Stochastic Frontier Analysis Model Research Articles

A spatial stochastic frontier model with fixed effects and endogenous environmental variables

ABSTRACT Stochastic frontier analysis (SFA) models often address fixed effects and endogeneity as separate issues. This study bridges this gap by proposing a unified approach within an ML (maximum likelihood) framework to manage both challenges simultaneously. The ML function is derived in a closed form, but its maximisation requires a numerical solution. To assess the properties of the ML estimator, a Monte Carlo simulation was performed. The simulation shows that the estimator performs reasonably well and gains efficiency asymptotically. The applicability of the estimator was further assessed through an empirical exercise that examined technical efficiency of the production function in a panel of European countries, managing spatiality, endogeneity, and fixed effects.

Regional efficiency analysis of fresh food cold chain logistics in China based on three-stage DEA model

PurposeAssessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China.Design/methodology/approachA three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results.Findings(1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions.Originality/valueThis study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Analysis of Industrial Water Use Efficiency Based on SFA–Tobit Panel Model in China

Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.

Voluntary sustainability standards and technical efficiency of Honduran smallholder coffee producers

Sustainable coffee production promises to improve production techniques and enhance the socioeconomic conditions of smallholder farmers. Using primary survey data from 659 coffee producers in Honduras, this study assesses the impact of voluntary sustainability standards (VSS) on the technical efficiency of smallholder coffee production. The article uses the Stochastic Production Frontier Analysis model to analyze and compare the technical efficiency (TE) of certified and non-certified coffee producers. To provide reliable comparability between groups, the dataset was balanced using Covariate Balancing Propensity Score (CBPS). The results show that the mean technical efficiency was 52.86% for pooled certified farmers and 55.56% for non-certified smallholder coffee producers. Specifically, the technical efficiency of 4C farmers was 51.58%, 53.82% for the Fairtrade group, 60.56% for RA farmers, and 60.15% for the UTZ group, indicating substantial inefficiencies in the coffee production of the different certified groups. Results from Tobit’s model for the determinants of TE indicated that variables such as the age of the household head, access to credit, and training attendance are among the main factors that significantly drive the technical efficiency of certified and non-certified farmers. Based on the findings, enhancing education opportunities, improving infrastructure for better market access and farm management, and expanding credit access are recommended to improve efficiency in the study area. Honduran smallholder coffee producers have considerable potential to increase output with existing technology by improving their technical efficiency. Therefore, stakeholders’ efforts should focus on enhancing efficiency levels and capitalizing on potential gains for both certified and non-certified farmers, to ultimately improve the farmers’ livelihoods.

Effect of Seed and Fertilizer Subsidies on the Technical Efficiency of Rice Farmers in Senegal

The aim of this study was to analyze the effect of seed and fertilizer subsidies (NPK and urea) on the technical efficiency of rice farmers in Senegal. Using data from the Annual Agricultural Survey (EAA) of the Directorate of Analysis, Forecasting and Agricultural Statistics of Senegal (DAPSA), the results of Stochastic Frontier Analysis (SFA) revealed that rice farmers in Senegal have on average a technical efficiency level of 0.545. This suggests that they could increase their current production by 45.5% while using the same level of inputs. Estimation of an SFA model for technical inefficiency revealed that seed and urea subsidies have a significant effect on reducing technical inefficiency. A farmer using subsidized seed saw a reduction in technical inefficiency level by 10.5% and using urea was associated with a 5.1% decrease in inefficiency. In contrast, the model showed no association between the use of subsidized NPK or the use of herbicides and technical inefficiency. And use of organic fertilizer was estimated to worsen technical inefficiency by 4.4% (perhaps reflecting greater reliance on lower-cost inputs among less productive farm households in Senegal). With regard to socio-demographic factors, the results further revealed older respondents experienced more severe technical inefficiency, and that women on average were 9.6% more inefficient than men. These barriers to improved efficiency among older farmers and women suggest targeted supports may be necessary alongside general subsidy programming.

How can agricultural water use efficiency be promoted in the North China Plain?

