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Talent, Craetivity And Regional Economic Performance


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Talent, Craetivity And Regional Economic Performance

  1. 1. Ann Reg Sci DOI 10.1007/s00168-008-0282-3 ORIGINAL PAPER Talent, creativity and regional economic performance: the case of China Haifeng Qian Received: 24 March 2008 / Accepted: 27 November 2008 © Springer-Verlag 2008 Abstract This paper investigates the geographic distribution of talent and its associations with innovation, entrepreneurship and regional economic performance in China. Two types of talent are examined: human capital in terms of the educa- tional attainment and the creative class in terms of the occupational skill. Explanatory variables of the talent distribution cover both market factors (jobs and wage levels) and non-market factors (amenities, openness and the university). The results of cor- relation analysis and multivariate analysis show that the university is the single most important factor that affects the talent distribution in China. Wage levels, service amenities and openness also contribute to talent attraction but to different extents. Furthermore, human capital outweighing the creative class exhibits positive effects on innovation, entrepreneurship and regional economic performance. Openness presents a direct influence not only on talent stock, but on innovation and regional economic performance as well. Explanations of empirical results in the Chinese context are offered. JEL Classification O30 · P25 · R12 1 Introduction There has been a growing concern over the role of talent in regional economic growth. Early in 1958, Ullman addressed the importance of human capital in regional devel- opment. Romer (1986) and Lucas (1988) theorize the impact of human capital on This paper was the winning entry of the 22nd Charles M. Tiebout prize in Regional Science. H. Qian (B ) School of Public Policy, George Mason University, 4400 University Drive, MSN 3C6, Fairfax, VA 22030, USA e-mail: 123
  2. 2. H. Qian economic growth in the endogenous growth theory. Their seminal work has begot numerous empirical studies, providing the evidence that human capital is a crucial driving force of economic growth (Barro 1991; Rauch 1991; Glaeser 1994, 1998, 2000; Glaeser et al. 1995; Glendon 1998; Simon 1998). Other studies (Florida 2002b; Florida et al. 2007; Lee et al. 2004; Acs and Armington 2006; Audretsch et al. 2006; Mellander and Florida 2007) show that human capital is associated with innovation or entrepreneurship, which further contribute to economic development (Schumpeter 1934; Baumol 1968). Talent geographically presents uneven distributions, both across countries and across regions or cities within a specific country. Talent appears to concentrate in cities, while cities play an important role in attracting, mobilizing, and organizing human capital for economic activity (Jacobs 1961, 1969; Lucas 1988; Glaeser 1994). The geographical dimension of talent spurs research on relationships between talent, creativity (including innovation and entrepreneurship) and regional economic devel- opment. To date, nearly all empirical studies on this topic are conducted based on cases of developed countries, especially the US, one of the best examples of a large and well developed “knowledge economy.” There has been much less research atten- tion on developing countries. This paper contributes to the literature by investigating the largest developing country, China. It attempts to identify geographically bounded factors associated with the talent stock in Chinese regions, and how the geographic concentration of talent may affect innovation, entrepreneurship, and further on regional economic performance. China’s economic success benefits greatly from its low-cost and available labor force; however, its growth appears to be more and more reliant on its talent. It can be seen that in China the government budget for higher educa- tion has undergone a significant increase during the last decade, and those with a college or higher-level degree play an increasingly important role in regional growth and especially the growth of cities. Li and Florida (2006) have examined the effects of non-market factors (amenities and diversity) on China’s talent production. Their work however suffers from data unavailability at the urban regional level and prob- lems in the measures used for their variables. Another limitation of their work is its focus on talent production rather than talent stock. Talent production, measured with a surrogate variable—the number of local universities—in their study to a large extent is exogenous of regional characteristics and depends instead on government policies and decisions in China. Additional efforts to build theoretical frameworks as well as seek appropriate measures are therefore needed so as to better understand China’s talent clustering at the sub-national regional level. Considering China’s low income per capita (roughly $1,700 in 2005), it is reason- able to hypothesize that market factors may play an important role in attracting talent in China. This paper accordingly investigates the effects of both market factors (includ- ing the wage level, wage change, and employment change) and non-market factors (including service amenities, openness, and the university) on the talent distribution, instead of focusing exclusively on non-market factors as Li and Florida (2006) did. The presence of universities, which may bear limited influence on local talent stock in the US due to the high level of population mobility (Florida et al. 2006), appear to be of great importance in China as a result of China’s exceptional institutional barriers to labor flow. Furthermore, by addressing the role of cities in the talent literature (Jacobs 123
