2012 may 4 mongolia
TRANSCRIPT
HOW SIMILAR ARE THE EAST ASIAN ECONOMIES? A CLUSTER ANALYSIS PERSPECTIVE
ON ECONOMIC COOPERATION IN THE REGION
May 4, 2012
Alexandre Repkine
INTERCONNECTION AND COOPERA-TION
1997 Asian financial crisis Japanese statement about interest rates Depreciation of Thai baht Capital flight and currency depreciation in
Asia Economic cooperation
Expanding the set of consumer choices Institutional framework helping to absorb
shocks
WHY ECONOMIC COOPERATION?
Comparative advantage Production possibility frontiers Gains from trade Examples of economic cooperation
Free Trade Agreement Regional Trade Agreement Customs Union Currency Union
ECONOMIC COOPERATION IN EAST ASIA
Growth in the number of FTAs from 2 in 1975 to 16 in 2000
By 2010: more than 45 FTAs concluded in the region
SCOPE AND SEQUENCING OF FTA
The scope of East Asian FTAs is con-stantly increasing
The problem of sequencing How do countries form economic coopera-
tion groups? How do smaller groups become larger
groups? What is the principle governing the process
of economic agglomeration?
ROLE OF ECONOMIC SIMILARITY
It makes sense for the economically simi-lar countries to form groups of eco-nomic cooperation prior to doing so with more different countries E.g. Philippines first forming alliance with
Thailand before concluding an FTA with EU How do we measure similarity Why are groups of similar countries better
off economically?
WHY ARE SIMILAR COUNTRIES BETTER OFF TOGETHER?
Gravity models Helpman and Krugman (1985) Geographically close countries trade more Countries with similar-sized GDPs trade more Similar countries in general trade more
(Bergstrand and Egger, 2007) Baier and Bergstrand (2004)
Cooperation and trade between economically simi-lar countries increases welfare
Similarity measured in terms of distance, GDPs, remoteness to ROW, and K/L ratios
DEFINING ECONOMIC SIMILARITY
What countries can be viewed as simi-lar and on what grounds? Language (Korea vs Japan) Historical heritage (Korea vs China) Current trade and political links (Korea vs
US) Once groups, or clusters, of similar
countries are identified, economic in-tegration can be based on those clusters
AN OUTLINE OF CLUSTER ANALYSIS
Every country is a collection of characteristics GDP size Population Human development index Trade openness
How does one compare collections of numbers? Cluster analysis employs a measure of
generalized distance based on several charac-teristics
AN EXAMPLE OF SIMILARITY MEASURE: EU-CLIDEAN DISTANCE
X and Y are any two economic parameters (GDP per capita, % urban popula-tion)
EUCLIDEAN DISTANCES IN ASIA
Real GDP per Capita, USD
Sh
are
of
Urb
an
Pop
ula
-ti
on
, %
• Number of distances grows quickly with more countries added
• Vietnam appears to be similar to Cambodia
• Should we include Malaysia in one group with China and Thailand?
• Dissimilarity matrix sum-marizes the informa-tion about economic distances between coun-tries
DATA SOURCES AND SUMMARY STA-TISTICS
Mean Stan-dard
Devia-tion
Min Max Source Computa-tion
Structural Shares
Agriculture, % 18.35% 0.13 1.46% 4.53% ADB SU501/SU499
Manufacturing, % 23.21% 0.12 5.96% 48.46% ADB SU504/SU499
Trading, % 13.98% 0.05 5.76% 21.66% ADB SU508/SU499
International Trade
Trade Openness, % 106.59% 46.84 24.31% 213.75% Penn openk
Economic Develop-ment
GDP per Capita, $ $10167 11010 $1707 $34223 Penn rgdpl
Share of Urban Popu-lation
44.10% 19.82 12.52% 84.68% ADB, Trading Economics
SU1223
Human Development Index, %
0.62% 0.14% 0.44 0.89% ADB SU1023
Economic Size
Population, mn people 61.2 65.6 2.9 240.3 Penn POP
GDP in constant 2005 prices, bn USD
1229 2195 7.46 9276 ADB SU499Note: ADB stands for the Asian Development Bank’s statistical database https://sdbs.adb.org/sdbs/index.jsp), Penn for the Penn World Table version 7 (http://pwt.econ.upenn.edu/php_site/pwt70/pwt70_form.php). Data on urban population shares in Papua New Guinea is taken from the Trading Economics In-dicators database (http://www.tradingeconomics.com/papua-new-guinea/urban-population-percent-of-total-wb-data.html). The “Computation” column is based on the variable names provided by the original databases. Population statistics are given for the subsample that excludes China.