Water resources are reliable guarantees for high quality development of agriculture in the North China Plain. Improving agricultural water use efficiency is one of the essential ways to solve agricultural water resources shortage. This study measured agricultural water use efficiency (AWUE) using a stochastic frontier analysis (SFA) model and estimated agricultural water-saving potential (AWP) for 74 prefecture-level cities in the North China Plain. The main factors that influenced AWUE were further identified by developing spatial econometric models. The results showed that: (1) From 2000 to 2020, the AWUE of the study area ranged from 0.701 to 0.755 and first rose then fell, and then leveled off. (2) Spatially, the best AWUE was in the central part, followed by the north, and the worst was in the south, and nearly 65% of prefectural cities had annual average AWUE below 0.8. (3) Significant gaps in AWUE and AWP were observed in different regions. Shijiazhuang, Anyang, Hebi and Xinxiang should be considered as key targets to improve AWUE. (4) The effective irrigation degree, per sown area annual precipitation, and annual average temperature had positive effect on AWUE. Increasing the corn sowing area appropriately and reducing the input of per sown area machinery power could also improve AWUE. Therefore, adopting deep plowing measures, developing semi-arid agriculture, and advancing water-saving irrigation technology were feasible ways to improve AWUE and contribute to sustainable agriculture in the North China Plain. These findings can provide a reference for the formulation of high-water efficiency agricultural management strategies in other similar major grain-producing areas.

Rethinking Regional High-Quality Development Pathways from a Carbon Emission Efficiency Perspective

Optimizing resource efficiency and mitigating climate change have become consensuses of human society. However, there is still a gap in assessing the carbon emission efficiency (CEE) and identifying the influence of various factors, especially in rapid urbanizing regions. In this paper, we built a stochastic frontier analysis model to assess CEE and conducted a case study in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA), a typical area of collaborative development in China. A comprehensive influencing factor index was constructed to analyze and identify the key influencing factors of CEE. The results revealed that the average CEE among the 13 cities increased in volatility from 2000 to 2019. The average CEE in Langfang was lowest, while that in Tangshan was highest. The input-related factors had a negative effect on CEE, including carbon emissions per capita, employment per ten thousand people, total assets per capita, and energy intensity. GDP per capita, the urbanization level, and the proportion of the tertiary sector’s GDP had positive impacts on CEE. Future policy formulation should focus on the transition from labor- and material-intensive industries to knowledge- and technology-intensive industries. All the results can contribute to achieving high-quality development and dual-carbon target of rapid-urbanizing areas.

Measurement and Influencing Factors of Total Factor Water Resources Utilization Efficiency in the Yellow River Basin

This paper focuses on the total factor water resources utilization efficiency (WRUE) as the research object, employing the Stochastic Frontier Analysis (SFA) model based on the transcendental logarithmic production function to measure the WRUE of nine provinces in the Yellow River Basin of China from 2012 to 2021, obtains WRUE values for each province. And then, we perform a regression analysis of influencing factors using the Tobit model. The results indicate that: (1) The average value of WRUE of nine provinces in the Yellow River Basin is 0.541, which deviates greatly from the fully effective frontier of SFA, with an overall downward trend during the study period. (2) There is a significant polarization of WRUE among the provinces. Inner Mongolia exhibits the highest WRUE at 0.978, close to the SFA efficient frontier, while Ningxia has the lowest WRUE at only 0.314; the remaining provinces display efficiency values between 0.4 and 0.6. (3) Among the influencing factors affecting the WRUE, the value added of the primary industry, per capita disposable income, and the number of reservoirs have a significant positive impact on the WRUE. In contrast, the value added of the secondary industry, local financial environmental protection expenditures, comprehensive water supply production capacity, and per capita daily water consumption negatively affect the WRUE.

Enhancing energy efficiency in Asia-Pacific: Comprehensive energy policy analysis

The Asia-Pacific (APAC) region has undergone remarkable economic growth over the past three decades, significantly reducing poverty levels. However, the concurrent rise in energy consumption and its environmental impact necessitate the development of a sustainable energy system to sustain and accelerate this progress. Recognizing the critical role of energy efficiency, governments in the region have increasingly formulated and implemented energy policies, encompassing laws, regulations, and action plans. However, understanding the precise influence of these policies on energy efficiency remains a challenge. This study employs an endogenous stochastic frontier analysis (SFA) model using data from the Asia Pacific Energy Portal Policy database covering 23 emerging economies from 2000 to 2017 to assess how energy policies affect energy efficiency in the APAC region. The findings indicate that implementing energy policies correlates with an average increase in energy efficiency by 0.158%. However, the impact varies depending on whether the policies are laws, regulations, or strategies. Though the region's average energy efficiency standing is at 0.34, there has been a consistent upward trend observed from 2000 to 2017. Furthermore, optimizing aggregate energy policies has resulted in substantial energy savings, averaging 0.15 quadrillion Btu. In light of these results, we proposed some policy actions.