  3. 3. Talent, creativity and regional economic performance: the case of China 1961, 1969; Lucas 1988; Glaeser 1994), the proportion of urban population in the province is another factor being considered. This study uses provincial level rather than city level data in order to obtain data in better quality. Based on those variables, descriptive analysis, correlation analysis and multivariate analysis are conducted. After exploring the factors that may affect the locality choice of China’s talent, this paper investigates how the distribution of talent is associated with the distributions of innovative and entrepreneurial activities. Talent has been suggested as a significant contributor to both innovation and entrepreneurship in such developed countries as the US, Germany and Sweden (Florida 2002b; Florida et al. 2007; Lee et al. 2004; Acs and Armington 2006; Audretsch et al. 2006; Mellander and Florida 2007). Our empirical investigation on China is hypothesized to provide similar evidence. The last analytical part focuses on the effects of talent, innovation and entrepreneurship on regional economic performance. The paper is organized as five sections. This introduction section has explained the background and necessity of this research. The literature on talent, creativity, and regional development is briefly reviewed in the second section. The third section describes data, variables and methods. It is followed by the fourth section that presents and interprets the findings. The last section concludes. 2 Theories The literature has distinguished two types of talent: human capital (Schultz 1963) and the creative class (Florida 2002c). Human capital is generally measured by the education attainment, and the creative class includes a certain group of occupational workers that are likely to undertake creative work.1 There is a plethora of research on the relations between talent, creativity and economic growth. This section provides a brief summary of this literature in three parts: the geographic distribution of talent; talent and creativity; and talent and regional development. 2.1 The geographic distribution of talent The literature shows that both market factors and non-market factors may affect the locality of talent. Economists traditionally focus on market factors, such as jobs, and economic and financial opportunities (Florida 2002b). Recently there have been two different approaches highlighting the role of non-market factors in attracting talent. One approach addresses the quality of life and examines the role of natural, cultural or service amenities. Amenities, according to Kotkin (2000), can attract high-tech industries and workers. Glaeser et al. (2001) argue that the growth of cities depends more and more on amenities they can provide to attract valuable human capital resi- dents. Lloyd and Clark (2001) depict the city as an “entertainment machine,” implying 1 (Florida 2002c) defines creative class as consisting of the super-creative core (including scientists and engineers, university professors, poets and novelists, artists, entertainers, actors, designers, architects, non- fiction writers, editors, cultural figures, think-tank researchers, analysts and other opinion-makers) and creative professionals (working in high-tech sectors, financial services, the legal and health care profes- sions and business management). 123
  4. 4. H. Qian the crucial role of consumption and aesthetic innovation in urban economic growth. Florida (2002a,c) shows that cultural amenities measured by the “Bohemian Index” are highly correlated with both human capital and high technology. Shapiro (2006) finds that “roughly 60% of the effect of college graduates on employment growth is due to productivity; the rest comes from the relationship between concentrations of skill and growth in the quality of life” (p 324). The other approach suggests that diversity, openness or tolerance can benefit regions in talent attraction and economic activity, although traditional wisdom favors special- ization rather than diversity. As Glaeser et al. (1992) have summarized, economists (Marshall 1890; Arrow 1962; Romer 1986) traditionally argue that knowledge spill- over is more likely to occur within an industry. Jacobs (1961), Glaeser et al. (1992) and Quigley (1998) by contrast note the importance of industrial diversity for urban economic growth. While there is no consensus over the role of industrial diversity, recent research has shifted to demographic or social diversity, based on the fact that talent is widely distributed across gender, race, nationality or sexual orientation. For instance, Saxenian (1999) has found that there are approximately 1/3 foreign-born sci- entists and engineers, and 1/4 new businesses with Chinese- or Indian-born founders in Silicon Valley. Regions labeled by social diversity—or low entry barriers—tend to attract talent with various backgrounds. Florida (2002b), Florida et al. (2007), Mellander and Florida (2007) reveal a positive relationship between diversity in terms of the gay index and the human capital stock. Diversity, according to Florida and Gates (2001), is also strongly associated with high-tech industry concentration. In addition, Ottaviano and Peri (2004) show that cultural diversity in terms of countries of birth contributes to the growths of rent and wage levels of US born citizens in US cities. In addition to those three approaches, cities and universities are closely related to talent. Talent appears to center in cities. As a consequence, nearly all the research men- tioned above is based on cities. Jacobs (1969) and Lucas (1988) have addressed the function of cities through which talent contributes to urban development. Universities are “human capital factories.” However, regions are not necessarily able to retain their local university graduates (Florida et al. 2006). 2.2 Talent and creativity Creativity in this study is divided into innovation (technological creativity) and entre- preneurship (market creativity). Talent has been observed to be an important contrib- utor to both innovation and entrepreneurship. One way to measure innovation is to examine high technology industries. Florida (2002b) presents human capital as a sig- nificant predictor for high technology. Mellander and Florida (2007) further display a positive relationship between the creative class and high technology. A knowledge spillover theory of entrepreneurship, developed by Acs and Armington (2006) and Audretsch et al. (2006), suggests that entrepreneurs play a primary role in commer- cializing new technologies from either research institutions or large companies that cannot or are not willing to commercialize their research fruits themselves. This the- ory implies an active role of human capital in entrepreneurial activity: to commercial- ize a new technology, the entrepreneur should at least understand it, which requires 123
