Strategic Party Government: Party Influence in Congress, 1789–2000

Why does the influence of Congressional parties fluctuate over time? Building on prevailing answers, we develop a model, Strategic Party Government, which highlights the electoral motives of legislative parties and the strategic interaction between parties. We test this theory using the entire range of House and Senate party behavior from 1789 to 2000 and find that the strategic behavior of parties complements members' preferences as an explanation for variation in party influence. Specifically, the strongest predictors of one party's voting unity are the unity of the opposing party and the difference between the parties in the preceding year. Moreover, we find strong links between party behavior in Congress and electoral outcomes: an increase in partisan influence on legislative voting has adverse electoral costs, while winning contested votes has electoral benefits.     

An Effective Approach to the Repeated Cross‐Sectional Design

Repeated cross‐sectional (RCS) designs are distinguishable from true panels and pooled cross‐sectional time series (PCSTS) since cross‐sectional units (e.g., individual survey respondents) appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate‐ and individual‐level analyses, available methods—from pooled ordinary least squares to PCSTS to time series—force researchers to choose one level of analysis. The PCSTS tool kit does not provide an appropriate solution, and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed by the use of multilevel modeling to estimate both aggregate‐ and individual‐level parameters simultaneously. We use Monte Carlo experiments and three applied examples to explore the advantages of our framework.     

The Electoral Costs of Party Loyalty in Congress

To what extent is party loyalty a liability for incumbent legislators? Past research on legislative voting and elections suggests that voters punish members who are ideologically “out of step” with their districts. In seeking to move beyond the emphasis in the literature on the effects of ideological extremity on legislative vote share, we examine how partisan loyalty can adversely affect legislators' electoral fortunes. Specifically, we estimate the effects of each legislator's party unity—the tendency of a member to vote with his or her party on salient issues that divide the two major parties—on vote margin when running for reelection. Our results suggest that party loyalty on divisive votes can indeed be a liability for incumbent House members. In fact, we find that voters are not punishing elected representatives for being too ideological; they are punishing them for being too partisan.     

Motivated Reasoning,Public Opinion,and Presidential Approval

Political Behavior - Presidential approval is a desirable commodity for US presidents, one that bolsters re-election chances and the prospects of legislative success. An important question, then,...     

Dynamic Conditional Correlations in Political Science

Time-varying relationships and volatility are two methodological challenges that are particular to the field of time series. In the case of the former, more comprehensive understanding can emerge when we ask under what circumstances relationships may change. The impact of context—such as the political environment, the state of the economy, the international situation, etc.—is often missing in dynamic analyses that estimate time-invariant parameters. In addition, time-varying volatility presents a number of challenges including threats to inference if left unchecked. Among time-varying parameter models, the Dynamic Conditional Correlation (DCC) model is a creative and useful approach that deals effectively with over-time variation in both the mean and variance of time series. The DCC model allows us to study the evolution of relationships over time in a multivariate setting by relaxing model assumptions and offers researchers a chance to reinvigorate understandings that are tested using time series data. We demonstrate the method's potential in the first example by showing how the importance of subjective evaluations of the economy are not constant, but vary considerably over time as predictors of presidential approval. A second example using international dyadic time series data shows that the story of movement and comovement is incomplete without an understanding of the dynamics of their variance as well as their means.     

Beyond the Unit Root Question: Uncertainty and Inference

A fundamental challenge facing applied time-series analysts is how to draw inferences about long-run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long-run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.