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Managing Environmental Risk in Presence of Climate Change: The Role of Adaptation in the Nile Basin of Ethiopia

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Abstract

This study investigates the impact of climate change adaptation on farm households’ downside risk exposure in the Nile Basin of Ethiopia. The analysis relies on a moment-based specification of the stochastic production function. We use an empirical strategy that accounts for the heterogeneity in the decision on whether to adapt or not, and for unobservable characteristics of farmers and their farm. We find that past adaptation to climate change (i) reduces current downside risk exposure, and so the risk of crop failure; (ii) would have been more beneficial to the non-adapters if they adapted, in terms of reduction in downside risk exposure; and (iii) is a successful risk management strategy that makes the adapters more resilient to climatic conditions.

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Notes

  1. It can be argued that if the production conditions become too challenging, farmers may see less of a scope for action (i.e., prospects are too gloomy) and be forced out of agriculture and migrate. However, this possibility (along with other non-crop related strategies) has not been observed in the sample used in our study.

  2. There are other very relevant studies addressing similar issues in different countries or at a different scale. The interested reader is referred to Mendelsohn et al. (1994), Gbetibouo and Hassan (2005), Seo and Mendelsohn (2008b), Hassan and Nhemachena (2008), Kurukulasuriya and Mendelsohn (2008), and Seo et al. (2009).

  3. To our knowledge there has not been a follow up survey yet.

  4. For complete information on the survey, please refer to IFPRI (2010).

  5. Note that the cross-section and plot level nature of the data does not allow an analysis of the dynamic aspects of farm-level management decisions. Panel data would be required to explore such issues. To our knowledge, there is no climate change survey where the same household has been interviewed in different point in time.

  6. We employed the OECD/EU conversion factor in the literature in developing countries, where adult female and child labor are converted into the adult male labor equivalent with the conversion factors 0.8 and 0.3, respectively.

  7. We thank a reviewer for emphasizing this aspect.

  8. Although a total of 48 annual crops were grown in the basin, the first five major annual crops (teff, maize, wheat, barley, and beans) cover 65 % of the plots. These are also the crops that constitute the staple foods of the local diet and are relevant in the context of self-subsistence farming. It should be also noted that including the other crops (e.g., perennials) would have implication for the specification of the production technology represented by the production function. We therefore limit the estimation to these primary, annual, crops.

  9. A more comprehensive model of climate change adaptation is provided by Mendelsohn (2000).

  10. Note that the production function can be estimated by OLS without making any normality assumptions regarding the error distribution. Indeed, if the errors were normally distributed, by construction the distribution would be symmetric, and the third central moment would be zero.

  11. It should be noted that the use of averages is conventional in this strand of literature (e.g., Mendelsohn et al. 1994; Deressa and Hassan 2009). Recently, however, a more precise agronomic measure of heat stress has been suggested: degree days. This is a piecewise-linear function of temperature captured by two variable degree days 10–30 \(^{\circ } {C}\) (Schlenker and Lobell 2010). The appropriate calculation of these requires a large amount of daily weather observations. Unfortunately, we do not have access to such detailed information.

  12. This does not provide information on the role of adaptation on farmer’s welfare under uncertainty.

  13. For notational simplicity, the covariance matrix \(\varvec{\Sigma }\) does not reflect the clustering implemented in the empirical analysis.

  14. The two-step procedure (see Maddala 1983, p. 224 for details) not only it is less efficient than FIML but it also requires some adjustments to derive consistent standard errors (Maddala 1983, p. 225), and it poorly performs in case of high multicollinearity between the covariates of the selection equation (1) and the covariates of the skewness equations (4a) and (4b) (Hartman 1991; Nelson 1984; Nawata 1994).

  15. We recognise that it is possible that the error terms of the switching regression model are correlated among the nearby geographical areas. As rightly pointed out by one of the reviewers, this may arise for several reasons. First, interpolation methods were applied to create spatial climate data sets. This procedure may introduce correlation in the errors. Unobserved soil characteristics are also spatially correlated. Therefore, standard errors should be adjusted for the spatial dependence in the residuals. However, we do not have the information on the distance between plots to adjust the standard errors for spatial dependence and we account for the correlation among plots within the same woreda by clustering the standard errors. Future research should account also for spatial dependence.

  16. We use the “movestay” command of STATA to estimate the endogenous switching regression model by FIML (Lokshin and Sajaia 2004). We rescaled and divided the skewness by 10 milliards to address convergence issues in the FIML estimation. Dividing a number by a constant does not affect the results.

  17. We refer the reader to Di Falco et al. (2011) for a discussion of the factors affecting the production functions of the adapters and non-adapters.

  18. Di Falco et al. (2011) use current weather as a proxy for climate (while we use climatic variables such as past rainfall and mean temperature), and they do not find an effect of weather on adaptation.

  19. We also have estimated models without the crop dummy variables. Results are robust, and available upon request.

  20. The exception is the variable “seeds” which displays some weak statistical significance of the positive portion of \(U\)-shape behaviour. The marginal impact is, however, negligible.

  21. See Di Falco et al. (2011). This paper investigated the potential endogeneity of access to credit. Testing procedure rejected this hypothesis at the 1 % statistical level.

  22. Note that BH\(_{2}\) is negative in Table 3 because it is calculated as the difference between (c) minus (d). However, it is positive if interpreted as (d) minus (c).

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Authors and Affiliations

  1. Department of Economics, University of Geneva, Geneva, Switzerland

    Salvatore Di Falco

  2. Grantham Research Institute on Climate Change and the Environment, London School of Economics, London, UK

    Salvatore Di Falco

  3. Department of Economics, University of Verona, Verona, Italy

    Marcella Veronesi

  4. Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland

    Marcella Veronesi

Authors
  1. Salvatore Di Falco
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  2. Marcella Veronesi
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Correspondence to Salvatore Di Falco.

Additional information

Previous versions of this paper have been presented at the 2012 Nordic Economic Development conference, the 2012 annual meeting of the Environment for Development Initiative, the 2011 EAERE (European Association of Environmental and Resource Economics) meeting, the 2011 AES (Agricultural Economics Society) Meeting, at the 2011 EAAE (European Association of Agricultural Economics), and at the 2010 Ascona Workshop on “Environmental Decisions: Risks and Uncertainties.” We would like to thank session participants for suggestions. We also would like to thank the three anonymous reviewers for their comments and suggestions. All remaining errors and omissions are our own responsibility. Funding from SIDA through the Environment for Development Initiative is gratefully acknowledged.

Appendix

Appendix

Table 4 Variables definition
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Table 5 Parameter estimates: test on the validity of the selection instruments
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Table 6 Parameters estimates of production function (2)
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Di Falco, S., Veronesi, M. Managing Environmental Risk in Presence of Climate Change: The Role of Adaptation in the Nile Basin of Ethiopia. Environ Resource Econ 57, 553–577 (2014). https://doi.org/10.1007/s10640-013-9696-1

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