  5. 5. Talent, creativity and regional economic performance: the case of China necessary knowledge and accordingly the entrepreneur to be part of the talent pool. The empirical analyses by Acs and Armington (2006) and Audretsch et al. (2006) as expected support a significant association between human capital and new firm formation. Lee et al. (2004) present a similar result in another empirical study. 2.3 Talent and regional development Ullman (1958) noticed the importance of human capital in regional development half a century ago. Recently, the endogenous growth model has theorized the impact of human capital on economic growth (Romer 1986; Lucas 1988) and spurred numerous empirical studies over their relationship. In a seminally important work, Barro (1991) provides evidence that human capital or education is a significant contributor to eco- nomic growth. Rauch (1991) finds a positive relationship between the human capital stock and property costs or the average wage in cities. Glaeser et al. 1995 note that the city with a higher initial level of human capital tends to have a higher population growth rate. Similarly, Glendon (1998) and Simon (1998) exhibit a strong relation- ship between human capital and regional employment growth. Additional evidence supporting the association between human capital and city growth can be found from (Glaeser 1998, 2000). Recently, some other studies (Florida et al. 2007; Mellander and Florida 2007) have found that the creative class as another type of talent exerts both direct and indirect impacts on regional development. 3 Methodology This paper identifies and seeks the test for the factors that are associated with the geo- graphic distribution of talent in China and how the talent geography influences regional innovation, entrepreneurship and economic performance. The literature presented in the previous section sheds light on a theoretical framework in Fig. 1. According to University Market Innovation Amenities Talent Regional Economy Entrepreneurship Openness City Fig. 1 Theoretical framework 123
  6. 6. H. Qian Table 1 Descriptive analysis of variables Variables Obs. Mean Std. Dev. Min Max GDP per capita (the log of ’04 GDP per 30 9.3476 0.5356 8.3134 10.6636 capita) Entrepreneurship index (# of ’04 new firms 30 0.8607 1.3540 0.1502 7.1791 per 1,000 employed) High-tech index (LQ of ’04 high-tech value 30 0.7508 0.8355 0.0374 3.2628 added) Innovation index (# of ’04 patents/’04 popu- 30 1.2035 1.5919 0.1299 6.0993 lation in 10,000) Creative class index (# of the ’04 creative 30 0.0280 0.0146 0.0154 0.0959 class /’04 population) Human capital index (# of the ’04 human 30 0.0790 0.0472 0.0424 0.2570 capital/’04 pop. of 15+) Average wage (the log of ’04 average wage) 30 9.6268 0.2572 9.3805 10.3118 Wage change (wage change ’97-04/wage ’97) 30 1.4169 0.1957 1.0642 1.8030 Employment Change (employment change 30 0.0486 0.0951 −0.1417 0.3545 ’97-04/employment ’97) Service amenities (LQ of ’04 employment in 30 1.0160 0.1686 0.7035 1.5574 five service industries) Openness (# of ’04 non-Hukou residents/’04 30 0.0942 0.0592 0.0292 0.3033 population) University (# of ’04 university students/’04 30 16.4102 11.2181 7.4521 62.0432 pop. in 1,000) City index (’00 urban population/’00 popula- 30 0.4010 0.1636 0.2320 0.8831 tion) this framework, the whole picture of the relationships between talent, creativity and economic performance is depicted in three stages: the factors associated with the talent stock; the impacts of talent on innovation and entrepreneurship; and the impacts of talent, innovation and entrepreneurship on region economic performance. 3.1 Variables Table 1 presents the descriptive statistics of variables employed in this analysis. Those variables serve as the proxies of those factors in Fig. 1. 3.1.1 Talent Two indicators—the human capital index and the creative class index—are used to measure regional stocks of two types of talent. The human capital index is defined as those holding a college or higher-level degree divided by the local population of 15 years old and older. The creative class index measures the proportion of professional and technical personnel (“Zhuanye Jishu Renyuan”) among the local population. Since there are no specific occupational data available in China’s official statistical materials, it is impossible to define the creative class following the exact Florida measurement 123
  7. 7. Talent, creativity and regional economic performance: the case of China methodology (Florida 2002c). However China’s “Zhuanye Jishu Renyuan”2 to a large extent mirrors the Florida-style creative class. Both variables use the 2004 data. 3.1.2 Market factors Market factors are expected to be significant contributors of talent attraction or reten- tion in China. Three variables are used to reflect the market force: the 2004 average wage in the natural logarithm form, the wage change rate from 1997 to 2004 and the employment change rate from 1997 to 2004. While employment growth indicates more opportunities for talent, a high wage level or wage increase implies better opportunities for talent. 3.1.3 Amenities In China, natural amenities, especially in terms of climate, can hardly influence tal- ent’s choice of localities. For instance, Beijing with a high density of human capital or the creative class is notorious for its pollution and sand storms. This study uses ser- vice amenities, gauged by the 2004 location quotient of employment in those service industries that directly contribute to human life and well being.3 3.1.4 Openness Most literature employs the diversity index or gay index to measure openness (Florida 2002a,b,c; Mellander and Florida 2007). Not surprisingly, statistical data on gays and lesbians are not available in China. As an alternative, a “Hukou index” is created in this paper as a proxy for openness. It is a compelling measure in the case of China, perhaps even better than the gay index. The Hukou system (or the household regis- tration system) is used to control the flow of population by the Chinese government. One’s Hukou (or resident registration) determines which city or county this person belongs to and whether she/he has rural or urban status. Those