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Climate Shocks and African Maize Prices
(Preliminary draft: Please do not quote or cite without permission of author. Comments welcome.)
Shun Chonabayashi
Cornell University
[email protected]
Selected Paper prepared for presentation at the Agricultural & Applied Economics
Association’s 2013 AAEA & CAES Joint Annual Meeting, Washington, DC, August 4-6, 2013.
Copyright 2013 by Chonabayashi. All rights reserved. Readers may make verbatim copies of
this document for non-commercial purposes by any means, provided that this copyright notice
appears on all such copies.
1
I. Introduction
Agriculture is the most affected sector by climate change. About 13% of GDP was
derived from agriculture in Sub-Saharan Africa in 2010 (WDI, 2012). It has been predicted that
the highest percentage losses owing to the direct impact of climate change on crops in the SubSaharan Africa region (Hertel et al., 2010). Another research result shows that, the mean
estimates of aggregate production changes in Sub-Saharan Africa are −22, −17, −17, −18, and
−8% for maize, sorghum, millet, groundnut, and cassava, respectively (Schlenker et al., 2010).
Although there have been studies on the climate shocks on African food prices, there are
many important questions at subnational scales that previous research has not addressed. To my
best knowledge, my study will be the first research that provides more careful study on the
impact of climate change on maize prices at subnational level in Africa.
In addition, although most studies focus on aggregate yield or production impacts, climate
change should affect not just food production but also demand variables, especially by affecting
incomes of many families who depend on farming or other climate-affected livelihoods. There
should be changes in equilibrium prices that are due to the confluence of changes in supply and
demand. Because there are global markets into which local markets may be imperfectly
integrated, those equilibrium prices effects may interact with global prices. By conducting spatial
price analysis and using household data, my research traces out the poverty and welfare impacts
of climate factors as transmitted through the economy of Africa. This is s an important
contribution, especially since food price movements can trigger social unrest as well as changes
in well-being.
The rest of the paper is structured as follows. Section 2 describes methodology I use to
analyze relationship between maize price and climate. Section 3 illustrates data that are used for
model estimation. Section 4 presents and discusses results. Finally, section 5 gives conclusion.
II. Methodology
Climate shocks affect maize prices in a few ways. They induce speculative storage
behavior before the realization of output shocks (Ravallion, 1986). They also have
contemporaneous effects due to expectations about future prices as well as lagged effects due to
realized supply shocks. African countries in particular are vulnerable to climate shocks as most
of farmers do not have either irrigation system or transportation system that can deliver food to
local regions.
I model quantitatively these relationships between local maize prices and climate
variables using a pooled semi-log model (Messetti et al. 2013).
′
′
′
𝑃𝑖,𝑡
= 𝛽ℎ(𝐶𝑖 ) + 𝛾𝑋𝑖,𝑡
+ 𝜃𝑍𝑖,𝑡
+ 𝜓𝑐 + 𝜀𝑖,𝑡
(1)
where P'i,t is the natural logarithm of the retail maize price in a regional market, X'i,t is a matrix
of time varying control variables, Zi is a vector of time invariant control variables, h(Ci) is a
vector of climate variables and ψ is country fixed effect. Subscript i, t and c represent regional
market, year and country respectively. β, γ, and θ are coefficient vectors. β provide sensitivity
information on the sensitivity of maize prices to climate and can be used to estimate the welfare
impact of climate change.
2
Time variant control variables, X'i,t, include global maize and crude oil prices. Dillon et
al. (2013) find that global maize and oil prices affect sub-national maize market prices through
their impacts on transport fuel prices.
Time invariant control variables, Zi, include irrigation, water scarcity and elevation since
the previous literature has made it clear that irrigation and water availability are important
variables in crop production. Irrigated land generally increases crop productivity and thus
negatively affects its prices. However, in Africa, nearly 80% of agricultural areas rely on rain
(Kurukulasuriya et al., 2008).
Since it is likely that maize prices at close markets are spatially correlated, a country
dummy variable accounts for the spatial autocorrelation. Also, countries have different policy on
maize price and import and export, which can be also explained by the variable.
