Market Facilitation by Local Government and Firm Efficiency:
                                      Evidence from China 1


                                  Robert Cull* World Bank
                                Lixin Colin Xu World Bank
                                        Xi Yang Johns Hopkins University
                                   Li-An Zhou Peking University
                                      Tian Zhu China Europe International Business School




                                                    Abstract
We use data from a large survey of Chinese firms to investigate whether local government
efforts to facilitate market development improve firm efficiency. Both government provision of
information about products, markets, and innovation and government assistance in arranging
loans are positively associated with firm efficiency, and those private firms with weak access to
and knowledge of financial, input, and product markets benefit most from such assistance. These
patterns are robust across multiple estimation approaches. Our examination of the determinants
of local government facilitation also suggests that it gravitates toward promoting efficiency,
though there are also indications that rent-seeking may play a role. Our evidence is consistent
with the notion that government facilitation can help some firms overcome market failures in the
early stages of a country’s private sector development. Though causality is difficult to establish,
we argue that changing fiscal dynamics that forced local governments to become increasingly
self-reliant in generating revenue, and a government promotion system based on local economic
performance, were key motivating factors for market facilitation by local government officials.

Key words: government facilitation, local government, market failures, China.

JEL codes: H1, H7, O1, O2, O5, P2, P5, G3.




1
  The views presented here are the authors’ own and not necessarily those of the World Bank, its member countries
or their executive directors. We have benefited from comments of Oliver Li, Bernard Yeung, Cheryl Long, and
other participants at workshops at National University of Singapore and Xiamen University. We are especially
grateful to an anonymous referee for thoughtful and detailed comments that have significantly improved the paper.
*
  Corresponding author. E-mail address: rcull@worldbank.org. Telephone 202-473-6365. Fax 202-522-1155.

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1. Introduction
       The great divergence in economic performance among developing and transition
economies in the past several decades has invigorated the debates on the proper role of the
government in economic development. As Rodrik (2008b) points out, in theory there is ample
room for governments to correct the rampant market failures in a developing country, but
identifying the conditions under which government intervention is likely to improve economic
performance remains an open empirical issue.
       A vast literature on the role of government in economic development focuses mostly on
national level government policies. However, in this paper we look at the issue of local
government activism in economic development. Using data from a large survey of Chinese
firms, we investigate what determines local government activism, and whether or when it
contributes to firm efficiency. We focus on two specific roles that local Chinese governments
have played in supporting local businesses: (1) an informational role in which they inform firms
about products, technologies and market opportunities, and (2) a financial role in which they help
in securing loans for firms from banks.
       In a nutshell, we find that both forms of local government activism were associated with
higher firm productivity in our sample period, and the effects varied by local income level,
industry and firm ownership. Specifically, we find that government activism was helpful in
relatively poorer regions and in industries that sold more to the domestic market, though activism
was also helpful for firms that sold heavily to overseas markets but also relied heavily on
innovation. Bank loan facilitation was also especially helpful for domestic non-state firms, while
both information and loan facilitation benefited pure foreign firms, and joint venture firms
benefited only from loan facilitation. These are the types of firms that were likely to have poor
access to and/or information on financial, product, and input markets, and thus stood to benefit
most from such forms of market facilitation. These results are therefore consistent with a
conditional proposition that government activism can help mitigate market failures when and
where they are severe.
       In addition to helping us to better understand the Chinese privatization experience, this
paper is important because it rounds out the discussion of private sector development in
transition economies. Much of that literature has focused on the effects of ownership, not only

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the distinction between government versus private ownership, but also the importance of
ownership concentration for firm performance (see Estrin et al., 2009 for an excellent review).
Within the group of private owners, special attention has been paid to the role of foreign owners
in spurring firm productivity and profitability, a topic that we also examine below. These issues
have also been studied in the Chinese context using samples of firms different from ours (Hu et
al., 2004; Jefferson and Su, 2006; Song and Yao, 2004; Sun and Tong, 2003; Tian and Estrin,
2008; Wang et al., 2004; Xu et al. 2005; Yusuf et al., 2006). For example, Tian and Estrin (2008)
show that the corporate value of Chinese publicly listed companies (PLCs) generally declines
with greater government ownership, except at high levels of government ownership
concentration, where the effect becomes positive. They also document that the government often
maintained large ownership shares in Chinese PLCs and remained the majority shareholder in
31% of the companies in their sample.
       The Chinese experience is also important in the context of the broader literature on
approaches to market reform in transition economies. Svejnar (2002) distinguishes between Type
I reforms, which include macro stabilization, price liberalization, opening to international trade,
and dismantling of the institutions of the communist system (including reductions in direct
subsidies to SOEs), and Type II reforms that involve market-supporting legal, regulatory, and
institutional improvements, and include protection of property rights and privatization. The
speed of reform has also been a salient issue in the literature. For example, Type II legal and
institutional reforms tended to progress slowly in the transition countries of Europe and Central
Asia (European Bank for Reconstruction and Development, various years). In the Chinese
context, there has generally been a more gradual, sequenced approach to both types of reforms,
including some Type I reforms that other transition economies rapidly undertook. There is also
variation in results across studies of the effects of private ownership on the productivity and
efficiency of Chinese firms (Estrin et al, 2009). In part, this variation could stem from
differences in methodology and firm samples, but the lack of robustly positive results could also
stem from China’s deliberate approach to reform, including the afore-mentioned reluctance (or
inability due to political considerations) to cede ownership shares in many companies and to
develop complementary market-supporting institutions to fully develop the private sector.
       The efforts to facilitate markets that we study therefore can be considered an alternative,
or perhaps a complementary, approach to privatization and more standard reforms for facilitating

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private sector development. We argue below that the combination of a government with strong
state capacity and the lack of market-supporting institutions after decades of central planning
motivated these efforts. To our knowledge, ours is the first micro-level study that attempts to
link specific forms of market facilitation by local governments to firm-level efficiency in the
context of the Chinese economy; such micro-level econometric evidence on the effect of specific
forms of market facilitation is also rare for other developing countries.
       The rest of the paper is organized as follows. Section 2 describes related literature, while
Section 3 provides background information that assists us in formulating key hypotheses. Section
4 describes the data and variables, and Section 5 presents the main analysis and results. In
Section 6, we assess the robustness of our main findings. We include additional controls to
address potential omitted variable bias and we test whether the relationships between local
government facilitation and firm productivity vary by firm ownership type, the level of local
economic development, or across industries. We also demonstrate the robustness of our findings
to alternative measures of government facilitation. The patterns that we uncover indicate that the
firms least equipped to access formal markets benefited most from these local government
interventions. Section 7 concludes.


2. Related Literature
       In a broad sense, our study concerns the role of government in fostering economic
development, an especially salient issue in the context of the so-called East Asian miracle
(Wade, 1990; World Bank, 1993; Page, 1994; Aoki, et al., 1996; Stiglitz, 1996, 2003; Shleifer,
1997; Stiglitz and Yusuf, 2001; Easterly, 2001; Rodrik, 2008a and 2008b; Brown et al., 2009;
Lin and Monga, 2010). Our study is closely related to the large literature on the role of local
governments in the rapid growth of China in the 1980s and early 1990s or in other
developing/transitional countries. Many authors (Byrd and Lin, 1990; Chung and Wang, 1994;
Che and Qian, 1998a, 1998b; Jin and Qian, 1998; Li, 1996; Nee, 1992; Song and Du, 1990;
Walder, 1995a and 1995b) argue that local governments contributed critical inputs, such as land,
initial collective assets, political connections and human capital to these collective enterprises,
helped in securing loans from state-owned banks and sometimes provided political protection for
these firms against the predatory behavior of the state. Even after the mass privatization of
township and village enterprises (TVEs) and small SOEs in the mid-1990s, local governments

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remained active players in China’s local economies (Jin et al., 2005; Montinola et al., 1996; Xu,
2011; Zhou, 2009). Several authors have characterized the Chinese government as a “helping
hand” to business firms in contrast to the Russian government as a “grabbing hand” during the
transition from planned to market economy (Frye and Shleifer, 1997; Li, 1998; Brown et al.,
2009). By and large, this literature has viewed the role of China’s local governments in a positive
light given the strong record of Chinese GDP growth. But firm-level micro-econometric studies
on whether and how local governments aided economic development are rare, in part because it
is difficult to identify and measure specific government interventions at the local level and to
uncover firm-level information on when and how those interventions provided assistance.
       Why would a Chinese local government go out of its way to help businesses instead of
expropriating them? We attribute this to the Chinese system of fiscal decentralization and its
performance-based political promotion system. During the 1980s, China's fiscal system moved
from a so-called “eating from the same big pot” (da guo fan) regime to the new “eating from
separate kitchens” (fen zao chi fan) regime. Under the old regime, local governments submitted
almost all fiscal revenues to the central government, which in turn dictated local fiscal
expenditures. Thus, they only passively followed central directives and had no fiscal incentives.
The new regime featured a set of fiscal revenue-sharing contracts that allowed local governments
to enjoy residual fiscal revenues after submitting a pre-specified amount or proportion of
revenues to the central government (Lin and Liu, 2000). This wave of fiscal decentralization
provided a close linkage between government spending and fiscal revenues generated at local
levels. Then, at the end of 1993, the central government launched a new round of fiscal reforms
featuring a tax-sharing scheme that is still in effect today.       These reforms pushed local
governments to rely increasingly on their own revenue sources (such as land sales) to finance the
provision of local public goods. In order to increase local fiscal revenues, local governments
were compelled to attract investment and help firms grow faster.
        Indeed, many authors have argued that fiscal decentralization provided strong financial
incentives for local governments in China to promote economic growth (Wong, 1992, 1997; Oi,
1992, 1995; Weingast et al., 1995; Montinola et al. 1996; Li, 1998; Berkowitz and Li, 2000; Lin
and Liu, 2000; Jin et al., 2005). They provide either qualitative or quantitative (cross-sectional
as well as longitudinal) evidence on the positive relationship between fiscal incentives and local
economic performance. The role of fiscal decentralization in providing incentives for local

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government officials to promote economic growth is implicit in these studies. In this respect, our
paper is related to the large literature on the economic implications of fiscal as well as political
decentralization in general and in the case of China (Qian and Weingast, 1997; Davoodi and
Zou, 1998; Oates, 1999; Bardhan, 2002; Besley and Coate, 2003). There are both benefits and
costs to decentralization. Many authors emphasize the benefits of decentralization including
more efficient provision of public services and constraining central government power and thus
preserving market incentives (Weingast, 1995; Oates, 1999; Jin et al., 2005; Montinola et al.,
1995), whereas others caution against the costs of decentralization such as a more skewed
distribution of income and greater macroeconomic volatility (Prud'Homme, 1995). Neither the
overall effect of decentralization nor its effect in a specific area is a settled issue yet. For
example, some authors argue that decentralization reduces corruption (Fisman and Gatti, 2002),
while others show that this effect may not exist or that it may actually increase corruption
(Treisman, 2000; Fan et al., 2009; Cheung et al., 2010). In the context of the Chinese economy, a
number of authors have challenged the argument that fiscal decentralization has been responsible
for China’s growth miracle (Zhang and Zou, 1998; Cai and Treisman, 2006).
       Another important incentive system motivating Chinese local officials is its performance-
based political competition (Maskin, Qian and Xu, 2000; Blanchard and Shleifer, 2001; Li and
Zhou, 2005; Chen, Li, and Zhou, 2005; Xu, 2011). Beginning in the late 1970s and early1980s,
the Chinese Communist Party shifted its priority from political campaigns to economic
development. As a result of this shift, local GDP growth became one of the key performance
indicators for local leaders' promotion evaluation, and it still is today (Chen, Li, and Zhou, 2005;
Xu, 2011, Li, 2013, Wu et al., 2013). The economic-performance-based promotion rules gave
rise to a tournament competition for economic growth among Chinese local officials.
       More specifically, China adopted a so-called "one-level down appointment" system in the
early 1980s, under which the central government appoints provincial leaders, provincial
governments appoint city leaders within provinces, and city governments appoint county leaders
within cities and so on (Li and Zhou, 2005). Most local leaders start their careers from low-level
governments and move up sequentially to a higher level, depending on their performance relative
to their peers. The GDP-growth based promotion rule applies to local leaders at provincial,
prefectural, and county levels. This means that Chinese local leaders are placed in a multi-layer
hierarchy and each competes with his or her local peers essentially on economic terms. Such a

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multi-layer political competition has generated strong incentives for local leaders to promote
regional economic growth by facilitating market development and nurturing local businesses,
especially for those at prefectural and county levels (Li et al., 2012). To the extent that China's
tournament competition relies on an authoritarian regime with one-party rule, it is unique to
China or similar settings and not applicable in those countries with democratic systems.
However, China's case highlights the positive role of economic performance-based political
competition among local politicians in incentive alignment of government officials, which has
general implications even for democratic countries (Besley and Case, 2003; Besley et al., 2010).


