Research

Abstract

We provide evidence that there are substantial racial and gender gaps among lobbyists. These gender and racial differences are also greater among conservative leaning groups. However, we show, these gaps are decreasing over time. Does demand for minority and female lobbyists play a role in these trends? Although previous work has highlighted the relative scarcity of women and minorities in positions leading to the lobbyist profession (supply), we know less about whether interest groups are interested in hiring qualified women and minorities for such positions (demand). Using a conjoint experiment embedded in a survey of individuals involved in hiring lobbyists, we find greater demand for female and minority lobbyists than for their male and White counterparts, especially among ideological liberals. Our work shows that the lobbying industry does not appear to discount the candidacies of potential female and minority lobbyists.

We apply tournament theory to congressional leadership to unify research on campaign finance with theories of endogenous party strength. Parties want to incentivize members to do costly work for the benefit of the party, such as fundraising. Accordingly, they make leadership offices attractive and award these leadership offices on the basis of who does the most work for the party. The more attractive the leadership office becomes, the harder party members work to win. We present a model to formalize this argument, derive its empirical implications, and find support for these implications using data from committee assignments, committee authorizations, and fundraising for leadership political action committees and congressional hill committees.

The accountability of police to the public is imperative for a functioning democracy. The opinions of police executives—pivotal actors for implementing oversight policies—are an understudied, critical component of successful reform efforts. We use a pre‐registered survey experiment administered to all U.S. municipal police chiefs and county sheriffs to assess whether police executives’ attitudes towards civilian oversight are responsive to 1) state‐level public opinion (drawing on an original n = 16,840 survey) and 2) prior adoption of civilian review boards in large agencies. Results from over 1,300 police executives reveal that law enforcement leaders are responsive to elite peer adoption but much less to public opinion, despite overwhelming public support. Compared to appointed municipal police chiefs, elected sheriffs are less likely to support any civilian oversight. Our findings hold implications for reformers: we find that existing civilian oversight regimes are largely popular, and that it is possible to move police executive opinion towards support for civilian oversight.

Abstract

Lobbyists are important agents of organized interests. While prior studies have investigated the observed hiring patterns of interest groups, conclusions about the demand for lobbyist characteristics may be confounded by the availability of lobbyists with certain characteristics. To assess the demand for lobbyists with expertise, connections, and who share groups’ preferences, we use a conjoint survey experiment to examine the hiring preferences for lobbyists. We find that organized interests prefer lobbyists with policy-specific expertise and the necessary connections to get access to decision-makers, but find little evidence that connections are more valuable than expertise. We also find that organized interests prefer lobbyists who share their political ideology, but that this preference diminishes when the hiring organization is not aligned ideologically with the party in unified control of government. Overall, our study paints a more nuanced picture of the role of preferences and connections in lobbying than many would expect.

Law enforcement agencies are increasingly adopting 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 \textit{institutio\nal} context. We administer a pre-registered survey experiment to 4,200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national FBI), 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.

Law enforcement agencies are increasingly adopting 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 \textit{institutional} context. We administer a pre-registered survey experiment to 4,200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national FBI), 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.

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 news viewership undergoes a small decline 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.

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.

Political 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.

Evidence 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.

“Revolving door” lobbying describes the back-and-forth transition of individuals between public service and employment in lobbying, raising normative concerns around the role of special interests in public policy. Little, however, is known about individuals who make the transition from lobbying into government. Using unique panel data from 2001-2020 of U.S. federal bureaucrats and congressional staff matched to lobbying records, we 1) provide important stylized facts on this phenomenon and 2) quantify the value to lobbying firms when their employees enter government service. Employing a matched difference-in-differences design appropriate for staggered treatment timing, we find a substantial increases in revenue to lobbying firms that gain government connections through departure of one of their lobbyists (36%, roughly $320,000 a year). Exploring the heterogeneity in this increase, we find larger premiums associated with lobbyists entering congressional offices (42\%) over government agencies (16%). Finally, we use a separate dataset accounting for monthly variation in the timing of the Trump administration’s appointment of lobbyists to agency positions, finding a large 36% increase in revenue for firms whose lobbyists were hired by the administration. These results shed light onto the political economy of the lobbying industry and the value of access in lobbying, and provide needed context surrounding policy debates on revolving door regulation.

Law enforcement agencies are increasingly adopting 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 \textit{institutio\nal} context. We administer a pre-registered survey experiment to 4,200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national FBI), 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.

Law enforcement agencies are increasingly adopting 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 \textit{institutio\nal} context. We administer a pre-registered survey experiment to 4,200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national FBI), 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.

Law enforcement agencies are increasingly adopting 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 \textit{institutio\nal} context. We administer a pre-registered survey experiment to 4,200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national FBI), 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.

The American states offer a wealth of variation across time and space to understand the sources, dynamics, and consequences of public policy. As laboratories of socio-economic 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 common 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.

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.

Building on previous work on lobbying and relationships in Congress, I propose a theory of staff-to-staff connections as a human capital asset for Capitol Hill staff and revolving door lobbyists. Employing lobbying disclosure data matched to congressional staff employment histories, I find that the connections these lobbyists maintain to their former Hill coworkers primarily drive their higher relative value as lobbyists. Specifically, a one standard deviation increase in the number of connections predicts $360,000 in additional revenue during an ex-staffer’s first year as a lobbyist. I also find that the indirect connections lobbyists maintain to legislators through knowing a staffer in a legislative office are of potential greater value than a direct connection to a Senator given a large enough number of connections. This paper sheds additional light onto the political economy of the lobbying industry, making an important contribution to the literature on lobbying and the revolving door phenomenon.