STUDY ON CREATIVE INDEX IN CHINA: A MODIFIED FLORIDA S 3Ts MODEL

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1 VERSLO IR TEISĖS AKTUALIJOS 2009, t. 3 CURRENT ISSUES OF BUSINESS AND LAW 2009, Vol. 3 STUDY ON CREATIVE INDEX IN CHINA: A MODIFIED FLORIDA S 3Ts MODEL Jianpeng ZHANG PhD student at Tomas Bata University in Zlin, Czech Republic Jitka KLOUDOVA Dr., Associate Professor at Tomas Bata University in Zlin, Bratislava School of Law, Slovakia Abstract With the increasing importance of creative economy, there are lots of research carried out on it. For assessing the regional and city creativity, Richard Florida established the 3T s model, that is, Technology, Talent and Tolerance. In this paper, we are firstly discussing whether the 3Ts model can be used for China; and then we are establishing the Creative Index for the 26 provinces in China mainland by a modified model which is based on the 3T s model combining the economic conditions in China. There are two new points in our model: One, we replace the Gay Index and Bohemian Index with the Creative Class Index to express the Tolerance in the region; Two, we calculate the number of Creative Class using a new method according to China s economy stage. Through the correlation analysis, the results show that there exists high a correlation between the Creative Index and GDP per capita. The model successfully explained the disparity of regional economic development. The findings show that the five provinces, Beijing, Shanghai, Tianjin, Zhejiang, Jiangsu, have the highest creative index being the most developed regions in China. Keywords: creative economy, creative Index, 3Ts model, creative class, China economy. Introduction Globalization and connectivity are new realities that have brought profound changes in peoples lifestyles worldwide. According to the Creative Economy Report from UN, 2008, the creative industries are among the most dynamic emerging sectors in world trade. This is reshaping the overall pattern of cultural production, consumption and trade in a world increasingly filled with images, sounds, texts and symbols. In this era of transformation, creativity and knowledge have become more important factors than ever before VILNIAUS TEISĖS IR VERSLO KOLEGIja 104 vilnius law and business college

2 in the development of economy and society. Traditional economic factors, however, such as land and natural resources, physical labor and capital have become either less crucial or more readily obtainable. In the economic development theory, the economists have put high emphasis on the effect of technology and talent. Solow, 1957 noted the effect of technology on economic growth; Schumpeter, 1982 believed that creative destruction caused continuous progress and improved the standards; subsequent research has found considerable regional differences in the level and utilization of innovation and high-tech industry (Markusen et al., 1986; DeVol, 1999). Barro, 1991 found a close relationship between human capital and economic growth at the country level. The endogenous growth model developed by Lucas, 1988 further clarified the role of human capital externalities in economic development. But whatever technology or talent, they are just one of the facets of economy development. Nowadays, the economists have been aware of the economy ecosystem in the economy development. Richard Florida is one of the representatives, who established the 3Ts model which emphasizes on the interaction and integrity of technology, talent and tolerance, especially in attracting and retaining creative people. The creative economy is an evolving concept centered on the dynamics of the creative industries. There is no single definition of the creative economy nor is there a consensus as to the set of knowledge-based economic activities on which the creative industries are based. At the heart of the creative economy lie the creative industries. Loosely defined, the creative industries are at the crossroads of the arts, culture, business and technology. Today s creative industries involve the interplay of traditional, technology-intensive and service-oriented subsectors (UN, 2008). According to the DCMS, the creative economy mainly focuses on the 13 creative industries, which are advertising, architecture, art and antiques, crafts, design, fashion, film, music, performing arts, publishing, software and computer games, television and radio. According to Florida, the creative economy is more extensive rather than lim- 105

