中国の地域間分業と地域の「位置」

25

【論文】

Firm Growth in China:
A Perspective of Informal Financing
Dongyang ZHANG*［キーワード］TFP, net working capital, trade credit［JEL 分類番号］D22, D24, G30

1 .Introduction and background

Strahan, 1996). However, China is an important
counterexample to the finding in the finance-Ever since the initiation of the “Opening-up

growth literature: Neither China’s legal nor

Policy” in the late 1970s, China has witnessedfinancial system is well developed, yet it hascontinuously spectacular economic growth, at

experienced the fastest economic growth (Allenan average rate of 9.8% over the past three

et al., 2005). Specifically, the under-developed

decades. As the largest emerging market withfinancial system is driven by the Chinese

many years of uninterrupted fast growth, China

government’s financial depressed policy, and it

presents us with an interesting case of study.

makes firms suffer from financial constraints.

While in the first quarter of 2016, the GDP

In China, financial constraints are prevalentfirms,

especiallynon-statefirms.

growth rate has declined to 6.7%. The growthamongpuzzle of China is not only an important topic

Financial constraints always harm Chinese

to discuss in the growth literature, but also aprivately owned firms’ growth (Poncet et al.,special topic for understanding the economics

2010; Firth, 2012; Chen and Guariglia, 2013;

of “New Normal”, which can help us understandDing et al., 2013), and the distorted financial

the sustainable development in China and theand institutional systems make financial

world better.

constraints even more severe. This situation is

However, one strand of literature champions

not exclusive to China; it has become a global

the view that the development of a financial

problem. China’s financial constraints cansystem with a stock market and intermediation

be categorized into three main types. First,

contributes to a country’s overall economic

financial depressed policy, which aims to drive

growth (e.g., McKinnon, 1973). Later literatures

rapid economic growth, and financing resource

provide support to this view by presenting

allocation is partial to local government,

empirical evidence at the country level (e.g.,

infrastructure industry and real estate market.

King and Levine, 1993; Levine and Zervos,

These industries are less sensitive to interest1998), as well as at the industry and firm levelrate, and they utilize financial resources(e.g., Rajan and Zingales, 1998; Jayaratne andinefficiently. Second, ownership’s discrimination───────────* E-mail:zhdyruc@gmail.com

and government controlled banking system
lead to the result that bank loan distribution

26

中国経済研究第13巻第 ２ 号

being favorable to state-owned enterprises,

more likely to exit due to poor performance.

and it has created barriers for non-state

Evidence of this productivity–survival link has

owned firms’ access to bank loans. The banking

been found in many pieces empirical research

discrimination against non-state owned firms

works. Studies of China (Song et al., 2011), USAalso insulates non-state firms from formal(Foster et al., 2008), Chile, Colombia (Tybout,financing (Brandt and Li, 2003; Lu et al, 2013).

1992; Eslava et al., 2004), Ghana (Frazer, 2005),

Third, because of the financial repression, theSpain (Farinas and Ruano, 2005), and Taiwan

shadow banking and underground finance(Aw et al., 2001) consistently demonstrate

have extended rapidly. Among these, thethat the productivity of exiting firms areusurious loan credit is the predominant andsignificantly lower than that of the surviving

has deep influences on social and economic

ones.

life. Therefore, firms who use these financial

Working capital management is particularly

resources have to bear high debt cost, and theimportant in the Chinese context, where firms

formal financial market has been distorted.

are limited to access long-term capital markets

financial(Ding et al., 2013; Zhang, 2016), or are facing

intermediations–firm

financial constraints. Such firms therefore need

performance nexus is intriguing and meaningful

to rely on internally generated funds, short-for exploring the mechanism of growth miracle

term bank loans, and trade credit to financein China. Particularly, the nexus applies totheir activities. In line with this argument,

the state sector and the listed sector, withit has been shown that effective working

arguably poorer applicable legal and financial

capital management has played a particularly

mechanisms, the private sector grows much

important role in alleviating the influence offaster than the others and contributes the mostthe 2008 financial crisis (KPMG China, 2011)

to the economy’s growth.

and investment behavior (Ding et al., 2013). In

Therefore,

understanding

constraints–financial

theRegarding the firm’s dimension, one concern

addition, the presence of financial constraints

is that China’s economic growth is strongly

and its effects on firms’ investment decisions

related to Total Factor Productivity (TFP).

and performance have received intense

Nobel Laureate Paul Krugman (1997) statedinterest in the corporate finance literature.

that “Productivity isn’t everything, but in

There is considerable evidence on that financial

the long run it is almost everything”. One

constraints are detrimental to the investmentline of research pays attention to examining

and growth of firms in developing economies

economy-wide productivity growth in China(Stein, 2003). To these firms, working capitalby adopting TFP as a measure of technologicalmay be used as an additional source of finance.

change (Jefferson et al., 2008). Research on firm

In fact, little is known about whether or how

survival and growth generally characterizes

working capital work or how much economic

that the likelihood of survival is essentially

activity they support in China, especiallydetermined by firms’ productivity (Ericson

under the financial constraints. In this chapter,and Pakes, 1995; Asplund and Nocke, 2006).

we focus on firm performance-productivity

Specifically, it is demonstrated that firms withand profitability, which are significantly

lower productivity are more likely to suffer

related to growth (Chen and Guariglia, 2013).

