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Gloria Ayee and Alicia Reyes-‐Barriéntez POLSCI 239 Final Paper May 5, 2011
GENDER AND PERCEPTIONS OF STATE LEGISLATORS
The presence of women in the realm of state legislative politics has increased dramatically
within the past few decades. Since 1971, the number of women serving in state legislatures
has quintupled. In 2011, women make up 23.3% of state legislators throughout the United
States (NFWL 2011). While there is an implicit assumption that as more women are elected
to political office their power and influence in policymaking will increase, a greater
presence of women in politics does not necessarily translate into a proportionate amount
of female power and influence (Kathlene 1994).
State governments influence the lives of their citizens through legislative
arrangements, which include creating laws and policies. Lawmaking is a complex process
that involves holding hearings, drafting bills, amending bills, and building coalitions.
Citizens vote for elected officials with the expectation that they will enact policies that are
in the best interests of the representative’s constituents, but there is variability in the
ability of legislators to effectively accomplish this objective. Scholars who study the
political behavior of men and women in public office have presented contradictory results.
As Reingold (1996:455) notes, some research suggests that women in office have a distinct
way of participating in politics, but equally compelling studies have revealed that there are
little or no differences between men and women in the way they express attitudes and
behave in terms of political power and influence. Although studies on state legislatures
have noted variations by gender in terms of the personal dynamics of legislative behavior
Ayee and Reyes-‐Barriéntez 2
(Blair and Stanley 1991), there is no conclusive and systematic research that examines the
role of gender in influencing perceptions of legislative effectiveness of elected officials.
While a bulk of the research has largely focused on the effects of gender on
perceptions of candidates running for office (Bledsoe and Herring 1990; Kahn and
Goldenberg 1991; Fox and Lawless 2004), few studies have addressed the effects of gender
on perceptions of elected officials. This project seeks to understand how perceptions of
political effectiveness vary depending on the gender of state legislators. That is, once
elected, to what extent does the gender of the legislator affect the way s/he is perceived?
OPERATIONALIZATION OF LEGISLATIVE EFFECTIVENESS
The factors that contribute to, or inhibit, legislative effectiveness have motivated the
research agendas of legislative scholars. What do political scientists mean when they refer
to legislative effectiveness? How can legislative effectiveness be quantified? Concepts like
“power,” “influence,” and “effectiveness” are frequently defined and used in political
science research, but they remain decidedly elusive and contextual in nature (Blair and
Stanley 1991). The extant literature operationalizes legislative effectiveness a number of
ways, using a mix of individual-‐level attributes and institutional-‐level factors as indicative
of political performance. Scholarship in the state politics literature has focused on
individual effectiveness by relying on elite surveys to generate individual reputational
rankings of legislative effectiveness (Meyer 1980; Hamm et al. 1983; Saint-‐Germain 1989;
Weissert 1991; Miquel and Snyder 2006). Legislative effectiveness assessments for
individual legislators can vary for several reasons. Each individual brings different personal
attributes to the chamber upon election. These personal attributes may affect how
successful or effective legislators are in carrying out their roles as members of the
Ayee and Reyes-‐Barriéntez 3
institution. In contrast to personal attributes, however, are institutional factors that could
also potentially affect legislative effectiveness. Institutional factors may include majority
party membership, or holding positions of institutional authority (for example, party
leaders and committee chairpersons).
In their study of women legislators in the U.S. House of Representatives, Jeydel and
Taylor (2003) measure legislative effectiveness as the ability of members to turn policy
preferences into law, and they find that legislative effectiveness is the product of length of
tenure, majority party membership, and membership in influential committees (26). Meyer
(1980) includes 12 variables in her two causal models of legislative effectiveness, finding
that education, seniority, and formal political leadership were determinants of legislators’
reputed influence. A number of scholars use a variety of measures dealing with bill
introductions and passage as another determinant of legislative effectiveness (Olson and
Nonidez 1972; Frantzich 1979; Anderson et al. 2003; Hasecke and Mycoff 2007). Other
studies have also included whether or not a legislator is a lawyer as a measure of
effectiveness (Derge 1959; Weissert 1991; Haynie 2002; Miquel and Snyder 2006).
Scholars, nonetheless, have overlooked the role of gender in perceptions of
legislative effectiveness. Given that the extant literature points to the differences in the way
political candidates are evaluated on the campaign trail (Bledsoe and Herring 1990; Khan
and Goldenberg 1991; Fox and Lawless 2004), it is important for scholarship to address the
potential role of gender in perceptions of the legislative effectiveness of state legislators.
This paper contributes to the current findings by focusing primarily on the effects of
gender on evaluations of legislative effectiveness.
Ayee and Reyes-‐Barriéntez 4
This project uses data from the North Carolina Center for Public Policy Research
(NCCPPR) to determine the extent to which gender influences perceptions of legislative
effectiveness. Three other articles–Weissert (1991), Haynie (2002), and Miquel and Snyder
(2006)–have examined legislative effectiveness in North Carolina, using data from NCCPPR.
