We now move to tests of our theoretical expectations. After describing the survey data, we present survey results illustrating the central puzzle of this research project. Consistent with previous research, we find that majorities of the American public support policies intended to aid homeless people. However, we also find widespread support for exclusionary policies that have become popular in U.S. cities and states in recent years. Following this section, we present our first key finding: that disgust sensitivity is a powerful predictor of support for exclusionary policies, but has no meaningful effect on aid policies. Our second contribution is to show that group affect cannot explain why so many people support both aid and exclusionary policies, nor can it explain the effects of disgust sensitivity. Finally, we show experimental evidence that disease cues in the media can amplify the effect of disgust sensitivity on support for banning panhandling.

Data

We rely on two separate surveys; exact question wording for each survey can be found in the Supplementary Material in Appendix 1. First, we collected data on attitudes towards the homeless in a national sample from a module in the 2014 Cooperative Congressional Election Study (CCES; N = 861).Footnote 8 In the pre-election wave of the survey, we measured pathogen disgust sensitivity using four items from the seven-item pathogen disgust subscale of the Three Domains of Disgust Scale (TDDS; Tybur et al. 2009).Footnote 9 We rely on this measure as it is argued to be the best available measure of pathogen disgust sensitivity and the behavioral immune system (Lieberman and Patrick 2014; Tybur et al. 2014). In addition, we measured attitudes towards the homeless using a 101-point feeling thermometer. In the post-election wave, approximately 1 month later, we randomly assigned subjects to one of four experimental conditions, described further below. Our key outcome measures consist of two classes of policy attitudes—exclusionary policies and aid policies. Exclusionary policies include support for banning sleeping in public and banning panhandling. Aid policies consist of support for subsidized housing for the homeless and increased government aid to the homeless.

Second, we replicated and extended our primary observational results with more robust measures using a convenience sample of adults recruited from Amazon’s Mechanical Turk (MTurk; N = 504). While MTurk samples are not nationally representative, they replicate key findings on the psychological correlates of political ideology (Clifford et al. 2015) and routinely replicate experimental results from nationally representative surveys (e.g., Berinsky et al. 2012; Mullinix et al. 2016; Weinberg et al. 2014). Furthermore, MTurk respondents tend to be more attentive than respondents in many common samples (Hauser et al. 2015; Weinberg et al. 2014). Subjects were asked about their support for the same four homelessness policies. At the end of the survey, we measured pathogen disgust sensitivity using the full seven-item TDDS subscale.

The Strange Popularity of Policies Excluding Homeless People from Public Life

We begin the empirical analyses by presenting distributions of policy attitudes in order to illustrate the central puzzle driving this project. We analyze only the 462 observations from the two control conditions (No Stimulus, Neutral; described further below) because results do not meaningfully differ across these two conditions and because doing so facilitates presentation and maximizes statistical power. We exclude data from our two treatment conditions because we expect the treatments to affect policy attitudes and our interest here is in baseline public opinion. Consistent with previous research, our CCES respondents strongly support helping homeless people, as shown in the left-hand panel of Fig. 1.Footnote 10 Sixty percent support increasing aid to the homeless, while only 19 % oppose it (the remainder neither support nor oppose it). Similarly, 65 % favor providing subsidized housing to homeless people, while only 17 % oppose it. At the same time, however, a substantial proportion of the public also supports exclusionary policies: 52 % support a ban on panhandling, while only 23 % oppose a ban (the remainder neither support nor oppose a ban). Meanwhile, 46 % support a ban on sleeping in public areas, and 30 % oppose the ban. In fact, even when we restrict the analysis to those who support increased aid, we still find that a plurality support exclusionary policies. The middle and right-hand panel of Fig. 1 displays support for exclusionary policies broken down by support for aid to homeless people. Even among those who support aid, a plurality (47 %) support banning panhandling and a plurality (44 %) also support banning sleeping in public. Given that, as previous research has found, aid to the homeless is such a popular proposition, it is puzzling that policies with punitive effects also attract such widespread support.

Fig. 1 Pluralities of those who support aiding homeless people also support exclusionary policies. Note Policy attitudes are trichotomized. The favor category in this figure includes respondents who 'strongly favor,' 'somewhat favor,' and 'slightly favor' each policy. The oppose category includes respondents who 'strongly oppose,' 'moderately oppose,' or 'slightly oppose' each policy. The neutral category represents respondents who 'neither favor nor oppose' the policy Full size image

