Scaling Politically Meaningful Dimensions Using Texts and Votes

Lauderdale, Benjamin E.; Clark, Tom

doi:10.1111/ajps.12085

Cited by 93 publications

(72 citation statements)

References 26 publications

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“…Thus, given sufficiently rich data, we could measure how legislators' positions vary by topic, and recover multidimensional preference estimates with topic labels, following recent work on roll-call analysis (Lauderdale and Clark 2014). As with disaggregating by topic, estimation of dynamic positions can also be achieved from a closely related model that does not change the lower-level model for the texts.…”

Section: Multidimensionality and Dynamicsmentioning

confidence: 99%

Measuring Political Positions from Legislative Speech

Lauderdale

Herzog²

2016

Polit. anal.

Self Cite

134

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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...

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Section: Multidimensionality and Dynamicsmentioning

confidence: 99%

Measuring Political Positions from Legislative Speech

Lauderdale

Herzog²

2016

Polit. anal.

Self Cite

134

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“…By incorporating coded labels and retweets as a relatively direct measure of polarization, text could then be included as an additional source of information about polarization. This approach is similar to the recent efforts to find ways of estimating ideal points for legislators that combine both the text of legislation and legislators' observed votes (Lauderdale and Clark 2014). For these reasons, our model does not preclude analysis of text but rather helps provide an estimation framework for anchoring estimates of noisy Twitter data.…”

Section: Discussionmentioning

confidence: 91%

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

Kubinec¹,

Owen²

2018

Preprint

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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.

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“…For discussion on the statistical bene ts of low-dimensionality, see Poole and Rosenthal (1997); Martin and inn (2002);and Clinton et al (2004). For a paper that treats estimation when dimensionality is higher, see Lauderdale and Clark (2014).…”

Section: Central Bank Preferencesmentioning

confidence: 99%

A textual Taylor rule: estimating central bank preferences combining topic and scaling methods

Baerg

Lowe

2018

PSRM

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Scholars o en use voting data to estimate central bankers' policy preferences but consensus voting is commonplace. To get around this, we combine topic-based text analysis and scaling methods to generate theoretically motivated comparative measures of central bank preferences on the U.S. Federal Open Market Commi ee leading up to the nancial crisis in a way that does not depend on voting behavior. We apply these measures to a number of applications in the literature. For example, we nd that FOMC members that are Federal Reserve Bank Presidents from districts experiencing higher unemployment are also more likely to emphasize unemployment in their speech. We also con rm that commi ee members on schedule to vote are more likely to express consensus opinion than their o schedule voting counterparts.

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Scaling Politically Meaningful Dimensions Using Texts and Votes

Cited by 93 publications

References 26 publications

Measuring Political Positions from Legislative Speech

Measuring Political Positions from Legislative Speech

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

A textual Taylor rule: estimating central bank preferences combining topic and scaling methods

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