Competition and Corporate Tax Avoidance: Evidence
from Chinese Industrial Firms∗
Hongbin Cai
∗
Qiao Liu
†
Abstract
This article investigates whether market competition enhances the incentives of Chinese industrial firms to avoid corporate income tax. We estimate the effects of competition on the relationship between firms’ reported accounting profits and their imputed
profits based on the national income account. To cope with measurement errors and
potential endogeneity, we use instrumental variables, exogenous policy shocks and other
robustness analysis. We find robust and consistent evidence that firms in more competitive environments engage in more tax avoidance activities. Moreover, all else equal,
firms in relatively disadvantageous positions demonstrate stronger incentives to avoid
corporate income tax.
JEL Classification: L10, D21, H26, G30
Keywords: Competition, firm behavior, tax avoidance and evasion, Chinese economy
∗
Guanghua School of Management and IEPR, Peking University, Beijing, China 100871. Contact: (86)10-
6276-5132, [email protected]
†
School of Economics and Finance, Faculty of Business and Economics, University of Hong Kong, Pokfulam,
Hong Kong. Contact: (852)2859-1059, [email protected].
1
Recently tax avoidance and evasion has received increasing attentions both practically and
in academic research.1 The literature has come to view tax avoidance and evasion as an important corporate strategy and has examined various determinants affecting tax avoidance
activities (see, e.g., Slemrod, 2004; Desai and Dharmapala, 2006). However, how industrial
characteristics such as competitive environment affect firms’ incentives to engage in tax avoidance activities has not been analyzed. Moreover, almost all empirical works in this literature
focus on the U.S. experiences (for a notable exception, see Sivadasan and Slemrod, 2006, on
India).
Using a large dataset of Chinese industrial firms, we examine empirically how product market competition affects firms’ incentives to avoid corporate income tax. We study corporate
“tax saving” behavior of Chinese firms, because it is an important economic phenomenon in
an increasingly important economy in the world.2 For example, the Chinese National Auditing
Office uncovered 11.89 billion Chinese yuan or RMB (about $1.6 billion) in 2003 based on
a four-month, nationwide investigation of 788 companies selected at random in 17 provinces
and cities (The Asian Wall Street Journal, A2, September 20, 2004). Fisman and Wei (2004)
also identify evidence of pervasive tariff evasion in China. It is safe to say that these cases of
uncovered tax evasion represent only a tiny fraction of tax avoidance and evasion by Chinese
firms.
Intuitively, firms under greater competition pressure are more motivated to avoid tax so
as to have more investment money to compete in the market place. Thus, firms in more
competitive industries and firms in relatively disadvantageous positions within an industry
should have stronger incentives to avoid tax. In an earlier working paper (Cai and Liu, 2007),
we demonstrate these results in a simple theoretical model. In this paper we focus on testing
these conjectures empirically.
The dataset we use is maintained by the National Bureau of Statistics of China (NBS)
and contains firm-level information based on the annual accounting briefing reports filed by all
“above scale” industrial firms in China from 2000 to 2005. On average we have about 190,000
firms per year in our sample. However, a main challenge for our empirical analysis, which is
common to the literature on tax avoidance and evasion, is that firms’ true accounting profits
1
are not observable. To overcome this difficulty, scholars often use book incomes as a proxy for
true profits and use the book-tax gap as a measure of tax avoidance, e.g., Desai (2003; 2006)
and Desai and Dharmapala (2006).3 But this approach works only for public companies since
book incomes for non-listed companies are usually unavailable.
Using a similar approach, we calculate an imputed corporate profit based on the national
income account— that is, by deducting intermediate inputs from gross output. For many
reasons, this imputed corporate profit can legitimately differ from a firm’s true accounting
profit based on the General Accepted Accounting Principles (GAAP). A main reason is the
differences in the revenue and expense recognition rules of the two systems, for example, not all
gross output in the current year necessarily converts into firm revenue in the same year. Asset
depreciation rules can also be different. Tax credits (such as earning-reinvestment credits)
and tax loss carryovers can be other sources of the gap between the imputed profit and true
accounting profit. For these reasons, the imputed corporate profit PRO based on the national
income account is probably not a good proxy for true accounting profit, and certainly not
as good as book income. Thus, using the gap between the imputed profit and the reported
accounting profit as a measure of tax avoidance is not appropriate in our context.
However, for our purposes, we only need to assume that the imputed profit and the true
accounting profit are positively correlated, which is likely to hold since both reflect a firm’s
economic fundamentals. The reason is that our theoretical predictions are mainly concerned
with comparative statics results about the sensitivity of reported profits to true accounting
profits. As long as the imputed profit and the true accounting profit are positively correlated,
our comparative statics results will carry over to the sensitivity of the reported profit to
the imputed profit. Therefore, our empirical strategy is to test hypotheses regarding how
competition (and other variables of interest) affects the sensitivity of the reported profits to
the imputed profits.
Our theoretical predictions are all confirmed by our empirical results. Specifically, we find
strong evidence indicating that competition in the product market enhances firms’ incentives
to engage in tax avoidance activities. The estimated effect on profit under-reporting has
the predicted sign and is statistically significant for several measures of competition (the
2
number of firms, concentration, or industry average profit margin) and alternative definitions
of industries (3-digit or 4-digit industry codes) and markets (national or regional).
Our main empirical results are robust to alternative specifications. Besides OLS regressions, we run 2SLS regressions instrumenting for both the imputed profit and competition.
In particular, we use the number of permissions required for establishing a new firm in a
four-digit industry as the instrument for competition, and the average imputed profit at the
four-digit industry level (excluding the firm itself) as the instrument for the imputed profit. In
all model specifications, we find supporting empirical evidence.4 In addition, we investigate a
natural experiment on competitive environment (lifting of restrictions on foreign investment)
for two industries. All these investigations yield the same result that competition encourages under-reporting profits by firms. This means that even though endogeneity (such as
competition) and measurement errors (such as the imputed profit) pose potentially serious
econometric issues in our estimations, they are unlikely to be the driving forces of our main
results.
The competition effect is also economically significant. Take the 2SLS regression in column
(4) of Table 4 as an example. When all independent variables take their mean values, the
reported profit will increase by 0.504 if the imputed profit increases by 1. If the competition
measure used in the regression (industry average profit margin in this case) increases from
its mean by one standard deviation (i.e., competition is less intensive by one standard deviation), then the responsiveness of the reported profit to the imputed profit increases to 0.584,
representing a 15.7% increase from its previous level. In other words, a representative firm in
an industry that is one standard deviation less competitive than the average industry reports
about 16% more profit for each unit of imputed profit than an identical firm in the industry
with the average degree of competition.
Our analysis also yields useful results regarding other factors that may affect firms’ incentives to engage in tax avoidance activities. After controlling for other characteristics, firms
facing higher tax rates or tighter financial constraints and smaller firms report less profits for
each unit of imputed profit. These are all consistent with our theoretical predictions. The
estimated effects of these factors have the predicted signs and are statistically and economi3
cally significant. Based on the 2SLS estimation of the baseline model (e.g., see model (4) in
Table 4), all else equal, for each unit of imputed profit, a one-standard-deviation increase in
tax rate reduces a firm’s reported profit by 5.6% from its mean level; a one-standard-deviation
increase in our measure of accessibility to capita markets leads to a 2.1% increase in the firm’s
reported profits; and lastly, a one-standard-deviation increase in firm employment size causes
the firm’s reported profits to increase by 27.4%.
Our paper builds on and contributes to the aforementioned growing literature on corporate
tax avoidance and evasion. Our main contribution is to present systematic evidence from
China that product market competition increases tax avoidance. Insofar as some of the tax
avoidance and evasion is illegal or socially undesirable, our paper is also related to papers
that point out the “dark side” of competition, e.g., Cummins and Nyman (2005) and Shleifer
(2004). In particular, Shleifer (2004) argues that competition encourages the spread of a wide
range of unethical behavior such as employment of child labor, corruption, excessive executive
pay, and corporate earnings manipulation. The basic idea is simple: the effects of competition
critically depend on the instruments firms use to compete in the product markets. If firms use
socially unproductive means to gain competitive advantage, then competition may not lead
to socially desirable outcomes. In this regard, ours is the first paper to our knowledge that
provides systematic empirical evidence consistent with that claim.