3 ited to the creative industries. Florida s opinion was based on a series of occupations to analyze the creative economy, the Creative Class adding economic value through their creativity. Specifically, the Creative Class includes two parts: 1. Creative professionals. These professionals are the classic knowledgebased workers and include those working in healthcare, business and finance, the legal sector, and education. 2. Super-creative core. These workers include scientists, engineers, techs, innovators, and researchers, as well as artists, designers, writers and musicians. As for China, the definition of creative economy is rich in variety. There are many scholars and local governments identifying culture industries with creative economy. Even in the relatively developed regions, the definitions are very deferent. Take Beijing and Shanghai as examples. In Beijing, culture and creative industries include nine items: Culture and Arts; Advertisement conference and Exhibition; Press and Publication; Arts and antique markets; Film and Video, Television and Radio; Design services; Computer services; Tourism, Recreation &entertainment; other support services. Shanghai creative industries point out that they can create wealth and provide extensive job opportunities through a series of value-added activities, such as production and consumption which mainly concentrate on knowledge-intensive factors, such as innovative ideas, skills and advanced technology. They mainly include research and development design, architecture design; culture and art; consulting; fashion, etc (SCIC, 2009). Both cases showed how big deference in the definition of creative economy does exist. The rest of this paper is structured as follows: in the second part, we discussed the effectiveness of Florida s 3T models to China and put forth the technical parameters, including technology index, talent index and creative index for China. In the third part, we ranked the overall Creative Index of 26 provinces in China 1 and analyzed the correlation of variables. And then, we discussed the advantages and disadvantages of our model. The last part of the paper is conclusions. 1 There are altogether 31 provinces in China mainland, but unfortunately we can t find the complete data of these 5 provinces, temporarily: Gan su, Hai nan, Ning xia, Shan xi, Yun nan. 106

4 1. Can Florida s 3Ts Model be Effective for China? Since Florida has established the 3Ts model, it has been widely used to assess the creativity in different countries and regions, e.g. US and Europe. In conventional assumption, the people moved to where the investment and technology are concentrated, but Florida has shown that corporations will increasingly follow the talent. Even more significantly, the new creative class is drawn to a particular quality of place: open, diverse communities where difference is welcomed and culture creativity is easily accessed. Therefore, Florida emphasized the combination of technology, talent and tolerance in his three T s model for the regional development. But the 3Ts model is established on the developed country, which has the deferent economy structure if compared with the developing country. First of all, the proportion of agriculture in GDP is very small, e.g. it is less than 1% in America. Secondly, most of the developed countries are of post industrial age. The Fordism has been taking place by the flexible production, and the latter need more creative people. Therefore, they have a high proportion of service class and creative class. According to the results of Florida, the Creative Class has taken up about 30% of national workforce in some developed counties, for example, the United States, Belgium, Netherlands, Finland, etc. (2004). In contrast with China, it is a developing country. The primary industry took up 11.26% of GDP in 2007 which is much higher than that of the developed country. Meanwhile, China has not finished the industrialization process. And then, there is very low creative class proportion in some region s workforce, especially in western region of China. Except for the different stage of economic development, China has a different culture than the Western countries. The marriage of gays or lesbians has not been admitted in the legal sense. But the openness and tolerance can be reflected in many aspects, for example, the diversity minorities, the diversity in culture, the respect to the art society, etc. So, we will use the ratio of creative people in the region s workforce to express the region s tolerance because 107

5 of the qualities of creative people, for example, an innovative cognitive style, rebellious spirit, acceptance of conflict, etc. It is proved that there is a high relationship between creative class and GDP per capita in our paper. 2. Methodology and Data Based on the analysis in the second part, we changed some parameters in our model. For example, the creative class takes place of tolerance, which explained the GDP per capita gap in deferent provinces very well and also has a high correlation with other Indexes. In this part, we constructed the three sub-indexes and ranked the overall Creative Index for the 26 provinces in China mainland. Table 1 summarizes the key descriptive statistics for these variables. Table 1. Descriptive Statistics of the Indexes Tech. Index CCI Talent Index Creative GDP per Index capita Observations Mean Standard Error Median Standard Dev Range Minimum Maximum Note: Table 1 is calculated by the Indexes that we will establish in the following parts. For the methods of calculation see the following parts. Technology Index The measure of technological innovation is based on the province s total amount of invention patents approved by the State Intellectual Property Office of China in The patents approved were taken as Innovation Index by Tairan Li and Florida, 2006 to analyze the regional growth of top100 cities in China and also were used to analyze the US cities, States rank and EU creativity (2002, 2003, 2004). 108

