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Economy-wide Effects of Climate Change in Ethiopia1

Amsalu W. Yalew

Dresden Leibniz Graduate School (DLGS)/

Leibniz Institute of Ecological and Regional Development (IOER)/

Technische Universität Dresden (TU Dresden)

Email: a.yalew@dlgs.ioer.de /amsalueth@gmail.com

Version: March 2016

Please do not quote

Abstract Climate change has turned to be a major economic threat in many developing countries. In this paper, we assess

the economy-wide effects of climate change induced productivity and labor supply shocks in agriculture in

Ethiopia. Overall, we find falling agricultural output but increasing prices for agricultural commodities. The net

effect is declining real consumption by households. The larger is the proportion of subsistence consumption

expenditure, the higher is the welfare loss due to climate change. This substantiates the argument that climate

change severely affects poor households in a given country and low-income countries in general. Therefore,

climate change and its likely impacts shall be accounted along with proactive measures in national economic

plans of the country.

Keywords: Climate Change, Agricultural Productivity, Migration, CGE Model, Ethiopia

1. Introduction The scientific discourse on climate change in the last decade has made two important conclusions. The first is

that climate change is unequivocal (IPCC, 2014). The second is that biophysical and economic impacts are

nonlinear and disproportionately affect low-income tropical countries as they heavily depend on agriculture

which is inherently sensitive to climate (IPCC, 2014; Stern, 2007; Cline, 2007; Mendelsohn et al., 2006).

Ethiopia is a typical tropical low-income country where agriculture plays a vital role in terms of

livelihood, employment, export revenue, and national income. Agriculture employs 83% of the population,

contributes to 42-45% of GDP, is the source of 80% of foreign earnings, and consists of 9 out of 10 top export

items in the country (NBE, 2015; NLFS, 2013). Factors affecting Ethiopian agriculture bear potential threats

to the macro-economy in general and households’ real consumption in particular. Climate change is one of

such factors. Quantifying the sectoral and economy-wide effects of climate change in countries like Ethiopia is

important at least in two main ways. First, it is a critical step to formulate national adaptation plans and strategies

(Hertel and Lobell, 2014; Bezabih et al., 2011). This in turn helps to mobilize resources for adaptation in

vulnerable sectors in advance. Second, it helps to examine and factor in the issue of climate change in the

present and future development plans of the country.

The present paper aims to assess the biophysical and economic effects of climate change in Ethiopia.

Yield changes projected by two Global Gridded Crop Models (GGCMs) of the Agricultural Model Inter-

comparison Project (AgMIP: www.agmip.org) are introduced as productivity shocks in grain and livestock

producing agricultural activities. Given the stylized facts of rural Ethiopian economy, we further assume that

climate change may result in movement between occupations. With dry climate change scenario, farmers may

move into elementary occupations (occupations with no specific skill requirements). In the context of the

1 Paper to be presented at International Conference on Economic Modeling (EcoMod2016), Lisbon, Portugal, July 6-8, 2016.

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Ethiopian CGE, the phenomenon is equivalent to reducing agricultural labor supply but increasing unskilled

labor supply by the same amount which we arbitrarily assumed for the lack of empirical evidence.

The CGE simulation results show that the effects on the macro-economy, sectoral output, and

households’ welfare are non-negligible. Apparently, the two activities – grain and livestock – would be the main

losers. Through competition for factors, the impacts also ripple through the rest of agricultural activities. Prices

of agricultural commodities will substantially increase. Due to the weak inter-sectoral linkage, however, the

effects do not spread towards many of industrial and services sectors. Nonetheless, impacts on real GDP may

reach up to -7.5% for agriculture is the dominant economic sector in the economy. Imports of agricultural

goods will increase while agricultural export do contract. Conversely, exports of non-agricultural goods increase

while their imports decrease. Thus, climate change may affect the trade mix of the country. The effects on

households’ welfare are immense ranging from -2% to -16% depending on the household type and scenario.

Adding labor supply shocks may worsen resource competition within agriculture. Whereas it may dampen the

negative impacts in non-agricultural activities. Our sensitivity analysis shows that the larger is the proportion of

subsistence consumption budget, the higher will be households’ welfare loss due to climate change. Thus, the

consumption structure of an economy is as important as production structure to influence the sign and size of

economic impacts of climate change.

The reminder of the paper is organized as follows. Section 2 reviews the related literature. Section 3

presents the research materials and methods. Section 4 focuses on the description of the CGE model and its

calibration. We present and analyze the results in Section 5 followed by a sensitivity analysis in Section 6. Section

7 concludes the paper.

2. Literature Review

2.1. Climate Change and Agriculture Climate change is unequivocal and disproportionately affects low-income tropical countries which heavily

depend on agriculture (IPCC, 2014; Stern, 2007; Cline, 2007; Mandelson et al., 2006). Agriculture in Ethiopia

employs more than 80% of its labor force (NLFS, 2013; 2005). Nine out of ten top export items are agricultural

items (NBE, 2015). Agriculture contributes about 45% of GDP (EDRI, 2009; NBE, 2015). However,

agriculture in Ethiopia is virtually rain-fed, less-diversified, and smallholders producing about 90% of total

agricultural production (AgSS, 2005-2015; IFPRI and CSA, 2006). These conditions make Ethiopian agriculture

and economy disposed to climate variability and climate change (Conway and Schipper, 2011). The observed

evidences in the past five decades substantiates the concern about the future.

National average temperature has increased by 1oC since the 1960s (FDRE, 2015) increasing by 0.37oC

per decade (Tadege, 2007). The number of hot days and nights in a year is increasing overtime (World Bank,

2016). On the other hand, the observed trend of mean annual rainfall is not clear (World Bank, 2016; Tadege,

2007). Despite the inter-seasonal and inter-annual rainfall variability, nationally rainfall remained more or less

constant in the second half of the twentieth century (FDRE, 2015; Cheung et al., 2008; Tadege, 2007; NMSA,

2001). In line with the meteorological evidences is that many farmers across Ethiopia perceive that increasing

temperature, decreasing and erratic rainfall in their villages in the past twenty to thirty years (Bryan et al., 2009;

Tessema et al., 2013; Kassie, 2014; Tesso et al., 2012; Hadgu et al., 2014). Farmers even perceive that the onset

of rainy season is delaying (Kassie, 2014; Hadgu et al., 2013; Tafesse et al., 2013) and soil moisture is declining

(Tesso et al., 2012). The observed meteorological evidence and farmers’ perception correspond with the

observed trends in Africa or Eastern Africa as a region (cf. IPCC, 2013: 4-12).

There are also observed impacts that can attribute to the observed climate change and variability in the

last five decades. Farmers reported that crop yields in their areas have declined over the last 20-30 years (Hadgu

et al., 2014; Tafesse et al., 2013; Teka et al., 2012). Per contra, drought is being recurrent and unpredictable

phenomenon (FDRE, 2015; Ali, 2012). For instance, 15 major droughts have stroke Ethiopia since 1950

(Robinson et al., 2013; Ali, 2012). More than half of households in the country experienced at least one major

drought shock in 1999-2004 period (Robinson et al., 2013 citing UNDP, 2007). A 10% seasonal rainfall drop

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from its long-term average implies a -4.4% food production in Ethiopia (von Braun, 1991) and -5% food

consumption in rural households (Dercon, 2004). Moreover, droughts do have long-term consequences as they

degrade assets owned by rural households (Dercon, 2004; von Braun, 1991).Droughts usually turn to be famine

which eventually increase the number of people looking for food aid (Robinson et al., 2013; Ali, 2012).

Government expenditure on food aid and emergency drought relief swung as droughts occur (Robinson et al.,

2013).Droughts and famines are also reasons for temporary and permanent migration in Ethiopia (NLFS, 2013;

Meze-Hauken, 2004; Ezra, 2001). Recent droughts toll about 1% to 4% of GDP (FDRE, 2015). The trajectory

of economic growth in Ethiopia is highly influenced by rainfall availability and variability (Ali, 2012; World

Bank, 2006). If there was no rainfall variability in 1960-2008, average Ethiopian income would have been at

least four times higher than what actually it is (Ali, 2012). These past experiences in Ethiopia reveal that adaptive

capacity in Ethiopian agriculture and economy are insufficient to cope up with environmental changes. Thus,

future climate change poses an apparent risk to Ethiopian economy.

Three main conclusions can be drawn from the existing literature on the future climate of Ethiopia.

