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The Evolution of the
International Trade
     Network
      Tim Kastelle
  IBUS2302 – 5 June 2009
Question


• Globalisation has changed
  everything, right?
A transformation, or a change in
                   degree?
                                       Exports/GDP 1820-2001

0.200


0.180


0.160


0.140


0.120


0.100                                                                                              Exports/GDP


0.080


0.060


0.040


0.020                                                                                          Source: Maddison, 1997, 2001, 2003

0.000
    1800   1820   1840   1860   1880   1900          1920   1940   1960   1980   2000   2020
                                              Year
What is Network Analysis
• Analytical tool for measuring network structure
  consisting of actors (people, firms, etc.) and the
  connections between them (social, financial
  exchange, technical collaboration, etc.)
• Draws from Social Network Analysis (sociology,
  psychology) and Complex Network Analysis
  (physics, economics)
• Theoretical justification in evolutionary
  economics
Data Set
• IMF Trade data, every 10 years from
  1938-98, and 2003 (8 sets)
• Calculate significant trade relations to
  build adjacency matrix
• If there is a significant trade flow from
  country A to country B, a 1 is recorded,
  otherwise the value is 0
1938 Network
1968 Network
1998 Network
Convergence?
Or divergence?
Degree Distribution
• The degree of a node is the number of
  edges that connect to it. An important
  characteristic of most graphs is the mean
  degree k, the average number of
  connections per node. The degree
  distribution of a graph is the measure of
  degree frequencies, in other words, it is a
  count of how many nodes have degree = k
  for each possible value of k.
Degree = 9
Average degree = 254/256 = 1.0
The Degree of a Node Roughly
     Correlates with GDP
                                  1000




                                  100
log Imports ($US billion)




                                   10




                                    1
                            0.1          1        10         100   1000




                                   0.1




                                  0.01
                                             log In Degree


Consequently, the degree distributions of the network
should be able to tell us whether GDP is converging or not
Frequency




                      0
                          2
                              4
                                  6
                                      8
                                            10
                                                  12
                                                       14
                                                            16
                                                                 18
                  0
                  5
              10
              15
              20
              25
              30
              35
              40
              45
              50
              55
              60
              65
              70
              75
              80
              85
              90




Degree (k)
                                                                      In Degree Histogram




              95
             10
                  0
             10
                  5
             11
                  0
             11
                  5
                                                                                                 histogram…




             12
                  0
             12
                  5
             13
                  0
             13
                  5
             14
                  0
             14
                  5
                                                                                            The degree distribution




             15
                  0
             15
                  5
             16
                  0
             16
                  5
…is Converted to a Cumulative
     Distribution Function
                                      1938 In Degree Distribution

                             1
                                  1        10                       100




                            0.1
Cumulative Distribution




                                                                          Cumulative Distribution




                           0.01




                          0.001
                                       Log Degree
Expected Changes
                                                2003 Random Graph Data

                               1
                                    1      10                     100    1000




                              0.1
  Cumulative Distribution




                                                                                Normal Distribution
                                                                                x^-1
                                                                                x^-0.5



                             0.01


Economic convergence will lead to a normal degree distribution,
while divergence will lead to a power law distribution
                            0.001
                                                  Log Degree
Actual Distributions
                                     1
                                          1        10                100   1000
Cumulative distribution - Pc(k)




                                    0.1
                                                                                  1938
                                                                                  1948
                                                                                  1958
                                                                                  1968
                                                                                  1978
                                                                                  1988
                                                                                  1998
                                                                                  2003
                                   0.01




                                  0.001
                                                        Degree - k
Results
• All of the CDFs are best described by a log-
  normal distribution with a power law tail
• The only change is that the curve has shifted to
  the right as the size of the network has
  increased over time
• This suggests that the reason that neither
  convergence nor divergence has gained
  overwhelming empirical support is that neither is
  actually happening
However, it is
actually
a James Dean
network:


Calm on the
outside….
…but seething underneath the
             surface
• Even though the overall network structure has
  been stable, we know that the roles of individual
  countries within the network have changed over
  time
• For example, China, Singapore, South Korea
  and Taiwan have become much more highly
  connected, while countries such as Portugal and
  Argentina have become less connected
Rank Clock Data – In Degree
Country          1938 1948 1958 1968 1978 1988 1998 2003
United States       1    1    2    3    2    1    1    1
United Kingdom      2    2    2    1    4    4    4    4
Germany             3    8    1    2    1    3    2    2
France              4    3    5    5    3    2    3    3
Netherlands         5    4    4    4    6    7    6    6
Belgium             6    6    7    8    8    8    8   10
Italy               7    5    6    6    5    5    5    5
Japan               8   21    8    7    7    6    9    9
Sweden              9    7    9   12   11   13   23   25
Argentina          10   17   23   34   47   59   61   96
Norway             11   18   21   17   28   28   38   41
South Africa       12   14   14   20   52   55   34   34
Poland             13   18   15   16   17   34   21   19
India              14   10   11   22   23   21   18   14
Rank Clock - International Trade Network 1938-2003
                                       1938
                                 100

