Ted Kennedy, Orin Hatch, and Other Strange Bedfellows?A Network Explanation of Legislative Voting

Jennifer N. VictorUniversity of Pittsburghjnvictor@pitt.edu

Gregory KogerUniversity of Miamigkoger@miami.edu

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

Sources of Campaign Finance in 2006

42%

10%

44%

3%

House Democrats

43%

9%

43%

2%4%

House RepublicansPACs

Small Individual Contributions

Large Individual Contributions

Self-Financing

Other

14%

20%

55%

3%8%

Senate Democrats

24%

11%56%

1%8%

Senate Republicans

Source: Center for Responsive Politics http://www.opensecrets.org/bigpicture/wherefrom.php?cycle=2006

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;

(A related question) Why Donate?Exchange/Access Theory

Campaign donations in exchange for votes or accessBut: Reneging? Small donations? Non-PAC organizations?

Information TheoryInformation persuades legislatorsBut: Why lobby allies?

Subsidy TheoryLobbyists subsidize legislatorsBut: Other resources? Why pay to play?

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.

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.

Research DesignFederal donations by lobbyists in the 2006 election cycle (109th Congress)

Obtained from the Center for Responsive Politics20,639 donations by 1,225 lobbyistsRecipients

Candidates for Congress National Party PACsPACs, including Leadership PACs

9,751 dyadic observations of lobbyist donations to MCs.

The Lobbyist-Legislator Network

2-mode network

1-mode network

2

1

C

B

A

LegislatorsLobbyists

ORA B C1 2

Legislators

1 2B

Lobbyists

The Two-Mode Lobbyist-Legislator Network , 2006

Descriptive Statistics: Number of lobbyist-donors

Mean Median SD Min MaxPer MC 18 10 23.8 0 220Per Dyad 0.68 0 2 0 76Per House Dyad

0.42 0 1.1 0 32

Per Senate Dyad

3.4 1 6.5 0 76

Incumbent dyads with the most lobbyist-donors

Member 1 Member 2Number of Common

Lobbyist Donors

Cantwell Clinton 76

Santorum Allen 67

Conrad Cantwell 61

Carper Cantwell 60

Cantwell Nelson, Ben

59

Menendez Clinton 56

Nelson, Bill Clinton 56

Nelson, Bill Cantwell 55

Conrad Clinton 52

Kennedy Cantwell 51

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]

B

A

3

2

1

LegislatorsLobbyists

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 Reinforce Cleavages

CB

321Legislators

LobbyistsA D

4

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:

Lobbyist Ties Link Legislators

CB

321Legislators

LobbyistsA D

4

Distribution of Point Connectivity

Distribution of Point Connectivity

Top Incumbent Recipients (by chamber), by Point Connectivity

Senate House

Member 1 Member 2(ln) Point

Connectivity

Number of Common Lobbyist-donors

Member 1 Member 2(ln) Point

Connectivity

Number of Common Lobbyist-donors

Cantwell (D-WA)

Nelson (D-NE)

5.945 59DeLay (R-TX 22)

Bonilla (R-TX 23)

5.724 22

Santorum (R-PA)

Nelson (D-NE)

5.894 17Lewis (R-CA 41)

Bonilla (R-TX 23)

5.724 10

DeWine (R-OH)

Santorum (R-PA)

5.889 43Lewis (R-CA 41)

DeLay (R-TX 22)

5.717 9

DeWine (R-OH)

Cantwell (D-WA)

5.878 15Lewis (R-CA 41)

Menendez (D-NJ 13)

5.697 4

DeWine (R-OH)

Nelson (D-NE)

5.875 11DeLay (R-TX 22)

Menendez (D-NJ 13)

5.694 0

Conrad (D-ND)

Cantwell (D-WA)

5.866 61Menendez (D-NJ 10)

Bonilla (R-TX 23)

5.694 0

Conrad (D-ND)

Nelson (D-NE)

5.864 46Lewis (R-CA 41)

Pombo (R-CA 11)

5.68 7

Santorum (R-PA)

Cantwell (D-WA)

5.861 10DeLay (R-TX 22)

Pombo (R-CA 11)

5.677 16

Santorum (R-PA)

Allen (R-VA)5.855 67

Pombo (R-CA 11)

Bonilla (R-TX 23)

5.677 8

DeWine (R-OH)

Allen (R-VA)5.852 35

Pombo (R-CA 11)

Menendez (D-NJ 13)

5.673 1

Measures—Dependent Variables

Voting AgreementThe probability legislator a voted the same as legislator b, given that they both voted.House: mean = 0.69, range: 0.1-1Senate: mean= 0.65, range: 0.26-0.98

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)

ExpectationsLegislators who are more connected through the lobbyist-donors network are more likely to vote together.

