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\title{The Policy and Electoral Implications of Increasing Gender Representation in Politics}
\author{Sarina Rhinehart}
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UNIVERSITY OF OKLAHOMA
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GRADUATE COLLEGE
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THE POLICY AND ELECTORAL IMPLICATIONS OF
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INCREASING GENDER REPRESENTATION IN POLITICS

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A DISSERTATION
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SUBMITTED TO THE GRADUATE FACULTY
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in partial fulfillment of the requirements for the
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DOCTOR OF PHILOSOPHY
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By
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SARINA RHINEHART
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Norman, Oklahoma
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2021
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THE POLICY AND ELECTORAL IMPLICATIONS OF
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INCREASING GENDER REPRESENTATION IN POLITICS
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A DISSERTATION APPROVED FOR THE
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DEPARTMENT OF POLITICAL SCIENCE
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BY THE COMMITTEE CONSISTING OF
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\  \  \  \  \  \  \  & Dr. Michael H. Crespin, Chair\\
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  & Dr. Charles J. Finocchiaro\\
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 & Dr.  Allyson Shortle\\
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\copyright \    Copyright by SARINA RHINEHART\   2021

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\chapter*{Acknowledgements}

The amount of support I have received during my Ph.D. experience and in writing this dissertation has been beyond incredible. My experience over the past five years at the University of Oklahoma has been a period of great personal growth, surrounded by amazing people willing to give of their time and energy to help me succeed. 

First, I thank my advisor Mike Crespin. Mike has been extremely giving of his time to help me learn the ropes of graduate school, coauthoring papers with me, editing my work, watching practice presentations, answering methods questions, and overall, being a great person to learn from. I could not have asked for a better advisor. Additionally, his vision for the Carl Albert Center graduate program made it an enjoyable place to come to work every day, and I will miss my time in Monnet Hall. 

I would also like to thank the rest of my committee – Chuck Finocchiaro, Allyson Shortle, Jill Edy, and Mackenzie Israel-Trummel for all their advice and support. Chuck has been giving of his time, especially in helping to answer tricky research design and methods questions in writing this dissertation. Allyson and Mackenzie have been great role models to me, who I admire greatly. I appreciate them supporting my research and coauthoring with me, as well as their openness to chat and offer much needed advice throughout graduate school. I have had so many positive experiences in and outside the classroom with many of the faculty in the OU Political Science Department and am grateful to have been part of such a supportive department. A special thanks to the department staff, Rhonda Hill and Jeff Alexander, for everything they do to make the department run more smoothly. 

Part of what made my experience at OU so spectacular was the people at the Carl Albert Center. The staff and faculty at the Carl Albert Center, including Katherine McRae, Kay Blunck, Kristen McMurray, Laurie McReynolds, and Cindy Simon Rosenthal were always so helpful and kind to me. I especially appreciate all the great chats with Lauren Schueler, as well as her endless supply of chocolate. 

I am also greatly indebted to all the support from Jessica Hayden and Matthew Geras. Jessica has been a role model, coauthor, and friend. I still remember coming out of one of my first graduate classes, feeling like I absolutely did not belong here, and Jessica took the time to sit and talk with me, giving me the confidence that I could be successful in graduate school. As a peer, Matthew has always been my go-to for getting feedback on papers, asking methods and coding questions, and as a coauthor. Personally, Matt has always been there for me, and I am lucky to call him my friend. 

To my parents, Janet and Jeff, for offering me an abundance of support from over 600 miles away. Weekly calls with my mom throughout graduate school were a welcome break from working. My parents' support and encouragement throughout my life has been constant, and for that, I’m so grateful. Additionally, I would like to thank the rest of my family – Vanessa, Keaton, Matt, Elise, and Cora. Visits back to Iowa are such a treat, and I hope now that I’m finished with graduate school I can visit you all more often!

Finally, I would like to thank my partner Daniel. We first met when I was preparing to take my comprehensive exams, and since then, he has been my biggest supporter in all aspects of life. In regards to graduate school, he is always willing to brainstorm ideas with me, engage in discussions to help me map out my theoretical arguments, and assist with coding. Beyond that, his emotional support has helped me through the most challenging parts of graduate school, for which I am so thankful.


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\chapter*{Abstract}
Despite more than a century since the first woman was elected to the U.S. Congress and women gained the right to vote, women remain underrepresented in Washington D.C., making up only about a quarter of lawmakers. In three separate articles, this dissertation examines gender representation in government and how the multiple identities of women candidates and politicians impact their electoral experiences and legislative behaviors. This dissertation examines how gender intersects with parental status, marital status, sexuality, race, and political party to shape women's experiences seeking and serving in political office. I find that while women continue to face obstacles in pursuing elected office and in achieving their preferred policy outcomes in government, there are also opportunities for women and other underrepresented groups to gain and exercise political power. 

In the first article, I examine candidate parental status and find that voters prefer candidates who are parents but perceive them as having less time and energy to devote to public service. My second article explores how voters respond to less conventional congressional candidates and finds that a preference for more conventional candidates is driven by a sense of shared values, perceived ability to govern, and perceived ideology. Article three looks at the influence of gender on how members of Congress vote, finding that as the role of parties has increased, gender has a diminishing impact on voting, even on women's issues. Collectively, this timely dissertation is important in understanding why representation in government matters, what obstacles remain that hinder the further inclusion of more diverse candidates in politics, and how gender intersects with other identities to shape political experiences.




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\chapter{Introduction}

Having a legislative body that better represents the identities and experiences of the people it serves has a fundamental influence on public trust in government and what types of policy issues are debated within the halls of the U.S. Congress. In recent years, we have witnessed more diverse types of candidates run and win seats in government, increasing our understanding of how the backgrounds and identities of politicians impact their campaign strategies, how voters perceive them, and ultimately, their behaviors serving in Congress. In three separate articles, this dissertation broadly examines representation in U.S. politics. Specifically, I explore the underrepresentation of women throughout the political process from women running for office to becoming legislators. Additionally, I aim to go beyond examining gender as a singular identity but as one that intersects in unique ways with other identities, including motherhood, political party, and sexuality, to shape candidates’ and politicians’ experiences and political behaviors. 

In Chapter 2, I examine how gender intersects with parental status to influence how voters perceive different types of political candidates. Specifically, I address if mothers with young children face electoral disadvantages in running for office. The article explores attitudes toward parents as political candidates and how that relationship may be conditional on gender, marital status, and race. I theorize mothers face increased skepticism about their ability to balance their career and household responsibilities. 

Using a survey experiment, I find respondents favor candidates with young children, rating parents as more relatable, better on family issues, superior on feminine and masculine traits, and more liberal. A clear disadvantage parents face is they are perceived as having less time and energy; however, immune from this parental penalty are White, married men. Gender and marital status condition parenthood, such that support is greatest for married mothers and lowest for single men without children. Ultimately, I find mothers do not face stronger voter biases than fathers, and these results highlight the advantages mothers can leverage to advance their electoral goals. 

Chapter 3 examines how voters use shortcuts, specifically information about candidates' personal lives, to evaluate political candidates who deviate from those who have historically served in Congress. Using a conjoint experiment, I vary candidates’ relationship status, children, and sexuality. I expect voters favor candidates from more traditional backgrounds, rating them as superior in terms of abilities, values, and perceived ideology. 

Overall, I find respondents favor more conventional candidate traits, as respondents prefer straight, married candidates who have older children. Republicans, women, and older respondents express a stronger preference for these more conventional candidates. At times, a shared sense of values, perceived ability to govern, and perceived ideology are all factors that explain this increased preference for more traditional candidates. However, I find that a sense of shared values with a candidate does not always align with vote choice, as Democratic respondents perceived they shared fewer values with queer candidates but this did not decrease the group’s overall willingness to vote for queer candidates, potentially because they were stereotyped as more liberal. Additionally, I find women respondents express a stronger preference than men for candidates who are parents. 

Furthermore, I find voters' evaluations of candidates are, at times, conditional on race and gender. For example, I find candidate gender conditions preferences for parental status, such that men candidates were evaluated similarly regardless of parental status, where women candidates were preferred when they had older children. Overall, this project sheds light on voters’ willingness to support nontraditional candidates and how this may impact the advancement of groups historically underrepresented in government.

My final chapter shifts to examine how lawmakers' identities influence legislative behavior. Chapter 4 adopts new methodologies to reevaluate the conventional wisdom that congressional women are more liberal than their male co-partisans. Using Bayesian item response theory, I scale legislators on 21 policy topics across 40 years. I expect women are more liberal in early years but that gender differences have diminished, driven by the replacement of moderate retirees with ideologically extreme women and a political agenda that increasingly divides legislators along party lines. 

I find House Republican women are more liberal in the 1980s and early 1990s, especially on women's issues. However, Speaker Newt Gingrich's reign marks a shift, such that GOP men and women are increasingly indistinguishable. Among Democrats, gender differences arise following the 1992 ``Year of the Woman," such that women voted more liberal. These differences persisted into 2010s; however, Democratic men and women legislators look more similar than decades prior. Ultimately, this project exposes how polarization has diminished gender differences in congressional voting, even on issues where gender has traditionally been thought of as a pivotal identity in shaping attitudes and policy preferences. 


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\chapter{How do you do it with Kids?: An Intersectional Analysis of Mothers Running for Political Office}

\section{Introduction}
``How do you do it with kids?" is a question Rep. Cathy McMorris Rodgers (R-WA) says she often hears on the campaign trail \cite{schaff2019mothers}. Nebraska state legislator Megan Hunt shares similar experiences, noting she too is often confronted with inquiries about her family such as, ``If you win and you have to go to Lincoln every day and be in the capitol, who's going to take care of your child?" \cite{five2020womenrun}. Rep. Grace Meng (D-NY) who would bring her young sons to campaign events, said, ``People will say to me, `Oh we didn't vote for you to be a babysitter,' where I don't sense [that] the same thing is said to men." \nocite{dittmar2018seat}(Dittmar, Sanbonmatsu \& Carroll 2018, pg. 79). Comments like these are just a small example of the environment mothers with young children face when running for political office. While 2018 and 2020 were record-breaking years for the number of women running for and elected to public office, there continues to be few mothers of young children within Congress. Compared to more than 100 men, only 25 women in the 116th Congress had school-aged children \cite{akin2019votemama}. 

As women are increasingly seeking political leadership, more women candidates are openly embracing motherhood on the campaign trail. For example, in 2018, Rep. Mikie Sherrill (D-NJ), brought her young children along with her to campaign events, and Zephyr Teachout ran for New York attorney general while visibly pregnant \cite{zernike2018}. Following the 2018 elections, former congressional candidate Liuba Grechen Shirley started the first political action committee devoted to helping mothers run for political office, and in Congress, Rep. Debbie Wasserman Schultz (D-FL) created the Moms in the House Caucus to bring together young mother lawmakers \cite{gibson2019moms,pepitone2019votemama}. Past research finds that having mothers represented within the halls of Congress matters, as mothers are more likely to prioritize legislation on family and child policy issues \cite{bryant2019working}.  

To understand why there are so few mothers of young children in Congress, this paper examines how voters evaluate parents running for office, conditional on gender, marital status, and race. Ample research has explored preferences for women candidates, but we still have much to learn about how gender intersects with other identities to shape voter preferences. As an individual's identities increasingly deviate from the stereotypical image of a politician, they are more likely to face uncertainty from voters about their strengths and overall ability to serve in political leadership. Specific to parenthood, stereotypes that women do or should do the bulk of household chores and child care responsibilities results in mothers running for office facing more questions from voters than fathers do about their time, energy, and abilities to serve in public office. I expect that while women with young children will face skepticism from voters about serving in office while also raising young children, male candidates will not face the same apprehension. Broadly, I expect parental status has four main influences on how voters evaluate a candidate -- overall ability, relatability, character traits, and ideology. 

Furthermore, much of the past research on parenthood and candidates hinges on the experiences of White, married mothers. This project aims to examine how parenthood intersects with other candidate identities including marital status and race. Specifically, because of the negative stereotypes of Black and single mothers, I expect these candidates will be increasingly disadvantaged as compared to White or married mothers of young children. 

To explore attitudes toward candidates with young children, I use a survey experiment on a high-quality sample, presenting respondents with a mock news article featuring a candidate running in a congressional primary race. Within the news story, the candidate's children, gender, race, and relationship are randomized. Overall, I find respondents are more likely to support candidates with young children as compared to those with no children. Respondents rated candidates with young children as more relatable, better able to address issues important to families, more warm, superior on both traditionally feminine and masculine character traits, and overall more liberal. These stereotypes may help or harm candidates based on their specific electoral context. However, a key disadvantage young parents face from voters is that parents are perceived to have less time and energy to devote to public service as compared to candidates without children. This disadvantage for having young children is consistent across gender, race, and relationship status; however, the one group immune from this parental disadvantage is White, married men. While I find limited differences based on a candidate's race, I do find gender and marital status interact with parenthood to influence voters' evaluations. Across all groups, respondents most favored married mothers and least favored single men who do not have children. This research builds on the breadth of literature devoted to exploring the opportunities and obstacles there are for campaigns of candidates who deviate from the ``ideal" image of a politician. Specifically, by exploring how parenthood impacts how voters perceive candidates, this project aims to examine how the multiple identities of women candidates shape success at the ballot box. 

\section{Mothers as Political Candidates}
While the number of women in politics has been on the rise, in 2019, women made up only 24\% of Congress and 29\% of state legislatures \cite{cawp2019womenstates,cawp2019womencongress}. An impediment to the further inclusion of women in politics is that women with political ambition, more often than men, wait until their children are grown before considering a run for office, stifling opportunities to advance to higher office \cite{carroll2013more,conway2005women,fulton2006sense}. Deason, Greenlee, and Langner (2015) coined the concept ``Politicized Motherhood," arguing that motherhood structures all aspects of women's pursuit of elected office, including when they decide to run, how they frame their campaign, and how they are perceived by voters. 

Both in the United States and globally, women politicians are less likely than their male colleagues to have children and are more likely to be unmarried, likely due to women making the decision to limit family responsibilities so they can pursue their career goals \cite{carroll2013more,deason2015mothers,joshi2020mothers}. Interviews with candidates find women more often mention balancing home life and career goals as a challenge in pursuing elected office \cite{gaddie2003born}. This phenomenon has materialized in state legislatures, where women are less likely than men to run in districts furthest away from the state capital, as this requires them to spend more time away from their families \cite{silbermann2015gender}. Counter to this narrative, some research does find one's marital or parental status does not predict likelihood of running for political office \cite{fox2014reconciling}.

Scholarship on parents running for office finds discernible differences in how men and women candidates choose to disseminate family details to voters. Men running for office are more likely than women to emphasize their family on their campaign websites, advertisements, and Twitter \cite{bystrom2004gender,campbell2018impact,cook2016gender,thomas2017privatemom}. Interviews with women candidates highlight they make strategic choices in how they frame their family in their campaign because they perceive voters will view mothers and fathers differently. For example, women candidates worry that while fathers of young children running for office will be thought of as charming and more relatable, mothers are often confronted with questions about how they will possibly manage their work-life balance \cite{neklason2018,stalsburg2016mom,zernike2018}. 

Analysis of campaign speeches finds women are more likely to talk about their families than are men, but that doing so hurts them electorally as women discussing their home life runs counter to traditional views of strong political leadership \cite{gimenezNDdoublebind}. Legislative staffers articulate they feel the media is hyper-focused on the family situations of women politicians, and evidence suggests their intuition is correct as women candidates receive more media coverage of their families than men \cite{burge2019family,niven2001does,smith2018politics}. Not only do women candidates receive more overall coverage of their families, but this coverage also tends to take a more negative tone than coverage of male candidates' families \cite{burge2019family}. Additionally, over the past several decades, analysis of media coverage and campaigns finds increasing references to candidates' family dynamics \cite{elder2012politics}. Therefore, regardless of if a voter bias against women with young children exists or not, candidates certainly seem to be acting in a way that reflects that they perceive voters will punish women candidates with young children. 

Experimental work finds voters tend to favor married candidates that have children, as parenthood reflects traditional values, cultural norms, and serves as an indicator that the candidate understands and will prioritize family policy issues (Barbara Lee Family Foundation 2017, Bell \& Kaufmann 2015, Campbell \& Cowley 2018, Greenlee 2014, Stalsburg 2010, Teele, Kalla \& Rosenbluth 2018).  \nocite{barbaralee2017,bell2015electoral,campbell2018impact,greenlee2014political,stalsburg2010voting,teele2018ties}This voter bias in favor of candidates with traditional families disproportionately impacts women as women, on average, still perform a majority of household chores and child care \cite{hook2017women,teele2018ties}. Therefore, this leaves mothers, as compared to fathers, with less time to devote to their political aspirations \cite{teele2018ties}. For example, per a 2010 survey of U.S. parents with two or more children, women spend nearly double the amount time in their week on child care as compared to men \cite{oecd2016oecd}. Additionally, parenting tasks tend to be gendered, with mothers doing more of the essential child care tasks, such as cooking or making doctor appointments, while fathers do more of the fun, leisure activities with their children \cite{musick2016parents}. The COVID-19 pandemic has only exacerbated this trend, with women being four times more likely than men to leave their jobs because of children not going to school or doing at-home learning \cite{gogoi2020}. Running for office requires long hours and working evenings and weekends, which based on average child care division of labor, leaves mothers with less time to campaign than fathers, dampening their electoral goals. 

Although voters tend to favor candidates with more traditional families, this perception is often conditional on gender. Specifically, mothers with young children are electorally disadvantaged as compared to fathers with young children (Hedlung, Freeman, Hamm \& Stein 1979, Pew Research Center 2008, Stalsburg 2010)\nocite{hedlund1979electability,pew2008,stalsburg2010voting}. These voter biases tend to be exacerbated within the Republican Party, as conservative voters are the least supportive of a mother with young children or single women without children running for office, preferring a mother with adult children (Bell \& Kaufmann 2015, Pew Research Center 2008).\nocite{bell2015electoral,pew2008} A 2018 poll found 51\% of U.S. adults believe it is better for women to have children before entering politics or early in their career if they want to reach high political office \cite{igielnik2019mothers}.

A shortcoming of past experimental research on candidates and parenthood is that it has almost exclusively looked at how respondents perceive White parents, as referenced by a photo of the hypothetical candidate or using distinctively White names to cue race. Additionally, much of the popular media features on mothers running for Congress have predominantly covered the campaign experiences of White mothers. Therefore, we have a limited understanding of how voters perceive mothers of different ethnicities and races. 

Additionally, apart from work at the Barbara Lee Family Foundation, no past research has examined how perceptions of parenthood hinges on marital status, specifically how married mothers and single mothers are viewed differently, which this paper aims to tackle.\nocite{barbaralee2017} By only examining the idealized version of motherhood -- White, married mothers -- most of the past work on parenthood and electoral politics draws much of its conclusions on the experiences on only a subset of parents and fails to consider how other identities may intersect with parenthood to shape voters' perceptions and biases. The goal of this project is to more carefully consider how stereotypes of motherhood may be conditional on other identities and the impacts this has on candidates' electoral prospects. 

\section{Theoretical Expectations}
Collectively, I expect voters prefer candidates who are married and have children. However, there may be a subgroup of that preferred group who faces disadvantages from voters -- mothers of young children. In the past several decades, women have increasingly entered the workforce, and in early 2020, women in the U.S. for the first time, held more jobs than men did \cite{kelly2020workforce}. Despite these gains, many still cling to norms of more traditional family dynamics and gender roles in the home. In a 2013 Pew survey, 51\% of respondents said children are better off with their mother at home, while only 8\% of respondents said the same about fathers \cite{wang2013}. Therefore, women with young children often face more societal pressures than men to stay at home with their children. 

These gender stereotypes and views of traditional gender roles ultimately influence how women are perceived when they seek elected office. The characteristics and traits typically thought to be most necessary for political leadership are often those stereotypically held by men. Furthermore, voters are more likely to ascribe these leadership traits to male candidates based solely on their gender. Therefore, women candidates may face a disadvantage as they do not fulfill the ``ideal" vision of who should be a politician in the eyes of some voters, and women candidates are less likely to be stereotyped as possessing the traits most valued in politics. 

Based on who has historically served in government and who continues to possess a majority of seats in politics, White, married fathers are likely viewed as the best overall fit or ideal image of a politician. Those who deviate from this ideal may suffer from voter biases about who belongs in politics. Pulling from psychology research, role congruity theory argues that because of the mismatch between women's traditional gender roles and the requirements for political leadership, women are less preferred by voters and are stereotyped as less likely to possess the skills and traits necessary for success in these leadership positions \cite{eagly2002role}. 

This often leaves women candidates caught in a catch-22, such that if they fulfill gender norms, they fail to meet expectations for political leadership, and if they embrace the traits valued in politics, they fall outside traditional expectations of how women should behave \cite{eagly2002role,gervais2011role}. Whereas, in comparison, because stereotypically masculine traits are more congruent with ideal visions of political leadership, men are favored for these roles and more positively evaluated once in these positions \cite{diekman2006rolling}. For example, women are stereotyped as more gentle and passive, traits not typically described as in line with successful political leadership, while men are more likely to be stereotyped as tough or assertive, characteristics viewed as much more compatible with political success \cite{huddy1993gender}. 

Expanding this theoretical framing to beyond just gender, candidates whose identities and characteristics increasingly deviate from this ``ideal" politician image likely face more reluctance from voters and greater obstacles to electoral success. Candidates' personal lives and family dynamics can lead to stereotypes about their values, skills, and traits. If the ideal image of a politician is a White, married father, as an individual's own identities deviates from this archetype, such as Black, single mothers, the more likely they are to face biases from voters because they are stereotyped as incongruent with the traits most desired for political leadership \cite{hoyt2013gender}. Within the dominant culture in the United States, the traditional heterosexual nuclear family is viewed as the norm or ideal family, and therefore, all who deviate from this norm likely face varying negative stereotypes \cite{ganong1990meta}. Only those perceived to have the ideal family receive an electoral advantage from their personal lives, and those who stray from this face voter biases about their fitness for office and have to make a stronger case to voters about their political leadership skills and values \cite{auer2020invoking}. 

Looking at parents in professional environments more broadly, the stereotypes of parenthood are not equally applied to men and women. In the workforce, mothers are often stereotyped as less committed to their work, less competent, and overly emotional \cite{bragger2002effects,correll2007getting,masser2007we,morgan2013field}. When women in the workforce become mothers, they are perceived as less competent than women without children, whereas the perceived competence of a man is not contingent on his parental status, thus boosting men's chances for career advancement \cite{cuddy2004professionals}.\footnote{These stereotypes of motherhood are predominately driven by men \cite{halpert1993pregnancy}.} Specifically, the stereotypical traits of motherhood, such as more gentle and warm, are counter to the traits desirable for political leadership \cite{huddy1993gender,smith2018politics}. 

In general, mothers are more harshly judged than fathers for being in the workforce. For example, stay-at-home mothers are viewed as more likeable and more loving of their children than working mothers, while judgments of fathers are not conditional on their employment status \cite{riedle1991exploring}. This is because mothers in the United States who take too much time away from their family for professional aspirations violate the expectations of ``intensive mothering," in which mothers face pressures to commit the largest chunk of their time and efforts to motherhood and managing the home \cite{collins2019making}.

The stereotype of mothers as less committed to working often does not translate to fathers, likely due to gender stereotypes that women do or should perform a majority of household work and child care. The perception that mothers are less committed to their careers likely shapes voters' evaluations of women candidates with young children, such that I expect voters view mothers as having less time than fathers to devote to public service. Mothers with young children running for office may be viewed as selfish for giving up time with their children to pursue their own professional gains in ways fathers are not. Even if male candidates have young children, they are not stereotyped as having less time, energy, or commitment to their work, likely because voters assume a male candidate's wife is providing the bulk of the child care. 

Furthermore, motherhood may exacerbate the stereotypes women candidates face, creating additional challenges for mother candidates \cite{deason2015mothers}. For example, women candidates are often stereotyped as possessing feminine traits, such as warmth and kindness \cite{fox2003,huddy1993gender}. Because these traits are also stereotypes of mothers, voters are increasingly likely to judge women with children as possessing feminine traits, which may disadvantage them in seeking office, as they are viewed as less likely to possess the leadership traits necessary for a career in politics and less likely to be competent in traditionally masculine policy issues \cite{rosenwasser1989gender,smith2018politics}. When primed to think about feminine stereotypes, voters view women candidates as less qualified and suffer electorally \cite{bauer2015}. However, it is also possible that as women have increasingly had electoral success, this historical disconnect between feminine traits and political leadership has diminished \cite{greenlee2017mothers}. Especially when campaigns emphasize feminine policy issues or the office is focused around a feminine policy area, women may actually be advantaged \cite{anziaNDfeminine,fox2003}. 

In particular, these stereotypes of women and traditional gender roles in the home may be especially damaging for Republican women running for office with young children. The Republican Party often frames itself as the party of traditional family values, expressed through the party's fight against abortion, the Equal Rights Amendment, and feminism, as well as its efforts to protect against the erosion of the nuclear family in U.S. culture \cite{dowland2015family,jost2008ideology,mansbridge2015we}. While Republican mothers are just as likely as Democratic mothers to be working, Republican fathers are more likely than Democratic fathers to believe in traditional parenting and believe that women should perform a majority of child care responsibilities \cite{elder2016red}. Because GOP voters are more likely to believe in more traditional gender roles, they may be especially critical of a woman seeking office while she has young children and prefer a woman to wait until her children are grown before pursuing her political aspirations. This leads to my first set of hypotheses. 

\singlespacing
\begin{hyp}
	Men candidates with young children will receive more electoral support than women with young children. 
\end{hyp} \vspace{2mm}	
\begin{hyp}
	Candidates with young children will be viewed as having less time and energy to devote to governing than candidates without children.
\end{hyp}	\vspace{2mm}	
\begin{hyp}
	Women candidates with young children will be viewed as more warm and feminine than women without children and men regardless of if they have young children or not. 
\end{hyp}\vspace{2mm}	
\begin{hyp}
	Republican respondents will be less likely than Democratic respondents to support women candidates with young children. 
\end{hyp}
\doublespacing 

\subsection{Intersectional Motherhood}
Taking up Burge, Hodges \& Rinaldi's (2020) call to take a more intersectional approach to understanding family dynamics in campaigns, I expect that in addition to gender differences, race and marital status also condition how voters perceive parents of young children running for office \cite{crenshaw1989demarginalizing}.\nocite{burge2019family} The idealized family is often depicted as two parents who are straight, cisgender, married, and White \cite{collins2012just,johnston2006constructing}. Yet, many children grow up in families that do not fit this strict ``ideal," including single parent households and Black families, which I examine in this paper.

While I expect married mothers of young children running for office will be viewed as violating gendered norms and as having less energy for politics, these stereotypes are likely applied even more harshly to single mothers. I expect single mothers will be judged as having to devote even more time to their children at the expense of professional success and further deviate from the ideal image of a politician. 

In general, single mothers, as compared to married mothers, are more likely to be negatively stereotyped. For example, single mothers are often evaluated as poor decision-makers and as worse mothers to their children than married mothers \cite{ganong1995content}. In 2018, approximately 1 in 5 children were living with a single mother, yet Americans continue to have an overall negative view of single mothers \cite{pew2018unmarried}. This upward trend of single mothers has even been publically denounced by politicians. For example, Rick Santorum during his 1994 Senate campaign said, ``We are seeing the fabric of this country fall apart, and it's falling apart because of single moms" \cite{sherman2018}. Focus group participants when asked about their opinions on single mothers running for office said they were concerned about how the campaign could negatively impact their children, which ultimately could impact one's willingness to vote for a single mother candidate (Barbara Lee Family Foundation). \nocite{barbaralee2017}

In light of the 2018 ``Year of the Woman," the candidacy of single mothers was highlighted by major media in the lead up to the 2018 midterm elections. Lily Espinoza Ploski, who ran for statewide office in California, spoke to reporters about the importance she felt for more single mothers, like herself, to run for office. Ploski said she feels single mothers are, ``one of the most disenfranchised groups of the population" \cite{pattillo2018mothers}. Other single mothers running for office in 2018 articulated how they heard voters' concerns over their children and their motherly responsibilities. For example, Katie Wilson, a single mother who unsuccessfully ran for Congress in New York, said she felt voters made assumptions about her decision to run for office coming at the expense of neglecting her motherly duties \cite{sherman2018}. This leads to my hypotheses about voter perceptions of candidates that are parents, conditional on marital status. 

\singlespacing
\begin{hyp}
	Married women candidates with young children will receive more electoral support than single women with young children, while electoral support for male candidates with young children will not vary by their marital status. 
\end{hyp} \vspace{2mm}	
\begin{hyp}
	Single women candidates with young children will be viewed as having less time and energy to devote to governing than married women with young children and men with young children regardless of if they are married or not. 
\end{hyp}
\doublespacing

I also expect race shapes how voters evaluate candidates and parenthood. Black mothers, both because of their race and gender, deviate from the typical vision of who belongs in politics. Therefore, I expect Black mothers will be more negatively evaluated as candidates than are White mothers or fathers. Not only are Black families incompatible with the ``ideal" family because of their race, but also Black women are stereotyped in different ways than White women, particularly when it comes to motherhood \cite{foster2008welfare,thomas2004toward}. For example, White mothers are often stereotyped as more feminine and more warm, but stereotypes of Black women are often related to their sexuality and place in the household \cite{harris2011sister}. Therefore, when race is coupled with parental status information, this likely exacerbates these negative stereotypes. 

One of the negative archetypes of Black mothers is that of a welfare queen, that Black mothers are perceived as lazier, more likely to receive public assistance, and more likely to have additional children \cite{ayee2019white,hancock2004politics,rosenthal2016stereotypes,woodard2005black}. Because Black mothers are more likely to be characterized in this way, people are less likely to support public assistance to Black mothers as compared to White mothers \cite{cassese2019intersectional,gilens1999welfare}. Black women are also stereotyped as playing a more dominant leadership role in managing their household than are White women, specifically when it comes to raising children \cite{chaney2011character,sewell2013mammies}. Because of this stereotype of the strong Black matriarchy, Black women are often held more responsible for their children's failures than are White women \cite{rosenthal2016stereotypes,woodard2005black}. These negative stereotypes of Black mothers likely have implications for Black woman seeking to run for office. Especially for those with young children, voters are likely to hold more negative views of Black mothers compared to White mothers.

\singlespacing
\begin{hyp}
	White women candidates with young children will receive more electoral support than Black women candidates with young children. 
\end{hyp} \vspace{2mm}	
\begin{hyp}
	Black women candidates with young children will be viewed as having less time and energy to devote to governing than White women candidates with young children. 
\end{hyp}
\doublespacing

\section{Experimental Design}
To test my expectations, I ran a survey experiment to gauge responses to political candidates and parenthood.\footnote{This research design was pre-registered with the Open Science Framework.} Respondents were presented with a mock news story about a candidate running in a U.S. Senate primary election participating in a campaign event. The candidate was described as either a Democrat or Republican, such that respondents were evaluating a candidate of their own political party, rather than of the opposite party.\footnote{Respondents who identified as independent received a follow-up question asking if they had to choose, do they think of themselves as more similar to Republicans or Democrats and then were assigned to a party treatment group based on their response to this question.} 

I chose to frame the scenario as a primary rather than a general election for two reasons. First, in primaries, voters lack the partisan cue available to them in general elections to guide their voting decision. Therefore, it is in primaries when I expect candidates' personal characteristics matter most in shaping how voters evaluate candidates \cite{hirano2019primary,kirkland2018candidate}. Secondly, in recent United States general elections, partisanship often trumps most other factors in shaping decisions at the ballot box, and therefore, I expect the effect of parenthood on voting decisions in general elections is limited, if not muted entirely.  

