Suchergebnisse

AbstractA large percentage of women receiving Temporary Assistance for Needy Families (TANF) — known colloquially as welfare—have experienced domestic violence in their lives, with studies reporting that upwards of 50% of welfare recipients have been abused at some point. Receiving TANF benefits can provide the financial stability that is necessary for a woman to leave an abusive relationship, though some TANF provisions, such as child support reporting requirements, may also put victims of domestic violence at risk. Under TANF, states may adopt the Family Violence Option and waive many program requirements, including time limits, work requirements, and child support reporting requirements, for victims of domestic violence. Given the importance of TANF for those trying to leave, or who have left, abusive situations, this article asks: Who represents the interests of these women in the states? To answer this question, I employ a mixed methods approach, combining a quantitative analysis of the diversity of interest groups in the states with a case study of Connecticut and New Jersey. I find that feminist and domestic violence organizations do not consistently have a significant effect on the adoption of policy accommodations for survivors, but these groups are speaking out.

We examine the role that local parties play in responding to and equipping local volunteers to work during campaign seasons. We use a field experiment during the 2020 U.S. general election to investigate whether local parties are more likely to respond to certain types of volunteers and to examine what factors are associated with local parties' responsiveness. We find that both Democratic and Republican local parties in competitive counties are more likely to respond to volunteers. Moreover, we find that both parties are more likely to respond to white volunteers and Democratic parties are more likely to respond to women. These differential response rates may be contributing to the increased demographic sorting between the parties.

In this research note we document changes to the rate of comments, shares, and reactions on local Republican Facebook pages. Near the end of 2018, local Republican parties started to see a much higher degree of interactions on their posts compared to local Democratic parties. We show how this increase in engagement was unique to Facebook and happened across a range of over a thousand local parties. In addition, we use a changepoint model to identify when the change happened and find it lines up with reported information about the change in Facebook's algorithm in 2018. We conclude that it seems possible that changes in how Facebook rated content led to a doubling of the total shares of local Republican party posts compared to local Democratic party posts in the first half of 2019 even though Democratic parties posted more often during this period. Regardless of Facebook's motivations, their decision to change the algorithm might have given local Republican parties greater reach to connect with citizens and shape political realities for Americans. The fact that private companies can so easily control the political information flow for millions of Americans raises clear questions for the state of democracy.

Political parties use the internet, and social media in particular, for fundraising, advertising, and mobilizing to achieve desirable ends. Local parties are first and foremost organizations, and so as they make their decisions, they have to use their resources wisely. Through our analysis of over 6000 county-level Democratic and Republican parties in the United States, we find a high degree of variation in the use of social media platforms (Facebook, Twitter, and Instagram) by parties. In explaining this variation, we focus on parties as organizations and so find the choice to use social media and their overall activity on it reflects the resources available to the party organization, as well as the size of potential audience and the competitiveness of their political environment. These variables explain local Democratic parties better than they explain local Republican parties.

Considerable research shows that welfare policies are stricter in states with large African American caseloads. We challenge the universality of this claim by extending Soss, Fording, and Schram's Racial Classification Model to account for the multidimensionality of policy, the constraints imposed by federal funding, and state legislators' ideological goals and racial stereotypes. Examining the work requirements, sanctions, time limits, and exemptions in the Temporary Assistance for Needy Families program (TANF), we test our hypotheses using the most detailed measures of state welfare policy yet examined. Consistent with our theory, we show that policy is more generous on some dimensions and less generous on others as the size of the African American caseload increases. This pattern reveals a complexity in welfare policy previously overlooked by research showing only negative effects. The results have important implications for theories addressing race in the context of TANF and other complex policy regimes.

Welfare policy is multidimensional because of the political compromises, competing goals, and federalist structure underpinning it. This complexity has hindered measurement and, therefore, the comparability of research on race and welfare policy. This paper describes a measurement strategy that is transparent, replicable, and attuned to matching the assumptions of statistical models to the policy process. We demonstrate that this strategy leads to more nuanced conclusions regarding the relationship between minority caseloads and the flexibility of state welfare policies. The strategy and recommendations are adaptable to research agendas that scholars bring to the comparative study of welfare in the U.S. states, countries, or other units—and to other complex policies enacted in federal systems.