CONTROLS: Service on the same committeesConstituent PreferencesParty membership (same party)Being from the same stateBeing electorally vulnerableBeing a party/committee leaderTerms servedDemographics

Coeff. Z Pr>|z| Coeff. Z Pr>|z| Coeff. Z Pr>|z|0.0026 0.0050(0.0001) (0.0003)

0.0014 0.0016 0.0013(0.0004) (0.0004) (0.0015)

-0.0033 -0.0032 0.0034(0.00002) (0.00002) (0.0001)

0.3535 0.3539 0.3532(0.0006) (0.0005) (0.0019)

0.3507 0.3540 0.3278(0.0005) (0.0005) (0.0019)

0.0111 0.0130 -0.0001(0.001) (0.001) (0.0035)

0.0005 -0.0005(0.0026) (0.0025)

0.0257 0.0274 0.0178(0.0006) (0.0006) (0.0019)

-0.0045 -0.0034 0.0102(0.0005) (0.0005) (0.0016)

-0.0009 -0.001 0.0019(0.0005) (0.0005) (0.0018)

-0.0034 -0.0025 0.0125(0.0006) (0.0006) (0.002)

0.0001 0.0000 -0.0000(0.00001) (0.00003) (0.0001)

0.5538 0.5410 0.5773(0.0008) (0.0014) (0.0026)

N 191,386 163,612 27,774Number of Groups 95,693 81,806 13,887Log Likelihood 702371.19 604889.38 82329.5

671.66 0.000

Woman (at least one) -7.40

-4.41

0.000

0.000 0.000

0.000 0.000

0.058 -1.96 0.050

0.20 0.843 -0.22

-161.13 0.000

Both Democrats 637.67 0.000 631.03 0.000

0.000Constituency Preferences (cd presidential vote difference)

-166.04

Both Republicans 670.84

Total Donors 4.53

Senior (at least one) -6.06

Black (at least one)

-9.98

Party Leader (at least 1) -1.90

Both in Competitive CDs

Dyad in Same State

0.826

41.85 0.000 42.69 0.000

Committee Service Coincidence 3.28 0.001 3.49 0.000

MODEL I - Full House MODEL II - Connected Dyads

(ln)Connectivity (via common lobbyist-donors)

19.41 0.000 15.51 0.000

9.16 0.000

-52.14 0.000

182.56 0.000

177.11 0.000

-6.24 0.000

1.08 0.281

-6.38 0.000

TABLE 5 HOUSE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election cycle

-

- - -

-0.04 0.968

MODEL III - Unconnected Dyads

- -

0.85 0.397

11.19 0.000 13.03 0.000

Numbers in parenthese are robust s tandard errors

-0.46 0.000

224.50 0.000

0.000 2.14 0.032

Constant 670.54 0.000 380.14 0.000

Coeff. Z Pr>|z| Coeff. Z Pr>|z| Coeff. Z Pr>|z|-0.001 0.0040(0.0007) (0.0017)

0.0040 0.0051 0.0007(0.0017) (0.0018) (0.0038)

-0.005 -0.0046 -0.0064(0.0002) (0.0002) (0.0005)

0.3805 0.3807 0.3767(0.0035) (0.004) (0.0077)

0.3752 0.3732 0.3778(0.0031) (0.0033) (0.0077)

0.0341 0.0202 0.0718(0.0128) (0.0141) (0.0286)

0.0204 0.0184(0.0056) (0.0054)

0.0069 0.0040 0.0367(0.0093) (0.0096) (0.0286)

0.0039 0.0054 -0.002(0.003) (0.0032) (0.007)

-0.0047 -0.0000 -0.014(0.0035) (0.0041) (0.0069)

0.0191 0.0078 0.0575(0.0042) (0.0047) (0.0096)

0.0001 -0.0000 0.0001(0.00002) (0.0000) (0.00007)

0.4947 0.4768 0.4843(0.005) (0.0086) (0.011)

N 10,098 7,656 2,442Number of Groups 5,049 3,828 1,221Log Likelihood 25796.165 19428.573 5188.0659

Constituency Preferences (cd presidential vote difference)