3. Background and Formulation of Hypotheses
       To assess what motivated local Chinese governments to assist firms, we first classify
three possible theories of government facilitation. The first is random facilitation in which the
local government facilitates loans and provides information randomly (and firms randomly seek
such assistance). While this theory may sound naïve, this is what we would expect in a
neoclassical world.    If this theory is true, we would expect no significant patterns in the
relationship between government facilitation and the list of potential determinants defined below
in section 5.a. A second theory is facilitation in exchange for rents. Similar to the capture
theory (Stigler 1971, Peltzman 1976), this theory suggests that government grants information
facilitation and loan help to firms that can provide more rents to it. This would predict that
government facilitation is more likely for larger firms because they can most affect employment
(a key goal of the government), for state-owned firms in order to protect less efficient firms and
due to their long-standing relationships with the government, and in places with more corruption
and worse institutions.     The third theory is facilitation for efficiency, which posits that
government facilitation is more likely where efficiency gains are likely to be greater. It implies a
greater likelihood of government facilitation for younger firms who likely need more assistance,
for non-state firms who tend to have less established relationships with state-owned banks, for
larger firms in which the same facilitation may yield a larger benefit due to scale economies, for
firms with stronger CEO leadership in which the same facilitation may yield a larger benefit, and
in locations with better institutions since those institutions constrain government officials and
channel government facilitation into more efficient activities.



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       Both the conjecture of facilitation in exchange for rents and that for efficiency imply that
local leaders’ tenure in their positions matters. Since local party secretaries tend to have short
tenures, on average 3 to 4 years on the job (Lu and Liu 2013), more experienced secretaries may
be better able to provide that facilitation. At the same time, party secretaries with longer tenures
are also more likely to behave like stationary bandits to internalize the benefits of such
facilitation (Olson 2000). Under either scenario, government facilitation should be more likely
in locations with longer-serving party secretaries.
       In their informational role, local governments provide firms with helpful contacts or
information on products, technologies or market opportunities. Information problems and other
market imperfections have hindered economic development in developing and transition
countries. For instance, how to match a large number of sellers and buyers that are
geographically dispersed is a serious challenge for developing countries like China. The lack of a
system which transmits information on product quality and sellers’ reputation limits the scope of
trade. Unlike developed countries where the private sector has developed multiple means of
overcoming asymmetric information, such as private credit and collateral registries, many
developing countries lack such market-supported, private-order institutions. China faces even
more serious challenges in these respects than many developing countries since it had a three-
decade period of a planned economy and markets did not exist when economic reforms started in
the late 1970s. However, Chinese local governments have helped firms overcome information
asymmetries by providing access to telecommunications infrastructure (e.g., telephone, fax, and
internet networks), improving matching efficiency in labor and product markets, monitoring
product quality, and exposing firms to new advances in technology.
       A prominent example of improving market matching and overcoming information
asymmetry is China’s rapid development of specialized markets under the strong support of local
governments. Zhejiang province perhaps serves as the best example. Between 1978 and 2003,
the total number of marketplaces in Zhejiang province, including specialized markets, rose from
1332 to 4036. Specialized markets enticed a large number of buyers to come on site and, by
pooling a large set of diverse demands, made it profitable to produce specifically for those
markets. By providing inputs for a large number of producers who cluster in the neighborhood of
the markets and specifically targeting those markets for their sales, the expansion of specialized



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markets contributed substantially to the emergence and growth of Zhejiang’s industrial clusters
(Ke, 2012, p. 32).
       To take an even more specific example, Yiwu is a county-level city located in the middle
part of Zhejiang province. It was a poor agriculturally-dominated region in the late 1970s.
However, it also had a long tradition of peddling even under strict regulations against commerce
during the planning regime, partly due to the tacit endorsement by the village and township
governments. The establishment of Yiwu Market in 1982 was a conscientious response to the
grassroots initiatives for market trade, as well as a focused government initiative to become a
leader in specialized markets. Initially small in scale (about 700 booths), Yiwu Market’s rapid
growth made it China’s No.1 market for industrial products by 1991, a position it has maintained
ever since. By 2006, Yiwu Market was home to 58,000 booths with total sales of over 30 billion
RMB, attracting business people from all over the world.
       In the successful story of specialized markets in Yiwu, the local government provided
initial capital to build physical structures for markets, lowered entry barriers for small,
inexperienced booth keepers, and maintained law and order to deter fraud. Construction of
specialized markets required land and capital investment, which was well beyond the financial
capacity of any private enterprise at that time. Some of these specialized markets occupied
100,000 to 500,000 square meters, and construction of each specialized market typically cost 10-
50 million RMB in 1998. As the trading volume of these markets rapidly increased, older
specialized markets had to be relocated and expanded. In the process of establishing and
expanding specialized markets, local governments repeatedly provided necessary funding and
other support.
       The role of local government in the evolution of specialized markets can be characterized
as that of a platform builder and facilitator. In order to maximize the number of sellers and
buyers meeting in the marketplaces, the local government levied very low taxes and fees on
booth-keepers compared to general tax rates in China. The overall rate of taxes and fees as a
percentage of sales in Zhejiang specialized markets was about 1 percent, while the general
turnover tax rate was about 5 percent of sales (Ke, 2012, p. 45). In addition, the local government
provided information facilitation by creating a price index for each product category which
reduced search costs for buyers. Reputation ratings were also provided for booth-keepers in the
marketplace to reward reputable sellers and punish cheaters. Yiwu Market’s reputation

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monitoring evaluation system classified the nearly 60,000 booths into six levels. The
administrative agency in that marketplace also fought counterfeit products and encouraged
product upgrading.
       In their financial role, local governments help businesses to obtain bank loans. As shown
in Appendix A, the Chinese banking sector is reasonably large (in terms of Deposits/GDP)
compared with other countries at a similar stage of economic development, but it is more state
dominated and has little foreign bank presence. In recent years, a shadow banking system has
grown rapidly, comprised of trust companies and other informal lenders to meet the needs of
private firms, presumably because the formal banking sector has done a poor job of allocating
credit to those firms (Wei and Davis, 2013). We speculate that these features could help explain
the need for local government assistance in arranging loans, and we note also that our findings
might be most applicable in banking environments with high shares of state ownership.
       In general, accessing finance is a major obstacle for SMEs in developing countries since
financial markets in those countries are typically underdeveloped, and SMEs usually have no
tangible assets to pledge as collateral for bank loans. This is especially the case for SMEs in
high-tech industries where innovation requires sizable, up-front investment in R&D, and
commercialization of patents takes time and may not bear fruit. As in other developing countries,
SMEs in China tend to rely on informal financing channels and new technology ventures develop
slowly due to credit constraints. But Chinese local governments have tried hard to facilitate
finance for SMEs. For example, local governments have helped provide easier access to finance
for private firms located in development zones. Government zone administrative committees, or
companies funded and run by the committees, often provide firms located in the development
zones with guarantees on loan repayment or subsidies on interest payments. They also strive to
facilitate the applications for specific government funding from central government ministries
(such as the Ministry of Science and Technology) for firms that meet the funding requirements.
From the late 1990s, China began cultivating a venture capital industry in order to promote
innovation and entrepreneurship in development zones. While continuing to attract and support
large manufacturing firms, local governments at different levels made substantial efforts to adapt
the financing channel to nurture R&D-oriented, small-sized ventures in high-tech industries, by
encouraging VC firms to invest in R&D-oriented SMEs located in development zones or by
establishing their own VC firms to make equity investments in technology ventures.

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        This second role in facilitating financing may be more controversial because a bank loan
for one firm might be allocated more efficiently to another firm in the absence of government
intervention. In China’s case, bank loan decisions became more centralized after the Asian
financial crisis in 1998, and thus the influence of local governments on loan decisions has
waned. Local governments cannot dictate that a state-owned national bank lend to a specific
firm, but can only act as a go-between or as a guarantor for the firm. Local government
facilitation may help secure loans for firms that have promising businesses but cannot get
sufficient credit, perhaps due to the lack of collateral or rigid national level bank policies that are
poorly adapted to local situations. On the other hand, we concede that it may also prop up
inefficient firms, especially SOEs (Bai, Hsieh and Song, 2014). And indeed, direct interventions
in credit markets by governments have generally not been a reliable means of improving firm
efficiency. Therefore, it remains an empirical question whether loan facilitation by local
governments in China improved firm efficiency.


   4.   Data, Variables
        Our data come from a survey of firms for the period between 2000 and 2002, conducted
in early 2003 by the World Bank on the investment climate in China. Firms were drawn from 18
cities that were selected to achieve balanced representation across five regions: Northeast
(including Benxi, Changchun, Dalian, and Harbin), coastal (including Hangzhou, Jiangmen,
Shenzhen, and Wenzhou), central (including Changsha, Nanchang, Wuhan, and Zhenzhou,
southwest (including Chongqing, Guiyang, Kunming, and Nanning), and northwest (including
Langzhou and Xi’an). The total sample is composed of 2,400 firms, 100 or 150 from each city.
        The questionnaire has two parts. Part one, based on interviews with the manager of a
firm, contains questions on general information about the firm and the manager, innovation,
market environment, relationships with clients and suppliers, location of manufacturing plant,
relations with government, and international trade. Part two is based on interviews with the
firm’s accountant and personnel manager, who provided quantitative information on production,
costs, employee training, schooling, and wages. While most of the qualitative questions pertained
only to the year 2002, many quantitative questions also requested information for 2000 to 2002.
Therefore, in the regressions the qualitative variables are time-invariant, while quantitative ones
vary over 2000-2002.

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         Firms were sampled randomly subject to a few constraints. First, the survey sampled only
the following industries: for manufacturing, apparel and leather goods, electronic equipment,
electronic components, consumer products, and vehicles and vehicle parts; for services,
accounting and related services, advertising and marketing, business logistics services,
communication services, and information technology services. 2 Second, the size restriction of
firms as measured by the number of employees was pre-specified. 3 Once these constraints were
roughly satisfied, the surveyors randomly drew the required number of firms from an electronic
list of firms in that city. The data contain domestic private firms, pure foreign firms, joint
ventures and state-owned enterprises. State-owned firms are those firms with a positive share of
state ownership, 4 (pure) foreign firms are those with 100 percent foreign ownership, joint
ventures are those firms with both positive foreign and domestic ownership, 5 and domestic
private firms constitute the rest of the sample. The four ownership categories are mutually
exclusive. An advantage of this data is that we have a large sample of small and medium firms
who are more likely to suffer from information and financing problems.
         The two key variables for government activism are: Info Facilitation, which is based on
the answer to the survey question regarding whether the firm obtained information on product
and technology advances from the government, and Loan Help, which is based on the answer to
the question regarding whether the government offered assistance in obtaining bank loans. We
call these government facilitation variables throughout. Because Loan Help is based on a
question about loans from banks, it is unlikely to contain information about access to other




2
  In certain cities, a few additional industries were included because of the inability to sample a sufficient number of
firms.
3
  For manufacturing (service) firms, the minimum number of employees was 20 (15) employees. When there were
not sufficient firms from a particular sector in a city, the size constraint was loosened.
4
  The results based on a 50% cutoff in state ownership are almost identical. Only 3% of the firms in the sample had
state ownership greater than zero but less than 50%.
5
  The survey also contains a variable that indicates whether the firm answered that it was a joint venture. However,
this variable is missing in 30 percent of the sample. Since it is reasonable to expect that a firm would know whether
it is a joint venture, we classify a firm as a JV when it would not be classified as one based solely on continuous
variables reflecting the share of ownership held by each ownership type at the time of the survey. However, in our
key table to test whether government facilitation affects firm efficiency based on ownership type, we also conduct a
robustness check to test whether the results are sensitive to alternative definitions of joint ventures and find that they
are not.

