Suchergebnisse

Abstract
Anemic demand for local news has contributed to an industry crisis. We consider whether local elections, which highlight the ability of local television stations and newspapers to provide information that is unavailable from national news outlets, increase local media use. While we show these elections are a time of increased attention to local politics in the news and among the public, we also find local media outlets do not benefit from this when considering behavioral news use measures. Relative to news outlets in cities without an election, local television remains substantively unchanged during local elections. Newspaper website traffic is largely stable, although it falls slightly the month after an election. In both cases these differences are small, even when considering close races and those happening off the federal election cycle. This shows limits on the ability of salient local political events to motivate local news use.

Abstract
The revolving door is a potential mechanism of private influence over policy. Recent work primarily examines the revolving of legislators and their staff, with little focus on the federal bureaucracy. To analyze decisions to turnover into lobbying, we develop an argument emphasizing the (1) policy expertise acquired from federal employment; (2) the proximity of employees to political decision-making; and (3) the agency policymaking environment. Leveraging federal personnel and lobbying data, we find the first two factors predict revolving whereas the policymaking environment has an inconsistent impact. We highlight the importance of studying selection into lobbying for estimating casual effects of lobbyist characteristics on revenue and contribute to the literature on bureaucratic careers and the nature of private influence in policymaking.

ABSTRACTPolitical scientists rely on complex software to conduct research, and much of the software they use is written and distributed for free by other researchers. This article contends that creating and maintaining these public goods is costly for individual software developers but that it is not adequately incentivized by the academic community. We demonstrate that statistical software is used widely but rarely cited in political science, and we highlight a partial solution to this problem: software bibliographies. To facilitate their creation, we introduce softbib, an R package that scans analysis scripts, detects the software used in those scripts, and automatically creates bibliographies. We hope that recognizing the contribution of software developers to science will encourage more scholars to create public goods, which could yield important downstream benefits.

AbstractMore than half of the current members of the US Congress served in their state legislature prior to holding federal office. We quantify the relationship between state legislative service and career progression to Congress. Using close elections for exogenous assignment of political experience across otherwise similar candidates, we show that serving in the state legislature more than doubles an individual's probability of eventually contesting a Congressional seat relative to a similar candidate who lost in a comparable election; it also doubles the individual politician's probability of eventually winning a Congressional seat. State legislatures thus create national politicians out of otherwise marginal political entrants. We then show that the effect of state legislative service on career progression is larger in more professionalized legislatures, highlighting the role of institutions in facilitating political career progression. Our results hold important implications for representation and accountability, and confirm that prevailing institutions can affect political selection via career progression.

The level of journalistic resources dedicated to coverage of local politics is in a long-term decline in the US news media, with readership shifting to national outlets. We investigate whether this trend is demand- or supply-driven, exploiting a recent wave of local television station acquisitions by a conglomerate owner. Using extensive data on local news programming and viewership, we find that the ownership change led to (1) substantial increases in coverage of national politics at the expense of local politics, (2) a significant rightward shift in the ideological slant of coverage, and (3) a small decrease in viewership, all relative to the changes at other news programs airing in the same media markets. These results suggest a substantial supply-side role in the trends toward nationalization and polarization of politics news, with negative implications for accountability of local elected officials and mass polarization.

AbstractEvidence suggests that well-funded, professional legislatures more effectively provide constituents with their preferred policies and may improve social welfare. Yet, legislative resources across state legislatures have stagnated or dwindled at least in part due to public antagonism toward increasing representatives' salaries. We argue that one reason voters oppose legislative resources, like salary and staff, is that they are unaware of the potential benefits. Employing a pre-registered survey experiment with a pre–post design, we find that subjects respond positively to potential social welfare benefits of professionalization, increasing support for greater resources. We also find that individuals identifying with the legislative majority party respond positively to potential responsiveness benefits and that out-partisans do not respond negatively to potential responsiveness costs. In a separate survey of political elites, we find similar patterns. These results suggest that a key barrier to increasing legislative professionalism – anticipated public backlash – may not be insurmountable. The findings also highlight a challenge of institutional choice: beliefs that representatives are unresponsive or ineffective lead to governing institutions that may ensure these outcomes.

AbstractThe American states offer a wealth of variation across time and space to understand the sources, dynamics, and consequences of public policy. As laboratories of socioeconomic and political differences, they enable both wide-scale assessments of change and studies of specific policy choices. To leverage this potential, we constructed and integrated a database of thousands of state-year variables for designing and executing social research: the Correlates of State Policy Project (CSPP). The database offers one-stop shopping for accurate and reliable data, allows researchers to assess the generalizability of the relationships they uncover, enables assessment of causal inferences, and connects state politics researchers to larger research communities. We demonstrate CSPP's use and breadth, as well as its limitations. Through an applied empirical approach familiar to the state politics literature, we show that researchers should remain attentive to regional variation in key variables and potential lack of within-state variation in independent and dependent variables of interest. By comparing commonly used model specifications, we demonstrate that results are highly sensitive to particular research design choices. Inferences drawn from state politics research largely depend on the nature of over time variation within and across states and the empirical leverage it may or may not provide.

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.