2. Preferences and Information-Seeking Questions in Committee Hearings

To substantively validate our new measurement strategy and demonstrate a use case of our measurement, we test hypotheses from theoretical lobbying models in the context of U.S. congressional committee hearings. In committee hearings, legislators publicly pose statements and questions regarding the hearing topic to outside agents such as lobbyists and policy specialists, who in this context are referred to as “witnesses,” and who typically have both preferences and expertise on the given policy topic (Esterling Reference Esterling2004). In general, legislators must rely on outside experts and lobbyists to subsidize their limited capacity to understand the complexities of legislation (Ban, Park, and You Reference Ban, Park and You2023; Hall and Deardorff Reference Hall and Deardorff2006).Footnote ²

Committee hearings are an excellent venue to understand communication between legislators and outside agents, for at least three reasons. First, committee hearings are a core component of the legislative process where we can observe communication patterns of legislators. Second, U.S. congressional committee hearings are fully transcribed while there is no written record of behind the scenes lobbying. Third, in hearings we can observe with which witnesses members choose to engage when given the opportunity to choose among witnesses with diverging preferences,Footnote ³ while in behind the scenes lobbying members only interact with groups to which they choose to give access.

Many theoretical frameworks of political communication suggest that communication patterns between actors tend to be shaped by the distribution of their preferences on an issue under consideration, and we expect that these theories apply to legislator–witness communication in committee hearings. For example, economic models of strategic information transmission suggest that when communication is costless, messages should be more informative to a receiver, and hence more valuable for updating beliefs if the preferences of the receiver (the legislator) and sender (the witness) are closer to each other (see Austen-Smith Reference Austen-Smith1992; Crawford and Sobel Reference Crawford and Sobel1982); in other strategic situations, such as some costly signaling or lobbying with verification game structures, witnesses with more distant preferences are more informative (e.g., Calvert Reference Calvert1985; Diermeier and Feddersen Reference Diermeier and Feddersen2000). Alternatively, an extensive psychological literature documents that individuals often engage in motivated reasoning where they search for information that is compatible with their existing views and preferences (Peterson and Iyengar Reference Peterson and Iyengar2021). Noninformational mechanisms may be at work as well. For example, an established sociological framework relies on the concept of homophily indicating legislators might be attracted to communicate with witnesses who are sociologically similar irrespective of any informational considerations (Bauer et al. Reference Bauer, de Sola Pool and Dexter1963).

While these frameworks lay out different mechanisms to explain patterns of communication in committee hearings, what is common is they all highlight that preference distance has a role in governing the interactions between legislators and witnesses. As we explain below, the weak, generic assumption that communication in hearings depends in any way on preferences is the only assumption that we need to motivate the method we propose to measure preference distance in the naturalistic setting of committee hearings. The identification of our model does not depend on the underlying mechanism that drives preference dependence in communication, although the revealed (estimated) direction of dependence can help adjudicate which of the mechanisms is at work.

As our primary outcome, we examine whether members of Congress condition their information-seeking behavior on preference distance in hearing communications. To capture members’ information-seeking behavior, we count the number of falsifiable sentences that each member directs to each witness, which is a measure of the extent to which the member engages the witnesses in informational, epistemic discourse (Esterling Reference Esterling2011). As secondary measures, we evaluate whether preference-distance matters even when legislators express non-falsifiable opinion or ask anecdotal questions, which indicate a non-epistemic discourse (Esterling Reference Esterling2007), such as when members engage in messaging behavior through grandstanding (Park Reference Park2021).

Comparing the preference-distance test across these different question types enables us to further analyze whether any observed preference dependence is primarily informational or noninformational. If preferences matter for all three types of sentences, that implies that the underlying mechanism is a sociological one because more frequent communications out of homophily would not necessarily apply only to falsifiable questions; if they matter only for falsifiable sentences, that implies that the legislators’ information-seeking incentives are motivating the observed communication pattern.

