Download On the Impact of Weather and Climate on

On the Impact of Weather and Climate on
Agriculture: Evidence from Ethiopia
Abstract
This paper assesses the impact of climate change and weather uncertainty in Ethiopia on
agricultural productivity of households based on plot level panel data from the Amhara
region. The major contribution of the analysis lies in distinguishing between weather
variability and climate change that has huge significance in Ethiopian agriculture but that has
not been assessed in previous related studies. The effects of the climate change and weather
uncertainty are identified the modified pseudo fixed effects estimations that recognizes the
possibility that climate change variables are fixed over time and varying across villages.
Overall, both climate and weather variables are found to significant determinants of
agricultural productivity, with farm level revenue being more responsive to seasonal climate
change variables. The major policy implication of the result is that policy efforts to mitigate
the permanent effects of weather uncertainty could be more important to the welfare of
Ethiopian farmers than transitory measures.
JEL classification: D2, Q12, Q15
Key words: farm revenue; climate change; weather variability, pseudo fixed effects method,
Ethiopia
1. Introduction
With their ultra weather-sensitive agrarian economy, developing countries and their
economic development are likely to suffer tremendously from the threat of climate change
(Mendelson and Dinar, 1999). Quantifying the impact of climate change on agricultural
sector guides appropriate adaptation measures (Sachs et al. 1999; Stage, 2010) and ensures
genuine participation of developing countries in climate change agreements (Cao, 2008).
One critical issue in assessing the links between agricultural productivity and climate
change is the actual measurement of climatic indicators. While it is a common practice to
assess the potential impacts of climate change on agriculture based the long term mean values
of expected climatic parameters, Roszenweig et al. (2009) argue that due consideration
should be given to shorter term weather-related measures as well. Such short term measures
would make visible extreme events and temporal spatial variations of more meteorological
conditions that have signficant deviations from climate change. Indeed, climate figures
generated from macro data for use in micro scales tend to poorly represent spatial-temporal
variability and extremes (Maraun et al., 2010). The effects of such variations and extremes
are likely to be captured through weather observations, as opposed to average climatic
paramters (Fisher et al., 2007), implying that controlling for short term weather related
factors is as important as the long term parameters in critically assessing the links between
agriculture and climate change1.
1
Climate scientists emphasize the distinction between weather and climate. Weather is what occurs at a
particular moment in time - typically, precipitation and temperature. Due to natural variability, weather
fluctuates from one hour to another, one day to another, one month to another, and one year to another. Climate,
by contrast, is the long-run pattern of weather over time. To climate scientists, therefore, climate change has a
very different significance from weather change. A change in weather is inherently short-run, while climate
change is a shift in the long-run pattern. Because these are different phenomena, it is not surprising that they
also have different economic implications (Fischer et al, 2007).
1
Accordingly, this paper sets out to contribute to the discussion on the impact of
climate change on agricultural productivity through distinguishing between long term climate
measures and more short term weather measures. The effects of the climate change and
weather uncertainty are identified the modified pseudo fixed effects estimations that
recognizes the possibility that climate change variables are fixed over time and varying
across villages. Distinguishing between weather variability and climate change has a huge
significance in Ethiopian agriculture that suffers from moisture constraints and general irratic
patterns even without the climate change impacts.
Traditional Ricardian analyses of the impact of climate change on agriculture,
introduced by Mendelsohn et al. (1994) assess the impact of future climate on farmers’
expected incomes by relying on the cross-sectional variation observed in current climate. The
attractiveness of the Ricardian method is that it automatically captures adaptation, since
farmers are adjusting inputs and outputs to match local conditions(Lang, 2007) and it can be
employed to fairly readily available data (Felezi, 2010), as evidenced by its application in
cases across the world2.
These advantages of the Ricardian analysis hinge on three critical implicit
assumptions. The first assumption is that average climatic parameters sufficiently capture the
impact of climate change on agricultural productivity. However, as (Mendelsohn et al., 2010)
argue, the model is not measuring the effects of year-to-year change in weather but a longterm change in climate. By doing so, the analysis fails to take account of change in climate
variation or extreme events (Salvo, 2010). More critically, to the extent that the climate
parameters capture the long term, average features of climate change, and not the short term
variations and extreme events, the approach would fail to account for the full range of farmer
2
adaptations. Second, the analysis could suffer from omitted variable bias, as such cross
sectional analyses insufficiently deal with unmeasured characteristics, that are important
determinants of output and land values in agricultural settings, (Deschenes and Greenstone
2007).
