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Assessing Systemic Trade Interconnectedness

Author(s):
Alexander Massara, and Luca Errico
Published Date:
September 2011
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I. Introduction

The cross-border transmission of shocks takes place through two main channels: the financial channel and the trade channel. The global financial crisis has drawn renewed attention to the former with recent IMF Executive Board documents discussing financial sectors of “systemic importance” and their inter-linkages in the context of Fund surveillance, underscoring the relevance of financial interconnectedness.2 Less emphasis has been placed on the trade channel, i.e., the real side of the equation. Nonetheless, understanding the impact that changes in domestic demand exert through the trade channel, especially in the case of systemically important trade sectors, is important in informing the analysis of cross-border spillovers and contagion.

Typically, considerations about the “systemic” importance of a trade sector have been based on its absolute (within jurisdiction) or relative (within the global trade system) size. Interconnectedness has, however, more recently emerged as a critical complementary consideration to gauge the systemic risk that may arise through direct or indirect inter-linkages among sectors in the global system. The idea is that the more and stronger linkages a given sector has to the global system, the higher the risk that distress in that sector may have repercussions on other jurisdictions or systemic stability.

Against this background, we develop an approach for assessing systemic trade interconnectedness by defining “systemic” trade sectors and identifying the jurisdictions hosting them.3 The approach draws from recent IMF work on financial interconnectedness and leverages the IMF’s Direction of Trade Statistics (DOTS) database.4 The use of DOTS lends robustness to the analysis by providing data that are not only uniform, but also available for the entire Fund membership. Additionally, the regular updating of DOTS by the IMF’s Statistics Department allows for dynamic analysis and recalibrations of the findings tracking global trade developments on a timely basis. This approach naturally complements financial interconnectedness analysis, providing a holistic view of the potential for spillovers and contagion at the bilateral, regional, and global levels.

The rest of the paper is organized in four parts. The next section introduces the analytical framework for our approach. Section III briefly describes the dataset. Section IV shows the results and elaborates on a few stylized facts so uncovered. Section V illustrates possible applications for, and extensions to, our approach. Section VI offers concluding remarks.

II. The Analytical Framework

Our approach is two-staged. In the first stage, jurisdictions are ranked based on trade size and interconnectedness indicators. In the second stage, the rankings of trade size and interconnectedness are combined into a composite index of systemic trade importance. Sensitivity analysis on the baseline composite index is performed to assess the robustness of the results.

A. First Stage

Size indicators

Three measures of the absolute size of a trade sector (in nominal U.S. dollars), namely: (i) total exports (X); (ii) total imports (M); and (iii) total turnover (X+M) are used to capture the importance of a jurisdiction’s trade sector in the global trade system. Being based on DOTS, trade in this analysis includes goods/merchandise, but excludes services. One measure of the relative size of a trade sector, namely: total turnover relative to nominal GDP (in U.S. dollars), is used to gauge the relative importance of the trade sector within a given jurisdiction.

These four trade size indicators then are combined into a single ranking for size by ranking all jurisdictions in each of the four trade size indicators separately and taking the median rank of the four indicators for each jurisdiction as the single ranking for trade size.

Interconnectedness indicators

Similar to the approach used for financial interconnectedness, network analysis is used to infer from the pattern of cross-border linkages among trade sectors the extent to which a trade sector in a jurisdiction is “central” in the global trade system (network).

The global trade network is defined as a set of bilateral trade relationships (links), either exports or imports, of different jurisdictions (nodes). We impose a materiality threshold to ensure that the analysis focuses only on economically meaningful links, i.e., trade relationships representing less than 0.1 percent of a jurisdiction’s GDP are excluded.

The network is expressed in matrix form where Aij represents the value of total turnover between jurisdiction i and jurisdiction j. The matrix has dimension n equal to the number of jurisdictions. Diagonal elements are zero. Off-diagonal elements are zero for jurisdiction pairs that have no link either as exporter or importer. The indicators are based on whether a link exists, that is, they are based on the indicator Nij=1 if Aij > 0, and 0 otherwise.