This experimental design is a 2x2x2x2 design. The vignette randomly varies the candidate's children (none or 2 young children), gender (woman or man), relationship status (married or single), and race (Black or White).\footnote{For consistency, all candidates are framed as in heterosexual marriages, but I encourage future research to examine how sexuality conditions perceptions of parenthood. Additionally, because of sample limitations, the candidate's race was limited to only Black or White, but I strongly encourage future research to examines other races and ethnicities in regards to parenthood and political candidacies.} Due to the number of treatment groups and sample size limitations, each respondent throughout the survey was presented with three separate news stories to evaluate.\footnote{The news stories were spread out throughout the survey, separated by other unrelated questions to avoid biasing how respondents evaluate each news article based on the prior news article. The news stories were always presented in the same order, and there was a separate randomization process for each news article.} The text of these three news stories varies but was written to be as similar as possible. They are all approximately the same length, cue the treatment variables in similar ways, and use nonpartisan language.\footnote{In each article, the age of the candidate is mentioned to avoid assumptions about the candidate's age and to keep it consistent throughout the treatment conditions.}

Within each news story, the candidate's first name and pronouns cue the candidate's gender. To cue if the candidate had children or not, much of the past experiments on parenthood have either shown a picture of the candidate's family or simply mentioned the candidate has children. However, because there has been a recent shift in candidates being more open in embracing their families with voters, particularly in media coverage of candidates, I chose to have the candidate's children more active in the individual's campaign. Each news story features the candidate at a campaign event and indicates the children were in attendance, creating the visual of the candidate in practice actually attempting to balance their professional and personal responsibilities.\footnote{More traditionally, young children could be considered any school-aged children or those under the age of 18. However, my expectations are specific to candidates having young children. While I expect that mothers of high-school aged children may also face pushback, I chose to frame the children as elementary or pre-elementary aged, because I expect the effects of motherhood to be most prominent with younger children. There is a perception that younger children require more care and time as compared to high-school aged children. Additionally, I decided to frame the candidate as having two children as this reflects the average number of children for women of this age. Based on data from 2014, for mothers in their early 40s, 41\% have two children \cite{livingston2015}.} For those without children, the story explicitly mentions the candidate does not have children, as it is possible respondents might make assumptions about if a candidate has children or not based on their marital status. The candidate's race is cued by their first and last name, as past research finds using racially distinctive names is an effective way to signal race in experiments \cite{bertrand2004emily,butler2011politicians}. To ensure the names cue race and not other characteristics about the candidate, I used past research to choose names that are nearly identically perceived as middle class, competent, and warm.\footnote{See the appendix for a full list of the names used in the experiment, as well as a more detailed description of the process for selecting names.} The candidate's marital status is stated as either married or single in the article. To increase the external validity of this experiment, all other components of the news articles, in particular the quotes, are taken from actual news coverage of various congressional candidates from both political parties.\footnote{This language is from news article written about Rep. Ayanna Pressley (D-MA), Rep. Abby Finkenauer (D-IA), Sen. Maggie Hassan (D-NH), Sen. Rick Scott (R-FL), Sen. Doug Jones (D-AL), Sen. Martha McSally (R-AZ). Before each vignette, respondents were given the following instructions: ``Please read the following news article about a political candidate. After which, you will be asked to evaluate this candidate."} 

\singlespacing
\begin{quote}
	\begin{center}
		\textbf{\textit{Experiment Treatment News Articles}} \vspace{3.5mm}
		
		\textbf{[Lillie, Ayanna, Travis, Antoine] [Novak/Smalls] announces bid for U.S. Senate
		}
	\end{center}
	[Lillie, Ayanna, Travis, Antoine] [Novak/Smalls], [Republican/Democratic] candidate for the U.S. Senate, was out on the campaign trail this week, speaking to a crowd at the local community center. 
	
	[Novak/Smalls], a [married/single] 45-year-old prosecutor, [holding one daughter on [her/his] hip and another by the hand/ exclude], introduced [herself/himself] to the crowd. [[Novak/Smalls] does not have children./exclude] 
	
	“I’m running because our country needs leaders, not politicians,” [she/he] said. “[As a single mother,/As a single father,/As a mother and wife,/As a father and husband,/As a wife,/ As a husband,/exclude] I know we all want the same thing. We want to work hard and make a good life for our family.” 
	
	[Novak/Smalls] said [she/he] decided to jump into this primary race because [she/he] is ready to serve in Washington D.C. as an advocate and a champion for [her/his] state in Congress. \\ 
	
	\begin{center}
		\textbf{[Sharon, Janelle, Charles, Cedric] [Steffen/Washington] launches campaign for U.S. Senate
		}
	\end{center}
	[Republican/Democrat] and local former mayor [Sharon, Janelle, Charles, Cedric] [Steffen/Washington] announced [he/she] is running for the U.S. Senate.
	
	[Steffen/Washington], who is 41 years old, [single/married], with [2 young children/ no children], said [she/he] decided to run for the Senate because [he/she] is ready to fight for [his/her] constituents in Congress.
	
	``Washington D.C. has lost its way on too many of the priorities that matter to our state,” [Steffen/Washington] said before a crowd of supporters on Tuesday. ``You can count on my commitment to problem-solving.”
	
	[Steffen/Washington], who was joined on stage by [his/her] [wife/husband/parents] [and children, ages 6 and 2/exclude], said this race isn’t about [him/her] or [his/her] opponents. “[As a single mother,/As a single father,/As a mother and wife,/As a father and husband,/As a wife,/ As a husband,/exclude] I know this race is about your kids, your families, your future.”
	\newpage
	\begin{center}
		\textbf{[Misty, Octavia, Ronald, Cornell] [Hofer/Cooks] set for U.S. Senate run}
	\end{center}
	Joining a crowded field for the open U.S. Senate seat is [Republican/Democrat] [Misty, Octavia, Ronald, Cornell] [Hofer/Cooks]. [Hofer/Cooks], a [single/married] 47-year-old [businessman/businesswoman] [with no children/exclude], was out speaking with voters on Friday, [joined by [his/her]/exclude] [wife/husband/exclude] [and/exclude] [two young sons,/exclude] about the stakes of this election. 
	
	``I’ll work across party lines, not to pick sides, but to find common ground,” [Hofer/Cooks] said. 
	
	[Hofer/Cooks] introduced [himself/herself] to voters as an [entrepreneur and proud American/ entrepreneur, proud American, [and single mother/and single father/mother, and wife/father, and husband/and wife/and husband]] who can’t wait for change to come in Washington D.C. 
	
	``When I see something is wrong, when I see someone struggling, I think about what am I going to be able to do today to make a difference,” [Hofer/Cooks] said. ``That’s why I’m running for the U.S. Senate.”  \\
	
	\footnotesize Note: In these news stories, the language in brackets [] is the text that varied based on which treatment group the respondent was randomized into or based on the respondent's political party. The randomization process was separate for each news article, so during this survey, each respondent was randomized into three separate treatment groups. 
\end{quote}
\doublespacing

After reading each news story, respondents answered several questions evaluating the candidate. I expect there are four broad potential explanations for how parenthood influences voters' candidate evaluations. First, I expect parenthood impacts voters' assessment of a candidate's overall ability to serve. This relates to their overall competence to serve in government, as well as their collective time, ability, and energy to get the job done, should they be elected. Secondly, parenthood likely sends a signal to voters about how relatable the candidate is, if the candidate is likely to share their values and if the candidate will represent them and their policy needs. Thirdly, those with children may be stereotyped as possessing different character traits than those without children. For example, a mother, simply because of her gender and parental status, may be stereotyped as more warm or more compassionate. Finally, voters may use a candidate's parental status to infer their ideological leanings. For example, maybe because it is more traditional for a politician to be married and have children, voters will perceive these candidates as more conservative than a single candidate without children. 

To gauge overall electoral support, I asked ``On a scale from 1 to 7, where 1 indicates that you would never support this candidate, and 7 indicates that you would always support this candidate, where would you place this candidate?"\footnote{Question wording is based on \cite{hainmueller2014causal}.} 
Several of my theoretical expectations center around the view that respondents will perceive women with young children as less committed to their careers. To operationalize this concept, I ask several questions. First, respondents answer, from strongly disagree to strongly agree, the question, ``This candidate has the time and energy to devote to public service." Additionally, I include two measures of competence, as I expect women with children, as compared to without children, may be stereotyped as less competent. Respondents rated each candidate on a 7-point scale how capable and how skillful they perceive the candidate to be.\footnote{Question wording is based on \cite{deason2020psychology,cuddy2004professionals}.} I expect women with young children to be viewed as more warm and feminine and include several questions to measure each concept. To measure warmth, respondents rated on a 7-point scale how sincere and selfish (reverse-coded) the candidate is. To examine both stereotypical feminine and masculine traits, I include four questions that ask respondents to rate the candidate on two feminine traits -- compassionate and emotional -- and two masculine traits -- decisive and tough.\footnote{Question wording is based on \cite{dolan2014takes,huddy1993gender}.}

One reason voters may favor candidates who are married and have children is that these candidates are viewed as overall more relatable. To measure this, I ask respondents on a 7-point scale from strongly disagree to strongly agree two questions -- ``This candidate shares similar values to my own," and ``This candidate represents people like me." Furthermore, I expect candidates with families may be preferred because they are perceived as having a better understanding of the issues and policies that most impact families and will be more likely to prioritize these issues should they be elected. To measure this explanation, I include the question, ``This candidate will prioritize issues important to families." Additionally, I am also curious if parenthood has any implications for the perceived ideology of a candidate, so I include the question, ``How would you rate this candidate?" and had respondents rate the candidate from very liberal to very conservative.\footnote{Question wording is based on \cite{jones2019gender}.} 

For my survey sample, I use Lucid, an online survey company that uses attention checks and open-ended questions to screen out low quality participants while also providing a sample that well reflects national demographics across age, gender, race, income, and political party \cite{coppock2019validating,fang2020perceptions}. See the appendix for a full breakdown of the demographics of my survey sample, but I find this sample has a good balance of ideologies and political parties, with 36\% of my sample identifying as Republicans, 38\% as Democrats, and 26\% as Independents. The sample is 70\% White and evenly split on gender. I requested 3,000 respondents, each of which evaluated three news stories for a total sample of 9,000.\footnote{The 3,000 represents the surveys that were completed 100\%. Other respondents did not answer all questions, but were included in the analysis when they responded to all variables included in the given model. Therefore, in most models, the sample size is larger than 9,000.}

My analysis is a combination of t-tests and OLS regression models. While most of my dependent variables are ordinal, linear regression has been recommended when estimating the causal effects of experimental treatments, even when the dependent variable is not continuous \cite{gomila2020logit}.\footnote{As a robustness check, in the appendix I include all models as ordered logit models. Results are nearly identical and any differences are noted in the appendix.} Each respondent has three separate observations within my data; therefore, I cluster standard errors by respondent in all models. Additionally, while I crafted the three news stories to be as similar as possible, it is likely there is some variation in how respondents evaluate each candidate based on the specific language in each vignette. Therefore, to accommodate for these variations, in most models I include a factored variable for which vignette was being evaluated.\footnote{I also ran all models subset out by vignette and nearly all results were consistently significant across the three vignettes, and for the few occasions when a treatment variable was not significant in one vignette, the coefficient was always in the same direction as the other vignettes. Therefore, I concluded it was appropriate to present in the body of the text the models the incorporate all vignettes.}

\section{Results}
To provide a complete view of the broader findings before jumping into more nuanced results, Table \ref{table_main_findings} displays the regression results for all dependent variables. It appears that across most dependent variables, parenthood shapes how voters evaluated candidates. The first model in Table \ref{table_main_findings} estimates respondents' overall willingness to support a candidate. Across all treatment groups, gender, race, and marital status do not significantly predict level of support, but I do find that respondents are significantly more likely to support candidates with young children as compared to candidates with no children. This finding is consistent with past research, which also finds voters prefer candidates who are parents \cite{campbell2018impact,teele2018ties}. 

In support of hypothesis 2, the second model in Table \ref{table_main_findings} shows those with no children are rated as significantly more likely to have the time and energy to serve as compared to those with young children. When holding other treatment variables constant and only varying if the candidate has children or not, I find the treatment group with young children is always rated as having significantly less time and energy. 

Parenthood also seems to impact how respondents ascribe certain character traits to candidates. Overall, I find support for hypothesis 3, such that mothers are viewed as more warm and more feminine. For my two measures of warmth -- sincere and selfish (reverse-coded) -- I find parents are perceived as more warm than non-parents. Women and parents are viewed as significantly more sincere, and parents are rated as less selfish than non-parents. For feminine traits, compassionate and emotional, I find parents, in particular mothers, are perceived as possessing more feminine traits. Women and parents are rated as significantly more compassionate, and parents are perceived as significantly more emotional. For masculine traits, I find no significant findings for my dependent variable of perceived decisiveness, but do find parents were rated as significantly more tough than candidates without children. 

	\begin{landscape}
		\begin{table}
			\begin{threeparttable}
				\footnotesize
				\caption{Models: Candidate Evaluations}
				\begin{tabular}{l c c c c c c c c c c c c}
					\hline
					\textbf{Candidate} & & \multicolumn{2}{c}{\textit{Warmth}} & \multicolumn{2}{c}{\textit{Feminine Traits}} & \multicolumn{2}{c}{\textit{Masculine Traits}} \\
					\textbf{Traits}& Support & Time & Sincere & Selfish & Compassion & Emotional & Decisive & Tough & Values & Rep Me & Fam Issues & Ideology \\
					\hline
					Woman       & $0.04$      & $-0.03$    & $0.06^{*}$  & $-0.02$    & $0.08^{*}$  & $0.02$      & $0.03$     & $0.07^{*}$  & $0.03$      & $0.08^{*}$  & $0.09^{*}$  & $-0.09^{*}$ \\
					& $(0.03)$    & $(0.03)$   & $(0.02)$    & $(0.03)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
					Black       & $-0.03$     & $0.00$     & $-0.01$     & $-0.02$    & $-0.01$     & $-0.03$     & $-0.01$    & $-0.00$     & $0.00$      & $-0.00$     & $-0.01$     & $-0.01$     \\
					& $(0.03)$    & $(0.03)$   & $(0.02)$    & $(0.03)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
					Single      & $-0.05$     & $-0.01$    & $0.00$      & $-0.01$    & $-0.02$     & $-0.01$     & $-0.01$    & $-0.01$     & $-0.04$     & $-0.07^{*}$ & $-0.05$     & $-0.08^{*}$ \\
					& $(0.03)$    & $(0.03)$   & $(0.02)$    & $(0.03)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
					No children & $-0.10^{*}$ & $0.29^{*}$ & $-0.14^{*}$ & $0.07^{*}$ & $-0.17^{*}$ & $-0.17^{*}$ & $-0.03$    & $-0.05^{*}$ & $-0.13^{*}$ & $-0.07^{*}$ & $-0.33^{*}$ & $0.10^{*}$  \\
					& $(0.03)$    & $(0.03)$   & $(0.02)$    & $(0.03)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
					Vignette 2  & $0.05^{*}$  & $0.04$     & $-0.04^{*}$ & $0.05^{*}$ & $-0.03$     & $0.02$      & $-0.01$    & $-0.01$     & $-0.00$     & $0.01$      & $-0.05^{*}$ & $-0.04$     \\
					& $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
					Vignette 3  & $0.01$      & $0.03$     & $-0.07^{*}$ & $0.11^{*}$ & $-0.08^{*}$ & $-0.01$     & $-0.03$    & $0.01$      & $-0.12^{*}$ & $-0.07^{*}$ & $-0.30^{*}$ & $-0.06^{*}$ \\
					& $(0.02)$    & $(0.03)$   & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.02)$    & $(0.02)$   & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
					Intercept   & $5.04^{*}$  & $4.21^{*}$ & $2.53^{*}$  & $1.08^{*}$ & $2.48^{*}$  & $2.05^{*}$  & $2.32^{*}$ & $2.20^{*}$  & $4.43^{*}$  & $4.27^{*}$  & $4.71^{*}$  & $2.81^{*}$  \\
					& $(0.04)$    & $(0.04)$   & $(0.03)$    & $(0.03)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
					\hline
					%R$^2$       & $0.00$      & $0.01$     & $0.01$      & $0.00$     & $0.01$      & $0.01$      & $0.00$     & $0.00$      & $0.00$      & $0.00$      & $0.03$      & $0.00$      \\
					Adj. R$^2$  & $0.00$      & $0.01$     & $0.01$      & $0.00$     & $0.01$      & $0.01$      & $0.00$     & $0.00$      & $0.00$      & $0.00$      & $0.03$      & $0.00$      \\
					Num. obs.   & $9404$      & $9401$     & $9368$      & $9371$     & $9369$      & $9369$      & $9369$     & $9373$      & $9401$      & $9396$      & $9336$      & $9377$      \\
					%RMSE        & $1.46$      & $1.34$     & $1.04$      & $1.24$     & $1.04$      & $1.12$      & $1.07$     & $1.07$      & $1.33$      & $1.37$      & $1.33$      & $1.59$      \\
					%	N Clusters  & $3242$      & $3242$     & $3234$      & $3235$     & $3238$      & $3235$      & $3235$     & $3236$      & $3242$      & $3242$      & $3237$      & $3239$      \\
					\hline
				\end{tabular}
				\begin{tablenotes}
					\item $^{*}p<0.05$. All models are OLS regression. 
				\end{tablenotes}
				\label{table_main_findings}
			\end{threeparttable}
		\end{table}
	\end{landscape}	
\normalsize

Across my two measures of relatability, respondents were also significantly more likely to say candidates with children share more similar values to their own, and that parents better represent people like themselves. This finding was also true for women and married candidates, who were evaluated as significantly better at representing people like me. As expected, parents were characterized as more likely to prioritize issues important to families, as were women candidates. 

Finally, candidates without children were overall perceived as more conservative than parents. Additionally, women and single candidates were rated as more liberal-leaning. Collectively, I find that parental status shapes much of how different types of candidates are evaluated, such that parents, as compared to non-parents, receive increased support, rated as having less time and energy to serve, are perceived as more warm, feminine, and masculine, more relatable, better on family issues, and more liberal. 

\subsection{Support Results}
Diving into more nuanced results, I turn first to overall willingness to support different types of candidates. Figure \ref{figure_support} shows the estimate and confidence interval for level of support for each of the 16 treatment groups, showcasing that respondents were most likely to support White, married mothers and expressed the lowest level of support for White and Black men who are single and do not have children. 

Ultimately, I do not find support for hypothesis 1, that support will be greater for men with young children than women with young children. For example, when I compare all treatments groups with children by gender, the average level of support for women is 5.05 and for men is 5.04. When I examine differences by race or marital status, I also do not find any support for hypothesis 1. In fact, across all treatment groups, it appears this preference for candidates who are parents is stronger for men candidates. In Figure \ref{figure_support_condensed}, I plot the results of basic t-tests for comparing candidates with and without children for all candidates and then broken down by women and men candidates. It appears respondents are significantly more likely to support a father compared to a man without children, but that support for women candidates is not significantly different between mothers and non-mothers.

While I encourage future research on why men benefit most from becoming parents in the eyes of voters, a possible explanation for this finding is that the traits often ascribed to parenthood, such as sincerity or compassionate are already traits which are often stereotypically associated with women. Therefore, while women who have children gain no extra bonus from becoming a parent, men who have children retain the positive stereotypes of being a man while gaining the positive stereotypes of parenthood. 

I do not find any support for hypothesis 4, that Republicans would be less supportive than Democrats of women with young children. Republican and Democratic respondents evaluated women with young children, regardless of her marital status or race, nearly identically in terms of willingness to support a given candidate. Furthermore, in the appendix, I separate out respondents by party and gender and run the same regression models, and find it is Democrats and women driving this stronger preference for candidates with children. The coefficient for both Democrats and Republicans is negative, meaning more support for candidates with children; however, it is only statistically significant for Democrats. Similarly, the coefficient for men respondents is substantively small and not statistically significant, while women respondents show a much stronger preference for parents.

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_support}
	\centering
	\textbf{\caption{Support for Candidates by Treatment Group} \label{figure_support}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the level of support for each treatment group. This question's responses ranged from 1, never support, to 7, always support.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_support_condensed}
	\centering
	\textbf{\caption{Support for Candidates by Parental Status} \label{figure_support_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the level of support for candidates who have young children and those who do not.}
	\end{description}
\end{figure}

In Table \ref{table_main_findings}, I find that across all groups, marital status does not predict level of support. However, I find some conditional support for hypothesis 5, that respondents favor married parents to single parents. In a t-test comparison, respondents demonstrated significantly higher support for White mothers who are married (5.15) as compared to single (4.93). However, I do not find a significant difference by marital status for parents who are White men or Black candidates. Furthermore, I find a lack of significant differences in level of support for White and Black candidates. White women with children have higher levels of support (5.15) than Black women with children (5.06), but the difference is not statistically significant. Additionally, the level of support does not significantly differ between Black men and Black women with young children. To summarize, I find respondents favor candidates who are parents, and that this preference is greater for men candidates. Respondents who demonstrate greater support for candidates with children are women and Democrats. I find that, at times, marital status conditions perception of parenthood, but do not find much support for race affecting levels of support for parents. 

To better understand why I did not have support for my hypotheses surrounding candidate's race, I examine if respondents were able to correctly identify the race of the candidate they read about. Past research has made strong arguments for the use of racially-distinctive name to cue race in survey experiments (e.g. Butler \& Homola 2017\nocite{butler2017empirical}). However, my lack of findings based on a candidate's race may be due to participants not correctly identifying the race of the treatment group they were assigned. To avoid biasing respondents' evaluations for each vignette, a manipulation check question was only included after the third vignette, and respondents were asked what was the race of the candidate in the article they just read. Only 27\% of respondents correctly answered this question, which is a low enough rate that puts into question if respondents correctly received the treatment as intended. 

Furthermore, when I restrict the sample to only responses to the third vignette and those who correctly identified the race of the candidate they evaluated, I find race did appear to condition responses. The sample size is small and the group who correctly identified the candidate's race is likely not a random sample from the sample as a whole, so I hedge drawing any strong conclusions from this subset of findings. However, I find that for this subset of respondents, they were most likely to support White candidates who were parents and least likely to support Black candidates who were not parents. Furthermore, Black parents were rated as having significantly less time and energy than White parents. For example, when comparing single mothers, White mothers were rated as having significantly more time and energy (4.6) than Black mothers (3.9), as well as were perceived as having significantly more shared values with respondents, 4.8 and 4.2 respectively. For married mothers, respondents showed significantly more support for White mothers (5.5) than Black mothers (4.9), and rated White married mothers as more compassionate, emotional, and decisive. I strongly encourage future research that incorporates stronger racial cues, such as a photo or specifically mentioning the race of the candidate, to ensure respondents correctly perceive the treatment group they are assigned. 

\subsection{Time \& Energy Results}
Overall, parents of young children receive higher levels of electoral support, but parents do face a key disadvantage in regards to their perceived time and energy. Figure \ref{figure_energy_condensed} plots results comparing candidates with and without children for the question, ``This candidate has the time \& energy to devote to public service." In Figure \ref{figure_energy_condensed}, I plot across all candidates, then by marital status, and then two ideal types to demonstrate some interesting intersectional results. The top two lines demonstrate that respondents perceived candidates with young children as having significantly less time and energy to serve as compared to candidates without children. When I separate out by candidate's marital status, I find that parents, regardless of marital status, are perceived as having less time and energy; however, the penalty is greatest single parents. Single candidates without children are perceived to have the most time and energy, 4.6, while single parents are perceived to have the least time and energy, 4.2.\footnote{In the appendix, I plot all treatment groups by perceive time and energy to serve, and the four bottom ranked candidates were single parents, signifying marital status conditions perceptions of parenthood and one's time and energy to serve.} 

When I examine perceived time and energy by specific treatment groups, I find the gap between parents and non-parents was greatest for Black, single women, such that the average perceived time and energy for Black single mothers was 4.0 and without children was 4.6. Holding all other variable constant, when comparing a candidate with and without children, the one without children was consistently ranked as having more time and energy. However, the one key group immune from this parental penalty is White, married, men, as demonstrated in Figure \ref{figure_energy_condensed}. While White, married men without children were estimated as having more time and energy than White, married fathers, the difference is not statistically significant. While I encourage future research to examine why White, married men are exempt from this parental penalty, it could be due to a perception that his wife is providing the majority of child care, and therefore, having children takes up less of his time and energy than other types of candidates. Straight, married men can pursue elected office while relying on their wives to fulfill household child care needs, a luxury women candidates often do not have. 

Looking at other specific treatments groups, it does appear gender conditions perceptions of parenthood. For example, White, single mothers are perceived as having significantly less time and energy (4.1) than White, single fathers (4.28). I find some support for hypothesis 6, such that single women with children are perceived as having less energy than married women. For White women, there is no significant difference in time and energy by relationship status; however, for Black mothers, those who are single are rated as having significantly less time and energy (4.05) than those who are married (4.28).\footnote{As an extension of perceived time and energy to serve, respondents also rated each candidate's perceived competence, measured by how capable and skillful they thought each candidate to be. Across all treatment groups, I found gender, race, relationship status, and children did not impact how competent a candidate is perceived to be. All results related to these dependent variables are included in the appendix.} 

\begin{figure}[t]
	\includegraphics[width=\textwidth]{figure_energy_condensed}
	\centering
	\textbf{\caption{Candidates' Time \& Energy to Serve} \label{figure_energy_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the question ``This candidate has the time and energy to devote to public service." Responses range from 0, strongly disagree, to 6, strongly agree.}
	\end{description}
\end{figure}

To offer more context to respondents' perceptions of different types of candidates, I included an open-ended question asking, ``Overall, what was your impression of this candidate? Why do you think this candidate would, or would not, make a good candidate?" Respondents' answers to this question highlight that mothers, particularly single mothers, were often questioned for their decision to run and their ability to balance home responsibilities with their political aspirations. These sample comments highlight these concerns. 
\newpage
\singlespacing 
\begin{quote}
	\textit{``Two young children. Should not run. Raise your children, don't go to DC.”} (Black, single mother treatment group) \\ \\
		\textit{``Don't think she will have the time needed to devote to her job. Her small children will suffer.”} (White, single mother treatment group) \\ \\
		\textit{``Would make a good candidate since she has time for running campaign. Not like other women, they are busy with families and kids. She is not."} (Black, married, woman, no children treatment group) \\ \\
		\textit{``I don't think it's appropriate to bring children out on the campaign trail."} (White, single mother treatment group)
\end{quote} 
\doublespacing

However, it is clear looking at the responses to the open-ended question that respondents had contradictory reactions to parenthood in politics. While some viewed parenthood as taking away a candidate's time and energy from serving in Congress, others viewed parenthood as a positive feature that aided the candidate's appeal, and that those without children are less able to understand the issues most important to families. Here are some examples of expressed positivity about parents in politics. 

\singlespacing 
\begin{quote}
	\textit{``Sounds like a great candidate. The fact that she is a single mother I think shows that she understands responsibility and hardships many people face.”} (Black, single mother treatment group) \\ \\
		\textit{``While she has plenty of career experience behind her she has no experience as to what it is like to raise children and take care of a family."} (White, married woman with no children treatment group) \\ \\
		\textit{``He is young and has ideas to try to help families...He has a young family but with a supportive wife he should be able to get things done."} (Black, married father treatment group) \\ \\
		\textit{``I think this candidate would make a good candidate based on him being a caring father."} (Black, single father treatment group) \\ \\ 
		\textit{``I think it would be great to have a fellow mom in office, fighting for family issues."} (White, single mother treatment group) 
	
\end{quote} 
\doublespacing

\subsection{Other Dependent Variable Results}
Overall, I find parents, particularly mothers, are evaluated as more warm and more likely to possess feminine traits. In Figure \ref{figure_warm_feminine_condensed} I plot perceive sincerity and compassion for candidates with and without children by the candidate's gender. For both traits, women with children are rated highest and men without children are rated lowest. It is clear from Figure \ref{figure_warm_feminine_condensed} that gender and parenthood condition perceived warmth and feminine traits, such that even though women are thought to stereotypically be more warm and feminine, men with children are rated more highly on these traits than women without children. In the appendix, I plot rated compassion for all treatment groups, and the top four rated groups are mothers and those rated least compassionate are single men without children. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_warm_feminine_condensed}
	\centering
	\textbf{\caption{Candidates' Warmth \& Feminine Traits} \label{figure_warm_feminine_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the questions ``How sincere/compassionate is this candidate?" Responses are on a 5-point scale from not at all to extremely.}
	\end{description}
\end{figure}

Past research suggests that being evaluated as more warm or more feminine may be a disadvantage in seeking political office, as these traits are often thought of as incongruent with the traits necessary for political leadership \cite{deason2015mothers,huddy1993gender}. However, I do not find that the candidates rated as more warm and feminine suffered in respondents' overall willingness to support these candidates. Therefore, it may only be when these traits are emphasized that they negatively impact mothers running for office. Warmth and femininity are incongruent with the traits thought to be most desired for political leadership; however, it appears the positives of being a parent, such as more relatable, seem to be overweighed in respondents' minds in making decisions about overall electoral support. 

In explaining why voters tend to favor parents for political office, I find that relatability is a key factor, such that respondents rate parents as sharing more values with them and as more likely to represent people like them. Across all treatment groups, single men without children were consistently rated as the least relatable. When I break down these results by respondents' gender, as presented in Figure \ref{figure_values_condensed}, I find this perception was predominately driven by women, who were significantly more likely to say they share more values with candidates who have children and are married. The coefficients for men respondents are in the same direction but substantively smaller and not statistically significant. 

\begin{figure}[h] 
	\includegraphics[width=\textwidth]{figure_values_condensed}
	\centering
	\textbf{\caption{Candidate Shares Values with Me} \label{figure_values_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the question ``This candidate shares similar values to my own." Responses range from 0, strongly disagree, to 6, strongly agree. The y-axis presents all respondents, as well as results subset out by respondents' gender.}
	\end{description}
\end{figure}

Similarly, I asked respondents if they felt the candidate represented people like them, and respondents were significantly more likely to rate women, married, and parent candidates as ``represents people like me." Respondents consistently rated married mothers as the most relatable and men with no children as the least relatable. In Figure \ref{figure_likeme_condensed}, I present this dependent variable on subsets of respondents by gender and political party, comparing how different groups rated single men without children to married mothers. Democrats and women seem to be driving much of this effect for married mothers. For example, when subset by respondents' gender, men did not significantly rate any of the treatment groups as better representing people like me, but women rated significantly higher candidates who were women, married, and parents. There is no significant difference in how Republicans rated single men without children and married mothers, but Democrats were significantly more likely to say they feel married mothers represent them better. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_likeme_condensed}
	\centering
	\textbf{\caption{Candidate Represents Me} \label{figure_likeme_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the question ``This candidate represents people like me." Responses range from 0, strongly disagree, to 6, strongly agree. The y-axis presents all respondents, as well as results subset out by respondents' gender or political party.}
	\end{description}
\end{figure}

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_fam_condensed}
	\centering
	\textbf{\caption{Candidate \& Family Issues} \label{figure_fam_condensed}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for the question ``This candidate will prioritize issues important to families" Responses range from 0, strongly disagree, to 6, strongly agree.}
	\end{description}
\end{figure}

Similar to the idea of relatability is the perception that voters may prefer certain types of candidates because they perceive that individual understands them, their needs, and will prioritize issues and policies most important to their lives. Figure \ref{figure_fam_condensed} demonstrates that both parental status and gender condition perceptions of issue ownership and competence on family issues. Candidates perceived as best able to handle family issues are women with children and those perceived as least capable at handling family issues are men without children. 

Finally, I asked respondents to place candidates on an ideological scale. Because respondents received a candidate that matches their own party, it is most useful to present these findings by party. Figure \ref{ideology_coefplot} showcases the regression results for perceived ideology by party. For Republican respondents, they rated men and married candidates as significantly more conservative, whereas Democrats perceived parents as more liberal than non-parents. Across both parties, the treatment group rated as most liberal was White, single mothers and the most conservative was Black, married men without children. This runs counter to past research which finds that Black candidates are often perceived as more liberal \cite{mcdermott1998race}, so this highlights that candidates' multiple identities interact in interesting way to influence how voters evaluate them. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{ideology_coefplot}
	\centering
	\textbf{\caption{Candidate \& Ideology} \label{ideology_coefplot}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the OLS regression results with confidence intervals by respondents' political party for the question ``How would you rate this candidate?" Responses range from 0, very liberal, to 6, very conservative.}
	\end{description}
\end{figure}

\section{Discussion}
In this study, respondents indicated they perceive parents of young children have less time and energy to devote to public service, but I ultimately find parents of young children are overall more likely to receive electoral support than candidates without children. Respondents consistently rated parents as more relatable, better on family issues, more warm, superior on both traditionally feminine and masculine traits, and overall more liberal. This suggests that while mothers of young children may not be overall disadvantaged in seeking political office, they may have to prove to voters that their family responsibilities will not detract from being a committed and successful politician. Therefore, while mothers in the past have often been less likely to highlight their family in their campaign, it may actually be advantageous for women politicians to more heavily showcase their family and their role as a mother on the campaign trail. However, consistent with arguments made by Teele, Kalla \& Rosenbluth (2018)\nocite{teele2018ties}, this voter preference for candidates who are parents does present unique challenges for women candidates. On average, women do perform more household chores and child care than men, leaving mothers of young children less time and energy to devote to their political aspirations than fathers.\footnote{While I frame this parental penalty of having less time and energy for politics as a disadvantage for parents running for office, I acknowledge this may not be considered a disadvantage among all voters. In particular, for voters who prefer a smaller government, having less time and energy to serve may not be viewed negatively.}

Another unexpected outcome of this experiment was that I found that often gender and marital status condition the effect of parenthood on voters' perceptions, but I surprisingly found limited examples of parenthood effects conditional on race. I theorized that because Black mothers are more negatively stereotyped than White mothers, this would present additional hurdles for Black mothers running for political office. However, it is possible that stereotypes of Black mothers, as a whole, do not apply to Black mothers who are politicians. Schneider \& Bos (2014)\nocite{schneider2014} find that often character traits traditionally ascribed to women are often not equally applied to women politicians. For example, they find women politicians are not stereotyped as warm or empathetic in the same way women as a whole are. Therefore, it is possible the stereotypes of Black mothers may not be equally ascribed to Black mothers who run for political office. I encourage future research on how stereotypes of Black women are applied to Black women politicians. Additionally, due to sample size limitations, this paper only examines differences between White and Black mothers. I expect Latina and Asian-American mothers may face unique stereotypes from voters and encourage future research that examines how motherhood intersects with other racial and ethnic identities. 

