from honeymoon to divorce: institution quality and … · index for each province-year from both...

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FROM HONEYMOON TO DIVORCE: INSTITUTION QUALITY AND

FOREIGN INVESTORS’ OWNERSHIP CONSOLIDATION IN CHINA

Qun Bao*

Yanling Wang**

Hongjun Xie***

Acknowledgement: Bao wishes to acknowledge the financial support from China’s National

Science Fund (71473136), the Huo-Yingdong Research Fund (141083) and Collaborative

Innovation Center for China Economy. Wang would like to thank School of Economics at

Henan University for the hospitality provided during her visit when the research work for this

paper was conducted while she was on sabbatical leave from Carleton University.

*: Nankai University, Department of International Economics. Tianjin, China. PC: 300071. E-

mail: [email protected]

**: The Corresponding Author. School of Economics, Henan University. And the Norman

Paterson School of International Affairs, Carleton University, Ottawa, Canada. E-mail:

[email protected]. Phone: 1(613)520-2600 ext. 2626. Fax: 1(613)520-2889.

***: Nankai University, Institute of International Economics. Tianjin, China. PC: 300071. E-

mail: [email protected].

mailto:[email protected]

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ABSTRACT

In China, joint ventures (JVs) between foreign investors and Chinese local firms were the

most popular form of foreign affiliates before 2001. Overtime, with policy space to operate as

foreign wholly owned (WOs), many foreign investors in JVs chose to consolidate ownership

and turned JVs into their WOs. Here, we examine how institution quality affects foreign

investors’ JV-to-WO ownership consolidation odds. For each province-year, we construct an

institution quality index from the business and judicial quality indicators, and further

compute a relative quality index to highlight provincial variations. Using more than 43,000

JVs operating in China’s 30 provinces over 1998 to 2007, we find that increases in institution

quality decrease the odds of foreign investors to divorce their Chinese local partners. The

odds for foreign investors in JVs to consolidate ownerships are significantly higher if they

operate in provinces with relatively weaker institution quality. The odds of foreign investors’

JV-to-WO decision vary with JVs’ local firms being SOEs and non-SOEs, with foreign

investors’ origins from Hong Kong, Macao and Taiwan (HMT) and other regions (Foreign),

and with foreign investors’ initial equity positions. Our results are not driven by FDI policy

shocks, and are robust to alternative measures of institution quality.

Keywords: Joint Ventures, Institution Quality, Ownership Consolidation

JEL codes: F23，L23

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I. INTRODUCTION

Partnering with local firms to form joint ventures (JVs) is one of the commonly observed

modes of entry of foreign direct investment (FDI) into many developing countries, and China

is a great case in point till 2001.1 In 1979, when the Chinese government enacted its first

piece of law on FDI—the Law of the People’s Republic of China upon Sino-Foreign Joint

Ventures, it was cautionary but also wanted to attract FDI through China’s huge market size

with the aim for facilitating within-firm knowledge spillovers.2 The 1979 Sino-foreign JV

law requires that foreign equity shares are between 25% and 49% so that Chinese local

partners (mainly state-owned enterprises—SOEs—at the time) have the majority equity

shares, but also with a good level of foreign involvement. When the first JV under the 1979

law was established in May 1980 (Beijing Air Catering Co., Ltd), it was a big turning point in

China and created a great momentum to attract FDI across the country.

In the following years, a full set of laws on FDI was enacted. In 1986, China enacted the

Law of the People’s Republic of China upon Sino-Foreign Cooperative Enterprises; in 1988,

Law of the People’s Republic of China upon Foreign Wholly Owned Enterprises; and in

1995, China promulgated the Provisional Regulations upon Guidance for Foreign Investment

Orientations and the Guiding Directory on Industries Open to Foreign Investment (the

Directory). The 1988 law on foreign wholly owned (WOs) sets performance requirements:

WOs should adopt advanced technology and/or equipment, or export a majority of their

products overseas or purchase a certain percentage of their intermediate inputs from local

firms (Long, 2005). With the stringent conditions for establishing and operating WOs, JVs

were the popular form for foreign investors to establish affiliates in China until 2001, when

China became a WTO member and removed these performance requirements. Since 2001,

establishing foreign WOs has been the predominant choice for foreign investors in China

(Table 1). For instance, among all foreign affiliates established cumulative to 2002, JVs

account for 53.25%, but that share decreased to 45.53% cumulative to 2006, and to 37.87%

cumulative to 2015. Among the newly yearly registered foreign affiliates, WOs are the stable

dominant form, accounting for 72.71% in 2006, 76.73% in 2015, and 75.75% in 2017.

(Insert Table 1 here.)

With the policy space post 1988 and especially after 2001 in China, we have observed

that many foreign investors initially bounded in JVs chose to divorce their local partners and

effectively turned these JVs to their WOs. This foreign JV-to-WO ownership consolidation is

widespread across industries (Figure 1) and regions (Figure 2), though with noticeable

variations. The consolidation also increases over time (Table 2), regardless of the ownership

structure of their Chinese local partners, or the origins of foreign investors, or foreign

investors’ initial equity shares in JVs. Inevitably, foreign ownership consolidation closes

some inherent spillover channels within JVs, not conducive to the original objective of

attracting FDI. From the honeymoon fanfare in forming JVs to divorces leading to foreign

1 There is a rich literature regarding FDI’s entry modes. Besides joint ventures, FDI also choose to operate as

wholly owned, either through acquiring existing firms, or by building new facilities as greenfield entries.

Foreign investors’ modes of entry depend on factors including firm size, experiences and legal framework

(Caves, 2007; Antràs, 2003; Antràs and Helpman, 2004; 2008). 2 Studies have generally found that FDI generates spillover effects in host countries, including improving

productivity and survival of local firms (Javorcik, 2004; Haskel et al., 2007; Wang, 2010; Wang, 2013).

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WOs lies the largely unanswered question: what would slow down the odds of foreign

investors’ JV-to-WO ownership consolidation; and what could explain the regional disparities

in foreign investors’ JV-to-WO decision?3 Here, we try to fill the void by focusing on firms’

operating external environment by building an index of institution quality, and study how

institution quality affects foreign investors’ JV-to-WO decision across time and region. This

novel approach undoubtedly enriches our understandings of FDI dynamics in China.

Institution quality is a broad concept with judicial quality at its core. Much of the

pioneering work into institution quality is by North (1981, 1990), who defines institutions as

humanly devised constraints that shape interactions between people. In North’s framework,

institutional quality improves with the limitations imposed on executive power to reduce the

de jure position of a country’s executives to put themselves above the law. Acemoglu et al.,

(2001, 2002, 2005) apply a wider perspective that takes into account both de jure and de facto

power to ensure that individuals, entrepreneurs, and challengers of the present economic

system are protected de facto in their ventures, their investments in human and physical

capital and new technological endeavors. An addition by Easterly (2013) stresses the rights

and opportunities of individuals, but also includes effective public services as an integral

element of high quality institutions, especially from the perspective of developing countries.

We adopt the Easterly approach as providing effective public service for firms has been a

constant theme of China’s ongoing economic reforms. We thus construct an institution quality

index for each province-year from both the legal (judicial quality) and the business sides

(business quality) to fully reflect the environment where foreign affiliates operate.

We borrow the insights developed by Hart and his co-authors regarding the effects of

judicial quality on firms’ ownership consolidation (Grossman and Hart, 1986; Hart and

Moore, 1990; Hart, 1995). They argue that firms’ ownership structure is consolidated if the

efficiency of enforcing contracts is compromised. Indeed, in cross-country studies, La Porta

et al. (1998, 2000) find that firm ownership is heavily concentrated in countries with a weak

rule of law. Interestingly though, we have seen twin increases in China during the sample

period between 1998 and 2007: the general improvement in institution quality across China

and the increasing incidences for foreign investors to turns JVs to WOs. It has been widely

acknowledged by the business world that China’s institution quality, both from the legal side

and from the business side, has been improving since 1980 (to be discussed in detail in

Section 3). Based on Hart’s theory, we would expect that increases in institution quality

would slow down foreign investors’ odds to turn JVs to WOs; and variations in institution

quality across provinces would explain differences in regional JV-to-WO incidences.

