Stochastic Frontier Analysis Research Articles

Re‐Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries

AbstractThe Association of Southeast Asian Nations (ASEAN) heavily relies on fossil fuels to drive economic growth, making it one of the world's largest greenhouse gas emitters. Promoting energy efficiency has been proposed as a solution to mitigate this issue. However, the potential negative impact of energy efficiency on economic growth remains unclear. Therefore, this study aims to re‐evaluate the dynamic impact of overall energy efficiency on economic growth in ASEAN countries, considering climate change factors. Unlike prior studies, this research measures overall efficiency based on both persistent and transient components of energy efficiency. This study employs two key techniques: stochastic frontier analysis (SFA) to estimate the overall energy efficiency and panel vector autoregression (PVAR) to analyze the dynamic causal effects of determinants' shocks. The findings suggest that enhancing ASEAN's overall energy efficiency could result in an 85.9% reduction in total energy consumption, indicating high levels of inefficiency in the region. While extensive energy efficiency measures might induce a temporary economic contraction in ASEAN countries, the rate of decline seems to abate over the long term. This implies that the impact of the energy efficiency shock will be most prominent during initial growth stages, progressively waning over time. Additionally, this study confirms the presence of the Environmental Kuznets Curve (EKC) for climate change and economic growth.

Growth Models and Influencing Mechanisms of Total Factor Productivity in China’s National High-Tech Zones

Total factor productivity (TFP) serves as a crucial indicator of high-quality development, showcasing a region’s utilization of technological advancements and innovative strategies to enhance production efficiency. With China’s regions experiencing varying levels of development, the paths to high-quality development in NHTZs also exhibit significant disparities. Analyzing data from 88 NHTZs between 2011 and 2018, this research aims to explore differences in TFP growth models and the mechanisms influencing these models. Employing stochastic frontier analysis (SFA) and the Tobit model, the study identifies three TFP growth models: technology-led, multi-efficiency improvement, and scale efficiency compensation. While factors such as capital accumulation, overseas market participation, and patent output moderately hinder TFP growth, innovation platforms, R&D investment, and overseas talent acquisition significantly contribute to TFP enhancement. Distinguishing itself from existing TFP studies, this paper focuses on NHTZs, highlighting their role in regional high-tech industry development. It introduces three TFP growth models and their characteristics while also uncovering influencing factors for each model. This research offers valuable insights into regional disparities in high-quality development and contributes to understanding TFP growth dynamics in NHTZs.

Toward green production practices: empirical evidence from Thai manufacturers' technical efficiency

PurposeThe development of green manufacturing has become essential to achieve sustainable development and modernize the nation’s manufacturing and production capacity without increasing nonrenewable resource consumption and pollution. This study investigates the effect of green industrial practices on technical efficiency for Thai manufacturers.Design/methodology/approachThe study uses stochastic frontier analysis (SFA) to estimate the stochastic frontier production function (SFPF) and inefficiency effects model, as pioneered by Battese and Coelli (1995).FindingsThis study shows that, on average, Thai manufacturing firms have experienced declining returns-to-scale production and relatively low technical efficiency. However, it is estimated that Thai manufacturing firms with a green commitment obtained the highest technical efficiency, followed by those with green activity, green systems and green culture levels, compared to those without any commitment to green manufacturing practices. Finally, internationalization and skill development can significantly improve technical efficiency.Practical implicationsGreen industry policy mixes will be vital for driving structural reforms toward a more environmentally friendly and sustainable economic system. Furthermore, circular economy processes can promote firms' production efficiency and resource use.Originality/valueTo the best of the authors' knowledge, this study is the first to investigate the effect of green industry practices on the technical efficiency of Thai manufacturing enterprises. This study also encompasses analyses of the roles of internationalization, innovation and skill development.

Spatio-temporal evolution of sports tourism industry integration: A empirical analysis of Xinjiang in China.

