Linear regression models are frequently used to analyze distributive politics in the U.S. Congress; however, authors have used a variety of specifications with different implicit assumptions about how bicameralism shapes legislative bargaining. I derive a model that describes district or state spending authorizations as the aggregation of spending secured by multiple legislators working on behalf of overlapping constituencies. This bicameral shares model allows the disaggregation of House and Senate influence through simultaneous estimation of the relative bargaining power of the two chambers and the advantages that accrue to legislators holding partisan, committee, and other relevant affiliations. In the 2005 transportation bill, the model better predicts the functional form of small state advantage than recently employed specifications in the literature.

Private information characteristics like resolve and audience costs are powerful influences over strategic international behavior, especially crisis bargaining. As a consequence, states face asymmetric information when interacting with one another and will presumably try to learn about each others' private characteristics by observing each others' behavior. A satisfying statistical treatment would account for the existence of asymmetric information and model the learning process. This study develops a formal and statistical framework for incomplete information games that we term the Bayesian Quantal Response Equilibrium Model (BQRE model). Our BQRE model offers three advantages over existing work: it directly incorporates asymmetric information into the statistical model's structure, estimates the influence of private information characteristics on behavior, and mimics the temporal learning process that we believe takes place in international politics.

For many years, committee chairs have been selected on the basis of seniority. Recent work has suggested that alternative factors, specifically financial support of party goals and party unity, have diminished the importance of seniority in committee chair selection. However, previous work has either failed to quantify these effects or has done so with inappropriate methods. This paper argues for the use of a Bayesian conditional logit estimator to correctly model committee chair selection in the U.S. House of Representatives. Results show a declining commitment to seniority throughout the Republican era and support the importance of fundraising as a determinant of committee chair selection. This paper shows that two other factors, financial support of party goals and party unity, have essentially replaced seniority as the central criteria for selecting committee chairs.

In many applications of instrumental-variables regression, researchers seek to defend the plausibility of a key assumption: the instrumental variable is independent of the error term in a linear regression model. Although fulfilling this exogeneity criterion is necessary for a valid application of the instrumental-variables approach, it is not sufficient. In the regression context, the identification of causal effects depends not just on the exogeneity of the instrument but also on the validity of the underlying model. In this article, I focus on one feature of such models: the assumption that variation in the endogenous regressor that is related to the instrumental variable has the same effect as variation that is unrelated to the instrument. In many applications, this assumption may be quite strong, but relaxing it can limit our ability to estimate parameters of interest. After discussing two substantive examples, I develop analytic results (simulations are reported elsewhere). I also present a specification test that may be useful for determining the relevance of these issues in a given application.

We measure legislative productivity for the entire history of the U.S. Congress. Current measures of legislative productivity are problematic because they measure productivity for a limited number of decades and because they are based on different aspects of productivity. We provide a methodology for measuring (1) a Legislative Productivity Index (LPI) and (2) a Major Legislation Index (MLI). We use the W-CALC algorithm of Stimson (1999, Public opinion in America: Moods, cycles, and swings. 2nd ed. Boulder, CO: Westview Press) to combine information from previously used indicators of productivity into measures of the LPI and the MLI. We provide examinations of content, convergent, and construct validity. The construct validity model includes potential determinants of legislative productivity. We conclude that the LPI and the MLI are superior measures of productivity than other measures used in the literature.

In this paper, we introduce a recently developed methodology for assessing the assumption of causal homogeneity in a time series cross-section Granger framework. Following a description of the procedure and the analytical contexts for which it is appropriate, we implement this new approach to examine the transformation of the post-World War II party system in the South. Specifically, we analyze the causal relationship between black mobilization and GOP growth in the region. We find that black mobilization Granger caused Republican growth throughout the South, whereas Republican growth Granger caused black mobilization only in the deep South. We discuss the substantive significance of our results and conclude with guidelines for the appropriate use of this procedure and suggestions for future extensions of the method.

In a recent issue of this journal, Larocca (2005) makes two notable claims about the best linear unbiasedness of ordinary least squares (OLS) estimation of the linear regression model. The first, drawn from McElroy (1967), is that OLS remains best linear unbiased in the face of a particular kind of autocorrelation (constant for all pairs of observations). The second, much larger and more heterodox, is that the disturbance need not be assumed uncorrelated with the regressors for OLS to be best linear unbiased. The assumption is unnecessary, Larocca says, because “orthogonality [of disturbance and regressors] is a property of all OLS estimates” (p. 192). Of course OLS's being best linear unbiased still requires that the disturbance be homoskedastic and (McElroy's loophole aside) nonautocorrelated, but Larocca also adds that the same automatic orthogonality obtains for generalized least squares (GLS), which is also therefore best linear unbiased, when the disturbance is heteroskedastic or autocorrelated.