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sources of financing such as the equity finance from venture capital firms described in the
introduction. 6
         We recognize that governments have multiple levers through which they can affect firm
productivity. To more precisely identify the effects of Info Facilitation and Loan Help we
therefore include as an explanatory variable the effective tax rate faced by each firm, measured
as total tax payments divided by sales. 7 In robustness checks, we also include firm managers’
perceptions of the effectiveness of the national tax bureau and the local tax bureau and of the
number days the firm spends per year dealing with government regulators (see Table 1 for
detailed definitions of those variables).
         We also have a series of variables that act as proxies for the local institutional
environment, which may be correlated with both government facilitation and firm efficiency. We
need to control for these variables in order to better isolate the effect of government help on firm
efficiency. Following Acemoglu and Johnson (2005), we separate the property rights variables
into two groups: those that measure the risk of government expropriation and those that measure
the ease and reliability of contract enforcement. 8 First, for contract enforcement, we have the
index for property rights protection, which is based on a firm’s answer to the following question:
of all the commerce or other conflicts that the company has experienced, what is the likelihood
that their contractual or property rights are protected by the legal system? This variable (which
appears in some but not all of our specifications) ranges from 0 to 1, with a higher value
implying better protection of property rights. A related measure is court development, proxied
by the share of a firm’s disputes that are resolved by the court system. A higher value of court
development implies a stronger legal system and better protection of property rights (Long,
2010). As a proxy for corruption and government expropriation, we rely on the share of


6
  As noted above, venture capital firms provide equity finance primarily to R&D-oriented, small-sized ventures in
high-tech industries. Because our sample covers a much wider array of industries and firm sizes, it is unlikely that
the “Loan Help” variable summarizes information about those equity investments for the vast majority of firms.
7
  Cai et al. (2011) provide evidence that effective tax burdens affect firm performance in China.
8
  Acemouglu and Johnson (2005) unbundle institutions into “property rights institutions” and “contracting
institutions.” Property rights institutions capture how much private property is secure from the “grabbing hand” of
the state, for example through outright expropriation or bribe extraction. Contracting institutions capture the
effectiveness of institutions that are used to resolve disputes between private contracting parties, such as the courts
and the judicial system. Based on cross-country evidence, they find that property rights institutions tend to be more
important than contracting institutions in facilitating economic development. Their interpretation is that it is easier
for private parties to use alternative mechanisms to get around the contracting issues, but it is harder to avoid
government expropriation. Cull and Xu (2005) provide evidence that both types of institutions mattered for firm re-
investment rates in China.

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entertainment and travelling costs over sales (ETC). Cai et al. (2011) provide evidence that this
is a good proxy for corruption in China: ETC is higher in regions that feature higher tax burdens
and worse government services, presumably because it was spent to reduce tax burdens and to
improve government services; the average ETC level in a locality also has a negative relationship
with firm efficiency, but the effect differs across regions, with less pronounced negative effects
in regions with higher taxes and worse government services. We also include local GDP per
capita as a general control for the level of institutional and economic development, and in
robustness checks we further include indicators of the development of professional services
markets, which constitute an important part of the local business environment and are proxied by
managers’ perceptions of the availability and affordability of professional services that could
assist their firms.
           Since the efficiency of government bureaucracy may also affect firm efficiency, we
include additional control variables. 9 We use two variables to measure local government
efficiency. Government Efficient Services is an index that reflects the manager’s assessment
about the share of government officials that offer efficient services. Official Helping Firms is an
index that reflects the manager’s assessment of the share of government officials that help rather
than hinder firm development. Finally, leadership turnover has been frequent in Chinese local
governments—the typical party secretary, the top leader of a locality, only has a 3 to 4 year
tenure. This may have an effect on local economic development (Lu and Liu, 2013). To the
extent that longer horizons for local government leaders may lead to more efficient government
policies and actions that internalize the benefits of current efforts or investments, or alternatively
behave as “stationary bandits” in the words of Mancur Olson (Olson, 2000), it may be important
to capture the time horizon of local party secretaries. We thus create a measure, PS Long Tenure,
a dummy variable that equals one if the current party secretary has held his/her position for three
or more years. 10
           In our main regression, we also control for variables measuring a locality’s financial
development because, presumably, government help with loans as well as information may be
more useful for firms in a locality or industry that has poor access to finance. There is a large
literature on the linkages between access to finance and economic development (see Levine


9
    All three variables are based on direct questions posed in the survey.
10
    The default is thus that the party secretary has held his/her position for one to two years.

                                                                                                   14
1997, 2005). While most of this literature focuses on access to formal finance such as bank
loans and overdraft facilities, there is a growing literature that examines the impact of access to
informal finance (Allen, Qian and Qian 2005; Cull and Xu 2005, Cull, Xu and Zhu 2009).
Following this literature, we include measures of access to both formal and informal finance, and
investigate whether they have different effects on firm efficiency. Access to formal finance is
measured as the city-industry share of firms with access to bank loans. Access to informal
finance is measured as the city-industry average of trade credits granted to other firms. Trade
credit by itself provides short-term financing for working capital. Some observers of the Chinese
economy suggest that trade credit is an indirect means of channeling bank loans to profitable
firms (Allen, Qian and Qian 2005; Cull, Xu and Zhu 2009). In robustness checks, we also
include two variables that describe financing conditions: city-industry average levels of interest
rates on loans and the city-industry share of loans that are guaranteed by the government. This
robustness check ensures that our variable for government facilitation of loans does not merely
capture government subsidies and/or government guarantees.
       In the regressions that summarize firm productivity we also include as explanatory
variables basic firm characteristics such as age, the number of employees, ownership type
(foreign, state, private domestic, joint venture) and the number of years of CEO schooling. In
robustness checks, we include additional CEO characteristics such as the length of his/her tenure
in that position in years and the share of the firm that he/she owns.
       One might expect little variation in survey responses in an authoritarian country such as
China. However, the means and standard deviations reported in Table 2 indicate wide variation
in our key variables. About 16 percent of the firms in our sample received loan help, and 35
percent of firms received government information facilitation. About 64 percent of firms view
property rights protection in conflict resolution as reasonable, but only 8 percent of disputes are
resolved in the court. The majority of disputes are therefore settled without resorting to the
formal legal system. On average, entertainment and travelling costs account for 3.1 percent of
sales, with a standard deviation of 2.4 percentage points. There are indications that firm
managers on average have reservations about the quality of the state machinery: roughly 35% of
managers think the government offers efficient services and only 34% of government officials
are viewed as helping rather than hindering firm development.



                                                                                                15
        State owned firms comprise 21% of the sample, domestic private, 32.4%, joint ventures,
16.2%, and pure foreign, 30.1%. This diversity in ownership allows us to examine whether
government facilitation differentially affects the efficiency of different types of firms. State and
non-state firms differed on several dimensions (see Table 3). For example, state firms were
significantly less productive, with a disadvantage in labor productivity of 50 log points. State
firms were slightly less likely to receive government assistance in securing loans (15.0% vs.
15.8%), and substantially more likely to receive information facilitation (40.3% vs. 33.8%).
They also had a stronger sense of property rights protection: they spent less on ETC, had higher
self-reported scores on the index of property rights protection, and used courts substantially more
(9.6% vs. 7.7%). Interestingly, and perhaps surprisingly, in judging the efficiency of the state
machinery, the state and the non-state sectors reached similar conclusions based on the indices of
government efficiency and officials helping firms. The picture that emerges is thus one of the
government being reasonably unbiased toward non-state firms—at least not outrageously biased.
        Since we have a keen interest in understanding how the role of government facilitation
differs in poor and rich regions, Table 4 reports the summary statistics for these two types
separately. Firms in the poor regions were substantially less productive, with labor productivity
lagging behind on average by 71 log points. They received slightly less loan assistance, but a
similar amount of information facilitation. They spent substantially more on ETC and found
government officials to be less helpful. In contrast, firms in the poor and rich regions reported
similar property rights protection, had similar tendencies to use the courts, and judged official
efficiency similarly.


   5.   Empirical Specifications, hypotheses, and results
        In this section, we first examine the key determinants of government facilitation to shed
light on why the local governments supply (and firms demand) government facilitation. We then
proceed to examine the relationship between government facilitation and firm efficiency.


           a. Determinants of government facilitation
        While our ultimate purpose is to investigate the possible effect of government activism
on firm efficiency, it is useful to understand what motivates governments to provide information
and loan facilitation. To this end, we present a linear probability model in Table 5 that links Loan

                                                                                                 16
Help and Information Facilitation to (i) firm characteristics and industry dummies, (ii) CEO
characteristics, (iii) the local income level, (iv) the local institutional environment, (v) the
efficiency of government bureaucracy, and (vi) regional dummies to control for broader cultural
and other differences that could affect the nature of government assistance. 11 Note that models 3
and 4 include regional dummy variables while models 1 and 2 do not.
         The results in table 5 are mostly consistent with the facilitation-for-efficiency conjecture,
though there is also some indication that private interest also plays a role in government
facilitation. Many variables that are related to firm characteristics and the local political and
institutional environment are statistically significant, which refutes the random facilitation
hypothesis. Consistent with the facilitation for efficiency conjecture, but inconsistent with the
rent-seeking hypothesis, government facilitation is more likely for young firms, for those that
have stronger CEO incentives (such as managerial ownership and longer CEO tenure), and that
are located in areas with better-developed court systems. However, we also find that in locations
with higher ETC expenditures—which are presumably more corrupt places (Cai et al., 2011), or
places with wiggle room for making deals between the government and firms (Hallward-
Driemeier et al. 2010)—government facilitation is also more likely, which is consistent with the
rent-seeking facilitation hypothesis.           But this could also be consistent with the efficiency
hypothesis if one adopts the “grease payment” argument that only the more efficient firms can
afford to pay bribes to get the necessary government facilitation, and thus bribes represent an
efficient mechanism for allocating government facilitation (Lui 1985; Bardhan 1997). However,
the significant negative coefficient for pure foreign ownership in the information facilitation
regressions casts doubt on the notion that the most efficient firms are more likely to receive
government assistance given the well-documented superior performance of those enterprises




11
  The results based on probit are very similar. The regional dummies represent large areas that cover multiple
provinces that are similar in terms of geography and culture. Southern and northern areas are in general considered
distinct, as are coastal versus inland areas. In addition, the Manchurian provinces have historically been more
similar in terms of culture and colonial history, and thus are classified as an area in our analysis. Specifically, our
regional dummies are classified as follows: the Manchurian region includes Benxi, Changchun, Dalian, and Haerbin.
The Southcoast region includes Hangzhou, Jiangmen, Wenzhou, and Shenzhen. The North inland region includes
Zhengzhou, Xi’an, and Lanzhou. The South Inland region includes Kunming, Nanchang, Nanning, Changsha,
Guiyang, Wuhan, and Chongqing. On the effects of culture on financial and economic outcomes see, for example,
Ahern et al. (2012) on how cultural similarities affect the frequency, terms, and price reactions to cross-border
mergers (and cross-regional mergers in the U.S.) or Stulz and Williamson (2003) on how culture and language
influence the enforcement of creditor rights across countries.

                                                                                                                    17
(Estrin et al., 2009; Xu et al., 2004). Joint ventures enjoy the same level of government
facilitation as state-owned enterprises ( the omitted category in the regressions). 12


             b. The base regression specification and related empirical issues
        We now examine whether government facilitation is associated with improved firm
efficiency. We rely on the following reduced form equation:
     ln( LPijt ) = α + δ j + β1' FIRM it + β 2' LOCALit + β 3 InfoFacilitationi + β 4' LoanHelpi + ε it (1)

        The subscripts i, j and t represent firm, industry and time, respectively. The dependent
variable is log labor productivity (i.e., value added per worker). Since in many specifications we
also include industry dummies and their interaction with log firm size and log capital-labor ratio,
we are essentially using total factor productivity (TFP) as the dependent variable. As noted
above, the key variables are Loan Help and Info Facilitation. In the base specification, we
control for industry dummies represented as δj, firm characteristics (‘FIRM’ in equation 1)
including log firm age, log firm size (as measured by the number of employees), the ownership
dummy variables for domestic private, foreign, and joint venture ownership (thus state
ownership is the omitted category), and the CEO’s number of years of schooling. ‘LOCAL’
represents features of the local environment that could affect firm productivity including log
GDP per capita (at the city level), a dummy variable indicating whether the local party secretary
has had a lengthy tenure (i.e., three or more years), and city-industry average levels of: (a) access
to bank loans and trade credit, (b) firm managers’ perceptions of the efficiency of government
services provision and the share of government officials that help rather than hinder firm
development, (c) the ratio of firms’ expenditures on entertainment and travel to their sales
(ETC), and (d) the effective tax rate. In sensitivity checks (discussed later), we control for more
of the covariates described in the previous section, to test whether the estimates of our main
coefficients remain robust.
        A key empirical issue is that firm-level Loan Help and Info Facilitation may be
endogenous. That is, firms that receive active government facilitation may differ systematically
from those that do not. A natural way to identify their effects is to find good instruments that are
correlated with the two facilitation variables but are otherwise not determinants of firm

12
  This could be because the local governments are more likely to benefit from joint ventures’ successes. Such direct
interest of government in business also motivates government facilitation (Bai et al. 2014).