Weissert (1991) focuses on issue specialization and finds that legislators who introduce
bills on “salient” issues are regarded as more effective than other legislators. Haynie (2002)
focuses on racial discrimination and finds that, on average, Black legislators are viewed as
less effective than White legislators, even when controlling for all other factors. Miquel and
Snyder (2006) find that seniority, being a member of the majority party, and holding a
position of power all increase legislative effectiveness ratings. Nevertheless, none of these
scholars include gender as a variable with a potentially influential role in perceptions of
legislative effectiveness.
HYPOTHESES Our hypotheses will consider the role of gender in perceptions of the legislative
effectiveness of state legislators. Specifically, we will examine whether or not journalists,
lobbyists, and other legislators rate state legislators differentially based on gender. We
propose the following hypothesis:
H1: Ceteris paribus, male legislators will be perceived as more effective than female legislators.
A number of other studies find that voters and elites are socialized to perceive that men are
better capable of being political leaders (Campbell et al. 1960; Kirkpatrick 1974; Hill 1981;
Sapiro 1983). Deber (1982) finds that for women candidates to be elected, they must
appear more qualified than their men counterparts in order to overcome electoral hurdles.
Ayee and Reyes-‐Barriéntez 5
Our hypothesis fits in with this line of literature. The second hypothesis considers the role
of qualifications in affecting perceptions of legislative effectiveness:
H2: When compared to male legislators with similar legislative qualifications (leadership position, whether or not the legislator is lawyer, length of tenure, and number of bill introductions), female legislators will receive lower legislative ratings.
Legislators who are chairs of committees or are in the majority are more likely to be
considered more successful at introducing and passing bills (Jeydel and Taylor 2003).
Jeydel and Taylor (2003), nonetheless, do not consider gender as a variable in their model
with a potentially significant affect on legislative effectiveness ratings. For women, these
qualifications may not have as positive an effect as it may for men, given that women are
perceived as being “out of place” in the political arena, generally seen as “a man’s world”
(Bullock and Heys 1972; Welch 1977; Costantini 1990). Additionally, women are perceived
as being less capable of raising sufficient funds and effectively introducing and passing bills
(Fox and Lawless 2004). Thus, we expect that despite similar qualifications and higher-‐
ranking statuses, women will be perceived as less effective than men.
DATA AND METHODS
We use data collected by the North Carolina Center for Public Policy Research (NCCPPR),
which contains evaluations of members of the North Carolina General Assembly by
legislators, lobbyists, and the journalists during the following years: 1983, 1985, 1987,
1989, and 1991. For the NCCPPR data, an average effectiveness score is computed for each
legislator based on assessments of legislators’ participation in committee work, their skill
in guiding bills through the floor debate, their expertise in special fields, the political power
they hold (either by virtue of formal office, longevity, or personal attributes), and their
ability to sway the opinion of their fellow legislators (NCCPPR 1978, 4). Registered
Ayee and Reyes-‐Barriéntez 6
lobbyists, members of the media who cover the news on the North Carolina state
legislature, and every member of the General Assembly provided a rating for each legislator.
The data contain legislative effectiveness ratings for the 120 members of the lower house,
the North Carolina General Assembly.
One potential limitation of our project has to do with our ability to generalize the
results from a study that focuses on data collected for North Carolina. State legislatures are
not monolithic institutions, and comparisons between different state legislatures are
complicated by a number of factors, including length of session, political culture, number of
standing committees, degree of party competition, size of chamber, and staff resources
(Kathlene 1994). Regardless, there are some commonalities across the different legislative
bodies. Thus, while our data is only available for the North Carolina General Assembly, the
state’s legislature is in many respects like other state legislatures:
Like all other states (except Nebraska), it has two houses, and most of its legislators are male lawyers, business [people], or farmers. Its members introduce approximately the same number of bills as the national average and give up their seats at approximately the same rate. Session length in North Carolina is typical of many states. Most or all [sic] members of the North Carolina legislature and the nation are part-‐timers, and like most states, have only very limited access to professional staff… Salaries of North Carolina legislators are in the lower range, but not the lowest. And as in other states, the legislative agenda is dominated by spending issues for schools, highways, healthcare for the poor, welfare and a variety of judicial issues (quoted in Haynie 2002 from Weissert 1989: 17).
We are thus confident that our findings have important implications for legislatures
outside of North Carolina.
We begin our methodological approach by providing a table with the descriptive
statistics of overall legislative effectiveness scores during the five sessions from 1983 to
1991 (Table 1). Table 2 looks specifically at ratings by varying qualifications (including
length of tenure [seniority], whether the legislator is a leader, number of bills introduced,
Ayee and Reyes-‐Barriéntez 7
and whether the legislator is a lawyer). We also include histograms that provide a visual
representation of the descriptive statistics in Table 2.
We then run an ordinary least squares (OLS) regression to examine the relative
effects of gender on perceptions of legislative effectiveness, including ratings by all three
groups (legislators, lobbyists, journalists). Given that our data contain repeated
observations of individuals over time, autocorrelation of variables is a potential
methodological concern in the development of our models. Thus, we clustered the
standard errors by legislator, which makes reaching levels of significance more rigorous.
