An economic efficiency study on different regions of Ghana via Slack-based data envelopment analysis and regression analysis

In this article, we evaluate the efficiency of the 10 different regions of Ghana using slack-based data envelopment analysis, a nonparametric linear programming technique. Furthermore, we analyse the variable effects on the efficiency of the regions by various regression models using bootstrap sampling technique. The data come from the 1991/1992 and 1998/1999 Ghana Living Standards Survey. Our results show that wealth is not strongly related to efficiency. For example, the study indicates that the Brong–Ahafo region is the most efficient region but not the most wealthy in Ghana. Generally, urban regions are not found to be among the most efficient regions due to the high expenditures. The regression analysis shows that female heads of household have an overall positive effect on efficiency. In addition, any form of education obtained is also found to have a significant positive effect on efficiency.     

Valuing environmental factors in cost-benefit analysis using data envelopment analysis

Environmental cost-benefit analysis (ECBA) is used for the social evaluation of investment projects and policies that involve significant environmental impacts. Economic valuation of environmental impacts forms one of the critical steps of ECBA. We develop a new method for this purpose, which does not require price estimation for environmental impacts using stated or revealed preference methods. Our approach is based on data envelopment analysis (DEA), which is modified to ECBA by using absolute shadow prices instead of relative prices. We also discuss how the method can be used for sensitive analysis in ECBA. We illustrate the method by means of a hypothetical numerical example.     

Environmental cost-benefit analysis of alternative timing strategies in greenhouse gas abatement: A data envelopment analysis approach

Assessing the benefits of climate policies is complicated due to ancillary benefits: abatement of greenhouse gases also reduces local air pollution. The timing of the abatement measures influences both the economic costs and ancillary benefits. This paper conducts efficiency analysis of ten alternative timing strategies, taking into account the ancillary benefits. We apply the approach by Kuosmanen and Kortelainen [Valuing Environmental Factors in Cost-Benefit Analysis Using Data Envelopment Analysis, Ecological Economics 62 (2007), 56-65], which does not require prior valuation of the environmental impacts. The assessment is based on synthetic data from a dynamic applied general equilibrium model calibrated to The Netherlands. Our assessment shows that if one is only interested in GHG abatement at the lowest economic cost, then equal reduction of GHGs over time is preferred. If society is willing to pay a premium for higher ancillary benefits, an early mid-intensive reduction strategy is optimal.     

Using data envelopment analysis to assess macroeconomic policy performance

It is common practice to summarize the macroeconomic performance of countries in terms of the four well-known dimensions captured by the magic diamond of the OECD. This study provides a comparison of several synthetic indicators that merge the four separate indicators into one single statistic. These indicators are inspired by Data Envelopment Analysis (DEA)-based models, which allow for unequal weighting of the different economic objectives. The calculated weights then act as proxies for the true policy priorities. Comparison of the models focuses on the underlying assumptions as well as on the empirical results they generate.     

Estimating most productive scale size with stochastic data in data envelopment analysis

This article estimates most productive scale size in stochastic data envelopment analysis (DEA). Jahanshahloo and Khodabakhshi [Jahanshahloo, G.R. and Khodabakhshi, M., Using input–output orientation model for determining most productive scale size in DEA. Applied Mathematics and Computation 2003, 146(2–3), 849–855.] studied most productive scale size in classic data envelopment analysis. The classic data envelopment analysis requires that the values for all inputs and outputs be known exactly. However, this assumption may not be true, because data in many real applications cannot be precisely measured. One of the important methods to deal with imprecise data is considering stochastic data in DEA. Therefore, this research studies most productive scale size with considering stochastic data in DEA. To that end, input–output orientation model introduced in Jahanshahloo and Khodabakhshi [Jahanshahloo, G.R. and Khodabakhshi, M., Using input–output orientation model for determining most productive scale size in DEA. Applied Mathematics and Computation 2003, 146(2–3), 849–855.] is extended in stochastic data envelopment analysis. To solve the stochastic model, a deterministic equivalent is obtained. Although the deterministic equivalent is non-linear, it can be converted to a quadratic program. Furthermore, data of software companies is used to apply the proposed approach. Performance of software companies are evaluated based on their scale sizes in classic and stochastic data envelopment analysis.     