In a significant deviation from earlier cross sectional approaches, Deschenes and
Greenstone (2007) assess the link between agricultural production and climate change by
distinguishing between the effects of weather and climatic parameters using a panel data
analysis. They argue that consistent estimation of the effect of climate change requires that
the observed covariates are orthogonal to the error term which will be invalid if unobserved
transitory or permanent factors co-vary with the climate variables. Accordingly, they estimate
the effect of weather on agricultural profits3, controlling for county fixed effects and stateyear interactions.This enabled controlling for time-invariant idiosyncratic features of the
county thereby removing long term climate variables as they are fixed over the duration of
the panel using county level fixed-effects thereby. The estimated impacts of temperature and
precipitation on agricultural profits are multiplied by the predicted change in climate to infer
their welfare impact.
While the use of panel data analysis is a significant improvement over previous
analyses, Fisher et al. (2007) argue that the approach by Deschenes and Greenstone (2007)
does not control for time-varying omitted variables which may be quite highly correlated
with weather shocks and, therefore, it may measure something different from the impact of
climate on long-run profit. As a result, their estimates may reflect the short-run response to
fluctuations in weather and therefore does not allow for longer-run adaptation.
3
The inclusion of the county fixed effects necessitates two substantive differences in the use of variables from
traditional Ricaridian analyses: the dependent variable, is agricultural profits, instead of land values and the
climate change impact is approximated by with annual realizations of weather.
3
Similarly Masseti and Mendhalson (2010) argue that intertemporal methods that
eliminate cross sectional variation and focus on year to year changes not as useful as the cross
sectional variation, as interannual changes in weather are a poor proxy for climate.
In
addition, the coefficients of the time varying variables should not change over time, as is the
case in Deschenes and Greenstone (2007)’s analysis. In an ideal panel data model, the
coefficients of the time invariant variables should also not change. Accordingly, they estimate
the Ricardian mode by interacting the climate variables with year dummies in order to allow
the climate coefficients to change over time. These are compared with a two stage estimation
in which land value is regressed on the time varying variables using the covariance method
with county fixed effects followed by a regression of the time-mean residuals on the time
invariant variables. Instead of a single set of coefficients for the climate variables, one has a
different estimate for each year.
Other recent panel data analyses of the relationship between climate change and
agricultural productivity include Fezzi et al (2010) who argue that the quadratic forms that
are typically used in modelling climatic variables are not restrictive and explore the
relationships of interst using Additive Mixed Model (AMM) of land value, that have flexible
smooth property, on a panel of farms situated throughout England and Wales (EW). Timmins
(2003) argues that traditional Ricardian analysis may yield biaed results when land-use
decisions depend on the climate attributes being valued and when land has unobserved
attributes that differ with the use to which it is put.
Our approach builds on these previous analyses of the use of panel data approaches to
exploring the links between climate change and agricultural profits 4. The major departure of
4
We follow many Ricardian studies in developing countries for using agricultural profit instead of land values.
the use of land values in such analyses requires land markets working perfectly such that land prices reflect the
present discounted value of land rents into the infinite future (Deschenes and Greenstone 2007) which does not
hold in Ethiopia lack of prices for land sales due to full state ownership of the land (Difalco et al. 2011;).
4
our approach lies in the use of (long term) climate measures along with short term (weather)
measures, at the same time controlling for unobserved heterogeneity using a four-wave
survey data from Ethiopia.
As we argued earlier, the essence of controlling for both climate and weather factors
is to ensure that the full range of adaptation options (to both weather variability and weather
change) are taken into account in the Ricardian analysis. While previous studies assume that
farmers do adapt to climate change but not to weather change (e.g. Masseti and Mendhalson
(2010), there exists evidence that farmers in developing countries indeed adapt to weather
changes as well.
