Voter turnout and the benefits of voting

A model of voting behavior is developed that predicts that individuals vote if the absolute value of voting for or against a referendum exceeds the cost of voting. The results obtained from examining voting on city-county consolidation referenda and in New York state (1) provide support for the relatively untested prediction that turnout rises as the absolute value of the mean gains resulting from an electoral outcome increase and (2) augment the evidence that turnout rises as the probability of altering an electoral outcome increases and falls as the cost of voting rises.     

Provider medicaid fraud: A note on white-coat crime

To date, relatively few empirical studies have been conducted of provider medicaid fraud or, more generally, of “white-coat crime”--offenses by health professionals. Based on data accumulated by a quality control unit on the duplicate billings for a single service by New York State providers, the present research attempts to help fill this void by assessing the prevalence of medicaid fraud. Two conclusions are suggested by the data. First, unlike other realms of upperworld criminality, provider fraud does not appear to be pervasive. Second, provider occupations with more of a business orientation appear to have a greater involvement in fraudulent activities than groups that are more fully professionalized.     

Merging content analysis and the policy sciences: A system to discern policy-specific trends from news media reports

In this article we set out to bridge a surprising methodological gap between two time-honored research traditions – news media content analysis and the policy sciences analytic framework. Lasswell, a recognized pioneer of both the method and the framework, discussed the mutual benefits of each decades ago. But few researchers, if any, have formally linked the two. To that end, in this article we (1) make the case for using news media content analysis to inform research studies using the policy sciences analytic framework; (2) introduce an original content analysis categorical system for that purpose; (3) demonstrate that system with a study of 90 national news articles about the stratospheric ozone hole; and (4) compare our system to others used to examine news content. We report that our system, used by human coders, is well geared to describing and mapping trends in the social process surrounding the development of the Montreal Protocol ozone treaty during the intelligence gathering and promotion phases encompassed by our data sample. We argue that other content analysis systems fall short – in structure and purpose – of meeting the promise ours holds to the policy scientist.     

Are Niche Parties Fundamentally Different from Mainstream Parties? The Causes and the Electoral Consequences of Western European Parties' Policy Shifts, 1976–1998

Do “niche” parties—such as Communist, Green, and extreme nationalist parties—adjust their policies in response to shifts in public opinion? Would such policy responsiveness enhance these parties' electoral support? We report the results of statistical analyses of the relationship between parties' policy positions, voters' policy preferences, and election outcomes in eight Western European democracies from 1976 to 1998 that suggest that the answer to both questions is no . Specifically, we find no evidence that niche parties responded to shifts in public opinion, while mainstream parties displayed consistent tendencies to respond to public opinion shifts. Furthermore, we find that in situations where niche parties moderated their policy positions they were systematically punished at the polls (a result consistent with the hypothesis that such parties represent extreme or noncentrist ideological clienteles), while mainstream parties did not pay similar electoral penalties. Our findings have important implications for political representation, for spatial models of elections, and for political parties' election strategies.     

An Overlapping Networks Approach to Resource Allocation for Domestic Counterterrorism

Motivated by the links between terror and crime and the difficulty in directly detecting terror activity, this article formulates and solves a resource allocation problem on overlapping networks to determine if interdiction efforts may be able to take advantage of these connections. The government, knowing only the general structure and overlap of the networks, allocates its scarce resources to investigate each terror and criminal network. There are two stages to the investigation: an initial investigation of all nodes (i.e., terrorists or criminals) and a secondary investigation of criminals identified during the initial investigation to determine if they are terrorists. Applying the model to data derived from a population of terrorists in the United States between 1971–2003 suggests that the government may be able to exploit the terror connections of crimes that are relatively uncommon, somewhat easy to detect, and are attractive to terrorists.     

The Political Budget Cycle and Subnational Debt Expenditures in Federations: Panel Data Evidence from India

What political variables explain variations in subnational fiscal expenditures on interest payments on the debt? The author argues that the political budget cycle and center‐right political party ideology—rather than the effective number of parties, alternation of power, ideological proximity between the central government and constituent units, or most forms of political party ideology—can help explain the level of expenditures on interest payment of subnational debt in India. The core empirical finding is that significant increases in expenditures on the debt occur the year in which a state assembly election is held in India.     

Comparative Forensic Soil Analysis of New Jersey State Parks Using a Combination of Simple Techniques with Multivariate Statistics,

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle‐size distribution, pH analysis in both water and 1 M CaCl₂, and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold‐one‐out cross‐validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications.