Taxation in a mixed economy: The case of China
Sinclair Davidson and Larry Li*
Abstract
The People’s Republic of China provides an interesting environment to examine the corporate
income tax. Government has significant ownership stakes in the for-profit economy and Stateowned Enterprises are liable to the corporate income tax. This is very different to most other
economies where State-Owned Enterprise tends to dominate the not-for-profit economy and pays
no corporate income tax. Government ownership also varies between the central government and
local government in addition to State Asset Management Bureaus. This provides a rich institutional
background to examining the corporate income tax. We report that effective tax rates do appear to
vary across the ownership types, but that SOEs pay a statistically higher effective tax rate than to
non-State-owned. In addition, Local government owned State-owned Enterprise pay higher effective
tax rates than central government and SAMB owned State-owned Enterprise. We also investigate
Zimmerman’s (1983) political cost hypothesis. Unfortunately these results are econometrically
fragile with the statistical significance of those results varying by empirical technique.
Key words: Corporate income tax, Effective tax rates, Ownership Structure.
JEL Classification: H2
*
School of Economics, Finance and Marketing, RMIT University, 445 Swanston Street, Melbourne, Australia,
3000.
1
Introduction
In this paper we examine the relationship between government and corporate income tax in the
People’s Republic of China. China provides an interesting environment to examine these issues as
government still controls a significant number of firms in China after three decades of economic
reform and privatization. In this paper we differentiate between the central government and ‘local’
government, i.e. any level of government that is not the central government. In addition to direct
share ownership, the Chinese authorities also have State Asset Management Bureaus (SABMs) that
can be understood as being equivalent to investment trusts, which represent the state in the
exercise of ownership rights with the mission of maximising the value of state assets. So within China
there are firms that are controlled by private individuals and foreign investors and then other firms
that are owned by three different forms of government ownership. See Chen, Firth and Xu (2009) for
a detailed discussion. Another consideration that makes China interesting is that government
ownership is diffuse across the economy. In many ‘western’ economies government ownership
tends to be highly concentrated in the not-for-profit sector and consequently not liable to the
corporate income tax.
We expect to observe substantial variation in the tax paying behaviour of those different firm types.
For example, we might expect non-SOE firms to behave more or less like any other firm where
management attempts to minimise corporate income tax liabilities. Of particular interest, however,
are SOE firms owned by local government. Taxes are collected by the central government while local
government owners face the same incentives as do private shareholders to divert resources.
Blanchard and Shleifer (2001) report that local government in China has tended to promote business
unlike, say, Russian local government. In part this has been due to the central government allowing a
substantial share of tax revenues to remain in the regions where that tax revenue was earned. To
that extent, local government SOEs might pay just as much tax as do central government SOEs. It is
not clear how SABM owned firms might behave.
We examine these issues using data drawn from China over the period 1999 – 2009 and over 10,000
firm years. In brief we find there is no significant difference between effective tax rates paid by
different types of Chinese firms. We do find that local government SOEs pay slightly higher rates of
corporate income tax than do those SOEs owned by the central government. This is consistent with
the Blanchard and Shleifer (2001) argument.
We also test for Zimmerman’s (1983) ‘political cost hypothesis’. He suggests that larger firms would
be subject to greater scrutiny from the taxation authorities leading to higher effective rates of
taxation for those firms. We test for this relationship following Davidson and Heaney (2012) –
however, we find no evidence for the political cost hypothesis.
This paper is organised as follows: Section II sets out our hypotheses. Section III describes the data
set and methodology employed in this study. Section IV presents the empirical findings and
discussion, and conclusions are reported in Section V.
Hypotheses
We investigate two sets of hypotheses in this paper. First we examine the data for evidence
consistent with Zimmerman’s (1983) ‘political cost hypothesis’. Zimmerman (1983) suggests his
political cost hypothesis to explain a positive relationship between size and effective tax rates. His
argument being that larger firms would be subject to greater scrutiny from the taxation authorities
2
leading to higher effective rates of taxation. It is important to be clear what Zimmerman is arguing.