First, projected changes in temperature and rainfall are sensitive to the assumed GHG emission and

concentration scenario (i.e. SRES) (cf. World Bank, 2010) and climate model (i.e. GCM) (cf. Adamassu et al.,

2013). Second, nevertheless, there is clear agreement that mean annual temperature will increase (Conway and

Schipper, 2011; Tadege, 2007). Even GCMs which disagree on precipitation (e.g. MIROC to increase and

CSIRO to decrease) predicts a warmer future (Admassu et al., 2013). The number of hot days and nights will

continue to rise (World Bank, 2016). Third, the changes in rainfall are uncertain (FDRE, 2015; Conway and

Schipper, 2011) which is in line with predictions on Africa continent in general and East African region in

particular (cf. IPCC, 2013). Despite ambiguous mean annual rainfall predictions, however, rainfall in the Kiremt

(Ethiopian summer or crop growing period) is most likely to decrease (World Bank, 2008; NMSA, 2001). It is

pointed that “seasonal predictions suggest significant drop in rainfall during the planting season” (World Bank,

2008:50). Evidence from northern Ethiopian substantiates the same (cf. Hadgu et al., 2014; Hadgu et al., 2013).

The combined effect of increasing temperature, increasing hot days and nights, uncertain rainfall (but likely to

decline in Kiremt) will increase the overall Vapor Pressure Deficit (VPD) which in turn leads to higher rates of

evaporation and plant transpiration (Hertel and Lobell, 2014; Admassu et al., 2013; Cline, 2007). Increasing

evapotranspiration eventually decreases soil moisture (Hertel and Lobell, 2014; Admassu et al., 2013) and

shortens the length of the growing period for crops and grasses. This pose a challenge to the Ethiopian

agriculture which virtually depends on rainfall.

Despite the importance of the subject, there exist only limited literature assessing the biophysical and

economic impacts of climate change in Ethiopia.2 Kassie (2014) reports -2% to -29% impacts on maize yields

in 2050s in the Central Rift Valley. In Ethiopian dry case scenario wheat, maize, sorghum, and barely yields may

decline by 2-5% in 2050s (World Bank, 2010). Depending on location, wheat yield may fall by 28-30% in 2070s

(NMSA, 2001). Under alternative SRES/GCM scenarios, Admassu et al. (2013) finds that wheat yield to

decrease throughout the country while maize and sorghum yields may decline only in present major growing

areas. World Bank (2008) reviewed some global and regional studies and inferred about -2.6% to - 5.8% for

wheat and -3.5% to -7.3% for maize/coarse grains in 2030. In 2050s, sugar cane yield may decline by 9%

(FDRE, 2015). Regional studies (cf. Müller et al., 2014:8; Waha et al., 2013:136) also document comparable

projections on crop yields in Ethiopia. For example, Müller et al. (2014) projects circa 20% decline in crop yield

in 2080s.3 With changing climate, soil moisture and length of growth period (LGP) changes. Thus, climate

change also affects suitability of different locations for crop cultivation. In effect, there may be net gain or loss

in areas suitable for growing crops in a country though new areas may be costly to cultivate (Admassu et al.,

2013; Nelson et al., 2010). Unfortunately, this case is not well researched in Ethiopia. Admassu et al. (2013) and

Evangelista et al. (2013) are exceptional in this regard. Admassu et al. (2013) uses DSSAT crop model and

projects net area loss in wheat, maize, and sorghum in major growing areas of Ethiopia in 2050s. Evangelista

2 We reported here only the dry scenario impacts if studies had considered different scenarios. 3 See p.8, Figure 3 (f) of the same study.

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et al. (2013) uses a Maxent Software and predicts the net area change suitable for growing: teff (-11%, -17%),

maize (-14%,-25%), sorghum (-7%,-7%), and barley (-31%,-46%) in (2020, 2050) compared to 2000.4 To

conclude, despite the difference in SRES, GCMs, time horizons (2030s, 2050s, and 2080s compared to the

present), and crop models (DSSAT, CliCRop, CERES) there exists consensus on negative (and increasing

overtime) impacts of climate change on crop yields in Ethiopia.

Climate change is expected to result in fall in productivity (World Bank, 2010; 2008; Maddison et al.,

2007), the quality of some crops (e.g. coffee) (FDRE, 2015), net farm revenue (Deressa and Hassan, 2009), and

increase in people looking for food aid due droughts (FDRE, 2015; World Bank, 2010). The production of wild

coffee may decline by 40 to 90 % in 2080s (FDRE, 2015). Crop productivity may fall by 1.3% (Maddison et al.,

2007) and by 7% (World Bank, 2008). Livestock productivity may be lower that by 30% (World Bank, 2010)

and 50% (FDRE, 2015) in 2050s compared to without climate change scenario. The net farm revenue per

hectare may fall by a range from -10% to -303 % in 2050 in the Nile Basin (Deressa and Hassan, 2009).

Agricultural GDP with climate change may be lower by 3% to 30% than without climate change agricultural

GDP in 2050 (FDRE, 2015). Climate change may increase the number of people looking for food aid by 30%

(FDRE, 2015), increase drought expenses by 72% (World Bank, 2010) in 2050s. Some economic studies went

further to feed the productivity shocks in agriculture into CGE model to assess economy-wide effects of climate

change. Crop productivity alone has a potential to induce a -6% fall in GDP in 2050 (Ferede et al., 2013), and

-10% in 2100 (Mideksa, 2010). Compared to the no climate change scenario, Ethiopian GDP with climate

change may be lower by 10% in 2050 (World Bank, 2010) and 46% in 2030 (World Bank, 2008). Ethiopian

agriculture is subsistence agriculture. Climate change dampens real household consumption (Ferede et al., 2013;

World Bank, 2008). The impacts are peculiarly sever in arid lowland production zones and worse for rural-poor

income group and (Robinson et al., 2013; Ferede et al., 2013; Arndt et al., 2011) and thus worsen income

inequality (by 20% in 2100) (Mideksa, 2010). Climate change reduces economic growth by 2.7% in 2030 and

retards structural change (World Bank, 2008). It may cut off a third of income that would have been obtained

due to economic growth with no climate change (Gebreegziabher et al., 2011).

However, the majority of the economic studies are based on elasticities of farm income with respect

to a unit percentage increase in temperature estimated through Ricardian approach (Deressa and Hassan, 2009;

Mideksa, 2010; Ferede et al., 2013; Gebreegziabher et al., 2011). The Ricardian approach is a highly criticized

approach for assuming perfect autonomous adaptation and adjustment in farming decisions and

implementations. Which is hardly possible in reality at least in Ethiopian context (cf. Wossen et al., 2015 and

references within). Therefore, it is argued that the sectoral impact estimates by Ricardian studies understate the

impacts of climate change (Adams et al., 1998). The World Bank (2010) and papers based on it (Robinson et

al., 2013; Robinson et al., 2012) are exceptional in this regard as they pursued structural approach. However,

the considered only five cereals – teff, wheat, barley, sorghum, and maize – crops. 5 Second, the World Bank

(2010) applied a dynamic CGE model thus projecting macroeconomic and industrial structure of the country

in addition to climate change and its primary impacts. The results in dynamic models are sensitive to the rate

and path of socio-economic variables overtime, and the discount rate assumed. It is hardly possible to

distinguish whether the future damages are high due to the socio-economic changes (or their uncertain

projections) or climate change (or its uncertain projection) per se (FDRE, 2015; Pielke, 2007). In addition, the

assumed socio-economic scenarios may overwhelm climate change impacts. For instance, assumed TFP growth

rate of the macro economy can substantially reduce the expected impacts of climate change (cf. Gebreegziabher

et al., 2011 for Ethiopia; Bezabih et al., 2011 for Tanzania). This may undermine the need for proactive

adaptation. Third, the World Bank (2010) gauged climate change uncertainty through GCMs while wider

uncertainty is implied by crop models (Rosenzweig et al., 2013).

4 Maxent (Maximum Entropy) model is a species distribution model (Evangelista et al., 2013). Figures reported here are an average of three GCMs of A2a SRES scenario. 5The studies modelled the productivity effects of climate change as predicted by the CliCrop (for crops) and Hybrid (for livestock). The Hybrid model partly depends on a Ricardian approach - automatic adjustment in choice of livestock species and automatic adjustment in ecosystems in response to climate change (Seo and Mendelsohn, 2008). It included the impacts on hydropower and road sectors in addition to agriculture.

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In the present study we attempt to address these gaps. We pursue structural approach and broadened

the number of impacted agricultural activities. Our method for livestock acknowledges the fact that mixed rain-

fed farming is the dominant livestock production system in Ethiopia. We couple the likely adverse effects of

climate change on agricultural sector that may further induce agricultural labor outmigration as discussed below.