The Divergent Effects of Disgust Sensitivity

We have argued that disgust sensitivity motivates support for exclusionary policies while leaving opinion about aid policies unaffected. If this is the case, we should observe positive associations between disgust sensitivity and support for exclusionary policies, and we should observe no such associations between disgust sensitivity and opinion about aid policies. In order to test these propositions, we conduct a series of OLS regressions predicting each policy attitude. Our control variables include partisan identification, ideological self-identification, and church attendance, variables that are likely correlated with both disgust sensitivity (Olatunji 2008; Terrizzi et al. 2013) and our outcome variables (e.g., Toro and McDonell 1992).Footnote 11 For the CCES analysis, since this dataset includes an experiment, we analyze data only from the two control conditions (No Stimulus, Neutral) in order to observe associations in the absence of a treatment effect. Our key independent variable of interest is disgust sensitivity, which is scored as an average of the items making up the TDDS pathogen disgust subscale (CCES: α = .75; MTurk: α = .85; the distribution of the variable can be found in the Supplementary Material in Appendix 2). For these analyses, as in all analyses throughout the document, all statistical tests are one-tailed and all variables are coded from 0 to 1 in order to facilitate interpretation.

In the left-hand side of Fig. 2, we plot the effect of shifting disgust sensitivity from the 10th percentile to the 90th percentile on exclusionary policy attitudes (full model details are shown in Supplementary Material in Appendix 3). The results indicate that, as expected, disgust sensitivity is positively and powerfully associated with support for exclusionary policies. Across both the CCES and MTurk studies, those who are more easily disgusted are more likely to support both banning sleeping in public areas and banning panhandling. This relationship is statistically significant, and the magnitude is meaningful in both samples, ranging from slightly more than one-half of a point to slightly more than one full point on the seven-point scale of the dependent variable in the CCES sample. Indeed, in the CCES sample the coefficient is the largest in both models, rivaled most closely by ideology.

Fig. 2 Disgust sensitivity predicts exclusionary, but not aid policy attitudes. Note CCES Module. Effects are estimated from models in the Supplementary Material in Appendices 3 and 4 and represent the effect of movement on the disgust sensitivity scale from the 10th percentile to the 90th percentile. 90 % confidence intervals are used to reflect the one-tailed (directional) hypothesis tests Full size image

Also as expected, we find null relationships between disgust sensitivity and opinion about policies intended to aid the homeless. The right-hand side of Fig. 2 shows the effect of shifting disgust sensitivity from the 10th percentile to the 90th percentile (model details shown in the Supplementary Material in Appendix 4). The findings reveal not merely absence of evidence but evidence of absence: the coefficients on disgust sensitivity are close to zero, and the standard errors are small, leading to a high level of confidence that those who feel disgust easily are no less likely than their counterparts to support government efforts to help the homeless.Footnote 12 Notably, these null findings are evident for both of the policies (aid to the homeless and subsidizing housing for the homeless) and across both the CCES module and the Mechanical Turk study.

Disgust sensitivity has demonstrated considerable explanatory power as we have attempted to explain support for exclusionary policies, but consistently has no effect on opinion about aid policies. Also notable is the failure of traditional explanatory factors to do the same. Ideology, for example, predicts consistency in policy attitudes; conservatives are less likely to support aid than liberals, but they are more likely to support exclusionary policies. Furthermore, interactive analyses reveal no meaningful difference in the disgust sensitivity coefficient across ideological groups: among liberals and conservatives alike, disgust sensitivity is positively associated with support for exclusionary policy but not associated with opinion about policies intended to aid the homeless. Finally, in our samples ideology is only weakly correlated with disgust sensitivity in any case (r = .06, p = .06; for similar results, see Tybur et al. 2010).

Building on Previous Research: The Limited Explanatory Power of Group Affect

So far, we have shown evidence from two separate surveys, including a large national sample, that disgust sensitivity predicts exclusionary attitudes towards the homeless without undermining support for aid. However, it is natural to wonder whether the group affect approach, which has been dominant in the literature, can explain these effects. According to our theory, this should not be the case. Disgust sensitivity should motivate the desire for physical distance without necessarily undermining the desire to help. Similarly, one might genuinely desire to help a sick friend, while still maintaining the physical distance necessary to avoid contracting the illness. We use the CCES data to test this claim in supplementary analyses in two ways.

As a first step, we predict feelings towards homeless people (rescaled to range from 0 to 1) as a function of disgust sensitivity and the same control variables used in previous models (full model results shown in the Supplementary Material in Appendix 5). Affect was measured in the first wave of the survey, along with disgust sensitivity, and thus we analyze the full sample. Disgust sensitivity does have a suggestive, but weak, relationship with affect towards homeless people (p = .09, one-tailed). While the coefficient is negative, as one might expect, it is substantively small (b = −.05) and dwarfed by the effects of many other variables in the model, including ideology (b = −.13), age (b = .10), and church attendance (b = .09). Thus, disgust sensitivity has little apparent effect on feelings towards the homeless, suggesting that antipathy cannot be a driver of the effects of disgust sensitivity.

