DBJ Discussion Paper Series, No.1002

Bureaucrats as Managers – Evidence from China

Joseph P.H. Fan(Faculty of Business Administration, The Chinese University of Hong Kong)
Jun Huang(School of Accountancy, Shanghai University of Finance & Economics)
Felix Oberholzer-Gee(Harvard Business School)andMengxin Zhao
(School of Business, University of Alberta)

September

2010

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors inorder to protect the tentative characters of these papers. Any opinions, findings,
conclusions or recommendations expressed in these papers are those of the authors and do
not reflect the views of the Institute.

Bureaucrats as Managers – Evidence from China
Joseph P.H. Fan
Faculty of Business Administration, The Chinese University of Hong Kongpjfan@cuhk.edu.hk
Jun Huang
School of Accountancy, Shanghai University of Finance & Economics
sufehuang@gmail.com
Felix Oberholzer-Gee
Harvard Business School
foberholzer@hbs.edu
Mengxin Zhao
School of Business, University of Alberta
mengxin.zhao@ualberta.ca

Abstract
Former civil servants lead a large number of companies in both developed and emerging
economies. In this paper, we ask whether these managers make effective chief
executives. To study this question, we examine strategic choices and the financial
performance of publicly listed companies in China during the 2001 to 2005 period. Our
results indicate that the bureaucratic past of CEOs has a profound influence on company
strategy and financial returns. Former civil servants are more likely than other corporate
leaders to enter new industries, and those who manage state-owned enterprises (SOEs)
diversify their firms in a manner that is largely inconsistent with the recommendations ofscholars of corporate strategy. As a result, the performance effects of diversificationdiffer sharply across SOEs and private firms. More generally, we find little evidence thatshareholders benefit from appointing former bureaucrats to the position of CEO despite
their extensive knowledge of government processes in an economy where the influenceof the state is all pervasive. Our findings add to the recent literature on the importance ofmanagerial style and CEO characteristics. In our study CEOs matter, but we also showthat private-sector incentives can largely mute the influence of personality. Specifically,
former bureaucrats who manage private companies show few of the weaknesses that arecharacteristic of their peers at state-owned enterprises. In the context of private firms
operating in China, private-sector incentives trump managerial style.
JEL Classifications: D23; G32; G38; K42; P26; P31
Key Words: Bureaucrat Managers, Corporate Diversification; Institutions; China

11.

Introduction
In many economies, former civil servants play an important role in the private

sector. In France, for example, former bureaucrats run more than 10% of all publicly
traded companies, and these companies represent almost 70% of the assets listed on the
French stock market (Bertrand et al., 2005). “Revolving doors” link the private andpublic sectors in the United States where many former government officials takeexecutive positions (Eckert, 1981; Salant, 1995). Former civil servants and elected
officials are perhaps even more prominent among the captains of industry in emerging
economies. Well-known examples include Taiwan’s Chungwa Telecom Company (withCEO Chen-Tan Ho, a former vice minister), the Korea Electric Company (with CEO
Wan-Gul Lee, a former deputy minister of energy), and Brazil’s Companhia Ener deMinas Ger (with CEO Djalma Bastos de Morais, a former minister of communications).
In China, executives with experience in the state bureaucracy make up close to 20% of
chief executive officers of publicly listed firms, and they manage about a fifth of theassets listed on the country’s exchanges.
Do former civil servants make effective CEOs? Do they favor particular strategic
choices? And how do these choices impact the financial performance of companies?
Despite the important role of former bureaucrats in private enterprise, we have little
evidence on these questions. We seek to fill this gap in the literature using the Chinese
economy as our example. China offers ample opportunity to document the managerial
success of former civil servants. In our sample, which comprises almost all publicly
traded non-financial companies during the 2001 to 2005 period, 1.6% of CEOs held acentral government position and 17% have experience working in the local bureaucracy.
Besides the ubiquity of former bureaucrats who serve as chief executives, China
also makes an interesting case study because the government owns a majority stake innumerous publicly traded companies. This allows us to investigate how the institutional
environment – public as opposed to private ownership – influences strategic decisions
and financial performance. We use companies’ diversification policies as our example
for an important decision that illustrates how managerial style and incentives shapestrategy.

2Our results indicate that the bureaucratic past of CEOs has a marked influence on
strategic decision-making and financial returns. We observe four broad patterns in ourdata. First, CEOs who served as bureaucrats are more likely than other executives toenter new industries. Second, former civil servants at the head of state-owned enterprises(SOEs) choose a mix of business activities that is largely at odds with therecommendations of scholars studying corporate strategy (Montgomery, 1994).

In

particular, these executives enter industries that are less profitable, have lower rates ofgrowth, and are less related to the firm’s core business. Third, given these regularities, itis perhaps not surprising that CEOs without government experience managing private
firms make the most successful leaders in our sample. Fourth, we find that private-sector
incentives can be even more important than executives’ career paths.

Specifically,

former civil servants in charge of private companies are statistically indistinguishable
from executives without a bureaucratic past. In our sample, it is the strategic choices
characteristic of private companies that tend to benefit shareholders, irrespective ofwhether these companies are run by former civil servants or by executives who lackgovernment experience.
Our study adds to two distinct bodies of research.

The literature on theimportance of CEO characteristics for company performance dates back at least to
Barnard (1938). But in the past two decades, many studies in strategic management andindustrial organization focused on firms’ competitive environment to explain thevariation in financial performance across companies, neglecting the influence of manager
personalities and human capital (Montgomery, 2008). More recently, there has been arenewed interest in the persona of chief executives and managerial styles, at times at theexpense of a careful accounting of managerial incentives and competition. 1 As our
results indicate, it is important to marry these two approaches to studying organizations:personality and incentives matter.

