Jennifer N. Victor
University of Pittsburgh
jnvictor@pitt.edu
Gregory Koger
University of Miami
gkoger@miami.edu
House Democrats
3%
42%
45%
10%
PACs
House Republicans
2% 4%
Small Individual
Contributions
Large Individual
Contributions
Self-Financing 43%
42%
Other
Senate Democrats
9%
Senate Republicans
1%
3%
8% 14%
8%
24%
20%
55%
11%
56%
Source: Center for Responsive Politics http://www.opensecrets.org/bigpicture/wherefrom.php?cycle=2006
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;
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?
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.
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.
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.
2-mode network
A
1
B
2
C
Lobbyists
Legislators
1-mode network
A
1
B
Legislators
2
C
OR
1
B
Lobbyists
2
Mean
Per MC
18
Per Dyad
0.68
Per
House
0.42
Dyad
Per
Senate
3.4
Dyad
Median
10
0
SD
Min
23.8
2
0
0
Max
220
76
0
1.1
0
32
1
6.5
0
76
Member 1
Number of Common
Member 2
Lobbyist Donors
Cantwell
Clinton
76
Santorum
Allen
67
Conrad
Cantwell
61
Carper
Cantwell
60
Cantwell
Nelson,
Ben
Menendez Clinton
59
Nelson, Bill Clinton
56
Nelson, Bill Cantwell
55
Conrad
Clinton
52
Kennedy
Cantwell
51
56
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
Lobbyists
3
Legislators
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
A
B
C
D
1
2
3
4
Lobbyists
Legislators
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
A
B
C
D
1
2
3
4
Lobbyists
Legislators
Senate
Member 1
Member 2
Cantwell (DWA)
Santorum (RPA)
DeWine (ROH)
DeWine (ROH)
DeWine (ROH)
Conrad (DND)
Conrad (DND)
Santorum (RPA)
Santorum (RPA)
DeWine (ROH)
Nelson (DNE)
Nelson (DNE)
Santorum (RPA)
Cantwell (DWA)
Nelson (DNE)
Cantwell (DWA)
Nelson (DNE)
Cantwell (DWA)
Allen (R-VA)
Allen (R-VA)
House
(ln) Point
Connectivity
Number of
Common
Lobbyistdonors
5.945
59
5.894
17
5.889
43
5.878
15
5.875
11
5.866
61
5.864
46
5.861
10
5.855
67
5.852
35
Member 1
Member 2
DeLay (R-TX
22)
Lewis (R-CA
41)
Lewis (R-CA
41)
Lewis (R-CA
41)
DeLay (R-TX
22)
Menendez (DNJ 10)
Lewis (R-CA
41)
DeLay (R-TX
22)
Pombo (R-CA
11)
Pombo (R-CA
11)
Bonilla (R-TX
23)
Bonilla (R-TX
23)
DeLay (R-TX
22)
Menendez (DNJ 13)
Menendez (DNJ 13)
Bonilla (R-TX
23)
Pombo (R-CA
11)
Pombo (R-CA
11)
Bonilla (R-TX
23)
Menendez (DNJ 13)
(ln) Point
Connectivity
Number of
Common
Lobbyistdonors
5.724
22
5.724
10
5.717
9
5.697
4
5.694
0
5.694
0
5.68
7
5.677
16
5.677
8
5.673
1
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
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)
Legislators who are more connected through the
lobbyist-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
TABLE 5 HOUSE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election cycle
MODEL I - Full House
MODEL II - Connected Dyads
MODEL III - Unconnected Dyads
Coeff.
Z
Pr>|z|
Coeff.
Z
Pr>|z|
Coeff.
