Identity in Campaign Finance and Elections: The Impact of Gender and Race on Money Raised in 2010

Abstract

Disproportionate rates of congressional representation based on gender and race are especially stark considering the symbolic and substantive meaning derived from descriptive representation (Mansbridge 1999). Using an original data set consisting of candidate demographics, district characteristics, and campaign finance reports, we analyze an understudied barrier to representation: unequal access to campaign receipts. We argue that it is the simultaneous gendering and racialization of the campaign finance system that produces gaps in campaign fundraising and representation (Crenshaw 1989). Our results underscore the limitations of unitary approaches which conclude that women no longer face a disadvantage in campaign fundraising. Unequal access to campaign receipts serve as a barrier to the descriptive representation of women of color. By analyzing the interaction of both race and gender on campaign receipt totals in U.S. House elections from 2010 to 2018, we assert the path to representation is not equal for all.

Keywords

gender race campaign fundraising congressional elections

Introduction

A democracy’s strength can be evaluated on its representation of historically marginalized groups, yet in the United States, descriptive representation has historically been limited (Dovi 2002; Mansbridge 1999). Despite the numerous substantive and symbolic benefits derived from descriptive representation, women constitute merely 23.7 percent of the United States Congress (and are roughly 50% of the population) and even though people of color are 39 percent of the population, they account for just 22 percent of Congress in 2019 (Center for American Women and Politics 2019; Lowande, Ritchie, and Lauterbach 2019). Scholars have posited numerous theories for why these representational gaps exist, ranging from gaps in ambition (Fox, Lawless, and Feeley 2001; Lawless and Fox 2010) and psychological stereotyping (Huddy and Terkildsen 1993; Schneider and Bos 2014; Sigelman et al. 1995) to attitudes about proper roles in society (Arceneaux 2001) and other contextual factors (Citrin, Green, and Sears 1990).

While we understand the importance of evaluating the mechanism behind gender and racial gaps in representation, the extant work falls short in two critical ways. First, by focusing purely on evaluations of candidates, scholars risk missing subtle but important effects of candidate identity on other measures of candidate success (in our case, fundraising totals). Second, most studies maintain focus on a single axis of identity (usually race or gender), potentially missing how compounding forms of systematic and attitudinal discrimination operate within an already marginalized group.

We argue that unequal campaign funding between candidate demographic groups is one factor yet to be fully analyzed as a barrier to the descriptive representation of marginalized groups in Congress. While previous quantitative research has determined that access to campaign funding is no longer an explanation for women’s current lack of descriptive representation, these conclusions result from the prioritization of one identity over another (i.e., concentration on a candidate’s gender over race or race over gender), have only been conducted at the state level, or exclusively focused on one election cycle (Bryner and Haley 2019; Burrell 2014; Carroll and Fox 2018; Grumbach and Sahn 2020; Hogan 2007; Sojka 2014).

Understanding that systems of oppression intersect to shape experiences, and that people with multiple marginalized identities tend to be the most institutionally oppressed, we argue that the exploration of unequal access to campaign receipts as a barrier to descriptive representation in the U.S. House of Representatives must consider how candidates with multiple marginalized identities are positioned (Bryner and Haley 2019; Crenshaw 1989; Hancock 2004; Hawkesworth 2003; Strolovitch 2006).

Fundraising as a Barrier to Representation

One of the strongest indicators of congressional viability is a candidate’s access to campaign contributions. Campaign fundraising and spending provide candidates with the necessary tools and resources to carry out a wide range of campaign-related activities such as advertising, voter mobilization, and polling, which have a strong empirical relationship with electoral success (Bond et al. 2012; Freedman, Franz, and Goldstein 2004; Hall and Bonneau 2008; Jacobson 2015; Kam and Zechmeister 2013). In fact, Box-Steffensmeier, Darmofal, and Farrell (2009) and Ferguson, Jorgensen, and Chen (2016) found campaign spending to have an isolated and significant effect on voter support. Expenditures can help candidates from underrepresented groups challenge gender and/or racial stereotypes through obtaining access to campaign advertising, mobilizing low turnout-voters, and consequently increasing levels of name recognition and campaign framing opportunities (Albright 2014; Bullock, Gaddie, and Ferrington 2002; Fraga 2018; Lieske 1989).

Thus, to the extent that campaign money accrues to certain types of candidates, it can exaggerate or negate opportunities for descriptive representation.

Gender Influences on Campaign Fundraising

At first glance, however, campaign fundraising does not appear to be particularly problematic for candidates from marginalized groups. Burrell’s most recent study on U.S. House elections from 1993 to 2010 found that women raise equal or more money compared with men (Burrell 2014, 144, 145). If fundraising gaps no longer exist, then we should not be concerned about the ability of campaign fundraising to exacerbate gaps in representation. On the other hand, while previous quantitative research has shown that fundraising no longer serves as a barrier to women’s numbers in Congress, the results of existing work are still inconclusive once we take interactions between a candidate’s race and gender into consideration (Carroll and Fox 2018; Green 2003; Hogan 2007; Kitchens and Swers 2016; La Cour Dabelko and Herrnson 1997).

Women serving in state legislatures cite numerous reasons as to why they believe fundraising is harder for women than men. Their explanations range from men and women having distinctive social networks to there being gendered differences in women and men’s comfort in asking for money (Sanbonmatsu, Carroll, and Walsh 2009). Compared with men, women tend to gain a broader range of financial support because they generally raise a higher percent of their funds from individual donors and in smaller amounts (Crespin and Deitz 2010; Green 1998; Herrick 1996; Jenkins 2007; La Cour Dabelko and Herrnson 1997). In fact, it was the success of female Political Action Committees (PAC) organizations, such as EMILY’s List, which ameliorated women’s campaign finance burden by efficiently bundling individual donations for women running for office. While Crespin and Deitz (2010) point out that women’s tendency to rely on individual donors might suggest they have a broader network of support, it can also mean women are required to work harder than men to raise similar amounts of money (Jenkins 2007). Interestingly, these gender-based challenges do not seem to automatically translate into fundraising disparities, as Burrell (2014) finds that women actually perform well with both smaller and larger donations. However, surveys of donors and PACs indicate that even with the rise of female PAC organizations, they have failed to address the overall fundraising challenges faced by Republican and some Democratic women (Crowder-Meyer and Cooperman 2018; Kitchens and Swers 2016; Thomsen and Swers 2017). Only 52 percent of Democratic women and 25 percent of Republican women received support from female donor PACs. The Republican women who received these funds still raised less than Republican men. Consequently, gender-neutral fundraising outcomes should not be equated to the absence of a male-dominated fundraising process. Women and men may raise equal amounts of money, but women could be working harder per each dollar raised.

