Stochastic Frontier Analysis Model Research Articles

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).

The impact of technical efficiency on firms' value: The case of the halal food and beverage industry in selected countries.

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company's TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry's strength by comparing the result of TE between 5 countries.

Performance evaluation of national healthcare systems in the prevention and treatment of non-communicable diseases in sub-Saharan Africa.

Non-communicable diseases (NCDs) remain a major public health concern globally, threatening the achievement of sustainable development goal 3.4 (SDG 3.4), which seeks to reduce premature NCD-related deaths by one-third by 2030. According to the World Health Organization (WHO), improving the efficiency of NCD spending (i.e., maximizing the impact of every dollar spent on NCDs) is one of the strategic approaches for achieving SDG target 3.4. This study aims to assess the efficiency and productivity of NCDs spending in 34 sub-Saharan African (SSA) countries from 2015 to 2019. The study employed the data envelopment analysis (DEA) double-bootstrap truncated and Tobit regressions, one-stage stochastic frontier analysis (SFA) model, the Malmquist productivity index (MPI), and spatial autocorrelation analysis to estimate NCDs spending efficiency, identify the context-specific environmental factors that influence NCDs spending efficiency, evaluate total productivity change and identify its components, and assess the spatial interdependence of the efficiency scores. The estimated average DEA bias-corrected NCD spending efficiency score was 87.3% (95% CI: 86.2-88.5). Additionally, smoking per capita, solid fuel pollution, alcohol use, governance quality, urbanization, GDP per capita, external funding for NCDs, and private domestic funding for NCDs healthcare services were found to be significantly associated with NCDs spending efficiency. The study also revealed a decline of 3.2% in the MPI, driven by a 10.6% technical regress. Although all countries registered growth in efficiency, except for the Central Africa Republic and DR Congo, the growth in efficiency was overshadowed by the decline in technical change. Global Moran's I test indicated the existence of significant positive spatial autocorrelation in the efficiency of NCDs spending across SSA countries. The study underscores the importance of efficient use of resources in NCDs treatment and prevention and increased investment in NCDs research and development in achieving the SDG target 3.4.

Comperative Efficiency using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) in the Banking Industry

This study’s objective is to employ data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to investigate the efficiency accomplishments of Indonesian commercial banking from 2018 to 2019. The first method of measuring efficiency employing a non-parametric data envelopment analysis (DEA) technique reveals that the average efficiency of 71 banks fell from 2018 (0.82) to 2019 (0.81). According to DEA findings, major banks outperform small banks on average. According to the approximated SFA Cobb-Douglas (CD) function, interest expenditure and labor expense have a positive and considerable influence on interest income. This occurs when deposit interest rates rise, banks gain interest revenue by raising lending rates, and banks increase non-interest income. According to the SFA of the Cobb-Douglas function, many banks are inefficient, particularly the first to 49th banks that arise from small banks. The Gamma value is near one (0.999), while the LR test yields a significant result of 36.14. The Cobb-Douglas SFA model is therefore applicable. The efficiency performance findings from the two models above reveal the same thing: large banks are more efficient than small banks.

Evidence of Malaysian Company Performance using Copula and Stochastic Frontier Analysis During the COVID-19 Pandemic

Traditional stochastic frontier analysis (SFA) assumes error independence, potentially leading to estimation and efficiency score errors. The purpose of this paper is to introduce the assumption of dependent errors into SFA to rank the performance of 12 Malaysian companies. In 2019 and 2020, during the global COVID-19 outbreak, the shockwaves it sent through various sectors, including healthcare and transportation, were profound. This study assesses company efficiency performance using the copula stochastic frontier analysis (CSFA) model. Seven Archimedean copulas are considered, and the most suitable copula is selected based on the lowest AIC (Akaike Information Criterion) value. The Cot copula, with an AIC value of -19.707, emerges as the best model. The results also reveal a relationship between random errors and inefficiency errors, as well as evidence that COVID-19 contributes to business inefficiency. According to the Cot copula results, Eita Resources Berhad (0.995), My E.G. Services Berhad (0.994), and KPJ Healthcare Berhad (0.857) are the top-performing companies, while Pansar Berhad (0.316), Suria Capital Holdings Berhad (0.319), and Hap Seng Consolidated Berhad (0.411) are the least efficient ones. Therefore, the primary contribution of this study is the proposition that the Cot copula and SFA are appropriate models for analyzing efficiency results. CSFA is a highly accurate model as it accounts for external factors, or random noise, in efficiency estimation and acknowledges the assumption of dependent errors in SFA, making it more realistic for real-world applications.

