Ann Reg SciDOI 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 itsassociations with innovation, entrepreneurship and regional economic performancein 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. Explanatoryvariables 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 mostimportant factor that affects the talent distribution in China. Wage levels, serviceamenities and openness also contribute to talent attraction but to different extents.Furthermore, human capital outweighing the creative class exhibits positive effects oninnovation, entrepreneurship and regional economic performance. Openness presentsa direct influence not only on talent stock, but on innovation and regional economicperformance as well. Explanations of empirical results in the Chinese context areoffered.

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, USAe-mail: hqian@gmu.edu

123

H. Qian

economic growth in the endogenous growth theory. Their seminal work has begotnumerous empirical studies, providing the evidence that human capital is a crucialdriving 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 innovationor entrepreneurship, which further contribute to economic development (Schumpeter1934; Baumol 1968).

Talent geographically presents uneven distributions, both across countries andacross regions or cities within a specific country. Talent appears to concentrate incities, while cities play an important role in attracting, mobilizing, and organizinghuman 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 oncases of developed countries, especially the US, one of the best examples of a largeand well developed “knowledge economy.” There has been much less research atten-tion on developing countries. This paper contributes to the literature by investigatingthe largest developing country, China. It attempts to identify geographically boundedfactors associated with the talent stock in Chinese regions, and how the geographicconcentration of talent may affect innovation, entrepreneurship, and further on regionaleconomic performance. China’s economic success benefits greatly from its low-costand available labor force; however, its growth appears to be more and more relianton 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 acollege or higher-level degree play an increasingly important role in regional growthand especially the growth of cities. Li and Florida (2006) have examined the effectsof non-market factors (amenities and diversity) on China’s talent production. Theirwork 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 itsfocus on talent production rather than talent stock. Talent production, measured witha surrogate variable—the number of local universities—in their study to a large extentis exogenous of regional characteristics and depends instead on government policiesand decisions in China. Additional efforts to build theoretical frameworks as well asseek appropriate measures are therefore needed so as to better understand China’stalent 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 inChina. 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 inthe US due to the high level of population mobility (Florida et al. 2006), appear to beof great importance in China as a result of China’s exceptional institutional barriers tolabor flow. Furthermore, by addressing the role of cities in the talent literature (Jacobs

123

Talent, creativity and regional economic performance: the case of China

1961, 1969; Lucas 1988; Glaeser 1994), the proportion of urban population in theprovince is another factor being considered. This study uses provincial level ratherthan 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, thispaper investigates how the distribution of talent is associated with the distributions ofinnovative and entrepreneurial activities. Talent has been suggested as a significantcontributor to both innovation and entrepreneurship in such developed countries asthe 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). Ourempirical investigation on China is hypothesized to provide similar evidence. The lastanalytical part focuses on the effects of talent, innovation and entrepreneurship onregional economic performance.

The paper is organized as five sections. This introduction section has explainedthe background and necessity of this research. The literature on talent, creativity,and regional development is briefly reviewed in the second section. The third sectiondescribes data, variables and methods. It is followed by the fourth section that presentsand 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 theeducation attainment, and the creative class includes a certain group of occupationalworkers that are likely to undertake creative work.1 There is a plethora of research onthe relations between talent, creativity and economic growth. This section provides abrief 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 thelocality of talent. Economists traditionally focus on market factors, such as jobs, andeconomic and financial opportunities (Florida 2002b). Recently there have been twodifferent approaches highlighting the role of non-market factors in attracting talent.One approach addresses the quality of life and examines the role of natural, culturalor service amenities. Amenities, according to Kotkin (2000), can attract high-techindustries and workers. Glaeser et al. (2001) argue that the growth of cities dependsmore 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 andengineers, university professors, poets and novelists, artists, entertainers, actors, designers, architects, non-fiction writers, editors, cultural figures, think-tank researchers, analysts and other opinion-makers) andcreative professionals (working in high-tech sectors, financial services, the legal and health care profes-sions and business management).

123

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 isdue to productivity; the rest comes from the relationship between concentrations ofskill and growth in the quality of life” (p 324).