Four measures of “centrality” of a jurisdiction’s trade sector within the global trade network are considered:

  • In-Degree” is the number of links that point to a node. It is given by the sum ∑j Nji;
  • Closeness” is the inverse of the average distance from node i to all other nodes. The distance between i and j, δij equals the shortest path. The average distance from i to all other nodes is given by ∑jδij/(n-1). Closeness is the inverse of this measure;
  • Betweenness” looks at the nodes that the shortest path goes through. Let gjk denote the number of shortest paths between j and k, and gjk(i) denote the number of such paths that go through node i. The probability that node i is on the shortest path from j to k is given by gjk(i)/gjk. “Betweenness” of node i is the sum of these probabilities over all nodes excluding i, divided by the maximum that the sum can attain: (∑j≠ik≠igik(i)/gjk)/(n-1)(n-2); and
  • Prestige” (or eigenvector centrality) considers the identity of counterparties. It is a measure of the importance of a node in the network. It assigns relative scores to all nodes in the network based on the principle that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. The “prestige” of jurisdiction i (vi) is obtained by taking the “prestige” of its exporters, weighted by a matrix of relationships with i, that is, vi=∑jRjivj. This defines a linear system v=R’v where R is the matrix of relationship. The solution to the system is the eigenvector associated with the unit eigenvalue.

Because we consider both exports and imports, the network is “undirected” and because we assign equal weights to the four measures of centrality, the network is “un-weighted” with binary values (0, 1). As with the ranking for trade size, a single ranking for trade interconnectedness is calculated from these four different indicators. All jurisdictions are ranked in each of the four interconnectedness indicators separately, taking the median of the four rankings as the single ranking for trade interconnectedness.

B. Second Stage

An overall composite index of trade systemic importance is calculated as a combination of the trade size and trade interconnectedness rankings calculated in the first stage. The rankings of size and interconnectedness are combined into a weighted average “baseline” index to allow the analysis of the relative significance of size and interconnectedness in systemic importance.

Sensitivity analysis of the composite index suggests that while weight changes affect some of the individual country rankings at the margin, they do not introduce significant changes in the listing of the jurisdictions in the upper echelons of the overall ranking. We tested for the following combinations of size and interconnectedness breakdowns: 0.8/0.2 (0.8 for size and 0.2 for interconnectedness), 0.7/0.3, 0.6/0.4, and 0.5/0.5, respectively.

Because we wanted to compare our findings with those of previous IMF work on financial interconnectedness (below), we maintained the same approach of giving relatively more weight to the size than the interconnectedness dimension, which reflects historical experience. Nonetheless, this needs not be the case—and indeed may not reflect future developments. Hence, future work could usefully explore the sensitivity of the composite index with reversed breakdowns, i.e., giving relatively more weight to interconnectedness than size.

III. The Dataset

Drawing from DOTS, we considered import and export data for 240 jurisdictions vis-à-vis each other in the years 2000 and 2010, which resulted in about 60,000 time series for each year. We then filtered out those jurisdictions for which GDP data was not available in either year, which resulted in a final sample of 169 jurisdictions representing almost 100 percent of total world trade in both years 2000 and 2010. Each of these 169 jurisdictions presented 240 possible bilateral trade relationships with the rest of the sample.

As subsequent steps, turnover (X+M) and turnover to GDP ratios were calculated for each bilateral relationship. Any relationship for which turnover was less than 0.1 percent of each jurisdiction’s GDP was given a zero value and filtered out. The remaining “significant” trade relationships were given values of one and run through a specialized software for network analysis—NodeXL. This software is designed to read data in binary form (0, 1)—or “edge” and “vertex”—to calculate the four indicators of centrality described previously.

IV. The Results

The results obtained from applying our approach to the dataset are illustrated in Figure 1, which shows the global trade network based on the 2010 rankings of the top 25 systemic jurisdictions and the top 10 systemic jurisdictions, respectively.

Figure 1.The Global Trade Network, 2010

Sources: IMF DOTS Database and IMF staff estimates.

Straight lines between jurisdictions reflect the connections (links) between the trade centers of two jurisdictions (nodes). The interconnectedness of each jurisdiction is reflected by each node’s distance from the center of the network and the size of each node reflects the size ranking of each jurisdiction.

The top 25 jurisdictions hosting systemically important trade sectors in the years 2000 and 2010 are summarized in Table 1. The individual overall rankings, as well as the rankings by each of the trade and interconnectedness indicators for all of the 169 sample jurisdictions in the years 2000 and 2010 are included in Appendix Tables A and B, respectively.

