Small Power: How Local Parties Shape Elections. By David Doherty, Conor M. Dowling, and Michael G. Miller. New York: Oxford University Press, 2022. 320p. $29.95 paper
In: Perspectives on politics, Band 20, Heft 4, S. 1465-1466
ISSN: 1541-0986
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In: Perspectives on politics, Band 20, Heft 4, S. 1465-1466
ISSN: 1541-0986
In: State politics & policy quarterly: the official journal of the State Politics and Policy section of the American Political Science Association, Band 20, Heft 3, S. 267-291
ISSN: 1946-1607
AbstractThe parties as networks approach has become a critical component of understanding American political parties. Research on it has so far mainly focused on variation in the placement of candidates within a network at the national level. This is in part due to a lack of data on state-level party networks. In this article, I fill that gap by developing state party networks for 47 states from 2000 to 2016 using candidate donation data. To do this, I introduce a backboning network analysis method not yet used in political science to infer relationships among donors at the state level. Finally, I validate these state networks and then show how parties have varied across states and over time. The networks developed here will be made publicly available for future research. Being able to quantify variation in party network structure will be important for understanding variation in party-policy linkages at the state level.
In: Politics, Groups, and Identities, Band 10, Heft 5, S. 788-806
ISSN: 2156-5511
In: Perspectives on politics, Band 17, Heft 2, S. 326-339
ISSN: 1541-0986
Donald Trump's success in the 2016 presidential primary election prompted scrutiny for the role of news media in elections. Was Trump successful because news media publicized his campaign and crowded out coverage of other candidates? We examine the dynamic relationships between media coverage, public interest, and support for candidates in the time preceding the 2016 Republican presidential primary to determine (1) whether media coverage drives support for candidates at the polls and (2) whether this relationship was different for Trump than for other candidates. We find for all candidates that the quantity of media coverage had significant and long-lasting effects on public interest in that candidate. Most candidates do not perform better in the polls following increases in media coverage. Trump is an exception to this finding, receiving a modest polling bump following an increase in media coverage. These findings suggest that viability cues from news media contributed to Trump's success and can be influential in setting the stage in primary elections.
In: Reuning, Kevin and Nick Dietrich. "Media Coverage, Public Interest, and Support in the 2016 Republican Invisible Primary" Perspectives on Politics, Forthcoming
SSRN
Working paper
In: Mobilization: An International Quarterly, Band 25, Heft 3, S. 339-363
We introduce a fine-grained method of categorizing protests by their strategies and tactics that places protests in a multidimensional space based on motivations—direct change towards a policy or goal; changing public discourse narratives; and building movement identities or communities. This technique recognizes that multiple motivations may exist and allows protests to be compared based on where they are in multiple dimensions. To test our method and the theoretical dimensions we hypothesize, we surveyed protesters at the 2016 Republican and Democratic National Conventions. Using questions about participant goals and targets, and confirmatory factor analysis, we corroborate the existence of three dimensions. We show these dimensions provide real information about the differences between protests outside the two conventions. We conclude by discussing how our multidimensional measure can be extended to other events, social movement organizations, or whole movements to facilitate comparisons of events, organizations, or movements across time and space.
In: Party politics: an international journal for the study of political parties and political organizations, Band 27, Heft 6, S. 1243-1253
ISSN: 1460-3683
Since the New Deal, labor has been a key member of the Democratic coalition. As unions decline, their centrality to the Democratic Party has also diminished. At the same time, state variation in party preferences, party strength, and the types of unions that remain has led some unions to become involved in Republican politics. In this manuscript we investigate how central unions are in party networks using state legislative donation data from 2000–2016. We find that union contributions are associated with increasing centrality to the Democratic Party, while business interest contributions are associated with unions being less central. Only union membership rates are related to labor's position in the Republican network. This work has implications for how we consider which groups are under a party's umbrella. While labor may spend more money, it cannot keep pace with business groups in the party coalition.
In: Political research quarterly: PRQ ; official journal of the Western Political Science Association and other associations, Band 76, Heft 2, S. 931-943
ISSN: 1938-274X
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: Research & politics: R&P, Band 9, Heft 2, S. 205316802211038
ISSN: 2053-1680
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.
In: Party politics: an international journal for the study of political parties and political organizations, Band 29, Heft 1, S. 164-175
ISSN: 1460-3683
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.
SSRN
Working paper
In: Journal of peace research, Band 57, Heft 6, S. 801-814
ISSN: 1460-3578
Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event. We demonstrate the utility of the model by focusing on a dataset that defines 'one-sided-killing' as government-caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths for each country-year in the dataset. These new event-based, count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time. Our modeling framework also provides a principled method for inferring unobserved count variables based on conceptually related categorical information.
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 27, Heft 4, S. 503-517
ISSN: 1476-4989
Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.
In: Political communication: an international journal, Band 39, Heft 2, S. 184-201
ISSN: 1091-7675