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Impact assessment of push-pull technology on incomes, productivity and poverty among smallholder households in Eastern Uganda

Author

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  • Chepchirchir, R.
  • Macharia, I.
  • Murage, A.W.
  • Midega, C.A.O.
  • Khan, Z.R.

Abstract

The paper evaluates the impact of adoption of push-pull technology (PPT) on household welfare in terms of productivity, incomes and poverty status measured through per capita food consumption in eastern Uganda. Cross sectional survey data was collected from 560 households in four districts in the region: Busia, Tororo, Bugiri and Pallisa, in November and December 2014. Tobit model was used to determine the intensity of adoption of the technology whereas generalized propensity scores (GPS) was applied to estimate the dose-response function (DRF) relating intensity of adoption and household welfare. Results revealed that with increased intensity of PPT adoption, probability of being poor declines through increased yield, incomes, and per capita food consumption. With an increase in the area allocated to PPT from 0.025 to 1 acre, average maize yield increases from 27 kgs to 1,400 kgs, average household income increases from 135 USD (UGX 370,000) to 273 USD (UGX 750,000) and per capita food consumption increases from 15 USD (UGX 40,000) to 27 USD (UGX 75,000). The average probability of being poor declines from 48% to 28%: This implies that increased investment on PPT dissemination and expansion is essential for poverty reduction among smallholder farmers.

Suggested Citation

  • Chepchirchir, R. & Macharia, I. & Murage, A.W. & Midega, C.A.O. & Khan, Z.R., 2016. "Impact assessment of push-pull technology on incomes, productivity and poverty among smallholder households in Eastern Uganda," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246316, African Association of Agricultural Economists (AAAE).
  • Handle: RePEc:ags:aaae16:246316
    DOI: 10.22004/ag.econ.246316
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    References listed on IDEAS

    as
    1. Mulubrhan Amare & Solomon Asfaw & Bekele Shiferaw, 2012. "Welfare impacts of maize–pigeonpea intensification in Tanzania," Agricultural Economics, International Association of Agricultural Economists, vol. 43(1), pages 27-43, January.
    2. Magingxa, Litha Light & Kamara, Abdul B., 2003. "Institutional Perspectives Of Enhancing Smallholder Market Access In South Africa," 2003 Annual Conference, October 2-3, 2003, Pretoria, South Africa 19077, Agricultural Economics Association of South Africa (AEASA).
    3. Ssewanyana, Sarah N. & Kasirye, Ibrahim, 2010. "Food security in Uganda: a dilemma to achieving the millennium development goal," Research Series 113614, Economic Policy Research Centre (EPRC).
    4. Guardabascio, Barbara & Ventura, Marco, 2013. "Estimating the dose-response function through the GLM approach," MPRA Paper 45013, University Library of Munich, Germany, revised 13 Mar 2013.
    5. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    6. Michael Burton & Dan Rigby & Trevor Young, 2003. "Modelling the adoption of organic horticultural technology in the UK using Duration Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(1), pages 29-54, March.
    7. Ferto, Imre & Forgacs, Csaba, 2009. "The Choice Between Conventional And Organic Farming. A Hungarian Example," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 5(5-6), pages 1-4.
    8. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    9. Timothy G. Conley & Christopher R. Taber, 2011. "Inference with "Difference in Differences" with a Small Number of Policy Changes," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 113-125, February.
    10. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    11. Liu, Jing & Florax, Raymond, 2014. "The Effectiveness of International Aid: A Generalized Propensity Score Analysis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169804, Agricultural and Applied Economics Association.
    12. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    13. Adeleke Oluwole Salami & Abdul Kamara & Zuzana Brixiova, 2010. "Working Paper 105 - Smallholder Agriculture in East Africa: Trends, Constraints and Opportunities," Working Paper Series 242, African Development Bank.
    14. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    15. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    16. Kassie, Menale & Shiferaw, Bekele & Muricho, Geoffrey, 2011. "Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda," World Development, Elsevier, vol. 39(10), pages 1784-1795.
    17. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    18. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LLC, vol. 8(3), pages 354-373, September.
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