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An Empirical Assessment of Chinese GlobalBillionaire Entrepreneurs: Social Origins, Foreign

Exposures, and Financing Decisions

Kezhou Xiao∗

This version: June 24, 2018

Abstract How do foreign exposures and social origins affect the financing choices ofglobal billionaire entrepreneurs from mainland China? Building a unique database, Ifind that (i) the politically unconnected billionaire entrepreneurs financed by foreign ven-ture capitalists outside mainland China are more likely to float their companies outsidemainland China (mainly in Hong Kong and the U.S), use offshore financing vehicles, andmake entries into the innovative sectors and (ii) the politically connected global billionaireentrepreneurs, however, are strongly associated with a record of state-owned enterprise(SOE) restructuring. (JEL: G10, L26, O31, P26, O53, F63)

∗Address: Department of Economics, London School of Economics, Houghton Street, London WC2A2AE, United Kingdom, e-mail: [email protected]. Kezhou Xiao is a Ph.D. Candidate in Economics at LondonSchool of Economics and Political Science (LSE). He is the academic entrepreneur behind this project. Thecurrent working paper is part of his Ph.D. thesis at LSE. The paper was originally titled, “Creative Destruc-tion with Chinese Characteristics: Becoming Global Billionaires in a Communist State”. The accompanyingcodebook was titled, “Becoming Global Billionaires from Mainland China: Theory and Evidence”. I thankChenxi Liu, Zhenzhi He, and Weiting Yuan for their excellent research assistance at Peking University, Bei-jing, China. In particular, without Ji Shen, an assistant professor at Peking University, this project would bemuch delayed.

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1 Introduction

A central feature of any functioning market economy is that grassroots entrepreneurscould become self-made billionaires if their enterprises are deemed successful by standardbusiness metrics, as Milton Friedman remarked, “The social responsibility of business isto increase its profits” (Friedman, 2007). On May 5, 2013, Jack Ma, the founder of AlibabaGroup, made a public speech at an event co-hosted by Alibaba Group and the StanfordProgram on Regions of Innovation and Entrepreneurship (SPRIE) of the Graduate Schoolof Business at Stanford University1. Three things took notice for the newly minted globalbillionaire entrepreneur. One, Ma said to his audience, without Silicon Valley, there couldnever be Alibaba. Two, before plunging into the internet business, Ma was an Englishteacher with humble social origin in a country assumed to be driven by political connec-tions. Three, according to the same Stanford news outlet, Ma never wrote a single line ofcode for his internet giant.

How do foreign exposures and social origins affect the financing choices of global bil-lionaire entrepreneurs from mainland China? Motivated by Jack Ma and other internetbillionaires from mainland China, this paper presents an empirical assessment of theseentrepreneurs from mainland China, triangulating social origins, foreign exposures, andfinancial decisions. Building on the World’s Billionaires, Forbes Feb 2017 version, I analyzethe financial choices and social and political origins of Chinese billionaires. The two corefindings of the paper are that (i) the politically unconnected billionaire entrepreneurs fi-nanced by foreign venture capitalists outside mainland China are more likely to float theircompanies outside mainland China (mainly in Hong Kong and the U.S), use offshore fi-nancing vehicles, and make entries into the innovative sectors and (ii) the politically con-nected global billionaire entrepreneurs, however, are strongly associated with a record ofstate-owned enterprise (SOE) restructuring. Figure 1 visualizes one of the key empiricalfindings: the exposure to foreign element combined with the high index of political uncon-nectedness predicts a higher probability of the presence of offshore vehicles underlyingthe billionaire entrepreneurs’ company. Other than those without any foreign exposure,the increase in the degree of political unconnectedness increases the predicted probabilityof using offshore vehicles.

Unlike cross-country empirical papers on global billionaires (Henrekson and Sanan-daji, 2013; Bagchi and Svejnar, 2015), to my knowledge, this paper is the first system-atic empirical assessment of global billionaire entrepreneurs from mainland China link-

1http://fsi.stanford.edu/news/jack_ma_ideas_and_technology_can_change_the_world_20130506

2

http://fsi.stanford.edu/news/jack_ma_ideas_and_technology_can_change_the_world_20130506

http://fsi.stanford.edu/news/jack_ma_ideas_and_technology_can_change_the_world_20130506

Figure 1: Predicted Probability of Using Offshore Vehicles

Note: The predicted response was generated from a logistical regression from a preferred specification.Using offshore vehicle is represented by a dummy, which captures whether or not an offshore vehicleis used behind the operation of the company chaired by the billionaire entrepreneur. Possible offshorelocations include the following combinations: Australia, Bermuda/Hong Kong, Cayman Islands, Cay-man Islands/Hong Kong, Hong Kong only, Singapore, Virgin Islands/Cayman Islands, and the Virgin Is-lands/Hong Kong.

ing social-political backgrounds to capital structure and financing decisions. The use ofoffshore vehicles and publicly listing companies outside mainland China are two majorempirical decisions made by these global billionaires given the institutional inefficienciesamong authoritarian settings (Minxin, 2009; Shambaugh, 2016; Bai et al., 2014; Acemogluet al., 2005).

One unique contribution of this paper is to document the usage of offshore vehicles sys-tematically, by highlighting the extensive application of the variable interest entity (VIE)structures from global billionaire entrepreneurs, epitomized by Jack Ma. Figure 2 repre-sents the VIE structure underpinning Alibaba2. The combination of an offshore holdingcompany in the Cayman Islands and a variable interest entity within mainland China un-derpins the financial structure of Alibaba, a public listed company in New York StockExchange (NYSE). These observable financing choices from billionaire entrepreneurs pro-vide available and accessible data to assess the financial as well as political economy condi-

2The VIE structure is used in floating China’s first generation of internet companies on the New YorkStock Exchanges and NASDAQ. The first case, relying on VIE structures, was the listing of Sina (NASDAQ:SINA) as early as in 2000. Another famous internet company, Sohu (NASDAQ: SOHU), founded by CharlesZhang, a graduate of MIT Ph.D. in experimental physics, was also listed in NASDAQ. As of 2017, most ofthe publicly listed Chinese companies on the NYSE, the NASDAQ, and the Hong Kong Stock Exchange, andothers were structured with VIE.

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tions to which these entrepreneurs would have to respond. The two decisions consideredby my paper include the choice to float the company publicly outside mainland China andwhether or not to apply offshore entities.

Figure 2: Alibaba’s VIE Structure

Source: Alibaba’s Registration Statement to the SEC, the U.S regulator, https: // www. sec. gov/ Archives/edgar/ data/ 1577552/ 000119312514184994/ d709111df1. htm

This paper continues the discussion on Chinese entrepreneurs (Djankov et al., 2006).The existence of self-made billionaire entrepreneurs in internet business from my database- Jack Ma (Alibaba), Pony Ma (Tencent), Robin Li (Baidu), William Ding (NetEase), RichardLiu (JD) and others - highlights the need to consider the nature of open economy a criticalassumption (Rodrik, 2015) for not only political survival (De Mesquita et al., 2005) but alsothe economic performance of an authoritarian state (Besley, 2005).

This paper also adds to the large literature of entrepreneurship and political econ-omy of finance. Early contributions from Baumol (1990) as well as recent work from Bau-mol et al. (2007) augments traditional view of entrepreneurship, broadening its concep-tual scope by incorporating “bad entrepreneurship” through investments in rent-seeking

4

https://www.sec.gov/Archives/edgar/data/1577552/000119312514184994/d709111df1.htm

https://www.sec.gov/Archives/edgar/data/1577552/000119312514184994/d709111df1.htm

(Krueger, 1974) activities. Concentrating on the reward structure of entrepreneurs, Ace-moglu (1995) links the ratio of good and bad entrepreneurship to talent allocation andeconomic performance. Building on a similar vein, my paper empirically assesses the cor-relation between social and political origins and financial decisions, and how that relation-ship is mediated by the degree of foreign exposure. Importantly, this research documentsthe capacity of politically unconnected billionaire entrepreneurs to circumvent credit mis-allocations in the capital market (Fisman, 2001; Faccio, 2006) by seeking financing fromventure capitalists outside the country.