-26.56 0.000 -22.57 0.000

Committee Service Coincidence 2.40 0.016 2.81 0.005

Both Republicans 122.88 0.000 114.82 0.000

Both Democrats 107.54 0.000 96.12 0.000

Both in Cycle 3.67 0.000 3.38 0.001

Dyad in Same State 2.66 0.008 1.43 0.153

0.093

Black (at least one) 0.74 0.457 0.42 0.676

Woman (at least one) 1.33 0.183 1.72 0.085

0.18 0.854

Total Donors 1.84 0.066 -0.04 0.972

Party Leader (at least 1) -1.34 0.181 -0.01 0.990

Senior (at least one) 4.53 0.000 1.68

TABLE 6 SENATE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election cycleMODEL III - Unconnected Dyads

- - -

MODEL I - Full Senate MODEL II - Connected Dyads

(ln)Connectivity (via common lobbyist-donors)

-1.47 0.143 2.40 0.016

1.28 0.199

-14.14 0.000

49.12 0.000

48.89 0.000

2.51 0.012

- - -

-0.25 0.803

-2.06 0.040

5.98 0.000

1.90 0.058

43.98 0.000

Numbers in parenthese are robust s tandard errors

Constant 99.36 0.000 55.76 0.000

HOUSE ResultsHOUSE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election cycle MODEL I - Full House

Coeff. Z Pr>|z|(ln)Connectivity (via common lobbyist-donors)

0.0026 19.41 0.000(0.0001)

Committee Service Coincidence 0.0014 3.28 0.001(0.0004)

Constituency Preferences (cd presidential vote difference) -0.0033 -166.04 0.000

(0.00002)

Both Democrats 0.3535 637.67 0.000(0.0006)

Both Republicans 0.3507 670.84 0.000(0.0005)

Dyad in Same State 0.0111 11.19 0.000

N 191,386 Number of Groups 95,693 Log Likelihood 702371.2

SENATE ResultsTABLE 6 SENATE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election cycle

MODEL I - Full Senate MODEL II - Connected Dyads

MODEL III - Unconnected Dyads

Coeff. Z Pr>|z| Coeff. Z Pr>|z| Coeff. Z Pr>|z|

(ln)Connectivity (via common lobbyist-donors)

-0.001-1.47 0.143

0.00402.40 0.016 - - -(0.0007) (0.0017)

Committee Service Coincidence

0.0040 2.40 0.016 0.0051 2.81 0.005 0.0007 0.18 0.854(0.0017) (0.0018) (0.0038)

Constituency Preferences (cd presidential vote difference)

-0.005-26.56 0.000

-0.0046-

22.57 0.000-0.0064

-14.14 0.000(0.0002) (0.0002) (0.0005)

Both Democrats 0.3805 107.54 0.000 0.3807 96.12 0.000 0.3767 49.12 0.000(0.0035) (0.004) (0.0077)

Both Republicans 0.3752 122.88 0.000 0.3732 114.82 0.000 0.3778 48.89 0.000(0.0031) (0.0033) (0.0077)

N 10,098 7,656 2,442 Number of Groups 5,049 3,828 1,221

Interpretation of Results

0.6

0.62

0.64

0.66

0.68

0.7

0.72

min 5% 25% 50% 75% 95% maxPred

icte

d Pr

obab

ility

of V

oting

A

gree

men

t

Connectivity via Common Lobbyists' Donations

Predicted Probability of Co-voting Among MCs Connected by Lobbyists' Donations

House Voting Coincidence Senate Voting Coincidence

House Mean Co-voting

Senate Mean Co-voting

Visualization of Results

Random Senators (N=38)Actual Data: Most Central Senators in Lobby-Donor Network (N=38)

Senate 38 random senators, opacity of tie indicates voting agreement, color party, squares are in-cycle, circles are not.

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.

Size of node = $ contributionsColor of node = Non-leader

= LeaderShape of node = in cycle = not up

Strange Bedfellows, HouseRepresentative Representative

Vote Agreement(μ = 0.69)

Ideological Difference(μ = 15.0)

Point Connectivity

(μ = 96.4)Edolphus Towns

(D-NY)Roy Blunt (R-MO) 0.45 54 246

Edolphus Towns (D-NY)

Chet Edwards (D-TX)

0.811 56 242

Edolphus Towns (D-NY)

John Carter (R-TX)

0.447 53 235

Edolphus Towns (D-NY)

Joe Barton (R-TX) 0.441 52 234

Charles Rangel (D-NY)

Chet Edwards (D-TX)

0.80 60 225

Charles Rangel (D-NY)

Tom DeLay (R-TX)

0.432 54 225

Charles Rangel (D-NY)

John Sullivan (R-OK)

0.412 55 225

Charles Rangel (D-NY)