For example, without considering the difference

between private companies and SOEs, we find that a firm’s return on assets declines bymore than 50 percent if it chooses a former bureaucrat as CEO. However, this result is1

Recent contributions to this quickly growing literature include Baranchuk, MacDonald and Yang (2007),
Bebchuk, Cremers, and Peyer (2007), Bertrand and Schoar (2003), Bolton, Brunnermeier and Veldkamp(2008), Hambrick, Cho and Chen (1996), Kaplan, Klebanov and Sorensen (2007), Malmendier and Tate(2005), Pérez-González (2006), and Van den Steen (2007).

3

misleading because it is entirely driven by SOEs appointing former civil servants. The
board of a private firm should expect a former bureaucrat to perform no worse than aprivate-sector candidate.
Our paper also complements the literature on the value of government expertiseand political relationships.

Former civil servants have detailed knowledge of thegovernment’s inner workings, knowledge that might be particularly valuable in an
economy like the Chinese where state influence is all pervasive and government
regulation is often ambiguous. Former bureaucrats also had an opportunity to personally
get to know other officials and regulators and these relationships might influence theirstrategic decisions. As our examples in the first paragraph illustrate, many former civil
servants lead regulated businesses in the telecom and energy industries, possibly because
they better understand the regulatory processes and know the regulators. While we havesubstantial evidence that close political ties can increase the overall financial
performance of companies (Fisman, 2001; Johnson and Mitton, 2003; Faccio, 2006), we
know much less about the mechanisms that lead to changes in profitability. In particular,
there are only a handful of papers that look at the competitive strategies of firms led bygovernment insiders. 2 We believe our paper is the first to study the diversification
policies of executives with substantial government expertise. As in our performance
results, we find that CEO characteristics as well as private-sector incentives matter forfirms’ choice of scope and profitability.

Former civil servants heading SOEs

systematically enter structurally unattractive industries. As a result, the performance
effects of diversification differ sharply across SOEs and private firms.
The paper proceeds as follows. We develop our hypotheses and describe our data
in section 2. Section 3 reports the empirical results. We offer concluding remarks in
section 4.

2

Political relationships appear to influence the timing of job creation (Bertrand et al., 2005), the decision tocross-list shares (Leuz and Oberholzer-Gee, 2006), and the vertical scope of the firm (Fan, Huang, Morck,and Yeung, 2008).

4

2.

CEOs and Corporate Performance
An extensive literature in management, finance, and economics documents thatboth industry characteristics and firm-internal resources influence companies’ financial
performance. The quality and managerial acumen of the firm’s top executive team isconsidered to be a particularly important resource (Hambrick and Mason, 1984;
Waldman, Ramirez, House and Puranam, 2001; Wasserman, Nohria and Anand, 2002).
Top managers matter both because they help set the company’s strategic direction andbecause more competent managers are better at executing a given strategy (Terviö, 2008).
Influenced by their education and career path, many managers exhibit a personal style ofmanagement.

For example, Bertrand and Schoar (2003) document that younger

executives and executives with MBA degrees tend to make a larger number ofdiversifying acquisitions.
In our models, we allow the type of manager (former civil servants and executives
without government experience) as well as executives’ strategic decisions (the company’s
diversification policy) to impact financial performance. There are several reasons why
one might expect former bureaucrats to exhibit a distinct managerial style. First, they area highly selected group with an unusual educational background, skill set, and work
experience. For example, as in many European countries, individuals who join China’s
civil administration tend to be highly educated (Nanjing Daily, 2005; Xiangfan News,
2008).

In addition, former bureaucrats have a deep understanding of government

processes, and many maintain close ties to their colleagues in government even after theirdeparture from the civil service (Wu, Wu, and Liu, 2008). In the Chinese context, close
ties to the bureaucracy are a mixed blessing. On the one hand, well-connected companies
benefit from preferential access to capital markets and reduced red tape (Li, Meng andZhang, 2006; Bai, Lu and Tao, 2006; Fan, Rui and Zhao, 2008).3 On the other hand,
there is also anecdotal evidence to suggest that government officials encourage connectedcompanies to pursue strategies that may not be in the best interest of shareholders. Someconnected companies are asked to absorb excess labor and help jump-start local
3

One of many examples is the publicly listed Create Technology & Science Co. Ltd, located in Suzhou.
Thanks to its excellent local connections, the firm receives preferential loans directly from the City Finance
Bureau.

5

industries (Lin, Cai and Li, 1998; Young, 2000).4 Because well-connected companies
face more attractive business opportunities and greater social obligations, the net impact
of bureaucratic ties on financial performance is not obvious. A prominent example is
Baosteel, China’s premier producer of steel. The company benefits from access to state
assets, subsidized research funding, state investments and protective tariffs. Being anational champion comes at a cost, however. In 1998 the government asked Baosteel tomerge with four loss-making companies, taking on 80,000 redundant workers. Baosteel
was not allowed to lay off more than 10,000 workers a year (Sirkin et al., 2008). By
studying the performance consequences of appointing a former bureaucrat to the positionof CEO, we hope to shed light on the net impact of bureaucratic ties on financial
performance.
Studying the managerial decisions of former civil servants in China is alsointeresting because the Chinese government maintains a controlling stake in a majority oflisted companies (Sun and Tong, 2003).5 Because we observe both types of companies –state-owned firms with former civil servants at the helm and SOEs with chief executives
that lack government experience – we can study, separately, how private-sector
incentives and CEO characteristics influence strategic decision-making and financial
performance.
Our example for a major strategic decision is companies’ diversification policy.
Diversification recommends itself because there is substantial interest in the relation
between diversification and corporate performance (Lang and Stulz, 1994; Stein, 1997;
Scharfstein, 1998; Rajan, Servaes, and Zingales, 2000; Maksimovic and Phillips, 2003;
specifically for China, see Lin and Su, 2008). Early studies on the performance effects ofdiversification tended to find that a broad horizontal scope was detrimental to firm value(Comment and Jarrell, 1995; Berger and Ofek, 1995; Servaes, 1996; Lins and Servaes,
1999). More recent papers that control for firm and industry heterogeneity often fail tofind significant effects (Hyland, 1999; Maksimovic and Phillips, 2002; Campa and Kedia,