The other approach suggests that diversity, openness or tolerance can benefit regionsin 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 urbaneconomic 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 thattalent is widely distributed across gender, race, nationality or sexual orientation. Forinstance, 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 foundersin Silicon Valley. Regions labeled by social diversity—or low entry barriers—tendto attract talent with various backgrounds. Florida (2002b), Florida et al. (2007),Mellander and Florida (2007) reveal a positive relationship between diversity in termsof 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 birthcontributes 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 totalent. 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 thefunction of cities through which talent contributes to urban development. Universitiesare “human capital factories.” However, regions are not necessarily able to retain theirlocal 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 toexamine high technology industries. Florida (2002b) presents human capital as a sig-nificant predictor for high technology. Mellander and Florida (2007) further displaya positive relationship between the creative class and high technology. A knowledgespillover theory of entrepreneurship, developed by Acs and Armington (2006) andAudretsch et al. (2006), suggests that entrepreneurs play a primary role in commer-cializing new technologies from either research institutions or large companies thatcannot 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

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 firmformation. 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 halfa century ago. Recently, the endogenous growth model has theorized the impact ofhuman capital on economic growth (Romer 1986; Lucas 1988) and spurred numerousempirical 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 capitalstock and property costs or the average wage in cities. Glaeser et al. 1995 note thatthe city with a higher initial level of human capital tends to have a higher populationgrowth rate. Similarly, Glendon (1998) and Simon (1998) exhibit a strong relation-ship between human capital and regional employment growth. Additional evidencesupporting the association between human capital and city growth can be found from(Glaeser 1998, 2000). Recently, some other studies (Florida et al. 2007; Mellanderand Florida 2007) have found that the creative class as another type of talent exertsboth 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 regionalinnovation, entrepreneurship and economic performance. The literature presented inthe previous section sheds light on a theoretical framework in Fig. 1. According to

Market

Amenities

Openness

Talent

Innovation

Entrepreneurship

Regional Economy

University

City

Fig. 1 Theoretical framework

123

H. Qian

Table 1 Descriptive analysis of variables

Variables Obs. Mean Std. Dev. Min Max

GDP per capita (the log of ’04 GDP percapita)

30 9.3476 0.5356 8.3134 10.6636

Entrepreneurship index (# of ’04 new firmsper 1,000 employed)

30 0.8607 1.3540 0.1502 7.1791

High-tech index (LQ of ’04 high-tech valueadded)

30 0.7508 0.8355 0.0374 3.2628

Innovation index (# of ’04 patents/’04 popu-lation in 10,000)

30 1.2035 1.5919 0.1299 6.0993

Creative class index (# of the ’04 creativeclass /’04 population)

30 0.0280 0.0146 0.0154 0.0959

Human capital index (# of the ’04 humancapital/’04 pop. of 15+)

30 0.0790 0.0472 0.0424 0.2570

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’97-04/employment ’97)

30 0.0486 0.0951 −0.1417 0.3545

Service amenities (LQ of ’04 employment infive service industries)

30 1.0160 0.1686 0.7035 1.5574

Openness (# of ’04 non-Hukou residents/’04population)

30 0.0942 0.0592 0.0292 0.3033

University (# of ’04 university students/’04pop. in 1,000)

30 16.4102 11.2181 7.4521 62.0432

City index (’00 urban population/’00 popula-tion)

30 0.4010 0.1636 0.2320 0.8831

this framework, the whole picture of the relationships between talent, creativity andeconomic performance is depicted in three stages: the factors associated with the talentstock; the impacts of talent on innovation and entrepreneurship; and the impacts oftalent, innovation and entrepreneurship on region economic performance.

3.1 Variables

Table 1 presents the descriptive statistics of variables employed in this analysis. Thosevariables 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 tomeasure regional stocks of two types of talent. The human capital index is definedas those holding a college or higher-level degree divided by the local population of15 years old and older. The creative class index measures the proportion of professionaland technical personnel (“Zhuanye Jishu Renyuan”) among the local population. Sincethere 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

Talent, creativity and regional economic performance: the case of China

methodology (Florida 2002c). However China’s “Zhuanye Jishu Renyuan”2 to a largeextent 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 averagewage in the natural logarithm form, the wage change rate from 1997 to 2004 and theemployment change rate from 1997 to 2004. While employment growth indicates moreopportunities for talent, a high wage level or wage increase implies better opportunitiesfor 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 capitalor 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 serviceindustries 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 (Florida2002a,b,c; Mellander and Florida 2007). Not surprisingly, statistical data on gays andlesbians are not available in China. As an alternative, a “Hukou index” is created inthis 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 personbelongs to and whether she/he has rural or urban status. Those with a local Hukou orregistration are always local permanent residents and can reap local economic, socialand political benefits, such as social welfare, education, and voting rights. Those wholive in a jurisdictional area however without a local Hukou are always “marginal”workers or visitors from the outside. Therefore, Hukou is a reasonable indicator ofopenness.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 thankone 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 facilitymanagement, 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

