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This paper evaluates the impact of adopting improved agricultural technologies (high yielding varieties, HYVs) on rural household welfare measured by consumption expenditure and poverty indices in two regions of rural Ethiopia (Amhara and Tigray) and 51 rural villages based on data drawn from the World Bank (2010). It applies two potential program evaluation techniques (propensity score matching, PSM, and endogenous switching regression, ESR). The analysis reveals that adoption of improved agricultural technologies has a robust, significant and positive impact on per capita consumption expenditure and a negative impact on the poverty status of households. The overall average gain in per capita consumption expenditure ranges from Birr 582.67 to Birr 606.69 annually. The estimated impact on poverty reduction as measured by the headcount index ranges from 6.7 to 8.3% points. The findings also indicate that this reduces the depth and severity of poverty. The estimated effect on reducing the depth of poverty is in the range of 0.5–0.6% points and it decreases inequality (severity) of poverty by about 0.1% points. This suggests the need for continued and broad public and private investments in agriculture research to address vital development challenges and the need for policy support for improving extension efforts and access to seeds and market outlets that encourage adoption of improved agricultural technologies.
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Adekambi, S.A., A. Diagne, F.P. Simtowe, and G. Biaou. 2009. The impact of agricultural technology adoption on poverty: The case of, NERICA rice varieties in Benin. Paper prepared for presentation at the International Association of Agricultural Economists’ conference, Aug 16–22, Beijing, China.
Alene, A.D., V. Manyong, G. Omanya, H. Mignouna, M. Bokanga, and G. Odhiambo. 2008. Smallholder market participation under transactions costs: Maize supply and fertilizer demand in Kenya. Food Policy 33 (4): 318–328. CrossRef
Bahadur, K.L., and B. Siegfried. 2004. Technology adoption and household food security. Analyzing factors determining technology adoption and impact of project intervention: A case of smallholder peasants in Nepal. Paper prepared for presentation at the Deutscher Tropentag, 5–7 Oct, Humboldt University, Berlin.
Balagtas, V., J.Y. Coulibaly, M. Jabbar, and A. Negassa. 2007. Dairy market participation with endogenous livestock ownership: Evidence from cˆote d’ivoire. AAEC annual meeting, Portland, Oregon TN.
Becerril, J., and A. Abdulai. 2010. The impact of improved maize varieties on poverty in Mexico: A propensity score-matching approach. World Development 38 (7): 1024–1035. CrossRef
Becker, S.O. 2009. Methods to estimate causal effects theory and applications. U Stirling, Ifo, CESifo and IZA last update: 21 Aug 2009. Stirling Management School, UK.
Becker, S.O., and A. Ichino. 2002. Estimation of average treatment effects based on propensity scores. The Stata Journal 2 (4): 358–377.
Blundell, R., and I. Preston. 1998. Consumption inequality and income uncertainty. Quarterly Journal of Economics 113: 603–640. CrossRef
Blundell, R., and M. Costa-Dias. 2000. Evaluation methods for non-experimental Data. Fiscal Studies 21 (4): 427–468. CrossRef
Bwalya, R., J. Mugisha, and T. Hyuha. 2013. Transaction costs and smallholder household access to maize markets in Zambia. Journal of Development and Agricultural Economics 5 (9): 328–336. CrossRef
Caliendo, M., and S. Kopeinig. 2008. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys 22 (1): 31–72. CrossRef
Datt, G., and M. Ravallion. 1996. How important to India’s poor is the sectoral composition of growth? World Bank Economic Review 10 (1): 1–26. CrossRef
Di Falco, S., M. Veronesi, and M. Yesuf. 2011. Does adaptation to climate change provide food security? A micro-perspective from Ethiopia. American Journal of Agricultural Economics 93 (3): 829–846. CrossRef
Doagostino, R.B. 1998. Tutorial in biostatistics propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Department of Public Health Sciences, Winston-Salem, USA.
Donaldson, D. 1992. On the aggregation of money measures of well-being in applied welfare economics. Journal of Agricultural and Resource Economics 17: 88–102.