4

For instance, to clean up the Chuanzi river, the city of Changde (Hunan) asked the publicly listed Hunan
Jinjian Cereals Industry Co., a producer of grains and oils, to enter the sewage control business.
5
There is a large literature on SOEs that documents how government ownership influences the resource
allocation and financial performance of these firms (for a survey, see Megginson and Netter, 2001).

6

2002; Graham, Lemmon, and Wolf, 2002; Villalonga, 2004a) or even support for the ideathat closely related diversification enhances financial returns (Villalonga, 2004b).

2.1. Empirical Specification and Hypotheses
We begin by analyzing whether former civil servants pursue distinct corporate
policies:(1)

Diversificationit = α1FCSit + α2Xit + α3Iit + λt + ηi + ε1it
In this model, the firm’s diversification policy is a function of the CEO’s career

path – the indicator FCSit equals one if the CEO is a former civil servant – a vector offirm characteristics Xit, and a vector of institutional variables Iit. Our models include atime trend λt, which we implement as year fixed effects. The firm fixed effects ηi control
for time-invariant unobserved heterogeneity among the sample companies. Includingthese fixed effects is important because companies’ diversification opportunities varyacross industries, locations and firm capabilities in ways that we cannot directly observe.
In model (1), α1 is identified from turnover in CEOs. In our sample, we observe 184
instances in which the FCS status of the new CEO differs from the status of his
predecessor.
We expect α1 to be positive because former civil servants can rely on theirexpertise in government affairs and personal ties to obtain preferential financing andfavorable regulatory treatment. This group of executives is also more likely to diversify
if bureaucrats encourage them to enter industries that are considered important from apublic point of view.
In all our models, we control for institutional variation that might influence
diversification policies.

As the literature on diversification in emerging markets

documents, companies with a wider scope might outperform more narrowly focused
firms because it is difficult to engage in arms-length exchange in markets with weak
institutions (for recent evidence on China, see Du, Lu and Tao, 2008; more generally, see
Khanna and Palepu, 2000; Almeida and Wolfenzon, 2006; Khanna and Yafeh, 2005).
While some of China’s provinces have developed institutions that support markets, others7

lag far behind (Fan and Wang, 2006).

A first control in our models reflects thedevelopment of a province’s financial market.

Better developed markets facilitate

mergers and acquisitions, easing changes in firm scope. We use the fraction of loans
extended to private enterprise as a proxy for financial market development. While better
developed financial markets encourage deals, increased bureaucratic interference and red
tape make it more difficult for companies to enter and exit industries. Our proxy for the
influence of the bureaucracy in a province is the fraction of the workforce that is
employed by the civil administration. An increase in this fraction is associated withgreater government influence.
Although financial markets and bureaucratic interference are likely to be related
to firm diversification, we do not have a strong prior as to the direction of their influence
because the effect of the institutional environment on diversification depends on the typeof adjustment that companies seek to make. To see this, consider a situation in whichfirms experience a shock that increases their optimal scope.

In this case, moresophisticated financial markets will facilitate greater diversification (a3finance>0) whilemore red tape will delay firms’ entry into new industries (a3bureaucracy<0). In contrast, if
firms experience a shock that leaves them too diversified, more red tape will slow down
firms’ exit from unattractive industries (a3bureaucracy>0) while better developed financial
markets can assist in selling assets (a3finance<0). Because the shocks that firms experience
are unobserved, it is difficult to predict the influence of the institutional environment on
our sample companies.
In some specifications, we will replace the variable Diversificationit withcharacteristics of the industries that companies enter. These models allow us to check
whether former civil servants enter industries that differ in systematic ways from theindustries that attract their peers. Specifically, we will test if former bureaucrats enterattractive industries, namely sectors of the economy that grow quickly and exhibit
favorable returns for the average firm.
In a second step, we study the performance consequences of former civil servants
taking on the role of CEO.
(2)

Performanceit = b1FSCit + b2Diversificationit + b2Xit + b3Iit + λt + ηi + ε2it8In this model, former bureaucrats influence performance directly and via theirchoice of diversification. As before, we include year and firm fixed effects as well as
time-varying controls at the level of the firm and the province. If the group of former
civil servants is positively selected or if its government expertise prove valuable to thefirm, we will find b1>0. The effect of diversification on performance is more difficult topredict. If former bureaucrats have access to attractive diversification opportunities thatare foreclosed to less-connected CEOs, the performance effect will be positive. On theother hand, if former civil servants are asked to enter unattractive industries for reasonsof public policy or local pride, the performance of their companies is likely to suffer.

