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What make a place attractive to haigui entrepreneurials? ----Behavior analysis on the destination choice of haigui entrepreneurs Feb. 2010 Xue PENG1 Graduate School of Social and System Studies, the University of Kitakyushu

Abstract This paper takes Chinese returnee entrepreneurs (i.e. haigui entrepreneur) as research object, who are a highly productive group and have great potential in promoting regional growth. Haigui entrepreneurs have attracted intensive attention of almost all the local governments but their distribution is extremely unbalanced. To reveal the reasons why some regions attracted more haigui entrepreneurs, a discrete choice approach (using McFaddens conditional logit model) was adopted. Analysis was conducted to find the affecting factors of haigui entrepreneurs destination choice behavior. The results show that economic level, technology power, social ties have positively increased a place to be chosen, while urban amenities, tolerance, and talent power is not relevant. Key words destination choice, haigui entrepreneurs, discrete choice model

1. Introduction

In contemporary urban economy, intellectual assets and knowledge has been recognized as the most important economic inputs, instead of physical resources or capital that had once been identified (APEC 2002; OECD, 2002; UNESCO 2005). Further discussion has distinguished creativity from knowledge, and arguing that mere knowledge does not lead to economic development without human creativity, which is &quot;the ultimate economic resource&quot; (Florida et al., 2008). In current China, the creativity of talents is particularly important since China is in the need of shifting the economic growth model and developing industries with higher added-value. In this process, returnees with study or training experience abroad (commonly referred as haigui in Chinese) has played significant role in starting companies with high technology or high creativities, and bring back advanced management experience, advanced technology, network with the international society etc.


Xue PENG(1984-), student of Phd program in the Graduate School of Social and System Studies,

the University of Kitakyushu; also a cooperative researcher in the International Centre for the Study of East Asian Development. Email: [email protected]; Tel:81-93-583-6209; FAX:81-93-583-4602; Address:11-4 Otemachi, Kokurakita, Kitakyushu, 803-0814 Japan Main research interests: international talent (migration), urban planning. 1/19

In China, haigui entrepreneurs are popular in the recruiting program of every local government. However, their distribution is extremely unbalanced. The purpose of this research is to find out which characters of a place can attract them. There have been empirical studies testing the affecting factors of talent distribution and its relationship with economic development, such as studies by Zhang and Fan (2006), Li and Florida (2006), Qian (2008). But these studies did not factor out the impact of hukou (registered residence status) system, which affect urban citizens rights in various ways, such as social welfares, employment opportunities, etc. By studying haigui, the restriction from hukou system can be much alleviated because most cities offered hukou for them under preferential policies. So the academic contribution is to offer a good example to the study of talent distribution in China. The other academic contribution is for the first time using individual level to study talent distribution in China. This paper adopted a discrete choice analysis method which deals with micro data. Existing studies on talent distribution or migration only looks at the stock of talents, and neglect individual differences which might be critical in a destination choice of a talent, such as social ties. By adopting discrete choice model, this paper allows us to study on the destination choice behavior of haigui entrepreneurs and finally reveal the affecting factors of their unbalanced distribution.

2. Literature review on affecting factors

2.1. Economic performance

In traditional migration and urban theory, economic reason is the basic factor reason for talents to choose their migration destinations. Entrepreneurs would like to choose a developed place with higher per capital GDP since it means a bigger local market. The effect of regional job opportunities on talents has been studied by researchers, such as Boschma and Fritsch (2009), and it is suggested that growing job market offers more job opportunities for employees. For employers, the growing job market means a dynamic economy which is important for an entrepreneur. So, economic performance here is represented by the economic level (measured by per capital gdp) and economic dynamics (employment growth rate).

2.2. Urban amenity

Fotheringham et al. (2000) has pointed out that the factors influencing talents destination choices and attracting people to particular places have been altered fundamentally during the late 1990s. While it used to be thought that choosing between places to live was solely decided by economic considerations (such as employment), other factors may have became important and start to show influence destination choices. Economically active people now appear to be able to give greater weight to other factors besides local job opportunities in making their residential choices, and these change can be dated to as early as 1970s (Svart, 1976; Rudzitis, 1989).


