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
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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).
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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
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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
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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
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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).
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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.
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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: http://www.stats.gov.cn/tjsj/
pcsj/rkpc/dwcrkpc/t20030819_402376364.htm, retrieved 17 April, 2007.
8 Data available at the website of the National Bureau of Statistics of China: http://www.sts.org.cn/sjkl/
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.
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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
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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
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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
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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.
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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. 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
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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
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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
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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: http://www.sts.org.cn/sjkl/gjscy/data2006/
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.
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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.
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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
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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
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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
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23. Talent, creativity and regional economic performance: the case of China
of most universities. Since government decisions are political, the development of
universities might be exogenous of economic development in China and the assump-
tion on their causal relation in this paper is therefore reasonable.
Acknowledgments The author would like to thank Richard Florida, Roger Stough, Bernard Weinstein
and two referees for their valuable comments.
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