The rest of the paper proceeds as follows. Section 1 describes briefly the institutional
background of corporate taxation in China. Then Section 2 develops theoretical conjectures
and empirical hypotheses. We describe the dataset and our empirical strategy in Section 3,
and then present the main empirical results in Section 4. Section 5 examines robustness of
our empirical analysis. Concluding remarks are in Section 6.
1
Institutional Background
In the central planning system before economic reforms started in 1978, Chinese industrial
firms were mostly state-owned (except small collective firms). They were not independent
accounting units and must send all surpluses to the government agencies controlling them.
4
There was no corporate income tax in the planning system (for a description of China’s
economic reforms, see Wu, 2003). Starting in 1979 and continuing in the 80’s, the Chinese
government introduced a number of enterprise taxation reforms. Large state-owned enterprises
(SOEs hereafter) were subject to 55% income tax, small SOEs were subject to a 10 – 55% tax
schedule, and private firms’s income tax rate was set at 35%. For large SOEs, there were also
complicated and evolving sharing formula to divide the after tax profits between SOEs and
government.
SOE reforms focused on “delegating decision power and giving incentives” (fang quan yang
li) in the 80’s, and then continued in the 90’s with “modern corporation system” emphasizing
corporatization and governance. Many small and medium sized SOEs were quietly privatized,
others were transformed into corporations. At the same time, non-state firms (private, foreign,
Hong Kong/Taiwan) grew fast and became more and more important in the economy. As a
part of the “systematic fiscal and taxation” reforms, in 1994 China enacted the “Corporate
Income Tax Code” that overhauled corporate taxation. All domestic firms, independent of
ownership types, pay 33% corporate income tax, except that small firms with taxable income
less than 30,000 RMB pay 18% income tax. Since the reforms, SOEs have been allowed to
keep the after-tax profits for re-investing and for covering “reform costs” (e.g., paying for layoff
workers).5 Most foreign invested industrial firms pay 15% corporate income tax. The Code
allowed various exemptions, tax credits (e.g., recycling, environmental protection, retained
earning reinvestment) and reductions (e.g., to high tech firms, new firms in poor areas), and
foreign investment firms in general enjoy much more favorable treatments (Cui, 2005).
The tax collection agencies were also reformed in 1994. Before 1994, provincial and local
tax collectors collected all taxes, and then the central government and provincial governments
divided the total tax revenue according to some previously negotiated sharing formula. In the
1994 reforms, taxes are classified into central and local taxes, and a National Taxation bureau
(Guoshuiju) and provincial bureaus (Dishuiju) are responsible for collecting central taxes and
local taxes separately. Both the National Taxation Bureau and provincial bureaus are under
the supervision of the State Administration of Taxation. Right now, corporate income tax
is classified as a “central tax”, thus is collected by the National Taxation Bureau and its
5
branches in all provinces.6
Along with impressive economic growth in the last three decades, the economic landscape
in China has changed dramatically over the reform years. Starting from a central planning
economy, the Chinese economy has become a mixed economy in which the non-state sector
plays an increasingly dominant role. Among all “above scale” industrial firms in our dataset,
in 2005 the SOEs and collective firms account for only 10.2% and 15.1% respectively, mixedownership firms for 21.4%, and private Firms (domestic, foreign, and Hong Kong and Taiwan)
account for 53.3%. Moreover, through many years of reforms, managers of the remaining SOEs
and collective firms are now given considerable decision power and relatively strong incentives
that tie their compensations with firm performance, giving rise to a common concern about
“insider control” in these firms (Wu, 2003).
The high economic growth and increasing tax collection efforts together produce impressive
growth of corporate income tax revenue for the Chinese government since the reforms in 1994.
In 2001, the total revenue from corporate income tax was 263 billion RMB, and in 2005 it
increased to 534.4 billion RMB (China Statistical Yearbook, 2006).7 However, enforcement and
collection of corporate income tax are still considered rather weak. For example, according to
one report by a city branch of the National Taxation Bureau , there are three main problems:
(1) insufficient manpower in the collection agency to deal with the increasing number of firms;
(2) lack of training and skills in the collection agency to collect corporate income tax (which
is much more complicated than other taxes such as VAT); and (3) ineffective management
system in the collection agency.8 In each city, tax collection offices usually identify large firms
as “important tax targets” (zhongdianhu) and each tax official is assigned to deal with a
certain number of these firms. Except this, tax collection officials do not appear to have
effective systematic ways of auditing and checking firms. For example, sampling tax evasion
cases reported in the industry magazine “China Taxation,” one finds that a vast majority of
the cases were reported by insiders to the tax authority.
Given the weak enforcement of corporate income tax, one would expect tax avoidance
and evasion to be quite pervasive. Even though there is no systematic study, that is quite
evident from anecdotal evidence and numerous cases reported in the media. Among the
6
many avoidance and evasion methods, the following seem to be among the most common:
(a) mis-recording sales revenue (e.g., under “accounts receivable”); (b) abusing tax credits
(e.g., claiming recycling materials); (c) transfer prices with “affiliated” firms; (d) earning
management (e.g., to smooth profit and loss); and (e) fake receipts.9 Moreover, there are also
numerous books of taxation planning that teach how to minimize corporate tax legally (e.g.,
Cui, 2005). The large demand of such knowledge indicates that firms do pay close attention
to tax efficiency strategies.
2
Theoretical Conjectures, Empirical Methodology and
Testable Hypotheses
Let πi,t be firm i’s true accounting profit in year t. We postulate that it reports a profit of
π̂i,t = di,t πi,t + ei,t + ζi,t
(1)
where π̂i,t is the profit firm i reports, di,t < 1 and ei,t ≤ 0 are two parameters, and ζi,t is a
mean zero error term. This means that firms under-report their profits. Clearly, the amount
of profit under-reported, πi,t − π̂i,t , is decreasing in di,t and ei,t . In other words, when di,t and
ei,t are greater, firm i reports more truthfully.
In an earlier working paper (Cai and Liu, 2007), we present a simple theoretical model
that yields a profit-reporting decision rule as in Equation (1). We also show that di,t and
ei,t depend on market competition and other firm characteristics. Intuitively, firms under
greater competitive pressure are more motivated to avoid tax so as to have more investment
money to compete in the market place. Thus, firms in more competitive industries and firms
in relatively disadvantageous positions within an industry should have stronger incentives to
avoid tax. Our theoretical predictions can be summarized into the following two conjectures
(for details, see Cai and Liu, 2007):
Conjecture 1 All else equal, profit under-reporting becomes more severe in more competitive
environments.
7
Conjecture 2 All else equal, profit under-reporting becomes more severe when tax rates or
marginal returns of capital are larger, or when the cost of tax avoidance is smaller.
Intuitively, with higher tax rates, one yuan of un-reported profit saves more tax, hence
profit under-reporting is more profitable. With higher marginal returns of capital, one yuan of
saved tax will generate more future profit. In either case, firms will tend to report less profits.
On the other hand, if the cost of tax avoidance is higher, firms will report more truthfully.
Note that we focus on the effect of competition on tax avoidance and do not explicitly
consider the possible agency problems in the tax avoidance decisions. Recently the effects
of managerial incentives on firms’ tax avoidance have been emphasized by several scholars
(Crocker and Slemrod, 2005; Chen and Chu, 2005; Desai and Dharamapala, 2006). These
studies clearly show that agency issues can have important implications for firms’ tax avoidance behavior. In this paper, as long as managers partially care about firm value, our qualitative results (Conjectures 1 and 2) should still hold. Specifically, if managers’ payoff functions
are increasing in firm value, competition pressure will force managers to engage in more tax
avoidance activities. Thus, to focus on the competition effect and also due to data limitation,
we abstract from agency issues in this paper. See Section 5.4 for more discussion on this.
Since πi,t is not observable, we cannot directly estimate Equation (1). To overcome this
difficulty, we adopt the following approach. Using the NBS database, which we will detail
in Section 3, we compute firm i’s corporate profit P ROi,t in year t according to the national
income accounting system as follows:
P ROi,t = Yi,t − M EDi,t − F Ci,t − W AGEi,t − CU RRDi,t − V ATi,t
(2)
where Yi,t is the firm’s gross output; M EDi,t measures its intermediate inputs excluding
financial charges; F Ci,t is its financial charges (mainly interest payments); W AGEi,t is the
firm’s total wage bill; CU RRDi,t is the amount of current depreciation, and V ATi,t is the
value added tax.