                                                                                                                 18
efficiency, but unfortunately we were unable to find good candidates. Instead, we address the
potential endogeneity of the two facilitation variables in several alternative ways. 13
            First, we do not directly use firm-level measures. Instead, we rely on city-industry
averages of firms’ answers to the relevant questions to gauge the local level of government
facilitation. This city-industry mean based measure is less subject to the reverse causality issue
associated with firm-level answers, and its effect on firm productivity should be viewed as
resulting from the level of government activism within a particular industry in a locality, not
necessarily from the direct government information or loan help received by the firm.
            Second, since there are obviously potential omitted variables at the city-industry or other
levels that may be correlated with our facilitation variables, we control for additional
determinants of firm efficiency, including measures of: the effectiveness of local-level
institutions such as courts, financing conditions, tax and regulatory burdens, the availability and
affordability of professional services needed by firms (such as accounting, legal, insurance) and
additional CEO characteristics, and show that our key results remain robust.
            Third, we conduct sub-sample analyses, and examine whether the effects of government
facilitation are stronger in certain subsamples in which market failures are supposed to be more
debilitating. If the results on government facilitation reflect purely omitted variables or other
concerns about endogeneity, there are no strong reasons that such correlations should be stronger
for firms in those subsamples. Confirmation of that conjecture would thus render support for our
basic hypothesis that active government facilitation may help firms in overcoming relevant
market failures.
            Finally, we rely on the Rajan-Zingales methodology to further ensure that the
government facilitation effects are not due to omitted variables (Rajan and Zingales, 1998).
Specifically, by controlling for city and industry dummy variables, we investigate whether the
effects of government facilitation are more pronounced in industries that would be expected to
benefit more from government facilitation. Those city and industry dummies are included to
control for all time-invariant factors specific to the local and the industry level, such as human
capital, culture, and governance. Our identification therefore comes from comparing firms in the
same industry, but in different cities that feature distinct levels of government facilitation. By



13
     See also Dollar et al. (2006), Aterido et al. (2011) and Xu (2011b).

                                                                                                    19
holding all city- and industry-level heterogeneity constant, this methodology goes a long way in
overcoming potential omitted variable bias.
       The similarity between the subsample analysis and the Rajan-Zingales analysis, in our
view, is consistent with a causal effect of government facilitation. However, with observational
data, endogeneity concerns can never be completely dismissed. We do not attempt instrumental
variable analysis since it can be especially problematic when potential instruments are correlated
through other channels in the residual of the performance equation (Bazzi and Clemens 2010;
Morck and Yeung 2011). We thus rely on a large number of robustness checks, and try to offer a
coherent argument to tie various findings together.


           c. Base results
       Table 6 presents the base results.       Columns (1) and (2) use firm-level government
facilitation variables directly; but our preferred results are in columns (3) and (4), because they
use city-industry average values of the facilitation variables which, as pointed out earlier, are less
subject to endogeneity concerns. Column (1) reports a labor productivity regression. Columns
(2)-(4) report TFP regressions: log labor productivity remains the dependent variable, but those
models also control for industry dummies and their interaction with the log capital-labor ratio
and log labor. For columns (2)-(4), we cluster the standard errors at the city-industry level to
avoid overstating the precision of our estimates (Moulton 1990). Column (4) also includes the
regional dummy variables described above.
       The results consistently show that government facilitation is associated with higher
productivity. In general, the results for labor productivity and TFP are qualitatively similar,
though magnitudes for the facilitation variables are a bit smaller in the TFP results in model (2),
especially for Loan Help. This makes sense since Loan Help would likely affect capital intensity,
and part of the effect of Loan Help on labor productivity is due to changes in capital intensity.
Interestingly, the coefficients for the city-industry average government facilitation are much
larger than for the firm-level government facilitation. This indicates that firm-level government
facilitation is negatively correlated with determinants of productivity, and thus government
facilitation is similar to a compensatory program in which the government picks lower
productivity (but presumably high-potential) firms when allocating limited government
facilitation resources. The magnitudes are large: in model 4, increasing local Loan Help by one

                                                                                                   20
standard deviation would increase TFP by 9.5 log points, and increasing local Info Facilitation
by one standard deviation would increase TFP by 11.2 log points.
           Since the results based on labor productivity and TFP tend to be qualitatively similar, and
those based on TFP perhaps are more defensible (because TFP is net of input changes), we focus
on TFP results in the rest of this paper. But the results in general are similar when using labor
productivity. Since our focus is on government facilitation, we just briefly note here that the
coefficients for our control variables indicate that TFP tends to be higher in larger, younger
firms, and in richer regions. Importantly, relatively to state-owned enterprises, domestic private
firms and foreign firms both have significantly higher productivity (around 23-27 log points), but
joint ventures are even more impressive – they have productivity 47 log points higher than state-
owned enterprises. 14 In addition, CEO schooling, the efficiency of government service provision,
and greater usage of trade credit are positively associated with productivity. In contrast, higher
tax rates are negatively linked to productivity. Productivity is also lower where firm managers
tend to perceive government officials as helpful. This is somewhat surprising, especially, for
example, in light of evidence in Hall and Jones (1999) that predatory, diversionary government
interventions are associated strongly with lower firm productivity. Perhaps governments needed
to be more active and helpful in low-productivity areas where market failures were likely to be
more severe in the Chinese context.


       6. Robustness Checks


                a. Omitted Variable Bias
            While our base regressions were designed to include an expansive set of control
variables, additional factors could conceivably affect firm productivity and their omission could
impact the coefficients we estimate for the government facilitation variables. In this sub-section
we therefore introduce controls related to: additional CEO characteristics, the effectiveness of
courts, tax and regulatory burdens, the availability and affordability of professional services
needed by firms (such as accounting, legal, insurance), and financing conditions. The results are
in Table 7.



14
     The results are consistent with Megginson and Netter (2001) and Estrin et al. (2009).

                                                                                                   21
       Adding CEO characteristics has modest effects on our estimates for the government
facilitation variables (column 2). The coefficient for Loan Help changes from 0.699 to 0.664 and
that for Info Facilitation from 0.560 to 0.486.      Similarly small changes in the facilitation
coefficients are found when we include the share of business disputes settled through courts, our
measure of judicial efficiency (column 3), or our measures of the effectiveness of local and
national tax bureaus and the burden imposed by dealings with regulators (column 4). Including
measures of the availability of professional services that assist firms (accounting, legal,
insurance, and others) reduces coefficients for both facilitation measures to near 0.5 (column 5).
The one for information facilitation remains significant at the 5 percent level, though that for
loan help just misses significance at the ten percent level. Adding variables describing local
credit conditions (city-industry means of interest rates on loans and the share of loans guaranteed
by government) actually increases the Loan Help coefficient to 0.728, though the coefficient for
Info Facilitation declines to 0.454 (column 6). Both coefficients are significant at the 10 percent
level (5 percent level for Loan Help). This check thus suggests that our Loan Help variable is not
merely reflecting the effect of government subsidies on loans or the effect of government
guarantees but rather its role as a matchmaker in credit markets. In almost all cases, the two key
variables achieve or approach statistical significance at the 10 percent level or better, though the
results are admittedly weakest when all five sets of additional controls enter the regression
(column 7). That could be due to the loss of observations and multicollinearity between
explanatory variables. All in all, however, the stability of our estimates is a good sign that our
key variables likely reflect independent sources of variation, not the influence of omitted
variables.


             b. Culture and Language
       Cross-country studies have shown that similarities in culture and language influence
financial outcomes such as the frequency of cross-border mergers and protection of creditor
rights (Ahern et al., 2012; Stulz and Williamson, 2003). Moreover, cultural differences within
the U.S. have also been shown to affect cross-regional mergers (Ahern et al, 2012) and, even
within a small country, Finnish investors were more likely to invest in stocks of firms that
communicated to them in their native tongue and had chief executives that shared their cultural
background (Grinblatt and Keloharju, 2001). In a country as large as China, therefore, one might

                                                                                                 22
expect cultural differences to affect firm productivity. And indeed, Gao and Long (2014) find
that economic growth is slower in Chinese cities that speak a different (main) dialect than those
that speak the same dialect as in the provincial capital. In only one of the cities in our sample is
the predominant dialect different than that spoken in the provincial capital (Dalian), which gives
us relatively little variation to exploit on that dimension. Still, as a simple check, we show that
the coefficients for the facilitation variables remain very similar when firms from Dalian are
dropped from the regression (Table 8, column 1).
         Table 8, column 2 provides a more stringent test by including city dummy variables to
account for local culture (and other factors). One could argue that this test is too stringent since
culture is not generally city-specific, and more importantly, city dummies are likely to drain
explanatory power from our government facilitation variables, which are measured as city-
industry means of firm reports of receiving government facilitation. However, the coefficient for
the information facilitation variable remains large (0.657) and highly significant, though the
Loan Help variable declines to 0.417 and is no longer significant.


             c. Government facilitation and firm ownership
         Active government help may have differential effects based on firm ownership. State,
domestic private, foreign, and joint venture firms differ in many aspects, which may alter the
costs and benefits of government assistance. SOEs, for instance, likely have more interactions
with local governments based on previous relationships, and can also conceivably contribute
more to the government’s agenda for political stability by preserving more jobs (Shleifer 1998;
Megginson and Netter 2001). SOEs also have better access to finance, and thus an efficiency-
oriented government may render more loan facilitation services to non-state firms. 15 Domestic
private firms in China, on the other hand, are shown in the literature to face more severe
financial constraints and have worse access to finance (Cull et al. forthcoming). Foreign firms,
in contrast, may have more informational problems in finding out what to produce. Joint
ventures, by forming an alliance between domestic firms and foreign firms, may have less need
for information facilitation. Alternatively, stronger incentives for local governments to help joint
ventures may imply that they offer better information to those firms, and they may therefore

15
  At the same time, a large literature suggests that such efficiency-oriented governments are rare, and that political
considerations are at the root of credit misallocation and firm/SOE under-performance (Shleifer and Vishny, 1994;
Sapienza, 2004; Johnson and Mitton, 2003; Dinc, 2005; Khwaja and Mian, 2005; Claessens, et al., 2008).