Finally, if committee members are motivated by economic incentives for learning information, we expect that the dependence of falsifiable questions on preference distance should be especially apparent among witnesses that have research-based expertise, when there is stronger policy information asymmetry between members and witnesses (Austen-Smith Reference Austen-Smith1993), but should be less apparent for nonexperts who provide less of an opportunity to update beliefs. Conversely, if committee members are primarily motivated by psychological desires to confirm their existing beliefs, the degree of expertise should not matter for information-seeking behavior.

3. Statistical Model

We propose a flexible model that measures witnesses’ policy preferences in order to test for preference dependence using data from hearings. Our model requires two types of data: (1) DW-Nominate scores to measure legislator preferences and (2) the count of legislators’ questions or statements directed to each witnesses within a committee hearing. Optionally, our model can exploit (3) data that construct a common space ideology measure such as survey responses or CF scores from Bonica (Reference Bonica2013). The common space measure can aid in describing the geometric relationship between legislator and witness preferences.

We use these data in a statistical model to measure witnesses’ latent preferences over policy topics for use in hypothesis tests regarding communication patterns in hearings. In our notation, Latin letters indicate observed data and Greek letters indicate parameters. Label legislators’ policy-relevant preferences regarding legislation as $L_j$ and agents’ policy-relevant preferences regarding the same legislation as $\zeta _i$ . Within a given hearing, each legislator (indexed by j) directs some number of each type of question to each witness as an outside agent (indexed by i). In this context, the outcome $O^m_{ij}$ is the count of sentences of type $m \in \{falsifiable, opinion, anecdotal\}$ within the $ij$ th member–witness dyad. We model the dyadic count outcomes as a function of the preference distance between legislator and agent using the equation set,

(1a) $$ \begin{align} O^m_{ij} \sim & \mbox{Poisson}(\widetilde{\lambda^m_{ij}})\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \end{align} $$

(1b) $$ \begin{align} &\ ln\widetilde{\lambda^m_{ij}} = \lambda^m_{ij} = \beta^m_{0} + \beta^m_{1}d(L_{j}, \zeta_{i}) + \beta^m_{2}R_i + \beta^m_{3}d(L_{j},\zeta_{i})R_i + \eta_{1_{ij}} + \eta_{2_j} + \eta_{3_i}, \nonumber \\ L_{j} \sim & \mbox{Normal}(\mu^L_{j}, \sigma^{L}) \end{align} $$

(1c) $$ \begin{align} & \mu^L_{j} = \alpha_0 + \alpha_1\psi_{j},\qquad\qquad\qquad\qquad\qquad \nonumber \\ \zeta_{i} \sim & \mbox{Normal}(\mu^{\zeta}_{i}, \sigma^{\zeta})\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \\ & \mu^{\zeta}_{i} = (\alpha_0 + \alpha_2) + (\alpha_1 + \alpha_3)\psi_{i}.\qquad\qquad\qquad\qquad \qquad\qquad\qquad\quad\nonumber \end{align} $$

Here, each count outcome is modeled in Equation (1a) using a Poisson stochastic process with parameter $\widetilde {\lambda ^m_{ij}}$ conditional on the data and normally distributed random effects to accommodate overdispersion.Footnote ⁴ The term $d(L_{j}, \zeta _{i})$ is a preference-distance function that measures the distance in preferences between the $j^{\mathrm {th}}$ committee member and the ith witness; we implement the distance function using a quadratic distance, $d(L_{j}, \zeta _{i}) = (L_{j} - \zeta _{i})^2$ . To complete the outcome model, $R_i$ is an observed indicator variable that equals one if the witness, i, comes from an organization that specializes in policy research, and equals zero otherwise. The parameter $\beta ^m_{1}$ tests the preference-distance hypothesis among witnesses that are not from research-based organizations and $\beta ^m_{3}$ tests whether preference distance matters differently for witnesses from research-based organizations. $\beta ^m_{0}$ and $\beta ^m_{2}$ are constants. Finally, the model includes one random effect at the witness level ( $\eta _3)$ , one at the legislator level ( $\eta _2$ ), and one at the dyad level ( $\eta _1$ ).Footnote ⁵

Equations (1b) and (1c) are the optional bridging equations that establish the functional relationship between legislator preferences L and agent preferences $\zeta $ ; $\psi $ is a bridging variable used only for identification. Consider each of L and $\zeta $ in turn.

