Mapping the Ideological Marketplace

Bonica, Adam

doi:10.2139/ssrn.2148801

Cited by 97 publications

(140 citation statements)

References 73 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…In our second application, we compare the estimates from our model to existing scaling methods for US Senators based on roll-call votes (Poole and Rosenthal 1985;Clinton, Jackman and Rivers 2004) and campaign donations (Bonica 2014). While estimates from all three methods are positively and similarly correlated within as well as across parties, we find a much larger increase in speech polarization compared to (already high) roll-call 2 polarization.…”

Section: Gender Differences In Speechmentioning

confidence: 94%

Measuring Political Positions from Legislative Speech

Lauderdale

Herzog²

2016

Polit. anal.

138

100

View full text Add to dashboard Cite

Table A1 provides an overview of the speeches and debates included in our analysis. In an average electoral period, there were 8,707 speeches across 779 debates in the Irish Dáil and 7,464 speeches across 584 debates in the US Senate. The 112th and 113th Senates stick out at as the two least productive Senates in terms of number of speeches and debates, which is most likely the result of legislative gridlock after the Republican party took over the House in the 2010 election.The average debate in our data set consists of 12 speakers, ranging from a minimum of 5 speakers (our lower threshold) to as many as 73 speakers. At the level of individual speeches, we find an average of 683 and 552 words per speech and legislature. The longest individual speech in our data set is 37,610 words long, which is senator Ted Cruz's (TX-R) 21-hour filibuster speech in September 2013. Sources Additional Analysis of Classification and Uncertainty versus WordfishIn the main text, we show plots of the relationships between individual TDs Wordshoal score and coalition membership, and uncertainty estimates for the 30th Dáil. In Figure A1 we show the corresponding plots for the 29th Dáil. Figure A1: The association between the estimated positions of each legislator and their status as members of the coalition versus opposition, with correlation and local linear smooth, under Wordfish (left) and our approach (right), for the 29th Dáil. In the bottom row, we show the 95% intervals associated with the estimates for each legislator under Wordfish (left) and Wordshoal (right). A5 Dail 29Type of debate Beta root mean square Analysis of Variation in Disagreement by Legislative StageThe Wordshoal model facilitates assessments of the relative extent to which different kinds of debates align with the estimated common dimension. The greater the magnitude of the β j , the greater the extent to which the common dimension predicts speech variation in a given debate. As our summary statistic for β j , we use the root mean square of the β j , weighted by the number of speeches in each debate speeches j .In the paper, we utilize this statistic only for the US Senate, here we assess it across different types of parliamentary debates in Ireland. The majority of debates in our data take place during the second reading of a bill, which is the most important legislative stage after which a bill is formally accepted or rejected. The second most common type are motions, which are an instrument of parliament to scrutinize the work of the government. This includes ad hoc motions on topical issues, seasonal adjournment debates, and (less frequently) A6 motions of confidence in the government or in individual cabinet members (Gallagher 2010). Figure A2 shows the weighted root mean square of β j for motions, second-stage bills, and for debates during the remaining legislative stages, which we group together because of the small number of observations at each stage. We find high government-opposition division during debates on motions, which are mostly used by...

show abstract

Section: Gender Differences In Speechmentioning

confidence: 94%

Measuring Political Positions from Legislative Speech

Lauderdale

Herzog²

2016

Polit. anal.

138

100

View full text Add to dashboard Cite

show abstract

“…We and 2012 (Bonica, 2014). 8 In order to reduce errors or discrepancies in these sources, we checked for conflicts between the four sources, and identified those elections where…”

Section: Municipal Election Datamentioning

confidence: 99%

Mayoral Partisanship and Municipal Fiscal Policy

Benedictis-Kessner

Warshaw

2016

The Journal of Politics

149

View full text Add to dashboard Cite

Does it matter for municipal fiscal policies which party controls the mayorship in municipal government? The bulk of the existing evidence says no. But there are a variety of theoretical reasons to believe that mayoral partisanship should affect municipal policy outcomes. In this paper, we examine this question using data on nearly 1000 elections in medium and large cities over the past 60 years. In contrast to previous work, we find that mayoral partisanship has a significant impact on the size of municipal government. Overall, Democratic mayors spend more than Republican mayors. The bulk of this additional spending comes on roads, housing, libraries, and interest. We find a modest impact of mayoral partisanship on city tax levels. However, Democratic mayors issue substantially more debt than Republican ones. They also spend much more to service interest on debt. Our findings show that mayoral partisanship matters for city policy. Moreover, our findings add to a growing literature indicating that the constraints imposed on city policymaking do not prevent public opinion and elections from having a meaningful impact on municipal policy.

show abstract

“…Item-response theory has been used in political science for estimating the latent positions of actors based on roll-call voting datasets (Clinton, Jackman, and Rivers 2004), and more recently, through social media and campaign finance contributions (Bonica 2014;Barberá 2015). Item-response theory (IRT) has also been applied to difficult measurement problems, in particular the construction of democratization indices from a variety of coding sources (Coppedge et al 2017;Treier and Jackman 2008).…”

Section: Modeling Ideological Polarization Over Timementioning

confidence: 99%

When Groups Fall Apart: Identifying Transnational Polarization with Twitter from the Arab Uprisings

Kubinec¹,

Owen²

2018

Preprint

View full text Add to dashboard Cite

Scholars continue to disagree as to what extent international social connections act as a conduit to influence contentious politics within states. To answer this question, we provide the first rigorous and real-time measure of transnational ideological diffusion across sectarian groups by employing a novel statistical method and new data to capture the transnational dynamics of polarization after the Arab Uprisings of 2011. As authoritarian governments fell, populations in several states polarized between secularists and Islamists over what kind of regime was to replace the ousted one. To examine these endogenous processes, we collected a comprehensive dataset on elite and citizen Twitter accounts in Cairo and Alexandria (Egypt) and Tunis (Tunisia) for a ten-month period during the critical year of 2013. Given the difficulty in directly measuring polarization, we also developed a new model, item response theory-vector autoregression (IRT-VAR), that allows us to incorporate measurement uncertainty while providing over-time estimates of transnational polarization. We show through our model that following catalytic events like regime ousters (such as the military coup against the Muslim Brotherhood in Egypt), we can separate the direct effects of these events on group polarization within a country from indirect transnational feedback happening through the channel of social media.

show abstract

Mapping the Ideological Marketplace

Cited by 97 publications

References 73 publications

Measuring Political Positions from Legislative Speech

Measuring Political Positions from Legislative Speech

Mayoral Partisanship and Municipal Fiscal Policy

When Groups Fall Apart: Identifying Transnational Polarization with Twitter from the Arab Uprisings

Contact Info

Product

Resources

About