2.2. Climate Change and Migration Climate has profound influence on human settlement patterns (McLeman and Smit, 2006). This is conspicuous

fact in developing countries where agriculture, fishing and tourism are the main means livelihood for the

majority of population. Thus, it is argued that the greatest impact that climate change may impose perhaps is

the displacement of millions of people (Brown, 2008 citing IPCC, 1990).6 Climate change may make some

places hardly inhabitable: by increasing incidence of climate sensitivities diseases like malaria, by making the

places hotter to live permanently, by degrading the asset base of rural households (through droughts, floods),

and by raising sea level. Climate change also exacerbates human migration by degrading the cropland, grazing

land, and water availability. Climate change also makes future rural livelihood prospects less predictable and

reliable (Brown, 2008). These all may impel rural population either to fight (for scare natural resources) or flight

from their usual surroundings (UNCCD, 2014). It may lead to dramatic rural-urban drift (Brown, 2008).7

Overall, such forced migration may increase pressure on urban infrastructure and services, undermine

economic growth, exacerbate risk of conflicts over natural resources, and disrupt the education and social life

of the migrants themselves (UNCCD, 2014; Brown, 2008).

Historically, migration has been common way of adaptation to environmental changes and natural

disasters (Burnham and Ma, 2015; Brown, 2008; McLeman and Smit, 2006). So is in Ethiopia (Ezra and Kiros,

2001). The decision to migrate may be driven by an objective of income earnings (as in neoclassical view) or to

spread out risks associated with climate change (as in new economics of labor migration) (McLeman and Smit,

2006). The decision may be made at individual or at household level. It may be temporary or permanent. Many

will remain as internal migrants (Narwotzki et al., 2015; Brown, 2008). However, the nexus between climate

change and migration (as impact or autonomous adaptation) is very complex (McLeman and Smit, 2006; Brown,

2008). Non-climate drivers – population growth, poverty, and governance – remain to be critical variables to

determine the time and scale of climate change induced migration (Brown, 2008). Mobility of human beings,

among others, are constrained by different institutional (legal) and physical (distance, cultural values), and

financial and social resources to move from one to another place. Moreover, migration as adaptation to climate

change depends on what else adaptation options are available (McLeman and Smit, 2006). With less diversified

rural economy (which offers low employment opportunities other than agriculture), and higher prevalence of

poverty (leaving households with no assets to sale); the probability of climate change induced migration is high.

Ethiopian rural economy is virtually agrarian. Nearly 90% of rural labor is employed in agriculture

(NLFS, 2013; 2005). Of the agricultural labor about 92% are full time agricultural workers (IFPRI and CSA,

2006). About 99% of agricultural labor is engaged in crop and livestock farming (NLFS, 2013) which are highly

susceptible to climate change. About 60% and 20% of annual crop produces go, respectively, to household

consumption and seeds (AgSS, 2014). More than 65% of income for consumption expenditure comes for rural

households from agricultural activities (HICES, 2000; 2005; 2011). Rural livelihood, thus, is inextricably linked

to agriculture in Ethiopia.

Climate change reduces suitable area to staple crops (Evangelista et al., 2013) and deleteriously affects

crop yields (World Bank, 2010). In contrast, population in Ethiopia is predicted to double in 2050 (FAO, 2015).

The pace of rural farm income will continue to be lower than rural population growth rate. This reduces

agricultural labor productivity which exacerbates rural-urban migration (Wondimagegnhu, 2015). Per capita

agricultural land availability dwindles with increasing population. Lack of land dissuades autonomous adaptation

6 Globally environmental migrants mounted to 25 million in mid-1990s, projected to be 50 million in 2010, and even further to rise to 200 million by

2050 (Brown, 2008 and references within). 7 To account this, Brown (2008:15) coined the term “forced climate migrant’’.

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(Burnham and Ma, 2015; Tessema et al., 2013). Taken together, climate change will affect employment in

agriculture (Narwotzki et al., 2015). In Ethiopia, the proportion of labor migration surpasses the non-labor

migration (marriage and other social reasons) in drought periods (Gray and Mueller, 2011; Ezra and Kiros,

2001). Per contra, the rural-to-urban migration is recently catching up the rural-to-rural migration which used

to be the dominant form of migration in the country for decades. Of all migrants, 35% (down from 46% in

2005) were rural-to-rural, 33% (up from 24 in 2005) were rural-to-urban migrants (NLFS, 2013). This may

partly attribute to population pressure, environmental degradation, and recurrent droughts and famines in rural

areas (Ezra and Kiros, 2001 and references within).

3. Materials and Methods Climate change in this study refers to the change in mean annual temperature and precipitation in future, i.e.,

2050s (2035-2065) compared to the present, i.e., 1990s (1980-2010). Partly the biophysical impacts are much

clearer in 2050s than the near-term periods, and partly the period is consistent with studies in the subject (cf.

World Bank, 2010; Nelson et al., 2010; Robinson et al., 2012). We consider three entry points of climate change:

crop productivity, livestock productivity, and agricultural labor outmigration. These are shocks to the CGE

model and we analyze the economy-wide effects.

3.1. Climate Change and Crop Productivity

Biophysical (or more specifically crop) models are widely used to simulate crop responses to climate change.

Simulations in crop models involve that:

“One provides the model with initial field conditions (e.g., for soil moisture and nitrogen status), crop

information (cultivar characteristics, planting arrangement, and fertilization and irrigation, if any), and the daily

weather and [CO2] data corresponding to the historic, current or future scenarios of interest; the simulation is

then run, and the outputs are compared to those of other simulations where different initial conditions,

management practices, or weather and [CO2] scenarios were used.”(White et al., 2011:357).

Variety of crop models have been used to assess the sign, magnitude, rate, and pattern of climate

change impacts on agricultural productivities for the last two decades (Rosenzweig et al. 2013; White et al.,

2011). The models basically differ in terms of their original purpose, structure, inclusion and parametrization

of soil and crop processes, management inputs and outputs (Rosenzweig et al., 2013).

The sign and size of climate change impacts on crops depend on: the projected changes of mean

temperature and precipitation, the crop variety, the location where it grows, and the crop model used. Our data

on biophysical impacts is already processed and readily available from AgMIP (Villora et al., 2014). Thus, here,

we only surface our choices not the methodological process behind them.8 We picked a climate change scenario

represented by HadGEM2-3M GCM for RCP8.5 emission scenario. Then, two impact (crop) models – LPJmL

and EPIC – are used to account uncertainty.9 While EPIC is a site-based crop model, the LPJmL is global

ecosystem based model. LPJmL and EPIC models are further different in terms of their structure, parameters,

modelling, and calibration (see Rosenzweig et al., 2013 for detail). The application of LPJmL and EPIC for

relatively larger number of crops is a plus. No carbon fertilization effect is assumed for including CO2

fertilization effects increases the uncertainties of effects (Weindl et al., 2015; Müller and Robertson, 2014;

Rosenzweig et al., 2013; White et al., 2011). In addition, accounting CO2 fertilization effects implicitly presumes

quicker adjustments in farm management practices which is not practical in most developing countries (Hertel

and Lobell, 2014; Müller and Robertson, 2014; Nelson et al., 2010). For instance, in Ethiopia there is clear

evidence that adoption of modern farm technologies and practices has been low and slow in Ethiopia (Wossen

8 We refer an interested reader to Rosenzweig et al. (2013), Müller and Robertson (2014), and Villora et al. (2014). 9 The choice is made after checking the uncertainties implied by impact models is wider than uncertainties implied either RCPs or GCMs. This has already been underlined by Rosenzweig et al. (2013). While Moss et al. (2010) pointed that climate change under different RCPs will not be visible in near-term.

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et al., 2015 and references within; AgSS, 2014). On the other hand, an elevated CO2 increases the susceptibility

of plants to pests and diseases (Müller and Robertson, 2014; Nelson et al., 2010); and it may interfere with plant

regulation of enzyme and hormone (Müller and Robertson, 2014; Hertel and Lobell, 2014). Consequently,

actual benefits of increased CO2 were found to be substantially below what is expected (Nelson et al., 2010;

Brookshire and Weaver, 2015). Likewise, there are no significant benefits foreseen from elevated CO2 for

Ethiopia (Robinson et al., 2013). The AgMIP-GGCMs simulate the impacts with full-or no-irrigation scenarios.

We picked the no-irrigation scenario for irrigation in Ethiopia is very low (AgSS, 2005-2015; Robinson et al.,

2013; Awulachew, 2010). Nor it is likely to have full irrigated agriculture in the country in the next two decades.

To sum, we take the case of SSP-2 (SSP), RCP8.5 (RCP), HadGEM2-ES (GCM), crop yields as simulated by

LPJmL and EPIC model (with no effects from elevated CO2 and with no irrigation). It, then compares, the

future (2035-2065) with the current (1980-2010) thirty years-average yields of each crops. We would like to

point here that out choice can be regarded as dry climate change (or high-end impact) scenario.