The role of amenities started to be recognized as a major attraction for people in their location decision. At first the amenity literature was mainly concerned with natural amenities like climate and environmental beauty (Ullman, 1954). Later, urban amenities caught researchers attention. Tiebout (1957) first implied the relationship of urban amenities with migration, and argues that people vote on their feet by choosing where the city provides better public goods. His followers find that talents are increasingly &quot;voting with their feet&quot;(Findlay and Rogerson, 1993) and attach high values to amenities leading to a more pleasant urban life, such as a variety of consumer services and goods, aesthetical and physical settings; good public services, and speed to make the city accessible. (Glaeser, Kolko and Saiz, 2001); or a &quot;particular packages of amenities&quot;, including cafes, galleries, music and a generally bohemian, tolerant atmosphere are strongly correlated with the presence of knowledge workers (Florida, 2002). Attempts have been done to measure the urban amenity in empirical research, mainly focused on the ability of a city to provide cultural offerings and public services. Buettner and Janeba (2009) proved the publicly provided goods such as cultural offerings act as important pull factor for talents in German. Fritsch (2007) also did a research about German and reached a similar conclusion that a high level of public supply in health care and education can explain the distribution of creative talents. Meanwhilel, opposite evidence were also found by Boschma and Fritsch (2009) that the provision of public facilities in health care and education has only a minor, if any, impact on the presence of the talents, which is also true for the regional supply of cultural and recreational amenities. In this paper, the urban amenity is represented by variables of public service (including heath care and education) and cultural goods and its effect on talents destination choice will be tested.

2.3. Creative milieu

The latest debate on talents distribution has focused on the creative milieu of a place, especially in the studies on creative class ­ a subgroup of talents who have received special attention from the academia as well as the urban managers. Florida (2002), who started the theory of creative class, has brought new elements into the debate of place attractiveness. He argues that a city should be characterized with &quot;3T&quot; power (tolerance, technology and talent power) to attract the creative class. Tolerance This recognition of tolerance can be derived to the works of Jacobs (1961) who stressed the importance of a diversity of individuals. Florida contributed to the economic development literature by introducing tolerance as a new answer to the question why some places are better able than others to generate, attract, and retain creative people. According to him, it is not (or not only) job opportunities or urban amenities that attract the creative class to a city (Florida , and Gates 2003). Competitive cities are those with &quot;low barriers to entry&quot; which are &quot;known for diversity of thought and openmindedness&quot; (Florida, 2004), in his termination, the &quot;Tolerance&quot;.


In empirical studies, tolerance of a place is represented by the diversity of specific type of residents, like gay, foreigner, or bohemian (magnitude or share of people who are engage in cultural and artistic occupation, such as musicians, writers, performing artists, photographers, designers, fashion models and so on). The rationale is that these people are sensitive and a place without tolerance to new ideas would appeal unfriendly, resulting in pushing them out. Gay index is stressed to be one of the best proxies for tolerance (Inglehart, 2003, 2005). But due to the lack of data availability, its adoption is only limited in empirical studies in the U.S. (Florida, 2002). Studies in European countries frequently adopted the foreigner/ethnic index (Hansen and Niedomysl, 2009; Fritsch, 2007) and bohemian index (Boschma and Fritsch, 2009; Hansen and Niedomysl, 2009) and use them to explain creative classs distribution. In Chinese context, it is difficult to use the above measurements of tolerance. But since the country has long restricted internal migration, the tolerance can be measured by the share of the population in a region who are from other parts of the country, i.e. without local registered residence (i.e. hukou), as proposed by Florida et. al.(2008). Technology A lot of studies have demonstrated that technology is the main source of productivity growth (Solow, 1956). This exogenous view of technology was later imbedded in the model by Romer (1990). Technology power (innovation) is the outcome of creativity, and in turn could be a good indication of an environment inductive to creative industry. High-tech entrepreneurs are supposed to be interested in cities with higher technology power. As the second indicator for creative milieu, technology can be measured either from the input side, such as R&amp;D expenditures, or from the output side, in the form of patents. The output side is more reliable in the sense that high input does not necessarily lead to high output (Florida et. Al. 2008). In China three types of patents are granted: inventions; utility models; and designs. To evaluate regional technology and innovation, this paper used officially approved patents per 10 thousand population to represent the technology power. Talent Talent power is considered as another feature of a competitive city. The role of talent in economic growth has been identified in many existing studies (Lucas, 1988; Mellander and Florida 2006). Subsequent studies based on the work of Baumol (1968) have improved our understanding of the role of human capital in relation to technology, technological innovation and entrepreneurship (Florida 2002b; Lee, Florida, and Acs 2004; Acs and Armington 2006; Audretsch, Keilbach, and Lehmann 2006). Talent can be understood as human capital or as the creative class. Generally the former is associated with educational and the latter with occupational measures. Human capital is represented by those graduating with a college or higher-level degree, standardized by the local population 15


years old or older.