Racial Influences on Campaign Fundraising

Gender is also not the only axis of identity on which access to campaign money is potentially more difficult. Reports of women and men raising equal campaign money fail to consider how unequal access to campaign receipts may serve as a barrier to descriptive representation based on race. Johnson, Oppenheimer, and Selin (2012) concluded that difficulty in raising campaign funds was one of the major barriers preventing African American House members from advancing into the conventional political pipeline of running for a Senate seat. In a study of fifteen state legislatures in 2006, candidates of color raised 47 percent less money than white candidates (Albright 2014). Unequal access to campaign receipts, based on a candidate’s race, is often explained by the donor class being predominantly white and, therefore, not proportional to public demographics (Grumbach and Sahn 2020). Regardless of the cause, the racial funding disparities reported do not even consider how people of color may need to raise more money than whites to challenge racial stereotypes with campaign information and framing.

In sum, existing explanations as to how campaign fundraising operates as a barrier to descriptive representation are inconclusive. While previous quantitative research has shown that fundraising no longer acts as a barrier to women’s descriptive representation, the combination of differences in fundraising sources, partisanship, and perceptions/experiences with fundraising between men and women suggests fundraising is still gendered in ways that disadvantage women running for office. Most of all, existing research on campaign finance fails to provide a full picture of how money affects descriptive representation because it neglects to address how fundraising is both racialized and feminized in different electoral contexts. Previous studies focusing solely on gender or race ignore that fundraising disparities are likely to exist on racial lines between women.

Interacting Gender and Race on Campaign Receipts

A primary issue with prior scholarship on the role of identity in raising money for U.S. House elections is that it fails to engage with intersectionality. A unitary methodological approach focuses on one identity and assumes that institutional racism or sexism operate statically and can be separated. But as Crenshaw (1989) outlines, people at the margins of multiple marginalized identities tend to be the most institutionally oppressed. Concentration on a single identity of race or gender (a unitary approach) therefore privileges those without multiple marginalized identities, reinforcing the existing hierarchy.

Previous literature describing how money operates as a barrier to descriptive representation at the congressional level reproduces the problems described by Crenshaw (1989). It assumes that institutional and attitudinal sexism cuts neatly across racial lines, erasing the experiences of women of color.¹ Thus, the fact that women of color are forced to confront both gender and racial stereotypes when running for office indicates not only that money is more important for congressional viability when compared with other demographic groups, but also that women of color face a unique set of fundraising challenges compared with white women (Bullock, Gaddie and Ferrington 2002; Dittmar 2014; McDermott 1998; Sanbonmatsu and Dolan 2009). For instance, precinct level, experimental, and national exit poll data for congressional elections find that Black women receive the most electoral support from other Black women (Philpot and Walton 2007). Unlike white women and men of color, Black women do not gain noteworthy support from white men, white women, Black men, and Black women unless they have prior political experience. Black women need more experience and are often required to work much harder than their opponents just to achieve the same electoral outcomes (Carey and Lizotte 2019).

The limitation of unitary approaches in empirical studies is thus in misrepresenting the plight of those who face more than one form of structural and attitudinal discrimination. In fact, we also believe that women of color face greater fundraising barriers because social identity is often used as an ideological cue for donors. Although the tendency for Republicans and Democrats to view women as more liberal than men should work in Democratic women’s favor, liberalism is not the “brand” of the Democratic Party (Grossman and Hopkins 2016). We thus expect Democratic women of color to raise fewer contributions than white women. Likewise, we expect Republican women of color’s perceived liberalness to operate as a disadvantage in fundraising because it is not congruent with their party’s “conservative” brand (Francia 2001; Huddy and Terkildsen 1993; Koch 2002; McDermott 1997).

Second, women of color will have a harder time raising funds than other social groups because shared social group membership is a predictor of whether an individual will donate to a specific candidate (Thomsen and Swers 2017). If donors’ decision making depends upon social affinity, women of color candidates are consequently at a disadvantage. Campaign donors and party leaders are disproportionately white and male. Furthermore, the demographic makeup of party leaders and donors does not assist women of color throughout the campaign process (Hassell and Visalvanich 2019; Mayersohn 2015). With a donor pool that lacks shared social group membership and has higher average levels of sexism and racial resentment than people of color (Francia et al. 2003; Grossman and Hopkins 2016; Tesler 2012; Wilson and Davis 2018), women of color candidates are forced to prove themselves as viable candidates to donors to an extent that other candidates do not.

Donor demographics also take on a distinctly raced-gendered frame when they are considered in combination with their motivations. As Francia et al. (2003) describe, donors can be broadly described as groups of investors (funding candidates friendly to their business), ideologues (funding candidates who share their policy preferences), and intimates (funding candidates with whom they have some connection). According to Francia and colleagues, 47 percent of donors state that knowing the candidate is always important in their decision to donate. This finding, compounded by social affinity and raced-gendered candidate emergence, means it is likely that the predominantly white male donor class will have fewer personal connections with women of color candidates.

