Ignorance Is Bias: The Effect of Latino Losers on Models of Latino Representation

Ignorance Is Bias: The Effect of Latino Loserson Models of Latino Representation

Eric Gonzalez Juenke Michigan State University

Nearly every aggregate study of minority legislative representation has observed outcomes of elections (officeholders), ratherthan the supply of minority candidates. Because of this, scholars have left a large amount of important data, the electionlosers, out of their models of minority representation. The evidence presented in this article demonstrates that voters in theUnited States cannot choose minority officeholders because there are rarely minority candidates on the ballot. I use statelegislative candidate data from Carsey et al. (2008) and Klarner et al. (2012) to test models of Latino representation thatcorrect for first-stage selection bias. Once candidate self-selection is taken into account, the probability of electing a Latinoincreases enormously. I then use data from 2010 to make out-of-sample predictions, which clearly favor the conditionalmodel. Thus, our current understanding of Latino representation is significantly biased by ignoring the first stage of anelection, a candidate’s decision to run.

The two questions that motivate most of the minor-ity representation research in the United Statesare “When and why do minority candidates get

elected to office?” Although they are not always givenexplicit or foremost attention, they remain fundamentalto understanding the processes of descriptive and sub-stantive representation, legislative redistricting, co-ethnicvoting, and related political phenomena. While scholarsappear to have some good answers to the first question,we have fairly muddled answers to the second. This is be-cause it is unclear to what extent the anti-minority prej-udice found in some experimental settings and on somesurveys hurts minority candidates in actual elections. Per-haps instead, partisanship, incumbency, and other elec-toral factors mitigate voters’ negative “affect.” If it turnsout that minority candidates often get elected in white dis-tricts when they run, then our understanding of minorityrepresentation requires revision.

Traditionally, minority representation research ex-amines the “population-seats” relationship in order to

Eric Gonzalez Juenke is an Assistant Professor of Political Science at Michigan State University, South Kedzie Hall, 368 Farm Lane, Rm.334, East Lansing, MI 48824 ([email protected]).

I wish to thank the editor and the anonymous reviewers who offered valuable comments and changes during the review process. I also wishto thank the following individuals for ideas and assistance with earlier versions of the article: Anthony Bertelli, Ryan Black, Gina Branton,David Ciuk, Michael Colaresi, Johanna Dunaway, Matt Eshbaugh-Soha, David Kelly, Valerie Martinez, Rob Preuhs, Cory Smidt, SophiaWallace, and the Jr. Faculty workshop participants at Michigan State University. The author received no financial funding for this projectbeyond that provided by the Department of Political Science at Michigan State University. All replication materials are available on theAJPS Data Archive on Dataverse (http://dvn.iq.harvard.edu/dvn/dv/ajps).

1I use the term white throughout to refer to “non-Hispanic whites” as defined by the U.S. Census Bureau. The shortened term is lessconfusing for the reader.

make inferences about the extent to which minoritygroups are represented in federal, state, or local legis-latures. Indeed, the minority population/minority seatsrelationship is one of the strongest in political science,impervious to other political controls, alternative model-ing strategies, or changes in level of analysis. The evidencesteadfastly supports the theory that voters (both white1

and nonwhite) are most responsible for the presence orabsence of minority officeholders.

The minority candidate emergence literature has itsown version of the population-seats relationship, andit too is remarkably strong. Minority candidates over-whelmingly run for legislative office in districts with largepercentages of co-ethnic and co-racial voters. Conversely,minority candidates are a rarity in predominantly whitedistricts. For example, in 2000, over 95% of black andLatino officeholders represented districts that were lessthan 25% white (Gonzalez Juenke and Preuhs 2012, Sup-plemental Information Figure 1). The problem is thata lack of a large sample of minority candidate data has

American Journal of Political Science, Vol. 58, No. 3, July 2014, Pp. 593–603

C©2014, Midwest Political Science Association DOI: 10.1111/ajps.12092

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594 ERIC GONZALEZ JUENKE

FIGURE 1 Latino Candidate Emergence in theStates

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prevented researchers from studying the population-seatsrelationship properly, in a way that accounts for minoritycandidate supply.