The dependent variable is the total effectiveness score given by journalists, lobbyists and
other legislators. A study of the ratings methods and procedures used by various states in
evaluating legislative effectiveness concluded that the effectiveness measures used by
NCCPPR are the most systematic, objective, and widely respected (quoted in Haynie 2002,
from Mahtesian 1996). The independent variables include gender, majority party, whether
the legislator holds a seniority position, whether the legislator holds a leadership position,
whether the legislator is a lawyer, the number of bills introduced by the legislator, whether
the legislator is a member of the rules committee and/or the finance committee, and a
dummy variable for each session year. We create three models with legislative
effectiveness ratings as the dependent variable. Model II includes lawyer as an independent
variable, while Model I does not. Model III looks at the effect of gender on qualifications
(including length of tenure [seniority], whether the legislator is a leader, number of bills
introduced, and whether the legislator is a lawyer). We also look at the differences
between perceptions among legislators, lobbyists, and journalists.
Ayee and Reyes-‐Barriéntez 8
RESULTS
TABLE 1: Descriptive Statistics of Overall Legislative Effectiveness Ratings
1983
1985
1987
1989
1991 Number of Women in the Legislature
19 15 20 19 20
Number of Men in the Legislature
111 105 100 111 100
Mean Rating of Women
42.9 41.7 39.3 43.3 42.5 Mean Rating of Men 46.0 45.2 46.2 46.6 45.1
Difference 3.1 3.5 6.9 3.3 2.6
Overall Mean Ratings 45.5 44.6 46.1 46.0 44.6
Table 1 demonstrates that during all sessions, the overall legislative effectiveness score for
men is higher than it is for women. The difference between the mean ratings of men and
women ranges from a minimum of 2.6 in 1991 to maximum of 6.9 in 1987. The descriptive
statistics provide limited support for our first hypothesis that all things being equal, men
legislators will receive higher legislative effectiveness ratings than women, but they do not
tell us anything about whether there is a difference between the ratings of men and women
with similar qualifications. Therefore, we created categories for length of tenure, whether
the legislator holds a leadership position in the legislature, whether the legislator is a
lawyer, and the number of bills introduced by each legislator. As mentioned earlier, the
existing literature largely finds that seniority, bill introductions, being a lawyer, and
holding a leadership position affect perceptions of legislator effectiveness. Thus, we should
expect to find increasing legislative effectiveness scores as length of tenure and bill
introduction increases, and for legislators who hold a leadership position and/or are
Ayee and Reyes-‐Barriéntez 9
lawyers. Table 2 confirms this expectation. With one exception,1 the descriptive statistics
demonstrate that the longer a member has served in the legislature and the more bill
introductions s/he has made, the higher his/her legislative effectiveness score. Likewise,
legislators who hold a leadership position receive higher legislative ratings than those who
are not leaders. Notably, however, the mean rating for male legislators is higher than the
mean rating for women legislators at every level of seniority and at every level of bill
introductions. Furthermore, men who are leaders receive much higher scores than women
holding a leadership position, with a marked difference of 9.1 points between the mean
score received by men and that received by women. A second disparity is the 13.3-‐point
difference in the mean legislative rating between men and women with 31 or more bill
introductions. Even more remarkable is the 14.1-‐point advantage that male lawyers have
over female lawyers. Thus far, the descriptive statistics provide some support for both of
our hypotheses. That is, men are perceived as more legislatively effective than women, and
even when compared to their male counterparts with similar qualifications, women receive
lower scores.
1 The exception we find is the decrease in legislative effectiveness ratings between seniority level II and seniority level III for women. However, we hesitate to focus on this effect since the total N (4 observations) in the seniority level III category may is small to be considered meaningful.
Tab
le 2
: D
escr
iptiv
e St
atis
tics f
or
Leg
isla
tive
Eff
ectiv
enes
s Rat
ings
by
Var
ying
Qua
lific
atio
ns
Law
yer
Wom
en: 4
4.9
Men
: 59.
0
Wom
en: 1
4
Men
: 75
Not
a L
awye
r
Wom
en: 4
42.3
Men
: 42.
2
Wom
en: 8
2
Men
: 392
Bill
In
trodu
ctio
ns
Leve
l III
31 b
ills o
r m
ore
Wom
en: 5
0.2
Men
: 63.
5
Wom
en: 1
4
Men
: 75
Bill
In
trodu
ctio
ns
Leve
l II
15-3
0 bi
lls
Wom
en: 4
7.5
Men
: 48.
0
Wom
en: 2
9
Men
: 156
Bill
In
trodu
ctio
ns
Leve
l I
0-14
bill
s
Wom
en: 3
7.8
Men
: 39.
4
Wom
en: 5
0
Men
: 271
Lead
er
Wom
en: 3
7.8
Men
: 38.
0
Wom
en: 4
4
Men
: 293
Not
a L
eade
r
9 ye
ars o
r m
ore
Wom
en: 4
0.0
Men
: 61.
7
Wom
en: 4
Men
: 34
Seni
ority
Le
vel I
I
5-8
year
s
Wom
en: 4
9.4
Men
: 53.