with a local Hukou or registration are always local permanent residents and can reap local economic, social and political benefits, such as social welfare, education, and voting rights. Those who live in a jurisdictional area however without a local Hukou are always “marginal” workers or visitors from the outside. Therefore, Hukou is a reasonable indicator of openness.4 The Hukou index or openness is defined as the proportion of population 2 “Zhuanye Jishu Renyuan” in China includes scientists and engineers, university professors, teachers, agricultural and sanitation specialists, aviators and navigators, economic and statistical specialists, accoun- tants, translators, librarians, journalists, publishers, lawyers, artists, broadcasts, athletes, and so on. I thank one referee for pointing out that librarians and accountants, among others, are unlikely to undertake crea- tive work and “professional class” might be a better expression to capture “Zhuanye Jishu Renyuan” than “creative class.” 3 The service industries included in this measure are hotels and restaurants, environment and public facility management, resident services and other services, sanitation, social security and social welfare, and culture, sports and entertainments. 4 For a detailed introduction of Hukou, see Modderman et al. (2007). 123
  8. 8. H. Qian without local Hukou or registration. The higher the Hukou index, the more open the region. The 2004 statistical data are used for this measure. 3.1.5 University Universities are places where most talent is produced. Regions with more universi- ties and university students present potential advantages in talent attraction, providing their ability to retain graduates in the future. University students are generally reluc- tant to seek a job in other places after graduation due to their well-established local network, advantages in accessing local job information and inertia. In China, institu- tional barriers (represented by the Hukou system) further prevent the flow of university graduates. Not every job opportunity is associated with a Hukou registered in the job location. Firms and organizations offering jobs may have a limited number of Hukou issuances under the pre-determined quota. If someone decides to be relocated, she/he may have to secure the Hukou in the new place in addition to obtaining a job there. The university therefore is hypothesized to play an exclusively important role in China’s talent geography. The university is measured by the 2004 university students divided by local population. 3.1.6 City The city index, measured by the proportion of the urban population in the total, is employed to examine the role of cities in attracting talent. Talent generally concentrates in cities, so do innovative and entrepreneurial activities. This city index accordingly could be an important factor that influences the talent stock. 3.1.7 Innovation Innovation can be measured either from the input side, such as R&D expenditures, or from the output side, in the form of patents. The innovation index, measured by the 2004 officially granted patents per capita, is used here. In China three types of patents are granted: inventions; utility models; and designs. In addition, since innovation is more likely to occur in high-tech industries, the high-tech index, defined as the loca- tion quotient of the value added in high-tech industries,5 is considered as a measure of innovation capacity. Talent hypothetically plays a critical role in innovation. 3.1.8 Entrepreneurship In consistence with the literature, the number of new firms established in 2004 divided by the 2004 employed population is introduced as a proxy for entrepreneurship. Entre- preneurs exploit market opportunities and commercialize new technologies. Talent and technological innovation may have significant impacts on entrepreneurial activity. 5 High-tech industries are officially defined as consisting of electronic and telecommunications, computers and office equipments, pharmaceuticals, medical equipments and meters, and aircraft and spacecraft. 123
  9. 9. Talent, creativity and regional economic performance: the case of China 3.1.9 Regional economic performance The effects of all those variables mentioned above are eventually exerted on regional economic performance, measured by the 2004 GDP per capita in the natural logarithm form. 3.2 Data This analysis uses China’s provincial-level data.6 Although theoretically it is more per- suasive to interpret the economic geography of talent based on city-level data, many of those city-level data corresponding to the variables of my interest are either unavail- able or unreliable. China’s official data are always criticized for their reliability, yet the series of China Statistics Yearbook provides the most reliable data among all the official sources and therefore is widely used by both Chinese and foreign scholars. All of our data, except for urban population, the value added of high-tech industries and new firm formation, are from this series of yearbooks which unfortunately offers only provincial-level data. The urban population data come from the 2000 (fifth) National Population Census7 , the value added of high-tech industries data from China Statistics Yearbook on High Technology Industry (2005),8 and new firm formation data from China Economic Census. This analysis covers all the 31 provinces of mainland China. Table 1 in this section and the statistical analysis in the following section exclude Tibet, which appears to be an outlier. Therefore there are 30 observations in total. All the static data except for urban population use the year of 2004, the latest data available when this research was launched, and all the dynamic changes cover from 1997 to 2004.9 The urban population data are for the year of 2000 when the latest population census was conducted. 3.3 Methods Primary methods are descriptive analysis, correlation analysis and regression analysis. For the regression part, ordinary least squares (OLS) estimations are first employed; with the presence of heteroskedasticity, weighted least squares (WLS) estimations are then used to report the p-value and adjusted R 2 . To avoid the problem of multicollin- earity, some variables appearing in the correlation analysis are not necessarily used in regression models. In the last stage where factors associated with regional economic performance are investigated, the simultaneous presence of talent, innovation and 6 Province here refers to all the provincial-level jurisdictions, including provinces, autonomous regions and municipalities directly under the central government (Beijing, Shanghai, Tianjin, and Chongqing). 7 Data available at the website of the National Bureau of Statistics of China: pcsj/rkpc/dwcrkpc/t20030819_402376364.htm, retrieved 17 April, 2007. 8 Data available at the website of the National Bureau of Statistics of China: gjscy/data2006/2006-1.htm, retrieved 17 April, 2007. 9 The starting year of changes is 1997 because Chongqing was separated from Sichuan province that year as a municipality directly under the central government. It was the latest jurisdictional adjustment of the Chinese central government. 123