Note that P ROi,t defined here, as an imputed profit, can legitimately differ from firm
i’s true accounting profit πi,t . A main reason is the differences in the revenue and expense
8
recognition rules of the two systems, for example, not all gross output in the current year
necessarily converts into firm revenue in the same year. Asset depreciation rules can also
be different. Tax credits (such as earning-reinvestment credits, recycling and environmental
credit) and tax loss carryovers can be other sources of the gap between P ROi,t and πi,t . For
these reasons, this imputed profit is at best a very noisy proxy for πi,t . In fact, a key challenge
for our empirical strategy is to effectively address the measurement errors in P ROi,t , which
we will detail in later sections.
On the other hand, the imputed profit from the national income account system and the
(unobservable) true profit should be positively correlated since they both reflect the firm’s
fundamentals. We suppose that the imputed profit and the true profit are related in the
following way:
πi,t = ηi,t + P ROi,t + θi,t
(3)
where ηi,t is an unknown parameter, and θi,t is a mean zero error term. The unobservable
ηi,t reflects the (firm-specific) differences in profit calculation between the accounting system
and the national income account system. A priori, we do not know the sign of ηi,t : it can be
positive or negative.
By substituting Equation (3) into Equation (1), we derive
RP ROi,t = di,t P ROi,t + Ei,t + i,t ,
(4)
where we use RP ROi,t to replace π̂i,t ; Ei,t = di,t ηi,t + ei,t ; and i,t = di,t θi,t + ζi,t . Furthermore,
we propose the following econometric specification for di,t (note that j denotes the industry
of firm i):
di,t = β0 + β1 ∗ Competj,t + β2 ∗ T axi,t + β3 ∗ F inancei,t + β4 ∗ F irm Sizei,t
(5)
+β5 ∗ Xi,t + i,t
where Competj,t measures the level of competition in industry j; T axi,t is firm i’s tax rate in
year t; F inancei,t is a measure of how easily firm i can access capital market; and Xi,t is a
9
set of control variables that includes other firm characteristics, time fixed effect, and location
fixed effects.
From Conjectures 1 and 2, we have the following hypotheses:
Hypothesis 1 β1 < 0, i.e., a firm’s incentives to engage in tax avoidance are positively
correlated with the degree of product market competition.
Hypothesis 2 β2 < 0, i.e., a firm’s incentives to engage in tax avoidance are positively
correlated with its tax rate.
Hypothesis 3 β3 > 0, i.e., a firm’s incentives to engage in tax avoidance are negatively
correlated with its accessibility to capital market.
The impact of firm size on firms’ tax avoidance is harder to determine. On the one hand,
as mentioned earlier, tax collectors in China pay more attention to larger firms, thus making
the expected cost of avoiding tax higher for larger firms. Moreover, firm size may also serve
as a proxy for easier access to capital market, which again reduces larger firms’ incentives
to avoid tax. On the other hand, one could argue that there are economies of scales in tax
avoidance activities, thus larger firms have stronger incentives to hide profits. While it remains
an empirical issue to find out which direction the net effect goes, our prior is that the former
is likely to be more important and hence we state Hypothesis 4 as follows:
Hypothesis 4 β4 > 0, i.e., larger firms have weaker incentives to engage in tax avoidance.
We have a specification for Ei,t , which is similar to that for di,t as in Equation (5). However,
since we cannot determine the sign of ηi,t in Equation (3) a priori, and since Ei,t = di,t ηi,t +
di,t ei,t , our model has no prediction about how Ei,t will be affected by competition or other
variables. Thus, we do not have predictions about the signs of the coefficients in the estimation
of Ei,t .
10
3
Data and Variable Definitions
3.1
Dataset
In order to study the impact of product market competition on corporate tax avoidance, we
analyze a large dataset developed and maintained by the National Bureau of Statistics of
China (NBS). The NBS data contains annual survey data of all “above scale” industrial firms
in China (i.e., industrial firms with sales above a certain level). On average, close to 190,000
firms per year for the period from 2000 to 2005 are included in the dataset, spanning 37 twodigit manufacturing industries and 31 provinces or province-equivalent municipal cities. They
account for most of China’s industrial value added and have 22% of China’s urban employment
in 2005.
The NBS collects the data to compute the Gross Domestic Product. For that purpose,
every industrial firm in the dataset is required to file with the NBS an annual report of production activities and accounting and financial information. The information reported to the
NBS should be quite reliable, because the NBS has implemented standard procedures in calculating the national income account since 1995 and has strict double checking procedures for
“above-scale” firms. Moreover, firms do not have clear incentives to misreport their information to the NBS, because such information cannot be used against them by other government
agencies such as the tax authorities.10 Misreporting of statistical data was commonly suspected for some time in China, the most notorious was local GDP data provided by local
governments. However, the national income accounting of above-scale firms is done by the
central NBS, hence is much less subject to manipulation by local governments.
The NBS designates every firm in the dataset a legal identification number and specifies
its ownership type. Firms are classified into one of the following six primary categories: stateowned enterprises (SOEs), collective firms, private firms, mixed-ownership firms (mainly joint
stock companies), foreign firms, and Hong Kong, Macao, and Taiwan firms. The NBS does not
treat publicly listed companies in China separately, which are all grouped under the mixedownership category. By the end of 2005, there are about 1,300 publicly listed companies in
China’s two stock exchanges, and only slightly over 700 of them are manufacturing firms.
11
To obtain a clean sample and to rule out outliers, we delete the following kinds of observations from the original data set:
• observations whose information on critical parameters (such as total assets, the number
of employees, gross value of industrial output, net value of fixed assets, or sales) is
missing;
• misclassified observations whose operation scales are clearly smaller than the classification standard of “above scale” firms, specifically, observations for which one of the
following is true: (i) the value of fixed assets is below RMB 10 million; (ii) the value of
total sales is below RMB 10 million; and (iii) the number of employees is smaller than
30;
• observations that have one of the following variables at a negative value: (i) total assets
minus liquid assets, (ii) total assets minus total fixed assets, (iii) total assets minus net
value of fixed assets, or (iv) accumulated depreciation minus current depreciation;
• observations with extreme variable values (the values of key variables are either larger
than the 99.5 percentile or smaller than the 0.5 percentile).
From this procedure, we obtain a sample of 514,394 observations representing 194,635 unique
firms.11 All monetary terms are in terms of 2000 constant Renminbi (RMB).
3.2
Profit Measures
The NBS dataset contains input and output information for every sample firm, which allows
us to compute the imputed profit (P RO) defined under the national income account system
as in Equation (2). The dataset also contains the pre-tax accounting profit reported by each
firm (RP RO). We normalize both imputed profits and reported profits by firms’ total assets
(TA). As shown in Table 2, the sample mean of the reported profit is 0.0515 and that of
the imputed profit is 0.1431 during 2000 — 2005. We define a variable GAP to denote the
difference between the two profit measures. The variable GAP has a sizable mean of 0.0916,
but a much smaller median of 0.027.12
12
As mentioned before, the positive difference between the two profit measures can simply
reflect the exogenous differences between the accounting system and the national income
account system, such as different expenses recognition rules, different asset depreciation rules,
different tax credit and tax loss carryovers rules, etc. But tax avoidance activities by firms can
also make additional contributions to the gap between the imputed profit and the reported
profit. It is the purpose of this paper to empirically detect the latter source of the gap and
analyze how market competition affects firms’ incentives to engage in tax avoidance activities.
An example may illustrate the difference between the imputed profit and the reported
profit in China. In 2005, Hudong Heavy-machines Co., a listed company in the Shanghai
Stock Exchange, reported a pre-tax operating profit of 141 Million RMB. However, the pretax profit under the national income account system according to Equation (2) is 382 million
RMB. The gap is mainly due to the differences in output vs. sales (RMB 1,744 million vs.
RMB 1,423 million), and intermediate inputs vs. expenses (RMB 1,351 million vs. 1,282
million). As discussed early, the differences inherent in revenue and expense recognition rules
between the two systems can lead to legitimate differences between outputs and sales and
between inputs and expenses, thus cause a substantial difference between the reported profit
and the imputed profit. The central question in our analysis is whether behind the legitimate
differences in the two systems, firms manage their accounting and reporting practices to reduce
their income taxes. And if they do, how do they do it differently across industries of different
competition?