Most of all, the consequences of analyzing race or gender in separate models or controlling for one social identity are exemplified by other recent studies relating to intersectionality and elections (Bryner and Haley 2019; Hassell and Visalvanich 2019; Sojka 2014). These studies found additional party support primarily accruing to white women candidates and distinct state-level fundraising disadvantages for women of color. Taken together, this work suggests multiple power dynamics by which the intersection of race and gender may influence campaign fundraising and candidate success in U.S. House elections.

Based on prior evidence, candidates with multiple marginalized identities should raise less money than candidates with a singular marginalized identity or white men (Hypothesis 1). While evidence from the unitary approach is mixed, the empirical results from studies that consider both race and gender, coupled with theories of intersectionality, point toward fundraising disadvantages for women of color candidates.

Electoral Context as a Moderator

One additional complicating factor is the role that electoral context plays in the fundraising process. We believe that marginalized candidates likely face the greatest fundraising disparities in the least competitive and lowest profile races. As high-quality candidates frequently delay running as challengers, waiting instead for incumbent retirements, we see evidence that the high-profile nature of open seat races erases many predictors of fundraising success, including prior legislative experience (Berkman and Eisenstein 1999).

Despite recent findings that women no longer face a disadvantage in terms of total funds raised, historically women have not been advantaged in fundraising and even more recent studies conclude their financial success is sensitive to electoral contexts. Women run in friendlier congressional districts and have more political experience than male candidates (Fulton and Dhima 2020; Lawless and Pearson 2008; Pearson and McGhee 2013), yet even with better campaign strategies women must still overcome numerous fundraising and electoral related challenges, to raise money at the same rates as men.

The effects of money on candidate viability also vary with a candidate’s status as an incumbent, challenger, or open seat contestant (Abramowitz 1988; Jacobson 1990, 2013, 2015; Kenny and McBurnett 1994). While both challengers and incumbents benefit from the name recognition that comes with campaign funds, challengers benefit disproportionately more than incumbents. This is because incumbents spend more money when the competition is tougher (Jacobson 2006). Access to campaign funding is therefore especially important to challengers and open seat contestants because these candidates lack the incumbency advantage.

Branton et al. (2018) and Fraga and Hassell’s (2020) research exemplifies the importance of considering how district-level characteristics and seat type influence the strength of party support toward congressional candidates. Women candidates are perceived to be less qualified than men when running against a man and women received less support from parties in less competitive congressional elections. This was especially true for differentiating support toward white women candidates and candidates of color. As competitiveness increases, party support increases for several reasons, potentially washing out raced-gendered fundraising disparities. Thus, as both subjective and objective candidate quality shifts with changes in the electoral context, we expect that the strongest effects for a candidate’s racial and gender identity will be when party support is lowest (i.e., when the candidate is the incumbent or challenger).

We also explore a second hypothesis, grown out of work that shows the varied effects of money based on race type and district competitiveness. We expect that candidate characteristics like race and gender will have their strongest influence when party structures are not incentivized to help all candidates in competitive races. Thus, in open seat races, we follow prior research (Berkman and Eisenstein 1999) in expecting the electoral context effects to outweigh other considerations. Thus, the effects of a candidate’s racial and gender identity should be stronger when the candidate is a challenger or incumbent than when the seat has no incumbent (Hypothesis 2).

In addition to our two hypotheses, we explore two alternative research questions: how does the effect of campaign fundraising on vote share differ by candidate identity (Research Question 1) and is the interaction between candidate race and gender driven by partisan affiliation (Research Question 2)? We have no a priori reason to expect that money should prove more consequential for some candidates versus others. However, if campaign dollars help women of color more than white women, we would be less worried about any possible fundraising gaps. Conversely, if men or white women benefit more from fundraising than women of color (or if the correlation between money raised and vote totals does not vary by candidate identity) then intersectional fundraising differences exacerbate representational gaps.

Likewise, we have no a priori reason to expect that the interaction between race and gender on total contributions received is conditional on a candidate’s partisan affiliation. But on the off chance that certain cycles enable Democrats or Republicans to raise money more easily, we include partisan identification as a control variable in our models. Furthermore, although women’s PAC organizations may help Democratic women access the necessary campaign funds to remain competitive in their elections to a greater extent than Republican women, the impact of women’s PAC organization can still have a heterogenous effect based on gender-ethnorace. We therefore run separate models for Democrats and Republicans, recognizing how candidate emergence and support from women’s organizations can be different between Republican and Democratic women (Carroll and Fox 2018; Crespin and Deitz 2010; Kitchens and Swers 2016; Sanbonmatsu 2002). In sum, while we acknowledge Democratic and Republican women of color have different fundraising experiences, we still predict women of color will raise less money than white women in both parties.

Data and Method

We test these expectations using a data set built from publicly available data from the 2010–2018 U.S. Congressional elections.² The data consist of every major party candidate who had at least $2,000 in campaign receipts. The minimum threshold was based on similar previous research, and it establishes that the general U.S. House candidate was actively campaigning, as opposed to serving as a party placeholder (Burrell 2014, 121). However, the low monetary threshold amount was chosen to avoid eliminating a candidate with difficulty fundraising in general. This means candidates running unopposed are excluded from the analysis.

We also used Federal Election Commission (FEC) 24-month campaign summary tables from 2010 to 2018 to find the total campaign receipts, receipts from individuals, and receipts from PACs reported by electoral cycle. We sorted through the FEC’s list of primary and general House candidates for the 2010–2018 congressional election cycles to include the Democratic and Republican U.S. House candidates from general elections, as reported by CNN, Huffington Post, and Washington Post election results (CNN 2010, 2016; Huffington Post 2014, 2018; RealClearPolitics 2018; The Washington Post 2019). In cases where more than one candidate from a party appeared on the ballot (as is fairly common in Louisiana, for example), we include the Democrat and Republican who received the highest vote share in the general election. For each candidate, we collected the candidate’s gender and race (coded as white or non-white). In addition, we include several candidate-level control variables such as partisan identification. To account for ease of fundraising in subsequent cycles for incumbents, we include a seniority variable measuring the number of years served in office. For models that include incumbents, we include dummy variables for whether the individual is a member of House leadership or a committee chair, to account for greater access and visibility to donors. Finally, to account for candidate quality, we include a measure of the candidate’s final vote share and whether or not a candidate has held an elected office (CNN 2010, 2016; GovTrack n.d.; Huffington Post 2014, 2018; RealClearPolitics 2018; The Washington Post 2019).³

