Cambridge University Press
The Dynamics of Racial Resentment Across the 50 U.S. StatesAppendix for ReflectionAppendix A: Detailed Figures Referenced in TextA1. Racial Resentment in Individual States, Over TimeA2. Racial Resentment by Region, Over TimeA3. Mapping Annual Racial Resentment in the States, Relative to Other StatesNote: This is similar to Figure 3, but with deciles in lieu of quintiles. Quintiles for each map are calculated using data from that year only. As such, the individual maps should not be directly compared to each other. However, it is appropriate to compare to maps to observe patterns in which states have racial resentment scores in the higher and low quintiles of the data.A4. Changes in Estimated Racial Resentment in Over Time, Sort by Racial Resentment in 2016Note: This is similar to Figure 4, but instead sorted by estimated racial resentment in 2016.A5. Mapping Annual Racial Resentment in the States, Relative to Other StatesNote: This is similar to Figure 5, but with deciles in lieu of quintiles. Quintiles for each map are calculated using data from that year only. As such, the individual maps should not be directly compared to each other. However, it is appropriate to compare to maps to observe patterns in which states have racial resentment scores in the higher and low quintiles of the data.Appendix B: The Use of MRP to Create Subnational Estimates of Public OpinionMultilevel regression with post-stratification weighting (MRP) is an approach to estimating public opinion that brings together three pieces of information: census data, survey data containing a measure of the attitude one is interested in measuring, and data on state-level variables that may have an impact on those attitudes. Public opinion is modeled as a function of demographic characteristics and state-level variables, and the responses are weighted using frequencies of demographic types from the census. The method has been vigorously tested and validated across a range of data, and several groups of scholars have created useful sets of guidelines and cautions to those using MRP ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2013</Year><RecNum>39</RecNum><DisplayText>(Lax and Phillips 2013, Buttice and Highton 2013)</DisplayText><record><rec-number>39</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519835380">39</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate sub-national opinion using MRP? Preliminary findings and recommendations</title><secondary-title>annual meeting of the Midwest Political Science Association, Chicago</secondary-title></titles><dates><year>2013</year></dates><urls></urls></record></Cite><Cite><Author>Buttice</Author><Year>2013</Year><RecNum>25</RecNum><record><rec-number>25</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519246359">25</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Buttice, Matthew K</author><author>Highton, Benjamin</author></authors></contributors><titles><title>How does multilevel regression and poststratification perform with conventional national surveys?</title><secondary-title>Political Analysis</secondary-title></titles><periodical><full-title>Political Analysis</full-title></periodical><pages>449-467</pages><volume>21</volume><number>4</number><dates><year>2013</year></dates><isbn>1476-4989</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2013, Buttice and Highton 2013). MRP can be used to create state level estimates with a single national survey of at least 1,400 or so respondents PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYXg8L0F1dGhvcj48WWVhcj4yMDA5PC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA (Raudenbush and Bryk 2002, Snijders 2011, Steenbergen and Jones 2002, Lax and Phillips 2009b, Park, Gelman, and Bafumi 2004). Park, Gelman and Bafumi (2006) compare MRP estimates of opinion to older approaches to modeling state opinion and show that MRP substantially outperforms approaches that do not partially pool information across respondents. The subsequent process of post-stratification corrects for over-sampling or under-sampling of demographic categories ADDIN EN.CITE <EndNote><Cite><Author>Voss</Author><Year>1995</Year><RecNum>40</RecNum><DisplayText>(Voss, Gelman, and King 1995)</DisplayText><record><rec-number>40</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519836439">40</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Voss, Stephen</author><author>Gelman, Andrew</author><author>King, Gary</author></authors></contributors><titles><title>The polls—A review: Preelection survey methodology: Details from eight polling organizations, 1988 and 1992</title><secondary-title>Public Opinion