3.3
Competition Variables
Following the standard practice in the Industrial Organization literature, we construct four
variables to measure competition intensity. The first is simply the number of above-scale
firms operating in a four-digit industry. We use its natural logarithm in our analysis. The
second measure is the Herfindahl index of total sales in a four-digit industry, which is the
sum of squares of the market shares (by sales) by all firms in industry j. As another way
of measuring concentration, we compute the total market share accounted for by the four
13
largest firms in industry j (by sales). Both the Herfindahl index and the concentration ratio
are negatively correlated with competition. The fourth measure of competition we use is the
industry average profit margin, defined as the ratio of total pre-tax profit to total sales in a
four-digit industry. As competition increases, one expects that firm profit on average will fall,
thus the industry average profit margin should be negatively correlated with competition.
All four competition variables are computed for each of the 503 four-digit manufacturing
industries in China. For brevity, we present the competition variables for the thirty-seven
two-digit manufacturing industries averaged over 2000-2005 in Table 1. All measures show
substantial variations across industries. We calculate the bivariate correlations among the
four competition measures of 4-digit industries. Not surprisingly, the Herfindahl index and
the concentration ratio are highly correlated with a correlation coefficient of 0.864, since both
measure concentration. The logarithm of firm number is negatively correlated with both
the Herfindahl index and the concentration ratio, the coefficients are −0.564 and −0.745,
respectively. Industrial average profit margin is negatively correlated with the logarithm of
firm number (-0.0241), and positively correlated with both the Herfindahl index and the
concentration ratio (the correlation coefficients are 0.013 and 0.011, respectively). Note that
the correlation between profit margin and the other measures has the expected sign, but is
very small. Overall, the four variables measure the degree of competition consistently, yet
offer somewhat differentiated perspectives.
We report the summary statistics of the competition variables based on both the two-digit
and four-digit industry levels in Table 2. The two concentration measures, the Herfindahl
index and the concentration ratio, are very sensitive to market definition. As one would expect,
concentration increases as the market becomes smaller (from 2-digit to 4-digit industry). On
the other hand, the industrial average profit margin is stable.
3.4
Other Variables
From our data set, we construct the following variables of firm characteristics and report their
summary statistics in Table 2.
14
We construct a variable T AX from the ratio of actual corporate income tax paid by a
firm to its reported pre-tax profit, and set it to zero for loss-making firms. Although the
standard corporate income tax rate is 33% in China, the Chinese government gives various
preferential tax treatments (e.g., tax reduction for a certain period of time) to various kinds
of firms (e.g., foreign firms, high-tech firms, joint ventures). Local governments also grant
tax holidays and rebates to various types of companies in order to promote local economic
development. Furthermore, tax collection and enforcement is quite discretionary, leaving large
room for distortion and bribery in exchange of tax reduction. As a result, there are substantial
variations in the effective tax rates across firms. From Table 2, the variable T AX has a sample
mean of 25.01% with a standard deviation of 13.14%. Note that a small fraction of firms have
very high effective tax rates (the sample max is 77.29%), which likely indicates that there are
large tax carryovers from previous years.
We use access to credit to measure firms’ marginal returns to capital. Firms that are
more financially constrained should have higher marginal returns to capital. However, it is
difficult to measure a firm’s access to credit markets. In our context, we use the following
strategy. In development economics it is well established that access to credit is crucial for
firm performance and economic growth in developing countries (e.g., Rajan and Zingales, 1998;
Banerjee and Duflo, 2004). In the case of China, as the Chinese economy has been growing at
a very fast pace, Chinese firms’ demand for credit has grown rapidly. But the banking sector
and stock market have not developed quickly enough to keep pace with this growing demand,
thus Chinese firms are usually facing binding credit constraints. Thus, the actual amount of
debt a firm has reflects mostly how much it manages to borrow, not its endogenously chosen
optimal capital structure. Consequently, we expect firms’ access to credit and their debt
to equity ratios to be positively correlated. Note that banks in China have little discretion
over interest rates they charge borrowing firms (the corporate lending rates were regulated
throughout our sample period). Also note that the corporate bond market is tiny due to strict
regulations. Therefore, interest payments on loans reflect how much a firm is able to borrow.
Therefore, we compute the ratio of total financial charges to total assets for each firm and
use it as a proxy for the firm’s access to credit markets.13 From Table 2, this variable has a
15
sample mean of 1.56% and a standard deviation of 1.43%.
We construct six dummy variables to represent a firm’s ownership status: DSOE , Dprivate ,
Df oreign , DHK/T W , Dmixed , and Dcollective . These binary variables take the value of one if a
firm falls into a corresponding ownership category and zero otherwise. From the means of
these dummy variables in Table 2, in our sample observations of (domestic) private firms,
Hong Kong or Taiwan invested firms, and foreign firms account for 25.5%, 14.8% and 13.1%,
respectively. This shows how far the Chinese economy has changed from the central planning
system since reforms started. Roughly speaking, more than 50% of the above scale industrial
firms are “pure” private firms, while SOEs and collective firms only account for 10.3% and
14.6% of our sample observations. The remainder of 21.7% consists of mixed ownership firms.
We measure firm size by the logarithm of the number of employees. The mean of employment in our sample is 501, with the maximum and minimum being 161,654 and 30 respectively.
In Table 2, one can see that on average, our sample firm has total assets of 163 million yuan.
Using the logarithm of total assets as a measure of firm size yields similar results.
We also include the ratio of sales to total output, as one control variable. To some extent,
this variable controls for the difference in the timing of when revenues are recognized into
income under the accounting system and the national income account system. Its sample
mean is 0.994 and standard deviation is 0.244 (Table 2).14
We create twenty-eight location dummies to capture the geographical effect. For the 31
provinces and province-equivalent municipal cities, we merge Tibet, Qianghai, and Ningxia
into one location because each of the three adjacent provinces has too few observations. We
also merge Chongqing and Sichuan because Chongqing was separated from Sichuan to become
a province-equivalent municipal city only since 1997. Finally, we create six year dummies to
capture time-varying effects.
4
Empirical Results
To get a feeling of how competition may affect a firm’s profit hiding incentive, we plot the difference between the imputed profit and reported profit against the logarithm of the number of
16
firms in an given two-digit industry in Figure 1. We observe a significantly positive correlation
between the gap of the two profit measures and the level of competition.15 There are no clear
outliers in the scatter plot, implying that the relation between the gap between the imputed
profit and reported profit and competition measures is unlikely due to outliers. Plotting the
gap against other competition measures yields a similar pattern. Although this quick look at
the data should be taken with caution, it does suggest that the difference between the imputed
and reported profits may be related to the degree of market competition.
Based on Equations (4) and (5), we estimate the following equation:
RP ROi,t = (β0 + β1 Compet + β2 T AX + β3 F IN AN CE + β4 LN LABOR + β5 RSALE
+β6 DOW N +
X
i=year
βi dyear +
X
βj dloc ) ∗ P ROi,t + α1 Compet + α2 T AX
j=loc
+α3 F IN AN CE + α4 LN LABOR + α5 RSALE + α6 DOW N +
X
αi dyear
i=year
+
X
αj dloc + i,t
(6)
j=loc
where DOW N is a set of five ownership dummy variables with the SOE dummy taken as the
benchmark; dyear is a set of year dummies; and dloc is a set of location dummies. Including dyear
and dloc controls for time-specific, and location-specific effects on profit reporting behavior.
In the above specifications, the signs and magnitudes of the coefficients of the interactive
terms (i.e., βs) indicate whether and to what extent each of the variables affects the sensitivity
of reported profits to imputed profits. Our main focus is on β1 . If it is negative and significant,
then firms in more competitive industries tend to report less profit (Hypothesis 1). Our other
hypotheses say that β2 < 0, β3 > 0 and β4 > 0. Note that we include all control variables and
dummy variables in the constant term in Equation (6) corresponding to Ei,t in Equation (4),
but as discussed before, we do not have predictions about the signs of the coefficient estimates
of αs.
4.1
OLS Regressions
We first use the OLS regression to estimate Equation (6), in which we use four alternative
measures of competition at the four-digit industry level. The results are reported in Table 3.