To that end, for each province-year, we build a composite index for institution quality

from indicators capturing China’s provincial judicial and business qualities—levels of

institution quality. From the levels, we further compute a relative institution quality index—

the distance from the best quality province, relative to the quality range across all provinces

for that year. While the levels of institution quality capture the general trend across the

sample period, relative institution quality index highlights variations across provinces for

3 A somewhat related study is Zhang et al., (2016), who investigate how political hazard affects foreign

investors’ share ratios in joint ventures in China. Apparently, researchers have long been interested in studying

FDI’s post-entry performance, including on productivity, wage structure, employment, innovation and others

(Aitken and Harrison,1999; Girma and Görg, 2007; Arnold and Hussinger, 2010).

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horizontal comparison.

We use a sample of over 43,000 JVs operating in 30 provinces in China between 1998

and 2007 and track foreign investors’ JV-to-WO ownership consolidation during the sample

period. In lieu of China’s ever-changing environment regarding FDI, we differentiate the

general more favorable environment for FDI and the effects from institution quality by

including year specific effects as well as an experiment with a FDI policy shock in 2004. We

find that the odds for foreign investors to turn JVs into WOs are significantly lower with

increases in institution quality. The odds for foreign investors to turn JVs into WOs are

significantly higher if they operate in provinces with relatively weaker institution quality. The

ownership structure of domestic partners and the origins of FDI in JVs generate meaningful

differences in foreign investors’ JV-to-WO decision. Ceteris paribus, if JVs’ local partners

are SOEs, external institution quality does not affect foreign investors’ JV-to-WO decision;

while it is significantly higher when JVs’ local partners are non-SOEs. If JVs’ foreign

partners are from Hong Kong, Macao or Taiwan (HMT), the odds of ownership consolidation

are higher than from other regions (Foreign). Further, foreign investors’ initial controlling

positions manifest into different odds for their JV-to-WO decision: the less they could exert

control in JVs, the larger the odds for them to consolidate ownerships to WOs. The results are

not driven by policy shocks, and are robust to different measures of institution quality. We

also address foreign investors’ endogeneity bias by using instrumental variables of provinces’

Christian primary school enrollments in 1919, and their colonial ties for China's provinces in

the early 1900s.

The remaining of the paper is organized as the following. Section II describes the trends

from JVs to WOs in China, Section III documents the main variables, Section IV discusses

the estimation strategy, Section V reports the main results and some robust analyses, Section

VI runs a policy shock test, Section VII uses alternative measures of judicial quality from

other data sources, and Section VIII concludes the paper.

II. OUR SAMPLE AND THE TREND FROM JVs TO WOs IN CHINA

Our sample is constructed from the Annual Survey of Manufacturing Enterprises

(ASME). The major focus for us is on firms’ foreign investors and on whether foreign

investors’ equity shares changed. The ASME dataset reports the volume of the registered

capitals from each investor. Domestic investors are categorized as SOEs, collective firms,

private firms, individuals and others including cooperated citizens. We group collective firms,

private firms and individuals as Non-SOEs (or Others). Foreign investors are clarified by

sources: from Hong Kong, Macao or Taiwan (HMT), from other foreign regions (Foreign

excluding HMT), and other sources including cooperated foreign affiliates operating in

China. For a firm, if the equity share of the sum of the registered capitals from all foreign

sources (including HMT and Foreign) is above 0% and below 99%, then the firm is a JV and

becomes a member of our sample. In ASME, most JVs have only one local partner and one

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foreign partner. Accordingly, it is easy to clarify the ownership structure of JVs’ local partners

as SOEs versus Non-SOEs, and the sources of FDI as from HMT versus from Foreign

(excluding HMT). We emphasize JVs’ local partners as SOEs and Non-SOEs, and foreign

investors as HMT and Foreign, because the ownership structure of the local firms, and the

sources of FDI embody significant differences in how JVs respond to institution quality, to be

examined in full detail in the econometric analyses. In cases of firms detected as JVs but with

several local partners or several foreign partners, we do not include them in our sample.4

When a JV was first detected in year t, that JV was included in our sample and was

tracked during the sample period. We exclude these JVs operating in mining, water and

electricity industries, and those operating in Tibet, both due to small sample sizes. In the

1998-2007 sample period, we identified 43,050 JVs. A JV-to-WO occurs in year t if a JV’s

foreign equity share increases from below 100% in year (t-1) to 100% in year t. When a JV

becomes a WO in year t, then observations after year t for that JV are discarded from the

sample. Those JVs without foreign ownership consolidation during the sample period are

kept throughout as the comparison base. In total, we identified 8,400 JVs that became solely

foreign owned. We have 146,128 observations, unbalanced observations among JVs.

Regarding the JV-to-WO trend, we first compute JV-to-WO incidence ratio for year t: we

divide the number of JVs in year t that became foreign WOs in (t+1) by all the JVs operating

in year t. We track the annual incidences of JV-to-WO across the ownerships of their Chinese

partners as SOEs and Non-SOEs; across the origin of their foreign partners as HMT, and

Foreign (excluding HMT); and across foreign investors’ initial controlling positions between

(0, 25%), [25%, 50%), and [50%, 100%). Differentiation of foreign investors’ controlling

shares as such is because JVs with under 25% foreign shares are not officially recognized as

foreign affiliates and thus are not eligible to enjoy China’s favorable FDI policies. The

associated JV-to-WO ratios are reported in Table 2. A few observations emerge.


First, for the whole sample, the JV-to-WO incidence ratio increases from 3.57% during

1998-1999 to 6.66% during 2006-2007. It has two periods with slight decreases in the ratio:

during 1999-2000 and 2001-2002 periods (Column 1), but records two significant increases:

one in 2000-2001 and another in 2003-2004. Both increases coincide with big economic

events in China. In 2001, China officially joined the WTO; and in 2004, China unveiled a

new version of the FDI laws. With a simple regression of JV-to-WO’s annual incidence on

time trend, it returns a coefficient of 0.103, statistically significant at the 1%. A similar

increasing time trend is observed across all subsamples, highlighting the ongoing increasing

odds of foreign investors’ JV-to-WO decision.

Second, there is a higher tendency of JV-to-WOs with Non-SOEs as domestic partners

4 For instance, a JV could have 25% equity shares from SOE, non-SOE, HMT and Foreign. It is not obvious

how to assign one ownership to the local partners, or to the foreign partners. These types of JVs are the minority.

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than with SOEs (Columns 2 and 3). The sample average with SOEs as partners is 3.90% and

5.63% for Non-SOEs. The difference is statistically significant.

Third, JV-to-WO ratios are higher with HMT investors than with investors from other

regions (Foreign) (Columns 4 and 5). It is averaged as 5.89% with HMT partners and 4.85%

with Foreign investors. The difference is also statistically significant.

Fourth, the larger the shares held by foreign partners in JVs to begin with, the higher the

incidence ratios (Columns 6—8). The average JV-to-WO incidence ratio is 2.67%, 3.89% and

8.68%, respectively for foreign share ranges in (0, 25%), [25%, 50%), and [50%, 100%). The

differences are also statistically significant.

We also compute JV-to-WO ratios across industries and regions to reflect China’s

changing industry preferences for FDI in the Directory, and its unbalanced regional

developments. The incidences of JVs-to-WOs are widespread across industries (Figure 1) and

provinces (Figure 2), though with variations.5 In general, low technology manufacturing

industries have seen higher JV-to-WO ratios than high technology ones, and Eastern and

Coastal areas have higher incidences than the central region and the west.6

(Insert Figure 1 and Figure 2 here.)