Besides candidate-level characteristics, we controlled for several district-level and race-level variables. Since competitive races will probably result in higher fundraising totals, we control for whether the seat is considered safe (according to the Rothenberg and Gonzales Political Report). We also expect that certain characteristics of the district, such as the average income or demographics, will affect fundraising totals. Therefore, we also control for whether the district is a majority/minority district, if the district is in the South, and the percent of women in the state legislature for the election cycles (Daily Kos, n.d.; Daily Kos, n.d.; National Conference of State Legislatures 2010, 2016). We also controlled for the percentage of the district’s population that is white non-Hispanic and the percentage that held a bachelor’s degree or higher using data from the American Community Survey 1-year estimates in 2010, 2015, and 2016. Finally, we account for money availability by including a measure of average household income in the district.

Using these candidate- and district-level variables, we used ordinary least squares regression to estimate the effects of race, gender, and the interaction of race and gender on fundraising totals and vote share. We cluster standard errors by candidate to account for auto-correlation over multiple cycles. We also include year dummies to account for different fundraising conditions based on presidential election cycles.

Fundraising Results

We begin with a test of Hypothesis 1, with results appearing in Table 1. As Table 1 shows, we find support for our first hypothesis as it relates to total campaign receipts and individual donations. While the direction of the interaction coefficient for PACs is correct, the effect fails to reach statistical significance. The main effect of race (which represents the effect of race for men candidates) fails to exert an effect on campaign donations, while the main effect of gender does positively affect donations (for white candidates), suggesting that white women raise more money than white men. What is interesting, however, is that the interaction of race and gender produces a negative and statistically significant effect on total receipts and individual contributions. Since our model uses indicator variable coding for both race and gender, the interaction term here represents the additive effect of a candidate being a woman of color, over and above the effects of race or gender alone. Thus, while gender exerts a positive, significant main effect, this is interpreted as the effect for white women. The effect of race and gender for a non-white woman is the additive combination of the gender coefficient and the gender by race interaction coefficient. It is interesting, therefore, that the size of the interaction coefficient is nearly twice the size as the gender coefficient and negatively signed.⁴

Table 1.

Effects of Race and Gender on Campaign Fundraising.

	Receipts	Individuals	PACs
Race (non-white)	68052.46 (111995.24)	104943.83 (96380.26)	13783.98 (23838.53)
Gender (female)	234129.81* (102906.30)	263376.67* (85620.95)	2694.41 (20196.49)
Race × Gender	−371834.15* (148767.18)	−318919.91* (124401.70)	−55641.82 (34646.47)
Republican	17796.77 (59927.90)	−51936.58 (45959.91)	24154.02 (16915.34)
Safe district	−1234360.31* (82841.87)	−808187.03* (57351.17)	−216424.73* (18714.96)
Open seat	442892.65* (76670.81)	217322.02* (53505.88)	142963.66* (13943.69)
Incumbent	882940.47* (106557.46)	338999.57* (80457.96)	688106.73* (28855.02)
District % college educated	1224080.69* (255207.56)	1137006.31* (180630.83)	−27833.63 (87687.02)
Household income	−2.67* (1.19)	−1.57 (0.95)	−0.75* (0.29)
District % non-white	4025.35 (2643.84)	2225.64 (2074.82)	2134.03* (725.37)
Majority-minority	−136912.89 (111793.81)	−120325.88 (81108.25)	24504.12 (32945.04)
State % women in legislature	9562.96 (5340.40)	8993.87* (4267.13)	1193.55 (1445.72)
South	−19509.37 (64506.63)	17251.01 (49305.45)	−33329.46 (18557.79)
Seniority	−19576.14* (3767.91)	−18152.66* (2471.37)	781.25 (1887.85)
Leadership	4070863.32* (945633.13)	1436569.71* (390122.01)	1191097.42* (248130.58)
Committee chair	407570.31* (151146.22)	188742.55 (101079.66)	201440.24* (61085.82)
Previous office	220129.12* (70850.80)	174479.18* (51335.88)	124714.45* (13124.66)
Vote total	1305194.59* (237303.11)	961749.66* (170863.07)	−65990.68 (72235.19)
Year (2012)	31897.15 (47401.81)	−12130.66 (34476.05)	40859.98* (11871.45)
Year (2014)	−7038.24 (50770.19)	−73315.70* (32813.66)	46596.56* (13930.51)
Year (2016)	−133815.56* (56733.76)	−174664.63* (37810.54)	38076.43* (14920.55)
Year (2018)	451111.85* (69041.76)	274583.71* (54120.17)	94262.04* (15817.34)
Constant	990169.61* (323439.21)	493284.82* (243949.06)	255567.76* (99199.74)
N	3730	3730	3730
R ²	.310	.214	.561

Standard errors clustered by candidate in parentheses. PAC = Political Action Committees.

p < .05.

We can calculate the predicted amount of money raised for candidates with various characteristics. If we look at a non-southern, Republican challenger for a safe seat with prior elected experience, with all other controls at their means, we can vary the race and gender of this hypothetical candidate. As will become obvious, white women are consistently the most successful fundraisers. In this scenario, a white woman with these characteristics is predicted to raise approximate $1.48 million for her electoral campaign. A woman of color is expected to raise only $1.17 million, a difference of $310,000, or a reduction of over 20 percent from the fundraising total of the white woman with equivalent candidate and district qualities. By way of comparison, a white male candidate is predicted to raise $1.24 million and a male candidate of color is predicted to raise $1.31 million. This underscores the interactive effects of race and gender in driving down fundraising totals.