                                                                                                                     23
benefit more from such information facilitation. Relatedly, local governments may directly
benefit from joint ventures to a greater extent than other ownership forms and may have
incentives to provide better loan support to them (Bai, Hsieh and Song, 2014). However, since
joint venture firms tend to have less severe financial constraints due to access to foreign capital
in general, the effect of local loan help may be less pronounced than for domestic private firms,
though the superior productivity of the joint ventures (as shown earlier) may keep the returns to
capital high and thus still render such loan help productivity enhancing. To understand how
government facilitation affects firms of different ownership types, we thus run separate
productivity regressions for each type (Table 9). We use the most stringent base specification,
based on the last column of Table 6. Again, we only report the TFP regressions, but the results
based on labor productivity are very similar.
       Loan Help is positively associated with productivity for all three types of non-state firms
(see columns (1) to (4)). The magnitude is largest for domestic private firms at 1.37, followed by
joint ventures (0.98), and then foreign firms at 0.86. SOEs do not benefit from local loan
facilitation at all, perhaps due to their non-binding financial constraints (Cull et al., forthcoming).
This order is roughly consistent with the degree of financial constraints across those ownership
types. Indeed, domestic private firms have less access to formal finance in China (Brandt and Li
2003; Huang, 2003; Cull, Xu and Zhu 2009). Our results suggest that government help in
matching up borrowers and lenders is especially useful in easing the relatively severe financial
constraints faced by some ownership types. Info Facilitation is also associated with productivity
positively and significantly, but only for SOEs and pure foreign firms, and not for domestic
private or joint venture firms. An interpretation is that local governments are eager to provide
useful information to the firms closest to them (i.e., SOEs), or to information-deficient firms that
the government is eager to lure to attract investment. Indeed, Huang (2003) provides evidence
that local governments often provide preferential treatment for foreign firms for this purpose.
Notice that joint venture firms do not benefit from local government information facilitation,
perhaps because they already have access to local information provided by their local partners.
       Our definition of joint ventures takes advantage of two sources of information: whether
the firm has both foreign and domestic ownership, and whether the firm self-identified as a joint
venture. It is perhaps useful to ensure that our results are robust when we rely on the joint
venture definition that depends solely on the continuous ownership share variables, that is, when

                                                                                                    24
both domestic and foreign ownership are positive. In the last column, we therefore define joint
ventures based on this procedure and find results similar to what we find with the baseline
definition.


              d. Development level and the effectiveness of government facilitation
         China is a large country with tremendous regional disparities in economic development.
In 2002, the GDP per capita of the city of Dongguan in Guandong Province was more than 17
times that of Tianshui in Gansu Province. Survey evidence indicates that variation in the quality
of regional governance is also quite large (Cull and Xu 2005; Long 2010; Wang, Xu and Zhu
2012). Some recent studies have suggested that countries at different stages of development face
distinct binding constraints and thus should have different optimal policies (Kremer 1993;
Hausman et al. 2005). 16
         Substantial regional variation also affords us an opportunity to examine how the
effectiveness of government facilitation differs across levels of development. A priori, firms in
poor regions are likely to be more constrained in their development potential. Firms there tend
to have less experience in long-distance, arms-length trade and have less information about
market demand and frontier technologies, and thus about which products are likely to sell well.
The informational environment for bank lending in poorer regions is also likely to be more
opaque, due to poor information infrastructure (such as credit and collateral registries). Thus we
expect that the local government in a poorer region has a stronger potential role to play in
reducing information asymmetries regarding products, technology, and between lenders and
borrowers.
         To see how the role of the government differs by income levels, and to test the conjecture
that contracting institutions become more important as an economy develops (McMillan and
Woodruff 2002), we divide our 18 sampled cities into two groups based on GDP per capita, with
poor cities defined to be those with average GDP per capita lower than the sample median
(12,467 yuan), and the rest of the cities grouped as rich. 17 We report results for both the pooled
sample and the subsamples by ownership type in Table 10.


16
  A summary of recent empirical support for this argument is found in Xu (2011b).
17
  An alternative way to check how government facilitation is related to development level is to interact our
government facilitation variables with measures of provincial telecom density, road density, and average years of
schooling in our productivity regression. We have tried this, and found that none of those interactions was robustly

                                                                                                                   25
         There are strong differences between the poor and the rich regions. For the richer regions,
Loan Help is significantly associated with firm productivity but Info Facilitation is not. In sharp
contrast, for poorer regions government information facilitation regarding product and
technology advances is strongly associated with firm productivity. Increasing this variable by
one standard deviation (0.2) is associated with a productivity improvement of 18 log points.
This is consistent with the notion that firms in backward regions suffer from the lack of
information about products and technology, perhaps due to too little exposure to mature markets
and a lack of local human capital. Similarly, government help in obtaining loans is positively
related to productivity in poor cities.           Increasing this by one standard deviation (0.136) is
associated with a boost in productivity by 11 log points. This is consistent with the notion that
information asymmetry is severe in poor regions, and thus government facilitation of loans is
particularly effective.
         Further analyzing the data by ownership types, we find that government facilitation plays
a stronger role in poor regions, but only for non-state firms. Indeed, SOEs do not benefit from
government information facilitation in either poor or rich regions. For domestic private firms in
poor regions, the benefits of Loan Help appear to be substantial. A one standard deviation
increase is associated with a productivity gain of 35 log points. This is in sharp contrast to the
smaller magnitude and statistical insignificance for this variable in the rich regions. Similarly,
Info Facilitation is significantly and positively associated with the productivity of domestic
private firms in poor regions but not in the rich ones.
         In general, foreign firms and joint venture firms benefit more from government
facilitation in poor regions than in rich regions. Foreign firms seem to benefit more from
government information facilitation in poor regions than in rich ones. Indeed, increasing Info
Facilitation by one standard deviation is associated with a productivity boost for foreign firms by
23 log points in poor regions, but there is no significant effect for these firms in rich regions. For


significant and thus we do not present them below. This finding, coupled with what we find below, suggest that the
mechanisms through which government facilitation affects firm performance are not physical infrastructure or
schooling; rather, they are through other factors related to being a poor region such as poor information service
provision (e.g., the lack of credit and collateral registries).
      In Appendix A, we show that China’s broader information infrastructure (telephone lines per capita, internet
usage, road density, and schooling) are comparable to or even better than that of countries at a similar stage of
development. Given the insignificance of the interaction terms described above, this suggests to us that developing
the broader information infrastructure is likely insufficient to account for the productivity gains that we find are
associated with the two specific forms of government facilitation studied in this paper.

                                                                                                                  26
joint ventures, information facilitation has little effect in both regions. Increasing Loan Help to
foreign firms in both rich and poor regions results in productivity gains of similar magnitude,
though the effect is not significant in either region. However, local loan help is significantly
associated with higher productivity of joint venture firms in poor regions.


             e. Industry Thirst for and Effectiveness of Government Facilitation
         Rajan and Zingales (1998) investigate how industry-level growth rates across countries
vary with both dependence on external sources of financing in an industry and the level of
financial sector development in a country. We adapt this framework for our analysis by focusing
on how firm productivity varies with both government facilitation efforts and industry features
that are likely to be particularly conducive to effective facilitation. We assume that in certain
types of industries, costs associated with the lack of government coordination and facilitation
may be particularly high. Industries that rely more on inter-provincial trade, export markets, and
technological innovation are likely to be in need of government facilitation to overcome
information problems and obtain external financing. We therefore construct three industry-level
variables: the average export/sales ratio, the average share of firm sales to other provinces, and
the average innovation index (see Table 1 for definitions), and use them as proxies for industry
thirst for government facilitation. 18 Our conjecture is that government facilitation should be
particularly effective in boosting productivity in facilitation-thirsty industries. The specification
we adopt is as follows:
ln LPijct = α + β j + γ c' + X ijct
                                '
                                    θ + δ FACILITATION c * THIRST j + ε ijkt                           (2)

         In the specification, as usual, we control for firm characteristics, the local income level,
CEO characteristics, and industry dummies. More importantly, we control for city dummies, and
thus essentially hold constant all city level variables, which include the local business
environment, the political environment, leadership characteristics, culture, and endowments. The
potential for omitted variable bias is thus substantially reduced relative to other types of
regressions. The key parameter is the interaction between government facilitation variables at


18
   In a further robustness check (not presented here), we also allow city level loan help to interact with industry-level
average sales growth (as a proxy for growth opportunities). The hypothesis is that in industries with more growth
opportunities, government facilitation might be more useful. The interaction terms (for the pooled sample and for
different subsamples) tend to be almost always positive, but are never statistically significant. This could be because
the industry sales growth measure is a poor proxy for future growth opportunities.

                                                                                                                      27
the city level and Thirst at the industry level. Since the interaction varies at the city-industry
level, the standard error is clustered at that level as well.
         Table 11, Panel A considers the pooled sample. We confirm that in industries that need
more government facilitation—that is, those with a strong export orientation, those with a need
for a large domestic market, and those that rely heavily on innovation—facilitation is more
strongly associated with higher productivity. In the case of Loan Help, its interaction with
industries’ tendencies to export, reliance on a large market, and on technological innovation are
all significant. In the case of Info Facilitation, the interaction terms are significant for reliance
on large markets and innovation, but not for the tendency to export.
         To better understand the magnitudes of these coefficients, we classify the characteristics
of an industry as “strong” if its average (taken across firms in that industry) for a given
characteristic ranks one standard deviation above the industry mean on that same measure.
Similarly, we call the government facilitation level high if the average for that facilitation
variable (across firms in a city) is one standard deviation above the mean across cities on that
measure. Using those definitions, in a “strongly” export-oriented industry, the difference in firm
efficiency between areas characterized by high versus average Loan Help is 15 log points larger
than the same difference (i.e., high versus average Loan Help) in an industry with the average
tendency to export. 19 Relative to the mean large-market-reliant industry, the productivity
differential between high and the mean Loan Help locations is 11.4 log points larger in strongly
large-market-reliant industries. Similarly, relative to an industry at the mean level of innovation
intensity, the productivity differential between high and mean Loan Help locations is 10 log
points larger in a strongly innovation-intensive industry.
          Similar results are found for the Info Facilitation variable. Relative to the mean large-
market-reliant industry, the productivity differential between high and mean Info Facilitation
locations is 4 log points larger in strongly large-market-reliant industries. Finally, relative to the
mean innovation-intensive industry, the productivity differential between high and mean Info
Facilitation locations is 3 log points larger in strongly innovation-intensive industries.
         Panel B presents the estimation results for each ownership type. Here, SOEs in large-
market-reliant or innovation-intensive industries benefit more from government loan help; but


19
  That is, 12.268*0.09*0.136, where 12.268 is the coefficient of the interaction term, 0.09 is the standard deviation
of industry-level export tendency, and 0.136 is the standard deviation of local Loan Help.

                                                                                                                   28
the impact of government information facilitation does not hinge on industry characteristics for
SOEs. For domestic private firms, both loan help and information facilitation have stronger
effects in industries that have greater need for government facilitation. For foreign firms and
joint venture firms alike, government loan help is particularly useful in export-oriented
industries, but there are no significant relationships for other industry characteristics.
       Panel C presents the results for the poor and the rich regions separately. Again, the effect
of government facilitation is much more keenly felt in facilitation-thirsty industries in poor cities,
as five of the six interaction terms are statistically significant. In contrast, none of the six
interaction terms are significant for the rich regions.
       Before closing this section, we note that the three industry characteristics, export
orientation, large market reliance, and innovation, are all strongly related to firm productivity.
Table 12 shows strong correlations between firm efficiency and each of these three variables
(measured at firm level). This is not surprising.         From Adam Smith through the current
endogenous growth literature, the key importance of large markets and innovation for
productivity through channels such as specialization and the development of growth-supporting
institutions have been emphasized (Jones and Romer 2009). Thus, our finding that government
facilitation is especially useful for export-oriented, long-distance-trade-oriented, and innovation-
intensive industries implies that such facilitation has been conducive to the development of an
especially productive part of the local economy.


   7. Conclusions
       We have presented a variety of evidence consistent with the notion that market
facilitation by local governments has been associated with improved efficiency for some Chinese
firms. Even if one takes those results and our interpretations at face value, natural questions arise
about why some firms benefit more than others, why local governments are compelled to provide
such assistance, and whether such results are replicable in other contexts. The pattern of our
results indicates strongly that those non-state firms with weak access to and knowledge of
financial, input, and product markets benefit most, which suggests that facilitation by local
governments was a deliberate attempt to resolve market failures stemming primarily from
information asymmetries. There are two specific factors in China’s political and economic
environments which improve the opportunities for local governments to add value through

                                                                                                   29
market facilitation. First, China started its economic reforms from a legacy of a planning regime
where market-supporting institutions were lacking. Market imperfections therefore necessitated
government facilitation. Second, with a prominent history of strong government (Fukuyama,
2011), China has a government with strong state capacity that enabled effective market
facilitation. We emphasize that it is not so much that the government sector is inherently better at
providing such information, but that the switch to a market-oriented economy after decades of
central planning meant that there were no suitable institutions available to assume those roles.
The government therefore was left to perform these roles largely by default.
        The extent to which the productivity benefits of such facilitation would be replicable in
other countries is important to consider, but beyond the scope of this paper. Our conjecture is
that the lack of strong private market-supporting institutions, the genuine desire to convert to a
market-oriented economy, and the capacity of a strong government have contributed to the
success of these government facilitation efforts in China. To the extent that these features are
shared by other developing countries, such efforts could bear fruit elsewhere. However, our
overall view is that the Chinese situation is probably quite unique and extrapolating too much
from it to other contexts would be premature based on the available research.
        Our explanation of why local governments pursued such policies is rooted in the
changing fiscal dynamics that forced those governments to become increasingly self-reliant in
generating revenue. The tax-sharing reform in 1993-94 dramatically decentralized the provision
of basic social services to the local government level without providing corresponding fiscal
revenues, forcing local governments to rely on the development of their local economies to
finance the provision of public goods (Jin et al., 2005). The government also imposed coherent
incentives that tied the promotion of local government officials to local economic performance
(Li and Zhou 2005), again reinforcing the incentives of local governments to develop their
economies.
        Given the right incentives, based on local fiscal self-reliance and a system of political
promotion that rewards economic performance, our results suggest that local government could
conceivably play a role in harnessing local information to facilitate credit market development
and in providing infrastructure that enables markets to work better and grow, though the
historical record from developed economies suggests that most of these activities were left to
private interests.

