This thesis examines banking efficiency and the productivity of thirteen transition Central and Eastern European banking systems during 1998-2003 using Data Envelopment Analysis (DEA). It proposes a non-parametric methodology for non-radial Russell output efficiency measure of banking firms, incorporating risk as an undesirable output. In addition, the proposed efficiency measure handles unrestricted data, i. e. both positive and negative. The Luenberger productivity index is suggested, which is applicable to technology where the desirable and undesirable outputs are jointly produced, and are possibly negative. Furthermore, the thesis addresses the main issue in the literature on banking performance measurement, which concerns the lack of consistency in the conceptual and theoretical considerations in describing the banking production process. Consequently, a metaanalysis tool, to examine the choice of inputs and outputs definitions in the banking efficiency literature, is suggested. In addition, the performance measures are estimated using three alternative definitions of the banking production process focusing on the risk and environmental dimensions of bank efficiency and productivity, with further comparative analysis using bootstrapping and kernel density techniques. Overall, the empirical results suggest that in Central and Eastern Europe Czech, Hungarian and Polish banks were the most technical efficient banks and the banking risk was mainly affected by external environmental factors during the analyzed period. Productivity analysis implies that the main driver of productivity change in the Central and Eastern European banks is the technological improvement. As meta-analysis revealed, the choice of particular approach of describing the banking production process is determined not by the availability of particular input or output variable information but the concepts of researcher's theoretical considerations. Statistical tests and density analysis indicate that efficiency scores, returns parameters and productivity indexes are sensitive to the choice of particular approaches.

The largest Florida manatee (Trichechus manatus latirostris) aggregation at a natural warm-water refuge occurs in Kings Bay, Crystal River, FL. Over the last 32 years, the U.S. Fish and Wildlife Service and the State of Florida have created a network of manatee protection areas within Kings Bay including a year-round refuge designation and seven Federal manatee sanctuaries during the winter manatee season (November 15 – March 31). Aerial survey data collected between 1983 and 2012 was used to examine the seasonal change in manatee distribution within Kings Bay in order to assess the effectiveness of current sanctuary sizes and locations. Regression analysis indicated a significant change in manatee abundance among the winter seasons (p < 0.05). The average winter manatee counts increased by 4.81 animals per year over the 30 year period. In contrast, no significant changes in average or peak manatee abundance was detected among the summer seasons (p = 0.71 and p = 0.45 respectively). The average manatee counts increased by only 0.109 animals per year over the summer periods. Spatially explicit models using Geographic Information System (GIS) analysis revealed a strong correlation between high manatee density and artesian springs during the winter seasons. Highest abundances were identified at three locations: King’s Spring, Three Sisters Springs, and Magnolia Springs. These three locations coincide well with pre-existing sanctuary designations, but additional coverage is needed to support the overflow of manatees outside of sanctuary boundaries. Manatees continued to use Kings Bay in the summer seasons but in lower numbers and densities. Because density patterns were not uniform across summer periods, a heavier reliance on boat speed regulation is recommended to provide adequate protection to the endangered Florida manatee. Within a habitat type, the Magnolia Springs, South Banana Island, and Three Sisters Springs sanctuaries exhibited a significant influence on manatee density, suggesting differences in quality among sanctuaries. Years coinciding with extreme cold weather events also had a significant influence on manatee density. Using GIS to investigate seasonal shifts in manatees can be very informative regarding many issues including habitat selection and may improve the design and management of protected areas.