Members of Congress (MCs) are concerned about reelection and act in office with this goal in mind. Whether citizens notice this behavior and how they respond remains an open question. We examine the relationships between legislators' characteristics and activity and their constituents' evaluations of their performance in office. We argue that MCs' behavior does filter down to citizens, but that their responses are conditioned by their partisanship and interest in politics. Our analyses combine 2006 and 2008 Cooperative Congressional Election Study survey data on citizens with information on legislators' activity in office in the 109th and 110th Congresses. The results reveal that there are widespread linkages between MC behavior and constituency approval, but that legislators' copartisan, outpartisan, and independent constituents respond in different, yet predictable, ways. Moreover, these effects are strongest among the most interested constituents. Our findings have important implications for our assessments of legislators' strategies and constituency representation.
Profound differences exist in how Americans from various racial and ethnic groups view police and court officials. We argue that vicarious experiences contribute to this racial and ethnic divide. Drawing on research on social communication, social network composition, and negativity biases in perception and judgment, we devise a theoretical framework to articulate why vicarious experiences magnify racial and ethnic disparities in evaluations of judicial actors. Four hypotheses are tested using original survey data from the state of Washington. Results provide strong evidence that vicarious experiences influence citizens’ evaluations of both police and courts, and they do so in a manner that widens racial divides in how those actors are perceived.
Why do COVID-19 social distancing policies vary so widely across states in federal countries? This mixed-methods study of Brazil, Mexico, and the United States finds that state-level variation in the stringency of social distancing policies is driven not by the epidemiological, demographic, or socioeconomic factors commonly emphasized in previous research, but largely by political factors. Introducing a novel framework for explaining pandemic policymaking, the study shows the central importance of political parties, presidential power, and governors’ coalitions in determining state-level policy stringency. In the United States and Mexico, statistical and qualitative evidence indicates that interstate collaboration among governors, combined with top-down pressures from national party elites and presidents, led to greater policy alignment among coordinated states. In Brazil, in contrast, where there is little evidence of either policy coordination or alignment, state-level policies resulted instead from intrastate factors and diffusion. Together, these findings highlight how a multilevel framework attuned to varied combinations of intra-unit, cross-unit, and cross-level causal factors strengthens our understanding of pandemic policymaking.
Collaborations between the academy and governments promise to improve the lives of people, the operations of government, and our understanding of human behavior and public policy. This review shows that the evidence-informed policy movement consists of two main threads: ( a) an effort to invent new policies using insights from the social and behavioral science consensus about human behavior and institutions and ( b) an effort to evaluate the success of governmental policies using transparent and high-integrity research designs such as randomized controlled trials. We argue that the problems of each approach may be solved or at least well addressed by teams that combine the two. We also suggest that governmental actors ought to want to learn about why a new policy works as much as they want to know that the policy works. We envision a future evidence-informed public policy practice that ( a) involves cross-sector collaborations using the latest theory plus deep contextual knowledge to design new policies, ( b) applies the latest insights in research design and statistical inference for causal questions, and ( c) is focused on assessing explanations as much as on discovering what works. The evidence-informed public policy movement is a way that new data, new questions, and new collaborators can help political scientists improve our theoretical understanding of politics and also help our policy partners to improve the practice of government itself.
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