The empirical question I examine here is “Does mi-nority candidate supply prior to the election bias thepopulation-seats relationship after the election?” I usedata from Carsey and colleagues’ “State Legislative Elec-tion Returns, 1967–2003” to test models of Latino can-didate supply in state legislative districts in 2000 (Carseyet al. 2008). These data are supplemented with informa-tion from the U.S. Census and from CQ Press’s “Almanacof State Legislatures” to test multivariate models of Latinocandidate success in thousands of general elections acrossa broad array of political, economic, and social contexts.I then use similar state legislative data from the 2010 gen-eral elections to make out-of-sample predictions of thedifferent models. I find that the research on minoritydescriptive representation in the United States has beensignificantly impaired by a disconnect between empiricalpractice and theory. It turns out that the venerable answerscholars give to the question, “When and why do minoritycandidates get elected to office?” is likely incomplete.

Descriptive Representation: MinorityVotes Equal Minority Seats

There has been a concerted effort among political scien-tists to test for descriptive legislative representation ef-fects for blacks, and to a lesser extent Latinos (Cameron,Epstein, and O’Halloran 1996; Canon 1999; Engstromand McDonald 1981; Epstein and O’Halloran 2006;

Karnig 1976; Karnig and Welch 1982; Lublin 1997, 1999;Meier et al. 2005; Robinson and Dye 1978; Sass 2000;Welch 1990). There has also been a long tradition of at-tention to electoral structures and vote dilution, as well astheir effects on the minority vote (Adams 2000; Behr 2004;Davidson 1989; Davidson and Grofman 1994; Grofmanand Davidson 1992; Grofman, Handley, and Niemi 1994;Key 1984). The overwhelming conclusion from this well-established research is that the size of the minority vote(or population) is the single biggest driver of minoritydescriptive representation in the United States. Canon’sassessment of the descriptive research in the 1980s and1990s similarly places the weight of African Americanrepresentation on the shoulders of black and white vot-ers (Canon 1999, 54). Empirically, electoral institutionsoften affect levels of descriptive representation, but voterrace/ethnicity is nonetheless dominant in every study thatfocuses on this phenomenon.2

Casellas (2011) updates this research by investigat-ing the presence of Latino officeholders in low Latinodistricts and explores the reasons for their success. Hiscombined qualitative and quantitative approach is oneof the most thorough studies of Latino elites, but he(admittedly) can only focus on Latino officeholders, notlosing Latino candidates (2011, 79). The focused small-N approach suggests a variety of individual candidateand party-level factors that might explain the success ofthese unique representatives, but it also reinforces the ideathat these officeholders succeeded largely despite majoritywhite voters in their districts (2011, 76–105).

The difficulty with the inferences drawn frompopulation-seats evidence is that the models are igno-rant of the losers on the ballot. Without looking at thesupply of minority candidates, it is unclear whether de-scriptive representation bias happens prior to the electionor on Election Day itself. A common approach to look-ing at minority descriptive representation comes froma study by Epstein and O’Halloran (2006). They findmany of the same population-seats patterns for blacks andLatinos as have been uncovered by work elsewhere (seealso Lublin et al. 2009). Like almost all other representa-tion scholars, Epstein and O’Halloran are forced to codethe race/ethnicity of the “winning candidate” only (2006,65). Coding the officeholder or winning candidate is aperfectly reasonable empirical strategy if no other elec-tion information is accessible, as is almost always thecase in large-N studies of descriptive representation.3 The

2Figure S1 in the Supplemental Information (SI) displays this tra-ditional relationship and will be discussed below.

3See Table 2 in the SI for a sample of work using the same method.

IGNORANCE IS BIAS 595

problem is that this common practice disregards first-stage selection bias.