Furthermore, in this study, I made the decision to frame each candidate as seeking a seat in the U.S. Senate. I made this decision as I expected that serving in Congress, which requires long work hours and spending time in Washington D.C., to be when parents with young children face the most skepticism from voters. However, I find that even at this high-level of political leadership, voters still prefer parents to non-parents. Therefore, I encourage future research on if this preference of parents as candidates is exacerbated in lower offices, particularly at the local level, where the time and energy requirements are lower and the desired character traits are different. 

Finally, I had expected there to be greater differences by respondents' political party but overall do not find Democrats and Republicans rate mothers of young children differently. This lack of a party dynamic could be due to the design, which only assigned respondents to a co-partisan candidate. Overall, I would expect Republican mothers are perceived very differently than Democratic mothers, and therefore, encourage future research on how party intersects with motherhood. Additionally, a limitation to this paper is that in the treatment design, the language was kept consistent across all conditions to isolate the effects of my main interests -- parenthood, gender, race, and marital status. However, in reality, I would not expect that Republicans and Democrats would use the same language or choose to frame themselves in the same way. The same is likely also true for White and Black candidates or women and men candidates. This limitation of an experimental design encourages more research on how Republican and Democratic mothers running for office speak about their identity as a mother and how this framing is likely partisan. 

Overall, I find that respondents favor candidates who are parents, but the open-ended responses highlight that, at times, voters can have opposite reactions to parenthood. Some view parenthood as a positive for candidates while others view it as a negative. The impacts of parenthood on a candidate's success if likely conditional on the specific region and cultural norms of the area where a candidate running, and candidates may have to adjust how their frame their families in their campaigns based on the specific context of their district. 

\newpage
\section{Appendix}
\subsection{Experiment Details}

\begin{table}[h!]
	\centering
	\begin{threeparttable}
		\label{names}
		\caption {Names used in Experiment}
		\begin{tabular}{llll}
			& Vignette 1                   & Vignette 2                      & Vignette 3 \\ \hline
			\multicolumn{1}{l|}{Black man}       & \multicolumn{1}{l|}{Antoine} & \multicolumn{1}{l|}{Cedric}     & Cornell    \\
			\multicolumn{1}{l|}{}                & \multicolumn{1}{l|}{}        & \multicolumn{1}{l|}{}           &            \\
			\multicolumn{1}{l|}{Black woman}     & \multicolumn{1}{l|}{Ayanna}  & \multicolumn{1}{l|}{Janelle}    & Octavia    \\
			\multicolumn{1}{l|}{}                & \multicolumn{1}{l|}{}        & \multicolumn{1}{l|}{}           &            \\
			\multicolumn{1}{l|}{Black last name} & \multicolumn{1}{l|}{Smalls}  & \multicolumn{1}{l|}{Washington} & Cooks      \\
			\multicolumn{1}{l|}{}                & \multicolumn{1}{l|}{}        & \multicolumn{1}{l|}{}           &            \\
			\multicolumn{1}{l|}{White man}       & \multicolumn{1}{l|}{Travis}  & \multicolumn{1}{l|}{Charles}    & Ronald     \\
			\multicolumn{1}{l|}{}                & \multicolumn{1}{l|}{}        & \multicolumn{1}{l|}{}           &            \\
			\multicolumn{1}{l|}{White woman}     & \multicolumn{1}{l|}{Lillie}  & \multicolumn{1}{l|}{Sharon}     & Misty      \\
			\multicolumn{1}{l|}{}                & \multicolumn{1}{l|}{}        & \multicolumn{1}{l|}{}           &            \\
			\multicolumn{1}{l|}{White last name} & \multicolumn{1}{l|}{Novak}   & \multicolumn{1}{l|}{Steffen}    & Hofer      \\ \hline
		\end{tabular}
	\end{threeparttable}
\end{table}

I use the politician's name in the treatments to cue both gender and race. In the experiment, the respondents are randomized into a treatment group of either a Black man, Black woman, White man, or White woman. The last names were selected using data from the 2000 Census \cite{census2014names}. I restricted the Census to only last names that occur at least 5,000 times. For the last names selected, 90\%+ of Census respondents were either White or Black, respectively. To select the first names, I used data that had respondents rate first names on race, as well as other traits and characteristics \cite{hayesname}. I first restricted the names to the first names most likely be identified as either a White or African American name. Then, from those names, I matched names across important characteristics, including perceived middle class, competence, and warmth. 
\newpage

\subsection{Survey Question Wording}
\begin{itemize} [noitemsep] \singlespacing
	\item Children: Are you the parent or guardian of any children? Yes, I have children under 18; Yes, all my children are 18 years old or older; No, I do not have children. 
	\item Marital: Are you currently... Single; Partnered, not living together; Partnered, living together; Married; Divorced; Widowed; Other (fill in blank)
	\item Gender: What is your gender? Man, Woman, Other (fill in blank)
	\item Race: Which racial or ethnic groups describe you? Mark all that apply. White; Black or African American; Hispanic or Latino(a); Asian American; Native American or American Indian; Other
	\item Ideology: In general, how would you describe your own political viewpoint? Very liberal, liberal, moderate, conservative, very conservative, not sure.
	\item Party: In general, do you think of yourself as a Republican, a Democrat, an independent, or something else? Republican; Democrat; Independent or other party
	\item Independents (only displayed for those who selected ``independent or other party" in the previous question): If you had to choose, do you think of yourself as more similar to Republicans or Democrats? More similar to Republicans than Democrats; More similar to Democrats than Republicans.  
	\item Income: What is the annual household income for all members of your family? This figure should include salaries, wages, pensions, dividends, interest, and all other income. Less than \$10,000; \$10,000 - \$19,999; \$20,000 - \$29,999; \$30,000 - \$39,999; \$40,000- \$49,999; \$50,000 - \$59,999; \$60,000 - \$69,999; \$70,000 - \$79,999; \$80,000 - \$89,999; \$90,000 - \$99,999; \$100,000 - \$149,999; More than \$150,000
	\item Education: What is the highest level of education you have completed? Less than high school diploma; High school graduate or GED; Some college; 2-year degree; 4-year degree; Post-graduate education
	\item Religion: How often do you attend religious services, apart from occasional weddings, baptisms or funerals? Every week; Almost every week; Once or twice a month; A few times a year; Never %Based on 2016 ANES wording
	\item Racial resentment 1: It's really a matter of some people not trying hard enough; if blacks would only try harder they could be just as well off as whites. 4-point scale from strongly disagree to strongly agree.\footnote{Racial resentment question wordings based on \cite{kinder1996divided,valentino2002cues}.}
	\item Racial resentment 2: Over the past few years, blacks have gotten less than they deserve. 4-point scale from strongly disagree to strongly agree. This measure is reverse-coded.
	\item Sexism 1: Most men are better suited emotionally for politics than are most women. 4-point scale from strongly disagree to strongly agree.\footnote{Sexism question wordings based on \cite{alexander1993gender,bracic2020teachers,sanbonmatsu2003gender}.}
	\item Sexism 2: A husband's job is to earn money; a wife's job is to look after home and family. 4-point scale from strongly disagree to strongly agree.
	\item Sexism 3: Women can handle job pressures as well as men. 4-point scale from strongly disagree to strongly agree. This measure is reverse-coded.
	\item Working mothers 1: A pre-school child is likely to suffer if his or her mother works. 4-point scale from strongly agree to strongly disagree.\footnote{Working mothers question wordings based on \cite{menaghan1991determining}.}
	\item Working mothers 2: A working mother can establish just as warm and secure a relationship with her children as a mother who does not work. 4-point scale from strongly agree to strongly disagree. This measure is reverse-coded.
	\item Support: On a scale from 1 to 7, where 1 indicates that you would never support this candidate, and 7 indicates that you would always support this candidate, where would you place this candidate?\footnote{Question wording is based on \cite{hainmueller2014causal}.} 
	\item Values: This candidate shares similar values to my own. 7-point scale from strongly disagree to strongly agree.
	\item Time: This candidate has the time and energy to devote to public service. 7-point scale from strongly disagree to strongly agree.
	\item Represent: This candidate represents people like me. 7-point scale from strongly disagree to strongly agree.
	\item Priorities: This candidate will prioritize issues important to families. 7-point scale from strongly disagree to strongly agree. 
	\item Candidate Ideology: How would you rate this candidate? Very liberal, liberal, somewhat liberal, middle of the road, somewhat conservative, conservative, very conservative.\footnote{Question wording is based on \cite{jones2019gender}.}
	\item Warmth measures: How sincere is this candidate? How selfish is this candidate? 5-point scale from not at all to extremely. The selfish measure is reverse-coded.\footnote{Warmth and competence question wordings based on \cite{deason2020psychology,cuddy2004professionals}.}
	\item Competence measures: How capable is this candidate? How skillful is this candidate? 5-point scale from not at all to extremely.
	\item Feminine trait: How compassionate is this candidate? How emotional is this candidate? 5-point scale from not at all to extremely.\footnote{Trait question wordings based on \cite{dolan2014takes,huddy1993gender}.}
	\item Masculine trait: How decisive is this candidate? How tough is this candidate? 5-point scale from not at all to extremely.
	\item Open ended question: Overall, what was your impression of this candidate? Why do you think this candidate would, or would not, make a good candidate?
\end{itemize} 

\newpage
\subsection{Survey Sample}

\begin{table}[!h]
	\centering
	\label{surveydemographics}
	\caption{Survey Sample Demographics}
	\begin{tabular}{lll}
		\\
		\textbf{Gender}                                                                                                      & \textbf{Race/Ethnicity}                                                                              & \textbf{Ideology}                                                                                                                                                                                  \\
		\begin{tabular}[c]{@{}l@{}}49\% Men\\ 51\% Women\end{tabular}                                                        & \begin{tabular}[c]{@{}l@{}}70\% White\\ 13\% Black\\ 7\% Latino\\ 7\% Other\\ 4\% Mixed\end{tabular} & \begin{tabular}[c]{@{}l@{}}12\% Very liberal\\ 19\% Liberal\\ 40\% Moderate\\ 18\% Conservative\\ 11\% Very conservative\end{tabular}    
		\\                                           \hline               \\ 
		\textbf{Party}                                                                                                       & \textbf{Region}                                                                                      & \textbf{Religious Attendance}                                                                                                                                                                      \\
		\begin{tabular}[c]{@{}l@{}}36\% Republicans\\ 26\% Independents\\ 38\% Democrats\end{tabular}                        & \begin{tabular}[c]{@{}l@{}}39\% South\\ 22\% West\\ 20\% Northeast\\ 19\% Midwest\end{tabular}       & \begin{tabular}[c]{@{}l@{}}28\% Never\\ 22\% A few times a year\\ 15\% Once or twice a month\\ 13\% Almost every week\\ 22\% Every week\end{tabular}                                               \\ \hline \\
		\textbf{Relationship}                                                                                                & \textbf{Children}                                                                                    & \textbf{Education}                                                                                                                                                                                          \\
		\begin{tabular}[c]{@{}l@{}}46\% Married\\ 27\% Single\\ 11\% Divorced\\ 11\% Partnered\\ 5\% Widowed\end{tabular} & \begin{tabular}[c]{@{}l@{}}37\% No Children\\ 37\% Young Children\\ 26\% Older Children\end{tabular} & \begin{tabular}[c]{@{}l@{}}3\% Less than high school diploma\\ 21\% High school graduate\\ 22\% Some college\\ 11\% 2-year degree\\ 24\% 4-year degree\\ 20\% Post-graduate education\end{tabular} \\  \hline \\
		\textbf{Income}                                                                                                             & \textbf{Age}                                                                                                  &                                                                                                                                                                                                    \\
		\begin{tabular}[c]{@{}l@{}}Median: \$50,000-\\ \$59,999\end{tabular}                                                   & \begin{tabular}[c]{@{}l@{}}Mean: 45\\ Median: 43\\ Min: 18\\ Max: 89\end{tabular}                    &                                                                                                                                                                                                   
	\end{tabular}
\end{table}

\newpage
\subsection{Additional Figures \& Results}
\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_energy}
	\centering
	\textbf{\caption{Candidates' Energy to Serve by Treatment Group} \label{figure_energy}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``This candidate has the time and energy to devote to public service." Responses range from 6, strongly agree, to 0, strongly disagree.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_sincere}
	\centering
	\textbf{\caption{How Sincere a Candidate is by Treatment Group} \label{figure_sincere}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How sincere is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_selfish}
	\centering
	\textbf{\caption{How Selfish a Candidate is by Treatment Group} \label{figure_selfish}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How selfish is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_capable}
	\centering
	\textbf{\caption{How Capable a Candidate is by Treatment Group} \label{figure_capable}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How capable is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_skillful}
	\centering
	\textbf{\caption{How Skillful a Candidate is by Treatment Group} \label{figure_skillful}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How skillful is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_compassionate}
	\centering
	\textbf{\caption{How Compassionate a Candidate is by Treatment Group} \label{figure_compassionate}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How compassionate is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_emotional}
	\centering
	\textbf{\caption{How Emotional a Candidate is by Treatment Group} \label{figure_emotional}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How emotional is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_decisive}
	\centering
	\textbf{\caption{How Decisive a Candidate is by Treatment Group} \label{figure_decisive}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How decisive is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_tough}
	\centering
	\textbf{\caption{How Tough a Candidate is by Treatment Group} \label{figure_tough}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``How tough is this candidate?" Responses range from 0, not at all, to 4, extremely.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_values}
	\centering
	\textbf{\caption{Candidate Shares Values with Me by Treatment Group} \label{figure_values}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``This candidate shares similar values to my own." Responses range from 0, strongly disagree, to 6, strongly agree.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_likeme}
	\centering
	\textbf{\caption{Candidate Represents Me by Treatment Group} \label{figure_likeme}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``This candidate represents people like me" Responses range from 0, strongly disagree, to 6, strongly agree.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=\textwidth]{figure_fam}
	\centering
	\textbf{\caption{Candidate \& Family Issues by Treatment Group} \label{figure_fam}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents the estimate and 95\% confidence interval for each treatment group for the question ``This candidate will prioritize issues important to families" Responses range from 0, strongly disagree, to 6, strongly agree.}
	\end{description}
\end{figure}

\FloatBarrier
\newpage
\subsection{Additional Dependent Variables Models}
\begin{table}[!h]
	\begin{center}
		\begin{threeparttable}
			\caption{Models: Competence Dependent Variables}
			\begin{tabular}{l c c}
				\hline
				& \multicolumn{2}{c}{Competence} \\
				\textbf{Candidate Traits}& Capable & Skillful \\
				\hline
				Woman       & $0.03$     & $0.01$     \\
				& $(0.02)$   & $(0.02)$   \\
				Black       & $-0.02$    & $-0.02$    \\
				& $(0.02)$   & $(0.02)$   \\
				Single      & $-0.00$    & $-0.03$    \\
				& $(0.02)$   & $(0.02)$   \\
				No children & $-0.01$    & $-0.01$    \\
				& $(0.02)$   & $(0.02)$   \\
				Vignette 2  & $0.06^{*}$ & $0.07^{*}$ \\
				& $(0.02)$   & $(0.02)$   \\
				Vignette 3  & $0.00$     & $0.04^{*}$ \\
				& $(0.02)$   & $(0.02)$   \\
				Intercept   & $2.43^{*}$ & $2.34^{*}$ \\
				& $(0.03)$   & $(0.03)$   \\
				\hline
				%R$^2$       & $0.00$     & $0.00$     \\
				Adj. R$^2$  & $0.00$     & $0.00$     \\
				Num. obs.   & $9366$     & $9372$     \\
				%RMSE        & $1.04$     & $1.04$     \\
				%N Clusters  & $3237$     & $3236$     \\
				\hline
			\end{tabular}
			\begin{tablenotes}
				\item $^{*}p<0.05$. These are OLS regression models for two measures of candidate competence -- capable and skillful. These were not included in the body of the text because I did not find that gender, race, marital status, or parental status influenced perceptions of candidate competence. 
			\end{tablenotes}
			\label{table_competence}
		\end{threeparttable}
	\end{center}
\end{table}

\FloatBarrier
\subsection{Models with Additional Respondent Covariates}
The three tables below are all dependent variables included in the body of the paper, but these models include additional respondent covariates. The inclusion of these additional independent variables does not significantly change many of the findings presented in the body of this paper. 

\begin{table}[!h]
	\begin{center}
		\caption{Models with Additional Covariates}
		{\scriptsize
			\begin{tabular}{l c c c c}
				\hline
				& Support & Time & Sincere & Selfish \\
				\hline
				Woman candidate       & $0.05$      & $-0.03$     & $0.06^{*}$  & $-0.01$     \\
				& $(0.03)$    & $(0.03)$    & $(0.02)$    & $(0.02)$    \\
				Black candidate       & $-0.03$     & $-0.00$     & $-0.01$     & $-0.02$     \\
				& $(0.03)$    & $(0.03)$    & $(0.02)$    & $(0.02)$    \\
				Single candidate      & $-0.06^{*}$ & $-0.03$     & $0.00$      & $-0.00$     \\
				& $(0.03)$    & $(0.03)$    & $(0.02)$    & $(0.02)$    \\
				No children candidate & $-0.12^{*}$ & $0.29^{*}$  & $-0.14^{*}$ & $0.06^{*}$  \\
				& $(0.03)$    & $(0.03)$    & $(0.02)$    & $(0.02)$    \\
				Vignette 2            & $0.04^{*}$  & $0.03$      & $-0.06^{*}$ & $0.05^{*}$  \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Vignette 3            & $-0.01$     & $0.01$      & $-0.09^{*}$ & $0.11^{*}$  \\
				& $(0.02)$    & $(0.03)$    & $(0.02)$    & $(0.02)$    \\
				Women                 & $-0.00$     & $-0.12^{*}$ & $0.02$      & $-0.07^{*}$ \\
				& $(0.05)$    & $(0.04)$    & $(0.03)$    & $(0.04)$    \\
				Black                 & $-0.18^{*}$ & $-0.13$     & $-0.11^{*}$ & $0.09$      \\
				& $(0.08)$    & $(0.07)$    & $(0.05)$    & $(0.06)$    \\
				Latinx                & $-0.03$     & $0.01$      & $-0.03$     & $0.04$      \\
				& $(0.09)$    & $(0.08)$    & $(0.06)$    & $(0.07)$    \\
				Mixed race            & $-0.01$     & $0.10$      & $0.04$      & $-0.07$     \\
				& $(0.11)$    & $(0.10)$    & $(0.08)$    & $(0.08)$    \\
				Other race            & $-0.21^{*}$ & $-0.15$     & $-0.12$     & $0.06$      \\
				& $(0.09)$    & $(0.08)$    & $(0.06)$    & $(0.07)$    \\
				Age                   & $0.01^{*}$  & $0.01^{*}$  & $0.01^{*}$  & $-0.02^{*}$ \\
				& $(0.00)$    & $(0.00)$    & $(0.00)$    & $(0.00)$    \\
				Ideology              & $-0.07^{*}$ & $-0.13^{*}$ & $-0.03^{*}$ & $-0.06^{*}$ \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Independent           & $-0.44^{*}$ & $-0.34^{*}$ & $-0.27^{*}$ & $0.05$      \\
				& $(0.06)$    & $(0.05)$    & $(0.04)$    & $(0.04)$    \\
				Republican            & $0.08$      & $0.15^{*}$  & $0.08$      & $-0.05$     \\
				& $(0.06)$    & $(0.05)$    & $(0.04)$    & $(0.05)$    \\
				Income                & $0.03^{*}$  & $0.01$      & $0.02^{*}$  & $0.00$      \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Education             & $0.08^{*}$  & $0.05^{*}$  & $0.04^{*}$  & $0.05^{*}$  \\
				& $(0.02)$    & $(0.02)$    & $(0.01)$    & $(0.01)$    \\
				Religion              & $0.09^{*}$  & $0.10^{*}$  & $0.06^{*}$  & $0.07^{*}$  \\
				& $(0.02)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Divorced              & $-0.07$     & $-0.11$     & $-0.06$     & $0.01$      \\
				& $(0.08)$    & $(0.07)$    & $(0.05)$    & $(0.05)$    \\
				Other                 & $-0.42$     & $-0.82^{*}$ & $-0.25$     & $0.31$      \\
				& $(0.23)$    & $(0.22)$    & $(0.18)$    & $(0.19)$    \\
				Partnered             & $0.07$      & $0.07$      & $0.03$      & $-0.16^{*}$ \\
				& $(0.08)$    & $(0.07)$    & $(0.05)$    & $(0.06)$    \\
				Single                & $-0.01$     & $-0.04$     & $-0.04$     & $-0.13^{*}$ \\
				& $(0.07)$    & $(0.06)$    & $(0.05)$    & $(0.05)$    \\
				Widowed               & $-0.27^{*}$ & $-0.16$     & $-0.17^{*}$ & $-0.00$     \\
				& $(0.12)$    & $(0.09)$    & $(0.08)$    & $(0.08)$    \\
				Older children        & $-0.10$     & $-0.02$     & $-0.12^{*}$ & $-0.05$     \\
				& $(0.07)$    & $(0.06)$    & $(0.04)$    & $(0.05)$    \\
				Young children        & $0.34^{*}$  & $0.30^{*}$  & $0.18^{*}$  & $0.02$      \\
				& $(0.06)$    & $(0.05)$    & $(0.04)$    & $(0.05)$    \\
				Sexism                & $0.04^{*}$  & $0.02$      & $0.02^{*}$  & $0.10^{*}$  \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Racism                & $-0.02$     & $-0.01$     & $-0.02$     & $0.00$      \\
				& $(0.02)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Working mothers       & $-0.05^{*}$ & $-0.06^{*}$ & $-0.04^{*}$ & $0.08^{*}$  \\
				& $(0.02)$    & $(0.02)$    & $(0.01)$    & $(0.01)$    \\
				Intercept             & $4.12^{*}$  & $3.90^{*}$  & $1.99^{*}$  & $1.28^{*}$  \\
				& $(0.15)$    & $(0.13)$    & $(0.11)$    & $(0.12)$    \\
				\hline
				%	R$^2$                 & $0.14$      & $0.14$      & $0.11$      & $0.19$      \\
				Adj. R$^2$            & $0.14$      & $0.13$      & $0.10$      & $0.19$      \\
				Num. obs.             & $8780$      & $8780$      & $8765$      & $8769$      \\
				%	RMSE                  & $1.34$      & $1.24$      & $0.98$      & $1.12$      \\
				%	N Clusters            & $2936$      & $2936$      & $2935$      & $2935$      \\
				\hline
				\multicolumn{5}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}}
		\label{table_extra_covariates1}
	\end{center}
\end{table}


\begin{table}[!h]
	\begin{center}
		\caption{Models with Additional Covariates}
		{\scriptsize
			\begin{tabular}{l c c c c}
				\hline
				& Compassionate & Emotional & Decisive & Tough \\
				\hline
				Woman candidate       & $0.09^{*}$  & $0.03$      & $0.04$      & $0.06^{*}$  \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Black candidate       & $-0.02$     & $-0.04$     & $-0.03$     & $-0.01$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Single candidate      & $-0.03$     & $-0.02$     & $-0.02$     & $-0.01$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				No children candidate & $-0.18^{*}$ & $-0.18^{*}$ & $-0.04$     & $-0.05^{*}$ \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Vignette 2            & $-0.05^{*}$ & $0.01$      & $-0.02$     & $-0.01$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Vignette 3            & $-0.09^{*}$ & $-0.02$     & $-0.04^{*}$ & $0.00$      \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Women                 & $0.04$      & $-0.09^{*}$ & $-0.00$     & $0.02$      \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				Black                 & $-0.09$     & $-0.01$     & $-0.19^{*}$ & $-0.04$     \\
				& $(0.05)$    & $(0.06)$    & $(0.05)$    & $(0.05)$    \\
				Latinx                & $-0.03$     & $0.08$      & $-0.05$     & $-0.04$     \\
				& $(0.06)$    & $(0.07)$    & $(0.07)$    & $(0.06)$    \\
				Mixed race            & $0.08$      & $0.02$      & $0.05$      & $-0.02$     \\
				& $(0.07)$    & $(0.07)$    & $(0.08)$    & $(0.08)$    \\
				Other race            & $-0.13^{*}$ & $-0.06$     & $-0.05$     & $-0.11$     \\
				& $(0.06)$    & $(0.07)$    & $(0.06)$    & $(0.06)$    \\
				Age                   & $0.01^{*}$  & $-0.00^{*}$ & $0.01^{*}$  & $0.00^{*}$  \\
				& $(0.00)$    & $(0.00)$    & $(0.00)$    & $(0.00)$    \\
				Ideology              & $-0.03^{*}$ & $-0.03$     & $-0.04^{*}$ & $-0.04^{*}$ \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Independent           & $-0.20^{*}$ & $-0.16^{*}$ & $-0.18^{*}$ & $-0.15^{*}$ \\
				& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
				Republican            & $0.08$      & $0.04$      & $0.01$      & $0.09^{*}$  \\
				& $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.05)$    \\
				Income                & $0.01^{*}$  & $0.02^{*}$  & $0.02^{*}$  & $0.02^{*}$  \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Education             & $0.04^{*}$  & $0.03^{*}$  & $0.05^{*}$  & $0.02$      \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Religion              & $0.06^{*}$  & $0.09^{*}$  & $0.05^{*}$  & $0.07^{*}$  \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Divorced              & $-0.06$     & $-0.03$     & $0.07$      & $-0.00$     \\
				& $(0.05)$    & $(0.06)$    & $(0.05)$    & $(0.05)$    \\
				Other                 & $-0.21$     & $0.09$      & $0.02$      & $0.05$      \\
				& $(0.18)$    & $(0.16)$    & $(0.15)$    & $(0.17)$    \\
				Partnered             & $0.09$      & $-0.02$     & $0.01$      & $0.04$      \\
				& $(0.05)$    & $(0.06)$    & $(0.06)$    & $(0.05)$    \\
				Single                & $-0.01$     & $-0.03$     & $0.01$      & $0.04$      \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
				Widowed               & $-0.11$     & $-0.11$     & $-0.13$     & $-0.16^{*}$ \\
				& $(0.08)$    & $(0.09)$    & $(0.08)$    & $(0.08)$    \\
				Older children        & $-0.12^{*}$ & $-0.04$     & $-0.10^{*}$ & $-0.08$     \\
				& $(0.04)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
				Young children        & $0.18^{*}$  & $0.15^{*}$  & $0.12^{*}$  & $0.17^{*}$  \\
				& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
				Sexism                & $0.02^{*}$  & $0.06^{*}$  & $0.00$      & $0.03^{*}$  \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Racism                & $-0.02^{*}$ & $-0.01$     & $-0.02$     & $-0.02$     \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Working mothers       & $-0.04^{*}$ & $0.01$      & $-0.01$     & $-0.04^{*}$ \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Intercept             & $2.04^{*}$  & $1.93^{*}$  & $1.92^{*}$  & $1.81^{*}$  \\
				& $(0.11)$    & $(0.12)$    & $(0.11)$    & $(0.11)$    \\
				\hline
				%	R$^2$                 & $0.08$      & $0.12$      & $0.06$      & $0.06$      \\
				Adj. R$^2$            & $0.08$      & $0.11$      & $0.06$      & $0.06$      \\
				Num. obs.             & $8764$      & $8762$      & $8762$      & $8770$      \\
				%	RMSE                  & $1.00$      & $1.06$      & $1.04$      & $1.03$      \\
				%	N Clusters            & $2936$      & $2935$      & $2935$      & $2936$      \\
				\hline
				\multicolumn{5}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}}
		\label{table_extra_covariates2}
	\end{center}
\end{table}

\begin{table}[!h]
	\begin{center}
		\caption{Models with Additional Covariates}
		{\scriptsize
			\begin{tabular}{l c c c c}
				\hline
				& Values & Rep Me & Fam Issues & Ideology \\
				\hline
				Woman candidate       & $0.02$      & $0.08^{*}$  & $0.08^{*}$  & $-0.09^{*}$ \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				Black candidate       & $0.00$      & $-0.00$     & $-0.01$     & $-0.01$     \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				Single candidate      & $-0.05^{*}$ & $-0.10^{*}$ & $-0.06^{*}$ & $-0.08^{*}$ \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				No children candidate & $-0.14^{*}$ & $-0.09^{*}$ & $-0.35^{*}$ & $0.10^{*}$  \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				Vignette 2            & $-0.03$     & $-0.01$     & $-0.05^{*}$ & $-0.04$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Vignette 3            & $-0.15^{*}$ & $-0.09^{*}$ & $-0.31^{*}$ & $-0.05^{*}$ \\
				& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
				Women                 & $-0.08^{*}$ & $-0.10^{*}$ & $-0.01$     & $0.06$      \\
				& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
				Black                 & $-0.16^{*}$ & $-0.16^{*}$ & $-0.14^{*}$ & $-0.08$     \\
				& $(0.06)$    & $(0.07)$    & $(0.07)$    & $(0.07)$    \\
				Latinx                & $-0.11$     & $-0.11$     & $-0.17$     & $-0.10$     \\
				& $(0.08)$    & $(0.08)$    & $(0.09)$    & $(0.08)$    \\
				Mixed race            & $0.03$      & $-0.04$     & $0.02$      & $-0.03$     \\
				& $(0.10)$    & $(0.11)$    & $(0.10)$    & $(0.10)$    \\
				Other race            & $-0.30^{*}$ & $-0.19^{*}$ & $-0.19^{*}$ & $-0.11$     \\
				& $(0.08)$    & $(0.08)$    & $(0.08)$    & $(0.08)$    \\
				Age                   & $0.01^{*}$  & $0.01^{*}$  & $0.01^{*}$  & $0.01^{*}$  \\
				& $(0.00)$    & $(0.00)$    & $(0.00)$    & $(0.00)$    \\
				Ideology              & $-0.12^{*}$ & $-0.13^{*}$ & $-0.10^{*}$ & $0.78^{*}$  \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Independent           & $-0.42^{*}$ & $-0.42^{*}$ & $-0.39^{*}$ & $0.18^{*}$  \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
				Republican            & $0.11^{*}$  & $0.11^{*}$  & $0.11^{*}$  & $0.16^{*}$  \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.06)$    \\
				Income                & $0.01$      & $0.01$      & $0.02^{*}$  & $0.01$      \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Education             & $0.06^{*}$  & $0.03^{*}$  & $0.03$      & $-0.03$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Religion              & $0.11^{*}$  & $0.13^{*}$  & $0.09^{*}$  & $-0.05^{*}$ \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Divorced              & $-0.07$     & $-0.06$     & $-0.08$     & $0.15^{*}$  \\
				& $(0.07)$    & $(0.07)$    & $(0.07)$    & $(0.06)$    \\
				Other                 & $-0.63^{*}$ & $-0.77^{*}$ & $-0.69^{*}$ & $0.47$      \\
				& $(0.23)$    & $(0.19)$    & $(0.23)$    & $(0.27)$    \\
				Partnered             & $0.13^{*}$  & $0.10$      & $0.08$      & $0.14^{*}$  \\
				& $(0.07)$    & $(0.07)$    & $(0.07)$    & $(0.07)$    \\
				Single                & $0.03$      & $-0.00$     & $0.01$      & $0.05$      \\
				& $(0.06)$    & $(0.06)$    & $(0.06)$    & $(0.06)$    \\
				Widowed               & $-0.13$     & $-0.20^{*}$ & $-0.20^{*}$ & $-0.06$     \\
				& $(0.09)$    & $(0.10)$    & $(0.10)$    & $(0.09)$    \\
				Older children        & $-0.03$     & $-0.02$     & $-0.07$     & $-0.05$     \\
				& $(0.06)$    & $(0.06)$    & $(0.06)$    & $(0.05)$    \\
				Young children        & $0.32^{*}$  & $0.32^{*}$  & $0.25^{*}$  & $-0.04$     \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
				Sexism                & $0.04^{*}$  & $0.05^{*}$  & $0.03^{*}$  & $-0.03^{*}$ \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Racism                & $-0.01$     & $-0.01$     & $-0.02$     & $-0.02$     \\
				& $(0.01)$    & $(0.01)$    & $(0.01)$    & $(0.01)$    \\
				Working mothers       & $-0.04^{*}$ & $-0.04^{*}$ & $-0.05^{*}$ & $-0.02$     \\
				& $(0.02)$    & $(0.02)$    & $(0.02)$    & $(0.02)$    \\
				Intercept             & $3.81^{*}$  & $3.86^{*}$  & $4.19^{*}$  & $1.15^{*}$  \\
				& $(0.13)$    & $(0.13)$    & $(0.14)$    & $(0.14)$    \\
				\hline
				%	R$^2$                 & $0.15$      & $0.14$      & $0.13$      & $0.34$      \\
				Adj. R$^2$            & $0.15$      & $0.14$      & $0.13$      & $0.34$      \\
				Num. obs.             & $8779$      & $8777$      & $8736$      & $8761$      \\
				%	RMSE                  & $1.22$      & $1.27$      & $1.25$      & $1.29$      \\
				%	N Clusters            & $2936$      & $2936$      & $2936$      & $2934$      \\
				\hline
				\multicolumn{5}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}}
		\label{table_extra_covariates3}
	\end{center}
\end{table}