2.2. Econometric Issues
A difficulty for all studies in this stream of research is that managers are notrandomly assigned to companies. As a consequence, our results must be interpreted withcare. We observe the strategic choices and the performance of companies led by former
civil servants, not the causal effect of appointing a former bureaucrat on diversificationand profitability. Formally, equations (1) and (2) form a recursive system in which theeffects of interest are identified if cov(ε1it, ε2it) = 0. This condition might be violated if,
for instance, boards match CEOs and firms using executive characteristics that we cannot
observe.
We pursue several strategies to address this issue. As discussed, we include firm
fixed effects in all our models. If companies in industries with more attractive business
opportunities are more likely to choose former civil servants as CEOs, we would tend tooverestimate the performance effect associated with appointing former bureaucrats. The
fixed effects in our models alleviate this concern.

In addition, the timing of CEO

appointments might be important. If boards select a specific type of executive at a time
when more attractive diversification opportunities become available, the coefficients inthe performance equation will still be biased. We explore the importance of this type ofmatching in three ways. First, we add firm-specific time trends to model (2), asking
whether the presence of a former civil servant shifts the (linear) trend in firm
performance. If the most knowledgeable managers are asked to run the companies facing

9the most complex regulatory environments, the firm-specific time trends help distinguishfirms with bright past and future performance from firms where incoming CEOs make adifference. In a second set of specifications we include lagged measures of profitability
to see whether CEOs proved successful conditional on a firm’s past performance. Finally,
we test whether the performance effects vary with CEO tenure. The matching argument
suggests that the bias in our estimates will be particularly strong right after the
installation of a new top executive because boards choose CEOs for particular tasks. For
instance, they appoint an M&A expert at a time when the firm faces attractive acquisition
opportunities. As it is more difficult for board members to predict future challenges andprospects, the bias introduced by matching should weaken with a CEO’s tenure. We
investigate the importance of matching by estimating CEO effects for the latter part oftheir career at a firm.

2.3. Data
Our initial sample includes all companies that are listed on the Shanghai Stock
Exchange and the Shenzhen Stock Exchange during 2001 to 2005 period. The China
Securities Regulatory Commission requires that publicly traded companies disclose
segment information for all business segments comprising more than 10% ofconsolidated sales, assets, or profits. The available information typically includes anindustry designation, a description of the company’s products and services, as well as
segment sales, costs and profits. We manually collect these data from annual reports
starting in 2001. Data for prior years are available, but the reporting quality is considered
poor.

From this sample, we exclude companies for which segment and industry

information is incomplete. We also omit financial services firms from our study because
their financial statements are not easily comparable to those of other companies. With
these criteria we obtain data for 1,352 firms.
We collect financial data for our sample firms from the China Stock Market andAccounting Research (CSMAR) financial statement database. The annual reports ofChinese companies contain a brief biographical sketch of the CEO, listing previous
positions in industry and government (Fan, Wong, and Zhang, 2007). From these reports,

10

we manually collect information on whether a CEO held a position in government. We
complement these data with provincial-level institutional data that come from various
sources, including the China Information Bank and the China National Bureau of
Statistics. Table 1 provides summary statistics and variable definitions.

3.

Results
Do former bureaucrats pursue more aggressive diversification strategies? We test

our first hypothesis in table 2. Former civil servants do in fact enter a larger number ofindustries. The main effect is moderate in size, representing not quite 10% of a standard
deviation in the number of business segments.

Former bureaucrats’ influence on

diversification policy is not significantly different across state-owned and private firms.
Growing firms and firms operating in provinces with more fully developed financial
markets compete in a larger number of industries. In table 2, we measure diversification
using 3-digit Standard Industry Classification (SIC) codes. We find similar results for the
2-digit and the 4-digit classification.
Former civil servants might use their bureaucratic ties and regulatory expertise toenter attractive industries and leave segments of the economy that provide little
opportunity.

We test this conjecture by looking at the mix of industries in which

diversified firms decide to compete. Our dependent variables are industry profitability(measured as the sales-weighted historical industrial three-year average ROS of all
segments in which the firm operates), growth (an equivalently constructed historical
growth rate) as well as a relatedness measure that is calculated from commodity flow
information in the national economy’s input-output matrix (see table 1 for details). 6
These variables do not vary by firm – the historical growth rate of the residential
construction industry is the same for all construction companies in a particular year – but
our measures vary within firm over time because companies enter and leave segments.
As a result, we can estimate the models in table 3 with firm fixed effects. We think of
our industry profitability and growth measures as broad indications of industry
attractiveness (Porter, 1980). The relatedness measure is of interest because a common
6

Our approach is a modified version of Fan and Lang (2000).

11

finding in the corporate strategy literature is that diversified firms perform better if they
operate in more closely related industries (Montgomery, 1994).
The results in table 3 indicate that, with a former civil servant in the corner office,the mix of industries in which the firm competes deteriorates. For example, in periods
when a former bureaucrat is CEO, sales-weighted industry profitability declines by 15%
of a standard deviation.

However, this decline only occurs in SOEs.

In privatecompanies, appointing a former bureaucrat to the position of CEO has no effect on
profitability and growth. 7 There is an interesting difference between our results forprofitability and growth and our findings for relatedness. Former bureaucrats always
enter less-related industries, irrespective of whether their company is a state-owned
enterprise or a private firm. On the other hand, private companies operate in moreclosely related segments, again irrespective of CEO type. Taken as a whole, the results intable 3 are inconsistent with our conjecture that former bureaucrats use their ties to enterattractive industries.
In table 4, we study the performance consequences of appointing a former civil
servant to the position of CEO. Although the results in table 3 indicate that former
bureaucrats who manage SOEs tend to enter more difficult segments of the economy, thebusiness acumen of these CEOs and their expertise in government affairs might perhaps
help them outperform their private rivals.