  10. 10. H. Qian Fig. 2 Human capital entrepreneurship as independent variables would cause multicollinearity due to their strong correlations between each other. I therefore put them separately into the regres- sion models, meanwhile using the two-stage least squares (2SLS) method whenever the problem of endogeneity resulting from this effort is confirmed. 4 Results and findings In accordance to the analytical framework exhibited in Fig. 1 and derived from the literature, this section is organized in three parts: geographically bounded factors associated with the talent distribution; the effects of talent on innovation and entre- preneurship; and the effects of talent, innovation and entrepreneurship on regional economic performance. Each part presents results and explanations of both correla- tion and regression analyses. 4.1 Geographically bounded factors associated with the talent distribution Figures 2, 3, 4, 5, 6, 7 show the provincial distributions of talent and its potential determinants. It can be seen that talent is unevenly distributed in terms of both human capital and the creative class. Beijing, Shanghai and Tianjin, three of the four munic- ipalities directly under the central government, have the largest proportions of talent. Beijing takes the lead with 26% of the population of 15 years old and older holding a college or higher-level degree and 9.6% of the total population belonging to the cre- ative class. Only four out of all provinces have higher than 10% of the population of 15 years old and older holding a college or higher-level degree, and seven of them have more than 3% creative class population. One interesting finding is that coast provinces 123
  11. 11. Talent, creativity and regional economic performance: the case of China Fig. 3 Creative class Fig. 4 Service amenities do not necessarily attract more talent than inland ones, even though the top three are all from the coast area. For instance, In Shaanxi Province, 9% of the population of 15 years old and older possesses a college or higher-level degree, higher than many coastal provinces. Xinjiang, one of the farthest provinces from the coast, ranks the fourth nationally in terms of both college graduates and the creative class. Table 2 presents the correlation matrix between variables. It provides preliminary evidence on what might affect the talent distribution in China. The framework in 123
  12. 12. H. Qian Fig. 5 Openness Fig. 6 University students Fig. 1 addresses possible effects of market factors, service amenities, openness, the university and urbanization. 4.1.1 Market factors The wage level is significantly correlated with both the human capital index and the creative class index and its coefficient with the former (0.746) is higher than 123
  13. 13. Talent, creativity and regional economic performance: the case of China Fig. 7 Average wage (no log) that with the latter (0.644). The difference is understandable to some extent. China’s professional and technical personnel are likely to be older than those who hold a col- lege or higher-level degree, in that the expansion of higher education did not occur substantially until the late 1990s. The wage is the dominant source of income in China, especially for young people who appear to be better educated. Therefore a region pro- viding a higher average wage tends to attract or retain more talent, especially young college graduates. The wage change is significantly correlated with the human capital index (with a coefficient of 0.402) but not with the creative class index. By contrast, the employment change is significantly correlated with the creative class index (with a coefficient of 0.401) but not with the human capital index. 4.1.2 Service amenities The coefficient between service amenities and the human capital index is 0.615, and between service amenities and the creative class index is 0.580; both are significant correlations. These results are consistent with most literature, showing that talent takes service amenities into account when choosing localities for living and working. 4.1.3 Openness Openness has a strong and significant correlation with both the human capital index and the creative class index. Their coefficients are 0.679 and 0.739, respectively. It echoes the empirical findings in the US (Florida 2002b), although openness is mea- sured in different ways. A high level of regional openness implies high tolerance and low barriers to entry of immigrants. Such a context encourages more outside talent to flow into the region. 123
  14. 14. 123 Table 2 Correlation matrix The log of Entrepreneurship High-tech Innovation Creative Human The log Wage Employment Service Openness University City GDP p.c. index index class index capital of wage change change amenities index index The Log of 1 GDP p.c. Entrepreneurship 0.7465*** 1 High-tech index 0.6861*** 0.6070*** 1 Innovation index 0.8268*** 0.8333*** 0.7289*** 1 Creative class 0.5958*** 0.5246*** 0.4483** 0.7098*** 1 index Human capital 0.6539*** 0.7668*** 0.5381*** 0.8008*** 0.9082*** 1 index The log of wage 0.7960*** 0.7840*** 0.7341*** 0.9276*** 0.6438*** 0.7459*** 1 Wage change 0.4089** 0.3898** 0.2814 0.4527** 0.3496 0.4017** 0.4852*** 1 Employment 0.0416 0.0970 0.0576 0.3793** 0.4006** 0.3304 0.4038** −0.0452 1 change Service 0.2424 0.3729** 0.2107 0.4519** 0.5796*** 0.6149*** 0.4270** 0.2058 0.3592 1 amenities Openness 0.7389*** 0.5388*** 0.6149*** 0.8050*** 0.7391*** 0.6792*** 0.7614*** 0.3333 0.4972*** 0.3665** 1 University 0.6919*** 0.6913*** 0.6093*** 0.7982*** 0.9159*** 0.9262*** 0.7605*** 0.4571** 0.2086 0.5625*** 0.6971*** 1 City index 0.8758*** 0.8307*** 0.7120*** 0.8687*** 0.7268*** 0.7988*** 0.7947*** 0.4709*** 0.0531 0.4281** 0.7095*** 0.8159*** 1 ** Significant at 0.05; *** Significant at 0.01 H. Qian