The evidence of global billionaire entrepreneurs from mainland China contributes tothe political economy of finance literature and its linkage to economic development (Kingand Levine, 1993; Levine, 1997; Rajan and Zingales, 1998; Levine, 2005; Aghion et al., 2005)from cross-country perspective. Studies on Chinese political economy (Minxin, 2009;Shambaugh, 2016; Bai et al., 2014) tend to characterize the Chinese state as more or less asystem of crony state capitalism, whereby billionaire entrepreneurs enriched themselvesthrough a cozy partnership with the state. While this observation is partly consistent withmy data, crony capitalism argument seems to be problematic at explaining both the real aswell as sustained total TFP growth (Brandt et al., 2012) and the increase in the number ofbillionaire entrepreneurs (Aghion and Howitt, 1990) from mainland China, particularlythose of humble social origins. My project is related to the recent paper by Song et al.(2011), who identify differentiated access to finance as major friction between private en-trepreneurs and state-owned firms. While the focus of Song et al. (2011) centers on privatesavings as a source of bootstrapping for high productivity private firms, my paper high-lights the possibility of soliciting venture capitalists outside mainland China among bil-lionaire entrepreneurs as a way of alleviating existing credit frictions when the politicallyunconnected entrepreneurs are priced out of the capital market, prevalent in the financialdevelopment literature (Rajan and Zingales, 2003; Haber et al., 2008). Relatedly, my paperadds to the discussion of Huang (2008) by stressing urban entrepreneurship financed byoutside venture capitalists.

Interests in the leadership of the management and firm policies go beyond the Chi-nese context. Bertrand and Schoar (2003) reports that manager fixed effects matter for awide range of firm policies in the context of the United States. Bertrand and Mullainathan(2003) investigates the relationship between corporate governance and managerial prefer-ence. CEOs payments are found to be less connected with luck for better-governed firms(Bertrand and Mullainathan, 2001).

The structure of the paper is as follows. Section II describes the institutional contextand the construction of my database. The following section presents the core empirical

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findings. Section IV considers a few theoretical preliminaries. The final section concludes.

2 Institutional Background and Database Construction

2.1 The Institutional Background

It has been 40 years since China orients herself towards market reforms through heropening and reform policy (Naughton, 2017). The increase in the number of global billion-aire entrepreneurs from mainland China provides the background for my research. Figure3 presents the major empirical foundation - the evolution of the number of Global billion-aires between 2003 and 2017, documented by the World’s Billionaires database, Forbes.In 2003, none in a country of more than 1.3 billion citizens could reach the threshold ofone billion U.S dollars, where India has 7, Russia 17, and Hong Kong, China 11. Leadingall other countries, United States claimed 222 global billionaires, about 47 (222/476) per-cent of the total (476). By 2017, fourteen years later, the number of global billionaires frommainland China goes up from zero to 319, being the second largest in this league. Panel Aof Figure 4 reports that while the number of billionaires for the United States grew from222 to 565, an increase of 154%, its share dropped from 47% (222/476) to 28% (565/2043)over the same period. The decline of about twenty percentage points in the share of globalbillionaires claimed by the United States was accompanied by the rising shares from main-land China from zero to 15.64%. Panel B of Figure 4 presents the evolution of the numberof top 100 billionaires by country between 2003 and 2017. While China witnessed thelargest increase in the number of top 100 global billionaires, United State, as of 2017, re-mained dominant within the top 100 league with 36 billionaires. Before 2010, none of thebillionaires from mainland China made it into top 100. By 2017 seven mainland Chinesejoined the league of top 100, which was on top of four global billionaires from Hong Kongand Macau. In sum, the absolute numbers and share of global billionaires from mainlandChina witnessed sharp increase between 2003 and 2017, according to Forbes. Within aspan of fourteen years, seven Chinese citizens rose from nowhere to the richest 100 menand women on earth.

Underlying the secular growth in the number of global billionaires, Figure 2 in theintroduction represents the operating VIE structure used by Alibaba, sourced from thecompany’s Registration Statement to the SEC, an independent agency of the U.S govern-ment responsible for the regulation of the securities industry. The VIE structure functionsthrough loan agreements, exclusive call option agreements, and others such that differentlegal entities within and outside the Chinese border can be glued together. For this paper, I

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briefly discuss five major reasons why billionaire entrepreneurs love to apply a VIE. First,it opens up an avenue for grassroots entrepreneurs to look for finance outside of Chinain the global capital market. Second, unlike a foreign-owned company registered withthe Chinese government, the VIE structure bypasses to some degree tedious bureaucraticred tapes over foreign capital regulations in some restricted areas of business in China,especially in the innovation sector. Third, the user (e.g., the entrepreneurs) of VIE struc-ture can easily leverage these financing vehicles to float the company outside of China’scapital market, in the Hong Kong Stock Exchange, the New York Stock Exchange (NYSE),the NASDAQ, and others. Fourth, as of 2017, both mainland China, Hong Kong, and Sin-gapore Stock Exchange do not allow the issuance of dual-class stock, which is commonamong high tech company in the United States. By listing themselves on the U.S stockmarket, these Chinese companies can benefit from the financial innovations available onlyin America. Finally, most of the high tech companies are backed by non-Chinese venturecapitalists, the floating of the company outside China makes a convenient financial exitfor these investors to reap their financial rewards.

Figure 3: Evolutions of Numbers of Global Billionaires by Country, 2003-2017

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Figure 4: Additional Figures for Evolution of Global Billionaires in a Communist State:2003-2017

Panel A

Billionaire Shares

Panel B

Top 100 Numbers

Source: The World’s Billionaires List, Forbes, 2003-2017. A billionaire is counted towards a country if he/she has thecitizenship of that country according to database.

2.2 Data

To construct a unique database for the research question, I first identify global billion-aires from mainland China using two lists: the World’s Billionaire List by Forbes3, 2017and Hurun’s China Rich List, 20164. The consideration of both lists allows me to conductrobustness tests to selection into billionaire lists. The core dataset includes the World’sBillionaire List by Forbes, Feb. 2017 version. To enlarge the sample size, I incorporate Hu-run’s top 300 billionaire entrepreneurs from his 2016 list. The priority is given to whethera certain entrepreneur is on the given list only, rather than the actual net worth, day-to-dayfluctuations, and the valuation techniques underlying the wealth measures. A codebookis available upon request for the process behind my database construction.

The World’s Billionaires is an annual ranking by the net worth of the world’s wealthi-est billionaires by Forbes, an American business magazine. Hurun China Rich List, on theother hand, compiled by Mr. Rupert Hoogewerft5, a British businessman and publisherin China, was constructed using a lower wealth threshold (approx. 300 Million USD) anda selection criteria that emphasizes the penetrating nature of Chinese businessman oper-

3The website link: https://www.forbes.com/billionaires/list/4The website link: http://www.hurun.net/EN/Article/Details?num=2B1B8F33F9C05See the robustness check section for details.

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https://www.forbes.com/billionaires/list/

http://www.hurun.net/EN/Article/Details?num=2B1B8F33F9C0

ating within mainland rather than holders of mainland passports.From these two billionaire lists, I have constructed a new dataset on the financing

decisions made by these global billionaire entrepreneurs. In doing so, I use the Orbisdatabase6, a global companywide database on financial and ownership information. Us-ing Orbis, I trace the billionaire entrepreneurs on the two lists to their ownership infor-mation. Careful attention is paid to the choice of listing locations (Hong Kong, Shanghai,Shenzhen, New York, NASDAQ, etc.) and the incidence of an offshore variable interest en-tity (VIE) structure, two major observable decisions made by the billionaire entrepreneurs.For private companies as of Feb. 2017, a dummy is created to capture listed companies.In addition, I supplement the billionaire lists with detailed company research on the of-ficial website to gather information on the founding year, founding headquarter, and thecurrent headquarter at the city level. Critical to my story, I assign two dummy variablesto two significant events: (a) whether or not the company was financed by venture capitaloutside mainland China and (b) whether or not the company was restructured (e.g., pri-vatized) from a state-owned enterprise (SOE). Given the fact that the primary leadershipof firm (“the entrepreneur”) and other cofounders share the same company level informa-tion, I distinguish them by a CEO dummy. Last but not the least, I construct a dummy toidentify the existence of a communist party organ within the company. To finalize con-struction of the foreign exposure index, I search for non-Chinese names on the board orsenior management of the company, using the latest information from Orbis. Along withan English speaking dummy and foreign education dummy, the foreign exposure indexis the summation of four variables: English speaking dummy, foreign education dummy,foreign-financed dummy, and foreign management dummy.