Congress scholars routinely take legislators’ preferences, L, as indicated by their observed roll-call votes, and hence recovered correctly through well-established scaling procedures such as IDEAL (Clinton, Jackman, and Rivers Reference Clinton, Jackman and Rivers2004) or DW-NOMINATE (Poole and Rosenthal Reference Poole and Rosenthal1991). Roll-call votes indicate legislators’ office-induced or operational preferences (Burden, Caldeira, and Groseclose Reference Burden, Caldeira and Groseclose2000; Rhode Reference Rhode1991), preferences which summarize the full vector of relevant influences on each legislator including party, constituency, interest group, and donor pressure along with her personal beliefs. Therefore, it is legislators’ operational preferences measured in roll-call vote scaling that are relevant to most institutional tests (Burden et al. Reference Burden, Caldeira and Groseclose2000).

The next statistical task is to measure outside agents’ preferences in a comparable legislative policy preference space $\zeta $ in order to establish the distance $d(L_{j}, \zeta _{i})$ between legislator and agent. Since agents are not legislators, their occupation neither requires nor enables them to vote on legislation, and so roll-call preferences do not and indeed cannot exist for them. In addition, while legislators vote on many bills, we typically only observe individual agents take a position on a single bill before the committee, if they take any position at all, and often witnesses’ support or opposition is to technical provisions in the legislation. As a result, the legislatively relevant preferences, $\zeta $ , that govern interaction in a hearing are missing data for all outside agents.

3.2. Unconstrained Flexible Model

Of course, witnesses are not legislators, and so it is better to measure witness preferences in a separate dimension. We offer a flexible solution to this identification problem by estimating a distance function, which includes a random effect ( $\zeta _i$ ), on the right-hand side of a regression. We rely on that distance function to estimate the parameter testing the relationship between the distance and the question rate.

Identification of the flexible model is straightforward and relies only on the nested structure of the dyadic data that naturally occurs in the context of committee hearings.Footnote ⁷ To see the identification, first note that we include witness-level ( $\eta _3$ ), member-level ( $\eta _2$ ), and dyad-level ( $\eta _1$ ) random effects, each of which is identified by nesting in the data.Footnote ⁸ Second, note that the witness preference parameter $\zeta _i$ is also a random effect at the witness level, and it is identified separately from $\eta _3$ because it is embedded inside of a nonlinear function.Footnote ⁹ Finally, we set the variance of $\zeta $ in the bridging equations to equal the variance of our observed DW-NOMINATE scores, and as a result, $\zeta $ behaves as a covariate in the outcome equation to estimate the preference-distance parameter $\beta _1$ .

Unlike the constrained-regression approach, the bridging equations are not necessary for inferring witness preferences in our flexible model. Instead, the bridging equation (1c) hierarchically models $\zeta $ as a witness-level random effect using witness-level covariates, and so each of the parameters is identified via hierarchical random effect regression, including the “shift” ( $\alpha _2$ ) and “rotation” ( $\alpha _3$ ) parameters. Empirically, in both our simulations and in the application, including this witness-level equation has virtually no effect on the estimates for witness preferences beyond increasing their precision. As a result, the analyst can choose to estimate the model with or without the bridging equations; they are fully optional. Substantively, estimating the shift and rotation parameters shows the geometric relationship between the witness preference space and the common space ideology score.

