DOES ADAPTATION TO CLIMATE CHANGE PROVIDE
FOOD SECURITY?AMICRO-PERSPECTIVE
FROM ETHIOPIA
SALVATORE DI FALCO,MARCELLA VERONESI, AND MAHMUD YESUF
We examine the driving forces behind farm households’ decisions to adapt to climate change, and the
impact of adaptation on farm households’ food productivity. We estimate a simultaneous equations
model with endogenous switching to account for the heterogeneity in the decision to adapt or not, and
for unobservable characteristics of farmers and their farm. Access to credit, extension and information
are found to be the main drivers behind adaptation. We find that adaptation increases food productivity,
that the farm households that did not adapt would benefit the most from adaptation.
Key words: adaptation, climate change, endogenous switching, Ethiopia, food security, productivity,
spatial data.
JEL classification: Q18, Q54.
At the core of the ongoing debate regard-
ing the implications of climate change in
sub-Saharan Africa, there is the issue of
food security. In this part of Africa, mil-
lions of small-scale subsistence farmers, gen-
erally with less than one hectare of land,
produce food crops in extremely challeng-
ing conditions. The production environment is
characterized by a joint combination of low
land productivity and harsh weather condi-
tions (e.g., high average temperature, scarce
and erratic rainfall). These result in very
low yields and food insecurity (Di Falco
and Chavas 2009).
Food security is a broad concept. It encap-
sulates availability, access, and utilization of
Salvatore Di Falco is a lecturer (UK equivalent for assistant
professor) at the London School of Economics; Marcella Veronesi
is a researcher and lecturer at ETH Zurich, Institute for Environ-
mental Decisions; Mahmud Yesuf is research assistant professor at
Kansas State University.
The authors would like to thank the Editor and two anonymous
referees for the useful and constructive comments. We also thank
for their comments on an earlier draft of the paper the partici-
pants at the 117th EAAE Seminar at the University of Hohenheim,
at the 2010 Environment for Development Annual Meeting held
in Debre Zeit (Ethiopia), and in seminars at ETH Zurich, the
University of Cambridge,the Centre for Study ofAfrican Economy
(University of Oxford), and at The Grantham Research Insti-
tute on Climate Change and the Environment (London School
of Economics). All remaining errors and omissions are our own
responsibility.
foodstuff.
1
In this paper we focus on one
of the most important determinants of food
availability in the Ethiopian subsistence farms
context: food productivity (FAO 2002). The
availability (and to some extent the access)
of food is crucially determined by the produc-
tivity of these farm households. They account
for about 95% of the national agricultural out-
put, of which about 75% is consumed at the
household level (World Bank 2006). With low
diversified economies and reliance on rainfed
agriculture, sub-Saharan Africa’s development
prospects have been closely associated with
climate. For instance, the World Bank (2006)
reported that catastrophic hydrological events
such as droughts and floods have reduced
Ethiopia’s economic growth by more than a
third.
Climate change is projected to further
reduce agricultural productivity (Cline 2007;
Parry et al. 2005; Rosenzweig and Parry 1994).
A plethora of climate models converge in fore-
casting scenarios of increased temperatures for
most of this area (Dinar et al. 2008). The fourth
Intergovernmental Panel on Climate Change
(IPCC) states that at lower latitude, in trop-
ical dry areas, crop productivity is expected
1
For a critical discussion of the different dimensions and metrics
of food security please refer to Barrett (2010),Gregory,Ingram, and
Brklacich (2005), and Jenkins and Scanlan (2001).
Amer. J. Agr. Econ. 93(3): 829–846; doi: 10.1093/ajae/aar006
Received February 2010; accepted January 2011; published online March 7, 2011
© The Author (2011). Published by Oxford University Press on behalf of the Agricultural and Applied Economics
Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
830 April 2011 Amer. J. Agr. Econ.
to decrease “for even small local tempera-
ture increases (1–2
◦
C)” (IPCC 2007, p. 11). In
many African countries, access to food will be
severely affected:“Yields from rain fed agricul-
ture could be reduced by up to 50% by 2020”
(IPCC 2007, p. 13). Given these discouraging
prospects, it is no surprise that the identifica-
tion of both “climate-proofing” technologies
and adaptation strategies are vital to support
the yields of food crops. These strategies can
indeed buffer against climate change and play
a crucial role in reducing the food insecurity of
farm households.
