Gender Differences in Cooperative Environments?
Evidence from the U.S. Congress
Stefano Gagliarducci and M. Daniele Paserman
University of Rome Tor Vergata and Boston University
Preliminary: suggestions welcome
April 2015
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
April
U.S.
2015
Congress1 / 44
Introduction
Main question:
Do women in the U.S. Congress engage in more cooperative behavior?
Specifically: do they build larger coalitions? Coalitions that cross party
lines?
Question is relevant for at least three literatures:
Gender differences in the performance of elected politicians.
Cooperation and partisanship in Congress.
More broadly: gender differences in cooperative behavior in a
real-world setting with high stakes → implications that go beyond the
political arena?
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Gender Differences in Cooperative Environments? Evidence from the
April
U.S.
2015
Congress2 / 44
Motivation 1 - Gender Differences in Cooperativeness
Commonly held view that women are better at building compromise (Pew
Research Center, 2015):
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April
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2015
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Motivation 1 - Gender Differences in Cooperativeness
Recent flurry of work in economics (mostly experimental) on gender
differences in psychological traits and preferences.
Niederle (2015) summarizes experimental (lab and field) literature on
three traits:
Competitiveness: Women shy away from competition.
Risk aversion: Most studies find women to be more risk averse, but
evidence more mixed with newer elicitation methods.
Altruism or cooperative attitudes: evidence very mixed, essentially no
field evidence.
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Motivation 1 - Gender Differences in Cooperativeness
Can these differences explain differences in labor market outcomes?
Much focus on competitiveness, but it is not the only trait employers
care about.
In some contexts, maybe more important to be able to reach out to
others, work together toward the common goal, build coalitions.
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Motivation 2: Bipartisanship in Congress
Large literature in political science on partisanship and polarization in
Congress.
Poole and Rosenthal’s NOMINATE score (1985) and its refinements
(D-NOMINATE, DW-NOMINATE), based on roll-call voting behavior
(http://www.voteview.com).
Party polarization has been on the increase since 1970s, acceleration
since the 1990s.
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Motivation 2 - Bipartisanship in Congress
Partisanship is often viewed as a major obstacle to good government.
Common view that more women would increase bipartisanship:
Women may be our best chance at breaking through disastrous
partisan gridlock in Congress...women in the Senate nodded
agreement when Susan Collins told Diane Sawyer on ABC World
News, “If [women] were in charge of the Senate and of the
administration, we would have a budget deal by now... With all
deference to our male colleagues, women’s styles tend to be more
collaborative.” [Swanee Hunt (former US ambassador), Boston
Globe, January 2, 2013].
Is there substance to these claims?
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Motivation 3 - Gender Differences in Policy Outcomes?
In the US Congress: Anzia and Berry (AJPS 2011): female congress
members secure more discretionary federal spending to their districts.
They interpret this as evidence of bias on the part of voters, who elect
only most talented and hard-working women.
Similar finding for Brazilian municipalities in Brollo and Troiano (2012).
In other contexts, evidence more mixed:
Female leaders choose more female-friendly laws (Chattophadyay and
Duflo, 2004; Clots-Figueras, 2011, 2012; Rehavi 2007; Funk and
Gathmann 2008).
Gagliarducci and Paserman (2012), Ferreira and Gyourko: no evidence
that female mayors (in Italy and the US) achieve better policy
outcomes.
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This paper
Focus on cosponsorship of congressional bills in the US House of
Representatives, 1989-2010.
Two measures of cooperative behavior:
Number of cosponsors on bills sponsored (coalition-building).
Number/fraction of cosponsors from opposite party (bipartisanship).
Also:
Type of coalitions formed (with women, with more senior legislators...)
Topic of the bill (”women-friendly” topics and not)
Outcomes of the bills (passed House, became public law)
Methodology:
First show OLS with varying sets of controls.
How to address selection of districts in which women are elected? RD
(with caution) and propensity-score weighting.
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April
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Unique contribution:
Test of gender differences in cooperative behavior using real-world
setting (US Congress), high stakes decisions.