We use two accounting measures of

performance – ROS and ROA – and we follow Berger and Ofek (1995) in ourconstruction of a market-based measure of firm value (see table 1). We present three
specifications. The first includes main performance effects for former bureaucrats andfor diversification along with firm and year fixed effects.

The second and third

specifications allow the CEO effect to vary by form of ownership and by diversification
policy. The third specification also includes firm-specific time trends.
As in tables 2 and 3, we find that former civil servants’ influence on performancediffers across public and private companies. Starting with the CEO main effects, ourresults show that accounting performance, but not a firm’s market value, declines
7

The sum of the main effect and the interaction (bureaucrat + bureaucrat × private) is statistically not
different from zero for the growth and profitability variables (Prob > F = 0.92 (profitability), Prob > F =0.69 (growth)), but is significant for the relatedness variable (Prob > F = 0.02 (relatedness)).

12

significantly when a former civil servant assumes the post of CEO in a state-owned
enterprise (models 2 and 5). There is no corresponding effect for private companies(F=0.49 and F=0.96, respectively).

In specifications with firm-specific time trends(models 3 and 6), we find results that are more favorable to former bureaucrats: theiraccounting performance no longer lags the achievements of executives without
government experience. And capital markets even attach a premium to SOEs run byformer bureaucrats (model 9).
The presence of multiple interaction effects in our specifications makes it difficult
to read table 4. We summarize our findings in table 5 which reports Wald tests for the
hypothesis that the sum of main and interaction effects is zero.

There are threeinteresting patterns in our data. Executives without government experience who manage
private firms pursue the most successful diversification policies. For these firms andexecutives, increases in the number of business segments significantly improve
accounting performance and firm value. Second, bureaucrats in charge of SOEs – thegroup that pursued a more challenging diversification strategy – never achieve adiversification-related premium in accounting performance or firm value.

Third, inprivate companies the performance of both types of executives is so similar that wecannot reject a hypothesis that there is no difference between the top two cells in thematrices in table 5. For our diversification-related measures of corporate success, theform of ownership appears to be more important than the career path of the CEO.

3.1. Robustness Tests
We conduct a number of tests to see whether our results are robust to changes in
specification. In table 6, we include a measure of lagged performance to see if the timingof CEO appointments drives our results. The estimates in table 6 are similar to ourearlier findings, although the main performance effects of former bureaucrats leading
SOEs are now less favorable. As before capital markets assign a premium to SOEs thatare run by former civil servants. The diversification results are little affected by the
inclusion of measures of past performance. In unreported specifications, we replaced thelagged variable with other measures of past success, including last year’s performance13

and separate annual measures for past performance. Our results are robust to thesechanges.
In table 7, we drop a CEO’s first year of tenure to get a sense of the importance oftemporal matching of CEO skills to business situations. As in our previous robustness
tests, we find more negative performance effects for former civil servants when they
manage SOEs. As before, financial success does not decline when a former bureaucrat
heads a private company. The results for diversification are summarized in table 8.
Private-sector executives leading private firms continue to be most successful.

Aninteresting change in these results is the improved accounting performance when former
civil servants diversify SOEs. However, capital markets appear to look at these improved
financials with skepticism. Unlike private companies under private leadership, which
achieve a diversification-related premium, the firm value of SOEs does not respond toincreases in firm scope.

3.2. Discussion
Chinese former civil servants are a highly selected group. They are well educated,
understand the government’s inner workings, and they have personal relationships withformer colleagues in an economy where government is all pervasive. Yet, we find little
evidence that shareholders benefit from the appointment of former bureaucrats to theposition of CEO.

This group of executives never outperforms managers without

government experience, and we find consistent evidence that former civil servants
underperform their peers when they are in charge of SOEs.

Because we observe

executives’ diversification policies, our study sheds direct light on one of the sources forthe observed differences in performance. Former bureaucrats choose to compete in morechallenging competitive environments, namely in sectors of the economy with lower
profitability and growth rates.
An interesting question is whether the diversification policies of SOEs are due topush or pull factors. Do bureaucrats push their former colleagues to compete in difficult
environments? Or is it the case that former civil servants see themselves pulled to takeadvantage of opportunities in these segments of the economy? Our results are more14

consistent with the second explanation. If Chinese bureaucrats wanted to push SOEs into
specific sectors of the economy, they could force entry irrespective of a CEO’s career
path, i.e. the bureaucratic or non-bureaucratic past. However, our results apply only toformer civil servants. Managers without government experience do not enter unattractive
industries even if they run a state-owned enterprise.
One possible explanation for the observed pull forces is that former bureaucrats enjoy
a competitive advantage in difficult markets, perhaps because they benefit from better
access to financing and reduced red tape. Our main results in table 5 (columns 1 and 2)
provide no support for this view. However, if we drop the CEO’s first year of tenurefrom our sample, the SOE’s accounting performance, but not its market value, ispositively related to diversification (table 5, column 3). One interpretation of this findingis that it reflects a quid pro quo. Former bureaucrats can do better in difficult markets –their accounting performance improves with greater diversification – but capital markets
discount these gains, perhaps because they expect the former bureaucrats to be forced tomeet social obligations of some sort in the future. Interestingly, this divergence ofaccounting performance and market value is absent in our results for private companies
managed by private managers. For these companies, increases in diversification lead to
improved accounting performance and greater market value.
More broadly, the results presented here speak to two areas of inquiry. First, ourfindings suggest that it is interesting to push beyond the general insight that government
relations can create value. In the Chinese context – and we suspect this might be true inother economies as well – bureaucratic ties influence the strategic choices of companies,
and these choices have consequences for short-run profitability and the sustainability ofcompetitive advantage. Opening up the black box of “political ties” can help us better
understand the trade-offs that executives face when they invest in improved government
relations.
A second insight relates to managerial style and organizational incentives. In thecurrent literature, some studies focus on organizational incentives and the firm’s
competitive environment to understand variation in financial performance.