Climate change impacts on crop productivity are usually represented by changes in yield of crops. The

AgMIP-GGCMs simulate for different globally important crops at 0.5ox0.5o grid cells. Using the AgMIP Tool

at GEOSHARE (Villoria et al., 2014) we obtained the aggregate mean annual crop yields overtime for Ethiopia.

Climate change effects on crop yield (∆Yc) is computed as the average yield of the future (Yfc) climate over the

present (Ypc) climate.

∆Yc =Yf

c−Ypc

Ypc x100-------------------------------------------------- (1)

AgMIP crop models simulate the impacts for selected globally important crops only. Of these globally

important crops, some are economically less important (e.g. rice and cassava) or not directly represented in the

economic accounts (e.g. potatoes) in Ethiopia. On the other hand, Ethiopia produces a lot of crops which are

grouped into 12 broad crop activities in the 2006 Ethiopian SAM. When the crop is not directly simulated by

a crop model (s), the common practice is to infer from the effects on ‘similar’ crops (Müller and Robertson,

2014). The similarity is defined by the type of photosynthetic pathway (e.g. C3 or C4 crops); main climate zone

suitable for the crop (e.g. temperate or tropical); their susceptibility to drought damage; and the economic

valuable part of the crop (Hertel and Lobell, 2014; Müller and Robertson, 2014; Bondeau et al., 2007). Likewise,

we first map GGCM and SAM crops on the basis of their photosynthetic pathway and main climatic zone the

crops mainly grew. We, then, calculated correlation coefficient between the yields of the ‘similar’ crops using

20 years yield (tones/hectare) data from the Annual Agricultural Sample Survey (AgSS) (AgSS, 1995-2014).

Thus, for example, wheat yield projections by LPJmL can be used as proxy for barley and teff; average of

soybeans and field peas for pulses; average of groundnuts, rapeseed, and sunflower for oilseeds; and the likes.

The procedure can give us yield changes for eight crops in the SAM. These are Teff, Barley, Maize, Sorghum,

Wheat, Pulses, and Oilseeds.10

The crops accounted here covers about 84% of total crop area and 66% of all land use (AgSS, 2014).11

Next, we impose the upper (+30%) and lower (-30%) limits to yield changes to control the sensitivity of

variations and model artifacts if the crop model simulate with very low reference productivity (cf. Nelson et al.,

2010; Müller and Robertson, 2014). The capped yield change impacts are then weighted by their area of

cultivation in 2004/05 harvest season which is used in creating the 2006 SAM (EDRI, 2009). Of the total

9,811,070 hectares of land cultivated by grains the share of crops was as follows: teff (23%), barley (11%), wheat

(15%), maize (15%), sorghum (13%), pulses (14%), and oilseeds (9%) (AgSS, 2005).12 We weighted and

aggregated as “grain” activity (AGRAIN) that produce an aggregate grain commodity (CGRAIN) in the CGE

10 “Pulses” is average yield of Faba beans, Field peas, Haricot beans, Chick-peas, Lentils, Grass peas, Soya beans, and Fenugreek, Gibto. “Oilseeds” is

average yield of Neug, Linseed, Groundnuts, Sunflower, Sesame, and Rapeseed. 11 All land use includes: Crop Area, Fallow land, Grazing land, Wood land and other land uses (AgSS, 2005-2014). 12 There is only slight change in the weights in 2014 harvest season (AgSS, 2014).

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model. The procedure yields us a weighted grain yields changes -10% (LPJmL scenario) and -26% (EPIC

scenario).

3.2. Climate Change and Livestock Productivity Climate change affects the livestock farming directly and indirectly (Nardone et al., 2010; Thornton et al., 2009;

Adams et al., 1998). It directly affects mortality, morbidity, reproduction, and physiological performance of

livestock and indirectly through its effects on feed quality and quantity, water availability, livestock diseases, and

loss of biodiversity.13 The direct and indirect effects jointly influence the stock of livestock per farm (location)

and the livestock species to be reared at each farm (Nardone et al., 2010; Seo and Mendelsohn, 2008). In effect,

livestock production (livestock stock and their products like meat, egg, and milk) and productivity (the value

per unit of inputs) are influenced by climate change. However, the direction and the magnitude of the effects,

and the main channel through which climate change affects livestock production system varies across regions

and the current production systems. The indirect effects are paramount in the tropics and subtropics (Nardone

et al., 2010). Smallholders’ livestock production system is vulnerable than the industrial system (Nardone et al.,

2010; Seo and Mendelsohn, 2008).

Ethiopia is a tropical country whose livestock agriculture is virtually smallholder mixed rain-fed

production system (AgSS, 2004-2014; Gebremariam et al., 2010; ILRI, 2015).Grazing land and crop residues

contribute more than 85% of animal feed (AgSS, 2004-2014). There is still apparent lack of attention to issues

of animal health and nutrition (MoARD, 2010) and livestock breeding policies, regulations, and strategies (ILRI,

2015). Only 0.23% of animal feed was improved feed in 2004-2014(AgSS, 2014). Only 26% of the total afflicted

livestock population is treated (AgSS, 2015). Only 1% of cattle and poultry were crossbred despite the efforts

implemented for decades to improve productivity through crossbreeding (ILRI, 2015). The Ethiopian livestock

sector is also under commercialized in Ethiopia (Gebremariam et al., 2010).14Livestock production system in

Ethiopia is thus susceptible to climate change.

Unlike for crops, there is no publicly available physiological model(s) to date to assess the changes

temperature, humidity, and precipitation on livestock productivity (Weindl et al., 2015; Robinson et al., 2013).

In effect we considered the indirect effects through feed availability only. We arbitrarily assume that from all

ways through which climate change affects livestock productivity (production), thirty percent is through forage

quality and quantity. About 87% animal feed (59% from grazing and 28% crop residues) in Ethiopia (AgSS,

2004-2014) is directly affected by climate change.

∆YL = 0.3 x (0.59x∆YG + 0.28x∆YC) --------------------------------------------------- (2)

Equation 2 states that climate change induced livestock productivity (ΔYL) depends on climate change

effects on managed grassland productivity (ΔYG), and grain productivity (ΔYC). The climate change impact

scenarios and crops are as discussed in Section 3.1.15 The procedure yields us livestock productivity impacts of

-2% (LPJmL scenario) and -5% (EPIC scenario).

3.3. Climate Change and Migration Climate change related migration can be from rural areas/agricultural sector to urban areas/non-agricultural

sectors (Narwotzki et al., 2015). Such migration would reduce rural labor and hence rural/agricultural output

(Lipton, 1982). Migration also affects the labor market and hence wages in receiving regions/sectors. As such,

migration either from regions/sectors to regions/sectors will have economy-wide implications.

13 See Thornton et al. (2009:115-117) and Nardone et al. (2010:58-63) for the channels through which climate change affect livestock production system; and on how these effects influence animal health, production (meat, milk, egg), and reproduction. 14 The influence of the socio-economic conditions on livestock productivity¸ however, may improve with recent moves by the Ethiopian government to pay attention to the sector on one hand and urbanization and increasing export demand for live animals on the other hand (ILRI, 2015; Gebremariam et al., 2010; MoARD, 2010). 15 Impacts on grassland is simulated by LPJmL model for the case of EPIC is missing in our data source (Villora et al., 2014).

9

Climate change induced migration is complex as it is influenced by institutional factors (Brown, 2008).

The current regulations in Ethiopia makes a decision to migrate permanently very hard. Sale of agricultural land

is prohibited by law, people who move from their rural livelihood would lose their entitlement over their farm

lands, and proof and registration is required to new migrants (Dorosh and Schmidt, 2010). On the other hand,

Narwotzki et al. (2015) argues that the climate change effect on migration will be through employment in

agriculture. In Ethiopia, in male labor migration dominates other forms of migrations in years of droughts

(Gray and Mueller, 2011; Ezra and Kiros, 2001). Therefore, we considered here migration as movement

between occupations which is equivalent to sectoral migration in Ethiopian CGE context. The Ethiopia SAM

(EDRI, 2009) specified agricultural labor as a specific labor category. It is employed only in crop, livestock, and

forestry and fishery activities. Unskilled labor is another labor category which is employed in elementary

occupations – occupations that do not require specific skill – of manufacturing and services. Again, as per SAM

recordings, unskilled labor is not employed in agriculture. In other words, with climate change farmers

(agricultural labors) may be forced to give up working in agriculture and move to work in non-agricultural

sectors. However, as they do not have specific skills they will remain in the unskilled labor category. Our

approach requires less information with regard to the specific activities that will receive (employs) the migrants.

