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Political Research Quarterly published Garcia and Stout 2020 "Responding to Racial Resentment: How Racial Resentment Influences Legislative Behavior". The article abstract indicates (emphasis added):

Through an automated content analysis of more than fifty four thousand press releases from almost four hundred U.S. House members in the 114th Congress (2015–2017), we show that Republicans from districts with high levels of racial resentment are more likely to issue press releases that attack President Barack Obama. In contrast, we find no evidence of racial resentment being positively associated with another prominent Democratic white elected official, Hillary Clinton. Our results suggest that one reason Congress may remain racially conservative even as representatives' cycle out of office may be attributed to the electoral process.

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Racial resentment conflates racial attitudes and political ideology, apparently even when controlling for factors such as partisanship and political ideology, so comparing how district racial resentment predicts the percentage of press releases attacking Barack Obama to how district racial resentment predicts the percentage of press releases attacking Hillary Clinton is a useful way to assess whether any association of racial resentment is due to the racial component of district racial resentment. But that comparison should involve a statistical test of whether the coefficient for district racial resentment in the Obama models differs from the coefficient for district racial resentment in the Clinton models. And my analyses indicate that, for results reported in the article, these coefficients don't differ at p<0.20.

My analyses indicated that the p-value is p=0.23 for a test of whether the coefficient on district racial resentment in an Obama model differs from the coefficient on district racial resentment in a Clinton model, using only a predictor of weighted district racial resentment and limiting the sample to Republican representatives. The p-value is about p=0.33 for a test comparing the key interaction coefficients in Models C and D in Garcia and Stout 2020 Table 1 (see the plot below).

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The Garcia and Stout 2020 abstract's claim that "…we find no evidence of racial resentment being positively associated with another prominent Democratic white elected official, Hillary Clinton" (p. 812) is contradicted in the main text of Garcia and Stout 2020:

Pearson's R for the relationship between the unweighted (.16) and the weighted (.14) district's racial resentment score and Republicans issuing of negative-Clinton press releases are statistically significant at .05.

I think that the "no evidence" claim refers to the lack of statistical significance for the Clinton models in Table 1 when adding statistical control, but the coefficient/standard error ratio is about 1.3 for the key coefficient for Clinton in Table 1 Model D, so that's some evidence. Adding "cluster(robust)" to the regression specification increases this t-statistic to 1.78, which is not no evidence. And then removing the control for candidate margin of victory gets the p-value under p=0.05.

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For the outcome variable codings of the percentage of press releases attacking Obama and the percentage of press releases attacking Clinton, 53% and 76% of the observations are zero, respectively. The outcome is a percentage, so I re-estimated the models using fractional logistic regression. As indicated in the output, the p-value for the interaction coefficient did not fall under p=0.15 in the Clinton models mentioned above or in the Obama models mentioned above.

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The key Table 1 coefficient is an interaction term that involves district racial resentment and the political party of the representative, but the abstract claims are limited to Republican representatives. I estimated the Table 1 models limited to Republican representatives: the p-value for racial resentment did not fall under p=0.80 for the Obama models. The p-value for racial resentment did not fall under p=0.40 for the Obama models limited to non-Republican representatives.

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So, in the fractional regression models discussed above, the key Table 1 interaction coefficient did not have a p-value under p=0.15; in the linear regression models discussed above with statistical control, district racial resentment did not predict at p<0.80 among Republican representatives the percentage of press releases attacking Obama; and in the linear regression models discussed above, the association of district racial resentment and the percentage of press releases attacking Obama did not differ at p<0.20 from the association of district racial resentment and the percentage of press releases attacking Clinton.

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NOTES

1. Thanks to Jennifer R. Garcia for sending me data for the article.

2. Results reported in the post are for the weighted models, but the Stata output contains results for unweighted models, in which the inferences or a lack of inferences are the same or similar.

3. Stata code. Stata output. R code for the plot.

4. For what it's worth, Republican members of Congress from districts with relatively low levels of racial resentment were more likely to issue press releases that attacked Obama than to issue press releases that attacked Clinton, measuring low district racial resentment as the bottom 10% of GOP districts by racial resentment; the same pattern held for Republican members of Congress from districts with relatively high levels of racial resentment, measured as the top 10% of GOP districts by racial resentment. Stata code for this analysis. Stata output for this analysis.