H. Qian

without local Hukou or registration. The higher the Hukou index, the more open theregion. 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, providingtheir 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 localnetwork, advantages in accessing local job information and inertia. In China, institu-tional barriers (represented by the Hukou system) further prevent the flow of universitygraduates. Not every job opportunity is associated with a Hukou registered in the joblocation. Firms and organizations offering jobs may have a limited number of Hukouissuances under the pre-determined quota. If someone decides to be relocated, she/hemay have to secure the Hukou in the new place in addition to obtaining a job there. Theuniversity therefore is hypothesized to play an exclusively important role in China’stalent geography. The university is measured by the 2004 university students dividedby local population.

3.1.6 City

The city index, measured by the proportion of the urban population in the total, isemployed to examine the role of cities in attracting talent. Talent generally concentratesin cities, so do innovative and entrepreneurial activities. This city index accordinglycould 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, orfrom the output side, in the form of patents. The innovation index, measured by the2004 officially granted patents per capita, is used here. In China three types of patentsare granted: inventions; utility models; and designs. In addition, since innovation ismore 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 measureof 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 dividedby the 2004 employed population is introduced as a proxy for entrepreneurship. Entre-preneurs exploit market opportunities and commercialize new technologies. Talent andtechnological innovation may have significant impacts on entrepreneurial activity.

5 High-tech industries are officially defined as consisting of electronic and telecommunications, computersand office equipments, pharmaceuticals, medical equipments and meters, and aircraft and spacecraft.

123

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 regionaleconomic performance, measured by the 2004 GDP per capita in the natural logarithmform.

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 ofthose 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, yetthe series of China Statistics Yearbook provides the most reliable data among all theofficial sources and therefore is widely used by both Chinese and foreign scholars. Allof our data, except for urban population, the value added of high-tech industries andnew firm formation, are from this series of yearbooks which unfortunately offers onlyprovincial-level data. The urban population data come from the 2000 (fifth) NationalPopulation Census7, the value added of high-tech industries data from China StatisticsYearbook on High Technology Industry (2005),8 and new firm formation data fromChina Economic Census.

This analysis covers all the 31 provinces of mainland China. Table 1 in this sectionand the statistical analysis in the following section exclude Tibet, which appears to bean outlier. Therefore there are 30 observations in total. All the static data except forurban population use the year of 2004, the latest data available when this research waslaunched, and all the dynamic changes cover from 1997 to 2004.9 The urban populationdata 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 arethen used to report the p-value and adjusted R2. To avoid the problem of multicollin-earity, some variables appearing in the correlation analysis are not necessarily used inregression models. In the last stage where factors associated with regional economicperformance are investigated, the simultaneous presence of talent, innovation and

6 Province here refers to all the provincial-level jurisdictions, including provinces, autonomous regionsand 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 yearas a municipality directly under the central government. It was the latest jurisdictional adjustment of theChinese central government.

123

H. Qian

Fig. 2 Human capital

entrepreneurship as independent variables would cause multicollinearity due to theirstrong correlations between each other. I therefore put them separately into the regres-sion models, meanwhile using the two-stage least squares (2SLS) method wheneverthe 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 theliterature, this section is organized in three parts: geographically bounded factorsassociated with the talent distribution; the effects of talent on innovation and entre-preneurship; and the effects of talent, innovation and entrepreneurship on regionaleconomic 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 potentialdeterminants. It can be seen that talent is unevenly distributed in terms of both humancapital 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 acollege 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 of15 years old and older holding a college or higher-level degree, and seven of them havemore than 3% creative class population. One interesting finding is that coast provinces

123

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 areall from the coast area. For instance, In Shaanxi Province, 9% of the population of15 years old and older possesses a college or higher-level degree, higher than manycoastal provinces. Xinjiang, one of the farthest provinces from the coast, ranks thefourth nationally in terms of both college graduates and the creative class.

Table 2 presents the correlation matrix between variables. It provides preliminaryevidence on what might affect the talent distribution in China. The framework in

123

H. Qian

Fig. 5 Openness

Fig. 6 University students

Fig. 1 addresses possible effects of market factors, service amenities, openness, theuniversity and urbanization.

