Assessing most productive scale size with fuzzy stochastic data envelopment analysis

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

The Relative Efficiency of Skilled Labor across Countries: Measurement and Interpretation

I study how the relative efficiency of high- and low-skill labor varies across countries. Using microdata for countries at different stages of development, I document that differences in relative quantities and wages are consistent with high-skill workers being relatively more productive in rich countries. I exploit variation in the skill premia of foreign-educated migrants to discriminate between two possible drivers of this pattern: cross-country differences in the skill bias of technology and in the relative human capital of skilled labor. I find that the former is quantitatively more important, and discuss the implications of this result for development accounting. (JEL I26, J24, J31, J61, L16, O15)

A Study of Gjestvang and Singh Randomized Response Model Using Ranked Set Sampling

In this chapter, the authors investigate the performance of the Gjestvang and Singh randomized response model for estimating the mean of a sensitive variable using ranked set sampling along the lines of Bouza. The proposed estimator is found to be unbiased, and a variance expression is derived. Then a simulation study is carried out to judge the magnitude of relative efficiency in various situations. At the end, the proposed model is assessed based on real secondary data applications. A set of SAS codes is also included.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

In this chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

In the chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use of ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

A Model Based on Data Envelopment Analysis for the Measurement of Productivity in the Software Factory

Productivity in software factories is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities. One of the pillars of competitiveness is productivity, and it is related to the effort required to accomplish the assigned tasks. However, there is no standard way to measure it, making it difficult to establish policies and strategies to improve the factory. In this work, a model based on data envelopment analysis is presented to evaluate the relative efficiency of the software factories and their projects, to measure the productivity in the software production component of the software factory through the activities that are carried out in their different work units. The proposed model consists of two phases in which the productivity of the software factory is evaluated and the productivity of the projects it conducts is assessed. Numerical tests on 6 software factories with 160 projects implemented show that the proposed model allows one to assess the software factories and the most efficient projects.

Applications of a “Carry-Over” Enhanced DSBM Intertemporal Technical Efficiency Model on the Taiwanese 5G Industry

Taiwan has proven itself successful at both inventing the key technologies leading to the development of 5G (fifth generation wireless technology)-related industries and serving as an indispensable link in the burgeoning 5G-industrial global supply chain. This study analyzes the current state of Taiwan’s 5G industry via the utilization the purpose of this study is to the Dynamic Slacks-Based Measure (DSBM). To achieve this purpose, a dynamic-data production process model was developed to analyze the 5G industry’s overall relative efficiency. Results indicate that (1) key chip-producing companies typically experience increased efficiency following 5G R&D industry development investment, and that said companies’ relative efficiency is, indeed, affected positively by R&D investment; and (2) key chip companies’ relative increases of efficiency were higher than those of brand terminal and downstream-industry-category companies, while companies with higher levels of R&D investment exhibited relatively higher and more significant levels of efficiency. Finally, it was discovered that the relative efficiency of Taiwan's 5G-related industries’ R&D investment was, indeed, statistically significant in terms of the Taiwanese government’s industrial policies regarding 5G R&D investment. Keywords: Dynamic Slacks-Based Measure (DSBM), 5G Industry, R&D Inputs.

Comparing the MSIS-29 and the Health Utilities Index Mark III in Multiple Sclerosis

Objective: Since the properties of health-related quality of life measures vary across samples, studies directly comparing the properties of different measures can be useful in understanding their relative strengths and limitations. We aimed to compare the psychometric properties of the Health Utilities Index Mark III (HUI3) and the Multiple Sclerosis Impact Scale-29 (MSIS-29).Methods: In Spring 2020, North American Research Committee on Multiple Sclerosis (NARCOMS) Registry participants completed the HUI3, MSIS-29, Patient Determined Disease Steps (PDDS) and SymptoMScreen. For the HUI3 and MSIS-29 we assessed floor and ceiling effects, construct validity, and internal consistency reliability. We used relative efficiency to compare the discriminating ability of the two measures with respect to disability.Results: We included 5,664 participants in the analysis, with mean (SD) age 63 (10.1) years; 4,579 (80.8%) were women. For the HUI3 the mean (SD) score was 0.44 (0.32), for the MSIS-29 physical it was 34.0 (24.2) and for the MSIS-29 psychological it was 25.9 (20.4). Neither of the measures had floor or ceiling effects, and internal consistency reliability was > 0.70 for both. The HUI3 and MSIS-29 physical were strongly correlated (r = −0.78; 95%CI:−0.79,−0.77). The correlation between the HUI3 and MSIS-29 psychological was weaker but remained moderately strong (r = −0.64; 95%CI:−0.66,−0.63). After adjusting for sociodemographic and clinical factors, relative efficiency to discriminate between disability (PDDS) groups was highest for the MSIS-29 physical scale, followed by the HUI3.Conclusion: Both measures had adequate validity and reliability. The MSIS-29 physical discriminated between disability groups better than the HUI3.

The Optimalization of Zakah Distribution During Covid-19 Pandemic Using Data Envelopment Analysis (DEA) Method at Badan Amil Zakat Nasional (BAZNAS) Indonesia

Purpose: The purpose of this paper is to assess the level of optimality of zakat institutions to deal with the economic crisis during the Covid-19 pandemic.Design/Method/Approach: In this research, the measurement of efficiency of zakat distribution during the Covid-19 pandemic at badan amil zakat nasional (BAZNAS) has been carried out using data envelopment analysis (DEA) method.Findings: The result is the mean of zakah distribution relative efficiency are 70 %. Finally, some suggestion for evaluating the distribution of zakat in the future has been discussed. Originality/Values: The contribution of this research is to obtain the efficiency value of zakat distribution during the Covid-19 pandemic. So that with the result, the management of zakat distribution at BAZNAZ can improved and evaluated.