As a second test, we re-analyze our policy models above, restricting the sample to the control conditions, while including a control for feelings towards the homeless (details shown Supplemenatry material in Appendix 5). As one would expect, positive feelings towards the homeless strongly predict support for aid policies (ps < .001). Notably, positive affect also strongly predicts opposition to exclusionary policies (ps < .01), providing evidence that these policies are widely seen as harmful (contrary to the “tough love” perspective).Footnote 13 Together, these results also reveal that group affect cannot explain the puzzle, as positive feelings towards the homeless predict supporting aid and opposing exclusionary policies. Most importantly though, controlling for group affect does not substantively affect any of the inferences we draw about the effect of disgust sensitivity on policy attitudes. Disgust sensitivity still has no apparent effect on attitudes towards aid policies (ps > .83), but large and statistically significant effects on supporting exclusionary policies (ps < .01). Moreover, the magnitudes of the effects of disgust sensitivity are on par with the effects of group affect, a remarkable finding given that we view group affect to be closer to the dependent variable in the causal chain. Thus, while support for exclusionary policies seems to be driven in part by negative feelings towards homeless people, this antipathy cannot explain the effects of disgust sensitivity.

What if Everyone were Low on Disgust Sensitivity?

We now examine another implication of our argument that disgust sensitivity helps explain the puzzling pattern of attitudes about homelessness policies: people low in disgust sensitivity should hold more consistent attitudes (e.g., support aid and oppose exclusionary policies). As a test of this hypothesis, we construct predicted values of policy support at different levels of disgust sensitivity (based on models in the Supplementary Material in Appendices 3 and 4), holding all control variables at their means. The results of these simulations are presented in Fig. 3: if our argument is correct, we should expect to see that it is among those high on disgust sensitivity that both aid policies and exclusionary policies are especially popular.

Fig. 3 Predicted policy attitudes, at high and low disgust sensitivity. Note CCES Module. Predicted values are constructed based on Supplementary Material in Appendix 3. “High DS” represents the ninetieth percentile of disgust sensitivity (.46 on the 0–1 scale); “Low DS” represents the tenth percentile (.96). All other variables set to their means. 95 % confidence intervals shown. Full size image

Indeed, we see that for a respondent scoring at the ninetieth percentile of the disgust sensitivity scale (.96 on the 0–1 scale), the results look similar to those described above: this respondent is predicted to strongly support both aid policies and exclusionary policies. For a respondent scoring at the tenth percentile (.46), in contrast, the results are quite different: support for aid policies is unaffected while support for exclusionary policies drops. Our simulations therefore suggest that if disgust sensitivity were low among the public at large, exclusionary policies would be less popular while aid policies would retain strong support. However, since many people in fact have a high propensity to feel disgust (indeed, 82 % scored above the theoretical midpoint of the scale; see the Supplemenatary Material in Appendix 2 for the distribution), this tendency motivates them to support policies that exclude the homelessness from public life—effectively criminalizing homelessness—even as disgust sensitivity leaves unaffected their support for policies intended to aid the homeless. Disgust sensitivity, in short, helps explain why so many Americans who support aid to homeless people also support exclusionary attitudes.

Can News Media Depictions of the Homeless Activate Disgust Sensitivity?

We have argued that media depictions of the homeless as diseased and unclean (Shields 2001) activate disgust sensitivity, shaping opinion about exclusionary policy. Such depictions are common: recent headlines include “Hospitals Discharging Sick Homeless Back onto the Street,”Footnote 14 “Homeless People Return to Camp in Same Area After Cleanup,”Footnote 15 “Bill Proposed for Shower Bus for Homeless,”Footnote 16 and “A Homeless Epidemic in New York?”Footnote 17.

To test whether such media depictions of the homeless exacerbate the effects of disgust sensitivity on policy opinion, our experimental manipulation embedded in the CCES survey consisted of four conditions. In three conditions, subjects received a short paragraph about cities struggling with how to cope with homelessness under shrinking budgets. In the fourth condition (No Stimulus), respondents did not read any text. In the Neutral condition, the issue was portrayed as a conflict between the desire to help the homeless and the desire to regulate homeless camps and maintain communities. This serves as an additional control condition to ensure that effects are not driven by standard discussion of the impact of homelessness on communities.Footnote 18 The Disease Cues condition was similar, but designed to prime disease concerns. The text mentioned concerns about public urination and littering, and keeping communities clean and sanitary; the purpose of this condition was to ascertain whether the association between disgust sensitivity and policy opinion can be magnified in the presence of disease cues. Finally, the Threat Cues condition mentioned concerns about aggressive panhandling and keeping communities safe and secure. This final treatment provides a placebo test, allowing us to rule out the possibility that any negative portrayal of the homeless would strengthen the impact of disgust sensitivity. In contrast, we expect that only the Disease condition will have this effect.

