The effects of climate change on African agricultural productivity growth revisited


This paper analyzes the effects of climate change on African agricultural total factor productivity (TFP) growth and test whether agricultural TFP levels are converging in the region. The study uses cross-country balanced panel data covering 35 countries from 1981 to 2010 and a technological catching-up model based on the Ricardian analysis estimated by Feasible Generalized Least Square (FGLS) regression. Historical country-wide rainfall and temperature are climate factors included in the model. Education, capital intensity, and arable land equipped with irrigation are other potential confounding variables in the regression. The empirical results show that levels of African agricultural TFP are converging over time, though the rate of convergence appears relatively slow in the region. We also find that rain significantly increases agricultural TFP growth, but temperature does not affect the study’s African agricultural TFP growth. Other results show that education, capital intensity, and arable land equipped with irrigation significantly increased agricultural TFP growth.

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Data availability

The data will be made available upon request from the lead author.


  1. 1.

    Term climate change in this paper refers to a change in the mean of temperature and rainfall for an extended period.

  2. 2.

    Although many studies have attempted to explore the effects of climate change on agricultural production or productivity for individual African countries or micro-level analysis (see Amare et al. 2018; Ochieng et al. 2016), the present study focuses on African context as a whole or macro-level analysis.

  3. 3.

    The countries included in the sample are Angola, Benin, Botswana, Burkina-Faso, Burundi, Cameroon, Chad, Cote’d’ Ivoire, Ethiopia, Gabon, Guinea, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

  4. 4.

    The data that support the findings of this study are available from the corresponding author upon reasonable request.

  5. 5.

    Beta-convergence does not necessarily mean sigma-convergence, but they are complementary.

  6. 6.

    Sigma-convergence has always been estimated using the absolute convergence framework.

  7. 7.

    We also estimate the unconditional or absolute beta-convergence in the study presented in model 1 of Table 3.

  8. 8.

    We follow Barrios et al. (2010) and Chieng et al. (2016) to specify the average long-run temperature and rainfall over 5- and 10-year intervals in time t-5 and t-10 to t, respectively. Abidoye and Abidoye and Odusola (2015) also used 5-year intervals to capture climate change in their study. All of these studies focus on Africa.

  9. 9.

    A similar approach has been used by Ball et al. (2014) and Ogundari and Aromolaran (2017) for cross-state and cross-country data analyses, respectively.

  10. 10.

    The estimated unconditional or absolute beta-convergence in model 1 is similar to Eq. 1A.

  11. 11.

    Lusigi et al. (1998) is the only known cross-country study that test the convergence hypothesis in African TFP.

  12. 12.

    Using model 4 of Table 3 as an example, convergence speed is equivalent to 1.1% (i.e., 0. 0106×100).

  13. 13.

    Martin and Mitra (2001) found strong evidence of a rapid convergence in levels and growth rates of TFP in agriculture as a result of the international dissemination of innovation.


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The authors thank the anonymous reviewer for the helpful comments on the earlier version of the manuscript.

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The first author (K.O) retrieves and analyzes the data and prepares the write up for the manuscript. The second author (R.O) checks the results, proofread the document, and provides policy guidance for the write up.

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Correspondence to Kolawole Ogundari.

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Table 5 Descriptive statistics of variables used in the regression
Table 6 Correlation matrix of the variables
Table 7 IVF and condition index for the variables

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Ogundari, K., Onyeaghala, R. The effects of climate change on African agricultural productivity growth revisited. Environ Sci Pollut Res (2021).

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  • Agriculture
  • Climate change
  • Convergence
  • Total factor productivity
  • Ricardian model
  • Africa