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.     

Virginia's Revenue Forecasting Process and Models

In Virginia, major differences between revenue forecasts and actual revenue collections raised questions from legislators about the adequacy of the state's econometric forecasting models and the forecasting process itself. Consequently, Virginia's process and models were examined by the Joint Legislative Audit and Review Commission (JLARC), a legislative watchdog agency. The review found most of the differences could be attributed to declining economic factors and not poor forecasting. However, a surprisingly large component of the forecast was based on undocumented judgmental inputs that required greater oversight. Other states in similar circumstances may wish to evaluate their own forecasting process and econometric models using the JLARC approach.     

Warriors in the “new world order”

He writes regularly on matters of strategy. His publications include The Strategic Dimension of Military Manpower;and Paradoxes of Power: The Military Establishment in the Eighties.     

Dimensions of the self-concept: A source of further distinctions in the nature of self-consciousness

This study distinguished between forms of self-consciousness (private self-consciousness and social anxiety) and investigated the effect of self-esteem, vulnerability to criticism, and the tendency to fantasize on each. Utilizing a sample of adolescents (age 12 to 19 years), a structural equation analysis using unobserved variables revealed that, as expected, vulnerability to others' criticism heightened both private self-consciousness and social anxiety. In contrast, the tendency to fantasize in everyday life increased only private self-consciousness, and low self-esteem led directly only to increased social anxiety. Further, results showed that self-esteem had an appreciable indirect effect on both private self-consciousness and social anxiety, as mediated by vulnerability and the tendency to fantasize, indicating that the effect of self-esteem (a primary motivator) was more complex than an analysis of direct effects would indicate. Implications for our understanding of self-consciousness and the self-concept are discussed.My deepest thanks go to Morris Rosenberg, whose insights into the self-concept were indispensable, both in conceptualization and in analysis. I also thank John A. Fleishman and Edward Z. Dager for their constructive comments on early drafts of this article. Any errors remaining are my responsibility.This study was supported by NIMH Grant R01 MH27747-06 awarded to Morris Rosenberg. The data were analyzed using the facilities of the Computer Science Center, University of Maryland, College Park.Professor Elliott received his Ph.D. in sociology at the University of Wisconsin, Madison. His current major interest is in the structure of the self-concept.     

Estimating Dynamic Panel Data Models in Political Science

Panel data are a very valuable resource for finding empiricalsolutions to political science puzzles. Yet numerous publishedstudies in political science that use panel data to estimatemodels with dynamics have failed to take into account importantestimation issues, which calls into question the inferenceswe can make from these analyses. The failure to account explicitlyfor unobserved individual effects in dynamic panel data inducesbias and inconsistency in cross-sectional estimators. The purposeof this paper is to review dynamic panel data estimators thateliminate these problems. I first show how the problems withcross-sectional estimators arise in dynamic models for paneldata. I then show how to correct for these problems using generalizedmethod of moments estimators. Finally, I demonstrate the usefulnessof these methods with replications of analyses in the debateover the dynamics of party identification.