Quarterly</secondary-title></titles><periodical><full-title>Public Opinion Quarterly</full-title></periodical><pages>98-132</pages><volume>59</volume><number>1</number><dates><year>1995</year></dates><isbn>1537-5331</isbn><urls></urls></record></Cite></EndNote>(Voss, Gelman, and King 1995). The pooling of information across states and years also allows for accurate estimation of opinion for smaller states, which are often excluded from national surveys or have a small state sample ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b, Pacheco 2011)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite><Cite><Author>Pacheco</Author><Year>2011</Year><RecNum>15</RecNum><record><rec-number>15</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245995">15</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Using national surveys to measure dynamic US state public opinion: A guideline for scholars and an application</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>415-439</pages><volume>11</volume><number>4</number><dates><year>2011</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b, Pacheco 2011). In general, MRP yields smaller errors, higher correlations, and more reliable estimates than disaggregation ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b).Because one doesn’t need as many survey responses to produce reliable state estimates compared to disaggregation, scholars have used MRP to create measures of sub-national and sub-state attitudes on a range of issues, including same-sex marriage ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>1779</RecNum><DisplayText>(Lax and Phillips 2009a, Lewis and Jacobsmeier 2017)</DisplayText><record><rec-number>1779</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1779</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>Gay rights in the states: Public opinion and policy responsiveness</title><secondary-title>American Political Science Review</secondary-title></titles><periodical><full-title>American Political Science Review</full-title></periodical><pages>367-386</pages><volume>103</volume><number>3</number><dates><year>2009</year></dates><isbn>1537-5943</isbn><urls></urls></record></Cite><Cite><Author>Lewis</Author><Year>2017</Year><RecNum>65</RecNum><record><rec-number>65</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">65</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lewis, Daniel C</author><author>Jacobsmeier, Matthew L</author></authors></contributors><titles><title>Evaluating Policy Representation with Dynamic MRP Estimates: Direct Democracy and Same-Sex Relationship Policies in the United States</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>441-464</pages><volume>17</volume><number>4</number><dates><year>2017</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009a, Lewis and Jacobsmeier 2017), ideology and partisanship PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Fbm5zPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (Kastellec, Lax, and Phillips 2010, Franko 2017, Caldarone, Canes-Wrone, and Clark 2009), income inequality ADDIN EN.CITE <EndNote><Cite><Author>Franko</Author><Year>2017</Year><RecNum>36</RecNum><DisplayText>(Franko 2017)</DisplayText><record><rec-number>36</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">36</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Franko, William W</author></authors></contributors><titles><title>Understanding public perceptions of growing economic inequality</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>319-348</pages><volume>17</volume><number>3</number><dates><year>2017</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Franko 2017), roll call voting PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYXN0ZWxsZWM8L0F1dGhvcj48WWVhcj4yMDE1PC9ZZWFy
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ADDIN EN.CITE.DATA (Kastellec et al. 2015, Kastellec, Lax, and Phillips 2010, Krimmel, Lax, and Phillips 2016), the Affordable Health Care act ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2017</Year><RecNum>77</RecNum><DisplayText>(Pacheco and Maltby 2017)</DisplayText><record><rec-number>77</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683897">77</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author><author>Maltby, Elizabeth</author></authors></contributors><titles><title>The Role of Public Opinion—Does It Influence the Diffusion of ACA Decisions?