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Dynamics of Farmers Income Growth: Regional and Sectoral Winners and Losers from Three-Time SAS Data

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  • A. Narayanamoorthy
  • Chandra, S. R.
  • Nuthalapati
  • Sujitha, K. S.
  • Suresh, R.

Abstract

An attempt is made in this paper to find out whose (state) and which (source) farm income increases in India by using SAS data of three-time points, namely 2002-03, 2012-13 and 2018-19. It shows that although the total annual income per farmer household has increased over time, significant changes have taken place in the share of different sources of income. Between 2002-03 and 2018-19, the share of wage income increased in 12 out of 18 states, while the same declined in 16 out of 18 states in crop production income. Strikingly the share of income from farming of animals has increased significantly in all the states, including the most advanced agricultural states. The analysis of the growth rate shows that among different sources of income, the income from farming of animals has registered the highest growth rate; 13 out of 18 states have registered a growth rate of over 5 per cent, which is not observed in any other source of income. Assam, J&K, WB and Jharkhand have registered a poor growth rate (less than one per cent) in the total annual of income of farmer households. The Univariate Regression Analysis shows that the factors determining each source of income are different. While the variables such as RELE (percentage of villages electrified), PIRA (percentage of irrigated area to cropped area), MECP (monthly expenditure of crop production) and ROAD (percentage of villages having pucca road) have positively and consistently determined the crop production income, RELE, HPLO (share of agricultural households possessing land less than 1.00 ha) and ROAD seem to be the important determinants of wage income. RELE, PIRA and ROAD seem to be the important determinants of the income from farming animals, while the total income of farmers is mainly determined by variables such as RELE, PIRA, MECP and ROAD.

Suggested Citation

  • A. Narayanamoorthy & Chandra, S. R. & Nuthalapati & Sujitha, K. S. & Suresh, R., 2022. "Dynamics of Farmers Income Growth: Regional and Sectoral Winners and Losers from Three-Time SAS Data," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 3), September.
  • Handle: RePEc:ags:inijae:345200
    DOI: 10.22004/ag.econ.345200
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    References listed on IDEAS

    as
    1. Narayanamoorthy, A., 2000. "Farmers' Education and Productivity of Crops: A New Approach," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 55(3), September.
    2. Bina Agarwal & Ankush Agrawal, 2017. "Do farmers really like farming? Indian farmers in transition," Oxford Development Studies, Taylor & Francis Journals, vol. 45(4), pages 460-478, October.
    3. Sitakanta Panda, 2015. "Farmer education and household agricultural income in rural India," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 42(6), pages 514-529, June.
    4. Narayanamoorthy, A. & Sujitha, .S., 2021. "Trends and Determinants of Farmer Households’ Income in India: A Comprehensive Analysis of SAS Data," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 4), December.
    5. Pratap S. Birthal & Devesh Roy & Md. Tajuddin Khan & Digvijay Singh Negi, 2015. "Farmers' Preference for Farming: Evidence From a Nationally Representative Farm Survey in India," The Developing Economies, Institute of Developing Economies, vol. 53(2), pages 122-134, June.
    6. Pratap S. Birthal & Shiv Kumar & Digvijay S. Negi & Devesh Roy, 2015. "The impacts of information on returns from farming: evidence from a nationally representative farm survey in India," Agricultural Economics, International Association of Agricultural Economists, vol. 46(4), pages 549-561, July.
    7. Narayanamoorthy, A., 2017. "Farm Income in India: Myths and Realities," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 72(1), March.
    8. Narayanamoorthy, A. & Hanjra, Munir A., 2006. "Rural Infrastructure and Agricultural Output Linkages: A Study of 256 Indian Districts," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 61(3), pages 1-16.
    9. Das, R., 2017. "Raising Farm Income in India: What Does a Simultaneous Quantile Regression Approach Tell?," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 30(Conferenc).
    10. Satyasai, K.J.S., 2016. "Farmer's Income: Trend and Strategies for Doubling," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 71(3), September.
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