Since the issuance of the "Guiding Opinions on Vigorously Developing Sports Tourism" in 2016, the integration of sports and tourism has become a strategy in regional economic development. It creates new economic growth points, enhances local images, and promotes cultural communication. In the context of the "Tourism Makes Xinjiang Thrive" strategy, quantitatively investigating the integration of the sports and tourism industries helps people to better understand their interaction which can serve as the valuable input in policy-making for the comprehensive development of a region. This paper uses entropy weight method, stochastic frontier analysis and coupling coordination model to quantitatively analyze the effect of sports tourism industry integration in Xinjiang from the perspective of integration path. Meanwhile, the Dagum Gini coefficient and nuclear density estimation were used to analyze the regional differences and dynamic evolution of industrial integration quality. The result shows that (1) The sports and tourism integration quality in Xinjiang has not reached the optimal goal of complete integration. In the process of mutual industrial promotion, tourism promotes a higher degree of integration with the sports industry. (2) The industrial integration quality shows a phenomenon of "imbalance and inadequacy" among the regions. The regions with high quality of industrial integration were Urumqi, Ili, Kashgar, Altay and Changji, which have rich sports tourism resources. (3) The overall spatial difference in the quality of industrial integration presented a fluctuation downtrend. The difference between the tourism industrial belts was very significant.

Adapting innovation capabilities in emerging economies: the role of learning and cultural and institutional distance in MNC subsidiaries in Korea

Purpose The rise of emerging economies in the innovation landscape has often been attributed to the positive spillovers of innovation capabilities from multinational corporations (MNCs). However, it is less certain that their innovative capabilities imported from the home country function effectively in the host country from the outset. This study examines the performance of the innovation capabilities of MNC subsidiaries in emerging economies over time by considering the gradual process of their learning about host countries.Design/methodology/approach We employed stochastic frontier analysis to measure innovation capabilities, our focal construct. For regression analysis, we applied the Mundlak estimator, a variant of the fixed-effects panel estimator, to a sample comprising subsidiaries of MNCs from technologically advanced nations operating in Korea between 2006 and 2016.Findings Our results indicate that the innovation capabilities of MNC subsidiaries initially underperform those of local firms but improve over time, eventually surpassing the capabilities of their local counterparts. Furthermore, our findings reveal that institutional distance amplifies the underperformance of the innovation capabilities of MNC subsidiaries.Originality/value This study contributes to the literature by extending both theoretical development and empirical measurement of innovation capabilities in cross-national settings. Additionally, it deepens our understanding of whether and how MNC subsidiaries adapt their innovation capabilities to the local market environment.

Effects of species mixing on maximum size-density relationships in Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.)-dominated mixed forests converted from even-aged pure stands.

Density management is a key silvicultural tool in management programs that enhances compositional and structural diversity and hence forest growth during the conversion of even-aged pure stands into mixed forests. To determine the optimum stand density, a model of maximum size-density relationships was employed to explore the relationship of the self-thinning trajectory with growth, species mixing, latitude, and site conditions during the transition of even-aged pure Chinese fir stands to Chinese fir-dominated mixed forests using stochastic frontier analysis. Data were obtained from a total of 591 permanent plots located in Fujian, Jiangxi, Zhejiang, and Anhui provinces in southern China. The results showed that (1) the slope of the maximum size-density relationship of Chinese fir-dominated mixed forests increased and plateaued over time; (2) the slope of the maximum size-density relationship of Chinese fir-dominated mixed forests did not deviate from Reineke's assumed universal slope of -1.605; and (3) mixing proportion had a positive effect on maximum size-density relationships, and latitude and site conditions had the opposite effect on maximum size-density relationships. Our findings will provide valuable guidance for the forest management of areas in which even-aged pure stands are being converted to mixed forests (i.e., when broadleaved tree species are planted after thinning to improve overall stand density and promote stand growth).

