Transcript Yemen

THE IMPACT OF TECHNOLOGY PROGRESS AND CLIMATE CHANGE
ON SUPPLY RESPONSE IN YEMEN
PASQUALE LUCIO SCANDIZZO
Centre for Economic and International Studies (CEIS), Faculty of Economics, University of Rome "Tor Vergata”
DANIELE CUFARI
Department of Economics Law and Institutions, Faculty of Economics, University of Rome “Tor Vergata”
Yemen
Source: WFP
Yemen is one of the poorest countries in the World:
• GDP per capita around 600 USD
• Small land based: around 1.2 Mln Has of arablel and against 24 Mln of
population
• Oil sector is dominant: around 27% of GDP and 90% of merchandise
exports
• Scarcity of water and infrastructure
Yemen Agroecological zones
Source: IFPRI
1. Upper Highlands (above 1,900 m): temperate, rainy summer and a cool, moderately dry
winter
2. Lower Highlands (below 1,900 m): Precipitation ranges from 0 mm to 400 mm and the
temperature in the summer reaches 40°C.
3. Red Sea and Tihama Plain: tropical, hot and humid climate, while
rainfall averages only 130 mm annually and occurs in irregular, torrential storms.
4. Arabian sea cost: average temperature of 25°C in January and 32°C in June, with an average
annual rainfall of 127 mm
5. Internal Plateau: characterized by a desert environment
6. Desert
Climate change poses a significant threat to Yemen’s development, with rising temperature
projections and increasing in variance of rainfall
Climate-related hazards in Yemen include extreme temperatures, floods, landslides, sea level
rise, and droughts.
Volatility increase and impact on agricultural
productivity
The volatility of the yield is negatively related with the productivity
This negative effect, is enhanced by the increase of the variation of the rain,
especially for the planting season
Dependent Variable: NET VALUE OF PRODUCTION
Method: Least Squares
Sample: 1 90
Included observations: 90
Volatility
increases
Average Effects on farm
productivity
Percentages
FARM_SIZE
((FARM_SIZE))^2
VARIABILITY OF SORGHUM YIELD
VARIABILITY OF WHEAT YIELD
HIGH RAINFALL VARIATION
R-squared
Adjusted R-squared
Coefficient
Std. Error
t-Statistic
Prob.
10661.47
-400.2723
-4.109969
-11.38326
-5380.643
1209.997
129.3618
1.349881
3.574484
2233.962
8.811153
-3.094208
-3.044690
-3.184589
-2.408565
0.0000
0.0027
0.0031
0.0020
0.0182
0.582808
0.563175
sorghum
Wheat
US dollars
20%
-4%
-270,4176
50%
-10%
-676,044
100%
-20%
-1352,088
20%
-4%
-283,93848
50%
-11%
-709,8462
100%
-21%
-1419,6924
Example of impact Impact of Climate Change
(Authors’ estimates on unbalanced Panel data)
Dependent variable: Logarithm of maize yield
Independent variables: logarithm of average quantity and variance of
rainfall in critical seasons
Coefficient
T-statistic
-2
-5.40
Winter average
0.65
5.67
Spring+Fall average
0.63
4.42
Winter+Fall variance
-0.25
-3.97
Variation of average spring rainfall
-0.22
-1.81
R-squared
0.87
Constant
Rainfall variance has a negative effect in the winter and the fall and the
variation of rainfall in the spring, a likely manifestation of climate change,
has also a negative effect
Field Survey Descriptive statistics
Farm size
Persons living from farm activity
Persons working in the farm
N° of cropping seasons
N° of cultivated crops
Value added
Ave. St. Dev. Value Added
Per capita Value Added
Log Value Added
St. Dev. Value Added
Cultivated land under cereals
Cultivated land under pulses
Cultivated land under vegetables
Cultivated land under fruits
Cultivated land under coffee
Cultivated land under qat
Cost of water
Cost of fertilizers
Cost of chemicals
Cost of hired work
Cost of land operations
Other costs
Total costs of production
Unit
Ha
Nb
Nb
Nb
Nb
USD
USD
USD
USD
USD
Ha
Ha
Ha
Ha
Ha
N°
observations
90
90
90
89
391
90
90
90
81
81
90
90
90
90
90
Ha
90
of
Mean
1.18
11
4
2
6
6261
5023
529
8
1
0.49
0.10
0.08
0.23
0.01
Std. Dev.
1.69
8
3
1
3
12910
7604
965
2
1
0.57
0.26
0.47
0.60
0.02
0.28
0.67
Unit
N° of observations Mean
Std. Dev.
