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Diversity Matters: The Election of Asian Americans to U.S. State and Federal Legislatures

Published online by Cambridge University Press:  31 March 2023

DAVID LUBLIN*
Affiliation:
American University, United States
MATTHEW WRIGHT*
Affiliation:
University of British Columbia, Canada
*
David Lublin, Professor, Department of Government, American University, United States, dlublin@american.edu.
Matthew Wright, Associate Professor, Department of Political Science, University of British Columbia, Canada, matthew.wright@ubc.ca.
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Abstract

Despite substantial research on descriptive representation for Blacks and Latinos, we know little about the electoral conditions under which Asian candidates win office. Leveraging a new dataset on Asian American legislators elected from 2011 to 2020, combined with pre-existing and newly conducted surveys, we develop and test hypotheses related to Asian American candidates’ ingroup support, and their crossover appeal to other racial and ethnic groups. The data show Asian Americans preferring candidates of their own ethnic origin and of other Asian ethnicities to non-Asian candidates, indicating strong ethnic and panethnic motives. Asian candidates have comparatively strong crossover appeal, winning at higher rates than Blacks or Latinos for any given percentage of the reference group. All else equal, Asian American candidates fare best in multiracial districts, so growing diversity should benefit their electoral prospects. This crossover appeal is not closely tied to motives related to relative group status or threat.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Figure 1. Asian American Legislators by Election Year and PartyNote: State legislators include only the 33 states as described in the text. Numbers based on election results and do not include appointments or special elections. Source: Data gathered by authors.

Figure 1

Table 1. Random Effects Logistic Regression Models of the Election of Asian American State Legislators

Figure 2

Figure 2. Ratio of Asian American State Legislators to Asian Population after the 2020 Elections, 33 StatesNote: Ratio equals the share of Asians in the state legislature divided by the share of Asians alone or in combination of the population. Includes the 33 states as described in the text. Excluded states are not outlined. Source: Data on legislator race gathered by authors. Data on Asian alone and combination population from the American Community Survey. See Supplementary Table 3 for the descriptive data used to create this map.

Figure 3

Figure 3. Asian American State Legislators by Ethnic Origin and YearNote: Includes only the 33 states as described in the text. Source: Data gathered by authors.

Figure 4

Figure 4. Percent Asian American State Legislators by Asian Alone or in Combination PopulationNote: See the text for information on the 32 included states. Source: Data on legislator race gathered by authors. Data on Asian alone and combination population from the American Community Survey.

Figure 5

Figure 5. Percent Asian and the Estimated Probability of an Asian State Legislator in Hawaii and 32 Other StatesNote: VAP is the voting-age population. CVAP is the citizen voting-age population. Each curve shows the estimated probability of an Asian American state legislator based on the models in Table 1. Asian American winner is the dependent variable in all models with the only independent variables being different measures of the share of Asians. The estimator is multilevel logistic regression with random effects by district to address error clustering. The estimates based on the models including Asian Alone VAP and total population as independent variables are virtually identical, so only the former is shown here.

Figure 6

Table 2. Random Effects Logistic Regression Models of the Election of Asian American State Legislators that Include Intergroup or Intragroup Diversity, Excludes Hawaii

Figure 7

Figure 6. Preferences on Different Kinds of Representations in Congress by GroupNote: Points represent means with 95% confidence intervals. Each x-axis value is a different item in the representation battery asked identically in both surveys. Possible scores range from 0 = not at all important to 1 = extremely important. Note that the “Paneth Asian” and “Asian Eth Origin” items were asked only of Asian respondents; these respondents saw either “Asian Americans” or their own ethnic origin, respectively, assigned at random. The figure was produced from the following tables, posted as individual files in the Harvard Dataverse associated with this article: “2021 Online Survey Aggregates for Figure 6.dta” and “NAAS 2016 Aggregates for Figure 6.dta.” Each table was generated from 2021 Online Internet Survey and 2016 NAAS (respectively) per detailed instructions provided in the posted do-file “Survey Analysis Code.do.” Source: 2016 NAAS and 2021 Online Internet Survey.

Figure 8

Figure 7. Asian Voter Candidate Preference by Race/Ethnicity of CandidateNote: This figure maps support for vignette candidate (and additive index of three favorability questions asked), with scores ranging from 0 = completely unfavorable to 3 = completely favorable. Only Asian respondents are included. “W,” “B,” and “L” stand for “white,” “Black,” and “Latino,” respectively. Spikes represent 95% confidence intervals. The figure was produced from the following table, posted in the Harvard Dataverse associated with this article: “2021 Online Survey Aggregates for Figure 7.dta.” This table was generated from the 2021 Online Internet Survey per detailed instructions provided in the posted do-file “Survey Analysis Code.do.” Source: 2021 Online Survey.

Figure 9

Figure 8. Estimated Probability of Asian, Black, or Latino Legislator by Group CVAPNote: CVAP is the citizen voting-age population. Each curve shows the estimated probability of either an Asian, Black, or Latino state legislator based on the appropriate models in Table 1 and Supplementary Table 9. In these models, the dependent variables are Asian, Black, or Latino winner with the corresponding CVAP for that group included as an independent variable. The model of Black winner also includes Hispanic CVAP. The estimator is multilevel logistic regression with random effects by district to address error clustering. Results are based on data from the 32 states included in the dataset as described in the text other than Hawaii.

Figure 10

Figure 9. Percent Asian American Legislators in 2020 by District Diversity, Excluding HawaiiNote: All states but Hawaii included for the U.S. House. See the text for information on the 32 included states for state legislators. The intergroup diversity index equals 1 − Σ(xi/population)2 with xi being the standard census racial categories with Hispanics excluded and treated as a separate category. The small non-Hispanic multiracial population is also treated as a distinct category. Source: Data on legislator race gathered by authors. Data on race and ethnicity from the American Community Survey.

Figure 11

Figure 10. Estimated Probability of an Asian American State Legislator as Intergroup Diversity Varies with 95% Confidence IntervalsNote: Estimated probability of an Asian American winner based on Model 1 in Table 2. The independent variables in the model are proportion Asian voting-age population, intergroup diversity, and an interaction of the two variables. The estimator is multilevel logistic regression with random effects by district to address error clustering. Results are based on data from the 32 states included in the dataset other than Hawaii.

Figure 12

Figure 11. Non-Asian Voter Preferences by Candidate Race/EthnicityNote: This figure maps support for vignette candidate (an additive index of three favorability questions asked), with scores ranging from 0 = completely unfavorable to 3 = completely favorable. Bars represent permutations of respondent (R) race (W for “white,” B for “Black,” and L for “Latino”) and candidate (C) race (W, A, B, and L). Only non-Asian respondents are included. Spikes represent 95% confidence intervals. The figure was produced from the following table, posted in the Harvard Dataverse associated with this article: “2021 Online Survey Aggregates for Figure 11.dta.” This table was generated from the 2021 Online Internet Survey per detailed instructions provided in the posted do-file “Survey Analysis Code.do.”

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Lublin and Wright Dataset

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