III. THE MAIN VARIABLES

A. Institution Quality

Regarding China’s institution quality, on the legal front, China’s legal system has been

refined to cover a wide range of rights in general; and laws on FDI are amended every three

to five years in particular to address the concerns of foreign investors, and to meet China’s

contemporaneous needs. In our context, understanding the importance of judicial quality on

foreign investors’ ownership consolidation is better through cost effects. First, firms might

have to bribe judges in order to have a favorable hearing, if there is no guarantee that judges

would act professionally. Second, the judicial process might be contaminated by political

factors favoring some firms over others through their political connections and lobbying.

Third, undue delays in hearing associated with weak judicial quality put firms in passive

situations that tend to create and compound financial problems. Increases in judicial quality

5 Industries are clarified using GB/T2002 codes. They are: 13-Processing of Food from Agricultural Products,

14-Foods, 15-Beverages, 17- Textile, 18-Textile Wearing Apparel, Footware and Caps, 19-Leather, Fur, Feather

and Related Products, 20-Processing of Timber, Wood, Bamboo, Rattan, Palm and Straw Products, 21-Furniture,

22-Paper and Paper Products, 23-Printing, Reproduction of Recording Media, 24-Articles For Culture,

Education and Sport Activities, 25-Processing of Petroleum, Coking, Processing of Nuclear Fuel, 26-Raw

Chemical Materials and Chemical Products, 27-Medicines, 28-Chemical Fibers, 29-Rubber, 30-Plastics, 31-

Non-metallic Mineral Products, 32-Smelting and Pressing of Ferrous Metals, 33-Smelting and Pressing of Non-

ferrous Metals, 34-Metal Products, 35-General Purpose Machinery, 36-Special Purpose Machinery, 37-

Transport Equipment, 39-Electrical Machinery and Equipment, 40-Communication Equipment, Computers and

Other Electronic Equipment, 41-Measuring Instruments and Machinery for Cultural Activity and Office Work,

and 42-Artwork and Other Manufacturing. 6 There are 11 provinces in the eastern and coastal region: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu,

Zhejiang, Fujian, Shandong, Guangdong, and Hainan. There are 8 in the middle part of China: Shanxi, Jilin,

Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. And 11 provinces are in the West: Inner Mongolia,

Guangxi, Chongqing, Guizhou, Yunnan, Shaanxi, Sichuan, Gansu, Qinghai, Ningxia, and Xinjiang.

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would decrease costs.

China’s highly decentralized FDI approval and policy implementation system creates

opportunities for healthy competition for FDI among local authorities; and local governments

have been increasingly seeking to ensure the administrative and operational efficiency of the

approval process. For instance, the most common practice is setting up “one-stop” facilities,

allowing investors to conduct all procedures in one place, very much embraced by foreign

investors. Increases in China’s judicial quality is a non-disputable fact.

Increase in China’s business quality has been transformative, and is a direct product of

China’s ongoing economic reforms to re-define firm-government relationships. In a nutshell,

the ultimate objective of the reforms is that firms should be solely responsible for their

management, and governments should be providing services for firms. The business

community has been applauding governments’ role transition from micromanaging firms

(under the planned economy) to service-providers (under the market economy). Foreign

investors have generally found China to be a good destination, and China continues to attract

huge amount of FDI inflows, though the majority being foreign WOs post 2001 (Table 1).

While at the same time, it is still well documented that firm-level decisions are often

affected indirectly by governments’ overt, but mostly covert, interventions (Bai et al., 2004;

Cai et al., 2011; Wang and You, 2012; Cull et al., 2017; Huang et al., 2015). And government

interventions are found to be important factors in resolving business disputes (Du et al.,

2014). Further, improvements on institution quality across China’s provinces are not even. In

fact, there are noticeable variations, caused largely by China’s policy environment. When

market institutions were not fully in place in the 1980s and 1990s, China experimented with

opening up to foreign investment in selected coastal cities and in special economic

zones/industrial parks with a focus on attracting export-oriented manufacturing FDI. Thus,

different provinces have enjoyed various levels of autonomy, and they have had a large

degree in interpreting and carrying out their own versions for the same regulations set by

China’s central government. These have caused variations in institution quality across

provinces both in levels and in the pace of change, though inland and western provinces have

been gradually catching up with the coastal regions in institution quality.

Given China’s context, we follow the concept of Easterly (2013) and construct an

institution quality index for each province-year from the indicators of both judicial quality

and business quality. Fan et al., (2003) developed the marketization index database (with

frequent updates) to measure each province’s yearly marketization progress with 23

indicators from 5 broad categories.7 Each indicator, depending on its content, has a different

value range, and higher values of an indicator imply a better quality. Among the 23

indicators, we choose five the most relevant to embody China’s legal and business quality

from two categories: legal environment and governments’ intervention on the market

(service).8 On the legal side, they are: protection of firms’ rights—constructed to reflect the

7 The indicators have also been used recently by Cai and Zheng (2016), Chen et al., (2015) and Yi et al., (2015). 8 The Fan et al. indices are in five broad groups: development of non-state owned economy; development of

product markets; development of factor markets; the relationship between governments and market; and legal

environment and market intermediaries.

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number of business disputes and its closing rate, and protection of intellectual property rights.

On the business side, they are the share of resources allocated by market force (in comparison

by central planning or other forms); the efforts to decrease direct involvement of government

in firms’ decision-making process; and fostering market-oriented business intermediaries.

Rather than taking a simple average, we use the principal component method to build the

composite index, 𝐶𝑝,𝑡, for each province-year, from the five chosen indicators, to reflect the

major factors affecting its institution quality in that province-year. C generally increases over

time, reflecting the common improvement of institution quality across China. Indeed, an OLS

regression of C on year returns a coefficient of 0.089, statistically significant at 1% level.

However, variations in C are worth noting, though the differences are shrinking,

indicating that inland and western provinces are catching up with the costal ones. We control

for the rate of improvement to highlight provincial variations for any given year by creating a

relative quality index, 𝑄𝑝,𝑡, as the following:

(1) 𝑄𝑝,𝑡 =𝑚𝑎𝑥{𝐶𝑞,𝑡}−𝐶𝑝,𝑡

𝑚𝑎𝑥{𝐶𝑞,𝑡}−𝑚𝑖𝑛{𝐶𝑞,𝑡} ,

While 𝐶𝑝,𝑡 is the composite index for province p in year t, derived from the five chosen

indicators using the principal component method. The terms, 𝑚𝑎𝑥{𝐶𝑞,𝑡} and 𝑚𝑖𝑛{𝐶𝑞,𝑡},

stand for respectively the best and the worst institution quality in year t across the 30

provinces, with the difference between the two being the range. For a given year t, 𝑄𝑝,𝑡

measures the distance of a province’s institution quality to the best one, relative to the range.

Clearly, given the range, a smaller value of a province’s composite indicator 𝐶𝑝,𝑡 would

return a larger 𝑄𝑝,𝑡, indicating relatively a lower institution quality in province p (a longer

distance to the best quality province) in year t, and vice versa.9

Defining relative institution quality as such has two advantages. First, it helps control the

improvement of institution quality for a given province for any two consecutive periods, and

highlights the variations across provinces both in levels and in the pace of change. For

instance, province p, even if there are no improvements from year (t-1) to t in its institution

quality (i.e., 𝐶𝑝,𝑡−1 = 𝐶𝑝,𝑡), but changes in the range (from the max and/or from the min

changes) are reflected in the variations of 𝑄𝑝,𝑡−1 to 𝑄𝑝,𝑡. Because, for instance, if the

maximum of institution quality increases from (t-1) to t, then no improvement in 𝐶𝑝 means

relatively a longer distance from the best quality province from (t-1) to t —a relative decrease

in institution quality in p. Similarly, proportional improvements in institution quality across

all the provinces from year (t-1) to t will return an unchanged Q from (t-1) to t. This is

exactly what we would like to capture in comparison of institution quality across provinces

for time t. Second, relative indicator also helps eliminate any potential measurement issues

arising from year-to-year changes in calibrating the indicators.