In this respect, we follow Difalco et al. (2011) who analyse the impact of adaptation
on farm households’ food productivity using a simultaneous equations model to account for
the heterogeneity in households’ decision to adapt to climate change or not. Their measure of
climate/weather variable distinguishes between the long term and short term patterns of
weather and climate. However, unlike our approach, their analysis is based cross section data,
and hence does not enable controlling for unobserved heterogeneity.
This paper is concerned with empirically assessing the differential impacts of weather
variability and climate change on productivity of Ethiopian farmers. Plot level panel data
from Ethiopia combined with 30 year monthly rainfall and temperature data are employed in
the analysis. Both moving average and time invariant long term mean measures are used as
climate variables while the weather variables were constructed as both averages for the years
corresponding to the survey years as well as lags from the survey years. Both annual and
seasonal precipitation and temperature measures are used in the analysis.
The study makes two major contributions to the existing literature on the impact of climate
change on productivity. First, while the effects of annual and seasonal weather patterns are
likely to differ from the long term patterns of climate change, these possible differentials
5
have not been thoroughly investigated. To the extent that the pattern of climate change
mimics weather uncertainty, policy measures aimed at mitigating the impacts of climate
change could also serve the same purpose as those as weather uncertainty. This distinction is
highly relevant in a setting like Ethiopia where both seasonal and yearly variations in rainfall
are huge and rainfall is hugely erratic.
Secondly, the study makes an important methodological contribution to the analysis of the
links between climate change and agriculture by applying a modified pseudo fixed effects
analysis that enables keeping the time invariant effects of climate change, the time variant
effects of weather and at the same time controlling for unobserved effects at a household
level that potentially lead to omitted variable bias in cross sectional Ricardian studies.
The rest of the paper is organized as follows. Section 2 presents an overview of the roles
of climate change and weather uncertainty in Ethiopia. In Section 3, the data employed in the
empirical analysis is presented. The econometric methodology employed is discussed in
section 4. Section 5 discusses the empirical findings and section 6 concludes the paper.
2.Weather variability, climate change and agricultural productivity in Ethiopia: a
Background
Agriculture remains one of the most important sectors in the Ethiopian economy for the
following reasons: (i) it directly supports about 83% of the population in terms of
employment and livelihood; (ii) it contributes over 40% of the country’s gross domestic
product (GDP); (iii) it generates about 85% of export earnings; and (iv) it supplies around
73% of the raw material requirement of agro-based domestic industries (MEDaC 1999; AfDB
2011). It is also the major source of food for the population and hence the prime contributing
6
sector to food security. In addition, agriculture is expected to play a key role in generating
surplus capital to speed up the country’s overall socio-economic development (MEDaC
1999).
Ethiopia has a total land area of about 112.3 million hectares. Of this, about 16.4
million hectares are suitable for producing annual and perennial crops. Of the estimated
arable land, about eight million hectares is used annually for rainfed crops. The country has a
population of about 80million with a growth rate of about 2.6% (AfDB 2011). Small-scale
farmers who are dependent on low input and low output rainfed mixed farming with
traditional technologies dominate the agricultural sector. The present government of Ethiopia
has given top priority to this sector and has taken steps to increase its productivity. However,
various problems are holding this back. Some causes of poor crop production are declining
farm size; subsistence farming partly due to population growth; land degradation due to
inappropriate land use such as cultivation of steep slopes; overcultivation and overgrazing;
and inappropriate polices. Other causes are tenure insecurity; weak agricultural research and
extension services; lack of appropriate agricultural marketing; an inadequate transport
network; low use of fertilizers, improved seeds and pesticides; and the use of traditional farm
implements. However, the major causes of low level of production are drought, which often
causes famine, and floods. These climate related disasters make the nation dependent on food
aid.
With agriculture almost completely dependent on rainfall, rain rules the lives and
well-being of many rural Ethiopians. It determines whether they will have enough to eat and
whether they will be able to provide basic necessities and earn a living. Indeed, the
dependence on rainfall and its erratic pattern has largely contributed to the food shortages and
crop crises that farmers are constantly faced with. Even in good years, the one-time harvest or
7
crop may be too little to meet the yearly household needs; as a result, the majority of
Ethiopia’s rural people remain food insecure (Devereux 2000)5.
