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Production Efficiency in Indian Agriculture: An Assessment of the Post Green Evolution Years

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  • Subhash C. Ray

    (University of Connecticut)

  • Arpita Ghose

    (Jadavpur University)

Abstract

In this paper we use the nonparametric approach of Data Envelopment Analysis (DEA) to obtain Pareto-Koopmans measures of technical efficiency of individual states in India over the years 1970-71 through 2000-01 in a multi-output, multi-input model of agricultural production. The Pareto-Koopmans measure is a complete measure of efficiency that reflects all unrealized potential for increasing \textit{any} output and decreasing \textit{any} input that the firm has failed to exploit. In our empirical analysis, we disaggregate overall efficiency into two distinct components representing output and input efficiencies and identify the contributions of individual outputs and inputs to the measured level of overall efficiency. Because introduction of modern inputs has been a major component of the process of modernization of Indian agriculture, we examine to what extent different states succeeded in utilizing the modern inputs compared to the traditional inputs. Finally, we use regression analysis to explain variations in efficiency across states in terms of differences in various infra-structural, institutional, and demographic factors.

Suggested Citation

  • Subhash C. Ray & Arpita Ghose, 2010. "Production Efficiency in Indian Agriculture: An Assessment of the Post Green Evolution Years," Working papers 2010-26, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2010-26
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    2. Agnes Gold & Stefan Gold, 2019. "Drivers of Farm Efficiency and Their Potential for Development in a Changing Agricultural Setting in Kerala, India," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 31(4), pages 855-880, September.
    3. Bhushan, S., 2016. "TFP Growth of Wheat and Paddy in Post-Green Revolution Era in India: Parametric and Non-Parametric Analysis," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 29(1).
    4. Subhash C. Ray, 2014. "Data Envelopment Analysis: An Overview," Working papers 2014-33, University of Connecticut, Department of Economics.
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    7. Dahlan, Hadi Akbar, 2021. "Trends and Food Technology Gap in Global Food Policy," SocArXiv 7r8sm, Center for Open Science.
    8. Veronese da Silva, Aline & Costa, Marcelo Azevedo & Lopes-Ahn, Ana Lúcia, 2022. "Accounting multiple environmental variables in DEA energy transmission benchmarking modelling: The 2019 Brazilian case," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    9. Singh, Sanjeet, 2016. "Evaluation of world’s largest social welfare scheme: An assessment using non-parametric approach," Evaluation and Program Planning, Elsevier, vol. 57(C), pages 16-29.
    10. Liu, Yansui & Zou, Lilin & Wang, Yongsheng, 2020. "Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years," Land Use Policy, Elsevier, vol. 97(C).
    11. Juo, Jia-Ching & Fu, Tsu-Tan & Yu, Ming-Miin & Lin, Yu-Hui, 2016. "Non-radial profit performance: An application to Taiwanese banks," Omega, Elsevier, vol. 65(C), pages 111-121.
    12. Jiale Yan & Zhengyuan Tang & Yinuo Guan & Mingjian Xie & Yongjian Huang, 2023. "Analysis of Measurement, Regional Differences, Convergence and Dynamic Evolutionary Trends of the Green Production Level in Chinese Agriculture," Agriculture, MDPI, vol. 13(10), pages 1-18, October.
    13. Lin Liu & Honggang Sun, 2019. "The Impact of Collective Forestland Tenure Reform on the Forest Economic Efficiency of Farmers in Zhejiang Province," Sustainability, MDPI, vol. 11(8), pages 1-15, April.
    14. Li, Nan & Jiang, Yuqing & Mu, Hailin & Yu, Zhixin, 2018. "Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA)," Energy, Elsevier, vol. 164(C), pages 1145-1160.
    15. Subhash C. Ray, 2018. "Data Envelopment Analysis with Alternative Returns to Scale," Working papers 2018-20, University of Connecticut, Department of Economics.
    16. Sanjeet Singh & Prabhat Ranjan, 2018. "Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education," Annals of Operations Research, Springer, vol. 269(1), pages 641-666, October.
    17. Bingfei Bao & Anli Jiang & Shengtian Jin & Rui Zhang, 2021. "The Evolution and Influencing Factors of Total Factor Productivity of Grain Production Environment: Evidence from Poyang Lake Basin, China," Land, MDPI, vol. 10(6), pages 1-21, June.
    18. Ghulam, Yaseen & Jaffry, Shabbar, 2015. "Efficiency and productivity of the cement industry: Pakistani experience of deregulation and privatisation," Omega, Elsevier, vol. 54(C), pages 101-115.
    19. Nodin, Mohd Norazmi & Mustafa, Zainol & Hussain, Saiful Izzuan, 2022. "Assessing rice production efficiency for food security policy planning in Malaysia: A non-parametric bootstrap data envelopment analysis approach," Food Policy, Elsevier, vol. 107(C).
    20. West, Steele, 2021. "The Estimation of Farm Business Inefficiency in the Presence of Debt Repayment," 2021 Conference, August 17-31, 2021, Virtual 315048, International Association of Agricultural Economists.

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    More about this item

    Keywords

    Data Envelopment Analysis; Pareto-Koopmans Efficiency; Modern and traditional inputs.;
    All these keywords.

    JEL classification:

    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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