Presented by Sika Gbegbelegbe to the Workshop on Trade Data Challenges in Eastern and Southern Africa, Nairobi, February 01, 2011

1. Formal Trade Data Quality Challenges: Selected Staple Foods in Eastern and Southern Africa Sika Gbegbelegbe Workshop on Trade Data Challenges in Eastern and Southern Africa, Nairobi, February 01, 2011

2. Outline Introduction Methodology: Illustrative approach to data consistency assessment: bilateral trade data Mathematical approach to data consistency assessment: trade data by country or product groups Results Summary: major findings Implications of results

3. Introduction Various initiatives to increase intra-regional trade in staple foods in Eastern and Southern Africa (ESA): ACTESA by COMESA, RATIN by EAGC, and COMPETE by USAID Assessment of impact of initiatives is difficult: paucity of quality trade data Remains unclear whether countries in ESA are making best use of available trade opportunities

4. Introduction (cont.) ReSAKSS: tasked to develop, in collaboration with partners, an indicator to track intra-regional trade in staple foods in ESA February 2010: workshop on tracking intra-regional trade in EAC and COMESA; worked with stakeholders in region Short- and long-term action points to facilitate development of trade indicator and also identify ways to improve trade data quality (formal and informal) June 2010: validation workshop for indicator to track intra-regional trade in staple foods in ESA Reported formal trade data is characterized by substantial discrepancies which are widespread across products and countries; informal trade data is inexistent; where it exists, it is incomplete Action points (way forward): 2 workshops, one on formal trade data and another on informal trade data Presentation: assessment of consistency of reported formal trade data in ESA

5. Methodology Illustrative approach to trade data consistency assessment: Figures on bilateral trade volumes for maize Tables on bilateral trade values for rice and dry legumes (beans and pulses) Mathematical approach to trade data consistency assessment: trade data for groups of countries or food products Data: COMTRADE, FAOSTAT and COMSTAT Countries: EAC countries Sub-COMESA region (Burundi, Congo DR, Djibouti, Ethiopia, Kenya, Malawi, Rwanda, Uganda, Zambia)

6. Illustrative approach to data consistency assessment: maize trade between Kenya and Tanzania Data source: COMTRADE, 2010

7. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - comtrade Data source: COMTRADE, 2010

8. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - faostat Data source: FAOSTAT, 2010

9. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - comstat Data source: COMSTAT, 2010

10. Illustrative approach to data consistency assessment: maize trade between Burundi and Tanzania Data source: COMTRADE, 2010

11. Illustrative approach to data consistency assessment: maize trade between Canada and USA Data source: COMTRADE, 2010

12. Illustrative approach to data consistency assessment: rice trade values (1000 US$) in ESA in 2008 Data source: COMSTAT, 2010

13. Illustrative approach to data consistency assessment: trade values (1000 US$) for dry legumes in ESA in 2008 Data source: COMSTAT, 2010

14. Mathematical approach to trade data consistency assessment: trade data by country or product groups Total discrepancy measure: consistency of trade data aggregated at regional level for product ‘p’; around 5% (ITC, 2005): Absolute average discrepancy measure: consistency of bilateral trade data per product ‘p’ on average; around 10% (ITC, 2005)

15. Mathematical approach to trade data consistency assessment – Results Data source: COMSTAT, 2010

16. Summary: major findings Study uses various approaches to assess consistency of reported trade data for staples foods in ESA: results indicate that discrepancies in reported formal trade data in ESA are substantial and widespread across countries and food products Causes of discrepancies (ITC, 2005; FAOSTAT, 2010): Inconsistencies across countries on product coverage: tax evasion; product misclassification Time lag in compilation of trade data Differing trade reporting systems: general vs. special trade Differing product classification systems across countries: HS codes Inconsistencies on country of origin or destination: transit, re-exports Loss of produce en route Inconsistencies on quantity measurements; gross vs. net weight Valuation systems: currency conversions Reporting errors: mistakes for calculation and typing

17. Implications of results Improving formal trade data quality: Harmonize the trade reporting and commodity classification systems across countries in the region: COMESA and EAC Accelerate the trade data harmonisation process in countries in ESA: national agencies

18. Implications of results – trade data harmonisation Trade data harmonisationaims at simplifying the declaration process in customs offices at borders (World Customs Organization, 2007): Implementation of a Single Window Environment where only one form is used in each country to capture the data required by the national agencies involved in external trade

19. Implications of results – trade data harmonisation Outcomes of successful trade data harmonisaton process (WCO, 2007): Reduce administrative procedures for importers and exporters Reduced costs for both traders and governments Improvement in the timeliness and accuracy of reported trade data

20. Implications of results – trade data harmonisation Steps for successful trade data harmonisation process (WCO, 2007): Identify lead agency and dedicate staff to conduct harmonisation Make inventory of current trade data per agency and identify information requirements (for all agencies) Harmonise data at national level Identify redundancies by comparing data definitions Harmonise inventory of information and data requirements to the international WCO Data Model standards