  15. 15. Talent, creativity and regional economic performance: the case of China 4.1.4 University The university presents the strongest association with talent through the correlation coefficient with the human capital index of 0.926 and with the creative class index of 0.916, highlighting the exclusively important role of local universities in the talent geography in China. This is in accordance with findings in the Western context by Berry and Glaeser (1968) and Mellander and Florida (2007). This result in China’s context can also be largely attributed to institutional barriers to labor flow brought by the Hukou system. Moreover, it is not uncommon that local employers prefer local college graduates, even if those from other localities might be more qualified. This phenomenon further prevents talent flow and empowers local universities as a deter- minant of regional talent stock. 4.1.5 Urbanization Not surprising, while talent concentrates in cities, the city index is strongly correlated with both the human capital index and the creative class index, with coefficients of 0.799 and 0.727, respectively. The results support the Jacobs-Lucas-Glaeser notion on the role of cities in attracting human capital. Tables 3 and 4 exhibit the results of regression analysis on the geography of tal- ent in China. More persuasive evidence is thus provided on what factors may affect the talent distribution under the ceteris paribus condition. Table 3 presents the results of several regression models with an identical dependent variable: the human capital index. The university is shown to be the strongest contributor to the stock of talent with a college or higher-level degree, consistent with the correlation analysis. Its coef- ficient is consistently positive and highly significant (at the 0.001 level). In Model 1, where the university is the sole explanatory variable, the adjusted R 2 still reaches 0.85, suggesting a robust explanatory power. The wage level is another independent Table 3 Regression models: human capital index as dependent variable Independent variables Dependent variable: human capital (college or above) Model 1 Model 2a Model 3a Model 4 The log of wage 0.074** 0.035 Wage change 0.008 −0.010 Employment change −0.050 0.050 Service amenities 0.118** 0.104** 0.024 Openness 0.418*** 0.221 −0.079 University 0.004*** 0.003*** Adjusted R-squared 0.85 0.45 0.52 0.87 ** Significant at 0.05; *** Significant at 0.01 a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas- ticity 123
  16. 16. H. Qian Table 4 Regression models: creative class index as dependent variable Independent variables Dependent variable: creative class Model 1a Model 2a Model 3a Model 4 The log of wage 0.002 −0.011 Wage change 0.005 −0.001 Employment change −0.003 0.031** Service amenities 0.031* 0.030 0.003 Openness 0.150*** 0.142** 0.040 University 0.001*** 0.001*** Adjusted R-squared 0.70 0.42 0.37 0.88 * Significant at 0.1; ** Significant at 0.05; *** Significant at 0.01 a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas- ticity variable presenting positive coefficients, but only significant for one out of two regres- sion models that include it. The coefficient of service amenities is positive in three models and indicates a significant relationship in two of them. Openness is a signifi- cant contributor to human capital only in one out of three models. Neither the wage change nor the employment change shows a significant effect in any given model. Regression models in Table 4 take the creative class index as the dependent vari- able. The results are partially different from Table 3. The university is continuously powerful and highly significant in presenting a positive association with the creative class. However its coefficients are not as high as in Table 3, implying its contribu- tion to the creative class is not as strong as to human capital. It is not surprising in that universities confer degrees but do not necessarily train students as professional or technical personnel. The coefficient of service amenities remains positive but only marginally significant in one out of three models. Meanwhile openness is a positive and significant contributor in two out of three models, likely to suggest that openness plays a relatively important role in attracting the creative class compared with human capital. Market factors exhibit controversial effects on the creative class, and most of their coefficients are insignificant. 4.2 Talent, innovation and entrepreneurship Talent is hypothesized to be important for innovation and entrepreneurship. Figures 8, 9, 10 present geographic distributions of innovation and entrepreneurship in China. The coast provinces in those figures appear to be more innovative and entrepreneurial than inland ones. According to the correlation matrix in Table 2, the human capital index is significantly correlated with the innovation index, the high-tech index and entrepreneurship, with coefficients of 0.801, 0.538, and 0.767, respectively. Similarly, the creative class index also has a significant correlation with those three variables. Their coefficients are 0.710, 0.448, and 0.525. A comparison among those coefficients shows that college graduates contribute more than professional and techni- cal personnel to China’s innovative and entrepreneurial activity. Another comparison 123
  17. 17. Talent, creativity and regional economic performance: the case of China Fig. 8 Patent production Fig. 9 Value added of high-tech industries indicates that college graduates and the creative class experience greater influences on patent production than on new firm formation and the value added of high-tech indus- tries. This can be explained as follows. Talent is distinguished based on its knowledge or ability to generate knowledge. Patents are likely to be knowledge-based. Lots of China’s new firms, in contrast, are hardly to be connected with new knowledge. Many entrepreneurs have a poor educational background yet a good sense in grasping market opportunities and then still establish their own businesses, for instance, a small firm 123
