Th4_Agricultural trade for food security in Africa: A Ricardian approach

Agricultural trade for food security
in Africa: A Ricardian approach
Mandiaye Diagne1a, Steffen Abeleb,
Aliou Diagnec, Papa A. Seckc
bThe

aAfrica

1m.diagne@cgiar.org

Rice Center (AfricaRice), Saint Louis, Senegal
Food Security Center, University of Hohenheim, Stuttgart, Germany
cAfrica Rice Center (AfricaRice), Cotonou, Benin

Outline






Introduction
Data
Methods
Results and Discussions
Conclusion

INTRODUCTION
 With food security becoming even more of a challenge in the
recent food crises, African governments have prioritized
domestic staple food production.

 Food insecurity arises from harvest failure due to climate
conditions, price volatility and low agricultural productivity
 Beside national level policies and commitments to tackle food
insecurity; and under international market uncertainty, facilitating
access to African regional markets could play a major role.
 Poorly integrated markets are one of the primary causes of food
supply shortages and price volatility.

 The study aims at showing how staple foods trade within Africa
could contribute to food security and overall welfare in Africa.

DATA
 The crops and staple foods in our model are: Rice, wheat,
other grains (maize, millet, sorghum), vegetables and fruits
(bananas/plantains, cassava/potatoes) and soybean
 Bilateral trade flows are from the GTAP 7 database and we
include 19 countries/regions.
 The total number of observations, considering bilateral
trade flows, is 342.

DATA
Table 1: Selected countries/regions from GTAP 7 database
Country/Region
( 1) Egypt
( 2) Ethiopia
( 3) Morocco
( 4) Madagascar
( 5) Mozambique
( 6) Malawi
( 7) Nigeria
( 8) Senegal
( 9) Tunisia
(10) Tanzania
(11) Uganda
(12) Rest of South Central Africa (Angola, DR of Congo)
(13) Rest of Central Africa (Central African Republic, Cameroon, Congo, Gabon, Chad etc.)
(14) Rest of Eastern Africa ( Burundi, Djibouti, Kenya, Rwanda, Sudan etc.)
(15) Rest of South Africa Customs Union (Lesotho, Namibia, Swaziland)
(16) Rest of West Africa (Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Gambia, Mali,
Niger, Togo etc.) `

(17) South Africa
(18) Zambia
(19) Zimbabwe

GTAP code
EGY
ETH
MAR
MDG
MOZ
MWI
NGA
SEN
TUN
TZA
UGA
XAC
XCF
XEC
XSC
XWF
ZAF
ZMB
ZWE

Methods

 We use an improved Ricardian trade model with multiple
goods and multiple countries specification (Eaton and
Kortum 2002; Reimer and Li 2009,2010) based on
technology differences and geographic barriers among
countries
 The practical concern is to estimate the parameters:
 Country estate of technology (Ti)
 Heterogeneity of technology (  )
 Geographic bariers (dni)

Methods
The equilibrium variables are represented by a system of three equations:

 X ni 
T
w
  ln i   ln i   ln d ni   ln d ni  Si  S n
(1) ln 
X 
Tn
wn
 nn 
, the share of the destination country n expenditure devoted to staple
foods from the source country i.
Where
- wi is land rental rate;
- dni are geographic barriers;
- Ti is the state of technology and
-  is the parameter of technology variability
- Si measures competitiveness
- lndni = mn + dk + b + l + c , the geographic barriers equation.
Where mn, represents the openness to imports
dk, distance in miles between countries
b, proximity if two countries share border
l, common language
c, use the same currency

Methods
 (2) P     1   

n
  

 

1/1



N

T ( wi d ni )
i 1 i





1/ 

,the overall price paid in

the purchaser country n linked to the yield distribution, geographic
barriers and land rental rate;
where σ the elasticity of substitution of agricultural product derived from the
Utility function, Γ is the Gamma function.