</title><secondary-title>Journal of health politics, policy and law</secondary-title></titles><periodical><full-title>Journal of health politics, policy and law</full-title></periodical><pages>309-340</pages><volume>42</volume><number>2</number><dates><year>2017</year></dates><isbn>0361-6878</isbn><urls></urls></record></Cite></EndNote>(Pacheco and Maltby 2017), smoking bans ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2012</Year><RecNum>56</RecNum><DisplayText>(Pacheco 2012)</DisplayText><record><rec-number>56</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">56</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>The social contagion model: Exploring the role of public opinion on the diffusion of antismoking legislation across the American states</title><secondary-title>The Journal of Politics</secondary-title></titles><periodical><full-title>The Journal of Politics</full-title></periodical><pages>187-202</pages><volume>74</volume><number>1</number><dates><year>2012</year></dates><isbn>0022-3816</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2012), abortion ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), death penalty ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), and welfare spending ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), to name a few. Indeed, MRP is “emerging as a widely used gold standard for estimating preferences from national surveys” ADDIN EN.CITE <EndNote><Cite><Author>Selb</Author><Year>2011</Year><RecNum>37</RecNum><Pages>456</Pages><DisplayText>(Selb and Munzert 2011, 456)</DisplayText><record><rec-number>37</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">37</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Selb, Peter</author><author>Munzert, Simon</author></authors></contributors><titles><title>Estimating constituency preferences from sparse survey data using auxiliary geographic information</title><secondary-title>Political Analysis</secondary-title></titles><periodical><full-title>Political Analysis</full-title></periodical><pages>455-470</pages><volume>19</volume><number>4</number><dates><year>2011</year></dates><isbn>1476-4989</isbn><urls></urls></record></Cite></EndNote>(Selb and Munzert 2011, 456).Implementing MRP to Measure Racial ResentmentRacial resentment is most frequently measured with a battery of four questions, and combined into an additive scale ADDIN EN.CITE <EndNote><Cite><Author>Kinder</Author><Year>1996</Year><RecNum>452</RecNum><DisplayText>(Kinder and Sanders 1996)</DisplayText><record><rec-number>452</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">452</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Kinder, Donald R.</author><author>Sanders, Lynn M.</author></authors></contributors><titles><title>Divided by Color: Racial Politics and Democratic Ideals</title></titles><dates><year>1996</year></dates><pub-location>Chicago</pub-location><publisher>The University of Chicago Press</publisher><urls></urls></record></Cite></EndNote>(Kinder and Sanders 1996). 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ADDIN EN.CITE.DATA (DeSante 2013, Feldman and Huddy 2005, Filindra and Kaplan , Tuch and Hughes 2011). Here, we use a multilevel model to create individualized scores of racial resentment based on demographic and geographic markers, and weight the occurrence of those factors appropriately to create a state level measure. That measure represents the level of racial resentment in each state in a particular year.In order to implement MRP, we collected public opinion survey data on racial resentment over time using the American National Election Survey (ANES) and pooled that data into a single dataset. The ANES uses a cluster sampling design. Some scholars have articulated concerns about the use of clustered data to create estimates of state opinion because of the non-representativeness of clustered data ADDIN EN.CITE <EndNote><Cite><Author>Brace</Author><Year>2002</Year><RecNum>3942</RecNum><DisplayText>(Brace et al. 2002)</DisplayText><record><rec-number>3942</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1556634164">3942</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Brace, Paul</author><author>Sims-Butler, Kellie</author><author>Arceneaux, Kevin</author><author>Johnson, Martin</author></authors></contributors><titles><title>Public opinion in the American states: New perspectives using national survey data</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>173-189</pages><dates><year>2002</year></dates><isbn>0092-5853</isbn><urls></urls></record></Cite></EndNote>(Brace et al. 2002). The problem of clustered sampling can be mitigated by including state level variables in MRP, and the final step of post-stratification in particular corrects for this bias ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b). Many other scholars have effectively used clustered data to create state estimates of opinion using MRP (for instance PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9sbHdlcms8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFy
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ADDIN EN.CITE.DATA (Stollwerk 2013, Koch and Thomsen 2017, Butz and Kehrberg 2016).The ANES asked the standard battery of four questions to gauge racial resentment in 1988, 1990, 1992, 1994, 2000, 2004, 2008, 2012, and 2016. Specifically, respondents were asked if they agree strongly, agree somewhat, neither agree nor disagree, disagree somewhat or disagree strongly with the following four statements: 1) Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors. 2) Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class. 3)Over the past few years, blacks have gotten less than they deserve. 4)It’s really a matter of some people not trying hard enough, if blacks would only try harder they could be just as well off as whites. We follow conventions established by both scholars of MRP and racial resentment in our treatment of these questions in our analysis. We code each of the four racial resentment questions such that agreement with the prompts are coded on a five-point scale, with higher scores indicating a higher level of racial resentment. We then combined responses to the four prompts and divided that number by 16, so that an individual’s racial resentment score ranges from 0 to 1, with 1 representing the highest possible level of racial resentment.Next, we use a multilevel linear regression model to predict the level of racial resentment using a series of demographic and state-level factors used in the extant MRP literature. This includes measures of a series of binary categorical measures for age (18-29, 30-44, 45-64, or 65+), education (less than high school graduate, high school graduate, some college, or college graduate), and state of residence (each of the 50 states). Rather than include a race-gender interaction, we follow scholars who have sought to take seriously the intersectionality literature in quantitative data analysis; specifically, we include indicators of each race-gender combination of respondents (non-Hispanic white man, non-Hispanic white woman, non-Hispanic black man, non-Hispanic black woman, men of “other” race, women of “other” race) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5jb2NrPC9BdXRob3I+PFllYXI+MjAwNzwvWWVhcj48
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ADDIN EN.CITE.DATA (Hancock 2007, Masuoka and Junn 2013, Reingold and Smith 2012). Scholars often exclude public opinion data from non-whites in their analysis of racial resentment. While blacks and other people of color may not exhibit the same levels of anti-Black animus, they are neither immune from relying on individual rather than structural explanations of racial inequality ADDIN EN.CITE <EndNote><Cite><Author>Smith</Author><Year>2014</Year><RecNum>1355</RecNum><DisplayText>(Smith 2014, Nunnally and Carter 2012)</DisplayText><record><rec-number>1355</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1355</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Smith, Candis Watts</author></authors></contributors><titles><title>Shifting From Structural to Individual Attributions of Black Disadvantage Age, Period, and Cohort Effects on Black Explanations of Racial Disparities</title><secondary-title>Journal of Black Studies</secondary-title></titles><periodical><full-title>Journal of Black Studies</full-title></periodical><pages>432-452</pages><volume>45</volume><number>5</number><dates><year>2014</year></dates><isbn>0021-9347</isbn><urls></urls></record></Cite><Cite><Author>Nunnally</Author><Year>2012</Year><RecNum>945</RecNum><record><rec-number>945</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">945</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Nunnally, Shayla C.</author><author>Carter, Niambi M.</author></authors></contributors><titles><title>Moving from Victims to Victors: African American Attitudes on the "Culture of Poverty" and Black Blame</title><secondary-title>Journal of African American Studies</secondary-title></titles><periodical><full-title>Journal of African American Studies</full-title></periodical><pages>423-455</pages><volume>`16</volume><number>3</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>(Smith 2014, Nunnally and Carter 2012) nor impervious to a reliance on the dominant racial logic of the time ADDIN EN.CITE <EndNote><Cite><Author>Bonilla-Silva</Author><Year>2014 [2003]</Year><RecNum>1045</RecNum><DisplayText>(Bonilla-Silva 2014 [2003])</DisplayText><record><rec-number>1045</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1045</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Bonilla-Silva, Eduardo</author></authors></contributors><titles><title>Racism without Racists: Color-blind Racism and the Persistence of Racial Inequality in the United States</title></titles><edition>4th</edition><dates><year>2014 [2003]</year></dates><pub-location>Lanham</pub-location><publisher>Rowman and LIttlefield Publishers, Inc.