Table 1.Jurisdictions with Systemically Important Trade Sectors: 2000-2010
20002010
JurisdictionOverall

Rank 1/
Size

Rank
Interconnectedness

Rank 2/
JurisdictionOverall

Rank 1/
Size

Rank
Interconnectedness

Rank 2/
United States118China, P.R.: Mainland122
France242Germany236
Japan338Netherlands361
Germany4211italy474
United Kingdom556France559
China, P.R.: Mainland675United States6119
Italy783Korea, Republic of797
Netherlands894Belgium8113
Canada9614Japan9420
Spain10151United Kingdom10107
Korea, Republic of11137China, P.R.:Hong Kong11817
Belgium121115Canada121114
China, P.R.:Hong Kong131020Spain13164
Switzerland141710Indial41714
Singapore151422Malaysia151718
Sweden161816Switzerland162211
Malaysia171621Thailand172114
Thailand182117Singapore181334
Austria191922Russian Federation191433
Ireland201926Brazil202021
Denmark212512Australia211925
Brazil222317Sweden222711
Australia232122Turkey232910
Mexico241244Austria242624
India252612Indonesia252822
Sources: IMF DOTS Database and IMF staff estimates.

Weighted average of the size and interconnectedness rankings using a 0.7/0.3 weight breakdown respectively.

Excludes links representing less than 0.1% of each jurisdiction’s GDP.

Sources: IMF DOTS Database and IMF staff estimates.

Weighted average of the size and interconnectedness rankings using a 0.7/0.3 weight breakdown respectively.

Excludes links representing less than 0.1% of each jurisdiction’s GDP.

Our results uncover a few stylized facts:

  • First, the composition of the top 25 systemic jurisdictions has remained virtually unchanged over the decade under review. With the exception of Canada and Spain, the composition of the top 10 jurisdictions in 2010 mirrored that of 2000; only three countries appear on the 2010 list that did not appear in 2000 (Indonesia, Russia and Turkey). Nonetheless, the relative rankings of individual jurisdictions have moved markedly. This is particularly the case for the emerging Asian economies, such as China Mainland and India, which rose by five and eleven positions, respectively.
  • Second, Europe and Asia have maintained their dominance at the top of the overall list. Europe has maintained its position mainly on account of its interconnectedness, whereas in Asia, size has been a more important factor. This suggests that while Asian countries are of importance to the absolute size of global trade, they are not (yet) “as central” in the global trade network as European jurisdictions.
  • Third, considering in particular the interconnectedness rankings included in Appendix Table B, African economies as a whole rose the most overall, however they still rank last on average. Conversely, European economies fell the most overall. In fact, the largest declines in interconnectedness are to be found in Eastern Europe, reflecting the fact that this region was hit hardest by the contraction in demand stemming from the global financial crisis.
  • Fourth, over the decade under review, China has increased its prominence in the global trade network not only in terms of size, by substantially raising its share in total world exports and imports, but also in terms of interconnectedness, by increasing its significant trading partners. China is the only non-European country in the top five for interconnectedness in both years 2000 and 2010.
  • Fifth, China’s relation to Japan as strategic export destinations has changed considerably over the past ten years (Figure 2). The country’s growing use of raw materials has enabled it to become a major destination for emerging market and developing economies’ exports over the past decade.

Figure 2.Top Imports into China and Japan, 2000-2010

Sources: IMF DOTS Database and IMF staff estimates.

Finally, the United States and Japan have fallen significantly in their centrality rankings, which was the driving force behind their decline in the overall rankings. While both countries increased their number of significant trading partners, several other countries in the top 25 added considerably more partners during the period under review.

V. Applications and Extensions

Our approach lends itself easily to a number of insightful exercises, including regional analyses of the data. For example, Figure 3 illustrates a possible use of the size indicators to better understand the change in regional trade dynamics over the decade under consideration. For this purpose, we have considered six systemic regions, namely: the United States, China, the United Kingdom, the GIIPS (Greece, Ireland, Italy, Portugal, and Spain), the Euro Area excluding the GIIPS (Core Euro Area), and Japan (collectively, the Systemic Regions).

Figure 3.Percent of Total Exports: 2000 vs 2010

Sources: IMF DOTS Database and IMF staff estimates.

The dynamics of trade in the Systemic Regions over the past decade uncover a few points worthy of note:

  • First, China’s role as a strategic importer has grown substantially over the decade as exports to China as a percentage of total exports have grown in the case of the United States, Japan, and Core Euro Area.
  • Second, the United Kingdom and the GIIPS have remained largely static in terms of their export profiles. In fact, the largest share of their exports throughout the decade went to each other and to the Core Euro Area. Such concentration suggests that the United Kingdom and the GIIPS are more susceptible to contagion and spillover through the trade channel from shocks emanating from the Core Euro Area than any of the other four regions.
  • Third, the Core Euro Area has decreased its share of exports to the Systemic Regions, an indication that it has diversified its trading base (with the rest of the world). This point is supported by Figure 4, which shows that the Core Euro Area has overtaken the United States as the region with the most diversified export structure.
  • Fourth, Figure 4 also shows that, while the United States and the Core Euro Area have more diversified export profiles, Japan and especially China have increased markedly their diversification towards the rest of the world over the decade under consideration.