There may be more evidence of bipartisanship in bill sponsorship and
cosponsorship:
Polarization literature has focused on roll call voting behavior.
But sample of bills that reach the floor misses part of the picture, party
leadership has interest in pushing forward divisive agenda (Harbridge,
2009).
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A note about cooperativeness
Three possible interpretations of cooperativeness:
1
Altruism: Legislator 1 wants to pass a bill, needs support from
legislator 2. Legislator 2 is indifferent to content of bill, offers support.
2
Public Good: Same as above, but legislator 2 cares about bill, but
would rather free ride on someone else’s work.
3
Battle of the Sexes:
Tax Hike
Spending Cut
Tax Hike
2,1
0,0
Spending Cut
0,0
1,2
In all three cases, we can think of acting cooperatively or inducing
cooperation.
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Preview of the Findings
Coalition-building:
Women have higher number of cosponsors on bills they sponsor.
Differences almost disappear after controlling for selection.
Bipartisanship:
No gender difference in number cosponsors of opposite party.
But large differences by party: female Republicans have more
opposite-party (and female) cosponsors, female Democrats fewer.
All effects amplified on female-issues.
Female-sponsored bills no more likely to become public law.
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Outline
Introduction and Motivation
Institutional Framework
Data and Methodology
Results
Conclusion
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Institutional Background
House of Representatives: lower chamber of US legislative branch.
435 congressional districts, each state is represented in proportion to
population.
Elections for all districts every two years.
Representatives draft legislation, must also pass Senate and be
approved by President before becoming law.
Different types of legislation:
Bills
Simple Resolutions
Concurrent Resolutions
Joint Resolutions
Will focus on House bills, have force of law and are primary vehicle
for members’ legislative efforts.
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Institutional Background - Sponsorship
Each bill has one primary sponsor. Not necessarily sole or most
important author of bill.
However, sponsor is identified with bill content.
Sponsor’s activities:
gathering and communicating information
coalition building
public relations
shepherding legislation through House.
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Institutional Background - Cosponsorship
Bill can be signed by any number of cosponsors.
Some debate about whether cosponsorship is just position-taking.
Wawro (2001): “The number of cosponsors a member can [attract] ... is
indicative of the member’s ability to convince others that the bill is worthy
of their support.”
Attracting numerous cosponsors can keep a bill moving through the
legislative process.
0 cosponsors
1-10 cosponsors
11+ cosponsors
Gagliarducci and Paserman
Passed the House
15.6%
20.6%
21.0%
Became Public Law
4.2%
5.4%
5.3%
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Data
Focus on Congresses 101-111 (1989-2010)
Information on bill sponsorship, content, outcome, number and
identity of cosponsors from THOMAS (Library of Congress).
Information on individual congress members (age, gender, tenure in
congress, committee membership) from ICPSR, Biographical
Directory of Congress.
Information on electoral outcomes (votes by party, identity of losing
candidates): House Clerk, Congressional Quarterly.
District demographics: US Census Summary Data Files.
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5
10
Pct. female
15
20
25
Results - Descriptive Analysis
1990
1995
2000
Year Congress Elected
All
Republicans
2005
2010
Democrats
Figure 1: Fraction of Female Congress Members
Figure 1: Fraction of Female Congress Members
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Results - Descriptive analysis
Table 1: Summary Statistics
Panel A: Sponsor Characteristics
All Sponsors
Democratic Sponsors
Republican Sponsors
All
4,778
Male
4,188
Female
590
All
2,505
Male
2,099
Female
406
All
2,273
Male
2,089
Female
184
12.8
(10.5)
12.5
(10.4)
14.5
(11.1)
13.2
(11.1)
12.9
(10.9)
14.8
(12.0)
12.4
(9.7)
12.2
(9.8)
14.0
(8.8)
All
61,161
Male
52,577
Female
8,584
All
33,043
Male
27,027
Female
6,016
All
28,118
Male
25,550
Female
2,568
Number of cosponsors: median
17.0
(35.9)
3
16.5
(35.5)
3
19.9
(38.3)
5
17.6
(35.8)
4
17.0
(35.6)
3
19.9
(36.8)
6
16.3
(36.0)
3
15.9
(35.4)
3
19.8
(41.7)
4
Percent cosponsors opposite party: mean (std.
dev.)