continuous decreases in market share and areSpecifically, we explore the role played byFirm Growth in China: A Perspective of Informal Financing

27

working capital management in explaining thecredit under the possible existence of financial

reason why Chinese firms are able to growconstraints. We first analyzed how trade credit

at high rates despite the significant financial

and net working capital as informal financial

constraints. Using an extensive and nationallyintermediations contribute to Chinese firms’

representative database of non-listed Chinese

TFP, focusing on four ownership types: state,firms (GUOTAIAN non-listed Chinese firms

collective, private and foreign. Second, we

database), we hope to fill a part of the gap bytested the extent to which firm heterogeneity,

examining the extension of working capitali.e., links to different ownership. We tried to

management, one of the mechanisms suggested

explain the Chinese growth puzzle from an

by Hale and Long (2011), Ding et al. (2013) andinformal financing perspective.

Aktas et al. (2015) as an important financial

2 .Literature review

intermediation in Chinese firms’ growth,
investment and profitability.
Furthermore, trade credit is a part of

2.1

Working capital management and firm
performanceworking capital. It is important to investigate

Net working capital (NWC) is defined as

the specific effect of trade credit. “Tradethe difference between firms’ current assets

credit is a financing intermediation offered(including accounts receivable, inventories, andby suppliers to their customers, and thecash) and current liabilities (including accounts

demand of trade credit (i.e., account of payable)

payable and short term debt). It represents

represents the credit that a firm owes its

the source and use of short-term capital.

suppliers for the goods which it has received

Working capital is often used to measure a

but not yet paid” (Lin and Chou, 2015). Trade

firm’s liquidity. Liquidity is a precondition

credit is typically regarded as a short-termto ensure that firms are able to meet theirinformal financing instrument (Wu et al., 2014).

short-term obligations. Insufficient liquidity is

Trade credit is more likely to be significant in

one of the reasons for bankruptcy (Dunn andexplaining firm growth in China, where there is

Cheatham, 1993). Yet, too much liquidity may

a relatively weak and capricious enforcement

harm firms’ profitability (Bhattacharya, 2001).

of contracts and discrimination in bank lending

Good management of working capital therefore(formal financing). China therefore offers an

requires a balance between liquidity and firm

opportunity to study trade credit in a state-

performance in order to maximize the value of

dominated banking environment notorious

the firm. The advantages of holding inventories

for misallocating credit. China, a typicallyand extending trade credit to customers aretransitional and developing economy, provides

outlined as the followings. Yet, the higher thea special perspective for other developing

inventories and account of receivable are, thecountries to study the relationship between

less the amount money is available to the firm

trade credit and firm productivity for us tofor profitable investment. It suggests that it

understand the mechanism of Chinese firms’

is difficult for firm managers to find out thegrowth better.

optimal level of working capital (Deloof, 2003).

In

this

paper,

we

investigatedtheThe literature proposes several theoretical

relationships between Chinese firms’ TFP

arguments to understand the relationshipand net working capital, TFP and trade

between working capital and firm performance.

28

中国経済研究第13巻第 ２ 号

Additional investment in net working capital

capital management has played a particularly

is expected to have positive effects, in particularimportant role in alleviating the effects of thefor firms with low level of working capital. It

recent financial crisis in China (KPMG China,

is because that working capital allows firms to2011). In addition, Hale and Long (2011a,

grow by increasing sales and earnings. More

2011b) argue that the spectacular growthinventories are known, among other issues, tocharacterizing Chinese private firms in recent

reduce supply cost, to provide hedge against

years is mainly due to their ability to manage

input price fluctuations, and to minimize theworking capital, their accounts receivable inloss of sales due to potential stock-outs (see,particular, more efficiently than other firms.

e.g., Blinder and Maccini, 1991; Fazzari and

Moreover, firms characterized by high working

Petersen, 1993; Corsten and Gruen, 2004). On

capital display high sensitivities to investment

the other hand, overinvestment in working

in working capital than to cash flows and low

capital may generate adverse effects and leadsensitivities to investment in fixed capital than

to value destruction for shareholders. Like

cash flows. An active management of working

any other investment, increases in working

capital may help firms alleviate the effects

capital require additional financing, which leadsof financing constraints on fixed investment

to further financing and opportunity costs(Ding et al., 2013). These findings suggest that(see, e.g., Kieschnick et al., 2013). Therefore,

working capital is an additional and importantceteris paribus, firms with high working

financial resource for Chinese firms, but thecapital on their balance sheet also potentiallywork on this issue is limited.

face high interest expenses and bankruptcy

Therefore, our analysis tried to bridge therisk. Moreover, too much cash tied up in net

gap on the literatures about the net working

working capital might also prevent firms from

capital management and Chinese firm

value-enhancing investment projects in theperformance from the perspective of financial

short run (see, e.g., Ek and Guerin, 2011). The

constraints. We discussed each of the four

existence of potential benefits and costs implies

distinct hypothesis.

therefore a non-linear relationship between
working capital level and firm performance,

2.2

Trade credit and firm performancewith the expected relationship being negative

Furthermore, regards to trade credit, afor firms with high level of working capital (i.e.,

number of studies adopt a micro perspective

overinvestment in net working capital) andand use firm-level data to understand the link

positive for the ones with low level of working

between financial development and growth

capital (i.e., underinvestment in net working

in China. However, only a few focus on thecapital) (Baños-Caballero et al., 2014; Mun andrelationship between specific sources of tradeJang, 2015; Aktas et al., 2015).