0
Wom
en: 2
6
Men
: 119
Se
nior
ity
Leve
l I
1-4
year
s
Wom
en: 4
0.1
Men
: 41.
7
Wom
en: 6
3
Men
: 349
Ove
rall
Mea
n R
atin
g
Num
ber o
f O
bser
vatio
ns
per C
ateg
ory
Figure 1: Relationship Between Legislative Effectiveness Scores and Gender by Varying Qualifications
The models in Table 4, which use overall legislative effectiveness scores as the
dependent variable, provide a more detailed analysis than the descriptive statistics alone.
As indicated earlier, the models include a number of explanatory variables commonly
employed as measures of legislative effectiveness: gender, seniority, whether a legislator is
a member of the majority party, whether a member holds a leadership position, the
number of bills introduced by the legislator, whether or not the legislator is a member of
the rules committee, whether or not the legislator is a member of the finance committee,
and dummy variables for each session year. Model II includes lawyer as an explanatory
0 10 20 30 40 50 60 70
Women Men
Legislative Effectiveness Score
Seniority
Seniority I
Seniority II
Seniority III
0 10 20 30 40 50 60 70
Women Men
Legislative Effectiveness Score
Leadership
Not Leader
Leader
0 10 20 30 40 50 60 70
Women Men Legislative Effectiveness Score
Bill Introductions
Introductions I
Introductions II
Introductions III
0 10 20 30 40 50 60 70
Women Men Legislative Effectiveness Score Lawyer
Not Lawyer
Lawyer
Ayee and Reyes-‐Barriéntez 12
variable, while Model I does not. In Model I, being female is statistically significant,
confirming our first hypothesis that overall, female legislators are perceived as less
effective than male legislators. But, once the lawyer variable is included, the significance
disappears (although the negative correlation between being female and overall legislative
effectiveness score remains the same in both models). This finding is important, and thus
Model III looks at the interaction between being a woman and a number of qualifications:
seniority, whether or not a legislator holds a leadership position, the number of bills
introduced by each member, and whether or not the legislator is a lawyer. The interaction
between being a woman and seniority is statistically significant and negatively correlated
with dependent variable. Likewise, the interaction between being a woman and a lawyer is
statistically significant and negatively correlated with overall legislative effectiveness score.
These two interactions provide some support for our second hypothesis, which states that
when compared to male legislators with similar legislative qualifications, female legislators
will receive lower legislative ratings. However, we are cautious about these findings given
that the data contains only 14 observations for women who are lawyers, and only a total of
4 women from 1983 to 1991 were also lawyers. A sample size with a larger size of women
might yield more conclusive results. Nonetheless, it is important to note that if we
eliminate the variable for lawyer, we find strong support for our hypotheses. If we do not
eliminate the lawyer variable, the interaction between being a woman and being a lawyer
is statistically significant. Either way, we find support for both of our hypotheses. Figure 2
plots the relationship between gender and legislative effectiveness ratings by seniority.
Overall, we find support for our second hypothesis, which states that when compared to
male legislators with similar legislative qualifications, female legislators will receive lower
Ayee and Reyes-‐Barriéntez 13
legislative ratings. The figure demonstrates that as length of tenure (seniority) increases,
legislative effectiveness ratings increase significantly for men but remain virtually the same
for women.
TABLE 4: Overall Legislative Effectiveness Scores
Variables Model I Model II Model III Intercept 27.66**
(1.73) 26.62** (1.63)
24.93** (1.63)
Female -‐2.37* (1.44)
-‐1.55 (1.34)
6.73** (2.64)
Majority Party 3.07* (1.37)
3.42** (1.29)
4.22** (1.26)
Seniority 1.17** (0.24)
1.41** (0.22)
1.62** (0.23)
Leadership Position 6.69** (1.50)
5.50** (1.40)
5.37** (1.47)
Bill Introductions 0.42** (0.05)
0.33** (0.04)
0.33** (0.05)
Lawyer —
10.05** (1.28)
11.71** (1.33)
Rules Committee Member
6.40** (1.28)
5.78** (1.20)
5.38** (1.16)
Finance Committee Member
0.74 (1.07)
0.10 (1.00)
-‐0.27 (0.98)
Year85 -‐1.88 (1.67)
-‐1.40 (1.55)
-‐1.08 (1.51)
Year87 -‐2.94* (1.71)
-‐2.28* (1.59)
-‐1.90 (1.54)
Year89 0.82 (1.68)
0.92 (1.58)
1.39 (1.54)
Year91 3.03* (1.70)
2.73** (1.58)
2.87* (1.53)
Female: Seniority — — -‐1.31* (0.66)
Female: Leader — — -‐0.90 (3.41)
Female: Bills — — -‐0.09 (0.12)
Female: Lawyer — — -‐13.57** (3.96)
N 580 580 580 Adj. R2 0.66 0.62 0.65 *p<0.10 **p<0.05 Clustered standard errors in parentheses
Ayee and Reyes-‐Barriéntez 14
Figure 2: Plotting the Relationship between Gender Legislative Effectiveness Ratings by Seniority
0 5 10 15
020
40
60
80
Length of Tenure in Years
Legis
lative E
ffectiveness R
ating
women
men
Ayee and Reyes-‐Barriéntez 15
The findings thus far, nonetheless, do not reveal whether legislative effectiveness
ratings differ among group (legislators, lobbyists, or journalists). One group could
potentially be driving the overall mean scores. Table 5 thus looks at each individual group,
with two models included for each group (one that includes the variable for lawyer and one
that does not). For legislative effectiveness ratings given by fellow legislators, the
relationship between being female and the dependent variable is negative and statistically
significant. That is, legislative effectiveness scores decrease for female legislators,
regardless of whether the variable for lawyer is included. For ratings given by journalists,
whether or not the variable for lawyer is included in the model does not matter;
nonetheless, there is a negative relationship between being female and legislative
effectiveness scores. For lobbyists, being female is statistically significant when the lawyer
variable is not included, but when the variable is included, the relationship does note reach
statistical significance. Overall then, legislative effectiveness scores given by journalists
appear to be the least influenced by gender than legislators, while legislators seem to be
the most influenced by gender.