HETEROSCEDASTICITY CORRECTION MEASURES IN STOCHASTIC FRONTIER ANALYSIS

The stochastic frontier analysis (SFA) model, designed to assess technical efficiency in production models, operates under the assumption of homoscedasticity. However, in practical scenarios, either the random error or the technical efficiency error, or both, can exhibit non-homoscedasticity. This research proposes heteroscedasticity correction measures for the random error (HCRE), technical efficiency error (HCTE), and both (HCRTE) within the SFA model. The study aims to determine which correction measure yields the most efficient parameter estimates when heteroskedasticity is present. The comparison involves evaluating the mean squared error (MSE) across different forms of heteroscedasticity and sample sizes through Monte Carlo simulations comprising 5000 replications. The findings indicate that attempting to correct for heteroskedasticity in the absence of such issues can adversely affect the parameter estimates of the SFA Model. Conversely, the HCRTE measure consistently produces the most efficient estimates when dealing with heteroskedasticity in terms of both random error and technical efficiency. Moreover, in cases where heteroskedasticity exists, applying the HCRTE measure not only enhances parameter estimates but also improves the technical efficiency measure of the SFA model.

Examining the persistent and transient technical efficiency of the Indian fertilizer industry

ABSTRACT This study examines the persistent and transient technical efficiency of the Indian fertilizer industry using the Stochastic Frontier Analysis (SFA) model developed by Kumbhakar et al. (2014). The firm-level data is obtained from the Centre for Monitoring Indian Economy (CMIE) for the time frame spanning from 2009 to 2019. The results demonstrate a potential scope to enhance technical efficiency, with an average increase of up to 20%. It varies across firms where state-owned enterprises (SOEs) can achieve maximum improvement through continuous efforts to enhance efficiency by restructuring operations. Similarly, leveraging advanced technology and management practices can elevate efficiency by 20% by minimizing transient inefficiency. The result from Tobit regression suggests that private firms (individual and part of conglomerate) are more technically efficient than SOEs. The firms’ age, financial performance, and R&D are positively related to persistent and transient TE. Meanwhile, the size and embodied technology are positive and significant in influencing persistent TE. Private firms with R&D or embodied technology activities have no extra premium of higher TE against the R&D activity of SOEs. It underscores that the effectiveness of innovative activity varies across ownership of the firms due to differences in structure and functioning.

PROPERTY RIGHTS GUARANTEES AS PREREQUISITES FOR ECONOMIC COMPETITION: AN EMPIRICAL STUDY

Economic competition by definition implies a choice between competing alternatives. But can economic agents always protect their rights to make this choice? In order to protect property rights, they should have a guarantor who imposes sanctions on the violator. However, the guarantor must be able to use violence, since the imposition of sanctions is often not voluntary. This paper shows that the uneven distribution of violence in society, which is not monopolized by one decision-making center and/or applied outside the rule of law, hinders economic competition. In such a situation, i.e. in a limited access social order, part of property rights remains unprotected, which prevents the entrepreneur from performing his/her function. The reason for this effect is the desire of the elite to maintain the status quo in distribution of rents: they try to avoid changes caused by the market process that may lead to a violation of existing political balance. In this study, the stated theses are confirmed on microdata of the BEEPS industrial enterprises survey by the European Bank for Reconstruction and Development - we test the hypotheses using stochastic frontier analysis (SFA) and discrete choice models. Empirical analysis allows us to argue that in the limited access order, the obstacle to participation in economic competition is a weak property rights protection - it forces firms to compete for limited resources not by satisfying the needs of consumers, but by following the interests of the property rights guarantor. The results of the study can be useful in developing competition policy measures and explaining the phenomenon of «regulatory bias» in antitrust policy of developing countries.

Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms

ABSTRACT This study utilizes micro-level farm panel data from 2007 to 2012 to measure the technical efficiency of grain production among farms using a stochastic frontier analysis model. Additionally, it employs a two-way fixed effects model to empirically investigate the impact of fiscal support for agriculture on grain production technical efficiency and its underlying mechanisms. The results reveal significant room for improvement in the technical efficiency of grain production among Chinese farms, with increased fiscal support for agriculture demonstrating a substantial enhancement of their efficiency. This promoting effect only exists in the major grain-producing areas and increases with the increase of farm size. The augmentation of fiscal support for agriculture achieves this goal by augmenting modern agricultural input factors, alleviating financing constraints faced by farms, and optimizing agricultural production infrastructure. Our findings provide guidance for optimizing fiscal policy to support agriculture, promoting agricultural modernization, and achieving food security.

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.

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.

