Technology pp 141-156 | Cite as

Impact of Agricultural Related Technology Adoption on Poverty: A Study of Select Households in Rural India

  • Santosh K. SahuEmail author
  • Sukanya Das
Part of the India Studies in Business and Economics book series (ISBE)


This paper applies a program evaluation technique to assess the causal effect of adoption of agricultural related technologies on consumption expenditure and poverty measured by headcount, poverty gap and poverty severity indices. The paper is based on a cross-sectional household level data collected in 2014 from a sample of 270 households in rural India. Sensitivity analysis is conducted to test the robustness of the propensity score based results using the “rbounds test” and the mean absolute standardized bias between adopters and non-adopters. The analysis reveals robust, positive and significant impacts of agricultural related technologies adoption on per capita consumption expenditure and on poverty reduction for the sample households in rural India.


Agriculture related technology adoption Propensity score matching Poverty Odisha India 

JEL Classification

C13 C15 O32 O38 



We would like to thank the participants of the workshop on “Harnessing Technology for Challenging Inequality” at Tata Institute of Social Sciences, Mumbai jointly organized with Forum for Global Knowledge Sharing. We gratefully acknowledge Prof. K. Narayanan and Prof. N.S. Siddharthan for comments and suggestions in the earlier draft of this paper. We are grateful to MSSRF-APM Project for the funding support of the sub-project on PDHED at MSE Chennai. We gratefully acknowledge inputs from Prof. U. Sankar, Prof. R.N. Bhattacharyya, Prof. K.R. Shanmugam, and Dr. A. Nambi for the insightful comments and suggestions on the project output. We also grateful acknowledge the respondents for their active participation during primary data collection.


  1. Ali, A., & Abdulai, A. (2010). The adoption of genetically modified cotton and poverty reduction in Pakistan. Journal of Agricultural Economics, 61(1), 175–192.CrossRefGoogle Scholar
  2. Becerril, J., & Abdulai, A. (2010). The impact of improved maize varieties on poverty in Mexico: A propensity score-matching approach. World Development, 38(7), 1024–1035.CrossRefGoogle Scholar
  3. Bellon, M. R., Adato, M., Becerril, J., & Mindek, D. (2006). Poor farmers’ perceived benefits from different types of maize germplasm: The case of creolization in lowland tropical Mexico. World Development, 34(1), 113–129.CrossRefGoogle Scholar
  4. Binswanger, H. P., & Von Braun, J. (1991). Technological change and commercialization in agriculture: The effect on the poor. The World Bank Research Observer, 6(1), 57–80.CrossRefGoogle Scholar
  5. David, C. C., & Otsuka, K. (Eds.). (1994). Modern rice technology and income distribution in Asia. International Rice Research InstituteGoogle Scholar
  6. De Janvry, A., & Sadoulet, E. (2002). World poverty and the role of agricultural technology: Direct and indirect effects. Journal of Development Studies, 38(4), 1–26.CrossRefGoogle Scholar
  7. De Janvry, A., Graff, G., Sadoulet, E., & Zilberman, D. (2001). Technological change in agriculture and poverty reduction. Concept paper for the WDR on Poverty and Development Google Scholar
  8. Evenson, R., & Gollin, D. (2003). Assessing the impact of the green revolution: 1960 to 2000. Science, 300(2), 758–762.CrossRefGoogle Scholar
  9. Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–766.CrossRefGoogle Scholar
  10. Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–476.CrossRefGoogle Scholar
  11. Just, R. E., & Zilberman, D. (1988). The effects of agricultural development policies on income distribution and technological change in agriculture. Journal of Development Economics, 28(2), 193–216.CrossRefGoogle Scholar
  12. Karanja, D. D., Renkow, M., & Crawford, E. W. (2003). Welfare effects of maize technologies in marginal and high potential regions of Kenya. Agricultural Economics, 29(3), 331–341.CrossRefGoogle Scholar
  13. Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.CrossRefGoogle Scholar
  14. Kijima, Y., Otsuka, K., & Sserunkuuma, D. (2008). Assessing the impact of NERICA on income and poverty in central and western Uganda. Agricultural Economics, 38(3), 327–337.CrossRefGoogle Scholar
  15. Lee, M. J. (2005). Micro-econometrics for policy, program and treatment effects. Advanced Texts in Econometrics, Oxford University Press.Google Scholar
  16. Mendola, M. (2007). Agricultural technology adoption and poverty reduction: A propensity score matching analysis for rural Bangladesh. Food Policy, 32(3), 372–393.CrossRefGoogle Scholar
  17. Minten, B., & Barrett, C. B. (2008). Agricultural technology, productivity, and poverty in Madagascar. World Development, 36(5), 797–822.CrossRefGoogle Scholar
  18. Moyo, S., Norton, G. W., Alwang, J., Rhinehart, I., & Demo, M. C. (2007). Peanut research and poverty reduction: Impacts of variety improvement to control peanut viruses in Uganda. American Journal of Agricultural Economics, 89(2), 448–460.CrossRefGoogle Scholar
  19. Mwabu, G., Mwangi, W., & Nyangito, H. (2006). Does adoption of improved maize varieties reduce poverty? Evidence from Kenya. Paper presented at the International Association of Agricultural Economists Conference, Gold Coast, AustraliaGoogle Scholar
  20. Rangarajan, C. (2014). Report of the expert group to review the methodology for measurement of poverty. Government of India Planning Commission. Available at
  21. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39(1), 33–38.Google Scholar
  22. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.CrossRefGoogle Scholar
  23. Sianesi, B. (2004). An evaluation of the Swedish system of active labour market programmes in the 1990s. Review of Economics and Statistics, 86(1), 133–155.CrossRefGoogle Scholar
  24. Winters, P., De Janvry, A., Saudolet, E., & Stamoulis, K. (1998). The role of agriculture in economic development: Visible and invisible surplus transfers. Journal of Development Studies, 345, 71–97.CrossRefGoogle Scholar
  25. Wu, H., Ding, S., Pandey, S., & Tao, D. (2010). Assessing the impact of agricultural technology adoption on farmers’ well-being using propensity score matching analysis in Rural China. Asian Economic Journal, 24(2), 141–160.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Madras School of EconomicsChennaiIndia
  2. 2.Department of Policy StudiesTeri UniversityNew DelhiIndia

Personalised recommendations