Financing Investment in Physical
and Intangible Capitals in China
Go Yano
Graduate School of Economics, Kyoto University
e-mail: [email protected]
Maho Shiraishi
Faculty of Foreign Studies, University of Kitakyushu
e-mail: [email protected]
1
CONTENTS
1.
2.
3.
4.
5.
6.
Motivation
Literature Review
Empirical Model
Data and overview
Estimation Results
Conclusions
2
1. Motivation (1)




Experience of China =An important paradox in
development
Poorly-developed financial system, whereas fast economic
growth (Allen et al., 2005).
Answer by Guariglia et al. (2011), Ding et al. (2013), and
Chen and Guariglia (2013): Internal cash flow and active
management of cash internally held have remarkably
contributed to financing investment in China.
However, to our best knowledge, no work have
econometrically compared internal finance with
external finance in the contribution to financing various
corporate activities including investment.
3
1. Motivation (2)




Furthermore, also relation between internal and external
finances in Chinese domestic firms.
Internal and external financings have different cost and,
therefore, pecking order from each other.
So, making clear their relationship rather than simply
comparing their contributions.
Likely facing financial constraints Chinese domestic
firms can be motivated to match carefully these
financing sources.
4
1. Motivation (3)



After all, China’s experience shows
 that economic development under not wellfunctioning financial intermediation is possible?
or
 that economic development needs a functioning
financial intermediation.?
Although obviously important research questions to
development policy, few studies have tackled it.
Our aim is to fill this gap using Chinese domestic firms
panel data for the period 2000-2009.
5
2. Literature Review (1)


Internal finance in China: Guariglia et al. (2011), Ding et al.
(2013), Chen and Guariglia (2013), He et al. (2013) <within
business group>
External finance in China: Zhang et al. (2012)
<development of financial intermediation in China after
the WTO entry in 2001>, Chang et al. (2010) <bank fund
reallocation and loans>, Firth et al. (2009) <banks allocate
loans to financially healthier and better-governed private
firms>.
6
2. Literature Review (2)




Fixed capital investment by Chinese domestic firms: not
only not only physical (tangible) capital but also
intangible one.
Intangible capital refer to R&D capital, human capital as a
result of worker training and so on, so intangible capital
formation = investment for broadly defined innovation.
Intangible capital as sources of economic growth (Buiges et
al., 2000; Corrado et al., 2005, 2009; Fukao et al., 2009;
Marrano et al., 2009; van Ark, Hao, Corrado, and Hulten,
2009). It is due to remarkable contribution to innovation.
Intangible capital’s nature related with its financing: Risky
(uncertain return), lack of collateral value, likely asymmetric
information between borrower firms and lenders
7
2. Literature Review (3)


R&D capital constitutes an important portion of
intangible capital.
R&D capital investment and the way of financing it:
Brown et al. (2009), Brown and Petersen (2011) < 1.
internal finance 2. equity finance>, Carpenter and
Petersen (2002), Brown et al. (2012), Chiu et al. (2012),
Shin and Kim (2014) <equity financing’s advantages
over debt financing >
8
2. Literature Review (4)


Informed finance by banks in Italy: Herrera and Minetti
(2007) <unsophisticated role of relationship banks in
innovation> Benfratello et al. (2008) < bank finance’s
positive impact on process innovations and only
modest impact on product innovations>
Banks’ expertise of internal R&D and other innovative
activities in actual and potential borrower firms could
be quite limited.
Possible advantage of trade credit finance over bank
finance on acquisition of information: Petersen and
Rajan (1997). <The supplier firms visit the customer
firms more often than banks>
9
3. Empirical model (1)
(I physical/K)it = α+αt + β1(I physical/K)it-1
+ β2(Net profits/K) it-1+ β3(Bank loans/K) it-1
+ β4(Accounts payable/K) it-1 + γXit +ε it, (1)

where i and t denote the firm and time (year) and
ε it = μi + μj + μjt + μp + eit.
10
3. Empirical model (2)




μi: firm-specific fixed effect
μj: industry specific effect
μjt captures industry-specific time effects
μp: province-specific effect where the firm is
located.
11
3. Empirical model (3)
(I physical/K)it = α+αt + β1(I physical/K)it-1
+ β2(Net profits/K) it-1+ β3(Bank loans/K) it-1
+ β4(Accounts payable/K) it-1
+ β5(Bank loans/K) it-1× (Net profits/K) it-1
+ β6(Accounts payable/K) it-1× (Net profits/K) it-1
+ γXit +ε it, (2)
12
3. Empirical model (4)

To consider the manner of financing intangible
capital investments,
I intangible/K in place of I physical/K



K = K physical + K intangible
I physicalit = K physicalit-1 - (1-0.05)K physicalit-1
I intangibleit = K intangibleit-1 - (1-0.05)K intangibleit-1
13
3. Empirical model (4)

The control variables (vector) X include growth
rate of sales (∆Sales it/salesit-1) and logarithms of
firm’s age and number of labors (ln(Age) and
ln(Labor)), where ∆Sales it= sales it - sales it-1.
14
4. Data and overview (1)

Our data are drawn from the Qin database over the period 2000-2009.

Industrial firms data filed by Chinese National Bureau of
Statistics (NBS industrial firms data) have been frequently
used by researchers. Our data has several merits over the
NBS industrial firms data.
More recent period than NBS industrial firms data (usually
available for 1998-2007). In the latter, usually available period
for empirical analysis is from.
Most importantly, longer (2000-2009) record of account
payable (trade credit received) than NBS industrial firms
data(2004-2007) .
More reliable intangible capitals information NBS industrial
firms data in which values of intangible capitals take zero
15
strangely for all firms in some year.
1.
2.
3.
4. Data and overview (2)

Industrial firms.
Domestic firms (foreign-owned firms are omitted)
Firms with more than 3 years’ consecutive data as a common
practice for system GMM estimation.
The final number of sample firms selected is 10,256.
The number of observations is 32,280 and the panel is
unbalanced.
All of sample firms are non-listed ones.

The descriptive statistics in Table 1.





16
Table 1
Descriptive Statistic
Variables
(1) Dependent variable
Mean
Std. Dev.
Obs. No.
I physical /K
0.286
0.656
32280
I intangible /K
0.020
1.111
32280
0.119
0.386
32280
(Bank loans /K)-1
0.038
0.369
32280
(Accounts payable /K)-1
0.565
2.511
32280
(Bank loans /K)-1× (Net profits /K)-1
0.011
0.144
32280
(Accounts payable /K)-1 × (Net profits /K)-1
0.208
3.265
32280
∆Sales/sales -1
0.239
0.454
32280
ln(Age )
2.090
0.163
32280
ln(Labor )
5.665
1.636
32280
(2) Source of funds
(Net profits /K)-1
(3) Control variables
17
5. Estimation results
Table 2
Estimates of Fhisical and Intangible Investment Functions (1): Baseline Results
Dependent Variable
(1) Lagged dependent variable
(I physical /K)-1
I physical /K
I physical /K
I intangible /K
I intangible /K
(1)-a
(2)-a
(3)-a
(4)-a
-0.003
(-0.25)
-0.007
(-0.60)
-0.0002
(-0.70)
-0.0007
(-0.94)
(I intangible /K)-1
(2) Source of funds
(Net profits /K)-1
(Bank loans /K)-1
(Accounts payable /K)-1
1
0.183**
(5.36)
0.426**
(3.80)
0.010
(0.51)
0.120**
(3.31)
0.355**
(3.36)
0.039**
(3.52)
0.054
(0.37)
0.034**
(3.94)
0.032
(0.71)
-0.220
(-0.69)
0.026**
(3.08)
0.027
(1.15)
-0.010
(-0.76)
0.093**
(4.42)
0.032
(0.29)
0.048**
(8.99)
Yes
0.077
0.347
114
10,256
32,280
Yes
0.092
0.362
158
10,256
32,280
Yes
0.643
0.704
114
10,256
32,280
Yes
0.491
0.942
158
10,256
32,280
(Bank loans /K)-1× (Net profits /K)-1
(Accounts payable /K)-1 × (Net profits /K)-1
(3) Control variables
(4) Year Dummy Variables
p-value of Hansen test
p-value of AR(2) test
Instruments No.
Groups No.
Obs. No.
1
The table presents Blundel and Bond's two-step system GMM results. The dependent variable is I/K
We report in parentheses the z statistics that based on the Windmeijer (2005)'s finite sample correction to
the standard errors in two-step estimation.
* Significant at 5%.
** Significant at 1%.
External finance via trade credit
indirectly finances physical capital
investment.
Direct and indirect financing intangible capital
investment by trade credit receipt. Trade credit
finance in China likely succeeds in mitigating
18
information problem.
Table 3
Estimates of Physical and Intangible Investment Functions (1): Financial Behavior of Private SMEs
Dependent Variable
(1) Lagged dependent variable
(I physical /K)-1
1
I physical /K
I physical /K
I intangible /K
I intangible /K
(1)-b
(2)-b
(3)-b
(4)-b
0.006
(0.42)
-.0008
(-0.05)
-0.0003
(-0.40)
-0.0009
(-0.46)
0.165**
(5.15)
0.250
(1.90)
0.285*
(2.35)
0.433*
(1.97)
0.042*
(2.28)
0.053*
(2.40)
0.132**
(3.66)
0.153
(1.19)
0.245*
(2.09)
0.449
(1.86)
0.037*
(2.12)
0.001
(0.04)
-0.087
(-0.66)
0.186
(0.67)
0.023*
(1.97)
0.013**
(3.70)
0.091
(1.08)
0.054
(0.52)
-0.380
(-0.47)
0.683
(0.86)
0.038**
(3.39)
-0.023*
(-1.97)
0.094
(0.63)
0.254
(1.16)
-0.716
(-0.82)
0.383
(0.41)
0.032**
(3.17)
0.062
(1.02)
0.053
(0.08)
0.450**
(5.16)
0.075**
(14.61)
-0.049**
(-8.61)
Yes
0.190
0.314
170
10,256
32,280
Yes
0.342
0.322
251
10,256
32,280
Yes
0.370
0.628
170
10,256
32,280
Yes
0.250
0.759
251
10,256
32,280
(I intangible /K)-1
(2) Source of funds
(Net profits /K)-1
(Net profits /K)-1× Private SMEs
(Bank loans /K)-1
(Bank loans /K)-1× Private SMEs
(Accounts payable /K)-1
(Accounts payable /K)-1× Private SMEs
(Bank loans /K)-1× (Net profits /K)-1
(Bank loans /K)-1× (Net profits /K)-1
× Private SMEs
(Accounts payable /K)-1 × (Net profits /K)-1
(Accounts payable /K)-1 × (Net profits /K)-1
× Private SMEs
(3) Control variables
(4) Year Dummy Variables
p-value of Hansen test
p-value of AR(2) test
Instruments No.
Groups No.
Obs. No.
Financially distressed private SME: stronger
tendency to use cash flow in physical capital
investment only when trade credit can be
received.
More risky intangible capital investment; More
serious information problem of SMEs’
intangible capital investment; Informed bank
finance likely works for financing intangible
19
capital investment by private SMEs.
5. Estimation results (2)
To check the robustness of findings shown in
Table 3, we adopt another category of financially
distressed firms than private SMEs; liquidity
constrained firms.
 But, the two categories actually overlap?
 Check!

