Stochastic Frontier Approach Research Articles

Effect of climate change vulnerability on farm technical efficiency in central Africa: the case of rural Cameroun

PurposeA major topical issue in the 21st century is climate change, and its impact on humanity cannot be quantified, as it affects agriculture, water levels, settlements and more. This study aims to evaluate the effect of climate change on farm technical efficiency in rural Cameroon and suggest ways for improvement.Design/methodology/approachWe employ a two-step approach to assess whether the efficiency of these farms is vulnerable to climate change. In the first step, we calculate efficiency using the stochastic frontier approach with a Cobb–Douglas specification. In the second step, we investigate the effect of climate change on efficiency by applying a Tobit model. The data used come from a questionnaire survey of 419 randomly selected farmers.FindingsThe findings reveal that the average level of technical efficiency is 62.9%. This implies that there are existing opportunities to increase technical efficiency if resources are used optimally. The results also show that increases in the climate change vulnerability index have statistically significant negative effects on the technical efficiency of farms. This negative relationship is attributable to the adverse influence of the exposure and sensitivity components of the climate change vulnerability index.Originality/valueWe provide an understanding of the effect of climate change vulnerability on the technical efficiency of farms in rural Cameroon and suggest means of improving it. Additionally, we examine whether the influence of climate change varies across agro-ecological zones.

Assessment of carbon emission reduction potential in new-type urbanization policy: A perspective on carbon rebound effect

In this research, we utilize a refined model for carbon emissions, known as the Stochastic Frontier Approach (SFA), to measure the extent of the carbon rebound effect across a sample of 231 urban areas in China, spanning the years 2011 through 2019. Additionally, the study explores the impact that policies associated with the advancement of new urbanization have had on the aforementioned carbon rebound phenomenon. The findings of this thesis indicate that this policy can reduce carbon emission volumes effectively; however, there is also a significant carbon rebound effect, making the reduction less efficient than expected. After several robustness tests, the main conclusions remain consistent and reliable. Our investigation into the underlying mechanisms reveals that policies aimed at fostering new urbanization may attenuate the carbon rebound effect by enhancing the efficiency and composition of industrial sectors. However, technological advancements and the degree of energy efficiency remain pivotal in potentially escalating the carbon rebound effect. The analysis of variations across different scenarios demonstrates that the policy’s impact on the carbon rebound effect is subject to variation depending on several factors, including geographical contexts like provincial boundaries, the administrative hierarchy of cities, transportation infrastructure, and the constraints imposed by resource availability. As a multi-target policy, although this policy has a carbon rebound effect, it also has a significant social welfare effect, which coincides with the development goal of China. Ultimately, some suggestions are offered to enhance the role of this policy in promoting carbon emission reduction and to foster a virtuous interaction between this policy and economic growth.

Productivity and growth decomposition: a novel single-index smooth-coefficient stochastic frontier approach

Abstract Our paper investigates productivity, output growth and total factor productivity (TFP) growth using a novel single-index smooth-coefficient stochastic frontier approach and two firm-level datasets respectively from the high technology (high-tech) manufacturing and Knowledge Intensive Business Services (KIBS) sectors in Norway. The approach considers input productivity and technical inefficiency to be flexible functions of production environmental variables indexed with unknown parameters for more precise estimation of marginal effects of these variables on the frontier and inefficiency. Output growth is decomposed into technical change (TC), input-driven component (IDC) and efficiency change (EC), while TFP growth is decomposed into TC, scale component and EC. The primary objective is to (i) maximise output through the frontier and efficiency channels and (ii) enhance productivity growth through such channels as technical progress and efficiency improvement, specifically tailored for the manufacturing and services industries. The empirical results reveal substantial heterogeneity in technology across firms. Overall speaking, geographical industrial concentration, export intensity and urbanisation positively influence output in both sectors. Technical progress contributes to TFP growth in both sectors; however, TC is biased towards capital in the high-tech sector and driven by labour in the KIBS sector. In addition to TC, TFP growth in the high-tech and KIBS sectors also benefits from EC and IDC, respectively.

The impact of digital economy on energy rebound effect in China: A stochastic energy demand frontier approach

The energy rebound effect threatens to realize the energy-saving effect of energy efficiency measures. The effect of the development of the digital economy on the energy rebound effect is still being determined. This paper is based on the panel data of 30 provinces in China from 2011 to 2021. A stochastic frontier model of energy demand is built to examine the influence of the digital economy on the energy rebound effect. The findings are as follows: (1) Digital economy significantly promotes the energy rebound effect. The average energy rebound impact is calculated to be 55.1%. (2) Urbanization strengthens the positive effect of the digital economy on the rebound effect. (3) Different provinces experience the rebound effect differently due to the digital economy. The digital economy has a more significant role in fostering the rebound effect in places with higher R&D intensity, lower coal share, and higher secondary sector share. This study argues that we should deepen the reform of energy prices, promote sophisticated energy-saving technology development, and promote energy structure transformation while expanding the digital economy to mitigate the negative impact of the rebound effect effectively.