\FloatBarrier
\newpage
\subsection{Models By Respondents' Political Party}
The three models below present the same models presented in the body of the paper but subset by respondents' political party. The Democrat models include those who identify as Democrats and those who identify as independents but more closely identify with the Democratic Party than the Republican Party. The Republican models include those who identify as Republicans and those who identify as independents but more closely identify with the Republican Party than the Democratic Party. 
\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Party}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Support} & \multicolumn{2}{c}{Time} & \multicolumn{2}{c}{Sincere} & \multicolumn{2}{c}{Selfish} \\
			\textbf{Candidate Traits}& Dems & Reps & Dems & Reps & Dems & Reps & Dems & Reps \\
			\hline
			Woman       & $0.07$      & $0.02$      & $0.00$     & $0.00$     & $0.06$      & $0.06^{*}$  & $-0.04$    & $0.01$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.04)$   \\
			Black       & $-0.03$     & $-0.02$     & $0.00$     & $0.00$     & $-0.03$     & $0.01$      & $-0.01$    & $-0.02$    \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.04)$   \\
			Single      & $-0.06$     & $-0.04$     & $0.00$     & $0.00$     & $0.04$      & $-0.04$     & $-0.04$    & $0.02$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.04)$   \\
			No children & $-0.16^{*}$ & $-0.05$     & $0.28^{*}$ & $0.28^{*}$ & $-0.16^{*}$ & $-0.11^{*}$ & $0.08^{*}$ & $0.05$     \\
			& $(0.04)$    & $(0.05)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.04)$   \\
			Vignette 2  & $0.14^{*}$  & $-0.04$     & $0.04$     & $0.04$     & $-0.05$     & $-0.04$     & $0.05$     & $0.04$     \\
			& $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.02)$    & $(0.03)$   & $(0.03)$   \\
			Vignette 3  & $0.08^{*}$  & $-0.07^{*}$ & $0.03$     & $0.03$     & $-0.07^{*}$ & $-0.08^{*}$ & $0.09^{*}$ & $0.12^{*}$ \\
			& $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   \\
			Intercept   & $4.93^{*}$  & $5.15^{*}$  & $4.11^{*}$ & $4.11^{*}$ & $2.48^{*}$  & $2.58^{*}$  & $1.04^{*}$ & $1.11^{*}$ \\
			& $(0.05)$    & $(0.06)$    & $(0.05)$   & $(0.05)$   & $(0.04)$    & $(0.04)$    & $(0.05)$   & $(0.05)$   \\
			\hline
			%	R$^2$       & $0.01$      & $0.00$      & $0.01$     & $0.01$     & $0.01$      & $0.00$      & $0.00$     & $0.00$     \\
			Adj. R$^2$  & $0.00$      & $-0.00$     & $0.01$     & $0.01$     & $0.01$      & $0.00$      & $0.00$     & $0.00$     \\
			Num. obs.   & $4837$      & $4567$      & $4834$     & $4834$     & $4815$      & $4553$      & $4822$     & $4549$     \\
			%	RMSE        & $1.41$      & $1.50$      & $1.32$     & $1.32$     & $1.01$      & $1.07$      & $1.19$     & $1.30$     \\
			%	N Clusters  & $1665$      & $1577$      & $1665$     & $1665$     & $1660$      & $1574$      & $1661$     & $1574$     \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_party1}
	\end{center}
\end{table}

\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Party}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Compassionate} & \multicolumn{2}{c}{Emotional} & \multicolumn{2}{c}{Decisive} & \multicolumn{2}{c}{Tough} \\
			\textbf{Candidate Traits}& Dems & Reps & Dems & Reps & Dems & Reps & Dems & Reps \\
			\hline
			Woman       & $0.07^{*}$  & $0.10^{*}$  & $-0.00$     & $-0.00$     & $0.03$      & $0.04$      & $0.10^{*}$  & $0.03$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			Black       & $-0.01$     & $-0.01$     & $-0.02$     & $-0.02$     & $-0.00$     & $-0.03$     & $0.01$      & $-0.01$    \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			Single      & $-0.03$     & $-0.01$     & $0.03$      & $0.03$      & $-0.01$     & $-0.01$     & $0.00$      & $-0.01$    \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			No children & $-0.18^{*}$ & $-0.15^{*}$ & $-0.17^{*}$ & $-0.17^{*}$ & $-0.07^{*}$ & $0.01$      & $-0.09^{*}$ & $-0.00$    \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			Vignette 2  & $-0.05^{*}$ & $-0.01$     & $0.03$      & $0.03$      & $0.02$      & $-0.03$     & $-0.02$     & $-0.00$    \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			Vignette 3  & $-0.07^{*}$ & $-0.09^{*}$ & $-0.01$     & $-0.01$     & $-0.00$     & $-0.07^{*}$ & $0.00$      & $0.02$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   \\
			Intercept   & $2.46^{*}$  & $2.50^{*}$  & $1.95^{*}$  & $1.95^{*}$  & $2.28^{*}$  & $2.36^{*}$  & $2.15^{*}$  & $2.25^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   \\
			\hline
			%	R$^2$       & $0.01$      & $0.01$      & $0.01$      & $0.01$      & $0.00$      & $0.00$      & $0.00$      & $0.00$     \\
			Adj. R$^2$  & $0.01$      & $0.01$      & $0.01$      & $0.01$      & $0.00$      & $-0.00$     & $0.00$      & $-0.00$    \\
			Num. obs.   & $4817$      & $4552$      & $4819$      & $4819$      & $4820$      & $4549$      & $4820$      & $4553$     \\
			%	RMSE        & $1.02$      & $1.06$      & $1.09$      & $1.09$      & $1.04$      & $1.10$      & $1.02$      & $1.11$     \\
			%	N Clusters  & $1663$      & $1575$      & $1662$      & $1662$      & $1662$      & $1573$      & $1661$      & $1575$     \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_party2}
	\end{center}
\end{table}


\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Party}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Values} & \multicolumn{2}{c}{Rep Me} & \multicolumn{2}{c}{Fam Issues} & \multicolumn{2}{c}{Ideology} \\
			\textbf{Candidate Traits}	& Dems & Reps & Dems & Reps & Dems & Reps & Dems & Reps \\
			\hline
			Woman       & $0.04$      & $0.02$      & $0.11^{*}$  & $0.11^{*}$  & $0.07$      & $0.11^{*}$  & $-0.03$    & $-0.14^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.05)$    \\
			Black       & $-0.00$     & $0.01$      & $-0.01$     & $-0.01$     & $-0.01$     & $-0.00$     & $0.01$     & $-0.00$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.05)$    \\
			Single      & $-0.04$     & $-0.04$     & $-0.06$     & $-0.06$     & $-0.06$     & $-0.04$     & $-0.02$    & $-0.14^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.05)$    \\
			No children & $-0.13^{*}$ & $-0.14^{*}$ & $-0.11^{*}$ & $-0.11^{*}$ & $-0.36^{*}$ & $-0.31^{*}$ & $0.12^{*}$ & $0.07$      \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.05)$    \\
			Vignette 2  & $0.06^{*}$  & $-0.07^{*}$ & $0.05$      & $0.05$      & $-0.02$     & $-0.08^{*}$ & $-0.03$    & $-0.05$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$    \\
			Vignette 3  & $-0.05$     & $-0.20^{*}$ & $-0.03$     & $-0.03$     & $-0.27^{*}$ & $-0.34^{*}$ & $0.03$     & $-0.14^{*}$ \\
			& $(0.04)$    & $(0.03)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$    \\
			Intercept   & $4.29^{*}$  & $4.58^{*}$  & $4.16^{*}$  & $4.16^{*}$  & $4.65^{*}$  & $4.77^{*}$  & $2.33^{*}$ & $3.29^{*}$  \\
			& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$   & $(0.07)$    \\
			\hline
			R$^2$       & $0.00$      & $0.01$      & $0.00$      & $0.00$      & $0.03$      & $0.03$      & $0.00$     & $0.00$      \\
			Adj. R$^2$  & $0.00$      & $0.01$      & $0.00$      & $0.00$      & $0.03$      & $0.02$      & $0.00$     & $0.00$      \\
			Num. obs.   & $4834$      & $4567$      & $4831$      & $4831$      & $4802$      & $4534$      & $4818$     & $4559$      \\
			RMSE        & $1.30$      & $1.36$      & $1.34$      & $1.34$      & $1.31$      & $1.34$      & $1.37$     & $1.71$      \\
			N Clusters  & $1665$      & $1577$      & $1665$      & $1665$      & $1663$      & $1574$      & $1663$     & $1576$      \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_party3}
	\end{center}
\end{table}


\FloatBarrier
\newpage

\subsection{Models By Respondents' Gender}
The three models below present the same models presented in the body of the paper but subset by respondents' gender. 

\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Gender}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Support} & \multicolumn{2}{c}{Time} & \multicolumn{2}{c}{Sincere} & \multicolumn{2}{c}{Selfish} \\
			\textbf{Candidate Traits} & Men & Women & Men & Women & Men & Women & Men & Women \\
			\hline
			Woman       & $-0.02$     & $0.09^{*}$  & $-0.06$    & $-0.01$    & $0.03$      & $0.08^{*}$  & $-0.02$    & $-0.03$    \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   \\
			Black       & $-0.02$     & $-0.04$     & $0.00$     & $0.01$     & $0.02$      & $-0.04$     & $-0.04$    & $0.01$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   \\
			Single      & $-0.03$     & $-0.07$     & $-0.02$    & $-0.02$    & $0.01$      & $-0.01$     & $0.02$     & $-0.04$    \\
			& $(0.05)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   \\
			No children & $-0.03$     & $-0.17^{*}$ & $0.20^{*}$ & $0.39^{*}$ & $-0.09^{*}$ & $-0.18^{*}$ & $0.04$     & $0.09^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   \\
			Vignette 2  & $0.01$      & $0.09^{*}$  & $-0.01$    & $0.08^{*}$ & $-0.04$     & $-0.05^{*}$ & $0.03$     & $0.06^{*}$ \\
			& $(0.03)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   & $(0.03)$    & $(0.02)$    & $(0.03)$   & $(0.03)$   \\
			Vignette 3  & $-0.09^{*}$ & $0.10^{*}$  & $-0.06$    & $0.11^{*}$ & $-0.09^{*}$ & $-0.05^{*}$ & $0.12^{*}$ & $0.10^{*}$ \\
			& $(0.04)$    & $(0.03)$    & $(0.04)$   & $(0.03)$   & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   \\
			Intercept   & $5.17^{*}$  & $4.92^{*}$  & $4.45^{*}$ & $3.98^{*}$ & $2.56^{*}$  & $2.50^{*}$  & $1.28^{*}$ & $0.89^{*}$ \\
			& $(0.06)$    & $(0.05)$    & $(0.05)$   & $(0.05)$   & $(0.04)$    & $(0.04)$    & $(0.05)$   & $(0.04)$   \\
			\hline
			%		R$^2$       & $0.00$      & $0.01$      & $0.01$     & $0.02$     & $0.00$      & $0.01$      & $0.00$     & $0.00$     \\
			Adj. R$^2$  & $-0.00$     & $0.01$      & $0.00$     & $0.02$     & $0.00$      & $0.01$      & $0.00$     & $0.00$     \\
			Num. obs.   & $4570$      & $4834$      & $4569$     & $4832$     & $4552$      & $4816$      & $4555$     & $4816$     \\
			%	RMSE        & $1.51$      & $1.39$      & $1.37$     & $1.29$     & $1.11$      & $0.97$      & $1.34$     & $1.12$     \\
			%	N Clusters  & $1576$      & $1666$      & $1576$     & $1666$     & $1574$      & $1660$      & $1574$     & $1661$     \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_gender1}
	\end{center}
\end{table}

\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Gender}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Compassionate} & \multicolumn{2}{c}{Emotional} & \multicolumn{2}{c}{Decisive} & \multicolumn{2}{c}{Tough} \\
			\textbf{Candidate Traits} & Men & Women & Men & Women & Men & Women & Men & Women \\
			\hline
			Woman       & $0.05$      & $0.11^{*}$  & $0.02$      & $0.01$      & $0.03$     & $0.03$     & $0.06$     & $0.07^{*}$  \\
			& $(0.03)$    & $(0.03)$    & $(0.04)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			Black       & $0.03$      & $-0.04$     & $-0.03$     & $-0.02$     & $-0.02$    & $-0.01$    & $0.02$     & $-0.02$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			Single      & $-0.05$     & $0.00$      & $0.02$      & $-0.05$     & $-0.01$    & $-0.01$    & $-0.01$    & $-0.01$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			No children & $-0.12^{*}$ & $-0.21^{*}$ & $-0.15^{*}$ & $-0.20^{*}$ & $-0.01$    & $-0.05$    & $-0.02$    & $-0.07^{*}$ \\
			& $(0.03)$    & $(0.03)$    & $(0.04)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			Vignette 2  & $0.02$      & $-0.08^{*}$ & $0.01$      & $0.02$      & $-0.01$    & $0.00$     & $-0.02$    & $-0.00$     \\
			& $(0.03)$    & $(0.02)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			Vignette 3  & $-0.06^{*}$ & $-0.09^{*}$ & $-0.01$     & $-0.00$     & $-0.05$    & $-0.02$    & $0.00$     & $0.01$      \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$   & $(0.03)$   & $(0.03)$   & $(0.03)$    \\
			Intercept   & $2.48^{*}$  & $2.48^{*}$  & $2.17^{*}$  & $1.93^{*}$  & $2.37^{*}$ & $2.27^{*}$ & $2.24^{*}$ & $2.17^{*}$  \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$   & $(0.04)$   & $(0.04)$   & $(0.04)$    \\
			\hline
			%		R$^2$       & $0.01$      & $0.02$      & $0.00$      & $0.01$      & $0.00$     & $0.00$     & $0.00$     & $0.00$      \\
			Adj. R$^2$  & $0.00$      & $0.02$      & $0.00$      & $0.01$      & $-0.00$    & $-0.00$    & $-0.00$    & $0.00$      \\
			Num. obs.   & $4552$      & $4817$      & $4555$      & $4814$      & $4551$     & $4818$     & $4554$     & $4819$      \\
			%RMSE        & $1.11$      & $0.98$      & $1.17$      & $1.06$      & $1.12$     & $1.02$     & $1.12$     & $1.01$      \\
			%N Clusters  & $1576$      & $1662$      & $1575$      & $1660$      & $1574$     & $1661$     & $1575$     & $1661$      \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_gender2}
	\end{center}
\end{table}


\begin{table}[!h]
	\begin{center}
		\caption{Models Subset by Respondents' Gender}
		\begin{tabular}{l c c c c c c c c}
			\hline
			& \multicolumn{2}{c}{Values} & \multicolumn{2}{c}{Rep Me} & \multicolumn{2}{c}{Fam Issues} & \multicolumn{2}{c}{Ideology} \\
			\textbf{Candidate Traits}& Men & Women & Men & Women & Men & Women & Men & Women \\
			\hline
			Woman       & $-0.02$     & $0.06$      & $0.02$      & $0.12^{*}$  & $0.05$      & $0.11^{*}$  & $-0.08$     & $-0.10^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			Black       & $-0.00$     & $0.02$      & $-0.02$     & $0.02$      & $0.02$      & $-0.03$     & $-0.01$     & $-0.01$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			Single      & $0.01$      & $-0.09^{*}$ & $-0.03$     & $-0.11^{*}$ & $-0.01$     & $-0.09^{*}$ & $-0.11^{*}$ & $-0.05$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			No children & $-0.08$     & $-0.19^{*}$ & $-0.04$     & $-0.10^{*}$ & $-0.25^{*}$ & $-0.41^{*}$ & $0.11^{*}$  & $0.09^{*}$  \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			Vignette 2  & $0.01$      & $-0.02$     & $-0.00$     & $0.02$      & $-0.05$     & $-0.04$     & $-0.05$     & $-0.03$     \\
			& $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    & $(0.03)$    \\
			Vignette 3  & $-0.14^{*}$ & $-0.11^{*}$ & $-0.11^{*}$ & $-0.03$     & $-0.34^{*}$ & $-0.26^{*}$ & $-0.07$     & $-0.04$     \\
			& $(0.04)$    & $(0.03)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Intercept   & $4.54^{*}$  & $4.34^{*}$  & $4.43^{*}$  & $4.12^{*}$  & $4.74^{*}$  & $4.68^{*}$  & $2.71^{*}$  & $2.90^{*}$  \\
			& $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.07)$    & $(0.05)$    \\
			\hline
			%		R$^2$       & $0.00$      & $0.01$      & $0.00$      & $0.01$      & $0.02$      & $0.04$      & $0.00$      & $0.00$      \\
			Adj. R$^2$  & $0.00$      & $0.01$      & $0.00$      & $0.00$      & $0.02$      & $0.03$      & $0.00$      & $0.00$      \\
			Num. obs.   & $4569$      & $4832$      & $4567$      & $4829$      & $4535$      & $4801$      & $4554$      & $4823$      \\
			%	RMSE        & $1.37$      & $1.29$      & $1.40$      & $1.33$      & $1.35$      & $1.31$      & $1.74$      & $1.42$      \\
			%	N Clusters  & $1576$      & $1666$      & $1576$      & $1666$      & $1574$      & $1663$      & $1575$      & $1664$      \\
			\hline
			\multicolumn{9}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{table_by_gender3}
	\end{center}
\end{table}

\FloatBarrier
\newpage

\subsection{Figures for Heterogeneous Treatment Effects}
\begin{figure}[!h]
	\includegraphics[width=13cm]{figure_support_partygender}
	\centering
	\textbf{\caption{Candidate Support by Respondents' Party \& Gender} \label{figure_support_partygender}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents support for candidates by respondents' political party or gender. This figure highlights that it was women and Democrats who demonstrated significantly more support for candidates with children than those without children.}
	\end{description}
\end{figure}

\FloatBarrier
\newpage
\subsection{Ordered Logit Models}
In the body of this paper, all models presented are OLS regression models. The decision to use OLS was made because linear regression offers more easily interpretable coefficients and is appropriate when used for experiments, even when the dependent variable is not continuous. However, as a robustness check, I also ran all models as ordered logit models, which are presented below. In comparing all coefficients between the OLS models and ordered logit models, results are nearly the same and there are only two significant differences. In the OLS model for the dependent variable tough, the coefficient for candidates with children is significant, and in the ordered logit, the p-value for this coefficient is 0.06. Secondly, in the OLS model for the dependent variable family issues, marital status is not significant, but in the ordered logit model it is statistically significant. 


\begin{table}[!h]
	\begin{center}
		\caption{Ordered Logit Models}
		\begin{tabular}{l c c c c c c}
			\hline
			& & & \multicolumn{2}{c}{\textit{Warmth}} & \multicolumn{2}{c}{\textit{Feminine Traits}} \\
			& Support & Time & Sincere & Selfish & Compassion & Emotional \\
			\hline
			Woman          & $0.06$      & $-0.03$     & $0.10^{*}$  & $-0.05$     & $0.14^{*}$  & $0.03$      \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Black          & $-0.04$     & $0.01$      & $-0.00$     & $-0.03$     & $-0.00$     & $-0.04$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Single         & $-0.06$     & $-0.01$     & $0.01$      & $-0.03$     & $-0.03$     & $-0.02$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			No children    & $-0.14^{*}$ & $0.41^{*}$  & $-0.25^{*}$ & $0.11^{*}$  & $-0.31^{*}$ & $-0.29^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Vignette 2     & $0.08$      & $0.07$      & $-0.07$     & $0.08$      & $-0.05$     & $0.02$      \\
			& $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			Vignette 3     & $0.03$      & $0.06$      & $-0.13^{*}$ & $0.17^{*}$  & $-0.13^{*}$ & $-0.03$     \\
			& $(0.04)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			%	Delete         & $-3.55^{*}$ & $-3.21^{*}$ & $-1.79^{*}$ & $0.62^{*}$  & $-1.74^{*}$ & $-0.91^{*}$ \\
			%	& $(0.08)$    & $(0.07)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			%	Delete         & $-2.98^{*}$ & $-2.38^{*}$ & $-0.08$     & $1.83^{*}$  & $0.04$      & $0.75^{*}$  \\
			%	& $(0.06)$    & $(0.06)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			%	Delete         & $-2.13^{*}$ & $-0.72^{*}$ & $1.47^{*}$  & $2.80^{*}$  & $1.54^{*}$  & $2.02^{*}$  \\
			%	& $(0.06)$    & $(0.05)$    & $(0.05)$    & $(0.06)$    & $(0.05)$    & $(0.06)$    \\
			%	Delete         & $-0.53^{*}$ & $0.10^{*}$  & $-3.16^{*}$ & $-0.14^{*}$ & $-3.10^{*}$ & $-2.18^{*}$ \\
			%	& $(0.05)$    & $(0.05)$    & $(0.07)$    & $(0.05)$    & $(0.07)$    & $(0.06)$    \\
			%	Delete         & $0.45^{*}$  & $1.51^{*}$  &             &             &             &             \\
			%	& $(0.05)$    & $(0.05)$    &             &             &             &             \\
			%	Delete         & $1.41^{*}$  & $-4.05^{*}$ &             &             &             &             \\
			%	& $(0.05)$    & $(0.10)$    &             &             &             &             \\
			\hline
			AIC            & $31650.26$  & $29863.64$  & $26639.60$  & $26082.14$  & $26629.64$  & $27864.10$  \\
			%BIC            & $31736.04$  & $29949.43$  & $26711.06$  & $26153.59$  & $26701.09$  & $27935.56$  \\
			%Log Likelihood & $-15813.13$ & $-14919.82$ & $-13309.80$ & $-13031.07$ & $-13304.82$ & $-13922.05$ \\
			%Deviance       & $31626.26$  & $29839.64$  & $26619.60$  & $26062.14$  & $26609.64$  & $27844.10$  \\
			Num. obs.      & $9404$      & $9401$      & $9368$      & $9371$      & $9369$      & $9369$      \\
			\hline
			\multicolumn{7}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{ordered_logit1}
	\end{center}
\end{table}

\begin{table}[!h]
	\begin{center}
		\caption{Ordered Logit Models}
		\begin{tabular}{l c c c c c c}
			\hline
			& \multicolumn{2}{c}{\textit{Masculine Traits}} \\
			& Decisive & Tough & Values & Rep Me & Fam Issues & Ideology \\
			\hline
			Woman          & $0.06$      & $0.11^{*}$  & $0.04$      & $0.10^{*}$  & $0.11^{*}$  & $-0.11^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Black          & $-0.02$     & $-0.01$     & $0.01$      & $0.00$      & $-0.01$     & $-0.01$     \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Single         & $-0.01$     & $-0.00$     & $-0.06$     & $-0.09^{*}$ & $-0.08^{*}$ & $-0.10^{*}$ \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			No children    & $-0.06$     & $-0.07$     & $-0.18^{*}$ & $-0.10^{*}$ & $-0.45^{*}$ & $0.11^{*}$  \\
			& $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    & $(0.04)$    \\
			Vignette 2     & $-0.00$     & $-0.02$     & $0.01$      & $0.02$      & $-0.06$     & $-0.06$     \\
			& $(0.05)$    & $(0.05)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			Vignette 3     & $-0.05$     & $0.01$      & $-0.15^{*}$ & $-0.08$     & $-0.42^{*}$ & $-0.05$     \\
			& $(0.05)$    & $(0.05)$    & $(0.04)$    & $(0.04)$    & $(0.05)$    & $(0.04)$    \\
			%		Delete         & $-2.58^{*}$ & $-2.56^{*}$ & $-4.36^{*}$ & $-4.18^{*}$ & $-4.82^{*}$ & $-2.33^{*}$ \\
			%		& $(0.06)$    & $(0.06)$    & $(0.10)$    & $(0.09)$    & $(0.11)$    & $(0.06)$    \\
			%		Delete         & $-1.48^{*}$ & $-1.26^{*}$ & $-3.58^{*}$ & $-3.31^{*}$ & $-3.96^{*}$ & $-1.23^{*}$ \\
			%		& $(0.05)$    & $(0.05)$    & $(0.07)$    & $(0.07)$    & $(0.08)$    & $(0.05)$    \\
			%	Delete         & $0.28^{*}$  & $0.54^{*}$  & $-2.75^{*}$ & $-2.46^{*}$ & $-3.12^{*}$ & $-0.52^{*}$ \\
			%	& $(0.05)$    & $(0.05)$    & $(0.06)$    & $(0.06)$    & $(0.06)$    & $(0.05)$    \\
			%	Delete         & $1.83^{*}$  & $1.96^{*}$  & $-1.08^{*}$ & $-0.83^{*}$ & $-1.39^{*}$ & $0.91^{*}$  \\
			%	& $(0.05)$    & $(0.06)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			%	Delete         &             &             & $-0.15^{*}$ & $0.06$      & $-0.57^{*}$ & $1.66^{*}$  \\
			%	&             &             & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.05)$    \\
			%	Delete         &             &             & $1.17^{*}$  & $1.33^{*}$  & $0.71^{*}$  & $2.78^{*}$  \\
			%	&             &             & $(0.05)$    & $(0.05)$    & $(0.05)$    & $(0.06)$    \\
			\hline
			AIC            & $26963.57$  & $27035.21$  & $30104.11$  & $30713.87$  & $29248.38$  & $33692.27$  \\
			%BIC            & $27035.02$  & $27106.67$  & $30189.90$  & $30799.65$  & $29334.08$  & $33778.02$  \\
			%Log Likelihood & $-13471.78$ & $-13507.61$ & $-15040.06$ & $-15344.93$ & $-14612.19$ & $-16834.13$ \\
			%	Deviance       & $26943.57$  & $27015.21$  & $30080.11$  & $30689.87$  & $29224.38$  & $33668.27$  \\
			Num. obs.      & $9369$      & $9373$      & $9401$      & $9396$      & $9336$      & $9377$      \\
			\hline
			\multicolumn{7}{l}{\scriptsize{$^{*}p<0.05$}}
		\end{tabular}
		\label{ordered_logit2}
	\end{center}
\end{table}

\FloatBarrier
\clearpage
\subsection{Models by Vignette}
In this experiment, respondents evaluated three separate vignettes. In most models, I analyze all vignette in the same model and include an independent variable for which vignette was being evaluated. However, as a robustness check, I ran all models separated out by vignette number, and present the results for two of my dependent variables below. All coefficients were in the same direction across vignette number. I find similar results across vignette number. 

\begin{table}[!h]
	\begin{center}
		\caption{Models by Vignette Num}
		\begin{threeparttable}
			\begin{tabular}{l c c c c c c}
				\hline
				& \multicolumn{3}{c}{\textit{Support}} & \multicolumn{3}{c}{\textit{Time}} \\
				& Vig 1 & Vig 2 & Vig 3 & Vig 1 & Vig 2 & Vig 3 \\
				\hline
				Woman       & $0.05$      & $0.03$      & $0.04$     & $-0.01$    & $-0.10^{*}$ & $0.02$     \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$   & $(0.05)$   & $(0.05)$    & $(0.05)$   \\
				Black       & $0.01$      & $-0.06$     & $-0.05$    & $0.00$     & $0.01$      & $0.00$     \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$   & $(0.05)$   & $(0.05)$    & $(0.05)$   \\
				Single      & $0.00$      & $-0.10$     & $-0.05$    & $-0.04$    & $-0.03$     & $0.02$     \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$   & $(0.05)$   & $(0.05)$    & $(0.05)$   \\
				No children & $-0.15^{*}$ & $-0.12^{*}$ & $-0.05$    & $0.37^{*}$ & $0.29^{*}$  & $0.22^{*}$ \\
				& $(0.05)$    & $(0.05)$    & $(0.05)$   & $(0.05)$   & $(0.05)$    & $(0.05)$   \\
				Intercept   & $5.01^{*}$  & $5.14^{*}$  & $5.03^{*}$ & $4.17^{*}$ & $4.29^{*}$  & $4.24^{*}$ \\
				& $(0.06)$    & $(0.06)$    & $(0.06)$   & $(0.05)$   & $(0.05)$    & $(0.05)$   \\
				\hline
				R$^2$       & $0.00$      & $0.00$      & $0.00$     & $0.02$     & $0.01$      & $0.01$     \\
				Adj. R$^2$  & $0.00$      & $0.00$      & $-0.00$    & $0.02$     & $0.01$      & $0.01$     \\
				Num. obs.   & $3230$      & $3129$      & $3045$     & $3230$     & $3128$      & $3043$     \\
				\hline
				\multicolumn{7}{l}{\scriptsize{$^{*}p<0.05$}}
			\end{tabular}
			\label{by_vignette}
		\end{threeparttable}
	\end{center}
\end{table}

\FloatBarrier
\newpage
\subsection{Models by Respondents' Sexist Attitudes}
\begin{figure}[!h]
	\includegraphics[width=12cm]{figure_sexism_index}
	\centering
	\textbf{\caption{Respondents' Level of Sexism} \label{figure_sexism_index}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents respondents' level of sexism, with higher scores equally more sexist attitudes. This measure is an additive index constructed from responses to three questions on a 4-point scale -- Most men are better suited emotionally for politics than are most women; Women can handle job pressures as well as men (reverse-coded); A husband's job is to earn money, a wife's job is to look after home and family.}
	\end{description}
\end{figure}

\begin{figure}[!h]
	\includegraphics[width=12cm]{figure_support_predict}
	\centering
	\textbf{\caption{Support by Respondents' Level of Sexism} \label{figure_support_predict}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents predict support for candidates by gender across the range of respondents' sexist attitudes. It does not appear that sexism significantly shaped candidate support.}
	\end{description}
\end{figure}

\FloatBarrier
\clearpage
\subsection{Models For Respondents Who Correctly Answered the Manipulation Check}

\begin{figure}[!h]
	\includegraphics[width=12cm]{correct_race_support_coplot}
	\centering
	\textbf{\caption{Coefficient Plot: Respondents Who Correctly Answered the Manipulation Check} \label{correct_race_support_coplot}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents only respondents who correctly answered the manipulation check. Because the manipulation check was only answered after the third vignette, this model only include responses to the third vignette. This subset of respondents were significantly less likely to support Black candidates.}
	\end{description}
\end{figure}






\newpage
\setcounter{hyp}{0}
\chapter{Making the Personal Political: Support for Unconventional Congressional Candidates}

\section{Introduction}
Recent years have been record-breaking for the diverse types of candidates seeking and winning seats on Capitol Hill. For example, in 2013, Kyrsten Sinema of Arizona was sworn into office as the first openly bisexual legislator to serve in Congress. Elected in 2018, Rep. Katie Porter was the first single mother of young children elected to the House of Representatives. While these legislators reflect a shift toward more diversity in Congress, the reality is that lawmakers continue to be rather homogenous in terms of backgrounds and identity groups. Even today, the life experiences and backgrounds of those serving in Washington D.C. do not well reflect the diversity of those they represent.  

Much of the past research exploring why Congress is unrepresentative of the populations it serves has focused on gender and race and ethnicity. This project builds on this line of research to examine how other identities impact how voters evaluate candidates. Specifically, I am interested in how candidates' personal lives, including their relationship status, children, and sexuality, impact how voters perceive them, and ultimately, if candidates who deviate from the personal background of a typical politician face electoral obstacles. 

When people think about politicians, they are most likely to visualize who has traditionally served in politics -- White, straight men who are married with older children. Yet, this image is incompatible with the growing diversity of the United States population. Over the past 50 years, the personal lives of Americans progressively look more varied. For example, more Americans than ever openly identify as LGBTQ+, there has been a dramatic decline in marriages, and increasingly individuals choose to postpone parenthood until later in life \cite{mortelmans2016changing}. Therefore, as the country diversifies in terms of lifestyles and living arrangements, we should expect that increasingly the types of candidates seeking elected office may come from less conventional backgrounds. 

Furthermore, we know diversity in government matters. For example, mothers in Congress are more likely to sponsor bills aimed at addressing family and children issues, and LGBTQ legislators prioritize the passage of bills that most directly impact the LGBTQ community as compared to their straight, cisgender colleagues \cite{bryant2019working,haider2010out,reynolds2013representation}. Therefore, we need a better understanding of the political environment these types of candidates with less traditional personal identities face and potential obstacles for creating a more representative institution. 