Table 4
Categorical Situation of Private SMEs and Liquidity Constrained Firms
Non Private SMEs
Private SMEs
Non Liquidity Constrained firms
50.84%
25.35%
Liquidity Constrained firms
12.61%
11.21%
Total
63.45%
36.55%
Total
76.18%
23.82%
100.00%
χ2(1)=638.226
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Table 5
Estimates of Physical and Intangible Investment Functions (1): Financial Behavior of Liquidity Constrainted firms
Dependent Variable
(1) Lagged dependent variable
(I physical /K)-1
1
I physical /K
I physical /K
I intangible /K
I intangible /K
(1)-c
(2)-c
(3)-c
(4)-c
0.001
(0.19)
-0.006
(-1.52)
-0.0003
(-0.48)
-0.0003
(-0.19)
0.166**
(4.34)
0.084
(1.90)
0.264*
(2.28)
0.334**
(2.79)
0.047**
(2.69)
0.057*
(2.11)
0.182**
(3.84)
0.026
(0.32)
0.309*
(2.24)
0.333
(1.31)
0.050*
(2.48)
-0.005
(-0.21)
0.278*
(2.17)
-0.265
(-2.60)
0.008
(0.55)
0.041*
(2.07)
0.070
(1.61)
0.187
(0.93)
-0.408
(-0.81)
-0.015
(-0.03)
0.036*
(2.80)
-0.027*
(-2.28)
0.113
(1.72)
0.065
(1.00)
-0.516
(-0.74)
0.442
(0.68)
0.035**
(2.73)
0.025
(0.83)
0.321
(0.89)
0.438
(1.92)
0.052*
(2.52)
0.035*
(-2.03)
Yes
0.390
0.286
180
10,256
32,280
Yes
0.700
0.333
268
10,256
32,280
Yes
0.500
0.352
180
10,256
32,280
Yes
0.401
0.962
268
10,256
32,280
(I intangible /K)-1
(2) Source of funds
(Net profits /K)-1
(Net profits /K)-1× Liquidity Constrained firms
(Bank loans /K)-1
(Bank loans /K)-1× Liquidity Constrained firms
(Accounts payable /K)-1
(Accounts payable /K)-1× Liquidity Constrained firms
(Bank loans /K)-1× (Net profits /K)-1
(Bank loans /K)-1× (Net profits /K)-1
× Liquidity Constrained firms
(Accounts payable /K)-1 × (Net profits /K)-1
(Accounts payable /K)-1 × (Net profits /K)-1
× Liquidity Constrained firms
(3) Control variables
(4) Year Dummy Variables
p-value of Hansen test
p-value of AR(2) test
Instruments No.
Groups No.
Obs. No.
largely parallel estimates to
counterparts using private SMEs
dummy shown in Table 3
for financially distressed firms, complementary
functions of trade credit and bank finances to
internal cash flow for financing physical and
21
intangible capital investments, respectively.
Table 6
Estimates of Physical and Intangible Investment Functions (1): High-Tech Industry 1 2
Dependent Variable
(1) Lagged dependent variable
(I physical /K)-1
I physical /K
I intangible /K
(2)-d
(4)-d
0.010
(1.46)
(I intangible /K)-1
(2) Source of funds
(Net profits /K)-1
(Bank loans /K)-1
(Accounts payable /K)-1
(Bank loans /K)-1 × (Net profits /K)-1
(Accounts payable /K)-1 × (Net profits /K)-1
(3) Control variables
(4) Year Dummy Variables
p-value of Hansen test
p-value of AR(2) test
Instruments No.
Groups No.
Obs. No.
Since physical capital investment in
high-tech industry is more risky
than that in other industries,
informed bank finance play
relatively large role.
-0.001
(-0.18)
0.173**
(3.03)
1.126*
(2.16)
0.056
(1.85)
0.160**
(4.74)
0.007*
(2.17)
0.085**
(9.69)
0.308**
(3.03)
0.008
(1.42)
0.399**
(6.17)
0.006
(1.30)
Yes
0.492
0.355
158
1,703
5,662
Yes
0.666
0.437
158
1,703
5,662
In quite risky intangible capital investment, high-tech
industry firms cannot use trade credit finance entailing
repayment to customers and have to rely on banks
having much information of the firm and further on
22
internal cash flow without repayment obligation.
Table 7
Estimates of Physical and Intangible Investment Functions (1): Private SMEs in High-Tech Industry 1 2
Dependent Variable
(1) Lagged dependent variable
(I physical /K)-1
I physical /K
I intangible /K
(2)-e
(4)-e
0.001
(0.06)
(I intangible /K)-1
0.026
(0.88)
(2) Source of funds
(Net profits /K)-1
(Net profits /K)-1 × Private SMEs
(Bank loans /K)-1
(Bank loans /K)-1× Private SMEs
(Accounts payable /K)-1
(Accounts payable /K)-1× Private SMEs
(Bank loans /K)-1× (Net profits /K)-1
(Bank loans /K)-1× (Net profits /K)-1
× Private SMEs
(Accounts payable /K)-1 × (Net profits /K)-1
(Accounts payable /K)-1 × (Net profits /K)-1
× Private SMEs
0.244**
(2.80)
-0.159*
(-2.26)
0.771
(1.95)
1.712*
(2.49)
0.029
(1.83)
0.028
(0.56)
0.265
(1.33)
1.034**
(3.53)
-0.003
(-0.29)
0.014*
(2.14)
0.065**
(7.23)
0.016*
(2.26)
0.657**
(8.09)
-0.449**
(-5.25)
0.004
(0.47)
0.003
(0.85)
0.454**
(4.27)
-0.365**
(-3.97)
0.006
(1.24)
0.001
(0.22)
Yes
0.377
0.340
212
1,703
5,662
Yes
0.260
0.380
212
1,703
5,662
(3) Control variables
(4) Year Dummy Variables
p-value of Hansen test
p-value of AR(2) test
Instruments No.
Groups No.
Obs. No.
1. Under exceedingly
risky conditions, firms
cannot but leave debt
finance and rely on
internal cash flow for
financing their
investments.
2. The situation where
internal cash flow
makes distinguished
contribution to financing
investment as depicted
by Guariglia et al. (2011)
and so on.
For financing physical capital investment, financially distressed hightech firms tend to rely on loans from informed banks more than
internal cash flow. =>reconfirming large role of informed bank finance
in physical capital investment by China’s high-tech industry firms.
23
6. Conclusions (1)

