Beijing Urban competitiveness in World’s Cities
Global Urban Competitiveness Report
(2007-2008)
Ni Pengfei
Global Urban Competitiveness Project (GUCP)
Institute of Finance and Trade Economics (CASS)
Beijing, China, 20 octo 2008
Method of Analysis
Global Urban Competitiveness:
Conceptual Framework
 The urban sustainable competitiveness implies a city’s ability in
relation to other cities in the world to attract and translate
resources, control and occupy markets, accumulate wealth as fast
as possible and offer urban residents material benefits, which is
determined by the combination of its enterprise operating factors
and industrial systems.
 UC1= F（the size of GDP, number of international patent
applications, the distribution of multinational corporations, price
advantages, economic growth rate, GDP per capita, GDP per
square kilometer, employment rate, and labor productivity）
 UC2= F（E、T、I、L、H、S、G）. UC2 means the input or
structure of the city’s competitiveness, E means the quality of
enterprise, T means human resource, I means industry structure,
L means the living environment, H means the business soft
environment, S means the business hard environment, G means
the global connectivity.
 UC1= UC2
Global Urban Competitiveness: Index System
Urban
Indicative
Competiti
veness
GDP per
Capita
Economi
c Growth
Rates
GDP per
Square
Kilometer
Employm
ent Rates
Labor
Productiv
ity
GDP Size
Price
Advantag
es
Internatio
nal
Patent
Applicati
ons
TNCs
Number
Explanatory components of urban
competitiveness
Company
Essence
Industry
Structure
Human
Resources
Living
Environment
Software
Environment
Hardware
Environment
Global
Connectivity
Global Urban Competitiveness: City Samples
 500 Cities for General Urban Competitiveness Measurement
Universality: cities from 5 continents, 130 countries and regions,
representing different areas and levels of development
Selection criterion: the numbers is determined according to national
population and income per capita; and then filtered by the scale, status and
the accessibility, accuracy and standardization of the statistical data.
 150 Cities for Detailed Analyses
Representativeness: from 5 continents, 47 countries and regions. Focus on
key cities of North America, Europe, Asia and Oceania，and some pivotal
cities located in South America and Africa.
Selection criterion: global influence, the social and economic position in its
area, the typicalness of its kind, special research value and accessibility of
the data, also some consideration of previous research.
 10 Cities for competitiveness case study
Selection criterion: Successful, creative, sustainable and usable urban
experience.
Global Urban Competitiveness: Data sources and
statistical methods
 Data for statistical index
 Global Urban Competitiveness Index system has 114 indexes.
The acquirement of the data is complicated.
Every sample city has the original statistics related to the
indexes, such as urban population and area, but the statistic
scopes are different nationally
Majority of the sample cities has related original statistics, and
some are living indexes published by consultancy services.
There is no related international or national statistical agency
yet, even no subjective survey data, such as industrial index,
city function index or enterprise quality
Global Urban Competitiveness: Data
Sources and Statistical Methods
 Data collection channel
International organizations and official statistical
publications, then processed for consistency (mainly
use statistics in 2005, time series data only from
2001 to 2005).
Internet provide index-related statistics, quantified
according to certain criteria (mainly use statistics in
2007, time series data from 2004 to 2007).
Global Urban Competitiveness: Data Sources
and Statistical Methods
 Processing of collected index-related data
 Data integration: In order to solve the difference between statistic
scopes and criterions, a study has been done about statistical
items and criterions of international organizations, such as
statistical distribution from United Nation Statistics Division,
World Development Indicators from World Bank, database of
Organization for Economic Co-operation and Development etc.
Then data transformation relations are established among
statistical items from different countries. Therefore, using this
most reasonable, comparable and complete statistical standard set
to process the collected data, we generated a unified data base
covering 500 cities around the globe.
Missing Data: if a city has the deficiency of certain indexes,
estimation is made according to the given national statistics, its
domestic position and corresponding performance.