Using a conjoint experiment of nearly 3,000 respondents, this project explores voters' preferences and attitudes toward candidates that do not fit the stereotypical image of a member of Congress. Respondents viewed a pair of hypothetical candidates, who were randomly assigned seven different characteristics, including relationship status, children, and sexuality. Respondents were then asked to make a binary choice on which of the two candidates they would be more likely to support in a congressional primary. 

Overall, I expect respondents will be more likely to support candidates with more traditional backgrounds, such that preferred candidates will be straight, married, and have older children. Beyond understanding baseline support, I want to examine three potential explanations for how and why respondents make their voting decisions and evaluation of candidates, including a sense of shared values, ability to govern, and perceived ideology. I expect voters may be less likely to support unconventional candidates because they view these candidates as less likely to share their values, less able to handle the responsibilities of governing, and less congruent with their own ideology. 

Ultimately, I find a candidate's sexuality, relationship status, and parental status do influence voters' evaluations. Respondents favor candidates who fit the more traditional politician mold, including a stronger preference for married, straight candidates with older children. Republicans and older respondents expressed a stronger preference for more conventional candidates. The biggest effect size of these three personal candidate characteristics was sexuality. Gay and bisexual candidates were viewed as sharing fewer values with voters than straight candidates and were stereotyped as overall more liberal. For Democratic respondents, similar to Republicans, they rated gay and bisexual candidates as sharing fewer values with themselves; however, this stereotype did not decrease overall support among Democrats, as it did for Republicans. This may be explained by the fact that gay and bisexual candidates were stereotyped as overall more liberal, which is a disadvantage among Republican voters but a potential advantage with Democrats. 

Furthermore, I find women respondents expressed a stronger preference for candidates who are parents. Gender of the candidate also conditioned preferences for parental status, such that respondents evaluated men candidates similarly regardless of their parental status, but preferred women candidates who had older children as compared to women without children. Ultimately, this project provides insight into opportunities and obstacles for the campaigns of unconventional congressional candidates, and how candidates' personal characteristics shape how voters perceive them and impact voting behavior.  

\section{Diversity in Congress}
The identities and life experiences of those who serve in government matters, as these legislators bring with them a perspective that influences their legislative priorities and voting behavior in Congress \cite{pitkin1967}. Much of the past literature on descriptive representation in politics has focused on the electoral challenges and congressional behavior of lawmakers by gender and race or ethnicity. However, increasingly research has examined the behavior and experiences of other identity groups in politics, including LGBTQ candidates and legislators. 

Crowned by the media as a "Rainbow Wave," 2018 saw gains in LGBTQ candidates winning seats in Congress, including Kansas Rep. Sharice Davids as the first openly gay woman of color in Congress \cite{stack2018lgbt}. Beyond recent successes, LGBTQ candidates often face challenges in winning votes \cite{golebiowska1999gays,golebiowska2001group,haider2017bringing,herrick1999gays,jones2019gender}. Voters are overall less willing to support LGBTQ candidates, a bias largely driven by Republicans, conservatives, and religious voters \cite{doan2010role,haider2010out}. When LGBTQ candidates do run, they tend to be strategic, running in more liberal, urban districts where they perceive voters are more accepting of LGBTQ candidates \cite{everitt2014versus,haider2010out}. Furthermore, rapid shifts in public opinion on LGBTQ issues has overall increased the general willingness to vote for LGBTQ candidates.  For example, a 2019 poll found 68\% of voters are either enthusiastic or comfortable with a gay presidential candidate \cite{dann2019gay}. 

When it comes to a politician's relationship and parental status, most members of Congress are married and wait until their children are grown before seeking elected office \cite{carroll2013more,conway2005women,fulton2006sense}. This is especially true for women lawmakers. Therefore, a majority of the research on candidates' family dynamics has predominantly been examined through a gendered lens, such that the impact of family dynamics is expected to be conditional on gender. The campaign experiences of mothers often differ from fathers, as mothers often face questions and criticism about who is taking care of their children while they are running for office, implying that by seeking elected office they are neglecting their motherly duties in a way fathers are not often confronted with \cite{zernike2018}. For example, former Massachusetts Governor Jan Swift recalled a time a voter asking if she had a boyfriend because the voter worried if Swift got married and had children, she would be unable to fulfill the requirements of the job \cite{five2020womenrun}. 

Past research finds voters prefer married parents as candidates, driven more strongly by conservative voters evaluating single candidates and those without children as less likely to earn their support    \cite{campbell2018impact,greenlee2014political,stalsburg2010voting,teele2018ties}. This preference for candidates with more traditional family dynamics disproportionately impacts women candidates, who are more likely to perform a majority of household task and childcare needs, leaving mothers with less time than fathers to devote to their political goals \cite{teele2018ties}. Therefore, it is clear that both sexuality and family dynamics impact how voters assess political candidates, and that gender may intersect in unique ways with these other identities. 

\section{Theoretical Expectations}
While a democracy functions best with an informed electorate who carefully analyzes the skills and policy positions of candidates for political office, voters often lack the time, resources, and interest to be fully informed about their options at the ballot box \cite{carpini1996americans}. Therefore, voters often use shortcuts to evaluate candidates and craft voting decisions \cite{crowder2020voting,lupia1994shortcuts} The most influential shortcuts guiding modern voting is political party, yet in primaries, voters lack this useful cue, making acquiring information about candidates and their policy preferences costly. Primaries are therefore likely when candidates' personal characteristics play a bigger role in shaping voting decisions \cite{hirano2019primary,kirklandforthcoming}. 

When voters have limited knowledge about candidates, they may incorporate information forged by their own past experiences or societal stereotypes to guide their voting decisions, regardless of if these stereotypes accurately apply to the candidate being evaluated or not \cite{popkin2020reasoning}. In applying this argument to aspects of candidates' personal lives, I outline below three potential explanations -- values, ideology, and abilities -- for how voters might use aspects of a candidate's personal identity to draw conclusions about the individual's fitness for office. 

\subsection{Candidate Values}
A candidate's personal attributes may be used as a shortcut by voters to make assumptions about the candidate's values and how those values may or may not align with one's own beliefs and principles. Voters tend to favor candidates who look and think like them, as individuals feel more comfortable around those with whom they share identity groups \cite{besley1997economic}. Voters who share an identity with a candidate are more likely to perceive that candidate is relatable and share their values. Ultimately, this perception may lead voters to assume candidates who are more similar to themselves will have a better understanding of what issues and policies are a priority for that voter and this will be reflected in their legislative behavior. 

Specifically, being married and having children may send a signal to some voters, especially those who are themselves married with children, that the candidate is more relatable and possesses traditional family values. Voters may also view candidates with families as better able to understand and prioritize policies that most directly impact families and thus may increase their likelihood of supporting these candidates. Even for those not married and without children, they may envision themselves one day fulfilling those identities or at a minimum likely view these characteristics as ``ideal" in a culture centered around the nuclear family.

Furthermore, much of the discussion around LGBTQ issues in the United States is framed as value-based, which likely has consequences for how voters perceive the values of LGBTQ candidates. Americans remain divided, with some believing being gay is a lifestyle choice and others that an individual is born gay \cite{pew2015gay}. For those who view a queer individual as choosing their sexuality, they view this as an active choice to abandon traditional lifestyle decisions and values. Therefore, some may view LGBTQ politicians as less able to serve ethically and perform their necessary professional duties, thus decreasing support \cite{jones2016trump}. 

\subsection{Candidate Ideology}
Secondly, voters may use candidates' personal characteristics to infer the candidate's ideology.\footnote{In this section, I present a candidate's perceived values and perceived ideology as two separate concepts, but in reality, it is likely these two concepts are intertwined. Especially for the most ideologically extreme voters, I expect a strong correlation between perceived values and perceived ideology.} Past research finds voters' perceived ideology of a candidate is shaped by the candidate's gender, race, and social class \cite{carnes2015mill,king2003sex,koch2000citizens,mcdermott1998race}. Based on a candidate's traits, voters may make assumptions about the individual's lived experiences and political background and how these events have shaped their political preferences and ideology. For example, because LGBTQ individuals are more likely to have experienced discrimination as compared to straight, cisgender people, one might assume LGBTQ politicians are more liberal on issues related to equality and anti-discrimination. At times, stereotypes about identity and ideology can supersede party, such that past research finds transgender candidates are perceived as more liberal than cisgender candidates, regardless of political party \cite{jones2019gender}. 

\subsection{Candidate Ability}
Finally, a candidate's personal traits may send a signal to voters about the candidate's quality and abilities to serve in government. Certain personal attributes may trigger a connection to either positive or negative trait evaluations. For example, past research finds voters often assign trait evaluations to candidates based solely on their gender \cite{fox2003}. Men are perceived as more competent and better able to handle crisis, while women are stereotyped as more compassionate and gentle \cite{branton2018impact,huddy1993gender}. A similar mechanism may be at play with other candidate identities, such that candidates who are married and are parents may be perceived as more hard-working, likeable, better time management skills, or as having more energy, while single candidates without children may be perceived as more selfish or as poor decision-makers \cite{ganong1995content}. Candidates who openly identify as LGBTQ may be stereotyped as less honest and less moral, leading to decreased electoral support \cite{doan2010role}. 

Stereotypes about what traits candidates do or do not possess may also lead voters to perceive some candidates are better able to handle certain types of policy issues. For example, because women are stereotyped as more compassionate, they are perceived as better able to handle social welfare issues than are men \cite{huddy1993gender}. This connection between traits and issue competence may be especially consequential for LGBTQ candidates. A prevalent obstacle for many LGBTQ candidates is being seen as a multidimensional candidate. Often queer politicians are stereotyped as solely focused on issues concerning the LGBTQ community, rather than as a candidate who is invested in a diverse array of policy concerns \cite{golebiowska2002political,kluttz2014outness}. Stereotypes of a candidate's traits and issue competence likely shape overall evaluations and ultimately voting decisions. 

\subsection{Heterogeneous Effects}
Collectively, I expect voters prefer candidates with more conventional characteristics; however, I expect this preference is more strongly driven by Republicans and older respondents. The Republican Party is often framed as the party of traditional family values \cite{dowland2015family,jost2008ideology}. Residents in conservative states are more likely to be married, and children in conservative states are more likely to live in two-parent households \cite{wilcox2015marriage,wilcox2015families}. Conservatives are also more likely to express they are troubled by the decline in marriage rates (Pew Research 2010)\nocite{pew2010marriage}. Therefore, I expect Republicans have a heightened preference for candidates who are married and have children. 

As related to sexuality, I expect that opposition to LGBTQ candidates is concentrated among Republicans, conservatives, and older voters. These demographics are the most likely to say that, ``homosexuality should be discouraged by society," while Democrats and younger people are more accepting of the LGBTQ community \cite{pew2017homosexuality}. Furthermore, because views on sexuality are so closely tied with the religious community, and White Evangelicals are a strong voting base of the GOP, I expect that queer candidates face greater obstacles in pursuing a primary run within the Republican Party. GOP voters may also stereotype LGBTQ candidates as more liberal, and thus, more ideologically distant from themselves, leading to decreased support.

The theory laid out above about how characteristics of candidates' personal lives influence how voters evaluate politicians' values, ideology, and ability leads to the following hypotheses. 

\singlespacing
\begin{hyp}
	Support will be greater for married candidates than unmarried candidates. 
\end{hyp}
\begin{hyp}
	Support will be greater for candidates with children than candidates without children. 
\end{hyp}
\begin{hyp}
	Support will be greater for straight candidates than gay or bisexual candidates. 
\end{hyp}
\begin{hyp}
	The preference for more conventional candidates will be driven by Republicans, conservatives, and older respondents. 
\end{hyp}
\doublespacing

\section{Data \& Methods}
This project explores voters' candidate preferences through a conjoint experiment that varies multiple character traits.\footnote{A potential limitation of using an online survey conjoint experiment is concern that respondents would infer the goals or hypotheses of the study, thus biasing responses; however, recent research finds that even when respondents in online survey experiments are told the researchers' goals, their responses do not change \cite{mummolo2019demand}.} Conjoint analyses, while more traditionally used in marketing research, have proven to be a useful methodological approach for political science, especially in examining multifaceted individual-level decision making \cite{hainmueller2014causal,hainmueller2015hidden,kirklandforthcoming}. In a conjoint experiment, respondents are presented with two options, both of which have been randomly assigned various characteristics. Respondents are then asked to select which of the two options they prefer. This design allows for the simultaneous estimation of the causal effect of multiple candidate characteristics on how respondents evaluate candidates \cite{hainmueller2014causal}. This method also provides more external validity as compared to more traditional survey experiments because rather than respondents assessing one candidate at a time, it better mimics voters' experience at the ballot box where voters often must decide based on comparing the various candidate options. 

Much of the prior conjoint experiments in political science have presented findings in terms of the Average Marginal Component-Specific Effect (AMCE); however, in analyzing my results, I follow the recommendations of Leeper, Hobolt \& Tilley (2019) and present my findings as marginal means.\nocite{leeper2019measuring}\footnote{Also see Neuner \& Wratil 2020 for a detailed explanation of the differences between AMCE and marginal means \nocite{neuner2020populist}} AMCEs, particularly when used for analysis on subsets of the sample, can present biased results because the attribute estimates are contingent on the reference category selected by the researcher.\footnote{While some authors have a strong theoretical justification for which attributes they select as their reference categories, this choice is often arbitrary or based on weak theoretical expectations.} In comparison, marginal means are estimates that do not require the selection of a reference category, and instead, can be interpreted as the probability a trait is favored by respondents, marginalized across all other attributes \cite{leeper2019measuring,saha2020ambitious}.  

In this conjoint experiment, respondents were shown an introduction where they are instructed they will be shown a pair of candidates running for their party's primary for an open seat for the U.S. House of Representatives. Each respondent evaluated two pairs of candidates, for a total of four candidates. I chose to frame this conjoint experiment as a primary because, as mentioned above, partisanship strongly drives general election voting decisions. Therefore, it is likely in primaries, when voters cannot rely on party to make their decision, where candidates' personal characteristics play a more prominent role in guiding or influencing vote choice. 

Respondents were shown two profiles that have randomized seven candidate attributes -- sexuality, children, relationship status, gender, race/ethnicity, occupation, and age. Table \ref{candidateattributes} displays the chosen character traits for each category, and Figure \ref{conjointexample} shows an example of how the candidates were presented to respondents in the survey. For each character category, one (or more) of the options represent a more ``traditional" candidate or a trait that represents the majority of members of Congress. The other options represent less traditional candidate traits. For example, most members of Congress are straight, which in this experiment represents the more conventional candidate trait, while gay and bisexual represent the less conventional sexuality traits. 

Regarding my theoretical expectations about family dynamics, I vary both the candidate's relationship status and children. For relationship status, married represents the conventional candidate, and the other three options -- single, cohabiting partner, and divorced -- were chosen to represent the growing diversity of households in the United States. The past several decades have witnessed a decline in two-parent households and more adults experiencing divorce or cohabitation \cite{pew2015family}. For children, most candidates tend to wait until their children are older before seeking elected office, so 2 children ages 17 \& 19 represents the conventional candidate. The less conventional children traits are having younger children (ages 3 \& 6) and no children. While there has been prior research which has examined voter support for diverse types of candidates, much of this literature has been limited in the diversity it explores. For example, past studies have not examined ages of children, cohabiting partner, or bisexuality. If we truly hope to understand the political environment diverse candidates face, this study expands our current understanding to examine these candidate traits ignored by past research. 

While sexuality and family dynamics are my primary theoretical interests, I incorporate other candidate traits into this experiment, including gender, race/ethnicity, and occupation, to explore potential interaction effects, as well as to throw off respondents from detecting the true intentions of the experiment and biasing the results. For the candidate's occupation, I incorporated six occupations, of which three fall more into the conventional candidate category as political insiders -- city council member, congressional staffer, and political consultant. Less conventional occupations included nurse, radio talk show host, and ironworker. The candidate's gender is varied between male and female, and race/ethnicity is varied between White, Black, Hispanic, and Asian. 

I include the ages of children in the conjoint, therefore was concerned respondents would infer the candidate's age based on this information. If this occurred, it would be unclear if an effect for the children attribute could be attributed to having children or the perceived age of the candidate. To potentially alleviate this concern, I incorporated the candidate's age into the conjoint. Because I do not have any theoretical expectations or interests related to or conditional on candidate's age, I selected three ages which are quite similar. 

\begin{table}[h]
	\centering
		\begin{threeparttable}
	\caption{Conjoint Experiment Candidate Attributes} 	\label{candidateattributes} 
	\begin{tabular}{@{}ll@{}}
		\toprule
		Relationship status  & Married; Single; Cohabiting partner; Divorced                                                                                   \\
		Children      & 2 children ages 17 \& 19; 2 children ages 3 \& 6; None          \\
		Sexuality            & Straight; gay; bisexual    \\
		Occupation           & City council member; Congressional staffer; Political consultant; \\
		& Nurse; Ironworker; Radio talk show host \\
		Race/Ethnicity                 & White, Black, Hispanic, Asian    \\
		Gender               & Male; Female           \\
		Age & 43; 45; 46 \\
		\bottomrule
	\end{tabular}
	\begin{tablenotes}
	\footnotesize \item Note: Each candidate profile was randomly assigned one of the characteristics from each attribute category. The order in which the attributes appeared was also randomized. Based on the number of attributes included in this conjoint, there are a total of 5,184 possible combinations of attributes.  
	\end{tablenotes}
\end{threeparttable}
\end{table}

After viewing the two profiles, respondents were asked to decide which of the two candidates they would be more likely to support in their party's congressional primary. A binary choice is traditional for conjoint experiments; however, for this project I wanted to incorporate other questions to better understanding the causal story behind respondents' candidate choices. For example, Teele, Kalla, and Rosenbluth (2018) use a conjoint experiment to explore preferences of women candidates, finding that voters prefer candidates with traditional households, but without further investigation, it is unclear why this is. The authors can only theorize why respondents selected the candidate they did. 

Therefore, to explore possible reasons behind respondents' voting decisions, respondents answered three further questions about each candidate to gauge how respondents assesses each of the hypothetical candidates. I asked respondents how strongly they perceive each candidate shares similar values to their own, how strongly they perceive each candidate has the time, energy, and ability to handle the responsibilities of governing, and their perception of each candidate's ideology.\footnote{See the appendix for exact question wording.} These three questions had respondents answer on a 7-point scale, and for the analysis, each was rescaled to range from 0 to 1. 

\begin{figure}[h]
	\includegraphics[width=13cm]{ConjointExample}
	\centering
	\textbf{\caption{Example of Conjoint Experiment} \label{conjointexample}}
	\begin{description}
		\item \noindent \footnotesize {Note: This is an example of what respondents' screens looked like during the survey conjoint experiment. Candidate characteristics were randomized, and the order in which the characteristics appeared was also randomized.}
	\end{description}
\end{figure}

This conjoint experiment was run in September 2020 with the online survey company Lucid, which provides a nationally representative sample on age, gender, race, income, and political party. The survey included nearly 3,000 respondents. For complete surveys, the analysis includes a sample size of more than 11,500. Because each respondent has multiple observations within each model, the standard errors are clustered by respondent. See the appendix for a complete table of the demographics of this survey sample. In this sample, 71\% of respondents identify as White. The sample includes a good mix along partisan and ideological lines, with 39\% identifying as Democrats, 36\% as Republicans, and 25\% as Independents. However, the sample does appear to be more educated than the county as a whole, as 44\% of this sample has a bachelor's degree or higher. Overall, this is a high-quality sample that well reflects many of the important political demographics relevant to this study. 

\section{Results}
In my analysis, I first present the findings for my four dependent variables -- binary choice, shared values, ability to govern, and ideology -- for the full sample, followed by models broken out by respondents' demographic characteristics and interaction effects between candidate attributes. Figure \ref{figure_winner} displays the results for all respondents when they had to make a binary choice between two candidates. In Figure \ref{figure_winner}, the x-axis reflects the probability that a respondent will select a candidate with that given characteristic as their preferred candidate. The dotted vertical line at 0.5 is a reference point, such that any attribute with a greater than 0.5 marginal mean signifies that a candidate that possesses that feature increases the probability that candidate will be selected as the respondent's preferred choice. Therefore, any attribute with a marginal mean of less than 0.5 means the given attribute decreases overall favorability toward candidates with that trait. 

As visualized in Figure \ref{figure_winner}, sexuality, children, and relationship status all appeared to have a significant effect on candidate selection. Respondents were significantly more likely to select straight candidates (0.57) and least likely to choose gay candidates (0.45). The probability of a gay candidate being selected was 0.12 lower than a straight candidate. While bisexual candidates were significantly more likely to picked than gay candidates, they were significantly less preferred as compared to straight candidates. 

For parental status, the most preferred was candidates with older children (0.52), significantly higher than support for candidates with no children (0.48). Respondents expressed a clear preference for married candidates, preferring this to all other candidate relationship types. The least preferred was divorced candidates (0.46), whereas single and cohabiting were nearly equally preferred, 0.49 and 0.50 respectively. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_winner_cropped}
	\centering
	\textbf{\caption{Marginal Means for Candidate Attributes: Binary Choice} \label{figure_winner}}
	\begin{description}
		\item \noindent \footnotesize {Note: The dependent variable is the binary choice of which candidate respondents would be more likely to support in their party's congressional primary. This figure presents marginal means with 95\% confidence intervals. The black dashed line indicates a probability of 0.5.}
	\end{description}
\end{figure}

Figure \ref{figure_values2} visualizes respondents' perception of shared values with candidates based on their characteristics. Because the remaining three of my four dependent variables are not binary as the first figure was, these variables were rescaled to range between 0 and 1. However, because these variables are not binary, the below figures are not as directly interpretable as the first figure. Rather than seeing if an attribute has a greater or less than 0.5 probability of being selected, in Figure \ref{figure_values2} it is most useful to compare attributes to each other. 

Whereas I found voters expressed an overall preference for parents and married candidates, in Figure \ref{figure_values2} I do not find that either parental or marital status influence perceive shared values with a candidate. Therefore, it does not appear shared values is the mechanism driving the effect for these attributes. However, candidates' sexuality did influence perceived shared values. Respondents were significantly more likely to say they share values with a straight candidate as compared to either a gay or bisexual candidate. There was not a significant difference between gay and bisexual candidate. Collectively, I find that respondents' perception of shared values for candidates based on sexuality aligned with respondents' binary choice selection of overall support. However, I did not find that a greater sense of shared values with parents or married candidates was what drove greater overall support for these candidates. These findings seem to highlight that while sometimes perceiving shared values with a candidate aligns with voting decisions, at other times, it does not entirely shape voting behavior.

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_values2_cropped}
	\centering
	\textbf{\caption{Marginal Means for Candidate Attributes: Shared Values} \label{figure_values2}}
	\begin{description}
		\item \noindent \footnotesize {Note: The dependent variable is a 7-point scale from strongly disagree to strongly agree rescaled to range from 0 to 1 for perceived shared values with a candidate. This figure presents marginal means with 95\% confidence intervals.}
	\end{description}
\end{figure}

Figure \ref{figure_govern} displays the marginal means for respondents' perceptions of a candidate's time, energy, and ability to handle the responsibilities of governing. Similar to the past two figures, I find queer candidates are disadvantaged, such that straight candidates are perceived as having significantly more ability to govern than are gay or bisexual candidates. While I do not find any significant effects for relationship, I do find those with no children were perceived to have significantly more time, energy, and ability to govern than parents. Candidates with young children were viewed as having the least time and energy to govern. While candidates who are parents were disadvantaged on this measure, it did not appear to influence respondents' willingness to vote for parents, as parents were preferred in overall support. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_govern_cropped}
	\centering
	\textbf{\caption{Marginal Means for Candidate Attributes: Ability to Govern} \label{figure_govern}}
	\begin{description}
		\item \noindent \footnotesize {Note: The dependent variable is a 7-point scale from strongly disagree to strongly agree rescaled to range from 0 to 1 for perceived time, energy, and ability to handle the responsibilities of governing. This figure presents marginal means with 95\% confidence intervals.}
	\end{description}
\end{figure}

The fourth dependent variable is perceived ideology of the candidate. Because respondents were instructed to assume each candidate was a member of the political party they most identify with, I do not present the findings for ideology for the full sample but only as conditional on the party of the respondent. Therefore, I next run some analysis that considers differences in findings across subgroups of respondents for all four of my dependent variables. 

\subsection{Results by Respondents' Traits}
First, I examine how self-identified Democrats, Republicans, and Independents perceived candidates, based on the candidate's sexuality. Figure \ref{figure_byparty_sexuality} presents this analysis for three of the four dependent variables. In the left most visualization in Figure \ref{figure_byparty_sexuality}, I find when respondents had to make a binary choice between two candidates, a respondent's political party conditioned their probability of selecting a candidate based on their sexuality. For Democratic respondents, there were not significant differences in which candidates they favored based on sexuality. Democrats were nearly equally likely to pick a straight, gay, or bisexual candidate. For Independents, there was no difference in their favorability toward gay and bisexual candidates, but they were significantly more likely to pick straight candidates. Republican respondents were the least likely to pick a gay candidate (0.4), and were most likely to pick a straight candidate (0.62), in-line with Hypothesis 4 that Republicans would drive the preferences for more conventional candidates. 

In the middle of Figure \ref{figure_byparty_sexuality} is perceived shared values by a candidate's sexuality conditional on a respondent's party. I find for both Republicans and Independents, they perceive they share significantly more values with straight candidates as compared to either gay or bisexual. While Democratic respondents were not less likely to vote for queer candidates, I do find Democrats perceive bisexual candidates share fewer values with them than do straight candidates; however, the effect size is much smaller than for Republicans or Independents. 

This finding for Democrats is notably interesting because in the flow of the survey, respondents first answered the binary choice of which candidate to support and then were immediately asked to rate how much they perceived they shared values with each candidate. Because these questions were shown sequentially, it could be expected that a respondent's first decision of which candidate to support might color how they answer other questions on the candidates. However, with the sexuality of the candidate, I find that Democratic respondents were willing to support the gay and bisexual candidates but then turn right around in the next question and say they did not share as many values with these candidates. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_byparty_sexuality}
	\centering
	\textbf{\caption{Marginal Means for Candidates' Sexuality by Respondents' Political Party} \label{figure_byparty_sexuality}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents marginal means with 95\% confidence intervals by respondents' political party.}
	\end{description}
\end{figure}

Finally, the right most visualization in Figure \ref{figure_byparty_sexuality} displays perceived ideology by respondents' party. Republicans and Independents rated straight candidates as significantly more conservative than both gay or bisexual candidates. Neither group of respondents perceived an ideological difference between gay and bisexual candidates. This stereotype likely disadvantages queer Republicans candidates, particularly in primary elections, such as this experiment is framed, as Republican primaries are where candidates must appeal to the more conservative wing of the party. 

Democratic respondents also perceive straight candidates as more conservative than either gay or bisexual candidates, but this is not the same disadvantage it is for queer Republican candidates. Being stereotyped as more liberal might advantage Democratic gay or bisexual candidates in their primary among more liberal voters. However, this does not mean queer candidates will not face challenges in winning a general election, when they often have to appeal to a more diverse voting bloc. Therefore, across several of the dependent variables, respondents' political party conditioned their responses to candidates' sexualities, with Republicans displaying the greatest differences in how they assess straight versus queer candidates. 

In looking at how respondents' party conditioned their response to candidates' parental and marital status, I find there were not as great of effect sizes as there were for sexuality. However, there was a distinction in how Republicans and Democrats perceived shared values with candidates based on relationship status. I find Democrats and Independents do not significantly differ in perceived shared values based on a candidate's marital status. However, Republicans significantly prefer married candidates as compared to divorced candidates. Counter to initial expectations, Republicans did not punish single or cohabiting candidates. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_byparty_values_relationship}
	\centering
	\textbf{\caption{Marginal Means for Candidates' Relationship by Respondents' Political Party} \label{figure_byparty_values_relationship}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents marginal means with 95\% confidence intervals by respondents' political party.}
	\end{description}
\end{figure}

Beyond respondent's party, I expect gender likely also conditions how respondents react to and evaluate candidates. In particular, I find men and women evaluated candidates differently based on their parental status. On the left side of Figure \ref{figure_bygender_children}, I find that men respondents did not vary their binary choice of candidates based on their parental status; however, women respondents expressed a significant preference for candidates with older or younger children compared to those with no children. 

Similarly, on the right side of Figure \ref{figure_bygender_children}, a respondent's gender influenced their perception of a candidate's time and energy to govern. For men respondents, they did not perceive a difference between candidates with younger and older children, but with a small effect size, perceived those with no children to have more time and energy to govern. For women respondents, the age of the children mattered more in perceive ability to govern. Women evaluated candidates with no children as having the most time and energy to govern and those with younger children as having significantly less time and energy to govern as compared to either having older or no children. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_bygender_children}
	\centering
	\textbf{\caption{Marginal Means for Candidates' Parental Status by Respondents' Gender} \label{figure_bygender_children}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents marginal means with 95\% confidence intervals by respondents' gender.}
	\end{description}
\end{figure}

Finally, in examining differing candidate evaluations by respondents' characteristics, I present marginal means across respondents' ages. To simplify the results, I divided respondents into two group -- Generation X and older compared to Millennials and younger.\footnote{I use the Pew Research Center classification of generations, which defines the beginning of Millennials as those born in 1981. Therefore, my first grouping includes the Silent Generation, Baby Boomers, and Generation X while my second grouping includes Millennials and Generation Z \cite{pew2019gen}.} Figure \ref{figure_byage_winner_sexuality} displays binary choice by respondents' generation for candidates' sexuality. I find that younger respondents significantly prefer straight candidates to gay candidates, but the effect size is small (0.52 to 0.47). The effect size of a candidate's sexuality was much greater for older respondents. Older respondents significantly preferred straight candidates (0.60) to both bisexual (0.47) and gay (0.44) candidates.

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_byage_winner_sexuality.pdf}
	\centering
	\textbf{\caption{Marginal Means for Candidates' Sexuality by Respondents' Age} \label{figure_byage_winner_sexuality}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents marginal means with 95\% confidence intervals by respondents' age.}
	\end{description}
\end{figure}

A respondent's age also had a significant effect on perceived ideology of candidates. Figure \ref{figure_byage_ideo_relationship} visualizes perceived ideology of candidates based on their relationship status. Younger respondents did not evaluate candidates differently based on their marital status, but older respondents were more likely to rate married candidates as more conservative. Older respondents rated candidates with a cohabiting partner as significantly more liberal than either married or divorced candidates. This section highlights that Republicans, women, and older respondents are the most likely to prefer more conventional candidates, and are the types of voters who less traditional candidate may face increased challenges in winning over. 

\begin{figure}[h]
	\includegraphics[width=\textwidth]{figure_byage_ideo_relationship}
	\centering
	\textbf{\caption{Marginal Means for Candidates' Relationship by Respondents' Age} \label{figure_byage_ideo_relationship}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents marginal means with 95\% confidence intervals by respondents' age.}
	\end{description}
\end{figure}

\subsection{Interaction Effects}
In designing this conjoint, I incorporated a candidate's gender and race, as I expect both gender and race may condition how respondents evaluated candidate's marital and parental status, as well as sexuality. When I interact a candidate's gender and parental status, I find gender does condition preferences for candidate's parental status. For men candidates, respondents did not significantly vary in how they evaluated men based on parental status, but for women, respondents expressed a significant preference for women candidates with older children as compared to no children. This presents a unique challenge for women who disproportionately handle more of child care needs, taking away time from their political ambitions, yet women without children are less preferred by voters. 

I also tested if a candidate's race conditioned perceptions of marital and parental status. For marital status, I do not find a significant interaction. Across racial and ethnic groups, respondents preferred married to divorced candidates. For parental status, I found a significant interaction, such that parental status was more important in how respondents evaluated Black candidates than other races and ethnicities. Respondents expressed a stronger preference for Black candidates with older or younger children than for Black candidates with no children. 

When I incorporate an interaction term between sexuality and gender, I find the least preferred candidates are gay men. Of the queer candidates, support was highest for bisexual women, and in the middle, respondents were nearly equally likely to support a bisexual man or a gay woman. Thus, is it clear that at times, gender and race both intersect with candidates' other personal identities to influence voters' perceptions of candidates. 

\section{Conclusion}
This project set out to explore how receptive voters are to unconventional congressional candidates. I find there were significant differences in voting preferences based on a candidate's sexuality and family dynamics. I find respondents were significantly more likely to vote for candidates that are straight, married, and have children. These preferences for more conventional candidates are more strongly driven by Republicans, women, and older respondents. At times, I find that a shared sense of values, perceived ability to govern, and perceived ideology all influenced how voters evaluated candidates. 