4.1.1 Market factors

The wage level is significantly correlated with both the human capital index andthe creative class index and its coefficient with the former (0.746) is higher than

123

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’sprofessional 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 occursubstantially 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 youngcollege graduates. The wage change is significantly correlated with the human capitalindex (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 (witha 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, andbetween service amenities and the creative class index is 0.580; both are significantcorrelations. These results are consistent with most literature, showing that talent takesservice 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 indexand the creative class index. Their coefficients are 0.679 and 0.739, respectively. Itechoes 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 andlow barriers to entry of immigrants. Such a context encourages more outside talent toflow into the region.

123

H. Qian

Tabl

e2

Cor

rela

tion

mat

rix

The

log

ofE

ntre

pren

eurs

hip

Hig

h-te

chIn

nova

tion

Cre

ativ

eH

uman

The

log

Wag

eE

mpl

oym

ent

Serv

ice

Ope

nnes

sU

nive

rsity

City

GD

Pp.

c.in

dex

inde

xcl

ass

inde

xca

pita

lof

wag

ech

ange

chan

geam

eniti

esin

dex

inde

x

The

Log

ofG

DP

p.c.

1

Ent

repr

eneu

rshi

p0.

7465

***

1

Hig

h-te

chin

dex

0.68

61**

*0.

6070

***

1

Inno

vatio

nin

dex

0.82

68**

*0.

8333

***

0.72

89**

*1

Cre

ativ

ecl

ass

inde

x0.

5958

***

0.52

46**

*0.

4483

**0.

7098

***

1

Hum

anca

pita

lin

dex

0.65

39**

*0.

7668

***

0.53

81**

*0.

8008

***

0.90

82**

*1

The

log

ofw

age

0.79

60**

*0.

7840

***

0.73

41**

*0.

9276

***

0.64

38**

*0.

7459

***

1

Wag

ech

ange

0.40

89**

0.38

98**

0.28

140.

4527

**0.

3496

0.40

17**

0.48

52**

*1

Em

ploy

men

tch

ange

0.04

160.

0970

0.05

760.

3793

**0.

4006

**0.

3304

0.40

38**

−0.0

452

1

Serv

ice

amen

ities

0.24

240.

3729

**0.

2107

0.45

19**

0.57

96**

*0.

6149

***

0.42

70**

0.20

580.

3592

1

Ope

nnes

s0.

7389

***

0.53

88**

*0.

6149

***

0.80

50**

*0.

7391

***

0.67

92**

*0.

7614

***

0.33

330.

4972

***

0.36

65**

1

Uni

vers

ity0.

6919

***

0.69

13**

*0.

6093

***

0.79

82**

*0.

9159

***

0.92

62**

*0.

7605

***

0.45

71**

0.20

860.

5625

***

0.69

71**

*1

City

inde

x0.

8758

***

0.83

07**

*0.

7120

***

0.86

87**

*0.

7268

***

0.79

88**

*0.

7947

***

0.47

09**

*0.

0531

0.42

81**

0.70

95**

*0.

8159

***

1

**Si

gnif

ican

tat0

.05;

***

Sign

ific

anta

t0.0

1

123

Talent, creativity and regional economic performance: the case of China

4.1.4 University

The university presents the strongest association with talent through the correlationcoefficient with the human capital index of 0.926 and with the creative class indexof 0.916, highlighting the exclusively important role of local universities in the talentgeography in China. This is in accordance with findings in the Western context byBerry and Glaeser (1968) and Mellander and Florida (2007). This result in China’scontext can also be largely attributed to institutional barriers to labor flow brought bythe Hukou system. Moreover, it is not uncommon that local employers prefer localcollege graduates, even if those from other localities might be more qualified. Thisphenomenon 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 correlatedwith both the human capital index and the creative class index, with coefficients of0.799 and 0.727, respectively. The results support the Jacobs-Lucas-Glaeser notion onthe 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 affectthe talent distribution under the ceteris paribus condition. Table 3 presents the resultsof several regression models with an identical dependent variable: the human capitalindex. The university is shown to be the strongest contributor to the stock of talentwith 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 Model1, where the university is the sole explanatory variable, the adjusted R2 still reaches0.85, 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 0.01a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas-ticity