Financing Efficiency and Influencing Factors of High-Tech Small and Medium-Sized Enterprises of Information Technology Industry in the China Yangtze River Delta

High-tech small and medium-sized enterprises (high-tech SMEs) play a driving role in the economic transformation of the China Yangtze River Delta. However, comparatively little research and attention has been paid to evaluating the financing efficiency and investigating both external and internal influencing factors on financing efficiency based on the information of these enterprises. This study evaluates the financing efficiencies in a homogeneous environment of high-tech SMEs of information technology industry in the China Yangtze River Delta for the period 2014 to 2020 employing the panel three-stage Data Envelopment Analysis model. We have concluded that the financing technical efficiency presents a U-shaped trend from 2014 to 2020, at a moderate level due to the low pure technical efficiency; financing efficiency shows different spatial distribution patterns; most decision-making units are in the quasi-relatively inefficient range, while only a minority are at the efficiency frontier. Furthermore, the external and internal environmental factors are investigated based on Panel Stochastic Frontier Analysis and Tobit model. According to the empirical results, the optimization of the financial and technical environment contributes to financing efficiency; by contrast, a good economic and policy environment appears to restrict it; internal environmental factors such as growth ability, debt solvency, and the average age of managers have significant impacts on financing efficiency.

Measuring efficiency of public hospitals under the impact of Covid-19: the case of Türkiye

The Covid-19 pandemic has had serious medical, administrative and financial effects on the health system and hospitals around the world. In Türkiye, compared to 2019 realizations, in 2020 and 2021 respectively there were 39% and 21% decrease in the number of outpatient services and 29% and 17% decline in total inpatient services of public hospitals. The main subject of this research is how the pandemic period affects the Turkish public hospitals’ efficiency. We have measured the technical efficiency of outpatient and inpatient care services of Turkish public hospitals using Stochastic Frontier Analysis (SFA). The dataset includes 563 hospitals for the years 2015 through 2021. Inputs of number of physicians, nurses and other medical staff, and number of beds and their interactions with each other are introduced to the SFA models of outputs of outpatient visits and inpatient discharges adjusted with case mix index are derived. Firstly, we found that the years associated with Covid-19 have a significant negative impact on the inpatient service efficiency. Training and Research and City Hospitals have low efficiency scores in outpatient services but high efficiency scores in inpatient services. In addition, the regions with high population rates have positive impact in outpatient efficiency and negative impact in inpatient efficiency. During the pandemic, city hospitals, have received large investments, gained a key role by increasing both the patient load and their efficiency. Future reforms can be guided by taking advantage of the efficiency differences of hospitals in different environmental factors.

Density-dependent mortality models for mono- and multi-species uneven-aged stands: The role of species mixture

The self-thinning law describes the progressive mortality of growing trees in crowded even-aged stands because of competition. In uneven-aged stands, however, density-dependent mortality is relatively poorly known. We developed density-dependent self-thinning models for mono-species and multi-species uneven-aged stands. Three models for mono-species Picea abies, Abies alba and Fagus sylvatica stands, four models for mixed conifer/broadleaf stands and a model for single tree selection stands as a specific type of uneven-aged forest were parametrized by Quantile Regression (QR) and Stochastic Frontier Analysis (SFA) using data from n = 31,612 forest inventory plots in Slovenia. The average slope of the self-thinning line in uneven-aged stands of −1.422 was statistically significantly lower than Reineke’s slope. Maximum stand density was higher in mono-species stands than in multi-species stands. The higher the proportion of beech in a stand, the higher the intercept and slope of the maximum stand density line. In single tree selection stands, the intercept and the slope of the maximum stand density line were highest and statistically different from all other types of uneven-aged forests. Species mixture had an impact on the slope and intercept of the self-thinning line. The slopes of the self-thinning lines differed between the studied types by up to 36% and 69% in the SFA and QR models, respectively, while the intercepts differed by up to 13% and 24%, respectively. However, maximum stand densities differed significantly only between compositionally very dissimilar stand types, such as Fagus sylvatica- or Picea abies-dominated mixtures versus other broadleaf mixtures (e.g., Quercus petraea, Carpinus betulus). The SFA predictions of maximum stand densities were systematically lower and less varying than the QR predictions, but not statistically different. The models can be used to constrain individual tree growth models in uneven-aged forests and to estimate the onset of inter-tree competition in irregular stands.