Explaining wine scores through stochastic frontier analysis. An intent to measure sensory non-observable attributes

ABSTRACT Experts give scores to wines, which are quality proxies for marketers and buyers. The production of scores proxying wine quality is explained by a set of observable objective attributes, plus another set of unobservable and subjective (sensory) features, and randomness. Is it possible to use a Stochastic Frontier Approach (SFA) to understand whether objective and subjective (sensory) characteristics of wines explain the differences in ‘producing’ wine scores? We estimate a wine quality stochastic frontier production function, using a database of 1800 top-scored wines, in 18 years-window encompassing objective determinants that largely explain the scores (price, production, year, grape, country, etcetera), being aware that sensory aspects related to wine grading are unobservable. We find that the observable variables explain part of the ‘efficiency’ in attaining scores and suggest that ‘inefficiency’ in score generation can be attributed to unobservable (sensory) variables.

Comparative efficiency of Chilean water utilities: a Bayesian stochastic frontier approach

ABSTRACT Assessing the potential relationship between ownership and performance of water utilities is relevant for water regulators. Incorporating technological heterogeneity among groups of water utilities could solve any incomparability issues and lead to more appropriate policy implications. This can be done with the use of the metafrontier framework which assumes that utilities operate under different technologies. The study applies this framework to compare the efficiency of private water utilities (PWU) and concessionary water utilities (CWU) in Chile using Bayesian Stochastic Frontier Analysis (SFA) techniques. The results indicated that PWU performed better than CWU. Nevertheless, both types of water utilities presented considerable cost inefficiency. PWU and CWU should considerably reduce costs by 34% and 48% using the industry technology as a whole, respectively. The methodological approach proposed in this study could be used to compare the performance of other groups of water utilities to support the decision making of water regulators.

Returns to scale, technical and efficiency changes in the Spanish hotel industry using technological heterogeneity models

This paper analyses returns to scale, productivity growth and its decomposition in the Spanish hotel industry (period 1997–2019). To do so, we consider that hotels can have technological heterogeneity and, therefore, parameters in their production function can differ between them. Also, we use a multiple input and output production function based on an output distance stochastic frontier approach with random parameters in a Bayesian framework. Results indicate that the percentage of hotels working under increasing returns to scale decreased during the period considered. Furthermore, productivity growth was low, indicating signs of stagnation. Efficiency change appears to be the main driving force behind productivity growth, although technical change had a positive effect on productivity after the start of the global financial crisis. Several practical implications are proposed based on these results.

Gender diversity and profit efficiency of microfinance institutions: A Sub-Saharan study.

Irrespective of the promising opportunity to improve profit efficiency by at least 73%, microfinance institutions operating in Sub-Saharan Africa are efficient only for 27%, far below the average value. The conclusion is drawn after analyzing the profit efficiency of the microfinance institutions using the stochastic frontier approach applied to data obtained from 128 microfinance institutions operating in 34 Sub-Saharan African countries. The study results suggest the presence of uniform profit efficiency experience across time among microfinance institutions. Microfinance institutions operating in low-income countries and credit union form microfinance are economically more efficient than their counterparts. Furthermore, the profit efficiency of microfinance institutions is significantly affected by total assets, cost per loan, loan per staff, legal status, and the county's income group of microfinance. Notably, the profit efficiency of microfinance institutions is adversely affected by the presence of female borrowers and female loan officers suggesting that gender diversity plays a role in the efficiency of microfinance institutions. Finally, we recommend that the managing body of microfinance work more on improving labor efficiency, earning asset utilization, loan collection efficiency, women's involvement and the hottest technology implementation.

Factors Affecting Technical Efficiency of Coffee Producers: The Case Dale Woreda, Sidama Region, Ethiopia