15.0
(21.3)
15.2
(21.4)
13.5
(20.6)
11.7
(18.1)
12.4
(18.6)
9.0
(15.32)
18.8
(24.0)
18.3
(23.6)
23.9
(26.8)
Number of Sponsors
Number of bills sponsored: mean (st. dev.)
Panel B: Bill Characteristics
Number of bills
Number of cosponsors: mean (std. dev.)
Notes: In Panel A the unit of observation is an individual-congress member, while in Panel B is a bill.
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14
Mean number of cosponsors
20
22
16
18
24
Results - Descriptive analysis
1990
1995
2000
Year Congress Elected
Total
Males
2005
2010
Females
ite Party
0
35
Figure 2a: Mean Number of Cosponsors
Gagliarducci and Paserman
Figure 2a: Mean Number of Cosponsors
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Males
Results - Descriptive Analysis
15
Pct. of Cosponsors of Opposite Party
25
30
35
20
Figure 2a: Mean Number of Cosponsors
1990
1995
2000
Year Congress Elected
Total
Males
2005
2010
Females
Figure 2b: Percent Cosponsors of the Opposite Party
Figure 2b: Fraction Cosponsors of Opposite Party
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OLS Analysis
Table 2a: OLS Regressions
(1)
(2)
(3)
(4)
(5)
A: Number of Cosponsors
All
s.e.
n. bills
3.308 ***
(0.667)
61,334
2.103 ***
(0.635)
61,334
2.737 ***
(0.650)
60,670
1.450 **
(0.651)
61,334
2.004 ***
(0.659)
60,670
Democrats
s.e.
n. bills
2.675 ***
(0.836)
33,043
1.686 **
(0.805)
33,043
3.012 ***
(0.772)
32,847
0.840
(0.841)
33,043
1.925 **
(0.789)
32,847
Republicans
s.e.
n. bills
4.082 ***
(1.135)
28,118
3.174 ***
(1.046)
28,118
3.791 ***
(1.127)
27,671
2.623 ***
(1.000)
28,118
3.162 ***
(1.068)
27,671
Year effects
Yes
Yes
Yes
Yes
Yes
Bill category fixed effects
No
Yes
Yes
Yes
Yes
Sponsor charactersistics
No
No
Yes
No
Yes
District characteristics
No
No
No
Yes
Yes
Notes: Each entry in the table represents the coefficient on the female dummy from separate regressions. Robust standard
errors, clustered at the individual-Congress level, in parentheses. The unit of observation is a bill. All estimates include
Congress fixed effects. Bill characteristics include 20 dummies for the major topic. Sponsor characteristics include: age,
tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or ranking member) or black, a
party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a dummy for
whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics include: 3 macro area dummies, the percentage of black, over-65, foreign and urban residents, the logarithm
of the median income, and the logarithm of the population density.
Table 2a: OLS Analysis - Number of Cosponsors
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OLS Analysis
Table 2b: OLS Regressions
(1)
B: Number of Cosponsors of Opposite Party
(2)
(3)
(4)
(5)
All
s.e.
n. bills
-1.539 **
(0.626)
61,331
-1.894 ***
(0.615)
61,331
0.047
(0.524)
60,667
-0.767
(0.577)
61,331
0.347
(0.522)
60,667
Democrats
s.e.
n. bills
-2.996 ***
(0.417)
33,042
-2.800 ***
(0.411)
33,042
-2.460 ***
(0.406)
32,846
-1.757 ***
(0.411)
33,042
-1.681 ***
(0.424)
32,846
Republicans
s.e.