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The Ellis and Faricy 2020 Political Behavior article "Race, Deservingness, and Social Spending Attitudes: The Role of Policy Delivery Mechanism" discussed results from Figure 2:

This graph illustrates that while the mean support for this program does not differ significantly by spending mode, racial attitudes strongly affect the type of spending that respondents would prefer: those lowest in symbolic racism are expected to prefer the direct spending program to the tax expenditure program, while those high in symbolic racism are expected to prefer the opposite (p. 833).

Data for Study 2 indicated that, based on a linear regression using symbolic racism to predict nonBlack participant support for the programs, controlling for party identification, income, trust, egalitarianism, White race, and male, as coded in the Ellis and Faricy 2020 analyses, the predicted level of support at the lowest level of symbolic racism with other predictors at their means was 3.37 for the tax expenditure program and 3.87 for the direct spending program, but the predicted level of support at the highest level of symbolic racism was 3.44 for the tax expenditure program and 3.24 for the direct spending program.

However, linear regression can misestimate treatment effects. Below is a plot of the treatment effect estimated at individual levels of symbolic racism, with no controls (left panel) and with the aforementioned controls (right panel).

There does not appear to be much evidence in these data that participants high in symbolic racism preferred one program to the other. For example, in the left panel, at the highest level of symbolic racism, the estimated support was 2.76 for the tax expenditure program and was 2.60 for the direct spending program (p=0.41 for the difference). Moreover, the p-value for the difference did not drop under p=0.4 if participants from adjacent high levels of symbolic racism are included (7 and 8, or 6 through 8, or 5 through 8, or 4 through 8), with or without the controls.

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NOTES

1. Code for my analyses and plot. Data for the plot.

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In a Monkey Cage post and Chapter 6 of their Ignored Racism book, Mark D. Ramirez and David A.M. Peterson reported on a conjoint experiment, in which White adult U.S. citizens were given a profile of two target persons and were asked "Which of these citizens do you prefer to keep registered to vote?". The experiment manipulated profile target characteristics such as race, gender, and criminal status.

Latina/o racism-ethnicism (LRE) was measured with responses to four "modern racism"-type items, such as "Many other ethnic groups have successfully integrated into American culture. Latinos and Hispanics should do the same without any special favors".

Results in Figure 6.7 indicated that high LRE participants favored White targets over Hispanic targets. But Figure 6.7 results also indicated that low LRE participants favored Hispanics targets over White targets. This experiment thus provided further evidence that a nontrivial percentage of participants at low levels of modern racism / modern sexism items have racial bias and/or gender bias. Here is prior post on a study indicating that persons at low levels of hostile sexism discriminated against men.

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Racial attitudes have substantially correlated with environmental policy preferences net of partisanship and ideology, such as here, here, and here. These results were from data collected in 2012 or later, so, to address the concern that this association is due to "spillover" of anti-Obama attitudes into non-racial policy areas, I checked whether the traditional four-item measure of racial resentment substantially correlated with environmental policy preferences net of partisanship and ideology in ANES data from 1986, which I think is the first time these items appeared together on an ANES survey.

I limited the sample to non-Hispanic Whites and controlled for participant gender, education, age, family income, partisanship, and ideology, and the race of the interviewer. The outcome variable concerns federal spending on improving and protecting the environment, which I coded so that 1 was "increased" and 0 was "same" or "decreased", with Don't Knows and Not Ascertaineds coded as missing; only 4 percent of respondents had indicated "decreased".

Other model variables at their means, the predicted probability of a reported preference for increased federal spending on improving and protecting the environment was 65% [54%, 76%] at the lowest level of racial resentment, but fell to 39% [31%, 47%] at the highest level of racial resentment. That's a substantial 26 percentage-point drop "caused" by racial attitudes, for anyone who thinks that such a research design permits causal inference.

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NOTES:

1. Kinder and Sanders 1996 used racial resentment to predict non-racial attitudes (pp. 121-124), but, based on reading that section, I don't think KS96 predicted this environmental policy preference variable.

2. Data source: Warren E. Miller and the University of Michigan. Institute for Social Research. American National Election Studies. ANES 1986 Time Series Study. Inter-university Consortium for Political and Social Research [distributor].