2.4. Social ties

Basically, the creative city theory proposed by Florida denied the role of social capital in making a city creative. Rather, he believes that homogeneous communities that have strong ties between their members can have an adverse effect on growth, claiming that such environments often tends to suppress new ideas and creativity. Therefore, the future is moving toward &quot;place with looser networks and weaker ties&quot; that &quot;are more open to newcomers and thus promote novel combinations of resources and ideas&quot; (Florida 2004, 273). However, social connection has been proved an important issue in peoples destination choice, either for the reason of social capital(Benson-Rea, Rwlinson, 2003) or emotional needs(Powdthavee, 2008). A wider social context with the focus on social capital and social networks has been made in the empirical studies of migration by (Benson-Rea, Rwlinson, 2003). Here significant role of social ties, which form migratory chain and self-sustaining migratory process, is highlighted. Dahl and Sorensens research (2009) even found entrepreneurial talent in Denmark place much more emphasis on being close to family and friends than on regional characteristics that might influence the performance of their ventures when deciding where to locate those businesses. This variable can only be tested by using discrete data. Discrete choice model allows us to test the influence of it on talents destination choice. Social ties with three types of places: birth place, studied place, and work place were tested in this paper.

3. The distribution pattern of haigui entrepreneurs in China

To demonstrate the unbalanced distribution of haigui entrepreneurs, this paper managed to approximate the number of Chinese haigui and haigui entrepreneurs in each province. The only officially published data relating haiguis distribution is released on the Exhibition of Chinese Returnees Entrepreneurship Achievements held at Beijing in 2004. After that, there is no systematically published data. But the number of haigui has grown very fast and their distribution may also have changed in recent years. Due to the limitation of data availability, the estimated distribution is just in year 2008. Limited by serious shortage of necessary information, the final data in each province were not estimated in the same method or collected from the same source. Officially released data are adopted primarily for provinces with available data. But for those without published data, haigui data were estimated based on information in the years nearest. The result of distribution pattern in each province is listed in Table 1.

Table 1. Distribution pattern of haigui and haigui entrepreneurs in China


2003 Beijing 40000

Haigui Entrepreneurs

2003 5000


2008 80000

Haigui Entrepreneurs

2008 13443

Alternative province Yes


Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

1500 3000 1331

50000 3000 3000 4000 4000

4000 10000 300 2600

345 69 100 26 380 210 146 4580 976 589 206 344 31 448 95 330 157 866 120 14 40 231 64 390 54 11 20

10000 3075 4000 2729 24000 3439 6200 75000 36000 9646 4000 8200 508 9600 1556 5404 8200 24000 1966 387 3600 5330 1100 1049 6387 2050 200 176 3690

800 142 200 201 2863 565 393 7158 1800 1595 547 1613 135 1115 255 1100 322 2079 89 35 90 463 148 2000 77 34 50 Not Not Not


Yes Not Not Not Not



Source: Data in 2003 is from Exhibition of Chinese Returnees' Entrepreneurship Achievements held at Beijing in 2004. Data in 2008 is estimated from various data source. Estimation process of haigui and haigui entrepreneurs in 2008 can be found in Table 5&amp;6 separately in the appendix. This is the latest update on the distribution of Chinese haigui as well as haigui entrepreneurs, after the officially published data in 2003. Although its accuracy still needs to be improved, the dataset can portray the general picture of haigui entrepreneurs distribution. The new distribution pattern of haigui reveals the following features: 1) Beijing and Shanghai has overwhelmingly high number of haigui than other provinces. 2) Coastal provinces have more haigui than inland provinces; 3) Among inland provinces, the central provinces have more haigui than western ones. Haigui entrepreneurs generally follow the same distribution pattern with haigui. But between Beijing and Shanghai, the differences on haigui entrepreneurs are much larger than on 6/19

haigui. Beijing has attracted much more haigui entrepreneurs than Shanghai although the number of overall haigui is almost the same in the two cities. The reason of this unbalance lies partly in the fact that Beijing is the political center of China. Central government is in charge of setting new industry standards, making important industry policies, etc. And when policy makers in the central government need to consult with experts in specific industry, entrepreneurs in Beijing obviously have more chances to be contacted than those in Shanghai. So locating in Beijing has a bigger chance to participate in the policy making and have more advantages. On the other hind, another reason is that there are more head quarters of multinational firms in Shanghai than Beijing. So Shanghai offers more positions in foreign invested companies which have employed a lot of haigui. As a result, the initiative to start a company is weaker for a haigui in Shanghai than in Beijing.