Z
Pr>|z|
0.0026
0.0050
(ln)Connectivity (via common
19.41
0.000
15.51
0.000
(0.0001)
(0.0003)
lobbyist-donors)
0.0014
0.0016
0.0013
Committee Service Coincidence
3.28
0.001
3.49
0.000
0.85
0.397
(0.0004)
(0.0004)
(0.0015)
Constituency Preferences (cd
presidential vote difference)
Both Democrats
-0.0033
(0.00002)
0.3535
(0.0006)
Both Republicans
0.3507
(0.0005)
Dyad in Same State
0.0111
(0.001)
Both in Competitive CDs
0.0005
(0.0026)
Black (at least one)
Woman (at least one)
Party Leader (at least 1)
0.0257
(0.0006)
-0.0045
(0.0005)
-0.0009
(0.0005)
Senior (at least one)
-0.0034
(0.0006)
Total Donors
0.0001
(0.00001)
Constant
0.5538
(0.0008)
N
Number of Groups
Log Likelihood
191,386
95,693
702371.19
Numbers i n pa renthes e a re robus t s ta nda rd errors
-166.04
0.000
637.67
0.000
670.84
0.000
11.19
0.000
0.20
0.843
41.85
0.000
-9.98
0.000
-1.90
0.058
-6.06
0.000
4.53
0.000
670.54
0.000
-0.0032
(0.00002)
0.3539
(0.0005)
0.3540
(0.0005)
0.0130
(0.001)
-0.0005
(0.0025)
0.0274
(0.0006)
-0.0034
(0.0005)
-0.001
(0.0005)
-0.0025
(0.0006)
0.0000
(0.00003)
0.5410
(0.0014)
163,612
81,806
604889.38
-161.13
0.000
631.03
0.000
671.66
0.000
13.03
0.000
-0.22
0.826
42.69
0.000
-7.40
0.000
-1.96
0.050
-4.41
0.000
2.14
0.032
380.14
0.000
0.0034
(0.0001)
0.3532
(0.0019)
0.3278
(0.0019)
-0.0001
(0.0035)
0.0178
(0.0019)
0.0102
(0.0016)
0.0019
(0.0018)
0.0125
(0.002)
-0.0000
(0.0001)
0.5773
(0.0026)
27,774
13,887
82329.5
-52.14
0.000
182.56
0.000
177.11
0.000
-0.04
0.968
-
-
9.16
0.000
-6.24
0.000
1.08
0.281
-6.38
0.000
-0.46
0.000
224.50
0.000
TABLE 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|
-0.001
0.0040
(ln)Connectivity (via common
-1.47
0.143
2.40
0.016
(0.0007)
(0.0017)
lobbyist-donors)
0.0040
0.0051
0.0007
Committee Service Coincidence
2.40
0.016
2.81
0.005
0.18
0.854
(0.0017)
(0.0018)
(0.0038)
Constituency Preferences (cd
presidential vote difference)
Both Democrats
-0.005
(0.0002)
0.3805
(0.0035)
Both Republicans
0.3752
(0.0031)
Dyad in Same State
0.0341
(0.0128)
Both in Cycle
0.0204
(0.0056)
Black (at least one)
Woman (at least one)
Party Leader (at least 1)
0.0069
(0.0093)
0.0039
(0.003)
-0.0047
(0.0035)
Senior (at least one)
0.0191
(0.0042)
Total Donors
0.0001
(0.00002)
Constant
0.4947
(0.005)
N
Number of Groups
Log Likelihood
10,098
5,049
25796.165
Numbers i n pa renthes e a re robus t s ta nda rd errors
-26.56
0.000
107.54
0.000
122.88
0.000
2.66
0.008
3.67
0.000
0.74
0.457
1.33
0.183
-1.34
0.181
4.53
0.000
1.84
0.066
99.36
0.000
-0.0046
(0.0002)
0.3807
(0.004)
0.3732
(0.0033)
0.0202
(0.0141)
0.0184
(0.0054)
0.0040
(0.0096)
0.0054
(0.0032)
-0.0000
(0.0041)
0.0078
(0.0047)
-0.0000
(0.0000)
0.4768
(0.0086)
7,656
3,828
19428.573
-22.57
0.000
96.12
0.000
114.82
0.000
1.43
0.153
3.38
0.001
0.42
0.676
1.72
0.085
-0.01
0.990
1.68
0.093
-0.04
0.972
55.76
0.000
-0.0064
(0.0005)
0.3767
(0.0077)
0.3778
(0.0077)
0.0718
(0.0286)
0.0367
(0.0286)
-0.002
(0.007)
-0.014
(0.0069)
0.0575
(0.0096)
0.0001
(0.00007)
0.4843
(0.011)
2,442
1,221
5188.0659
-14.14
0.000
49.12
0.000
48.89
0.000
2.51
0.012
-
-
1.28
0.199
-0.25
0.803
-2.06
0.040
5.98
0.000
1.90
0.058
43.98
0.000
HOUSE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006
election cycle
MODEL I - Full House
(ln)Connectivity (via common lobbyistdonors)
Coeff.
