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26-05-2020 | Original Paper

Who Benefits? Race, Immigration, and Assumptions About Policy

Author: Jake Haselswerdt

Published in: Political Behavior | Issue 1/2022

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Abstract

Existing scholarship suggests that attitudes about the real or imagined beneficiaries or targets of public policies shape public opinion about those policies, with racial and ethnic stereotypes driving policy evaluations for many Americans. Despite the importance of these assumptions, we lack strong evidence about how and why people form such assumptions in the first place. In a pre-registered survey experiment, I demonstrate that elements of policy design (e.g., a work requirement) significantly affect the assumptions that individuals make about policy beneficiaries (their race and national origin). These assumptions shape individuals’ evaluations of the policy, conditional on existing attitudes (e.g., racial resentment). Importantly, existing attitudes do not condition the effects at the assumption stage: even those who profess not to believe in racial stereotypes about work ethic still assume that the absence of a work requirement makes a policy more likely to benefit blacks and immigrants.

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There is some degree of endogeneity in this process, as perceptions of deservingness also guide the design of policy itself.

Of course, traditional cash welfare (now TANF) is no longer unconditional following the revisions of 1996, but this fact is apparently little-appreciated by the public (Soss and Schram 2007).

See Haselswerdt (2016) for all pre-registration details and the full pre-analysis plan. Note that all EGAP registrations have been migrated to the Open Science Framework website.

The wording of these hypotheses has been changed from the pre-analysis plan for clarity.

I return to this later, with reference to analyses displayed in Appendix 4.

Respondents who are familiar with the EITC may assume that the policy includes a work requirement even if they are in the “no work requirement” condition. This would bias the effect size downward, making this a tougher test of the hypotheses than it would otherwise be.

I include information about cost (based on the Joint Committee on Taxation’s budget estimate for the EITC) in order to fix this information across conditions. Since this study does not focus on fiscal attitudes, I want to avoid a situation in which respondents in one condition assume that the policy is more costly than those in other conditions.

These perception questions are deliberately placed after the approval question, to avoid explicitly priming considerations of race or immigration that would alter the respondent’s policy attitude.

There were only 17 such respondents (less than 1% of the sample). Excluding them from the analysis does not appreciably change the results.

This response option does not distinguish between illegal and legal immigrants. This was a deliberate choice, as the phrase “illegal immigrants” is a politically loaded term. Including it in the list may have undermined my effort to avoid aggressively priming issues of race or immigration.

Respondents were also asked whether they thought this policy was likely to be supported by Democrats, Republicans, both, or neither (see Appendix 1). I return to these questions in footnote 27.

Only three of these items were repeated on the 2016 ANES.

Analysis of the 2012 ANES finds that these five items scale well (α = .71).

Since the 2016 ANES included only three of the five immigration policy items, a three-item scale was used for that analysis. I used survey weights for all regressions using ANES data.

These control variables are part of my pre-registered design (Haselswerdt 2016). Simple regressions show that none of the attitudinal independent variables (symbolic racism, anti-immigration attitudes, ideology, and party identification) were affected by the experimental treatments.

Replication data and code can be accessed on the Political Behavior Dataverse at https://doi.org/10.7910/DVN/X4EUKO. Workers on MTurk voluntarily select tasks and complete them in exchange for payment (45 cents in this case). 1865 unique workers accepted this task. Those that completed the survey on Survey Monkey were given a code to enter on MTurk to receive payment. Five were rejected for failing to enter the correct code. Another 61 were excluded due to missing data.

Appendix 3 provides more detail.

See Table 9.

I also present robustness checks related to representativeness in Appendices 6 and 7.

See Appendix 2 for more information on what groups respondents identified as likely to benefit, including cross-tabulations.

Tables 11 and 12 in Appendix 4 substitute ideology and party identification for these group-specific variables in the interactions. Overall, no clear patterns emerge, though the negative coefficient of the conditional tax credit treatment was stronger for conservatives in the “immigrants only” model, and that of the conditional cash treatment was weaker for Republicans in the “blacks only” model.

This is somewhat in contrast to the findings of Ellis and Faricy (2019) on delivery mechanism and symbolic racism.

About 27% of TANF households are non-Hispanic white (Administration of Children and Families 2019), compared to about half of EITC recipients (Murray and Kneebone 2017).

There is also some evidence here of the interactive effects predicted by the hypotheses, particularly in the race analysis (see Tables 14 and 16). Since these specific models were not part of my initial hypotheses, I consider this to be only suggestive evidence for such patterns.

An additional multinomial analysis, displayed in Table 17, establishes that the effects of the conditional tax credit treatment on each “exclusive” group assumption are statistically significant even when considered in the same model.

Tables 18 and 19 and Figs. 5 and 6 in Appendix 5 display the results of alternative specifications that treat the assumptions that the minority groups will benefit as separate from the assumption that the majority groups will not benefit, with triple interactions between the assumptions and the attitudinal variables. Consistent with the main results, the negative interaction is strongest when the respondent assumes that the minority benefits to the exclusion of the majority. Tables 20 and 21 demonstrate that the interactive findings reported in the main results are robust to the inclusion of interaction terms of the assumptions with ideology and party identification.

One possible confounding factor here is assumptions about partisanship—it could be that the apparent interaction between assumptions about target groups and preexisting attitudes is just an artifact of assumptions about which of the major parties supports the proposal. Using questions included in the survey (see Appendix 1), I am able to rule out this possibility—see Table 22 in Appendix 5.

This scenario is a case of “Model 3” in Preacher et al. (2007) since the relationships between the mediator (group assumptions) and the dependent variable (policy approval) are conditioned by other variables (symbolic racism and anti-immigration attitudes). These analyses use “normal-theory” standard errors; bootstrapped standard errors for selected values are nearly identical. See Appendix 8 for details.

For nonwhite respondents, the interaction effect of the “immigrants only” assumption and anti-immigrant sentiment was actually larger than for white respondents (p = .08 for the triple interaction).

Note that there is some evidence of such effects in the multinomial logit results in Appendix 4.

Both of these components are important to attitudes—see Figs. 5 and 6.

Administration of Children and Families. (2019). Characteristi and financial circumstances of TANF recipients Fiscal Year (FY) 2018. https://www.acf.hhs.gov/ofa/resource/characteristics-and-financial-circumstances-of-tanf-recipients-fiscal-year-2018.

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Title: Who Benefits? Race, Immigration, and Assumptions About Policy
Author: Jake Haselswerdt
Publication date: 26-05-2020
Publisher: Springer US
Published in: Political Behavior / Issue 1/2022
Print ISSN: 0190-9320
Electronic ISSN: 1573-6687
DOI: https://doi.org/10.1007/s11109-020-09608-3

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