Figure 4.Share of Total Exports to the Systemic Regions

Sources: IMF DOTS Database and IMF staff estimates.

Additional insights may be gained from comparing our findings on systemic trade interconnectedness with earlier findings on systemic financial interconnectedness. To this end, we have calculated the overall ranks of the jurisdictions with systemically important trade sectors shown in Table 1 using a weighted average of the size and interconnectedness rankings with a 0.7/0.3 weight breakdown (0.7 for size and 0.3 for interconnectedness). This is the same size and interconnectedness weight breakdown that had been used for determining the overall ranks of the jurisdictions hosting systemic financial centers.

As Table 2 and Figure 5 show, there is a very strong overlap between jurisdictions hosting trade and financial sectors of systemic importance. In fact, there is an almost perfect overlap between the top 25 jurisdictions with systemic financial sectors and the top 25 jurisdictions with systemic trade sectors in 2010.5

Figure 5.Jurisdictions with Systemic Trade and Financial Sectors

Source: IMF staff estimates.

Table 2.Composite Index Ranking: the Top 25 Systemic Jurisdictions, 2010
Systemic Trade

Sector Rank
JurisdictionSystemic Financial

Sector Rank 1/
Jurisdiction
1China, P.R. Mainland1United Kingdom
2Germany2Germany
3Netherlands3United States
4Italy4France
5France5Japan
6United States6Italy
7Korea, Republic of7Netherlands
8Belgium8Spain
9Japan9Canada
10United Kingdom10Switzerland
11China, P.R. Hong Kong11China, P.R. Mainland
12Canada12Belgium
13Spain13Australia
14India14India
15Malaysia15Ireland
16Switzerland16China, P.R. Hong Kong
17Thailand17Brazil
18Singapore18Russian Federation
19Russian Federation19Korea, Republic of
20Brazil20Austria
21Australia21Luxembourg
22Sweden22Sweden
23Turkey23Singapore
24Austria24Turkey
25Indonesia25Mexico
Sources: IMF DOTS Database and IMF staff estimates.

As identified in “Integrating Stability Assessments Under the Financial Sector Assessment Progran into Article IV Surveillance: Background Material”

Sources: IMF DOTS Database and IMF staff estimates.

As identified in “Integrating Stability Assessments Under the Financial Sector Assessment Progran into Article IV Surveillance: Background Material”

The only exceptions are: Luxembourg, Ireland, and Mexico whose systemic importance is limited to the financial sector; and Indonesia, Malaysia, and Thailand whose systemic importance is limited to the trade sector.

Finally, our approach may be extended by relaxing either one or both of the assumptions imposed on the network, namely that it be “undirected” and “un-weighted.” For example, the analysis could focus on exports or imports only and/or give more weight to the eigenvector centrality relative to the other three interconnectedness indicators, or any combinations thereof. Additionally, future work could explore the sensitivity of the composite index with reversed breakdown, i.e., giving relatively more weight to the interconnectedness than the size dimension.

VI. Concluding Remarks

The paper has laid out our approach for assessing systemic trade interconnectedness using network analysis and the IMF’s DOTS database. Our results uncover several stylized facts offering additional insights into the changing patterns of global trade over the decade 2000-2010. We also have shown possible applications of our approach to gain a better understanding of trade dynamics across world regions and the overlapping of trade and financial sectors of systemic importance in the top 25 jurisdictions. Our approach lends itself easily to a wide range of analytical exercises addressing specific global trade issues, as well as global (trade and financial) interconnectedness issues.

The use of DOTS has lent robustness to our analysis by providing uniform data for 169 jurisdictions representing almost 100 percent of total world trade in both the year 2000 and the year 2010. Additionally, the quarterly updating of DOTS makes it possible to recalibrate our findings to track global trade developments on a timely basis.