USD/year
USD/year
USD/year
USD/year
USD/year
USD/year
USD/year
37
51
46
65
35
11
90
1649
82
126
489
292
1002
1692
1421
71
104
233
223
539
998
Adapting to climate change: Mathematical model
Assuming that each option underlying value evolves as a Brownian Motion with zero
drift and constant variance
(1)
𝑑 𝑉𝑖 𝑦𝑗 = 𝜎𝑗 𝑉𝑖 𝑦𝑑𝑧𝑗
where j denote the j-th option and i denote the i-th farmer. The economic value of
the ith farm can be represented by the equation:
𝑊𝑖 =
(2)
𝑉𝑖
𝜌
+
𝐽
𝑗=1 𝐴𝑖𝑗
(𝑉𝑖 𝑦𝑗 )𝛽
where 𝐴𝑖𝑗 (𝑉𝑖 𝑦𝑗 )𝛽 is the value of the jth option to adapt of the ith farmer and β =
1
2
+
(3)
1
2𝜌
+ 2
2
𝜎
(Dixit and Pindyck, 1994). For adoption To be acceptable for option j:
𝑉𝑖 𝑦𝑗∗ =
𝛽1
𝛽1 −1
∗ 𝐼𝑗
where 𝐼𝑗 represents the cost of adoption. At farmer level, the option value over an
infinite time horizon for farmers who have not adopted (yet) is given by:
(4)
𝑉𝑖 𝑦𝑗
𝛻𝑗∗ 𝑦𝑗∗
𝛽1
∗
𝛻𝑗∗ 𝑦𝑗∗
𝜌𝛽1
where 𝛻𝑗∗ 𝑦𝑗∗ = 𝑏𝑖 i. e. the coefficient estimated in the regression on an estimate of
the increment of value added due to the adoption
Econometric Results: Value Added equations
Constant
p-value
Standard Deviation of value added
Gender (0=female, 1=male)
Dummy high value crops
(farmers growing qat, coffee, fruits and vegetables)
Dummy terrace irrigation
Dummy land partly owned and partly rented
Dummy changes of agricultural practices
in response to climate change
Age group 15-29
Education from 4 to 8 years
VALUE ADDED IN USD
OLS
TLS
-12587.00
-14278.00
0.00
0.00
1.45
1.54
0.00
0.00
1829.00
0.07
1353.29
0.00
2056.57
0.05
4264.50
0.00
1315.21
0.01
3075.57
0.00
1908.04
0.01
Dummy alternative form of irrigation
apart from terrace
R-squared
Adjusted R-squared
3134.16
0.097
4727.64
0.049
13123.55
0.00
0.96
0.95
0.86
0.85
Adapting to climate change: option values (US $
per year)
Item
Opportunity option:
high value crops (qat,
coffee, fruits and
vegetables)
Growth option: terrace
rehabilitation
Coping option: changes
of practices in response
to climate change
Opportunity option:
education
Underlying
(increase in
Value
Added per
farm)
Estimates
of strike
prices
Volatility
Value of
Option
1353
835
0.33
815
2057
1269
0.50
1373
1315
811
0.39
787
1908
1897
0.30
985
Option values for introducing Drought Tolerant maize
(US dollars/ha)
Variable
Obs
Mean
Std. Dev.
Min
Max
7849.05
Valueadded/ha
32
2257.081
1980.829
73.7
Val. added GM/ha
32
2708.498
2376.994
88.44
9418.86
Difference of VA
32
451.4163
396.1647
14.74
1569.81
Beta
32
1.37
0
1.37
1.37
hurdle
32
3.69
0
3.69
3.69
Underlying
32
15377.96
13495.78
502.14
53477.16
Estim. investments
32
4166.212
3656.293
136.04
14488.09
option value 35%
32
11320.98
9935.358
369.6668
option value 55%
option value 75%
32
32
11958.85
12507.17
10495.16
10976.37
390.4953
408.3998
39368.94
41587.15
43493.96
Adapting to climate change: Option Values (US dollars
per year)
Underlying
(increase
in Value
Added per
farm)
Estimates
of strike
prices
Volatility
Option
value
Volatility
+
Option
value
+
Volatility
++
Option
value
++
1353
835
33%
815
53%
916
73%
988
Opportunity
option:
adoption of
drought
tolerant
maize
1358
416
35%
1131
55%
1196
75%
1250
Growth
option:
terrace
rehabilitation
2057
1269
50%
1373
70%
1485
90%
1594
1315
811
39%
787
59%
898
79%
993
1908
1897
30%
985
50%
1188
70%
1335
Opportunity
option: high
value crops
(qat, coffee,
fruits and
vegetables)
Coping
option:
changes of
practices in
response to
climate
change
Opportunity
option:
education
Option values contribution
7000
6000
5000
Opportunity option: education
Coping option: changes of practices in
response to climate change
4000
Growth option: terrace rehabilitation
3000
Opportunity option: adoption of drought
tolerant maize
Opportunity option: high value crops (qat,
coffee, fruits and vegetables)
2000
1000
0
Option value
Option value +
Option value ++
CONCLUSIONS
• Climate Change threats provide the incentives to adapt trough a class of
projects, which construct capabilities and open real options as a major
source of opportunities.
• The options to adapt to climate change in Yemen, exist not only as a
reactive and coping responses of existing farming system, but also as
accumulation of capabilities to flexibly create a whole set of new
farming systems
• The adoption of the GM technology appears to be an especially valuable
option for the country to adapt to some of the harshes conditions that
may be determined by climate change
Thank you for your attention