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Adoption and Impact of Improved Agricultural Technologies on Rural Poverty

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Economic Growth and Development in Ethiopia

Abstract

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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Notes

  1. 1.

    The outcome variable here is log of households’ income.

  2. 2.

    Birr is the official currency of Ethiopia.

  3. 3.

    What would have happened to participating units if they had not participated?

  4. 4.

    D is the treatment variable and Y for the outcome variable.

  5. 5.

    For a detailed description of the methods see Blundell and Dias (2000).

  6. 6.

    See Caliendo and Kopeinig (2008) for some practical guidance in the implementation of propensity score matching.

  7. 7.

    An alternative estimation method is the two-step procedure (see Maddala 1983: 224, for details). However, this method is less efficient than FIML, it requires some adjustments to derive consistent standard errors (Maddala 1983: 225), and it shows poor performance in case of high multicollinearity between the covariates of the selection Eq. 2.6 and the covariates of the regression Eqs. 2.7a and 2.7b.

  8. 8.

    A further discussion on this can be found in Hentschel and Lanjouw (1996), Blundell and Preston (1998) and Donaldson (1992).

  9. 9.

    The default is logit, but probit could also be used by specification.

  10. 10.

    P0 P1, P2 stands for headcount index, poverty gap index and severity index respectively of the FGT values.

  11. 11.

    The ‘movestay’ command of Stata was used to estimate the endogenous switching regression model by FIML (Lokshin and Sajaia 2004).

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Authors and Affiliations

  1. Department of Economics, Addis Ababa University, Addis Ababa, Ethiopia

    Tsegaye Mulugeta Habtewold

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  1. Tsegaye Mulugeta Habtewold
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Correspondence to Tsegaye Mulugeta Habtewold .

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Editors and Affiliations

  1. Department of Economics, Sogang University, Seoul, Korea (Republic of)

    Almas Heshmati

  2. Department of Economics, Sogang University, Seoul, Korea (Republic of)

    Haeyeon Yoon

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Habtewold, T.M. (2018). Adoption and Impact of Improved Agricultural Technologies on Rural Poverty. In: Heshmati, A., Yoon, H. (eds) Economic Growth and Development in Ethiopia. Perspectives on Development in the Middle East and North Africa (MENA) Region. Springer, Singapore. https://doi.org/10.1007/978-981-10-8126-2_2

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