He is not suggesting a monotonic relationship between size and effective tax rates – he is clear that
the positive relationship exists only for the very largest companies. By contrast much the literature
finds a negative relationship between effective tax rates and firm size. This could be explained by a
‘political influence hypothesis’ – the argument here being that large firms are better able to lobby
government for preferential tax regimes that allows them to reduce their effective tax burden.
Conversely there may be large fixed costs associated with tax planning with large firms being in a
better position to incur those fixed costs. That too could explain an observed negative relationship
between firm size and effective tax rates.
Our second set of hypotheses relate to the impact government ownership (or, at least, control) has
on effective tax rates. The privatisation literature suggests that government ownership leads to
relatively poor performance compared to private ownership (see Megginson 2005 for a description
of that literature). To that extent state owned enterprises may have lower effective tax rates relative
to their privately owned peers. On the other hand there may well be ‘political influence hypothesis’
at work whereby state-owned enterprises have access to tax shelters that privately owned firms
cannot access. These sorts of argument suggest that state-owned enterprises will have lower
effective tax rates than their privately owned peers.
On the other hand, it is possible that state-owned enterprises could have higher effective tax rates.
Here the state could act like most other controlling shareholders and divert resources to its own
private benefit by not minimising tax liabilities within the firms that it controls. It is important to
recognise that this is a corporate governance problem like any other situation where the controlling
shareholder diverts resources. Our argument here, however, is subtly different from the usual
discussion surrounding the relationship between corporate governance and corporate taxation (see
Desai, Dyck and Zingales (2007) and the collection of papers in Schön (2008) – especially the chapter
by Desai and Dharmapala (2008).
The usual argument suggests that the taxation authorities monitor corporate performance and limit
the extent that controlling shareholders can divert resources for private gain. The possibility that we
explore here is that payment of corporate income tax could itself constitute diversion of resources
for private gain. In this case ‘private gain’ is the expansion of resources available to the state. At the
same time, different types of government ownership exist in China and different levels of
government may face different incentive structures around the payment of corporate tax. As such,
we would expect to see variations in effective rates that correspond to variations in the level of
government ownership.
Methodology
Within the literature there is considerable variation in the way that effective tax rates (ETR) are
calculated. The ETR is generally based on information provided in a firm’s annual reports, taking the
form:
There is a range of alternative ETR measures (Plesko, 2003) with variation found in both the measure
of taxation and the measure of income selected for calculation. In this paper we focus on one such
definitions; The ratio of tax payable to accounting profit before tax.
3
We broadly replicate the empirical analysis in Richardson and Lanis (2007, 2008) and Davidson and
Heaney (2012). They employ ordinary least squares regression analysis to examine the determinants
of the variability of effective tax rates. We replicate the variables Richardson and Lanis (2007, 2008)
employ in their analysis and employ their basic model as follows:
ETRi,j = α0 + β1SIZEi,j + β2LEVi,j + β3CINTi,j + β4INVINTi,j + β5ROAi,j + εi,j
(1)
Where ETRi = the effective tax rate for firm i estimated from financial statements in year j;
SIZE is the natural logarithm of total assets for firm i in year j;
LEV is the ratio of long-term debt to total assets for firm i in year j;
CINT is the ratio of net property, plant and equipment to total assets for firm i in year j;
INVINT is the ratio of inventory to total assets for firm i in year j; and,
ROA is the ratio of pre-tax income to total assets for firm i in year j.
α, β, ε are estimated parameters.
Richardson and Lanis (2007, 2008) and Davidson and Heaney (2012) also use a measure of Research
and Development expenditure in their model; unfortunately our data does not include that data,
because most Chinese companies are not required to report R&D data in their report until recent
years.. Nor does it include a variable for Foreign Sales – a variable Davidson and Heaney (2012)
employ.
Following Richardson and Lanis (2007, 2008) and Davidson and Heaney (2012) we expect LEV, and
CINT to have negative coefficients and INVINT to have a positive coefficient. In contrast to
Richardson and Lanis (2007, 2008) but consistent with Davidson and Heaney (2012) we expect Size
to have a positive coefficient and the square of Size to have a negative coefficient.
Our initial data sample include all public firms in China from 1999 – 2009. To deal with outliners and
the (extremely) misreported data, we winsorize all firm level variable at 1% level in both tails of the
distribution. Corporate governance data is obtained from the China Corporate Governance
database, and financial data is obtained from the China Stock Market Trading database.