Airports’ public infrastructure and sources of inefficiency

PurposeThe Spanish airport system contains several regional airports within an amenity distance and alternative travel modes. Profitable airports cross-subsidise small airports, which are not required for regional development or connectivity. Airports are government-owned and centralised-managed by Spanish Airports and Air Navigation (AENA, for its Spanish acronym). This study aims to analyse the probability of an under-used public infrastructure and the AENA’s managerial ability as per the financial sustainability of the network in the long term.Design/methodology/approachThe national regulatory framework determines the airports’ environment. Six airports revealed unobserved heterogeneity, avoiding model misspecification. The framework is defined through proxies of the singularities of the Spanish framework: public investments and geographical specifications. The stochastic frontier analysis model follows two time-varying specifications, accounting for airports’ environmental factors, to ensure the robustness of the results to differ from the inefficiency caused by AENA and external factors.FindingsAirports’ infrastructure capacity and traffic are not correlated; regional airports become a financial burden for the system unless they specialise or differentiate. Proxies defining the airports’ context are relevant. Because airports do not compete for airlines and passengers, there are too many regional airports with little traffic, resulting in disused public infrastructure that falls far short of improving connectivity and regional development.Originality/valueThis study contributes to paying attention to the characteristics of the regulatory framework, such as management strongly centralised in AENA, airport charges decided by the owner, lack of competition and lack of an independent regulatory entity. Another original contribution considers reliable capital measures (airports’ infrastructure).

Technical efficiency evaluation and ranking of container terminal operators through DEA

In today’s competitive business environment the evaluation and ranking of container terminals plays a crucial role. Data Envelopment Analysis (DEA) models are suitable to measure the efficiencies of alternatives specified with multiple inputs with multiple outputs, where as Stochastic Frontier Analysis (SFA) models are suitable to measure the efficiencies of alternatives specified by multiple inputs with single output. In this four inputs and four outputs are considered for the evaluation of technical efficiency of container terminals and DEA is implemented to evaluate the performance of these container terminals based on technical efficiency. Also Regression analysis is performed to identify the factors which affect the technical efficiency mostly.

The Impact of Internet Use on Production Efficiency of Animal Husbandry: Based on the Evidence of 340 Herdsmen in Inner Mongolia, China

Production efficiency is a standard to evaluate the high-quality development of animal husbandry. As an important tool for herdsmen to collect and use information for animal husbandry production and innovation, the internet is not only an inner motivation for improvement of production efficiency but also an important engine for transformation of animal husbandry modernization. It is of practical significance to analyze the impact of internet use on the production efficiency of animal husbandry from the micro-level. This paper, based on the survey data of 340 herdsmen in Inner Mongolia, adopts the Stochastic Frontier Analysis (SFA) model to measure the production efficiency of animal husbandry and selects the Tobit model and moderation effect model to analyze the relationship between internet use and animal husbandry production efficiency under the influence of different capitals. Results show that the production efficiency of animal husbandry is relatively low and internet use has a positive and statistically significant effect on production efficiency; moreover, social capital and financial capital have a negative moderation effect when internet use affects production efficiency. Our findings suggest that the use of internet technology should be paid more attention in order to increase the production efficiency of grass-based animal husbandry in Inner Mongolia.

Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis.

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.