We estimate similar models as those described above, but for the full sample, and we also include dummy variables indicating the Disease and Threat conditions. We also allow the effect of disgust sensitivity to vary by experimental condition by including interactions between disgust sensitivity and both the Disease and Threat conditions. We expect a positive interaction between disgust sensitivity and the Disease condition, indicating that in this condition—and only in this condition—the effect of disgust sensitivity on exclusionary policy attitudes was amplified. As shown in the Supplementary Material in Appendix 7, the effect of disgust sensitivity is substantively identical across the Control and Neutral conditions, so for the purposes of statistical power we do not model an interaction between disgust sensitivity and the Neutral condition.

Figure 4 displays the effect of shifting disgust sensitivity from the 10th to 90th percentile by experimental condition for each policy outcome (coefficient estimates are presented Supplementary Material in Appendix 5).Footnote 19 Starting at the left-hand side of Fig. 4, disgust sensitivity has a strong effect on banning panhandling in the pooled control conditions (No Stimulus and Neutral), about one and one-half points of the seven-point scale of the dependent variable (b = .22, p < .01). Yet, the effect of disgust sensitivity is more than twice the size in the Disease condition, more than three points on the seven-point scale of the dependent variable (b = .47, p < .001). The interaction term between disgust sensitivity and experimental condition (details shown in the Supplementary Material in Appendix 5) demonstrates that the effect of disgust sensitivity is significantly different across conditions (p < .05).Footnote 20 In contrast, the effect of disgust sensitivity in the Threat condition (b = .28, p < .05) is nearly identical to its effect in the control conditions (b = .22, p < .01) and the two effects are not statistically distinguishable (p = .64). Thus, the results suggest that the Disease cue dramatically increases the impact of disgust sensitivity on opinion about panhandling, while the placebo Threat condition has no such effect.

Fig. 4 Disease cues increase the effect of disgust sensitivity on exclusionary attitudes. Note CCES Module. Effects and 90 % confidence intervals are estimated from models in the Supplementary Material in Appendix 5 and represent the effect of movement on the disgust sensitivity scale from the 10th percentile to the 90th percentile. “Control” pools the effects across both the No Stimulus and the Neutral conditions Full size image

Moving to the right in Fig. 4, the next set of plots displays the effects of disgust sensitivity on opinion about banning sleeping in public. Disgust sensitivity again has a large effect in the pooled control conditions (b = .33, p < .001). Consistent with our expectations, this effect increases by nearly half in the Disease condition (b = .46, p < .001), though the two effects are not statistically distinguishable (p = .17). Finally, the effect of disgust sensitivity actually decreases in the placebo Threat condition (b = .28, p < .05) relative to the control (b = .33, p < .001) and the effect is not statistically distinguishable from the control (p = .71). Once again, we find suggestive evidence that the Disease cue, but not Threat, increases the magnitude of the effect of disgust sensitivity on exclusionary policies, though the results for this policy outcome are not definitive.

Finally, we turn to the effect of disgust sensitivity on aid policies. Here we do not expect to find any effects of disgust sensitivity, nor any interactions between the treatment conditions. Consistent with these expectations, the effect of disgust sensitivity on aid to the homeless is small and statistically indistinguishable from zero across all experimental conditions (ps > .46). Moreover, the effect of disgust sensitivity is nearly identical across the control and Disease conditions (b = −.02, b = −.04, respectively) and there is no evidence of an interaction effect (p = .85). The results are similar for subsidized housing, with disgust sensitivity again having null effects across all experimental conditions (ps > .33). And again, the effect of disgust sensitivity does not vary meaningfully across the control and Disease conditions (b = −.01, b = −.07, respectively), nor is there any statistical support for an interaction (p = .55).

Overall, although disgust sensitivity already has powerful effects on exclusionary attitudes, our experiment provides some evidence that simple disease cues in media coverage, such as mentions of public urination and sanitation, can dramatically amplify this effect. Indeed, our Disease cue more than doubled the effect of disgust sensitivity on support for banning panhandling, and we also found suggestive evidence that it increased the effect of disgust sensitivity on support for banning sleeping in public by almost fifty percent. Notably, we did not find any evidence that the Disease cue simply increased negative feelings towards the homeless, as it did not change the already null effect of disgust sensitivity on either aid policy. This finding reinforces our argument that the effects of disgust sensitivity on attitudes towards the homeless are primarily about avoiding possible pathogens, rather than simple antipathy. We also did not find any evidence that a placebo threat cue increased the impact of disgust sensitivity on attitudes towards any of the policies. Thus, consistent with our argument, it is not any negative portrayal of homeless people, but disease cues in particular, that activate dispositional differences in disgust sensitivity.