Implementing Generalized Panel Data Stochastic Frontier Estimators

2019 ◽  
pp. 225-249
Author(s):  
Subal C. Kumbhakar ◽  
Christopher F. Parmeter
Keyword(s):  
Panel Data ◽  
Stochastic Frontier

Technical efficiency and its determinants in the Indian textile garments industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdulla ◽  
Shiv Kumar
Keyword(s):  
Panel Data ◽  
Technical Efficiency ◽  
Textile Industry ◽  
Small And Medium Enterprises ◽  
Stochastic Frontier ◽  
Efficiency Score ◽  
Value Chains ◽  
Small Scale ◽  
Content Type ◽  
Medium Enterprises

Purpose This paper aims to examine technical efficiency and its determinants in Indian textile garments industry in post-agreement on textiles and clothing regime and evaluate the technical efficiency among micro, small and medium enterprises (MSMEs) firms. Design/methodology/approach This study uses unbalanced panel data for the period 2005–2010 to 2015–2016. The stochastic frontier function is used to estimate technical efficiency and its determinants. Findings The results show that the overall ecosystem of textile garments’ value chains could be improved to enhance the technical efficiency thereof. The result also reveals that small-scale firms have the highest technical efficiency scores, and medium-scale firms have the least technical efficiency score among all the categories of MSMEs. Research limitations/implications The textile garments industry needs to define its innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations. Practical implications This study has shown that to offset India’s cost disadvantage in the international markets, there is a need to develop an ecosystem of textile manufacturing and value chains, eliminate the inverted duty structure (where inputs are taxed at a higher rate than the final product) and switch over from shuttle looms toward shuttle-less looms. This would unleash the potential of textile and garments industry and make it globally competitive and technically efficient. Further, there will be an alignment with the ease of doing business with an appropriate mix of policy, technology, institution, infrastructure, information and services. Originality/value Using frontier production function takes stochastic context into account for the dynamic character of technical efficiency and its components. Most of the past studies have assessed technical efficiency at the aggregate level using three-digit National Industrial Classification (NIC) or four-digit NIC code. An analysis at higher levels of aggregation masks the variation in technical efficiency. This study used five-digit NIC data to measure the firm-specific technical efficiency of the textile industry. According to the authors’ knowledge, this study is the first of its kind in the Indian textile industry using stochastic frontier approach and panel data. Further, it also looks at the contribution of different determinants in technical efficiency to the firms.

scholarly journals Determinants and Differences of Grain Production Efficiency Between Main and Non-Main Producing Area in China

2019 ◽  
Vol 11 (19) ◽  
pp. 5225
Author(s):  
Furong Chen ◽  
Yifu Zhao
Keyword(s):  
Panel Data ◽  
Technical Efficiency ◽  
Production Function ◽  
Production Efficiency ◽  
Stochastic Frontier ◽  
Grain Production ◽  
Marginal Productivity ◽  
Rural Labor ◽  
Stochastic Frontier Production Function ◽  
Frontier Production Function

This paper investigated the determinants, especially labor transformation, and differences of technical efficiency between main and non-main grain-producing area in China based on a panel data from 30 provinces in the period of 2001–2017. Stochastic frontier production function was used to estimate the level of technical efficiency and the marginal productivity of different inputs. The estimated results showed that land is the most important factor to improve China’s grain output, followed by fertilizers, labor, and machinery inputs. There was a significant 4.6 percent gap of production efficiency between main and non-main producing provinces. Influence of rural labor transformation was confirmed to be positive to improve technical efficiency.

scholarly journals An Analysis of Energy Use Efficiency in China by Applying Stochastic Frontier Panel Data Models

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1892 ◽  
Author(s):  
Xiaoyan Zheng ◽  
Almas Heshmati
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Energy Policy ◽  
Energy Use ◽  
Stochastic Frontier ◽  
Control Variables ◽  
Energy Use Efficiency ◽  
Level Data ◽  
Use Efficiency ◽  
Province Level

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China’s energy efficiency and a transition to cleaner energy sources and production.

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