Summary statistics for the variables are shown in table 1, while table 2 shows the industry and
annual distribution of the data.
4
Table 1: Summary Statistics. ETR = the effective tax rate; SIZE is the natural logarithm of total assets,
LEV is the ratio of long-term debt to total assets, CINT is the ratio of net property, plant and
equipment to total assets, INVINT is the ratio of inventory to total assets, and ROA is the ratio of pretax income to total assets for firm i in year j. SOE = 1 indicates that the firm is a State-Owned
Entreprise, SOECG = 1 indicates a State-Owned Enterprise where the central government is the
single largest shareholder and SOELG = 1 indicates a State-Owned Enterprise where a local
government (i.e. not the central government) is the single largest shareholder and SAMB = 1
indicates a State-Owned Enterprise where a State Asset Management Bureau is the single largest
shareholder. N = the number of firm years in the final sample.
All
Mean
Std.Dev.
N
SOE = 1
Mean
Std.Dev.
N
SOE = 0
Mean
Std.Dev.
N
SOECG = 1
Mean
Std.Dev.
N
SOELG = 1
Mean
Std.Dev.
N
SAMB = 1
Mean
Std.Dev.
N
ETR
SIZE
CINT
INVINT
LEV
ROA
0.1796 21.3057 0.2940 0.1603 0.0583 0.0568
0.1837 1.1136 0.1846 0.1438 0.0889 0.1510
12707 12707 12707 12707 12707 12707
ETR
SIZE
CINT INVINT
LEV
ROA
0.1861 21.4809 0.3140 0.1548 0.0641 0.0557
0.1805 1.1080 0.1888 0.1377 0.0924 0.0940
8487
8487
8487
8487
8487
8487
ETR
SIZE
CINT INVINT
LEV
ROA
0.1667 20.9534 0.2539 0.1713 0.0466 0.0590
0.1894 1.0388 0.1688 0.1550 0.0802 0.2255
4220
4220
4220
4220
4220
4220
ETR
SIZE
CINT INVINT
LEV
ROA
0.1613 22.3224 0.3367 0.1435 0.0794 0.0669
0.1870 1.6989 0.2155 0.1243 0.1226 0.0762
750
750
750
750
750
750
ETR
SIZE
CINT INVINT
LEV
ROA
0.1894 21.4222 0.3088 0.1576 0.0627 0.0565
0.1782 1.0012 0.1857 0.1403 0.0892 0.0858
6977
6977
6977
6977
6977
6977
ETR
SIZE
CINT INVINT
LEV
ROA
0.1799 21.1870 0.3389
0.1921 0.9179 0.1858
760
760
760
0.1411
0.1234
760
0.0616 0.0375
0.0848 0.1580
760
760
The first thing to note in Table 1 is that there does not seem to be much variation in effective tax
rates across organisational ownership structures. The overall effective tax rate is 17.96 per cent with
State-owned Enterprises slightly lower at 18.61 per cent and Non-SOEs with an effective tax rate of
16.67 per cent. This result is somewhat surprising – there are good reasons to believe that ETRs
would be different across ownership structures. Where there is a difference in within SOEs. Those
SOEs that are owned by the central government have a lower ETR (16.13 per cent) compared to
those SOEs owned by local government. The Welsh t-test for unequal variances returns a t-value of
3.34 indicating that the ETRs for different types of SOEs (central government or local government)
are statistically significantly different from each other. It appears that SOEs controlled by local
government pay significantly more corporate income tax than do central government SOEs.
5
Similarly, there does not appear to be large size differences between different ownership types,
there does appear to be size difference with SOEs. Those SOEs owned by the central government are
larger than those owned by local government.
Table 2 Panel A shows the industry distribution of the sample used in this study. The industry
classification is based on the “Guidelines on Industry Classification of Listed Companies” issued by
the China Securities Regulatory Commission (CSRC). Following Richardson and Lanis (2007, 2008)
and Davidson and Heaney (2012) we have excluded the Banking sector from the analysis.
Table 2: Industry and annual ETR statistics. ETR = the effective tax rate, N = the number of firm years
in the final sample.