The population-seats empirical work finds much ofits theoretical support from research done at the individ-ual level of analysis. Most recently, this evidence has beengenerated through the use of experiments (Kam 2007;McConnaughy et al. 2010; Sigelman et al. 1995; Terkildsen1993). The effects of white prejudice toward Latino can-didates is mixed, however, and depends on the presenceof partisan cues (Kam 2007; Sigelman et al. 1995). That is,most studies find that many voters (both white and non-white) hold co-ethnic preferences, but these preferencesare unstable when partisan and other information cues areavailable to subjects (Kam 2007). Citrin, Green, and Searstheorize that pure racist voting (voting against a candidatebecause of his or her race/ethnicity) is the wrong way tounderstand how prejudice affects vote choice (1990, 75–79). However, these theoretical specifics are often brushedaside in light of the overwhelming empirical evidenceof representational bias from the aggregate population-seats research. Yet early racial bias scholars warned againstextrapolating experimental evidence to explain electoraloutcomes (Sigelman et al. 1995, 259–60). The approachhere takes this caveat seriously.

The Bias of Minority Candidate Supply

While the objective here is not a strict analysis of mi-nority candidate emergence, it is important to briefly re-view why minority voters correlate with the presence ofa minority candidate on the ballot.4 Few large-N studiesexamine minority candidates at all, but those that havetend to focus on individual characteristics (Stone 1980;Stone and Maisel 1997). Krebs (1999) looks at black can-didates in Chicago’s City Council wards over time, findinga relationship between the presence of more experiencedcandidates and open seats, as well as black populationeffects. In a separate study, Krebs (1998) finds that partysupport, incumbency, and campaign spending play im-portant roles in the minority representation process. Foxand Lawless (2004, 2005) observe individual qualities likepoliticized upbringing, ideological motivations, compet-itive traits, and occupation to develop a more completeunderstanding of why some people run for office and oth-ers do not. They find that blacks of equal qualificationsto other potential candidates are less likely to considerrunning for office, all else equal (they find the same for

4The interest here is not why minority candidates emerge; rather,it is the effects on representation models when they do not. SeeCarson (2005) for a good review of the broader strategic candidateliterature and some important extensions.

females, but not Latinos; Fox and Lawless 2005). Thus,there are a variety of “individual-resource” reasons to ex-pect fewer minority candidates than white candidates atthe state legislative level. U.S. history has not favored mi-nority candidates or officeholders, and thus the politicalexperience (in particular, incumbency), networks, andorganizational capacities that come with holding electiveoffice have not been passed on to minority individualsand groups relative to their white counterparts.

The political context also incentivizes strategic mi-nority candidates to run in elections where they can puttheir limited resources to more efficient and effectiveuse. One of these environments is when minority votersare concentrated into districts where they achieve influ-ence status for a particular seat (Canon, Schousen, andSellers 1996; Johnson, Oppenheimer, and Selin 2012).Canon, Schousen, and Sellers show that African Amer-ican elites were deeply involved in the battle over thecreation of new majority-minority districts in North Car-olina, in part because of the consequent substantive ef-fects, but also because of the potential effects for latentblack candidates (Canon 1999, 116–18). Their elite inter-views show that candidates preferred black majority dis-tricts because of the electoral opportunity they promised(117).

Candidate strategy prior to the election is thus partlyresponsible for the dearth of minority choices outside ofminority-populated districts. Branton (2008) finds a sig-nificant relationship between minority population andminority primary candidates, and the results are sta-ble across both Republican and Democratic primaries(Branton 2008, 463–70). It is clear from this and other re-search (Canon, Schousen, and Sellers 1996; Krebs 1999)that for a variety of reasons, candidates and parties aremaking strategic decisions before the general election abouttheir chances of winning in white districts, and they typ-ically decide not to risk it. Often, these are tactical deci-sions made by elites who are slotting opportunities to ateam of associates (Canon, Schousen, and Sellers 1996).Casellas’s work on Latino officeholders supports the ideathat party elites play a significant role in the emergence ofLatino candidates in Latino minority districts, reinforc-ing recruitment and support as the drivers of opportunity(2011, 89–90; Leighley 2001). Thus, candidate/party pre-dictions about the racial/ethnic outcomes of a generalelection, whether they are correct or not, are constrainingthe choices given to voters.

Consequently, we want to systematically assesswhether including observations where Latinos are notpresent on the ballot (the “naı̈ve” model) biasesthe population-seats relationship. An empirical link-age between the emergence and minority representation


research has not yet been made in the literature, but thefollowing explicit expectations allow us to do that here.

