REGIONAL TRADE INDICATORS FOR STAPLE FOODS IN COMESA. Gbegbelegbe Sika, Gelan A., Wanjiku J., Karugia J., Massawe S., Wambua J.

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1 TOWARDS DEVELOPING INTRA- REGIONAL TRADE INDICATORS FOR STAPLE FOODS IN COMESA Gbegbelegbe Sika, Gelan A., Wanjiku J., Karugia J., Massawe S., Wambua J.

2 OUTLINE Introduction Goal and objectives of study Literature review Approaches and methods Results Conclusions and policy implications

3 INTRODUCTION Various initiatives to increase intra-regional trade in staple foods in Eastern and Southern Africa (ESA) Need of mechanism to measure and evaluate impact of trade initiatives

4 GOAL AND OBJECTIVES OF STUDY Overall goal of the study: develop trade indicators to assess dynamics of intra-regional formal trade in main staple foods in the region Objectives: Assess consistency of reported trade data for selected COMESA countries Develop trade flow matrix based on the most accurate data Develop indicators to assess dynamics of formal intraregional trade in staple foods in region

5 LITERATURE REVIEW Most studies: arithmetic indicators; difference between reported exports/imports and mirror records ITC (2005): comprehensive set of arithmetic indicators Total discrepancy measure: assesses consistency of trade data reported at aggregated level Absolute average discrepancy measure: assesses consistency of the trade data reported per product or between 2 countries

6 APPROACHES AND METHODS Data source: Comtrade

7 APPROACHES AND METHODS Consistency of reported trade data: Total discrepancy measure; absolute average discrepancy measure (ITC, 2005) Indicators Dynamic indicators: validation; evolution of intraregional trade Static indicators: net trade indicators of specialization or net normalised share balance (NSB)

8 DATA Data source: import and export values and volumes from Comstat COMESA countries: Burundi, Congo DR, Djibouti, Ethiopia, Malawi, Rwanda, Uganda and Zambia Products: maize grain, rice, dry legumes (beans and pulses), plantains, live bovine animals, milk and cream, bovine meat, fish and crustaceans, tomatoes, maize and wheat flour, cassava, sweet potato, onions, cabbage Period: 2008 and 2009

9 RESULTS CONSISTENCY OF TRADE DATA Discrepancy measures: discrepancies in reported trade data are substantial and widespread across products and countries Options: use average, or maximum between reported exports and mirror records; use imports/exports only Maximum between reported exports and mirror records (bilateral basis): Data analysis: export values are higher than import values in about 35% of the cases for COMESA countries Imports are under-recorded (tax evasion): Yeats (1990); Bouët et al. (2008) Statistical tests revealed no outliers in reported export and import values and volumes over time (2005 and 2008)

10 MATRIX - TOTAL EXPORT VALUES IN 2008 (1000 US$) Maize grain Rice Dry legumes (beans and pulses) All products Burundi Congo DR Djibouti Ethiopia 4.2 3, ,596.8 Kenya 1, ,766.2 Malawi 1, , , ,661.2 Rwanda , ,604.5 Uganda 5, , , ,911.5 Zambia 11, , ,693.3 Total 20, , , ,040.2

11 DYNAMIC INDICATORS - VALUE (1000 US$) AND VOLUME OF TRADE FOR ALL PRODUCTS (%) Burundi Congo DR Djibouti Ethiopia 3, , Kenya 1, , Malawi 5, , Rwanda 3, , Uganda 27, , Zambia 12, , Total (value) 56, , Total (volume) 176, ,

12 DYNAMIC INDICATORS CHANGE IN INTRA- REGIONAL TRADE Factors affecting intra-regional trade in COMESA region: Factors affecting national food production and demand (climatic shocks, national policies); level of food stocks; trade policies (export bans); level of food aid; level of trade with rest of world Why has intra-regional trade stalled in 2009? East Africa Low food production in East Africa: below-average harvests in Kenya, Ethiopia and Tanzania; Uganda registered relatively good harvests Continued failed harvests in pastoral and agro-pastoral areas in east Africa depleted food stocks in these areas Export bans in East Africa: the little food produced could not move much between countries Southern Africa Bumper harvests in southern Africa Restrictive trade policies: export bans in southern Africa till late 2009

13 STATIC INDICATORS Net normalized share balance (comparative advantage): Ethiopia (6), Kenya (9), Uganda (7), Zambia (8): comparative advantage for substantial number of products Djibouti: no comparative advantage for any of the products under study

14 CONCLUSION AND POLICY IMPLICATIONS Reported trade data for COMESA countries: discrepancies are substantial and widespread across products and countries Proposed methodology: use maximum between reported exports/imports and mirror records Methodology is consistent with economic events (including trade policies) Limitation of methodology: we have not converted all values into either FOB and CIF values; over- or underestimation of change in intra-regional trade Policy implications: proposed methodology should not be used to estimate level of intra-regional trade; countries should rather strive at improving trade data collection systems