The migration can temporary or permanent. Agricultural labor is now released from working on crop and

livestock farming, and forestry and fishery. It can still stay in rural areas but only to work in fetching water for

own household consumption, cottage industries like weaving, tanning, grain milling, and the likes.

Due to lack of empirical evidence, we arbitrary assume that climate change may induce ‘migration’ of

0.5 million laborers under mild (LPJmL) scenario and about 1 million in high (EPIC) scenario. Though arbitrary,

it is within empirical ranges. Of the 3.9 million recent migrants, about 0.15 million are due to displacements

caused by droughts (and conflicts) and land scarcity, around 1 million migrants are motivated by ‘search for

jobs’ (NLFS, 2013). Nearly a third of total migrants are rural-urban migrants (NLFS, 2013; 2005).

3.4. Modeling into the CGE model We left with a critical question of modeling the primary shocks in agriculture (crop productivity, livestock

productivity, and labor outmigration) into an economic model. The exercise involves mapping the primary

effects with exogenous parameters or variables of the CGE model.

How productivity effects of climate change are modeled is critical. Other things remaining constant,

climate change is regarded as exogenous technological change leading to change in total factor productivity

(TFP) or individual factor productivity (PFP) in agriculture (Robinson et al., 2014; Frontier Economics, 2008;

Adams et al., 1998). Both approaches are pursued in the empirical literature. For instance, Bosello et al. (2013),

Bezabih et al. (2011), and some global models (e.g. GTEM, MAGNET) (Robinson et al., 2014) have modeled

yield changes as shocks to land productivity: Whereas Wiebelt et al. (2015) and Robinson et al. (2012) modeled

it as shocks to total factor productivity (or scale) parameter in the CGE model. We followed the latter approach

as the policy implication of partial productivity changes are not clear (Benin et al., 2011; Zapeda et al., 2001).

We shocked the shift (efficiency) parameter of the value-added component of grain (AGRAIN) and livestock

(ALIVST) production activities of the calibrated CGE model. As of the 2005/6 Ethiopian SAM, these

aggregated production activities account 40 of the 65 detailed agricultural activities, 67% agricultural GDP, and

32% of national GDP measured at factor cost (EDRI, 2009).

In the CGE model, labor supply of each skill category is fixed at initial level. Economy-wide wage rate

is set to clear labor market. All labor skill categories are assumed to be mobile across activities which employ

them as indicated in the SAM. The initial labor supply (QFS) of agricultural labor was about 26 million while

that of unskilled workers was about 3 million.

Table 1: Description of the Simulations

Simulation Description of the experiment

PRD_L LPJmL scenario productivity effects

PRD_E EPIC scenario productivity effects

10

MIG_L LPJmL scenario migration effects

MIG_E EPIC scenario migration effects

PRD_M_L LPJmL scenario combined effects (PRD_L + MIG_L)

PRD_M_L EPI scenario combined effects (PRD_E + MIG_E)

For convenience, one can consider LPJmL impacts as ‘mild’ dry scenario and EPIC impacts as ‘high’

dry scenario impacts.

4. Description and Calibration of the CGE Model We use the standard IFPRI CGE Model (Lofgren et al., 2002).16 All markets are assumed to be competitive

and hence all agents are price takers. Endogenous prices clear factor and commodity markets. A producer is

engaged with an economic activity which produces one or more commodities. The aggregate final demand for

commodities comes from households, activities (for intermediate inputs), government, investment, and rest of

the world (RoW). Ethiopia is a small-open economy with no influence on the world price of its imports and

exports. Imports are imperfect substitutes to domestic varieties. Likewise, there exists imperfect transformation

between exports and domestic sales.

The decision of a producer at every step is dictated by profit maximization goal given the production

technology and market prices for outputs, factors, and intermediate inputs. Every producer faces nested

production technology. At the top of the nest, producers combine composite primary factors (aggregate valued-

added) with aggregate intermediate inputs at fixed ratio (i.e. Leontief technology). The aggregate value-added is

specified by Constant Elasticity of Substitution (CES) function over land, labor, and capital. The intermediate

inputs are aggregated by Leontief function. An activity can produce one or more commodities. Commodities

can either be consumed at home or marketed. Marketed commodities from different producers are aggregated

using CES function. This aggregated output (QX) of a commodity can be sold at domestic (QDS) or foreign

market (QE) as specified by the Constant Elasticity of Transformation (CET) function.

Households maximize the Stone-Geary utility subject to their budget constraint resulting in a Linear

Expenditure System (LES) demand system. Households consumption include home commodities (valued at

producer price), and market commodities (valued at demand prices). Households’ consumption comprises

domestic and imported goods. How much to consume of domestic and imported variety is dictated by a CES

function as in the Armington tradition.

Households, Enterprises, Government, and RoW are the four institutions of the model. Households

receive income from factors, and transfers from other institutions. Households make payments: direct taxes,

transfer to rest of the world, and save. The leftover income is spent on consumption. The transaction pattern

of enterprises is very similar to that of households expect the former do not consume. Government collects

direct and sales taxes, and import tariffs. It also receives income from factors owned by public enterprises.

Government transfers to households and to the rest of the world. It spends on public services – public

administration, education, and health services. Investment demands investment goods. Investment is financed

through savings from households, enterprises, governments, and rest of the world. The rest of the world

demand represents foreign demand for Ethiopian exports. In addition to payments to Ethiopian exports, RoW

makes transfer to Ethiopian government and households. However, it collects debt repayments, payments for

imports, and transfers made by households.

The model is calibrated to the 2005/06 Social Accounting Matrix (SAM) of Ethiopia. The SAM is

constructed by the Ethiopian Development Research Institute (EDRI, 2009). The aggregated SAM used here

consists of 16 production activities of which 5 are agriculture, 5 are industrial, and 6 are services. There are 5

agricultural, 6 industrial, and 6 services commodities making 17 total commodities. There are five labor

16 The standard IFPRI CGE model is primarily meant for developing economies. The model is also consistent with the SAM compared to alternative

models. The full list of equations is presented in Annex C.

11

categories which are agricultural labor (FLAB0), administrative workers (FLAB1), professional and technical

workers (FLAB2), unskilled workers (FLAB3), and skilled workers (FLAB4). The other factors are land

(FLND), livestock (FCAP1), and non-agricultural capital (FCAP2). There are three household groups based on

their location: rural households (RURLH), small urban households (SURBH), and big urban households

(BURBH). Three are three broad tax categories – direct taxes (DTAX), commodity taxes (STAX), and import

tariffs (MTAX). There are six other accounts in the SAM: Trade and Transport Margins (TRC), Enterprises

(ENT), Government (GOV), Rest of the World (RoW), Saving-Investment (S-I), and Stock (Inventory)

changes (DSTK). The full list and description the SAM are given in Annex A.

The share and shift parameters of the model depend on the SAM and functional forms used in the

model. Income and trade elasticities, the Frisch parameter17, and factor substitution of factors are drawn from

the empirical literature elsewhere. We draw the physical unit employment statistics from surveys used for

constructing the SAM (EDRI, 2009). Accordingly, observed employment in each activity and total factor supply

are based on National Labor Survey (NLFS, 2005) for labor; from Annual Agricultural Sample Survey (AgSS,

2005) for land and Tropical Livestock Units (TLU).18 Land is employed only in crop agriculture whereas TLU

is used only in livestock activity (EDRI, 2009).

All factor supplies are fixed at observed level and fully employed. Economy-wide wage (WF) clears the

factor market if the factor is mobile across activities: Activity-specific wage distortion (WFDIST) clears the

factor market if the factor is activity-specific. Labor is assumed to be mobile across activities. Whereas land,

TLU, non-agricultural capital are activity specific. The Consumer Price Index (CPI) is the numeraire of the

model. The macro closures combination is Johansen type. In the public sector balance, tax rates and real

government consumption are fixed. Government saving is the residual that maintains the balance between

government expenditure and revenue. Real investment is fixed at initial level, and thus, savings will adjust to

investment to keep the savings-investment (S-I) balance. In the external balance closure, foreign saving is fixed

but exchange rate is flexible. This combination of the macro closures is highly recommend for single-period

models aimed at analyzing households’ welfare effects of a policy or exogenous changes (Lofgren et al., 2002;

Hosoe et al., 2010).

5. Results and Discussion In the subsequent sections we present and discuss the results on macroeconomic variables, sectoral output, and

households’ welfare. The results show that economy-wide effects are proportional to the agricultural

productivity shocks.

5.1. Macroeconomic Effects The macroeconomic effects of an exogenous or policy change are analyzed through macroeconomic variables.

It is customary to focus on GDP, aggregate private consumption, exports, imports, government budget surplus,

and exchange rate. Figure 1 depicts the macroeconomic consequences of different climate change scenarios.

