Jennifer N. Victor University of Pittsburgh jnvictor@pitt.edu Gregory Koger University of Miami

1. Ted Kennedy, Orin Hatch, and Other Strange Bedfellows?A Network Explanation of Legislative Voting Jennifer N. Victor University of Pittsburgh jnvictor@pitt.edu Gregory Koger University of Miami gkoger@miami.edu

2. Do Lobbyists “influence” legislators’ votes? The media say “yes:”

3. Sources of Campaign Finance in 2006 Source: Center for Responsive Politics http://www.opensecrets.org/bigpicture/wherefrom.php?cycle=2006

4. Influences on Voting • Constituents/public opinion (Achen 1978; Hill and Hurley 1999; Miller and Stokes 1963) • Representation of subgroups (Arnold 1990; Bartels 2008; Bishin 2000, 2009; Fenno 1978) • Parties/Party loyalty (Cox and Poole 2002; Lebo, McGlynn and Koger 2007; Lee 2008; Sinclair 2002) • Organized Interests (Mansbridge 2003; Ansolahebere, de Figueiredo, and Snyder 2003;

5. (A related question) Why Donate? • Exchange/Access Theory • Campaign donations in exchange for votes or access • But: Reneging? Small donations? Non-PAC organizations? • Information Theory • Information persuades legislators • But: Why lobby allies? • Subsidy Theory • Lobbyists subsidize legislators • But: Other resources? Why pay to play?

6. Lobbying, Networks, and Contributions • Legislators’ relationships with the lobbying community influence their voting behavior. • Emphasize the system of connections between legislators and lobbyist-donors, rather than the “transaction.” • Existing evidence that legislators and lobbyists desire long term relationships (Snyder 1990; Berry and Wilcox 2009). • Donations are observable evidence of relationships and common interests.

7. Expectations • Ceteris paribus, we expect legislators who are more connected through the lobbying-donation network (directly or indirectly) to be more likely to vote the same way.

8. Research Design • Federal donations by lobbyists in the 2006 election cycle (109th Congress) • Obtained from the Center for Responsive Politics • 20,639 donations by 1,225 lobbyists • Recipients • Candidates for Congress • National Party PACs • PACs, including Leadership PACs • 9,751 dyadic observations of lobbyist donations to MCs.

9. The Lobbyist-Legislator Network • 2-mode network • 1-mode network A 1 B 2 C Lobbyists Legislators OR A B C 1 2 1 B 2 Legislators Lobbyists

10. The Two-Mode Lobbyist-Legislator Network , 2006

11. Descriptive Statistics: Number of lobbyist-donors

12. Incumbent dyads with the most lobbyist-donors

13. Point Connectivity • We aren’t just interested in the number of common donors legislators share, but how legislators are connected through the network. • Ties come in different forms: • Lobbyists [A,B] indirectly connect legislators [1,3] 1 A 2 B 3 Lobbyists Legislators

14. Point Connectivity Lobbyists • We aren’t just interested in the number of common donors legislators share, but how legislators are connected through the network. • Ties come in different forms: • Lobbyists Reinforce Cleavages B D A C 1 2 3 4 Legislators

15. Point Connectivity Lobbyists • We aren’t just interested in the number of common donors legislators share, but how legislators are connected through the network. • Ties come in different forms: • Lobbyist Ties Link Legislators B D A C 1 2 3 4 Legislators

16. Distribution of Point Connectivity

17. Distribution of Point Connectivity

18. Top Incumbent Recipients (by chamber), by Point Connectivity

19. Measures—Dependent Variables • Voting Agreement • The probability legislator a voted the same as legislator b, given that they both voted. • House: mean = 0.69, range: 0.1-1 • Senate: mean= 0.65, range: 0.26-0.98

20. Regression, Inference, and Network Data • Analysis of Social Network data requires particular attention to: • Sampling • Autocorrelation • We want to model the relationships between observations. • Use a mixed model: (legislators nested in dyads). • Dyads (level 1, i); Legislators (level 2, j). • Include a legislator-specific random intercept, ζ1j,to capture unobserved heterogeneity between observations. • We assume the random intercept and residual are normally distributed ζj~N(0, ψ); εij ~N(0,θ)

21. Expectations • Legislators who are more connected through thelobbyist-donors network are more likely to vote together. • CONTROLS: • Service on the same committees • Constituent Preferences • Party membership (same party) • Being from the same state • Being electorally vulnerable • Being a party/committee leader • Terms served • Demographics

24. HOUSE Results

25. SENATE Results

26. Interpretation of Results

27. Visualization of Results Random Senators (N=38) Actual Data: Most Central Senators in Lobby-Donor Network (N=38) Size of node = $ contributions Color of node = Non-leader = Leader Shape of node = in cycle = not up • Senate 38 most central actors (those with greater than mean degree centrality), opacity of tie indicates voting agreement, color indicates leadership, squares are in-cycle, circles are not. Compared to random data: more GREEN, more dark ties, more SQUARES, and LARGER nodes. Senate 38 random senators, opacity of tie indicates voting agreement, color party, squares are in-cycle, circles are not.

