Predictors of Public Support for Social Safety Net Policy During the COVID-19 Pandemic. Academic Article uri icon

Overview

abstract

  • INTRODUCTION: U.S. residents had varying experiences of the COVID-19 pandemic and social safety net policy in 2020. Past research has suggested that partisanship, ideology, racial attitudes, and personal experience may each influence policy attitudes. In this study, we explore whether variation in support for social safety net policy in 2020 is predicted by negative experiences of the pandemic when controlling for racial attitudes, partisanship, and ideology. METHODS: Support for 12 social safety net policies in 2020 was estimated using data from a nationally representative panel survey of U.S. adults conducted in 2020 (n=1,222). Logistic regression was used to examine differences in the predicted probability of supporting a majority of social safety net policies related to health, housing, and employment by partisanship, ideology, racial attitudes, and negative experiences of the pandemic. Analyses were conducted in 2021. RESULTS: Higher levels of symbolic racism was a consistently strong predictor of lower social safety net policy support across health, housing, and employment policies; as was identifying as either Conservative or Republican. Negative experiences of the pandemic were generally unpredictive of support for the social safety net policy. CONCLUSIONS: Despite the pandemic's consequences as well as the potential for social safety net policy to address these consequences, negative experiences of the pandemic failed to predict policy support, even as racial attitudes, partisanship, and ideology strongly predicted these preferences in 2020. Building public support for social safety net policy requires communication strategies that identify the shared benefits of these policies.

publication date

  • February 18, 2022

Research

keywords

  • COVID-19
  • Racism

Identity

PubMed Central ID

  • PMC8853750

Scopus Document Identifier

  • 85127357759

Digital Object Identifier (DOI)

  • 10.1017/S1537592717004182

PubMed ID

  • 35337693

Additional Document Info

volume

  • 63

issue

  • 1