This paper aims to contribute to the lit-
erature on climate change on agriculture by
providing a micro perspective on the issue of
adaptation and food security. We investigate
how farm households’ decision to adapt, that
is to implement a set of strategies (e.g., chang-
ing crop varieties, adoption of soil and water
conservation strategies) in response to long
run changes in key climatic variables such as
temperature and rainfall,affects food crop pro-
ductivity in Ethiopia. This seems particularly
relevant because most of the debate on climate
change in agriculture has been focusing on the
impact of climate change rather than on the
role of adaptation.
The links between climate change and food
productivity have largely been explored focus-
ing on the relation between climate vari-
ables and agriculture. There is, indeed, a
large and growing body of literature that
uses either agronomic models or Ricar-
dian analysis to investigate the magnitude
of these impacts (e.g., Deressa and Hassan
2010; Kurukulasuriya and Rosenthal 2003; Seo
and Mendelsohn 2008). Agronomic models
attempt to estimate directly,through crop mod-
els, the impacts of climate change on crop
yields. They rely on experimental findings that
indicate changes in yield of staple food crops
(i.e., wheat) as a consequence of warming
temperatures (e.g., Amthor 2001; Fuhrer 2003;
Gregory et al. 1999). Then, the results from
the model are fed into behavioral models that
simulate the impact of different agronomic
practices on farm income or welfare.
The Ricardian approach (pioneered by
Mendelsohn et al. 1994) purports to iso-
late, through econometric analysis of cross-
sectional data, the effects of climate on farm
income and land value, after controlling for
other relevant explanatory variables (e.g., fac-
tor endowment, proximity to markets). The
Ricardian approach implicitly incorporates the
possibility of the implementation of adaptation
strategies by farmers.
2
Since it is assumed
that farmers have been adapting optimally
to climate in the past, the regression coef-
ficients are estimating the marginal impacts
on outputs of future temperature or rainfall
changes already incorporating farmers’ adap-
tive response. Thus, adaptation choices do not
need to be modeled explicitly. They have been
efficiently implemented. One of the obvious
shortcomings of this approach is that it is a
“black box” that fails to identify the key adap-
tation strategies that reduce the implication of
climate on food production.
Disentangling the productive implications of
adaptation to climate change is of paramount
importance. Besides determining the impact
of climatic variables on food productivity, it is
necessary to understand how the set of strate-
gies implemented in the field by the farmers
(e.g.,changing crops,adopting water harvesting
technologies or, soil conservation measures) in
response to long term changes in environmen-
tal conditions affects crop productivity. More
specifically, it is necessary to assess whether the
farm households that actually did implement
those adaptation strategies are indeed getting
benefits in terms of an increase in the produc-
tivity of food crop. This is central if adaptation
strategies need to be put in place.
As mentioned earlier, our focus on the pro-
ductivity of food crops (and not on land val-
ues) is motivated by its implications for the
achievement of food security. Moreover, using
productivity seems particularly appropriate in
the Ethiopian context. A key assumption of the
Ricardian approach is that land markets are
working properly.
3
Under this circumstance
land prices will reflect the present discounted
value of land rents into the infinite future
(Deschenes and Greenstone 2007). Properly
working land markets, however, may not be
operating in areas of the developing world
where land property rights are not perfectly
assigned. This is the case of Ethiopia. In this
country in 1975 a land reform was imple-
mented. As result all land was made state
property, land rentals as well as labor hiring
were made illegal under the regime of Derg
(1974 – 1991). After the change in the gov-
ernment land rentals and labor hiring were
2
The Ricardian approach has been recently widely adopted in a
series of country level analyses (see Dinar et al. 2008; Mendelsohn
2000). Global scale analysis can, however, mask tremendous local
differences.
3
An alternative approach would be to use farm net revenues
(i.e., Deressa and Hassan 2010).