Duclos, J.-Y., and A. Araar. 2010. Poverty and equity: Measurement, policy and estimation with DAD. Economic studies in inequality, social exclusion and well-being. Berlin: Springer.
Esquivel, G., and A. Huerta-Pineda. 2006. Remittances and poverty in Mexico: A propensity score matching approach. Unpublished.
Faltermeier, L., and A. Abdulai. 2006. The adoption of water conservation and intensification technologies and farm income: A propensity score analysis for rice farmers in Northern Ghana. Unpublished.
Feder, G., R.E. Just, and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries. Chicago Journal, Economic Development and Cultural Change 33 (2): 255–298. CrossRef
Foster, J., J. Greer, and E. Thorbecke. 1984. A class of decomposable poverty measures. Econometrica 52 (3): 761–766. CrossRef
Gertler, P.J., S. Martinez, P. Premand, L.B. Rawlings, and C.M.J. Vermeersch. 2011. Impact evaluation in practice. Available at: http://www.worldbank.org/.
Hailemariam, T., M. Alemu, G. Köhlin, and S. Di Falco. 2016. Does adoption of multiple climate-smart practices improve farmers’ climate resilience? Empirical evidence from the Nile Basin of Ethiopia. Discussion Paper Series, August.
Haughton, J., and S.R. Khandker. 2009. Handbook on poverty and inequality. Washington DC: The World Bank.
Hausman, J.A. 1978. Specification tests in econometrics. Econometrica 46: 1251–1272. CrossRef
Heckman, J., H. Ichimura, J. Smith, and P. Todd. 1998. Characterizing selection bias using experimental data. Econometrica 66 (5): 1017–1098. CrossRef
Hentschel, J. and P. Lanjouw. 1996. Constructing an indicator of consumption for the analysis of poverty: Principles and illustrations with reference to Ecuador. Working Paper No. 124, Living Standards Measurement Study. Washington, DC: The World Bank.
Hossain, M. 1989. Green revolution in Bangladesh: Impact on growth and distribution of income. Dhaka: University Press Ltd.
Hundie, B., and A. Admassie. 2016. Potential impacts of yield-increasing crop technologies on productivity and poverty in two districts of Ethiopia. Unpublished.
Jung, S. 2014. Does education affect risk aversion?: Evidence from the British education reform. Thema Working Paper, Université de Cergy Pontoise, France.
Kassie, M., B. Shiferaw, and G. Muricho. 2011. Agricultural technology, crop income, and poverty alleviation in Uganda. World Development 39 (10): 1784–1795. CrossRef
Kassie, M., B. Shiferaw, and G. Muricho. 2010. Adoption and impact of improved groundnut varieties on rural poverty. Evidence from rural Uganda. Discussion Paper Series, May.
Kelsey, J. 2011. Market inefficiencies and the adoption of agricultural technologies in developing countries. Unpublished.
Lee, L.F., and R.P. Trost. 1978. Estimation of some limited dependent variable models with application to housing demand. Journal of Econometrics 8: 357–382. CrossRef
Lokshin, M., and Z. Zurab-Sajaia. 2004. Maximum likelihood estimation of endogenous switching regression models. The Stata Journal 4 (3): 282–289.
Maddala, G.S. 1983. Limited dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press. CrossRef
Maddala, G.S., and F.D. Nelson. 1975. Switching regression models with exogenous and endogenous switching. In Proceeding of the American statistical association ( Business and Economics Section), 423–426.
Mendola, M. 2003. Agricultural technology and poverty reduction: A micro-level analysis of causal effects. Development Studies Working Papers No. 179 November, University of Milan-Bicocca, Italy.
Mendola, M. 2007. Agricultural technology adoption and poverty reduction: A propensity score matching analysis for rural Bangladesh. Food Policy 32 (3): 372–393. CrossRef
MOFED (Ministry of Finance and Economic Development). 2012. Ethiopia’s progress towards eradicating poverty: An interim report on poverty analysis study (2012/13). Addis Ababa: The Federal Democratic Republic of Ethiopia.