n. bills
5.847 ***
(1.177)
28,116
4.669 ***
(1.053)
28,116
5.830 ***
(1.017)
27,669
3.560 ***
(0.941)
28,116
4.345 ***
(0.972)
27,669
Year effects
Yes
Yes
Yes
Yes
Yes
Bill category fixed effects
No
Yes
Yes
Yes
Yes
Sponsor charactersistics
No
No
Yes
No
Yes
District characteristics
No
No
No
Yes
Yes
Notes: Each entry in the table represents the coefficient on the female dummy from separate regressions. Robust standard
errors, clustered at the individual-Congress level, in parentheses. The unit of observation is a bill. All estimates include
Congress fixed effects. Bill characteristics include 20 dummies for the major topic. Sponsor characteristics include: age,
tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or ranking member) or black, a
party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a dummy for
whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics include: 3 macro area dummies, the percentage of black, over-65, foreign and urban residents, the logarithm
of the median income, and the logarithm of the population density.
Table 2b: OLS Analysis - Percent Cosponsors of the Opposite Party
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OLS Analysis
Number of cosponsors:
Women attract higher number of cosponsors.
In all specifications, female coefficient attenuated with inclusion of
district characteristics.
Number of cosponsors opposite party:
Democratic women less cooperative, Republicans more.
Coefficients attenuated with inclusion of district characteristics.
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Methodology
From OLS analysis, two main facts emerge:
1
2
Controls matter, especially district characteristics.
Important differences by party.
Districts that elect female representatives different in terms of
observables. Perhaps also unobservables?
Solution: RD Analysis?
In mixed-gender races decided by narrow margin, gender of elected
representative essentially randomized.
But, in RD analysis separate by party, races on two sides of the
threshold come from very different pools:
Compare close races in which female R narrowly beats male D to races
in which male R narrowly beats female D (and vice versa for D).
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RD analysis - Distribution of Margin of Victory
Kernel Density of the Running Variable
0 .005 .01 .015 .02
Party = All, bandwidth = 15
-100
-50
0
50
100
Kernel Density of the Running Variable Kernel Density of the Running Variable
Party = Republicans, bandwidth = 15
.04
0
0
.01
.02
.03
.005 .01 .015 .02
Party = Democrats, bandwidth = 15
-100
-50
0
50
100
-100
-50
0
50
100
Figure 3: McCrary Density Tests
Figure 3: Distribution of Margin of Victory: All, Democrats and Republicans
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Within party, substantial discontinuity in density of the running
variable at threshold.
Within party, electoral races on two sides of threshold come from
different pools, not necessarily similar in terms of observables or
unobservables.
RD is very specific form of matching estimator, where we try to
match on a single value of a single variable.
Calculate standardized difference in covariates (Imbens and
Wooldridge, 2009), for full sample and RD sample: does RD achieve
better balance?
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Balancing Tests
Table 3A: Balancing Tests
(1)
OLS - Full
Sample
ALL
(2)
(3)
RD PS
matching optimal
bandwidth Full Sample
(4)
OLS - Full
Sample
Democrats
(5)
(6)
RD - optimal PS matching
bandwidth
- Full
Sample
Republicans
(7)
(8)
(9)
OLS - Full RD - optimal PS matching
Sample
bandwidth
- Full
Sample
A: District Characteristics
Northeast
-0.038
-0.003
0.035
-0.114
-0.354 *
-0.028
0.051
Midwest
-0.053
-0.218
-0.017
-0.022
0.242
0.003
-0.097
-0.655 *
0.061
South
-0.204
-0.152
-0.020
-0.304 *
-0.413 *
0.030
0.003
0.120
-0.033
0.005
0.418 *
0.525 *
-0.022
-0.005
0.048
0.563 *
-0.004
% Black
0.094
-0.077
-0.030
-0.172
0.017
-0.200
-0.271 *
-0.120
% Urban
0.429 *
0.094
0.036
0.435 *
0.216
0.016
% Foreign Born
0.421 *
0.050
-0.002
0.435 *
0.164
-0.179
-0.209
0.013
-0.308 *
0.121
0.093
0.024
0.207
Log(Population Density)
-0.048
0.025
-0.003
-0.066
Lagged Democratic Share
0.248
0.087
0.016
0.010
Campaign Expenditures Ratio (D/R
0.227
0.054
0.053
-0.033
0.000
-0.059
West
0.293 *
% Over 65
Log(Median Income)
Margin of victory
0.445 *
-0.036
0.057
0.260 *
-0.240
0.027
-0.026
0.241
-0.225
0.003
-0.260 *
0.037
0.013
-0.276 *
-0.023
0.363 *
0.014
0.070
-0.205
-0.001
0.345 *
0.058
-0.091
0.025
-0.075
-0.228
-0.069
0.122
0.369 *
0.018
0.044
-0.001
0.047
0.019
0.113
-0.021
0.000
-0.054
-0.149
0.000
-0.040
0.089
Notes: Each coefficient represents the standardized difference between treated and control units. All specifications control for Congress fixed effects. The unit of
observation is an individual-congress member. Leader is a dummy for being a committee chair or ranking member. Camp. Exp. D/R is the percentage of total campaign
spending by the Democratic candidate. In column (4) the optimal bandwidth is 25.
