Friday, December 19, 2014

What Predicts the Spatial Distribution of IAT Scores in the United States?

On December 15, an article by Chris Mooney on the Washington Post’s Wonkblog presented an interesting spatial distribution of state-level IAT scores across the United States. Based on a sample of more than 1.5 million participants from Project Implicit, IAT scores appear to be systematically higher in the southeastern and eastern US. (For an interactive version of the map, click here.)

The map above raises (at least!) two questions. First, what might predict this distribution of IAT scores? And second, given that the differences between scores (though significant with this large sample) are quite small (range = .341 - .456), are they meaningful?

To address these questions, we examined a number of potential predictors of state-level IAT scores. We selected predictors either because they were theoretically plausible (e.g., state level indices of racial segregation, median income, income inequality, the ratio of White:Black state residents, political preferences) or because they are known to share a similar spatial distribution to the observed IAT scores (e.g., ‘social capital’, status as a former slave-holding state). In addition, we examined whether state-level IAT scores could be used to predict actual behaviors – specifically, numbers of racist tweets recorded after the 2012 Presidential election.

What Predicts this Distribution of IAT Scores? 

Correlational analyses revealed several significant correlates of state-level IAT scores.  Note that where possible we examined variables around the year 2009 because this was the modal year of IAT data collection.

·         We used the ‘Civic Life Index’as a measure of social capital.  This index captures a composite of volunteering behavior, neighborhood engagement, voter participation and civic infrastructure.  There was a strong negative correlation between this measure of social capital and IAT scores (r (49) = -.576, p < .001)
·         State levels of income inequality (GINI) were positively correlated with IAT scores (r (49) = .409, p < .01).
·         The ratio of White to Black state residents (log transformed because it is skewed) was a strong negative predictor of IAT scores (r (49) = -.733, p < .001) – the more White relative to Black state residents, the lower the IAT score.
·         We indexed political preferences in terms each state’s Electoral College vote in the 2008 Presidential election.  States that voted for McCain (M = .405, SD = .030) vs. Obama (M = .398, SD = .027) did not have significantly different IAT scores (F (1, 49) = 0.79, p > .30).
·         In contrast, status as a former slaveholding state (in 1860) was a strong predictor of current day IAT scores.  Former slave states (M = .426, SD = .022) had significantly higher IAT scores than non-slaveholding states (M = .390, SD = .023; F (1, 49) = 27.45, p <. 001).
·         An index of Black/White segregation and state-level median income did not significantly correlate with IAT scores (rs < |.17|, ps > .20)

Of course, many of these variables were correlated with each other, so we ran a series of regression analyses that allowed us to identify the unique effects of each potential predictor.  Entering all of the variables EXCEPT population composition (ratio of White to Black residents) into a regression equation showed that two predictors remained significant – the Civic Life Index negatively predicted IAT scores (β = -.449, p < .01), whereas status as a former slaveholding state positively predicted IAT scores (β = .439, p < .01). 

When, however, the ratio of White to Black state residents (log transformed) was entered into a regression equation with these two variables, the contributions of the Civic Life Index (β = -.173, p > .10) and slaveholding status (β = .214, p > .10) dropped to non-significance.  By far the strongest single predictor of state-level IAT scores was the ratio of White:Black state residents (β = -.470, p < .01), accounting for a rather astonishing 53.8% of the variance.  States where Whites outnumber Blacks substantially in the population have lower average IAT scores.  In contrast, states where Blacks make up proportionally more of the population have higher average IAT scores.

Figure: Relationship between White:Black Population Ratio (log) and IAT Scores

Interpretation.  The strong association between population composition and IAT scores is consistent with the idea that bias increases when and where there is greater perceived competition between groups.  In the political science and sociology literatures this sort of population composition effect is often termed the “racial threat hypothesis” (Key, 1949).  In states where the African American outgroup comprises a larger proportion of the population, Whites may perceive greater competition for political, cultural and economic resources (e.g., Tolbert & Grummel, 2003).  Further, due to negative stereotypes about African Americans, they may also perceive greater risk for cross-race crime (e.g., Eitle, D'Alessio & Stolzenberg, 2002).

In contrast, these findings are inconsistent with any simple hypothesis that contact between members of different racial groups will lead to reduced bias.  (Of course, social psychologists know this anyway!)