To relate the financing decisions to the social origins and personal attributes of the bil-lionaire entrepreneurs on my lists, I compile information on the educational attainmentsof a given entrepreneur, which include a dummy on whether or not the entrepreneur hasa college education, whether his/her college is an elite college according to official stan-dards, and whether or not his/her education is taught in foreign university outside main-land China. To construct measures on political (un)connectedness, using public informa-tion, three measures are coded: (a) the social origin/family background (e.g. whether ornot the entrepreneur is connected with the top circles of political elites at birth); (b) the oc-cupational experience before founding his/her own company (e.g., whether or not his/herprior occupation is related to the state); (c) the experience of first scoop of gold (e.g. thedegree of which the first scoop of gold is made via political connections), which capturesthe DNA of the business model, reflective of the potential type of an entrepreneur. To

6Its official website: https://www.bvdinfo.com/en-gb/our-products/data/international/orbis.

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https://www.bvdinfo.com/en-gb/our-products/data/international/orbis

simplify the empirical specification, all these measures are converted into measures of un-connectedness, with a value of one denoting political unconnectedness. The compositemeasure of social origins aggregates all three sub-metrics from above.

Table 1 summarizes the global billionaire entrepreneur data from Forbes, Feb. version,2017. Among 317 billionaires from mainland China, about 95% of them is identified withquite complete information7. The proportion of politically connected types from familybackground, job connections, and first scope of gold experience are about 6%, 29%, and17% respectively. Foreign exposures are low, averaging at 0.24 with a possible maximumof four. Only 2% of them have foreign degrees if they do have college degrees at all. 7%of them are co-founders and investors rather than CEOs of the firm. For about 70% ofthe firms, I have found a party organ within the firm. About 80 percent of companies arepublic, but the fraction of listing outside mainland China is around one quarter in total,which is roughly the fraction of existence of offshore vehicles.

Figure 5 presents the heatmap diagrams for the central message of my paper, triangu-lating social origins, foreign exposures, and financial decisions. Panel A documents thefraction of billionaires using offshore vehicles. At low values of the politically unconnectedindex (e.g., the entrepreneur is connected somehow), increasing foreign exposure does notnecessarily increase the chances of public listing outside mainland China. At higher lev-els of political unconnectedness, the positive correlation between foreign exposures andlisting outside becomes strengthened. A similar pattern holds for using offshore vehicles,as seen by the heat map on the right panel.

3 An Empirical Assessment of Billionaire Entrepreneurs:Evidence from Mainland China

In this section, I proceed with my core empirical investigation in four steps. First, I doc-ument the strong association between foreign exposure and financing decisions. Second, Itrace the extensive usage of offshore vehicles and the action of public listing outside main-land China to the social origins and foreign exposures of billionaire entrepreneurs. Third,I link the politically unconnected billionaire entrepreneurs with some degrees of foreignexposures to entry into the innovative sectors and those that are politically connected withan incidence of state-owned enterprise restructuring. Finally, I empirically assign billion-aire entrepreneurs according to their potential types to discuss its relationship to Baumol(1990).

7Table A.5 gives a short definition of the underlying variables.

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Table 1: Summary Statistics for Global Billionaires from Mainland China

Mean SD Min Max

Family background dummy 0.94 0.24 0 1Job (un)connectedness dummy 0.71 0.45 0 1First scoop of gold dummy 0.83 0.38 0 1Political unconnectedness index 2.49 0.82 0 3English speaking dummy 0.05 0.22 0 1Foreign education dummy 0.02 0.15 0 1Foreign financing dummy 0.08 0.28 0 1Foreign management dummy 0.09 0.28 0 1Foreign exposure index 0.24 0.69 0 4College education dummy 0.53 0.50 0 1Elite college dummy 0.27 0.44 0 1CEO dummy 0.93 0.26 0 1Listing dummy 0.79 0.41 0 1Foreign listing dummy 0.25 0.44 0 1SOE restructuring dummy 0.16 0.37 0 1Offshore vehicle dummy 0.26 0.44 0 1Party organ dummy 0.69 0.46 0 1TMT sector entry dummy 0.36 0.48 0 1N: 301Unidentified: 16 (5% of

total)Notes: Billionaire list: Feb, 2017 version, Forbes. Political connection measures are converted into uncon-nectedness where a value of one is coded for not having strong political ties. Political unconnected indexsums all three measures: family background, job (un)connection, and first scoop of gold. Foreign exposureindex aggregates all four measures: english speaking, foreign financing, foreign management, and foreigneducation. These measures of political unconnectedness have not been converted.

My core empirical specification follows a linear probability model, a la Besley andReynal-Querol (2011). I primarily focus on the determinants of financing decisions (De,c,t)made by the billionaire entrepreneurs (e) at the founding year (t) based in city (c) [e.g. thecity of the founding headquarter] with respect to measures political unconnectedness (Pe)and foreign elements (Fe) associated with the entrepreneur (e) in the following fashion:

De,c,t = µc + ωt + θPe + βFe + γXe + εc (1)

Where µc captures the founding headquarter fixed effects of the city, ωt is a year dummy,and Xe are other control variables associated with the entrepreneurs (e.g., college degreedummy). I cluster the standard errors, εc, by the city of the current (as of 2017) head-quarter (c, as opposed to c in the fixed effect) of the company to allow for some degree ofwithin-city correlations. Unlike the empirical papers on leadership and growth (Besley

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Figure 5: A Heat Map Representation: Social Origins, Foreign Exposures, and FinancialDecisions

Panel A

Public Listing outside Mainland China

Panel B

Using Offshore Vehicles

Source: Author’s Database.

and Reynal-Querol, 2011; Jones and Olken, 2005; Besley et al., 2016), my dataset on bil-lionaires consists of cofounders, investors, and minor shareholders, which became global

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billionaires as the company grows larger. Other than variations in social origins, the co-founders share the same company level characteristics as that of the entrepreneur. Toaddress this concern, I consider two alternative strategies: (i) removing cofounders andfocusing only on the founding CEOs; (ii) using billionaire net worth as analytical weightsin our regressions. Although strategy (ii) is preferable to (i) by keeping the sample as intactas possible, the core story is robust to both treatments.

The main coefficient of interests are placed on θ, an estimate of the effect of a politi-cally (un)connected entrepreneur on the probability of making certain financing decisions,and β, which relates the foreign exposures of an entrepreneur to financing decisions. Byadding both year fixed effects and first headquarter fixed effects, the specification controlsfor the unobservable but fixed responses from different cities to China’s opening and re-form policies and the common national policy shocks for a given year in which a companywas founded by that entrepreneur.

3.1 Main Result I: Foreign Exposure and Financing Decisions

In the first step, I explore the relationship between foreign elements of the billionaireentrepreneur and the associated financing decisions. The following specification is con-sidered:

De,c,t = µc + ωt + β Foreign Exposure Indexe + εc (2)

The core results of equation 2 are presented in Table 2. In column (1), the empiricalspecification investigates the relationship between the incidence of foreign financing be-fore IPO and the probability of public listing outside mainland China, controlling foryear and founding headquarter effects only with analytical weights on billionaire networth whenever possible. The possible listing locations include the following categories:Hong Kong/Shanghai, Hong Kong/Shenzhen, Hong Kong/Shenzhen/London/KualaLumpur, Hong Kong/Shenzhen/Singapore, New York Stock Exchange, New York StockExchange/Shanghai, NASDAQ, Shanghai/Singapore, and Shenzhen/Hong Kong, Shen-zhen/Hong Kong/Australia. Global billionaire entrepreneurs are 78.9% more likely, in astatistically significant way, to go public outside mainland China, conditioning on beingfinanced by investors outside mainland China. This result is robust to using another mea-sure of foreign exposure as shown in column (2), the existence of foreign management,which is coded as a dummy according to whether or not the company has a foreign man-ager (e.g., identified by non-Chinese names) on the senior team or board, ascertained byname list from the Orbis database. Conditional on the incidence of foreign management,the probability of listing outside is close to 80% higher than those without.