H1: Latino population is positively associated with Latinocandidates being on the ballot.

H2: Conditional on a Latino candidate being on the ballot,district ethnic population will have a positive, butattenuated, association with a Latino winning theelection (compared to a naı̈ve model).

Data and Analyses

The election data come from a variety of sources. Thecandidate data for the 2000 state legislative generalraces are from Carsey and colleagues’ (2008) “StateLegislative Election Returns, 1967–2003.” These data con-tain the name, party, vote totals, election characteristics,and other pertinent information for nearly every sin-gle candidate running for state legislative office in theUnited States in 2000 (N = 4,881). I start by using the U.S.Census Bureau’s Spanish surname list (U.S. Census Bu-reau 2000) to code the Latino ethnicity of each candidateusing computer matching. I then use multiple sourcesto verify Latino identities using elite self-identificationfrom the National Association of Latino Elected and Ap-pointed Officials (NALEO 2001, 2010a, 2010b), candi-date websites, and other Internet sources. A surnamematch on its own has been shown to be 90% accurate(Word and Perkins 1996). The additional use of eliteself-identification methods further reduces the numberof false positives and false negatives.5 This procedurematches much of the experimental work on Latino can-didates and voter “affect,” which involves the specific useof Latino surnames as the experimental condition (Kam2007; McConnaughy et al. 2010). Self-identification isalso the method used in officeholder models.

I supplement the candidate data with demo-graphic and socioeconomic district information from theAlmanac of State Legislatures (Lilley, Defranco, andBernstein 1999).6 The demographic information includesmeasures of the racial and ethnic populations in the dis-

5The false positive and negative rate of the surname-matching com-puter procedure on its own was 1% in 2000, and between 5% and7% in 2010. I began with a list of Spanish-surnamed candidatesfrom the computer matching and was able to subtract many falsepositives using the additional information from NALEO and Inter-net sources. I was also able to include a number of Latino candidatesand winners whom NALEO did not include as part of their list, par-ticularly third-party candidates. I discuss each procedure in moredetail in the SI.

6Latino voting age population (VAP) and citizen voting age pop-ulation (CVAP) data do not exist for all states in the 2000 sample,

tricts (i.e., percent white, black, Latino, and Asian), thedistrict unemployment rate, education level (percent-age over age 25 with “some college”), income (averagehousehold income), and a measure of Republican votingstrength in the district.7 These variables serve as controlsfor the Latino emergence and Latino election models.Descriptive statistics can be found in the supplementalinformation (Table SI 2).

The individual-level candidate data are aggregated tothe level of the election in order to capture the overalleffects of context on the pool of candidates. While there isreluctance to give up information in moving from a lowerlevel of analysis to a higher one, the aggregate approach isthe most theoretically and empirically appropriate here.The question of an individual’s ethnicity (the probabilityof a given candidate being Latino) and the question ofthe ethnicity in the overall candidate pool are similar, butthe latter is more of a function of system-level variables(information we have) rather than individual-level vari-ables (information we do not have). It is possible thatfuture examination of the data would involve a multilevelmodeling of individual- and system-level factors, but thisawaits the discovery of other sources of (candidate-level)information. In short, unlike some of the very creativeand unique candidate emergence data sets used in pre-vious work (Branton 2008; Fox and Lawless 2004, 2005;Stone and Maisel 1997, 2003), candidate-level measuresof candidate quality, personal or occupational character-istics, or socioeconomic status, which would allow fora multilevel modeling approach, do not yet exist. Thetrade-off here is ignorance of candidate-level variation(aside from race/ethnicity, party, and incumbency) forbreadth of contextual variation (information about thedistrict population and the election itself). It is importantto reiterate that the main interest here is to condition thenaı̈ve population-seats model based on whether a Latinois actually on the ballot, rather than modeling individualcandidate emergence for its own sake. The former can bedone with these data but not the latter.

Candidate Supply

To account for the first stage of representation, I es-timate a probit regression model of Latino candidate

thus Latino population is used instead. I explain why using thepopulation measure biases against my hypotheses in the SI.