From a policy perspective, jurisdictions hosting both systemic trade and financial sectors would seem to be the natural focus of risk-based surveillance on cross-border spillovers and contagion. The analysis underscores that these jurisdictions display the strongest inter-sectoral interconnectedness to the global economy. As such, they have the highest potential for transmitting disturbances to other jurisdictions or to systemic stability via either the trade or financial channel or indeed both channels simultaneously. These jurisdictions would thus seem to warrant particular attention and further analysis on the risks associated with their activities, especially when carried out through systemically important financial institutions and non-financial corporations.

Appendix I: Trade and Interconnectedness Rankings for 169 Jurisdictions, 2000 and 2010

The Appendix includes details on the individual rankings of all the 169 jurisdictions in our dataset that are summarized in two tables, Appendix Table A and Appendix Table B.

Appendix Table A focuses on the four trade size indicators: (i) exports; (ii) imports; (iii) turnover; and (iv) the turnover to GDP ratio. It shows the overall rank, as well as the rankings for each of the four trade size indicators, for each jurisdiction in the year 2000 and the year 2010.

Appendix Table B focuses on the four trade interconnectedness indicators: (i) in-degree; (ii) closeness; (iii) betweenness; and (iv) prestige. It shows the overall rank, as well as the rankings for each of the four trade interconnectedness indicators, for each jurisdiction in the year 2000 and the year 2010.