As expected, candidate-level control variables such as leadership membership, seat status, committee chairpersonship, and previous officeholding also positively contribute to fundraising totals, as do district-level variables such as the percentage of the district who hold a college degree, average household income, and percentage of women in the state legislature. The effects of the year fixed effects are variable and inconsistently signed.

While the evidence is fairly clear for our first hypothesis, we turn next to our examination of Hypothesis 2. Table 2 presents the same analysis from Table 1 subdivided into challengers, open seat candidates, and incumbents. Here, we find mixed evidence to support Hypothesis 2. Looking at challengers, we see a consistent pattern, with the main effect of gender exerting a positive effect on individual contributions (again, for white candidates), but the interaction of race and gender producing lower fundraising totals for individual contributions (among women of color candidates). Once again, the interaction coefficients should be interpreted in conjunction with the gender coefficient. Similarly, the same pattern emerges for open seats, but the effect fails to reach conventional levels of statistical significance. For incumbents, the pattern holds, and the interaction is significant for both total receipts and individual contributions.

Table 2.

Effects of Race and Gender on Campaign Fundraising, by Race Type.

	Challenger			Open Seat			Incumbent
	Receipts	Individuals	PACs	Receipts	Individuals	PACs	Receipts	Individuals	PACs
Race (non-white)	68867.34 (97728.95)	125615.69 (83368.45)	25610.05* (12810.71)	−203991.31 (212114.14)	−101436.57 (159772.69)	−5738.76 (27717.20)	348178.20 (215780.02)	291329.42 (186503.98)	39852.72 (57085.18)
Gender (female)	136675.21 (76388.64)	175068.17* (59874.86)	13102.43 (8886.29)	70038.17 (170682.20)	104825.96 (121664.39)	40843.90 (23916.68)	319497.05 (219466.93)	338068.42 (187765.17)	−12455.70 (46409.04)
Race × Gender	−285446.79 (156230.55)	−290769.33* (132910.10)	−26990.78 (21312.96)	−228444.22 (299399.01)	−56507.09 (222225.49)	−79952.53 (42591.44)	−552604.32* (255884.09)	−438901.56* (219194.72)	−126712.73 (67354.83)
Republican	−219943.29* (71348.26)	−229345.17* (52145.41)	−31698.72* (10307.39)	−334287.89* (123234.04)	−282104.33* (81929.69)	−30006.84 (16783.27)	215787.24 (116586.64)	124696.30 (92580.66)	15814.33 (41080.86)
Safe district	−1106782.54* (127930.30)	−767793.33* (91426.91)	−78414.83* (13723.37)	−1035909.49* (189760.56)	−749960.69* (119046.33)	−146132.29* (22062.76)	−720936.56* (117738.51)	−381004.82* (82309.44)	−239031.03* (39497.73)
District % college educated	963097.24* (287965.69)	859603.86* (244062.79)	−21823.20 (42997.37)	3118936.96* (680337.88)	2237476.36* (417701.83)	126247.07 (95935.50)	1172173.84* (398503.13)	1254497.27* (265226.49)	−45965.71 (177807.76)
Household income	2.82* (1.11)	2.53* (0.86)	0.40* (0.15)	−3.82 (2.16)	−2.42 (1.84)	−0.39 (0.29)	−1.45 (2.03)	−0.91 (1.54)	−0.85 (0.60)
District % non-white	−5824.56* (2649.84)	−3084.05 (1966.23)	−1188.09* (400.35)	5181.83 (4409.58)	1937.21 (3259.57)	1054.06 (811.56)	3753.14 (4716.67)	820.90 (3751.85)	3697.48* (1549.24)
Majority-minority	53091.66 (130678.43)	97248.32 (99243.62)	6135.72 (15009.79)	32736.16 (269008.32)	−94519.74 (189128.92)	17362.39 (44977.61)	−88994.74 (178610.44)	−143911.64 (131029.72)	94017.65 (62884.80)
State % women in legislature	−4192.18 (5497.28)	851.19 (3982.63)	−711.16 (613.57)	14614.33 (10318.39)	14399.10* (7047.46)	2231.98 (1510.82)	10640.22 (9457.74)	8680.44 (7824.66)	461.44 (3005.71)
South	−226460.72* (78282.42)	−144494.99* (59338.48)	−40655.46* (10131.55)	56097.70 (145438.14)	5303.94 (89518.33)	−40256.58* (18778.27)	−72485.30 (105942.34)	7550.37 (79680.89)	−64390.14 (40959.28)
Previous office	100846.07 (78292.29)	107296.28 (61499.61)	63626.51* (11641.15)	−279274.81* (131213.96)	−151504.07 (87427.66)	65743.91* (17585.16)	—	—	—
Vote total	5316160.65* (395054.26)	3622988.95* (333454.03)	913884.34* (75212.06)	3777054.62* (389503.48)	2239965.14* (270445.69)	718050.87* (59419.71)	−2775714.81* (477471.84)	−1809920.21* (341760.01)	−765203.43* (176073.18)
Year (2012)	−94007.19 (63454.72)	−106853.50* (47082.15)	13160.43 (9336.71)	−97863.23 (124235.47)	−109631.20 (91282.26)	−16671.00 (22914.69)	143644.39 (81037.81)	58262.36 (62408.23)	76005.25* (21847.05)
Year (2014)	−164861.78* (70994.74)	−150741.93* (46253.67)	−10954.96 (9217.37)	49040.44 (154266.07)	−15002.07 (110272.05)	14372.37 (28977.38)	152759.99* (72686.83)	−13885.88 (47519.91)	119869.66* (25039.93)
Year (2016)	−408403.99* (72351.41)	−304063.31* (53965.40)	−15392.40 (10208.72)	51822.33 (182990.27)	−127888.81 (108716.57)	−45482.10 (26376.10)	84508.23 (80465.89)	−70376.31 (55172.58)	113187.97* (27352.16)
Year (2018)	510887.03* (98512.37)	442741.35* (77609.08)	11308.54 (9504.29)	295194.85 (156858.58)	143383.13 (118707.11)	−60758.72* (24067.52)	412400.75* (90728.03)	133095.81 (76370.07)	199334.99* (28968.09)
Seniority	—	—	—	—	—	—	−12851.06* (4279.68)	−13436.94* (2470.19)	1688.71 (2218.40)
Leadership	—	—	—	—	—	—	4411953.02* (990702.91)	1613678.00* (409972.91)	1218714.92* (254847.79)
Committee chair	—	—	—	—	—	—	444160.35* (145653.92)	208679.93* (96338.49)	210805.10* (60659.68)
Constant	−768550.31* (376028.64)	−874396.21* (294676.27)	−188364.06* (50239.27)	66925.21 (742783.70)	185587.92 (598574.14)	−11128.36 (109053.51)	3375331.89* (540816.68)	1915279.03* (375095.26)	1288484.61* (215806.45)
N	1493	1493	1493	486	486	486	1751	1751	1751
R ²	.418	.401	.479	.303	.322	.418	.255	.154	.241