6 Figure 1. Technology Index in China (Source: State Intellectual Property Office of China) Figure 1 shows the patents per 10 thousand people in China. Shanghai, Beijing and Zhejiang rank the top three; Xizhang, Guangxi and Qinghai are the last three. We can see a huge gap between the top three and the three bottom provinces; the former is 29 times higher than that of the later for the patents per capita. Among all observations, the total patents granted for the 6 eastern provinces and municipalities, including Guangdong, Zhejiang, Jiangsu, Shanghai, Shandong and Beijing, took up 69.64% of the total patents granted in Figure 2. Talent Index in China Note: The data come from the statistic year book of all provinces. The data of the following provinces are of 2006: Liaoning, Zhejiang, Shanxi, Hebei, Neimenggu, Guangxi, Guizhou; An hui are of 2005 the others are of

7 Talent Index We use the Student of Enrolment as a Talent Index, which include Students of Regular College Course and Specialized Subject, and postgraduate students. We believe it is a good parameter for a regional talent production. The students of enrolment is not only a talent pool in the region but it also reflects the size of the university. Florida has researched the university s important role in the Creative economy (2006). It should be better than to use the number of colleges to explain the talent production, because the sizes of colleges vary greatly. Figure 2 shows the patents per 10 thousand people in China. The top three municipalities are Beijing, Tianjin and Shanghai; the last three are Guizhou, Guangxi and Qinghai. The number of top three is 4.89 times higher than the last three ones. Creative Class Index (CCI) In our model, we use the CCI as the proxy of Tolerance Index which was used in the Florida s model because of the high correlation between creative class and tolerance index, such as openness, diversity, lower barriers to entry, etc. If there is more openness and diversity in some place, it will get high creative class index; vice versa, if some place gets a high creative class index, we can summarize, it is more open and diverse. We also gave up using the population of minority nationality as a tolerance index which was used by Tairan Li and Florida, 2006 to analyze the Chinese economic growth, because most of the people are minorities in some regions, for example, Xizang, Neimenggu, Guangxi, etc. According to Florida s definition of the Creative Class, the economic stage of China and the data availability, we use data from the China Statistical Yearbooks of all provinces to build comparable measures of the Creative Class for 26 provinces. Specifically, the Creative Class includes the employees work in the following seven industries: Information Transmission, Computer Services and Software; Banking and Insurance; Lease and Business Affairs Services; Scientific Research, Polytechnic Services and Geological Prospecting; Education; Health Care, Social Security and Social Welfare; Culture, Sports and Recreation. 110

8 Figure 3. Creative Class Index in China Note: the data of the following provinces are of 2006: Liaoning, Zhejiang, Shanxi, Hebei, Neimenggu, Guangxi, Guizhou, and An hui are of 2005; the others are of These industries have the following common characteristics: (1) they are in line with the definition of Florida and all the occupations mentioned in Florida s model are included. (2) These industries are knowledge-based industries. Their work needs more creative and independent decision of mind. Overall, these occupations are more complex than the primary and secondary industries. (3) The average salary of these industries is higher than that of other industries, even than in secondary industry or the sub-industries in the tertiary industry. Although the creative class we selected is more extensive than Florida s classification, such as there being no Lease, Sports and Social Welfare, they are still highly related to the GDP per capita (the correlation between CCI and GDP per capita is ). Figure 3 shows the creative occupations in percentage of total employment in China. We can read from the figure that Beijing, Shanghai and Tianjin rank the top three, their creative class are 22.95%, 17.47% and 12%, respectively. Guizhou, Hubei and Henan provinces rank the bottom three. A surprising phenomenon is Xinjiang which ranks the sixth place considering its technology index and talent index lags relatively behind. Figure 4 and Figure 5 show the relationship between CCI and Technology Index, and Talent Index. The coef- 111