Global Urban Competitiveness: Data
Sources and Statistical Methods
 Solution for index-related data which can not be collected
Grading method to replace Index. In the light of unified standard, such
index will be replaced by another related index which is most
identical and typical, grading by indirect factors. For example, use
distribution of transnational financial company to indicate the urban
financing development status.
Typical sample comparative method. According to the standard,
typical samples will be selected and compared within a sample city,
to represent, indicate, and standardize certain aspect of this city. For
example, enterprises are represented by an example of a typical
industry.
Grading method using related information. According to the aspect of
the index, find the key point and class standard, then collect related
data which can be used to indicate such index.
m
y  xj j
w
j 1
Global Urban Competitiveness : Method of
Quantitative Analysis

Global urban competitiveness index (GUCI) of 500 cities in world.
 Choosing non-linear weighted integration method to deal with data .
 Choosing clustering analysis method for comparative research.

Global Urban component competitiveness index (GUCSI) of 500 cities in world.
The explanatory component competitiveness sub-indices are divided into three
levels, where the tertiary level indices can be integrated into secondary level
indices and then integrate the secondary-level indices into primary level indices
using equal weighting.

Cause and effect analysis of competitiveness in 150 cities from world
 Employ the non-linear fuzzy curve analysis and the linear regression analysis
methods

The case study of top 10 cities in world
Research Results
Beijing Urban competitiveness in
World’s 500 Cities
City
Competitivenes
s Score
Rank
New York
1
1
London
0.944185
2
Tokyo
0.790169
3
Paris
0.759375
4
Washington
0.696406
5
Los Angeles
0.668836
6
Stockholm
0.647921
7
Singapore
0.645897
8
San Francisco
0.642095
9
Chicago
0.629848
10
Beijing
0.457567
66
GUCI distribution and comparison of
500 cities (Unit: index)
Global Cities: Which city is the most
competitive?
 This report measures competitiveness of 500 cities in the world
with 9 indexes, namely GDP, GDP per capita, GDP per square
kilometer, labor productivity number of multinational
corporation headquarters, number of international patent
applications, price advantage, economic growth rate, and
employment rate.
 The top 20 competitive cities are New York, London, Tokyo,
Paris, Washington, Los Angeles, Stockholm, Singapore, San
Francisco, Chicago, Toronto, Seoul, Boston, San Diego,
Oakland, Helsinki, Madrid, Vienna, Philadelphia, and Houston.
 The top 20 cities are the strongest ones in terms of economy
size, development level, technical innovation, and economic
control. Among the top 20 cities, 10 located in North America,
7 in Europe, and 3 in Asia. In conclusion, the regions with the
strongest urban competitiveness are North America, Europe
and Asia.
Beijing Urban competitiveness in
World’s 500 Cities
City
Gross Domestic
Product
Rank
$ billions
Tokyo
584.9532
1
Paris
525.0543
2
New York
502.51
3
London
446.2
4
Mexico City
220.08
5
Los Angeles
180.08
6
Hongkong
179.78
7
Seoul
176.6
8
Sydney
171.69
9
Melbourne
134.76
10
Beijing
82.71
23
Beijing Urban competitiveness in
World’s 500 Cities
City
Ratio of Nominal Exchange Rate
to Real Exchange Rate
Rank
Score
Yangon
11.11111
1
Harare
8.333333
2
Addis Ababa
6.25
3
Phnom Penh
5.555556
4
Pyongyang
5.263158
5
Accra
5.263158
6
Kinshasa
5.263158
7
Ho Chi Minh City
5
8
Hanoi
5
9
Kampala
5
10
4.347826
60
Beijing
Beijing Urban competitiveness in
World’s 500 Cities
City
GDP Per Capita
Rank
$
Geneva
62676.92
1
New York
61178.19
2
