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The non-life insurance companies indemnify the properties from the risk of being damaged due to unforeseen events like natural calamity or accidents. The probability of bankruptcy is imminent on account of large, unprecedented claims. As a risk saver of various society stakeholders, these insurers must be efficient while managing the insurance business. The present research thrusts upon to evaluate the efficiency and decomposition that would further direct the insurers towards achieving optimal scale. Thus, the captioned research aims to measure and rank the technical efficiency of the general insurance firms of Ethiopia and evaluate and analyze their relative efficiencies. The research adopts a quantitative approach and deploys descriptive analysis by a panel data of 17 Ethiopian general insurers for the period 2005-2016 on the input-output-oriented approach of Data Envelopment Analysis (DEA). The data of general insurance are obtained using stratified sampling from the mix of life and general category. The inputs employed are total expenses, total liabilities, and shareholder’s fund, while net premiums earned and income from investments are used as outputs. The findings reveal that the public insurer is technically efficient by operating at an optimal scale as compared to all private insurers who, in turn, experience pure technical inefficiency to scale inefficiency due to poor management practices and erroneous utilization of input materials. Increasing Returns to Scale (IRS) witnessed a major form of scale inefficiency in 2016. Private insurers should increase capital and size of assets, cost efficiency, and improve key management skills. AcknowledgmentThe authors express their thanks of gratitude for the support extended by Ethiopia’s insurance companies’ officials to provide the hard copies of published annual reports up to 2016 as the secondary data are not available after that year’s analysis.

In practical applications of data envelopment analysis, inputs and outputs are often stated as ratio measures, including various percentages and proportions characterizing the production process. Such ratio measures are inconsistent with the basic assumptions of convexity and scalability required by the conventional variable and constant returns-to-scale (VRS and CRS) models. This issue has been addressed by the development of the Ratio-VRS (R-VRS) and Ratio-CRS (R-CRS) models of technology, both of which can incorporate volume and ratio inputs and outputs. In this paper, we provide a detailed standalone development of the special case of the R-CRS technology, referred to as the F-CRS technology, in which all ratio inputs and outputs are of the fixed type. Such ratio measures can be used to represent environmental and quality characteristics of the production process that stay constant while simultaneously allowing the scaling of the volume of production. We illustrate the use of the F-CRS technology by an application in the context of school education.

Economists tend to believe that production technology should exhibit increasing returns to scale first and then constant and finally decreasing returns to scale, called regular variable returns to scale (RVRS) in this paper. Further, a special pattern of RVRS production technology when there is only one output is the production function that has an S-shaped curve along any ray of inputs from the origin. In the literature on efficiency analysis, the most frequently used empirical technology is the variable returns to scale (VRS) production technology. Although it exhibits RVRS, it is unable to model nonconvex production technologies, such as the S-shaped production function. Recently, a new empirical production technology has been introduced to capture RVRS with partial convexity. This paper explores its relationship with efficiency measurement. Furthermore, a novel empirical production technology that can better capture the characteristics of the S-shaped production function is proposed. These two new production technologies provide better alternatives to the commonly used Free Disposal Hull (FDH) production technology in non-convex production with RVRS. Our new production technology is illustrated using US manufacturing industry data. If one believes that the production technology is partially convex and exhibits RVRS, it is found that the conventional VRS production technology overestimates the technical inefficiency of small production units under this belief.

This study proposes a new flexible functional form for distance functions. This generalizes the existing functional form of Diewert (1992b) by allowing for α-returns to scale technology and employing the quadratic mean of order r aggregator function. Because we allow parameter α to be any strictly positive real number and r to be any non-zero real number, it includes a variety of functional forms as special cases, and all of them can now be considered as flexible functional forms.