7Because there is no standard partisanship measure for state leg-islative districts across states, this variable is a proxy. The modelsperform just as well without it, but it is included for readers whowant to see a control (any control) for partisanship in the district.It is explained in detail in the SI.


supply. There are a number of ways to model the broaderemergence/election process, but none are demonstra-tively better than the sequential probit used here.8 Inthis first step, I model the aggregate outcomes of choicesmade by candidates, parties, and primary voters to bepresent on the ballot. This will assess the relationship be-tween the size of the Latino population in the district andLatino candidate supply (Hypothesis 1). In subsequentsections, I estimate corrected and conditional models ofpopulation-seats during the election (Hypothesis 2).

The tabular results for the first stage (Latino emer-gence) are in the SI. The details are relegated to the SIin order to reiterate that this stage is not the primary fo-cus of the analysis. Instead, I display the main effects inFigure 1. Latino population is positively and significantlyassociated with a Latino candidate being on the ballot. Afew other contextual factors influence this decision, butit is clear that Latino district population drives Latinocandidate emergence. This is clear from both the tabularresults, where the marginal effects for Latino populationdwarf those of any other variable, and from the predictedprobabilities plotted in Figure 1.9 The comparison to thepopulation-seats relationship in Figure SI 1 is notable.Latino candidates are choosing to run for office for avariety of reasons, but the clearest contextual driver forthese decisions is the percentage of Latino voters in thedistrict. Conversely, Latino candidates are rarely on bal-lots in low Latino population districts, holding all otherrelevant political factors constant.

Latino Population and Latino Winners

Having modeled the first step of the population-seatsrelationship, I move to the election stage. The questionhere is to what extent the absence of Latino candidateson the ballot affects the venerable population-seats rela-tionship. Table 1 presents results of four probit regres-sion models of whether the election winner is Latino(1) or not (0). Column 1 displays the Latino populationmodel on its own. This model is ignorant about whethera Latino is on the ballot, and it corresponds with tradi-tional population-seats models. Latino district popula-tion is significant, positive, and drives the findings in this

8Alternative approaches including rare events logit (King and Zeng2001), a Heckman selection model (Heckman 1979), and a sequen-tial logit with correlated errors (Buis 2011) produce substantivelysimilar results, even though diagnostics suggest that correlated er-ror models are inappropriate. These results are presented in theSI.

9Probabilities and confidence intervals in all of the figures are esti-mated using CLARIFY (King, Tomz, and Wittenberg 2000; Tomz,Wittenberg, and King 2003).

naı̈ve model. The controls work as expected but are over-whelmed by the population effects. Of note is the negativeand significant black population coefficient, a finding thatwould almost certainly be interpreted as black voters be-ing less likely than whites to support Latino candidates,or more precisely as further evidence of a black-browndivide (Vaca 2004). In Figure 2, I display the indepen-dent effects of the naı̈ve Latino population model fromcolumn 1 to make this point clearer. Its similarity to theLatino supply model in Figure 1 is notable. It is the firstindication that what the naı̈ve population model ascribesto voters may instead be related to candidate decisions torun.

To test whether the population-seats relationship re-mains strong after accounting for candidate selection, Iestimate three election models that control for, or arecensored by, Latino candidate supply. The results in thesecond column of Table 1 substitute a measure of the per-centage of Latino candidates on the ballot (candidate “sup-ply”) for Latino district population (voter “demand”).This censoring variable dominates the model, improvesthe explanatory power of the regression considerably, andon its own explains almost all of the variation in Latinowinners (in results not displayed, the pseudo-R2 usingonly this variable is .83). This makes sense. When thisvariable has a value of 0, there is no chance for a Latinoto win the election. When it is 100%, there is certaintyabout the ethnic outcome of the race. This is an obvi-ous point, but one that is ignored in previous large-Nempirical work.

The last two columns in Table 2 present two variationsof this censored approach, and the evidence is the same;the supply of Latino candidates substantially moderatesthe population-seats relationship. In column 3, both thecandidate supply and Latino population demand vari-ables are included simultaneously. As the percentage ofLatino candidates on the ballot increases, the probabilityof a Latino winner increases substantially. After control-ling for this censoring variable, however, Latino popula-tion increases matter much less in regard to whether aLatino wins the race.