0.0026
Z
Pr>|z|
19.41
0.000
3.28
0.001
-166.04
0.000
637.67
0.000
670.84
0.000
11.19
0.0111
191,386
95,693
702371.2
0.000
(0.0001)
Committee Service Coincidence
0.0014
Constituency Preferences (cd
presidential vote difference)
Both Democrats
-0.0033
(0.0004)
(0.00002)
0.3535
(0.0006)
Both Republicans
0.3507
(0.0005)
Dyad in Same State
N
Number of Groups
Log Likelihood
TABLE 6 SENATE VOTING AGREEMENT: Random Intercept Mixed Regression Model, 2006 election
cycle
MODEL II - Connected MODEL III - Unconnected
MODEL I - Full Senate
Dyads
Dyads
Coeff.
Z
Pr>|z| Coeff.
Z Pr>|z| Coeff.
Z Pr>|z|
-0.001
0.0040
(ln)Connectivity (via
-1.47 0.143 (0.0017) 2.40 0.016
(0.0007)
common lobbyist-donors)
0.0040
Committee Service
(0.0017)
Coincidence
Constituency Preferences -0.005
(0.0002)
(cd presidential vote
difference)
Both Democrats
0.3805
(0.0035)
Both Republicans
0.3752
(0.0031)
N
Number of Groups
10,098
5,049
2.40
0.016
0.0051
(0.0018)
-0.0046
-26.56
0.000
107.54
0.000
122.88
0.000
(0.0002)
0.3807
(0.004)
2.81 0.005
0.000
22.57
96.12 0.000
0.3732 114.8
0.000
(0.0033)
2
7,656
3,828
0.0007
(0.0038)
0.18 0.854
-0.0064
(0.0005)
0.3767
(0.0077)
0.3778
(0.0077)
2,442
1,221
-14.14 0.000
49.12 0.000
48.89 0.000
Predicted Probability of Voting
Agreement
Predicted Probability of Co-voting Among MCs
Connected by Lobbyists' Donations
0.72
House Mean Co-voting
0.7
0.68
0.66
0.64
0.62
Senate Mean Co-voting
0.6
min
5%
25%
50%
75%
95%
Connectivity via Common Lobbyists' Donations
House Voting Coincidence
Senate Voting Coincidence
max
Actual Data: Most Central Senators in
Lobby-Donor Network (N=38)
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 = $ contributions
Color of node
= Non-leader
= Leader
Shape of node
= in cycle
= not up
Random Senators (N=38)
Senate 38 random senators, opacity of tie indicates voting
agreement, color party, squares are in-cycle, circles are not.