ESG investment and bank efficiency: Evidence from China

Although environmental, social, and governance (ESG) investment is essential to achieving sustainable development, previous studies usually focused on the relation between ESG investment and non-financial firms but neglected its impact on bank efficiency. To fill this research gap, we use a stochastic frontier analysis (SFA) model to investigate the influence of ESG investment on banks' profit efficiency and to explore whether financial technology (fintech) can strengthen the relationship between them. Results show that increasing ESG investment is beneficial to bank efficiency. We also find structural differences in the influence of ESG investment on bank efficiency. Specifically, environmental (E) and governance (G) investments enhance bank efficiency, while social (S) variables reduce bank efficiency. In addition, fintech has a moderating effect. When the level of fintech is high, ESG investment rises, and the increase in bank efficiency is more significant. Finally, by conducting a time-varying analysis of bank efficiency, we find that banks with higher ESG investments are more efficient. Under the goal of sustainable development, strengthening ESG investment and focusing on the empowering role of fintech are essential ways for commercial banks to increase efficiency.

Effectiveness of Higher Education Financing: DEA and SFA Modelling

The article aims to assess the effectiveness of financial expenditures in higher education for 31 provinces in China, namely overall, technical and scale efficiency, and to determine whether the returns to scale are increasing or decreasing for each province. The main method used is the three-stage DEA analysis / Data Envelopment Analysis, and software DEAP2.1 and Frontier 4.1 are used for modelling. Financial expenditures on higher education form an input parameter in the DEA model. The output parameters (determining the efficiency of higher education) are the number of students, the number of full-time teachers and the new value added to fixed assets in higher education institutions. The modelling was carried out for 2008-2013 and 2014-2018 (the COVID-19 pandemic is not considered to avoid uncommon trends and unpredictable disturbances in the model). A comparison of the analysis results for these periods allowed the identification of new trends and patterns. The study takes into account three main groups of parameters: 1) endogenous (inefficiency of internal management processes); 2) exogenous (gross regional product per capita of each province in China; the extent of university clustering, which is determined by political and market decisions and does not depend on educational institutions; and the degree of fiscal decentralisation, which is estimated in the article as the ratio of local and state budget expenditures per capita, taking into account the distribution of total budget expenditures for each district and the total population of the respective territory); 3) random disturbances. At the first stage of the study, a BCC model focused on inputs (to assess the technical efficiency of financial expenditures on higher education in different provinces) was built. It does not consider exogenous factors and random disturbances. In the second stage, SFA (stochastic frontier analysis) was applied to account for the impact of all three groups of factors (endogenous, exogenous and statistical noise) on the efficiency of financial expenditures in higher education for each province in China. In the third stage, the adjusted and standardised input and output parameters are incorporated into the BCC models, and the actual value of efficiency caused by exogenous parameters and the influence of random factors is determined. A comparison of the results of each modelling stage for each stage in the study and each province in China allowed the establishment of how the influence of the determinants of higher education financing efficiency changed in chronological and spatial contexts.

Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction

Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating relative efficiency measurement models with machine learning algorithms. Data analyses were conducted using data provided by the energy assessment project offered to small and medium-sized manufacturing companies in the United States ( n 7,548) using sales as the output, with the inputs being the number of employees, hours of operation, electricity, natural gas, cost of electricity, and cost of natural gas. Performance was estimated differently, employing parametric (SFA) and non-parametric (DEA) methods. The prediction benchmarking process occurred by adopting machine learning algorithms: regression (LM), support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT). The findings showed that it is possible to identify the best prediction algorithm associated with a performance model. However, the performance prediction may differ if different strategies for measuring performance or machine learning model configurations are used. In addition, SFA-LOG and SVM had the best performance for regression, and DEA-VRS/IRS excelled with random forest; the RF algorithm was the best fit across all performance approaches. The error rate depends on the algorithm and the performance model, and the number of classes must be reduced to obtain a higher success rate.