To see how much bias may be present in the naı̈vemodel, I estimate the exact same population model fromcolumn 1 while excluding elections where a Latino can-didate was not on the ballot. The results are displayedin column 4. Latino district population is still corre-lated with Latino winners, but not nearly as much as thepopulation-seats research suggests, and it is overwhelmedby the conditional effects of Latino candidate supply.

It is inappropriate to directly compare the coefficientsfrom column 1 and column 4, as they describe two dif-ferent samples. Thus, the conditional population effects


TABLE 1 Predicting a Latino Winner, Population, Candidates, and Selection Bias

Population Candidate Mixed CensoredDependent Variable: Pr (Latino Winner) (Naı̈ve) Model Model Model (Conditional) Model

Latino candidate pool 5.91∗∗ 4.99∗∗

(.44) (.48)Latino population 6.32∗∗ 3.91∗∗ 4.96∗∗

(.42) (.63) (.77)Black population −1.54∗∗ −1.02 −.72 −.43

(.56) (.67) (.73) (1.12)Candidates/seat .14 .41∗∗ .36∗∗ −.56∗∗

(.08) (.13) (.15) (.18)Open seat .12 .16 .05 −.23

(.14) (.17) (.20) (.27)Republican strength −.001 −.003∗∗ −.002 −.002

(.001) (.001) (.001) (.002)Upper chamber −.16 −.10 −.15 −.22

(.15) (.28) (.32) (.30)Constant −3.66∗∗ −2.41∗∗ −4.38∗∗ −.71

(.35) (.54) (.74) (.75)

N 4,881 4,881 4,881 233Wald chi2 535.02∗∗ 259.06∗∗ 211.87∗∗ 84.73∗∗

Pseudo R2 .66 .82 .86 .47

Note: Unstandardized probit coefficients with robust standard errors in parentheses. Clustering for errors by state produces no significantor substantive differences in the results. All of the controls from Table 1 are included in these models, but their results are not displayed heredue to space constraints (results are available from the author). Further, using a 5% Latino population threshold does not substantivelychange the results in any of the models.∗p < .05, ∗∗p < .01 (two-tailed tests).

FIGURE 2 Descriptive Representation: Naı̈veModel

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(column 4) are displayed in Figure 3, and two results areprominent. First, the predicted probabilities are muchhigher for Latino candidates in low Latino populationdistricts than the population-only model implies (com-

pare to Figure 2). The best estimated range of predictedprobabilities for success in Latino minority districts is be-tween 20% and 80% Latino. More importantly, the 50%threshold for a Latino winner is crossed at 20% Latinopopulation (the 95% confidence interval is between 10%and 30% Latino), substantially lower than in the naı̈vemodel. Second, Latino candidates do extremely well indistricts that are more than 30% Latino population (70%likelihood of winning), most likely due to the partisanand ethnic makeup of these districts.10 Third, the un-certainty in this model is larger on the low end of theLatino population distribution (which is where most ofthe observations lie). In short, Latino candidates are ex-pected to do fairly well in many districts in the sample,and the factors that determine how well these candidatesperform on Election Day have much more to do withLatino candidate supply than with white voter demand.

10This is also why Latino population still has a significant relation-ship with Latino victory: Latino candidates are almost guaranteedvictory in districts with more than 35% Latino population. Any-thing short of this guarantee will create a positive and significantcoefficient for Latino population.


TABLE 2 Latino Winners and Losers and Their District Contexts

2000 Latino Winners Latino Losers

Districts with Latino on ballot (233) 167 66Latino incumbents 121 0Races with non-Latino incumbent 5 45“Open” races in Latino majority districts 31 1“Open” races in Latino minority districts 10 202010 Latino Winners Latino LosersDistricts with Latino on ballot (285) 198 87Latino incumbents 127 6Races with non-Latino incumbent 5 54“Open” races in Latino majority districts 43 7“Open” races in Latino minority districts 23 20

These supply-censored models are an important step infinding out what happens when Latino candidates runoutside of Latino majority districts. They do very well.