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Balancing Tests
Table 3A: Balancing Tests
(1)
OLS - Full
Sample
ALL
(2)
(3)
RD PS
matching optimal
bandwidth Full Sample
(4)
OLS - Full
Sample
Democrats
(5)
(6)
RD - optimal PS matching
bandwidth
- Full
Sample
Republicans
(7)
(8)
(9)
OLS - Full RD - optimal PS matching
Sample
bandwidth
- Full
Sample
B: Member Characteristics
Democrat
0.276 *
0.617 *
0.042
-0.304 *
-0.283 *
-0.307 *
Rookie
0.110
0.181
0.089
0.155
Age
0.139
0.128
0.099
0.124
-0.173
Leader
-0.204
-0.079
-0.261 *
-0.197
-0.208
Born in State
-0.363 *
Ivy League College
Tenure in Congress
-0.367 *
-
-
-
-
-
-0.541 *
-0.348 *
-0.265 *
0.156
-0.330 *
0.400 *
0.133
0.053
-0.494 *
0.029
0.015
0.129
0.395 *
-0.291 *
-0.222
-0.082
-0.457 *
0.112
-0.234
-0.370 *
-0.330 *
-0.308 *
-0.263 *
-0.137
-0.497 *
-0.124
-0.100
-0.157
-0.168
-0.195
-0.177
-0.094
No occupation
0.147
-0.050
0.164
0.126
-0.069
0.157
0.107
0.380 *
Education
0.254 *
0.165
0.169
0.225
0.115
0.165
0.281 *
0.301 *
0.101
Lawyer
-0.436 *
-0.176
-0.430 *
-0.455 *
-0.191
-0.458 *
-0.518 *
-0.380 *
-0.502 *
Professional
-0.110
-0.065
-0.135
-0.048
-0.100
-0.042
-0.267 *
-0.251 *
-0.275 *
Business
-0.175
0.178
-0.213
-0.007
0.636 *
-0.097
-0.299 *
0.127
-0.313 *
0.532 *
-0.252 *
Other
0.283 *
-0.079
0.380 *
0.170
-0.387 *
0.264 *
Black
0.209
-0.009
0.059
0.189
-0.078
0.065
-0.058
0.063
0.000
-0.550 *
-0.132
0.104
0.676 *
-0.063
Notes: Each coefficient represents the standardized difference between treated and control units. All specifications control for Congress fixed effects. The unit of
observation is an individual-congress member. Leader is a dummy for being a committee chair or ranking member. Camp. Exp. D/R is the percentage of total campaign
spending by the Democratic candidate. In column (4) the optimal bandwidth is 25.
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Solutions
RD augmented with inverse propensity score weighting to account for
differences in covariates on two sides of the threshold.
Inverse propensity score weighting on full sample (including non
mixed-gender races), controlling also for margin of victory of winning
candidate.
Propensity score can be based on district characteristics only, or on
both district and individual characteristics.
Different interpretation of the results.
Gagliarducci and Paserman
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AprilU.S.