For climate measurement, I use two different sets of variables. First, I use a quadratic
functional form for average temperature and precipitations in four seasons as below:
′
2
2
′
′
𝑃𝑖,𝑡
= 𝛽0 + � 𝛽1,𝑘 𝑇𝑖,𝑘 + � 𝛽2,𝑘 𝑇𝑖,𝑘
+ � 𝛽3,𝑘 𝑅𝑖,𝑘 + � 𝛽4,𝑘 𝑅𝑖,𝑘
+ 𝛾𝑋𝑖,𝑡
+ 𝜃𝑍𝑖,𝑡
+ 𝜓𝑐 + 𝜀𝑖,𝑡
𝑘
𝑘
𝑘
(2)
𝑘
where k represents four seasons, including spring, summer, fall and winter and T and R are
seasonal mean temperature and precipitation. I use the quadratic form since (Mendelsohn et al.,
20).
I explored several ways of defining three-month average seasons. Comparing the results,
I found that defining winter in the northern hemisphere as the average of December, January and
February provided the most robust results for Africa. This means that March, April and May
would be spring, June, July and August would be summer, and September, October and
November would be fall in the northern hemisphere. I adjusted for the fact that seasons in the
southern and northern hemispheres occur at exactly the opposite months of the year.
As a second climate measurement, I use Degree Days (DD). DD is equal to the sum of
daily mean temperatures within a given time interval R during the growing season. Denoting
with dd8-30i,r, the contribution of day r ∈ R in location i to 8-30 ̊C DD (DD8-30i), I calculate
degree days as follows:
𝑑𝑑8– 30𝑖,𝑟 = �
0 𝑖𝑓 𝑡𝑖,𝑟 ≤ 8
𝑡𝑖,𝑟 − 8 𝑖𝑓 8 < 𝑡𝑖,𝑟 ≤ 30
𝐷𝐷8– 30𝑖 = �
𝑟∈𝑅
(3)
𝑑𝑑𝑖,𝑟
In addition to DD, Extreme Degree Days (EDD) have negative effects on crop yield in
the US according to a recent study by Lobell et al. (2013). EDD is defined in the same way as
DD:
𝑒𝑑𝑑30𝑖,𝑟 = �
0 𝑖𝑓 𝑡𝑖,𝑟 ≤ 30
𝑡𝑖,𝑟 − 30 𝑖𝑓 30 < 𝑡𝑖,𝑟
𝐸𝐷𝐷30𝑖 = �
𝑟∈𝑅
𝑒𝑑𝑑𝑖,𝑟
(4)
I do not include Cold Degree Days (Massetti et al., 2013) since we do not observe them
in countries of this study. Total growing season mean precipitations are also calculated. Using
these measures described above, the resulting equation is below:
3
′
′
′
𝑃𝑖,𝑡
= 𝛽0 + 𝛽1 𝐷𝐷8– 30𝑖 + 𝛽2 𝐷𝐷8– 302𝑖 + 𝛽3 𝐸𝐷𝐷30𝑖 + 𝛽4 𝑃𝑖 + 𝛽4 𝑃𝑖2 + 𝛾𝑋𝑖,𝑡
+ 𝜃𝑍𝑖,𝑡
+ 𝜓𝑐 + 𝜀𝑖,𝑡
(5)
DD8-30i represents a typical measure used to predict maize development rates (Kiniry et
al., 1991) and is closely related to average growing-season temperature. The same is true for
precipitation. Too much or too little rain is harmful.
III. Data
Monthly retail maize prices data for Benin, Burundi, Cape Verde, Chad, Côte d'Ivoire,
Ethiopia, Ghana, Kenya, Malawi, Mauritania, Mozambique, Nigeria, Rwanda, Senegal, Somalia,
South Sudan, Tanzania, Togo, Uganda, Zambia and Zimbabwe and monthly US dollar exchange
rate are obtained from the USAID Famine Early Warning System (FEWS) 1. Table 1 and Map 1
show a list of regional markets and spatial distribution of the price series. All price data were
converted into US dollars using the exchange rate.
Global maize and crude oil price series come from the World Bank Global Economic
Monitor (GEM) database of commodity prices. Crude oil prices are expressed in nominal USD
per barrel, and are the equally weighted average spot price of Brent, Dubai, and West Texas
Intermediate crude, as calculated by the World Bank. Maize prices are expressed in nominal
USD per metric ton.
We use the monthly CPI extracted from the U.S. Department of Labor Bureau of Labor
Statistics to convert all price series for regional and global maize and global oil to real prices in
US dollar with a base year of 2005.