While the model is theoretically identified, there are two core empirical requirements for identification. First, because we recover witness preferences as a random effect, witnesses must be nested in repeated dyads—that is, each witness must testify before a committee with more than one member, which is virtually always the case, for example, in the U.S. Congress. Second, preference distance must in fact be relevant to the pattern of communication observed in committee hearings. If this condition is not met, then the patterns of communication in committee hearings will not reveal witnesses’ preferences.

Geometrically, our approach allows the agent preference dimension $\zeta $ to rotate away from L. We define $\zeta ^0$ as constrained-regression agent preferences that are imputed from the unidimensional constraint which sets $\alpha _2=\alpha _3=0$ and so forces $\zeta $ and L to be the same dimension, and the flexible, unconstrained preferences $\zeta ^1$ are imputed from the flexible model. The rotation indicated by $\alpha _3$ can be converted to the angle $\theta $ (measured in radians) governing the direction of the relationship through a Cartesian coordinate space, and one can retrieve $\theta $ post-estimation via the cosine rule and the vectors of constrained and unconstrained preferences (Binmore and Davies Reference Binmore and Davies2001, 18),

(2) $$ \begin{align} \theta = \cos^{-1}{\frac{\langle \boldsymbol{\zeta}^{\mathbf{0}}, \boldsymbol{\zeta}^{\mathbf{1}} \rangle}{\| \boldsymbol{\zeta}^{\mathbf{0}} \|\| \boldsymbol{\zeta}^{\mathbf{1}} \|}}. \end{align} $$

Setting aside any shift between the two lines, the angle $\theta =0$ is the parallel case; $\theta =\pi /2$ is orthogonal; and $0<\theta <\pi /2$ is oblique.Footnote ¹⁰ The constrained-regression method forces the rotation to always and often implausibly equal the parallel case with $\theta \equiv 0$ .

4. Application to Medicare Hearings

We use our flexible model to test for the relationship between preference distance and questioning in congressional committee hearings on the Medicare program. To select the sample, we randomly drew 29 hearings from the universe of all hearings on the Medicare program that were held between 2000 and 2003 across 12 different congressional committees and subcommittees. In all, across all of the 29 hearings, there are 67 witnesses, 87 committee members, and 669 dyads. The replication package is Esterling and Park (Reference Esterling and Park2024).

To capture legislators’ speaking patterns $(O_{ij}^m)$ , we construct three dependent variables. We follow Esterling’s (Reference Esterling2007) coding rules for classifying member statements in committee hearing transcripts into the three mutually exclusive sentence types: falsifiable, opinion, and anecdotal.Footnote ¹¹ We count the number of each of these three types of sentences that legislators make within each legislator–witness dyad. That is, we concatenated each legislator’s statements directed to each witness, and then counted the number of occurrences of each type of sentence. Table 1 shows the descriptives for the 669 dyads in the sample.

Table 1 Member–lobbyist dyadic outcome data.

Note in Table 1 that under our coding, at these hearings, members are most likely to make falsifiable inquiries. Most members ask zero questions to most witnesses, and so these counts have low means and high standard deviations. We accommodate this over dispersion in the count variables in the statistical model by including dyad-level random effects (Harrison Reference Harrison2014; Skrondal and Rabe-Hesketh Reference Skrondal and Rabe-Hesketh2007).

We have two key independent variables. First, to measure legislator preferences $(L)$ , we use first dimension DW-NOMINATE scores (Lewis et al. Reference Lewis, Poole, Rosenthal, Boche, Rudkin and Sonnet2018; Poole and Rosenthal Reference Poole and Rosenthal1991) from the $108$ th Congress to measure legislators’ roll-call preferences L.Footnote ¹² Second, we construct an indicator for witnesses from research-based organizations $(R)$ which is coded as 1 for witnesses from universities, foundations, and think tanks, and 0 otherwise.Footnote ¹³ Under this coding, 37% of the sample is research-based.

