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

[email protected] Rice Center (AfricaRice), Saint Louis, Senegal

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

mailto:[email protected]

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.

DATATable 1: Selected countries/regions from GTAP 7 database

Country/Region GTAP code ( 1) Egypt EGY ( 2) Ethiopia ETH ( 3) Morocco MAR ( 4) Madagascar MDG ( 5) Mozambique MOZ ( 6) Malawi MWI ( 7) Nigeria NGA ( 8) Senegal SEN ( 9) Tunisia TUN (10) Tanzania TZA (11) Uganda UGA (12) Rest of South Central Africa (Angola, DR of Congo) XAC

(13) Rest of Central Africa (Central African Republic, Cameroon, Congo, Gabon, Chad etc.) XCF (14) Rest of Eastern Africa ( Burundi, Djibouti, Kenya, Rwanda, Sudan etc.) XEC (15) Rest of South Africa Customs Union (Lesotho, Namibia, Swaziland) XSC (16) Rest of West Africa (Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Gambia, Mali, Niger, Togo etc.) `

XWF

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

MethodsThe equilibrium variables are represented by a system of three equations:

, 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

nininin

i

n

i

nn

ni SSddw

w

T

T

X

X

lnlnlnlnln (1)

Methods ,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.

, returns to land;

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

/1

1

1/1

)(1

)2(

N

i niiin dwTP

N

n N

i niii

niiin

ii

dwT

dwTX

Lw

1

1)(

)(1)3(

Results and Discussions1. 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 %.

Paddy

RiceWheat

Oth.

gr.(a)

Veg.

frt.(b) Soybean

Ti

(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

Table 1: Yield parameters of crops and foodsResults and Discussions

Results and Discussions1. 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

Results and Discussions2. Table 2: Determinants of bilateral trade flows

Source of barrier Coefficient Estimate p-value dist1 [0,375] -θd1 -7.16 0.00 dist2 [275,750] -θd2 -8.80 0.00 dist3 [750,1500] -θd3 -10.43 0.00 dist4 [1500,3000] -θd4 -12.06 0.00 dist5 [3000, max] -θd5 -12.98 0.00 Border -θb 1.38 0.00 Language -θl 0.71 0.01 Currency -θc 0.53 0.47 Destination country Source countryCountry Coefficient Estimate p-value Coefficient Estimate p-valueEgypt -θm1 2.68 0.00 S1 1.88 0.00Ethiopia -θm2 -0.40 0.54 S2 -1.84 0.00Morocco -θm3 2.89 0.00 S3 1.77 0.00Madagascar -θm4 -4.84 0.00 S4 -2.10 0.00Mozambique -θm5 -0.16 0.81 S5 -0.44 0.31Malawi -θm6 -1.36 0.03 S6 -1.64 0.00Nigeria -θm7 -2.89 0.00 S7 -0.09 0.83Senegal -θm8 0.28 0.68 S8 0.04 0.93Tunisia -θm9 1.25 0.05 S9 0.69 0.10Tanzania -θm10 -0.14 0.83 S10 -0.03 0.94Uganda -θm11 -3.09 0.00 S11 -2.38 0.00Rest of South Central Africa -θm12 -2.96 0.00 S12 -0.30 0.49Rest of Central Africa -θm13 -0.88 0.18 S13 0.52 0.24Rest of Eastern Africa -θm14 3.30 0.00 S14 2.39 0.00Rest of Sth African Custom Union -θm15 0.05 0.94 S15 -0.32 0.45Rest of West Africa -θm16 1.36 0.03 S16 1.45 0.00South Africa -θm17 6.65 0.00 S17 3.14 0.00Zambia -θm18 -0.91 0.16 S18 -1.43 0.00Zimbabwe -θm19 -0.84 0.18 S19 -1.30 0.00







Results and Discussions3. 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

Results and Discussions3. 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%.

Results and Discussions3. 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%).

Results and Discussions4. 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.

Results and Discussions4. 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.

AcknowledgementMany 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)

Thank you! Merci!

Technology

Th4_Agricultural trade for food security in Africa: A Ricardian approach