Predicting State Allocation of Funds: The Case of John Rowland and his Hometown of Waterbury, Connecticut.

This study attempts to analyze the underlying factors and motives influencing the allocation of discretionary state expenditures. The fact that some cities receive more money than other cities begs the question of what accounts for this variation. After framing the provision of state money within the theoretical framework of political patronage, a case study of Governor Rowland’s tenure in office and the accompanying expenditures to Connecticut’s 17 largest cities from 1995 to 2004 was conducted to evaluate whether a disproportionate amount of money was given to Rowland’s hometown of Waterbury, Connecticut. Besides employing a statistical analysis that determined that cities with similar characteristics received different amounts of money, interviewing was conducted to identify reasons for such variation. The results indicate that Waterbury received a greater amount of money than was predicted based on the city’s economic and demographic characteristics, and that non-objective and biased factors such as favoritism, the need to reward political support, or the desire to increase political loyalty sometimes take precedence over more objective factors.

Transition Zones of Forested Inland Wetlands in Northeastern Connecticut

Inland wetlands are valuable natural resources intimately associated with the hydrologic cycle. This study was designed to (1) investigate vegetation distribution and selected physical and chemical properties of wetland and bordering upland soils and the interface between the two, and (2) provide the ground truth necessary for the identification and delineation of deciduous wetland forests using false-color infrared (FCIR) imagery.

Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

Explaining Recent Connecticut Bank Failures

Significant numbers of U.S. commercial bank failures in the late 1980s and early 1990s raise important questions about bank performance. We develop a failure-prediction model for Connecticut banks to examine events in 1991 and 1992. We adopt data envelopment analysis to derive measures of managerial efficiency. Our findings can be briefly stated. Managerial inefficiency does not provide significant information to explain Connecticut bank failures. Portfolio variables do generally contain significant information.