credit and firm growth, and the role trade credit

In the Chinese context, firms have limited

playing on sustainable firm growth remains

access to long-term capital markets. Therefore,

controversial (Garmaise and Moskowitz, 2003).

working capital management is especially

One current firm-level approach is to research

important. However, related studies aretrade credit based on the behavior of bothquite limited. In line with this argument,

suppliers and receivers. Using NBS industrial

it has been shown that effective working

firm-level data (1998-2003) to investigate theFirm Growth in China: A Perspective of Informal Financing

29

efficiency of trade credit in China by analyzing

However, the previous literature has not

the attributes of trade credit from supplierreached a consensus on whether or not

firms, Cull et al. (2009) argue that trade credit

trade credit presents an efficient financial

does not play an economically significant role inintermediation in China. In particular,

China. This is because that SOEs are the only

understanding the mechanism between tradeones who redistribute trade credit obtained

credit and firm growth in China can help us

from bank loans to non-state firms incidentally.

understand the reason why that China grows

However, non-SOEs actively exploit tradeso fast in spite of its underdeveloped financial

credit as an informal financing channel tosystem better. Therefore, there is a large gap

overcome liquidity problems (Garnaut et al.,needs to be filled in the literature, related to2001) and financial growth in China, where

how trade credit influences firms’ productivity

the formal financial sector is underdevelopedby analyzing firm-level data in China. Based on(Brandt et al., 2003; Ge and Qiu, 2007).

the previous literature, our research focuses on

Another approach is to research thewhether financial intermediation through trade

substitution and complementary effect of tradecredit can promote the performance of Chinese

credit and bank loan. Some studies document

non-listed firms or not, and whether tradethat privately controlled firms tend to rely

credit can ease financial constraints under the

more on informal finance and trade credit,

heterogeneity (ownership, firm age and size) of

whereas SOEs do not, not even during financial

enterprises and institutional environments incrisis (Wu et al., 2014; Lin and Chou, 2015). Yano

China or not.

and Shiraishi (2012) also argue that financial

3 .Empirical specifications andestimation methodology

intermediation through trade credit is more
efficient than bank finance in economicallydeveloped coastal areas in China. However, Du

3.1

Baseline specification

et al. (2012), who explicitly compare the relative

As one of the most important measurements

importance of bank loans and trade credit inof growth, TFP (total factor productivity)

promoting firm performance, and find out thatreveals differences in economic growth anddue to bank discrimination in China (Brandt

income levels across countries and regionsand Li, 2003), bank loans can improve firm

(Caselli and Gennaioli, 2005; Hsieh andperformance more than trade credit. Yano et al.

Klenow, 2010). Based on Chen and Guariglia’s(2013) find that ethnic minority firms are more(2013) empirical model of cash flow and firm

unwilling to repay trade credit than bank loans.

performance, we establish a model to find

Therefore, financial intermediation throughthe determinants of TFP and reveal whether

trade credit to ethnic minority firms leads tofinancial factors exert any effect. Specifically,

inefficient financial allocation in Xinjiang.

we estimate our model as follows:
(1)
(2)

1
indicates the performance ofwhere───────────1

Due to the serial correlation of TFP (TFP
follows a first-order Markov process (Levinsohn

firm i at time t; Net working capital (NWK) is───────────
and Petrin (2003)), lagged TFP should be
included to control for this problem.

30

中国経済研究第13巻第 ２ 号

our key explanatory variable2. D and (1-D) are(Generalized Method of Moments) allows us

dummy variables, if WK is negative (positive),to address the potential endogeneity problem

and D (or 1-D) equals to 1, or else equals to

because firms adjust inputs according to

0. Trade credit is another key explanatory

anticipants of shocks to productivity orvariable. Accounts payable scaled by total

profitability throughout the production processassets is used to evaluate trade credit . All data

(Coricelli et al., 2012). Unobservable corporate-in this paper has been deflated by deflators .

specific fixed effects affect ownership and3

4

is a firm-specific effect, which we control

financial constraints; simultaneous causality forfor regressing Eqs. (1 and 2) in first differences,financial constraints (Shailer and Wang, 2015);

is an industry-specific effect measured

dynamic panel bias (Nickell, 1981; Arellano andandby an industry dummy. There are 37 industries,
the basic industry being textile manufacturing(Brandt et al., 2012).

is a time-specific effect,

Bond, 1991; Bond, 2002) exist.
In addition, we use two criteria to test that
our estimations are reasonable.

isFirst, we access the presence of nth-orderused to control industry-specific businessserial correlation in the different residuals,

cycle effects (Guariglia et al., 2011; Chen andwhich is denoted as m(n) test. The estimations

is an idiosyncraticthat we regress can be reasonable if theseand we control it using a time dummy.

Guariglia, 2013). Finally,
error term.

specifications are exempted from derail

These specifications enable us to test

correlation in 1st-difference residuals. In thewhether net working capital and trade credit

presence of serial correlation of order n, lags

influence Chinese firms’ TFP.

n+1 and deeper are required strictly in the
instrument set (Brown and Petersen, 2009;

3.2

Endogeneity problems

Roodman, 2009). m(n) test is asymptotically

In examining the relation between net

distributed as a standard normal distribution

working capital and firm performance, one

under the null hypothesis for no n-order serial

issue is the potential endogeneity of explanatory

correlation of the differenced residuals.

variables with respect to TFP. System GMM───────────
2 Consistently with previous studies (Fazzari
and Petersen, 1993; Ding et al., 2013), current
assets minus current liabilities scaled by total
assets is used to evaluate working capital.
3 Consistently with previous studies (Choi andKim, 2005; Lin and Chou, 2015), the trade credit
variable is scaled by total assets.
4 Our data have been deflated by the deflators
taken from the China Statistical Yearbook(various issues) published by the National
Bureau of Statistics of China. We use theprovincial capital goods deflator to deflate thecapital variable and the gross domestic product(GDP) deflator to deflate other variables.

Second, to evaluate whether our instruments
are legitimate and our model is correctly
specified, we assess whether the variables in the
instrument set are uncorrelated with the error
term in the relevant equations. We use Hansen
test (J test) to test overidentifying restrictions.
The result of this test for instrument validity
is asymptotically distributed as a chi-squarewith degrees of freedom equal to the numberof instruments less the number of parameters.
However, using system GMM to estimate a
production function based on a large panel
data, Sargan test tends to over-reject the null
hypothesis of instrument validity (Blundell etal., 2000). As our panel data is of large number,Firm Growth in China: A Perspective of Informal Financing

we choose Hansen test as a major reference.

4 .Data and summary statistics
4.1

Data

We use data drawn from the annual
financial accounts filed by industrial firms from
non-listed firms in the GTA (GUOTAIAN)

31

et al., 2012). Finally, our panel covers 131,482
non-listed firms, corresponding to 757,423 firmyear observations (trade credit), and 118,356
non-listed firms, corresponding to 625,618 firmyear observations6 (NWK).
4.2

Summary statistics

database via CSMAR (China Securities Market

Table 1 displays the descriptive statistics of& Accounting Research) over the period 2005-

our key variables by year. With respect to firm

2009. This covers 41 industries and includes

performance, there is a steady increase in TFP

enterprises with annual sales of five million(3.588~4.795) and ROS (0.015~0.031), separately.

yuan (approximately US $650,000) or more.

Turning to the financial variables, net working

Due to data restrictions, “we have omitted

capital increases steadily (0.036~0.084). By

observations with negative sales, negative

further dividing the net working capital

total assets minus total fixed assets, negative

according to positive and negative, we findtotal assets minus liquid assets, and negativethat generally the positive (negative) working

accumulated depreciation minus current

capital working capital increases (decreases)

depreciation. Firms that do not have complete

steadily (0.036~0.084/-0.088~-0.073). Cash flow

records on our main regression variables are

increases from 0.080 to 0.119, while leveragealso omitted. To control the potential influence

decreases from 0.616 to 0.566.

5of outliers, we exclude observations by the one-

Table 2 displays the descriptive statistics ofpercent tails of each of the regression variables.

our key variables by ownership. With respect

We also omit all firms with less than 5 years of───────────
6 In fact, our original panels cover 134,768 nonlisted firms, corresponding to 935,623 firm-year
observations during 1999-2007. As we identify
five different types of firm ownerships: state,collective, legal person, domestic private, andforeign. “Collective firms are distinct fromstate-owned in that they are either owned bytownship-villages governments or collectively
by the employees. Legal-person share is a
mixture of ownership by state legal persons
and private legal persons (Cull et al., 2009)”. By
the end of 1990s, nearly two-thirds of all TVEs
had been privatized in the provinces, althoughconsiderable differences exist across townships
as to the extent of privatization (Brandt etal., 2003; Brandt and Li, 2003). Therefore, we
discuss three typical ownerships state, privateand foreign in this paper, as the ownerships ofcollective and legal person are not clear andambiguous.

consecutive observations” (Guariglia et al., 2011).
In addition to the above treatment, we further
match the address, telephone number andindustry code of firms and omit observations offirms with fewer than eight employees (Brandt───────────
5 The GTA database’s full name is “China Nonlisted Enterprise Database”, collected by “GTA
Information Technology Company”, which
covers 41 manufacturing industries and includes
firms with annual sales over five million yuan
(approximately US $650,000) from 1998-2009.The
total number of firms is over two million, and
101 variables, including firms’ basic informationand financing information, are increased inthis database. Because our main explanatory
variable is denoted by accounts payable, which
begins from 2004, our data sample’s time period
is 2004-2009.

