Economic growth and environmental efficiency: Evidence from U.S. regions

This paper proposes a conditional directional distance function model in order to examine the link between regional environmental efficiency and GDP per capita levels. As an illustrative example we apply our model to USA regional data revealing an inverted ‘U’ shape relationship between regional environmental efficiency and per capita income. The results derived from a non-parametric regression indicate a turning point at 49,000 dollars

Economic growth and environmental efficiency: Evidence from U.S. regions

This paper proposes a conditional directional distance function model in order to examine the link between regional environmental efficiency and GDP per capita levels. As an illustrative example we apply our model to USA regional data revealing an inverted ‘U’ shape relationship between regional environmental efficiency and per capita income. The results derived from a non-parametric regression indicate a turning point at 49,000 dollars

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

A generalized directional distance function in data envelopment analysis and its application to a cross-country measurement of health efficiency

Economic activity produces not only desirable outputs but also undesirable outputs that are usually called negative externalities in economic theory. Negative externalities are usually omitted from efficiency assessments (i.e., applications of Data Envelopment Analysis) which fail to express the true production process. In the present paper we develop a generalized directional distance function method for handling asymmetrically both desirable and undesirable outputs in the assessment process. Unlike the existing directional distance function-based approaches, the proposed method is units-invariant even in case assumptions for the direction vectors are relaxed. The new method is applied to data from national health systems of 160 countries. Desirable and undesirable outputs are incorporated to obtain a clear view of the efficiency status of the national health systems

Monitoring bank performance in the presence of risk

This paper proposes a managerial control tool that integrates risk in efficiency measures. Building on existing efficiency specifications, our proposal reflects the real banking technology and accurately models the relationship between desirable and undesirable outputs. Specifically, the undesirable output is defined as nonperforming loans to capture credit risk, and is linked only to the relevant dimension of the output set. We empirically illustrate how our efficiency measure functions for managerial control purposes. The application considers a unique dataset of Costa Rican banks during 1998–2012. Results’ implications are mostly discussed at bank-level, and their interpretations are enhanced by using accounting ratios. We also show the usefulness of our tool for corporate governance by examining performance changes around executive turnover. Our findings confirm that appointing CEOs from outside the bank is associated with significantly higher performance ex post executive turnover, thus suggesting the potential benefits of new organisational practices.Peer ReviewedPostprint (author’s final draft

A generalized directional distance function in data envelopment analysis and its application to a cross-country measurement of health efficiency

Economic activity produces not only desirable outputs but also undesirable outputs that are usually called negative externalities in economic theory. Negative externalities are usually omitted from efficiency assessments (i.e., applications of Data Envelopment Analysis) which fail to express the true production process. In the present paper we develop a generalized directional distance function method for handling asymmetrically both desirable and undesirable outputs in the assessment process. Unlike the existing directional distance function-based approaches, the proposed method is units-invariant even in case assumptions for the direction vectors are relaxed. The new method is applied to data from national health systems of 160 countries. Desirable and undesirable outputs are incorporated to obtain a clear view of the efficiency status of the national health systems

The hybrid returns-to-scale model and its extension by production trade-offs: an application to the efficiency assessment of public universities in Malaysia

Most applications of data envelopment analysis (DEA) employ standard constant or variable returns-to-scale (CRS or VRS) models. In this paper we suggest that these models may sometimes underutilize our knowledge of the underlying production process. For example, in the context of higher education considered in the reported application, individual universities often maintain a certain student-to-staff ratio which points that there should be an approximately proportional relationship between students and staff, at least in the medium to long run. A different example is an observation that the teaching of postgraduate students generally requires more resources than the teaching of the same number of undergraduate students. In order to incorporate such information in a DEA model, we propose a novel approach that combines the recently developed hybrid returns-to-scale DEA model with the use of production trade-offs. The suggested approach leads to a better-informed model of production technology than the conventional DEA models. We illustrate this methodology by an application to Malaysian public universities. This approach results in a tangibly better efficiency discrimination than would be possible with the standard DEA models

Regional economic growth and environmental efficiency in greenhouse emissions: A conditional directional distance function approach

By using conditional directional distance functions this paper investigates the effect of regional economic growth on regions’ environmental efficiency in greenhouse gas emissions. A sample of ninety eight regions (NUTS 2 level) from Germany, France and the U.K. has been used and regional environmental inefficiencies have been obtained using both the unconditional and conditional output directional distance functions. The results reveal that German regions have the highest environmental efficiency levels. In addition it appears that the effect of regional economic growth on regions’ environmental efficiency levels varies between regions and countries due to different national administrative arrangements on the implementation of environmental policies