Panel A: Industry
All
Agriculture, forestry, livestock faming, fishery
Conglomerates
Construction
Electronic
Electric power, gas and water production
Financial service
Food and beverage
IT
Machinery, equipment and instrument
Media
Medicine, healthcare and biological products
Metal and non-metal
Mining
Other Manufacturing
Paper making and printing
Petroleum, chemistry, rubber and plastic
Real estate
Social service
Textile, clothes, and furniture
Transport and storage
Wholesale and retail trade
Panel B: Annual
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
6
ETR
N
0.1796 12707
0.0886
233
0.1969
724
0.2092
247
0.1953
591
0.1257
425
0.1862
222
0.1963
552
0.1365
776
0.1538 1926
0.0935
63
0.1857
853
0.1719 1096
0.2186
298
0.1397
149
0.1591
243
0.1815 1367
0.2218
855
0.1985
295
0.1831
606
0.1951
551
0.2440
635
ETR
N
0.1513
7
0.1528
966
0.1535 1029
0.2082 1081
0.2019 1146
0.2009 1241
0.1977 1226
0.2046 1298
0.1873 1519
0.1402 1566
2009
0.1585
1628
There is substantial variation in ETR across the 21 industries. This is to be expected. In the formal
analysis we do not investigate industry effects per se but include the industries in the regression
analysis as control variables. We do investigate whether there are industry ownership variations
within the data. There are some small variations, but not enough in our opinion, to drive any large
differences in the ETR.
There are two years where ‘low’ ETRs are recorded in the data; 2000 and 2001. All other years see
an average ETR above 20 per cent. The last two years of the sample show lower ETRs than most of
the post 2002 period – just above 20 per cent. This could be due to the global slowdown that
occurred at that time, or it could be due to tax reforms that occurred in the Chinese economy. We
do not pursue that question here but leave it for detailed examination in a companion paper.
Results
Table 3 shows the results of estimating equation (1). We first set of the basic result and test for the
political cost hypothesis as per Davidson and Heaney (2012). We estimate equation (1) using OLS
and White (1980) consistent standard errors. Column (1) shows the expected signs that Richardson
and Lanis (2007, 2008) posit. Columns (2), (3) and (4) show the model estimated using pooled OLS
while columns (5), (6) and (7) show the model estimated using fixed effects. Columns (4) and (7)
show a test of the Political Cost hypothesis by including quadratic and cubic size effects.
A negative coefficient on the quadratic size term and a positive coefficient on the cubic size term
would be consistent with the Political Cost hypothesis.
7
Table 3: Base case results. Table shows results of equation (1) estimated using OLS and a test of the Political
Cost Hypothesis. SIZE is the natural logarithm of total assets, LEV is the ratio of long-term debt to total assets,
CINT is the ratio of net property, plant and equipment to total assets, INVINT is the ratio of inventory to total
assets, and ROA is the ratio of pre-tax income to total assets for firm i in year j. Column (1) shows the expected
sign of coefficients from Richardson and Lanis (2007, 2008). The base industry is Machinery, equipment and
instrument and the base year is 2004. T-values are shown in parenthesis.