Ayee and Reyes-‐Barriéntez 16
TABLE 5:
Overall Legislative Effectiveness Scores by Legislators, Journalists, and Lobbyists
Legislators Model I
Journalists Model I
Lobbyists Model I
Legislators Model II
Journalists Model II
Lobbyists Model II
Intercept 30.17** (1.65)
20.52** (2.62)
32.51** (1.70)
29.24** (1.56)
18.94** (2.03)
31.70** (1.62)
Female -‐2.96** (1.37)
-‐2.11 (1.80)
-‐2.40* (1.41)
-‐2.24* (1.29)
-‐1.09 (1.68)
-‐1.71 (1.33)
Majority Party 2.93** (1.31)
3.15* (1.72)
3.18* (1.35)
3.22* (1.24)
3.69** (1.61)
3.38** (1.28)
Seniority 1.22** (1.23)
1.01** (0.30)
1.33** (0.23)
1.43** (0.22)
1.31** (0.28)
1.53** (0.22)
Leadership Position 5.39** (1.43)
8.97** (1.89)
5.41** (1.47)
4.36** (1.35)
7.43** (1.76)
4.44** (1.40)
Bill Introductions 0.39** (0.05)
0.49** (0.06)
0.39** (0.05)
0.31** (0.05)
0.37** (0.06)
0.32**
(0.05) Lawyer — — — 8.70**
(1.23) 12.60** (1.61)
8.54**
(1.28) Rules Committee Member 6.26**
(1.22) 6.84** (1.60)
6.12** (1.25)
5.76** (1.15)
6.07** (1.50)
5.57**
(1.19) Finance Committee Member 0.54
(1.09) 1.00 (1.90)
0.61 (1.05)
0.01 (0.96)
0.43 (1.25)
0.02
(1.00) Year85 0.51
(1.59) -‐1.45 (2.09)
-‐4.64** (1.63)
0.91 (1.49)
-‐0.84 (1.94)
-‐4.26**
(1.54 Year87 -‐2.81*
(1.63) -‐2.97 (2.13)
-‐2.89* (1.67)
-‐2.24 (1.52)
-‐2.13 (1.98)
-‐2.36 (1.58)
Year89 2.48 (1.60)
-‐0.49 (2.12)
-‐0.62 (1.64)
2.32 (1.52)
0.71 (1.98)
-‐0.59 (1.57)
Year91 5.29** (1.62)
2.55 (2.13)
1.14 (1.66)
4.98** (1.52)
2.16 (1.97)
0.9 (1.57)
N 580 588 588 588 580 580 Adj. R2 0.60 0.51 0.55 0.55 0.57 0.60 *p<0.10 **p<0.05 Clustered standard errors in parentheses
Ayee and Reyes-‐Barriéntez 17
CONCLUSION While a number of studies have focused on the effects of gender on perceptions of
candidates running for political office, no systematic research has been carried out to
determine whether perceptions of legislative effectiveness of elected officials varies by
gender. This research project attempts to fill the lacuna in the scholarship by examining
how perceptions of political effectiveness vary depending on the gender of state legislators.
It is important for scholars to understand the dynamics that affect perceptions of legislative
effectiveness, because legislative effectiveness is a necessary precondition for political
career advancement (Mayhew 1991).
The work described here is important for several reasons. First, it extends previous
research that examines the factors that affect perceptions of candidates running for office
by including an analysis of whether or not these factors continue to remain relevant once
these political candidates are voted into office. Second, our models include gender as an
explanatory variable that affects perceptions of legislative effectiveness. Finally, the
analysis reveals that being a lawyer plays a significant role in evaluations of male
legislators but not for female legislators.
We began this study with the expectation that men serving in the legislature would
be perceived to be more effective than women, regardless of seniority, leadership position,
number of bills introduced, and whether or not the legislator is a lawyer. The analysis
presented in this article supports both of our hypotheses, which state that ceteris paribus,
male legislators will receive higher legislative effectiveness ratings than female legislators,
and that when compared to male legislators with similar legislative qualifications, female
legislators will receive lower legislative ratings. Nonetheless, as we noted earlier, the data
Ayee and Reyes-‐Barriéntez 18
is limited in that the sample size of women is not ideal. An analysis of data from other state
legislatures is needed to expand on this project.