28. Strange Bedfellows, House

29. Strange Bedfellows, Senate

30. Conclusions • Our innovations on the question of how/whether lobbyists influence legislators: • Look at lobbyists’ personal donations, not PACs • Use network analysis. • We find that, ceteris paribus, the stronger the connection between legislators in the lobbying network, the more likely the are to vote together. • Effect is stronger in the House than the Senate

31. Conclusions • At the very least, lobbyists’ donation are indicative of legislators latent policy preferences. • Our data are also consistent with the relatively unsupported claim that lobbyists buy votes.

32. Future Work • Representation • Which has more explanatory power: donations or constituents? • Power • Who is most central in the legislator network? • Ties • Can we predict who will donate/receive? • If lobbyists primarily seek relationships, there will be evidence of ties over time.

33. Why Donate? Prof. Jennifer N. Victor

34. EXTRA SLIDES

35. Measures—Dependent Variables • Voting Agreement--House

36. Measures—Dependent Variables • Voting Agreement--Senate

37. The Network Approach • Why networks, and why now? • Not inconsistent with methodological individualism. • Network analysis considers the unit of analysis to be a relationship rather than the individual. • Politics is naturally about relationships. • Technology now makes it possible.

38. The Network Approach • Network tools are particularly useful when we want to understand: • Flow of information i.e., voter contagion: Nickerson APSR 2008 • Coordination and cooperation i.e., collective action problems: Siegel AJPS 2009 • Informal institutions i.e., Caucuses: Victor & Ringe 2009 • Multiple levels of organizations i.e., international capitalism: Lazer 2005

39. The Network Approach • Senate Co-sponsorship (Fowler 2006)

40. The Network Approach • 2004 A-list Bloggers (Adamic and Glance 2005)

41. The Network Approach: An Increasing Trend

42. Anecdotal Support for Network Perspective • Quotes from lobbyists: ‘I don't usually give out my personal money unless I know the person and I feel like I've got some kind of respect and relationship with that person’ - Republican lobbyist Richard F. Hohlt as quoted in Carney 2007.

43. Anecdotal Support for Network Perspective • Quotes from lobbyists: ‘I do not give for the purpose of having access. Virtually everyone I deal with in representation of a client I know personally and I have known personally for 10, 15, 20 years. So, when I enter, I enter on the basis of my credibility and the issues at hand, and not based upon the fact that I have contributed to an individual and am seeking access to that individual.’ -Former Rep. Tom Loeffer (R-TX) quoted in Carney 2007.

44. Anecdotal Support for Network Perspective • Quotes from lobbyists: Tony Podesta says that personal relations, not a desire for access, drive his donations. ‘In every case, they are people I know, people who are friends, people I have a relationship with,’ he says. ‘It’s not a door-opener kind of thing. It’s rather an effort to keep in office or send to office people who are doing a good job.’ - Tony Podesta, Democratic lobbyist as quote in Carney 2007.

46. Measures—Independent Variables • Common Lobbyist-Donors • Committee Coincidence • House: mean = 0.2, range: 0-3 • Senate: mean = 0.73. range: 0-4 • Ideological Distance • House: mean = 0.5, range: 0 – 1.9 • Senate: mean = 0.5, range: 0 – 1.9 • Same State: 0 (139,457) or 1 (4,996) • Electoral Vulnerability • House (Cook Competitive District):

47. Measures—Independent Variables • Electoral Vulnerability, at least 1 • House (Cook Competitive District): 0 (81,406); 1 (14,297) • Senate (in cycle 2006): 0 (2,628); 1 (2,422) • Leadership (party, committee, cardinal) , at least 1 • House: 0 (69,378); 1(26,325) • Senate: 0 (1,275); 1 (3,775) • Senior, at least 1 greater than mean terms served • House: 0 (16,117); 1 (79,586) • Senate: 0 (828); 1 (4,222)

48. Measures—Independent Variables • African-American, at least 1 • House: 0 (79,401); 1 (16,302) • Racial Minority, at least 1 • Senate: 0 (4,465); 1 (585) • Woman, at least 1 • House: 0 (69,378); 1 (26,325) • Senate: 0 (3,741); 1 (1,309)