</publisher><urls></urls></record></Cite></EndNote>(Bonilla-Silva 2014 [2003]). Henry and Sears ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Henry</Author><Year>2002</Year><RecNum>30</RecNum><DisplayText>(2002)</DisplayText><record><rec-number>30</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">30</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Henry, P.J.</author><author>Sears, David O.</author></authors></contributors><titles><title>The Symbolic Racism 2000 Scale</title><secondary-title>Political Psychology</secondary-title></titles><periodical><full-title>Political Psychology</full-title></periodical><pages>253-283</pages><volume>23</volume><number>2</number><dates><year>2002</year></dates><urls></urls></record></Cite></EndNote>(2002) show that the racial resentment measure is reliable for Blacks, and Kam and Burge ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Kam</Author><Year>2018</Year><RecNum>2075</RecNum><DisplayText>(2018)</DisplayText><record><rec-number>2075</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522552616">2075</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kam, Cindy D</author><author>Burge, Camille D</author></authors></contributors><titles><title>Uncovering Reactions to the Racial Resentment Scale across the Racial Divide</title><secondary-title>The Journal of Politics</secondary-title></titles><periodical><full-title>The Journal of Politics</full-title></periodical><pages>314-320</pages><volume>80</volume><number>1</number><dates><year>2018</year></dates><isbn>0022-3816</isbn><urls></urls></record></Cite></EndNote>(2018) find that white and Black Americans relate to the four questions of the measurement in similar ways. Finally, we include a state-level variable representing state ideology ADDIN EN.CITE <EndNote><Cite><Author>Berry</Author><Year>1998</Year><RecNum>18</RecNum><DisplayText>(Berry et al. 1998)</DisplayText><record><rec-number>18</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519246096">18</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Berry, William D</author><author>Ringquist, Evan J</author><author>Fording, Richard C</author><author>Hanson, Russell L</author></authors></contributors><titles><title>Measuring citizen and government ideology in the American states, 1960-93</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>327-348</pages><dates><year>1998</year></dates><isbn>0092-5853</isbn><urls></urls></record></Cite></EndNote>(Berry et al. 1998) and an indicator variable for the year of the survey. The results of the model are used to predict the level of racial resentment for each of “types” of individuals based on combinations of the demographic and geographic identifiers. By “type” we are referring to the 4,800 permutations of individuals (3 race groups x 2 genders x 4 age groups x 4 education groups x 50 states) that we are able to ascertain within the constraints of the U.S. Census data. For instance, we predict the level of racial resentment for the average non-Hispanic white, male, between the ages of 18-29, with less than a high school education in Alabama, as well as the level of racial resentment of a non-Hispanic black, woman, older than 65, with a college education in Wyoming. We are interested in estimating racial resentment in the states over time. Previous work with dynamic MRP takes two different approaches to incorporating an over-time element to the analysis. One approach to incorporating time is to run the analysis iteratively with small windows of time. For instance, Pacheco (2011) pools survey data in three and five year floating blocks to create an annual estimate of opinion. On the one hand, this approach may have the benefit of incorporating information from neighboring years to mitigate potential effects of outlier survey years. On the other hand, this approach is relatively inefficient and may ignore as significant amounts of data ADDIN EN.CITE <EndNote><Cite><Author>Gelman</Author><Year>2016</Year><RecNum>8</RecNum><DisplayText>(Gelman et al. 2016)</DisplayText><record><rec-number>8</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245720">8</key></foreign-keys><ref-type name="Unpublished Work">34</ref-type><contributors><authors><author>Gelman, Andrew</author><author>Lax, Jeffrey</author><author>Phillips, Justin</author><author>Gabry, Jonah</author><author>Trangucci, Robert</author></authors></contributors><titles><title>Using