  18. 18. H. Qian Fig. 10 Entrepreneurship Table 5 International comparison on R&D expenditures as a percentage of value added of high-tech industries China US Japan Germany France UK Italy Korea 2004 2002 2002 2002 2002 2002 2002 2003 Total high-tech industries 4.6 27.3 29.9 24.1 28.6 26 11.6 18.2 Pharmaceutical products 2.4 21.1 27 – 27.2 52.4 6.6 4.4 Aircraft and spacecraft 16.9 18.5 21.6 – 29.4 23.8 23.4 – Electronic and 5.6 25.4 20.4 39.2 57.2 23.6 19.4 23.4 telecommunications equipment Computers and office 3.2 32.8 90.4 18.1 15.8 5.9 8.8 4.4 equipment Medical equipment and 2.5 49.1 30.1 14 16.1 8.3 6.4 10.7 meters Source: China Statistics Yearbook on High Technology Industry (2005) OECD STAN Database 2005; OECD, Research and Development Statistics 2005. Available at: 2006-1.htm, retrieved 1 May, 2007 producing buttons by taking advantage of low-cost labor. China’s high-tech industries are not necessarily knowledge-based either, since their value added stems generally from assembling and manufacturing, not from innovation. Table 5 is an international comparison on R&D intensity. In China, R&D expenditures in 2004 only account for 4.6% of the total value added in high-tech industries, much lower than 27.3% in the US in 2002, 29.9% in Japan in 2002 and 18.2% in Korea in 2003. This percentage for most knowledge economies is above 20%. China therefore is still far from a knowledge economy in which talent is expected to play a central role in industrial innovation and entrepreneurial activity. 123
  19. 19. Talent, creativity and regional economic performance: the case of China Table 6 Regression models: innovation as dependent variable Independent variables Dependent variable: Dependent variable: innovation index (patents) high-tech index Model 1a Model 2a Model 3a Model 4a Creative class 18.668 −0.153 Human capital 16.490*** 5.842 Service amenities −0.240 1.134 −0.775 −0.079 Openness 12.978*** 17.067*** 6.328** 8.794*** Adjusted R-squared 0.66 0.60 0.34 0.30 ** Significant at 0.05; *** Significant at 0.01 a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas- ticity Table 7 Regression models: entrepreneurship as dependent variable Independent variables Dependent variable: entrepreneurship Model 1a Model 2a Model 3a Model 4a High-tech index 0.478** Innovation index (patents) 0.764*** Creative class 17.202 Human capital 24.406*** 11.813** 21.616*** Service amenities −1.260 1.096 −1.077 −0.890 Openness 0.425* 8.045** −9.485 −2.598 Adjusted R-squared 0.54 0.35 0.70 0.70 * Significant at 0.1; ** Significant at 0.05; *** Significant at 0.01 a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas- ticity Besides talent, the wage level, openness, the university and the city index all demonstrate strong, significant and positive correlations with innovation or entre- preneurship. Openness has stronger correlations with the innovation index (with a coefficient of 0.805) and the high-tech index (with a coefficient of 0.615) than with entrepreneurship (with a coefficient of 0.539). Openness contributes to regional crea- tive activity by lowering barriers to entry of outside talent. Moreover, an open society appears to have talent with different backgrounds and thinking perspectives, encour- aging births of new ideas. Tables 6, 7 present the results of regression analysis. According to Table 6, col- lege graduates contribute significantly to patent production but not to the value added of high-tech industries, holding service amenities and openness constant. This may also result from the difference between patents and high-tech industries explained in correlation analysis. The effects of the creative class are somehow counterintuitive. Neither coefficient is significant, suggesting that professional and technical personnel cannot be used to explain patent production or the value added of high-tech industries. 123
  20. 20. H. Qian Fig. 11 GDP per Capita (no log) Openness, whose coefficient is consistently positive and significant in all the regression models, demonstrates its power in accounting for innovative activity. The coefficient of service amenities is not significant in any model. Table 7 provides the empirical evidence that human capital is significantly asso- ciated with entrepreneurial activity. The coefficient of the human capital index is positive and significant in all models. The innovation index and the high-tech index are also positively and significantly associated with entrepreneurship. This suggests that in China high-tech entrepreneurship is not negligible despite the wide spread of less knowledge-based entrepreneurial activity. It also supports the knowledge spill- over theory of entrepreneurship (Acs and Armington 2006; Audretsch et al. 2006). The effect of openness on entrepreneurial activity is positive and significant (or marginally significant) in two out of four models. Neither service amenities nor the creative class is a significant contributor to entrepreneurship. 4.3 Regional economic performance Regional economic performance is the last stop of this analysis. The coastal provinces, according to Fig. 11, have higher GDP per capita than the inland ones in most cases. In the correlation matrix, there are significant and positive correlations between the logarithm of GDP per capita and all other variables except the employment change and service amenities. Among them, the city index and the innovation index present the strongest association with regional economic performance, with coefficients of 0.876 and 0.827, respectively. Table 8 shows the results of regression analysis. In Model 5 where the effect of entrepreneurship on regional economic performance is examined, the simultaneous presence of entrepreneurship, high technology, innovation and human capital would 123
