Stochastic Nonparametric Envelopment Of Data Research Articles

A comparison of chance-constrained data envelopment analysis, stochastic nonparametric envelopment of data and bootstrap method: A case study of cultural regeneration performance of cities

This study comprehensively compares three efficiency measurement methods—chance-constrained data envelopment analysis (CCDEA), stochastic nonparametric envelopment of data (StoNED), and the bootstrap method—in the context of the cultural regeneration performance of cities. The research examines these methods’ methodological differences, advantages, and disadvantages with a focus on uncertainty handling, production function assumptions, and computational requirements. The analysis reveals that CCDEA and the bootstrap method yield similar efficiency scores, while StoNED tends to produce lower efficiency scores. Furthermore, regions exhibit higher value-creation efficiency of cultural and creative industry than operational management efficiency, thus highlighting the untapped potential for improving value creation in cultural regeneration projects. This comprehensive comparison enables researchers and practitioners to further understand the nuances among these methods and select the most suitable method for their specific needs and objectives when evaluating the performance of cultural regeneration projects or other applications.

Cost and quality of service performance in the Chilean water industry: A comparison of stochastic approaches

The evaluation of efficiency can be of great value to water companies and regulators to adopt policies and design incentives to enhance performance. This study delves into the implications of employing distinct methodologies, namely the classical Stochastic Frontier Analysis (SFA), Bayesian SFA, and Stochastic non-parametric Envelopment of Data (StoNED), to evaluate cost and quality of service efficiency within the water industry. Chilean water companies reported average efficiencies of 0.623, 0.583, and 0.522 using the SFA, BSFA, and StoNED approaches, respectively. Furthermore, the SFA analysis suggested that the performance of water companies experienced a decline of −0.59% per year from 2010 to 2018. In contrast, the BSFA and StoNED estimations indicated an opposite trend, with annual performance improvements of 0.51% and 0.17% respectively, over the same period. These findings underscore the critical role of selecting appropriate methodologies when interpreting and comparing efficiency results for making informed long-term decisions.

Metafrontier frameworks for estimating solar power efficiency in the United States using stochastic nonparametric envelopment of data (StoNED)

Solar energy is one of the most promising energy sources as it its significantly reduce greenhouse gas (GHG) emissions compared to fossil fuels. In this study, we employ the meta frontier framework to estimate US solar energy performance in 2019 using stochastic non-parametric envelopment of data (StoNED) under the convex and non-convex frameworks. This estimation allows us to monitor operating inefficiencies and technological gaps in each observation. In addition, we investigate the potential impact of the specification of a convex production technology in relation to the use of a nonconvex technology in the comparative analysis. This methodological reflection is mainly supported by the recent engineering literature that provides evidence of the non-convex hypothesis. The results indicate that a multifaceted approach must be taken to ensure the supply of energy. Given that sunny states have the potential to transmit energy to other states, the drawbacks, such as environmental concerns and high investment expenses, drive policymakers to look for other alternatives, such as adapting panels that are suitable for specific conditions.

Evaluation of dynamic eco-efficiency of water companies: the influence of non-revenue water and water supply interruptions

Benchmarking the dynamic performance of water companies is a powerful tool to develop sound policies. However, it requires the use of reliable and robust methods and quality of data. In this study, the stochastic nonparametric envelopment of data (StoNED) method was employed to assess productivity change and eco-productivity change of a sample of Chilean water companies over the period 2010–2018. The StoNED approach overcomes the main limitations of stochastic frontier analysis and data envelopment analysis techniques which are the most commonly used to evaluate performance of water companies. Productivity change metric focused on the economic performance, whereas eco-productivity change evaluated economic and quality of service performance by including non-revenue water and unplanned water supply interruptions. Hence, this study analyzed the impact of quality of service variables on the dynamic eco-efficiency of water companies. The annual average productivity change and eco-productivity change estimations were 0.18% and −0.14%, respectively. It was evidenced that on average the Chilean water companies have put as priority to reduce operational costs than to improve quality of service. In both performance metrics, efficiency change was the main contributor to performance changes, whereas the role of technical change was negligible. It was illustrated that full private water companies performed better than concessionary companies and the public company. Reducing non-revenue water and unplanned water supply interruptions was particularly costly for concessionary water companies. The methodology and conclusions of this study are of great interest for water regulators and water utilities to improve the benchmarking process in the provision of drinking water services.