Ayee and Reyes-‐Barriéntez 19
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APPENDIX: R CODE rm=(list=ls()) options(scipen=3) options(digits=3) ncga=read.table("/Users/aliciareyes-barrientez/Desktop/NCGA_ORIGINAL_updated.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE) names(ncga) ### RECODING VARIABLES library(car) summary(ncga$senior) senior.recode=recode(ncga$senior, "1:4=1; 5:8=2; 9:18=3") summary(ncga$gender) gender.recode=recode(ncga$gender, "1=0; 0=1") #1=female summary(ncga$intros) intros.recode=recode(ncga$intros, "0:14=1; 15:30=2; 31:69=3") ### NEW DATA FRAME FOR RECODED VARIABLES attach(ncga) new.data=as.data.frame(cbind(name, senior.recode, senior, gender.recode, pid, leader, apco, finco, ruco, intros, intros.recode, edu, lawyer, legeffect, lobbyeffect, mediaeffect, totaleffect, year83, year85, year87, year89, year91)) detach(ncga) ### OLS REGRESSIONS regTOTAL=lm(totaleffect~gender.recode+pid+senior+leader+intros+lawyer+ruco+finco+year85+year87+year89+year91, data=new.data) summary(regTOTAL) regTOTAL2=lm(totaleffect~gender.recode+pid+senior+leader+intros+ruco+finco+year85+year87+year89+year91, data=new.data) summary(regTOTAL2) regTOTALi=lm(totaleffect~gender.recode+pid+senior+leader+intros+lawyer+ruco+finco+year85+year87+year89+year91+gender.recode:senior+gender.recode:leader+gender.recode:intros+gender.recode:lawyer, data=new.data) summary(regTOTALi) regLEG=lm(legeffect~gender.recode+pid+senior+leader+intros+lawyer+ruco+finco+year85+year87+year89+year91, data=new.data) regLEG2=lm(legeffect~gender.recode+pid+senior+leader+intros+ruco+finco+year85+year87+year89+year91, data=new.data) regMEDIA=lm(mediaeffect~gender.recode+pid+senior+leader+intros+lawyer+ruco+finco+year85+year87+year89+year91, data=new.data) regMEDIA2=lm(mediaeffect~gender.recode+pid+senior+leader+intros+apco+ruco+finco+year85+year87+year89+year91, data=new.data) regLOBBY=lm(lobbyeffect~gender.recode+pid+senior+leader+intros+lawyer+ruco+finco+year85+year87+year89+year91, data=new.data) regLOBBY2=lm(lobbyeffect~gender.recode+pid+senior+leader+intros+ruco+finco+year85+year87+year89+year91, data=new.data) ### CLUSTERED STANDARD ERRORS### #regTOTAL library(sandwich) #required for clustering s.e.'s options(scipen=3) options(digits=3) Mtotal=length(unique(new.data$name)) dfcw.total=regTOTAL$df/(regTOTAL$df-(Mtotal-1)) library(lmtest) coeftest(regTOTAL, dfcw.total*vcov(regTOTAL)) library(apsrtable) apsrtable(regTOTAL) #N=580, R2=0.62 #regTOTAL2 library(sandwich) #required for clustering s.e.'s Mtotal2=length(unique(new.data$name))
dfcw.total2=regTOTAL2$df/(regTOTAL2$df-(Mtotal2-1)) library(lmtest) coeftest(regTOTAL2, dfcw.total2*vcov(regTOTAL2)) apsrtable(regTOTAL2) #N=180, R2=0.66 #regTOTALi library(sandwich) #required for clustering s.e.'s Mtotali=length(unique(new.data$name)) dfcw.totali=regTOTALi$df/(regTOTALi$df-(Mtotali-1)) library(lmtest) coeftest(regTOTALi, dfcw.totali*vcov(regTOTALi)) apsrtable(regTOTALi) #R2=0.65,#N=580 #regLEG M=length(unique(new.data$name)) dfcw=regLEG$df/(regLEG$df-(M-1)) library(lmtest) coeftest(regLEG, dfcw*vcov(regLEG)) apsrtable(regLEG) #regLEG2 M2=length(unique(new.data$name)) dfcw2=regLEG2$df/(regLEG2$df-(M2-1)) library(lmtest) coeftest(regLEG2, dfcw*vcov(regLEG2)) #regMEDIA M.media=length(unique(new.data$name)) dfcw.media=regMEDIA$df/(regMEDIA$df-(M.media-1)) library(lmtest) coeftest(regMEDIA, dfcw.media*vcov(regMEDIA)) apsrtable(regMEDIA) #regMEDIA2 M.media2=length(unique(new.data$name)) dfcw.media2=regMEDIA2$df/(regMEDIA2$df-(M.media2-1)) library(lmtest) coeftest(regMEDIA2, dfcw.media2*vcov(regMEDIA2)) apsrtable(regMEDIA2) #regLOBBY M.lobby=length(unique(new.data$name)) dfcw.lobby=regLOBBY$df/(regLOBBY$df-(M.lobby-1)) library(lmtest) coeftest(regLOBBY, dfcw.media*vcov(regLOBBY)) apsrtable(regLOBBY) #regLOBBY2 