Table A.Size Rankings
20002010
JurisdictionRankExportsImportsTurnoverTurnover/GDPRankExportsImportsTurnoverTurnover/GDP
Albania135139120128137129127119128124
Algeria50445746935048534997
Angola71649974176451755869
Argentina4042414116642414941152
Armenia12613712313179131135127131114
Aruba135146124141105154158152153125
Australia2124182014019201819151
Austria19232019562628262662
Azerbaijan, Rep. of10697117110978364937592
Bahamas, The861107891338411877916
Bahrain, Kingdom of66618071106659867110
Bangladesh6970586614573746169135
Barbados122138118124114145143141146104
Belarus6163596185562566233
Belgium1111121171110121211
Belize1451451451464914814615315145
Benin14414313814414411214210211830
Bolivia10610110310412811298113107116
Bosnia and Herzegovina100121931068610410910310598
Brazil2326222416420211920167
Brunei Darussalam88811119546968613310155
Bulgaria71746672346568636644
Burkina Faso147148142145157145141140143157
Burundi164161160165152163163159162148
Cambodia1111071121125898102929723
Cameroon10995108107143118105115112143
Canada66665411121011113
Cape Verde159168156157116156165155155100
Central African Republic149141164154136160156158159155
Chad164154161160167132113148139121
Chile434544441104142464088
China, P.R.: Mainland77871342122117
China, P.R.: Hong Kong1010101028119102
China, P.R.: Macao9086989247132138109122165
Colombia5149535315652555455159
Comoros167167167167126166166166166129
Congo, Democratic Republic of12310613311812510510012011177
Congo, Republic of102911431113892801329831
Costa Rica66676569447163807436
Côte d’Ivoire77779281737977968757
Croatia737562689075767276112
Cyprus102109799311212012897106139
Czech Republic32373036182929293019
Denmark252827268234353433101
Djibouti143147137143111411441361404
Dominica161159159164551571541571587
Dominican Republic636955606385937479132
Ecuador807283771097470717382
Egypt5766424914953614250137
El Salvador8183757883102101989990
Equatorial Guinea11810815212619958313910268
Estonia7678717657778858129
Ethiopia12413011611916011512195103150
Fiji1211191271223613912914314247
Finland302932326837403938103
France44541135655136
Gabon838011387251039113710970
Gambia, The1531621491527715516215415446
Georgia14213513013814111712010711986
Germany222298333376
Ghana95999097811011048290107
Greece4654394314758664353161
Grenada1571561551564816516416216565
Guatemala8285727913081848180109
Guinea13912314113413812111912212320
Guinea-Bissau1581511651618416015016516484
Guyana1261241351302014113114614532
Haiti13913612813615413014012313273
Honduras961029199859395889340
Hungary36383538133134333216
Iceland9693949610610910312411480
India2630252816317191316154
Indonesia272534278928242829140
Iran, Islamic Republic of4241464212435333834123
Ireland19202423123832483559
Israel343429349943464746110
Italy88781237887131
Jamaica981048594107125136108121122
Japan33331654444163
Jordan791057683597794768364
Kazakhstan61577363395150555293
Kenya929686891298997788691
Korea, Republic of13121313769711954
Kuwait54466351616245705489
Kyrgyz Republic Lao People’s Democratic134127140137431071301011138
Republic1331321311356712111513012649
Latvia88948888718181848453
Lebanon98118678013187106737796
Libya695177591215354695751
Lithuania74796975406867666826
Luxembourg52605255276769656763
Macedonia, FYR1051031011053010610710611042
Madagascar124112129120135137132135136120
Malawi139133139142100144133145144115
Malaysia161717163171720189
Maldives1481571461485214715914714987
Mali11914011412191141151128138138
Malta84888484912612412112774
Mauritania1291261341322112811713813324
Mauritius1011001001034512112311812495
Mexico121311127815151615105
Moldova1261291251292612112611612541
Mongolia1291251361332311911212612035
Morocco6062515710869715765108
Mozambique13213412112514211411111011660
Myanmar919289909494929092146
Nepal114117106114132140139129134160
Netherlands999915657617
New Zealand4850495011155575860134
Nicaragua1101221041138011511011411725
Niger13714212614088152153142147153
Nigeria494068455743375043102
Norway3127362910132303531118
Oman58557462415956686443
Pakistan5558545615362655259149
Panama10811382101139459040565
Papua New Guinea94841151021687791129413
Paraguay111111971081209699919656
Peru6465616415159585961145
Philippines29323133294047363971
Poland323526311152426252581
Portugal3943283710345523744111
Qatar59538765375543675178
Romania53565054744953454879
Russian Federation241633211021491713133
Rwanda154153154153162148147149148164
Samoa155158153155616215516116134
São Tomé and Príncipe16716616816851168168168168106
Saudi Arabia28213725952518302367
Senegal11712010911611711111610411583
Seychelles1501501481495015014815115015
Sierra Leone1521491511506215114915015275
Singapore141415141131415143
Slovak Republic47524752143844414214
Slovenia55595658315960606322
Solomon Islands1561521621588715314516315628
South Africa3836383911836383237128
Spain1515141512216161417144
Sri Lanka687164705379857978126
St. Kitts and Nevis1601641571596016316116016339
St. Lucia151163147151961351601251371
St. Vincent & Grens.1611601581627515915715615718
Sudan1119811010915589828988162
Suriname1381281441392413612514414158
Sweden18181918702727272885
Switzerland17191617692223232272
Syrian Arab Republic7573707316872726270168
Tajikistan120114132123412713711712948
Tanzania11411610711515811011499104127
Thailand21222122222122222127
Togo14614415014713313813413413521
Tonga16616516616610416716716716794
Trinidad and Tobago848295866676751008237
Tunisia65686067646973647250
Turkey3539233014829312127141
Turkmenistan92871059835108108105108130
Uganda129131122127159134122131130158
Ukraine45474848324549444761
United Arab Emirates37314035722325242438
United Kingdom5545127101368142
United States11111611211166
Uruguay8789818515085878385119
Uzbekistan10290102100146100969495147
Vanuatu1611551631634215815216416052
Venezuela, Rep. Bol.4033434011945365145156
Vietnam44484547283339313612
Yemen, Republic of77769682658988878999
Zambia11611511911792988911110066
Sources: IMF DOTS Database and IMF staff estimates.
Sources: IMF DOTS Database and IMF staff estimates.
Table B.Interconnectedness Rankings
20002010
JurisdictionRankDegreeBetweennessClosenessPrestigeRankDegreeBetweennessClosenessPrestige
Afghanistan, Islamic Republic of178178169177178170170164169170
Albania151148167154145133133130132131
Algeria76769677756463776463
American Samoa190188187191190192189187192192
Angola130130150128125126126151125122
Antigua and Barbuda176176165176179178177175179178
Argentina36364936362927423026
Armenia, Republic of143140164141141120120138119118
Aruba125125114124135144141131144146
Australia22212521252525352527
Austria22233323222424322324
Azerbaijan, Republic of100100103999695941149297
Bahamas, The95931019197109106106110113
