Sustainable efficiency drivers in Eurasian airports: Fuzzy NDEA approach based on Shannon's entropy

This research explores the physical infrastructure and flight consolidation efficiency drivers of Eurasian airports regarding their infrastructure and movement productivity levels. A novel Fuzzy Double-Frontier Network DEA (FDFNDEA) model is proposed to investigate the relationship between desirable (freight and passenger turnovers) and undesirable (pollutant emission levels due to aircraft movements) outputs against the respective infrastructure usage, fuel consumed, and movements performed at each of the 23 Eurasian airports from 2000 to 2018. This balance between desirable and undesirable outputs emerges spatially and temporally due to the evolution of the airport system's productive resources at each one of the Eurasian countries over the period observed. Shannon's entropy is used as the cornerstone to quantify the input and output vagueness of this evolution in Triangular Fuzzy Numbers (TFN), thus allowing the accurate building of alternative optimistic and pessimistic double-frontier efficiency. Differently from previous research, Shannon's entropy is the key for measuring input and output vagueness levels in light of the maximal entropy principle. This principle states that the distribution that best represents the current state of knowledge is the one with largest entropy. Maximal entropy yields bias-free decision-making in the sense that the input/output distributional profiles for Eurasian airports contain the maximal possible heterogeneity, working as a robust or best/worst-case scenario against eventual unconsidered assumptions. Hence, optimistic and pessimistic Malmquist Productivity Indexes (MPI) for overall and each stage productivity results are subsequently regressed against contextual variables related to airport characteristics and regional socio-demographic and economic indicators of each Eurasian country using bootstrapped Cauchy regressions. The findings revealed the spatial heterogeneity of productivity factors and airport performance across Eurasia. Results also demonstrated the negative impact of income inequality and the positive impact of private participation on technological progression in the Eurasian airport industry.     

Efficiency outcomes of market concentration and managed care

The paper examines how hospital cost efficiency has reacted to extensive horizontal integrations of hospitals and rapid growth of managed care in the US health care industry. Cost efficiency is estimated by using panel data approaches to relax the assumptions for the hospital effects imposed in earlier studies. The paper shows that higher managed care penetration over time is associated with greater hospital efficiency, and higher market concentration is positively associated with efficiency when markets are highly competitive or highly concentrated.     

Ill-behaviour of transformation-frontiers with environmental quality versus pollution

If one looks at environmental quality N as a renewable natural resource (with its own natural dynamics) which is changed by emissions Z, then one gets a better understanding of the shape of the damage function. In the case of a logistic growth dynamics one obtains a well-shaped transformation frontier N(Z), even in a two-species natural dynamics. For a more general natural interaction in the framework of a prey-predator model one obtains the possibilities of a cusp catastrophe which makes the transformation frontier N(Z) ill-shaped for the standard arguments of economists and their most favoured environmental policy instruments.     

Urban Influence on Costs of Production in the Corn Belt

This article determines the relative technical efficiency of rural- and urban-influenced crop/livestock enterprises in the Corn Belt. Farmers in urban-influenced locations are less technically efficient than farmers in rural locations. During 1998–2000, stochastic production frontier procedures indicate that a 10% increase in urban influence leads to a close to 4% decrease in technical efficiency. The most successful urban-influenced farms have controlled costs as effectively as rural farms. They have tended to de-emphasize that nondairy livestock activities—particularly beef and hogs—do not rely extensively on off-farm income, and have relatively large, less residential/lifestyle operations compared to less successful urban-influenced farmers. However, our statistical analysis clearly bears out the refrain in popular literature that urban proximity raises the cost for, and decreases the viability of, traditional farms.     

The mean-variance investment problem in a constrained financial market

This paper extends the mean-variance analysis and the two-fund separation theorem to a market with some constraints, such as, the incompleteness, prohibition of short-selling, and partial information, with stochastic interest rate, and with stochastic volatility for risky assets. By maximizing a quadratic utility of terminal wealth, we show that the efficient frontier for the problem is a straight line in the mean-standard-deviation diagram. The quadratic utility function exhibits mean-variance efficiency. Our results apply to portfolios of claims in a single period, multiperiod, and continuous time.     

Hypothesis tests using data envelopment analysis

A substantial body of recent work has opened the way to exploring the statistical properties of DEA estimators of production frontiers and related efficiency measures. The purpose of this paper is to survey several possibilities that have been pursued, and to present them in a unified framework. These include the development of statistics to test hypotheses about the characteristics of the production frontier, such as returns to scale, input substitutability, and model specification, and also about variation in efficiencies relative to the production frontier.     

Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis

Improving productive efficiency is an increasingly important determinant of the future of the swine industry in Hawaii. This paper examines the productive efficiency of a sample of swine producers in Hawaii by estimating a stochastic frontier production function and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models. The technical efficiency estimates obtained from the two frontier techniques are compared. The scale properties are also examined under the two approaches. The industry's potential for increasing production through improved efficiency is also discussed.     

An Average Derivative Estimation of Stochastic Frontiers

This paper utilizes the average derivative estimation of Stoker (1986) and the pesudo-likelihood estimation of Fan, Li, and Weersink (1996) to estimate a semiparametric stochastic frontier regression, y = g(x) + , where the function g(.)is unknown and is a composite error in a standard setting. The proposed semiparametric method of estimation is applied to data on farmers' credit unions in Taiwan. Empirical results show that the banking services of the farmers' credit unions is subject to economies of scale, but high degree of cost inefficiency in operation.     

How Fast Do Banks Adjust? A Dynamic Model of Labor-Use with an Application to Swedish Banks

This paper deals with a dynamic adjustment process in which adjustment of a key variable input (labor) towards its desired level is modeled in a panel data context. The partial adjustment type model is extended to make the adjustment parameter both firm- and time-specific by specifying it as a function of firm- and time-specific variables. Desired level of labor use is represented by a labor requirement function, which is a function of outputs and other firm-specific variables. The catch-up factor is defined as the ratio of actual to desired level of employment. Productivity growth is then defined in terms of a shift in the desired level of labor use and the change in the catch-up factor. Swedish banking data is used as an application of the above model.     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.