Technical efficiency and its determinants in the Indian meat processing industry

ABDULLA ABDULLA; SHIV KUMAR

doi:10.56093/ijans.v91i3.114149

Authors

ABDULLA ABDULLA ICAR-National Institute of Agricultural Economics and Policy Research, Pusa, New Delhi 110 012 India
SHIV KUMAR ICAR-National Institute of Agricultural Economics and Policy Research, Pusa, New Delhi 110 012 India

https://doi.org/10.56093/ijans.v91i3.114149

Keywords:

Determinants, Meat, Processing industry, Technical efficiency

Abstract

The stochastic frontier approach captures the technical efficiency and its determinants of meat processing industry using unit level data from 2002-2016 of annual survey of industries in India. The study concludes substantial (73%) scope of improvement in technical efficiency. Better management of inputs by private limited firms have achieved higher technical efficiency compared to firms of other sectors. Cross learning among best performing states and worst performing states would bridge the gap of technical efficiency in meat processing industry. The matching skill set of labour force with state of art technology is the way out to arrest the deterioration of technical efficiency in the industry.

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References

Abdulla and Mohd Izhar Ahmad. 2017. Technical efficiency and its determinants: A stochastic frontier analysis of sugar mills in Uttar Pradesh. The IUP Journal of Applied Economics XVI(4): 29–40.

Bartz-Zuccala, Wiebke, Pierre Mohnen and Helena Schweiger. 2018. The role of innovation and management practices in determining firm productivity. Comparative Economic Studies 60(4): 502–30. DOI: https://doi.org/10.1057/s41294-018-0075-3

Battese G E and T J Coelli. 1992. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India.’ Journal of Productivity Analysis 3(1–2): 153– 69. DOI: https://doi.org/10.1007/BF00158774

Battese G E and T J Coelli. 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20(2): 325–32. DOI: https://doi.org/10.1007/BF01205442

Khan, Md Riyazuddin and Abdulla. 2019. Dynamics of MSME Industry in Uttar Pradesh and Maharashtra: An analysis of sugar firms. SEDME (Small Enterprises Development, Management and Extension Journal) 46(4): 1–3. DOI: https://doi.org/10.1177/0970846419894775

Kumbhakar Subal C. 1991. Estimation of technical inefficiency in panel data models with firm- and time-specific effects. Economics Letters 36(1): 43–48. DOI: https://doi.org/10.1016/0165-1765(91)90053-N

Ministry of Fisheries, Animal Husbandry and Dairying. 2019. 20th Livestock Census.

MoFPI. 2017. Opportunities in Meat and Poultry Sector in India. http://foodprocessingindia.gov.in/pdf/poultry.pdf.

Negi Digvijay S, Pratap S Birthal, Devesh Roy and Md Tajuddin Khan. 2018. Farmers’ choice of market channels and producer prices in India: Role of transportation and communication networks. Food Policy, Elsevier 81(c): 106–21. DOI: https://doi.org/10.1016/j.foodpol.2018.10.008

Pinstrup-Andersen P, Pandya-Lorch R and Rosegrant M W. 1999. World Food Prospects: Critical Issues for the Early Twenty- First Century. http://library.cgiar.org/handle/10947/1622.

Reifschneider David and Rodney Stevenson. 1991. Systematic departures from the frontier: A framework for the analysis of firm inefficiency. International Economic Review 32(3): 715. DOI: https://doi.org/10.2307/2527115

Wang Luqi, Xiaolong Xue, Zebin Zhao and Zeyu Wang. 2018. ‘The Impacts of Transportation Infrastructure on Sustainable Development: Emerging Trends and Challenges.’ International Journal of Environmental Research and Public Health 15(6): 1172. DOI: https://doi.org/10.3390/ijerph15061172