Bahrain, Kingdom of78787975778484878279
Bangladesh60611360695155404960
Barbados89914489105999484104112
Belarus51486051514846484648
Belgium151210151532425
Belize128128115127137999110097102
Benin10510211710610098941139798
Bermuda177176168177177181180171180180
Bhutan195194187195195191191187190190
Bolivia138135151137133145142160144139
Bosnia and Herzegovina165158183166150176175183175174
Botswana185185187184185182182181181181
Brazil17172817172121282120
Brunei Darussalam170169180169164166166177164163
Bulgaria49496551494242454242
Burkina Faso160158171161159137135158136129
Burundi168169162166169150149149149148
Cambodia158158166156153127126110132126
Cameroon90908391947575867576
Canada14122412131414231414
Cape Verde154153154160151145146125144142
Central African Republic140138127141136149149147151148
Chad147144158147142153154167152150
Chile50495249524545554547
China, P.R.: Hong Kong20202120201717221715
China, P.R.: Macao133135153132127158159162152154
China, P.R.: Mainland54164522922
Colombia62596859656058755962
Comoros162162173161158151152169149145
Congo, Democratic Republic of133133147134132131131124127133
Congo, Republic of1091088911311410810612110595
Costa Rica848393848379771057978
Côte d’Ivoire54536354554949524951
Croatia63616464631019111192109
Cuba148144129149163157149137162164
Cyprus63648463616666656565
Czech Republic39395039383939513939
Denmark1212312141313101313
Djibouti115119107113116118122118119117
Dominica1321301161341458890888996
Dominican Republic88868887998182727991
Ecuador747287747673711027372
Egypt48496948483636123638
El Salvador121119144121121123120142123123
Equatorial Guinea170168177170165165164176164161
Eritrea189188187192189189186187191187
Estonia5858615858161159153160162
Ethiopia142140157141134134133148134127
Falkland Islands192194187189193194194187189194
Faroe Islands188188187188188187185187186185
Fiji14514761411561411421139155
Finland29283229282323362423
France21412991598
French Territories: French Polynesia184184179183184183182170181183
French Territories: New Caledonia179180178179180179179179176177
Gabon115111126117112115113128115114
Gambia, The10710511910710193941169292
Georgia10810813710811092911179189
Germany1110151011641446
Ghana72729471727171977170
Gibraltar179179181180176173173173171172
Greece30303530303838443837
Greenland187186187187187190189187188189
Grenada173173152172173138138129137141
Guam186186187185186188186187184186
Guatemala79789077808686988685
Guinea10910813311310793941239286
Guinea-Bissau159158159163157155154154152153
Guyana9897829810397949497108
Haiti137133118137143118118141118120
Honduras11511112410811810394126105101
Hungary46466646435252745349
Iceland91935991919194579288
India1212231212141471416
Indonesia19192919182222332222
Iran, Islamic Republic of56577254546161806261
Iraq157153174156152158157168152156
Ireland26263626212627412625
Israel43435642454444474345
Italy331131441743
Jamaica9491100919887887186100
Japan8820882020252018
Jordan65664565666566436564
Kazakhstan71719271708281648284
Kenya73744371795252395357
Kiribati194191184194194196194187195195
Korea, Republic of775710773712
Kuwait979713297897779817777
Kyrgyz Republic12311913512511712512489125132
Lao People’s Democratic Republic151151176151144164163172163158
Latvia7578377774147147144148157
Lebanon60617660604950614950
Lesotho196194187196196186186184184188
Liberia175175175175175172171166170171
Libya127125146128123111111135109103
Lithuania79761098273139138136141144
Luxembourg676881656475751047675
Macedonia, FYR10110013910487177176180177176
Madagascar11411710811311311010968110110
Malawi101104102911021031099697111
Malaysia21213121231818201817
Maldives166164185163155142142163141138
Mali118115148117111124125156123121
Malta69697469687474297374
Mauritania9693125999095941229787
Mauritius82831481826364466066
Mexico44445845436161856258
Moldova939310689928382828282
Mongolia136135141134130185184185183184
Morocco47476247474647584646
Mozambique14914810414614911311379110114
Myanmar135138120137131153154157152152
Namibia181182170181183175173165174173
Nauru191191187190191196194187195195
Nepal151151172151147156157178152147
Netherlands441844111111
Netherlands Antilles168164143171172168165146168168
New Zealand404024040313263134
Nicaragua172169155172171117116134116119
Niger119119145119119128126115127130
Nigeria53545154575252535352
North Korea14614898141170152152108152166
Norway31311230313535273533
Oman81817877815757605855
Pakistan33323832353132263132
Panama70698669717271917173
Papua New Guinea13814012813214014714890147151
Paraguay141140140147138106104101105107
Peru68657568676864736866
Philippines54545553565655634954
Poland33334134333636493636
Portugal28282628292625242928
Qatar10610512299104122122145119116
Romania42425443415550665353
Russian Federation35334234343331343331
Rwanda154153160151154142142132141143
Saint Helena193191186193192195191186194193
Samoa16216770156167163161150160159
São Tomé and Príncipe16216216116316016141374041
Saudi Arabia41414841424066696868
Senegal928699919369113133110105
Seychelles15015313414914811310411910299
Sierra Leone11911713012111510334133335
Singapore22238232734881128990
Slovak Republic565471575388861078693
Slovenia656697656288168152166167
Solomon Islands167172149166168168181182186181
Somalia18318218218518118327302629
South Africa3233463332284244
Spain11113458705756
Sri Lanka515267505057135127137135
St. Kitts and Nevis15615313815516113612699127137
St. Lucia11311485111124128118109122128
St. Vincent and the Grenadines124123121123128121131155127125
Sudan111111142111108131116103116124
Suriname10410295104109116178161178179
Swaziland1811811631821821791151111
Sweden1616271616111119119
Switzerland1010171091110314010294
Syrian Arab Republic8786123888410227382630
Taiwan Province of China252330232629138139140140
Tajikistan12912813112812914066316571
Tanzania8585348488671481421
Thailand1717917191484838581
Togo999711399958516776166165
Tonga1741748017417416777597983
Trinidad and Tobago82817382867747624644
Tunisia595977605946918910
Turkey27273927241012692127134
Turkmenistan130130105128139128194187195195
Tuvalu198194187197197196111143110104
Uganda12212313611912011240544140
Ukraine37374737374043504442
United Arab Emirates45445343464371677
United Kingdom64746718211819
United States8822871970677069
Uruguay8686111868570169174171169
Uzbekistan126125156125122171161120159160
Vanuatu16016411015616616079957780
Venezuela, República Bolivariana de77749176788060786059
Vietnam383840373959191187193191
Yemen, Republic of101105579910610710656105105
Zambia1121151910812613513593134136
Zimbabwe144144112140162173172159173175
Sources: IMF DOTS Database and IMF staff estimates.
Sources: IMF DOTS Database and IMF staff estimates.
References