On tariff elimination, trade harmonization, and household well‐being: A study of the GST rollout in India

AbstractThe transformative research agenda encourages the pursuit of questions emphasizing societal welfare and well‐being outcomes rather than (just) focusing on narrow, incremental business‐related outcomes. This paper addresses that premise by examining those households at the social and economic bottom of the pyramid (BOP) that are intended to benefit most from the removal of trade barriers. The removal of these barriers should enable supply chains to yield commercial and welfare efficiency in terms of economic surpluses. The Goods and Services Tax legislation implemented in India in 2017 sought to do just that, serving as the experimental testbed for our investigation. Yet, as our analysis of more than 50,000 unique households in the Consumer Pyramids Household Survey data indicates, those at the social and geographic BOP see less benefit than others from the same policy intervention. This runs counter to the intended consequences of such policies. We employ a middle‐range theorizing approach grounded in the Theory of Customs Unions to inform and substantiate our hypotheses, which are tested using a stochastic frontier analysis and truncated regression. The results hold implications for policymakers and organizations seeking to elevate the efficacy of supply chains orchestrated by and serving BOP populations.

Efficiency Assessment on Codified Knowledge Products: An SFA Approach

Knowledge applied to innovation is increasingly recognized as an explanatory factor of economic growth. Innovation derives from applying knowledge to generate new products or processes. National Innovation Systems (NIS) performs as the formal or informal network of people within institutions, interacting to produce and apply knowledge to innovation. NIS can be understood as two subsystems: one based on scientifical and technological work, producing codified products (publications and patents), and the other centered on practical actions to diffuse, apply, and use knowledge. Our objective is to assess cost efficiency in the production of codified knowledge outputs (CKO), being our unit of analysis NIS (countries). To attain our goal, we apply a Stochastic Frontier Analysis (SFA) to estimate a cost frontier of CKO. The sample is a panel that includes 1189 observations, for 23 years (1996-2019), and 82 countries. Our main results identify determinants and patterns of efficiency and productivity, tendencies, and specifics of countries and groups of them.

Efficient Tariff System in the Electricity Distribution: Evidence from Uganda

This study investigates incentive regulation to foster an efficient tariff system in the electricity distribution subsector in Uganda. This study seeks to find empirical evidence to support the argument that regulation is associated with efficiency among distributors. It seeks to design an appropriate model of incentive regulation within the distribution subsector. It assesses the efficiency of existing tariff setting system with a view of guiding policy on how best incentives should be appropriated. It uses the data envelopment analysis and stochastic frontier analysis to investigate how distribution firms use input costs to come up with an efficient end user tariffs. Quarterly data used is from Electricity Regulatory Authority (ERA) covering the period 2013-2019. The findings are that distribution firms cost inputs are inconsistent with the way they their operational and maintenance costs are generated and transmitted to end user tariff. The regulator should be keen on the way tariff is set such that it is fair to all players in the electricity markets. Incentive regulation has a positive influence on cost efficiency and end user tariff. A reduction in energy losses and energy purchases from transmitter makes up the most efficient cost drivers. Lastly, tariff regulation has increased efficiency in operations through in improved quality and reliability of power distribution. First and foremost is reduced load shedding, secondly is more reliable power distribution to end users. Appropriate Incentive regulation has a direct effect on cost of utility and in increasing access of vulnerable groups.

A Stochastic Production Frontier Analysis of Factors That Affect Productivity and Efficiency of Logging Businesses in Virginia

Abstract Understanding the effect of the relationship between timber harvesting attributes on loggers’ productivity and efficiency is crucial for the feasibility and expansion of sustainable forest management and logging. We applied a stochastic production frontier model to firm-level operational data collected from 202 loggers in Virginia, United States, in 2019. Logging equipment value, physiographic region, tract area, number of workers and crews in the woods, college education level, and harvest type statistically increase harvesting productivity. Harvesting productivity in the Coastal Plain was the greatest of all physiographic regions, and pine clearcut productivity was statistically greater than that of hardwood thinning. On the other hand, manual felling reduces harvesting productivity. We found an average efficiency rate of 67% among firms in our sample, which is similar to that found in the literature. The estimated values can show factors that improve forest harvest productivity through better planning and investments while improving the sustainable use of inputs and resources. Study Implications: We empirically analyzed factors affecting logging productivity and efficiency in the southern US state of Virginia. Increased productivity was associated with working in the Coastal Plain physiographic region, investing in logging equipment, increasing the number of workers and crews in the woods, increasing pine clearcut as opposed to hardwood thinning, choosing optimal harvesting tract size, and having a college education as opposed to no high school degree. Manual felling reduces harvesting productivity, and average BMP implementation time does not affect harvesting productivity. Our results can be used as a guide in planning future decisions to increase logging productivity.