17 Frisch parameter measures the proportion of a household expenditure that should be kept for subsistence consumption. The higher is the absolute value of Frisch parameter, the higher is the share of subsistence in total consumption budget. The richer is the household group, the lower is the budget share of subsistence consumption. Alternatively, the higher is the marginal propensity to save, the lower is the minimum (floor) consumption budget

share. 18 1TLU=1*(Camels) +0.7*(Cattles) + 0.1*(Sheep) + 0.1*(Goats) + 0.01*(Poultry)

12

Source: CGE Simulation

Prices of agricultural commodities increase while those of non-agricultural commodities decline

(unreported here). This makes domestic markets attractive for agricultural commodities compared to industrial

goods. Hence, agricultural commodities export decline while export of industrial commodities increase.

Conversely, agricultural commodities imports increase whereas industrial commodities imports decrease.

Exchange rates (EXR) - expressed as local currency to foreign currency appreciate. In effect, exports are

adversely affected higher than imports. In sum, trade (import and export) mix of the country will be affected.

Part of the trends are explained because of the macro-closure. The external balance foreign savings (FSAV) is

fixed. As other forms of international transactions (transfers to and from RoW) are fixed, trade balance is also

fixed in the model (Lofgren et al., 2002). Hence, the fall in net exports of some commodities shall be

compensated by increasing net exports from other commodities.

Agricultural production in Ethiopia is mainly for consumption. Consequently, the aggregate private

(PRVCON) is a highly impacted macroeconomic component. Of course, the impacts on private consumption

partly depends on the macro-closures. In the Johansen closure, the highest saving adjustment is levied on

households (Lofgren, 2001; Lofgren et al., 2002). This influences households’ consumption as spending on

consumption is residual of transfers and taxes.

The impacts on total absorption (ABSORP – total spending on domestic consumption) are slightly

lower than that of GDP. This implies the role imports to smoothen impacts (Robinson et al., 2013; Arndt et

al., 2011). Government saving (GSAV) is the only macro variable with positive change. This should be

interpreted with caution, however. GSAV is only residual of government revenue and government expenditure.

Positive GSAV shows here partly that the government expenditure has declined higher than government

revenue. Partly, another macroeconomic saving, i.e., foreign savings are constant, part of the savings adjustment

(towards fixed investment) will be met by public savings.

5.2. Sectoral Effects Macroeconomic effects, however, represent aggregate impacts. We further need to investigate the effects on

different activities of the economy. Productivity and labor supply shocks in agriculture can spread into other

sectors in three main channels. The first is through reallocation of land, labor, and capital to more rewarding

sectors. Such effects depend on the type and extent of factors of production that agricultural and non-

agricultural activities compete for. The second is through the backward and forward interlinkage between

agriculture and the rest of sectors. That is through the size of agricultural commodities used as intermediate

inputs in industrial and services production and vice versa. The third way is through households’ demand for

non-agricultural goods as relative price of agricultural commodities to price of non-agricultural goods changes.

Apparently such effects depend on own price and cross price elasticities, and the budget share of different

commodities in consumption basket.

-12

-9

-6

-3

0

3

6

9

12

15

18

21

24

ABSORP PRVCON EXPORTS IMPORTS GDPMP GSAV EXR

Figure 1:Macroeconomic effects of climate change in Ethiopia (% changes)

PRD_L PRD_E MIG_L MIG_E PRD_MIG_L PRD_MIG_E

13

Figures 2 to 4 depict the climate change effects on different sectors of the economy. The productivity

shocks discussed in Section 3 are introduced into grain producing activity (AGRAIN), and livestock producing

(ALIVST) activities. Consequently, the impacts in the two activities are higher. However, the impacts also ripple

through the rest of agricultural activities – vegetables, fruits, and cash crop (ACCROP), enset (AENSET), and

forestry and fishing activities (AFISFOR). This is due to the fact that these resources compete for primary

factors (agricultural labor and land especially), and intermediate inputs.

Source: CGE Simulation

The indirect effects on industrial and services activities are not visible as such. Only with increasing

productivity shock, negative impacts may be felt in some industrial and services activities which use agricultural

commodities as inputs. The list includes, Private Services (APVTS – which includes hotels and restaurants and

trade business), Primary Manufacturing activities (APMAN-which includes food processing industries), and

construction (ACONS- and which uses commodities from forests and grain activities).19

Source: CGE Simulation

Effects on secondary manufacturing industries is exceptional as it exhibits visible positive changes.

This may be partly because of the fact that export demand shall be meet from industrial activities, to maintain

the trade balance, as exports of agricultural commodities are severely affected. And, partly with decreasing

imports of the same commodity category, this activity has to meet the domestic demand.

19 In Ethiopia it is common to use grain straw with muds in rural huts and even peri-urban areas houses.

-27

-24

-21

-18

-15

-12

-9

-6

-3

0

AGRAIN ACCROP AENSET ALIVST AFISFOR

Figure 2: Climate Change Effects on Agricultural GDP at factor cost (%)

PRD_L PRD_E MIG_L MIG_E PRD_MIG_L PRD_MIG_E

-3

0

3

6

9

12

15

AMINQ ACONS APMAN ASMAN AUTL

Figure 3: Climate Change Effects on Industrial GDP at factor cost (%)

PRD_L PRD_E MIG_L MIG_E PRD_MIG_L PRD_MIG_E

14

Source: CGE Simulation

The fact that the indirect effects to non-agricultural activities are low confirms the weak inter-sectoral

linkages in the Ethiopian economy. The Ethiopian agriculture is factor intensive many of which (i.e., agricultural

labor, cropland, and TLU) are employed only in agriculture. The use of modern agricultural (intermediate)

inputs which would have been supplied by non-agricultural sectors is low. Fertilizer is entirely imported while

seeds are produced and used by the agricultural activities themselves. On the supply side, the majority of what

is agricultural production is consumed by the rural households themselves. In other words, only a fraction of

agricultural products enter into the market. Therefore, the impacts would be contained rather being transferred

to the rest of sectors through the supply and prices of market commodities. However, it is important to note

that total GDP is highly shaped by impacts on agricultural GDP.

From figures 1 to 4, one can see that impacts due to migration are not comparable with productivity

shocks. This attributes to the agricultural labor surplus in Ethiopia. Likewise, Wondimagegnhu (2015) finds

marginal product of labor of temporary migrant sending households to remain positive despite fall in

agricultural labor supply. The migration scenario, however, reduces the range of impacts across activities with

the agricultural sector. Furthermore, the indirect effects on non-agricultural activities will cease with migration.

Exogenous labor supply of unskilled labor will reduce wage rates and hence costs of production in non-

agricultural sectors. This compensates the indirect effects accruing to productivity shocks.

5.3. Households’ Welfare Effects Climate change can impact household welfare through income (i.e. through changes in factor incomes) and on

the expenditure side (i.e. changes commodity prices) (Wiebelt et al., 2015). CGE models are appreciated for

having a mechanism to assess the effects of exogenous or policy changes on human welfare (Burfisher, 2011;

Piermartini and Teh, 2005). Equivalent Variation (EV) is widely used measure of the changes in consumer’s

wellbeing. EV compares “the costs of pre- and post-shock levels of consumer utility, both valued at base year

prices’’ (Burfisher, 2011:97). A positive EV implies a welfare gain due to the new policy/shock: a negative EV

indicates welfare loss.

-3

0

3

6

APVTS ATRNCOM APADMN APAGRI AEDUC AHEAL

Figure 4: Climate Change Effects on Services GDP at factor cost (%)

PRD_L PRD_E MIG_L MIG_E PRD_MIG_L PRD_MIG_E

15

Source: CGE Simulation

Agriculture is the main supplier of food in Ethiopia. As one would expect, the impacts on consumption

and hence welfare are paramount (see Figure 1 also). Small urban households seem to lose more from climate

change. Rural households are producers but also consumer of their own commodities. Their home

consumption is valued at producer price compared to urban households who pay demand prices. Demand

prices include commodity taxes and trade margins on the top of producer prices. Households in big urban areas

are least affected compared to the other two groups partly due to relatively lower share of food consumption

expenditure and partly due to better access to imported foods and commodities. The fact that changes in

agriculture production directly affects consumption has also be underlined in Arndt et al. (2011) and Dercon

(2004). This attributes to the fact that the majority of agricultural production goes to rural households’

consumption (AgSS, 2005; 2014).

6. Sensitivity Analysis Results from CGE simulations are highly influenced by the functional forms, macro and micro closures, and

parameters (elasticities) used in calibration process. Therefore, sensitivity analysis is important to test the

robustness of the results. In addition, comparing the responses to changes in a specific parameter(s) can be

considered as experiments with important policy implications (Burfisher, 2011).