1
C
SIZE
(+)
2
Pooled Model
3
4
5
-0.104
-2.374
-5.659
-0.253*
-2.285***
-0.377
(-1.38)
(-8.29)
(-4.81)
(-2.12)
(-3.75)
(-0.20)
0.0107***
0.221***
0.692***
0.0225***
0.218***
-0.0732
(6.75)
(8.62)
(4.18)
(4.52)
(3.86)
(-0.26)
-0.0049***
-0.0272***
-0.0047***
0.00993
(-3.50)
(-3.56)
2
SIZE
(-8.22)
3
Size
Fixed Effects
6
7
(0.74)
0.000351**
-0.00242
(2.88)
(-1.13)
LEV
(-)
-0.0216
(-1.06)
-0.0162
(-0.80)
-0.0128
(-0.63)
-0.00339
(-0.11)
0.00806
(0.27)
0.00630
(0.21)
CINT
(-)
-0.0038
-0.0016
0.0017
-0.0608**
-0.0633**
-0.0633**
(+)
(-0.34)
0.160***
(-0.14)
0.150***
(-0.16)
0.151***
(-3.00)
0.0820**
(-3.12)
0.0752**
(-3.13)
0.0733*
(11.59)
(10.89)
(10.94)
(2.87)
(2.64)
(2.57)
0.132***
0.147***
0.158***
0.098***
0.114***
0.109***
(12.53)
(13.82)
(14.01)
(3.46)
(4.14)
(3.88)
Yes
Yes
Yes
Yes
Yes
Yes
NO
Yes
NO
Yes
NO
Yes
Adj-R2
0.0751
0.08
0.0806
0.0351
0.0371
0.0372
N
12707
12707
12707
12707
12707
12707
INVINT
ROA
(+)
Industry Dummies
Annual Dummies
Looking first at columns (2), (3) and (4) we see that the signs of the coefficients are consistently
positive or negative and are always consistent with the expected signs. We observe that SIZE is
significantly positively related to ETRs across all regressions, implying that large firms are more likely
to pay more tax in China. In addition, ROA has a significantly positive impact on ETRs, which is
consistent with the expected signs and existing literature. As expected, INVINT is significantly
positively related to the effective tax rate, showing that inventory intensive firms are associated with
higher ETRs (Richardson and Lanis, 2007; 2008). CINT has a positive sign but not significant, implying
that asset quality does not play an important role in terms of effective tax rate determination in
China. All up we are confident that the Richardson and Lanis model (2007, 2008) provides a good
explanation of the ETRs in China.
Column (4) provides a test of the Political Cost hypothesis. Under that hypothesis we would expect
to observe significant differences in the ETRs for very large corporations. As expected under that
hypothesis the quadratic and cubic Size terms are statistically significantly different from zero –
providing evidence to support the view that very large Chinese firms would face higher ETRs than do
smaller Chinese firms. Unfortunately the economic significance of the cubic Size term in column (4)
8
is small. This can be seen in figure 1 below where we trace out the predicted relationship between
Size and ETR given the coefficients in column 4.
Figure 1: Relationship between ETR and Size
Source: Author calculations and column 4 Table 3.
We calculate the turning points to be 22.66 and 29.00 respectively. While those very large firms
(with Size above the natural logarithm of 29) do pay slightly higher effective tax rates than do firms
that are slightly smaller – they still pay lower effective tax rates than do average Chinese firms.
In columns (5), (6) and (7) we employ a fixed effects model to re-estimate equation (1). This is to
account for potential time invariant heterogeneity across firms. The Hausman test (not reported)
confirms our suspicion that a fixed effects model is appropriate for our data. Most of our key
findings are consistent with that of Columns (2), (3) and (4). ROA and INVENT still have positive and
statistical significance impact on ETRs. CINT, on the other hand, is negatively significantly related to
ETRs in columns (5), (6) and (7), show that the variance of CINT does have significant impact on ETRs.
SIZE remains its statistical significance in column (5) and (7), however, it has lost its explanatory
power in column 7. What is particularly interesting, however, is that the Size coefficients in columns
(3) and (6) are very similar. This suggests that large Chinese firms are able to reduce their ETRs to
below that of smaller (and average) Chinese firms. This result is inconsistent with Zimmerman’s
political cost hypothesis.
9
Figure 2: Relationship between ETR and Size
Source: Author calculations and columns 3 and 6 Table 3.
The economic significance of this reduction can be seen in figure 2. Here we show, everything else
being equal, that large Chinese firms are able to substantially reduce their effective tax rates relative
to average sized Chinese firms. At this point we cannot untangle whether this relationship is driven
by a political influence hypothesis effect or simply the fixed costs associated with tax planning.
To investigate the influence of ownership structure on ETRs, we extend the basic model (shown in
equation (1)) and include dummy variables denoting different forms of corporate ownership. We
differentiate first between SOEs and non-SOEs, and further divide SOEs into three sub-categories:
firms controlled by the State Asset Management Bureau (SAMB), SOEs affiliated to central
government, and SOEs affiliated to the local government. Chen et al (2009) argue that SOEs perform
best when directly controlled by central government and perform worse if controlled by the SAMB.