M.lobby2=length(unique(new.data$name)) dfcw.lobby=regLOBBY2$df/(regLOBBY2$df-(M.lobby2-1)) library(lmtest) coeftest(regLOBBY2, dfcw.media*vcov(regLOBBY2)) apsrtable(regLOBBY2) ### Finding out how many lawyers by gender in legislature lawyerandgender=data.frame(new.data$gender.recode, new.data$lawyer) lawyerandgender=na.omit(lawyerandgender) table(lawyerandgender) lawyerandgender=data.frame(new.data$gender.recode==1, new.data$lawyer==1, new.data$year83==1) lawyerandgender=na.omit(lawyerandgender) table(lawyerandgender) summary(lawyerandgender) names(lawyerandgender) ### DESCRIPTIVE STATISTICS: TOTAL MEAN RATINGS mean(new.data$totaleffect[new.data$year83==1], na.rm=TRUE) #45.5 mean(new.data$totaleffect[new.data$year85==1], na.rm=TRUE) #44.6 mean(new.data$totaleffect[new.data$year87==1], na.rm=TRUE) #45.1 mean(new.data$totaleffect[new.data$year89==1], na.rm=TRUE) #46.0 mean(new.data$totaleffect[new.data$year91==1], na.rm=TRUE) #44.6 ### DESCRIPTIVE STATISTICS: LEG MEAN RATINGS mean(new.data$legeffect[new.data$year83==1], na.rm=TRUE) #46.7 mean(new.data$legeffect[new.data$year85==1], na.rm=TRUE)
Ayee and Reyes-‐Barriéntez 22
#48.1 mean(new.data$legeffect[new.data$year87==1], na.rm=TRUE) #46.2 mean(new.data$legeffect[new.data$year89==1], na.rm=TRUE) #48.7 mean(new.data$legeffect[new.data$year91==1], na.rm=TRUE) #48.6 ###MEAN TOTALEFFECT BY GENDER THROUGH THE YEARS #1983 mean(new.data$totaleffect[new.data$year83==1][new.data$gender.recode==1], na.rm=TRUE) #42.9 F mean(new.data$totaleffect[new.data$year83==1][new.data$gender.recode==0], na.rm=TRUE) #46.0 M #1985 mean(new.data$totaleffect[new.data$year85==1][new.data$gender.recode==1], na.rm=TRUE) #41.7 F mean(new.data$totaleffect[new.data$year85==1][new.data$gender.recode==0], na.rm=TRUE) #45.2 M #1987 mean(new.data$totaleffect[new.data$year87==1][new.data$gender.recode==1], na.rm=TRUE) #39.3 F mean(new.data$totaleffect[new.data$year87==1][new.data$gender.recode==0], na.rm=TRUE) #46.3 M #1989 mean(new.data$totaleffect[new.data$year89==1][new.data$gender.recode==1], na.rm=TRUE) #43.3 F mean(new.data$totaleffect[new.data$year89==1][new.data$gender.recode==0], na.rm=TRUE) #46.6 M #1991 mean(new.data$totaleffect[new.data$year91==1][new.data$gender.recode==1], na.rm=TRUE) #42.5 F mean(new.data$totaleffect[new.data$year91==1][new.data$gender.recode==0], na.rm=TRUE) #45.1 M ###MEAN LEGEFFECT BY YEAR #1983 mean(new.data$legeffect[new.data$year83==1][new.data$gender.recode==1], na.rm=TRUE) #44.8 F mean(new.data$legeffect[new.data$year83==1][new.data$gender.recode==0], na.rm=TRUE) #47.0 M #1985 mean(new.data$legeffect[new.data$year85==1][new.data$gender.recode==1], na.rm=TRUE) #44.0 F mean(new.data$legeffect[new.data$year85==1][new.data$gender.recode==0], na.rm=TRUE) #48.9 M #1987 mean(new.data$legeffect[new.data$year87==1][new.data$gender.recode==1], na.rm=TRUE) #42.0 F mean(new.data$legeffect[new.data$year87==1][new.data$gender.recode==0], na.rm=TRUE) #47.1 M #1989 mean(new.data$legeffect[new.data$year89==1][new.data$gender.recode==1], na.rm=TRUE) #47.0 F mean(new.data$legeffect[new.data$year89==1][new.data$gender.recode==0], na.rm=TRUE) #49.0 M #1991 mean(new.data$legeffect[new.data$year91==1][new.data$gender.recode==1], na.rm=TRUE) #46.9 F mean(new.data$legeffect[new.data$year91==1][new.data$gender.recode==0], na.rm=TRUE) #48.9 M ### DESCRIPTIVE STATISTICS BY LEADERSHIP POSITION leader.female=new.data[new.data$leader==1 & new.data$gender.recode==1,] mean(leader.female) #total:48.2, leg:49.4 length(leader.female$name) #44 notleader.female=new.data[new.data$leader==0 & new.data$gender.recode==1,] mean(notleader.female) #total:37.8, leg:40.3 length(notleader.female$name) #49 leader.male=new.data[new.data$leader==1 & new.data$gender.recode==0,] mean(leader.male) #total:57.3, leg:57.8 length(leader.male$name) #206 notleader.male=new.data[new.data$leader==0 & new.data$gender.recode==0,] mean(notleader.male, na.rm=TRUE) #total:38.0, leg:41.4 length(notleader.male$name) #292
