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To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.0038-4941.2006.00367.x Byline: Andrew B. Whitford (1), Jeff Yates (1), Holona L. Ochs (2) Abstract: Objectives. Does aggregate ideological extremism reduce public participation? Does participation in governance processes fall when the social environment shifts to the extreme left or the extreme right of the political spectrum? Our main hypothesis is that the aggregate ideological orientation of the social environment constrains volunteerism in social regulatory programs. Methods. We test our hypothesis using a panel tobit analysis of data from the federal Long-Term Care Ombudsman Program. Results. Our model of public participation (expressed as volunteerism) shows that participation expands when the ideological position of a state's citizens is at the extreme left or right of the political continuum. We show the differential effects of two types of aggregate ideological orientation: of citizens and their political leaders. We further find that participation is greatest in states with extremely liberal citizen ideological positions. Conclusions. These findings paint a more complex picture of the effect of extremism in the social environment on public participation measured as production volunteerism. In sum, public participation is greatest when the social environment is ideologically polarized, and social regulation is strongest when volunteerism is greatest. Author Affiliation: (1)University of Georgia (2)University of Kansas

I examine how the legislature and the president sequentially enable and constrain agencies in a tug-of-war over the exercise of bureaucratic discretion, partly in response to past political interventions. I provide evidence from a duration analysis of the U.S. Environmental Protection Agency's enforcement of hazard waste law for the acceleration and deceleration of policy implementation in response to sequential interventions by multiple, competing principals. I document the use of agenda-setting and solution-forcing statutes by Congress and case clearance mechanisms by the president. Sequenced political control means that agencies face shifting political expectations, caused in part by how the agency responds to past control attempts. While previous empirical research has portrayed a largely static world in which Congress and the president have influence, this study reveals a dynamic portrayal in which there is move and countermove from these principals.

One consequence of the president's use of rhetoric to shape the public agenda, the media, and congressional attention is less recognized: Presidential rhetoric shapes the priorities of the administrative agents over whom he seeks managerial control. We present statistical tests of the managerial power of presidential policy signals in the case of the United States Attorneys' implementation of the federal 'War on Drugs.' We find that presidential policy signals shifted the composition of the Attorneys' caseload, although not to the exclusion of other pertinent local, national, and internal factors. Yet, the consequences of presidential rhetoric for executive governance remain real and substantial.

We contrast social-structural theories of voting behavior with spatial theories of voting behavior to explain voter choice in the Netherlands and Great Britain. We hypothesize that voting behavior is best explained by the spatial theory of voting. Markov chain Monte Carlo (MCMC) simulation is used to estimate multinomial probit (MNP) and multinomial logit (MNL) models of voter choice, for which we calculate Bayes factors for the purpose of model comparison. We find that the joint social-structural/spatial model is the best explanatory model in the Netherlands. Our results indicate that the MNP model outperforms the MNL model in our Dutch sample. In Great Britain, on the other hand, a purely spatial model is the best explanatory model, and our MNL model outperforms our MNP model. These results suggest the question of whether to employ MNL or MNP depends crucially on the data at hand.