John Carter (R-TX)

0.444 57 225

Charles Rangel (D-NY)

Eric Cantor (R-VA)

0.43 52 225

Charles Rangel (D-NY)

John Boehner (R-OH)

0.452 55 225

Strange Bedfellows, SenateSenator Senator

Vote Agreement(μ = 0.65)

Ideological Difference(μ = 9.7)

Point Connectivity

(μ = 95.7)Edward Kennedy

(D-MA)Orin Hatch (R-UT) 0.359 35.94 319

Edward Kennedy (D-MA)

Ben Nelson (D-NE)

0.583 29.26 318

Orin Hatch (R-UT)Hillary Clinton (D-

NY)0.408 32.37 304

Orin Hatch (R-UT)Richard Durbin

(D-IL)0.38 33.42 245

Orin Hatch (R-UT)Lincoln Chafee

(R-RI)0.626 33.42 245

Thomas (R-WY)Lincoln Chafee

(R-RI)0.608 30.35 241

Craig Thomas (R-WY)

Hillary Clinton (D-NY)

0.374 29.3 240

Edward Kennedy (D-MA)

Craig Thomas (R-WY)

0.324 32.87 240

Patrick Leahy (D-VT)

Craig Thomas (R-WY)

0.35 29.87 215

Patrick Leahy (D-VT)

Orin Hatch (R-UT) 0.39 32.94 215

ConclusionsOur 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

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.

Future Work

RepresentationWhich has more explanatory power: donations or constituents?

PowerWho is most central in the legislator network?

TiesCan we predict who will donate/receive?

If lobbyists primarily seek relationships, there will be evidence of ties over time.

Why Donate?

Prof. Jennifer N. Victor

EXTRA SLIDES

Measures—Dependent Variables

Voting Agreement--House0

12

34

5D

ensi

ty

0 .2 .4 .6 .8 1House Coincidence of Voting

Measures—Dependent Variables

Voting Agreement--Senate0

12

34

5D

ensi

ty

.2 .4 .6 .8 1Senate Coincidence of Voting

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.

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 cooperationi.e., collective action problems: Siegel AJPS 2009

Informal institutionsi.e., Caucuses: Victor & Ringe 2009

Multiple levels of organizations i.e., international capitalism: Lazer 2005

The Network Approach

Senate Co-sponsorship (Fowler 2006)

The Network Approach

2004 A-list Bloggers (Adamic and Glance 2005)

The Network Approach: An Increasing Trend

1971-

1997

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 200902468

101214161820

Number of Papers Published in Major Political Science Journals with the word "Network" in the Title

(APSR, AJPS, JOP, IO, LSQ, BJPS, APR, PSQ, PA, PS, PC)(Data Complied by author)

Num

ber o

f Pub

licati

ons

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.

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.

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.

Coeff. Z Pr>|z| Coeff. Z Pr>|z|-0.490 0.244(0.229) (0.313)

0.500 1.328(0.107) (0.145)

-0.322 -0.285(0.233) (0.339)

-0.029 -0.148(0.010) (0.227)

0.724 0.388(0.149) (0.158)

0.008 -0.064(0.012) (0.042)

2.523 3.065(0.103) (0.156)

-0.394296 -0.394629(0.089) (0.194)

0.674154 0.673930(0.060) (0.131)

N 437 101Log Restricted-Likelihood -1522 -454robust standard errors in parentheses, clustered on state

Negative Binomial Predicting Number of Lobbyist-Donors, 2006 cycleHouse Senate

Number of Lobbyist-Donors Number of Lobbyist-Donors

Competitive District/In Cycle

4.47 0.000 9.19 0.000

Distrance from Median -2.14 0.032 0.78 0.435

Woman -0.29 0.771 -0.65 0.514

African-American/Minority -1.38 0.167 -0.84 0.401

0.000

Party/Committee Leader 4.87 0.000 0.014

Terms Served 0.65 0.516 -1.51 0.132

ln(alpha)

alpha

2.45

19.67Constant 24.43 0.000

Measures—Independent Variables

Common Lobbyist-DonorsCommittee Coincidence

House: mean = 0.2, range: 0-3Senate: mean = 0.73. range: 0-4

Ideological DistanceHouse: mean = 0.5, range: 0 – 1.9Senate: mean = 0.5, range: 0 – 1.9

Same State: 0 (139,457) or 1 (4,996)Electoral Vulnerability

House (Cook Competitive District):

Measures—Independent VariablesElectoral 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)

Measures—Independent VariablesAfrican-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)