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In 2006, the Massachusetts legislature passed a landmark health care reform bill (the Reform) that has served as a model for the national health care reform. By aiming to provide 'access to affordable, quality, accountable health care,' the goals of this reform were to reduce the number of uninsured Massachusetts residents while containing the growth of health care costs and improving the quality of health care services. The current paper examines the impact of the Reform on the quality of care in addition to hospital costs simultaneously from a perspective of efficiency analysis. We develop an integer-valued non-radial Russell data envelopment analysis (DEA) model, which is unit variant and calculate the hospitals' efficiency directly. However, the proposed integer-valued model is non-linear. The current paper thus transforms this model into a parametric integer linear programming. We develop a method to derive its optimal solutions. We then use the new DEA model to calculate and compare the efficiency scores of hospitals in Massachusetts and Connecticut pre- and post-Reform. The analysis shows that the Reform has achieved its cost containment and quality improvement goals at the same time. These analyses provide potentially useful information to hospital regulators and government regulators, especially in light of the national interest on health care legislation. [ABSTRACT FROM AUTHOR]

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user's preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a broadly used non-parametric technique for performance evaluation and data analytics. While conventional single-stage DEA models overlook the internal interactions of decision making units (DMUs), network DEA opens this black box to investigate the internal structure of DMUs. Practically, many network DEA models involve shared performance measures that are not easily divisible among individual components of a network. Based upon a two-stage network DEA model, the current study treats such performance measures as inseparable links, implying that no proportions are optimized and allocated to the two stages of the network. The shared and unsplittable links in the proposed two-stage DEA model manifest integrality while both ends of the link are maximized or minimized simultaneously, and this setting has not been modeled in any existing DEA studies. The shared and unsplittable links in our model can be considered intermediate measures, but they are different from the two existing types of dual-role intermediate measures, which are traditional intermediate measures and feedback measures. Our performance link is a new type of intermediate measure that is minimized or maximized in both stages of the network. The resulting network DEA model is highly non-linear. To address the non-linearity, a parametric linear model is adopted. The proposed approach is construed in four variants, and then illustrated using a set of 100 banks in the United States. [ABSTRACT FROM AUTHOR]