We argue that SOEs affiliated to the local government are more likely to pay higher tax, because tax
revenue is the major source of income for local government. Therefore, SOE is included in the
equation. SOE is a dummy variable with a value of one if the government is one of its shareholders.
To further differentiate the government ownership, we also employ three other dummy variables,
including SOELG, SOECG and SAMB. SOELG is with a value of one if SOEs are controlled by the local
government. SOECG is with a value of one if SOEs are controlled by central government. SAMB is
with a value of one if the company is controlled by SAMB. We also use pooled OLS and fixed effects
model to analysis the sample data. Table 4A presents the findings using pooled OLS and Table 4B
shows the results of equation (1) estimated using fixed effects.
ETRi,j = α0 + β1SIZEi,j + β2LEVi,j + β3CINTi,j + β4INVINTi,j + β5ROAi,j + β6 Ownershipi,j + εi,j
(2)
Where variables are defined as before in equation (1) and Ownership is a dummy variable (1, 0) to
show whether a firm is controlled by local government, central government or SAMB.
Table 4A presents the results of equation (2) estimated using pooled OLS. Overall the empirical
findings are fairly consistent. Columns (1), (2) and (3) show that SOEs generally pay higher levels of
corporate income tax with the SOE coefficients being positive and statistically significantly different
10
from zero. If we take a closer look to SOEs controlled by different government authorities, the
difference among firms controlled by SOECG, SOELG and SAMB is noticed in columns (4)-(12). As
expected, we find that firms affiliated with local government ownership tend to pay more tax as tax
revenue is the major source of income for the local government. On the other hand, firms controlled
by central government are less taxed, implying that these firms are in favour of tax policies. SAMB is
negatively related to ETRs, but not statistically significant. Overall the result from Table 4A seems to
suggest that those SOEs owned by local government in China pay higher levels of corporate income
tax than do other types of firm. Yet the coefficient is likely to be economically small – while being
highly statistically significantly different from zero. Local government owned SOEs would pay an
effective tax rate of 18.94 per cent while the average effective tax rate for non-state firms is 16.67.
Overall, SOEs firms tend to pay more tax, because most SOEs are controlled by local government,
which is confirmed by the number of observations of SOELGs in the sample data.
Table 4B shows the results of equation (2) estimated using panel data fixed effects approach.
However, our key variables of interests, ownership dummy variables have lost their explanatory
power. The explanations here is simple, ownership structure changes very slowly over time in China.
The state shares normally cannot be freely traded on the stock exchanges before 2005, and were
transferable only by approval of the authorities. Market liquidity is severely impeded due to trading
restrictions, which has significantly reduced market liquidity and has become a major obstacle to
market efficiency in Chinese market. In 2005, the Chinese authorities introduced a program of
gradual floatation of non-tradeable shares for all domestically listed companies. While the bulk of
state shares are now technically tradeable, the overall trading evidence shows that the original nontradeable shareholders gradually reduce their shareholdings but still retain control through the
creation of a relatively more dispersed ownership structure. Therefore, it is difficult to observe
robust empirical results if the ownership dummy variables is fairly stable.
Looking just at the pooled OLS results we could argue that SOEs that are owned and controlled by
the central government are able to use their influence to reduce their tax burdens, while those SOEs
owned and controlled by local government are diverting resources away from non-controlling
shareholders and paying too much tax. Unfortunately that neat story is not supported by the data
when we examine the results of the fixed effects estimation shown in table 4B.
11
Table 4: Corporate goverance results. Table shows results of equation (2) estimated using OLS and fixed effects. SIZE is the natural logarithm of total assets,
LEV is the ratio of long-term debt to total assets, CINT is the ratio of net property, plant and equipment to total assets, INVINT is the ratio of inventory to
total assets, and ROA is the ratio of pre-tax income to total assets for firm i in year j. The base industry is Machinery, equipment and instrument and the
base year is 2004. T-values are shown in parenthesis. Table 4A contains OLS results and table 4B contains Fixed Effects results.