9 ficient of correlation tells us that there is a positive linear relationship between CCI and two other Indexes. Figure 4. Scattergraph of CCI and Tech. Index Figure 5. Scattergraph of CCI and Talent Index Overall Creative Index The Methodology of Creative Index was used by many economists, except for Florida and Tairan, Kloudova, 2008, a, who used this method to research the relationship between the creative economy and economic growth in the Czech Republic. The steps of constructing overall Creative Index are as follows: 1. We established the Technology Index, Talent Index and Creative Class Index, separately. Let us take the Technology Index as an example: First, we rank the patents granted. The patents per 10 thousand in the first place are denoted by p 1 and Talent Index is x 1. Here, p 1 =13.18, x 1 =26. The province in the second place is p 2 and x 2 ; the province in the third place is p 3 and x 3, and so on. Second, the x 2 p 2 / p 1 * x 1 ; x 3 = p 3 / p 1 * x 1. Thus, we get the Technology Index. We can use the same method get the other two indexes. 2. We add the Technology Index, Talent Index and Creative Class Index to get the province s overall Creative Index. Table 1 shows the overall province creativity ranking. We can see the top three being Beijing, Shanghai and Tianjin - their Creative Indexes are 70.07, and 43.73, respectively; the bottom three are Guangxi, An hui and Guizhou. The top three Creative Index are far higher than that in the most of other provinces. We will examine the correlation between them in the next part. 112

10 Table 2. Overall Province (include 4 municipalities) Creativity Ranking Rank Province Tech. In. CCI Talent In. Creative In. 1 Beijing Shanghai Tianjin Zhejiang Jiangsu Guangdong Heilongjiang Liaoning Jilin Shandong Hunan Hubei Chingqing Fujian Hebei Jiangxi Xinjiang Sichuan Shanxi Neimenggu Henan Xizang Qinghai Guangxi An hui Guizhou Note: the data of the following provinces are of 2006: Liaoning, Zhejiang, Shanxi, Hebei, Neimenggu, Guangxi, Guizhou, and An hui are of 2005; the others are of Figure 6. Overall Province (include 4 municipalities) Creativity Ranking 113

11 3. The Correlation Analysis Between Overall CI and Other Variables In this part, we examined the correlation between overall and other variables and conducted their bivariate and multivariate regression analysis. Table 3 shows the correlation matrix for key variables, and indicates the bivariate correlations which have a high value between these variables. Figure 7 shows that there are strong positive linear relationships between overall Creative Index and Technology Index, Talent Index, Creative Class Index. Figure 8 shows the linear relationship between Creative Index and GDP per capita. The Creative Index is higher; the GDP per capita is higher. It shows that creativity has an important influence on the regional development; at least, we can say they are highly related. From the Table 3, we can see that the coefficient of correlation between CI and GDP per capita is as high as Table 3. Correlation Matrix for Key Variables Tech. index CCI Talent Index Creative Index GDP per capita Tech. index 1 CCI Talent Index Creative Index GDP per capita Figure 7. Scattergraph of CI and other Indexes Technology Index and CI Talent Index and CI 114

12 CCI and CI Figure 8. Scattergraph of CI and GDP per capita Conclusions This article has explored the economic geography of 26 China provinces Creativity Index with a modified 3T model. The model explained the relationship between the regional economy disparity and its creativity successfully. From the Creative Index, it may be concluded that the top 6 provinces are Beijing, Shanghai and Tianjin, Zhejiang, Jiangsu, Guangdong, which are all located in the southeast China and also are the most economically developed regions in China. In addition, three northeast provinces, including Heilongjiang, Liaoling, Jinlin, rank in the seventh, eighth and ninth, respectively, which shows the potential strength for the future economic development. There are several aspects which need to be discussed concerning this model. First, we just took the cross-section data. Therefore, it is a statistic analysis so it cannot tell us about the past and the future, what are their rates of growth as technology and creative class. Second, regarding the definition of creative class, there is no unity and consistent definition for it in the world, especially in the developing countries, such as China. The definition in our model is an experiment. Meanwhile, we know that some industries, such as social welfare and sports, could not be considered as creative industries, but we disposed data approximately because of the data availability. However, the model shows that there exists a high relation between Technology Index, Talent Index and Creative Index through the correlation analy- 115

