3,110 research outputs found

    DEA-Based Incentive Regimes in Health-Care Provision

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    A major challenge to legislators, insurance providers and municipalities will be how to manage the reimbursement of health-care on partially open markets under increasing fiscal pressure and an aging population. Although efficiency theoretically can be obtained by private solutions using fixed-payment schemes, the informational rents and production distortions may limit their implementation. The healthcare agency problem is characterized by (i) a complex multi-input multi-output technology, (ii) information uncertainty and asymmetry, and (iii) fuzzy social preferences. First, the technology, inherently nonlinear and with externalities between factors, yield parametric estimation difficult. However, the flexible production structure in Data Envelopment Analysis (DEA) offers a solution that allows for the gradual and successive refinement of potentially nonconvex technologies. Second, the information structure of healthcare suggests a context of considerable asymmetric information and considerable uncertainty about the underlying technology, but limited uncertainty or noise in the registration of the outcome. Again, we shall argue that the DEA dynamic yardsticks (Bogetoft, 1994, 1997, Agrell and Bogetoft, 2001) are suitable for such contexts. A third important characteristic of the health sector is the somewhat fuzzy social priorities and the numerous potential conflicts between the stakeholders in the health system. Social preferences are likely dynamic and contingent on the disclosed information. Similarly, there are several potential hidden action (moral hazard) and hidden information (adverse selection) conflicts between the different agents in the health system. The flexible and transparent response to preferential ambiguity is one of the strongest justifications for a DEA-approach. DEA yardstick regimes have been successfully implemented in other sectors (electricity distribution) and we present an operalization of the power-parameter p in an pseudo-competitive setting that both limits the informational rents and incites the truthful revelation of information. Recent work (Agrell and Bogetoft, 2002) on strategic implementation of DEA yardsticks is commented in the healthcare context, where social priorities change the tradeoff between the motivation and coordination functions of the yardstick. The paper is closed with policy recommendations and some areas of further work.Data Envelopment Analysis, regulation, health care systems, efficiency, Health Economics and Policy,

    Quota Trading and Profitability: Theoretical Models and Applications to Danish Fisheries

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    Using Data Envelopment Analysis (DEA), we provide a framework to analyze the potential gains from quota trading. We compare the industry profit and structure before and after a free trade reallocation of production quotas. The effects of tradable production quotas depend on several technological and behavioral characteristics, including the ability to learn best practice (catch-up) and the ability to change the input and output composition (mix). To illustrate the usefulness of our approach, we analyze a dataset from the Danish fishery. We study the industry profit and structure under each of four sets of technological and behavioral characteristics.Data Envelopment Analysis (DEA), Individual Transferable Quotas (ITQ), reallocation, technical efficiency, allocative efficiency, fishery, Agribusiness, C61, L51, Q22, Q28,

    Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

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    Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

    Measuring Hospital Efficiency through Data Envelopment Analysis when Policy-makers’ Preferences Matter. An Application to a sample of Italian NHS hospitals

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    In this paper we show how both the choice of specific constraints on input and output weights (in accordance with health care policy-makers’ preferences) and the consideration of exogenous variables outside the control of hospital management (and linked to past policy-makers’ decisions) can affect the measurement of hospital technical efficiency using the Data Envelopment Analysis (DEA). Considering these issues, the DEA method is applied to measure the efficiency of 85 (public and private) hospitals in Veneto, a Northern region of Italy. The empirical analysis allows us to verify the role of weight restrictions and of demand in measuring the efficiency of hospitals operating within a National Health Service (NHS). We find that the imposition of a lower bound on the virtual weight of acute care discharges weighted by case-mix (in order to consider policy-maker objectives) reduces average hospital efficiency. Moreover, we show that, in many cases, low efficiency scores are attributable to external factors, which are not fully controlled by the hospital management; especially for public hospitals low total efficiency scores can be mainly explained by past policy-makers’ decisions on the size of the hospitals or their role within the regional health care service. Finally, non-profit private hospitals exhibit a higher total inefficiency while both non-profit and for-profit hospitals are characterised by higher levels of scale inefficiency than public ones.Hospital performance, Technical efficiency, Data envelopment analysis, NationalHealth Service

    Technical efficiency gains from port reform : the potential for yardstick competition in Mexico