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Table 2: The Effect of Foreign Exposure on Probability of Public Listing Outside MainlandChina: OLS and Logit Estimation

(1) (2) (3) (4) (5) (6)list foreign list foreign list foreign list foreign list foreign list foreign

finance foreign 0.789∗∗∗ 6.309∗∗(0.060) (2.251)

foreign management 0.799∗∗∗ 6.807∗∗(0.071) (2.529)

total foreign 0.290∗∗∗ 2.176∗∗∗(0.026) (0.473)

Method OLS OLS OLS Logit Logit LogitYear Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 156 156 156R2 0.794 0.807 0.796Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). AllOLS regressions include analytical weights by the net worth of the billionaire. Possible foreign list-ing locations include the following categories: Hong Kong/Shanghai, Hong Kong/Shenzhen, HongKong/Shenzhen/London/Kuala Lumpur, Hong Kong/Shenzhen/Singapore, New York Stock Exchange,New York Stock Exchange/Shanghai, NASDAQ, Shanghai/Singapore, and Shenzhen/Hong Kong, Shen-zhen/Hong Kong/Australia.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

To aggregate all four dummy measures of foreign elements by summation into oneindex, I aggregate (i) English speaking dummy, (ii) foreign education dummy, (ii) foreignfinancing, and (iv) foreign management dummy. The column (3) confirms a positive andstatistically significant association between total foreign exposure and the choice of listingoutside mainland China, although the magnitude of the effect drops as a result of theconstruction of the index variable, from 78% to 30%. To ensure against misspecificationerrors, from column (4) to (6), I repeat the analysis using a logistic fit to the specification2 and reach similar results: the odds ratio of listing outside mainland China goes up in astatistically significant way across all measures of the defined foreign exposures.

To confirm the fact that the strong and positive associations between foreign elementsand financing decisions involve not only listing decisions but also the utilization of off-shore vehicles, I next consider a binary variable indicating the presence of offshore vehi-cles underpinning the multi-billionaire dollar company founded by these billionaire en-trepreneurs. Table 3 considers the effect of different measures of foreign exposure on theprobability of using offshore vehicles. I find comparable magnitudes linking the incidenceof foreign financing and management to the utilization of offshore vehicles in the previoustable.

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Column (3) and (4) show that the probability of using offshore vehicles goes up by76.3% and 81.3% conditioning on the incidence of foreign financing events and manage-ment, respectively. This strong association remains in the logistical regression. Englishspeaking dummy, as seen in column (1) and (5), is significant in OLS but logit regressions.A close inspection reveals that, as indicated by column (2), the holder of a degree fromforeign universities outside mainland China does not necessarily translate into a strongassociation between exposure and utilization of offshore vehicles. In unreported tables,the fact that a degree from outside mainland China is less significant as a predictor of usingoffshore vehicles and listing outside might explain why the composite measure of foreignindex reduces the magnitude of the association. This connection holds across specificationfrom OLS to logistic regression for the foreign education dummy, as verified by column(2) and (6). Taken together, the strong association between foreign exposures and two ma-jor financing decisions is driven mostly by the incidence of the event of foreign financing,through which the event of foreign management ensues.

Completing the first step of my argument, these results suggest that global billionaireentrepreneurs backed up by foreign financing are more likely to utilize offshore vehiclesand go public outside mainland China. Moreover, these results are transferable to differentmeasures of the foreign element and estimation techniques in our specification. Unlike En-glish or foreign education dummies, the incidence of foreign financing captures the mostpreferred measure of foreign exposure by linking the foreign element to the relaxation ofliquidity constraints in a financially underdeveloped country like mainland China.

3.2 Main Result II: The Social and Political Origin of Financing Deci-sions

Having established the first step of my argument that the incidence of foreign financ-ing events is critical to the subsequence firm decisions by the billionaire entrepreneur, inthis section, the attempt is to link the social origin of billionaire entrepreneurs to theseexposures, using specification 3.

De,c,t = µc + ωt + κ college degree + θ Political Unconnectedness Indexe

+ β Foreign Exposure Indexe × Political Unconnectedness Indexe + εc (3)

To capture the essence of political connectedness, I construct three dummies to eval-uate separate dimensions. A dummy family unconnect measures the inherited social andpolitical connection, gathered from publicly available resources through our codebook.

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16

Table 4: The Effect of Social Origin and Foreign Exposure on Probability of Public ListingOutside Mainland China: OLS and Logit Estimation


total unconnectedness 0.027 0.013 -0.023 0.082 0.091 -0.078(0.060) (0.055) (0.047) (0.257) (0.273) (0.333)

college education 0.243 0.099 1.139 0.380(0.131) (0.128) (0.596) (0.790)

total unconnectedness 0.100∗∗∗ 0.940∗∗∗× total foreign (0.008) (0.156)

Method OLS OLS OLS Logit Logit LogitYear Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 156 156 156R2 0.703 0.717 0.799Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). All OLSregressions include analytical weights by the net worth of the billionaire. The variable total unconnectednessaggregates three dummys of political unconnectedness by summation, discussed in the text. We treat theinteraction term as a product of continuous measure × continuous measure.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Similarly, two other dummies, job unconnect and first scope unconnect, are constructed tomeasure the acquired political connections through the perspective of prior occupationbefore the founding of successful enterprise and the initial profit-making model.

To be clear about my terminology regarding political (un)connectedness the definingcriteria is the extent to which the inherited or acquired ties with respect to the communiststate are perceptively strong. Take Liu Yonghao 8 as an example. Before plunging into achicken farming business with his three brothers, Liu worked as a public school teacher;His billionaire brother and early partner briefly at the county government. Unlike WangJianlin (another case study) who served as a manager of a state-owned enterprise beforeits privatization program into his personal hands, Liu’s brothers are peasants of a poorlocality with no real public authority even though they might have taken a public jobbefore their entrepreneurship. It is these considerations that I construct several empiricalmeasures of political (un)connectedness from three factors: family and social origin, joband occupation, and the DNA of business model (via the first scope of gold). Similar tomy treatment of the aggregate foreign exposure index, I aggregate these three measuresof political unconnectedness into one index via summation.

In column (1) and (4), Tabel 4, I fit our specification using OLS and logistic regression,

8He would be part of a comprehensive case for the theory part of the project, Xiao and Shen (2018).

17

respectively. The small but insignificant estimates of the coefficient provide the first proofof our conceptual framework: among the politically unconnected entrepreneurs, there aretwo types - the foreign exposed and those with limited influence. This statement is robustfor controlling the presence of college degree in column (2) and (5). In my most preferredspecification (3) and (6), the existence of two mixed types of entrepreneurs among thepolitically unconnected becomes evidence through the inclusion of an interaction termbetween the total measure of political unconnectedness and that of foreign exposure. Tofurther the robustness checks, Tabel 5 reports the effect of political unconnectedness onthe probability of using offshore vehicles. Column (1) and (4), applying OLS and logisticregression separately, find a small but insignificant effect of political unconnectedness.This result holds, as in column (2) and (5), through inclusion of the college educationdummy for global billionaire entrepreneurs. Finally, by controlling an interaction termbetween foreign exposure and political unconnectedness, the table concludes, as a resultof column (3) and (6), that the extensive utilization of offshore vehicles remains stronglyand positively associated with the politically unconnected grassroots entrepreneurs withsufficient amount of foreign exposure.