                                  90

                                  80                        1948

                                  70

                                  60

                                  50

                                  40

                                  30

                                  20
2003                                                                      1958
                                  10

                                   0




       1998                                                        1968




                       1988                       1978
Rank Shift Dynamics 1938-2003


                     1938
                  100
                   90
                   80                   1948
                   70                                      Singapore
                   60                                      United States
                   50                                      Argentina
                   40                                      Ireland
                   30                                      China - Mainland

2003               20                               1958
                   10
                    0




   1998                                          1968




          1988                  1978
Conclusions
• The overall structure is stable
• Changes at the country level reflect the
  impact of policy – and these are quite
  substantial

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Ibus2302 Kastelle 2009

  • 1. The Evolution of the International Trade Network Tim Kastelle IBUS2302 – 5 June 2009
  • 2. Question • Globalisation has changed everything, right?
  • 3. A transformation, or a change in degree? Exports/GDP 1820-2001 0.200 0.180 0.160 0.140 0.120 0.100 Exports/GDP 0.080 0.060 0.040 0.020 Source: Maddison, 1997, 2001, 2003 0.000 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 2020 Year
  • 4. What is Network Analysis • Analytical tool for measuring network structure consisting of actors (people, firms, etc.) and the connections between them (social, financial exchange, technical collaboration, etc.) • Draws from Social Network Analysis (sociology, psychology) and Complex Network Analysis (physics, economics) • Theoretical justification in evolutionary economics
  • 5. Data Set • IMF Trade data, every 10 years from 1938-98, and 2003 (8 sets) • Calculate significant trade relations to build adjacency matrix • If there is a significant trade flow from country A to country B, a 1 is recorded, otherwise the value is 0
  • 11. Degree Distribution • The degree of a node is the number of edges that connect to it. An important characteristic of most graphs is the mean degree k, the average number of connections per node. The degree distribution of a graph is the measure of degree frequencies, in other words, it is a count of how many nodes have degree = k for each possible value of k.
  • 12. Degree = 9 Average degree = 254/256 = 1.0
  • 13. The Degree of a Node Roughly Correlates with GDP 1000 100 log Imports ($US billion) 10 1 0.1 1 10 100 1000 0.1 0.01 log In Degree Consequently, the degree distributions of the network should be able to tell us whether GDP is converging or not
  • 14. Frequency 0 2 4 6 8 10 12 14 16 18 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 Degree (k) In Degree Histogram 95 10 0 10 5 11 0 11 5 histogram… 12 0 12 5 13 0 13 5 14 0 14 5 The degree distribution 15 0 15 5 16 0 16 5
  • 15. …is Converted to a Cumulative Distribution Function 1938 In Degree Distribution 1 1 10 100 0.1 Cumulative Distribution Cumulative Distribution 0.01 0.001 Log Degree
  • 16. Expected Changes 2003 Random Graph Data 1 1 10 100 1000 0.1 Cumulative Distribution Normal Distribution x^-1 x^-0.5 0.01 Economic convergence will lead to a normal degree distribution, while divergence will lead to a power law distribution 0.001 Log Degree
  • 17. Actual Distributions 1 1 10 100 1000 Cumulative distribution - Pc(k) 0.1 1938 1948 1958 1968 1978 1988 1998 2003 0.01 0.001 Degree - k
  • 18. Results • All of the CDFs are best described by a log- normal distribution with a power law tail • The only change is that the curve has shifted to the right as the size of the network has increased over time • This suggests that the reason that neither convergence nor divergence has gained overwhelming empirical support is that neither is actually happening
  • 19. However, it is actually a James Dean network: Calm on the outside….
  • 20. …but seething underneath the surface • Even though the overall network structure has been stable, we know that the roles of individual countries within the network have changed over time • For example, China, Singapore, South Korea and Taiwan have become much more highly connected, while countries such as Portugal and Argentina have become less connected
  • 21. Rank Clock Data – In Degree Country 1938 1948 1958 1968 1978 1988 1998 2003 United States 1 1 2 3 2 1 1 1 United Kingdom 2 2 2 1 4 4 4 4 Germany 3 8 1 2 1 3 2 2 France 4 3 5 5 3 2 3 3 Netherlands 5 4 4 4 6 7 6 6 Belgium 6 6 7 8 8 8 8 10 Italy 7 5 6 6 5 5 5 5 Japan 8 21 8 7 7 6 9 9 Sweden 9 7 9 12 11 13 23 25 Argentina 10 17 23 34 47 59 61 96 Norway 11 18 21 17 28 28 38 41 South Africa 12 14 14 20 52 55 34 34 Poland 13 18 15 16 17 34 21 19 India 14 10 11 22 23 21 18 14
  • 22. Rank Clock - International Trade Network 1938-2003 1938 100 90 80 1948 70 60 50 40 30 20 2003 1958 10 0 1998 1968 1988 1978
  • 23. Rank Shift Dynamics 1938-2003 1938 100 90 80 1948 70 Singapore 60 United States 50 Argentina 40 Ireland 30 China - Mainland 2003 20 1958 10 0 1998 1968 1988 1978
  • 24. Conclusions • The overall structure is stable • Changes at the country level reflect the impact of policy – and these are quite substantial