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Results - Coalition Building
Table 4: Gender and the Number of Cosponsors
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
All
s.e.
n. bills
n. individuals
optimal bandwidth
2.004 ***
0.659
60,670
4,746
2.308
2.597
4,871
403
25
0.575
3.290
11,749
883
1.173
2.434
4,871
403
25
Democrats
s.e.
n. bills
n. individuals
optimal bandwidth
1.925 **
0.789
32,847
2,492
2.182
3.609
2,343
193
30
1.065
4.684
6,141
441
-1.201
3.441
2,343
193
30
Republicans
s.e.
n. bills
n. individuals
optimal bandwidth
3.162 ***
1.068
27,671
2,244
6.101
4.796
1,227
100
13
0.709
4.442
5,608
442
4.768
4.203
1,227
100
13
1.290 *
0.696
55,672
4,403
0.972
0.758
55,008
4,358
1.809
1.116
29,560
2,278
2.090 *
1.150
29,364
2,265
1.825 **
0.898
26,089
2,121
3.116 ***
1.171
23,818
1,953
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy for whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics include: 3 macro area dummies, the percentage of black, over-65, foreign and urban residents, the logarithm of the median
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
31 / 44
Results - Bipartisanship
Table 5: Gender and the Percent Cosponsors of the Opposite Party
(1)
OLS - Full Sample
All
s.e.
n. bills
n. ind.
optimal bandwidth
0.347
0.522
60,667
4,746
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
0.822
3.876
2,781
232
16
0.222
3.445
11,748
883
-1.681 ***
0.424
32,846
2,492
-3.351 *
1.910
1,978
167
24
-0.907
1.907
6,140
441
4.345 ***
0.972
27,669
2,244
12.514 *
6.673
1,043
88
11
5.819
7.133
5,608
442
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
0.332
3.540
2,781
232
16
-6.885 ***
2.115
1,978
167
24
7.533
5.769
1,043
88
11
1.181
0.789
55,670
4,403
0.730
1.010
55,006
4,358
-0.960
0.634
29,559
2,278
-1.332 **
0.550
29,363
2,265
3.374 ***
0.931
26,088
2,121
3.400 ***
1.072
23,817
1,953
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy for whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics include: 3 macro area dummies, the percentage of black, over-65, foreign and urban residents, the logarithm of the median
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
32 / 44
Basic Results - Summary
Women of both parties recruit more cosponsors:
Strong in OLS (+5-20%, w.r.t. 16).
Not precise in RD or matching.
On average, no evidence that women are more bipartisan.
But large differences by party:
OLS: Female Republicans recruit more cosponsors (+25%, w.r.t. 16),
and are more bipartisan (+30%, w.r.t. 5)
RD + matching: similar qualitatively to OLS.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
33 / 44
Additional Analysis
Do women recruit more female cosponsors?
Any differences on female-relevant issues?
Definition 1: Major Topic: Health, Labor/Employment/Immigration,
Education, Law/Crime/Family, Social Welfare.
Definition 2: Rank minor topics by fraction of female sponsors, take
top 25%.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
34 / 44
Results - Coalition Building with other Women
Table 6: Gender and the Percent Female Cosponsors and Female Cosponsors of the Opposite Party
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
A: Percent Female Cosponsors
All
s.e.
n. bills
n. ind.
optimal bandwidth
2.772 ***
0.303
60,667
4,745
2.926 **
1.454
3,311
271
18
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
2.977 ***
0.402
32,845
2,492
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
1.485 ***
0.406
27,670
2,243
2.053
1.436
11,748
883
1.899
1.328
3,311
271
18
2.414 ***
0.306
55,670
4,402
2.880 ***
0.383
55,006
4,357
2.186
1.615
1,795
152
22
3.208
1.969
6,140
441
2.190
1.768
1,795
152
22
2.460 ***
0.396
29,559
2,278
3.033 ***
0.469
29,363
2,265
2.355
2.576
1,157
95
12
-0.189
2.741
5,608
442
1.302
2.201
1,157
95
12
1.621 ***
0.421
26,088
2,120
1.902 ***
0.480
23,817
1,952
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy
for whether
sponsor was born Gender
in the state
of election,
the total number
of bills sponsored
within
congress.
