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Applied Social Networks

Applied Social Networks. James Fowler University of California, San Diego. Who is the Best Connected Legislator in the U.S. Congress?. Who cares? Social connections have an important effect on political behavior and outcomes among voters

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Applied Social Networks

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  1. AppliedSocial Networks James Fowler University of California, San Diego

  2. Who is the Best Connected Legislator in the U.S. Congress? • Who cares? • Social connections have an important effect on political behavior and outcomes among voters • influencing the flow of political information(Huckfeldt et al. 1995) • voter turnout behavior(Fowler 2005; Highton 2000; Straits 1990) • vote choice (Beck et al. 2002)

  3. Who is the Best Connected Legislator in the U.S. Congress? • Social connections may also have an important effect on legislators • well-connected legislators may be more influential with their peers • better able to influence policy • Methodological challenge: observability • Social relationships are conducted in private • Based on partisan, ideological, institutional, geographic, demographic, and personal affiliations.

  4. Recent large scale social network studies • Hyperlink network between political interest groups • Hindman, et al. 2003 • Email networks • Ebel, Mielsch, and Bornholdt 2002 • Scientific collaboration networks • Newman 2001 • Network of committee assignments in the U.S. Congress • Porter et al. 2005

  5. Here: the network of legislative cosponsorships • A directional link can be drawn from each cosponsor of a piece of legislation to its sponsor • These links provide a rich source of information about the social network between legislators

  6. Cosponsorship and Social Connectedness • Large literature analyzes • which bills receive support • individual motivations to cosponsor • Mayhew 1974; Campbell 1982; Kessler and Krehbiel 1996; Koger 2003; Wilson and Young 1997; Panning 1982; Pellegrini and Grant 1999; Talbert and Potoski 2002 • No literature considering which legislators receive support • Yet several argue that bill sponsorship is a form of leadership • Caldeira, Clark, and Patterson 1993; Hall 1992; Kessler and Krehbiel 1996; Krehbiel 1995; Schiller 1995

  7. Cosponsorship and Social Connectedness • Some argue that cosponsorships = “cheap talk” • Kessler and Krehbiel 1996; Wilson and Young 1997 • However, there may be substantial search cost involved in deciding which bills to cosponsor • From 1973-2004 the average House member cosponsored only 3.4% of all proposed bills and the average Senator only cosponsored 2.4% • Legislators expend considerable effort recruiting cosponsors and talking about them on the floor and with constituents (Campbell 1982) • Oddly, only one published study of Cosponsorship networks • Faust and Skvoretz (2002) interspecies comparison finds that Senate cosponsorship network most resembles the network of mutual licking between cows!

  8. Cosponsorship Data • 280,000 “bills” proposed in the U.S. House and Senate from 1973 to 2004 (93rd-108th Congresses) recorded in Thomas • over 2.1 million cosponsorship signatures • partitioned by chamber and Congress to create 32 separate cosponsorship networks • http://jhfowler.ucsd.edu/cosponsorship.htm

  9. Cosponsorship in the House

  10. Cosponsorship in the Senate

  11. Mutual Cosponsorship Relations

  12. Connectedness: An Alternative Measure • Traditional measures of centrality generate plausible names • None takes advantage of information about the strength of social relationships • Total number of cosponsors on each bill • Legislators recruit first those legislators to whom they are most closely connected. • More cosponsors = lower probability of direct connection • Bills with fewer total cosponsors more reliable • Strength of the connection between i and j = 1/cij • Total number of bills sponsored by j and cosponsored by i • More bills in common = stronger relationship • Weighted cosponsorship distance

  13. Weighted cosponsorship distance

  14. Legislative connectedness • Suppose direct distance from legislator j to legislator i is simple inverse of the cosponsorship weight • Then use Dijkstra’s algorithm (Cormen et al. 2001) • Starting with legislator j, identify from a list of all other legislators the closest legislator i • Replace each of the distances with • Remove legislator i from the list and repeat until there are no more legislators on the list. Connectedness is the inverse of the average of these distances from all other legislators to legislator j.

  15. Results for the House

  16. Results for the Senate

  17. Quality of Strongest Weighted Relationships • Institutional Ties • House committee chairs and ranking members • Senate majority and minority leaders • Regional Ties • From the same state • In the House they are often from contiguous districts • Issue Ties • Rep. Jim DeMint and Sue Myrick -- Republican Study Committee • Sen. George Mitchell and Jim Sasser -- Federal Housing Reform • Sen. Kay Bailey Hutchinson and Sam Brownback -- marriage penalty relief and bankruptcy reform • Personal Ties • Senator John McCain chaired Senator Phil Gramm’s 1996 Presidential campaign • McCain has told the media that they have been friends since 1982 when they served together in the House (McGrory 1995)

  18. 108th House Top 20

  19. 108th Senate Top 20

  20. External Validity: Legislative Influence • Widely used measure of legislative influence is number of successful floor amendments • Hall 1992; Sinclair 1989; Smith 1989; Weingast 1991 • 1 SD increase in connectedness increases successful floor amendments • 53% in House • 65% in Senate