9 Relative is a concept to be used more recently in a few studies regarding FDI and institutions in their cross-

country comparison (Aleksynska and Havrylchyk, 2013; Choi et al. 2016); and even in cultural distance (Meyer

and Nguyen, 2005; Buckley et al. 2007; Du et al., 2008).

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Table 3 gives us a broad look of the institution quality composite indices both in levels

and in relative forms. The reported summary statistics indicate that among all the provinces,

the best quality environment is in the eastern region. Further, an OLS regression of Q on time

trend leads to a coefficient of -0.011, statistically significant at 1%. That implies an ongoing

shrinking distances for provinces from the best one, an expected outcome given that other

regions are catching up with the eastern provinces.

In the table, we also report two other indicators which we would use individually as

alternatives for robustness analyses. The first one is efforts to decrease the direct involvement

of government in firms’ decision-making process—ratio of time allocated by firms’ manager

to lobby governments and to manage firms’ business (T)—included in C. The second one is

the ratio of lawyers in provincial population (L)—a higher ratio suggesting the easiness of

obtaining a lawyer in solving legal disputes—not included in C. The two indicators also

increase over time, with shrinking distances from the best provinces, evidenced by their

relative forms (computed using the same method as C to Q).

B. Other Control Variables

We include a set of firm-level variables to control their influences on the decision of

foreign investors to buy out the equity shares from their local partners, ranging from

productivity to competitiveness in the labor market. The data sources for these firm-level

controls are ASME. Here, we briefly discuss each in turn. Firms’ labor productivity, defined

as output over number of employees in logs—lnLP, is to capture productivity effects on

foreign investors’ ownership consolidation, as some studies have found that foreign investors

tend to cherry-pick more productive firms to acquire (Arnold and Javorcik, 2009; Gelübcke,

2013). Capital stock in logs (lnK) is computed from fixed capital formation using the

perpetual inventory method with a depreciation rate of 0.09 (following Brandt et al., 2012),

and it aims to look into the impact of capital on foreign investors’ decision to consolidate

ownerships. Wages (defined as total wages over the number of employees in logs) are a sign

of firms’ competitiveness in the labor market. Debt (ratio of total debt over total asset) is to

capture firms’ financial distress. Export Intensity (the ratio of exports to firms’ production) is

to control firms’ integration with the overseas markets. ShareDistance, which is measured as

the difference between foreign investors’ and local partners’ equity shares (foreign investors’

equity share minus domestic partners’) is to control the pre-WO foreign investors’ equity

position in JVs. Finally, we include firm age (Age) to control for firms’ business

experiences—long survival might signal firms’ strength in the market. Table 4 provides

summary statistics for each of them.


IV. THE ESTINATION STRATEGY

To capture foreign investors’ ownership consolidation to turn JVs to WOs, we use a

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binary variable, WO, with 1 for the JV-to-WO ownership consolidation, and 0 otherwise. We

adopt a logit model to explain the JV-to-WO decision, as the following.10

(2) 𝑙𝑜𝑔𝑖𝑡(𝑊𝑂𝑓,𝑖,𝑝,𝑡 = 1) = 𝛽𝐶𝑝,𝑡−1 + 𝛾𝑋𝑖,𝑡−1 + 𝛿 ∗ 𝐼𝑛𝑑 + 𝜃 ∗ 𝑃𝑟𝑜𝑣 + £ ∗ 𝑌𝑒𝑎𝑟 + 휀𝑖,𝑡,

Where f indexes firm, i industry, p province, and t year. X is a vector including firm-level

variables. Ind, Prov and Year are vectors of dummy variables for industry, province and year

respectively. Ind is to capture the effects from time-invariant FDI policies across industries, at

the two-digit level. Prov is to control provincial fixed effects not captured in the institution

quality index, and Year is for year-specific fixed effects not only arising from different

versions of FDI policies in certain years, but also arising from China’s ongoing economic

reforms with year-specific focus. Together, the inclusion of fixed effects from province,

industry and year captures all the non-varying effects originating from provinces, industry

and year. C captures the effects of institution quality on foreign investors’ tendency to

consolidate ownerships. In all the regressions, we use values at (t-1) for the continuous

variables to control for potential endogeneity. We correct the standard errors by clustering

firms by provinces, as firms operating in the same province have the same institution quality.

V. THE RESULTS

A. Institution Quality and Foreign Investors’ JV-to-WO Decision

We start with examining how the general improvements of institution quality over time

affects the ongoing odds for foreign investors to turn JVs to WOs. The results are in Column

(1) in Table 5. Reported are the logistic coefficients, with figures in parentheses the clustered

standard errors (clustered by provinces).


The coefficient on C is negative and statistically significant at the 5% level. It implies that

foreign investors’ odds of JV-to-WO decision are significantly lower with increases in

institution quality. A coefficient of -0.102 indicates that one unit increase in C leads to about

10% decrease in the odds [exp(-0.102)-1≈-0.10], others held at the mean. This result is

implied by Hart’s theory that increases in institution quality reduces firms’ ownership

consolidation. In China’s context, our results also indicate that were there no improvements

in institution quality across provinces, given foreign investors’ clear preference to establish

WOs, those initially bounded in JVs would have had higher incidences to turn JVs to WOs.

Regarding the effects of firm-level controls, it merits some discussions. Ceteris paribus,

more productive JV affiliates exhibit higher odds of becoming foreign WOs—signaling

foreign investors’ intent to cherry-pick more productive firms to acquire, which is consistent

with many findings in the FDI literature including Arnold and Javorcik (2009), Baldwin and

Wang (2011) and Gelübcke (2013). The larger the difference between the foreign investor’s

10 While logit and probit yield similar inferences, we choose logit as the coefficients can be interpreted in terms

of the odds, given the generally rather low JV-to-WO ratios for the whole sample at 5.35%.

12

and domestic partner’s equity shares, the higher the odds of foreign investors turning the JVs

to their WOs—highlighting foreign investors’ intent to exert more control. Increases in JVs’

debt-equity ratio also raise their odds to become foreign WOs—a result which might

indirectly suggest that foreign investors have more financial resources to draw on than their

Chinese partners. Also, the longer a JV has been in business, the larger its capital stock, and

the higher its wage levels, the less the odds for foreign investors to turn these JVs to their

WOs. Long survival in business might signal that the current ownership structure has been

doing well; and higher wages indicate firms’ competitiveness in the labor market.

It is worth noting that in the Fan et al. (2003) database, a province’s judicial quality and

business quality indicators are closely correlated. While we choose to use five indicators

closely reflecting firms’ external institution quality, we also believe that individual institution

quality measure, whether it is judicial quality indicator or business quality indicator, might

generate some effects on foreign investors’ decision to turn a JV to a foreign WO, though

using only one indicator can’t adequately capture the richness of institution quality. Here, we

experiment with a business indicator—T—decrease in government’s interference in firm-

level decision—included in the composite measure; and a legal indicator—L— lawyers’ ratio

in population—not included in the composite measure. Results are reported in Columns (2)

and (3) respectively in Table 5. In the case of T, the coefficient is negative and significant at

the 5%, and it is negative but not significant for L. Results on firm-level controls are nearly

identical as in Column (1). The results corroborate those from C, and offer additional

evidences between institution quality and JV’s foreign ownership consolidation tendency.

B. Variations in Institution Quality across Provinces

While it is useful to know that improvements in institution quality slows down foreign

investors’ odds to turn JVs to their WOs, here, we continue to examine how variations in C

across provinces, embodied in Q, affect foreign investors’ JV-to-WO odds. Results are

reported in Column (1) in Table 6. We focus on the coefficient on Q, as the results on firm-

level covariates are nearly identical as those in Table 5.


The positive and statistically significant coefficient on Q indicates that the odds for

foreign investors to turn JVs to their WOs is significantly higher if they operate in provinces

with relatively weaker institution quality (relatively longer distances to the best). In terms of

the odds, the coefficient of 0.806 indicates that one unit increase in relative institution quality

(Q) increases the odds of JVs to become WOs by 124% (e^0.806-1). The finding directly

confirms those in La Porta, et al., (1998; 2000) where firms concentrate their ownerships in

countries with weaker legal quality.