Standard errors clustered by candidate in parentheses. PAC = Political Action Committees.

p < .05.

Under our hypothesis, we find no evidence that race, gender, or the interaction of race and gender matter for open seat races, which are frequently competitive and high-profile. The interaction of race and gender is never significant in these models, nor are the main effects of race or gender, suggesting that some other force may be driving donations. One possibility is that the competitiveness of the district, which is captured by the safe district variable, exerts an overwhelming influence on fundraising. We see in the open seat models that the coefficient for safe districts is much larger than it is for either incumbents or challengers. Our evidence cannot offer more insight into why open seat races show a reduced importance for candidate identity, but we note that the competitiveness and profile of these races likely play a role in this effect.

Turning our focus to the challenger and incumbent models, we can again construct predictions about fundraising totals. Here, we set all control variables at the mode or median for all challengers or incumbents.⁵ Rather than present individual predictions, we graph the differences between candidates. These appear in Figure 1. As Figure 1 shows, women candidates of color consistently raise less money than white women with the same district and candidate characteristics.

When looking at individual contributions to challengers, we see that a woman of color is predicted to raise just under $800,000 in individual contributions, while a similarly positioned white women is projected to raise nearly $950,000 and a man of color is expected to raise nearly $900,000. Interestingly, white male candidates are projected to raise approximately the same amount as women of color candidates ($770,000 versus $780,000), suggesting that the systems in place to boost women and minority candidates may be working for those on single axes of marginalization, but not for those with intersecting axes. For a challenger seeking national office for the first time, a difference of over $150,000 can be important for running a successful campaign.⁶

A similar pattern obtains for incumbents. When looking at individual contributions, a white woman incumbent could be expected to raise around $1.4 million, while a woman of color incumbent is predicted to raise only $1.25 million. Meanwhile, a male candidate of color would raise an average of $1.35 million, while white men raise the least money ($1.06 million). While the relative difference is smaller, this is still a consequential effect for candidates. Similarly stark differences emerge for total campaign receipts, as a white woman incumbent can expect to raise around $2.3 million, a man of color can expect around $2.35 million, but a woman of color incumbent will only raise $2.1 million. Once again, white men raise the least money, at around $2 million. The pattern and size of the effects is remarkably consistent regardless of how we measure fundraising success. Women of color are systematically disadvantaged by the campaign finance system compared with white women and male candidates of color. It is of interest that white men consistently raise equivalent or smaller amounts of money to women of color, though further investigation beyond the scope of this article is necessary to understand the nature of this dynamic.

Vote Total Results

It is possible, however, that women of color get more “bang for the buck” when it comes to fundraising. That is, money could exert a stronger positive effect for women of color than for other candidates. Of course, the reverse could also be true (and fits more closely with what we know of the disadvantages faced by women of color) and it could take more money to win votes for women of color than for other candidates. Regardless, we can test the effects of race and gender on the effects of fundraising by interacting race, gender, and fundraising and examining its effect on vote total. To test for the varied effects of fundraising totals on vote share, we ran models examining the interaction of race, gender, and total money raised on vote share. These appear in the first three columns of Table 3. Note here we use a logged calculation for fundraising totals, although the results are substantively similar when we use the non-logged independent variable.

Table 3.

Effect of Money Raised on Vote Percentages, by Race/Gender or Race Status.

