Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India

1992 ◽  
Vol 3 (1-2) ◽  
pp. 153-169 ◽  
Author(s):  
G. E. Battese ◽  
T. J. Coelli
Keyword(s):  
Panel Data ◽  
Technical Efficiency ◽  
Production Functions ◽  
Paddy Farmers

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.

Using Panel Data to Estimate Risk Effects in Seemingly Unrelated Production Functions

1992 ◽  
pp. 35-49 ◽  
Author(s):  
Guang H. Wan ◽  
William E. Griffiths ◽  
Jock R. Anderson
Keyword(s):  
Panel Data ◽  
Production Functions ◽  
Risk Effects

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.

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