4. Methodology and Data

4.1. Review on the analysis methods

In the discussion of why some place is more attractive to talents, there is a popular research method - rating pace attractiveness for various aspects of performance (Rogerson 1999; Malecki 2004; McCann 2004), such as business climate, quality of life and so on. Such ratings have been questioned because there seldom is agreement on the variables to be included, or the relative weight, for achieving a proper measure of attractiveness (Rogerson, 1999). The arbitrariness could easily lead to criticism that it facilitates policies with problematic priorities (McCann, 2004). Instead of rating, migration perspective is considered more convincing in finding the factors for place attractiveness (Niedomysl, 2010). Micro data were usually adopted in the analysis, using the following dependent variables: 1) the stock of the talents; 2) the share of talents in the population (taking the magnitude of the whole population in consideration); 3) the in-migration rate or net migration rate (taking both the flow as well as the magnitude of whole population in consideration). These analyses can explain partly the reason for the distribution of talents. But people with the same skill/educational/occupational/etc. background are treated as a group, the difference between individuals are ignored in the analyses. However, the actual location selection process is likely to be a quite complex one, during which individuals reason and weigh different alternatives according to ones own preferences and ultimately reach the final decision favoring certain destination at the expenses of others. The discrete choice approach allows the researchers to study the destination choice behavior on individual level.

4.2. Discrete choice model

Discrete choice model assumes one make a choice from a designated choice set. The numbers of options has been quite restricted because of the difficulty to calculate. Although researchers have long been equipped with theoretical tools, which is pioneered by McFaddens (1973, 1976) contribution in econometric models, to deal with multiple choice problems, it was difficult to


calculate at that time. This restriction has been largely loosened by the improvement of computing technology dramatically thanks to the quick developing information technology. It becomes possible to do choice analysis on a larger choice set. By including personal information in the model, their differences were admitted and considered in the model. This model deals with information of both the subjects (i.e. individuals) and objects (i.e. the alternatives) in a choice behavior. Alternative places are set at province level. For an individual who faces a choice set of J provinces. Let yij be an indicator variable for the alternative actually chosen by the ith individual (case). So, yij =1 if individual i chose alternative j and yij =0 otherwise.

U ij Vij ij xij ij xaij a xcij c ijj J , i I

Uij ­ the utility that ith individual obtains by choosing jth alternative destination Vij ­ the observed utility xa ­ a vector of attributes of destinations characteristics xc ­ a vector of attributes of individual characteristics ij ­ the stochastic utility which remains unobserved a ­ a vector of alternative-specific regression coefficients c ­ a vector of case-specific regression coefficients J ­ the set of alternatives I - the set of all individuals


Among types of discrete choice model, Mixed Logit model fit best for the need of this research because it allows and to be random in addition to . The randomness in parameters accommodates random taste variation over people and correlation across alternatives that generate flexible substitution patterns. The probability that individual i chooses destination j is the probability that the utility of destination j exceeds that of all the other destinations.

Prij Pr(U ij U ik ), k where k J and k j Pr(xaij a xcij c ij ) Pr(xaij a xcij c ik ), k


The individuals objective is to choose the destination that maximizes his utility(Sjjastad, 1962). It has been shown (McFadden,1973; 1978) that if the systematic portion of utility can be represented by an additively separable and linear in parameters functional form, and the residuals ij are independently and identically distributed with a type I extreme value distribution (also called Gumbel and type I extreme value distribution), then the probability that an individual i will choose destination j is given by


Pr(Yi j ) Li ( )

exp( xij )

exp( x

k 1





The probability of yi=(yi1...yit) is conditional on

Ji j1

yij 1 . The integral for this choice

probability does not have a closed form, so the probability is approximated by simulation.