Multilevel Regression and Poststratification to Estimate Dynamic Public Opinion</title></titles><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(Gelman et al. 2016). Not all scholars using this iterative approach create short “windows” of time. For example, in their creation of state level partisanship and ideology, Enns and Koch (2013) create annual estimates by only using data for a single given year. By using only information from a single year, this approach makes use of an even smaller percent of the total data. The other approach to dynamic MRP pools all of the survey data into one analysis and includes indicator variables for years PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HZWxtYW48L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (Gelman et al. 2016, Franko 2017). This approach generates comparable estimates to those generated in this first method, but because it incorporates the complete set of information, is a more efficient model with tighter standard errors ADDIN EN.CITE <EndNote><Cite><Author>Gelman</Author><Year>2016</Year><RecNum>8</RecNum><DisplayText>(Gelman et al. 2016)</DisplayText><record><rec-number>8</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245720">8</key></foreign-keys><ref-type name="Unpublished Work">34</ref-type><contributors><authors><author>Gelman, Andrew</author><author>Lax, Jeffrey</author><author>Phillips, Justin</author><author>Gabry, Jonah</author><author>Trangucci, Robert</author></authors></contributors><titles><title>Using Multilevel Regression and Poststratification to Estimate Dynamic Public Opinion</title></titles><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(Gelman et al. 2016). We take the latter approach here, pooling all survey data and including indicator variables for specific years.The results in the model reflect our expectations and the broad literature on predicting racial resentment. In brief, we find that age has a positive relationship with racial resentment, meaning that older people are more racially resentful (Nteta and Greenlee 2013). We find that higher education is associated with lower racial resentment. Finally, the results corroborate studies that show that white men tend to have higher levels of racial animus than other race-gendered groups (e.g. ADDIN EN.CITE <EndNote><Cite><Author>Smith</Author><Year>2013</Year><RecNum>134</RecNum><DisplayText>(Smith, Senter, and Strachan 2013)</DisplayText><record><rec-number>134</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1525110367">134</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Smith, Justin M</author><author>Senter, Mary</author><author>Strachan, J Cherie</author></authors></contributors><titles><title>Gender and white college students' racial attitudes</title><secondary-title>Sociological Inquiry</secondary-title></titles><periodical><full-title>Sociological Inquiry</full-title></periodical><pages>570-590</pages><volume>83</volume><number>4</number><dates><year>2013</year></dates><isbn>1475-682X</isbn><urls></urls></record></Cite></EndNote>(Smith, Senter, and Strachan 2013). The state-level ideology variable is also significant.Table B1. Predicting Racial Resentment CoeffStd. Err.Age (30-44)0.43885***0.06361Age (45-64)0.57282***0.06307Age (65+)0.46422***0.0719Educ (HS grad)0.25779*0.10734Educ (some college)-0.058440.1116Educ (college grad)-1.55261***0.11268Race - Black-3.82843***0.09855Race - Other-0.82888***0.8933Female-5.85958***1.38478Female*BlackFemale*OtherIdeology-0.01383*0.00537Intercept13.1919***1.41610 '***' 0.001 '**' 0.01 '*' 0.05?N: 25138 (787 obs missing due to missingness)Multiple R-squared:? 0.1616, Adjusted R-squared:? 0.1592?State indicator variables omitted from table for brevityAfter estimating the multilevel linear regression model, we predicted the level of racial resentment for each of the 4800 “types” of people using the full slate of independent variables in the model. We then weight these person types by the frequency of those person types by each state, incorporating this information from the U.S. Census. By combining the predicted level of racial resentment for each “type” of person by how many of those “types” of people there are in each state, we are able to construct a measure of the state-level racial resentment. The estimates of state racial resentment are reported in Table B2.Table B2. Racial Resentment Scores in the US States, 