Rainfall contributes to poverty both directly, through actual losses from rainfall
shocks, and indirectly, through responses to the threat of crisis. The direct impacts
particularly occur when a drought destroys a smallholder farmer’s crops. Under such
circumstances, not only will the farmers and their families go hungry, but they also will be
forced to sell or consume their plough animals in order to survive. They are then significantly
worse off than before because they can no longer farm effectively when the rains return
(Barrett et al. 2007).
Ethiopia is one of the African countries that have recently started paying particular attention
to climate change as a national as well as a global problem. Ethiopia is active in climate
change negotiations representing Africa. There are also a number of activities undertaken
over the past decade to address climate change. These include submission of initial national
communications on GHG emissions to UNFCCC, submission of National Adaptation
Program of Action (NAPA) as well as the Nationally Appropriate Mitigation Actions
(NAMA) to UNFCCC, preparation of a REDD readiness plan, and ongoing preparation of a
national level adaptation program as well as a green growth strategy as part of a climate
resilient green economy (CRGE) strategy. Such attempts should be supported by rigorous
empirical studies on various aspects of climate change including its impacts which are very
limited.
5
Ethiopia has experienced at least five major national droughts since 1980, along with literally dozens of
localized ones (World Bank 2008b). These cycles of drought create poverty traps for many households,
constantly consuming any build up of assets and increase in income. Evidence shows that about half of all rural
households in the country experienced at least one major drought during the five years preceding 2004 (Dercon
2009). The evidence also suggests that these shocks are a major cause of transient poverty. That is, had
Ethiopian households been able to smooth consumption, then poverty in 2004 would have been at least 14
percent lower, which translates into 11 million fewer people falling below the poverty line.
8
3. The data
Our data was collected through a rural household survey conducted by the Ethiopian
Development Research Institute and Addis Ababa University in collaboration with
Gothenburg University, and through financial support from the Swedish International
Development Agency (Sida). The survey sites include households in two Zones (South Wollo
and East Amhara) of the Amhara National Regional State, a region that encompasses part of
the Northern and Central Highlands of Ethiopia and was conducted in the years 2000, 2002,
2005 and 2007 cropping seasons on the same households. The rainfall and temperature data
obtained from the Ethiopian Meteorology Authority includes monthly observations from the
years 1976 to 2006, collected in stations close to the study villages (kebeles).
The farming system is a mixed crop-livestock system, with a household having
several field plots for crop cultivation, and livestock grazing mainly on communal fields. The
crop production system consists of cereals, pulse and legume crops, oil seeds and others. The
major cereal crops include teff, wheat, barley, sorghum and maize. Pulses cover several kinds
of beans and peas as well as lentils and vetch. Perennials include coffee, fruit trees (orange,
mango, papaya, banana, avocado, guava, and pineapple) and spices.
It is evident that cereal crops account for the majority of the crops grown in the study
area. This followed by a considerable number of plots covered by pulse crops and legumes.
Oil crops also account for a small share of the crops grown, followed by vegetable crops,
spices, and perennials. While these are the major crops, several different crops are grown by
households albeit in much smaller quantities.
9
Table 2 presents description of the variables along with their descriptive statistics. It
should be noted that since the regressions are based on household and not plot level
observations, all the variables are household level or averaged at a household level.
3.2. Variables used in the analysis
Dependent variable: Farm level crop revenue
The dependent variable is measured as the total revenue from the different crops grown on a
given farm.
Weather and climate change measures6
The climate change and weather measures are constructed from the respective monthly from
the stations. Average annual and seasonal rainfall and temperature measures corresponding to
the survey years are used as weather indicators. For climate change measures we used two
measures: moving average and overall average measures. For the year 2007, for instance, the
rainfall and temperature moving average climate change measures are calculated as the
average of the monthly observations for the years 1982 to 2006. Similarly moving average
climate measures for the years 1980 to 2004, 1978 to 2002 and 1976 to 2000 represent the
climate variables for the years 2005, 2002 and 2000 respectively. Similarly, the long term
annual average measures are calculated as the average of the observations between the years
1976 and 2006 for the respective villages.
We chose the spring (the Belg) and the summer (the Kiremt) months in the seasonal
weather and climate measures as they correspond to the minor and major rainy seasons
6
The rainfall and temperature data are obtained from eight meteorological stations close to the twelve study
villages. In consultation with the Meteorology Authority, the rainfall and temperature values assigned to the
villages were based on proximity. Hence, the rainfall data we have is village level i.e. households in two villages
sharing the same rainfall values, in some instances.