17
In each column (except columns (1) and (2) where competition measures are excluded), the
heading identifies the measure of competition used in the regression. To save space, only the
estimates of interest are reported. We report robust standard errors in parentheses under the
estimates.
In column (1), we only include the constant and the imputed profit in the regression. The
imputed profit explains about 20.2% of cross-sectional variations in the reported profit. The
estimated coefficients for the imputed profit and intercept are 0.202 and 0.06 respectively, and
are both significant. If (i) the imputed profit is a good proxy for the true accounting profit,
(ii) there are no measurement errors in the imputed profit, and (iii) firms do not under-report
profit, then the coefficient of the imputed profit should be one and that of the intercept should
be zero. Not surprisingly, this is not the case.
In column (2), we add all variables specified in Equation (6) except the competition variables and their interactions with the imputed profit. The model fitness improves as the
adjusted R-squared increases from 20.2% to 24.2%, indicating that these covariates do help
explain the divergence between the reported profit and the imputed profit.
In columns (3) and (4), we only consider the impact of competition (measured by the
Herfindahl index and the profit margin, respectively) on the relationship between the reported
profit and the imputed profit. The interactions of the imputed profit with both competition
variables are significantly positive, showing that competition reduces the sensitivity of the
reported profit to the imputed profit. Compared to column 1, adding competition variables
increases the adjusted R-squared of the OLS regression by 1–2 percentage points, indicating
that competition variables are important in explaining firms’ profit reporting decisions.
In columns (5)-(8), we estimate Equation (6) with OLS regressions using different competition measures. The estimated coefficient of the interaction of competition with the imputed
profit, β1 in Equation (6), is negative when the logarithm of the number of firms is used, and is
positive in all other cases. In all the regressions, the estimated β1 is precisely estimated with a
standard error ranging from 0.005 to 0.0188, and is statistically very significant. The evidence
here shows that firms tend to hide more profits in industries that are less concentrated, have
more firms, or have lower average profit margin.
18
The results on other variables of interest are also largely consistent with our conjectures.
Table 3 shows that in all regressions, the estimated coefficient of the interaction of effective tax
rate with the imputed profit is negative and statistically significant. This is consistent with
Hypothesis 2. All else equal, a firm’s incentives to hide profits are negatively correlated with
its access to external financing (Hypothesis 3). It is evident from Table 3 that this hypothesis
is strongly supported by the OLS results. In all the regressions, the estimated coefficient of
the interaction of access to credit with the imputed profit is positive, statistically significant,
and quite stable across all regressions. Table 3 also shows that in all regressions, the estimate
of the interaction of the logarithm of the number of employees (our proxy for firm size) with
the imputed profit is positive and statistically significant. The estimates are in a close range
from 0.011 and 0.012. These results suggest that on the balance larger firms are less motivated
to avoid corporate tax (Hypothesis 4).
Table 3 also reports the estimated coefficients of ownership dummies. From the sensitivity
of the reported profit to the imputed profit, private firms and Hong Kong and Taiwan firms
report less profits than SOEs, collective firms and foreign firms report more profits than SOEs,
while mixed ownership firms are not significantly different from SOEs.
4.2
The Impact of Competition on Profit Hiding: IV Estimates
While the OLS regression results support our hypotheses, they should be interpreted with
caution. First of all, there might be omitted variables in our model specifications, which
could cause an endogeneity problem if these omitted variables are related to observed righthand-side variables such as competition variables and the imputed profit (we defer a more
detailed discussion of potential omitted variables to Section 5). Second, the imputed profit
and the competition variables in our empirical analysis are likely measured with error, in which
case OLS will yield biased and inconsistent estimates. Third, we mainly rely on industrylevel variations in competition to capture the effect of competition on firms’ tax avoidance
behavior. It may lead to concerns because of the difficulty of comparing competition levels
across industries.
19
These concerns justify the need to identify sources of variations in both the industry-level
competition and the imputed profit that are likely to be independent of corporate choice
variables. We use two approaches in this paper to address these concerns. Our primary
approach is to use the method of instrumental variables. We instrument for both the imputed
profit and the measure of competition — the two variables of interest that arguably are
subject to an endogeneity problem. We identify appropriate instrument variables and use the
2SLS regression to estimate the β1 ’s in Equation (6). In particular, we conduct several tests to
study the validity of instrumental variables used in our empirical analysis. We show that these
instruments are indeed exogenous and relevant, and are not subject to the weak instrument
problem (see e.g., Stock, Wright, and Yogo, 2002). For a second approach, we use variations
introduced by a natural experiment in China in 2002, i.e., China’s WTO entrance, which
exposed several Chinese manufacturing industries to heightened product market competition.
We examine how firms in those industries change their tax avoidance behavior in Section 4.3.
We start by discussing the method of instrumental variables. We use the four-digit industry average imputed profit (excluding the firm itself) as the instrument for a firm’s imputed
profit. Furthermore, we instrument for the competition variables with the number of application procedures a firm has to go through in order to enter a four-digit industry (ST EP ).16
This variable is a direct measure of entry barriers to a four-digit industry imposed by the
government. It should be correlated with the intensity of competition, but is unlikely to be
related to factors affecting firms’ profit-reporting once they have entered.17
Using the above IVs, we perform the 2SLS estimations of Equation (6). To save space, we
only report the results of using the profit margin as the measure of competition. We report the
second stage regression results in Table 4. Column (1) repeats the OLS results from Table 3 for
comparison. Columns (2) – (4) present the IV results. In column (2), only the profit margin
and its interaction with the imputed profit are instrumented with ST EP and the interaction
of ST EP with the imputed profit. In column (3), we instrument for the imputed profit with
its four-digit industry average. The interactions of the imputed profit with other covariates
are instrumented with the interactions of industry average imputed profit with corresponding
covariates. Column (4) presents the IV estimates in which the profit margin, imputed profit,
20
and their interactions with other covariates are all instrumented. Each regression includes
ownership dummies, location dummies, year dummies and their interactions with the imputed
profit.
Table 5 presents the results of the first-stage regressions for models (2)– (4) of Table 4.
Because the implications drawn on the first stage regression results for different models are
similar, we take the first-stage regression of model (2) from Table 4 as the example. Here,
only the profit margin and its interaction with the imputed profit are instrumented. The
instrumented variables are used as dependent variables in the first stage regressions. The
independent variables include the two instruments — ST EP and its interaction with the
imputed profit — and all exogenous variables used in the second stage regressions (we only
report the coefficient on the two instruments for brevity). The estimates of ST EP in the first
column and the interaction of ST EP with the imputed profit in the second column are all
statistically significant. Following the suggestion of Bound, Jaeger and Baker (1995), Table
5 also presents F-statistics with the null hypothesis that instruments jointly equal zero. The
F statistics in all of our first-stage regressions are significantly larger than the critical values
suggested in the literature (e.g., Bound et al., 1995; Staiger and Stock, 1997).
It should be emphasized that in case of multiple endogenous variables, the equation by
equation F statistics might be misleading. The instruments can be weak although they are
very significant in each first stage regression. As pointed out by Stock and Yogo (2005), the
reason for this is that when the predicted endogenous explanatory variables are close to be
collinear, it is difficult to separate their effects. Stock and Yogo (2005) tabulate critical values
that enable the use of the Cragg-Donald (1993) statistic to test whether a set of instruments
are weak in models with more than one endogenous variable. The bottom of Table 4 reports
the results of the weak identification test (the Cragg-Donald statistics, see e.g., Stock and
Yogo, 2005).
We now discuss the IV estimation results in Table 4. In all model specifications (note
that the profit margin is negatively correlated with the competition level), we find that β1
from Equation (6) is always positive and statistically significant, indicating that a higher
level of competition increases firms’ incentives to hide profits. We report the Cragg-Donald
21
(1993) statistics for our IV estimations at the bottom of Table 4. Their values passes the five
percent critical values proposed by Stock and Yogo (2005).18 The results thus show that the
instruments used in our 2SLS estimations are not subject to the weak identification problem.