1988-2016State198819901992199420002004200820122016AK0.67930.62630.66840.68920.70320.71450.71250.72710.6589AL0.69680.65510.69100.70720.71690.70050.69340.71580.6597AR0.71380.66680.70780.73100.73610.72930.74160.75980.7045AZ0.68000.63110.66730.69120.68830.70270.71470.71630.6609CA0.62830.58220.62400.64490.64460.64660.65580.66610.6082CO0.62500.57500.61500.63400.63510.65240.66280.67050.6131CT0.64780.59480.63910.66340.66760.67430.67420.69220.6216DE0.66500.59900.63350.65960.67130.65720.65700.66790.6117FL0.68910.63810.67860.69470.69810.70340.70360.71290.6554GA0.68900.63730.67800.70100.70480.68250.68680.69910.6296HI0.61740.57050.61350.62520.63620.63750.64540.65790.5943IA0.62520.57530.61820.63930.65060.67740.68360.69610.6370ID0.60660.55800.58930.61270.62330.65550.65550.66360.6105IL0.64990.59860.63610.65990.66040.66330.66860.67690.6267IN0.66040.61400.65470.68000.68330.69940.70630.71940.6571KS0.63590.58280.62190.64880.65870.67090.67640.68320.6297KY0.65770.60180.64610.67160.68760.69640.69970.71670.6560LA0.70190.64270.69120.70840.71780.69330.69670.69570.6436MA0.59500.54710.59340.62450.62090.64020.64210.65530.5926MD0.63220.58870.63120.65670.65430.63530.64100.65320.5906ME0.64990.60800.64580.66680.67580.70540.70070.71880.6589MI0.64250.59610.64240.66650.66780.67820.67760.69380.6334MN0.60770.56520.60200.63050.63310.66380.65990.67570.6144MO0.65450.60470.64660.66910.67450.69210.69530.70530.6484MS0.68770.64140.68700.70200.70890.68050.67340.67590.6288MT0.63090.57870.61300.63960.64040.67630.67530.68060.6295NC0.66590.61980.65910.67510.68200.67560.67160.68870.6329ND0.64450.59430.64140.66180.66690.69800.70400.71840.6622NE0.66460.60810.63440.66630.67970.70550.70740.71480.6560NH0.61230.55940.59900.61680.62080.64690.65240.66210.6024NJ0.64750.60570.64870.67110.66780.66570.67760.69020.6260NM0.64390.58650.63130.65410.65620.66070.66300.67810.6200NV0.68470.63870.67340.68750.68490.69880.70370.71190.6584NY0.62700.57500.61420.63940.64440.64090.64540.66170.5971OH0.66480.61470.65660.68230.68280.69200.70350.71670.6575OK0.65040.60470.64320.67360.67630.68950.69900.71410.6584OR0.59010.53370.57110.59340.59880.62400.62700.63970.5834PA0.64950.60540.64870.66570.67630.69200.69460.71040.6472RI0.58650.54880.59840.61640.60410.63070.63370.64290.5824SC0.67210.63180.67290.69030.69350.67160.68220.69150.6344SD0.64790.60520.64450.66880.67400.70250.71140.72100.6626TN0.68410.63750.67570.70980.70800.70910.72680.73560.6693TX0.66900.61980.65770.68430.67900.68280.69010.69960.6406UT0.58790.53750.58130.60630.60540.63640.64920.66200.6024VA0.63740.58920.63180.65570.65630.64580.64910.66270.6062VT0.54520.50800.54810.55140.56300.58970.60040.60350.5436WA0.59900.55520.58670.61820.62660.64150.64390.65850.5993WI0.63790.59210.62420.65090.65530.67790.68700.69860.6445WV0.67730.62820.66160.68100.69370.72190.72790.74480.6894WY0.64530.59160.62670.65360.65850.69040.70280.71150.6503Notes on Change Over TimeThe correlation of state racial resentment scores within a state over time is very high, ranging from .80 to .99. This means that the average level of racial resentment in states does not vary substantially, and varies more in some states than others. We also compared the relative ranking of states (from 1 to 50) in terms of level of racial resentment. The rankings of state racial resentment are also high over time, though the correlation is lower than the racial resentment scores themselves. That means that there is some variability in the relative rank ordering of states’ racial resentment, though not a lot. Tables B3 and B4 show correlation for racial resentment scores and rankings, respectively, below.Table B3. Correlation within Racial Resentment Scores1988199019921994200020042008201220161988119900.987119920.9750.99119940.9760.9830.986120000.9820.9810.9780.986120040.840.8390.8130.840.864120080.8170.8190.7930.8250.8380.982120120.8010.8060.7820.8170.8340.9780.988120160.8140.8170.7880.8180.8360.9810.9860.9881 Table B4. Correlation within Racial Resentment Ranking1988199019921994200020042008201220161988119900.981119920.9660.985119940.9680.9820.987120000.9770.9780.9750.981120040.80.8140.7860.7980.833120080.7560.7710.7460.7640.7890.972120120.7370.7540.740.7570.7830.9660.979120160.7470.7670.7440.7590.7830.9650.9750.981It is common in MRP scholarship to assess validity by comparing the newly generated scores with widely accepted measures of the same concept. We are unable to do so here, as there are not other state-level measures of racial animus over time. Instead, we compare the estimated racial resentment to presidential vote (in presidential voting years), and to the Enns and Koch (2013) measures of state partisanship and ideology. These scores are more highly correlated with the Enns and Koch scores of ideology (which ranges from correlated at .33 in 1992 to .65 in 2008) than their measure of state partisanship (which ranges from around 0 in 1992 to .56 in 2008). 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