Representative
Edolphus Towns
(D-NY)
Edolphus Towns
(D-NY)
Edolphus Towns
(D-NY)
Edolphus Towns
(D-NY)
Charles Rangel (DNY)
Charles Rangel (DNY)
Charles Rangel (DNY)
Charles Rangel (DNY)
Charles Rangel (DNY)
Charles Rangel (DNY)
Representative
Vote Agreement
(μ = 0.69)
Ideological
Difference
(μ = 15.0)
Point
Connectivity
(μ = 96.4)
Roy Blunt (R-MO)
0.45
54
246
0.811
56
242
0.447
53
235
0.441
52
234
0.80
60
225
0.432
54
225
0.412
55
225
0.444
57
225
0.43
52
225
0.452
55
225
Chet Edwards (DTX)
John Carter (RTX)
Joe Barton (R-TX)
Chet Edwards (DTX)
Tom DeLay (RTX)
John Sullivan (ROK)
John Carter (RTX)
Eric Cantor (RVA)
John Boehner (ROH)
Senator
Senator
Edward Kennedy
(D-MA)
Edward Kennedy
(D-MA)
Orin Hatch (RUT)
Orin Hatch (RUT)
Orin Hatch (RUT)
Orin Hatch (RUT)
Ben Nelson (DNE)
Hillary Clinton
(D-NY)
Richard Durbin
(D-IL)
Lincoln Chafee
(R-RI)
Lincoln Chafee
(R-RI)
Hillary Clinton
(D-NY)
Craig Thomas (RWY)
Craig Thomas (RWY)
Orin Hatch (RUT)
Thomas (R-WY)
Craig Thomas (RWY)
Edward Kennedy
(D-MA)
Patrick Leahy (DVT)
Patrick Leahy (DVT)
Vote Agreement
(μ = 0.65)
Ideological
Difference
(μ = 9.7)
Point
Connectivity
(μ = 95.7)
0.359
35.94
319
0.583
29.26
318
0.408
32.37
304
0.38
33.42
245
0.626
33.42
245
0.608
30.35
241
0.374
29.3
240
0.324
32.87
240
0.35
29.87
215
0.39
32.94
215
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
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.
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.
Prof. Jennifer N. Victor
3
2
1
0
Density
4
5
Voting Agreement--House
0
.2
.4
.6
House Coincidence of Voting
.8
1
0
1
2
Density
3
4
5
Voting Agreement--Senate
.2
.4
.6
Senate Coincidence of Voting
.8
1
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.
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
Senate Co-sponsorship (Fowler 2006)
2004 A-list Bloggers (Adamic and Glance 2005)
Number of Papers Published in Major Political Science
Journals with the word "Network" in the Title
Number of Publications
(APSR, AJPS, JOP, IO, LSQ, BJPS, APR, PSQ, PA, PS, PC)
(Data Complied by author)
20
15
10
5
0
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.
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.
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.
Negative Binomial Predicting Number of Lobbyist-Donors, 2006 cycle
House
Senate
Number of Lobbyist-Donors
Number of Lobbyist-Donors
Coeff.
Z
Pr>|z|
Coeff.
Z
Pr>|z|
-0.490
0.244
Distrance from Median
-2.14
0.032
0.78
0.435
(0.229)
(0.313)
Competitive District/In
Cycle
African-American/Minority
0.500
(0.107)
-0.322
(0.233)
Woman
Party/Committee Leader
Terms Served
Constant
-0.029
(0.010)
0.724
(0.149)
0.008
(0.012)
2.523
(0.103)
4.47
0.000
-1.38
0.167
-0.29
0.771
4.87
0.000
0.65
0.516
24.43
0.000
1.328
(0.145)
-0.285
(0.339)
-0.148
(0.227)
0.388
(0.158)
-0.064
(0.042)
3.065
(0.156)
ln(alpha)
-0.394296
-0.394629
(0.089)
(0.194)
alpha
0.674154
0.673930
(0.060)
(0.131)
N
437
Log Restricted-Likelihood
-1522
robust standard errors in parentheses, clustered on state
101
-454
9.19
0.000
-0.84
0.401
-0.65
0.514
2.45
0.014
-1.51
0.132
19.67
0.000
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):
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)
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)