EAST ASIAN COUNTRIES COVERED
1. Mongolia2. Korea3. China4. Taiwan (China)5. Cambodia6. Laos
7. Papua New Guinea8. Vietnam9. Indonesia10. Malaysia11. Philippines12. Thailand
Japan is excluded because of its special status of the second largest economy in the world (until recently) and its currency being the only hard currency in the region.
CLUSTERING BY K-MEANS
Specify the number of groups in advance
Make sure that the overall distance within each clus-ter of the individual observations from the cluster center (i.e. centroid) is minimized
Proceed in iteration so that each country may change its cluster several times in the process
Random assignment of group centers initially
Realistic group membership: clusters of 2, 3, and 4 countries
K-MEANS CLUSTERING, EUCLIDEAN DISTANCE MEASURE
2 groups 3 groups 4 groupsGroup 1
ChinaIndonesia
Korea Korea KoreaMalaysia Malaysia
PhilippinesTaiwan Taiwan Taiwan
Thailand
Group 2Cambodia Cambodia Cambodia
Laos Laos LaosMongolia Mongolia Mongolia
Papua New Guinea Papua New Guinea Papua New GuineaVietnam Vietnam
IndonesiaThailand
Philippines
Group 3China China
Group 4IndonesiaMalaysiaVietnam
Philippines
Thailand
• Results similar to the case when Man-hattan (c-ity block) measure is used
• China forms one –country cluster
• Korea and Taiwan
• Cambodia, Laos, Mongolia, Papua New Guinea
• Indonesia, Philip-pines, Thailand
K-MEDIAN CLUSTERING, EUCLIDEAN DISTANCE MEASURE
2 groups 3 groups 4 groupsGroup 1
ChinaIndonesia
Korea Korea KoreaMalaysia Malaysia
PhilippinesTaiwan Taiwan Taiwan
ThailandGroup 2
Cambodia Cambodia CambodiaLaos Laos Laos
Papua New Guinea Papua New GuineaPapua New
GuineaVietnam Mongolia Mongolia
Group 3China
Indonesia Indonesia Malaysia Mongolia Philippines Philippines Thailand Thailand Vietnam Vietnam
Group 4 China
• Mongolia joins more advanced group with Thailand and In-donesia in 4-group solution
• China is still forming a separate cluster in 4-group so-lution
• Korea and Taiwan still stick together
• Indonesia, Philippines and Thailand
HOW MANY CLUSTERS TO CHOOSE?
Both K-means and K-median clustering pro-cedures need a priori the number of clusters
Hierarchical procedures start with each coun-try being its own cluster, then agglomerating up
Stopping rules Pseudo-F value Duda-Hart value
AGGLOMERATION INTO CLUSTERS
Based on dissimilarity matrices (normally Euclidean distances)
Merging clusters that are similar Single-linkage Complete linkage Average linkage Cluster centroid Ward’s method
OPTIMAL NUMBER OF CLUSTERS
Average Linkage
Single Linkage
Complete Linkage
Centroid Ward’ sMethod
Number of Clusters
F DH F DH F DH F DH F DH
25.33 0.6 5.33 0.92 5.33 0.58 5.33 0.79 4.47 0.46
36.99 0.55 2.98 0.75 7.3 0.41 4.26 0.56 7.3 0.41
48.94 0.54 3.19 0.68 9.13 0.47 6.62 0 9.13 0.47
59 0.33 3.91 0.64 9 0.33 5.02 0.77 9 0.48
68.34 0 4.86 0.68 8.34 0.48 4.86 0.49 8.6 0.33
77.76 0.48 5.36 0.82 8.93 0 7.76 0 8.93 0
89.54 0 4.62 0.58 9.54 0.23 6.72 0.65 9.54 0.23
Four clusters appears to be the op-timal solu-tion
Single link-age is ex-ceptional
Average number of clusters 4.2
CLUSTER SEQUENCING: DENDRO-GRAMS
4-cluster group-ing coincides with K-means solution (Euclidean distance)China staying sepa-rately
Korea and Tai-wan
Cambodia, Laos, Papua New Guinea
Mongolia ambiguous
CONCLUSIONS
Economic theory suggests similar countries should trade and cooperate more since such cooperation increases their total welfare
Cluster analysis determines what countries are similar and suggests two approaches
Composing clusters if number of groups known
Hierarchical approach (dendrograms)
Results surprisingly stable over various procedures
Results could be used as background for future policy mak-ing on regional economic cooperation in East Asia