Temperature data are obtained from ERA-Interim, which is a global reanalysis of
recorded climate observations over the past 3.5 decades. It is presented as a gridded data set at
approximately 0.7 degrees spatial resolution (approximately 78 km) and 6-hourly temporal
frequencies. ERA-Interim is produced by the European Centre for Medium-Range Weather
Forecasts (ECMWF).
Precipitation data are from Modern ERA-Retrospective Analysis for Research and
Applications (MERRA) produced by National Aeronautics and Space Administration (NASA).
MERRA output data resemble other global reanalyses, with several key advances, including
output at frequencies of the 6-hourly analyses. These data are available at the spatial resolution
of 0.5 degrees latitude and 0.667 degrees longitude (approximately 56 km by 74 km).
Temperature and precipitation data for 2030 are also obtained from GFDL-ESM2M
RCP6 (run 1) experiment output for CMIP5 AR5 produced by NOAA’s Geophysical Fluid
Dynamics Laboratory (GFDL). The data set is available at the spatial resolution of 0.89 degrees
latitude and 0.143 degrees longitude (approximately 99 km by 16 km) and 3-hourly temporal
frequencies.
Data on irrigation are from Global Map of Irrigation Areas version 4.0.1. produced by
FAO (2007). This map provides area under irrigation in percentage of land area for each 5 by 5
minute grid cell globally (approximately 9km). Map 2 shows percentage of area under irrigation
in Africa.
1
For more details, refer http://www.fews.net.
4
Data on water availability are obtained from a map of global distribution of physical
water scarcity by major hydrological basin produced by Land and Water Division of Food and
Agriculture Organization (FAO) of the United Nations. Depending on percentage of
evapotranspiration due to irrigation of the total renewable water resources (less than 10%,
between 10% and 20% and more than 20%), water scarcity in river basins is classified as low,
moderate and high respectively.
Data on elevation for each regional market are obtained from the United States
Geological Survey (USGS, 2004), which was derived from a global digital elevation model with
a horizontal grid spacing of 30 arc seconds (approximately 1 km).
Data on beginning dates and duration of growing season for each regional maize market
are from FAO.
Since all of these data sets are spatially detailed, values that are geographically closest are
extracted to each regional market for the model estimation.
IV. Results and discussion
Table 4 and 5 reports estimation results from the equations (2) and (5).
V. Simulation
Table 6 shows current and predicted maize prices for 2012 and 2030. Map 3 shows
predicted maize price change from 2012 to 2030.
VI. Conclusion
The results of this study imply that it is likely that climate variables affect regional maize
prices in Africa.
5
References
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7
Map 1: Spatial distribution of the maize price series data
8
Map 2: Percentage of area under irrigation in Africa
9
Table 1: List of regional maize markets by country
Country
Benin
Burundi
Cape Verde
Chad
Côte d'Ivoire
Ethiopia
Ghana
Kenya
Malawi
Mauritania
Mozambique
Nigeria
Rwanda
Senegal
Somalia
South Sudan
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Market location
Bohicon, Come, Cotonou, Malanville, Parakou
Bujumbura, Gitega, Kirundo, Muyinga, Ngozi, Ruyigi
Praia
Bol, Moussoro, N'Djamena
Bouake
Addis Ababa, Jijiga, Jimma, Shashemene, Soddo, Yebelo
Tamale
Garissa, Kitui, Lodwar, Mandera, Marsabit, Moyale, Wajir
Bangula, Karonga, Lilongwe, Lunzu, Mitundu, Mzuzu, Ngabu, Salima
Nouakchott
Chokwe, Gorongosa, Manica, Maputo, Maxixe, Nampula, Tete, Xai-Xai
Bodija Market, Giwa, Kaura, Saminaka
Bugesera, Burera, Byumba, Gakenke, Gatsibo, Gisagara, Huye, Kamonyi,
Karongi, Kayonza, Kigali, Kirehe, Muhanga, Ngoma, Ngororero, Nyabihu,
Nyagatare, Nyamagabe, Nyamasheke, Nyanza, Nyaruguru, Rubavu, Ruhango,
Ruhengeri, Rulindo, Rusizi, Rutsiro, Rwamagana
Ziguinchor
Afgoi Addo, Afmadow, Baidoa, Buaale, Burao, Doble Yare, El Wak, Galkayo,
Hargeisa, Jamaame, Jilib, Jowhar, Kismayo, Luuq, Merca, Mogadishu,
Qoryooley, Wanle Weyne
Aweil, Juba
Dar Es Salaam, Iringa
Dapaong, Kara
Kampala, Masindi
Chipata, Choma, Kabwe, Kasama, Kitwe, Lusaka, Mansa, Mongu, Solwezi
Bulawayo, Gwanda, Gweru, Harare, Masvingo, Mutare
10
Table 2: Summary statistics of variables
Variable
Obs Mean Std. Dev.