4.3. Data

Sampling method This research uses hand-collecting data by collecting information of individual haigui entrepreneurs. The name of the sample entrepreneurs were drawn from various sources, including rewarding programs, Yearbooks of Returned Chinese Scholars Pioneer, publications about haigui, websites of Start-up Parks of Returned Students (&quot;&quot;). There are national rewarding programmes which recruited entrepreneurial talents, such as the Recruitment Program of Global Experts (&quot;&quot; in Chinese ­ literally means 1000 experts plan), &quot;Reward for the outstanding returnee entrepreneurs&quot; (&quot;&quot;) etc. Local governments also carried out various intensive plans, such as 1000 experts plan in Zhejiang province or 100 experts plan. Rewards issued by local governments are also chased to identify haigui entrepreneurs. Also, many Start-up Parks of Returned Students have introduced the successful cases to attract new comers. Haigui related federations or associations are also releasing information on successful members. All these sources are used to enlarge the name list of eligible samples. Then their personal experience was checked to collect individual attributes, using the reports on newspapers, magazines, books, etc. Definition of haigui entrepreneurs A haigui entrepreneur should meet the requirements for haigui and entrepreneurs at the same time. &quot;Haigui&quot;is the abbreviation of haiwai guiguo liuxuesheng(, student returnees from overseas) at first. Later its definition expands to refer all the highly qualified returnees, who have at least a tertiary educational background and have been abroad for study/training/work for at least 6 months. Entrepreneurs are defined based on their position in a company. To be specific, only those in the position of CEO, general manager, chairman of the board etc. were considered as entrepreneurs. The assumption here is only those people can make the final decision of where to locate the firm. Starting up a company will with no doubt make the founder a qualified research object, but this is not a requirement. Those who take over an existing company are also recognized as entrepreneurs and will be included in our dataset. When one accepted to take over an company, he accepted its location.


If an entrepreneurs business includes headquarters and branch companies, then the destination choice of him is defined as where the headquarters are. If someone opened start-ups for multiple times, his last choice would be adopted. We assume he is learning from the trial and error, in the location of firm as well as many other ways. The last trial is assumed to be a deliberate decision. If he has moved, the new place would be taken consideration. But if he is just opening a branch company, the location would still be his original choice. Description of sample dataset Totally 361 samples collected to build up the database, distributing in 24 Chinese provinces which forms the choice alternative set.7 out of 31 provinces in mainland China were dropped since there few or no samples to be collected2. We consider this process reasonable since including very unpopular alternative will not contribute much to result. Dropping alternatives seldom chosen will make the final alternative set approach the real choice set (which an individuals are likely to have in mind when he is looking for a location for his business). So it is assumed that each individual (sample) were selecting from 24 provinces and choose his return destination. Values are assigned to the variables describing ones alternative sets (the 24 provinces) according to the year when the destination choice was made. The time period ranges from year 1991 to 2008.To illustrate, if a haigui was looking for a place to locate his company in year 2000, then he chosen from 24 provinces with variables in year 2000. The values assigned to the variables (such as wage level, employment growth rate, etc.) of an alternative (such as Beijing), will be exactly from year 2000. The variables of other 23 alternatives were processed under the same principle. Variable list Variables used in this paper are listed in Table 2 and the statistic summaries in Table 3. Besides the variables discussed in the literature review, individual variables was also included to measured the numbers of years during which the individual stays abroad.

Table 2. Description of variables Varibles ln_pgdp growth_employment tolerance talent technology culture Description the logarithm of per capital GDP the average growth rate of employment opportunity in the preceding 3 years. the percentage of residents without local hukou (registered residence status) among the total population the percentage of people with college or higher level educational background within the population over 15 years old the number of patents granted per 10000 persons. the number of museums and art performance troupes per million population

Please find the 6th column in Table 1 to see whether a province is in the alternative set.



ln_doc ln_teacher if_birth if_study if_work timeabroad Variable if_birth if_study if_work timeabroad ln_pgdp tolerance talent technology growth_employ culture ln_doc ln_teacher Obs

the logarithm of the number of doctors per 10000 population the logarithm of the number of teachers per 10000 population =1, if the alternative province is ones birth place; =0 otherwise =1, if the individual has ever studied (for tertiary education or higher) in the alternative province; =0 otherwise =1, if the individual has ever worked in the alternative province; =0 otherwise the number of years one has stayed road Mean 0.026 0.039 0.013 10.869 7.113 0.094 0.072 1.657 1.014 3.979 2.823 4.523 Std. Dev. 0.158 0.194 0.114 5.936 0.768 0.076 0.054 2.522 0.023 2.186 0.307 0.307 Min 0 0 0 1 4.749 0.012 0.010 0.044 0.940 0.804 2.258 3.240 Max 1 1 1 33 8.966 0.455 0.326 18.172 1.129 11.534 3.881 5.430

