Asymmetry in IMTS & partner country attribution Habibur Rahman Khan United Nations Statistics Division Based on paper by Vladimir Markhonko, UNSD Consultant Joint GCC-STAT/UNSD Regional Workshop on International Merchandise Trade Statistics 7-10 February 2016, Muscat, Oman What is asymmetry in IMTS • The difference/discrepancy between the reported export of country A to country B, and the reported import of country B from country A A’s export to B X B’s import from A Y Asymmetry |X-Y| • Similarly: B’s export to A P A’s import from B Q Asymmetry |P-Q| • Also called Mirror Statistics Concerns • The UN Statistical Commission in its 45th session in 2014: “Reiterated the importance of obtaining more clarity in the complex measurement issues of cross-border economic relations….” “Requested the Friends of the Chair group to pay special attention to issues such as discrepancies in mirror statistics….” Major sources of asymmetry • Three main and well-known reasons for asymmetries are: I. Different criteria of partner attribution in import and export statistics II. Use of CIF-type values in import statistics and FOB-type values in export statistics III. Different trade systems (e.g., General vs Special) Asymmetries due to partner attribution • 8 annual data sets reported by 5 countries were analyzed: – – – – – Argentina (2011), Bosnia and Herzegovina (2013 -2014) Norway (2013-2014) Paraguay (2013-2014) Slovakia (2013) • These countries compiled IMTS by both “Country of Origin” and “Country of Consignment” for partner country • Analyses was limited to those records for which the partner country also reported trade with these countries Example: Norway (2013 & 2014) Norway 2013 2014 # of partners by country of origin 181 186 # of partners by country of consignment 181 183 2 0 128 128 # of partners with the same imports by country of origin and by country of consignment # of compared data triplets (imports by country of origin, imports by country of consignment and exports reported by partner Example: Norway (2013 & 2014) Norway 2013 2014 Sum of partner exports in the dataset (SPEX) 86345549033 85804435240 Sum of absolute values of asymmetry between imports by origin and corresponding partner exports (SOA) 27089218845 28224162535 Sum of absolute values of asymmetry between imports by consignment and corresponding partner exports (SCA) 13890411842 11294032487 Relative origin asymmetry (SOA/SPEX, in %) 31.4% 32.9% Relative consignment asymmetry (SCA/SPEX, in %) 16.1% 13.2% Example: Norway (2014) Top 5 asymmetries by the type of partner attribution By Origin By Consignment Sweden 5716132407 Poland 1879980865 China 5523409501 Netherlands 1857113217 Denmark 1868290677 Sweden 1389678886 Netherlands 1446953263 USA 1178488494 Poland 1176433151 Singapore 1170389558 Top 5 asymmetries as % of total asymmetry 58.1% 53.8% Main findings • More than a half of total asymmetry is concentrated in top 5 partners sorted by origin asymmetry • Asymmetry is noticeably reduced when country of consignment is used instead of country of origin • Asymmetry in the group of 5 partners with the greatest asymmetry is reduced even more than the asymmetry in all data • This reduction is uneven; it does happen in most countries, but not in all. Points for discussion • What are the major sources of bilateral asymmetry in the region? • How to address this issue/reduce asymmetry for countries in the region? • What are the needs for assistance? Thank you Please send your comments to [email protected]
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