Master's defense presentation, in partial fulfillment of the requirements of master diploma in statistics and applied economics. Major applied statistics and econometrics. Titled by : The impact of climate change on wheat yields, in North Africa.

1. The Impact of Climate Change on
Wheat Yield in North Africa
(1961-2019)
HOURIA BOUCHAGOUR
Master’s Thesis Defense Presentation
National Higher School of Statistics And Applied
Economics (ENSSEA), Kolea, Algeria
September 2020

2. Outline
1. Introduction
2. Motivation
3. Research question
4. Methods and materials
5. Hypotheses
6. Results and discussion
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3. 1. Introduction
Climate change has occurred
throughout many time scales. Since the
pre-industrial era, human activities are
speeding-up the changes in the climate.
It has already had observable effects on
the environment.
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4. 4
2. Motivation
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5. 5
3. Research Question
27/09/2020 THE IMPACT OF CLIMATE CHANGE ON WHEAT YIELD IN NORTH AFRICA (1961-2019)
How much wheat yields change
over climate change in North Africa ?

6. 6
4. Methods & Materials
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• Just &Pope stochastic production function
𝑓 𝑋𝑖𝑡, 𝛽 = 𝛽0 + 𝛽𝑡 𝑇 + 𝑥𝑖
𝛽𝑖
𝑖
• Cobb-Douglas Econometric specification
𝑌 = 𝐴 𝑥𝑖
𝜃𝑖𝐿
𝑖

7. 7
• Long Balanced Panel Data (N=5, T=59)
• Random Coefficient Model
𝒍𝒐𝒈𝒀𝒊𝒕 = 𝜷 𝟏 𝒍𝒐𝒈𝑻𝒊𝒕 + 𝜷 𝟐 𝒍𝒐𝒈𝑷𝒊𝒕 + 𝜷 𝟑 𝒍𝒐𝒈𝑨𝑳𝑨𝒊𝒕 + 𝜷 𝟒 𝒍𝒐𝒈𝑭𝒊𝒕
+𝜷 𝟓 𝑻𝒓𝒆𝒏𝒅 + 𝜶𝒊 + 𝝐𝒊𝒕
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4. Methods & Materials

8. 8
Year Country Y T P ALA F
1961 Algeria 410 16,07 6,3 6472 7,57
…
…
…
…
…
…
…
2019 Algeria 1900 21,08 59,92 7069,48 18,15
…
…
…
…
…
…
…
1961 Egypt 2470 15,81 4,95 2499 96,95
…
…
…
…
…
…
…
2019 Libya 1210 23,75 40,82 1769,19 28,3
…
…
…
…
…
…
…
1961 Morocco 380 12,86 21,63 6590 4,39…
…
…
…
…
…
…
2019 Tunisia 2440 21,08 59,92 2996,35 31,07
[Year]: time scale (T=59)
[Country]: cross-sections
or panels (N=5)
[Y]: average annual wheat
yields
[T]: average annual
temperature
[P]: a. a. precipitation
[ALA]: arable land area
[F]: fertilizers

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4. Methods & Materials

10. 10
5. Hypotheses
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• The 5 countries have different average levels of the
endogenous variables (slopes);
• Higher temperature will affect the wheat yield negatively
• Precipitation has beneficial effects;
• Control variables (F and ALA) and time trend have a
positive effect on wheat yields.

11. IPS(Im, Pesaran & Shin )
𝐻0: All panels contain unit root
𝐻1: Some panels are stationary
𝐻0 is rejected whenever
[ t-stat < t-tab ]
Chow
𝐻0: 𝛽𝑖𝑘 = 𝛽𝑘
𝐻0 rejected whenever
[ F-stat > P-value ]
11
IPS Unit
Root test
Chow
Poolability
test
Random
Coefficient
Model

12. 12
The estimated Random Coefficient Panel Model
𝑙𝑜𝑔𝑌𝑖𝑡 = 15.15 − 0.66 𝑙𝑜𝑔𝑇𝑖𝑡 + 0.20 𝑙𝑜𝑔𝑃𝑖𝑡 − 0.98 𝑙𝑜𝑔𝐴𝐿𝐴𝑖𝑡
+ 0.10 𝑙𝑜𝑔𝐹𝑖𝑡 + 0.02 𝑇𝑟𝑒𝑛𝑑 + 𝜖𝑖𝑡
6. Results & discussion
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6. Results & discussion
• All the variables in the model are stationary
• Our Panel data are not poolable. That is, each Country has
its own slopes for all regressors
• Both fixed and random effect models may be misleading
• [T], [P], [Trend], and [intercept] are significant variables in
the model; [F] and [ALA] are not.

14. Thank You!
14
Questions?
27/09/2020 THE IMPACT OF CLIMATE CHANGE ON WHEAT YIELD IN NORTH AFRICA (1961-2019)