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Formal Trade Data Quality Challenges: Selected Staple Foods in Eastern and Southern Africa

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

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Formal Trade Data Quality Challenges: Selected Staple Foods in Eastern and Southern Africa

  1. 1. Formal Trade Data Quality Challenges: Selected Staple Foods in Eastern and Southern Africa<br />Sika Gbegbelegbe<br />Workshop on Trade Data Challenges in Eastern and Southern Africa, Nairobi, February 01, 2011<br />
  2. 2. Outline<br />Introduction<br />Methodology:<br />Illustrative approach to data consistency assessment: bilateral trade data<br />Mathematical approach to data consistency assessment: trade data by country or product groups<br />Results<br />Summary: major findings<br />Implications of results<br />
  3. 3. Introduction<br />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<br />Assessment of impact of initiatives is difficult: paucity of quality trade data<br />Remains unclear whether countries in ESA are making best use of available trade opportunities<br />
  4. 4. Introduction (cont.)<br />ReSAKSS: tasked to develop, in collaboration with partners, an indicator to track intra-regional trade in staple foods in ESA<br />February 2010: workshop on tracking intra-regional trade in EAC and COMESA; worked with stakeholders in region<br />Short- and long-term action points to facilitate development of trade indicator and also identify ways to improve trade data quality (formal and informal)<br />June 2010: validation workshop for indicator to track intra-regional trade in staple foods in ESA<br />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<br />Action points (way forward): 2 workshops, one on formal trade data and another on informal trade data<br />Presentation: assessment of consistency of reported formal trade data in ESA<br />
  5. 5. Methodology<br />Illustrative approach to trade data consistency assessment:<br />Figures on bilateral trade volumes for maize<br />Tables on bilateral trade values for rice and dry legumes (beans and pulses)<br />Mathematical approach to trade data consistency assessment: trade data for groups of countries or food products<br />Data: COMTRADE, FAOSTAT and COMSTAT<br />Countries:<br />EAC countries<br />Sub-COMESA region (Burundi, Congo DR, Djibouti, Ethiopia, Kenya, Malawi, Rwanda, Uganda, Zambia)<br />
  6. 6. Illustrative approach to data consistency assessment: maize trade between Kenya and Tanzania<br />Data source: COMTRADE, 2010<br />
  7. 7. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - comtrade<br />Data source: COMTRADE, 2010<br />
  8. 8. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - faostat<br />Data source: FAOSTAT, 2010<br />
  9. 9. Illustrative approach to data consistency assessment: maize trade between Kenya and Uganda - comstat<br />Data source: COMSTAT, 2010<br />
  10. 10. Illustrative approach to data consistency assessment: maize trade between Burundi and Tanzania<br />Data source: COMTRADE, 2010<br />
  11. 11. Illustrative approach to data consistency assessment: maize trade between Canada and USA<br />Data source: COMTRADE, 2010<br />
  12. 12. Illustrative approach to data consistency assessment: rice trade values (1000 US$) in ESA in 2008<br />Data source: COMSTAT, 2010<br />
  13. 13. Illustrative approach to data consistency assessment: trade values (1000 US$) for dry legumes in ESA in 2008<br />Data source: COMSTAT, 2010<br />
  14. 14. Mathematical approach to trade data consistency assessment: trade data by country or product groups<br />Total discrepancy measure: consistency of trade data aggregated at regional level for product ‘p’; around 5% (ITC, 2005):<br />Absolute average discrepancy measure: consistency of bilateral trade data per product ‘p’ on average; around 10% (ITC, 2005)<br />
  15. 15. Mathematical approach to trade data consistency assessment – Results<br />Data source: COMSTAT, 2010<br />
  16. 16. Summary: major findings<br />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<br />Causes of discrepancies (ITC, 2005; FAOSTAT, 2010):<br />Inconsistencies across countries on product coverage: tax evasion; product misclassification<br />Time lag in compilation of trade data<br />Differing trade reporting systems: general vs. special trade<br />Differing product classification systems across countries: HS codes<br />Inconsistencies on country of origin or destination: transit, re-exports<br />Loss of produce en route<br />Inconsistencies on quantity measurements; gross vs. net weight<br />Valuation systems: currency conversions<br />Reporting errors: mistakes for calculation and typing<br />
  17. 17. Implications of results<br />Improving formal trade data quality:<br />Harmonize the trade reporting and commodity classification systems across countries in the region: COMESA and EAC<br />Accelerate the trade data harmonisation process in countries in ESA: national agencies<br />
  18. 18. Implications of results – trade data harmonisation<br />Trade data harmonisationaims at simplifying the declaration process in customs offices at borders (World Customs Organization, 2007):<br />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<br />
  19. 19. Implications of results – trade data harmonisation<br />Outcomes of successful trade data harmonisaton process (WCO, 2007):<br />Reduce administrative procedures for importers and exporters<br />Reduced costs for both traders and governments<br />Improvement in the timeliness and accuracy of reported trade data<br />
  20. 20. Implications of results – trade data harmonisation<br />Steps for successful trade data harmonisation process (WCO, 2007):<br />Identify lead agency and dedicate staff to conduct harmonisation<br />Make inventory of current trade data per agency and identify information requirements (for all agencies)<br />Harmonise data at national level<br />Identify redundancies by comparing data definitions<br />Harmonise inventory of information and data requirements to the international WCO Data Model standards<br />

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