Joint production in stochastic non-parametric envelopment of data with firm-specific directions

We propose a likelihood-based approach to Stochastic Non-Parametric Envelopment of Data (StoNED) estimator using a directional distance function with firm-specific directional vectors. Additionally, we show how to estimate firm-specific inefficiency estimates instead of focusing on their average only. Moreover, we propose models that are robust to misspecification in general and the use of unit-information-priors in this class of models. These priors control the amount of information to be exactly equal to one observation. In this context, we propose the use of Bayesian Bootstrapping to further mitigate possible misspecification. We also propose empirical tests for identification of the model. Monte Carlo experiments show the good performance of the new techniques and an empirical application to the technology of large U.S. banks shows the feasibility of the new techniques.

Assessing the impact of digital economy on green development efficiency in the Yangtze River Economic Belt

From principal component analysis (PCA) of 2011–2019 panel data for 108 cities along China's Yangtze River Economic Belt (YREB), this article reports findings on digital economy (DIE) and green development efficiency (GDE). We used a stochastic nonparametric envelopment of data (stoNED) model to measure green development efficiency and a mediating effect model to test the impact of DIE on GDE. We also assessed its mechanisms from a Chinese perspective, finding that (1) DIE significantly promotes GDE in China. (2) Moreover, DIE promotes the GDE in YREB through technological innovation, through the accumulation of human capital and, through upgrades to industrial structures. (3) Furthermore, DIE has heterogeneous effects on GDE. DIE in upstream and downstream regions and in large- scale cities and medium-scale cities can improve GDE, while the effect of DIE in midstream and small-scale cities is not obvious. These results contribute empirical support and a decision-making basis for promoting the win-win situation where DIE and GDE work together. We recommend speeding up the construction and improvement of digital infrastructure, enhancing the enabling capacity of DIE, and implementing a differentiated DIE strategy.

Influence of environmental variables on the energy efficiency of drinking water treatment plants

Evaluating and benchmarking the energy efficiency of drinking water treatment plants (DWTPs) is essential to improve the environmental and economic performance of these facilities. However, robust and reliable methods should be used to compute energy efficiency scores to avoid biased conclusions. In this study, the stochastic non-parametric envelopment of data (StoNED) method was employed to assess the energy efficiency of a sample of 146 DWTPs. This methodological approach also allowed us to explore the influence of several environmental variables on the energy performance of DWTPs. The average energy efficiency score of the facilities evaluated was 0.461. It means that they could save 54% of the current energy consumption. 42% of the DWTPs evaluated presented an average energy efficiency score varied between 0.41 and 0.61. None of the 146 water treatment facilities was identified as fully energy efficient which evidences that all DWTPs could save energy. The source of raw water, the age of the DWTP and the main technology used to treat raw water were factors that statistically affected the energy efficiency of DWTPs. It was also estimated that a 1% increase in the removal of total dissolved solids and sulfates result in an increase in energy consumption by 0.340 and 0.409%, respectively, ceteris paribus. The information and conclusions of this study are of great interest for water managers and regulators to improve the operation of DWTPs and plan the design of new facilities.

Evaluation of energy efficiency of wastewater treatment plants: The influence of the technology and aging factors