Coffee is the major source of income for smallholder farmers and is a leading export crop for Ethiopia. Even though a great proportion of coffee production comes from smallholder farmers with farm sizes below two hectares, productivity in these farms remained very low. To identify the causes of low productivity this study identified the technical efficiency level of smallholder coffee farmers and the factors that influence technical efficiency in coffee production in Dale Woreda, Sidama Region, Ethiopia using the stochastic frontier approach. In addition, this study attempts to determine whether the agricultural and some of the socio-economic characteristics of farmers can influence the efficiency of their technical efficiency coffee production. By exploiting the information that was gathered in a survey of 150 farmers selected randomly from three kebeles using stratified random sampling in the woreda, these farmers' technical efficiency was estimated using a trans-long-type stochastic frontier model and two-limit Tobit model. The mean technical efficiency of the study area is 65.43%. The results suggest that, factors like the land cover, labor in hours, compost used as well as their cross product and the interaction effects of labor with sufficient compost used, labor with proper capital-labor force, and minimum cost for agricultural equipment and composite with proper capital significant determinants of coffee technical efficiency in the area. Furthermore, variables like educational status, age of the coffee tree, membership of cooperative associations, size of livestock holding, presence of additional off-farm incomes, and age of household head are significant predictors of the technical efficiency of farmers in the study area. Thus, by improving their education, improving the frequency of extension service, increasing livestock holdings, improving membership in cooperative associations, and replacing the aged coffee tree, someone can improve his/her technical efficiency directly and coffee production indirectly.

Estimation of technical efficiency of maize farmers in Wa East District in Ghana: translog stochastic frontier approach

ABSTRACT Efficiency analysis among maize farmers is crucial, especially when maize output continues to decline, even under improved technology. The aim of this study was to estimate the level of technical efficiency among maize farmers in Wa East District in Ghana and to assess the determinants of their technical efficiency. Data were collected from 273 household heads using the multistage sampling method. The study applied the stochastic translog production function to do the estimation and assessment. The results revealed a variation of 60.2%–89.5% efficiency levels among maize farmers. In addition, maize farmers achieved about 71.5% of the required technical competences. Factors contributing significantly were labour, land, age, farming experience, household size, hired labour and seed quality. The results showed an increasing returns-to-scale parameter being significantly different from unit. Recommendations are improvement in the capacity of farmers by agriculture extension service and accessibility of farmers to affordable production inputs in real time.

Towards sustainable development goals: An analysis of environmental efficiency and the impacts of self-purification capacity across diverse income levels

The increasing urgency of global environmental degradation, particularly across diverse economic development stages, underscores a critical need for nuanced understanding and targeted strategies to achieve Sustainable Development Goals. Our study examines environmental efficiency trends over 27 years in 163 countries, utilizing greenhouse gases and particulate matter 2.5 as indicators. We address the challenge by developing and applying a two-stage method that combines a hyperbolic distance function with a stochastic meta frontier approach to assess environmental meta-efficiency. The average meta efficiency of these countries is 0.464, which remains at a relatively low level. Our model indicates that the high-income country group needs to reduce greenhouse gas and pollutant emissions by 25% and increase non-fossil energy usage by 33% to improve environmental efficiency. This suggests these countries must transition towards more sustainable energy sources and practices. Moreover, recognizing that existing income grouping inadequately characterizes each country, we use k-means cluster analysis for regrouping, more accurately reflecting individual differences. The regrouping results show that some high-income countries are classified into inactive groups, implying serious environmental problems. Our findings advocate for collaborative and tailored strategies to address these disparities. We conclude that income levels cannot solely drive environmental efficiency but must also consider geographical and climatic factors, which are pivotal in shaping a country’s environmental policies and efforts. This approach offers a clearer understanding of current inefficiencies and sets the stage for more informed policy-making that can better address the specific needs and capabilities of different countries.

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.

Determinants of technical efficiency of pig production systems in northern Uganda: a Stochastic Frontier approach

Determinants of technical efficiency of pig production systems in northern Uganda: a Stochastic Frontier approach

How efficient are Indian banks? An application of stochastic frontier analysis

Purpose This study aims to investigate the efficiency of Indian commercial banks from 2002 to 2018 using the stochastic frontier analysis. Design/methodology/approach This study uses the parametric approach of the stochastic frontier to examine the technical efficiency of banks acknowledging exogenous shocks, omitted variables and measurement errors, filling a gap in the existing financial literature. The scope of this study was constrained to 71 scheduled commercial banks to make it manageable and productive with 1,036 observations. Findings The results show that the mean technical efficiency of new private banks remained constant at 92.7% during the study period because of technology diffusion in banking systems. The technical efficiency of the nationalized, old private and foreign banks has enhanced over the period because of the efficient utilization of various innovative information technology services such as mobile banking, cheque truncation system, magnetic ink character recognition. However, the foreign banks are still laggards with a mean technical efficiency of 81.7%. The empirical findings suggest that new private sector banks depict higher efficiency than nationalized, old private and foreign banks. Research limitations/implications This study’s sample represents all categories of banks (public, private and foreign) including the banks that merged or consolidated during the period of study. To achieve the desired results, the authors incorporate the consolidated and merged banks in their data set. Further, the authors excluded all scheduled small finance banks and scheduled payment banks from their analysis, as these entities commenced operations post-2015. Additionally, the authors also excluded regional rural banks because of their distinct mandate aimed at servicing the rural populace and agricultural sector. Originality/value This study contributes to the literature on the performance of conventional banks in general and emerging markets, in particular, using the most recent data and covering a relatively long period using the stochastic frontier approach.

