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Agricultural Technology Adoption and Rural Poverty: Application of an Endogenous Switching Regression for Selected East African Countries


  • Asfaw, Solomon
  • Shiferaw, Bekele A.


Achieving agricultural growth and development and thereby improving rural household welfare will require increased efforts to provide yield enhancing and natural resources conserving technologies. Agricultural research and technological improvements are therefore crucial to increase agricultural productivity and thereby reduce poverty. However evaluation of the impact of these technologies on rural household welfare have been very limited by lack of appropriate methods and most of previous research has therefore failed to move beyond estimating economic surplus and return to research investment. This paper evaluates the potential impact of adoption of modern agricultural technologies on rural household welfare measured by crop income and consumption expenditure in rural Ethiopia and Tanzania. The study utilizes cross-sectional farm household level data collected in 2007 from a randomly selected sample of 1313 households (700 in Ethiopia and 613 in Tanzania). We estimate the casual impact of technology adoption by utilizing endogenous switching regression and propensity score matching methods to assess results robustness. This helps us estimate the true welfare effect of technology adoption by controlling for the role of selection problem on production and adoption decisions. Our analysis reveals that adoption of improved agricultural technologies has a significant positive impact on crop income although the impact on consumption expenditure is mixed. This confirms the potential direct role of technology adoption on improving rural household welfare, as higher incomes from improved technology translate into lower income poverty.

Suggested Citation

  • Asfaw, Solomon & Shiferaw, Bekele A., 2010. "Agricultural Technology Adoption and Rural Poverty: Application of an Endogenous Switching Regression for Selected East African Countries," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 97049, African Association of Agricultural Economists (AAAE);Agricultural Economics Association of South Africa (AEASA).
  • Handle: RePEc:ags:aaae10:97049

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    References listed on IDEAS

    1. Arega Alene & V. Manyong, 2007. "The effects of education on agricultural productivity under traditional and improved technology in northern Nigeria: an endogenous switching regression analysis," Empirical Economics, Springer, vol. 32(1), pages 141-159, April.
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    3. Nelson, Forrest D., 1984. "Efficiency of the two-step estimator for models with endogenous sample selection," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 181-196.
    4. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    5. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
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    Cited by:

    1. Hailu, Getu & Weersink, Alfons & Minten, Bart J., 2015. "Rural Organizations, Agricultural Technologies and Production Efficiency of Teff in Ethiopia," 2015 Conference, August 9-14, 2015, Milan, Italy 211702, International Association of Agricultural Economists.
    2. repec:eee:wodepe:v:4:y:2016:i:c:p:19-23 is not listed on IDEAS
    3. El-Shater, Tamer & Yigezu, Yigezu A. & Mugera, Amin & Piggin, Colin & Haddad, Atef & Khalil, Yaseen & Loss, Stephen & Aw-Hassan, Aden, 2015. "Livelihoods Effects of Zero Tillage among Small and Medium Holder Farmers in the Developing World," 89th Annual Conference, April 13-15, 2015, Warwick University, Coventry, UK 204303, Agricultural Economics Society.

    More about this item


    rural household welfare; technology adoption; propensity score matching; endogenous switching; Ethiopia; Tanzania; Food Security and Poverty; Research and Development/Tech Change/Emerging Technologies; C13; C15; O32; O38;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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