                                                                                                 30
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              Table 1
     Variable definitions. This table provides detailed descriptions of variables used in the paper.

Variables                   Definition
ln(LP)                      Firm-level log labor productivity, as measured by value added (in constant value) per worker.
loan help                   A dummy variable indicating whether the government offered help in obtaining bank loans (based
                            on a survey question).

                            A dummy variable indicating whether the firm receives its information from the government on
Info facilitation
                            product and technology advances (based on a survey question).
ETC                         Entertainment and traveling expenditure over sales, as a proxy for corruption.
                            A firm’s answer to the following question: of all the commerce or other conflicts that the company
Property rights
                            has experienced, what is the likelihood that their contractual or property rights are protected by the
protection
                            legal system?
Share of disputes by
                            The share of a firm’s disputes that are resolved by the court system.
courts

Gov efficient services      The manager’s assessment of the share of government officials that offer efficient services.
                            The manager’s assessment of the share of government officials that are helping rather than
Official helping firms      hindering firm development.
                            The dummy variable indicating whether a city party secretary has remained in his current job for 3
PS_longTenure
                            years or more.

CEO schooling               The number of years of schooling that the CEO has attained.

CEO tenure                  The number of years that the CEO has held his current CEO position.

CEO ownership               The share of the firm owned by its CEO.
Ln(firm age)                Logarithm of firm age.
Ln(L)                       Logarithm of the number of employees of the firm.
State                       The dummy variable indicating whether a firm is state-owned, i.e., with positive state ownership.
Domestic private            The dummy variable indicating whether a firm is a domestic non-state firm.
Foreign                     The dummy variable indicating whether a firm is a pure foreign-owned firm.
                            The dummy variable indicating whether a firm is a joint venture between domestic and foreign
JV
                            owners.
Sell to other province      A dummy variable indicating whether the firm sells its product to other provinces.
Ln(GDP per capita)          Logarithm of GDP per capita at the city level (in RMB).
Export/sales                The ratio of the value of a firm’s exports to its sales.
Innovation index            In the questionnaire, a firm is asked whether it has introduced innovation in the following areas: (1)
                            new product or services; (2) entry into new industries; (3) new technological innovation or
                            techniques; (4) new management methods; (5) new quality control methods. This index is the
                            summation of the five dummy variables divided by 5.
Local effective tax         City-industry average of the effective tax rate of a firm, as measured by total tax payments over
rates                       sales.
Access to bank loans        City-industry share of firms that had bank loans in the recent past.
Trade credit                City-industry average of the share of a firm’s input that was purchased by supplier credit.



                                                                                                                           40
                         City-industry average of the effectiveness score of the national tax bureau. The score is a
Effectiveness of         summation of the indicators for having no issues in each of the following areas: being impolite,
national tax bureau      setting artificial obstacles, too many forms, demands for gifts or bribes, delay or refusal to pay tax
                         rebates, and the incompetence of bureau officials.
                         City-industry average of the effectiveness score of the local tax bureau. The score is a summation
Effectiveness of local   of the indicators for having no issues in each of the following areas: being impolite, setting
tax bureau               artificial obstacles, too many forms, demands for gifts or bribes, delay or refusal to pay tax rebates,
                         and the incompetence of bureau officials.
                         Total days that the firm spent in a year in dealing with government regulators in areas such as
Days with regulators     taxation, labor and social security, health, public safety, environmental protection, and technical
                         standards.
                         City-industry mean of the score of the availability of professional services. The score is a
Access to professional   summation of indicators for whether the firm can obtain services in each of the following areas:
services                 R&D services, management consultant services, market research, accounting, law, insurance, and
                         IT services.
                         City-industry mean of the score of the affordability of professional services. The score is a
Affordability of         summation of indicators for whether the firm can afford services in each of the following areas:
professional services    R&D services, management consultant services, market research, accounting, law, insurance, and
                         IT services.


Average interest rate    City-industry average of the interest rate that was paid by a firm with a bank loan.
Government guarantee     City-industry share of firms whose loans were guaranteed by the government.




                                                                                                                       41
Table 2
Summary statistics.
This table presents the number of observations, mean, standard deviation, median, minimum, and maximum for
the variables used in the paper. The full sample period is from 2000 to 2002.

                                 Observations   Mean       S.D.        Median     Minimum      Maximum

Ln(GDP per capita)               7,194          9.475      0.408       9.422      8.547        10.745
PS_longTenure                    9,569          0.219      0.413       0.000      0.000        1.000
ln(LP)                           9,334          4.161      1.583       4.181      -4.255       16.454
loan help                        9,569          0.157      0.363       0.000      0.000        1.000

Info facilitation                6,478          0.351      0.477       0.000      0.000        1.000
loan help, city-indavg           9,569          0.157      0.136       0.126      0.000        0.700
Info facilitation, city-indavg   6,478          0.351      0.201       0.323      0.000        1.000
ETC                              9,266          0.031      0.024       0.024      0.002        0.163
Property rights protection       8,246          0.640      0.163       0.629      0.000        1.000
share of disputes by courts      9,281          0.081      0.078       0.069      0.000        0.500
Gov efficient services           8,646          0.355      0.138       0.343      0.018        0.800
Official helping firms           8,876          0.344      0.125       0.337      0.020        0.853
ln(firm age)                     9,569          2.211      0.983       2.079      0.000        3.970
ln(L)                            9,512          4.806      1.552       4.673      0.000        11.358
Domestic private                 9,565          0.368      0.465       0.000      0.000        1.000

State                            9,565          0.213      0.409       0.000      0.000        1.000
Domestic private                 9,565          0.324      0.468       0.000      0.000        1.000
Foreign                          9,565          0.301      0.459       0.000      0.000        1.000
JV                               9,565          0.162      0.369       0.000      0.000        1.000
CEO schooling                    9,498          14.639     2.403       15.000     0.000        18.000
CEO tenure                       9,506          5.767      4.260       5.000      0.000        33.000
CEO ownership                    9,486          9.792      21.683      0.000      0.000        100.000
Access to bank loans             9,569          0.571      0.282       0.571      0.000        1.000
Trade credit                     8,821          0.093      0.085       0.066      0.000        0.386
Local effective tax rates        9,343          0.077      0.034       0.072      0.016        0.312
Exp/sales, indavg                9,569          0.082      0.090       0.038      0.000        0.273
Sell to other province,
                                 9,569          0.562      0.220       0.632      0.141        0.944
    ind avg
Innovation index, ind
                                 9,569          0.406      0.119       0.408      0.230        0.601
   avg
sell to other province           9,379          0.574      0.495       1.000      0.000        1.000

Export/sales                     9,348          0.082      0.251       0.000      0.000        1.000

innovation index                 9,397          0.407      0.357       0.400      0.000        1.000




                                                                                                             42
Table 3
Summary statistics by state and non-state ownership.
This table presents the number of observations, mean, standard deviation, and median for state owned firms and
non-state firms. The non-state firms include domestic private firms, joint venture firms, and pure foreign firms.
                                             State                                          Non-State
                                N          Mean       S.D.       Median    N          Mean       S.D.       Median
 Loan Help                      2037       0.150      0.357      0.000     7528       0.158      0.365      0.000
 Info facilitation              1359       0.403      0.491      0.000     5115       0.338      0.473      0.000
 Loan Help, city-ind avg        2037       0.151      0.128      0.114     7528       0.158      0.138      0.126
 Info facilitation, city-ind
                                1359       0.352      0.192      0.325     5115       0.351      0.203      0.320
    avg
 CEO tenure                     2007       5.218      4.235      4.000     7495       5.915      4.255      5.000
 CEO ownership                  2020       1.366      8.258      0.000     7462       12.078     23.552     0.000
 PS_longTenure                  2037       0.261      0.439      0.000     7528       0.207      0.405      0.000
 lngdppc                        1502       9.429      0.349      9.412     5689       9.487      0.421      9.422
 ln(LP)                         1991       3.767      1.409      3.842     7339       4.269      1.610      4.298
 ln(firm age)                   2037       2.926      0.953      3.258     7528       2.017      0.898      1.946
 ln(L)                          2022       5.530      1.673      5.545     7486       4.609      1.458      4.489
 state                          2037       1.000      0.000      1.000     7528       0.000      0.000      0.000
 Domestic private               2037       0.000      0.000      0.000     7528       0.411      0.492      0.000
 foreign                        2037       0.000      0.000      0.000     7528       0.383      0.486      0.000
 JV                             2037       0.000      0.000      0.000     7528       0.206      0.404      0.000
 sell to other province         1960       0.623      0.485      1.000     7415       0.560      0.496      1.000
 Innovation index               1985       0.380      0.344      0.400     7408       0.414      0.360      0.400
 access to bank loans           2037       0.579      0.267      0.590     7528       0.569      0.286      0.571
 trade credit                   1826       0.078      0.080      0.060     6995       0.097      0.086      0.074
 ETC                            1983       0.028      0.019      0.024     7279       0.032      0.025      0.024
 Property rights protection     1777       0.662      0.158      0.680     6469       0.634      0.164      0.617
 share of disputes by
                                1981       0.096      0.079      0.093     7296       0.077      0.077      0.063
    courts
 Gov efficient services         1821       0.346      0.136      0.333     6825       0.357      0.139      0.351
 Official helping firms         1865       0.334      0.113      0.335     7007       0.346      0.128      0.337




                                                                                                                    43
Table 4
Summary statistics by income level.
This table presents the number of observations, mean, standard deviation, and median for firms in poor and rich
cities. The 18 cities in the sample are divided into two groups by GDP per capita, with poor cities defined to be
those with average GDP per capita lower than the sample median (12,467 yuan) in 2005, and the rest of the cities
defined as rich. Rich cities include Changchun, Changsha, Dalian, Hangzhou, Jiangmen, Kunming, Shenzhen,
Wenzhou, Wuhan, and Zhengzhou. Poor cities include Benxi, Chongqing, Guiyang, Haerbin, Lanzhou, Nanchang,
Nanning, and Xi’an.
                                                   Poor                                       Rich
                                N           mean       S.D.      Median N              mean        S.D.        Median
Ln(GDP per capita)             3,444     9.204       0.249      9.262      3,750      9.724       0.365      9.602
PS_longTenure                  4,573     0.294       0.456      0.000      4,996      0.150       0.357      0.000
ln(LP)                         4,406     3.786       1.623      3.851      4,928      4.495       1.469      4.470
Loan help                      4,573     0.147       0.354      0.000      4,996      0.166       0.372      0.000
Info facilitation              2,963     0.350       0.477      0.000      3,515      0.352       0.478      0.000
Loan help, city-indavg         4,573.    0.147       0.139      0.088      4,996      0.166       0.133      0.157
Info   facilitation,   city-
                               2,963     0.350       0.204      0.325      3,515      0.352       0.198      0.314
indavg
ETC                            4,353     0.037       0.087      0.013      4,913      0.025       0.057      0.010
Property rights protection     3,890     0.636       0.392      0.800      4,356      0.644       0.387      0.800
Share of disputes by courts    4,309     0.084       0.246      0.000      4,972      0.078       0.235      0.000
CEO schooling                  4,545     14.605      2.274      15.000     4,953      14.670      2.516      15.000
CEO tenure                     4,531     5.610       4.193      5.000      4,975      5.909       4.314      5.000
CEO ownership                  4,518     9.054       21.193     0.000      4,968      10.463      22.099     0.000
Gov efficient services         4,034     0.346       0.154      0.313      4,612      0.363       0.123      0.366
Official helping firms         4,160     0.318       0.125      0.300      4,716      0.366       0.121      0.371
Access to bank loans           4,573     0.522       0.255      0.528      4,996      0.616       0.297      0.604
Trade credit                   4,089     0.087       0.082      0.062      4,732      0.098       0.087      0.085
Local effective tax rates      4,416     0.089       0.140      0.059      4,927      0.067       0.024      0.063
Sell to other province         4,455     0.542       0.498      1.000      4,924      0.602       0.490      1.000
ln(firm age)                   4,573     2.296       1.033      2.197      4,996      2.133       0.928      2.079
ln(L)                          4,522     4.722       1.571      4.505      4,990      4.882       1.531      4.787
State                          4,569     0.241       0.427      0.000      4,996      0.188       0.391      0.000
Domestic private               4,569     0.300       0.458      0.000      4,996      0.345       0.475      0.000
Foreign                        4,569     0.322       0.467      0.000      4,996      0.282       0.450      0.000
JV                             4,569     0.137       0.344      0.000      4,996      0.185       0.388      0.000
Export/sales                   4,416     0.043       0.183      0.000      4,932      0.117       0.294      0.000
Innovation index               4,445     0.388       0.359      0.400      4,952      0.423       0.355      0.400




                                                                                                                     44
Table 5
Determinants of Government Facilitation.
This table presents the results from linear probability models that link government facilitation to firm characteristics,
CEO characteristics, local income levels and institutional environments, efficiency of government bureaucracy, and
industry and regional dummy variables. The dependent variables are Loan Help and Information Facilitation.
Columns (3) and (4) include regional dummy variables while columns (1) and (2) do not. *, ** and *** represent
statistical significance at the 10, 5 and 1 percent levels, respectively. White-heteroskedasticity-corrected standard
errors, clustered at the city level, are reported in parentheses.