Out-of-Sample Predictions

To further evaluate the difference between a naı̈ve popula-tion model and the conditional approach endorsed here,I investigate how well they do in predicting out-of-sampleelection results. I use a sample of elections in 2010 (N =5,052), incorporating candidate and election data fromKlarner et al. (2012), and the U.S. Census (2010) for dis-trict demographic controls.11 The coefficients from thenaı̈ve population model (Table 1, column 1) and the con-ditional model (Table 1, column 4) are compared in theirability to make predictions about the 2010 elections.

The differences between the two approaches arestark. Spike plots that are modified from “separationplots” (Greenhill, Ward, and Sacks 2011) are presented inFigure 4. Like separation plots, these figures can moreeasily transmit goodness-of-fit information when com-pared to PRE, ROC, and other diagnostic tools. ActualLatino winners (the lines) are plotted against the predic-tion made by each model (the x-axis). A perfect modelwill predict all of the Latino winners correctly and will dis-play lines along the far right side of the figure. Conversely,more lines to the left of the figure indicate that Latinosare winning in districts where the model predicts littlechance of victory. The naı̈ve model’s 2010 predictions arescattered, with a large spread of winners falling below50% probability. Indeed, many of the actual winners in

11I used the same procedures as I did for the 2000 sample, includingNALEO’s (2010a, 2010b) “Races to Watch.”

FIGURE 3 Descriptive Represenation:Conditional Model

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2010 have less than a 5% chance of winning accordingto the naı̈ve model. The conditional model is superb incomparison. Not only are most of the actual winners in2010 predicted to be winners by the model (184 of 198 fallabove 50% probability), but also they are concentrated atthe high end of the probability scale. That is, most of theactual winners have above an 80% chance of winning ac-cording to the model.12 Knowing who is running clearsup a large amount of uncertainty in models of ethnicrepresentation.

12I report traditional cross-tabulations of actual versus predictedwinners in Table SI 5 in the SI. I also display spike plots of the“misses” for each model and discuss the implications for the find-ings here (Figure SI 4). I relegate them to the SI to keep the focus hereon how pessimistic the naı̈ve population model is toward Latinocandidates, and how badly this pessimism performs in predictingwinners in 2010.


FIGURE 4 Modified Separation Plots of Out-of-Sample Predictions: PredictingLatino Winners in 2010

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Pr(Latino Winner)

Lines Equal 'Actual Latino Winner'

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Pr(Latino Winner)

Lines Equal 'Actual Latino Winner'

Conditional Model

Note: These are modified “separation plots” (Greenhill, Ward, and Sacks 2011). The black lines represent actualLatino winners, and the x-axis represents the predicted probabilities for these winners.

The results of the out-of-sample predictions favorthe supply-side hypotheses. Latino candidates could bequite successful outside of Latino majority districts if theywere encouraged to run. To make the point clearer, cross-tabulations of Latino candidates and election outcomesin both 2000 and 2010 are shown in Table 2. The win ratefor Latino incumbents is 100% in 2000, and 95% in 2010.In contrast, Latino challengers do not do very well againstnon-Latino incumbents (10% win rate). Neither of theseoutcomes is surprising, as all incumbents win about 95%of all races in the sample. What about open races with noincumbents? In both years, Latino candidates do very wellin Latino majority areas, winning almost all of these races.

In Latino minority elections, the results are not as robust,but not unfavorable either. In 2000, Latino candidateswin about 33% of the open races in Latino minority dis-tricts, and in 2010 they win more than 50% of these openraces.

Looking at the actual elections in 2000, it is easy to seewhy many of these Latinos lost. They ran as the underdogpartisans in partisan districts that just happened to havefew Latino voters. For example, AnnaJane Arroyo ran asa Democrat in a very Republican state house district inUtah (District 13, 7% Latino) where Democrats average33% of the vote (Snyder 2011). Arroyo received 30% ofthe vote. In Michigan’s 87th District (2% Latino), Henry


Sanchez ran as a Democrat even though the average voteshare for Democrats in the past three elections for thisseat was 32% (Carsey et al. 2008). Sanchez’s vote sharein 2000? It was 33%. James Auffant ran in Florida’s 35thDistrict (18% Latino), where Democrats garnered 40%of the vote in the previous election in 1996. Auffant lostwith 45% of the vote. Of the 20 Latino “open” race losersin Latino minority districts in 2000, 14 were Democratsrunning in strong Republican districts, two were Repub-licans running in strong Democratic districts, and fourwere third-party candidates with little chance of winning.By 2010, Latinos were winning more than 50% of theseopen races in Latino minority areas (Table 2).