Ravallion, M., S. Chen, and P. Sangraula. 2007. New evidence on the urbanization of global poverty. Population and Development Review 33 (4): 667–701. CrossRef
Rosenbaum, P.R. 2002. Observational Studies. New York: Springer. CrossRef
Rosenbaum, P.R., and D.B. Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician 39 (1): 33–38.
Rosenbaum, P.R., and D.B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1): 41–55. CrossRef
Sahu, S.K., and S. Das. 2015. Impact of agricultural related technology adoption on poverty: A study of select households in Rural India. Madras School of Economics Working Paper 131.
Sanchez, P.A., G.L. Denning, and G. Nziguheba. 2009. The African green revolution moves forward. Food Security 1: 37–44. CrossRef
Setotaw, F., G. Ayele, and H. Teklewold. 2003. Impact of technology on households food security in tef and wheat farming systems of Moretna Jiru woreda. Ethiopian Agricultural Research Organization (EARO), Research Report No. 48.
Shiferaw, B., M. Kassie, M. Jaleta, and C. Yirga. 2014. Adoption of improved wheat varieties and impacts on household food security in Ethiopia. Food Policy 44: 272–284. CrossRef
Simtowe, F., A. Solomon, B. Shiferaw, and L. Lipper. 2012a. Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy 37: 283–295. CrossRef
Simtowe, F., M. Kassie, S. Asfaw, B. Shiferaw, E. Monyo, and M. Siambi. 2012. Welfare effects of agricultural technology adoption: The case of improved groundnut varieties in rural Malawi. Paper prepared for presentation at the international association of agricultural economists (IAAE) Triennial Conference, 18–24 Aug, Foz do Iguaçu, Brazil.
Smith, J., and P. Todd. 2005. Does matching overcome LaLonde’s critique non-experimental estimators? Journal of Econometrics 125 (1–2): 305–353. CrossRef
Solomon, A., and S. Bekele. 2010. Agricultural technology adoption and rural poverty: Application of an endogenous switching regression for selected East African Countries. Cape Town, South Africa, September 19–23, 2010.
Solomon, A., B. Shiferaw, and F. Simtowe. 2010. Does technology adoption promote commercialization? Evidence from Chickpea Technologies in Ethiopia. Unpublished.
Solomon, A., M. Kassie, F. Simtowe, and Leslie Lipper. 2012. Poverty reduction effects of agricultural technology: A Micro-evidence from Tanzania. Unpublished.
Tesfaye, S., B. Bedada, and Y. Mesay. 2016. Impact of improved wheat technology adoption on productivity and income in Ethiopia. Wheat regional centre of excellence, Kulumsa Agricultural Research Centre, Ethiopia Department of Agricultural Economics, Pretoria University, South Africa. African Crop Science Journal 24: 127–135. CrossRef
Tsegaye, M., and H. Bekele. 2012. Impacts of adoption of improved wheat technologies on households’ food consumption in Southeastern Ethiopia. Selected poster prepared for presentation at the International Association of Agricultural Economists (IAAE) Triennial Conference, 18–24 Aug, Foz do Iguaçu, Brazil.
The World Bank. 2008. World development report 2008: Agriculture for development. Washington, DC: The World Bank.
Vance, C., and J. Geoghegan. 2004. Modeling the determinants of semi-subsistent and commercial land uses in an agricultural frontier of Southern Mexico: A switching regression approach. International Regional Science Review 27 (3): 326–347. CrossRef
Winter-Nelson, A., and A. Temu. 2005. Impacts of prices and transactions costs on input usage in a liberalizing economy: Evidence from Tanzanian coffee growers. Agricultural Economics 33 (3): 243–253. CrossRef
Wu, H., S. Ding, S. Pandey, and D. Tao. 2010. Assessing the impact of agricultural technology adoption on farmers’ well-being in Rural China. Asian Economic Journal 24 (2): 141–160. CrossRef
- Adoption and Impact of Improved Agricultural Technologies on Rural Poverty
Tsegaye Mulugeta Habtewold
- Springer Singapore
- Chapter 2
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