### DESCRIPTIVE STATISTICS BY LAWYER/NOT LAWYER lawyer.female=new.data[new.data$lawyer==1 & new.data$gender.recode==1,] mean(lawyer.female) #total:44.9, leg:44.6 length(lawyer.female$name) #11 notlawyer.female=new.data[new.data$lawyer==0 & new.data$gender.recode==1,] mean(notlawyer.female) #total:42.3, leg:44.6 length(notlawyer.female$name) #82 lawyer.male=new.data[new.data$lawyer==1 & new.data$gender.recode==0,] mean(lawyer.male, na.rm=TRUE) #total:59.0, leg:60.1 length(lawyer.male$name) #117 notlawyer.male=new.data[new.data$lawyer==0 & new.data$gender.recode==0,] mean(notlawyer.male, na.rm=TRUE) #total:42.2, leg:45.1 length(notlawyer.male$name) #392 ### DESCRIPTIVE STATISTICS BY BILL INTRODUCTIONS # WOMEN intros1.female=new.data[new.data$intros.recode==1 & new.data$gender.recode==1,] mean(intros1.female, na.rm=TRUE) #total:37.8, leg:41.0 length(intros1.female$name) #N=50 intros2.female=new.data[new.data$intros.recode==2 & new.data$gender.recode==1,] mean(intros2.female, na.rm=TRUE) #total:47.5, leg:48.7 length(intros2.female$name) #N=29 intros3.female=new.data[new.data$intros.recode==3 & new.data$gender.recode==1,] mean(intros3.female, na.rm=TRUE) #total:50.2, leg:49.2 length(intros3.female$name) #N=14 # MEN intros1.male=new.data[new.data$intros.recode==1 & new.data$gender.recode==0,] mean(intros1.male, na.rm=TRUE) #total:39.4, leg:42.6 length(intros1.male$name) #271 intros2.male=new.data[new.data$intros.recode==2 & new.data$gender.recode==0,] mean(intros2.male, na.rm=TRUE) #total:48.0, leg:50.4 length(intros2.male$name) #156 intros3.male=new.data[new.data$intros.recode==3 & new.data$gender.recode==0,] mean(intros3.male, na.rm=TRUE) #total:63.5, leg:64.3 length(intros3.male$name) #75 ### DESCRIPTIVE STATISTICS BY SENIORITY # WOMEN sr1.female.data=new.data[new.data$senior.recode==1 & new.data$gender.recode==1,] mean(sr1.female.data, na.rm=TRUE) #totaleffect=40.1 #legeffect=41.8 #mediaeffect=34.5 #lobbyeffect=43.9 length(sr1.female.data$name) #63 sr2.female.data=new.data[new.data$senior.recode==2 & new.data$gender.recode==1,] mean(sr2.female.data, na.rm=TRUE) #totaleffect=49.4 #legeffect=51.6 #mediaeffect=44.6 #lobbyeffect=50.8 length(sr2.female.data$name) #26 sr3.female.data=new.data[new.data$senior.recode==3 & new.data$gender.recode==1,] mean(sr3.female.data, na.rm=TRUE) #totaleffect=40.00 #legeffect=44.5 #mediaeffect=31.5 #lobbyeffect=44.0 length(sr3.female.data$name) #4 # MEN sr1.male.data=new.data[new.data$senior.recode==1 & new.data$gender.recode==0,] mean(sr1.male.data, na.rm=TRUE) #totaleffect=41.7 #legeffect=44.5
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#mediaeffect=36.0 #lobbyeffect=44.8 length(sr1.male.data$name) #349 sr2.male.data=new.data[new.data$senior.recode==2 & new.data$gender.recode==0,] mean(sr2.male.data, na.rm=TRUE) #totaleffect=53.0 #legeffect=55.3 #mediaeffect=47.3 #lobbyeffect=56.2 length(sr2.male.data$name) #119 sr3.male.data=new.data[new.data$senior.recode==3 & new.data$gender.recode==0,] mean(sr3.male.data, na.rm=TRUE) #totaleffect=61.7 #legeffect=63.7 #mediaeffect=56.2 #lobbyeffect=65.0 length(sr3.male.data$name) #34 ### INTERACTIONS plot.seniority=plot(y=c(0,90), x=c(0,18),xlab="Length of Tenure in Years", ylab="Legislative Effectiveness Rating", type="n") abline(a=31.66, b=0.31, col="orange", lwd=3) abline(a=24.93, b=1.62, col="lightblue", lwd=3) legend(1, 90, c("women", "men"), lwd=3, lty=1, col=c("orange","lightblue"))