123

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 0.01a 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 threemodels 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 wagechange 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 continuouslypowerful and highly significant in presenting a positive association with the creativeclass. 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 inthat universities confer degrees but do not necessarily train students as professionalor technical personnel. The coefficient of service amenities remains positive but onlymarginally significant in one out of three models. Meanwhile openness is a positiveand significant contributor in two out of three models, likely to suggest that opennessplays a relatively important role in attracting the creative class compared with humancapital. Market factors exhibit controversial effects on the creative class, and most oftheir 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 entrepreneurshipin China. The coast provinces in those figures appear to be more innovative andentrepreneurial than inland ones. According to the correlation matrix in Table 2, thehuman capital index is significantly correlated with the innovation index, the high-techindex 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 threevariables. Their coefficients are 0.710, 0.448, and 0.525. A comparison among thosecoefficients shows that college graduates contribute more than professional and techni-cal personnel to China’s innovative and entrepreneurial activity. Another comparison

123

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 onpatent 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 knowledgeor ability to generate knowledge. Patents are likely to be knowledge-based. Lots ofChina’s new firms, in contrast, are hardly to be connected with new knowledge. Manyentrepreneurs have a poor educational background yet a good sense in grasping marketopportunities and then still establish their own businesses, for instance, a small firm

123

H. Qian

Fig. 10 Entrepreneurship

Table 5 International comparison on R&D expenditures as a percentage of value added of high-techindustries

China US Japan Germany France UK Italy Korea2004 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 andtelecommunicationsequipment

5.6 25.4 20.4 39.2 57.2 23.6 19.4 23.4

Computers and officeequipment

3.2 32.8 90.4 18.1 15.8 5.9 8.8 4.4

Medical equipment andmeters

2.5 49.1 30.1 14 16.1 8.3 6.4 10.7

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 industriesare not necessarily knowledge-based either, since their value added stems generallyfrom assembling and manufacturing, not from innovation. Table 5 is an internationalcomparison on R&D intensity. In China, R&D expenditures in 2004 only account for4.6% of the total value added in high-tech industries, much lower than 27.3% in theUS in 2002, 29.9% in Japan in 2002 and 18.2% in Korea in 2003. This percentage formost knowledge economies is above 20%. China therefore is still far from a knowledgeeconomy in which talent is expected to play a central role in industrial innovation andentrepreneurial activity.

123

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 0.01a 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 0.01a 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 alldemonstrate strong, significant and positive correlations with innovation or entre-preneurship. Openness has stronger correlations with the innovation index (with acoefficient of 0.805) and the high-tech index (with a coefficient of 0.615) than withentrepreneurship (with a coefficient of 0.539). Openness contributes to regional crea-tive activity by lowering barriers to entry of outside talent. Moreover, an open societyappears 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 addedof high-tech industries, holding service amenities and openness constant. This mayalso result from the difference between patents and high-tech industries explained incorrelation analysis. The effects of the creative class are somehow counterintuitive.Neither coefficient is significant, suggesting that professional and technical personnelcannot be used to explain patent production or the value added of high-tech industries.

123

H. Qian

Fig. 11 GDP per Capita (no log)

Openness, whose coefficient is consistently positive and significant in all the regressionmodels, demonstrates its power in accounting for innovative activity. The coefficientof 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 ispositive and significant in all models. The innovation index and the high-tech indexare also positively and significantly associated with entrepreneurship. This suggeststhat in China high-tech entrepreneurship is not negligible despite the wide spread ofless knowledge-based entrepreneurial activity. It also supports the knowledge spill-over theory of entrepreneurship (Acs and Armington 2006; Audretsch et al. 2006). Theeffect of openness on entrepreneurial activity is positive and significant (or marginallysignificant) in two out of four models. Neither service amenities nor the creative classis 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 thelogarithm of GDP per capita and all other variables except the employment changeand service amenities. Among them, the city index and the innovation index presentthe strongest association with regional economic performance, with coefficients of0.876 and 0.827, respectively.