Min
Max
Local maize price (USD/kg)
945
0.400
0.273
0.041 1.755
Global maize price (USD/kg)
945
0.199
0.096
0.077 0.351
Local fuel price (USD/bbl)
945
0.077
0.036
0.011 0.123
Temperature (°C)
945 24.331
4.171
13.413 35.878
Precipitation (cm/month)
945
7.358
8.179 7.67E-10 48.243
Area under irrigation (%)
945
2.028
4.929
0 45.84
Dummy for high water scarcity 945
0.168
0.374
0
1
Elevation (m)
945 671.181
665.588
1
2427
11
Table 3: Growing season period by country
Country
Benin
Benin
Benin
Benin
Benin
Burundi
Burundi
Burundi
Burundi
Burundi
Burundi
Côte d'Ivoire
Cape Verde
Chad
Chad
Chad
Ethiopia
Ethiopia
Ethiopia
Ethiopia
Ethiopia
Ethiopia
Ghana
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Malawi
Mauritania
Mozambique
Mozambique
Market
Bohicon
Come
Cotonou
Malanville
Parakou
Bujumbura
Gitega
Kirundo
Muyinga
Ngozi
Ruyigi
Bouake
Praia
Bol
Moussoro
N'Djamena
Addis Ababa
Jijiga
Jimma
Shashemene
Soddo
Yebelo
Tamale
Garissa
Kitui
Lodwar
Mandera
Marsabit
Moyale
Nairobi
Wajir
Bangula
Karonga
Lilongwe
Lunzu
Mitundu
Mzuzu
Ngabu
Salima
Nouakchott
Chokwe
Gorongosa
Beginning month Duration (days)
1
140
1
140
1
140
4
140
2
140
1
140
11
200
7
140
11
200
11
200
11
200
1
140
6
110
5
85
5
110
5
140
1
230
11
140
3
230
1
200
1
200
2
170
3
140
9
85
9
140
3
140
2
85
1
140
2
140
1
170
2
85
9
140
1
140
10
140
9
140
10
140
1
170
9
140
10
140
6
110
11
140
9
140
12
Mozambique
Mozambique
Mozambique
Mozambique
Mozambique
Mozambique
Nigeria
Nigeria
Nigeria
Nigeria
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Rwanda
Senegal
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Manica
Maputo
Maxixe
Nampula
Tete
Xai-Xai
Bodija Market
Giwa
Kaura
Saminaka
Bugesera
Burera
Byumba
Gakenke
Gatsibo
Gisagara
Huye
Kamonyi
Karongi
Kayonza
Kigali
Kirehe
Muhanga
Ngoma
Ngororero
Nyabihu
Nyagatare
Nyamagabe
Nyamasheke
Nyanza
Nyaruguru
Rubavu
Ruhango
Ruhengeri
Rulindo
Rusizi
Rutsiro
Rwamagana
Ziguinchor
Afgoi Addo
Afmadow
Baidoa
Buaale
Burao
Doble Yare
9
11
10
10
10
10
1
4
3
3
7
5
5
5
11
7
7
7
5
7
11
5
4
7
4
5
11
5
4
7
4
4
7
5
5
4
5
11
5
2
2
2
2
2
2
140
140
140
140
140
140
140
140
140
140
140
9
9
230
200
140
200
200
9
200
200
230
200
200
230
230
200
9
200
200
9
230
200
9
200
9
230
200
140
85
140
85
85
110
140
13
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
Somalia
South Sudan
South Sudan
Tanzania
Tanzania
Togo
Togo
Uganda
Uganda
Zambia
Zambia
Zambia
Zambia
Zambia
Zambia
Zambia
Zambia
Zambia
Zimbabwe
Zimbabwe
Zimbabwe
Zimbabwe
Zimbabwe
Zimbabwe
El Wak
Galkayo
Hargeisa
Jamaame
Jilib
Jowhar
Kismayo
Luuq
Merca
Mogadishu
Qoryooley
Wanle Weyne
Aweil
Juba
Dar Es Salaam
Iringa
Dapaong
Kara
Kampala
Masindi
Chipata
Choma
Kabwe
Kasama
Kitwe
Lusaka
Mansa
Mongu
Solwezi
Bulawayo
Gwanda
Gweru
Harare
Masvingo
Mutare
2
2
1
2
2
2
2
2
2
2
2
2
5
6
11
10
3
2
11
8
10
2
10
9
9
10
9
11
2
10
10
1
1
11
1
85
85
110
140
140
85
140