Given the horizontal differences in provincial institution quality across China, the results

allow us to ask how the odds would change if a JV were to locate in a different province than

its current one for a given year. We take the following counterfactual to illustrate the point.

13

For instance, what would be a JV’s odds to become a foreign WO if it were to move its

location from Shandong, with a relative distance from the best quality province at the 25

percentile (its mean of Q being 0.61), to Inner Mongolia, at the 75 percentile (its mean of Q

being 0.77)? While holding everything else constant, the odds would increase by 13%

[(𝑒0.806∗0.77

𝑒0.806∗0.61⁄ ) − 1] with a 26% increase in Q [(0.77-0.61)/0.61].

We also experiment with T (a business quality indicator) and L (a legal quality indicator)

after converting them individually into their relative forms, with the corresponding empirical

results reported in Columns (2) and (3) in Table 6. First on relative T, the results indicate that

foreign investors’ odds of JV-to-WO increases if they operate in provinces with relatively

weaker business quality (more time spent by managers to lobby the government). On the

legal quality indicator, decreases in the relative distance in lawyers’ ratios from the best

province would increase the odds of foreign investors to go fully foreign owned, but the

coefficient is not significant. With the two experiments, coefficients from the firm-level

variables are not affected, signaling the stable and important effects they have on foreign

investors’ decision. Given that Q embodies provincial variations in institution quality, in the

following analysis, we focus on Q to see how the results change.

C. Endogeneity Bias

It could be argued that foreign investors with a would-be higher tendency to consolidate

JVs’ ownership into their own hands might choose to invest in regions with relatively weaker

institution quality in the first place, though empirical evidences pointing the other way.11 We

choose to use two instrumental variables (IVs): Christian primary school enrollments in 1919

from Fang and Zhao (2009), and the colonial ties for China's provinces in the early 1900s,

from Feenstra et al (2013).12

Christian primary schools were established by western missionaries in the early 1900s.

Besides academic teaching, these missionaries, and similarly, the colonizers in the provinces,

tended to generate undeniable influences on the then political elites and thus affected the then

institutional qualities. Assuming institutions are path-dependent following Acemonglu et al.,

(2001), then these IVs can be argued to be linked with today’s institution quality in these

provinces, but are not related to foreign investors’ decision to divorce their JV local partners.

The advantage of the first one is that variations in school enrollments can better explain the

variations in current institution quality across provinces, given that C (Q) slows increases

11 Awokuse and Yin (2010) find that IPR protection in China has positive and significant effects in attracting

FDI, and Du et al., (2008) show that US multinationals choose to invest in China with better property protection

of intellectual property rights. 12 The former British colonies are Anhui, Guizhou, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Sichuan, Zhejiang

and Chongqing. The former French colonies include Guangdong, Guangxi, Hainan and Yunnan. The former

Russian colonies are Heilongjiang, Jilin, Liaoning, Inner Mongalia as well as Xinjiang. Additionally, Shandong,

Shanghai and Tianjin are ruled by some foreign countries together (Feenstra et al., 2013).

14

(decreases) over time. The second one traces the provincial ties to their colonizers in the past.

To test the validity of the instruments, we perform the weak identification test by reporting

the Cragg-Donald Wald F statistic, and determine its validity according to the criterion

suggested by Stock and Yogo (2005).13 The associated results are reported in Table 7.


In Table 7, in the first stage (Column 1), the Cragg-Donald Wald F statistics is far larger

than the criterion suggested by Stock and Yogo (2005), lending support for the validity of the

instruments. Results on firm-level data from the second stage are generally in line with the

baseline results obtained earlier, but the coefficient on Q is much larger. The much larger Q

seems to suggest that the baseline regressions capture a lower bound of the effects of the

variations on institution quality on the odds of foreign investors’ decision to turn JVs to WOs.

Using Sargan’s test for overidentification, we fail to reject the null hypothesis that over-

identifying restrictions are valid at the 5% significance level. In the following sections, to be

on the conservative side, we use Q, the relative institution quality index, for further analyses.

D. Partners of Joint Ventures

JVs in our sample involve local partners as SOEs or Non-SOEs, and foreign investors

from HMT or other regions (Foreign). In the above analysis, the implicit assumption is that

ownership types of JVs’ local partners or the origins of foreign investors would not influence

foreign investors’ JV-to-WO decision. However, as is often argued in the literature regarding

Sino-foreign JVs, the ownership structure of Chinese local firms, and the origins of foreign

investors often exhibit different patterns in their relationship with the (local) Chinese

governments. Thus, they often have different experiences in terms of the degree of

government interventions and/or government protégé (Bao et al., 2015; Huang et al., 2015;

Lu et al., 2017). As to JVs’ local partners, SOEs might enjoy certain favorable treatments

from local governments, including expedite entries into particular industries and relatively

easily getting loans from state-owned banks, which Non-SOEs do not often have. But it is

also generally true that government intervention is the strongest for SOEs, to which

governments at different levels tend to ask to shoulder some forms of social services with no

or very little compensation. For collectively or privately owned firms (Non-SOEs), while

they do not enjoy special ownership relationships with governments, but they can largely

make firm-level decisions on their own, relatively free of direct government interventions.

Government’s intervention for foreign affiliates is the least, especially for solely foreign

owned affiliates. Further, regarding foreign investors, those from HMT are often grouped

together as “overseas Chinese investors”, and are perceived to know the Chinese culture

better, as they speak similar dialects as certain provinces in the mainland, and they are viewed

13 Stock and Yogo (2005) stated that F statistic typically exceeding 10 in the first stage would be large enough

to reject the weak-instrument hypothesis.

15

to be more familiar with Chinese business networking than investors from other regions

(Foreign). Whether the differences across local partners’ ownership structure or origins of

FDI manifest differently for foreign investors to turn JVs to WOs is an empirical question.

Econometrically, we choose to split the whole sample with the focus of the study:

according to local partners as SOEs or Non-SOEs (two sub-samples) to look at the effects of

local partners’ ownership structures, and then according to their foreign partners as HMT or

Foreign (two sub-samples). For instance, along the SOE/Non-SOE dichotomy, the SOE

subgroup consists of all JVs with their local partners as SOEs, and the other group as non-

SOEs. We run two parallel regressions, one for the SOE subgroup and one for the Non-SOE

subgroup, and then compare their respective coefficients. Similar subgroupings and

regressions are done for HMT versus Foreign subgroups. Results are reported in Table 8.


We first look at the results along the ownership structure of JVs’ local partners. When

JVs’ local partners are SOEs, the insignificant coefficient on Q indicates that foreign

investors’ odds of JV-to-WOs are not affected by variations in institution quality. But when

JVs’ local partners are Non-SOEs, external institution quality plays an important role in

shaping foreign investors’ odds to turn JVs into their WOs; and the odds are significantly

higher when they operate in provinces with relatively weaker institution quality. An F-test

shows that the coefficients along the two parallel regressions are statistically different. The

results imply that foreign investors bounded with Non-SOE partners are more sensitive to

firms’ external institution quality than with SOE partners, a difference often noted in some

other studies regarding Chinese firms (Huang, et al., 2015).

Second, along the origins of JVs’ foreign partners from HMT or Foreign, the odds of JV-

to-WO are higher when foreign partners are from HMT than from other regions (Foreign).

The difference arising from FDI sources might signal that HMT investors’ general familiarity

with the Chinese culture could give them some additional confidence in operating in

mainland China on their own, and thus more likely to turn JVs to their WOs, ceteris paribus.

E. Equity Power Imbalance and Foreign Investors’ JV-to-WO Decision

Any JVs with foreign investors’ equity shares below 25% are not officially recognized as

foreign affiliates and thus are not eligible for the special favorable treatments towards FDI. If

foreign investors’ equity share sits between 25% and 49%, their JVs enjoy China’s favorable

policies towards FDI, but they don’t control firms’ management. If, ceteris paribus, increases

in ownership consolidation is motivated by Hart’s theory, then firms’ external institution

quality would generate larger effects on foreign investors’ tendency to consolidate JVs’

ownership, if they have a lower equity share. To econometrically show the potential

differences, we choose to divide the whole sample into three subgroups according to foreign

investors’ equity shares: below 25%, between 25-49%, and between 50-99%. We run three

parallel regressions, one for each subgroup. Results are also in Table 8.