  21. External Validity: Roll Call Votes • Model roll call votes as in Poole and Rosenthal, adding connectedness score of sponsor • 1 SD increase in connectedness of sponsor increases votes for bill by • 5.2 in House • 8.2 in Senate • 2 SD increase would change 16% of House votes and 20% of Senate votes

  22. Landmark Legislation:An Alternative to Mayhew

  23. Modularity

  24. Polarization in the 108th Senate

  25. 108th House by Party

  26. 108th House by Ideology

  27. 108th House by State

  28. 108th House by Committee

  29. Polarization Over Time in the House

  30. Polarization Over Time in the Senate

  31. Black Legislators in the 103rd House

  32. Black Legislators in the 104th House

  33. Black Legislators in the 108th House

  34. Poor Districts in the 108th House

  35. The Quantitative Judicial Literature • Has focused on ideology of decisions and judgesGeorge and Epstein 1992; Segal 1985 • “continues to present an underdeveloped theoretical and empirical understanding of why and when law changes”Hansford and Spriggs 2006 • What if we could quantify the strength of a precedent’s importance?

  36. Questions We Might Answer • How has the norm of stare decisis evolved over time? • Does the Court consider the importance of a case when it decides whether or not to reverse it? • Do reversed cases decline in importance once they are reversed? • When the Court must reverse an important case, does it ground the reversing decision in important precedents? • Which issues and cases does the Court prioritize? • How do these priorities change over time?

  37. Quantifying Precedent • Each judicialcitation is a latent judgment about the cases cited (and not cited) • When justices write opinions, they spend time researching the law and selecting precedents to support their arguments • We can utilize the quantityand qualityof judicial citations to measure the importance of a precedent

  38. Past Attempts to Quantify Precedent • Measurements of the prestige of judges (Kosma 1998; Landes, Lessig, and Solimine 1998) • Citation behavior of appellate courts (Caldeira 1985; Harris 1985) • Role of legal rules in specific issue domain(Landes and Posner 1976; McGuire 2001; Ulmer 1970)

  39. Past Attempts to Quantify Precedent • Large scale network analysis(Chandler 2005; Smith 2005) • Legal vitality(Hansford and Spriggs 2006) • None consider • quality of citations • dynamics of legal change

  40. Data Collection • Generate List of Supreme Court “Decisions” • Shepard’s citations to Supreme Court decisions from • Other Supreme Court decisions • Appellate courts • District courts • State courts • Law journals • Other secondary sources • Majority, concurring, dissenting opinions

  41. Data Collection • Shepard’s data includes types of citations • String cite or treatment • Positive or negative • Will (eventually) help distinguish between • Salience (string-cite network) • Authority (treatment network)

  42. Types of Citations • A cited case may be • an important ruling • salient to the citing case • a reversed opinion • Regardless of content, each citation is a latent judgment about which cases are most important • an overruled case like Plessy v. Ferguson (1896) is probably more important than an overruled case like Crain v. United States (1896) • Thus, we include all judicial citations in our analysis

  43. Extended Network of Abortion Decisions

  44. Mean Inward & Outward Citations by Year

  45. Citations and Stare Decisis • Prior to 19th century, both inward and outward citations rare • during this period there was no “firm doctrine of stare decisis”(Kempin 1959, 50) • Outward citations slowly rise in the 19th century • norm takes hold • number of previous cases that could potentially be cited increases • Inward citations also rise • justices begin writing more broadly applicable legal rules • Inward citations fall in recent years

  46. Citations and Stare Decisis • Goodhart (1930) argues that by 1900, the doctrine of stare decisis was in full effect • Inward and outward citations continued to rise in the20th century • To what extent does this rise signify a further strengthening of the norm? • How many cases cite at least one other case?

  47. Percentage of Cases with At Least One Outward Citation by Year stare decisisin full effect Warren Court

  48. Stare Decisis and the Warren Court • Warren Court (1953-1969) departs from stare decisis • Sharp decrease in outward citiations • Sharp decrease in cases that do not cite any precedents • Consistent with argument Warren Court was “activist” • overruled more precedents than any other Court(Brenner and Spaeth 1995) • revolutionized Constitutional law(Horwitz 1998; Powe 2000; Schwartz 1996) • Warren Court also experiences sharp drop in inward citations • Surprising, given Burger (1969-1986) and Rehnquist (1986-2005) Courts cases contain highest outward citations in history

  49. Possible Explanations • Weak legal basis of Warren Court precedents • “Warren Court decisions did not articulate specific doctrinal analyses, and therefore did not provide firm guidance for future Courts” (Strossen 1996, 72). • Subsequent Courts would have trouble following Warren Court’s “many ambiguities, loopholes, and loosely formulated rules” (Emerson 1980, 440). • Justices as policy oriented actors • More conservative Burger and Rehnquist Courts unable to justify policy choices with liberal Warren Court precedents • Forced to cite their own or pre-Warren precedents

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