32

中国経済研究Table 1

第13巻第 ２ 号

Summary Statistics by Year

VARIABLES
TFPLP

2000
2001
2002
2003
2004
2005
2006
2007
3.588
3.602
3.719
3.846
4.057
4.296
4.525
4.795
(100)
(100)
(104)
(107)
(113)
(120)
(126)
(134)
1.677
1.693
1.722
1.766
1.797
1.826
1.854
1.881
TFPOP
(100)
(101)
(103)
(105)
(107)
(109)
(111)
(112)
ROS
0.015
0.015
0.017
0.020
0.021
0.023
0.026
0.031
ROA
0.042
0.044
0.047
0.050
0.050
0.056
0.060
0.067
WK
0.036
0.045
0.049
0.052
0.054
0.064
0.075
0.084
WK (Positive)
0.126
0.134
0.137
0.140
0.140
0.146
0.154
0.158
-0.088-0.088-0.086-0.086-0.084-0.081-0.077-0.073WK (Negative)
Cash flow
0.080
0.083
0.087
0.091
0.093
0.104
0.110
0.119
Leverage
0.616
0.602
0.595
0.587
0.591
0.578
0.571
0.566
Sales growth
0.018
0.003
0.054
0.047
0.018
0.033
0.026
0.041
Size
9.939
9.870
9.860
9.861
9.984
10.09410.20410.331
Total assets
20723.011 19341.339 19148.889 19168.048 21676.847 24197.389 27011.014 30668.765
Age
2.268
2.168
2.170
2.115
2.160
2.265
2.360
2.440
Real age
9.660
8.741
8.758
8.290
8.671
9.631
10.591
11.473-3.095-3.100-3.102-3.099-3.087-3.078-3.067-3.051SA
Observations
77,51995,677
111,564
134,786
119,761
115,811
109,806
102,757

Note: See Appendix 1 for precise definitions of all variables. This table reports the summary statistics forthe sample firms by year during 2000-2007. The variables of ROS, ROA, WK, Cash flow, andLeverage are ratios. The units of assets variables are thousand RMB, and the unit of Real age is
year.

Table 2
VARIABLES
TFPLP
TFPOP
ROS
ROA
WK
WK (Positive)
WK (Negative)
Cash flow
Leverage
Sales growth
Size
Age
SA
Observations

Full Sample
4.026
1.772
0.020
0.051
0.056
0.141
-0.083
0.095
0.590
0.031
10.011
2.239-3.085890,987

Summary Statistics by Ownership

SOEs
3.653
1.700
0.002
0.033
0.013
0.121
-0.1040.071
0.647-0.024
10. 407
2.707-3.038106,038

Private
3.952
1.776
0.027
0.059
0.048
0.133
-0.084
0.104
0.594
0.052
9.847
2.108
-3.105554,669

Foreign
4.304
1.806
0.023
0.053
0.110
0.175
-0.064
0.098
0.544
0.031
10.1902.251-3.063
170,280

Diff.(S/P)

Diff.(P/F)

Diff.(F/S)-0.295***-0.075***-0.025***-0.025***-0.034***-0.012***-0.020***-0.033***0.052***-0.076***0.210***
0.599***
0.010***

0.352***
0.030***-0.005***-0.006***
0.062***
0.043***
0.020***-0.006***-0.050***-0.029***0.344***
0.143***
0.042***-1.117***-0.154***-0.020***-0.016***-0.129***-0.075***-0.052***-0.024***0.151***-0.058***-0.456***
0.638***-0.090***Note: See Appendix 1 for precise definitions of all variables. This table reports the summary statistics forthe sample firms during 1999-2007 and tests comparing SOEs and private firms, private and foreign
firms, SOEs and foreign firms. The variables of ROS, ROA, WK, Cash flow, and Leverage are ratio.
The units of assets variables are thousand RMB. The significance of test statistic for the equalityof variables’ mean are shown by ***, ** and *. ***, ** and * represent statistical significance at the
1%, 5% and 10% percent level respectively.

Firm Growth in China: A Perspective of Informal Financing

33

to productivity, the average value for firms

Leverage of the foreign group is the lowest

with different ownership types generally(0.544) while that of SOEs is the highest (0.647).

fluctuates around 4.0, the foreign group has theThis is highly consistent with the reality thathighest TFP (4.304) while the SOEs group has

in China SOEs can easily get access to loans

the lowest (3.653). Regarding profitability, ROS

and other debts from banks and other financialof the private group is the highest (0.027) while

institutions while foreign firms may rely morethat of SOEs is only as low as 0.002. This echoeson their own country’s capital market rather

the fact that in China SOEs generally havethan debt financing. Additionally, turning tolower efficiency and profitability compared to

the financial constraints proxies, cash flow,

firms with other types of ownership . Turning

firm size and SA index, the SOE group is ofto the financial variables, net working capital

the weakest financial constraints (0.071, 10.407

of the foreign group is the highest (0.110) whileand -3.085) while the private group is of thethat of SOEs is the lowest (0.013). In addition,strongest (0.104, 9.847 and -3.105). These three

we find that generally the positive working

proxies are consistent with one another.

7

capital fluctuates around 0.141 whereas the

Table 3 shows the correlation coefficients

negative working capital accounts for aboutof the key variables used in NWK regressions.

-0.083. Consistent with the previous results, in

All the correlation coefficients are smaller thanterms of both positive and negative working

0.6 and most of them are very small, which

capital, the SOEs group is the lowest (0.121 andcan alleviate the concern of multi collinearity-0.104 respectively) while the foreign groupproblem when they are used simultaneously inis the highest (0.175 and -0.064 respectively).

the same regression.

Table 3

Correlation Matrix between Independent Variables
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(1) WK (Positive)

1(2) WK (Negative)

0.438

1

(3) Cash flow

0.205

0.149

1

(4) Leverage-0.578-0.519-0.304(5) Sales growth

0.002

0.049

0.177

-0.033

1(6) Size-0.089-0.002-0.129

0.021

0.042

1

(7) Age-0.033-0.054-0.1060.101-0.0860.124

1

Note: See Appendix 1 for precise definitions of all variables. This table reports the correlation matrix
between independent variables of the sample firms during 1999-2007.