From a theoretical point of view, -returns to scale is a relevant alternative to traditional DEA models for estimating production technologies under global returns to scale assumptions such as strictly increasing or strictly decreasing returns to scale. However, from a methodological and empirical point of view, a remaining question is the estimation of . This contribution proposes an effective methodology to estimate an optimal value of based upon a goodness-of-fit strategy. A global method using a grid search is presented first. Second, for generalised FDH technologies, a minimum extrapolation principle is developed to estimate directly the optimal -returns from a linear program. An illustration on 63 US industries over the period 1987-2012 shows the relevancy of our approach.

Background Medical diagnostic laboratories are an essential work environment that plays an important role in diagnosing, treating, and being sensitive to diseases. One way to evaluate laboratories’ performance is to calculate their efficiency. This study investigates the efficiency of laboratories that are related to health centers in the south of Iran. Methods This study was conducted in 2021. The input numbers include: the number of technical personnel and the number of cell counters, and the output data includes: the scores obtained from the level 2 health laboratory evaluation list. And efficiency was calculated with DEAP software. The analysis is accomplished by the assumption of input-oriented. Findings The efficiency of laboratories of Orzueeyeh and Ravar Cities had the highest efficiency with the assumption of variable returns to scale efficiency 1, and the model of all laboratories is the laboratory of Ravar City. The laboratories of Kuhbanan and Rabor cities had the lowest efficiency with the assumption of variable returns to scale efficiency of 0.859 and 0.899, respectively. The average scale efficiency, Variable returns to scale, and constant returns to scale for laboratories in the cities of Kerman province are 0.842, 0.943, and 0.895, respectively. Conclusions To increase the efficiency of laboratories, significant resources and funds should be used, as well as few studies have been done on the efficiency of laboratories, which requires more attention.

Efficiency measurement is a key and strategic factor in improving an organization’s performance and increasing their competitive advantage. Nevertheless, measuring efficiency in settings with multicomponent production technologies is a major issue with the existing approaches in the literature. The main contribution of the current paper is to develop a novel nonparametric approach to evaluate efficiency and obviate some of the theoretical barriers in multi-output settings. To this end, for the first time, new technologies assuming multiple hybrid returns-to-scale (MHRTS) with output-specific inputs, joint inputs, and outputs are developed. The new technologies are based on some of the axiomatic principles in data envelopment analysis (DEA) for forming a new production possibility set (PPS) to measure the efficiency of decision-making units (DMUs). By implementing the MHRTS technologies with output-specific inputs, joint inputs, and outputs, the proposed models can deal with undesirable outputs. Compared with the existing technologies in the DEA literature, the new technologies not only can incorporate output-specific inputs, joint inputs, and outputs for the performance evaluation of DMUs but also obviate existing theoretical barriers in the MHRTS technology. The applicability and usefulness of the proposed method are validated using a case study in the energy sector.

Attempts can be found in the data envelopment analysis (DEA) literature to identify returns to scale at efficient interior points of a face on the basis of returns to scale at points of the corresponding reference sets of the production possibility set. The purpose of this paper is to show that only an interior point of a face can identify returns-to-scale properties of points lying on this face. We consider all possible cases of dispositions of faces from this point of view. Returns-to-scale properties of the corresponding reference units are then established. We also show that to find returns-to-scale at an interior point of a face is a much easier problem than to find all vertices of this face.

The paper examines the present fintech ecosystem in India in general and assesses the efficiency of fintech firms operating in lending and payment verticals. Twelve companies were randomly selected from the top-rated fintech companies in India having an operational track record of four years and more. Financial data of these firms for three years (from 2017-2020) was used for evaluating the efficiency by applying the DEA model. The results indicate that 42 percent of the companies were unable to achieve profitability. The interest obligation (36%) on short-term borrowings which constitute 68 percent of current liabilities is the major contributor to the operating cost of lending firms. Companies from payment verticals are less homogeneous in their component-wise distribution of operating cost. The current ratio of two DMUs is excessively higher while it is below the benchmark for the other six DMUs. To make the fintech a revenue generating model by scaling up their operation, this study suggests the business areas where fintech firms could collaborate with traditional financial institutions.