District35 / 44
Gagliarducci
and the
Paserman
Differences
in and
Cooperative
Environments?
Evidence
fromthe
the
April
U.S.
2015
Congress
Results - Coalition Building with other Women
Table 6: Gender and the Percent Female Cosponsors and Female Cosponsors of the Opposite Party
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
B: Percent Female Cosponsors of the Opposite Party
All
s.e.
n. bills
n. ind.
optimal bandwidth
0.408 ***
0.142
60,669
4,746
-0.398
0.978
3,144
258
17
-1.025
0.974
11,748
883
-1.084
1.074
3,144
258
17
0.799 ***
0.198
55,671
4,403
0.650 ***
0.202
55,007
4,358
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
0.096
0.107
32,846
2,492
-0.410
0.535
1,927
163
23
-0.462
0.580
6,140
441
-0.463
0.622
1,927
163
23
0.340 ***
0.119
29,559
2,278
0.287 **
0.137
29,363
2,265
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
1.367 ***
0.328
27,671
2,244
1.740
2.128
1,140
94
12
0.290
2.144
5,608
442
0.639
2.108
1,140
94
12
1.323 ***
0.321
26,089
2,121
1.414 ***
0.366
23,818
1,953
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy for whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics include: 3 macro area dummies, the percentage of black, over-65, foreign and urban residents, the logarithm of the median
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
36 / 44
Results - Coalition Building with other Women
Women attract a higher number of female cosponsors (+25%, w.r.t.
2.6).
Both R and D women more likely to attract female cosponsors of the
opposite party (+60%, w.r.t. 0.7), but not estimated precisely.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
37 / 44
Results - Coalition Building on Female Issues
Table 7a: The Number of Cosponsors on Women's Issues
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
A: Number of Cosponsors
All
s.e.
n. bills
n. ind.
optimal bandwidth
3.921 ***
1.021
20,072
1,504
9.013
6.260
1,058
97
18
4.945
6.107
4,240
328
4.046
5.366
1,058
97
18
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
4.223 ***
1.162
11,721
836
-0.705
8.821
613
61
21
-0.104
8.839
2,524
189
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
4.365 **
1.912
8,292
665
14.172
9.161
508
44
16
11.176
8.900
1,716
139
1.955 *
1.055
18,136
1,383
3.567 ***
1.096
17,973
1,367
-2.458
8.338
613
61
21
1.395
1.343
10,316
749
4.475 ***
1.603
10,277
745
11.981
7.815
508
44
16
1.714
1.696
7,811
631
3.631 *
2.045
6,980
572
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy for whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
characteristics
include:
macro area dummies, the
percentage
of black, over-65,
foreign andEnvironments?
urban residents, theEvidence
logarithm of
the median
Gagliarducci
and 3Paserman
Gender
Differences
in Cooperative
from
the
AprilU.S.
2015
Congress
38 / 44
Results - Bipartisanship on Female Issues
Table 7b: Percent Cosponsors Opposite Party on Women's Issues
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
bandwidth with PS
Sample
Sample
matching
B: Percent Cosponsors of the Opposite Party
All
s.e.
n. bills
n. ind.
optimal bandwidth
2.012 ***
0.721
20,070
1,504
2.964
4.630
1,710
156
27
5.089
6.246
4,240
328
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
-0.967 **
0.485
11,721
836
-2.014
2.623
710
71
25
0.069
2.796
2,524
189
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
7.750 ***
1.600
8,290
665
24.394 **
10.462
383
36
12
24.395 **
11.033
1,716
139
1.317
4.582
1,710
156
27
-5.309 **
2.622
710
71
25
16.527 *
9.574
383
36
12
4.066 ***
1.509
18,135
1,383
3.708 *
1.995
17,972
1,367
-0.268
0.743
10,316
749
-0.891
0.582
10,277
745
7.350 ***
1.245
7,810
631
7.059 ***
1.570
6,979
572
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy
for whether
sponsor was born in
the state
of election,inand
the total number
of bills sponsored
withinfrom
the congress.