4.3
A Natural Experiment
Besides the instrumentation strategy, another way to deal with endogeneity and identify
causality is to use exogenous shocks to competition intensity (natural experiment). During
our sample period, the following event arguably produces exogenous variations in the otherwise endogenous competitive intensity. The Chinese government has taken a “gradualist”
approach to open its various industries to foreign investment. In 1995 the State Development
and Planning Commission of China (SDPC) issued its first investment catalogue, the “Catalogue Guiding Foreign Investment in Industries”, that specified what investment projects were
encouraged, permitted, restricted or prohibited for foreign investors. The SDPC revised the
catalogue in 2002 to loosen up restrictions on a number of industries in order to be compliant
with the WTO regulations. Among the 37 four-digit industries that were re-classified into the
“permitted” category in 2002 from “restricted to foreign investment” in 1995, large-sized refrigerator (4063) and power plant equipment (4011) were in manufacturing while most others
were in the service sector. As a result, the number of firms jumped substantially from 2002 to
2003 in large-sized refrigerator (from 86 to 129) and in power plant equipment (from 156 to
240). Similarly, the Herfindahl index in the large-sized refrigerator (power plant equipment)
decreased substantially from 0.299 (0.042) in 2002 to 0.101 (0.03) in 2003.
The exogenous shock to the competition intensity of the two industries provides us with a
unique opportunity to identify the effect of competition on firm-level profit reporting behavior.
We define a time dummy variable P OST , which takes the value of 1 after 2002 and 0 otherwise.
To control for potential confounding effects, we pair each of the two four-digit industries with
another four-digit industry that shares the same first three digits and has been permitted
to foreign investment since 1995. We identify electrical fans (4064) as the control group for
large-sized refrigerator (4063), and electrical generator (4012) as the control group for power
22
plant equipment (4011). We create another dummy T REAT to specify the treatment firms
in the large-sized refrigerator and the power plant equipment. The coefficient of T REAT ×
P OST × Imputed P rof it thus captures the difference between the treatment and control
groups in the changes in the sensitivity of the reported profit to the imputed profit before and
after 2002.
We study the impact on firms’ tax avoidance behavior of the change in the competitive
environment due to the lift-up of restrictions to foreign firms. We report the results in Table
6, which is structured as follows. Columns (1) – (3) report the results from three model
specifications for large sized refrigerator (4063) and electrical fan (4064) (N = 969). Columns
(4) – (6) report the results from the same three model specifications for power plant equipment
(4011) and electrical generator (4012) (N = 2,201).
Column (1) reports the OLS estimates for the refrigerator/fans pair, and column (4) reports
the results for the power plant equipment/electrical generator pair. In both cases, we also
include location dummies, ownership dummies and their interactions with the imputed profit.
The estimate on the variable of interest, T REAT × P OST × Imputed P rof it, is negative and
statistically significant in both cases. Thus, compared with firms in electrical fans (electrical
generator), firms in large-sized refrigerator (power plant equipment) reported less profit after
2002 for each yuan of imputed profit, which supports our main hypothesis that competition
increases firms’ incentives to hide profit.
We then depart slightly from the model specifications as those in Table 4 to the two pairs
of 4-digit industries — besides the variables used in Table 4, we also include P OST , T REAT
and their interactions with the imputed profit. For obvious reasons, we do not include year
dummies and their interactions with the imputed profit. We focus on using the profit margin
as the competition measure (using the other competition measures yields similar results).
We report the OLS estimates for the two pairs of industries in columns (2) and (5). The
interactive term of profit margin with impute profit is positive and marginally significant
in both columns, suggesting that competition increases firms’ incentives to avoid tax. The
interactions between the imputed profit and P OST and T REAT are not significant in both
cases. Columns (3) and (6) report the second stage regression results of the IV estimations.
23
We instrument for the profit margin with T REAT × P OST and for the interactive term
of profit margin with imputed profit with T REAT × P OST × Imputed P rof it under the
assumption that opening up those industries changes the competitive landscape. We report
the first stage regression result at the bottom of Table 6. The equation by equation F statistics
provide support for the validity of the instruments used.
Column (3) of Table 6 presents the second stage regression results for refrigerator/electrical
fan industries. Consistent with the OLS results, the IV estimate of the interaction between
the profit margin and the imputed profit is positive and marginally significant. The result is
in line with the finding in Table 4, where the 2SLS regressions are applied to the whole sample
and ST EP is used to instrument the competition. The Cragg-Donald statistic is 43.09, which
is higher than the 5% critical value tabulated in Stock and Yogo (2005), suggesting that the
instruments used in column (3) are not weak.
Column (6) presents the second stage regression results for power plan equipment/electrical
generator industries. While the IV estimate on the variable of interest — the interactive term
of the profit margin with the imputed profit — is positive and marginally significant, the
Cragg-Donald statistic is as high as 34.63 and passes the 5% critical value.
5
Robustness Checks and Further Discussions
In this Section, we conduct several robustness tests of our main empirical results and discuss
caveats of our empirical analysis.
5.1
Robustness Test: Profit Smoothing
In our analysis so far, we assume that a firm’s reported profit only depends on its contemporaneous imputed profit. However, firms may try to smooth profit over time in managing their
accounting to save tax, thus the reported profit may depend on imputed profits in other time
periods. If reported profits are indeed a function of imputed profits in previous or even future
time periods, our use of the contemporaneous imputed profit may lead to biased estimates.
24
To investigate this issue, we modify our estimation strategy by allowing reported profits to be
related to imputed profits in other periods. We report the results in Table 7.
In Panel A of Table 7, we include the one-year lagged imputed profit as an additional
independent variable in the baseline model (6). The coefficients of the lagged imputed profits
are statistically significant in all regressions, suggesting that firms’ reported profits do depend
on their imputed profits in the previous year. However, all of our previous results hold after
controlling for the lagged imputed profit. In all cases, the estimated coefficients have the
predicted signs and are statistically significant. In fact, the point estimates are similar to
those in the baseline model (Table 3).
As a further investigation, we suppose that firms smooth profits over two years so their
reported profits respond to both last year’s and this year’s imputed profits in the following
way:
RP ROi,t = (β0 + β1 Compet + β2 T AX + β3 F IN AN CE + β4 LN LABOR + β5 DOW N
+β6 RSALE +
X
i=year
βi dyear +
X
βj dloc ) ∗ P ROi,t + (λ0 + λ1 Compet + λ2 T AX
j=loc
+λ3 F IN AN CE + λ4 LN LABOR + λ5 DOW N + λ6 RSALE +
X
λi dyear
i=year
+
X
loc
λj d ) ∗ P ROi,t−1 + α1 Compet + α2 T AX + α3 F IN AN CE
j=loc
+α4 LN LABOR + α5 DOW N + α6 RSALE +
X
i=year
αi dyear +
X
αj dloc + i,t (7)
j=loc
where the sum of β and λ measures the sensitivities of reported profits to imputed profits of
current and last years. (In unreported analysis, we allow the reported profit to also respond to
the imputed profits two years earlier, one year later, or both. We then sum up the estimates
on these interactive terms. These experiments greatly reduce our sample size, but yield
qualitatively similar results.)
The regression results are reported in Panel B of Table 7, where we suppress the estimated
coefficients of other variables and only report βk + λk for k = 0, 1, 2, 3, 4, 6. As shown in Panel
B, β1 + λ1 is significantly different from zero in all regressions and have the expected signs.
This implies that even if firms smooth profit over two years, competition pressure enhances
25
firms’ incentives to hide profits. The estimated coefficients of other variables are all consistent
with our main results.
Finally, we suppose that a firm may smooth its profit over a much longer time period by
introducing the concept of “permanent profits” (see, e.g., Feldman and Slemrod, 2007 for a
similar analysis of the US data). For firms appearing in all six years in our sample period
2000 – 2005 (N=46,211), we obtain an estimate of permanent imputed and reported profits
by taking the average of imputed and reported profits for each firm over the six years. The
other variables (e.g., competition measures, effective tax rate, etc.) are computed in the same
way. We then estimate the baseline model (6) and report the OLS results in Panel C of
Table 7. For brevity, we only report the estimates on imputed profits and its interaction with
competition measures. Clearly, the results in Panel C are consistent with those reported in
Table 3. Therefore, the effect of competition on corporate tax avoidance is robust to the
possibility that firms smooth profit over time.
5.2
Robustness Test: Market Definition
Throughout our empirical analysis, we define an industry according to the four-digit industry
codes specified by the NBS in the national market. Of course, not all firms compete in the
national market. In particular, regional protectionism and the incremental reform process
in China may sometimes lead to the fragmentation of the domestic market (Young, 2000).