Critically, however, this pattern should be interpreted within its historical context.  It is not a coincidence that bias is greater in former slaveholding states.  In these and other states with large African American communities, for example, emancipation and extension of full voting rights during the civil rights era posed a significant threat to White’s political power and historical dominance. Understandings of racial identity and the nature of intergroup relationships forged in the past persist today and likely continue to exert influence (see Acharya, Blackwell & Sen, under review; Jackman, 1994). In a different historical context, however, the relative size of racial groups might not have the same relationship to bias. 

Do State-Level IAT Scores Predict Anything?

After the 2012 election, geographers at the University of Kentucky plotted the distribution of racist Tweets geolocated by state.  They created a racist tweet location quotient “that indicates each state's share of election hate speech tweet relative to its total number of tweets”. We found that the distribution of tweets they observed can be predicted by state-level IAT scores, such that states with higher average IAT scores tended to emit more racist tweets (r (49) = .326, p <. 05). 

We further examined whether this relationship varied as a function of which candidate (Romney or Obama) won the Electoral College vote in each state.  Perhaps unsurprisingly, numbers of racist tweets were greater in states that voted for Romney (who lost the election; β = .409, p < .01; R2 = .25).  However, there was also a significant interaction between state vote and IAT scores (β = 6.78, p < .01; ΔR2 = .14).  The positive relationship between IAT scores and racist tweets was significant among states that voted for Romney (β = .48, p < .05), but was non-significant among states that voted for Obama (β = -.25, p > .20).

 Figure: Relationships between IAT Scores and Racist Tweet Location Quotient for States that Voted for Romney Vs. Obama

Interpretation.  This pattern is consistent with contemporary theories of racial bias (e.g., aversive racism). Given that social norms generally prohibit overtly racist behavior, biased attitudes often only predict behavior when people feel they have an excuse to behave in a biased fashion or perhaps when something (like frustration or anger about a preferred candidate losing an election) reduces control over biased responses.  Alternately, the normative climate in states that voted for Romney might be have felt sufficiently accepting of prejudice to some individuals following the election to sanction the translation of biased attitudes into behavior.


We started with two initial questions. 

What predicts the spatial distribution of IAT scores in the United States?  A cluster of variables are correlated with state-level IAT scores, including indices of social capital, income inequality and historical status as a slaveholding state.  Notably, however, the strongest correlate was the ratio of White to Black state residents, consistent with the idea that intergroup competition and identity threat contributes to the type of  bias indexed by the IAT.

Given that the differences between states’ IAT scores are quite small, are they meaningful?  The answer to this second question appears to be yes.  First, the fact that IAT scores are related to variables like social capital and population composition suggests that they vary in a systematic fashion.  Second, we found that state-level IAT scores predicted a form of biased behavior – namely the number of racist tweets produced in each state following the 2012 election of President Barack Obama. Thus, although the observed differences between states are indeed small, the very large sample (1.5 million+) from which these estimates of bias are derived means that they are stable, predictable and predictive.

Finally, it is important to note that the current analyses sample in a limited way from a much larger universe of possible predictors.  We believe it was an informed sampling, but there are certain to be other important predictors out there.  Further, a more sophisticated approach would examine effects at the county-level.  We are currently engaged in some county-level analyses, and hope to report findings in the near future. 

The Data

If you'd like to take a look yourself or have ideas about additional predictors (urban/rural differences being an obvious contender), the data for these analyses can be downloaded here.  And many thanks to Brian Nosek and Project Implicit for providing open access to the IAT data!

Monday, August 25, 2014

What is our purpose? What are we working for?

As we start a new academic year, it's hard to imagine a better mission statement than these concluding words from Gunnar Myrdal's first volume of An American Dilemma...

"Social study is concerned with explaining why all these potentially and intentionally good people so often make life a hell for themselves and each other when they live together, whether in a family, a community, a nation or a world...

The rationalism and moralism which is the driving force behind social study, whether we admit it or not, is the faith that institutions can be improved and strengthened and that people are good enough to live a happier life.

With all we know today there should be the possibility to build a nation and a world where people's propensities for sympathy and cooperation would not be so thwarted."

- Gunnar Myrdal, 1942, An American Dilemma