	Receipts	Individuals	PACs	Receipts	Individuals	PACs
Money raised (logged)	0.02* (0.00)	0.02* (0.00)	0.01* (0.00)	0.02* (0.00)	0.02* (0.00)	0.01* (0.00)
Race (non-white)	−0.02 (0.04)	0.01 (0.03)	−0.03 (0.02)	0.04* (0.01)	0.04* (0.01)	0.03* (0.01)
Race × Money Raised	0.00 (0.00)	0.00 (0.00)	0.01* (0.00)	—	—	—
Gender (female)	−0.05 (0.03)	−0.01 (0.03)	−0.01 (0.01)	0.00 (0.01)	0.00 (0.01)	0.00 (0.01)
Gender × Money Raised	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	—	—	—
Race × Gender	−0.10 (0.08)	−0.04 (0.06)	−0.02 (0.03)	—	—	—
Race × Gender × Money Raised	0.01 (0.01)	0.01 (0.00)	0.00 (0.00)	—	—	—
Open seat	0.07* (0.01)	0.08* (0.01)	0.06* (0.01)	−0.14* (0.06)	−0.14* (0.05)	−0.03 (0.02)
Incumbent	0.20* (0.01)	0.21* (0.01)	0.17* (0.01)	0.57* (0.16)	0.56* (0.13)	0.28* (0.06)
Open Seat × Money Raised	—	—	—	0.02* (0.00)	0.02* (0.00)	0.01* (0.00)
Incumbent × Money Raised	—	—	—	−0.03* (0.01)	−0.03* (0.01)	−0.01 (0.00)
Partisanship (Republican)	−0.00 (0.00)	−0.00 (0.00)	0.01 (0.00)	−0.00 (0.00)	0.00 (0.00)	0.00 (0.00)
Safe district	0.04* (0.00)	0.03* (0.00)	0.04* (0.00)	0.03* (0.00)	0.03* (0.00)	0.04* (0.00)
District % college educated	−0.03 (0.02)	−0.03 (0.02)	0.01 (0.02)	−0.03 (0.02)	−0.03 (0.02)	0.00 (0.02)
Household income	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)
District % non-white	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00* (0.00)
Majority-minority	0.00 (0.01)	0.00 (0.01)	0.00 (0.01)	−0.00 (0.01)	−0.00 (0.01)	−0.00 (0.01)
State % women in legislature	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)	−0.00 (0.00)
South	0.00 (0.01)	0.00 (0.01)	0.01 (0.01)	0.00 (0.01)	0.00 (0.01)	0.01 (0.01)
Seniority	0.00* (0.00)	0.00* (0.00)	0.00* (0.00)	0.00* (0.00)	0.00* (0.00)	0.00* (0.00)
Leadership	0.03 (0.03)	0.04 (0.03)	0.04 (0.03)	0.06* (0.03)	0.07* (0.03)	0.05 (0.03)
Committee chair	−0.01 (0.01)	−0.01 (0.01)	−0.01 (0.01)	−0.00 (0.01)	−0.00 (0.01)	−0.00 (0.01)
Previous office	0.06* (0.01)	0.06* (0.01)	0.04* (0.01)	0.05* (0.01)	0.05* (0.01)	0.04* (0.01)
Year (2012)	0.02* (0.00)	0.01* (0.00)	0.00 (0.00)	0.02* (0.00)	0.01* (0.00)	0.00 (0.00)
Year (2014)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)
Year (2016)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)	0.02* (0.00)
Year (2018)	0.01* (0.00)	0.01* (0.00)	0.01* (0.00)	0.01* (0.00)	0.01* (0.00)	0.02* (0.00)
Constant	0.09* (0.03)	0.14* (0.03)	0.22* (0.03)	0.07* (0.03)	0.13* (0.03)	0.22* (0.03)
N	3730	3730	3730	3730	3730	3730
R ²	.675	.666	.688	.685	.681	.689

Standard errors clustered by candidate in parentheses. PAC = Political Action Committees.

p < .05.

As Table 3 shows, while the effect of money raised is positive and significant for total receipts, individual receipts, and PAC contributions, the effect on vote share is not contingent on race or gender (as evidenced by the lack of significant interactions, either the two-way interactions between fundraising and race/gender or the three-way interaction between race, gender, and fundraising). The last three columns of Table 3 show that, in our data set, the effect of money on vote total is instead contingent on the race status (incumbent, open seat, or challenger). When we look at the interaction of fundraising totals with seat status, we see that money has the strongest influence in open seat races, followed by challengers. In incumbent races, the effect of money raised actually reverses and exerts a negative effect on vote totals, which accords with prior literature to suggest that vulnerable incumbents need to raise more money, which creates a negative correlation between fundraising and vote share. Ultimately, these results show that the effect of money does not vary by race and gender.

What we can do, however, is use the projected fundraising totals to evaluate the effect of race and gender on eventual electoral success. Based on our previous estimates, a woman of color challenger is predicted to raise approximately $412,000 in individual contributions, compared with a white woman challenger expected to raise $570,000 in individual contributions. Based on the model in column two of Table 3, this projects to a predicted vote share of 63.1 percent. If this same woman of color challenger were to be on a level playing field with a white woman challenger, her predicted vote share would increase to 63.9 percent. While the difference is by no means massive, eight-tenths of a percentage point difference in vote share can be extraordinarily consequential for candidates.⁷ In 2018, for example, six congressional races were decided by less than half a percentage point (GA-7, MN-1, NY-27, PA-15, TX-23, UT-4), as was the U.S. Senate race in Florida.

Partisanship, Gender, and Ethnorace

Finally, we recognize potential concerns about model specification, especially around partisanship and district heterogeneity. While we include a control for the party of the candidate, we recognize these models might be capturing effects primarily for one party. To alleviate these concerns, we re-ran our analysis from Table 1, subdivided by partisanship. For space reasons, we present the full models in the online appendix in Table A4. We describe the results below.

While not all of the interactions achieve statistical significance, the race by gender interaction is significant for PAC fundraising for Republicans and individual contributions for Democrats, while total receipts for Democrats is marginally significant. In the other Republican models, the coefficient is correctly signed but insignificant. To illustrate the partisan differences, we constructed predicted probabilities and find that Republican women of color raise approximately $10,000 less from PACs than white Republican women. By contrast, Democratic women of color raise only about $2,000 less than white Democratic women. Similarly, across total campaign contributions, Republican women of color are outraised by white Republican women by about $10,000, while the difference grows to nearly $40,000 for Democratic women.

It is quite possible the race by gender interaction is only significant for PAC fundraising in the Republican model because women’s PAC organizations have benefited Democratic women more than Republican women (Crespin and Deitz 2010). Given that most influential women’s PAC organizations have a liberal ideological leaning, Republican women of color have fewer opportunities than Democratic women of color to gain support from women’s PAC organizations. However, with that being said, it is still evident that the support from women’s PAC organizations has not been extensive enough for even Democratic women of color to overcome total and individual level fundraising disparities in House elections. We should therefore be hesitant to interpret the insignificant race by gender coefficients as a signal that women of color now have similar fundraising experiences in comparison to other candidate social groups. As the Democratic party coalition has increasingly become more diverse, along with its donor pool, Democratic white men might have lost their fundraising advantages as Democratic white women have gained a fundraising edge. Candidates historically facing a single axis of marginalization in elections may no longer face disproportionate access to contributions. Yet, in contrast to white women, women of color, and men of color, white men running for U.S. House races haven’t needed to rely on a specific “male donor network” to ensure their competitiveness in elections. This is because the “general campaign donor pool” is already male-dominated and racialized white for both Democrats and Republicans. Thus, while Democratic and Republican women of color may have different fundraising experiences, the combination of these different findings illuminate that it is not solely partisanship driving the outcome of fundraising totals. Based on these results, we believe our findings are applicable to both political parties, though future work should examine the exact nature of the three-way relationship between race, gender, and partisanship.⁸