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    The authors show how relatively standard methodologies can help to measure the efficiency gains from reforming the organization of port infrastructure, how those measures can be used to promote competition between ports, and how competition can be built into an incentive-driven regulatory regime. As illustration, they use a case study of port reform in mexico in 1993, the first efficiency analysis of port restructuring in a developing country. Their analysis, which covers 1996-99 and relies on a stochastic production frontier, shows that overall, Mexico has achieved annual efficiency gains of 6-8 percent in the use of port infrastructure since assigning its management to independent, decentralized operators. Changes in relative performance ratings are revealing. They identify consistent sets of leaders and laggards, including some that would not have been identified by partial productivity indicators commonly used in the sector. The authors'main conclusions: 1) Reforms have significantly improved average port performance. 2) The analytically sound performance rankings allowed by the port-specific efficiency measures can help to promote yardstick competition in the sector. These rankings are superior to those that would emerge from use of partial productivity indicators. They account for the joint effects of all inputs on outputs--which is crucial, because it avoids the risk of inconsistent rankings based on different partial indicators, arbitrarily chosen. Developing the database method to measure efficiency in countries with no strong tradition of database development is an enormous task--especially in transport sectors, where the tradition of generating databases useful to policymakers is in its infancy. The most immediate effect of this exercise was to reveal the poverty of the database in the Mexican port sector and the need for regulators to invest in its development.Transport and Trade Logistics,Environmental Economics&Policies,Labor Policies,Economic Theory&Research,Common Carriers Industry,Environmental Economics&Policies,Ports&Waterways,Transport Security,Economic Theory&Research,Transport and Trade Logistics

    Opening the 'black box' of efficiency measurement: input allocation in multi-output settings.

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    We develop a new Data Envelopment Analysis (DEA)-based methodology for measuring the efficiency of Decision Making Units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and joint inputs in the efficiency evaluation. This contributes to opening the „black box‟ of efficiency measurement in two different ways. First, including information on the input allocation substantially increases the discriminatory power of the efficiency measurement. Second, it allows to decompose the efficiency value of a DMU into output-specific efficiency values which facilitates the identification of the outputs the manager should focus on to remedy the observed inefficiency. We demonstrate the usefulness and managerial implications of our methodology by means of a unique dataset collected from the Activity Based Costing (ABC) system of a large service company with 290 DMUs.

    Opening the 'black box' of efficiency measurement: input allocation in multi-output settings

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    We develop a new Data Envelopment Analysis (DEA)-based methodology for measuring the efficiency of Decision Making Units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and joint inputs in the efficiency evaluation. This contributes to opening the „black box? of efficiency measurement in two different ways. First, including information on the input allocation substantially increases the discriminatory power of the efficiency measurement. Second, it allows to decompose the efficiency value of a DMU into output-specific efficiency values which facilitates the identification of the outputs the manager should focus on to remedy the observed inefficiency. We demonstrate the usefulness and managerial implications of our methodology by means of a unique dataset collected from the Activity Based Costing (ABC) system of a large service company with 290 DMUs.

    CAN FISCAL POLICY EXPLAIN TECHNICAL INEFFICIENCY OF PRIVATISED FIRMS? A PARAMETRIC AND NONPARAMETRIC APPROACH

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    The massive interests of economic literature about the privatisation gave a notable impulse to the discussion about this theme in the pre and post privatisation firms performance. Basically in every case after privatisation the level of profit increases. Does this mean that privatisation is certainly able to increase efficiency? In this field a large part of the literature leave out the complex problem that public firms usually are subject to objectives and constraints that differently from private firms can affect the overall economic efficiency. Unfortunately many authors ignore the effects of taxation during the process of privatisation, but in real term there are significant tax issues that must be considered by public and private decision maker. In this paper we concentrate the attention on the efficiency measures with the purpose to identify and measure sources of successful performance that can be used in policy planning and allocation of resources. Several techniques to calculate these frontier functions have been used, some of them parametric, others non-parametric to empirically investigate the relationship between taxation on firm’s income and efficiency in the period pre and post-privatisation. In this work we use both econometric and mathematical programming approaches for measuring efficiency. The econometric tool provide maximum likelihood estimates of a stochastic production and cost functions to distinguish noise from inefficiency. Instead, the mathematical programming approaches are nonstochastic and they do not make strict assumptions on the functional form of production and the statistical properties of the data. The general results obtained from the 3 different tools (Stochastic Frontier, Data Envelopment Analysis and Neural Network) are consistent. In fact, we see that privatization enhanced efficiency in three out of four sample firms.Privatization, Fiscal policy, Data Envelopment Analysis, Stochastic Frontier, Neural Network
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