16

It is clear that the coefficients on Q for all three subgroups are statistically significant and

positive. The magnitudes of the coefficient on Q decreases with the increases in foreign

investors’ equity shares. The odds are highest when foreign investors have the least weight in

firms’ management prior to the change, and the lowest when they have the controlling

positions in JVs already. The results corroborate our argument that foreign investors would

consolidate ownership control when operating in an environment with relatively weaker

institution quality, and the needs are greater the fewer power they have to begin with.

F. Export Intensity

Export-oriented firms in general might face more stringent competition, either due to high

transportation costs between production facility and customers, or due to the uncertainties

associated with exporting. Export oriented firms would prefer to have a reliable legal and

business environment to carry out their daily business so that they can have cost advantage,

which translates into comparative advantage in exporting. If JVs are more export-oriented, it

is expected that they would have higher odds to experience ownership consolidation, if they

operate in regions with weaker institution quality.

In our case though, the sample only includes Sino-foreign affiliates operating in China. It

is a well-known fact that foreign affiliates in China generally locate in industrial parks or

other related theme parks in each city, and those with a primary focus to export their products

locate in export processing zones. Further, during the sample period, the majority of exports

from foreign affiliates are processing trade, meaning that these foreign affiliates import

intermediate inputs, assemble them in China, and then export the final products overseas.14

Clearly processing exports do not rely that much on China’s legal system to resolve legal

disputes involving suppliers or customers as ordinary exporters do, and local governments’

intervention to them would be minimal. Thus, we would not expect significant differences.

Nonetheless, we build export intensity, measured as the share of exports in sales for each

firm, and add an interaction term of Q*export intensity in the regression, in addition to Q and

export intensity. The coefficient on the former would capture the interaction effects between

firms’ export share and institution quality. The results are reported in Table 9.


While the coefficient on Q is consistent with the baseline result, the coefficient on the

interaction term is negligible (even at the thousandth, it is still 0) and not significant. It is thus

reasonable to say that institution quality where JVs operate does not exert additional

influence in foreign investors’ decision to consolidate ownership from JVs to WOs.

14 Ma and Van Assche (2011) note that “processing exports are predominately conducted by foreign invested

enterprises (FIEs), where non-processing exports are largely conducted by local firms. Between 1992 and 2007,

the share of processing exports conducted by FIEs has varied from a high of 89.7% in 1995 to a low of 75.0% in

2007. Conversely, FIEs’ share of non-processing exports has consistently remained below 25% (page 129).”

17

VI. INSTITUTION QUALITY VERSUS FDI POLICY CHANGE

It is worth noting that wholly foreign-owned affiliates are allowed in China only after

1988. With each round of amendments of FDI policies, foreign investors have enjoyed space

to operate as WOs (Table 1). The inclusion of year- and industry-fixed effects would control

these inherent effects associated with time and industry. However, in a broad context, one can

still ask to what extent our results were driven by improvements and variations across firms’

external institution quality across provinces or by specific FDI policy shocks. Below, we

answer that question by focusing on a particular FDI policy shock during the sample period,

and see how our results are affected.

During the sample period, there are three amendments in China’s FDI laws, respectively

in 2002, in 2004 and in 2007. The potential effects from the 2007 amendments would be felt

beyond our sample period. The 2002 version take places five years after the 1997 one.

Compared with the 1997 version, the 2002 amendment did not usher in major changes, but

the 2004 version did—it listed additional industries as “encouraged” for FDI, and introduced

certain clauses to prevent low-technology content FDI. The presents a policy shock, and we

thus decide to see how this policy change affects our results.

Specifically, in the 2004 version, among the listed 492 industries in the Directory, 117

industries have experienced regulatory changes, accounting for 23.8%. Among the 117

affected industries, 80 witnessed loosen restrictions by getting rid of some previous

restrictions, accounting for 68.4% (16.3% in all the listed industries in the Directory), while

37 industries saw added restrictions for FDI, accounting for 31.6% (7.5% in all industries in

the Directory). The vast majority, 285 industries, maintain their previous categories in the

Directory as “Limited” (14 industries), “Permitted” (323 industries), or “Encouraged” (38

industries) respectively. The loosened relaxations in the 2004 revision, three years’ after

China’s accession to the WTO, were to open up additional industries for FDI. Those policy

changes—affecting nearly 24% of all industries—provide a natural experiment to test the

importance of firms’ external environment on foreign investors’ decision to change the

control structure in JVs. While firms have a reasonable expectation regarding revisions to the

Directory by the central government every 3 to 5 years, but the content of the changes,

especially those industries to be affected are unknown to firms. If foreign investors’ odds to

go from JVs to WOs are largely driven by external policy changes, we might expect a

decreased effect for those JVs that saw loosed regulations; and the opposite for those that

experienced more stringent regulations.

To empirically differentiate FDI policy effects from those of institution quality, we build a

set of dummy indicators. Let indicator PC be 1 for those industries which experienced a

policy change starting from year 2004 and onward, and 0 otherwise. Further, PC_severe is to

indicate stringent regulatory changes for affected industries, and PC_loose is to indicate

loosened ones. Similarly, we let NPC be 1 for those industries which did not experience a

policy change, also starting from year 2004 and onward, with NPC_limited, NPC_permitted,

and NPC_encouraged respectively indicating their categories in the Directory of being

limited, permitted or encouraged. We run two sets of regressions for comparison. The first set

18

is to employ dummy indicators for industries with the policy change, and the second set is for

other industries without the policy change. The two sets of the results would be reinforcing

each other, albeit from the opposite direction. We do this for the composite measure of

institution quality C, and the relative quality Q. The new results are in Table 10.


We first look at the results in Columns (1)-(3) with C, and then (4)-(6) with Q. Note that

when we introduce interaction terms, we also add the dummy indicators as stand-alone

variables to control for their separate fixed effects. The coefficient on C itself is always

significant at the 1%, and is comparable with what is obtained before. The coefficient on the

interaction term, C*PC, is not significant, and much smaller in magnitude (Column 1). The

coefficient on the interaction term, C*PC, is not significant, but is negative at -0.010. In

terms of the odds, for firms operating in industries with no regulatory changes, one unit

increase in C leads to 10% [i.e., exp(-0.108)-1] decrease in the odds of foreign investors to

consolidate ownerships to WOs. For firms operating in industries with regulatory changes,

one unit increase in C is associated with a 11% [i.e., [exp(-0.108-0.010)-1] decrease in the

odds. The ratio of these two odds ratios for firms operating in industries with regulatory

changes over those without is reduced to exp(-.010)=0.99, not significantly different from 1,

as the coefficient -0.01 is not statistically significant.

Column (2) further differentiate the direction of the regulatory changes as PC_severe and

PC_loose respectively, and include the interaction terms of C with both of them. The

insignificant and much smaller coefficients on C* PC_severe or on C* PC_loose imply that

the effects of institution quality on foreign investors’ odds to consolidate ownership are not

statistically different between those operating in industries positively or negatively affected

by the 2004 FDI policy changes. Column (3) runs the opposite experiment, and instead

utilizes dummy indicators for industries without any policy changes, as NPC_limited,

NPC_permitted and NPC_encouraged respectively. Here, the comparison groups are those

with policy changes post 2004. The coefficients on the interaction terms of C with the three

dummy indicators are not statistically significant, while the coefficient on C is comparable

with those in Columns (1) and (2). These results indicate that foreign investors, operating in

industries not affected by the 2004 FDI policy changes, do not appear to have different odds

to consolidate ownerships, compared with those which have either enjoyed more freedom or

faced with more stringent regulations.