3. Stata code and output.

4. The post title is about 1986, but some ANES 1986 interviews were conducted in Jan/Feb 1987. The key result still holds if the sample is limited to cases with an "86" year for the "Date of Interview" variable, with respective predicted probabilities of 67% and 37% (p=0.002 for racial resentment). About 4 or so dates appear to be incorrect, such as "01-04-86", "12-23-87", and "11-18-99". Code:

logit env2 RR4 i.female i.educ age i.finc i.party i.ideo i.V860037 if NHwhite==1 & substr(V860009, 7, 8)=="86"
margins, atmeans at(RR4=(0 1))

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This post discusses three unpublished studies that I don't expect to be working on in the foreseeable future.

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1.

I posted at APSA Preprints "The Yuge Effect of Racist Resentment on Support for Donald Trump and…Attitudes about Automobile Fuel Efficiency Requirements?". This paper reports evidence indicating that a published measure of "racist resentment" does a remarkably good job predicting non-racial outcome variables such as environmental policy preferences. Sample results are at this prior post.

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2.

I posted at OSF a write-up of results for a preregistered study "Belief in Genetic Differences and Support for Efforts to Reduce Inequality". I reported these data in an unaccepted proposal for a short study with the Time-sharing Experiments for the Social Science.

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3.

Data for the second study and this third study are from a 2017 YouGov survey that I had conducted using funds from Illinois State University New Faculty Start-up Support and the Illinois State University College of Arts and Sciences. My initial plan was to run a version of my 2014 TESS proposal, but I saw the Carney and Enos paper (current version) in the 2015 MPSA program and realized that their experiments were similar to my plan, so I changed the survey.

Here is an early version of the planned survey.

One element of the new survey was an experiment involving attitudes about food stamps. I planned for the final three slides to each include an item about poor Americans, with the third item being randomly assigned to be about either poor White Americans or poor Black Americans. The third item was:

Most [randomize: poor Black Americans/poor White Americans] who receive government welfare could get along without it if they tried.

Carney and Enos had done something similar with the traditional racial resentment items, but these traditional racial resentment items aren't particularly good at measuring resentment (such as "Over the past few years, blacks have gotten less than they deserve"). The "could get along without it if they tried" is an old racial resentment item that wasn't included on the traditional four-item battery, but I think it does a nice job of capturing resentment.

I posted at OSF a write-up of results from this "unnecessary welfare experiment". I submitted to a journal a more extensive analysis and discussion, but the manuscript was rejected in peer review.

The "unnecessary welfare experiment" research design might have caused the estimated differences to be underestimates, given that the prior two items had the same response scale and were about poor Americans in general. Nonetheless, the results provide evidence that, in 2017, non-Hispanic White conservatives and non-Hispanic Whites in general reported more agreement that most poor Black Americans who receive government welfare could get along without it if they tried, compared to their reported agreement to the same statement about poor White Americans who receive government welfare.

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Racial resentment and symbolic racism are terms used to describe a set of measures used in racial attitudes research, including statements such as "Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors". This item and at least some of the other racial resentment items confound racism and nonracial ideology; in this "special favors" item, an individualist who believes that everyone should work their way up without special favors would select a response on the same side of the scale as an antiBlack racist who believes that only Blacks should work their way up without special favors.

Feldman and Huddy (2005) concluded that "racial resentment is an inadequate measure of prejudice because it confounds prejudice and political ideology" (p. 181), which is consistent with factor analysis of racial resentment items (Sears and Henry 2003: 271). Some research has addressed this confounding with what Feldman and Huddy (2005: 171) call the multivariate approach, in which the analysis includes statistical control for related ideological values. The logic of this multivariate approach is that racial resentment confounds ideology and antiBlack animus so that controlling for ideology should permit the residual association of racial resentment to be interpreted as the association due to antiBlack animus.

The analysis below approaches from the opposite direction: racial resentment confounds ideology and antiBlack animus so that controlling for antiBlack animus should permit the residual association of racial resentment to be interpreted as the association due to ideology. Moreover, if controls for ideology and for antiBlack animus are both included, then the association of racial resentment with an outcome variable should be zero. But this is not even close to being true, as illustrated below in a figure that reports the association of racial resentment with racial or possibly racialized outcome variables, using different sets of statistical control.