Di Falco, Veronesi, and Yesuf Does Adaptation to Climate Change Provide Food Security 831
legalized. However, the predominance of oral
contracts and agreements has prevented the
formation of well-defined property rights, and
large areas of this country are still plagued
by tenure insecurity. Recent land certification
reforms, in some areas, seem to be contributing
to more secure tenure and the enhancement of
land markets (Deininger et al. 2007; Holden et
al. 2007).
There is existing literature on the estimation
of the impact of climate change on food pro-
duction at country, regional, and global scale
(McCarthy et al. 2001; Parry et al. 2004; Pearce
et al. 1996; Stern 2007). Insights from these
studies are crucial in appreciating the extent
of the problem and designing appropriate mit-
igation strategies at global or regional level.
The aggregate nature of these studies, how-
ever, makes it very difficult to provide insights
in terms of effective adaptation strategies at
micro or farm household level.
4
Micro evi-
dence on the impact of rainfall, temperature,
and climate related adaptation strategies on
crop yield is very scanty.
Our study tries to fill the gap in the litera-
ture by examining how the decision to adapt or
not to adapt to climate change affects agricul-
tural productivity in the Nile Basin of Ethiopia.
We have access to a particularly rich database,
which contains both farm households that did
and did not adapt plus a very large set of con-
trol variables. Lack of enough spatial variation
on key climatic variables (rainfall and tem-
perature) in cross sectional data is one major
issue to conduct micro level studies on cli-
mate change. This can be particularly true in
developing countries where one meteorolog-
ical station is set to cover a wide geographic
area. To address this issue we employ house-
hold specific rainfall and temperature data
generated by the Thin Plate Spline method
of spatial interpolation. This method imputes
the farm specific values using latitude, longi-
tude, and elevation information of each farm
household (see Wahba 1990 for details).
We take into account that the differ-
ences in food productivity between those
farm households that did and those that
did not adapt to climate change could be
due to unobserved heterogeneity. Indeed, not
4
To the best of our knowledge, Temesgen (2006) is the only
economic study that attempts to measure the impact of climate
change on farm profit. This study applies the Ricardian approach
where the cost of climate variability is imputed from capitalized
land value. However, this study was conducted using subregional
(agro-ecology) agricultural data, not farm household level data.
distinguishing between the casual effect of
climate change adaptation and the effect of
unobserved heterogeneity could lead to mis-
leading policy implications. We account for the
endogeneity of the adaptation decision by esti-
mating a simultaneous equations model with
endogenous switching by full information max-
imum likelihood estimation. For the model
to be identified, we use as selection instru-
ments the variables related to the information
sources (e.g., government extension, farmer-
to-farmer extension, information from radio
and neighborhood).
Finally, we build a counterfactual analysis,
and compare the expected food productivity
under the actual and counterfactual cases that
the farm household adapted or not to climate
change. Treatment and heterogeneity effects
are calculated to understand the differences
in food productivity between farm households
that adapted and those that did not adapt, and
to anticipate the potential effects of changes
in agricultural policy. To our knowledge, con-
sidering the existing literature, this is a novel
exercise.
We find that there are significant and non-
negligible differences in food productivity
between the farm households that adapted and
those that did not adapt to climate change.
We also find that adaptation to climate change
increases food productivity. The impact of
adaptation on productivity is smaller for the
farm households that actually did adapt than
for the farm households that did not adapt
in the counterfactual case that they adapted.
In addition, if the nonadapters adapted, they
would produce the same as the adapters.
We control for the role of both rainfall and
temperature. We follow the existing literature
and include nonlinear terms for both these
variables (Mendelsohn et al. 1994). We find
that the estimated coefficients for rainfall in the
main rain season (Meher) are statistically sig-
nificant only for the group of farm households
that did not adapt. The same variables display
estimated coefficients that are not statistically
significant when we consider only the group of
farm households that implemented adaptation
strategies. This may indicate that this group
of farm households, through adaptation, is less
reliant on the rainfall in the Meher season.