These positive and highly significant estimates from both OLS and logit, respectively,reveals one of the essential insights of my empirical paper: merely being unconnected isnot enough for publicly listing the company outside mainland China or using offshorefinancing vehicles. It is the combination of some degrees of foreign exposure and politicalunconnectedness that drives the observed financing decisions of either going public orusing offshore vehicles outside the country. Combining my first and second step of theargument the association between foreign exposure and financing decisions can be tracedback to the politically unconnected cohorts of the entrepreneurs.

To visualize the key results from my paper graphically, Figure 6 is constructed by treat-ing the connectedness measure as a factor rather than a continuous variable. The upperand lower panel of Figure 6 confirms, respectively, that (i) except for those with zero for-eign exposure the higher the political unconnectedness index the billionaire entrepreneursare more likely to apply offshore vehicles; and (ii) condition on political unconnectednessindex the more exposed to foreign elements the more likely are the global billionaire en-trepreneurs to list their companies outside the country.

18

Figure 6: Logistical Regression: The Interactive Effect of Social Origin and Foreign Expo-sure

Figure 7: Predicted Probability of Going Public Outside Mainland China

Notes: The specifications used to generate these two graphics are logistic regressions similiar to those underlying Table5 and Table 4 except for the fact that the former takes index as factor variable rather than continuous variable.Regressions include fixed year effects and founding headquarter effects.

3.3 Main Result III: Beyond Decisions Regarding Public Listing andOffshore Vehicles

In the previous two sections, I have established (i) a strong positive association betweena foreign element and financing decision (e.g., utilization of offshore vehicles and listingoutside mainland China), and (ii) that sourced that association to thee entrepreneurs withhumble social origin with some degrees of foreign exposure. In this section, I consider twodifferent margins of variation, which would have critical interpretative effects on produc-tivity and talent allocation (Acemoglu, 1995): (a) an incidence of state-owned restructuringover the company’s history before IPO, and (b) the entry decision made by these billionaireentrepreneurs - whether or not the industry their companies have been operating belongsto the technology, media, and telecom (TMT) sector.

Table 6 reports the results to a specification similar to equation 3. Column (1) findsa negative association between political unconnectedness index and an incidence of the

19

Table 5: The Effect of Social Origin and Foreign Exposure on Probability of Using OffshoreVehicles: OLS and Logit Estimation

(1) (2) (3) (4) (5) (6)offshore offshore offshore offshore offshore offshore

total unconnectedness 0.034 0.017 -0.018 0.006 -0.029 -0.213(0.067) (0.064) (0.056) (0.299) (0.316) (0.399)

college education 0.302∗∗ 0.161 1.447∗ 0.826(0.106) (0.102) (0.576) (0.813)


Method OLS OLS OLS Logit Logit LogitYear Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 146 146 146R2 0.675 0.698 0.786Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). All OLSregressions include analytical weights by the net worth of the billionaire. The variable total unconnectednessaggregates three dummys of political unconnectedness by summation, discussed in the text. We treat theinteraction term as a product of continuous measure × continuous measure.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

state-own enterprise (SOE) restructuring with a significant level at 10% level. In column(2), this negative association persists after inclusion of a college education dummy. Col-umn (3) conducts a similar decomposition exercise by interacting the politically uncon-nected index with the index of foreign exposure, as in Table 5. The result suggests that,even after absorption of the foreign element, the politically unconnected index is still neg-atively associated with an SOE restructuring event at 10% level. The results from column(1) to (3) are robust to switching specifications from OLS to logistic regressions. Unlike anincidence of SOE restructuring, the sectoral decision made by billionaire entrepreneursshows a strong positive relationship between a college degree and entering into TMT sec-tors, as seen in column (5). Being politically unconnected translate only weakly into apotential entry into innovative sectors, reports column (4). Taking interaction terms withforeign exposure index, the correlation is absorbed at a highly statistically significant level.

In this margin of variation, I look for evidence of the impact of foreign exposure onentering into TMT sectors and associated with a record of SOE restructuring. Taken asa whole, the evidence on global billionaires from mainland China suggests that SOE re-structuring is strongly driven by connections with the communist state, while successes inTMT sectors depend crucially on college education and some degrees of foreign exposure.The mere presence of humble social origins would have to work on either channel.

20

Table 6: The Effect of Social Origin and Foreign Exposure on Probability of State-ownedEnterprise (SOE) Restructuring and Entry into Inovative (TMT) Sectors: OLS and LogitEstimation

(1) (2) (3) (4) (5) (6)SOE SOE SOE TMT Entry TMT Entry TMT Entry

OLS Estimation

total unconnectedness -0.068 -0.068 -0.070 0.061 0.046 0.031(0.041) (0.039) (0.041) (0.047) (0.043) (0.046)

total unconnectedness 0.004 0.043∗∗∗× total foreign (0.008) (0.009)

college education -0.004 -0.010 0.257∗∗ 0.195∗(0.066) (0.070) (0.083) (0.091)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 301 301 301R2 0.857 0.850 0.857 0.776 0.791 0.792

Logit Estimation



college education -0.004 -0.010 0.257∗∗ 0.195∗(0.066) (0.070) (0.083) (0.091)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 65 65 65 151 151 151Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). All OLSregressions include analytical weights by the net worth of the billionaire. The dummy SOE represents anincidence of state-owned restructuring along the firm’s capital accumulation process. The dummy TMTrepresents the technology, media, and telecom (TMT) sector into which a billionaire entrepreneur madeentry.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

3.4 Main Results IV: Locating Mixed Entrepreneurship from ChineseGlobal Billionaire Entrepreneurs

In previous sections, I have linked foreign exposure index of billionaire entrepreneursto their financing decisions, in particular, their choices of offshore financing vehicles andpublic listing decisions. Followed by this association, I have traced the social and politicalorigins of these billionaires. Last but not the least, the politically unconnected index andforeign exposure index have broader implications beyond financing choices.

Having established the relationship between foreign exposure and financing decisions

21

and locating that relationship to the unconnected grassroots entrepreneurs in previoussections, in this section, I am about to deal with one of the hardest questions concerningChinese political economy: who are the bad entrepreneurs, the rent seekers, and the cronycapitalists, if there are any? The existence of bad billionaire entrepreneurs (i.e., the rentseekers) can be profiled along the process of the data construction. The emphasis, how-ever, should not be placed on the mere existence of bad entrepreneurs, but their coexistencewith good types. It is this coexistence of good and bad entrepreneurs that lend supports toBaumol (1990), which will be discussed in the preliminary theory section.

To empirically relate my data to the proposed conceptual framework, I assign a billion-aire entrepreneur as belonging to CB type (shorthand for “connected with bank support”)using a cutoff rule which equals to two or higher in the aggregate measure for politicalconnectedness, out of a maximum of three9. Following this procedure, a billionaire en-trepreneur of UF (shorthand for “unconnected with foreign financing”) type is defined tobe having equal or higher than two in the aggregate measure of the foreign exposures orhaving a foreign capital injection before IPO10. I code the rest as belonging to UI (shorthandfor “unconnected with internal financing”) type. In the appendix Table A.6 I report themean and standard deviations of major variables conditional on the empirically assignedtype of a billionaire entrepreneur.

In this section, I proceed with two steps. First, I trace the ideal types of these billion-aire entrepreneurs with their financing choices. Second, I relate the existence of bad en-trepreneurs with state-owned restructuring the process of which is prone to local capture,authority leakage, and corruption11. Relatedly, I present evidence linking foreign-financedunconnected grassroots entrepreneurs with entries into the innovative sectors. Given thateach billionaire entrepreneur is assigned an empirical type, the following specification isfit:

De,c,t = µc + ωt + σ Ideal Typee + εc (4)

Table 7 reports the results of the effect of each empirical type of entrepreneur on the

9In the language of politically unconnected measure, the threshold value is one. In other words, anybillionaire entrepreneurs having an unconnected measure less than one would be designated into this type.

10The exception has been made to three billionaires, who could speak English and obtained universitydegrees from universities outside mainland China. However, they are better treated as being part of the UIgroup given their financing behavior and decisions through detailed profiling, cases which amount to about1% of our global billionaire space.