Table 8 shows the results of regression analysis. In Model 5 where the effect ofentrepreneurship on regional economic performance is examined, the simultaneouspresence of entrepreneurship, high technology, innovation and human capital would

123

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 0.01a Weighted least squares estimation is used together with OLS estimation due to presence of heteroskedas-ticityb Regression results of the second stage in a 2SLS process, predicted residues from the first stage are usedfor those variables presenting their coefficients in parentheses

result in multicollinearity. One way to solve this problem is to drop high technology andinnovation from the regression. It however would cause endogeneity since innovationand high technology as suggested in previous regressions affect both entrepreneur-ship and regional economic performance. I therefore employ a 2SLS approach forModel 5. In the first stage, high technology, innovation and human capital are sep-arately regressed on two exogenous variables service amenities and openness. Theirresiduals 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 serviceamenities and openness. The regression results show that the innovation index, thehigh-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 outof four models. These results overall suggest that China’s regional economic perfor-mance has become reliant on knowledge, innovation and entrepreneurship, althoughthe direct link between education- based human capital and GDP is weak. Moreover,openness presents a direct effect on regional economic performance with a positiveand significant coefficient in four out of five models.

5 Conclusion and discussion

The new growth theory has produced revolutionary impacts on understanding regionaleconomic growth. Talent has been considered as an increasingly important factor forsurvival and further development of regional economies. The determinants of the tal-ent distribution and the effects of talent on regional economic development have beenempirically investigated by a large body of literature, but mainly for the cases ofdeveloped countries or knowledge economies. China as a developing country is an

123

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 bothsimilarities and differences from developed countries. China’s talent as elsewhereconcentrates in cities. The single most important contributor to the talent distributionin China is the presence of universities. While this is reasonable even in knowledgeeconomies, the effect of the university is particularly amplified by the institutionalbarriers to population flow in the Chinese context. The Hukou system increases thecosts of migrating to other places. Among market factors, the average wage is mostlikely to be a significant contributor to the talent distribution, yet only to human capitalbut not to the creative class. It is supported by the fact that young college graduatesrely more on the wage as an income source than the elder creative class. The roles ofservice amenities and openness in attracting talent are less important in China than inthe US This difference might be attributed to their different stages of development.

One of the two measures of talent, human capital, presents significant associationswith 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 counterintuitivefinding: those industries, with very limited R&D investments, are basically specializedin manufacturing and assembling, and require less knowledge than those innovativehigh-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 innovationor on entrepreneurship. From the measurement perspective, some occupations cov-ered by this creative class index (or “Zhuanye Jishu Renyuan”), notably librarians andaccountants, can hardly be creative. If the measure is appropriate, the regression resultssupport 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 regionalinnovative activity, consistent with Florida’s theory on diversity.

Granted patents, the concentration of high-tech industries and entrepreneurship areall significantly associated with China’s regional economic performance. This is inline with the percepts of the endogenous growth theory. It also implies that economicgrowth 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 papercan only suggest correlations but not causalities. The causal relations implied in Fig. 1and the entire analysis are rather hypothetical based on the existing literature andobservation to the real world. A panel data analysis might be helpful in confirmingthose causalities but data unavailability and data inconsistencies cross years make suchan effort difficult and unreliable. One relationship deserving attention is between theuniversity and regional economic performance. This paper addresses indirect effectsof the former on the latter. The economic development theory on the other hand showsthat income growth may allow families to invest more on higher education and thusinversely affect the university as well. In China, this inverse causality is minimizedwhile the government subsidizes higher education and controls the location and scale

123

Talent, creativity and regional economic performance: the case of China

of most universities. Since government decisions are political, the development ofuniversities 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 Weinsteinand two referees for their valuable comments.

References

Acs ZJ, Armington C (2006) Entrepreneurship, geography, and American economic growth. CambridgeUniversity Press, New York

Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 29(3):155–173. doi:10.2307/2295952

Audretsch DB, Keilbach MC, Lehmann EE (2006) Entrepreneurship and economic growth. OxfordUniversity Press, New York

Barro RJ (1991) Economic growth in a cross-section of countries. Q J Econ 106:407–444. doi:10.2307/2937943

Baumol WJ (1968) Entrepreneurship and economic theory. Am Econ Rev 58(2):64–71Berry CR, Glaeser EL (2005) The divergence of human capital levels across cities. Pap Reg Sci 84(3):407–

444. doi:10.1111/j.1435-5957.2005.00047.xFlorida R (2002a) Bohemia and economic geography. J Econ Geogr 2:55–71. doi:10.1093/jeg/2.1.55Florida R (2002b) The economic geography of talent. Ann Assoc Am Geogr 92(4):743–755. doi:10.1111/

1467-8306.00314Florida R (2002c) The rise of creative class: and how it’s transforming work, leisure, and everyday life,

Basic Books, New YorkFlorida R, Gates G (2001) Technology and tolerance: the importance of diversity to high-tech growth.