Wastewater treatment is an energy-intensive process and therefore, improving the energy efficiency of wastewater treatment plants (WWTPs) is relevant from an economic and environmental perspective. Water companies managing WWTPs operate under monopolistic conditions and thus, benchmarking their performance using robust methods is fundamental for regulation. This study evaluates the energy efficiency of a sample of Chilean WWTPs using a newly developed technique, called stochastic non-parametric envelopment of data (StoNED). This technique integrates non-parametric (non-linear programming) methods with stochastic noise (parametric techniques) and therefore, it overcomes the limitations of both methodological approaches. Moreover, it allows exploring the influence of operating environment on the energy performance of WWTPs. Results evidenced that the Chilean WWTPs were considerably inefficient (average score was 0.433) presenting therefore significant opportunities to save energy (average savings were 203,413 MWh/year). Only 8 out of 203 facilities reported an average energy efficiency score which was greater than 0.81. It was also found that the age of the facilities negatively affected energy efficiency. Recently built WWTPs showed an average energy efficiency score of 0.489, whereas older facilities were found to be considerably inefficient, 0.340 on average. WWTPs using suspended-growth processes, i.e., conventional activated sludge and extended aeration, as secondary treatment were those with the lowest levels of energy efficiency on average. The results and conclusions of this study are useful for policy makers and WWTP operators who want to reduce energy consumption of WWTPs and therefore, their operational costs and environmental impacts.

Benchmarking the economic and environmental performance of water utilities: a comparison of frontier techniques

PurposeThe purpose of this paper is to estimate and compare the efficiency of several water utilities using three frontier techniques. Moreover, this study estimates the impact of several qualities of service variables on water utilities’ performance.Design/methodology/approachThe paper utilizes three frontier techniques such as data envelopment analysis (DEA), stochastic frontier analysis (SFA) and stochastic non-parametric envelopment of data (StoNED) to estimate efficiency scores.FindingsEfficiency scores for each methodological approach were different being on average, 0.745, 0.857 and 0.933 for SFA, DEA and StoNED methods, respectively. Moreover, it was evidenced that water leakage had a statistically significant impact on water utilities’ costs.Research limitations/implicationsThe choice of an adequate and robust method for benchmarking the efficiency of water utilities is very relevant for water regulators because it affects decision making process such as water tariffs and design incentives to improve the performance and quality of service of water utilities.Originality/valueThis paper evaluates and compares the performance of a sample of water utilities using three different frontier methods. It has been revealed that the choice of the efficiency assessment method matters. Unlike SFA and DEA, a lower variability was shown in the efficiency scores obtained from the StoNED method.

Nonlinearity in the relationship between intellectual capital and corporate performance: evidence from Vietnamese listed companies

PurposeConsensus on how intellectual capital (IC) affects corporate performance is limited because of various measurement models of IC and corporate performance. This study thus aims to further the debate on the relationship between IC and corporate performance from the perspectives of nonlinearity, the capital values of IC and the use of a holistic measure of corporate performance.Design/methodology/approachUsing 1,395 firm-year observations derived from Vietnamese listed companies from 2010 to 2018, this study focuses on (1) presenting an IC model benchmarked on value-creating expenses; (2) using a directional distance function (DDF)-based stochastic nonparametric envelopment of data (StoNED) framework to scrutinize multiple performance indicators and the capital values of people, structures and relationships simultaneously; and (3) adopting firm-year cluster-robust regressions to analyze the nonlinear association between IC and corporate performance empirically with an appropriate U test.FindingsResults suggest that human capital (HC), structural capital (SC) and relational capital (RC) are the main contributors of high corporate efficiency, whereas only HC and RC contribute to high corporate profitability. These results are absent when this study employs the conventional data envelopment analysis (DEA), which is also a multidimensional framework, as the dependent variable. More importantly, IC and its components can improve corporate performance, namely, both corporate efficiency and corporate profitability up to a critical point, after which the effects would drop.Practical implicationsOverall, this study highlights not only the need to invest in IC but also its associated costs. That is, policymakers also need to note the marginal cost of investing in IC, which may in the end outweigh the benefits from IC.Originality/valueThis study extends IC-related studies by investigating the nonlinear relationship between IC and corporate performance. Moreover, the value of this study also lies in the multidimensional DDF-based StoNED framework.

