This document discusses balancing trade statistics between countries. It describes Eurostat's FIGARO approach which uses weighted averages to consolidate trade flows when exports and imports between countries are not equal. It examines challenges like differences in valuation, coverage or data sources that can cause trade asymmetries. The document outlines Eurostat's process for developing Quarterly Data Reports which distinguish domestic, re-export and quasi-transit trade to help correct biases and improve the estimates of trade flows between countries.

1. ‘Solving’ trade in goods
asymmetries at Eurostat
Pedro MARTINS FERREIRA
Eurostat, Unit C5, International Global Accounts (IGA) team
OECD’ Regional Global Trade in Value Added Webinar
9 June 2021

2. • What does ‘balancing trade’ really means
• FIGARO’ approach
• Challenges / Improvements
Structure of my talk

3. Balancing trade
What does it really mean?

4. • Different measurement?
• Different coverage?
• Are both estimates equally
trustworthy?
• Two sample points of the same
phenomena?
A B
100 150
?
Balancing trade
cif/fob
non-alloc trade
weights
(weighted) average

5. FIGARO’s approach
Agricultural goods for France

6. Agricultural products
Crops
Live animals
Meat
Dairy products

7. Challenges
Trade asymmetry
CIF / FOB
exports (fob) mirror (cif)
agrProd 15 154.5 14 811.5
animals 2 139.5 1 701.0
crops 3 650.9 3 763.5
dairy 6 263.5 6 328.6
meat 3 100.6 3 018.4
Eurostat UN
agrProd 15 154.5 14 811.5
animals 2 139.5 2 149.8
crops 3 650.9 3 667.7
dairy 6 263.5 6 288.9
meat 3 100.6 3 112.3
Different valuations Non-allocated trade
Different data sources

8. re-exports
Challenges / Opportunity (?)
xEU
FR
EU
13.0 3.2
Other
EU
Other
xEU
Same
xEU
0.1
Other
EU
Same
EU
Other
EU
1.2
re-exports

9. Non-allocated trade

10. • FOB-type prices (OECD)
• Relative asymmetry of trade flow
𝑖 → 𝑗
• Exports and imports weights
Computation of weights
𝐴𝑖𝑗 =
𝑋𝑖𝑗 − 𝑀𝑖𝑗
𝑋𝑖𝑗 + 𝑀𝑖𝑗
𝜃𝑖 =
𝑗
𝐴𝑖𝑗 𝑋𝑖𝑗 + 𝑀𝑖𝑗
𝑗 𝑋𝑖𝑗 + 𝑀𝑖𝑗
𝜙𝑗 =
𝑖
𝐴𝑖𝑗 𝑋𝑖𝑗 + 𝑀𝑖𝑗
𝑖 𝑋𝑖𝑗 + 𝑀𝑖𝑗

11. • 3-year average weights
• Consolidated trade as a weighted
average between exports and mirror
exports
Consolidation
𝑇𝑖𝑗 =
1 − 𝜃𝑖 𝑋𝑖𝑗 − 1 − 𝜙𝑗 𝑀𝑖𝑗
1 − 𝜃𝑖 + 1 − 𝜙𝑗
240.4
“Brazil nuts, in shell, fresh or dried”
(HS = 081070)
322.4
FR IT
309.8
5.1
“Persimmons, fresh”
(HS = 080121)
71.3
FR IT
21.4

12. QDR & triangular trade
Breaking ‘gross consolidated flows’

13. • 𝑋𝐶: (gross) exports according to the
community principle;
• 𝑋𝑁: (gross) exports according to the
national principle;
• 𝑋𝐷: domestic component of gross exports;
• 𝑋𝑅: re-exports component of gross exports;
• 𝑋𝑄: quasi-transit component of gross
exports.
• 𝑋𝑄 = 𝑋𝐶 − 𝑋𝑁
• 𝑋𝐷 =
𝑀𝐷
𝑀𝐶
𝑋𝐶
• 𝑋𝑅 = 𝑋𝑁 − 𝑋𝐷 = 𝑋𝑁 −
𝑀𝐷
𝑀𝐶
𝑋𝐶
QDR (1)

14. Consistency between data sources
𝑀𝐷
𝑀𝐶
𝑋𝐶 ≤ 𝑋𝑁 ≤ 𝑋𝐶
Correcting bias for domestic exports
• domestic share of exports: ‘use table of domestic inputs’ / ‘use table’
• For each HS6 product within one CPA, create a matrix of HS6 products (row),
domestic / non-domestic (columns): RAS to match SUIOT domestic exports
QDR (2)

15. Quasi-transit and re-export partners
• Partners are taken from the distribution of original imports for which country of
origin is different from country of consignment.
QDR (3)

16. Triangular trade
A
B C
50
55
100
A
B C
50
5
100
A
B C
50
50
100
5

17. From orignal data to QDR
Q D R
agrProd 713.9 13 833.3 673.8
animals 71.9 1 918.0 18.1
crops 355.8 2 976.5 393.5
dairy 189.9 6 058.3 148.7
meat 96.2 2 880.6 113.5
exports (fob) non-alloc xfob
agrProd 15 154.5 279.6 15 429.2
animals 2 139.5 43.2 2 182.6
crops 3 650.9 130.3 3 780.0
dairy 6 263.5 56.6 6 317.4
meat 3 100.6 49.5 3 149.2
xfob mfob flow
agrProd 15 429.2 14 377.4 15 221.0
animals 2 182.6 1 666.2 2 008.0
crops 3 780.0 3 614.1 3 725.8
dairy 6 317.4 6 159.0 6 397.0
meat 3 149.2 2 938.1 3 090.2
imports (cif) ciffob mfob
agrProd 14 811.5 434.0 14 377.4
animals 1 701.0 34.8 1 666.2
crops 3 763.5 149.4 3 614.1
dairy 6 328.6 169.6 6 159.0
meat 3 018.4 80.2 2 938.1
ICIO

18. From orignal data to QDR
Q D R
agrProd 713.9 13 833.3 673.8
animals 71.9 1 918.0 18.1
crops 355.8 2 976.5 393.5
dairy 189.9 6 058.3 148.7
meat 96.2 2 880.6 113.5
exports (fob) non-alloc xfob
agrProd 15 154.5 279.6 15 429.2
animals 2 139.5 43.2 2 182.6
crops 3 650.9 130.3 3 780.0
dairy 6 263.5 56.6 6 317.4
meat 3 100.6 49.5 3 149.2
xfob mfob flow
agrProd 15 429.2 14 377.4 15 221.0
animals 2 182.6 1 666.2 2 008.0
crops 3 780.0 3 614.1 3 725.8
dairy 6 317.4 6 159.0 6 397.0
meat 3 149.2 2 938.1 3 090.2
imports (cif) ciffob mfob
agrProd 14 811.5 434.0 14 377.4
animals 1 701.0 34.8 1 666.2
crops 3 763.5 149.4 3 614.1
dairy 6 328.6 169.6 6 159.0
meat 3 018.4 80.2 2 938.1
To be added to ITSS
for a better estimate of “trade”
Geographical distribution of
re-export trade margins

19. Challenges / improvements

20. • What if asymmetry is ‘huge’?
• Operational: algorithm vs expert
assessment
• Product or partner miss
classification
• From ITGS to NA
• Better articulation between
asymmetry exercises and
compilation process
• Research on an algorithm that can
spot product / partner miss
classification
Challenges / improvements

21. Keep in touch
FIGARO
ESTAT-IGA@ec.europa.eu
https://ec.europa.eu/eurostat

22. Thank you
© European Union 2020
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