In each panel above, the top estimate indicates the association of racial resentment with the outcome variable controlling for only demographics. The second and third estimates respectively indicate the association of racial resentment with outcome variables after controls for demographics and racial attitudes and after controls for demographics and ideology. The fourth and fifth estimates respectively indicate the association of racial resentment with outcome variables after controls for demographics, ideology, and racial attitudes and after controls for demographics, ideology, and racial animus. The key comparison is between the third estimate and the fourth and fifth estimates: the measures of racial attitudes and racial animus had relatively little impact on the racial resentment estimate once the controls for ideology were included in the analysis. For example, in the top left panel, the coefficient for racial resentment was 0.51 controlling for demographics and ideology, was 0.48 controlling for demographics, ideology, and racial attitudes, and was 0.52 controlling for demographics, ideology, and racial animus. In a common racial resentment association analysis, the 0.51 coefficient controlling for demographics and ideology would be assigned to antiBlack animus, but the addition of seven racial attitudes controls accounted for only 0.03 of the 0.51 coefficient and the inclusion of six antiBlack animus controls did not even reduce the 0.51 coefficient. (see the Notes below for more description on the measurements).

A reasonable critique of the above analysis is that racial resentment taps a form of antiBlack racism that is not captured or is not well captured in the included measures of racial attitudes and racial animus. But, from what I can tell, that is an equally valid criticism of analyses that control for ideology: the nonracial ideology captured in racial resentment measures is not captured or not well captured in the included measures of ideology.

NOTES

1. The sample for the analysis was the 3,261 non-Hispanic Whites who completed face-to-face or online the pre- and post-election surveys, conducted between 8 September 2012 and 24 January 2013, and who were not listwise deleted from a model due to missing data for a variable. Each variable in the analysis was coded to range from 0 to 1. Linear regressions without weights were used to predict values of the outcome variables.

The racial resentment measure summed responses to the four ANES 2012 racial resentment items. Models included demographic controls for participant sex, marital status, age, education level, and household family income. Ideological controls were self-reported partisanship, self-reported ideology, an item about guaranteed jobs, an index of attitudes about the role of government, a moral traditionalism index, an authoritarianism index, and an egalitarianism index.

One set of models included seven controls for racial attitudes: a feeling thermometer difference of ratings of Whites and ratings of Blacks, a rating difference for Blacks and for Whites in general on a laziness stereotype scale, a rating difference for Whites and for Blacks in general on an intelligence stereotype scale, an item rating admiration of Blacks, an item rating sympathy for Blacks, an item measuring the perceived political influence of Blacks relative to Whites, and a difference in ratings of the level of discrimination in the United States today against Whites and against Blacks. Another set of models included six dichotomous controls that attempted to isolate antiBlack animus: a more than 20-point feeling thermometer rating difference in which Whites were rated higher than Blacks and with Whites rated at or above 50 and Blacks rated below 50, a rating of Blacks as lazier in general than Whites, a rating of Whites as more intelligent in general than Blacks, an indication of never feeling sympathy for Blacks, an indication that Blacks have too much influence in American politics but Whites don't, and an indication that there is no discrimination against Blacks in the United States today but that there is discrimination against Whites in the United States today.

2. Code for the analysis is here.

3. Results for the 2016 ANES are below:

4. Code for the 2016 ANES analysis is here.

5. Citations:

American National Election Studies (ANES). 2016. ANES 2012 Time Series Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-05-17. https://doi.org/10.3886/ICPSR35157.v1.

American National Election Studies, University of Michigan, and Stanford University. 2017. ANES 2016 Time Series Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-09-19. https://doi.org/10.3886/ICPSR36824.v2.

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I previously discussed Filindra and Kaplan 2016 in terms of the current state of political science research transparency, but this post will discuss the article more substantively.

Let's start with a re-quote regarding the purpose and research design of the Filindra and Kaplan 2016 experiment:

To determine whether racial prejudice depresses white support for gun control, we designed a priming experiment which exposed respondents to pictures of blacks and whites drawn from the IAT. Results show that exposure to the prime suppressed support for gun control compared to the control, conditional upon a respondent's level of racial resentment (p. 255).

Under the guise of a cognitive test, we exposed 600 survey participants who self-identified as white to three pictures of the faces of black individuals and another three of white individuals (p. 261).

For predicting the two gun-related outcome variable scales for the experiment, Table 1 indicates in separate models that the treatment alone, the treatment and a measure of symbolic racism alone, and the interaction of the treatment and symbolic racism all reach statistical significance at at least p<0.10 with a two-tailed test.