This project highlights how candidates' various identities can influence their electoral environment and how these identities can intersect in unique ways. For example, I find that overall voters prefer candidates who are parents, but that these attitudes are conditional on a candidate's gender and race or ethnicity. Additionally, this project highlights that candidates who most deviate from the conventional politician image likely face electoral challenges, especially when facing more conservative and older voters. 

These findings suggest that candidates who hold identities that stray from the norm in politics may have to actively address stereotypes and develop campaign tactics that frame their appeal to voters and highlight their ability to be successful in elected office. We have seen successful example of candidates actively addressing these types of issues in their campaigns. For example, when Stacey Abrams, who is single and does not have children, ran for Georgia governor in 2018, she ran an ad titled ``Foundation," which highlighted the importance of her parents in her life, spoke about the importance of family values, and showed a scene of Abrams sitting down for family dinner with her nieces and nephews. Additionally, Danica Roem of Virginia was the first openly transgender woman elected to a state legislature in 2017. In Roem's campaign, she did not shy away from her gender identity but she focused much of her time speaking with voters about infrastructure needs, thus avoiding being stereotyped as a one-issue candidate because of her gender identity and embracing a policy issue typically framed as less partisan. 

While Congress has diversified in recent years, there are still many firsts yet to be seen, including the first woman of color Republican senator or first openly transgender member of Congress. Moving forward, I encourage more research that examines the experiences of less conventional congressional candidates, including media coverage, campaign strategies, and voter perceptions. We need more work on where more diverse candidates are running, what challenges they face, and what impact having more diversity in Congress can have on the policy making process. Additionally, I encourage more research that expands our definition of diversity, including research on perception of non-binary and asexual candidates. 

\newpage
\singlespacing
	\section{Appendix}
	\subsection{Survey Questions}
	\noindent\textbf{\textit{Gender}}: What is your gender? 0= Male, 1= Female, 2= Other (text box fill-in). \\
	\textbf{\textit{Race}}: What racial or ethnic groups describe you? Mark all that apply. 0= White, 1= Black or African American, 2= Hispanic or Latino(a), 3= Asian American, 4= Native American or American Indian, 5= Other. Variable was recoded to White, Black, Latino, Other, and Mixed. \\
	\textbf{\textit{Ideology}}: In general, how would you describe your political viewpoint? 0= Very liberal, 1= Liberal, 2= Moderate, 3= Conservative, 4= Very conservative, 5= Not sure. ``Not sure" was recoded to ``moderate." \\
	\textbf{\textit{Party}}: In general, do you think of yourself as a Republican, a Democrat, an independent, or something else? 0= Republican, 1= Democrat, 2= Independent or other party. \\
	\textbf{\textit{Income}}: What is the annual household income for all members of your family? This figure should include salaries, wages, pensions, dividends, interest, and all other income. 1= Less than \$10,000, 2= \$10,000-\$19,999, 3= \$20,000-\$29,999, 4= \$30,000-\$39,999, 5= \$40,000-\$49,999, 6= \$50,000-\$59,999, 7= \$60,000-\$69,999, 8= \$70,000-\$79,999, 9=\$80,000-\$89,999, 10= \$90,000-\$99,999, 11= \$100,000-\$149,999, 12= More than \$150,000. \\
	\textbf{\textit{Education}}: What is the highest level of education you have completed? 1= Less than high school diploma, 2= High school graduate or GED, 3= Some college, 4= 2 year degree, 5= 4 year degree, 6= Post-graduate education \\
	
	\subsection{Experiment Questions}
	\textit{\textbf{Introduction:}} Suppose the individuals below are candidates running in your party's primary for an open seat for the U.S. House of Representatives. Examines their profiles and answer the questions below. \\
	
	\textit{\textbf{Questions given after seeing the candidate profiles:}} \\
	\begin{itemize}
		\item Based on the information in these profiles, which of these two candidates would you be more likely to support in your party's congressional primary? Candidate 1; Candidate 2 
		\item Do you agree or disagree with the following statements on Candidate [1/2]? Candidate [1/2] shares similar values to my own. Strongly agree; Agree; Somewhat agree; Neither agree nor disagree; Somewhat disagree; Disagree; Strongly disagree 
		\item Do you agree or disagree with the following statements on Candidate [1/2]? Candidate [1/2] has the time, energy, and ability to handle the responsibilities of governing. Strongly agree; Agree; Somewhat agree; Neither agree nor disagree; Somewhat disagree; Disagree; Strongly disagree 
		\item How would you rate Candidate [1/2]? Very liberal, Liberal, Somewhat liberal, Middle of the road, Somewhat conservative, Conservative, Very conservative
	\end{itemize}
	
	\newpage
		\begin{landscape}
	\subsection{Survey Sample Demographics}

	\begin{table}[!h] % Updated 1/13/21
		\centering
		\begin{threeparttable}
			\caption{Characteristics of Lucid Survey Sample}
			\label{surveydemographics2} 
			\begin{tabular}{llll}
				\\
				\hline
				Gender  & Race   & Ideology   & Party    \\ \hline
				52\% women   & 71\% White   & \begin{tabular}[c]{@{}l@{}}32\% liberal \end{tabular}    & 36\% Republicans                       \\
				48\% men   & 12\% Black    & \begin{tabular}[c]{@{}l@{}}29\% conservative \end{tabular}     & 39\% Democrats           \\
				& 7\% Latino   & 39\% moderate    & \begin{tabular}[c]{@{}l@{}}25\% Independent \end{tabular} \\
				& 10\% other/mixed    &          &                                                                               \\ \hline
				Income     & Education          & Age & Region                      \\ \hline
				\begin{tabular}[c]{@{}l@{}} Mean: \$50,000-\$59,999 \end{tabular}                 & \begin{tabular}[c]{@{}l@{}}3\% Less than high school diploma \end{tabular} & Mean: 45 & 38\% South   \\
				\begin{tabular}[c]{@{}l@{}} \end{tabular} &     21\% high school graduate &      Median: 44  & 22\% West    \\ 
				\begin{tabular}[c]{@{}l@{}} \end{tabular} &     22\% some college &    & 21\% Northeast       \\
				\begin{tabular}[c]{@{}l@{}} \end{tabular} &     11\% 2-year degree &     & 19\% Midwest      \\
				\begin{tabular}[c]{@{}l@{}} \end{tabular} &     24\% 4-year degree &           \\
				\begin{tabular}[c]{@{}l@{}} \end{tabular} &     20\% post-graduate education &           \\
				\hline   	                                      
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These demographics include all respondents who answered at least one dependent variable associated with the conjoint experiment. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
\end{landscape}
	
	
	\newpage
	\subsection{Additional Tables}
	
	\begin{table}[!h]
		\centering
		\caption{Candidates Traits for the Full Sample}
		\begin{threeparttable}
			\begin{tabular}{lllllll}
				& \multicolumn{1}{c}{\textbf{Binary}} &        & \textbf{Values} &        & \textbf{Govern} &        \\
				\hline
				Straight              & 0.57                                & (0.01) & 0.66            & (0.00) & 0.65            & (0.00) \\
				Bisexual              & 0.49                                & (0.01) & 0.58            & (0.00) & 0.61            & (0.00) \\
				Gay                   & 0.45                                & (0.01) & 0.59            & (0.00) & 0.61            & (0.00) \\
				Older children        & 0.52                                & (0.01) & 0.62   & (0.00) & 0.62            & (0.00) \\
				No children           & 0.48                                & (0.01) & 0.61            & (0.00) & 0.65            & (0.00) \\
				Young children        & 0.50                                & (0.01) & 0.61            & (0.00) & 0.61            & (0.00) \\
				Married               & 0.54                                & (0.01) & 0.62            & (0.00) & 0.63            & (0.00) \\
				Cohabiting            & 0.50                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				Divorced              & 0.46                                & (0.01) & 0.60            & (0.00) & 0.62            & (0.00) \\
				Single                & 0.49                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				City council member   & 0.57                                & (0.01) & 0.61            & (0.01) & 0.63            & (0.01) \\
				Congressional staffer & 0.51                                & (0.01) & 0.62            & (0.01) & 0.63            & (0.01) \\
				Ironworker            & 0.49                                & (0.01) & 0.62            & (0.01) & 0.62            & (0.01) \\
				Nurse                 & 0.52                                & (0.01) & 0.62            & (0.01) & 0.62            & (0.01) \\
				Political consultant  & 0.52                                & (0.01) & 0.60            & (0.01) & 0.63            & (0.01) \\
				Radio talk show host  & 0.39                                & (0.01) & 0.59            & (0.01) & 0.61            & (0.01) \\
				Male                  & 0.48                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				Female                & 0.52                                & (0.01) & 0.62            & (0.00) & 0.62            & (0.00) \\
				White                 & 0.50                                & (0.01) & 0.62            & (0.00) & 0.63            & (0.00) \\
				Asian                 & 0.48                                & (0.01) & 0.60            & (0.00) & 0.62            & (0.00) \\
				Black                 & 0.53                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				Hispanic              & 0.49                                & (0.01) & 0.61            & (0.00) & 0.62            & (0.00) \\
				45                    & 0.50                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				43                    & 0.51                                & (0.01) & 0.61            & (0.00) & 0.63            & (0.00) \\
				46                    & 0.49                                & (0.01) & 0.61            & (0.00) & 0.61            & (0.00) \\ \\
				\textit{N} & 11754 & & 11750 & & 11737 & 
				\\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the full sample for three dependent variables, which are visualized in Figures \ref{figure_winner}, \ref{figure_values2}, and \ref{figure_govern}. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	\begin{table}[]
		\centering
		\caption{Binary Winner Dependent Variable By Respondents' Party}
		\begin{threeparttable}
			\begin{tabular}{llllllll}
				& \textbf{Dem.} &        & \textbf{Rep.} &        & \textbf{Inde.} &        &  \\ 
				\hline
				Straight              & 0.51               & (0.01) & 0.62                 & (0.01) & 0.57                  & (0.02) &  \\
				Bisexual              & 0.51               & (0.01) & 0.48                 & (0.01) & 0.46                  & (0.02) &  \\
				Gay                   & 0.49               & (0.01) & 0.40                 & (0.01) & 0.47                  & (0.02) &  \\
				Older children        & 0.52               & (0.01) & 0.51                 & (0.01) & 0.54                  & (0.02) &  \\
				No children           & 0.47               & (0.01) & 0.48                 & (0.01) & 0.47                  & (0.02) &  \\
				Young children        & 0.51               & (0.01) & 0.50                 & (0.01) & 0.49                  & (0.02) &  \\
				Married               & 0.53               & (0.01) & 0.54                 & (0.02) & 0.55                  & (0.02) &  \\
				Cohabiting            & 0.52               & (0.01) & 0.49                 & (0.02) & 0.50                  & (0.02) &  \\
				Divorced              & 0.46               & (0.02) & 0.45                 & (0.02) & 0.47                  & (0.02) &  \\
				Single                & 0.49               & (0.01) & 0.51                 & (0.02) & 0.49                  & (0.02) &  \\
				City council member   & 0.60               & (0.02) & 0.56                 & (0.02) & 0.54                  & (0.02) &  \\
				Congressional staffer & 0.54               & (0.02) & 0.50                 & (0.02) & 0.49                  & (0.02) &  \\
				Ironworker            & 0.45               & (0.02) & 0.52                 & (0.02) & 0.50                  & (0.02) &  \\
				Nurse                 & 0.53               & (0.02) & 0.49                 & (0.02) & 0.55                  & (0.02) &  \\
				Political consultant  & 0.51               & (0.02) & 0.51                 & (0.02) & 0.53                  & (0.02) &  \\
				Radio talk show host  & 0.36               & (0.02) & 0.42                 & (0.02) & 0.40                  & (0.02) &  \\
				Male                  & 0.48               & (0.01) & 0.50                 & (0.01) & 0.46                  & (0.01) &  \\
				Female                & 0.52               & (0.01) & 0.49                 & (0.01) & 0.54                  & (0.01) &  \\
				White                 & 0.45               & (0.01) & 0.56                 & (0.01) & 0.49                  & (0.02) &  \\
				Asian                 & 0.48               & (0.01) & 0.47                 & (0.02) & 0.47                  & (0.02) &  \\
				Black                 & 0.54               & (0.01) & 0.49                 & (0.02) & 0.55                  & (0.02) &  \\
				Hispanic              & 0.52               & (0.01) & 0.47                 & (0.02) & 0.48                  & (0.02) &  \\
				45                    & 0.50               & (0.01) & 0.49                 & (0.01) & 0.49                  & (0.02) &  \\
				43                    & 0.51               & (0.01) & 0.52                 & (0.01) & 0.51                  & (0.02) &  \\
				46                    & 0.49               & (0.01) & 0.48                 & (0.01) & 0.49                  & (0.02) & 
				\\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary winner dependent variable by political party of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	\begin{table}[]
		\centering
		\caption{Values Dependent Variable By Respondents' Party}
		\begin{threeparttable}
			\begin{tabular}{llllllll}
				& \textbf{Dem.} &        & \textbf{Rep.} &        & \textbf{Inde.} &        &  \\
				\hline
				Straight              & 0.66               & (0.01) & 0.69                 & (0.01) & 0.61                  & (0.01) &  \\
				Bisexual              & 0.63               & (0.01) & 0.57                 & (0.01) & 0.54                  & (0.01) &  \\
				Gay                   & 0.65               & (0.01) & 0.56                 & (0.01) & 0.53                  & (0.01) &  \\
				Older children        & 0.65               & (0.01) & 0.62                 & (0.01) & 0.56                  & (0.01) &  \\
				No children           & 0.63               & (0.01) & 0.61                 & (0.01) & 0.56                  & (0.01) &  \\
				Young children        & 0.65               & (0.01) & 0.60                 & (0.01) & 0.56                  & (0.01) &  \\
				Married               & 0.65               & (0.01) & 0.63                 & (0.01) & 0.57                  & (0.01) &  \\
				Cohabiting            & 0.64               & (0.01) & 0.62                 & (0.01) & 0.54                  & (0.01) &  \\
				Divorced              & 0.65               & (0.01) & 0.59                 & (0.01) & 0.55                  & (0.01) &  \\
				Single                & 0.64               & (0.01) & 0.61                 & (0.01) & 0.57                  & (0.01) &  \\
				City council member   & 0.63               & (0.01) & 0.62                 & (0.01) & 0.56                  & (0.01) &  \\
				Congressional staffer & 0.66               & (0.01) & 0.61                 & (0.01) & 0.56                  & (0.01) &  \\
				Ironworker            & 0.65               & (0.01) & 0.63                 & (0.01) & 0.56                  & (0.01) &  \\
				Nurse                 & 0.66               & (0.01) & 0.62                 & (0.01) & 0.57                  & (0.01) &  \\
				Political consultant  & 0.63               & (0.01) & 0.60                 & (0.01) & 0.56                  & (0.01) &  \\
				Radio talk show host  & 0.62               & (0.01) & 0.59                 & (0.01) & 0.54                  & (0.01) &  \\
				Male                  & 0.64               & (0.00) & 0.61                 & (0.01) & 0.55                  & (0.01) &  \\
				Female                & 0.65               & (0.00) & 0.61                 & (0.01) & 0.57                  & (0.01) &  \\
				White                 & 0.64               & (0.01) & 0.63                 & (0.01) & 0.57                  & (0.01) &  \\
				Asian                 & 0.64               & (0.01) & 0.61                 & (0.01) & 0.54                  & (0.01) &  \\
				Black                 & 0.64               & (0.01) & 0.60                 & (0.01) & 0.57                  & (0.01) &  \\
				Hispanic              & 0.65               & (0.01) & 0.60                 & (0.01) & 0.56                  & (0.01) &  \\
				45                    & 0.65               & (0.01) & 0.61                 & (0.01) & 0.56                  & (0.01) &  \\
				43                    & 0.64               & (0.01) & 0.62                 & (0.01) & 0.56                  & (0.01) &  \\
				46                    & 0.64               & (0.01) & 0.61                 & (0.01) & 0.56                  & (0.01) & \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the values dependent variable by political party of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	\begin{table}[]
		\centering
		\caption{Govern Dependent Variable By Respondents' Party}
		\begin{threeparttable}
			\begin{tabular}{llllllll}
				& \textbf{Dem.} &        & \textbf{Rep.} &        & \textbf{Inde.} &        &  \\
				\hline
				Straight              & 0.65               & (0.01) & 0.68                 & (0.01) & 0.60                  & (0.01) &  \\
				Bisexual              & 0.63               & (0.01) & 0.62                 & (0.01) & 0.57                  & (0.01) &  \\
				Gay                   & 0.65               & (0.01) & 0.61                 & (0.01) & 0.57                  & (0.01) &  \\
				Older children        & 0.64               & (0.01) & 0.63                 & (0.01) & 0.58                  & (0.01) &  \\
				No children           & 0.66               & (0.01) & 0.66                 & (0.01) & 0.61                  & (0.01) &  \\
				Young children        & 0.62               & (0.01) & 0.62                 & (0.01) & 0.56                  & (0.01) &  \\
				Married               & 0.64               & (0.01) & 0.64                 & (0.01) & 0.58                  & (0.01) &  \\
				Cohabiting            & 0.65               & (0.01) & 0.65                 & (0.01) & 0.57                  & (0.01) &  \\
				Divorced              & 0.65               & (0.01) & 0.62                 & (0.01) & 0.58                  & (0.01) &  \\
				Single                & 0.64               & (0.01) & 0.63                 & (0.01) & 0.60                  & (0.01) &  \\
				City council member   & 0.64               & (0.01) & 0.65                 & (0.01) & 0.61                  & (0.01) &  \\
				Congressional staffer & 0.65               & (0.01) & 0.65                 & (0.01) & 0.59                  & (0.01) &  \\
				Ironworker            & 0.65               & (0.01) & 0.63                 & (0.01) & 0.58                  & (0.01) &  \\
				Nurse                 & 0.64               & (0.01) & 0.64                 & (0.01) & 0.58                  & (0.01) &  \\
				Political consultant  & 0.65               & (0.01) & 0.63                 & (0.01) & 0.58                  & (0.01) &  \\
				Radio talk show host  & 0.62               & (0.01) & 0.61                 & (0.01) & 0.56                  & (0.01) &  \\
				Male                  & 0.64               & (0.00) & 0.64                 & (0.01) & 0.58                  & (0.01) &  \\
				Female                & 0.64               & (0.00) & 0.63                 & (0.01) & 0.58                  & (0.01) &  \\
				White                 & 0.65               & (0.01) & 0.65                 & (0.01) & 0.60                  & (0.01) &  \\
				Asian                 & 0.64               & (0.01) & 0.63                 & (0.01) & 0.57                  & (0.01) &  \\
				Black                 & 0.64               & (0.01) & 0.64                 & (0.01) & 0.59                  & (0.01) &  \\
				Hispanic              & 0.65               & (0.01) & 0.63                 & (0.01) & 0.57                  & (0.01) &  \\
				45                    & 0.65               & (0.01) & 0.64                 & (0.01) & 0.59                  & (0.01) &  \\
				43                    & 0.64               & (0.01) & 0.65                 & (0.01) & 0.59                  & (0.01) &  \\
				46                    & 0.64               & (0.01) & 0.62                 & (0.01) & 0.57                  & (0.01) & \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the govern dependent variable by political party of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	\begin{table}[]
		\centering
		\caption{Ideology Dependent Variable By Respondents' Party}
		\begin{threeparttable}
			\begin{tabular}{llllllll}
				& \textbf{Dem.} &        & \textbf{Rep.} &        & \textbf{Inde.} &        &  \\ \hline
				Straight              & 0.59               & (0.01) & 0.51                 & (0.01) & 0.51                  & (0.01) &  \\
				Bisexual              & 0.65               & (0.01) & 0.62                 & (0.01) & 0.61                  & (0.01) &  \\
				Gay                   & 0.67               & (0.01) & 0.62                 & (0.01) & 0.61                  & (0.01) &  \\
				Older children        & 0.63               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				No children           & 0.64               & (0.01) & 0.58                 & (0.01) & 0.57                  & (0.01) &  \\
				Young children        & 0.64               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				Married               & 0.63               & (0.01) & 0.57                 & (0.01) & 0.57                  & (0.01) &  \\
				Cohabiting            & 0.65               & (0.01) & 0.59                 & (0.01) & 0.58                  & (0.01) &  \\
				Divorced              & 0.63               & (0.01) & 0.59                 & (0.01) & 0.57                  & (0.01) &  \\
				Single                & 0.64               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				City council member   & 0.63               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				Congressional staffer & 0.64               & (0.01) & 0.58                 & (0.01) & 0.57                  & (0.01) &  \\
				Ironworker            & 0.64               & (0.01) & 0.59                 & (0.01) & 0.57                  & (0.01) &  \\
				Nurse                 & 0.63               & (0.01) & 0.59                 & (0.01) & 0.58                  & (0.01) &  \\
				Political consultant  & 0.64               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				Radio talk show host  & 0.63               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				Male                  & 0.64               & (0.00) & 0.58                 & (0.01) & 0.57                  & (0.01) &  \\
				Female                & 0.64               & (0.01) & 0.59                 & (0.01) & 0.58                  & (0.01) &  \\
				White                 & 0.64               & (0.01) & 0.57                 & (0.01) & 0.57                  & (0.01) &  \\
				Asian                 & 0.64               & (0.01) & 0.58                 & (0.01) & 0.57                  & (0.01) &  \\
				Black                 & 0.64               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				Hispanic              & 0.64               & (0.01) & 0.59                 & (0.01) & 0.58                  & (0.01) &  \\
				45                    & 0.64               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) &  \\
				43                    & 0.64               & (0.01) & 0.59                 & (0.01) & 0.57                  & (0.01) &  \\
				46                    & 0.63               & (0.01) & 0.58                 & (0.01) & 0.58                  & (0.01) & \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the ideology dependent variable by political party of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	
	\begin{table}[]
		\centering
		\caption{Binary \& Values Dependent Variables By Respondents' Gender}
		\begin{threeparttable}
			\begin{tabular}{lllllllll}
				&              & \textbf{\textit{Bin.}} &                &        &              & \textbf{\textit{Val.}} &                &        \\
				& \textbf{Men} &                 & \textbf{Women} &        & \textbf{Men} &                 & \textbf{Women} &        \\ \hline
				Straight              & 0.59         & (0.01)          & 0.55           & (0.01) & 0.68         & (0.01)          & 0.64           & (0.00) \\
				Bisexual              & 0.48         & (0.01)          & 0.49           & (0.01) & 0.61         & (0.01)          & 0.56           & (0.01) \\
				Gay                   & 0.44         & (0.01)          & 0.46           & (0.01) & 0.60         & (0.01)          & 0.58           & (0.01) \\
				Older children        & 0.51         & (0.01)          & 0.53           & (0.01) & 0.63         & (0.01)          & 0.60           & (0.01) \\
				No children           & 0.49         & (0.01)          & 0.46           & (0.01) & 0.63         & (0.01)          & 0.59           & (0.01) \\
				Young children        & 0.50         & (0.01)          & 0.50           & (0.01) & 0.62         & (0.01)          & 0.60           & (0.01) \\
				Married               & 0.54         & (0.01)          & 0.54           & (0.01) & 0.64         & (0.01)          & 0.60           & (0.01) \\
				Cohabiting            & 0.49         & (0.01)          & 0.51           & (0.01) & 0.62         & (0.01)          & 0.60           & (0.01) \\
				Divorced              & 0.47         & (0.01)          & 0.45           & (0.01) & 0.62         & (0.01)          & 0.59           & (0.01) \\
				Single                & 0.50         & (0.01)          & 0.49           & (0.01) & 0.63         & (0.01)          & 0.59           & (0.01) \\
				City council member   & 0.55         & (0.02)          & 0.59           & (0.02) & 0.64         & (0.01)          & 0.59           & (0.01) \\
				Congressional staffer & 0.50         & (0.02)          & 0.52           & (0.02) & 0.64         & (0.01)          & 0.60           & (0.01) \\
				Ironworker            & 0.49         & (0.02)          & 0.49           & (0.02) & 0.64         & (0.01)          & 0.60           & (0.01) \\
				Nurse                 & 0.51         & (0.02)          & 0.53           & (0.02) & 0.63         & (0.01)          & 0.61           & (0.01) \\
				Political consultant  & 0.52         & (0.02)          & 0.51           & (0.02) & 0.62         & (0.01)          & 0.59           & (0.01) \\
				Radio talk show host  & 0.42         & (0.02)          & 0.36           & (0.02) & 0.60         & (0.01)          & 0.58           & (0.01) \\
				Male                  & 0.50         & (0.01)          & 0.47           & (0.01) & 0.63         & (0.01)          & 0.59           & (0.00) \\
				Female                & 0.50         & (0.01)          & 0.53           & (0.01) & 0.63         & (0.01)          & 0.60           & (0.00) \\
				White                 & 0.51         & (0.01)          & 0.50           & (0.01) & 0.64         & (0.01)          & 0.60           & (0.01) \\
				Asian                 & 0.48         & (0.01)          & 0.47           & (0.01) & 0.62         & (0.01)          & 0.59           & (0.01) \\
				Black                 & 0.51         & (0.01)          & 0.54           & (0.01) & 0.62         & (0.01)          & 0.59           & (0.01) \\
				Hispanic              & 0.50         & (0.01)          & 0.49           & (0.01) & 0.63         & (0.01)          & 0.60           & (0.01) \\
				45                    & 0.50         & (0.01)          & 0.49           & (0.01) & 0.62         & (0.01)          & 0.60           & (0.01) \\
				43                    & 0.51         & (0.01)          & 0.52           & (0.01) & 0.64         & (0.01)          & 0.59           & (0.01) \\
				46                    & 0.49         & (0.01)          & 0.49           & (0.01) & 0.62         & (0.01)          & 0.59           & (0.01) \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary and values dependent variables by gender of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	\begin{table}[]
		\centering
		\caption{Govern \& Ideology Dependent Variables By Respondents' Gender}
		\begin{threeparttable}
			\begin{tabular}{lllllllll}
				&              & \textbf{\textit{Gov.}} &                &        &              & \textbf{\textit{Ideo.}} &                &        \\
				& \textbf{Men} &                 & \textbf{Women} &        & \textbf{Men} &                   & \textbf{Women} &        \\ \hline
				Straight              & 0.66         & (0.01)          & 0.64           & (0.00) & 0.56         & (0.01)            & 0.53           & (0.00) \\
				Bisexual              & 0.62         & (0.01)          & 0.61           & (0.01) & 0.62         & (0.01)            & 0.63           & (0.01) \\
				Gay                   & 0.61         & (0.01)          & 0.61           & (0.00) & 0.64         & (0.01)            & 0.63           & (0.01) \\
				Older children        & 0.62         & (0.01)          & 0.62           & (0.00) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				No children           & 0.65         & (0.01)          & 0.65           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				Young children        & 0.62         & (0.01)          & 0.59           & (0.00) & 0.61         & (0.01)            & 0.60           & (0.01) \\
				Married               & 0.64         & (0.01)          & 0.62           & (0.01) & 0.60         & (0.01)            & 0.58           & (0.01) \\
				Cohabiting            & 0.63         & (0.01)          & 0.63           & (0.01) & 0.61         & (0.01)            & 0.61           & (0.01) \\
				Divorced              & 0.62         & (0.01)          & 0.61           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				Single                & 0.63         & (0.01)          & 0.62           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				City council member   & 0.64         & (0.01)          & 0.63           & (0.01) & 0.60         & (0.01)            & 0.60           & (0.01) \\
				Congressional staffer & 0.65         & (0.01)          & 0.62           & (0.01) & 0.62         & (0.01)            & 0.59           & (0.01) \\
				Ironworker            & 0.63         & (0.01)          & 0.62           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				Nurse                 & 0.63         & (0.01)          & 0.62           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				Political consultant  & 0.64         & (0.01)          & 0.62           & (0.01) & 0.60         & (0.01)            & 0.60           & (0.01) \\
				Radio talk show host  & 0.61         & (0.01)          & 0.60           & (0.01) & 0.60         & (0.01)            & 0.60           & (0.01) \\
				Male                  & 0.64         & (0.00)          & 0.62           & (0.00) & 0.61         & (0.00)            & 0.59           & (0.00) \\
				Female                & 0.63         & (0.00)          & 0.62           & (0.00) & 0.61         & (0.01)            & 0.60           & (0.00) \\
				White                 & 0.64         & (0.01)          & 0.63           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				Asian                 & 0.63         & (0.01)          & 0.61           & (0.01) & 0.61         & (0.01)            & 0.60           & (0.01) \\
				Black                 & 0.63         & (0.01)          & 0.62           & (0.01) & 0.60         & (0.01)            & 0.60           & (0.01) \\
				Hispanic              & 0.62         & (0.01)          & 0.62           & (0.01) & 0.62         & (0.01)            & 0.60           & (0.01) \\
				45                    & 0.63         & (0.01)          & 0.62           & (0.01) & 0.61         & (0.01)            & 0.59           & (0.01) \\
				43                    & 0.64         & (0.01)          & 0.62           & (0.00) & 0.61         & (0.01)            & 0.60           & (0.01) \\
				46                    & 0.62         & (0.01)          & 0.61           & (0.01) & 0.60         & (0.01)            & 0.60           & (0.01) \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the govern and ideology dependent variables by gender of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	\begin{landscape}
	\begin{table}[]
		\centering
		\footnotesize
		\caption{Binary \& Values Dependent Variables By Respondents' Generation}
		\begin{threeparttable}
			\begin{tabular}{lllllllll}
				&                         & \textbf{\textit{Bin.}} &                                &        &                         & \textbf{\textit{Val.}} &                                &        \\
				& \textbf{Gen X} &                 & \textbf{Millennial} &        & \textbf{Gen X} &                 & \textbf{Millennial} &        \\
				& \textbf{\& Older} &                 & \textbf{\& Younger} &        & \textbf{\& Older} &                 & \textbf{\& Younger} &       
				\\ \hline
				Straight              & 0.60                    & (0.01)          & 0.52                           & (0.01) & 0.66                    & (0.00)          & 0.65                           & (0.01) \\
				Bisexual              & 0.47                    & (0.01)          & 0.51                           & (0.01) & 0.56                    & (0.01)          & 0.62                           & (0.01) \\
				Gay                   & 0.44                    & (0.01)          & 0.47                           & (0.01) & 0.57                    & (0.01)          & 0.62                           & (0.01) \\
				Older children        & 0.53                    & (0.01)          & 0.51                           & (0.01) & 0.60                    & (0.01)          & 0.63                           & (0.01) \\
				No children           & 0.48                    & (0.01)          & 0.47                           & (0.01) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				Young children        & 0.49                    & (0.01)          & 0.52                           & (0.01) & 0.60                    & (0.01)          & 0.63                           & (0.01) \\
				Married               & 0.55                    & (0.01)          & 0.53                           & (0.01) & 0.61                    & (0.01)          & 0.64                           & (0.01) \\
				Cohabiting            & 0.49                    & (0.01)          & 0.52                           & (0.01) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				Divorced              & 0.47                    & (0.01)          & 0.44                           & (0.01) & 0.60                    & (0.01)          & 0.62                           & (0.01) \\
				Single                & 0.49                    & (0.01)          & 0.50                           & (0.01) & 0.59                    & (0.01)          & 0.64                           & (0.01) \\
				City council member   & 0.57                    & (0.01)          & 0.57                           & (0.02) & 0.60                    & (0.01)          & 0.62                           & (0.01) \\
				Congressional staffer & 0.52                    & (0.01)          & 0.50                           & (0.02) & 0.60                    & (0.01)          & 0.65                           & (0.01) \\
				Ironworker            & 0.49                    & (0.01)          & 0.49                           & (0.02) & 0.62                    & (0.01)          & 0.62                           & (0.01) \\
				Nurse                 & 0.52                    & (0.01)          & 0.52                           & (0.02) & 0.60                    & (0.01)          & 0.64                           & (0.01) \\
				Political consultant  & 0.52                    & (0.01)          & 0.51                           & (0.02) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				Radio talk show host  & 0.38                    & (0.01)          & 0.41                           & (0.02) & 0.57                    & (0.01)          & 0.62                           & (0.01) \\
				Male                  & 0.49                    & (0.01)          & 0.48                           & (0.01) & 0.59                    & (0.00)          & 0.63                           & (0.01) \\
				Female                & 0.51                    & (0.01)          & 0.52                           & (0.01) & 0.60                    & (0.00)          & 0.63                           & (0.01) \\
				White                 & 0.52                    & (0.01)          & 0.48                           & (0.01) & 0.61                    & (0.01)          & 0.63                           & (0.01) \\
				Asian                 & 0.48                    & (0.01)          & 0.46                           & (0.01) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				Black                 & 0.52                    & (0.01)          & 0.54                           & (0.01) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				Hispanic              & 0.48                    & (0.01)          & 0.52                           & (0.01) & 0.59                    & (0.01)          & 0.63                           & (0.01) \\
				45                    & 0.49                    & (0.01)          & 0.50                           & (0.01) & 0.59                    & (0.01)          & 0.64                           & (0.01) \\
				43                    & 0.51                    & (0.01)          & 0.51                           & (0.01) & 0.60                    & (0.01)          & 0.63                           & (0.01) \\
				46                    & 0.49                    & (0.01)          & 0.48                           & (0.01) & 0.60                    & (0.01)          & 0.62                           & (0.01) \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary and values dependent variables by generation of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	\begin{table}[]
		\centering
		\footnotesize
		\caption{Govern \& Ideology Dependent Variables By Respondents' Generation}
		\begin{threeparttable}
			\begin{tabular}{lllllllll}
				&                   & \textbf{\textit{Gov.}} &                     &        &                & \textbf{\textit{Ideo.}} &                     &        \\
				& \textbf{Gen X}    &                 & \textbf{Millennial} &        & \textbf{Gen X} &                   & \textbf{Millennial} &        \\
				& \textbf{\& Older} &                 &  \textbf{\& Younger}          &        & \textbf{\& Older}       &                   & \textbf{\& Younger}          &        \\ \hline
				Straight              & 0.67              & (0.00)          & 0.61                & (0.01) & 0.54           & (0.01)            & 0.55                & (0.01) \\
				Bisexual              & 0.62              & (0.00)          & 0.60                & (0.01) & 0.64           & (0.01)            & 0.61                & (0.01) \\
				Gay                   & 0.63              & (0.00)          & 0.60                & (0.01) & 0.65           & (0.01)            & 0.62                & (0.01) \\
				Older children        & 0.65              & (0.00)          & 0.59                & (0.01) & 0.61           & (0.01)            & 0.58                & (0.01) \\
				No children           & 0.66              & (0.00)          & 0.63                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Young children        & 0.62              & (0.00)          & 0.59                & (0.01) & 0.61           & (0.01)            & 0.60                & (0.01) \\
				Married               & 0.65              & (0.01)          & 0.59                & (0.01) & 0.60           & (0.01)            & 0.59                & (0.01) \\
				Cohabiting            & 0.64              & (0.01)          & 0.61                & (0.01) & 0.63           & (0.01)            & 0.59                & (0.01) \\
				Divorced              & 0.64              & (0.01)          & 0.59                & (0.01) & 0.60           & (0.01)            & 0.59                & (0.01) \\
				Single                & 0.64              & (0.01)          & 0.61                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				City council member   & 0.65              & (0.01)          & 0.62                & (0.01) & 0.60           & (0.01)            & 0.60                & (0.01) \\
				Congressional staffer & 0.65              & (0.01)          & 0.61                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Ironworker            & 0.65              & (0.01)          & 0.59                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Nurse                 & 0.64              & (0.01)          & 0.60                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Political consultant  & 0.64              & (0.01)          & 0.60                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Radio talk show host  & 0.61              & (0.01)          & 0.59                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Male                  & 0.64              & (0.00)          & 0.60                & (0.01) & 0.60           & (0.00)            & 0.59                & (0.00) \\
				Female                & 0.64              & (0.00)          & 0.60                & (0.01) & 0.62           & (0.00)            & 0.59                & (0.01) \\
				White                 & 0.65              & (0.01)          & 0.62                & (0.01) & 0.60           & (0.01)            & 0.59                & (0.01) \\
				Asian                 & 0.64              & (0.01)          & 0.59                & (0.01) & 0.62           & (0.01)            & 0.58                & (0.01) \\
				Black                 & 0.64              & (0.01)          & 0.60                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				Hispanic              & 0.64              & (0.01)          & 0.59                & (0.01) & 0.62           & (0.01)            & 0.59                & (0.01) \\
				45                    & 0.64              & (0.00)          & 0.62                & (0.01) & 0.61           & (0.01)            & 0.59                & (0.01) \\
				43                    & 0.65              & (0.00)          & 0.60                & (0.01) & 0.62           & (0.01)            & 0.58                & (0.01) \\
				46                    & 0.64              & (0.00)          & 0.58                & (0.01) & 0.60           & (0.01)            & 0.59                & (0.01) \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the govern and ideology dependent variables by generation of the respondent. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	\end{landscape}
	