Oakland(US)
60638.41
3
Edinburgh
59540.23
4
Washington
58548.98
5
London
57948.69
6
57931.4
7
Belfast
56105.86
8
Basel
55247.85
9
Zurich
54056
10
Beijing
6309.51
277
Oslo
Incomes per capita of cities worldwide
(Unit: US $)
Beijing Urban competitiveness in
World’s 500 Cities
City
GDP Per Square
Kilometre
Rank
$ thousands
New York
643498.2
1
Geneva
633715.1
2
Victoria(CA)
565083.3
3
Macao
482636.2
4
Lyon
337620.8
5
San Francisco
326156.5
6
Manchester
309761.2
7
San Juan
302016.4
8
Nottingham
300355.8
9
Kawasaki
296998.8
10
6785.89
358
Beijing
GDP per square kilometre of cities
worldwide (Unit: US $ thousands)
Beijing Urban competitiveness in
World’s 500 Cities
City
Employment Rate
Rank
%
Moscow
99.2
1
Tijuana
99.1
2
Baku
99.02
3
Acapulco
99
4
Quanzhou
98.83
5
Oakland(US)
98.67
6
Al Kuwayt
98.51
7
Minsk
98.5
8
Shenzhen
98.4
9
Huizhou
98.2
10
Beijing
97.92
14
Beijing Urban competitiveness in
World’s 500 Cities
City
Real Economic
Growth Rate（for
5 years）
Rank
%
Huhehaote
0.2
1
Baotou
0.2
2
Yantai
0.195727
3
Dongguan
0.192492
4
0.19
5
Zhongshan
0.184408
6
Huizhou
0.181123
7
Weifang
0.179826
8
Wuhu
0.179669
9
Manaus
0.179569
10
Baku
Economic growth rates of cities
worldwide (Unit: %)
Beijing Urban competitiveness in
World’s 500 Cities
City
Labor Productivity Rank
$
London
161120.7
1
New York
141880.7
2
Detroit
141259.2
3
New Orleans
126097.1
4
Philadelphia
124986.8
5
Boston
121893.5
6
Cleveland
119658.1
7
Oslo
118069.9
8
San Jose
116237.8
9
Baltimore
113666.5
10
Beijing
11698.64
291
Labor productivities of cities worldwide
(Unit: US $ )
Beijing Urban competitiveness in
World’s 500 Cities
City
Number of International
Patent Applications
Rank
number
Tokyo
89445
1
Osaka
39718
2
Paris
20364
3
London
17968
4
New York
16915
5
Seoul
16651
6
Stuttgart
15277
7
San Diego
14338
8
San Jose
12309
9
Stockholm
11785
10
Beijing
3012
56
Internatioanl patent applications by
cities worldwide (Unit: number)
Beijing Urban competitiveness in
World’s 500 Cities
City
Multinational
Corporation
Distribution
Rank
Score
New York
522
1
London
501
2
Hongkong
378
3
Paris
342
4
Tokyo
332
5
Singapore
317
6
Beijing
311
7
Shanghai
295
8
Moscow
289
9
Sydney
289
10
The distribution of multinational
companies worldwide (Unit: index )
Global Urban Economic Control Center:
Evolution in Progress
 Relationship between global space and economic decision-
making: the US,UK and Australia are the forerunners in the
ranking, and some Asian cities like Shanghai, Beijing and
Seoul are also in high rankings, which indicate that the global
economic decision-making center is changing.
 Relationship between Urban Scale and Economic Decision:
New York , London and Tokyo, are the world top cities and are
still the economic-decision making centers, however, the
headquarters of many multinationals are located in small cities,
like Geneva and Brussels, these cities are high-level
international cities and have good economic decision-making
abilities.
Global Urban Cities: The Future of Cities is
Uncertain
 According to the clustering analysis method with 9 index data of
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500 cities, we find that:
Some top cities located in the world economic core areas are
getting stronger and stronger. The gap between them and other
cities in the world becomes wider and wider.
Some developed cities located in economic core-areas of the
world slowed-down and even declined.
Some cities located in the edge zones of the economic core-areas
are rising rapidly and even exceeding their competitors.
Some less developed cities in periphery areas are declining
further.
Some less developed cities in the periphery areas rising rapidly.
Some less developed cities in the periphery rose rapidly and then
declined again.