1
(3) wi 
Li

 

  Ti ( wi d ni )
n1  X n  N

  Ti ( wi d ni )
  i 1

N







, returns to land;

where Xn is total expenditure in staple food un country n.

1. Trade flows and yield variability in Africa
 Considering total imports of crops and foods, each
African country imports from the others African
countries 9.96 % on average.
 Considering total spending on crops and foods, the
share of intra-African import is only 2.29 %.

Table 1: Yield parameters of crops and foods
Paddy

Oth.

Veg.

Wheat
gr.(a)

Rice

Ti
Soybean

frt.(b)

(Std. error)

Egypt

9.84

6.56

7.18

24.10

3.03

3.49 (0.89)

Ethiopia

1.85

1.49

1.11

5.47

0.42

0.72 (0.28)

Morocco

6.70

1.81

1.16

16.87

1.03

1.55 (1.08)

Madagascar

2.45

2.38

1.77

5.68

2.40

0.94 (0.19)

Mozambique

0.96

1.11

0.76

6.01

0.33

0.66 (0.35)

Malawi

1.17

0.75

1.02

13.09

0.64

0.73 (0.51)

Nigeria

1.42

1.07

1.37

8.33

0.90

0.78 (0.28)

Senegal

2.48

0.00

0.85

8.42

0.00

1.05 (0.42)

Tunisia

0.00

1.66

0.71

10.50

0.00

1.08 (0.65)

Tanzania

1.73

1.95

1.31

6.13

0.64

0.68 (0.21)

Uganda

1.30

1.67

1.48

7.09

1.01

0.80 (0.21)

Rest of South Central Africa

0.76

1.39

0.63

8.70

0.48

0.55 (0.28)

Rest of Central Africa

1.15

1.33

1.00

5.42

1.61

0.59 (0.10

Rest of Eastern Africa

3.33

2.17

0.81

8.27

0.79

0.82 (0.32)

Rest of South African Custom Union

3.40

0.90

0.53

8.56

0.00

0.96 (0.55)

Rest of West Africa

1.60

2.05

0.71

7.55

0.58

0.72 (0.23)

South Africa

2.29

2.03

2.96

20.88

1.61

1.84 (1.28)

Zambia

1.38

6.12

1.74

6.12

1.40

1.26 (0.84)

Zimbabwe

2.41

3.50

0.99

5.55

1.38

1.05 (0.49)

Average

2.64

2.30

1.53

8.71

1.08

1. Trade flows and yield variability in Africa
 In our model the yield variability parameters governing
comparative advantage are 2.62 and 2.84
 In the world crop sector, the yield parameter variability
is between 2.52 and 4.96 (Reimer and Li, 2010)
 This reflects crop and food productivity is more
heterogeneous in Africa than in the world as a whole

2. Table 2: Determinants of bilateral trade flows
Source of barrier
dist1 [0,375]
dist2 [275,750]
dist3 [750,1500]
dist4 [1500,3000]
dist5 [3000, max]
Border
Language
Currency
Country
Egypt
Ethiopia
Morocco
Madagascar
Mozambique
Malawi
Nigeria
Senegal
Tunisia
Tanzania
Uganda
Rest of South Central Africa
Rest of Central Africa
Rest of Eastern Africa
Rest of Sth African Custom Union
Rest of West Africa
South Africa
Zambia
Zimbabwe

Coefficient
Estimate
-θd1
-7.16
-θd2
-8.80
-θd3
-10.43
-θd4
-12.06
-θd5
-12.98
-θb
1.38
-θl
0.71
-θc
0.53
Destination country
Coefficient
Estimate
p-value
-θm1
2.68
0.00
-θm2
-0.40
0.54
-θm3
2.89
0.00
-θm4
-4.84
0.00
-θm5
-0.16
0.81
-θm6
-1.36
0.03
-θm7
-2.89
0.00
-θm8
0.28
0.68
-θm9
1.25
0.05
-θm10
-0.14
0.83
-θm11
-3.09
0.00
-θm12
-2.96
0.00
-θm13
-0.88
0.18
-θm14
3.30
0.00
-θm15
0.05
0.94
-θm16
1.36
0.03
-θm17
6.65
0.00
-θm18
-0.91
0.16
-θm19
-0.84
0.18