Columns (4) to (6) use relative institution quality Q to experiment. The coefficient on Q

itself is significant at the 5% level, and is comparable with what is obtained before. The

coefficient on the interaction terms are not significant and small in magnitudes either on

Q*PC (Column 4), Q*PC_severe and Q*PC_loose (Column 5), or Q *NPC_limited,

Q*NPC_permitted and Q*NPC_encouraged (Column 6).

Together, results in Table 10 imply that our treatment of including province, industry and

year fixed effects could adequately capture the different effects on foreign investors’ decision

19

arising from industry policy and yearly changes. Our results are not affected by policy

changes regarding FDI.

VII. SENSITITY IN MESUREMENTS FOR INSTITUTION QUALITY

As we have discussed earlier, institution quality is a macro measure. While we utilize the

Fan et al. database on China’s marketization index to indicate provincial institution quality,

there are other measures available, such as the one from the China Investment Climate Survey

(2005) by the World Bank. The World Bank indicators are at the city-level for 120 Chinese

cities, though only available for one year and only on the legal side. They include “disputes

protection” and “trust in judicial system”. The former is a direct measure of a city’s judicial

quality, and the second one is people’s perception of judicial quality in a city. These legal

measures, at a more detailed city level, could be used as a robustness check for our main

conclusion, given that judicial quality and business quality are often highly correlated.

To be consistent with our approach in treating variations, for both measures, we use the

same approach as from C to Q to calculate the relative index for “disputes protection” and

“trust in judicial system”. Thus, a higher value of the relative index implies a worse judicial

quality in that city. We document the new results in Table 11. The positive and significant

coefficients on Q indicate that the odds for foreign investors to turn JVs to WOs are

significantly higher if foreign investors operate in cities with relatively worse judicial quality,

though the magnitudes are smaller than those obtained with Q.


VIII. CONCLUSIONS

With the massive amount of inward FDI, China is often referenced as a useful case by

policy makers and academics worldwide to study the effects of FDI on the host economy.

Since China’s first regulation on FDI in 1979, the Chinese government has gradually

developed a full set of laws governing FDI’s activities, and has been amending the set of FDI

laws every three to five years. What has emerged from these amendments is a more favorable

environment towards FDI, allowing foreign investors to choose the appropriate mode of

operation as Sino-foreign joint ventures, Sino-foreign Cooperatives and foreign wholly

owned affiliates, with no performance requirements post 2001. To a large extent, China’s FDI

policies are closely related with China’s ongoing economic reforms with the objective of

transforming governments’ role from micromanaging firms (under the planned economy) to

providing services to firms (under the market economy). With the ongoing economic reforms,

we have observed twin increases: the improvement across China in institution quality

including on the legal and business sides; and the increasing incidences of foreign investors

initially bounded in JVs to divorce their Chinese local partners.

This paper examines the role of external institution quality on foreign investors’ JV-to-

WO decision. We capture institution quality from both the legal and business perspectives to

20

fully reflect China’s business environment, by constructing a composite institution quality

index for each province-year. While business and judicial quality have been increasing in

general over the years, there are discrepancies in levels and in the rate of increase across

provinces. We then construct a relative institution quality index to highlights these variations.

Using more than 43,000 JVs operating in China’s 30 provinces over 1998 to 2007, we

find that increases in institution quality decrease the odds of foreign investors to divorce their

Chinese local partners. The odds for foreign investors in JVs to consolidate ownerships are

significantly higher if they operate in provinces with relatively weaker institution quality. The

effects of institution quality on foreign investors’ odds of turning JVs to WOs vary with JVs’

local firms being SOEs and Non-SOEs; with foreign investors’ origins from Hong Kong,

Macao or Taiwan (HMT) and other regions (Foreign); and with foreign investors’ initial

controlling shares. Our results are not driven by policy shocks, and robust to alternative

measures of institution quality.

21

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24

TABEL 1

Established Foreign Affiliates by Type in China (Selected Years)

Joint Ventures

Wholly

Owned Cooperative Others Total

Cumulative

to 2002 225,883 145,165 52,965 183 424,196

(53.25) (34.22) (12.49) (0.04) Cumulative

to 2006 270,640 265,288 58,057 398 594,445

(45.53) (44.63) (9.77) (0.07) Cumulative

to 2015 316,803 458,125 60,662 414 836,595

(37.87) (54.76) (7.25) (0.05)

Year 2006 10,223 30,164 1,036 50 41,485

(24.64) (72.71) (2.50) (0.12)

Year 2011 5,005 22,388 284 35 27,712

(18.06) (80.79) (1.02) (0.13)

Year 2015 5,989 20,398 110 87 26,584

(22.53) (76.73) (0.41) (0.33)

Year 2016 6,662 21,024 126 86 27,900

(23.88) (75.35) (0.45) (0.31)

Year2017 8,364 27,007 124 127 35,652

(23.46) (75.75) (0.35) (0.36) Note: Figures in parentheses are the corresponding shares for each row year (data source:

China’s Ministry of Commerce website). The shares are calculated by the authors.

25

TABLE 2

The Incidences from JVs to WOs by Type (%) （1）（2）（3）（4）（5）（6）（7）（8）

All

Sample Local Partners Origins of FDI Foreign Equity Shares

Year SOE Non-

SOEs HMT Foreign (0,25) [25,50)

[50,100

)

1998-1999 3.57 2.03 4.11 3.98 3.18 1.36 2.45 6.27

1999-2000 3.04 1.83 3.44 3.24 2.84 0.95 1.88 5.70

2000-2001 4.65 3.29 5.01 5.59 3.64 1.06 4.36 6.86

2001-2002 4.44 2.86 4.80 4.97 3.89 1.44 3.13 7.64

2002-2003 4.83 3.87 5.02 4.92 4.75 1.98 2.69 9.15

2003-2004 6.55 4.34 6.89 7.49 5.69 2.61 4.36 11.29

2004-2005 7.67 6.01 7.83 8.32 7.13 5.82 6.22 10.59

2005-2006 6.73 5.61 6.84 7.30 6.29 4.73 5.11 10.09

2006-2007 6.66 5.28 6.76 7.20 6.24 4.09 4.80 10.51

Mean 5.35 3.90 5.63 5.89 4.85 2.67 3.89 8.68

s.e 1.60 1.53 1.49 1.76 1.55 1.78 1.43 2.10

Note: The yearly ratio of the statistics is interpreted as the following, with the first row of

All Sample as an example. The ratio 3.57% is calculated as the number of joint ventures in

1998 which became solely foreign owned in 1999 divided by the total number of joint

ventures in 1998.

26

FIGURE 1

Foreign Ownership Consolidation Incidence by Industry

FIGURE 2

Foreign Ownership Consolidation Incidence by Province

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0

100

200

300

400

500

600

700

800

900

1000

number of firms incidence ratio

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0

500

1000

1500

2000

2500

3000

Guan

gdon

g

Fuji

an

Shan

ghai

Hai

nan

Zhej

ian

g

Shan

dong

Tin

ajin

Lia

on

ing

Jian

gsu

Bei

jing

Hei

bei

Hun

an

Jili

n

Jian

gxi

Hub

ei

Anh

ui

Shan

xi

Hei

long

jian

g

Hen

an

Guiz

hou

Sic

huan

Shaa

nxi

Guan

gxi

Qin

gh

ai

Nin

gx

ia

Yun

nan

Nei

men

gg

u

Xin

jian

g

Ch

ong

qin

g

Gan

sunumber of firms incidence ratio

27

TABLE 3

Institution Quality Index across Regions

Regions Index Min Max Mean Std.