	\normalsize
	\begin{table}[]
		\centering
		\caption{Binary Dependent Variable with Interaction Between Relationship \& Race/Ethnicity}
		\begin{threeparttable}
			\begin{tabular}{lll}
				&      & \textbf{Binary} \\ \hline
				Straight              & 0.57 & (0.01)          \\
				Bisexual              & 0.49 & (0.01)          \\
				Gay                   & 0.45 & (0.01)          \\
				Older children        & 0.52 & (0.01)          \\
				No children           & 0.48 & (0.01)          \\
				Young children        & 0.50 & (0.01)          \\
				City council member   & 0.57 & (0.01)          \\
				Congressional staffer & 0.51 & (0.01)          \\
				Ironworker            & 0.49 & (0.01)          \\
				Nurse                 & 0.52 & (0.01)          \\
				Political consultant  & 0.52 & (0.01)          \\
				Radio talk show host  & 0.39 & (0.01)          \\
				Male                  & 0.48 & (0.01)          \\
				Female                & 0.52 & (0.01)          \\
				45                    & 0.50 & (0.01)          \\
				43                    & 0.51 & (0.01)          \\
				46                    & 0.49 & (0.01)          \\
				Married * White       & 0.54 & (0.02)          \\
				Cohabiting * White    & 0.51 & (0.02)          \\
				Divorced * White      & 0.46 & (0.02)          \\
				Single * White        & 0.50 & (0.02)          \\
				Married * Asian       & 0.53 & (0.02)          \\
				Cohabiting * Asian    & 0.46 & (0.02)          \\
				Divorced * Asian      & 0.45 & (0.02)          \\
				Single * Asian        & 0.46 & (0.02)          \\
				Married * Black       & 0.57 & (0.02)          \\
				Cohabiting * Black    & 0.54 & (0.02)          \\
				Divorced * Black      & 0.48 & (0.02)          \\
				Single * Black        & 0.51 & (0.02)          \\
				Married * Hispanic    & 0.52 & (0.02)          \\
				Cohabiting * Hispanic & 0.49 & (0.02)          \\
				Divorced * Hispanic   & 0.45 & (0.02)          \\
				Single * Hispanic     & 0.51 & (0.02)         \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary dependent variable with an interaction term between relationship status and race/ethnicity. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	
	
	\begin{table}[]
		\centering
		\caption{Binary Dependent Variable with Interaction Between Children \& Race/Ethnicity}
		\begin{threeparttable}
			\begin{tabular}{lll}
				&      & \textbf{Binary} \\ \hline
				Straight                  & 0.57 & (0.01)          \\
				Bisexual                  & 0.49 & (0.01)          \\
				Gay                       & 0.45 & (0.01)          \\
				Married                   & 0.54 & (0.01)          \\
				Cohabiting                & 0.50 & (0.01)          \\
				Divorced                  & 0.46 & (0.01)          \\
				Single                    & 0.49 & (0.01)          \\
				City council member       & 0.57 & (0.01)          \\
				Congressional staffer     & 0.51 & (0.01)          \\
				Ironworker                & 0.49 & (0.01)          \\
				Nurse                     & 0.52 & (0.01)          \\
				Political consultant      & 0.52 & (0.01)          \\
				Radio talk show host      & 0.39 & (0.01)          \\
				Male                      & 0.48 & (0.01)          \\
				Female                    & 0.52 & (0.01)          \\
				45                        & 0.50 & (0.01)          \\
				43                        & 0.51 & (0.01)          \\
				46                        & 0.49 & (0.01)          \\
				Older children * White    & 0.52 & (0.02)          \\
				No children * White       & 0.48 & (0.02)          \\
				Young children * White    & 0.51 & (0.02)          \\
				Older children * Asian    & 0.50 & (0.02)          \\
				No children * Asian       & 0.45 & (0.02)          \\
				Young children * Asian    & 0.48 & (0.02)          \\
				Older children * Black    & 0.55 & (0.02)          \\
				No children * Black       & 0.48 & (0.02)          \\
				Young children * Black    & 0.55 & (0.02)          \\
				Older children * Hispanic & 0.52 & (0.02)          \\
				No children * Hispanic    & 0.50 & (0.02)          \\
				Young children * Hispanic & 0.46 & (0.02)        \\ \hline 
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary dependent variable with an interaction term between parental status and race/ethnicity. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	
	
	\begin{table}[]
		\centering
		\caption{Binary Dependent Variable with Interaction Between Children \& Gender}
		\begin{threeparttable}
			\begin{tabular}{lll}
				&      & \textbf{Binary} \\ \hline
				Straight               & 0.57 & (0.01)          \\
				Bisexual               & 0.49 & (0.01)          \\
				Gay                    & 0.45 & (0.01)          \\
				Married                & 0.54 & (0.01)          \\
				Cohabiting             & 0.50 & (0.01)          \\
				Divorced               & 0.46 & (0.01)          \\
				Single                 & 0.49 & (0.01)          \\
				City council member    & 0.57 & (0.01)          \\
				Congressional staffer  & 0.51 & (0.01)          \\
				Ironworker             & 0.49 & (0.01)          \\
				Nurse                  & 0.52 & (0.01)          \\
				Political consultant   & 0.52 & (0.01)          \\
				Radio talk show host   & 0.39 & (0.01)          \\
				White                  & 0.50 & (0.01)          \\
				Asian                  & 0.48 & (0.01)          \\
				Black                  & 0.53 & (0.01)          \\
				Hispanic               & 0.49 & (0.01)          \\
				45                     & 0.50 & (0.01)          \\
				43                     & 0.51 & (0.01)          \\
				46                     & 0.49 & (0.01)          \\
				Older children * Man   & 0.50 & (0.01)          \\
				No children * Man      & 0.46 & (0.01)          \\
				Young children * Man   & 0.49 & (0.01)          \\
				Older children * Woman & 0.55 & (0.01)          \\
				No children * Woman    & 0.49 & (0.01)          \\
				Young children * Woman & 0.51 & (0.01)         \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary dependent variable with an interaction term between parental status and gender. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}
	
	\begin{table}[]
		\centering
		\caption{Binary Dependent Variable with Interaction Between Sexuality \& Gender}
		\begin{threeparttable}
			\begin{tabular}{lll}
				&      & \textbf{Binary} \\ \hline
				City council member   & 0.57 & (0.01)          \\
				Congressional staffer & 0.51 & (0.01)          \\
				Ironworker            & 0.49 & (0.01)          \\
				Nurse                 & 0.52 & (0.01)          \\
				Political consultant  & 0.52 & (0.01)          \\
				Radio talk show host  & 0.39 & (0.01)          \\
				White                 & 0.50 & (0.01)          \\
				Asian                 & 0.48 & (0.01)          \\
				Black                 & 0.53 & (0.01)          \\
				Hispanic              & 0.49 & (0.01)          \\
				45                    & 0.50 & (0.01)          \\
				43                    & 0.51 & (0.01)          \\
				46                    & 0.49 & (0.01)          \\
				Older children        & 0.52 & (0.01)          \\
				No children           & 0.48 & (0.01)          \\
				Young children        & 0.50 & (0.01)          \\
				Married               & 0.54 & (0.01)          \\
				Cohabiting            & 0.50 & (0.01)          \\
				Divorced              & 0.46 & (0.01)          \\
				Single                & 0.49 & (0.01)          \\
				Straight * Male         & 0.55 & (0.01)          \\
				Bisexual * Male         & 0.47 & (0.01)          \\
				Gay * Male              & 0.43 & (0.01)          \\
				Straight * Female       & 0.58 & (0.01)          \\
				Bisexual * Female       & 0.50 & (0.01)          \\
				Gay * Female            & 0.47 & (0.01)         \\ \hline
			\end{tabular}
			\begin{tablenotes}
				\footnotesize
				\item Note: These models are the estimates and standard errors for the binary dependent variable with an interaction term between sexuality and gender. 
			\end{tablenotes}
		\end{threeparttable}
	\end{table}



\doublespacing
\newpage
\setcounter{hyp}{0}
\chapter{When Gender Matters: Using Bayesian Scaling to Examine Gender and Voting Behavior in a Polarizing Congress}

\section{Introduction}
Following dozens of failed attempts to repeal the Affordable Care Act, Republican victories in the 2016 election gave the GOP their best opportunity yet to quash President Obama's signature legislation. However, GOP Senators Lisa Murkowski and Susan Collins, alongside John McCain, cut short Republicans' efforts to repeal Obamacare. The media labeled these women senators as ``moderates" within the Republican Party and were credited with ``saving" Obamacare \cite{cassidy2017obamacare,parlapiano2017obamacare}. This anecdote fits within the conventional wisdom in political science, as well as the popular media, that women in Congress are more liberal than their male colleagues, and that this trend is mostly driven by GOP women who are more liberal than their male co-partisans, especially on women's issues \cite{carey1998women,frederick2009female}. 

This project re-evaluates this phenomenon by adopting new methodological approaches to analyze congressional voting, as well as looking at how this trend has varied over time. Much of the current literature on gender and roll call voting in Congress has used DW-NOMINATE or women's interest group scores as the measure of ideology to compare men and women lawmakers, both of which have their limitations. DW-NOMINATE, which uses all votes to construct a member's ideology score, is an aggregate measure that may conceal gender differences because women lawmakers may vote differently than men on some policy issues and not on others \cite{crespin2010,roberts2009}. As for interest group scores, these measures tend to only use a tiny number of votes to construct members' scores and tend to reflect the partisan biases of the organization crafting the measure \cite{binder1999uncovering}. To address the limitations of past measures of ideology in examining gender differences in Congress, I use Bayesian item response theory on roll call votes to scale legislators on 21 policy topics. This method improves upon interest group scores because it allows for a larger number of votes to be examined and does not pick and choose which votes to include or not. Additionally, this method can offer new insights obscured by DW-NOMINATE by taking into consideration possible policy topic differences. 

A second limitation of the past literature on women legislators' vote records has been this research often only examines a short period of time. This project seeks to place a strong emphasis on the role of time and how polarization has effected legislative behavior. Due to changing dynamics in Congress and increased polarization, I expect that the influence of gender is not consistent across time or political parties. I expect there are two factors driving these temporal changes. First, I theorize that more moderate women have exited Congress and newly elected women tend to be more ideologically extreme than their predecessors. Secondly, I expect that increasingly the majority party controls the agenda, such that policies that do reach a floor vote increasingly divide legislators along partisan lines rather than gender. 

To examine these expectations, this project uses U.S. House roll call data from 1975 to 2016. After generating ideal point estimates for all members of Congress by policy topic across this time period, I find significant differences across party, issue area, and time. I find House Republican women had significantly more liberal voting records than Republican men in the 1980s and early 1990s, especially on traditionally defined women's issues; however, in 1995, when Newt Gingrich took over as speaker marks a significant shift in Republican women's voting behavior. Republican women have polarized since that time, such that no significant gender differences persist in voting behavior among House Republicans in the 2010s, even on women's issues. 

For House Democrats, significant gender differences did not surface until the mid-1990s. Democratic women did not appear to vote differently from their male co-partisans until after the 1992 ``Year of the Woman," which was a record-breaking year for women elected to Congress. Following 1992, Democratic women were significantly more liberal than their male colleagues, most notably on women's issues, continuing into the early 2010s. Despite more homogeneity within parties in recent years, I find Democratic women continue to vote slightly more liberal than Democratic men on some policy topics, including energy and science. 

This project highlights that polarization has disproportionately impacted Republican women, who over the past several decades have grown more conservative and are more in-line ideologically with their male colleagues, and that Democratic women remain more polarized than their male co-partisans. This counters the popular narratives that Republican women politicians are moderates and Republican men are the most conservative lawmakers. Additionally, these findings suggest that even women's issues, where gender identity would be expected to play a stronger role in shaping legislative behavior, have not been immune from the effects of polarization. Collectively, this project combines past research on gender and politics, agenda setting, and polarization to understand the changing effects of gender on congressional roll call voting. 

\section{Women in Congress}
As the first woman elected to Congress in 1916, Jeannette Rankin used her legislative power to prioritize and address women's issues, including introducing legislation to give women the right to vote. In the years since Rankin's election, scholars have examined how women differ in their legislative behavior from their men colleagues, especially in addressing and bringing attention to women's issues. As visualized in Figure \ref{housewomennumber}, the number of women in Congress has slowly and steady increased over time, more strongly driven by an increase in Democratic women elected to Congress. 

Early research on the effect of gender on legislative behavior did find significant differences. For example, women in the House in the 1960s and 1970s consistently voted more liberal than their male colleagues \cite{frankovic1977sex,welch1985women}. Looking at roll call voting in the late 1980s and early 1990s, a legislator's gender is significantly related to various interest group scores, and this effect was strongest among GOP women on women's issues \cite{dolan1998support,norton1999uncovering,swers1998women}. Other research on this time finds women lawmakers vote differently on other types of policy issues, including women being more likely to vote in favor of supporting the arts \cite{boles2007beyond}. 

\begin{figure}[h]
	\includegraphics[width=12cm]{housewomennumber}
	\centering
	\textbf{\caption{Women in the U.S. House of Representatives} \label{housewomennumber}}
	\begin{description}
		\item \noindent \footnotesize {Note: Data from Brookings ``Vital Statistics on Congress." https://www.brookings.edu/multi-chapter-report/vital-statistics-on-congress/}
	\end{description}
\end{figure}

More recent research on gender differences in Congress finds no gender effect on voting behavior or finds a gender effect is limited to only Republican women senators voting more liberal than Republican men (Evans 2005, Frederick 2009, 2010, 2013). \nocite{evans2005women,frederick2009female,frederick2010gender,frederick2013gender} Even looking at specific policy topics, such as military issues, evidence suggests women do not differ from their male colleagues \cite{bendix2020gender}. Similar research on state legislatures finds that women are more liberal than men on social issues and women's issues \cite{carey1998women,jenkins2012gender,thomas1994women}. A comprehensive analysis of state legislators from 1999 to 2014 finds that women of both parties are not more liberal, but rather women are more polarized than their men co-partisans, such that Democratic women are more liberal than Democratic men and Republican women are more conservative than Republican men \cite{osbornideology}.\footnote{The evidence is mixed on if the bigger gender difference in state legislatures is within the Democratic or Republican Party \cite{osborn2012women,poggione2004exploring}.} Furthermore, cross-national research on right wing parties finds women candidates tend to be more liberal overall than men candidates \cite{de2020female}.

Beyond voting, scholars have noted how women differ in terms of other legislative behavior. When interviewed, women in Congress often express they feel their presence makes a difference, as they offer a unique perspective as a woman in the legislative process \cite{brown2014sisters,dittmar2018seat}. Women lawmakers are more likely than men to sponsor legislation on women's issues \cite{atkinson2020gender,swers2013women}. Furthermore, women lawmakers sponsor more legislation overall and are more successful at pushing their bills through the process as compared to their male colleagues \cite{schmitt2019gender,volden2013women}. 

Therefore, if women lawmakers differ from their male colleagues on a variety of legislative behaviors, it would also be expected they behave differently on floor votes. Or perhaps, while stages earlier in the legislative process, such as bill sponsorship, offer lawmakers more freedom in what policies they pursue, the negative agenda control mechanisms at play in what makes it to the House floor restrict the opportunities for gender differences to arise in roll call voting \cite{cox2005setting}. Ultimately, past research on the role of gender identity in understanding congressional voting behavior is mixed, in part because much of this work is limited in scope, such as examining a single policy topic or gender differences within a single congress. This paper aims to offer a more comprehensive examination of how gender identity influences voting behavior by looking at votes across congresses and policy topics. 

\section{Theoretical Expectations}
A robust body of research has examined gender differences in congressional voting records. This paper seeks to extend this line of research by utilizing new methodological approaches to examine voting behavior by policy topic, in addition to examining how gender effects have waxed and waned across time. 

Among the public, party identity plays a dominating role in understanding policy preferences and political behavior; however, gender differences persist \cite{barnes2017american,howell2000complexities,lizotte2020gender}. While women are overall more likely than men to identify as Democrats, gender differences within parties also exist \cite{kanthak2003gendergap}. For example, recent research finds Republican women have more liberal views on education and health care than GOP men \cite{barnes2017american}.

Turning to political elites, models of congressional voting behavior have often been condensed down to a single dimension, as partisanship can explain a large majority of voting decisions \cite{poole1991}. With partisanship shaping much of legislative behavior, this leaves limited opportunities for gender differences to arise within parties. However, there are several reasons to suspect gender identity may influence legislative voting. First, women legislators articulate that they feel they bring a unique perspective that influences their policy preferences, especially on issues that disproportionately impact women \cite{brown2014sisters,dittmar2018seat}. Their life experiences as women give them different insights on issues such as reproductive rights, child care, and family leave than their male colleagues \cite{burrell1994woman,rhinehart2020women}. 

Additionally, women have historically had less power in society, shaping how women think about and value equality. Broadly, women tend to be more altruistic and egalitarian, while men tend to more highly value individualism \cite{carroll1988women,howell2000complexities,schlesinger2001gender}. This leads women to be more supportive of government programs that promote equality, including expanding the social welfare safety net and policies that extend rights to disenfranchised groups \cite{pratto1997gender}. Furthermore, women, especially as they have increasingly entered the workforce, are more likely to experience discrimination, thus leading women to be more supportive of policies that promote equal opportunity and support historically underprivileged groups \cite{carroll1988women,schlesinger2001gender}. 

Overall, I expect because of life experiences and value differences between men and women, women in Congress are more liberal than their male colleagues. However, I also expect this difference has fluctuated over time. As political elites have polarized and parties in Congress have grown more internally homogenous, the dominating role of parties leaving less room for gender effects in voting behavior. Since the 1970s, polarization in Congress has surged, and therefore, across all issues, party differences can explain a higher proportion of voting decisions \cite{jochim2013,mccarty2008polarized}. Across both parties, I except women increasingly vote more similar to their male co-partisans. However, I except this trend is more notable within the GOP, as the polarization of Congress has disproportionately been driven by Republicans shifting to the right \cite{grossmann2016asymmetric}. In particular, I expect this shift within the GOP, in which women's voting looks increasingly more similar to men's, began with the leadership of Speaker Newt Gingrich. 

After 40 years of Democratic rule, Gingrich and the 1994 Republican Revolution earned the GOP huge gains in the House \cite{rosenbaum1994republicans}. Gingrich, a self-described transformational leader, pushed an aggressive agenda that demanded conformity to a more conservative ideology focused on limited government and individual liberty \cite{armey5616,sinclair1999transformational,strahan2007leading}. Gingrich wrote in 1989 if the GOP ever wanted a majority in the House, they needed to coalesce around a ``conservative philosophy" \cite{edwards441}. 

Following the 1994 election, the new Republican Party House leadership emphasized this unity. For example, in a 1995 letter to all freshman Republicans, House Majority Leader Dick Armey (TX) specified, ``We have fundamentally changed the image of House Republicans and we have made great progress toward a better America...Let's stay solid as a team" \cite{armey5418}. In a note to all House Republicans later that year, Armey wrote, ``This Congress is truly historical...We ran on a unified program and we kept our word" \cite{armey557}. Once again stressing the importance of a cohesive conservative bloc, Armey wrote in a 1996 letter to GOP House members about upcoming votes, ``If we work together in a spirit of unity, we can successfully complete our work for this Congress" \cite{armey5418}. 

Overall, the Republican Party at this time shifted away from seeking bipartisan collaboration on legislation and toward a more adversarial style in pushing their own political agenda \cite{theriault2013gingrich}. For example, Gingrich cut funding to groups, including the Congressional Caucus for Women's Issues, which were dedicated to fostering bipartisan collaborations, as these groups threatened his ability to promote party unity \cite{hawkesworth2001yearwoman}. Gingrich further emphasized party unity by violating seniority norms in assigning committee chairs, and instead appointed chairs who were loyal to him and the party \cite{aldrich1997transition}.

As to why polarization has led to the decline of gender differences in roll call voting behavior, I offer two potential explanations. First, I expect a legislator's personal ideology is fairly consistent across their tenure in Congress \cite{poole2007changing}. Therefore, I expect much of the change in how women vote in Congress does not come from individual women changing their ideologies over time but rather through the replacement of retiring moderate women with more ideologically extreme women. Since the last 1970s, Republicans who have entered Congress have overall been more ideologically extreme than Republicans who served prior \cite{theriault2013gingrich}. Polarization has also made a career in Congress less appealing to moderate candidates, as they are less likely to think they can win and less likely to think they can accomplish policy goals should they be elected \cite{thomsen2017opting}. Looking at state legislators, the most common pipeline to running for Congress, more moderate state legislators are less likely to seek a congressional run as compared to their more ideologically extreme colleagues \cite{thomsen2017opting}. Therefore, I expect that since the 1990s, women who have left Congress have tended to be more moderate, while women newly elected to the House are more ideologically extreme.

Additionally, I expect changes in the composition of the political agenda have led to a decline in gender differences in roll call voting. While women legislators may be more liberal on some policies, if these policies are voted on less frequently, it gives the appearance that Congress is more polarized than it actually is while masking gender effects. It is likely that issue areas are more multidimensional than roll call outcomes would suggest \cite{crespin2010}. Starting with Gingrich, speakers were less likely to allow bills to make it to a floor vote if they expect it would split their party. Following the Republican Revolution, GOP leadership was more aggressive than their predecessors in controlling the legislative agenda \cite{carson2011unpacking}. Since then, House majority party leadership has been increasingly more successful at controlling what does and does not make a floor vote \cite{stiglitz2010agenda}. 

Therefore, the bills that do reach the floor are more likely to be decided along party lines, and consequently less likely to split the majority party along another dimension. One of those dimensions could be gender differences; therefore, the types of bills most likely to highlight gender differences are increasingly less likely to reach the House floor. Roll call votes reveal where each legislator stands on a particular policy alternative, but roll call records cannot reveal the majority party strategies behind what does and does not receive a floor vote \cite{lee2009beyond}. Even on women's issues, parties have increasingly framed these policies along different dimensions that are more likely to divide legislators along party lines rather than gender. For example, health care has traditionally been framed as a caregiving and women's issue, but in the past decade has been reframed as an economic issue, leading to more partisan unity in voting \cite{hayden2018dissertation}. These expectations about women in Congress and the influence of polarization leads to my first set of expectations.  

\singlespacing
\begin{hyp}
	Within both political parties, women in Congress vote more liberal than their male co-partisans. 
\end{hyp} \vspace{2mm}	
\begin{hyp}
	Gender differences in voting behavior have diminished over time. This decline has been greater within the Republican Party, especially since the 104th Congress. 
\end{hyp}
\doublespacing

The importance of studying roll call voting by policy topic is that, theoretically, I expect women are not more liberal than men on all issues, but rather this effect is greatest on women's issues. For example, I do not expect women to vote differently on national park conservation or highway infrastructure bills, but rather that gender differences are most prominent on issues such as reproductive rights or legislation advancing gender equality. I adopt Swers' (2002)\nocite{swers2002} definition of women's issues as those that, ``are particularly salient to women because they seek to achieve equality for women; they address women's special needs, such as women's health concerns or child care issues; or they confront issues with which women have traditionally been concerned in their role as caregivers, such as education, health care, or the protection of children." Therefore, because women are more likely to prioritize women's issues and offer a unique perspective on these policies, I expect these issues to be when gender differences are most prominent. 

\singlespacing
\begin{hyp}
	Within both political parties, women in Congress vote more liberal than their male co-partisans on women's issues. This effect has diminished over time. 
\end{hyp}
\doublespacing

\section{Data \& Methods}
Prevalent in research on gender and Congress is the use of the annual score produced by the American Association of University Women (AAUW). The AAUW is an interest group dedicated to advocating for women, and each congress they score lawmakers based on how they voted on the organization's policy priorities. For example, scores in the 112th Congress included votes on the Violence Against Women Reauthorization Act and the Paycheck Fairness Act. However, a shortcoming of using AAUW scores is this measure uses so few votes. For example, scores in the 103rd Congress were based on six bills. While this score provides a look at specific types of bills, it is limited in providing a broader understanding of voting behavior as legislators voted on more than 1,000 bills in the 103rd Congress.\footnote{Other women's interest group scores that have been used by researchers include those from the National Organization of Women and the National Right to Life Committee, both of which also use a similar small number of votes to construct members' scores.} 

Another concern with using interest group scores is introducing bias based on the partisan motivations of the organizations creating these scores. The political agenda and policy goals of a given interest group often more closely align with one party over the other. Thus, in creating these scores, these organizations have a partisan motivation in picking which votes they decide to include in their scorecard, likely picking hot-button legislation that sharply divides the parties \cite{binder1999uncovering}. Additionally, in looking at the distribution of most interest group scores, they tend to be bimodal, assigning most lawmakers an ideologically extreme score and few a moderate score \cite{jeong2018measuring,snyder1992artificial}. Therefore, these measures tend to exaggerate the true distribution of members' preferences.

While interest groups use too few votes to understand the complexity of legislators' preferences, DW-NOMINATE scores fall on the opposite extreme. DW-NOMINATE scores, which utilize scaling methods on all votes to craft members' scores, are a widely-used measure in congressional research. This method produces a parsimonious model that explains a majority of voting behavior (Poole \& Rosenthal 1991, 1997)\nocite{poole1991,poole1997congress}. While DW-NOMINATE scores suggest that voting is one-dimensional, a drawback of using this measure for my specific research question is that members are often not ideologically consistent across different policy topics \cite{crespin2010,hurwitz2001distributive}. A legislator could vote extremely conservative on one issue area but more moderate on others. For example, in Figure \ref{stripplot}, I plot three ideology scores for Rep. Ileana Ros-Lehtinen (R-FL) in the 114th Congress. Ros-Lehtinen has a 0.88 ideology score for voting on international affairs issues, making her a more conservative member in the House, a 0.4 ideology score for foreign trade bills, placing her around the median of Republicans, and an ideology score of -0.05 for environment issues, making her much more moderate on this issue than her GOP colleagues. This example highlights that aggregate scores, such as DW-NOMINATE, may mask the true dimensionality of members' preferences \cite{aldrich2014polarization,roberts2009}.

\begin{figure}[!h]
	\includegraphics[width=13cm]{stripplot}
	\centering
	\textbf{\caption{Ideological Inconsistency: Rep. Ileana Ros-Lehtinen (R-FL) in the 114th Congress} \label{stripplot}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure highlights an example of a legislator who is not ideologically consistent across policy topics, which leads me to use policy specific ideological scores for this analysis rather than DW-NOMINATE.}
	\end{description}
\end{figure}

To address the shortcomings of using either interest group scores or DW-NOMINATE to examine gender differences in Congress, I use Bayesian item response theory (IRT) to scale members by policy topic.\footnote{While Bayesian IRT was originally developed for education research, it has been adopted to roll call votes to scale members of Congress \cite{clinton2004statistical}.} Bayesian IRT assumes that lawmakers have policy preferences, and that they act in a way to maximize their utility \cite{riker1973introduction}. Members vote in favor of legislation if the proposed policy is closer to their ideal policy than the status quo and vote against the bill if the status quo is closer to their ideal point. While DW-NOMINATE and Bayesian IRT tend to produce similar ideal point estimates for legislators, Bayesian IRT offers several advantages over DW-NOMINATE \cite{carroll2009comparing}. First, while the DW-NOMINATE process is computationally intensive, the Bayesian approach simplifies the process using a routine Markov chain Monte Carlo (MCMC) simulation \cite{clinton2004statistical}.\footnote{Bayesian IRT assumes a quadratic utility function for legislators, and errors are assumed to follow a normal distribution \cite{clinton2004statistical}.} Furthermore, Bayesian IRT allows for the incorporation of data points that are traditionally excluded from DW-NOMINATE, including lopsided votes and legislators with short voting records, improving the estimates of members' ideal points \cite{clinton2004statistical}. Finally, Bayesian IRT is better able to scale members when few roll call votes are available, which is important for this project because for some policy topics, there are a limited number of votes per congress. 

In addition to using Bayesian IRT, another methodological decision to better address my hypotheses and to gauge gender differences in Congress is to scale members by policy topic rather than across all votes. This approach is useful for my research question because I expect gender differences to be greater on women's issue and for there to be limited to no gender differences on other issue areas. Therefore, if I used DW-NOMINATE, it may camouflage gender differences in voting behavior. While interest group scores tend to be centered around a single policy topic, these scores only incorporate a handful of votes. Bayesian IRT allows me to incorporate all votes on a given policy topic to estimate a lawmakers' policy-specific ideal point. This is a less biased estimate, as it does not pick and choose which votes are included. 