85
140
140
110
110
140
140
140
170
140
140
140
140
140
110
140
140
140
140
140
140
110
140
140
140
140
140
170
14
Table 4: Estimation results from equations (2)
Log of global maize price
Log of global oil price
Temperature winter (°C)
Temperature winter squared
Temperature spring (°C)
Temperature spring squared
Temperature summer (°C)
Temperature summer squared
Temperature fall (°C)
Temperature fall squared
Precipitation winter (°C)
Precipitation winter squared
Precipitation spring (°C)
Precipitation spring squared
Precipitation summer (°C)
Precipitation summer squared
Precipitation fall (°C)
Precipitation fall squared
Area under irrigation (%)
High water scarcity
Elevation (m)
Adjusted R-squared
0.564***
(0.0481)
0.107***
(0.0387)
-0.386***
(0.0878)
0.00740***
(0.00176)
0.168*
(0.0892)
-0.00433***
(0.00167)
-0.111
(0.0731)
0.00402***
(0.00154)
0.265**
(0.114)
-0.00457**
(0.00222)
0.0383***
(0.00622)
-0.00121***
(0.000199)
-0.0244***
(0.00775)
0.000760***
(0.000286)
0.0131**
(0.00604)
0.00000641
(0.000164)
0.00938
(0.00714)
-0.000841***
(0.000265)
-0.00558**
(0.00226)
0.190***
(0.0590)
0.0000428
(0.0000471)
0.770
Notes: *** significance at 1%; ** significance at 5%; * significance at 10%.
(Standard errors below coefficients)
15
Table 5: Estimation results from equation (4)
Log of global maize price
Log of global oil price
Degree Days
Degree Days squared
Extreme Degree Days
Growing season total precipitation
(mm)
Growing season total precipitation
squared
Area under irrigation (%)
High water scarcity
Elevation (m)
Adjusted R-squared
0.588***
(0.0496)
0.126***
(0.0405)
0.0000419
(0.0000611)
-3.59e-09
(7.58e-09)
0.000314
(0.000358)
0.000340***
(0.0000762)
0.000000175***
(4.13e-08)
-0.00648***
(0.00240)
0.178***
(0.0563)
0.0000405
(0.0000352)
0.735
Notes: *** significance at 1%; ** significance at 5%; * significance at 10%.
(Standard errors below coefficients)
16
Table 6: Current and predicted maize prices for 2012 and 2030
(1)
(2)
(3)
(3)-(1)
(3)-(2)
2012 actual 2012 predicted 2030 predicted Change 1 Change 2
Benin
0.48
0.59
0.62
0.14
0.03
Burundi
0.56
0.53
0.66
0.10
0.13
Cape Verde
0.62
0.74
0.52
-0.10
-0.22
Chad
0.60
0.64
0.88
0.27
0.24
Cote d'Ivoire
0.39
0.39
0.33
-0.06
-0.06
Ethiopia
0.37
0.41
0.25
-0.11
-0.15
Ghana
0.40
0.44
0.42
0.02
-0.02
Kenya
0.76
0.58
0.54
-0.21
-0.04
Malawi
0.34
0.39
0.59
0.24
0.20
Mauritania
0.91
0.78
0.67
-0.23
-0.11
Mozambique
0.40
0.44
0.42
0.01
-0.02
Nigeria
0.46
0.55
0.54
0.08
-0.02
Rwanda
0.47
0.45
0.56
0.09
0.11
Senegal
0.65
0.73
0.51
-0.13
-0.21
Somalia
0.45
0.49
0.39
-0.05
-0.10
South Sudan
1.19
0.93
0.86
-0.33
-0.07
Tanzania
0.55
0.61
1.14
0.60
0.54
Togo
0.42
0.47
0.44
0.02
-0.03
Uganda
0.49
0.45
0.51
0.02
0.06
Zambia
1.17
1.64
2.27
1.10
0.63
Zimbabwe
0.42
0.37
0.41
-0.01
0.04
Country
17
Map 3: Predicted maize price change from 2012 to 2030
18