11This, however, does not imply that all SOE restructuring events should be treated as corruption. Myviews on this are rather complicated. In my future work, I would discuss potentially two types of state-owned restructuring in my data: an efficiency improving exit scheme arranged by the local governmentsthrough an agreed management buyout (MBO) and a pernicious state-asset expropriation via insiders andlocal business and government elites. The extent to which this happens should be a topic of future researchso that early assessments can be put into a new perspective (Cao et al., 1999; Oi, 2005).

22

Table 7: The Effect of Type of Entrepreneur on Probability of Using Offshore Vehicles andPublic Listing outside Mainland China: OLS Estimation

(1) (2) (3) (4) (5) (6)offshore offshore offshore list foreign list foreign list foreign

OLS Estimation

college education 0.299∗∗ 0.271∗ 0.162 0.241 0.209 0.096(0.105) (0.108) (0.100) (0.129) (0.131) (0.132)

Dummy: type==CB -0.088 -0.066(0.154) (0.149)

Dummy: type==UI -0.364∗∗∗ -0.394∗∗∗(0.107) (0.084)

Dummy: type==UF 0.875∗∗∗ 0.912∗∗∗(0.096) (0.094)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 301 301 301R2 0.699 0.734 0.798 0.717 0.755 0.813Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). All OLSregressions include analytical weights by the net worth of the billionaire. The type of entrepreneurs isassigned according to a rule discussed in the text.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

probability of using offshore vehicles and listing outside mainland China via an OLS es-timation. Controlling for a college education, the unconnected grassroots entrepreneurswith limited foreign exposures (UI type) are significantly less likely to utilize offshore ve-hicles and float their companies outside mainland China. Conceptually, they are financedby bootstrapping on their internal cash flows, represented by the E firms from Song et al.(2011). Compared with those with some degrees of foreign exposure, these indigenousentrepreneurs prefer either to hold their companies privately or publicly float them inShenzhen or Shanghai. Foreign-financed UF type of entrepreneurs, however, are 87.5%

and 91.2% more likely to use offshore vehicles and float outside the country, respectively.The connected entrepreneurs are neither strongly associated with an offshore vehicle, norwith public listing outside. Table 9 in the appendix reports similar results via a logisticalregression.

Beyond financing choices, in Table 8, I find strong evidence supporting an intuitive factthat connected billionaire entrepreneurs are more likely to be associated with a record ofstate-owned enterprise (SOE) restructuring event, while foreign-financed billionaire en-trepreneurs have significantly higher chances of entering into innovative sectors. For apolitically connected type, the probability of associating with an SOE restructuring event

23

Table 8: The Effect of Type of Entrepreneur on Probability of State-owned Enterprise Re-structuring and TMT Sectoral Entry: OLS Estimation

(1) (2) (3) (4) (5) (6)TMT Entry TMT Entry TMT Entry SOE SOE SOE

OLS Estimation

college education 0.248∗∗ 0.260∗∗ 0.187∗ -0.004 -0.029 -0.022(0.089) (0.079) (0.091) (0.064) (0.069) (0.070)

Dummy: type==CB -0.262∗ 0.189∗(0.103) (0.091)

Dummy: type==UI -0.072 -0.127(0.081) (0.065)

Dummy: type==UF 0.482∗∗∗ 0.027(0.070) (0.051)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 301 301 301 301 301 301R2 0.699 0.734 0.798 0.717 0.755 0.813Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). All OLSregressions include analytical weights by the net worth of the billionaire. The type of entrepreneurs isassigned according to a rule discussed in the text.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

increases by 18.9% and the chance of entering into TMT sectors drops by 26.2%, signifi-cant at 5% level. The empirical evidence suggests that these unconnected grassroots en-trepreneurs financed by their internal cash flows are weakly associated with both a state-owned enterprise restructuring event and entry into innovative sectors. All these behav-iors can be compared to that of UF type, those billionaires who have been financed byforeign venture capitalists outside the country, are more likely to enter into innovative sec-tors and less associated with SOE restructuring. Indeed, whether or not the foreign ven-ture capitalists backed and self-made internet billionaire entrepreneurs, Jack Ma, RobinLi, Pony Ma, and others, are drivers of the creative destruction (Schumpeter, 1942) withChinese characteristics should be a topic for future research. Combined with logistic fit inTable 9 from the appendix, these results drive a significant finding of my paper: the con-nected entrepreneurs are more incentivized to leverage their political resources to theirmarket success through a plausibly profitable SOE restructuring event12.

12Have established the root of bad entrepreneurship, I make further observations about the role a state-owned enterprise restructuring event would play among the detailed database. Broadly speaking, there aretwo types among billionaire entrepreneurs with histories of SOE restructuring: (i) an efficient improvingtype: the previous managers or party secretaries of the same SOE, after marked performance improvementover the market reform period, were given the opportunity to negotiate a management buyout (MBO) fromlocal governments; (ii) a questionable type: billionaire entrepreneurs with limited public profile and career

24

Table 9: Locating Mixed Entrepreneurship: Logistic Estimation

(1) (2) (3) (4) (5) (6)offshore offshore offshore list foreign list foreign list foreign

Logistic Estimation

college education 1.420∗ 1.479∗ 0.883 1.133 1.009 0.342(0.582) (0.639) (0.714) (0.592) (0.711) (0.761)

Dummy: type==CB -0.265 -0.288(0.761) (0.701)

Dummy: type==UI -1.659∗∗ -1.754∗∗∗(0.507) (0.477)

Dummy: type==UF 34.612∗∗∗ 37.467∗∗∗(1.127) (3.872)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 146 146 146 156 156 156

(1) (2) (3) (4) (5) (6)TMT Entry TMT Entry TMT Entry SOE SOE SOE

college education 1.336∗∗ 1.426∗∗ 1.297∗ -1.746 -1.960 -1.188(0.482) (0.489) (0.527) (2.557) (2.996) (1.385)

Dummy: type==CB -0.262∗ 0.189∗(0.103) (0.091)

Dummy: type==UI 0.069 -5.529(0.430) (3.910)

Dummy: type==UF 1.443∗ 0.000(0.575) (omitted)

Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 151 151 151 65 65 64Notes: Robust standard errors in parentheses are clustered at the city level for current headquarters. Allregressions include year and first headquarter dummies a la Besley and Reynal-Querol (2011). The type ofentrepreneurs is assigned according to a rule discussed in the text.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

In sum, according to the empirical classification, among 317 global billionaire en-trepreneurs recorded by Forbes, Feb. 2017, after accounting for all the unidentifiedtypes as part of the connected group, the share of CB, UI, and UF type of billionaire en-trepreneurs are 20.25%, 72.78%, and 7.28% respectively.

history became owners of state assets out of nowhere through undisclosed deals with the local government,mostly from Northeast or other inner provinces, part of which most of the 5% (16/317) unidentified typesbelong.

25

3.5 Robustness Checks: CEOs only and An Expanded Dataset

The four steps of my argument presented thus far have established the main findingsof this paper. In this section, I address two potential challenges underlying my robustnesschecks. One worrying concern is that the small sample size driven by using only Forbes(Feb. 2017) only. Another concern relates to a mixture of both founding CEOs and othercofounders/investors. In this section, the goal is to assess the robustness of these findingsusing two alternative strategies: (i) restricting the sample to founding CEOs only and (ii)expanding the original sample to incorporate a more extensive database.

Table 10: The Effect of Foreign Exposure on Probability of Public Listing Outside MainlandChina: OLS and Logit Estimation, CEOs Only


finance foreign 0.778∗∗∗ 5.569∗∗(0.060) (2.014)

foreign management 0.786∗∗∗ 6.040∗∗(0.073) (2.315)

total foreign 0.284∗∗∗ 1.915∗∗∗(0.027) (0.412)

Method OLS OLS OLS Logit Logit LogitYear Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 280 280 280 143 143 143R2 0.786 0.796 0.789Notes: The same specification behind Table 2 is used other than conditioning on founding CEOs. Robuststandard errors in parentheses are clustered at the city level for current headquarters.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Repeating the step I of my argument, Table 10 and Table A.3 report the fit to specifica-tion 2, conditioning on the sample of founding CEOs. Despite a decrease in sample size,the strong association between foreign exposure and public listing as well as offshore de-cision remains strong via both OLS and logistic regressions. Removing cofounders, thesignificant level of having a degree outside mainland China increases from about 10% toless than 5% regarding the use of offshore financing vehicles. These results strengthen theargument that the connection between foreign exposures and financial decisions is trans-lated through the leadership of the company, the principal founders of the firm. Across allthe measures of foreign exposure, the estimated coefficients are significant at 10% level atleast if not stronger, for two outcome variables: utilization of offshore vehicles and publiclisting outside the country.