Inefficiency Analysis of Tax Efforts in Special Category States of India: Evidence from a Stochastic Frontier Model

The current article examines the tax efforts of India’s main mountainous states that have been accorded special category status by the National Development Council. These states have been under persistent budgetary pressure and are partially reliant on central funds in the form of grants in ‘aid’ and other central transfers. The present article employs the Stochastic Frontier Analysis (SFA) model to assess ‘tax effort’ of special category states. The model results implied that ‘tax effort’ rises with increase in the share of secondary sector, revenue expenditure and road infrastructure. Further, the level of debt lowers ‘tax effort’, whereas the introduction of FRBM and Goods and Service Tax Regime (GST) has raised the inefficiency in own tax mobilization. On the basis of tax effort index, computed through John Drew (JLMS) methodology, this study also indicates that Assam, Uttarakhand, and Jammu and Kashmir are in a far better position in tax efforts score compared to the north eastern special category states. JEL Codes: H2, H21, H22, H71

Obstacles to the development of China's marine economy: Total factor productivity loss from resource mismatch

The total factor productivity (TFP) of the marine economy loss due to the resource mismatch remains a major obstacle to the development of China's marine economy. This study analyzes the intrinsic mechanism of the four types of capital mismatches, including natural, knowledge, social, and institutional, affecting the TFP of the marine economy. Firstly, the Stochastic Frontier Analysis model is used to obtain the TFP in 11 Chinese coastal provinces from 2000 to 2020. Then the four types of capital are unified into the HK model to study their spatio-temporal characteristics, and the extended Syrquin decomposition is used to analyze the sources of the changes in the marine economic output during the whole study period. At the same time, we estimate the output gap and the contribution of distortionary changes in each type of resource. Finally, we use Bootstrap to measure the impact of each type of resource mismatch on TFP. The results show that ① TFP is on the rise and belongs to the middle level. Nevertheless, there is a slow growth rate, noticeable regional differences, with a specific "path dependence". ② All capitals are distorted, and only the distortion coefficient of natural capital is greater than 1. Spatially, resources are under-allocated in the economic pioneer regions and over-allocated in the latecomer regions. ③ At present, China's marine economic growth is mainly driven by resource inputs. Correcting the inter-regional resource distortion can raise output by 21%. ④ The contribution of knowledge and social capital distortions is positive but small, while institutional and natural capital contribute more but fluctuate sharply. Resource allocation tends to be rationalized in Shanghai, Zhejiang, Fujian, Shandong, and Guangdong. ⑤ The mismatch of natural capital shows an “inverted U-shaped” trend, with 2006 as the "inflection point". The mismatch of knowledge, social, and institutional capital shows a decreasing trend and remains stable. Mismatch of all capitals harms the TFP of the marine economy, and institutional capital mismatch has the greatest impact. This assessment framework can measure the extent of other resource mismatches in other regions of the world, and the results of the study can provide lessons for localities to promote efficient resource allocation and enhance TFP in the marine economy.

Analysis of Growth Factors of the Four Asian Dragons Following the Asian Financial Crisis

Purpose - This study aims to identify implications from a factor decomposition analysis of the growth structure of four Asian dragons, Korea, Singapore, Hong Kong, and Taiwan, which have re-emerged as key regions in the fourth industrial revolution and the global value chain.
 Design/Methodology/Approach - With the Translog Production Function employed as the analysis model, the Stochastic Frontier Analysis model was used to derive total factor productivity (TFP), which was decomposed into four factors including returns to scale, technological changes, technical efficiency, and others.
 Findings - The results of the analysis conducted over different periods indicate that during periods of high growth, the economy’s scale effect largely contributed to total factor productivity. However, it became evident that the effect of technological changes had become increasingly important after going through the foreign exchange and financial crises.
 Research Implications - The new growth momentum in these four regions suggests a need to invest in human capital, research and development, and other areas to cope with the low-growth era. Although past high-growth was attributed to the returns to scale effect, the economic structures shifted in the direction of enhancing efficiency, and the effect of technological changes became a driving force while going through the crises. These results show a need to focus on technological advancement through active research and development investment to increase total factor productivity.

How smart city construction affects destination tourism efficiency

To investigate the impact of smart city construction (SCC) on destination tourism efficiency (TE), this study analyzed the impact paths and mechanisms of SCC on destination TE from 3 aspects: technological innovation, informatization, and industrial structure upgrading. Based on panel data of Chinese prefecture-level cities from 2004–2019, we combined a stochastic frontier analysis model with a difference-in-differences model to conduct an empirical analysis to assess the policy effects of SCC on destination TE and validate the impact paths and mechanisms. The results show that the tourism of 153 sample cities show a low TE level overall, an increasing trend year by year, a distribution pattern of high in the east, and low in the middle and west. SCC can significantly improve destination TE, and the impact of SCC on destination TE has spatially heterogeneous.

A frontier-based parametric framework for exploring the competition–efficiency nexus in commercial banking: insights from an emerging economy

PurposeThe present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).Design/methodology/approachLerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.FindingsThe results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.Originality/valueLiterature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).