The synergy effect of energy security and carbon-haze collaborative management: From the perspective of biased technological progress

It is important to ensure energy security and achieve carbon-haze collaborative management for sustainable development. Reducing imported energy dependence is necessary to maintain energy security, while its impact on environmental quality remains unclear. From the perspective of biased technological progress, this paper estimates the level of biased technological progress towards self-sufficient energy by a heterogeneous stochastic frontier analysis (SFA) function, and then empirically examines whether self-sufficient energy biased technological progress has a dampening effect on haze pollution and carbon emissions. It is found that: (1) Self-sufficient energy biased technological progress can effectively reduce haze pollution and carbon emissions, achieving a synergistic effect between energy security and carbon-haze collaborative management. (2) “Efficiency enhancement” and “quality improvement” are the essential mechanisms for the synergistic effect. (3) Environmental regulation, abundant resource and technology endowments can enhance the haze reduction effect. And the lower dependence on foreign trade and stable global economic policy environment are more conducive to achieving carbon-haze collaborative control. (4) In the Eastern and Western regions, self-sufficient energy biased technology can be sped up to alleviate haze pollution. The findings can enrich the research exploring pollution control from the perspective of biased technological progress, and provide policy recommendations for promoting high-quality development.

Exploring social indicators in smallholder food systems: modeling children’s educational outcomes on crop-livestock family farms in Madagascar

Promoting smallholders’ sustainable development in Africa means addressing agronomic and economic factors but also highly relevant social influences shaping farmers’ production and affecting household well-being. Holistic, integrated analyses can help to meet this need, informing more effective policies and interventions for smallholder farming systems. The authors apply a transdisciplinary, quantitative approach to analyzing social impacts in the smallholder context, using milk-producing crop-livestock family farms in central Madagascar as a test case. First, stochastic frontier analysis is leveraged to confirm education as a social indicator linked to production efficiency. Then, linear regression is used for exploratory modeling of children’s educational outcomes. Findings from the Malagasy case emphasize the influence of rural infrastructure, parental education, chronic poverty, family planning, and crop-livestock diversification on children’s educational outcomes among one region’s farming households. Taken together, results suggest that Madagascar’s policymakers should consider comprehensive territorial planning for simultaneously promoting agricultural development and human well-being. This study illustrates how a transdisciplinary approach to social impacts analysis can integrate agronomic, economic, and social dynamics and help anticipate potential outcomes in support of smallholders’ sustainable development.

Innovation Strategies and Technical Efficiency

In the innovation literature, little attention has been paid to technical efficiency as a measure of performance, despite the fact that technical efficiency is key to explaining firm productivity. In this study we analyze the influence of internal R&D, external R&D and R&D cooperation on the technical efficiency of the firm. The data used come from the Panel de Innovación Tecnológica 2016 for Spanish companies, from whose database we have selected the set of innovative manufacturing firms. To estimate the coefficients of interest we have constructed a knowledge production function, estimated using Stochastic Frontier Analysis (SFA) and the Heckman correction. The results indicate that both internal R&D and R&D cooperation have a positive and significant influence on technical efficiency, while external R&D has a significant negative relationship. Knowing this type of information in advance is very important for business managers and policy makers, since it facilitates decision-making on innovation strategies, as well as the formulation of public policies to support innovation leading to an efficient allocation of public resources.

Urban green total factor productivity in China: A generalized Luenberger productivity indicator and its parametric decomposition

In this study, by taking the distance elasticity shares as the input weights in the production function, the generalized Luenberger productivity indicator (GLPI) was proposed to analyze the urban GTFP in China, and the stochastic frontier analysis (SFA) was used for parametric decomposition to obtain the technological change (TC), technical efficiency change (TEC), and scale efficiency change (SEC). The results show that the entire model and specific parameters passed the test, and the production function equation has good economic and statistical significance, which shows that it is feasible to use the parameter approach to measure and decompose the GLPI. Second, the value of the GLPI showed a rapid growth trend between 2000 and 2019, with a cumulative increase of 140.59%, indicating that various inputs were saved by 3.43% in a specific proportion, and that remarkable achievements have been made in the sustainable development of Chinese cities. Third, TC, TEC, and SEC all showed significant spatiotemporal heterogeneity, in which SEC was the main numerical component and TC was the driving force of the GLPI. Through mathematical proof in theory and data matching in reality, it is confirmed that the GLPI proposed in this study is scientific and applicable.