Table 4 shows the descriptive statistics of───────────
7 Our results are consistent with previous
studies (e.g., Chen and Guariglia, 2013), foreign
and private firms have higher TFP and
profitability.

the variables used in the estimation related
to trade credit. We present the statistics forall samples of state, collectively, privately andforeign-owned firms. As expected, the TFPvaries considerably across ownership groups.
Specifically, foreign firms display the highest

34

中国経済研究第13巻第 ２ 号

TFP (2.280), whereas SOEs display the lowest8

youngest in age9 (1.847). Finally, from the SA(1.668). State-owned firms are the lowest inindex, we find that SOEs suffer the least fromtrade credit (0.098), whereas foreign firms arefinancial constraints (-2.938), and private firms

the highest (0.185). Furthermore, SOEs are thesuffer the most10 (-3.117).

lowest in cash flow (0.028) and sales growthIn summary, these summary statistics(0.177), the largest in size (11.023) and the oldest

suggest that firms owned by different

in age (3.012), whereas private firms are theownerships perform differently in terms ofhighest in cash flow (0.110) and sales growth

productivity, financial constraint levels and(0.284), the smallest in size (9.688) and thegeneral financial health.

VARIABLES

All

State

Table 4

Descriptive Statistics-OwnershipCollective

PrivateForeign

Diff.(S/P) Diff.(F/P)

Diff.(C/P)

Diff.(F/S)

Diff.(C/S)

Diff.(C/F)

op

2.227

1.668

2.230

2.238

2.280

0.000

0.000

0.089

0.000

0.000

0.000

TFPfe

3.364

2.685

3.378

3.344

3.500

0.000

0.000

0.000

0.000

0.000

0.000

Trade Credit

0.152

0.098

0.146

0.143

0.185

0.000

0.000

0.000

0.000

0.000

0.000

Cash Flow

0.093

0.028

0.084

0.110

0.072

0.000

0.000

0.000

0.000

0.000

0.000

Debt

0.558

0.597

0.583

0.567

0.511

0.000

0.000

0.000

0.000

0.000

0.000

Sales Growth

0.251

0.177

0.248

0.284

0.191

0.000

0.000

0.000

0.000

0.000

0.000

TFP

Assets Growth

0.112

0.131

0.114

0.116

0.098

0.000

0.000

0.320

0.000

0.000

0.000

Size

10.00411.02310.0989.688

10.501

0.000

0.000

0.000

0.000

0.000

0.000

Age

1.956

3.012

2.094

1.847

1.920

0.000

0.000

0.000

0.000

0.000

0.000

SA Index-3.074-2.938-3.068-3.117-3.000

0.000

0.000

0.000

0.000

0.000

0.000

Obs.

75742328407

149565403687175764

Note: See Appendix 1 for precise definitions of all variables. Trade Credit, Cash Flow, Debt, Sales Growthand Assets Growth are ratios. The units of assets variables are thousand RMB. Diff. is the p-valueof the test statistic for the equality of variables’ mean, where “S”, “C”, “P” and “F” stands for state,collective, private and foreign, respectively.

Table 5 reports the correlation coefficientsof all the key independent variables used in
trade credit regressions. All the coefficientsare smaller than 0.4 and most of them arevery small, which can alleviate the concern ofmulti collinearity problem when they are used───────────
８ This distribution is consistent with Chen andGuariglia, 2013.

simultaneously in the same regression.
───────────
9 This is consistent with Guariglia et al., 2011;
Ding et al., 2013.
10 Our SA index confirms that Chinese firms
face financial constraints, but state- and foreignowned firms tend to face less severe ones (Chowand Fung, 1998; 2000; Chen et al., 2008; Héricourt
and Poncet, 2009; Poncet et al., 2010; Guariglia etal., 2011; Cull et al., 2015).

Firm Growth in China: A Perspective of Informal Financing

Table 5

Correlation Matrix between Independent Variables(1)(1)Trace Credit

35

(2)

(3)

(4)

(5)

(6)

(7)

1

(2)Cash Flow

-0.1291(3)Total Debt

0.350

-0.2781(4)Assets Growth-0.019-0.0670.041

1(5)Sales Growth-0.0180.192-0.0270.285

1(6)Size-0.057-0.153

0.026

0.186

-0.031

1(7)Age-0.041-0.0650.014-0.084-0.1750.188

1Note: See Appendix 1 for precise definitions of all variables.

5 .Estimation results(columns 5-6); in contrast, the coefficients of
TFP on WK for private and foreign firms are

5.1 Regression results-NWK
Table 6 presents the results of systemsignificantly positive for the negative WK group

GMM to investigate the relationship between(column 4). Table 6 indicates that relationship

WK and TFP using Eq. (1) by ownerships.

between WK and TFP is an inverted-U shaped

Each column with WK2 tests the effect of thewith optimal working capital levels in private

square term of WK on TFP to investigate

and foreign firms, but not in SOEs. Particularly,

the curvilinear relationship between WK andpositive (negative) WK plays a significantly

TFP. The results of private and foreign firms

negative (positive) role on TFP in private andof the WK2 model show a significant inverted

foreign firms, but not in SOEs, indicating thatU-shaped relationship between WK and TFP/

firms can boost their productivity by increasing

ROS (columns 2-3), while SOEs do not (column).

working capital efficiency for private andFurther, the positive and negative WK groups

foreign firms, i.e., minimizing receivable andare also examined separately to identify theinventory or maximizing payable when WK

unique relationship patterns of the two groups.

is positive, or maximizing receivable andAs presented in Table 6, for the positive WK

inventory or minimizing payable when WK isgroup the coefficients of TFP on WK for private

negative. However, WK is not an efficient tooland foreign firms are significantly negative

to enhance the firm performance in SOEs.