Table 4A
C
SIZE
SIZE
1
2
3
4
5
6
7
8
9
10
11
12
-0.0950
-2.349***
-5.784***
-0.132
-2.258***
-5.885***
-0.101
-2.253***
-5.986***
-0.102
-2.375***
-5.653***
(-1.25)
(-8.20)
(-4.91)
(-1.74)
(-7.79)
(-4.99)
(-1.33)
(-7.83)
(-5.08)
(-1.35)
(-8.29)
(-4.80)
0.00978***
0.219***
0.711***
0.0120***
0.210***
0.730***
0.00992***
0.210***
0.745***
0.0106***
0.222***
0.691***
(6.07)
(8.52)
(4.29)
(7.43)
(8.04)
(4.40)
(6.25)
(8.12)
(4.49)
(6.72)
(8.62)
(4.18)
-0.0293***
-0.00464***
-0.0300***
-0.00490***
-0.0272***
(-3.75)
(-7.75)
(-3.85)
(-8.23)
(-3.49)
2
-0.0485***
(-8.16)
Size3
-0.0282***
-0.0046***
(-3.62)
(-7.59)
0.000367**
0.000389**
(3.01)
-0.0119
(3.17)
-0.0233
-0.0176
-0.0140
0.000350**
(3.27)
-0.0208
(-1.03)
(-0.77)
(-0.59)
(-1.15)
(-0.87)
(-0.69)
(-1.02)
(-0.78)
(-0.59)
(-1.05)
(-0.79)
(-0.62)
CINT
-0.00562
-0.00328
-0.00356
-0.00238
-0.000790
-0.000888
-0.00462
-0.00237
-0.00264
-0.00335
-0.00107
-0.00126
(-0.50)
(-0.29)
(-0.32)
(-0.21)
(-0.07)
(-0.08)
(-0.41)
(-0.21)
(-0.24)
(-0.30)
(-0.10)
(-0.11)
INVINT
0.159***
0.149***
0.150***
0.159***
0.150***
0.151***
0.157***
0.149***
0.149***
0.160***
0.150***
0.151***
(11.49)
(10.81)
(10.85)
(11.52)
(10.89)
(10.94)
(11.40)
(10.77)
(10.82)
(11.59)
(10.89)
(10.93)
ROA
0.132***
0.148***
0.159***
0.132***
0.147***
0.158***
0.132***
0.147***
0.159***
0.131***
0.147***
0.157***
(12.57)
(13.85)
(14.09)
(12.56)
(13.75)
(14.05)
(12.57)
(13.77)
(14.10)
(12.50)
(13.79)
(13.99)
SOE
0.0101**
0.00949**
0.0100**
(2.82)
(2.65)
(2.79)
-0.028***
-0.0180*
-0.0204**
(-4.04)
(-2.56)
(-2.89)
0.0161***
0.0135***
0.0144***
SOELG
12
-0.0207
-0.0158
-0.0119
(2.87)
LEV
SOECG
-0.0155
0.000400**
-0.0213
-0.0159
-0.0125
(4.93)
(4.12)
(4.40)
SAMB
-0.00654
-0.00710
-0.00699
(-0.97)
(-1.05)
(-1.04)
Industry Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Annual Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adj-R
0.0757
0.0805
0.0812
0.0763
0.0805
0.0812
0.0769
0.0813
0.082
0.0752
0.0801
0.0807
N
12707
12707
12707
12707
12707
12707
12707
12707
12707
12707
12707
12707
1
2
3
4
5
6
7
8
9
10
11
12
2
Table 4B
C
SIZE
-0.252*
-2.283***
-0.377
-0.255*
-2.281***
-0.382
-0.260*
-2.294***
-0.434
-0.254*
-2.270***
-0.480
(-2.10)
(-3.76)
(-0.20)
(-2.12)
(-3.74)
(-0.20)
(-2.17)
(-3.79)
(-0.23)
(-2.12)
(-3.72)
(-0.25)
0.0225***
0.218***
-0.0731
0.0226***
0.218***
-0.0723
0.0225***
0.218***
-0.0656
0.0226***
0.217***
-0.0566
(4.52)
(3.87)
(-0.26)
(4.52)
(3.85)
(-0.26)
(4.52)
(3.89)
(-0.24)
(4.52)
(3.84)
(-0.20)
SIZE2
-0.00469***
0.00992
-0.00469***
0.00988
-0.00470***
0.00955
-0.00466***
0.00906
(-3.57)
(0.74)
(-3.54)
(0.74)
(-3.59)
(0.72)
(-3.53)
(0.68)
Size3
-0.000242
-0.000241
(-1.12)
-0.000236
(-1.12)
-0.000227
(-1.10)
(-1.06)
LEV
-0.00330
0.00808
0.00631
-0.00338
0.00805
0.00629
-0.00328
0.00819
0.00646
-0.00216
0.00915
0.00747
(-0.11)
(0.27)
(0.21)
(-0.11)
(0.27)
(0.21)
(-0.11)
(0.28)
(0.22)
(-0.07)
(0.31)
(0.25)
CINT
-0.0606**
-0.0633**
-0.0634**
-0.0605**
-0.0632**
-0.0633**
-0.0607**
-0.0632**
-0.0633**
-0.0602**
-0.0628**
-0.0629**
(-2.99)
(-3.12)
(-3.13)