Table 3. Statistic summaries 12191 12191 12191 10632 11903 11903 11903 11903 11903 11828 11903 11903

Note (1): The model takes each case's choice on each alternative as one observation. Thus, the total number of observations is supposed to be number of cases multiplied by the number of alternatives. Since we have 464 cases and each case in this analysis faces 24 alternatives.

5. Results and discussions

The result was summarized in Table 4.


Table 4. Regression results

Alternative variables if_birth if_study if_work ln_pgdp growth_employ tolerance talent technology culture ln_doc ln_teacher No. of cases(1) Coefficients 2.832 1.526 1.801 5.572 -0.929 -0.933 -5.696 0.109 -0.059 0.644 1.029 361 p 0.000 0.000 0.000 0.000 0.793 0.648 0.309 0.052 0.367 0.620 0.539

Note (1): The model will automatically neglect duplicated cases and only one remains if there is no difference among all the variables between cases. Also, case that has missing values in variables will also be dropped. So, if the explanatory variables were used in the model, several cases might be dropped. As a result, the number of cases adopted by the model might be different across specifications.

This paper found that haigui entrepreneurs destination choices are positively and significantly related with economic level, social ties, and technology power of a place. The results is shown quite conservative since the new factors raised by urban researchers are found not relevant. Among urban amenities, neither the cultural offerings nor the public services (including the health and educational service) are found related to haigui entrepreneurs location behavior. Talent power is also not found significantly related. Haigui entrepreneurs are more likely to locate in a economically well developed place. The higher GDP level represents a higher probability of production which may be beneficial to start ones business. In China, there are severe economic differences among regions. Haihui entrepreneurs preference for a higher economic level may result in a more intensive aggregation in well developed regions. The result offers fare reason to believe that the distribution of them will be even more unbalanced without proper government intervention. Their further aggregation will finally exacerbate the regional differences. Social ties is found strongly attract haigui entrepreneurs to locate their business. The strength of social ties influence can be ranked as follows: birthplace&gt; worked-place&gt; studied-place. Social connections, as emotional comfort or social resources, can increase the probability of haigui entrepreneur to locate at a place. This suggest governments to target those who have studied/worked /been born when recruiting haigui entrepreneurs. This implication will be especially important for less developed regions.


Since haigui entrepreneurs are mainly in industry of high-tech or new services which rely largely on innovations. A place with higher technology power will have a spill over effect and offer them inspirations. Although the relationship of talents aggregation and technology innovation may actually be reciprocal causation, the behavior analysis here at least confirms that a haigui entrepreneurs is more likely to choose province with higher technology power. Encouraging technological innovation is a good approach to attract haigui entrepreneurs for local governments. The result also suggests that better urban amenities do not increase the possibility of a place to be chosen. The urban amenities in China are not as good as those in developed countries. For haigui entrepreneurs, they are ready prepared for bearing the loss in amenities. But their need for better amenities can be compensated elsewhere, such as in foreign countries. This is why many of haigui entrepreneurs return alone and leave the family members abroad. They come back to China just for business, but their life is still left abroad. So they dont mind whether the place can provide good culture offerings, good education or health environment. Lastly, it is noteworthy that tolerance is found not related. One possible reason might be that creative class theory - which Florida had advocated - does not apply to high-tech oriented creative talents. Instead, the creative class of cultural creativities is his focus. The second possible explanation is that tolerance does not apply to the creative class in China at current stage. Of course, in the context China, tolerance is measured in different ways with the United States and European countries. It is necessary to re-measure the tolerance and redo this analysis when necessary data become available.