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respectively7.
Accordingly, the moving average climate measure for the kiremt season
includes the mean rainfall and temperature values for the 26 years in the kiremt season.
Similarly, the Belg (spring) long term moving average measure is calculated as the mean
rainfall and temperature values for the spring months. Accordingly, the long term Belg and
Kiremt seasonal moving average measures are computed as the seasonal means for the years
1976 to 2000, 1978 to 2002, 1980 to 2004, and 1982 to 2006 for the years 2000, 2002, 2005
and 2007 respectively.
The mean seasonal rainfall and temperature observations corresponding to the survey
years are used as seasonal weather measures. Similarly, the annual weather measure is
computed as the mean rainfall and temperature observations corresponding to the survey
years .
Other independent variables
Female headed households make up around 19% of the respondents. An average percentage
of the respondents that are able to write is 39%. The average age of the respondents is 47. On
average male and female adult members within the household are 2 and 1.9 respectively. This
is not surprising considering that there are limited off-farm opportunities and limited mobility
out of agriculture in the study area and in rural Ethiopia in general. The average livestock
holding was 5.182 units (tropical livestock units) and oxen ownership is around 1.87, for the
average household. The average land holding per household in the area is below 1.18 ha. The
proportion of fertile plots in 0.42.
flat sloped plots represent 0.67 share of the plots,
compared with red plots at 0.5.
7
Meher season (approximately June-September) crops harvested in September-December make up the bulk of
food production (90-95%), the belg is the short rainy season, which extends from February to May and Belg
production typically accounts for only 5-10% of total annual production (CSA, 2001).
11
4. Estimation Procedure
This section sets up a framework for analyzing the link between the farm level and
weateher and climate variables. We frame our analysis within the standard Ricardian
analysis, which is represented by equation 1:
rht   xht   vht   h  ht ,
(1)
where ht is farm household h in period t. Farm household-level revenue at time t is denoted
by Rht ,8 X ht represents the socioeconomic and farm-level characteristics, and Vht stands for
weather and climate variables at time t.  ,  , and  represent the respective vector of
parameter estimates, and  it represents the error term. The composite error term it  i  uit
is composed of a normally distributed random error term u ij ~ n(0,  u2 ) and an unobserved
household specific effect,  h .
Under the assumption that  h is orthogonal to the observable covariates, a random
effects estimator can be employed as an effective estimator of equation (2) (Baltagi 2001;
Wooldrige
2002).
However,
allowing
arbitrary
correlation
between
 h and the
regressors/observed covariates requires a fixed effect, as it takes  h to be a group-specific
constant term and uses a transformation to remove this effect prior to estimation (Wooldrige
2002).
To remedy the major drawback of removing the household specific effects of the
fixed effects estimator, Mundlak (1978) and Chamberlain (1982; 1984)9 suggest replacing the
Rit takes two distinct measures in our analysis: the beta coefficient and the risk ranking measure.
8
Note that
9
Also note that the strict exogeneity assumption on the observed covariates conditional on
h
is maintained,
although the arbitrary correlation between the two is allowed in this case. This implies that the observed
covariates only contain time-varying explanatory variables.
12
unobserved effect, with its linear projection onto the explanatory variables, in all time periods
plus the projection error. Allowing for correlation between  h and xh , and assuming a
conditional normal distribution with linear expectation and constant variance, implies that  h
can be approximated by the linear function in equation (3):
_
 h    x h  eh
_
eh | x h ~ N (0,  2 )
,
(2)
_
where x h is the average of the time varying variables in xht , and 𝜎𝑒2 is the variance of eh in
equation (3). Substituting the expression in equation (3) for  h in equation (2) gives:
_
rht   xht   vht   x h   ht ,
 ht ~ N (0,  2 ) .
(3)
This approach of adding the means of time-varying observed covariates as controls
for the unobserved heterogeneity without the data transformation in the fixed effects
estimator is commonly known as pseudo fixed effects or the Mundlak-Chamberlain random
effects model (Wooldridge 2002).
