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This article analyzes the strategic use of public policy as a tool for reshaping public opinion. In the 1990s, “progressive revisionists” argued that, by reforming welfare, liberals could free the Democratic Party of a significant electoral liability, reduce the race-coding of poverty politics, and produce a public more willing to invest in anti-poverty efforts. Connecting this argument to recent scholarship on policy feedback, we pursue a quantitative case study of the potential for new policies to move public opinion. Our analysis reveals that welfare reform in the 1990s produced few changes in mass opinion. To explain this result, we propose a general framework for the analysis of mass feedback effects. After locating welfare as a “distant-visible” case in this framework, we advance four general propositions that shed light on our case-specific findings as well as the general conditions under which mass feedback effects should be viewed as more or less likely.

Welfare sanctions are financial penalties applied to individuals who fail to comply with welfare program rules. Their widespread use reflects a turn toward disciplinary approaches to poverty management. In this article, we investigate how implicit racial biases and discrediting social markers interact to shape officials' decisions to impose sanctions. We present experimental evidence based on hypothetical vignettes that case managers are more likely to recommend sanctions for Latina and black clients—but not white clients—when discrediting markers are present. We triangulate these findings with analyses of state administrative data. Our results for Latinas are mixed, but we find consistent evidence that the probability of a sanction rises significantly when a discrediting marker (i.e., a prior sanction for noncompliance) is attached to a black rather than a white welfare client. Overall, our study clarifies how racial minorities, especially African Americans, are more likely to be punished for deviant behavior in the new world of disciplinary welfare provision.

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