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The Synergies and Trade-Offs of Planned Adaptation in Agriculture: a General Equilibrium Analysis for Ethiopia

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Abstract

Adaptation costs and finance are topical issues in many developing countries. In this study, we apply a computable general equilibrium (CGE) model to examine the economy-wide and regional effects of planned adaptation aimed at maintaining the current level of agricultural productivity in the face of climate change in Ethiopia. We derived the direct costs for planned adaptation in agriculture with respect to three alternative scenarios of climate change-induced productivity shocks of −5%, −10%, and − 15% under three adaptation policy effectiveness scenarios. The results show that such adaptation may require incremental public budget equivalent to 25–100% of the current public expenditure for adaptation relevant measures in agriculture. This will increase urban households’ welfare by 1% to 5% due to the incremental demand for skilled labor types. It will however reduce the government saving by 33% to 173%, and pull factors of production from the private sector which eventually decreases manufacturing output (by 2% to 10%), other private services (by 3% to 13%) and GDP of urbanized regions (by 0.2% to 3%). Such trade-offs may strain the current macroeconomic endeavors of the country, e.g. the aim to reduce fiscal deficits and the effort to foster structural transformation driven by public investment. Policies that promote urban and commercial agriculture may help to reduce the country’s reliance on rain-fed smallholder agriculture (and hence the need for planned adaptation) while fostering structural transformation.

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Notes

  1. Adaptation gap can be defined as “the difference between the level of adaptation actually implemented and a societally set target or goal, which reflects nationally determined needs related to climate change impacts, as well as resource limitations and competing priorities” (UNEP 2016).

  2. See Watkiss and Cimato (2016), Watkiss (2015), and ECONADAPT (2015) for a comprehensive review of adaptation economics literature. There are also a lot of studies focusing on adaptation at the city scale (see e.g. Hallegatte and Corfee-Morlot, 2011; Hirte et al., 2018).

  3. Likewise, some studies (e.g. Nozawa et al. 2018; Wing and Fisher-Vanden 2013; references in Agrawala et al. 2011b) apply theoretical frameworks to examine the relationship between adaptation and mitigation.

  4. Such public activities fall in public administration account in the original SAM (UNDESA 2008; MoFED 2005; EDRI 2009). Thus, we split the original public administration account into two - 80% (public administration, general) and 20% (public administration, agriculture) – on the basis of information from different policy documents on rural feeder roads (e.g. NBE 2016), on irrigation (e.g. MoWE 2001), agricultural R&D and extension services (e.g. Lanos et al. 2014; ReSAKSS 2014), and total public budget for agriculture and natural resources (MoARD 2010; MoFED 2014, 2015).

  5. The new public service account corresponds to agriculture, natural resources, and roads in national budget accounts (e.g., MoFED 2014, 2015). We found the amount to be reasonable compared with the total annual budget for agriculture and rural development which is estimated to be about 15% of total government spending and 5% of GDP (MoARD 2010).

  6. See Yalew et al. (2017) and the Appendix for further discussion.

  7. CGE models are able to consider second-best efficiencies, i.e., increasing a specific tax rate may increase or decrease excess burden, if any, due to other tax types in the model (Burfisher 2011).

  8. See Appendix Fig. 3 in the Appendix for an illustration.

  9. We have, for example, provided the sensitivity of households’ welfare in the Appendix.

  10. Urbanized regions include Addis Ababa, Dire Dawa, and Harari in which more than 50% of their population lives in urban areas.

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Acknowledgments

A.W.Y received a doctoral fellowship (2014-2017), which provided the basis for writing this article, from the Dresden Leibniz Graduate School and Leibniz Institute of Ecological Urban and Regional Development.

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Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 601203, 14412, Potsdam, Germany

    Amsalu Woldie Yalew & Hermann Lotze-Campen

  2. Institute of Transport and Economics, Technische Universität Dresden, 01062, Dresden, Germany

    Georg Hirte & Stefan Tscharaktschiew

  3. Department of Agricultural Economics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Hermann Lotze-Campen

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  1. Amsalu Woldie Yalew
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Correspondence to Amsalu Woldie Yalew.

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Appendix

Appendix

Adaptation Policy Effectiveness

We used the elasticity of agricultural productivity with respect to (w.r.t) public spending to capture the effectiveness of adaptation. However, elasticities from the literature are commonly available per measure (see Appendix Table 4 below). We hardly find previous studies to interpolate directly the elasticity of agricultural productivity w.r.t public budget to our composite adaptation measure.

Table 4 Elasticities of agricultural productivity w.r.t public spending in different measures 
Full size table

Therefore, to arrive at the average elasticity (i.e., w.r.t the composite measure), we first generated thousands of random variables (or elasticities) between the minimum and maximum elasticities w.r.t each measure. Then, we took the average of the four random elasticities (η), which is also a random variable, to represent the elasticity of agricultural productivity w.r.t a unit spending in the composite adaptation measure. The overall procedure yields average elasticities of 0.05 (minimum), of 0.2 (mean), and of 0.35 (maximum).

Regional Module

The main purpose of the regional module is to depict the economic structure of different regional states of Ethiopia. Appendix Fig. 3 below, as an example, illustrates the case for Addis Ababa (ADD) and Amhara (AMH) against the case for Ethiopia (ETH). Yalew et al. (2018) provides the details of the procedures we followed to construct the regional module used in this paper.

Fig. 3
figure 3

Shares of different sectors in total value-added GDP of Addis Ababa, Amhara, and Ethiopia. Note: AGRAIN- Grain crops, ACCROP- Cash crops, AENSET- Enset crop, ALIVST- Livestock, AFISFOR- Fish & forest, AMINQ-Mining & quarrying, ACONS-Construction, AMAN-Manufacturing, ATSER-Wholesale & retail trade, AHSER-Hotels & restaurants, ATRNCOM-Transport & communications, AFSER-Financial intermediaries, ARSER- Real estate & renting, APADMN- Public administration (general), APAGRI-Public administration (agriculture), ASSER- Social services, AOSER- Other services, and TOTAL- Total GDP at factor cost. Source: Yalew et al. (2018, 2017).

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Sensitivity Results

In general, the CGE results are robust to ±25% of different elasticities used in the calibration process. Appendix Table 5 below illustrates the sensitivity results of selected simulations on households’ welfare.

Table 5 Sensitivity of households’ welfare effects
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Yalew, A.W., Hirte, G., Lotze-Campen, H. et al. The Synergies and Trade-Offs of Planned Adaptation in Agriculture: a General Equilibrium Analysis for Ethiopia. EconDisCliCha 3, 213–233 (2019). https://doi.org/10.1007/s41885-019-00041-3

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