To define policy topics, I use the Policy Agendas Project, which codes all bills into one of 20 major topics and 220 subtopics.\footnote{Roll Call Votes, The Policy Agendas Project at the University of Texas at Austin (accessed October 10, 2019).\nocite{comparativeagendasproject}} For this project, I scale members on all votes in addition to by each of these 20 major policy topics.\footnote{I use the \textit{pscl} package in R to scale members. For the Markov chain Monte Carlo simulation, 10,000 iterations were run, with a burnin of 500 and the thinning interval set at 100. See Appendix Figure \ref{housecorrelations} for how my measures correlate with DW-NOMINATE. My ideology scores on all votes correlates with DW-NOMINATE 0.94.} Therefore, each legislator has an ideal point for each policy topic for each term they served in Congress. Lawmakers' ideal points are normalized, with a mean of 0 and a standard deviation of 1. Keeping in the same tradition as DW-NOMINATE, negative scores are more liberal and positive scores are more conservative. 

Although Bayesian IRT can better handle a small number of votes than can the DW-NOMINATE, using ideal points based on too few votes produces large credible intervals and limits the ability to feel confident in the conclusions I draw from this analysis. This is a challenge for this project because often within a single session of Congress, there may not be many votes on a specific policy topic. To determine the cutoff for the minimum number of votes for a policy topic to be included in this analysis, I look to the AAUW interest groups scores. By using the average number of votes included in the AAUW scores as my minimum cutoff, this means my ideal points are providing at least as much information as these interest group scores. The average number of votes used to create these scores across the same years used in this analysis is 8.5 votes; therefore, I decide to exclude from my analysis any policy topic in a single session of Congress that has less than nine votes.\footnote{Based on the nine votes threshold, I exclude 18 policy topics in a single Congress from this analysis, as well as four congresses on women's issues.} After excluding these policy topics, the mean and median number of votes used to scale per congress is 56 and 37, respectively.\footnote{See Appendix Figure \ref{numbervoteshouse} for the distribution of number of votes used by policy topic.}

This project is also specifically interested in how women lawmakers may differ from men on women's issues. Looking at the 20 major policy topics, I identify four which I operationalize as women's issues -- civil rights, education, health, and social welfare. Past research finds men are perceived as better able to address the economy, foreign affairs, crime, and terrorism; however, women are stereotyped as better to handle poverty, children, social welfare, health care, and education \cite{bauer2019feminine,bracic2020teachers,huddy1993gender,schneider2014effects}. Volden, Wiseman, Wittmer (2018)\nocite{volden2018women} define women's issues as those in which women legislators sponsor bills at a greater rate, which they find includes civil rights, education, and health. Therefore, based on how past research has define women's issues, I select these four policy topics as women's issues and those for which I expect gender differences to be greatest. 

In addition to these four major policy topics, I identify five of the coded subtopics that are exclusively women's issues. These five subtopics are gender and sexual orientation discrimination, infants and children, parental leave and child care, child abuse and child pornography, and family issues. While there is no clear definition of what constitutes a women's issue, these issues fit within how past scholars have strived to outline women's issues, including Swers' (2002) definition outlined above. I combine these five subtopics and scale members to give each an ideal point on my measure of women's issues.

This analysis runs from the 94th Congress (1975-76) to the 114th Congress (2015-16).\footnote{Roll call data was collected from Lewis, Poole, Rosenthal, Boche, Rudkin, \& Sonnet 2019 (accessed August 27, 2019)\nocite{lewis2019voteview}. Additional variables on roll call votes were collected from Crespin \& Rohde 2019 (accessed October 8, 2019)\nocite{crespin2019pipc}.} The 94th Congress was selected as the start year for two reasons. Before this time, there were not many women in Congress to study. Secondly, several rules changes in the House, including voting by electronic device and recorded voting in the Committee of the Whole, occurred in the early 1970s, making comparison of voting before and after this period challenging \cite{roberts2003procedural}. My dependent variable is my measure of ideology by policy topic, by Congress. Because this is a continuous variable, I run OLS regression. The main independent variables of interest are the party and gender of the lawmaker. Therefore, I construct a 4-level factored variable that labels Republican women, Republican men, Democratic women, and Democratic men.\footnote{I change the reference category depending on if I am comparing Republican women and Republican men versus comparing Democratic women and Democratic men. Throughout the paper, all analysis is presented as comparing women and men within parties. I do not examine gender effects across both parties because historically, the number of Democratic women in Congress has swamps the number of Republican women, so any analysis on Congress has a whole would just be picking up the liberal-leanings of Democratic women.} I incorporate a number of other covariates into my models that I expect impact members' voting behavior.\footnote{Several of these variables were provided by Volden \& Wiseman 2014 (accessed September 10, 2019)\nocite{volden2014legislative}.} Vote share is measured as the percent of the vote the legislator received in their most recent election. Several binary variables are incorporated, labeling if the lawmaker is Black or Latinx, as well as if the legislator represented a state from the South.\footnote{The coding of Southern states was incorporated based on data from the Bureau of Labor Statistics\nocite{bureau2014south}.} Seniority is a measure of the total number of terms a member has served in Congress. Congressional leadership often votes differently than rank-and-file members, so I include binary variables for majority party leadership, minority party leadership, and those that chair a committee. As general measures of district demand for certain types of policies, I include several common measures used in past research from the U.S. Census to measure district differences (e.g. Frederick 2010, Swers 1998)\nocite{frederick2010gender,swers1998women}. I include three measures --- percent urban, median household income, and percent Black.

\section{Results} 
\begin{figure}[H]
	\includegraphics[width=\textwidth]{housemeans}
	\centering
	\textbf{\caption{House Mean Ideal Points By Policy Topic} \label{housemeans}}
	\singlespacing
	\begin{description}
		\item \noindent \footnotesize {Note: For these ideal points, positive numbers are more conservative and negative numbers are more liberal. For ease of interpretation, these measures are standardized, such that each has a mean of 0 and a standard deviation of 1. The policy topics shaded in gray are the four which I have operationalized as women's issues.}
	\end{description}
\end{figure}
\doublespacing

As an initial look at the data, Figure \ref{housemeans} displays the average ideal point by policy topic, as well as all votes, for four groups in the U.S. House of Representatives -- Democratic men, Democratic women, Republican men, and Republican women. First, the most obvious trend that stands out is that partisanship consistently matters significantly in understanding voting behavior from the 1970s to now. Across nearly all policy topics and all congresses, men and women of the same party vote more similar than do all women or all men across partisan lines. There are a few exceptions to this, for example Republican women appear more closely aligned with Democrats than Republican men on civil rights in the 97th Congress, but these outliers are rare.

For Republicans, Figure \ref{housemeans} seems to offer some initial support for my expectation that since the 104th Congress (1995-96), when Gingrich took over the speakership, gender differences appear to have diminished. This shift has been so stark that in the most recent congresses, there appears few voting differences between Republican women and men on most issues. Voting on environmental issues is a good example of this trend, as it appears Republican women in the 1970s and 1980s voted more liberal than GOP men, but the 104th Congress marks a clear shift toward less divergence between men and women. 

Looking at House Democrats, there  are overall fewer gender differences across time than within the GOP. For example, looking at voting on labor legislation across 40 years, Democratic men and women appears to vote very similarly. Democratic women appear slightly more liberal than Democratic men on some policy topics in the 1990s and 2000s. For example, since the 103rd Congress (1993-94) and into the 2000s, Democratic women appear more liberal than Democratic men on civil rights and crime. However, in the most recent congresses, there appears few gender differences among Democrats. Overall, Figure \ref{housemeans} highlights that gender differences have fluctuated over time, and that polarization, especially within the past decade, has played a role in diminishing the influence of gender identity on congressional voting behavior. Figure \ref{housemeans} also suggests this shift has been particularly consequential for Republican women.      

\begin{figure}[H]
	\includegraphics[width=12cm]{housemeanswomen}
	\centering
	\textbf{\caption{House Mean Ideal Points on Women's Issues} \label{housemeanswomen}}
		\singlespacing
	\begin{description}
		\item \noindent \footnotesize {Note: For these ideal points, positive numbers are more conservative and negative numbers are more liberal. This measure of women's issues is a combination of voting on gender and sexual orientation discrimination, infants and children, parental leave and child care, child abuse and child pornography, and family issues.}
	\end{description}
\end{figure}  
	\doublespacing

Figure \ref{housemeanswomen} compares men and women of both parties on women's issues, using my measure constructed by scaling roll call voting on five policy subtopics. In this figure, Republican women stand out as the most interesting trend across time. On average, Republican women in the 1970s and 1980s appear more liberal on women's issues than Republican men but shift toward more conservative voting in the 1990s. For example, in the 98th Congress, Republican women voted as liberal as Democratic men on women's issues. However, since that time, GOP women have slowly grown more conservative. Since the 110th Congress, Republican women appear identical to GOP men in voting on women's issues. Across time, Republican men appear rather consistent in voting on women's issues, and it has been Republican women that have shifted toward voting more conservative and more in-line with their male colleagues. Because I expect women's issues are going to be when gender differences are most apparent, this seems to offer little evidence of gender differences within the Republican Party persisting into the 2010s. 

Among Democrats on women's issues, there are fluctuations in gender differences over time, but the 103rd Congress, the first session after the 1992 ``Year of the Woman" appears to mark a more liberal shift in how Democratic women voted. Similar to Republicans, I find Democratic women and men have grown more similar in how they vote on women's issues since the 1990s; however, Democratic women have not been the sole drivers of this shift. For example, it appears since the 1990s, Democratic men have on average moved slightly more liberal on women's issues. In the 2010s, Democratic men and women appear to vote similarly on women's issues.   

As a more stringent test of my theoretical expectations, I run regression models with my ideology scores as the dependent variable and the independent variables outlined above. I run a model by policy topic for each session of Congress, in addition to a model for all votes and women's issues. This gives me a total of 462 models (21 sessions of Congress x 22 policy topics). To simplify my results, in Figures \ref{houserepcoef1}-\ref{housedemcoef3}, I plot the coefficient and confidence interval for my variable of interest -- the factored variable of the legislator's gender and party. Because I am primarily interested in within party gender differences, Figures \ref{houserepcoef1}, \ref{houserepcoef2}, and \ref{houserepcoef3} present the coefficients for Republican women as compared to Republican men and Figures \ref{housedemcoef1}, \ref{housedemcoef2}, and \ref{housedemcoef3} present the coefficients for Democratic women as compared to Democratic men. In these figures, the dot represents the coefficient and the line visualizes the 95\% confidence interval. For ease of interpretation, all coefficients that are statistically significant (p$<$0.05) are bolded. 

Turning first to the results for Republican women, I find interesting patterns across time. In the late 1970s (94th-96th congresses), I find few significant gender differences, although most policy topic coefficients are in the negative direction, meaning women tended to be more liberal. The lack of findings in the 1970s is likely a result of the shortage of Republican women in the House at the time.\footnote{For example, in the 94-96th congresses (1975-1980), there were only five Republican women in the House.} Therefore, I cannot draw strong conclusions on GOP gender differences in the 1970s. However, in the 1980s, there is an increase of Republican women in the House, and with this comes significant gender differences in voting behavior. In support of hypotheses 1 and 3, I find the peak of Republican gender differences appears to be in the 101st-103rd congresses (1987-1994), in which Republican women voted more liberal than their male colleagues on several policy topics, including on women's issues. For the four major policy topics I operationalize as women's issues -- civil rights, health, education, and social welfare -- in the 101st-103rd congresses, Republican women are on average 0.46 more moderate than Republican men. 

However, as outlined in hypothesis 2, it is in the mid-1990s when Gingrich took over the speakership, that there is a drop in gender differences in the GOP. For example, in the 103rd Congress (1993-94), right before the Republican Revolution, Republican women legislators were significantly more liberal across 13 policy topics; however, in the following congress, when Gingrich was in charge, Republican women only voted significantly more liberal on three policy topics. Therefore, this shift was not restricted to women's issues but was across a wide variety of policy areas. Before the 104th Congress, Republican women were significantly more liberal than Republican men on the four major women's issues 42\% of the time, but in the 104th and beyond, women were only significantly different 24\% of the time. Therefore, it appears women's issues were not immune from these polarizing effects.   

Since the mid-1990s, Republican women in the House have become increasingly more in line ideologically with their male colleagues. This trend continued into the 2000s and 2010s, such that Republican women look nearly indistinguishable from GOP men. In the most recent congresses, while not statistically significant, Republican women appear to be moving more conservative than their male colleagues on some policy topics, including civil rights, labor, and defense. These results highlight a serious shift for Republican women in the House, such that in the 1980s and early 1990s Republican women were more liberal than their male co-partisans, but due to the asymmetric polarization of the GOP, Republican women have grown more conservative in their voting records on nearly all issues. 

\begin{figure}[H]
	\includegraphics[width=\textwidth]{houserepcoefficients1}
	\centering
	\textbf{\caption{House Republican women vs. Republican men (Policy Topics 1:8)} \label{houserepcoef1}}
	\begin{description}
		\item \noindent \footnotesize {}
	\end{description}
\end{figure} 

\begin{figure}[H]
	\includegraphics[width=\textwidth]{houserepcoefficients2}
	\centering
	\textbf{\caption{House Republican women vs. Republican men (Policy Topics 9:16)} \label{houserepcoef2}}
	\begin{description}
		\item \noindent \footnotesize {}
	\end{description}
\end{figure} 

\begin{figure}[H]
	\includegraphics[width=\textwidth]{houserepcoefficients3}
	\centering
	\textbf{\caption{House Republican women vs. Republican men (Policy Topics 17:22)} \label{houserepcoef3}}
	\begin{description}
		\singlespacing
		\item \noindent \footnotesize {Note: These figures plot the coefficient and 95\% confidence interval for the factored variable \textit{Republican women} as compared to \textit{Republican men}. Each coefficient is estimated from a separate OLS regression model. For ease of interpretation, all statistically significant estimates (p$<$0.05) are bolded. If the 95\% confidence interval crosses the dotted vertical line, the estimate is not statistically significant. If the 95\% confidence interval does not cross the dotted vertical line and is to the left of the line, this means for that policy topic, in that Congress, Republican women voted significantly more liberal than Republican men. Control variables included in all models are vote share, Black, Latino, South, seniority, majority party leadership, minority party leadership, committee chair, district percent urban, district median household income, and district percent Black.} 
	\end{description}
\end{figure} 
\doublespacing

\noindent These findings also support the notion that much of the change in the Republican Party came after Gingrich took over as Speaker and promoted a more unified conservative ideology in Congress.

Beyond looking at how House Republican women have changed over time, I am also interested in how gender differences may be greater on some policy topics than others. In support of hypothesis 3, I find Republican women are most often different from their male colleagues on my measure of women's issues, as well as civil rights. However, other policy topics that I had defined as women's issues, including education, health, and social welfare, do not appear to stand out as compared to other policy topics in highlighting Republican women voting significantly more liberal. Therefore, the stereotype that women are more liberal on these issues does not seem to hold up for Republican women. I find the fewest gender differences within voting on labor, finance, trade, and government operations, which makes sense as I would not expect gender identity to be a dominating influence in voting for these types of policies. 

Figures \ref{housedemcoef1}-\ref{housedemcoef3} highlight the role of gender in understanding voting within the Democratic Party. These results differ from findings on House Republicans both across time and by policy topic. For Democratic women, I find few significant differences in their voting behavior from Democratic men in the late 1970s and into the 1980s, whereas for Republicans, the 1980s were a significant period where GOP women were voting more liberal. However, it is in the mid-1990s, when gender differences are diminishing with the Republican Party, that there is a spike in gender differences within the Democratic Party. It is in the 103th Congress (1993-94), the first session after the 1992 ``Year of the Woman," when Democratic women appear to shift toward voting more liberal than Democratic men, in support of hypothesis 1. 

Past the mid-1990s, I find that gender differences within the Democratic Party persist into the 2000s and early 2010s. In the most recent years in this data, the voting behavior of Democratic women appears to have shifted a bit more in line with Democratic men, similar to how Republican women have, yet they remain significantly more liberal on several policy topics, including agriculture, energy, and science. Unsurprisingly, I find that one of the policy topics where Democratic women often vote more liberal than Democratic men is women's issues; however, I find that other policy topics where I expected Democratic women to vote more liberal on, including education, health, social welfare, and civil rights, do not stand out as compared to other policy topics as votes where women tend to differ significantly from their male colleagues. Surprisingly, I find Democratic women often vote more liberal on agriculture and energy bills. Across all congresses, the policy topics that demonstrate the fewest gender differences for Democrats are labor and housing. 


\begin{figure}[H]
	\includegraphics[width=\textwidth]{housedemcoefficients1}
	\centering
	\textbf{\caption{House Democratic women vs. Democratic men (Policy Topics 1:8)} \label{housedemcoef1}}
	\begin{description}
		\item \noindent \footnotesize {}
	\end{description}
\end{figure} 

\begin{figure}[H]
	\includegraphics[width=\textwidth]{housedemcoefficients2}
	\centering
	\textbf{\caption{House Democratic women vs. Democratic men (Policy Topics 9:16)} \label{housedemcoef2}}
	\begin{description}
		\item \noindent \footnotesize {}
	\end{description}
\end{figure} 

\begin{figure}[H]
	\includegraphics[width=\textwidth]{housedemcoefficients3}
	\centering
	\textbf{\caption{House Democratic women vs. Democratic men (Policy Topics 17:22)} \label{housedemcoef3}}
	\begin{description}
		\singlespacing
		\item \noindent \footnotesize {Note: These figures plot the coefficient and 95\% confidence interval for the factored variable \textit{Democratic women} as compared to \textit{Democratic men}. Each coefficient is estimated from a separate OLS regression model. For ease of interpretation, all statistically significant estimates (p$<$0.05) are bolded. If the 95\% confidence interval crosses the dotted vertical line, the estimate is not statistically significant. If the 95\% confidence interval does not cross the dotted vertical line and is to the left of the line, this means for that policy topic, in that Congress, Democratic women voted significantly more liberal than Democratic men. Control variables included in all models are vote share, Black, Latino, South, seniority, majority party leadership, minority party leadership, committee chair, district percent urban, district median household income, and district percent Black.} 
	\end{description}
\end{figure} 
\doublespacing 

Overall, the results for House Democrats highlight that while polarization has clearly dampened gender differences within the GOP, such that Republican women have grown more conservative over time, gender identity continues to play an important role in understand the voting behavior of Democrats. Democratic women remain slightly more polarized than their male co-partisans. Additionally, for Republicans, I find most often gender differences are concentrated to women's issues, but for Democrats, gender differences appear to cover a wider variety of policy topics. 

\subsection{Explanation: Is it Replacement?}
In my theory, I outline two potential factors at play that help explain how gender differences in Congress have changed over time. First, I expect more ideologically extreme women have replaced moderate women retirees. Second, I theorize the changing political agenda has shifted toward roll call votes that highlight partisan divides rather than gender differences. 

To better understand why gender differences changed so dramatically within the Republican Party between the 103rd and 104th congresses, I look more closely at the data to explain this shift. First, from the 103rd to the 104th congresses, there were only two GOP women who either retired or ran for higher office. Swept in as part of the 1994 Republican Revolution were seven new Republican women in the House, who appear to offer the best explanation for this shift. Using a t-test to examine differences in voting behavior in the 104th Congress between returning Republican women and those newly elected, I find a significant difference between the two on all votes (p$<$0.05). In the 104th Congress, the average ideology score on all votes for Republican women who had previously served in the House was 0.53, and the average for newly elected GOP women was 1.02, meaning the new women were much more conservative than previous women who had served. This seems consistent with anecdotal stories of the women who won in 1994. For example, Andrea Seastrand of California was first elected in 1994 and was dubbed a ``Newt clone" who was publically supportive of the goals of the Contract with America \cite{drummond1996gingrich}. Her opponent in her 1996 reelection campaign even attacked her as a legislator who could not think independently from Gingrich. This explanation also fits with research which finds since the 1990s, moderate Republican women are significantly less likely to run for Congress as compared to more ideologically extreme GOP women \cite{thomsen2015so}. 

\singlespacing
\begin{figure}[H]
	\includegraphics[width=12cm]{NewOldRetiringRepWomen}
	\centering
	\textbf{\caption{Average Ideology of New, Retiring, and Returning Republican Women} \label{newoldretiringrepwomen}}
	\begin{description}
		\item \noindent \footnotesize {This figure plots the average ideology on all votes by Congress for freshman Republican women, returning Republican women, and Republican women leaving at the end of the congress due to retiring, losing reelection, or running for higher office.}
	\end{description}
\end{figure} 
\doublespacing

In Figure \ref{newoldretiringrepwomen}, I plot the average ideology score on all votes for Republican women in each Congress. This figure starts in the 103rd Congress, when gender differences appear to start shifting. Looking at Republican women in each congress, I plot the average ideology score for freshman, non-freshman, and those serving their last term in the House, due to either retirement, losing reelection, or running for higher office. I find that overall, across the 1990s and 2000s, freshman Republican women tended to be more conservative than Republican women who had previously served. In 7 out of the 12 congresses plotted, freshman Republican women are more conservative than both returning and retiring GOP women. Thus, Figure \ref{newoldretiringrepwomen} seems to offer some evidence that the replacement of more moderate Republican women with more conservative Republican women is part of the story in understanding why this polarization of Republican women in the House has occurred.  

To examine how replacement might have played a role in the Democratic Party, I look to changes around 1992. This year was record-breaking for the number of Democratic women who ran for and won seats in Congress. Many of the women who ran in 1992 said they were inspired to run because of the saliency of women's issues at the time, in part due to Anita Hill's testimony before an all-male congressional committee bringing forward allegations of sexual harassment against Supreme Court nominee Clarence Thomas. Women's issues were also at the political forefront due to several Supreme Court decisions on abortion access, political debate surrounding the adoption of parental leave policies, and an election that emphasized traditional women's issues including education and health care \cite{dolan1998voting}. 

Looking at historical accounts of the 103rd Congress and trends in my data, it appears, the momentum for women in politics from the 1992 election carried into the 103rd Congress, resulting in an increase of women's issues legislation being considered and women of both parties working together to pass progressive women's issue bills \cite{dolan1998support}. Between the 102nd and 103rd congresses, Democratic women did not shift to voting more liberally than Democratic men on all policy topics, but rather, this shift was concentrated around women's issues, including civil rights, health, education, and my own defined subset of women's issues. For these four policy topics, Democratic women did not vote significantly different than Democratic men in the 102nd Congress but did vote significantly more liberal than Democratic men in the 103rd Congress, after the ``Year of the Woman."

There are a couple possible explanations for why the 103rd Congress stands out as a critical deviation in how Democratic women voted. First, it could be that Democratic women who served in the 102nd House and chose to retire were more moderate than the women who chose to run for reelection. Between the 102nd and 103rd congresses, there were five women who decided to either retire or run for higher office, and I do find that these five women tended to overall be more moderate than those who decided to stay in the House, especially on women's issues. For example, for my definition of women's issues, the average ideology score for the retiring women in the 102nd Congress was -0.42 while the average for women who did not retire was -0.85. Therefore, I find some evidence that retirements played a difference in understanding why the 103rd Congress marked a shifted in Democratic women's voting behavior. A second explanation could be that the women elected in the ``Year of the Woman" were more liberal than women who had previously served. There were 20 new Democratic women in the 103rd Congress. Looking at ideology scores differences, particularly on women's issues, between Democratic incumbent women and freshman women in the 103rd Congress, I find these new women were not substantially more liberal than incumbent women; however, the new women were more liberal than the women who had retired.\footnote{A third possible explanation for understanding the 103rd Congress is perhaps the women who served in both the 102nd and 103rd congresses shifted their attitudes and started voting more liberally on women's issues. Comparing ideology scores between the two congresses, I do not find much support for this potential explanation, as individual lawmakers tend to be consistent in how they vote across time \cite{poole2007changing}.}

\singlespacing
\begin{figure}[H]
	\includegraphics[width=12cm]{NewOldRetiringDemWomen}
	\centering
	\textbf{\caption{Average Ideology of New, Retiring, and Returning Democratic Women} \label{newoldretiringdemwomen}}
	\begin{description}
		\item \noindent \footnotesize {This figure plots the average ideology on all votes by Congress for freshman Democratic women, returning Democratic women, and Democratic women leaving at the end of the congress due to retiring, losing reelection, or running for higher office.}
	\end{description}
\end{figure} 
\doublespacing 

In Figure \ref{newoldretiringdemwomen}, I plot the average ideology on all votes for freshman, non-freshman, and retiring Democratic women in each congress. I do not find much evidence that new Democratic women are more liberal than non-freshman Democratic women. However, I do find that overall Democratic women who retire tend to be more moderate. In seven of the 12 plotted congresses, retiring Democratic women are more moderate than both freshman and non-freshman Democratic women. The 109th Congress is a bit misleading to this trend as only one Democratic women retired that year. The 111th Congress was a big year for retirements with 15 Democratic women retiring, who on average were much more moderate than Democratic women who stayed in the chamber. Overall, this seems to suggest that the replacement of retiring moderate women with more ideologically extreme women played a role in both parties in understanding why gender differences have changed over time. 

\subsection{Explanation: Is it a Changing Agenda?}
For both parties, I find some evidence that driving the diminishing effect of gender on roll call voting is the replacement of more moderate retiring women with more ideologically extreme women on both sides. However, I also expect a changing political agenda may have contributed to the dwindling influence of gender. In the early 1990s, both Republican and Democratic women voted more liberal than their male co-partisans on women's issues, but in the 2010s, gender no longer predicts voting behavior on women's issues. Therefore, in addition to how the composition of the women who serve has changed, I examine how the political agenda around women's issues has shifted. 

In looking at Democratic women changes in the 1990s, I find women became more liberal from the 102nd to the 103rd Congress, especially on women's issues. This may be in part due to a changing political agenda. The 1992 election highlighted women's issues, and this political focus translated into women's issues capturing a larger portion of the legislation agenda in the 103rd Congress. For example, in the 102nd Congress, there were a combine 64 votes on civil rights, health, and education bills, but in the 103rd Congress there were nearly double that with 115 votes on these women's issue policy topics. The 103rd Congress saw the passage of major women's issue legislation, including the Family and Medical Leave Act and amendments that expanded access to breast and cervical cancer preventative care. Looking across parties, both Republican and Democratic women in the 103rd Congress voted more liberal than their male co-partisans on women's issues, highlighting some bipartisan efforts between women to prioritize and pass significant women's issue legislation. 

Looking at how women's issues have changed over time, I compare my subtopic coded women's issues in the 103rd Congress to those in the 114th Congress and find some substantive differences, especially related to the issue of abortion. In the 103rd Congress, lawmakers voted on 34 women's issue bills of which only 6\% were explicitly about abortion. However, fast-forward to the 114th Congress and legislators voted on 22 women's issue bills, of which 50\% were related to abortion. This included a bill banning federal funding for abortion and legislation stopping funding to Planned Parenthood. This is an important shift that demonstrates an overall trend in the changing political agenda. Voting on women's issues in the 103rd Congress included bills on family leave and strengthening protections for victims of domestic violence, which are issues where one might expect more bipartisan support from women lawmakers. However, abortion is one of the most divisive partisan issues in today's politics \cite{wolbrecht2010}. An agenda that has shifted toward issues that are more partisan and away from those more likely to highlight gender differences means roll call votes are less likely to demonstrate any notable differences between men and women of the same political party. Therefore, I find some evidence that two factors are both at play in explaining my findings. The effect of gender on voting has changed over time in part due to moderate women being replaced by more ideologically extreme women and a changing political agenda that highlights partisan differences as the expense of gender differences. 

\section{Discussion}
Using more than 22,000 congressional roll call votes, this project provides new insight into how women in Congress differ from their men colleagues. By addressing the shortcomings of past methodological approaches, this paper provides insight into how gender differences in Congress vary across party, issue, and time. I find support for my expectation that while women in Congress in decades prior used to vote more liberal than their male co-partisans, this trend has diminished over time due to polarization. Republican women more strongly demonstrate this trend, as asymmetric polarization of the GOP has increasingly encouraged more homogenous voting among congressional Republicans, leaving little room for gender differences to be observed in voting behavior. For Democratic women in the House, they appear to have polarized more than Democratic men. Even in the most recent congresses, Democratic women continue to vote significantly more liberal than their male colleagues. Additionally, I find support that women's voting records across time are most different than their men co-partisans when it comes to legislation addressing women's issues. 

Past research on women in Congress has often only used interest group scores, which look specifically at women's issues, or use DW-NOMINATE, which is an aggregate measure of all votes. By scaling members by policy topic, this project provides new insight into how some policy topics, such as civil rights, tend to highlight policy preference differences between men and women, while other policy issues, such as labor, are not significantly influenced by a legislator's gender identity. This project highlights that relying on established congressional measurements may mask some of the variance in behavior and the importance of adopting measurements that best address the research question at hand. In future research, acknowledging that legislators are often not ideologically consistent and using scaling methods that allow for a more nuanced understanding of legislators' behavior can better help us address policy specific research questions. This type of ideology measurement can be used to understand how members of Congress differ by policy topic from their colleagues including African Americans, Latinx, younger generations, parents, and other identities expected to impact a legislators' perspective and policy preferences. 

Furthermore, this line of research is important because women are increasingly running for and winning seats in Congress, and this project provides insight into what difference having more women in politics can have on the policymaking process. Popular media accounts, especially in the wake of the 2018 elections and the record number of women elected to Congress, highlighted the differences women make as they are increasingly present in politics. While women likely play an important role in elevating women's issues onto the legislative agenda through sponsorships and speaking on these issues, this project highlights how polarization and party control have limited the number of opportunities for women to differ from their men colleagues, especially when it comes to the final stages of the policymaking process. 

This project also pushed back against the stereotype that Republican women are more liberal than Republican men. Past research finds that voters often stereotype Republican women as more liberal, which is an electoral obstacle for Republican women, especially in primaries \cite{king2003sex,mcdermott1998race}. While Republican women in the general public may be more liberal than Republican men, I find the same is not true for Republican women who win seats in Congress. This stereotype of Republican women being more liberal does not actually hold up in recent congresses, as the voting records of GOP women that seek and win seats in Congress are not significantly different than their male colleagues. This also signifies that the types of Republican women in Congress are not representative of Republican women in general, highlighting the current electoral challenges for more moderate GOP women seeking political office. 

A limitation of this project is that its scope is restricted to only the House of Representatives. Future research on gender and Congress should examine if similar trends exist in the Senate. The U.S. Senate has certainly polarized; however, because the power of party leadership to control the agenda is different than the House, there may be some interesting differences between chambers. However, I acknowledge the challenges of replicating this study in the Senate due to the small sample of women who have ever served in the Senate. 

The research question of this project was to examine gender differences across time, party, and policy issues. I offer potential explanations for why these gender effects exists and have diminished, but acknowledge this project is limited in providing a thorough exploration and test of these ideas. I encourage future research that examines how the congressional agenda has shifted and if the types of policies that are most likely to divide legislators along gender are increasingly less likely to receive a floor vote. 

Finally, we know district attributes and constituents influence members of Congress, and that some districts are more or less likely to elect women lawmakers. In my analysis, I account for some general measure of district demands; however, I recognize I cannot hold constant all differences between congressional districts. I acknowledge that perhaps women are getting elected from different types of districts than men and that this is what makes their voting behavior different, rather than their personal gender identity. I would suggest that because I find gender differences most prominent on women's issues, it does at least to some degree appear to be a gender effect, rather than just overall more liberal or conservative districts which women lawmakers represent. Additionally, the recent influx of women in Congress actually now allows for this type of analysis -- to examine if a male lawmaker replaced by a woman lawmaker representing the same district behavior similarly, or if, when holding district-level differences constant, gender differences persist. I encourage future research that utilizes the recent rise of women in Congress to further explore this line of research. 

\newpage
\section{Appendix}

\begin{figure}[h]
	\includegraphics[width=\textwidth]{NumberVotesHouse}
	\centering
	\textbf{\caption{House Number of Votes Used for Scaling} \label{numbervoteshouse}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure displays the distribution by policy topic of the number of votes in each session of the House of Representatives that were not unanimous, and were therefore, included in the Bayesian IRT process to produce legislators' ideal points by policy topic. The mean and median number of votes used to scale per congress is 56 and 37, respectively.}
	\end{description}
\end{figure}

\begin{figure}[h]
	\includegraphics[width=\textwidth]{housecorrelations}
	\centering
	\textbf{\caption{House Ideology Scores Correlations with DW-NOMINATE} \label{housecorrelations}}
	\begin{description}
		\item \noindent \footnotesize {Note: This figure presents a Pearson correlation matrix that compares DW-NOMINATE scores against my policy specific Bayesian IRT ideology scores. As expected, my ideology measure that uses all votes, by Congress, is highly correlated with DW-NOMINATE at 0.94. For specific policy topics, some correlate more highly with DW-NOMINATE than others. For example, economy and government operations correlate the most with DW-NOMINATE and foreign trade and agriculture correlate the least with DW-NOMINATE. Looking at just the 20 policy topics from the Policy Agendas Project, my ideology measures on average correlate with DW-NOMINATE at 0.86.}
	\end{description}
\end{figure}


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