Technical efficiency in the Malaysian electric and electronic manufacturing industry: A stochastic frontier analysis approach

This study measured the level of technical efficiency (TE) that exerted a significant impact on the Malaysian electric and electronic manufacturing industry, which consists of 13 sub-industries, from 2010 to 2015. Unbalanced panel data from 1880 firms was obtained from the Department of Statistics Malaysia. We used stochastic frontier (SFA) analysis with the transcendental logarithmic approach to estimate the empirical analysis parametric. The maximum likelihood estimated regression of the SFA parameters revealed a high total average TE of 0.944%. Both the manufacturing sub-industries for communications equipment (MSIC 263) and irradiation, electromedical, and electrotherapeutic equipment (MSIC 266) demonstrated the highest average TE value and consistency (0.989%). The consumer electronics manufacturing sub-industry (MSIC 264) yielded a notably lower TE level (0.846%). Changes in TE for all industries were monitored via standard deviation analysis. The low standard deviation (SD) for MSIC 263, 271, and 275 indicated continuous progress and demands for these industries, while the higher SD for MSIC 264, 266, 272, and 273 pointed out major changes that happened within the period. The findings demonstrated that studies on the TE levels indicate that the E&E manufacturing industry has highly expended and continuous efforts to update technologies and human capital innovations. Therefore, the practical implication is to achieve Sustainable Development Goal 9 for sustainable industrialization and fostering innovation by 2030.

Sustainable economic growth and energy security nexus: A stochastic frontier analysis across OECD countries

This study aims to provide a comprehensive picture of the sustainable economic growth undertaken by 22 OECD countries from 2005 to 2021. Sustainable economic growth is conceived as environmental-economic efficiency, that is the ability to increase GDP while reducing greenhouse gas (GHG) emissions. An efficiency effect frontier model is employed to simultaneously estimate the production frontier, technical inefficiency, and the effects of a composite set of institutional drivers that includes environmental taxation, economic globalization, and energy security. This study makes several contributions. Firstly, it expands upon the traditional single-output stochastic frontier model by incorporating a multi-output distance function. Secondly, it includes GHG emissions as an undesirable output in the analysis, providing unbiased assessments of sustainable economic growth. Thirdly, it explores the effects of an under-investigated institutional variable that has become crucial amid the current geopolitical uncertainty: the energy self-sufficiency represented by the energy import independence. Empirical results shed light on a decreasing trend in environmental-economic efficiency that started in 2009 and worsened during the Covid-19 pandemic. Furthermore, environmental taxation and economic globalization have curbed efficiency. Lastly, energy self-sufficiency has been revealed as a crucial driver to enhance the green growth of OECD countries.

Analyzing Common Market Options in the Scope of OIC

This paper initially employs the gravity model in order to analyze the trade data of selected 23 OIC countries and its 4 different subgroups. Then it applies stochastic frontier analysis to the model to gain efficiency scores and uncapped trade potential among countries in order to determine whether the group would have trade creation effect once they go for a Common Market structure. According to the statistical results, the 23 country group gives statistically significant results while deriving a %62 uncapped trade potential among group countries. Additionally, the 13-country subgroup also gives statistically significant results while deriving a %59 uncapped trade potential among them.
 While there are a number of studies which applies stochastic frontier analysis to the gravity model and gains efficiency scores, this study is the first to employ the method for OIC countries in order to determine the best possible country group for a Common Market without having trade diversion affect. The purpose of this study is to determine the Common Market options for Turkiye in the scope OIC in order to be a reference point for future Common Market initiatives.