Elementary education outcome efficiency of Indian state governments: a generalized stochastic frontier approach

PurposeThis paper aims to analyze the technical efficiency of Indian State governments in providing elementary education (EE) and to identify the determinants of their technical inefficiency.Design/methodology/approachThe Generalized Stochastic Frontier Approach (GSFA) is used in the context of the Inefficiency Effects Model to simultaneously estimate the frontier production function and the technical inefficiency model. Panel data of 28 Indian States from 2009–10 to 2018–19 is used.FindingsThe mean efficiency of States stands at 86%. Efficiency varied between 67 and 97%. 96% of the inter-state disparity in EE outcomes can be explained by inefficiency. Arunachal Pradesh is the least efficient State, followed by Sikkim and Tripura. Efficiency estimates were observed to change across States over the study period. Proportion of government schools, rural population, and proportion of Schedule Caste and Schedule Tribe children are the major determinants of inefficiency.Practical implicationsThis study emphasizes that efficient resource management is as important as adequate resource allocation for achieving positive EE outcomes. It distinguishes resource-poor States from inefficient ones, providing insights to enhance States’ efficiency, and aiding policymakers in formulating strategies for ensuring equitable and quality EE.Originality/valueThis is the first paper to apply GSFA (for Indian States), providing a more robust estimation of efficiency. The Inefficiency Effects Model is employed that address the limitations inherent in the two-stage approach.

Peran Green Economy dalam Meningkatkan Aspek Kesejahteraan Sosial Masyarakat (Studi Literatur)

Stochastic Frontier Approach (SFA) is a technique of measuring the level of efficiency with a This study aims to review the role of green economy in improving people's social welfare. Green economy is defined as a sustainable and environmentally friendly economic model that balances economic, environmental, and social aspects. Social welfare is measured by various indicators, such as health, education, income, and security. Based on the literature review, the green economy has the potential to improve social welfare through various mechanisms, including: green job creation, poverty reduction, improved health, improved education quality, improved security. This research also discusses some of the challenges and opportunities in implementing the green economy to improve social welfare. In conclusion, the green economy has significant potential to improve people's social welfare. Effective green economy implementation requires collaborative efforts from various stakeholders, including the government, private sector and civil society.

Production efficiency of internet-only banks and conventional banks: Evidence from China and Japan

In recent years, Internet-only banks have begun participating in many countries' financial markets; however, their impact on these markets requires clarification. This study focused on China and Japan and covered the period from 1995 to 2019. We utilized the stochastic frontier approach to estimate the output distance function with a view to comparing production efficiency before and after the entry of Internet-only banks into the financial markets of both countries. The results revealed that China's Internet-only banks are more efficient than conventional banks, while the opposite is true in Japan. Furthermore, although Japan's Internet-only banks outperformed China's, Japan's conventional banks were less efficient than were those in China; this finding suggests that Japan's Internet-only banks adopt more advanced production technologies. Finally, the results of stochastic meta-frontier regression supported the notion that national banks in both China and Japan outperformed private banks between 2015 and 2019.

Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis

The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

Technical efficiency of rice seed production and their determinants in Chitwan, Nepal – A one-step scaling stochastic frontier approach

Technical efficiency of rice seed production and their determinants in Chitwan, Nepal – A one-step scaling stochastic frontier approach

The Impact of Changes in Rural Family Structure on Agricultural Productivity and Efficiency: Evidence from Rice Farmers in China

Over the past three decades, China has shifted from a relatively immobile society to one where rural migrant workers are dispersed throughout urban areas, resulting in significant changes in rural family structure. Previous studies have tended to approach migrant workers as homogeneous groups within families. In contrast, our attention turns to the diversity among individuals and the complex interactions within families. Based on a survey of rice farmers in five provinces of China, this study aims to explore the heterogeneous impact of changes in rural family structure on the single-factor (i.e., land, labor, and capital) productivity and technical efficiency (TE) of rice production. Methodologically, we calculated the productivity indicator through the Cobb–Douglas production function. Following this, a one-step stochastic frontier approach (SFA) was employed to assess the production frontier and estimate inefficiency. To address self-selection bias in family migration behavior, we applied the propensity score matching method (PSM). The results reveal that significant outcomes are observed only with certain types of changes in rural family structure. The production decisions of rural families are influenced by the migration regions of their family members. Compared to non-migrating families (NM), families with couples’ joint migration outside the province show higher single-factor productivity and TE. We used multiple approaches to examine the results and came to similar conclusions. Therefore, enhancing social security measures and employment opportunities for migrant workers, with specific attention to supporting migrant couples, can have a positive impact on sustainable urban and rural development, as well as food security.