Discussion and Conclusion

Our findings offer an important corrective to literature examining the influence of race or gender on campaign fundraising. The unitary approach, which examined gender in isolation, found that women candidates no longer faced discrimination in the amount of money they are able to raise. Our results show this picture was incomplete. White women’s social positioning misrepresented the outcomes of the relationship between gender and campaign finance, neglecting to point out that women of color have a distinctly negative experience in fundraising. In other words, even though white women are not disadvantaged in their fundraising totals, the same cannot be said for women of color.

As suggested by work on intersectionality, the influence of multiple systems of oppression (in this case, gender and race) intersect to produce noticeably lower fundraising totals for women of color than white women in various races. These results clearly demonstrate that unitary approaches are insufficient for the study of race or gender as they relate to political candidates. Models predicting women’s fundraising success in U.S. House elections suffer from omitted variable bias without acknowledging the interaction between structural racism and sexism. Despite women initially appearing to raise more money than men, women of color continue to face structural barriers which inhibit their descriptive representation in Congress. While it may be true that a single axis of marginalization no longer disadvantages a candidate, our results demonstrate continued fundraising disadvantages for those who face more than one type of marginalization in both the Republican and Democratic parties.

Importantly, voters do not seem to overtly punish women of color, either directly through vote share or by responding less strongly to campaign tactics (proxied by money spent). That is, the effect of money on vote share is constant across race and gender. But this constancy belies the fact that women of color often raise 70 to 80 percent of the amount raised by other candidates. Women of color face an uphill battle as they attempt to raise money. In effect, this struggle to raise money further exacerbates disparities in descriptive representation. The downstream consequences of these fundraising disadvantages are real reductions in vote share. Due to the fundraising disadvantages faced by women of color, we project that women of color challengers receive approximately half a percentage point fewer votes than they would if they raised the same amount as white women challengers.

In addition, campaign receipts are an important marker of viability for candidates. If women of color candidates are systematically disadvantaged by the campaign finance system, as our results suggest, this could exacerbate existing issues with representation on ballots, as Juenke and Shah (2016) and Shah, Scott, and Gonzalez Juenke (2019) find. Although we cannot speak directly to the relationship between fundraising totals and candidate emergence, our results, coupled with prior work, suggest that representation suffers as a result of multiple points of conflict within the system.

These results, of course, are not without limitations. One important limitation and avenue for future research is uncovering the psychological or attitudinal foundations that explain these fundraising differences. This is beyond the scope of this study, but as we seek to understand and explain why certain groups remain underrepresented in politics, we must show not only that they face structural disadvantages (as these results show) but also what drives those disadvantages and, hopefully, how we overcome them. Future work should examine a variety of explanations, including overt sexism and racism, implicit attitudes, and the possibility that women candidates of color are seen as less qualified or viable than other candidates. Furthermore, elections do not occur in a vacuum, and other research can explore how the race and/or gender of one’s opponent has a salient effect on the amount of funds raised.

In addition, future scholars should look to understand the ways that institutional efforts to support underrepresented groups created fundraising advantages for candidates along a single axis of marginalization (men of color, white women). Our results point to an interesting pattern whereby women of color are disadvantaged in fundraising, but white men are as well. Of course, white men are advantaged by numerous other structural and political forces, potentially reducing the importance of campaign fundraising. Nonetheless, further investigation of the underlying causes of these fundraising patterns can help us understand whether white women and men of color are truly benefiting from greater acceptance from the donor class or if they are benefiting from efforts by PACs and party organizations intended to overcome structural and attitudinal barriers to success.

Overall, our findings point to an important problem facing efforts to improve descriptive representation in the United States Congress. In addition to attitudinal barriers, ambition gaps, and other well-known disadvantages faced by women of color candidates, they are specifically disadvantaged by the fundraising system. When running as challengers, women of color raise significantly less money than other candidates in similar positions. Because of existing disparities in the composition of Congress, the fact that challengers face the greatest fundraising barriers is particularly problematic, as it effectively supports privileged groups (especially white women) over marginalized groups (namely women of color).

As we look to address issues around the representativeness of Congress along lines of race and gender, scholars would be wise to consider how systems of oppression intersect to create disadvantages for those who are positioned at the margins of multiple identities. Our results show one area where this interaction creates disparities, but it is highly unlikely to be the only such arena where these problems arise. Turning our attention to a more holistic view of institutional discrimination in campaign finance improves both our theoretical and methodological understanding of the challenges we face in fighting for greater descriptive representation in politics.

Supplemental Material

sj-pdf-1-prq-10.1177_10659129211022846 – Supplemental material for Identity in Campaign Finance and Elections: The Impact of Gender and Race on Money Raised in 2010–2018 U.S. House Elections

Supplemental material, sj-pdf-1-prq-10.1177_10659129211022846 for Identity in Campaign Finance and Elections: The Impact of Gender and Race on Money Raised in 2010–2018 U.S. House Elections by Ashley Sorensen and Philip Chen in Political Research Quarterly

Footnotes

Acknowledgements

We thank members of the Beloit College Political Science Department and several anonymous reviewers for their helpful suggestions. We are especially grateful to John McMahon for his extensive feedback and support on this project.

Authors’ Note

A previous version of this article was presented at the annual meeting of the Southern Political Science Association in 2018, our thanks to our discussant, Sarah Fulton, and panel participants for their thoughtful recommendations.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ashley Sorensen

Philip Chen

Supplemental Material

Supplemental materials for this article are available with the manuscript on the Political Research Quarterly (PRQ) website.

Notes

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