Optionally, in case a researcher wants to use the bridging function, the common space ideology measure for a personal ideology scale $(\psi )$ , which is the core component of the bridging equation, needs to be estimated. For this, we administered a survey containing a battery of ideology items to the witnesses who appeared at the hearings in the data set, and to former members of Congress, who are not in the hearings sample. That is, we use survey responses from former members of Congress who did not attend the sampled hearings rather than the committee members from the sampled hearings, most of whom at the time of the data collection were current incumbents. Since former members’ DW-NOMINATE scores are in the same roll-call preference space L as the committee members in the sample, it is not necessary to use the same legislators in the bridging component as in the outcome component of the model; it is the same preference dimension for both. We estimate $\psi $ via an IRT model.

Administering the survey to former members rather than current members is preferable for two reasons. First, while in office, many members have a policy not to respond to academic surveys, and even those who respond would likely assign staff to complete and return the survey which would introduce measurement error in the measure of personal ideology $\psi $ . Former members are either retirees or they are employed where it is unlikely that staff support would fill out surveys relevant to their past legislative work. Second, like the witnesses, former members are typically private citizens, not elected officials, and so former members are more likely to fill out the responses based on their own, personal ideology rather than on an office-induced preference that is conditioned by pressure from constituents, party, donors, or groups. Measuring office-induced preferences would undermine bridging equation (1b), which is a mapping from legislators’ personal ideology $\psi $ to their office-induced preference L. However, here we assume that individuals’ ideology is constant over time so that their personal ideology is the same once they are former members as it was when they were members.

To measure the common space scale $\psi $ , we administered the following survey items to both a set of former members of Congress (FMC)Footnote ¹⁴ and to the witnesses in the sample. These questions are validated to measure each individual’s left–right ideology.Footnote ¹⁵

• • [Markets] The protection of consumer interests is best insured by a vigorous competition among sellers rather than by federal government regulation on behalf of consumers.

• • [Companies] There is too much power concentrated in the hands of a few large companies for the good of the country.

• • [HelpPoor] One of the most important roles of government is to help those who cannot help themselves, such as the poor, the disadvantaged, and the unemployed.

• • [Access] All Americans should have access to quality medical care regardless of ability to pay.

• • [Incomes] The differences in income among occupations should be reduced.

The labels in square brackets were not included in the survey question wording.

To complete the bridging data set, we merged in the most recent DW-NOMINATE scores for each former member, that is, the score from the Congress just prior to the member separating from the institution.Footnote ¹⁶ The descriptives of the survey responses and DW-NOMINATE scores are in Table 2. In this rectangular dataset, we have responses to the ideology survey questions from both former members and the witnesses, but the first-dimension DW-NOMINATE scores are missing for every witness. We estimate $\psi $ via an IRT model; SM Section A.4 shows the full model and we suppress the IRT model in Equations (1b) and (1c) to simplify the presentation.

Table 2 Ideology indicator descriptives for former members and witnesses.

In the outcome model, we include legislator-, witness-, and dyad-specific random effects to address any omitted covariates at each level. The legislator-specific random effect, $\eta _2$ , captures the committee member’s propensity to ask questions and make statements of all types to witnesses; a witness-specific random effect, $\eta _3$ , captures the witness’s propensity to attract questions and comments from legislators; and a dyad-specific random effect, $\eta _1$ , captures latent causes for the extent of the interaction between a legislator and a witness, such as if something a witness says leads to additional questions from a member. Furthermore, $\eta _1$ also accounts for overdispersion that comes from added variance in the count data (Skrondal and Rabe-Hesketh Reference Skrondal and Rabe-Hesketh2007).

We estimate both the single equation “constrained-regression” model and our flexible model using Bayesian MCMC estimation with uninformative priors.Footnote ¹⁷ The constrained-regression approach estimates the bridging model and the outcomes model separately and so the outcome equations do not update the preferences of agents, yielding agent preference estimates $\zeta ^0$ that are derived under the assumption that legislator and agent preferences are constrained to a single dimension. The flexible model estimates both models simultaneously so that they jointly inform the posterior distribution over each $\zeta ^1$ . More details about our statistical model and estimation are in SM Section A.4.