Othersconcentrate on CEO personality and executive careers. As our results indicate, we can15

gain a fuller understanding by combining these two approaches. Our CEO’s career paths
influence their strategic thinking. But whether these intuitions come to bear largely
depends on companies’ form of ownership. Admittedly, our measure for differences inincentives is crude. What is it about private firms that largely mutes the influence ofCEO type? Differences in executive compensation? CEO career concerns? These areinteresting questions for future research.

4. Conclusion
In this paper, we ask whether former civil servants make effective chief executives.
We study a sample of CEOs leading publicly traded firms in China, an economy in which
the influence of the state is all pervasive and government expertise might be particularly
valuable.

Despite this premise, we find little evidence to recommend former civil

servants for the post of CEO. When they lead SOEs, these executives systematicallyunderperform their peers, in part because their strategic choices differ significantly fromthe choices of executives without government experience. Interestingly, the differences
between the two types of executives largely vanish in private firms. In the context of thisstudy, private-sector incentives appear to be more important for strategic decisionmaking and financial performance than differences in managerial style.

16

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20

Table1 Summary Statistics

CEO was bureaucrat
Private company
Number of segments
Profitability of portfolio

Growth of portfolio

Relatedness of portfolio

Return on sales (ROS)

Definition
CEO served in state bureaucracy at
one point in his career (indicator)
Company is owned by private
investors (indicator)
Number of business segments withdifferent 3-digit SIC codes
Three-year historical industrial mean
ROS of industry segments, weighted
by salesThree-year historical industrial mean
growth rate of industry segments,
weighted by sales
Sales-weighted average of therelatedness coefficients of industry iand j, calculated as the average of thecorrelation coefficient of the two
industries’ input and output flows.
Ratio of net earnings to total sales

Return on assets (ROA)

Ratio of net earnings to total assets
The natural logarithm of the ratio offirm market value to its imputed
value. A firm’s imputed value is thesale-weighted sum of each segment’s
industry median ratio of market value
Excess Valueto total assets. The industry medianratio is based on the narrowest SIC
grouping that includes at least three
single-segment firms.
The natural logarithm of firm total
Firm size
assetsProvincial financial market Ratio of bank loans to the private
development
sector to total bank loans (index)
Provincial
Ratio of workforce employed by civil
bureaucratization
service to total workforce (index)

21

Mean

Std. Dev

Min

Max

0.185

0.388

0

1

0.241

0.428

0

1

2.663

1.614

1

13

-0.1020.432-2.813

1.236

0.440

0.737

-0.32613.910

0.415

0.397

-0.0951.000-0.0570.626-4.919

0.492

0.032

0.093

-0.499

0.195

0.103

0.358

-0.2102.280

21.1100.966

16.88426.978

6.831

3.156

0.000

12.220

5.954

2.481

-11.94

10.44

Table 2 – Determinants diversification

CEO was bureaucrat(1)# Segments
0.178***(2)# Segments
0.182***(3)# Segments
0.162***(0.053)(0.053)(0.062)-0.145*-0.162*(0.083)(0.088)

Private company
Bureaucrat × private company

0.0664(0.11)

Firm size

0.411***

0.411***

0.411***(0.041)(0.041)(0.041)0.0269**0.0268**(0.011)(0.011)-0.0191-0.0189(0.023)(0.023)

Provincial financial market
development
Provincial bureaucratization
Year fixed effects?

Yes

Yes

Yes

Firm fixed effects?

Yes

Yes

Yes

Observations

5724

5724

5724

R-squared
0.86
0.86
0.86
The dependent variable is the number of segments in which the firm is active. Variable definitions are
given in table 1. Standard errors are in parentheses.
***, ** and * denote significance at 1%, 5% and 10% level, respectively.

22

Table 3 – Characteristics of Target Industries

CEO was bureaucrat(1)
Profitability(2)
profitability(3)
profitability(4)growth

(5)
growth

(6)
growth(7)
relatedness(8)
relatedness(9)
relatedness-0.0693**-0.0704**-0.103***-0.113**-0.113**-0.176***-0.0618***-0.0645***-0.0643***(0.030)(0.030)(0.035)(0.049)(0.049)(0.057)(0.016)(0.016)(0.019)0.03940.0123-0.00250-0.05470.0903***0.0905***(0.047)(0.050)(0.077)(0.081)(0.026)(0.027)

Private company

Bureaucrat ×
private company
Firm size

0.108*

0.209**-0.000917(0.061)(0.10)(0.033)0.0475**0.0483**0.0472**0.172***

0.171***

0.169***-0.0766***-0.0773***-0.0772***(0.023)(0.023)(0.023)(0.038)(0.0378)(0.0378)(0.013)(0.013)(0.013)-0.00484-0.00492-0.00491-0.00508-0.00470-0.00470(0.0060)(0.0060)(0.0099)(0.0099)(0.0033)(0.0033)

0.01380.0141-0.00838-0.00774

0.00479

0.00479(0.013)(0.013)(0.022)(0.022)(0.0072)(0.0072)

Provincial financial
market development
Provincial
bureaucratization
Year fixed effects?

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Firm fixed effects?