Benchmarking the efficiency of water and sewerage companies: Application of the stochastic non-parametric envelopment of data (stoned) method

Many regulators employ frontier techniques to measure the performance of regulated monopolistic companies. This study estimates the cost efficiency of the water sector using the stochastic non-parametric envelopment of data (StoNED) method which brings together the qualities of both data envelopment analysis (DEA) and stochastic frontier analysis (SFA). This technique is applied to several Chilean private and public water companies without and with the inclusion of several environmental variables. The results indicated that the water industry demonstrated high levels of cost efficiency. Based on average results, it appeared that the public water company performed slightly better than private ones, with full private water companies being more efficient than concessionary. However, the analysis of the trend in companies’ efficiency revealed that full private water companies’ efficiency increased over the years 2010–2018, whereas the opposite was true for the public water company. It is also found that environmental variables might have a higher impact on public water company costs and inefficiency than private ones.

The cubic S-curve relationship between board independence and intellectual capital efficiency: does firm size matter?

PurposeFirst, this study assesses firms’ efficiency of transforming intellectual capital (IC) components into firm performance. Second, this study examines (1) cubic S-curve relationship between board independence and IC efficiency and (2) how firm size moderates the cubic S-curve relationship.Design/methodology/approachThis study employs a stochastic nonparametric envelopment of data (StoNED) framework to estimate IC efficiency, which is derived from the estimation process of transforming structural, relational and human capitals into accounting- and market-based performance indicators. This study conducts regression analyses on 1,104 firm-year observations of Taiwanese semiconductor firms over the period of 2011–2018.FindingsStoNED results suggest that sample firms' IC efficiency can be relatively improved by approximately 80%. Regression results indicate that a cubic S-curve relationship between board independence and IC efficiency exists, and firm size moderates the nonlinear effects.Practical implicationsOverall, this study highlights the importance of examining the nonlinear effect of board independence on IC efficiency from the perspective of agency theory, and the moderating effect from firm size, which may suggest availability of resources from the resource-based view of the firm.Originality/valueThis study contributes to the literature through the innovative application of an efficiency-based tool for evaluating IC efficiency. The cubic S-curve relationship between board independence and IC efficiency also points to the policy concerning the appropriate number of independent directors on board.

Stochastic semi-nonparametric frontier approach for tax administration efficiency measure: Evidence from a cross-country study

Under globalisation, countries all over the world have been impacted differently by both their macroeconomic conditions and other random factors particular to them. As such, governments and policymakers require a comparison of tax systems across different countries and, consequently, a study on the performance of Tax administration (TA) at a cross-country level would be a necessary reference for governments when designing tax policy. This paper seeks to measure the performance of TA across 44 countries, while considering the presence of contextual variables, using the recently developed and advanced frontier estimators, such as the semi-nonparametric StoNED (Stochastic Nonparametric Envelopment of Data) approach by Johnson and Kuosmanen (2011, 2012) and the conditional order-m (Daraio and Simar, 2005, 2007) approach, for two periods between 2008–2011 and 2012–2015. The results show that Tax agencies in these countries could have increased tax revenue, on average, by about 58.7% and 34.2% for the two periods, respectively. Equivalently, $7,737 and $4,667 PPP (purchasing power parity) per capita of tax revenue could have been increased for the two periods, respectively. It is also suggested, in general, the latter period (2012–2015) shows a higher level of efficiency than the former period (2008–2011), justified by both estimators.

Production efficiency of maize farmers under contract farming in Laos PDR

Maize is the second largest commercial crop in Lao PDR, especially in the northern and southern regions. Traditionally, farmers in Lao PDR produced and distributed products in the market on their own. Recently, however, contract farming has gained in popularity and has become more widely accepted, especially after the country entered international collaborations under the Ayeyawady-ChaoPhraya-Mekong Economic Cooperation Strategy (ACMECS) and the Greater Mekong Sub-region (GMS). This study analyzed the technical efficiency of maize production under the contract farming system. Data were collected from the survey responses of 302 contract farmers in Lao PDR's northern and southern regions and were analyzed using stochastic nonparametric envelopment of data (StoNED). Maize production efficiency estimations were calculated using the Tobit model. The results revealed that the efficiency of contract maize farmers in Lao PDR was on average 0.85 and that the main factors affecting maize production efficiency were the age and education level of the farmer and area of planted land. Age and education level had significantly positive impacts, whereas planted land area had negative impacts.