We run three alternative sensitivity scenarios to test the robustness of our results (See Annex B). In

the first sensitivity scenario, we replaced the Leontief specification at the top of production technology nest of

all activities by CES function. With CES aggregation at the top of the production technology, the ratio aggregate

value-added to aggregate intermediate input is no more fixed. In addition, land is allowed to be mobile across

activities increasing competition for agricultural factors as now both labor and land are mobile. Under this

sensitivity experiment, effects (output as well as prices) will spread to other agricultural activities more than the

base scenarios. In addition to being test of robustness, this would show us whether production flexibility could

dampen the negative effects of climate change. This, however, did not happen in our case. The impacts are

even slightly higher. The impacts in other sectors of the economy is indeterminate – it reduces for some while

it worsen for the others.

In the second test we altered the Frisch parameter. In the base scenarios, we used the Frisch parameter

of (-2) for rural and small urban households but (-1.5) for big urban households. In the sensitivity scenarios,

we set the values to be (-1) for all household groups. Many of the negative impacts from macro to household

level dampens with this experiment. This tells us that climate change impacts will be higher in societies or

household groups where consumption is mainly for subsistence. Further investigation into our results reveals

this. The response in EV of urban households is much higher than that of rural households. This is because

rural households own consumption of agricultural commodities is higher than marketed consumption

-18

-15

-12

-9

-6

-3

0

RURLH SURBH BURBH TOTAL

Figure 5: Climate Change Effects on Households' Welfare (% EV)

PRD_L PRD_E MIG_L MIG_E PRD_MIG_L PRD_MIG_E

16

expenditure. Households’ consumption from own production apparently constitutes subsistence consumption.

This also goes with previous studies (cf. Robinson et al., 2012; Arndt et al., 2011) which find climate change to

severely affect poor households. The impacts on the poor are not only due to lack of factor ownership, but,

also because of poor’s spending is more on subsistence consumption.

Thirdly, we rerun our experiment with ‘balanced’ macroeconomic closure rule. Under these closures,

the real investment and government consumption are flexed. Instead, the shares of investment and government

in total absorption are fixed. Any change in total absorption would, in nominal terms, be spread evenly across

government, investment, and households’ consumption according to their base share (Lofgren et al., 2002;

Lofgren, 2001). In our balanced macro-closure, both real government (thus GSAV declines) and real

investment consumptions increase. Thus, the savings adjustment burden of the households’ is higher now as

households’ consumption expenditure is what is left after from savings, taxes and other forms of transfers.

Compared to the Johansen closure, in the balanced closure, thus, private household slightly worsen further.

Other macroeconomic variables show improvement. As government consumption increases, output from

public services increases. Construction (ACONS) (as construction goods are investment goods) followed by

Mineral and quarrying (AMINQ) (as construction activities are the main user of mineral goods) clearly increases.

Whereas private services decline with balance closure. In the households’ welfare section, the rural households’

welfare slightly worsens whereas that of urban households dampened slightly.

In conclusion, our sensitivity analysis shows that the consumption structure of the economy is also

important in determining the sign and size of impacts. Effects on the agricultural, and rural households are

stable under alternative sensitivity tests compared to others. Again as in the case of the base simulations, the

aggregate welfare effects follow the effects on the rural households and aggregate output follows the effects on

agricultural output. Thus, it can be argued that our conclusions hold under alternative sensitivity analysis.

7. Conclusions and policy implications Under dry climate change scenario, crop and livestock productivities decline which in turn may trigger

outmigration of farmers from agricultural activities. We assessed the economy-wide consequences of these

primary effects in Ethiopia. Agricultural outputs fall while agricultural commodities prices increase. These

affects the imports and exports composition. Due to the weak inter-sectoral linkage between agriculture and

non-agriculture sectors, however, the effects are mainly contained within agricultural sector and the rural

households. Agriculture in Ethiopia is the main contributor to national income, employment, and food supply.

Thus, the effects of climate change on aggregate GDP and private consumption (or welfare loss) resembles

that of the effects on agricultural GDP and rural households. Our sensitivity analysis shows that households’

welfare effects would get worse when the proportion of subsistence consumption increases in households’

budget. Therefore, the consumption structure of households has also important role in determining sign and

magnitude of climate change impacts. Future research in this direction may pay off.

However, the study is not without limitation. There are ample sources of uncertainty of climate change

and its impacts. Nevertheless, we focused only at high-end impacts gauged by two models. For livestock, we

only considered impacts via feed availability and quality which we assumed is proportional to changes to

grassland and grains yields. Climate change induced agricultural labor outmigration are entirely assumed as we

could find no empirical data on migration in general and climate change induced migration in particular. Of

course, even if data were available, it would hardly been possible to disentangle climate change induced

migration from other motives of migration (Brown, 2008; McLeman and Smit, 2006). All impact scenarios did

not take into account the effects of climate variability and related impacts like outbreak of animal and plant

diseases which all may increase with climate change. Nor climate change impacts though changes in area

suitability for crops and indirect effects through international food prices are considered.

Even though we used different materials and methods, our findings substantiates the findings of

previous studies (cf. World Bank, 2010; Robinson et al., 2012; Arndt et al., 2011). We argue thus impacts are

17

enough to worry about and call for public action. Late responses of adaptation in countries like Ethiopia may

leave narrow window of opportunity to adapt in later stages.

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Annex A: List and Description of Accounts of the 2006 Ethiopian SAM (Aggregated for our purpose)

Activities Description Commodities Description

AGRAIN Grain: Cereals, Pulses, Oilseeds CGRAIN Grain: Cereals, Pulses, Oilseeds

ACCROP Vegetables, Fruits, Cash Crops, Crops n.e.c CCROP Vegetables, Fruits, Cash Crops, Crops n.e.c

AENSET Enset Crop CENSET Enset Crop

ALIVST Livestock farming CLIVST Livestock products

AFISFOR Forestry and Fishing CFISFOR Forestry and Fishing products

AMINQ Mining and Quarrying CMINQ Minerals

ACONS Construction CCONS Construction

APMAN Primary manufacturing CPMAN Primary manufactured products

ASMAN Secondary manufacturing CSMAN Secondary manufactured products

AUTL Utilities: Water, and Electricity CUTL Utilities: Water, and Electricity

APVTS Private Services CPVTS Private services products

ATRNCOM Transport and Communications CTRNCOM Transport and Communications Services

APADM General Public Administration and Defense CPADM General Public Admin and Defense

APAGRI Public Administration (Agriculture related) CPAGRI Public Administration (Agriculture related)

AEDUC Education and Training CEDUC Education

AHEAL Health and Social Works CHEAL Health

CMMAN Totally imported Manufactured goods

Factors Institutions

FLAB0 Agricultural labor RURLH Rural Households

FLAB1 Administrative workers SURBH Small Urban Households

FLAB2 Professional and technical workers BURBN Big-Urban Households

FLAB3 Unskilled workers ENT Enterprises

FLAB4 Skilled workers GOV Government

FLND Cropland ROW Rest of World

FCAP1 Livestock (in TLU) Other Accounts

FCAP2 Non-agricultural capital STAX Sales taxes

MTAX Import Tariffs

DTAX Direct taxes

TRC Trade and Transport Margins

S-I Savings-investment

DSTK Stock(Inventory Changes)

Source: EDRI (2009)

22

Annex B: Sensitivity Analysis (PRD_MIG_E Scenario)

Sensitivity Scenarios

BASE Sim0 Sim1 Sim2 Sim3

Macroeconomic Effects

ABSORP 162.5 -6.1 -6.6 -4.7 -5.9

PRVCON 114.8 -8.7 -9.3 -6.7 -10.0

EXPORTS 16.8 -6.3 -10.3 -3.2 -5.6

IMPORTS -46.9 -2.3 -3.7 -1.1 -2.0

GDPMP 132.3 -7.6 -8.1 -5.8 -7.2

GSAV 5.4 20.5 13.1 10.1 -14.7

EXR 1.0 -4.5 -4.5 -2.3 -3.7

Real Sectoral GDP(at Factor Costs)

AGRAIN 21.9 -25.4 -25.2 -27.5 -25.4

ACCROP 12.1 -10.9 -15.9 -2.8 -10.2

AENSET 1.4 -8.0 -11.1 -3.0 -8.5

ALIVST 17.6 -11.5 -11.3 -7.5 -11.6

AFISFOR 5.8 -9.5 -10.2 -4.3 -9.7

AMINQ 0.7 3.5 1.8 2.6 5.6

ACONS 5.3 0.0 2.3 0.1 3.4

APMAN 4.3 1.7 2.1 1.3 0.3

ASMAN 1.5 13.4 11.3 5.7 11.7

AUTL 2.3 2.8 2.4 3.7 2.2

APVTS 31.7 -0.4 -0.4 0.5 -1.3

ATRNCOM 6.4 4.1 3.2 1.6 3.1

APADMN 4.9 0.0 1.0 0.0 5.2

APAGRI 1.0 0.0 1.0 0.0 5.2

AEDUC 4.3 0.3 0.6 0.5 4.0

AHEAL 1.1 1.0 1.0 1.3 2.8

TOTAL 122.2 -7.4 -7.8 -6.1 -7.2

Households’ Welfare Change (EV %)

RURLH 86.7 -8.9 -10 -8.1 -10.8

SURBH 15.3 -15.8 -14 -6.4 -13.3

BURBH 12.7 -6.9 -6 -3 -6.7

TOTAL 114.8 -9.6 -10 -7.3 -10.7

Source: CGE Simulations

Notes:

All Base Values are in Billion Ethiopian Birr (ETB)

Sim0= Is the original experiment, Sim1=Is where all production activities are in CES production technology plus land is assumed to be fully mobile across crop

activities, Sim2=Is Sensitivity test to Frisch Parameters, and Sim3=is sensitivity test under ‘’balanced’’ closure.

