1. 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 Beijing, China, 20 octo 2008 Beijing Urban competitiveness in World’s Cities

2. Method of Analysis

3. 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

4. Global Urban Competitiveness: Index System Urban Indicative Competitiveness GDP per Capita Economic Growth Rates GDP per Square Kilometer Employment RatesLabor ProductivityGDP SizePrice Advantages International Patent Applications TNCs Number

5. Explanatory components of urban competitiveness Company Essence Industry Structure Human Resources Living Environment Software Environment Hardware Environment Global Connectivity

6. 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.

7.  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

8.  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

9.  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

10.  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. Global Urban Competitiveness: Data Sources and Statistical Methods

11. 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

12. Research Results

13. Beijing Urban competitiveness in World’s 500 Cities City Competitivenes s Score Rank New York11 London0.9441852 Tokyo0.7901693 Paris0.7593754 Washington0.6964065 Los Angeles0.6688366 Stockholm0.6479217 Singapore0.6458978 San Francisco0.6420959 Chicago0.62984810 Beijing0.45756766

14. GUCI distribution and comparison of 500 cities (Unit: index)

15. 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.

16. City Gross Domestic Product Rank $ billions Tokyo584.95321 Paris525.05432 New York502.513 London446.24 Mexico City220.085 Los Angeles180.086 Hongkong179.787 Seoul176.68 Sydney171.699 Melbourne134.7610 Beijing82.7123 Beijing Urban competitiveness in World’s 500 Cities

17. City Ratio of Nominal Exchange Rate to Real Exchange Rate Rank Score Yangon11.111111 Harare8.3333332 Addis Ababa6.253 Phnom Penh5.5555564 Pyongyang5.2631585 Accra5.2631586 Kinshasa5.2631587 Ho Chi Minh City58 Hanoi59 Kampala510 Beijing4.34782660 Beijing Urban competitiveness in World’s 500 Cities

18. CityGDP Per CapitaRank $ Geneva62676.921 New York61178.192 Oakland(US)60638.413 Edinburgh59540.234 Washington58548.985 London57948.696 Oslo57931.47 Belfast56105.868 Basel55247.859 Zurich5405610 Beijing6309.51277 Beijing Urban competitiveness in World’s 500 Cities

19. Incomes per capita of cities worldwide (Unit: US $)

20. City GDP Per Square Kilometre Rank $ thousands New York643498.21 Geneva633715.12 Victoria(CA)565083.33 Macao482636.24 Lyon337620.85 San Francisco326156.56 Manchester309761.27 San Juan302016.48 Nottingham300355.89 Kawasaki296998.810 Beijing6785.89358 Beijing Urban competitiveness in World’s 500 Cities

21. GDP per square kilometre of cities worldwide (Unit: US $ thousands)

22. CityEmployment RateRank % Moscow99.21 Tijuana99.12 Baku99.023 Acapulco994 Quanzhou98.835 Oakland(US)98.676 Al Kuwayt98.517 Minsk98.58 Shenzhen98.49 Huizhou98.210 Beijing97.9214 Beijing Urban competitiveness in World’s 500 Cities

23. City Real Economic Growth Rate （ for 5 years ） Rank % Huhehaote0.21 Baotou0.22 Yantai0.1957273 Dongguan0.1924924 Baku0.195 Zhongshan0.1844086 Huizhou0.1811237 Weifang0.1798268 Wuhu0.1796699 Manaus0.17956910 Beijing0.11680579 Beijing Urban competitiveness in World’s 500 Cities

24. Economic growth rates of cities worldwide (Unit: %)

25. CityLabor ProductivityRank $ London161120.71 New York141880.72 Detroit141259.23 New Orleans126097.14 Philadelphia124986.85 Boston121893.56 Cleveland119658.17 Oslo118069.98 San Jose116237.89 Baltimore113666.510 Beijing11698.64291 Beijing Urban competitiveness in World’s 500 Cities

26. Labor productivities of cities worldwide (Unit: US $ )

27. City Number of International Patent Applications Rank number Tokyo894451 Osaka397182 Paris203643 London179684 New York169155 Seoul166516 Stuttgart152777 San Diego143388 San Jose123099 Stockholm1178510 Beijing301256 Beijing Urban competitiveness in World’s 500 Cities

28. Internatioanl patent applications by cities worldwide (Unit: number)

29. City Multinational Corporation Distribution Rank Score New York5221 London5012 Hongkong3783 Paris3424 Tokyo3325 Singapore3176 Beijing3117 Shanghai2958 Moscow2899 Sydney28910 Beijing Urban competitiveness in World’s 500 Cities

30. The distribution of multinational companies worldwide (Unit: index )

31. 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.

32. Global Urban Cities: The Future of Cities is Uncertain  According to the clustering analysis method with 9 index data of 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.