Eastern and

Coastal Areas

(11 provinces)

C -1.03 5.31 1.16 1.43

Q 0.00 0.80 0.53 0.18

T 0.21 12.07 5.81 2.46

Relative T 0.00 0.92 0.48 0.19

L 0.45 11.28 3.80 2.91

Relative L 0.00 0.92 0.64 0.25

Middle Area (8

provinces)

C -1.91 0.70 -0.60 0.62

Q 0.58 0.92 0.75 0.08

T 0.00 7.07 3.06 1.67

Relative T 0.39 0.93 0.70 0.13

L 0.27 6.56 2.00 1.79

Relative L 0.40 0.94 0.79 0.15

The West (11

provinces)

C -2.58 0.86 -0.79 0.81

Q 0.56 1.00 0.77 0.10

T -0.84 8.34 3.33 1.79

Relative T 0.29 1.00 0.68 0.14

L -0.47 5.30 1.90 1.61

Relative L 0.51 1.00 0.80 0.14

Note: Definitions of the indices are in Section 3.1.

28

TABLE 4

Summary Statistics of the Main Variables

Obs Mean Std. Dev. Min Max

ln(labor productivity):

lnLP 146,128 9.082 1.467 3.709 14.827

Age 146,112 8.051 7.407 2 69

lncapital stock: lnK 146,128 10.435 1.444 7.037 16.22

lnWage 146,128 2.481 0.68 -0.911 4.924

Debt 146,128 0.428 0.173 0 1.244

ShareDistance 146,128 -10.525 45.586 -99.29 98.912

Export Intensity 146,128 0.355 0.416 0 1

Note: Definitions of the variables are in Section 3.2.

29

TABLE 5

The Composite Measure of Institution Quality

(Dependent variable: foreign ownership consolidation WO)

Variables

(1)

The Composite

Measure, C

(2)

Decrease in

government’s

Interference, T

(3)

Lawyers’ Ratio in

Population, L

Measure of

Institution Quality -0.102** -0.061** -0.009

(0.04) (0.03) (0.02)

lnLP 0.052*** 0.052*** 0.054***

(0.01) (0.01) (0.01)

Age -0.043*** -0.043*** -0.043***

(0.00) (0.00) (0.00)

lnK -0.097*** -0.097*** -0.099***

(0.02) (0.02) (0.02)

Wage -0.163*** -0.163*** -0.161***

(0.05) (0.05) (0.05)

Debt 0.193 0.194 0.190

(0.12) (0.12) (0.12)

ShareDistance 0.011*** 0.011*** 0.011***

(0.00) (0.00) (0.00)

Fixed Effects

Province Yes Yes Yes

Industry Yes Yes Yes

Year Yes Yes Yes

Observations 146,112 146,112 146,112

Note: *** and ** indicate the significance levels of 1% and 5% respectively. Figures in

parentheses are standard errors, clustered by province. Regression results on constant and

on province, industry and time dummies are not reported for brevity.

30

TABLE 6

Variations in Institution Quality across Provinces


Variables (1)

Q

(2)

Relative T

(3)

Relative L

Relative Institution

Quality Index 0.806** 0.785** 0.101

(0.35) (0.33) (0.25)

lnLP 0.052*** 0.052*** 0.054***

(0.01) (0.01) (0.01)

Age -0.043*** -0.043*** -0.043***

(0.00) (0.00) (0.00)

lnK -0.097*** -0.097*** -0.099***

(0.02) (0.02) (0.02)

Wage -0.163*** -0.163*** -0.161***

(0.05) (0.05) (0.05)

Debt 0.193 0.194 0.190

(0.12) (0.12) (0.12)

ShareDistance 0.011*** 0.011*** 0.011***

(0.00) (0.00) (0.00)

Fixed Effects

Province Yes Yes Yes

Industry Yes Yes Yes

Year Yes Yes Yes

Observations 146,112 146,112 146,112


parentheses are standard errors, clustered by province. Regression results on constant and on

province, industry and time dummies are not reported for brevity.

31

TABLE 7

Results after Correcting Endogeneity


parentheses are standard errors, clustered by province. Regression results on constant and

on province, industry and time dummies are not reported for brevity.

(1) First Stage (2) Second Stage

Variables (Dependent var: Q) (Dependent var: WO)

Q 2.421***

(0.44)

lnLP -0.003*** 0.060***

(0.00) (0.01)

Age -0.000** -0.043***

(0.00) (0.00)

lnK 0.013*** -0.130***

(0.00) (0.02)

Wage -0.086*** 0.050

(0.00) (0.09)

Debt 0.000 0.185

(0.00) (0.13)

ShareDistance -0.000*** 0.011***

(0.00) (0.00)

Britain -0.205***

(0.00)

France -0.276***

(0.00)

Russia -0.029***

(0.00)

Multi -0.210***

(0.00)

School Enrollment -0.110***

(0.00)

constant 0.843*** -4.695***

(0.00) (0.39)

Fixed Effects

Province Yes Yes

Industry Yes Yes

Year Yes Yes

F-stat 7544.17

Degree of freedom 5

Over identification test

(Sargan’s statistics) 8.8

Adjusted 𝑅2 0.31

Observations 142,299 142,299

32

TABLE 8

Nationalities of the Partners and the Range of Foreign Equity Shares


Note: Figures in parentheses are robust standard errors, clustered by province. ** and *

indicate significance levels of 5% and 10% respectively. Regression results on other controls

are not reported for brevity.

Variables\sub

samples

(1) (2) (3) (4) (5) (6) (7)

Local partners Foreign Partners Range of Foreign Equity

Shares (%)

SOE Non-

SOEs

HMT Foreign (0,25) [25,50) [50,100)

Q -0.164 0.815** 0.975** 0.644** 1.479** 1.129** 0.507* (0.41) (0.40) (0.45) (0.29) (0.73) (0.53) (0.28)

Other

controls:

Firm-level

variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fixed Effects

Province Yes Yes Yes Yes Yes Yes Yes

Industry Yes Yes Yes Yes Yes Yes Yes

Year Yes Yes Yes Yes Yes Yes Yes

Observations 21,351 124,691 69,745 76,211 22,193 73,017 50,398

33

TABLE 9

Firm Heterogeneity—Export Intensity


Variables (1)

Q 0.796**

(0.35)

Q*Export Intensity 0.196

(0.18)

Export Intensity 0.049

(0.09)

Other controls:

Firm-level variables

Yes

Fixed Effects

Province Yes

Industry Yes

Year Yes

Observations 146,112

Note: Figures in parentheses are robust standard errors, clustered by province. **

indicates significance level 5%. Regression results on other controls are not

reported for brevity

34

TABLE 10

The Policy Shock


Note: Figures in parentheses are robust standard errors, clustered by province. **

indicates significance level 5%. Regression results on other controls are not reported

for brevity.

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

C Q

Institution Quality -0.108** -0.108** -0.117** 0.850** 0.851** 0.921**

(0.04) (0.04) (0.05) (0.35) (0.35) (0.36)

Institution Quality*PC -0.010 0.078

(0.02) (0.15)

Institution Quality

*PC_severe -0.011 0.085

(0.04) (0.34)

Institution Quality

*PC _loose -0.009 0.073

(0.02) (0.15)

Institution Quality

*NPC _limited 0.024 -0.191

(0.04) (0.33)

Institution Quality

*NPC _permitted 0.009 -0.071

(0.02) (0.16)

Institution Quality

*NPC _encouraged

0.007

-0.057

(0.03) (0.22)

Other controls:

Firm-level variables Yes Yes Yes Yes Yes Yes

Fixed Effects

Province Yes Yes Yes Yes Yes Yes

Industry Yes Yes Yes Yes Yes Yes

(N)PC indicators Yes Yes Yes Yes Yes Yes

Year Yes Yes Yes Yes Yes Yes

Observations 124,676 124,676 124,676 124,676 124,676 124,676

35

TABLE 11

The Alternative Measure of Judicial Quality


Variables (1) (2)

Disputes Protection Trust in Judicial System

Q 0.516*** 0.712*** (0.07) (0.09)

Other controls:

Firm-level variables

Yes

Yes

Fixed Effects

Province Yes Yes

Industry Yes Yes

Year Yes Yes

Observations 122,668 122,668

Note: Figures in parentheses are robust standard errors, clustered by city. *** indicates

significance level of 1%. Regression results on other controls are not reported for brevity.

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