We also analyzed the drivers behind
adaptation. Econometric results show that
information on both farming practices (irre-
spective of its source) and climate change
is crucial in affecting the probability of
adaptation. In addition, we find that farm
832 April 2011 Amer. J. Agr. Econ.
households with access to credit are more
likely to undertake strategies to tackle climate
change.
Description of the Study Sites and Survey
Instruments
Ethiopia is a very interesting case study. A
recent mapping on vulnerability and poverty
in Africa listed Ethiopia as one of the coun-
tries most vulnerable to climate change with
the least capacity to respond (Orindi et al.
2006; Stige et al. 2006). The country’s econ-
omy heavily relies upon the agricultural sector,
which is mostly rainfed. The agricultural sec-
tor accounts for about 40% of national GDP,
90% of exports, and 85% of employment.
Ethiopia’s vulnerability is indeed largely due
to climatic conditions. This has been demon-
strated by the devastating effects of various
prolonged droughts in the twentieth century
and recent flooding. The productive perfor-
mance of the agricultural sector has been very
low. For instance, agricultural GDP and per
capita cereal production has been falling over
the last forty years with cereal yield stagnant at
about 1.2 tons per hectare. Direct implication
is that large areas of Ethiopia are plagued by
food insecurity.
This study relies on a survey conducted
on 1,000 farm households located within the
Nile Basin of Ethiopia in 2005. The sampling
frame considered traditional typology of agro-
ecological zones in the country (namely, Dega,
Woina Dega, Kolla and Berha), percentage of
cultivated land, degree of irrigation activity,
average annual rainfall, rainfall variability, and
vulnerability (number of food aid dependent
population). The sampling frame selected the
woredas (an administrative division equivalent
to a district) in such a way that each class in the
sample matched to the proportions for each
class in the entire Nile basin. The procedure
resulted in the inclusion of twenty woredas.
Random sampling was then used in selecting
fifty households from each woreda.
One of the survey instruments was in par-
ticular designed to capture farmers’ percep-
tions and understanding on climate change,
and their approaches on adaptation. Ques-
tions were included to investigate whether
farmers have noticed changes in mean tem-
perature and rainfall over the last two decades,
and reasons for observed changes. About 90%
of the sample perceived long term changes
in mean temperature or/and rainfall over the
last twenty years. About 68%, 4%, and 28%
perceived mean temperature as increasing,
decreasing and remaining the same over the
last twenty years, respectively. Similarly, 18%,
62%, and 20% perceived mean annual rain-
fall increasing, declining, and remaining the
same over the last twenty years, respectively.
Overall, increased temperature and declining
rainfall are the predominant perceptions in our
study sites.
Furthermore, some questions investigated
whether farm households made some adjust-
ments in their farming in response to long
term changes in mean temperature and rain-
fall by adopting some particular strategies. We
define the undertaken strategies as “adapta-
tion strategies,” and create the dummy variable
adaptation equal to 1 if a farm household
adopted any strategy in response to long-term
changes in mean temperature and rainfall, 0
otherwise. Changing crop varieties, adoption
of soil and water conservation strategies, and
tree planting were major forms of adaptation
strategies followed by the farm households in
our study sites. These adaptation strategies are
mainly yield-related and account for more than
95% of the adaptation strategies followed by
the farm households who actually undertook
an adaptation strategy. The remaining adap-
tation strategies accounting for less than 5%
were water harvesting, irrigation, non–yield
related strategies such as migration, and shift
in farming practice from crop production to
livestock herding or other sectors. About 58%
and 42% of the farm households had taken no
adaptation strategies in response to long term
shifts in temperature and rainfall, respectively.
More than 90% of the respondents who took
no adaptation strategy indicated lack of infor-
mation, land, money, and shortages of labor, as
major reasons for not undertaking any adap-
tation strategy. Lack of information is cited
as the predominant reason by 40–50% of the
households.
In addition, detailed production data were
collected at different production stages (i.e.,
land preparation, planting, weeding, harvest-
ing, and post harvest processing). The area is
almost totally rainfed. Only 0.6% of the house-
holds are using irrigation water to grow their
crops. Production input and output data were
collected for two cropping seasons, i.e., Meher
(long rainy season), and Belg (the short rainy
season) at the plot level. However, many plots
have two crops grown on them annually (one
during each of the Meher and Belg seasons).