Table 11 repeats step II of my argument by fitting specification 3, conditioning on

26

Table 11: The Effect of Social Origin and Foreign Exposure on Probability of Public ListingOutside Mainland China and Using Offshore Vehicles: OLS and Logit Estimation, CEOsonly


total unconnectedness 0.033 0.019 -0.014 0.169 0.175 0.033(0.058) (0.054) (0.046) (0.242) (0.262) (0.312)



R2 0.695 0.709 0.792



college education 0.313∗∗ 0.165 1.368∗ 0.672(0.108) (0.111) (0.589) (0.789)



founding CEOs with a similar result: the financing decision by billionaire entrepreneursto list publicly outside mainland China and using offshore vehicles is driven by the in-teraction term between the politically unconnected index and foreign exposure measures.Being unpolitical unconnected without sufficient degree of exposure will not result in theobserved pattern of going public outside the country. This result is further confirmed byfitting the same specification to outcome variable of offshore vehicles. Beyond the twofinancing decisions, Table 12 verifies the robustness of step III of the argument by remov-ing the cofounders and investors. After absorbing the effect from foreign exposures incolumn (3), one standard deviation increase in the unconnectedness measure decreasesthe chance of having a record of SOE restructuring by about 3%, significant at about the10% level. Without the interaction term, neither the presence of a college degree nor be-ing politically unconnected would adversely affect the chance of an SOE restructuring at

27

a level beyond 10% statistical significance. However, column (1) indicates the relationshipbetween political unconnectedness and an incidence of SOE restructuring is negative. Onthe innovation side, the interaction term captures most of the variability between foreignexposure and entry into the innovative sectors, mediated by the index of political uncon-nectedness, as seen in column (6). Without this interaction term, the decision to make anentry into a TMT sector is strongly associated with the presence of a college degree.

Table 12: The Effect of Social Origin and Foreign Exposure on Probability of SOE Restruc-turing and TMT Entry: OLS Estimation, CEOs only



college education -0.001 -0.007 0.259∗∗ 0.193(0.070) (0.075) (0.097) (0.109)


Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 280 280 280 280 280 280R2 0.857 0.857 0.858 0.757 0.771 0.785Notes: The same specification behind Table 4 is used other than conditioning on founding CEOs. Robuststandard errors in parentheses are clustered at the city level for current headquarters.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Another worry concerns the sample selection of global billionaire entrepreneurs,which are determined by Forbes, Feb. 2017 versions only. To address this concern andfurther the robustness checks, I enlarge my Forbes sample by incorporating the top 300billionaires from Hurun China Rich List, 2016 version. Hurun China Rich List is compiledby Mr. Rupert Hoogewerft, a British businessman, and publisher based in China. Hurun(Chinese character: 胡润) represents his Chinese name. Mr. Rupert Hoogewerft currentlyis the publisher of the Hurun Report, best known for its “China Rich List”, a ranking of therich people in China13. Expanding the list of global billionaires to the top 300 from Hurunmy identified sample size increases from 301 to 376 entrepreneurs by about 25%. The gainin sample sizes should be cautiously balanced by the potential increase in measurement

13Alumni from Eton College and a graduate of Chinese and Japanese from Durham University, the UnitedKingdom in 1993, Mr. Rupert Hoogewerft worked for seven years at Arthur Andersen, an accounting firm,before founding the China Rich List in 1999. Fluent in Mandarin Chinese with a diploma at the RenminUniversity of China, he is frequently featured in media and news outlets for interviews on China’s emergingrich class. The website of Hurun (English version) is available at http://www.hurun.net/EN/Home/Index.Along with China Rich List, Mr. Rupert Hoogewerft publishes regularly Hurun Philanthropy/Charity Listand Hurun Art List.

28

http://www.hurun.net/EN/Home/Index

errors (e.g., the net worth used to weight billionaire in my OLS estimates).Table A.1 reports the results of step I of my argument on an expanded dataset. Adding

new billionaires into the list does not change the strong associations between my mea-sures of foreign exposure and two major financing decisions underlying billionaire en-trepreneurs. The magnitudes of the variation are no different than the original as well asthe CEOs sample.

To check the robustness of step II of my argument on an expanded dataset, TableA.2 repeats the specification 3, which delivers similar results both in sign and magni-tude. In short, the interaction term between political unconnectedness measure and to-tal measure of foreign exposure captures most of the variations linking the two financingdecisions to these billionaire entrepreneurs. Across both OLS and logistic regressions,the central story remains consistent and robust. Table A.4 reconsiders the relationshipbetween SOE restructuring and TMT entry and the characteristics of the billionaire en-trepreneurs. The general message remains similar to the original table, other than one im-portant thing which strengthens the previous argument. The incidence of an SOE event isdriven strongly by the political unconnectedness measure, even after absorption throughinteraction term with foreign exposures. This finding lends further supports to the ideathat billionaire entrepreneurs with low political unconnectedness index are the likely rentseekers.

4 Theoretical Preliminaries

Extensive theoretical work has been done on the linkages between financial develop-ment and economic growth (see Levine (1997) for a survey). In this section, I briefly out-line a few channels which I believe to be critical in explaining the emergence of billionaireentrepreneurs from mainland China.

The contractual behavior between venture capitalists outside China and indigenousentrepreneurs inside the country might be one of the future theoretical avenues from workin the U.S market (Jovanovic and Szentes, 2013). In addition to the standard considerationsof optimal partnership with VCs through specification of time and money, the contextwith Chinese characteristics invariably forces additional attention on financial exit andinstitutional risks, taking into account the weak property rights environments in China(Bai et al., 2006; Besley and Ghatak, 2010).

In one of the following-up papers (Xiao and Shen, 2018), we propose a political econ-omy theory to formalize Baumol (1990), augmenting the liquidity premium asset pric-ing theory (Holmstrom and Tirole, 2001) to the political economy of development context

29

within China. The goal of our work is to rationalize the behaviors of foreign venture capi-talists backed billionaire entrepreneurs, who are politically unconnected. From there, weattempt to contribute, at a theoretical level, to the literature of political economy of finance(Rajan and Zingales, 2003; Haber et al., 2008).

5 Conclusion

The number of global billionaire entrepreneurs, documented by Forbes, witnesses anoticeable increase from mainland China. In this paper, I have developed a new metric toinvestigate the relationship linking the social origins and foreign exposures of these globalbillionaires and the observed financial choices. In doing so, this is the first paper tracingobserved financial structures to human factors in an empirical assessment of billionaireentrepreneurs from mainland China. The evidence reported in this paper pinpoints theexistence of mixed entrepreneurship within mainland China.

Many previous papers are skeptical of the capacity of a regime run by CommunistParty of China to commit fully to the creative destruction processes unleashed by polit-ically unconnected entrepreneurs. I show that these concerns can be partially circum-vented by the foreign-financed and politically unconnected entrepreneurs using offshorevehicles. The codebook and database used by this paper may bear three different areasof future research. First, this paper suggests the need to understand the response of en-trepreneurs’ financial decisions despite living in a financially underdeveloped environ-ment. How that response drives the channels between finance and growth is an interestingtopic to explore.

Second, in the context of the literature on globalization, this paper provides evidencethat financial market imperfections might be partially offset by ingenious choices madeby entrepreneurs given a country’s policy framework. Future research might explore thehow linkages between financially developed and underdeveloped countries could openup new space for global innovation.