p-value
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.47
Source country
Coefficient
Estimate
1.88
S1
-1.84
S2
1.77
S3
-2.10
S4
-0.44
S5
-1.64
S6
-0.09
S7
0.04
S8
0.69
S9
-0.03
S10
-2.38
S11
-0.30
S12
0.52
S13
2.39
S14
-0.32
S15
1.45
S16
3.14
S17
-1.43
S18
-1.30
S19

p-value
0.00
0.00
0.00
0.00
0.31
0.00
0.83
0.93
0.10
0.94
0.00
0.49
0.24
0.00
0.45
0.00
0.00
0.00
0.00

3. Counterfactual 1: Yield increase effects
 A yield increase of 30% in Western Africa (Nigeria,
Senegal and the Rest of West Africa) would increase net
welfare by 5.66 % due to prices drop of 8.59-8.75% and
intra-African trade would slightly improve by 0.54%
 A rice yield increase of 30% in Africa would increase net
welfare by 1.23% with a price decrease of 2.03%.
 The percentage change in Africa home production of all
staple foods would decrease by 9.5%,
 There is no significant change in Africa staple food
trade even if only 2 countries/regions would record a
drop in imports of all staple foods

3. Counterfactual 2: Effects of increased yield variability
 Almost African countries would have welfare decrease with
a minimum of 1.5 % for Morocco and a maximum of 10.7
% for Zimbabwe, due to a crop and food price increase of
2.3 % and 58.2 %, respectively.
 Only Egypt and South Africa would have a welfare
increase of 5.9% and 2.2%, respectively. The highest
decrease in crop and food prices would offset the
decrease in land rental rate (-0.41 % for Egypt and -10.8 %
for South Africa).
 The intra-African crop and food trade would only increase
by 2.7%.

3. Counterfactual 3: Land increase effects
 A 30% increase in cultivated land in Tanzania would rise
its net welfare by at least 16 % mainly due to a drop of
crop and foods prices and a respective decrease of the
land rental rate of 17 %. The Rest of Eastern Africa would
benefit the most from this situation with a decrease of
domestic food price of around 2 %.
 The intra-African trade would increase by 3% with an
export rise of 67% for Tanzania.

 The highest imports increase are recorded by Malawi
(32%) and the Rest of Eastern Africa (25%).

4. Food security implications
 On average these crops and foods provided 1419
Kcal/capita/day in Africa in 2004.
 We found a positive and significant correlation (66%)
between quantities of crop and food imported and total
Kcal/Pers/Day.
 We found, as well, a positive and significant correlation
(43%) between GDP per capita and total Kcal/Pers/Day.

4. Food security implications
From these evidences agricultural trade in Africa could play a
major role for Food Security in the continent:
 When the other African countries reduce their import trade costs to
the level of South Africa,
 African trade would increase by 1525%.
 Net welfare would increase on average by 38 %

Doubling intra African Trade volume:
 A welfare increase of 1.3%
 Decrease of crop and food price of 6%

Conclusion
 Productivity is still more heterogeneous across African
countries than in the world as a whole

 Distance is the main impediment for African trade and
makes prohibitive barriers costs for trading partners.
 Common borders and languages have a positive impact
on trade in Africa
 An improvement of competitiveness could highly contribute
to food security by stimulating trade and increasing total
income in the agricultural sector.

Acknowledgement
Many thanks to


DAAD (German Academic Exchange Service) and the Food Security
Center (University of Hohenheim, Germany)



Associate Prof. Jeffrey Reimer (Oregon State University, USA)



Prof Martina Brockmeier and Beyhan Bektasoglu (Assistant of Prof.
Brockmeier) (University of Hohenheim, Germany)

Th4_Agricultural trade for food security in Africa: A Ricardian approach

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