13 sis. The correlation analysis between the Creativity Index and GDP per capita also shows us that there is a close relation between them, which justifies the important influence of creativity on the regional development. As a result, there are at least two applications in this paper: theoretically, it provides a methodology to analyze the Creativity Index in other developing countries. However, practically, the result of our research could be beneficial to the government because it suggests how to reduce the regional economy disparity while making use of the reasonable innovation policy Reference: Creative Economy Report. (2008). The challenge of Assessing the Creative Economy towards Informed Policy-making. United Nations. China Statistical Yearbooks. (2008). National Bureau of Statistics. DeVol, R. (1999). America s high-technology economy: Growth, development, and risks for metropolitan areas. Santa Monica, CA: Milken Institute. Florida, R. (2002). The Rise of the Creative Class. New York: Basic Books. Florida, R. (2003). First ever rankings of the 50 states on the Creativity Index. Creative Intelligence, February 2003, 1 (4), 2. Florida, R.; Tinagli, I. (2004). Europe in the Creative Age. Florida, R.; Gates, G.; Knudsen, B.; Stolarick, K. (2006). The University and the Creative Economy. Kloudova, J. (2008a). Developing Creative Economy and its Impact on Regional Economic Growth in the Czech Republic. First Research Seminar. Measuring and Understanding the Creative Economy in the Regions, September School of Geography, University of Southampton, UK. Lucas, R. E, Jr. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22, Markusen, A.; Hall, P.; Glasmeier, A. (1986). High-tech America: The what, how, where and why of the sunrise industries. Boston: Allen and Unwin. Schumpeter, J. (1982). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle. Transaction Publishers. Shanghai Creative Industry Center (SCIC). (2009). Solow, R. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39, State Intellectual Property Office of China. (2008). The Work Foundation. (2007). Staying ahead: the economic performance of the UK s creative industries. London: DCMS. Tairan, L.; Florida, R. (2006). Talent, Technological Innovation and Economic Growth in China

14 Kokybiškumo indekso Kinijoje tyrimas: modifikuotas Florida 3T modelis Jianpeng Zhang, Jitka Kloudova Santrauka Auganti kūrybiškos ekonomikos svarba sąlygoja daugelį naujų tyrimų šioje srityje. Regioniniam ir miesto kūrybiškumui vertinti Richard as Florida sukūrė 3T (Technologijų, Talento ir Tolerancijos) modelį. Straipsnyje aptariamas galimas 3T modelio panaudojimas Kinijoje bei modifikuoto modelio, sukurto remiantis anksčiau minėtuoju, pagalba nustatomas kūrybiškumo indeksas 26-ioms Kinijos provincijoms atsižvelgiant į šalies ekonomikos būklę. Autorių pateikiamas modelis turi du naujus aspektus: pirma, džiaugsmo ir bohemijos indeksas yra pakeičiamas kūrybiškosios klasės indeksu, išreiškiančiu toleranciją regione; antra, apskaičiuojamas kūrybiškosios klasės skaičius atsižvelgiant į Kinijos ekonomikos būklę. Panaudojus koreliacijų metodą, gauti rezultatai rodo stiprų ryšį tarp kūrybiškumo indekso ir BVP vienam gyventojui. Modelis sėkmingai paaiškina ekonominės plėtros skirtumus tarp regionų. Tyrimo rezultatai rodo, kad penkios provincijos Pekino, Šanchajaus, Tiandžino, Zhejiang, ir Jiangsu turi aukščiausią kūrybiškumo indeksą ir yra labiausiai išsivystę regionai Kinijoje. Reikšminiai žodžiai: kūrybiška ekonomika, kūrybiškumo indeksas, 3T modelis, kūrybiškoji klasė, Kinijos ekonomika. Information about the authors Jianpeng Zhang is a Ph. D student of Faculty of Management and Economics Tomas Bata University in Zlin. Jitka Kloudova is a doctor, Associate Professor at Faculty of Management and Economics in Tomas University in Zlin and the Vice-Rector for Research, Bratislava School of Law. Her research interests include Chinese economy, Creative economy, etc. 117