The shadow prices and demand elasticities of agricultural water in China: A StoNED-based analysis

Based on stochastic nonparametric envelopment of data, this paper estimates the shadow prices of agricultural water and the technical efficiencies, using an input-output data of 30 provincial units in Mainland China from 2002 to 2012. The results show that the average shadow price estimates for agricultural water range between 2.57 yuan/m3 and 3.88 yuan/m3; the estimated price elasticity of agricultural water is 0.12, and that improving technical efficiencies of the agricultural sector has a significant effect on the water demand. That means the prospect of reducing the amount of agricultural water depends on the improvement of technical efficiency and the spread of water-saving irrigation techniques.

Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data

This paper suggests how stochastic nonparametric envelopment of data (StoNED) can be extended as an estimator in the metafrontier efficiency analysis. Both convex and non-convex metatechnologies are formed and a semi-nonparametric estimation technique for the corresponding metafrontiers is developed. Remaining consistent with the metafrontier theory, the resulting estimated metafrontiers always envelope the estimated group frontiers.

Modeling joint production of multiple outputs in StoNED: Directional distance function approach

Estimation of joint production technologies involving multiple outputs has proved a vexing challenge. Existing methods are unsatisfactory as they either assume away stochastic noise or restrict to functional forms that have incorrect output curvature. The first contribution of this paper is to develop a new probabilistic data generating process that is compatible with the directional distance function. The directional distance function is a very general functional characterization of production technology that has proved useful for modeling joint production of multiple outputs. The second contribution of this paper is to develop a new estimator of the directional distance function that builds upon axiomatic properties and does not require any functional form assumptions. The proposed estimator is a natural extension of stochastic nonparametric envelopment of data (StoNED) framework to multiple output setting. We examine the practical aspects and usefulness of the proposed approach in the context of incentive regulation of the Finnish electricity distribution firms.

On the Distributional Assumptions in the StoNED Model

In a recent paper Johnson and Kuosmanen (2011) propose a new, semi-parametric, general cost-frontier model, the stochastic nonparametric envelopment of data (StoNED). The model is semi-parametric in the sense that the cost function is estimated nonparametrically, while the functional form of the distribution for the error term is parametrically specified. A common assumption for this distribution is that it is a convolution of a truncated normal distribution, representing inefficiency, and a normal distribution, representing noise. This parametric form has the drawback that a negative skewness implies a negative expected inefficiency. It can thus never capture a negatively skewed distribution with a positive expectation. In this paper we investigate this assumption and its consequences for an analysis of inefficiency. Furthermore, we propose a solution to the problem and investigate its performance by means of a Monte Carlo simulation.

Malmquist Productivity Analysis Based on StoNED

We construct a Malmquist productivity index based on stochastic non-parametric envelopment of data (StoNED) method, and we study how the distributional assumptions in the second StoNED stage affect productivity change and its decompositions. Our discussion show that the distributional assumptions do not affect the estimates of overall productivity change and scale efficiency change, but that estimates of efficiency change and technical change are affected. Data on Norwegian electricity distribution companies is used to illustrate our discussion.

Optimal Scale in Different Environments The Case of Norwegian Electricity Distribution Companies

We study returns to scale in Norwegian electricity distribution companies. The scale issue of this sector has become an important political question, and it was for instance discussed by the Reiten commission (OED, 2014) in a study about the future structure and organization of the Norwegian electricity network industry. We use panel data from the Norwegian Water Resources and Energy Directorate (NVE) for the period from 2004 to 2010. The Data Envelopment Analysis (DEA) method and the Stochastic Nonparametric Envelopment of Data (StoNED) approach are applied to examine the scale issue. We show that a majority of the companies are smaller than the optimal size, in line with Kumbhakar et al. (2014). The performance of Norwegian distribution companies are influenced by a number of environmental factors, and some of these factors are negatively correlated with company size. However, our results show that controlling for environmental factors when estimating returns to scale does not have a big effect on the estimated optimal sizes.