  21. 21. Talent, creativity and regional economic performance: the case of China Table 8 Regression models: gross provincial product as dependent variable Independent variables Dependent variable: the log of GDP per capita Model 1a Model 2a Model 3 Model 4 Model 5 a,b Entrepreneurship 0.153* High-tech index 0.196* (0.100) Innovation index (patents) 0.228*** (0.070) Creative class 6.378 Human capital 4.849* 1.093 3.705 (−0.940) Service amenities −0.678 −0.301 −0.623 −0.526 −0.401 Openness 4.771*** 5.839*** 1.815 3.532** 4.953*** Adjusted R-squared 0.53 0.51 0.68 0.62 0.78 * Significant at 0.1; ** Significant at 0.05; *** Significant at 0.01 a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas- ticity b Regression results of the second stage in a 2SLS process, predicted residues from the first stage are used for those variables presenting their coefficients in par entheses result in multicollinearity. One way to solve this problem is to drop high technology and innovation from the regression. It however would cause endogeneity since innovation and high technology as suggested in previous regressions affect both entrepreneur- ship and regional economic performance. I therefore employ a 2SLS approach for Model 5. In the first stage, high technology, innovation and human capital are sep- arately regressed on two exogenous variables service amenities and openness. Their residuals are predicted and used for the second stage regression. This effort signifi- cantly reduces multicollinearity caused by high correlations between high technology, innovation, and human capital, and their potential exogenous determinants service amenities and openness. The regression results show that the innovation index, the high-tech index and entrepreneurship all contribute significantly to GDP per capita. The coefficient of the human capital index is positive and significant only in one out of four models. These results overall suggest that China’s regional economic perfor- mance has become reliant on knowledge, innovation and entrepreneurship, although the direct link between education- based human capital and GDP is weak. Moreover, openness presents a direct effect on regional economic performance with a positive and significant coefficient in four out of five models. 5 Conclusion and discussion The new growth theory has produced revolutionary impacts on understanding regional economic growth. Talent has been considered as an increasingly important factor for survival and further development of regional economies. The determinants of the tal- ent distribution and the effects of talent on regional economic development have been empirically investigated by a large body of literature, but mainly for the cases of developed countries or knowledge economies. China as a developing country is an 123
  22. 22. H. Qian interesting case considering its consistently high growth rate in the past two decades. The educational investment of the Chinese government has also experienced a sky- rocketing expansion especially since the late 1990s. According to this study, the economic geography of talent in China shows both similarities and differences from developed countries. China’s talent as elsewhere concentrates in cities. The single most important contributor to the talent distribution in China is the presence of universities. While this is reasonable even in knowledge economies, the effect of the university is particularly amplified by the institutional barriers to population flow in the Chinese context. The Hukou system increases the costs of migrating to other places. Among market factors, the average wage is most likely to be a significant contributor to the talent distribution, yet only to human capital but not to the creative class. It is supported by the fact that young college graduates rely more on the wage as an income source than the elder creative class. The roles of service amenities and openness in attracting talent are less important in China than in the US This difference might be attributed to their different stages of development. One of the two measures of talent, human capital, presents significant associations with both innovation and entrepreneurship, when innovation is measured by patents. It is not a significant contributor to the concentration of high-tech industries in China. The characteristics of the Chinese high-tech industries can explain this counterintuitive finding: those industries, with very limited R&D investments, are basically specialized in manufacturing and assembling, and require less knowledge than those innovative high-tech industries in the US and other knowledge economies. It is somewhat surpris- ing that the Chinese creative class exerts no significant influence either on innovation or on entrepreneurship. From the measurement perspective, some occupations cov- ered by this creative class index (or “Zhuanye Jishu Renyuan”), notably librarians and accountants, can hardly be creative. If the measure is appropriate, the regression results support a more important role of human capital than the creative class in innovation. Another interesting finding is that openness may play an important role in regional innovative activity, consistent with Florida’s theory on diversity. Granted patents, the concentration of high-tech industries and entrepreneurship are all significantly associated with China’s regional economic performance. This is in line with the percepts of the endogenous growth theory. It also implies that economic growth in China relies on more than its low-cost labor. Talent, innovation and entrepre- neurship are playing an increasingly important role in China’s regional development. Openness also shows its direct and positive impact on regional output. It is worth noting that the results derived from cross sectional analysis in this paper can only suggest correlations but not causalities. The causal relations implied in Fig. 1 and the entire analysis are rather hypothetical based on the existing literature and observation to the real world. A panel data analysis might be helpful in confirming those causalities but data unavailability and data inconsistencies cross years make such an effort difficult and unreliable. One relationship deserving attention is between the university and regional economic performance. This paper addresses indirect effects of the former on the latter. The economic development theory on the other hand shows that income growth may allow families to invest more on higher education and thus inversely affect the university as well. In China, this inverse causality is minimized while the government subsidizes higher education and controls the location and scale 123
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