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

5661

5661

5661

5661

5661

5661

5724

5724

5724

R-squared

0.39

0.39

0.39

0.44

0.44

0.44

0.78

0.78

0.78

Variable definitions are given in table 1. Standard errors are in parentheses.
***, ** and * denote significance at 1%, 5% and 10% level, respectively

23

Table 4 – Corporate Performance

CEO was bureaucrat
Private company
Number of segments(1)
ROS-0.0664*(0.039)-0.136**(0.061)0.0396***(0.011)

Bureaucrat ×
private company
Bureaucrat × segments
Private × segments
Bureaucrat ×
private × segments
Firm size
Provincial financial
market development
Provincial
Bureaucratization
Year fixed effects?
Firm fixed effects?
Firm × Year FE?
Observations
R-squared

0.410***(0.031)0.00434(0.0078)0.0252(0.017)
Yes
Yes
No
5724
0.497

(2)
ROS
-0.153**(0.078)-0.437***(0.091)0.00678(0.013)0.0679(0.14)
0.0252(0.022)0.109***(0.024)-0.00520(0.042)0.397***(0.031)0.00493(0.0078)
0.0235(0.017)
Yes
Yes
No
5724
0.500

(3)
ROS
-0.0794(0.109)-0.402***(0.131)0.0173(0.0193)
0.256(0.190)0.0176(0.0301)
0.131***(0.0346)-0.0464(0.0587)0.701***(0.0555)-0.0163(0.0133)
0.0131(0.0228)
Yes
Yes
Yes
5724
0.713

(4)
ROA
-0.0171***(0.0056)-0.00400(0.0089)0.00149(0.0016)0.0627***(0.0044)0.00110(0.0011)-0.000206(0.0025)
Yes
Yes
No
5689
0.518

Variable definitions are given in table 1. Standard errors are in parentheses.
***, ** and * denote significance at 1%, 5% and 10% level, respectively

24

(5)
ROA
-0.0351***(0.011)-0.0426***(0.013)-0.00234(0.0019)0.0342(0.021)0.00410(0.0032)
0.0125***(0.0035)-0.00462(0.0061)
0.0612***(0.0044)0.00115(0.0011)-0.000352(0.0025)
Yes
Yes
No
5689
0.520

(6)
(7)
(8)
(9)
ROA
Excess Value Excess Value Excess Value-0.02400.01100.0342
0.0948**(0.0154)(0.017)(0.035)(0.0451)-0.0342*-0.0428-0.0862**0.0542(0.0186)(0.027)(0.040)(0.0531)-0.00206
0.0211***0.0166***0.0114(0.00273)(0.0050)(0.0060)(0.00791)
0.00947-0.0110-0.0501(0.0270)(0.064)(0.0772)
0.00451-0.000695-0.0155(0.00427)(0.0098)(0.0124)0.0141***
0.0229**0.00497(0.00491)(0.011)(0.0141)-0.00619-0.0219-0.0181(0.00833)(0.019)(0.0237)0.101***-0.0995*** -0.0999*** -0.203***(0.00787)(0.014)(0.014)(0.0228)0.00113-0.0154*** -0.0154***0.00418(0.00189)(0.0035)(0.0035)(0.00547)-0.00582*
0.0142*0.0137*0.00423(0.00323)(0.0077)(0.0077)(0.00939)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
5689
5568
5568
5568
0.737
0.706
0.706
0.861

Table 5 – Summary of Diversification-Related Performance Effects
Return on Sales
Firm and Year FE

Including Firm × Year FE

Bureaucrat

Private

yes

no

Yes

0.135***

0.115***

No

0.032

0.007

Bureaucrat

Private

yes

no

yes

0.119**

0.148***

no

0.035

0.017

Return on Assets
Firm and Year FE

Including Firm × Year FE

Bureaucrat

Private

yes

no

Yes

0.010**

0.010***

No

0.002

-0.002

Bureaucrat

Private

yes

no

yes

0.010

0.012***

no

0.002

-0.002

Excess Value
Firm and Year FE

Including Firm × Year FE

Bureaucrat
yesPrivateBureaucrat

no

Yes

0.017

0.039**

No

0.016

0.017

Private

yes

no

yes

-0.0170.016

no-0.0040.011

For each cell, we report Wald tests for the hypothesis that the sum of the main and interaction effects is
not different from zero.
***, ** and * denote significance at 1%, 5% and 10% level, respectively.

25

Table 6 – Corporate Performance, Controlling for Past Performance
CEO was bureaucrat
Private company
Number of segments(1)
ROS-0.0704(0.043)-0.155**(0.068)0.0489***(0.013)

Bureaucrat ×
private company
Bureaucrat × segments
Private×segments
Bureaucrat ×
private × segments
Lagged average
performance (2 years)
Firm size
Provincial financial
market development
Provincial
bureaucratization
Year fixed effectFirm fixed effectFirm × Year FE?
Observations
R-squared-0.295***(0.030)0.520***(0.035)0.00799(0.0090)0.0269(0.020)
Yes
Yes
No
4966
0.509

(2)
ROS
-0.198**(0.088)-0.530***(0.10)
0.00790(0.015)0.0840(0.16)
0.0354(0.024)0.130***(0.027)-0.000920(0.047)-0.306***(0.030)0.505***(0.035)0.00902(0.0089)0.0246(0.020)
Yes
Yes
No
4966
0.514

(3)
ROS
-0.0633(0.109)-0.395***(0.130)0.0135(0.0191)0.325*(0.189)0.00957(0.0300)
0.113***(0.0340)-0.0485(0.0578)-0.882***(0.0332)
0.819***(0.0551)-0.0184(0.0136)0.0247(0.0234)
Yes
Yes
Yes
4966
0.777

(4)
ROA
-0.0195***(0.0062)-0.00254(0.0098)
0.00189(0.0018)-0.228***(0.028)0.0798***(0.0052)
0.00218*(0.0013)
6.20e-05(0.0028)
Yes
Yes
No
4941
0.524