Di Falco, Veronesi, and Yesuf Does Adaptation to Climate Change Provide Food Security 833
The farming system in the survey sites is
very traditional with plough and yolk (ani-
mals’ draught power). Labor is the major
input in the production process during land
preparation, planting, and post harvest pro-
cessing. Labor inputs were disaggregated as
adult male’s labor, adult female’s labor, and
children’s labor. This approach of collecting
data (both inputs and outputs) at different
stages of production and at different levels of
disaggregation should reduce cognitive burden
on the side of the respondents,and increase the
likelihood of retrieving a better retrospective
data. The three forms of labor were aggregated
as one labor input using adult equivalents. We
employed the standard conversion factor in the
literature on developing countries where an
adult female and children labor are converted
into adult male labor equivalent at 0.8 and 0.3
rates, respectively.
Monthly rainfall and temperature data were
collected from all the meteorological stations
in the country. Then, the Thin Plate Spline
method of spatial interpolation was used to
impute the household specific rainfall and tem-
perature values using latitude, longitude, and
elevation information of each household. By
definition, Thin Plate Spline is a physically
based two-dimensional interpolation scheme
for arbitrarily spaced tabulated data. The
Spline surface represents a thin metal sheet
that is constrained not to move at the grid
points, which ensures that the generated rain-
fall and temperature data at the weather sta-
tions are exactly the same as data at the
weather station sites that were used for the
interpolation. In our case, the rainfall and tem-
perature data at the weather stations are repro-
duced by the interpolation for those stations,
which ensures the credibility of the method
(see Wahba 1990). This method is one of the
most commonly used to create spatial climate
data sets. Its strengths are that it is readily avail-
able, relatively easy to apply,and it accounts for
spatially varying elevation relationships. How-
ever, it only simulates elevation relationship,
and it has difficulty handling very sharp spatial
gradients. This is typical of coastal areas. Given
that our area of the study is characterized by
significant terrain features, and no climatically
important coastlines, the choice of the Thin
Spline method is reasonable (for more details
on the properties of this method in comparison
to the other methods, see Daly 2006).
Finally, although a total of forty-eight 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 are the cornerstone of the
local diet. We limit the estimation to these pri-
mary crops. The final sample includes twenty
woredas, 941 farm households (i.e., on aver-
age about forty-seven farm households per
woreda), and 2,807 plots (i.e.,on average about
three plots per farm household). The scale
of the analysis is at the plot level. The basic
descriptive statistics are presented in table 1,
and the definition of the variables in table A1
of the appendix.
Modeling Adaptation to Climate Change
The climate change adaptation decision and
its implications in terms of food productivity
(our metric of food security) can be modeled
in the setting of a two-stage framework. In the
first stage, we use a selection model for climate
change adaptation where a representative risk
adverse farm household chooses to implement
climate change adaptation strategies if it gener-
ates net benefits.
5
Let A
∗
be the latent variable
that captures the expected benefits from the
adaptation choice with respect to not adapting.
We specify the latent variable as
(1) A
∗
i
= Z
i
α + η
i
with A
i
=
1 if A
∗
i
> 0
0 otherwise,
that is farm household i will choose to adapt
(A
i
= 1), through the implementation of some
strategies in response to long term changes in
mean temperature and rainfall, if A
∗
> 0, and 0
otherwise.
The vector Z represents variables that affect
the expected benefits of adaptation. These fac-
tors can be classified in different groups. First,
we consider the characteristics of the operating
farm (e.g., soil fertility, erosion). For instance,
farms characterized by more fertile soil might
be less affected by climate change and there-
fore relatively less likely to implement adap-
tation strategies. Then, current climatic fac-
tors (e.g., rainfall, temperature) as well as the
experience of previous extreme events such
as droughts and floods (in the last five years)
can also play a role in determining the prob-
ability of adaptation. It is also important to
address the role of access to credit. Households
that have limited access to credit can have less
5
A more comprehensive model of climate change adaptation is
provided by Mendelsohn (2000).