A panel data analysis of productive efficiency in freestanding health clinics

By using a panel data on a number of freestanding health clinics in New York State over 1984–1987, we have estimated the production function for ambulatory care after controlling for unmeasured clinic-specific managerial efficiency. We found significant differences in management efficiency, which peaks around 50500 visits per year. The optimal staffing ratio between doctors, extenders and nurses was found to be around 1∶1/3∶1, in order for a clinic to be on the efficiency frontier.     

Economies of scale in the US computer industry: An empirical investigation using data envelopment analysis

Up to recently, economists have had no good tools to measure the returns to scale of individual corporations in an industry. Data envelopment analysis (DEA) is a linear programming technique for determining the efficiency frontier (the envelope) to the inputs and outputs of a collection of individual corporations or other productive units. While DEA offers an avenue for calculating the returns to scale of individual corporations, the approach has been riddled by mathematical complications arising from the possibility of alternate optima. The present paper develops theory for calculating the entire range of these alternate optima. Furthermore, in a quite ambitions empirical application, DEA is employed to determine the time path of returns to scale of all publicly held U.S. computer companies over the time period 1980–1991. For the great majority of companies, a unique time path is obtained; only in less than 4 percent of the linear programming calculations is an entire range of alternate optima obtained. The results indicate that the computer industry was polarized into two camps: large aging corporations with decreasing returns to scale, and swarms of small upstart companies with advanced technology exhibiting increasing returns to scale.     

Testing the relationship between competition and efficiency in banking: A panel data analysis

The relationship between competition and efficiency in the banking sectors of five EU countries is investigated using Granger-type causality test estimations. We find positive causation between market power and efficiency, whereas the causality running from efficiency to competition is weak.     

Using data envelopment analysis approach to estimate the health production efficiencies in China

By using Data Envelopment Analysis approach, we treat the health production system in a certain province as a Decision Making Unit (DMU), identify its inputs and outputs, evaluate its technical efficiency in 1982, 1990 and 2000 respectively, and further analyze the relationship between efficiency scores and social-environmental variables. This paper has found several interesting findings. Firstly, provinces on frontier in different year are different, but provinces far from the frontier keep unchanged. The average efficiency of health production has made a significant progress from 1982 to 2000. Secondly, all provinces in China can be divided into six categories in terms of health production outcome and efficiency, and each category has specific approach of improving health production efficiency. Thirdly, significant differences in health production efficiencies have been found among the eastern, middle and western regions in China, and among the eastern and middle regions. At last, there is significant positive relationship between population density and health production efficiency but negative relationship (not very significant) between the proportions of public health expenditure in total expense and efficiency. Maybe it is the result of inappropriate tendency of public expenditure. The relationship between abilities to pay for health care services and efficiency in urban areas is opposite to that in rural areas. One possible reason is the totally different income and public services treatments between rural and urban residents. Therefore, it is necessary to adjust health policies and service provisions which are specifically designed to different population groups. __________ Translated from Jingji Yanjiu 经济研究 (Economic Research Journal), 2006, (7): 92–105     

The social dimension of firm performance: a data envelopment approach

A data envelopment model is proposed to quantify the social dimension of firm performance. Corporate social responsibility activities are treated as netputs to production. Production of good outputs and unintended by-products are combined in the overall measurement of firm performance. Our model is illustrated using a sample of global firms and suggests strikingly low social performance levels.     

Productivity growth in passenger-bus transportation: A heteroskedastic error component model with unbalanced panel data

This paper considers an econometric approach to measure total factor productivity (TFP) growth and technical change (TC) for 31 publicly-owned passenger-bus companies in India during 1983–1987. A translog variable cost function is used to represent the production technology. Firm heterogeneity is incorporated in the cost function using an error component model with firm-specific variances. TFP growth is decomposed into TC and economies of scale components. The TC component is further decomposed into pure, non-neutral, scale, and quasi-fixed factors/network components. An ownership group-wise comparison reveals that the public undertakings exhibit the highest rate of productivity growth, followed by the units operated by the state and local governments. The main source of TFP growth for the public undertakings and government-operated units is economies of scale, while the main source of falling TFP growth for corporations is technological regress.     

