Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress

We introduce the Hierarchical Ideal Point Topic Model, which provides a rich picture of policy issues, framing, and voting behavior using a joint model of votes, bill text, and the language that legislators use when debating bills. We use this model to look at the relationship between Tea Party Republicans and “establishment” Republicans in the U.S. House of Representatives during the 112th Congress. 1 Capturing Political Polarization Ideal-point models are one of the most widely used tools in contemporary political science research (Poole and Rosenthal, 2007). These models estimate political preferences for legislators, known as their ideal points, from binary data such as legislative votes. Popular formulations analyze legislators’ votes and place them on a one-dimensional scale, most often interpreted as an ideological spectrum from liberal to conservative. Moving beyond a single dimension is attractive, however, since people may lean differently based on policy issues; for example, the conservative movement in the U.S. includes fiscal conservatives who are relatively liberal on social issues, and vice versa. In multi-dimensional ideal point models, therefore, the ideal point of each legislator is no longer characterized by a single number, but by a multi-dimensional vector. With that move comes a new challenge, though: the additional dimensions are often difficult to interpret. To mitigate this problem, recent research has introduced methods that estimate multi-dimensional ideal points using both voting data and the texts of the bills being voted on, e.g., using topic models and associating each dimension of the ideal point space with a topic. The words most strongly associated with the topic can sometimes provide a readable description of its corresponding dimension. In this paper, we develop this idea further by introducing HIPTM, the Hierarchical Ideal Point Topic Model, to estimate multi-dimensional ideal points for legislators in the U.S. Congress. HIPTM differs from previous models in three ways. First, HIPTM uses not only votes and associated bill text, but also the language of the legislators themselves; this allows predictions of ideal points from politicians’ writing alone. Second, HIPTM improves the interpretability of ideal-point dimensions by incorporating data from the Congressional Bills Project (Adler and Wilkerson, 2015), in which bills are labeled with major topics from the Policy Agendas Project Topic Codebook.1 And third, HIPTM discovers a hierarchy of topics, allowing us to analyze both agenda issues and issue-specific frames that legislators use on the congressional floor, following Nguyen et al. (2013) in modeling framing as second-level agenda setting (McCombs, 2005). Using this new model, we focus on Republican legislators during the 112th U.S. Congress, from January 2011 until January 2013. This is a particularly interesting session of Congress for political scientists, because of the rise of the Tea Party, a decentralized political movement with populist, libertarian, and conservative elements. Although united with “establishment” Republicans against Democrats in the 2010 midterm elections, leading to massive Democratic defeats, the Tea Party was—and still is—wrestling with establishment Republicans for control of the Republican party. The Tea Party is a new and complex phenomenon for political scientists; as Carmines and D’Amico (2015) observe: “Conventional views of ideology as a single-dimensional, left-right spectrum experience great difficulty in understanding or explaining the Tea Party. ” Our model identifies legislators who have low (or high) levels of “Tea Partiness” but are (or are not) members of the Tea Party Caucus, and providing insights into the nahttp://www.policyagendas. org/ ture of polarization within the Republican party. HIPTM also makes it possible to investigate a number of questions of interest to political scientists. For example, are there Republicans who identify themselves as members of the Tea Party, but whose votes and language betray a lack of enthusiasm for Tea Party issues? How well can we predict from someone’s language alone whether they are likely to associate themselves with the Tea Party? Our computational modeling approach to “Tea Partiness”, distinct from self-declared Tea Party Caucus membership, may have particular value in understanding Republican party politics going forward because, despite the continued influence of the Tea Party, the official Tea Party Caucus in the House of Representatives is largely inactive and its future uncertain (Fuller, 2015). 