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Table 5. Information source or estimation method of haigui in 2008 (by province)

Province Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang H2008 80000 5650*** 3075** 4000 2729** 24000 3439*** 2391*** 75000 33690* 9646*** 4000 8200** 508*** 8984* 1556*** 5404*** 8200** 22460* 1966*** 387 3369 5330** 200 1049*** N/A 6387*** 2050** 200 176 3690** Estimation method or source From Returned Chinese Scholars Pioneer Yearbook (2009) = H2008Shanghai* (HE2003Tianjin/ HE2003Shanghai) = H2003Hebei*growth_rate_2008_to_2003 From Returned Chinese Scholars Pioneer Yearbook (2009) = H2003InnerMongolia*growth_rate_2008_to_2003 From Returned Chinese Scholars Pioneer Yearbook (2009) = H2008Shanghai* (HE2003Jilin/ HE2003Shanghai) = H2008Shanghai* (HE2003Heilongjiang/ HE2003Shanghai) From Returned Chinese Scholars Pioneer Yearbook (2009) = H2007Jiangsu*growth_rate_2008_to_2007 = H2008Shanghai* (HE2003Zhejiang/ HE2003Shanghai) From Returned Chinese Scholars Pioneer Yearbook (2009) = H2003Fujian*growth_rate_2008_to_2003 = H2008Shanghai* (HE2003Jiangxi/ HE2003Shanghai) = H2007Shandong*growth_rate_2008_to_2007 = H2008Shanghai* (HE2003Henan/ HE2003Shanghai) = H2008Shanghai* (HE2003Hubei/ HE2003Shanghai) = H2003Hunan*growth_rate_2008_to_2003 = H2007Guangdong*growth_rate_2008_to_2007 = H2008Shanghai* (HE2003Guangxi/ HE2003Shanghai) From Returned Chinese Scholars Pioneer Yearbook (2009) From Returned Chinese Scholars Pioneer Yearbook (2009) = H2003Sichuan*growth_rate_2008_to_2003 From Returned Chinese Scholars Pioneer Yearbook (2009) = H2008Shanghai* (HE2003Yunnan/ HE2003Shanghai) = H2008Shanghai* (HE2003Shaanxi/ HE2003Shanghai) = H2003Gansu*growth_rate_2008_to_2003 From Returned Chinese Scholars Pioneer Yearbook (2009) From Returned Chinese Scholars Pioneer Yearbook (2009) = H2003Xinjiang*growth_rate_2008_to_2003

Source: 2003 data is from the Exhibition of Chinese Returnees' Entrepreneurship Achievements (held at Beijing in 2004, by the PDC, MOP, MOE); data for 2007, 2008 are from Returned Chinese Scholars Pioneer Yearbook (2008, 2009). Note (1): [n] represents data officially published data in major cities of the province. Note (2): The estimation is done based on the following principles. The number in the latest year is of higher priority to be chosen as estimation base. Specifically, data in 2008 were firstly adopted, if it's not available, then estimation from 2007 will be used; for provinces still lack of data, estimation are made based on data in 2003.



The numbers with superscript * are estimated from the published data of 2007. It is already known that the growth rates of returnees in 2008 comparing to 2007 are as follows: Beijing = 1.103, Shanghai = 1.143, China (national wide) = 1.123. Then, the average 1.123 is used to calculate


The numbers with superscript ** are estimated from the published data of 2003. It is already known that the growth rates of returnees in 2008 comparing to 2003 are as follows: average of provinces (with data available in both year) = 1.75, China (national wide) = 2.35. The mean value 2.05 is set as the growth value for the estimation. (Beside, the number of Sichuan province is actually data in Chengdu city.) (It is worth noting that Zhejiang's returnee number is 6150 after the 2 step's calculation, and turns out to be too low (especially comparing to its neighboring province Jiangsu). Thus the third step's result is adopted for Zhejiang provinces.)


After step 1) and 2), there are some provinces lacking of data. The third step is to estimate the numbers for them according to their proportion to Beijing and Shanghai, using entrepreneur data in 2003. It is found out that these results are usually underestimated, comparing to provinces with data already known. So the relatively higher number (proportional to Shanghai's data) is adopted to complete the final dataset. The result numbers are noted with superscript ***.

Guizhou's data is calculated by none of the previous 3 ways. According to unofficial data source (, there are about 200 returnees in Guizhou and this number is adopted. Tibet's data is also missing after the aforementioned 3 steps were done. Since haigui001 database does not include returnees who currently reside there either, it reflects the fact that Tibet is rarely chosen by the haigui. Thus, case database will not contain cases in Tibet and it is dropped from the alternative set.

