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Perspectives of productivity growth in Indian food industry: a data envelopment analysis

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  • Mukesh Kumar
  • Partha Basu

Abstract

Purpose - The purpose of this paper is to measure the Malmquist Productivity Index and its different components such as technological change, technical efficiency change and the change in scale efficiency in the Indian food industry during the period spanning 1988‐1989 to 2004‐2005. Further, it examines the variation in productivity and its components with respect to the factors internal to the firms. Design/methodology/approach - The technique of data envelopment analysis has been used to measure productivity index and its different components under the assumption of variable returns to scale. Further, log‐linear regression model has been used to explain the variation in productivity and its components with respect to certain factors internal to the firms. Findings - In spite of a strong agricultural base and being the third largest producer of food products in the world, India's food processing industry is far from tapping its full potential as a result of a low rate of technological progress/regress on the one hand and increasing inefficiencies of the firms on the other hand. It is necessary to encourage imports along with R&D to ensure faster technological progress in the Indian food industry. However, the technological possibilities depend on the mode of organization and various economic and institutional factors. Therefore, bold institutional changes are to be made side‐by‐side in order that inefficiency is substantially reduced. Originality/value - The present study evaluates the contribution of technological change, technical efficiency change and scale efficiency change to total factor productivity growth in the Indian food processing industry by using the firm‐level data, collected from the Centre for Monitoring Indian Economy (CMIE). It further examines the impact of some common factors internal to the firms on their performance.

Suggested Citation

  • Mukesh Kumar & Partha Basu, 2008. "Perspectives of productivity growth in Indian food industry: a data envelopment analysis," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 57(7), pages 503-522, September.
    • Handle: RePEc:eme:ijppmp:v:57:y:2008:i:7:p:503-522
    DOI: 10.1108/17410400810904001
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    Citations

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    Cited by:

    1. Zulfiqar, Salyha, 2012. "Analyzing the Input Output Relationship of Small and Medium Enterprises in Pakistan: An Econometric Approach," MPRA Paper 50069, University Library of Munich, Germany.
    2. Eucabeth Majiwa & Boon L. Lee & Clevo Wilson & Hidemichi Fujii & Shunsuke Managi, 2018. "A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya’s rice processing industry," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 631-648, June.
    3. Kapelko, Magdalena & Oude Lansink, Alfons & Stefanou, Spiro E., 2015. "Analyzing the impact of investment spikes on dynamic productivity growth," Omega, Elsevier, vol. 54(C), pages 116-124.
    4. Kang, Hye-Jung & Choi, Ji-Hyeon, 2013. "What Drives the Productivity Change of the Korean Food Processing Industry?: A Stochastic Frontier Analysis Using Firm-Level Panel Data, 2001-2009," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 36(1), pages 1-20, May.
    5. Gaurvendra Singh & Yash Daultani & Rajendra Sahu, 2022. "Investigating the barriers to growth in the Indian food processing sector," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 441-459, June.
    6. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.
    7. Yasemin Keskin Benli & Suleyman Degirmen, 2013. "The Application of Data Envelopment Analysis Based Malmquist Total Factor Productivity Index: Empirical Evidence in Turkish Banking Sector," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 60(2), pages 139-159, April.
    8. Rupesh Kumar & Rajat Agrawal & Vinay Sharma, 2013. "e-Applications in Indian Agri-Food Supply Chain: Relationship among Enablers," Global Business Review, International Management Institute, vol. 14(4), pages 711-727, December.
    9. Apostolos Christopoulos & Ioannis Dokas & Sofia Katsimardou & Eleftherios Spyromitros, 2022. "The Malmquist Productivity measure for UK-listed firms in the aftermath of the global financial crisis," Operational Research, Springer, vol. 22(2), pages 1617-1634, April.
    10. Magdalena Kapelko & Alfons Oude Lansink, 2015. "An international comparison of productivity change in the textile and clothing industry: a bootstrapped Malmquist index approach," Empirical Economics, Springer, vol. 48(4), pages 1499-1523, June.
    11. Muhammad AFZAL & Maryam AYAZ, 2013. "Efficiency of Food Sector of Pakistan-A Dea Analysis," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 3(10), pages 1310-1330, October.
    12. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang & Kuo-Ren Lou, 2019. "Measuring Technical Efficiency and Returns to Scale in Taiwan’s Baking Industry―A Case Study of the 85 °C Company," Sustainability, MDPI, vol. 11(5), pages 1-14, February.
    13. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang, 2019. "Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    14. Nithyashree, M L, 2021. "The role of the macro environment and firm-specific characteristics in improving the resource use efficiency of the Indian food processing industry," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 34(Conferenc), October.

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