5. Discussion of results and conclusion
Table 2 presents the estimation results of the productivity analysis, controlling for the impacts
weather variables. This is followed up in Table 3 and Table 4 by the estimation results
controlling for the impacts of climate variables and both weather and climate variables
respecitively.
13
The objective of this analysis presented in Table 2 is to see the differential impacts of weather
and climate variables on farm level revenue.
The coefficient corresponding to the seasonal rainfall and temperature variables is
insignificant. However, the climate measures are significant determinants of productivity.
Overall, these results confirm that climate related variables have significant impact on
productivity than weather related variables. Education has a positive and significant impact
on productivity. Farm size is negative and significant, conforming to many previous findings
that relate to the inverse farm-size productivity relationship. The number of male adults per
ha is positive and significant across all the regressions, while its square is negative and
significant. This indicates that male labour availability has a strong positive, albeit non linear,
impact on productivity. As would be expected, chemical fertilizer and manure contribute
positively to productivity; the magnitudes are small, however. Oxen has a negative and
significant impact on productivity. Of the soil characteristics, soil slope appears to be the
most significant determinant of productivity, while soil colour and fertility are insignificant.
In a heavily rainfed agriculture like that of Ethiopia, agricultural incomes may
understandably be constrained by weather uncertainty. To the extent that weather variability
is stable over time, it may also mimic climate change, making the impact of climate change
on agricultural productivity akin to that of weather variability. However, if yearly weather
variabtion is by itself irratic, climate change needs to be measured separately and its imact
needs to be analysed on its own right.
This article assesses the importance of rainfall patterns, and long term weather
averages measuring climate change, on farm level crop productivity. The analysis employs
plot level panel data from Ethiopia,combined with a 30 year meteorolgical data
corresponding to the survey villages, used to construct seasonal and yearly rainfall variability
as well as a measure of long term averages. Results using both random and pseudo fixed
14
effects analyses showed that annual rainfall variability is a significant and positive
determinant of productivity, although the effect of seasonal rainfall variability and climate
change appears to be stronger. This implies that households income is highly conditioned on
weather variability. Similarly, climate change variables are significant determinants of
productivity while their effect is not uniform for the seasonal measures. Rural development
policies should take into account the farmers weather risk management in the face of weather
uncertainty. The results highlight that the differential impacts of weather and climate change
on productivity. This further points to the need to carefully distinguish between climate
change adapation and weather change mitigation/adaptation measures.
15
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Maria De Salvo (2010). The impact of climate change on agriculture in an Alpine region: A
Ricardian analysis. Mimeo. University of Catania
Stage, J. Economic valuation of climate change adaptation in developing countries. Annals of
the New York Academy of Sciences. 1185 (2010) 150–163 c_ 2010 New York Academy of
Sciences
Seo, N., and R. Mendelssohn. (2008) ’Measuring Impacts and Adaptations to Climate
Change: A Structural Ricardian Model of African Livestock Management’ Agricultural
Economics 38:1–15.
World Bank, 2008a. Ethiopia: Climate Factsheet, Washington DC: World Bank,
http://siteresources.worldbank.org/INTAFRICA/Resources/Ethiopia_Country_Note.p
df. Accessed 29 April 2010.
World Bank, 2008b. Rural Households and Their Pathways Out of Poverty. In World
Development
Report
2008.http://siteresources.worldbank.org/INTWDR2008/-
Resources-/2795087-1192112387976/WDR08_06_ch03.pdf. Accessed 23 February
2010.
