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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


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.


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.


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


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,


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.


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


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


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


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


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


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.


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


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


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.


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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)


#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


#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"))

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