1.
2.
Our main findings are as follows:
External finances via bank loan and trade credit as well as
internal finance play significant roles in investment by Chinese
domestic firms. Contribution by trade credit in financing
intangible capital investment is distinguished in particular. It can
be attributed to trade credit offerer’s superior ability to acquire
information of trade credit receiver firms.
Not only internal cash flow and external finance via bank loan
and trade credit directly finance physical capital investment but
also external finance via trade credit indirectly finances physical
capital investment. That is, receiving more trade credit enables
receiver firms to allocate more internal cash flow to physical
capital investment instead of day-to-day operations. This
evidences a kind of complementary relation between internal
24
and external finances.
6. Conclusions (2)
3.
Complementary relation between internal and external finances
changes from that between cash flow and trade credit to that
between cash flow and bank loans as investment has higher risk
and more serious information problem between borrower firms
and lenders: intangible capital investment by financially
distressed firms and physical capital investment by high-tech
industry firms. Informed bank finance plays large role under
middle level of risk.
4.
However, firms come to need to leave debt finance including
bank loans and rely on internal cash flow for financing their
investments under exceedingly risky conditions such as
intangible capital investment by financially distressed high-tech
firms. There appears situation as depicted by Guariglia et al. and
so on. This is due to the fact that internal finance by cash flow
25
does not have repayment obligation and so default risk.
6. Conclusions (2)


This paper points out appropriate combination of
internal and external finances which differs according
to risk and information problem associated by each
kind of investment.
This has implications for policy makers in transition or
developing economies such as China in that to develop
trade credit finance and to raise banks with ability to
collect detailed information on customer firms are
indispensable for economic development, while to
ensure high-tech industry firms internal cash flow from
their high profitability by protecting their intellectual
property right such as patent is important, too.
26
Thank you for your attention!
谢
谢
大
家
!
 This
research is a part of the project
“Research for Economics of Sustainable
Human Development” (An Asia-core
project).
27