(columns 5-6), while SOEs are not significantly

36

中国経済研究Table 6
(1)

第13巻第 ２ 号

Regression results-NWK

(2)

(3)

(4)

Dependent variable: TFP

(5)

(6)

Dependent variable: TFP

VARIABLES

SOEs

PrivateForeign

SOEs

PrivateForeign

Dependent i, t-1

0.902***

0.958***

0.920***

0.916***

0.951***

0.898***(0.064)(0.017)(0.063)(0.065)(0.018)(0.063)0.059

0.061***

0.238***-0.405-0.772***-0.529**(0.382)(0.022)(0.074)(0.361)(0.098)(0.263)-0.135-0.206***-0.223***0.221

0.563***

0.241***(0.137)(0.058)(0.082)(0.138)(0.038)(0.046)0.575

1.215***

2.480***

0.347

1.403***

1.285***(0.893)(0.092)(0.316)(0.916)(0.096)(0.182)0.037

0.290***

0.452***-0.0770.154***

0.037(0.232)(0.111)(0.074)(0.258)(0.037)(0.128)0.288*

0.501***

0.142

0.278*

0.355***

1.505***(0.153)(0.083)(0.291)(0.160)(0.093)(0.065)

Size

0.135

0.111***

0.131

0.109

0.106***

0.165(0.109)(0.026)(0.124)(0.109)(0.028)(0.125)Age-0.038***-0.041***-0.118***-0.043***-0.041***-0.021(0.013)(0.005)(0.026)(0.012)(0.005)(0.017)-0.830-0.934***-0.990-0.329-0.633***-1.074

WK (WK D)
WK2 (WK

1-D)

Cash flow
Leverage
Sales growth

Constant(0.915)(0.199)(1.057)(0.963)(0.225)(1.095)

Year Dummy

YES

YES

YES

YES

YES

YES

Industry Dummy

YES

YES

YES

YES

YES

YES

0.404

0.080

0.365

M(2)
M(3)

0.433

0.074

0.332

0.221

0.106

0.278

Hansen (J) Test

0.169

0.120

0.084

0.069

0.000

0.003

Observations

64,249438,282

130,778

63,659432,579

129,380

Note: See Appendix 1 for precise definitions of all variables. This table presents the results from
regressions using two-step GMM model. M(n) is a test for n-order serial correlation in the firstdifferenced residuals, asymptotically distributed as N(0,1) under the null of no serial correlation.
The Hansen (J) statistics is test of overidentifying restrictions, distributed under the null of
instrument validity. Robust standard errors are in parentheses. ***, ** and * represent statisticalsignificance at the 1%, 5% and 10% percent level respectively.

5.2

Regression results-Trade credit

and significantly associated with TFP, whereas

The estimation results of Eq. (2) are

age exhibits a negative and significant result.

presented in columns 1-5 in Table 7. According

Notably, trade credit, our main explanatory

to column 1, we obtain the estimations of ourvariable of interest, is a positive and significant

full data sample and find that size is positively

contributing factor to productivity, revealingFirm Growth in China: A Perspective of Informal Financing

37

that trade credit is an efficient financialfrom formal financing, such as banks (Dingintermediation for firm productivity .

et al., 2013), particularly state-owned banks,

11

We further find that trade credit has a positive

which typically discriminate other types ofeffect on productivity, which is significant

ownership (Allen et al., 2005), trade credit isin collective, private and foreign firms but

not a significant indicator of state-owned firms,

insignificant in state-owned enterprises. Cashand Cull et al. (2015) also find trade credit isflow has the most significant effect (under 1%)

not sensitive to state-owned enterprises’

on firm productivity in private firms, indicating

investment behavior. The significance derives

that private firms rely more on internal financefrom the similar properties of collectively andand are more financially constrained (Poncet etprivately owned firms (Abraham et al., 2010).

al., 2010; Guariglia et al., 2011; Ding et al., 2013;

According to World Bank (2005), fully foreign-

Chen et al., 2013). Therefore, trade credit is an

owned firms in China have limited access toefficient supplement informal financial tool indomestic direct finance, although foreign firms

Chinese private firms (column 4). Because SOEs

suffer less from financial constraints thancan obtain access to formal financial sources

privately and collectively owned firms do. As

easily and benefit from soft budget constraints

indicated in column 5, which shows that trade───────────
11 This result can support some previous
studies by illustrating trade credit is an efficientfinancial tool in China, such as Brandt and Li,
2003; Ge and Qiu, 2007; Lin and Chou, 2015.

credit positively and significantly affects firm
productivity, trade credit is a useful financial
tool when foreign firms’ direct domestic financeis blocked.