Variable definitions are given in table 1. Standard errors are in parentheses.
***, ** and * denote significance at 1%, 5% and 10% level, respectively

26

(5)
ROA
-0.0428***(0.013)-0.0530***(0.015)-0.00291(0.0021)0.0469**(0.023)0.00514(0.0035)
0.0158***(0.0039)-0.00709(0.0068)-0.234***(0.028)0.0780***(0.0052)
0.00229*(0.0013)-0.000114(0.0028)
Yes
Yes
No
4941
0.527

(6)
ROA
-0.0162(0.0151)-0.0227(0.0180)-0.00330(0.00264)
0.00548(0.0263)
0.00299(0.00416)
0.0120**(0.00472)-0.00485(0.00803)-0.886***(0.0315)
0.143***(0.00774)
0.00112(0.00189)-0.00506(0.00324)
Yes
Yes
Yes
4941
0.801

(7)
(8)
(9)
Excess Value Excess Value Excess Value
0.004190.02740.112**(0.019)(0.039)(0.0462)-0.0408-0.0863*
0.104*(0.029)(0.045)(0.0541)0.0189***0.0140**0.00593(0.0054)(0.0065)(0.00801)-0.0232-0.0398(0.070)(0.0785)-0.000367-0.0209*(0.011)(0.0126)
0.0236**-0.00314(0.012)(0.0142)-0.0181-0.0210(0.020)(0.0238)
0.0699***0.0703***-0.516***(0.022)(0.022)(0.0290)-0.113***-0.114***-0.172***(0.015)(0.015)(0.0234)-0.0161*** -0.0161***
0.00352(0.0039)(0.0039)(0.00573)
0.0177**0.0174**0.0197**(0.0085)(0.0085)(0.00989)
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
4798
4798
4798
0.701
0.702
0.876

Table 7 – Corporate Performance – First Year of CEO Tenure Omitted

CEO was bureaucrat
Private company
Number of segments(1)
ROS-0.113***(0.043)-0.117(0.072)0.0429***(0.012)

Bureaucrat ×
private company
Bureaucrat × segments
Private × segments
Bureaucrat ×
private × segments
Firm size
Provincial financial
market development
Provincial
bureaucratization
Year fixed effects?
Firm fixed effects?
Firm × Year FE?
Observations
R-squared

0.362***(0.032)0.000523(0.0085)0.0161(0.018)
Yes
Yes
No
4752
0.550

(2)
ROS
-0.234***(0.085)-0.437***(0.11)
0.00932(0.014)0.143(0.17)
0.0415*(0.023)0.113***(0.027)-0.0468(0.049)0.350***(0.032)0.00141(0.0085)0.0147(0.018)
Yes
Yes
No
4752
0.553

(3)
ROS
-0.366***(0.131)-0.480***(0.169)-0.00263(0.0199)
0.0992(0.257)0.0835**(0.0334)
0.127***(0.0391)-0.112(0.0738)
0.715***(0.0556)-0.00721(0.0135)
0.0136(0.0225)
Yes
Yes
Yes
4752
0.822

(4)
ROA
-0.0237***(0.0065)
0.00607(0.011)0.00275(0.0018)

0.0578***(0.0049)0.00127(0.0013)-0.00218(0.0027)
Yes
Yes
No
4717
0.586

Variable definitions are given in table 1. Standard errors are in parentheses.
***, ** and * denote significance at 1%, 5% and 10% level, respectively

27

(5)
ROA
-0.0454***(0.013)-0.0405**(0.016)-0.00161(0.0021)0.0356(0.025)0.00542(0.0035)0.0144***(0.0040)-0.00407(0.0073)
0.0563***(0.0049)0.00140(0.0013)-0.00234(0.0027)
Yes
Yes
No
4717
0.588

(6)
(7)
(8)
(9)
ROA
Excess Value Excess Value Excess Value-0.0561***
0.0137-0.00300
0.0796(0.0206)(0.021)(0.042)(0.0660)-0.0435-0.0732**-0.142***-0.0712(0.0265)(0.034)(0.051)(0.0809)-0.00216
0.0150***0.00750-0.000167(0.00313)(0.0058)(0.0069)(0.00973)
0.01970.07520.0416(0.0405)(0.081)(0.124)0.0116**0.0119-0.00601(0.00525)(0.011)(0.0166)
0.0158**0.0312**0.0306(0.00615)(0.013)(0.0190)-0.0121-0.0513**-0.0706**(0.0116)(0.023)(0.0353)
0.0964*** -0.0646*** -0.0664*** -0.165***(0.00875)(0.016)(0.016)(0.0273)0.00168-0.0127*** -0.0126***
0.0143**(0.00212)(0.0041)(0.0041)(0.00660)-0.00581
0.0150*0.0147*-0.00443(0.00354)(0.0087)(0.0087)(0.0111)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
4717
4600
4600
4600
0.820
0.733
0.734
0.896

Table 8 – Summary of Diversification-Related Performance
Effects – First Year of CEO Tenure Omitted
Return on Sales
Bureaucrat
yesPrivatePrivate

no

yes

0.095

0.124***

no0.081***-0.003Return on Assets
Bureaucrat
yesPrivatePrivate

no

yes

0.013

0.014**

no0.009**-0.002

Excess Value
Bureaucrat

PrivatePrivate

yes

no

yes

-0.0460.030*

no-0.0060.000

For each cell, we report Wald tests for the hypothesis that thesum of the main and interaction effects is not different fromzero.
***, ** and * denote significance at 1%, 5% and 10% level,
respectively.

28