Table 6. Information source or estimation method of haigui entrepreneurs in 2008 (by province)

Province Beijing Tianjin Hebei HE2008 13443 800 142 Estimation method or source From Returned Chinese Scholars Pioneer Yearbook (2009) =HE2003Tianjin*H2008Tianjin/H2003Tianjin =HE2003Hebei*H2008Hebei/H2003Hebei 170=H2007 in Taiyuan City; 233= HE2003Shanxi*H2009Shanxi/H2003Shanxi; 200=HC2009Shanxi*( HE2003 China / HC2003 China) &lt;HE2008Shanxi is estimated to be 200 - the relatively smaller data estimated for year 2009.&gt; = HE2007IM + (HE2010 IM- HE2007IM)/(2010-2007) =HC2008Liaoning*( HE2003 China / HC2003 China) &lt;proportional to Beijing&gt;



InnerMongolia Liaoning Jilin

201 2863 565



393 7158 1800 1595 547 1613 135 1115 255 1100 322 2079 89 35 90 463 N/A 148 N/A 2000 77 N/A 34 50

= ( HE2003Jilin/ HE2003Jilin) * HE2008Beijing &lt;proportional to Beijing&gt; = ( HE2003Heilongjiang/ HE2003Heilongjiang) * HE2008Beijing =HC2008Shjanghai*( HE2003 China / HC2003 China) From =HC2008Zhejiang*( HE2003 China / HC2003 China) =HC2008Anhui*( HE2003China/ HC2003 China) &lt;Underestimated, because HC is limited to companies in science parks.&gt; =HC2008Xiamen*( HE2003China/ HC2003China) &lt;Underestimated, because HC is limited to companies in Xiamen. &gt; = HE2007Jiangxi*H2008China/H2007China 1076= HE2008Shandong*H2008China/H2007China 1115=HC2007Shandong*( HE2003China/ HC2003China) &lt;HE2008Shandong is estimated to be 1115. &gt; &lt;proportional to Beijing&gt; = ( HE2003Henan/ HE2003Jilin) * HE2008Beijing From = HE2003Hunan*H2008Hunan/H2003Hunan = HE2003Guangdong*H2008Guangdong/H2003Guangdong 89 = HE2008Guilin+ HE2008Nanning From Returned Chinese Scholars Pioneer Yearbook (2009) &lt;Underestimated&gt; From Returned Chinese Scholars Pioneer Yearbook (2009) From = HE2003Sichuan*H2008Sichuan/H2003Sichuan =HC2008Yunnan*( HE2003China/ HC2003China) From Returned Chinese Scholars Pioneer Yearbook (2009) &lt;Underestimated. The data only includes Xian.&gt; 77=HE2011Lanzhou 111= HE2003Gansu*H2008Gansu/H2003Gansu. &lt;77 is adopted.&gt; From &lt;Underestimated. The data only includes HE in Ningxia until 2007.&gt; From

Note: HE ­ the number of haigui entrepreneurs; H ­ the number of haigui; HC ­ the number of haigui companies. Subscript 2003 and 2008 is used to indicate the year. The other subscript of province names is used to indicate the place.


Table 7. The result of collinearity diagnosis of variables in discrete choice analysis

if_birth if_birth if_study if_work timeabroad degree ln_pgdp growth_employ tolerance talent technology culture ln_doc ln_teacher 1 0.378 0.239 -0.006 -0.005 0.014 0.005 0.023 0.014 0.020 0.006 0.019 -0.018 if_study 1 0.314 -0.008 0.022 0.108 0.075 0.172 0.206 0.154 0.141 0.168 -0.004 if_work time abroad degree ln_pgdp growth_ employ tolerance talent technology culture ln_doc ln_teacher

1 -0.045 -0.005 0.067 0.049 0.117 0.123 0.087 0.105 0.109 0.010

1 0.333 0.290 0.123 0.090 0.143 0.144 0.037 -0.002 0.040

1 0.143 0.046 0.055 0.061 0.060 0.015 0.008 0.022

1 0.438 0.664 0.734 0.710 0.328 0.413 0.122

1 0.450 0.443 0.509 0.351 0.020 -0.017

1 0.780 0.744 0.584 0.503 0.026

1 0.761 0.661 0.690 0.089

1 0.462 0.439 -0.037

1 0.516 0.191

1 0.110





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