Estimating SUR system with random coefficients: the unbalanced panel data case

A system of regression equations for analyzing panel data with random heterogeneity in intercepts and coefficients, and unbalanced panel data is considered. A maximum likelihood (ML) procedure for joint estimation of all parameters is described. Since its implementation for numerical computation is complicated, simplified procedures are presented. The simplifications essentially concern the estimation of the covariance matrices of the random coefficients. The application and ‘anatomy’ of the proposed algorithm for modified ML estimation are illustrated by using panel data for output, inputs and costs for 111 manufacturing firms observed up to 22 years.     

Adjustment of inputs and measurement of technical efficiency: A dynamic panel data analysis of the Egyptian manufacturing sectors

The purpose of this article is to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the speed of adjustment of output, and to compare the technical efficiency estimates from this dynamic model to those from a static model. By assuming instantaneous adjustment of all inputs, a static model may underestimate technical efficiency of a production unit in the short-run. However, in this article I show that under the assumption of similar adjustment speed for all inputs, a linear partial adjustment scheme for output characterizes the dynamic production frontier. The dynamic frontier with time-invariant technical efficiency is estimated using the system GMM (generalized method of moments) estimator. Applying the model and estimation method on a panel data set spanning 9?years of data on private manufacturing establishments in Egypt, I find that (1) the speed of adjustment of output is significantly lower than unity, (2) the static model underestimates technical efficiency by 4.5 percentage points on average, and (3) the ranking of production units based on their technical efficiency measures changes when the lagged adjustment process of inputs is taken into account.     

A Markov analysis of social learning and adaptation

A number of recent contributions to the literature have modelled social learning and adaptation in an economic context. Understanding the processes driving these models is important in order to explain and predict the behaviour of the economy. In this paper, we analyze the economic applications for a class of adaptive learning models with bounded rational agents. The dynamics of these economies can be thought of as arising from discrete-time Markov chains. In particular, conditions for uniqueness of equilibria, convergence and stability in the economic systems follow from the accessibility and communication structures of these Markov chains. We establish a correspondence between absorbing states of the Markov chains and economic equilibria, whether stable or unstable, and develop theorems giving conditions for absorption and recurrence. Furthermore, we develop practical applications of these theorems using a cobweb model. We use a genetic algorithm, operating under election, as an example of a well known adaptive learning process.     

Contagion effects of national innovative capacity: Comparing structural equivalence and cohesion models

The effective promotion of national innovation performance is a crucial component of national innovation policy. This study examines network contagion effects of national innovative capacity via the international diffusion of embodied and disembodied technology by two different social network models: the cohesion model, based on diffusion by direct communication, and the structural equivalence model, based on diffusion by network position similarity. This investigation then utilizes data of 42 countries during 1997 to 2002 to empirically examine their network relationship. The analytical results demonstrate that international technology diffusion influences national innovation performance through contagion effects, but that the international similarity of national innovative capacity performance is more accurately predicted by network position than by interactions with others; and this study result provides a new perspective for science and technology policy makers.     

Germany before and after unification: A structural VAR analysis

Recent theoretical research in business cycle modelling has aimed at putting forward a unified framework for studying both short-term cycles and long-term growth. Empirical research based on structural vector-autoregression has established that the same factors which drive long-run growth also explain a large proportion of the movements of key macro variables at business cycle frequencies. The present paper aims at applying this approach to study the determinants of the post-unification downturn in Germany. The results suggest that German business cycles were not all alike. Whilst adverse supply shocks clearly matter before unification, it is primarily adverse aggregate demand shocks and a too tight monetary policy which dominate the German post-unification decline in output growth rates.