. Note that the social message and the informational message are identi- cal save for the faces of up to six friends who had previously self- reported voting, the names of up to 3 additional friends who had previously self-reported voting and the number of previously vot- ing friends shown at the bottom of the social message. 7 Figure 1.2: Self-reported voting and information seeking effects disaggregated by age. The left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by age. The right panel shows the difference in treatment effect on information seeking due to the inclusion of social information by age. In each, points indicate the average treatment effect for the group and bars represent 95% confidence intervals. 11 Figure 1.3: Self-reported voting and information seeking effects disaggregated by education. The left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by education level. The right panel shows the difference in treat- ment effect on information seeking due to the inclusion of social information by education level. In each, points indicate the average treatment effect for the group and bars represent 95% confidence intervals. 12 Figure 1.4: Self-reported voting and information seeking effects disaggregated by number of friends and close friends. The upper left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by the number of close friends. The upper right panel shows the difference in treatment effect on infor- mation seeking due to the inclusion of social information by the number of close friends. The lower left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by the number of friends. The lower right panel shows the difference in treatment effect on information seeking due to the inclusion of social information by the number of friends. In each, points indicate the average treatment effect for the group and bars represent 95% confidence intervals. 14 vii Figure 1.5: Self-reported voting and information seeking effects disaggregated by number of friends and close friends. The upper left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by the number of close friends. The upper right panel shows the difference in treatment effect on infor- mation seeking due to the inclusion of social information by the number of close friends. The lower left panel shows the difference in treatment effect on self-reported voting due to the inclusion of social information by the number of friends. The lower right panel shows the difference in treatment effect on information seeking due to the inclusion of social information by the number of friends. In each, points indicate the average treatment effect for the group and bars represent 95% confidence intervals. 15 Figure 2.1: Simulated effects of alter turnout on ego turnout, separated by decile. 30 Figure 2.2: ROC curves for predictions of voting. 31 Figure 3.1: The left panel shows the distribution of the number of pages that each user likes. The right panel shows the distribution of the number of fans that each page has. 47 Figure 3.2: Proportion of the variance explained by each dimension of the scaled agreement matrix. 50 Figure 3.3: Density plots of ideological estimates of 1,223 pages and 6.2 mil- lion users. 52 Figure 3.4: Scatter plot showing the relationship between the Facebook based ideology measure and DW-NOMINATE. 53 Figure 3.5: Average Facebook ideology score of users grouped by the users’ stated political views. Note that the category labeled ‘none’ is the group of users that actually wrote the word ‘none’ as their political views. The point labeled ‘(blank)’ is the group of users that has not entered anything in as their political views. Note also that the 95% confidence intervals for each of the estimates is smaller than the point. The color of the points is on a scale from blue to red that is proportional to each group’s average ideology score. 55 Figure 3.6: In each panel the points show the average ideology of a a specific age group for individuals age 18 through 80, and the lines repre- sent the 95% confidence interval of the estimate. The upper left panel shows the average ideology of all users in our sample. The upper right panel shows the average ideology of men and women by age. The lower left panel shows the average ideology of mar- ried and unmarried individuals by age. The lower right panel shows the average ideology of college attendees and those who have not attended college by age. 57 viii Figure 3.7: The correlation in ideology for familial and romantic relationships. 60 Figure 3.8: The correlation in ideology for friendship relationships. Each decile represents a separate set of friendship dyads. Decile of interaction is based on the proportion of interaction between the pair during the three months before ideology was scaled. 62 Figure C.1: The correlation of the Facebook measure of ideology with DW- NOMINATE as the minimum number of political pages liked by users increases. 83 ix LIST OF TABLES Table 1.1: Contingency table showing the relationship between validated voting and self-reported voting. “I voted” click represents the group that did click the “I voted” button on Election Day. No “I voted” click represents the group that was presented with the “I voted” button on election day but did not click it. It is important to note that the majority of individuals are on the diagonals and that the majority of those off the diagonal represent under-reports of voting rather than over-reports. 9 Table 1.2: OLS regression results using pre-election demographic variables avail- able from Facebook to predict each of the three political behaviors. 16 Table 1.3: OLS regression results of treatment status interacted with predicted probability of each of the three political behaviors. 17 Table 2.1: Match criteria. 27 Table 3.1: The first ten rows of the user by political page matrix. Entries in the matrix are dichotomous, where a 1 means that the user has liked the page and a 0 means that the user has not. 46 Table 3.2: The first ten rows of the affiliation matrix. Diagonal entries are the number of user that are a fan of the page. Off-diagonal entries are the number of users who like both pages. 48 Table 3.3: The first ten rows of the ratio of affiliation matrix. Because each page has a different number of fans, the denominator changes and the off diagonal entries are not identical. 48 Table 3.4: The effect of friend ideology on ego turnout. Results of logistic re- gression of ego validated voting in 2010 on ego covariates and al- ter characteristics of ideology and turnout.
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