18
Table 1: Definition of variables used in the regressions and descriptive statistics
Variable
Description
Socio economic characteristics of the household
Sex
Sex of the household head
0.192 0.394
Age
Age of household head
46.763 18.006
Write
Head’s formal education (1=read and write ;
0=otherwise)
0.392 0.488
Adult male
The number of male working-age family member
of the household
1.957 1.235
Adult female
The number of female working-age family member
of the household
1.876 1.056
Oxen
The number of oxen
1.877 1.393
Livestock
The number of livestock ( in tropical livestock
units)
5.182 4.244
Physical farm characteristics of the household
Landsize
Total farm size of the household in hectares
1.777 14.841
avg_fertile
Proportion of highly fertile plot in the total plots
managed by the household
0.420 0.372
avg_red
propotion of red soil plots in the total plots
managed by the household
0.501 0.370
avg_flat slope
proportion of flat slope plot in the total plots
managed by the household
0.675 0.335
Time variant
variables (averaged
over the survey
years)
mean_female
Number of female adults averaged over years
5.769 8.719
mean_male
The number of livestock averaged over years
22.496 25.729
mean_ox
The number of oxen averaged over years
8.963 10.027
mean_livestock
Number of male adults averaged over years
3.969 3.022
Weather and climate
change variables
Annual mean
the mean annual rainfall corresponding to the
survey year
0.972 1.117
Summer mean
the mean summer rainfall corresponding to the
survey year
0.271 0.085
Spring mean
the mean spring rainfall corresponding to the
survey year
0.496 0.162
Long term annual
the long term mean annual rainfall corresponding to
mean (by survey
the survey year
year)
Long term summer
the long term mean summer rainfall corresponding
mean (by survey
to the survey year
year)
Long term spring
the long term mean spring rainfall corresponding to
mean (by survey
the survey year
year)
19
Long term annual
mean (by kebele)
Long term summer
mean (by kebele)
Long term spring
mean (by kebele)
Total farm revenue
the long term mean annual rainfall corresponding to
the survey kebele
the long term mean summer rainfall corresponding
to the survey kebele
the long term mean spring rainfall corresponding to
the survey kebele
The sum of all revenues from the different crops
grown by the household
0.484
0.323
Table 2: Ricardian analysis of the impact of climate variables on farm level revenue
farm_output1
age
Coef.
Std. Err.
z
P>z
[95% Conf.
-0.57305
1.312125
-0.44 0.662
sex
-73.4218
48.91548
maleadult
156.7915
18.75093
femaleadult
164.9335
write
Interval]
-3.14477
1.998666
-1.50 0.133
-169.294
22.45078
8.36 0.000
120.0404
193.5427
18.37549
8.98 0.000
128.9182
200.9488
-2.80815
33.33372
-0.08 0.933
-68.1411
62.52475
livestock
-0.24937
2.758662
-0.09 0.928
-5.65625
5.15751
oxen
0.113002
2.768466
0.04 0.967
-5.31309
5.539095
landarea
0.397093
0.704515
0.56 0.573
-0.98373
1.777916
avg_red
178.7846
44.01943
4.06 0.000
92.50808
265.0611
avg_white
-115.338
122.4915
-0.94 0.346
-355.417
124.7407
avg_flatslop
167.5325
72.73298
2.30 0.021
24.97849
310.0865
avg_mslope
200.4785
78.40171
2.56 0.011
46.814
354.1431
avg_fertile
139.632
33.79677
4.13 0.000
73.39159
205.8725
telma
-195.439
107.9793
-1.81 0.070
-407.075
16.19631
yamed
-426.838
125.7327
-3.39 0.001
-673.27
-180.407
mfemale
-162.604
40.65977
-4.00 0.000
-242.296
-82.9126
mmale
-18.1014
36.37587
-0.50 0.619
-89.3968
53.19402
mox
-109.555
11.03473
-9.93 0.000
-131.182
-87.9268
109.535
10.99509
9.96 0.000
87.98501
131.085
kebele_spr~p
171.1743
24.94281
6.86 0.000
122.2873
220.0613
kebele_sum~p
-150.179
22.83675
-6.58 0.000
-194.938
-105.419
kebele_spr~f
-2.33866
4.590694
-0.51 0.610
-11.3363
6.65894
kebele_sum~f
11.76492
0.84419
13.94 0.000
10.11034
13.4195
_cons
-2189.91
619.7544
-3.53 0.000
-3404.61
-975.213
sigma_u
844.8592
sigma_e
1158.592
rho
0.347152
mlivestock
(fraction of variance due to u_i)
20
Table 3: Ricardian analysis of the impact of weather variables on farm level revenue
farm_output1
Coef.
Std. Err.
z
P>z
[95% Conf.