However, we believe this concern is alleviated due to the following reasons. First, our sample
period is from 2000 to 2005, during which China continued to broaden and deepen its marketoriented reforms. The trend is especially pronounced in the industrial sectors (e.g., see Holz,
2006). Second, we focus on “above scale” industrial firms, who are more likely to operate and
compete in the national market.
Still, as a robustness check, we consider an alternative definition of a product market: a
four-digit industry in one of China’s eight economic regions specified by the State Council of
China.19 Here we make an assumption that the geographical boundaries of a product market
are regional. Using this new market definition, we compute competition measures and estimate
26
the baseline models accordingly. We obtain empirical results qualitatively similar to those
based on the national market assumption. Thus, our main results are robust to different
geographic definitions of product markets.
5.3
An Alternative Possibility
We argue that firms in more competitive industries report less profits for each unit of imputed
profit in order to save tax. One alternative possibility is that firms in more competitive
industries have more difficulties converting imputed profits into accounting profits. An ideal
way to address this concern is to control for all potential factors that distort the relationship
between the reported profit and the imputed profit. For example, Desai and Dharmapala
(2006) use the component of the book-tax gap not attributable to firm accounting accruals as
a proxy for tax sheltering. However, firm accounting information in our dataset is not detailed
enough to allow us compute accounting accruals or abnormal accounting accruals.
As a substitute, we obtain from the our dataset the available components of working capital
accruals (see, e.g., Dechow et al., 1998), and variables that may account for the discrepancies
between reported profits and imputed profits. These variables include the change in inventories
scaled by total assets for firm i in year t (DIN Vi,t ); the change of current liabilities (liquid
liabilities in the NBS database) scaled by total assets (DCLi,t ); the ratio of depreciation in the
current year to total assets (DCU RRDi,t ); the ratio of sale charges (including advertisement
fees, packaging fees, delivery fees, etc) to total sales (SCi,t ); and the ratio of administrative
charges over total sales (ADCi,t ).
We then add the above five variables and their interactions with imputed profit to the
baseline regression model (6).20 The OLS results with this specification are reported in Table
8, where to save space only the estimated coefficients of competition variables and their
interactions with the imputed profit are reported. As way of comparison, the OLS results for
the baseline model (6) taken from Table 3 are included as a part of Table 8 (columns 1 and 2).
After controlling for the effects of these additional variables, the effect of competition on the
sensitivity of the reported profit to the imputed profit remains statistically significant. Even
27
the point estimates do not change much from those in Table 3. Therefore, our main results
are robust to the possibility that firms in more competitive industries face more technical
difficulties in converting imputed profits into accounting profits.
5.4
Other Variables Affecting Tax Avoidance
In this paper we focus on the effect of competition on firms’ tax avoidance behavior. Due to
this focus and more importantly due to data limitation, our analysis ignores several factors that
the recent literature on tax avoidance has identified as determinants of firms’ tax avoidance
behavior in the U.S. We briefly discuss some of those factors here.
In recent years, the agency approach to tax avoidance in the literature emphasizes managerial incentives and corporate governance in affecting firms’ tax avoidance behavior, e.g.,
Crocker and Slemrod (2005), Chen and Chu (2005), Desai and Dharamapala (2006). These
are likely to be important factors in the context of China as well, but probably through
somewhat different channels. Since our dataset does not contain information about managerial incentives and corporate governance structure, we cannot directly analyze their effects
on firms’ tax avoidance activities. Indirectly, we may be able to infer some effects from the
differences of tax avoidance behavior between different types of firms. As shown in Table 3,
relative to SOEs and mixed firms, private firms and Hong Kong and Taiwan firms tend to
hide more profits while collective firms and foreign firms tend to hide less profits. In general,
managerial incentives are ranked from the weakest to strongest as follows: SOEs and mixed
firms, Collective, private and Hong Kong and Taiwan firms, and foreign firms. Thus, our
result seems to suggest that weaker managerial incentives make managers less likely to engage
in tax avoidance activities (just as in undertaking other risky business investments). However,
further analysis is clearly needed to establish the link, which exceeds the scope of this paper.
Another important factor of tax avoidance that is not directly considered in our paper
is enforcement actions by tax authorities (see, e.g., Desai et al., 2007). To some extent, our
location dummies capture any location-specific enforcement characteristics that may affect
firms’ tax avoidance behavior (e.g., “soft” enforcement by a region may lead to more tax
28
avoidance by firms in that region). We do not have information about enforcement actions
relevant to each individual firms. This raises one question. An alternative interpretation
of our main results is that tax authorities in China “take it easy” on firms in competitive
industries and hence these firms engage in more tax avoidance activities. Even though we
cannot completely dismiss this interpretation, we think it is highly unlikely in our context.
First, as we explained before, tax collecting agencies in China are not very sophisticated yet.
Secondly, even if tax officials do enforce tax laws differentially across industries, they will most
likely do so by targeting profitable industries and taking it easy on less profitable ones. One
of our competition measures, the industry-level profit margin, captures the “profitability” of
industries. Thus, our results using profit margin are consistent with both our interpretation
and the alternative one. However, note that profit margin is almost uncorrelated with the
other three competition measures. The results using the other competition measures support
our interpretation but not the alternative. Thirdly, we calculate the effective tax rate for each
4 digit industry (total income tax/total pre-tax profit), and then calculate the correlations
between the effective industry tax rate and the competition measures. It turns out that
the effective industry tax rate is positively correlated with competition. Specifically, the
correlations between the effective industry tax rate with the Herfindahl index, profit margin,
concentration ratio, and the logarithm of the number of firms are respectively -0.0483, -0.0257,
-0.0664, and 0.0668. Even though the correlations are not large, they all point to the same
direction. This suggests that tax enforcement is not necessarily more lax in competitive
industries.
It may be useful to compare strategies to avoid tax by firms in the U.S. and in China. The
existing literature has identified three main strategies of tax avoidance by U.S. firms: depreciation provisions, overseas tax haven/shelters, and deductions on employee stock options.21
Due to the vast differences in tax laws and institutional backgrounds between the two countries, these are not commonly used by Chinese firms to avoid tax except perhaps depreciation
provisions (in Section 5.3 we control for depreciation). Common strategies of avoiding tax by
Chinese firms are discussed in Section 1. Outbound investments by Chinese firms are not very
active for most of our sample period. The firms with sizable overseas/offshore incomes are
29
mainly foreign firms and Hong Kong and Taiwan firms. We have controlled for the impact of
ownership throughout our empirical analysis. Moreover, we estimate the reported profit equations on a domestic-firm-only sub-sample and obtain qualitatively similar results. During our
sample period and until today, employee stock options are rarely used in Chinese industrial
firms, and are not commonly used even for those publicly listed firms in China. Thus, overseas
tax haven and deductions of employee stock options are unlikely to be important factors for
tax avoidance of Chinese firms.
Due to our identification strategy, if a tax avoidance activity always affects a firm’s revenue
or cost in exactly the same way in the accounting system and the national income account
system, then this will not be captured by our analysis. Among the common tax avoidance
strategies listed in Section 1, “abusing tax credits” clearly leads to different effects in the two
systems and hence is fully reflected in the our analysis. “Mis-recording sales revenue” is also
likely to result in different measures of profits under the two systems, for instance, hiding an
actually completed transaction into the account receivable reduces the reported profit in the
accounting system but has no effect on the profit measure in the national income account. It
is not obvious whether any of the other three common strategies: transfer pricing, earning
management and fake receipts, always have identical effects in the two systems. Since firms
can combine these strategies with other legal and illegal tax avoidance strategies in order to
be most effective in minimizing their tax liabilities, a “neutral” strategy that directly reduces
sales or increases costs by the same amounts in the two systems may have different effects. For
instance, fake receipts (inflated costs) of equipments that are subject to different depreciation
rules under the two systems lead to larger gaps between depreciation costs. Unfortunately it
is extremely hard to disentangle the specific avoidance strategy being used.