                                                                Loan            Info           Loan           Info
                                                                 help        facilitation       help       facilitation
Ln(firm age)                                                  -0.032***          -0.033**    -0.035***          -0.026*
                                                                 (0.009)          (0.014)       (0.010)          (0.014)
Ln(L)                                                          0.045***         0.049***      0.044***         0.044***
                                                                 (0.010)          (0.011)       (0.009)          (0.011)
Domestic private                                                   0.022           -0.051         0.020           -0.064
                                                                 (0.025)          (0.047)       (0.025)          (0.046)
Foreign                                                           -0.008         -0.066**        -0.003        -0.073**
                                                                 (0.022)          (0.028)       (0.022)          (0.029)
Joint Venture                                                      0.034           -0.037         0.033           -0.044
                                                                 (0.028)          (0.035)       (0.027)          (0.036)
Ln(GDP per capita)                                                -0.017            0.021         0.028           -0.033
                                                                 (0.029)          (0.054)       (0.030)          (0.036)
PS long tenure                                                    -0.009        0.121***         -0.037          0.066*
                                                                 (0.026)          (0.029)       (0.033)          (0.037)
CEO schooling                                                      0.006           -0.001         0.005            0.003
                                                                 (0.004)          (0.008)       (0.004)          (0.008)
Entertainment & travelling costs/sales, city-indavg            0.893***             0.464     1.390***           1.132*
                                                                 (0.304)          (0.636)       (0.433)          (0.628)
Property rights, city-indavg                                       0.017           -0.112        -0.045           -0.061
                                                                 (0.083)          (0.110)       (0.073)          (0.112)
Share of disputes through the court, city-indavg                   0.060        0.455***          0.131            0.280
                                                                 (0.100)          (0.170)       (0.098)          (0.181)
Gov efficient services, city-indavg                            0.227***             0.160     0.222***             0.086
                                                                 (0.056)          (0.188)       (0.055)          (0.172)
Official helping firms, city-indavg                               -0.018           -0.070         0.062           -0.145
                                                                 (0.113)          (0.180)       (0.105)          (0.169)
CEO tenure                                                        0.005*           -0.003      0.005**            -0.003
                                                                 (0.003)          (0.003)       (0.003)          (0.003)
CEO ownership share                                                0.000           0.001*         0.000          0.001*
                                                                 (0.000)          (0.000)       (0.000)          (0.000)
Regional dummies                                                  no              no            yes             yes
Industry dummies                                                  yes            yes            yes             yes
R squared                                                        0.087          0.081          0.095           0.095
Number of observations                                           5,563          3,865          5,563           3,865


                                                                                                                       45
Table 6
Firm Efficiency and Government Facilitation: Base Results.
This table presents the estimation results of equation (1) which studies the effect of government facilitation on firm
efficiency. For dependent variables, column (1) uses log labor productivity while columns (2)-(4) use total factor
productivity. For government facilitation variables, columns (1) and (2) use firm-level variables while columns (3)
and (4) use city-industry average values of facilitation variables. All four columns control for firm characteristics
(Industry dummies, Ln(firm age), ln(L), ownership), the interaction between industry dummies and both log(K/L)
and log(L), CEO characteristics, local access to finance, and the efficiency and quality of governance of local
government (including effective tax rates). Column (4) includes the regional dummy variables while columns (1)-(3)
do not. *, ** and *** represent statistical significance at the 10, 5 and 1 percent levels, respectively. White-
heteroskedasticity-corrected standard errors, clustered at the firm level in column (1), and at the city-industry level
in columns (2)-(4), are reported in parentheses.
                                        (1) Ln(LP)             (2) Ln(LP)            (3) Ln(LP)           (4) Ln(LP)
                                      LProd style            TFP style             TFP style            TFP style
Domestic private                         -0.044                0.232**               0.226**              0.227**
                                        (0.120)                (0.112)               (0.107)              (0.107)
foreign                                   0.114              0.264***              0.265***             0.266***
                                        (0.105)                (0.096)               (0.095)              (0.096)
Joint venture                          0.483***              0.465***              0.469***             0.469***
                                        (0.114)                (0.105)               (0.097)              (0.097)
Ln(GDP per capita)                     0.674***              0.466***              0.441***             0.454***
                                        (0.084)                (0.079)               (0.074)              (0.103)
PS long tenure                            0.049                 0.116                 0.072                0.077
                                        (0.116)                (0.110)               (0.095)              (0.110)
CEO schooling                          0.104***              0.049***              0.049***             0.049***
                                        (0.019)                (0.015)               (0.015)              (0.015)
Bank loan, city-ind mean               0.614***              0.439***                 0.228                0.251
                                        (0.149)                (0.132)               (0.140)              (0.172)
Trade credit, city-ind mean            2.140***                1.180**             1.348***             1.366***
                                        (0.518)                (0.496)               (0.438)              (0.475)
Gov efficient services, city-          1.156***              1.044***                0.744**              0.759**
    Ind avg                             (0.325)                (0.308)               (0.346)              (0.348)
Entertainment, travelling/sales,         -1.567                 0.303                 -0.872               -0.893
   city-ind mean                        (2.465)                (2.141)               (2.325)              (2.318)
Official helping firms, city-          -0.900**               -0.856**              -0.787**             -0.772**
   Ind avg                              (0.355)                (0.335)               (0.342)              (0.374)
Effective tax rate, city-ind          -5.695***              -6.212***             -5.739***            -5.812***
   Avg                                  (1.439)                (1.262)               (1.275)              (1.393)
loan help                              0.342***              0.228***
                                        (0.085)                (0.076)
info facilitation                         0.112                 0.090
                                        (0.074)                (0.066)
loan help, city-indavg                                                             0.714**               0.699**
                                                                                    (0.318)              (0.335)
info facilitation, city-ind                                                        0.541***              0.560**
   Avg                                                                              (0.207)              (0.219)
industry dummies * (ln(K/L), lnL)         no                    yes                   yes                  yes
regional dummies                          no                    no                    no                   yes
Industry dummies, ln(firm age),
                                          Yes                   Yes                  Yes                   Yes
ln(L)
Adj. R squared                           0.254                 0.397                 0.398                0.397
Number of observations                   4,138                 4,053                 4,053                4,053


                                                                                                                    46
Table 7
Firm Efficiency and Government Facilitation: Adding Other Local Controls.
This table provides robustness checks for the baseline results by adding different groups of controls. The dependent variable is total factor productivity. Column
(1) reports the baseline results as in column (4) of table 6. Column (2) adds additional CEO characteristics; Column (3) adds variables measuring the
effectiveness of courts; Column (4) adds variables for tax and regulatory burdens; Column (5) adds variables measuring the availability and affordability of
professional services needed by firms; Column (6) adds variables for financial conditions; Column (7) adds all the variables above. *, ** and *** represent
statistical significance at the 10, 5 and 1 percent levels, respectively. White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, are
reported in parentheses.

                                                                          (1) Base          (2)           (3)           (4)           (5)           (6)           (7)
loan help, city-ind mean                                                    0.699**       0.664**         0.664*        0.600*         0.530       0.728**          0.540
                                                                             (0.335)        (0.332)      (0.353)        (0.344)       (0.379)       (0.328)       (0.346)
info facilitation, city-ind mean                                            0.560**       0.486**       0.546**        0.543**       0.474**        0.454*          0.378
                                                                            (0.219)         (0.222)      (0.219)       (0.230)        (0.229)       (0.240)       (0.263)
Other controls as in column (4) of Table 6                                 Yes              Yes          Yes           Yes            Yes           Yes           Yes
Further CEO characteristics: CEO tenure, CEO ownership                     No               Yes          No             No            No            No            Yes

Courts: Share of disputes resolved through courts, city-ind mean           No               No           Yes            No            No            No            Yes

Taxation & regulation: City ind means of: effectiveness of                 No               No           No            Yes            No            No            Yes
         national taxation bureau, effectiveness of local tax
         bureau, days in dealing with regulators
Professional services: City ind mean of: access to professional            No          No                No             No            Yes           No            Yes
         services, affordability of professional services
Loan conditions: City ind means of: interest rate of loan,                 No               No           No             No            No            Yes           Yes
         whether loan was guaranteed by the government.
Adj. R squared                                                            0.397           0.401         0.385         0.398          0.391         0.407         0.393
Number of observations                                                    4,053           4,033         3,898         3,943          3,892         3,781         3,411




                                                                                                                                                                    47
Table 8
Firm Efficiency and Government Facilitation: The Role of Language and Culture.
This table provides robustness checks for the baseline results by controlling for the influence of language and culture. The dependent variable is total factor
productivity. Column (1) reports estimation results when we drop firms from Dalian, the only city in the sample whose predominant dialect is different from that
spoken in its provincial capital. Column (2) provides a more stringent test by including city dummy variables to account for local cultural factors. *, ** and ***
represent statistical significance at the 10, 5 and 1 percent levels, respectively. White-heteroskedasticity-corrected standard errors, clustered at the city-industry
level, are reported in parentheses.

                                                                                         (1) Ln(LP)                                        (2) Ln(LP)
                                                                                    TFP style                                            TFP style
                                                                   dropping non-mainstream language city Dalian                  include all city dummies

loan help, city-ind mean                                                                 0.658**                                           0.417
                                                                                         (0.334)                                           (0.340)
info facilitation, city-ind mean                                                        0.572***                                          0.657***
                                                                                         (0.211)                                           (0.234)
industry dummies * (ln(K/L), lnL)                                                          Yes                                               yes
Other controls as in column (4) of Table 6                                                 Yes                                              Yes
R squared                                                                                 0.399                                            0.401
Number of observation                                                                     3,914                                            4,053




                                                                                                                                                                   48
Table 9
 Firm Efficiency and Government Facilitation: By Ownership Type.
This table shows the relationship between government facilitation and firm efficiency by ownership type. The
dependent variable is total factor productivity and the regression specification is the same as in column (4) of table
6. Columns (1)-(4) report estimation results for state firms, domestic private firms, foreign firms, and joint venture
firms, respectively. Column (5) reports estimation results for joint ventures firms using a different definition of joint
ventures. In Column (4), joint ventures firms include those that have both foreign and domestic ownership and those
that are self-identified as joint venture firms. In Column (5), joint venture firms include only those with positive
domestic and foreign ownership shares. *, ** and *** represent statistical significance at the 10, 5 and 1 percent
levels, respectively. White-heteroskedasticity-corrected standard errors, clustered at the city-industry-ownership
level, are reported in parentheses.