Interval]
age
-0.57305
1.312125
-0.44 0.662
-3.14477
1.998666
sex
-73.4218
48.91548
-1.50 0.133
-169.294
22.45078
maleadult
156.7915
18.75093
8.36 0.000
120.0404
193.5427
femaleadult
164.9335
18.37549
8.98 0.000
128.9182
200.9488
write
-2.80815
33.33372
-0.08 0.933
-68.1411
62.52475
livestock
-0.24937
2.758662
-0.09 0.928
-5.65625
5.15751
oxen
0.113002
2.768466
0.04 0.967
-5.31309
5.539095
landarea
0.397093
0.704515
0.56 0.573
-0.98373
1.777916
avg_red
178.7846
44.01943
4.06 0.000
92.50808
265.0611
avg_white
-115.338
122.4915
-0.94 0.346
-355.417
124.7407
avg_flatslop
167.5325
72.73298
2.30 0.021
24.97849
310.0865
avg_mslope
200.4785
78.40171
2.56 0.011
46.814
354.1431
avg_fertile
139.632
33.79677
4.13 0.000
73.39159
205.8725
telma
-48.4462
106.7838
-0.45 0.650
-257.739
160.8463
yamed
-427.409
128.7094
-3.32 0.001
-679.674
-175.143
mfemale
-162.604
40.65977
-4.00 0.000
-242.296
-82.9126
mmale
-18.1014
36.37587
-0.50 0.619
-89.3968
53.19402
mox
-109.555
11.03473
-9.93 0.000
-131.182
-87.9268
109.535
10.99509
9.96 0.000
87.98501
131.085
4.011996
0.470906
8.52 0.000
3.089037
4.934954
ksummer_te~2
-3.47323
0.413502
-8.40 0.000
-4.28368
-2.66278
kspring_rf2
0.010397
0.026978
0.39 0.700
-0.04248
0.063273
ksummer_rf2
0.027823
0.001863
14.93 0.000
0.024171
0.031475
_cons
-1172.25
374.5671
-3.13 0.002
-1906.39
-438.111
sigma_u
844.8592
sigma_e
1158.592
rho
0.347152
mlivestock
kspring_te~2
21
Table 4: Ricardian analysis of the impact of climate and weather variables on farm level
revenue
arm_output1
Coef.
Std. Err.
z P>z
[95% Conf.
Interval]
age
-0.57305
1.312125
-0.44 0.662
-3.14477
1.998666
sex
-73.4218
48.91548
-1.50 0.133
-169.294
22.45078
maleadult
156.7915
18.75093
8.36 0.000
120.0404
193.5427
femaleadult
164.9335
18.37549
8.98 0.000
128.9182
200.9488
write
-2.80815
33.33372
-0.08 0.933
-68.1411
62.52475
livestock
-0.24937
2.758662
-0.09 0.928
-5.65625
5.15751
oxen
0.113002
2.768466
0.04 0.967
-5.31309
5.539095
landarea
0.397093
0.704515
0.56 0.573
-0.98373
1.777916
avg_red
178.7846
44.01943
4.06 0.000
92.50808
265.0611
avg_white
-115.338
122.4915
-0.94 0.346
-355.417
124.7407
avg_flatslop
167.5325
72.73298
2.30 0.021
24.97849
310.0865
avg_mslope
200.4785
78.40171
2.56 0.011
46.814
354.1431
avg_fertile
139.632
33.79677
4.13 0.000
73.39159
205.8725
mfemale
-162.604
40.65977
-4.00 0.000
-242.296
-82.9126
mmale
-18.1014
36.37587
-0.50 0.619
-89.3968
53.19402
mox
-109.555
11.03473
-9.93 0.000
-131.182
-87.9268
109.535
10.99509
9.96 0.000
87.98501
131.085
kebele_spr~p
-181.304
76.74917
-2.36 0.018
-331.73
-30.8784
kebele_sum~f
25.13217
12.61348
1.99 0.046
0.410208
49.85413
kspring_te~2
6.728456
2.186762
3.08 0.002
2.442482
11.01443
ksummer_te~2
-1.89459
0.862317
-2.20 0.028
-3.5847
-0.20448
kspring_rf2
-0.01034
0.038895
-0.27 0.790
-0.08657
0.065897
ksummer_rf2
-0.02465
0.028999
-0.85 0.395
-0.08149
0.032187
_cons
-1852.12
1310.821
-1.41 0.158
-4421.29
717.0406
sigma_u
844.8592
sigma_e
1158.592
rho
0.347152
mlivestock
22