    AhujaR.K.T.L.Magnanti and J.B.Orlin1993Network Flows: Theory Algorithms and ApplicationsPrentice HallUpper Saddle River, N.J.

    • Search Google Scholar
    • Export Citation

    ClausetA.M.E.J.Newman and C.Moore2004Finding community structure in very large networksPhysical Review E 70 066111.

    FreemanL.C.1977A set of measures of centrality based upon betweennessSociometry4035-41.

    International Monetary Fund2010Direction of Trade Statistics (Washington: International Monetary Fund)

    International Monetary Fund2010Integrating Stability Assessments Under the Financial Sector Assessment Program into Article IV Surveillance: Background Material” (August) available at: www.imf.org/external/np/pp/eng/2010/082710a.pdf

    • Export Citation

    International Monetary Fund2010Understanding Financial Interconnectedness” (October) available at: http://www.imf.org/external/np/sec/pn/2010/pn10150.htm

    • Export Citation

    International Monetary Fund2011Changing Patters of Global Trade” (June) available at: http://www.imf.org/external/pp/longres.aspx?id=4578

    • Export Citation

    NewmanM.E.J.2004Analysis of weighted networksPhysical Review E 70 056131.

    NewmanM.E.J.2005A measure of betweenness centrality based on random walksNetworks2739-54.

    NewmanM.E.J.2008Mathematics of networks” in L.E.Blume and S.N.Durlaufeds.The New Palgrave Encyclopedia of Economics2nd Edition Palgrave MacmillanBasingstoke.

    • Search Google Scholar
    • Export Citation
1

We wish to thank, without implications, the participants of the “Changing Patterns in Global Trade” seminar in February 2011, as well as the IMF colleagues with whom we prepared the Executive Board document “Changing Patterns in Global Trade,” where our approach was introduced in June 2011, for helpful comments. Any errors are our own.

2

For example, see “Understanding Financial Interconnectedness” (October 2010), available at: http://www.imf.org/external/np/sec/pn/2010/pn10150.htm

3

Our approach was introduced in the IMF’s Executive Board document “Changing Patters of Global Trade” (June 2011), available at: http://www.imf.org/external/pp/longres.aspx?id=4578

4

Integrating Stability Assessments Under the Financial Sector Assessment Program into Article IV Surveillance: Background Material” (August 2010), available at: www.imf.org/external/np/pp/eng/2010/082710a.pdf

5

The top 25 jurisdictions with systemic financial sectors as identified in “Integrating Stability Assessments Under the Financial Sector Assessment Program into Article IV Surveillance: Background Material” (August 2010), available at: www.imf.org/external/np/pp/eng/2010/082710a.pdf

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