Finally, in the context of the political economy of development, my paper suggests apotential role for a developmental state (Woo-Cumings, 1999) in shaping the course offinancial development. To what extent these state-led policies and institutions are suc-cessful in determining and promoting benign entrepreneurial responses are interestingtopics for future research.

30

A Appendix Tables:

Table A.1: The Effect of Foreign Exposure on Probability of Public Listing Outside Main-land China and Using Offshore Vehicles: OLS and Logit Estimation, An Expanded Dataset


Exposure Index 0.711∗∗∗ 0.748∗∗∗ 0.255∗∗∗ 3.902∗∗ 6.589∗∗ 1.492∗∗∗(0.093) (0.068) (0.043) (1.199) (2.438) (0.422)

Observations 375 375 375 215 215 215R2 0.722 0.750 0.727


Exposure Index 0.733∗∗∗ 0.766∗∗∗ 0.256∗∗∗ 3.551∗∗∗ 5.811∗∗∗ 1.507∗∗∗(0.074) (0.069) (0.041) (0.985) (1.665) (0.431)

Measure financing management total financing management totalMethod OLS OLS OLS Logit Logit LogitYear Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 375 375 375 224 224 224R2 0.733 0.765 0.734Notes: The same specification behind Table 2 is used other than conditioning on founding CEOs. Robuststandard errors in parentheses are clustered at the city level for current headquarters.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

31

Table A.2: The Effect of Social Origin and Foreign Exposure on Probability of Public ListingOutside Mainland China and Using Offshore Vehicles: OLS and Logit Estimation, AnExpanded Dataset


total unconnectedness 0.044 0.042 0.005 0.154 0.189 0.096(0.043) (0.041) (0.038) (0.199) (0.209) (0.232)



R2 0.645 0.653 0.725






32

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33

Table A.4: The Effect of Social Origin and Foreign Exposure on Probability of SOE Restruc-turing and TMT Entry: OLS Estimation, An Expanded Dataset


total unconnectedness -0.076∗ -0.077∗ -0.079∗ 0.060 0.058 0.040(0.035) (0.035) (0.036) (0.042) (0.036) (0.037)

college education 0.020 0.012 0.214∗ 0.154(0.057) (0.061) (0.086) (0.101)


Year Effect Yes Yes Yes Yes Yes YesFirst Headquarter Yes Yes Yes Yes Yes YesObservations 375 375 375 375 375 375R2 0.814 0.814 0.815 0.724 0.737 0.753Notes: The same specification behind Table 4 is used other than conditioning on founding CEOs. Robuststandard errors in parentheses are clustered at the city level for current headquarters.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

34

Table A.5: Variable Descriptions

Measures of Political (un)Connectedness/Social Origins

Family background A dummy variable which equals to one if the billionaire entrepreneurcomes from a family unconnected with communist party elites, and zerootherwise.

Job (un)connectedness A dummy variable which equal to one if the billionaire entrepreneur’sprior occupation has limited linkages with the party-state (e.g., seniormanagers at a state-owned enterprise), zero otherwise.

The first scoop of gold A dummy variable which equal to one if the billionaire entrepreneur’sfirst scoop of gold has limited linkages with the party-state (e.g., seniormanagers at a state-owned enterprise), zero otherwise.

Political unconnectednessindex

An aggregate measure by summation of three measures above.

Measures of Foreign Exposures

English speaking A dummy variable which equal to one if the billionaire entrepreneur iscapable of speaking fluent English, zero otherwise.

Foreign education A dummy variable which equal to one if the billionaire entrepreneurhas college degrees from universities outside mainland China, zero oth-erwise.

Foreign financing A dummy variable which equal to one if the company has been backedup by venture capitalists outside mainland China before its IPO, zerootherwise.

Foreign management A dummy variable which equal to one if the company (as of 2017) hasnon-Chinese names on the senior management or the board of directors,zero otherwise. Source: Orbis.

Foreign exposure index An aggregate measure by summation of four measures above.

Main Outcome Variables

Public Listing A dummy variable which equal to one if one of the companies controlledby the billionaire entrepreneur is a public company, zero otherwise.

Foreign listing A dummy variable which equal to one if one of the listed company con-trolled by the billionaire entrepreneur is floated outside mainland China(e.g., New York Stock Exchange), zero otherwise.

SOE restructuring A dummy variable which equal to one if the company has a record ofstate-owned enterprise restructuring before its IPO, zero otherwise.

Offshore vehicle A dummy variable which equal to one if the company (as of 2017) usesan offshore vehicle, zero otherwise. Source: Orbis.

TMT sector entry A dummy variable which equal to one if party organ is found within thecompany, zero otherwise.

Control and Other Variables

College education A dummy variable which equal to one if the entrepreneur has a collegedegree, zero otherwise.

Elite college A dummy variable which equal to one if the entrepreneur has an elitecollege degree, zero otherwise. (Rule: Key universities using 211 or 985projects.)

CEO A dummy variable which equal to one if the entrepreneur is the found-ing CEO, zero otherwise.

Founding year This comes from the company’s website for each of the billionaire.Founding headquarter The unit: city level. The company’s website.Current headquarter The unit: city level. The company’s website.Source: Author’s codebook and database unless stated otherwise. Most of the data collection workis done by gathering public information and building a detailed profile.

35

Table A.6: Means and Deviations of Major Variables for Global Billionaires in aCommunist State: the World’s Billionaires, Forbes, 2017

I. Types of Entrepreneurs II. Pooled Sample

UI CB UF All

(1) (2) (3) (4)

A measure of political connection: 0.004 0.333 0.043 0.060family background dummy (0.066) (0.476) (0.208) (0.238)

A measure of political connection: 0.183 0.896 0 0.282job connection dummy (0.387) (0.309) (0) (0.451)

A measure of political connection: 0.030 0.938 0 0.173first scoop of gold dummy (0.172) (0.245) (0) (0.379)

An aggregate measure: 0.213 2.188 0.043 0.515political connection, range: [0, 3] (0.410) (0.394) (0.209) (0.831)

A measure of foreign element: 0.030 0 0.348 0.050English speaking dummy (0.172) (0) (0.487) (0.218)

A measure of foreign element: 0.023 0 0.130 0.023foreign education dummy (0.131) (0) (0.344) (0.151)

A measure of foreign element: 0.004 0.041 0.957 0.083foreign financing dummy (0.066) (0.202) (0.209) (0.276)

A measure of foreign element: 0.022 0.062 0.783 0.086foreign management dummy (0.146) (0.244) (0.422) (0.281)

An aggregate measure: 0.074 0.104 2.217 0.242foreign element, range: [0, 4] (0.308) (0.371) (0.850) (0.686)

A measure of education: 0.500 0.500 0.870 0.528college education dummy (0.501) (0.505) (0.344) (0.500)

A measure of education: 0.261 0.208 0.522 0.272elite college dummy (0.440) (0.410) (0.511) (0.446)

A measure of financing decisions: 0.817 0.708 0.739 0.794listing dummy (0.387) (0.459) (0.449) (0.405)

A measure of financing decisions: 0.213 0.229 0.696 0.252foreign listing dummy (0.410) (0.425) (0.470) (0.435)

A measure of financing decisions: 0.104 0.500 0 0.159SOE restructuring dummy (0.306) (0.505) (0) (0.366)

A measure of financing decisions: 0.209 0.188 0.870 0.255offshore vehicle dummy (0.407) (0.394) (0.344) (0.437)

A measure of innovation industry: 0.335 0.271 0.826 0.362TMT sector dummy (0.473) (0.449) (0.388) (0.481)

N: 230 48 23 301Notes: Statistics in column 1 to 3 are based on the type of the billionaire entrepreneur, consistentwith our conceptual framework outlined in the paper. For the total number of 319 global billion-aires on the the World’s Billionaire List, Forbes, 2017 from mainland China, the missing rate isaround 5 percent (18/319) for the fact that we cannot identify the type of the entrepreneur as aresult of the lack of sufficient information from public sources.

36

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an empirical assessment of chinese global billionaire ... · billionaire entrepreneurs (aghion and...

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