But the outcome variable scales are built from a subset of measured gun-related items. Filindra and Kaplan 2016 reported an exploratory factor analysis used to select items for outcome variable scales: 7 of 13 policy items about guns and 8 of 9 belief items about guns were selected for inclusion in the scales. The dataset for the article uploaded to the Dataverse did not contain data for the omitted policy and belief items, so I requested these data from Dr. Filindra. I did not receive access to these data.

It's reasonable to use factor analysis to decide which items to include in a scale, but this permits researcher flexibility about whether to perform the factor analysis in the first place and, if so, about whether to place all items in a single factor analysis or to, as in Filindra and Kaplan 2016, separate the items into groups and conduct a factor analysis for each group.

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But the main problem with the experiment is not the flexibility in building the outcome variable scales. The main problem is that the research design does not permit an inference of racial prejudice.

The Filindra and Kaplan 2016 experimental design of a control and a single treatment of the black/white photo combination permits at most only the inference of a "causal relationship between racial considerations and gun policy preferences among whites" (p. 263, emphasis added). However, Filindra and Kaplan 2016 also discussed the experiment as if the treatment had been only photos of blacks (p. 263):

Our priming experiment shows that mere short exposure to pictures of blacks can drive opposition to gun control.

The Filindra and Kaplan experimental design does not permit assigning the measured effect to the photos of blacks isolated from the photos of whites, so I'm not sure why peer reviewers would have permitted that claim, which appeared in exactly the same form on page 9 of Filindra and Kaplan's 2015 MPSA paper.

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Filindra and Kaplan 2016 supplement the experiment with a correlational study using symbolic racism to predict the ANES gun control item. But, as other researchers and I have noted, there is an inferential problem using symbolic racism in correlational studies, because symbolic racism conflates racial prejudice and nonracial attitudes; for example, knowing only that a person believes that blacks should not receive special favors cannot tell us whether that person's belief is motivated by antiblack bias, nonracial opposition to special favors, or some combination of the two.

My article here provides a sense of how strong a residual post-statistical-control correlation between symbolic racism and an outcome variable must be before one can confidently claim that the correlation is tapping antiblack bias. To illustrate this, I used linear regression on the 2012 ANES Time Series Study data, weighted and limited to white respondents, to predict responses to the gun control item, which was coded on a standardized scale so that the lowest value is the response that the federal government should make it more difficult to buy a gun, the middle response is that the rules should be kept the same, and the highest value is that the federal government should make it easier to buy a gun.

The standardized symbolic racism scale produced a 0.068 (p=0.012) residual correlation with the standardized gun control item, with the model including the full set of statistical control as described in the note below. That was about the same residual correlation as for predicting a standardized scale measuring conservative attitudes toward women (0.108, p<0.001), about the same residual correlation as for predicting a standardized abortion scale (-0.087, p<0.001), and about the same residual correlation as for predicting a standardized item about whether people should be permitted to place Social Security payroll taxes into personal accounts (0.070, p=0.007).

So, based on these data alone, racial prejudice as measured with symbolic racism has about as much "effect" on attitudes about gun control as it does on attitudes about women, abortion, and private accounts for Social Security. I think it's unlikely that bias against blacks causes conservative attitudes toward women, so I don't think that the 2012 ANES data can resolve whether or the extent to which bias against blacks causes support for gun control.

I would bet that there is some connection between antiblack prejudice and gun control, but I wouldn't argue that Filindra and Kaplan 2016 provide convincing evidence of this. Of course, it looks like a version of the Filindra and Kaplan 2016 paper won a national award, so what do I know?

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NOTES:

1. Code for my analysis reported above is here.

2. The full set of statistical control has controls for: respondent sex, marital status, age group, education level, household income, employment status, Republican party membership, Democratic Party membership, self-reported political ideology, and items measuring attitudes about whether jobs should be guaranteed, limited government, moral traditionalism, authoritarianism, and egalitarianism.

3. Filindra and Kaplan 2016 Table 2 reports a larger effect size for symbolic racism in the 2004 and 2008 ANES data than in the 2012 ANES data, with respective values for the maximum change in probability of support of -0.23, -0.25, and -0.16. The mean of the 2004 and 2008 estimate is 50% larger than the 2012 estimate, so increasing the 2012 residual correlation of 0.068 by 50% produces 0.102, which is still about the same residual correlation as for conservative attitudes about women. Based on Table 6 of my article, I would not be comfortable alleging an effect for racial bias with anything under a 0.15 residual correlation with a full set of statistical control.

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