2 Polarization across Dimensions Ideal point models describe probabilistic relationships between observed responses (votes) on a set of items (bills) by a set of responders (legislators) who are characterized by continuous latent traits (Fox, 2010). A popular formulation posits an ideal point ua for each lawmaker a, a polarity xb, and popularity yb for each bill b, all being values in (−∞,+∞) (Martin and Quinn, 2002; Bafumi et al., 2005; Gerrish and Blei, 2011). Lawmaker a votes “Yes” on bill b with probability p(va,b = Yes |ua, xb, yb) = Φ(uaxb + yb) (1) where Φ(α) = exp(α)/(1 + exp(α)) is the logistic (or inverse-logit) function.2 Intuitively, most lawmakers vote “Yes” on bills with high popularity yb and “No” on bills with low yb. When a bill’s popularity is lower, the outcome of the vote va,b depends more on the interaction between the lawmaker’s ideal point ua and the bill’s polarity xb. Multi-dimensional ideal point models replace scalars ua and xb with K-dimensional vectors ua and xb (Heckman and Jr., 1997; Jackman, 2001; Clinton et al., 2004). Unfortunately, as Lauderdale and Clark (2014) observe, the binary data used for these models are “insufficiently informative to support analyses beyond one or two dimensions”, and the additional dimensions are difficult to interpret. To address this lack of interpretability, recent work has proposed multi-dimensional ideal point models to jointly capture both binary votes and the associA probit function is also often used where Φ(α) is instead the cumulative distribution function of a Gaussian distribution (Martin and Quinn, 2002). ated text (Gerrish and Blei, 2012; Gu et al., 2014; Lauderdale and Clark, 2014; Sim et al., 2015). 3 Hierarchical Ideal Point Topic Model Bringing topic models (Blei and Lafferty, 2009) into ideal-point modeling provides an interpretable, text-based foundation for political scientists to understand why the models make the predictions they do. However, both the topic—what is discussed— and the framing—how it is discussed—also reveal political preferences. We therefore introduce framespecific ideal points, using a hierarchy of topics to model issues and their issue-specific frames. Although the definition of “frame” is itself a moving target in political science (Entman, 1993), we adopt the theoretically motivated but pragmatic approach of Nguyen et al. (2013): just as agenda-issues map naturally to topics in probabilistic topic models (e.g., Grimmer (2010)), the frames as second-level agenda-setting (McCombs, 2005) map to secondlevel topics in a hierarchical topic model. Our model’s inputs are votes {va,b}, each the response of legislator a ∈ [1, A] to bill b ∈ [1, B]. Two types of text supplement the votes: floor speeches (documents) {wd} from legislator ad, and the text w′ b of bill b. While congressional debates are typically about one piece of legislation, we make no assumptions about the mapping between wd and w′ b. In principle this allows wd to be any text by legislator ad (e.g., not just floor speeches about this bill, but blogs, social media, press releases) and—unlike Gerrish and Blei (2011)—this permits us to make predictions about individuals even without vote data for them. Figure 1 shows the plate notation diagram of HIPTM, which has the following generative process: 1. For each issue k ∈ [1,K] (a) Draw k’s associated topic φk ∼ Dir(β, φk) (b) Draw issue-specific distribution over frames ψk ∼ GEM(λ0) (c) For each frame j ∈ [1,∞) (specific to issue k) i. Draw j’s associated topic φk,j ∼ Dir(β, φk) ii. Draw j’s regression weight ηk,j ∼ N (0, γ) 2. For each document d ∈ [1, D] by legislator ad (a) Draw topic (i.e., issue) distribution θd ∼ Dir(α) (b) For each issue k ∈ [1,K], draw frame distribution ψd,k ∼ DP(λ, ψk) (c) For each token n ∈ [1, Nd] i. Draw an issue zd,n ∼ Mult(θd) ii. Draw a frame td,n ∼ Mult(ψd,zd,n) iii. Draw word wd,n ∼ Mult(φzd,n,td,n) 3. For each legislator a ∈ [1, A] on each issue k ∈ [1,K] (a) Draw issue-specific ideal point ua,k ∼ N (∑Jk j=1 ψ̂a,k,jηk,j , ρ) weighting ηk,j by how much the legislator talks about that frame