23

Annex C: Equations of the Model (Lofgren et al., 2002)

PMc = pwmc. (1 + tmc). EXR + ∑ PQc′ . icmc′ cc′∈ CT c ϵ CM (1)

PEc = pwec. (1 − tec). EXR − ∑ PQc′ . icec′ cc′∈ CT c ϵ CE (2)

PDDc = PDSc + ∑ PQc′ . icdc′ cc′∈ CT c ϵ CD (3)

PQc(1 − tqc). QQc = PDDc. QDc + PMc. QMc c Є CD ∪ CM (4)

PXc. QXc = PDSc. QDc + PEc. QEc c ϵ CX (5)

PAa = ∑ PXACa c. θa cc∈C a ϵ A (6)

PINTAa = ∑ PQc. icac ac∈C a ϵ A (7)

PAa. (1 − taa). QAa = PVAa. QVAa + PINTAa. QINTAac a ϵ A (8)

CPI̅̅̅̅̅ = ∑ PQc. cwtscc∈C (9)

DPI = ∑ PDSc. dwtscc∈C (10)

QAa = αaa. [δa

a. QVAa−ρa

a

+ (1 − δaa). QINTAa

−ρaa

]−

1

ρaa

a ϵ ACES (11)

QVAa

QINTAa= [

PINTAa

PVAa .

δaa

1−δaa]

1

1+ρaa

a ϵ ACES (12)

QVAa = ivaa. QAa a ϵ ALEO (13)

QINTAa = intaa. QAa a ϵ ALEO (14)

QVAa = aava. [∑ δf a

va. QFf a−ρa

va

f∈F ]−

1

ρava

a ϵ A (15)

WFf. WFDISTf a̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅ = PVAa(1 − tvaa). [∑ δf a

va. QFf a−ρa

va

f∈F ]−1

. δf ava. QFf a

−ρava−1

a ϵ A; f ϵ F (16)

24

QINTc a = icac a. QINTAa a ϵ A; c ϵ C (17)

QXACa c + ∑ QHAa c hh ∈H = θa c. QAa a ϵ A; c ϵ CX (18)

QXc = αcac. [∑ δa c

ac . QXACa c−ρc

ac

a ϵ A ]

−1

ρcac−1

c ϵ CX (19)

PXACa c = PXc. QXc. [∑ δa cac . QXAC a c

−ρcac

a∈A ′ ]−1

. δa cac . QXACa c

−ρcac−1

a ϵ A; c ϵ CX (20)

QXc = αct . [δc

t . QEcρc

t

+ (1 − δct ). QDc

ρct

]

1

ρct c ϵ CE ∩ CD (21)

QEc

QDc= [

PEc

PDSc.

1−δct

δct ]

1

ρct −1

c ϵ CE ∩ CD (22)

QXc = QDc + QEc c ϵ (CD ∩ CEN) ∪ (CE ∩ CDN) (23)

QQc = αcq

. [δcq

. QMc−ρc

q

+ (1 − δcq

). QDc−ρc

q

]−

1

ρcq

c ϵ CM ∩ CD (24)

QMc

QDc= [

PDDc

PMc.

δcq

1−δcq ]

1

1+ρcq

c ϵ CM ∩ CD (25)

QQc = QDc + QMc c ϵ (CD ∩ CMN) ∪ (CM ∩ CDN) (26)

QTc = ∑ icmc c′. QMc′ + icec c′. QEc′ + icdc c′. QDc′c′∈C′ c ϵ CT (27)

YFf = ∑ WFf. WFDIST̅̅ ̅̅ ̅̅ ̅̅ ̅̅f̅ a. QFf af∈F f ϵ F (28)

YIFi f = shifi f . [(1 − tff). YFf − trnsfr row f. EXR] i ϵ INSD; f ϵ F (29)

YIi = ∑ YIFi ff ∈F + ∑ TRIIi i′i′∈INSDGNG′ + trnsfr i gov. CPI̅̅̅̅̅ + trnsfr i row. EXR i ϵ INSDNG; i′ ∈ INSDGNG′ (30)

TRIIi i′ = shiii i′. (1-MPSi′). (1 − TINSi′). YIi′ i ϵ INSDNG; i′ ∈ INSDGNG′ (31)

EHh = [1 − ∑ shiii hi ∈INSDNG ](1 − MPSh). (1 − TINSh). YIh h ϵ H (32)

PQc. QHc h = PQc . γc hm + βc h

m . [EHh − ∑ PQc′ . γc′ hm c′ϵC − ∑ ∑ PXACa c′. γa c′h

hc′∈Ca∈A ] c ϵ C; h ϵ H (33)

PXACa c. QHAa c h = PXACa c . γa c hm + βa c h

m . [EHh − ∑ PQc′ . γc′ hm c′ϵC − ∑ ∑ PXACa c′. γa c′h

hc′∈Ca∈A ] a ϵ A; c ϵ C; h ϵ H (34)

25

QINVc = IADJ̅̅ ̅̅ ̅̅ . qinv̅̅ ̅̅ ̅̅ c c ϵ C (35)

QGc = GADJ̅̅ ̅̅ ̅̅ ̅. qg̅̅ ̅c c ϵ C (36)

YG = ∑ TINSi. YIi

i∈INSDNG

+ ∑ tff. YFf

f∈F

+ ∑ tvaa. PVAa. QVAa

a∈A

+ ∑ taa. PAa. QAa

a∈A

+ ∑ tmc. pwmc. QMc. EXR

c∈CM

+ ∑ tec. pwec. QEc. EXR

c∈CE

+ ∑ tqc. PQc. QQc + ∑ YIFgov ff∈Fc∈C + (trnsfrgov row. EXR) (37)

EG = ∑ PQc. QGcc∈C + ∑ trnsfri gov . CPI̅̅̅̅̅i ∈INSDNG (38)

∑ QFf aa ∈A = QFSf̅̅ ̅̅ ̅̅ f ϵ F (39)

QQc = ∑ QINTc aa∈A + ∑ QHc ah∈H + QGc + QINVc + qdst + QTc c ϵ C (40)

∑ pwmc. QMcc ∈CM + ∑ trnsfr row ff∈F = ∑ pwec. QEcc ∈CE + ∑ trnsfr i row i∈INSD + FSAV̅̅ ̅̅ ̅̅ ̅ (41)

YG = EG + GSAV (42)

TINSi = tinsi̅̅ ̅̅ ̅̅ . (1 + TINSADJ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅. tins01i) + DTINS̅̅ ̅̅ ̅̅ ̅̅ . tins01i i ϵ INSDNG (43)

MPSi = mpsi̅̅ ̅̅ ̅̅ . (1 + MPSADJ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅. mps01i) + DMPS. mps01i i ϵ INSDNG (44)

∑ MPSii∈INSDNG (1 − TINSi). YIi + GSAV + EXR. FSAV̅̅ ̅̅ ̅̅ ̅ = ∑ PQc . QINVc c∈C + ∑ PQc . qdstc c∈C (45)

TABS = ∑ ∑ (PQcQHch)cϵChϵH + ∑ ∑ ∑ (PXACacQHAach)hϵHcϵCaϵA + ∑ PQc. QGccϵC + ∑ PQc . QINVc c∈C + ∑ PQc . qdstc c∈C (46)

INVSHR. TABS = ∑ PQc . QINVc c∈C + ∑ PQc . qdstc c∈C (47)

GOVSHR. TABS = ∑ PQc. QGccϵC (48)

26