🧭 What the model does

A new model extends the standard vote-choice framework to jointly generate roll-call votes and legislative speech from a common set of underlying preference parameters. Both votes and text are modeled as manifestations of the same latent preferences, allowing speech to inform estimates that traditionally rely only on votes.

🧰 How preferences are estimated

• Estimation uses a sparse Gaussian copula factor model that:
• Infers the number of latent dimensions automatically
• Is robust to outliers in the data
• Accounts for zero inflation common in text counts
• This approach links spatial preference estimation (from votes) with latent factor structure recovery (from text) in a single statistical framework.

📦 What data was analyzed

• Roll-call votes and floor speeches from recent sessions of the U.S. Senate were used to illustrate the estimator's performance.

🔎 Key findings

• Two stable latent dimensions consistently emerge:
• A conventional ideological dimension
• A second dimension that reflects Senators' leadership roles
• The method can leverage shared speech patterns to impute missing vote information, recovering reliable preference estimates for rank-and-file Senators even when only leadership votes are available.

💡 Why it matters

• Jointly modeling votes and text provides a fuller, more robust picture of legislative preferences.
• The estimator is practical for research settings with noisy text data or incomplete voting records, enabling better inference about legislators' positions and role-based behavior.