District
Gagliarducci
and the
Paserman
Gender
Differences
Cooperative
Environments?
Evidence
the
AprilU.S.
2015
Congress
39 / 44
Female Issues
On female-issues, R women recruit more cosponsors (+20-80%, w.r.t.
24), more of the opposite party (+100%, w.r.t. 6), more female
(+50%, w.r.t. 3.8), and more female of the opposite party (+200%,
w.r.t. 1).
Democrats: some evidence of more cooperation with other women on
female-relevant issues, but not as robust.
Large effect on R women possibly explained by absence of women
within their party, or R men reluctant to cooperate on women issues.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
40 / 44
Results - Bill Outcomes
Table 10: Gender and Bill Outcomes
(1)
OLS - Full Sample
(2)
RD - optimal
bandwidth
(3)
RD - 3rd order
polynomial
(4)
(5)
(6)
RD - optimal
PS matching - Full PS matching - Full
Sample
Sample
bandwidth with PS
matching
A: Passed the House
All
s.e.
n. bills
n. ind.
optimal bandwidth
-0.014 **
0.007
60,670
4,746
0.023
0.025
3,457
282
19
-0.018
0.028
11,749
883
0.007
0.027
3,457
282
19
-0.032 ***
0.007
55,672
4,403
-0.035 ***
0.007
55,008
4,358
Democrats
s.e.
n. bills
n. ind.
optimal bandwidth
-0.013 *
0.008
32,847
2,492
0.014
0.039
1,393
117
18
-0.003
0.037
6,141
441
0.066 *
0.039
1,393
117
18
-0.035 ***
0.007
29,560
2,278
-0.032 ***
0.008
29,364
2,265
Republicans
s.e.
n. bills
n. ind.
optimal bandwidth
0.002
0.012
27,671
2,244
0.038
0.055
1,353
108
14
-0.016
0.041
5,608
442
0.026
0.057
1,353
108
14
-0.012
0.012
26,089
2,121
-0.001
0.013
23,818
1,953
Sponsor characteristics
Yes
No
No
No
No
No
Bill charactertistics
Yes
No
No
Yes
Yes
Yes
District characteristics
Yes
No
No
No
No
No
Propensity Score
Distr.
Distr.+MV
Distr.+MV+Spon.
Notes: Entries in the table represent the coefficient on the female dummy. Robust standard errors, clustered at the individual-Congress level, in
parentheses. The unit of observation is a bill. All estimates include Congress fixed effects. Bill characteristics include 20 dummies for the major
topic. Sponsor characteristics include: age, tenure in Congress, a dummy for whether the sponsor is a rookie, a committee leader (chair or
ranking member) or black, a party dummie, 5 occupational dummies, a dummy for whether the sponsor has an Ivy League college degree, a
dummy for whether the sponsor was born in the state of election, and the total number of bills sponsored within the congress. District
Gagliarducciinclude:
and Paserman
Differences
Cooperative
Environments?
Evidence
April
U.S.
2015
Congress
41 / 44
characteristics
3 macro area dummies,Gender
the percentage
of black,inover-65,
foreign and
urban residents, the
logarithmfrom
of thethe
median
Bill outcomes
No effect of gender on bill outcomes, if not negative.
The advantage for women in recruiting cosponsors not enough for a
bill to move further in the legislative process.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
42 / 44
Conclusion
We have studied cooperative behavior in a real-world setting with
high stakes.
Findings: After controlling for the characteristics of the district in
which they are elected, not much evidence that women are inherently
more cooperative/bipartisan.
However, fairly robust evidence that female Republicans build larger
coalitions and attract more cosponsors from the opposite party,
especially on female issues.
⇒ Cooperation driven by common interest, rather than gender per se.
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
43 / 44
Thank you!
Gagliarducci and Paserman
Gender Differences in Cooperative Environments? Evidence from the
AprilU.S.
2015
Congress
44 / 44