These examples reinforce the fact that Latinos rarelyrun in Latino minority districts, and when they do com-pete, the ethnic population of the district has little todo with the outcome of the race. Most of these can-didates are running as Democrats in white Republicandistricts. In Figure SI 5 (in the SI), I demonstrate thatin these low Latino districts, all Democrats have abouta 35% win rate, all else equal. That is, in 2000, Latinolosers in Latino minority districts are doing just about aswell as other Democrats overall. This makes sense givenhow often Latino candidates run as Democrats (60% ofthe time versus 23% as Republicans). These districts willelect more Latinos if the parties recruit and support morecompetitive Latino candidates.

Discussion

The most important reason that Latino representationis low in state legislatures may not be because voterswill not support them, but because Latino candidatesare not showing up on the ballot in the general election.Representation scholars need to examine why this is thecase (candidate emergence and supply) in a much moreearnest fashion.

Is there white and minority preference for co-ethniccandidates? Yes—the literature (and popular polling) isclear that in many cases this favoritism exists. However,minority representation researchers assume that this realprejudice found in some individual races (and experi-mental settings) (a) is consistent across most electoraldistricts in the United States, (b) is so dominant thatit overrides partisanship and incumbency in most leg-islative elections, and (c) consistently overwhelms elec-toral competitiveness. Given the empirical fact of a strongpopulation-seats relationship, these assumptions becomeinferences, and subsequently conclusions that have im-portant policy consequences in the courts and state redis-

tricting plans. The results here do not change the fact thatminorities rarely hold office outside of heavily populatedminority districts. They do force us to come up with amore rigorous understanding of why that is the case.

The evidence here should convince researchers to doat least two things. First, minority representation schol-ars must be careful about the inferences they make basedoff of officeholder and election-winner data—careful tothe point of skepticism. Some of the current debate overvoting rights has to do with estimating moments of equalvoting opportunity and influence, and minority popu-lation tipping points in different electoral contexts. Thedegree of specificity with which we can trust this evidenceis severely hampered by these findings.

Second, linking emergence research with representa-tion research must be the norm when possible. The lackof connection between these two research areas has beenprimarily a “lack of data” problem. The more informationwe have about the losers of elections and the choices vot-ers are provided, the better our evidence and conclusionswill be. Thus, the research concerning the parties’ roles inminority candidate recruitment should be better incor-porated, theoretically and empirically, into the descriptiverepresentation story. The officeholder evidence from pastwork is unaffected by the evidence presented here. Whatneeds to change, and where this research breaks so acutelyfrom the past, is the inferences we draw from it.

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Supporting Information

Additional Supporting Information may be found in theonline version of this article at the publisher’s website:

Figure SI 1: Typical Population-Seats Relationship.Figure SI 2: Descriptive Representation, ConditionalModelFigure SI 3: Descriptive Representation, Conditional Ef-fects (Box Plots)Figure SI 4: Sequential Logit with Correlated ErrorsFigure SI 5: Modified Separation Plots of Out-of-SamplePredictions: Predicting Latino Losers in 2010Figure SI 6: Partisan BaselineTable SI 1: Descriptive Statistics: 2000 and 2010Table SI 2: A Sample of Recent Minority Descriptive Rep-resentation ResearchTable SI 3: Latino Candidate Emergence in the StatesTable SI 4: Predicting a Latino Winner in 2000, Popula-tion, Candidates, and Selection-Bias (alternative models)Table SI 5: Predicted Latino Winners and Losers in 2010Table SI 6: All Latino Winners in Districts with Less than30% Latino Population in 2000Table SI 7: All Latino Winners in Districts with Less than20% Latino Adult Citizen Population in 2010

Documents

Ignorance Is Bias: The Effect of Latino Losers on Models of Latino Representation