38

中国経済研究Table 7

L.TFP

Trade Credit
Cash Flow
Debt
Assets Growth
Sales Growth
Size
Age

Regression Results with Trade Credit
(1)

(2)

(3)

(4)

(5)

All

State

Collective

PrivateForeign

0.848***

0.962***

0.901***

0.845***

0.870***(0.012)(0.081)(0.024)(0.015)(0.033)0.234***

0.124

0.212***

0.249***

0.200**(0.028)(0.436)(0.036)(0.034)(0.102)

1.247***

0.490

1.042***

1.298***

1.203(0.161)(1.241)(0.099)(0.196)(1.145)0.282***

0.066

0.258***

0.274***

0.369*(0.037)(0.361)(0.019)(0.012)(0.202)

VARIABLES
op

第13巻第 ２ 号

-1.020***-0.427***-0.554***-0.704***-1.247(0.383)(0.135)(0.014)(0.087)(1.205)

1.096***

2.295**

1.156***

1.059***

1.181(0.255)(1.132)(0.019)(0.205)(1.274)0.146***

0.085

0.045***

0.172***

0.051(0.014)(0.098)(0.008)(0.024)(0.152)0.008-0.117-0.0040.013-0.006(0.040)(0.276)(0.026)(0.024)(0.126)-1.552***0.800-0.655***-1.833***-0.103(0.241)(2.546)(0.058)(0.163)(1.445)

Year Dummy

Yes

Yes

Yes

Yes

Yes

Industry Dummy

Yes

Yes

Yes

Yes

Yes

0.935

0.244

0.122

Constant

0.769

m(2)
m(3)

0.070

Hansen Test

0.094

0.565

0.101

0.453

0.674

Observations

541,546

16,765

105,638

292,399

125,122

Note: See Appendix 1 for precise definitions of all variables. This table presents the results from
regressions using two-step GMM model. M(n) is a test for n-order serial correlation in the firstdifferenced residuals, asymptotically distributed as N(0,1) under the null of no serial correlation.
Hansen statistics is test of overidentifying restrictions, distributed under the null of instrument
validity. Robust standard errors are in parentheses. ***, ** and * represent statistical significanceat the 1%, 5% and 10% percent level respectively.

6 .Summary

capital management impacts on firm TFP.
We found that there is an inverted U-shaped

We used the data of Chinese non-listed

relationship between working capital and TFP.

firms over the period of 1999-2007 to study

If a firm’s working capital is positive (negative),the relationship between working capital andthe firm’s working capital will have a negative

firm performance. Moving beyond the existing(positive) influence on its TFP. Working capital

literature, we investigated that how working

is significantly associated with TFP in privately

Firm Growth in China: A Perspective of Informal Financing

39

and foreign owned firms, while not being

positively affected by trade credit. In particular,significantly associated with TFP in SOEs. It

as SOEs tend to have better access to externalshows that working capital management is anfinance, trade credit has an insignificant

efficient tool to enhance firm performance ofeffect on the productivity of state-owned

non-SOEs.

enterprises. However, trade credit is found toFurthermore, this paper also investigatedhave a significant effect on the productivity of

the relationship between trade credit and TFPcollectively, privately and foreign-owned firms.

for Chinese non-listed manufacturing firms. By

China is the largest transitional andemploying a large and representative firm-

developing

level dataset, we empirically examined that

underdeveloped financial system and financial

whether working capital and trade credit asdepression policy. Informal finance is an

non-standard financial intermediations affectefficient and important financial tunnel tofirm TFP or not. Our empirical findings

help with Chinese firms’ growth in the future.

suggest that Chinese firms face strong financial

Informal finance will still work in China, andconstraints, which strangle their productivity.

we need to pay attention to their development

Chinese firms’ productivity is significantly andin the “New Normal” background.

economy,regardingtotheAppendix 1.Variable Name Definition
TFP opTotal Factor Productivity by Olley and Pakes (1996)

TFP feTotal Factor Productivity by fixed effect method

Working Capital Current assets minus current liabilities divided by total assets
NWK

Difference between working capital Year t-2 to Year t-1 divided by total assets

Trade Credit

Account of Payable Divided by Total Assets

Cash Flow

Net Profit plus Depreciation Divided by Total Assets

Debt

Long Term and Short Term Debt Excludes Account of Payable Divided by Total Assets

Assets Growth

Proportion of Change in Assets from Year t-2 to Year t-1

Sales Growth

Proportion of Change in Sales from Year t-2 to Year t-1

Firm Size

Natural Logarithm of Total Assets

Firm AgeNatural Logarithm of the Number of Years since the Open YearSA Index-0.737*Size+0.043*Size2-0.040*Age

40

中国経済研究

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Firm Growth in China: A Perspective of Informal Financing

43

Firm Growth in China: A Perspective of Informal Financing
Dongyang ZHANG (School of Economics, Capital University of Economics
and Business Graduate School of Economics, Kyoto University）
Keywords: TFP, net working capital, trade credit
JEL Classification Numbers: D22, D24, G30
China has experienced continuously spectacular economic growth, at an average rate of nearly
10% over the past decades. While in the first quarter of 2016, the GDP growth rate has declined to
6.7%. As the largest developing and transitional economy with many years of uninterrupted fast
growth, China’s financial system is underdeveloped, and the legal protection environment is weak.
There are no consensus results to explain this Chinese growth puzzle. Under the “New Normal”
policy background, firm’s growth is focused on, this paper tries to discuss and answer this puzzle
from the informal financing perspective. I investigate the relationships between TFP and net
working capital, TFP and trade credit, and support a micro-level evidence that Chinese firms’ TFP
is significantly related to informal financing, particularly the non-state factors, but not state-owned
enterprises.