Abstract
Recent scholarship argues that citizens’ support for specific government programs in the United States is affected by the means through which benefits are delivered as well as the distributional consequences of these policies. In this paper, we extend this literature in two ways through a series of novel survey experiments, deployed on a nationally representative sample. First, we directly examine differences in public support for prospective government spending when manipulating the mode of delivery. Second, we examine whether information about the distributional consequences of two existing government programs affects their popularity. We find that citizens have a preference for indirect spending that is independent of the distributional consequences of a given policy and identify mechanisms that may explain this view. Furthermore, we find little evidence that highlighting the regressive effects of current government programs significantly reduces the demand for their policy benefits. Our findings have implications for understanding the political calculus of policy design and the potential for public persuasion.
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Notes
Still another possibility, not addressed in this paper, is that indirectly delivered (regressive) programs could become more popular among program beneficiaries if they were delivered directly because individuals would better understand that they benefited personally. Any change in popularity due to direct program delivery could indeed be smaller or larger than the potentially countervailing effect of the overall distribution of program costs and benefits becoming more transparent.
It is important to note that indirect delivery of government benefits can also be accomplished by using private sector intermediaries. For example, in the case of Medicare, the national health insurance system for seniors, the federal government contracts services and the monitoring of those services to private health care providers. In an extensive examination of Medicare, Morgan and Campbell (2011) argue that policymakers “delegate” governance due to citizens’ divergent preferences for both small government and greater social provision.
For a general discussion of the potential biases in within-subject designs, see, Charness et al. (2012).
For a general discussion of the challenges interpreting the effects of bundled treatments, see, Dunning (2012, pp. 300–302).
For information regarding Lucid’s specific recruitment method, see: https://luc.id/wp-content/uploads/2017/07/IRB-Methodology_.Lucid_.pdf.
This household-income targeted subsample is constructed due to the requirements of an unrelated study with questions that are fielded on this survey.
We use a raking procedure to match the marginal distributions of our sample to the ACS margins of age, education, household income, gender, and minority identification. See, Battaglia et al. (2009) for a an overview of raking as well as practical considerations for implementing the procedure.
Analyses of the Lucid sample and SSI sample (discussed in Experiment 2) reflect decisions made prior to data collection and that were documented in two pre-analysis plans registered with Evidence in Governance and Politics (EGAP). The pre-analysis plans are filed jointly under EGAP ID 20170725AA. Materials to replicate the analyses presented in this article are available here: https://doi.org/10.7910/DVN/XYYAQC.
WIC is a federally funded program that targets low-income pregnant and postpartum women, infants, and children up to 5 years of age. Depending on the State agency administering the benefit, participants receive WIC benefits through checks, vouchers, or as electronic benefit transfers (see, https://www.fns.usda.gov/wic/). A typical example of an existing US Department of Labor job training policy is the Trade Adjustment Assistance Program (TAA). The TAA targets workers that have lost their jobs or have their jobs threatened due to trade-related circumstances. Eligible individuals receive some combination of classroom training, on-the-job training, income support, and reimbursement for job search related expenses (see, https://www.doleta.gov/tradeact/docs/program_brochure2014.pdf).
Note, that this method of transfer is similar to Electronic Benefit Transfer (EBT) cards currently used by many states.
Note, however, that certain benefits under TAA are transferred to eligible individuals as a reimbursement for a qualified expense.
Given that exposing respondents to both direct and indirect framings of the policy could generate biases in subsequent responses, we present this question last, and only in regards to the final policy that is shown. E.g., a respondent assigned to view the nutrition policy followed by the job training policy is only asked this question about the job training policy at the end of the corresponding question-block.
For the job training policy respondents are asked about “Citizen/business misuse and abuse” so as to include any funds diverted by educational institutions.
HMID benefit figures are based on authors’ calculations using the following data: Estimates of Federal Tax Expenditures for Fiscal Years 2015–2019 (Joint Committee on Taxation), Table 1.2. All Returns: Adjusted Gross Income, Exemptions, Deductions, and Tax Items, by Size of Adjusted Gross Income and by Marital Status, Tax Year 2014 (IRS). Both figures are conditional on take-up, but higher income households are much more likely to take up the policy.
UI benefit figures are based on authors’ calculations using US Department of Labor data: Significant Provisions of State Unemployment Insurance Laws, Effective January 2014.
We find that treatment effect on preferences to modify the HMID are statistically significant (\(\alpha = 0.05\)) when estimate the SATE without using weights. See, rows 3–4 of Online Appendix Figure B.1c.
We do find, however, that the unweighted effect of the regressive frame is to increase support for lower-income individuals paying a lower UI premium is (\({\widehat{SATE}} = 0.08\), \(p < 0.05\)). See, row 5 of Online Appendix Figure B.1d.
For a comparison between MTurk, student convenience, and ANES samples, see Berinsky et al. (2012).
The complete wordings of the distributional treatments employed in the MTurk sample are shown in Online Appendix Figure B.8.
The complete wordings of the distributional treatments employed in the SSI sample are shown in Online Appendix Table B.5.
The experiment conducted on MTurk was intended as a pilot study, thus we did not pre-register a plan for analysis.
The differences in results between samples could be the result of a number of factors. We call attention to two such factors. First, the type of respondent in each sample: MTurk respondents complete the survey as a “Human Intelligence Tasks (HITs)” in an online labor market whereas the average SSI respondent is usually compensated to take part in marketing research. Second, as the distributional treatments used in the MTurk sample are much different than those used in the SSI sample, the information about HMID beneficiaries presented to MTurk respondents could interact with the direct/indirect treatment in ways that are different than we observe with SSI respondents.
We note that even when restricting our attention to the regressive framing of the HMID, respondents in both the direct and indirect treatment conditions favor, on average, increasing spending on the program.
We do find, however, that the unweighted effect of the regressive frame is to increase support for lower-income individuals paying a lower UI premium among Republicans is (\(\widehat{SATE_{Rep}} = 0.12\), \(p < 0.05\)). See, row 5 of Online Appendix Figure A.4d.
A potential real-world example can be found in recent changes to the HMID that lowers the limit on qualifying mortgages from $1,000,000 to $750,000. See: http://wapo.st/2zMo5QV?tid=ss_mail&utm_term=.e57003cdcdc7.
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Acknowledgements
We thank Sarah Anzia, Christopher Berry, Natália S. Bueno, Alan Gerber, Jacob Hacker, John Henderson, Reuben Kline, Nolan McCarty, Patrick Tucker, Ebonya Washington, the three anonymous reviewers of this article and seminar participants at Yale University, the 2018 Emory University Conference on Institutions and Law Making, the 2018 New York University CESS Experimental Political Science Conference, and the 2017 American Political Science Association annual meeting for helpful comments and advice.
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Ashok, V.L., Huber, G.A. Do Means of Program Delivery and Distributional Consequences Affect Policy Support? Experimental Evidence About the Sources of Citizens’ Policy Opinions. Polit Behav 42, 1097–1118 (2020). https://doi.org/10.1007/s11109-019-09534-z
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DOI: https://doi.org/10.1007/s11109-019-09534-z