                                                                                                               Joint
                                                              Domestic                          Joint        venture:
                                                   State                         Foreign
                                                               private                         venture      alternative
                                                                                                            definition
                                                 TFP style     TFP style        TFP style      TFP style     TFP style
loan help, city-industry-ownership mean            0.285       1.374***          0.856*        0.977**        0.737*
                                                  (0.450)       (0.472)          (0.506)        (0.448)       (0.426)
info facilitation, city-industry-ownership         0.567*       0.075           0.946***        0.458          0.440
   mean                                           (0.316)       (0.230)          (0.339)        (0.376)       (0.489)
Ln(firm age)                                     -0.315***    -0.396***         -0.497***     -0.384***      -0.399***
                                                  (0.087)       (0.102)          (0.091)        (0.096)       (0.101)
Ln(GDP per capita)                                 0.389*      0.409***          0.410**       0.770***       0.584**
                                                  (0.212)       (0.141)          (0.194)        (0.223)       (0.267)
CEO schooling                                      0.030        0.031           0.092***        0.048*         0.040
                                                  (0.051)       (0.024)          (0.035)        (0.027)       (0.030)
Bank loan, city indavg                             -0.404       0.098             0.318         0.507         1.028**
                                                  (0.388)       (0.254)          (0.346)        (0.512)       (0.483)
Trade credit, city indavg                         2.519**      2.148***           -0.000        0.602         -0.676
                                                  (0.998)       (0.667)          (0.989)        (1.147)       (1.337)
Gov efficient services, city-indavg                -0.293       -0.250            1.046        1.890**        2.156**
                                                  (0.768)       (0.671)          (0.654)        (0.875)       (0.984)
Entertainment, travelling/sales, city-ind mean     -3.386       2.342             -0.900        -6.092         0.933
                                                  (6.594)       (2.533)          (4.679)        (4.997)       (5.387)
Property rights, city-indavg                       -0.023       0.517             -0.158        -0.266         0.234
                                                  (0.591)       (0.438)          (0.503)        (0.836)       (0.698)
Official helping firms, city-indavg                -0.850       -0.525            -0.769        0.203         -0.250
                                                  (0.862)       (0.626)          (0.688)        (0.751)       (0.908)
Effective tax rate, city-indavg                  -10.719***    -4.114*          -4.622**        -5.292       -8.563**
                                                  (2.307)       (2.340)          (2.340)        (3.303)       (4.005)
Firm size, PS long tenure                           Yes          Yes               Yes           Yes            Yes
Regional dummies                                    Yes          Yes               Yes           Yes            Yes
Adj. R squared                                     0.373        0.403             0.455         0.498          0.492
Number of observations                              785         1,166             1,114          725            501




                                                                                                                        49
Table10
Firm Efficiency and Government Facilitation: By Income Level.
This table shows the relationship between government facilitation and firm efficiency in rich and poor cities. The dependent
variable is total factor productivity and the regression specification is the same as in column (4) of table 6. Columns (1)-(5)
report estimation results for the pooled poor sample, and sub-samples of firms by ownership in poor cities. Columns (6)-(10)
report estimation results for the pooled rich sample, and sub-samples of firms by ownership in rich cities. Poor cities are
defined to be those with average GDP per capita lower than the sample median (12,467 yuan) in 2005, leaving firms in the
rest of the cities in the rich sub-sample. Rich cities include Changchun, Changsha, Dalian, Hangzhou, Jiangmen, Kunming,
Shenzhen, Wenzhou, Wuhan, and Zhengzhou. Poor cities include Benxi, Chongqing, Guiyang, Haerbin, Lanzhou,
Nanchang, Nanning, and Xi’an. *, ** and *** represent statistical significance at the 10, 5 and 1 percent levels, respectively.
White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, are reported in parentheses.


                                                         Poor                                                 Rich

                                                        Domestic                                             Domestic
                                   Pooled     State                 foreign     JV       Pooled    State                 foreign     JV
                                                         private                                              private
loan help, city-ind mean             0.826*    0.395    2.553***       0.884   1.700**   0.831**    -0.176      0.615      0.848     1.094
                                    (0.427)   (0.987)     (0.835)    (1.199)   (0.772)   (0.375)   (1.214)     (0.552)    (0.557)   (0.924)
info facilitation, city-ind        0.926***    -0.630     0.817*    1.690***     0.568     0.224   -1.391*     -0.039      0.290     0.567
    mean                            (0.327)   (0.636)     (0.440)    (0.527)   (0.720)   (0.280)   (0.817)     (0.330)    (0.543)   (0.713)
Other controls as in
                                     Yes      Yes         Yes         Yes       Yes       Yes      Yes         Yes        Yes       Yes
   column (4) of Table 6.
Adj. R squared                      0.403     0.471      0.577       0.469     0.523     0.400     0.403      0.329      0.515      0.535
Number                        of    1,765     379         503         582       301      2,288     448         760        622       458
observations




                                                                                                                                       50
Table 11
Firm Efficiency and Government Facilitation: The RZ-style Estimates.
This table presents the estimation results of equation (2) which studies how firm productivity varies with both government
facilitation efforts and industry characteristics that are likely to be particular conducive to effective facilitation. The
dependent variable is total factor productivity. We report the results on the interaction variables “government facilitation *
industry characteristics”, with government facilitation being either loan facilitation or information facilitation, and industry
characteristics being either export tendency, large domestic market tendency, or innovation tendency. We also include the
other controls as in column (4) of Table 6 except without variables on government facilitation. Panel A reports the estimation
results for the pooled sample. Panel B reports the estimation results for each ownership type. Panel C reports the estimation
results for the poor and rich cities separately. *, ** and *** represent statistical significance at the 10, 5 and 1 percent levels,
respectively. White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, are reported in
parentheses.


 Panel A. Pooled sample                            Industry characteristics:
                                         exportind        Large_mktind         Innovateind
 Loan helpc * industry charind           12.268***           3.795**           6.365***
                                          (4.298)            (1.499)            (2.224)
 R squared                                 0.351              0.350              0.351
 Info facilitationc * industry charind     1.333             1.196*             1.858**
                                          (2.987)            (0.620)            (0.924)
 R squared                                 0.348              0.349              0.349
 observations                              6,872              6,872              6,872
 Panel B. By ownership:                                      State                                       Domestic private
                                         exportind        Large_mktind         Innovateind   exportind    Large_mktind      Innovateind
 Loan helpc * industry charind             6.422             8.114**           16.441***       7.203         5.668**         6.728**
                                          (9.824)            (3.160)            (4.902)       (5.136)        (2.245)         (3.048)
 R squared                                 0.283              0.292              0.298         0.279          0.284           0.282
 Info facilitationc * industry charind     1.091              0.260              1.014        5.139**       2.006***        2.921***
                                          (7.436)            (2.002)            (3.018)       (2.420)        (0.690)         (1.040)
 R squared                                 0.282              0.282              0.282         0.281          0.284           0.284
 Number of observations                    1,458              1,458              1,458         2,200          2,200           2,200
             By ownership                                  Foreign                                        Joint Venture
                                         exportind        Large_mktind         Innovateind   exportind     Large_mktind     Innovateind
 Loan helpc * industry charind           13.850**             -1.319             -1.950      18.244***        3.069           5.402
                                          (6.119)            (2.244)            (3.136)       (6.407)        (2.702)         (4.041)
 R squared                                 0.377              0.374              0.374         0.501          0.497           0.497
 Info facilitationc * industry charind    -4.003              0.798              1.055         2.126          0.739           1.161
                                          (3.467)            (1.076)            (1.560)       (4.104)        (1.287)         (1.865)
 R squared                                 0.375              0.374              0.374         0.496          0.496           0.496
 Number of observations                    2,059              2,059              2,059         1,155          1,155           1,155
 Panel C. By income level                                    poor                                              rich
                                         exportind        Large_mktind         Innovateind   exportind     Large_mktind     Innovateind
 Loan helpc * industry charind           20.258***          8.530***           19.287***       5.477          0.588           1.040
                                          (4.752)            (3.230)            (5.697)       (4.064)        (1.406)         (1.991)
 R squared                                 0.308              0.308              0.312         0.364          0.363           0.363
 Info facilitationc * industry charind     0.094            5.658***           10.690***       3.121          0.645           1.133
                                          (7.491)            (1.687)            (2.672)       (2.135)        (0.556)         (0.809)
 R squared                                 0.302              0.311              0.313         0.364          0.364           0.364
 Number of observations                    3,230              3,230              3,230         3,642          3,642           3,642


                                                                                                                                          51
Table 12
 Firm Efficiency and the Three Industry Characteristics.
This table shows the correlation between firm efficiency and each of the three industry characteristics: export tendency, large
domestic market tendency, and innovation tendency. The dependent variable is total factor productivity. We also include the
other controls as in column (4) of Table 6 except without variables on government facilitation. Column (1) reports the
estimation results for the pooled sample. Column (2)-(5) report the estimation results for each ownership type. Column (6)-
(7) report the estimation results for the poor and rich cities separately. *, ** and *** represent statistical significance at the
10, 5 and 1 percent levels, respectively. White-heteroskedasticity-corrected standard errors, clustered at the firm level, are
reported in parentheses.


                           Pooled          State       Dom private       Foreign      Joint venture      Poor            Rich
Export/sales              0.202**        0.786***        0.465**          0.140          -0.230          0.196          0.143
                          (0.102)         (0.289)        (0.181)         (0.178)        (0.197)         (0.164)        (0.132)
Sell to other provinces   0.283***       0.460***         0.201*         0.199*         0.459***       0.301***        0.233***
                          (0.064)         (0.139)        (0.114)         (0.118)        (0.137)         (0.101)        (0.087)
Innovation index          0.493***        0.472**        0.399***       0.652***         0.094         0.542***        0.443***
                          (0.084)         (0.190)        (0.138)         (0.164)        (0.186)         (0.126)        (0.113)

Other controls the
   same as in column
   (4) of Table 6
   except without           Yes             Yes            Yes             Yes            Yes             Yes            Yes
   variables on
   government
   facilitation

ind dummies *
                            Yes             Yes            Yes             Yes            Yes             Yes            Yes
    ln(K/L), lnL
Adj. R squared             0.390           0.377          0.312           0.427          0.513           0.382          0.374
Number of
                           5,548           1,099          1,830           1,642           977            2,473          3,075
   observations




                                                                                                                                52
APPENDIX A.
The information infrastructure and financial environment in China.
This table compares the information infrastructure (telephone lines per capita, internet usage, road density, and schooling) and financial environment in China
with countries at a similar development stage. Data Sources: The first six columns, based on averages over 1999 to 2002, come from World Bank “World
Development Indicators” and “Education Statistics.” Percentage of foreign bank assets is averaged over 2004-2007 due to data availability; Percentage of
banking assets held by government banks comes from the World Bank “Bank Regulation and Supervision Survey,” version 3 (2007). The cells with dots imply
the number is not available.
                                                                                                                                                 Percentage of
                                                                                                                             Percentage of       Banking Assets
                                                                      Road density                           Bank            foreign bank        in Majority-
                                  Telephone lines   Internet users    (km of road per                        deposit/GDP     assets among        Government
                 GDP per capita   (per 100          (per 100          100 sq. km of     ave years of                         total bank          owned banks,
 Country         (current US$)    people)           people)           land area)        schooling                            assets (%)          2005

 Albania               1189.721         5.400588          0.227854          46.95435               9.91              41.55               93                   .

 Algeria               1681.965         5.625659          0.732246          1.091639               6.29            30.9225                   7             90.0

 Bulgaria              1737.614         36.00484          6.242844          12.93681               9.37              23.14               74                   .

 China                 997.7485         12.59868          2.429864          13.39654               7.11            36.9225                   2             68.8

 Ecuador               1778.881         10.06076             2.3042         12.62474               7.23             20.025               12                   .

 Egypt                 1410.498         9.216088          1.122664          1.597683               5.91             64.725               17                66.7

 Honduras              1101.277         4.798078          1.447529          3.022491               6.14            35.5025               35                 0.0

 Indonesia             784.1045         3.287689          1.380682            13.875               5.03             45.475              30.5               38.5

 Jordan                1807.545         13.11026          3.970373                  .              8.05            86.1825            12.25                 0.0

 Philippines           1025.487         4.004465          2.567536          50.35424               8.27            47.8775               1.5               12.1

 Russia                1897.506         22.50694          2.517214          3.876001              11.08             14.205          9.33333                38.5

 Swaziland             1324.168          3.09037          1.123676          14.90006                   6.9         18.9625              81.5                  .

 Syria                 1176.548         10.86117          0.688214          6.017793               7.68             38.405                   .                .

 Thailand              1944.792         9.325265          4.800715                0.0              6.11             104.25              3.25               14.5




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