Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The counterfactual is a condition in which the same household is observed under treatment and without treatment. In reality a household can only be observed under either of the two conditions at a time and not under both.
References
Ali, A., & Abdulai, A. (2010). The adoption of genetically modified cotton and poverty reduction in Pakistan. Journal of Agricultural Economics, 61(1), 175–192.
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.
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.
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.
David, C. C., & Otsuka, K. (Eds.). (1994). Modern rice technology and income distribution in Asia. International Rice Research Institute
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.
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
Evenson, R., & Gollin, D. (2003). Assessing the impact of the green revolution: 1960 to 2000. Science, 300(2), 758–762.
Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–766.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–476.
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.
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.
Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.
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.
Lee, M. J. (2005). Micro-econometrics for policy, program and treatment effects. Advanced Texts in Econometrics, Oxford University Press.
Mendola, M. (2007). Agricultural technology adoption and poverty reduction: A propensity score matching analysis for rural Bangladesh. Food Policy, 32(3), 372–393.
Minten, B., & Barrett, C. B. (2008). Agricultural technology, productivity, and poverty in Madagascar. World Development, 36(5), 797–822.
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.
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, Australia
Rangarajan, C. (2014). Report of the expert group to review the methodology for measurement of poverty. Government of India Planning Commission. Available at http://planningcommission.nic.in/reports/genrep/pov_rep0707.pdf
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.
Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.
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.
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.
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.
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this chapter
Cite this chapter
Sahu, S.K., Das, S. (2016). Impact of Agricultural Related Technology Adoption on Poverty: A Study of Select Households in Rural India. In: Siddharthan, N., Narayanan, K. (eds) Technology. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-10-1684-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-1684-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1683-7
Online ISBN: 978-981-10-1684-4
eBook Packages: Business and ManagementBusiness and Management (R0)