(-2.98)
(-3.11)
(-3.12)
(-2.99)
(-3.12)
(-3.12)
(-2.96)
(-3.09)
(-3.10)
0.0816**
0.0751**
0.0732*
0.0817**
0.0751**
0.0732*
0.0832**
0.0764**
0.0745**
0.0822**
0.0755**
0.0737*
INVINT
(2.85)
(2.63)
(2.56)
(2.85)
(2.63)
(2.56)
(2.89)
(2.67)
(2.60)
(2.87)
(2.65)
(2.58)
ROA
0.0986***
0.114***
0.109***
0.0987***
0.114***
0.109***
0.0987***
0.114***
0.109***
0.0983***
0.113***
0.109***
(3.45)
(4.13)
(3.88)
(3.46)
(4.14)
(3.88)
(3.45)
(4.14)
(3.88)
(3.45)
(4.14)
(3.88)
SOE
-0.00220
-0.000528
-0.000346
(-0.22)
(-0.05)
(-0.03)
-0.00631
-0.00281
-0.00213
(-0.32)
(-0.14)
(-0.11)
SOECG
13
SOELG
0.00829
0.00841
0.00819
(0.97)
(0.98)
(0.96)
SAMB
Industry Dummies
Yes
Annual Dummies
Yes
Adj-R2
N
Yes
Yes
Yes
0.0351
12707
Yes
Yes
0.0371
12707
Yes
Yes
0.0372
12707
Yes
Yes
0.0351
12707
Yes
Yes
0.0371
12707
Yes
Yes
0.0372
12707
14
Yes
Yes
0.0351
12707
Yes
0.0371
12707
-0.0216
-0.0207
-0.0202
(-1.71)
(-1.63)
(-1.59)
Yes
Yes
Yes
Yes
0.0372
12707
Yes
0.0355
12707
Yes
0.0374
12707
0.0376
12707
Conclusion
We investigate the Chinese corporate income tax. The Chinese case is interesting given the diversity
of ownership structures within China. Together with private ownership of firms (the situation in
‘western’ economies) China also has extensive state ownership of for-profit firms. These firms are
subject to the corporate income tax, in addition, there are multiple types of government ownership.
Some firms are owned directly while others are owned indirectly via State Asset Management
Bureaus. Then different levels of government have ownership of firms.
We find some evidence that effective tax rates do vary across ownership types, which is consistent
with our expectation. Generally speaking, SOEs tend to pay more tax than firms controlled by private
sectors. We argue that firms with private shareholders also have more incentive to aggressively
manage their tax affairs, so reducing their tax liability. On the other hand, local government does
have incentive to collect more tax revenue from affiliated SOEs at the expense of other
shareholders’ interest, as tax revenue is the major source of income for local government. This is
consistent with an argument first posited by Blanchard and Shleifer (2001) where local government
is better off having its firms paying more tax and then sharing the tax revenues with the central
government. This provides an incentive to local government to promote business and this probably
adds to economic growth in China. On the other hand, it does still constitute an agency problem.
That neat and plausible explanation, however, is econometrically fragile. When we estimate our
equations using fixed effects and not OLS the ownership variable coefficients are no longer
statistically significantly different from zero. This suggests, everything else being zero, that the
corporate tax regime in China has no impact on firms based on their ownership characteristics. That
outcome, however, is not intuitively obvious.
15
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