Brookings Institution, Center for Urban and Regional Growth, WashingtonFlorida R, Gates G, Knudsen B, Stolarick K (2006) The university and the creative economy. Available:

http://www.creativeclass.org/rfcgdb/articles/University_andthe_Creative_Economy.pdf. Extractedon 3 July, 2007

Florida R, Mellander C, Stolarick K (2007) Inside the black box of regional development: human cap-ital, the creative class and tolerance. Available on http://www.creativeclass.com/rfcgdb/articles/Inside_the_Black_Box_of_Regional_Development.pdf. Extracted on 20 September, 2007

Glaeser EL (1994) Cities, information, and economic growth. Cityscape 1(1):9–47Glaeser EL (1998) Are cities dying? J Econ Perspect 12:139–160Glaeser EL (2000) The new economics of urban and regional growth. In: Clark G, Gertler M, Feldman M

(eds) The Oxford handbook of economic geography. Oxford University Press, OxfordGlaeser EL, Kallal HD, Scheinkman JA, Shleifer A (1992) Growth in cities. J Polit Econ 100:1126–1152.

doi:10.1086/261856Glaeser EL, Scheinkman JA, Shleifer A (1995) Economic growth in a cross-section of cities. J Monet Econ

36:117–143 doi:10.1016/0304-3932(95)01206-2Glaeser EL, Kolko J, Saiz A (2001) Consumer city. J Econ Geogr 1:27–50. doi:10.1093/jeg/1.1.27Glendon S (1998) Urban life cycles. Working paper, Harvard University, CambridgeJacobs J (1961) The death and life of great American cities. Random House, New YorkJacobs J (1969) The economy of cities. Random House, New YorkKotkin J (2000) The new geography. Random House, New YorkLee SY, Florida R, Acs ZJ (2004) Creativity and entrepreneurship: a regional analysis of new firm formation.

Reg Stud 38(8):879–891. doi:10.1080/0034340042000280910Li T, Florida R (2006) Talent, technological innovation, and economic growth in China. Available on http://

www.creativeclass.org/rfcgdb/articles/China%20report.pdf. Extracted on 3 July, 2007Lloyd R, Clark TN (2001) The city as entertainment machine. In: Gatham KF (ed) Research in urban

sociology, vol 6, Critical perspectives on urban redevelopment. JAI/Elsevier, OxfordLucas RE Jr (1988) On the mechanism of economic growth. J Monet Econ 22:3–42. doi:10.1016/

0304-3932(88)90168-7Marshall A (1890) Principles of economics. Macmillan, London

123

H. Qian

Mellander C, Florida R (2007) The creative class or human capital? CESIS Electronic working paperseries #79

Modderman E, Gorter C, Dalhuisen J, Nijkamp P (2007) Labour manoeuvrability and economic perfor-mance: a test on township village enterprises in China. Int J Soc Econ 34(4):220–236. doi:10.1108/03068290710734190

Ottaviano G, Peri G (2004) The economic value of cultural diversity: evidence from US cities. NBERworking paper #10904

Quigley J (1998) Urban diversity and economic growth. J Econ Perspect 12(2):127–138Rauch JE (1991) Productivity gains from geographic concentration of human capital: evidence from the

cities. NBER working paper #3905Romer PM (1986) Increasing returns and long-run growth. J Polit Econ 94(5):1002–1037. doi:10.1086/

261420Romer PM (1990) Endogenous technological change. J Polit Econ 98(5):S71–S102. doi:10.1086/261725Saxenian A (1999) Silicon valley’s new immigrant entrepreneurs. Public Policy Institute of California,

BerkeleySchultz TW (1963) The economic value of education. Columbia University Press, New YorkSchumpeter JA (1934) The theory of economic development. Oxford University Press, OxfordShapiro JM (2006) Smart cities: quality of life, productivity, and the growth effects of human capital. Rev

Econ Stat 88:324–335. doi:10.1162/rest.88.2.324Simon CJ (1998) Human capital and metropolitan employment growth. J Urban Econ 43:223–243. doi:10.

1006/juec.1997.2048Ullman EL (1958) Regional development and the geography of concentration. In: Papers and proceedings

of the regional science association, vol 4, pp 179–198

123