6
Conclusion
In this paper, we have shown that competition pressure drives Chinese industrial firms to
engage in more tax avoidance activities. Our results highlight the importance of industrial
characteristics in understanding firms’ tax avoiding behavior. Our study provides strong
30
evidence that in a market environment with poor institutional infrastructure, competition
may very well encourage socially wasteful activities as firms use all possible instruments to
gain competitive advantage. Thus, policies intended to promote competition in developing
and transition economies must be accompanied by reforms which improve the institutional
infrastructure. Such reforms should include improved tax enforcement, strengthened financial
market regulation, and policies that ensure all market participants have a level competitive
field. Our empirical findings can potentially be useful in identifying types of firms as more
likely suspects of tax avoidance (e.g., those in more competitive industries), and thus may
help tax authorities improve their auditing strategies in the future.
31
Footnotes
∗
We are grateful to Jörn-Steffen Pischke (the editor), and three anonymous referees for
numerous constructive comments that greatly improved the paper. We have also benefited
from the comments of Paul Devereux, Amar Hamoudi, Joseph Fan, Huixing Huang, Ginger
Jin, Sainan Jin, Jean-Laurent Rosenthal, James Vere, and participants at the HKUST Finance Seminar, 2006 AEA annual meeting, and 2006 International Conference on Corporate
Governance in Asia. We thank Geng Xiao for help in the early stage of this project, and Yong
Wei for excellent research assistance. The first author acknowledges support from NSFC grant
(Project No. 70725002) and from the Chinese Education Ministry’s NCET. The second author’s research has been supported by grants from the University Grants Committee of Hong
Kong (Projects No. AOE/H-05/99, HKU 7472/06H, and HKU747107H).
1
For example, the U.S. Internal Revenue Service estimated that about 17% of income tax
liability is not paid (Slemrod and Yitzhaki, 2002). Citing the U.S. General Accounting Office,
Desai and Dharmapala (2006) state that “By 2000, 53% of large U.S. corporations reported
tax liabilities lower than $100,000.” Feldman and Slemrod (2007) find that “On average reported positive self-employment, non-farm small business and fame income must be multiplied
by a factor of 1.54, 4.54 and 3.87, respectively, in order to obtain true income.”
2
Following the literature, we do not distinguish tax avoidance (e.g., taking advantage of
loopholes in tax laws, earning management) and illegal evasion (e.g., failing to report revenue
or profit). Moreover, “tax avoidance and evasion”, “tax avoidance”, “tax saving” and “tax
efficiency” are used interchangeably.
3
Following the literature, we do not distinguish tax avoidance (e.g., taking advantage of
loopholes in tax laws, earning management) and illegal evasion (e.g., failing to report revenue
or profit). Moreover, “tax avoidance and evasion”, “tax avoidance”, “tax saving” and “tax
efficiency” are used interchangeably.
4
In an earlier working paper Cai and Liu, 2007, we also run GMM estimations on a balanced
panel of a subsample, and find qualitatively similar results.
5
See the World Bank policy note “SOE Dividends: How Much and to Whom?” (Kuijs et al.,
32
2005).
6
The classification of corporate income tax has changed over time since 1994. Initially the
National Taxation Bureau collected corporate income tax on SOEs that belonged to the central government and on foreign firms, while provincial bureaus were responsible for collecting
income tax on other SOEs, collective firms and domestic private firms. On January 1, 2002,
the State Council enacted the “Income Tax Sharing Reform Act” that changed the classification. This feature will not create problems for our empirical analysis, because the location
fixed effect in our regressions should reflect any enforcement difference across locations.
7
The data on the total revenue from corporate income tax before 2001 is not available. The
Yearbook has information about the total revenue from corporate income tax from SOEs and
Collective firms, which increased from 70.8 billion RMB in 1994 to 100 billion RMB in 2000.
8
“Current Situations and Suggestions for Improvement of Corporate Income Tax Collection”,
by Yushen Li and Xin Li, on webpage http://www.czsgsj.gov.cn/llyj/ShowArticle.asp?ArticleID=104.
9
On the internet there is a widely circulated article without source titled “60 Methods to
Evade Corporate Taxes,” see, for example, http://www.amteam.org/k/itsp-Software/20069/0/530239.html. In one case that involved 39 firms in three provinces in 2004, firms used
fake receipts of recycling materials to evade taxes totaling 1.239 billion RMB. In another
case in Hunan province in 2004, one company used transfer price to avoid tax of 228 million
RMB. For details of these cases, see the webpage of the State Administration of Taxation
http://finance.sina.com.cn/g/20060420/1614658846.shtml.
10
Article 33 of “Regulations on National Economic Census” states that “The use of mate-
rials regarding units and individuals collected in economic census shall be strictly limited to
the purpose of economic census and shall not be used by any unit as the basis for imposing
penalties on respondents of economic census.”
11
While it is common to exclude extreme observations to prevent outliers from driving the
regression results, Bollinger and Chandra (2005) point out that this is not always desirable.
As a robustness check, we run the regressions with the original sample (N = 525,184), and
obtain similar results.
12
Desai (2003) uses the unexplained gap between book income (the income reported to capital
33
markets) and tax income (the income reported to the tax authorities) as a proxy for corporate
tax avoidance. He finds evidence that these ”book-tax” gaps are large and increasing across
time. For example, the ratio of book income to tax income is 1.63 in 1998 for firms with
greater than $250 million in assets in the U.S.
13
Without tight financing constraints and fixed interest rates, a higher level of financial
charges may indicate lower future borrowing ability because banks are reluctant to lend to
firms with high levels of debts and will charge higher interest rates on firms with lower credit
ratings. As further evidence supporting our use of this variable as a measure of a firm’s borrowing capability in our setting, we calculate autocorrelations of both the variable and its
change from year to year for each firm. We find that the averages of both autocorrelations
are significantly positive. This evidence suggests that firms with high financial charges in the
current year can sustain their borrowing capability to the next year.
14
As a robustness check, we also conduct our empirical analysis without including the ratio
of sales over output as a control variable and yield similar results.
15
In an unreported OLS regression in which the gap is regressed against the logarithm of the
number of firms, the R squared is 0.26 and the estimated coefficient is significantly positive
at 0.023 (t-statistic is 8.79). Plotting this gap against competition measures at the four-digit
industry level or other competition variables yields similar patterns.
16
We thank Yupeng Shi for kindly providing us with the data. See Yupeng Shi (2006) for
more details.
17
In an earlier version of the paper (Cai and Liu, 2007), we also use the competition variables
at the two-digit industry level (excluding the four-digit industry instrumented) as instruments
for corresponding competition variables at the four-digit industry level. The IV results of using these instruments also yield evidence strongly supportive of our conjectures.
18
In models (3)–(4), there are more than three endogenous variables. Stock and Yogo (2005)
do not provide critical values for such cases. See Stock and Yogo (2005) for details about how
the critical values are tabulated.
19
The eight economic regions are Northern Coastal (ShangDong, HeBei, Beijing, and Tianjin),
Southern Coastal (GuangDong, FuJian, and HaiNan), Eastern Coastal (ShangHai, JiangSu,
34
and ZheJiang), North East (LiaoNing, HeLongJiang, and JiLin), Mid-Yangze Range (HuNan,
HuBei, JiangXi, and AnHui), Mid-Huanghe Range (ShaanXi, HeNan, ShanXi, and Inner
Mongolia), South West (GuangXi, YunNan, SiChuan, ChongQing, and GuiZhou), and finally,
North West (GanSu, Qinghai, NingXia, Tibet, and XinJiang).
20
For brevity, we only report the results of using the Herfindahl index and the profit margin
as the competition measures. Using the other two competition measures yields qualitatively
similar results.
21
We thank an anonymous referee for pointing this out to us.
35
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39
Table 1
Competition Intensity across the Two-Digit Industries in China (2000-2005)
Two-Digit Industry
[06]Coal Mining
[07]Petroleum Extraction
[08]Ferrous Mining
[09]Nonferrous Mining
[10]Nonmetal Mining
[13]Food Processing
[14]Food Production
[15]Beverage
[16]Tobacco
[17]Textile
[18]Garments
[19]Leather
[20]Timber
[21]Furniture
[22]Papermaking
[23]Printing
[24]Cultural
[25]Petroleum Processing
[26]Raw Chemical
[27]Medical
[28]Chemical Fiber
[29]Rubber
[30]Plastic
[31]Nonmetal Products
[32]Pressing Ferrous
[33]Pressing of Nonferrous
[34]Metal Products
[35]Ordinary Machinery
[36]Special Equipment
[37]Transport Equipment
[39]Electric Machinery
[40]Electric Equipment
[41]Electronic and Telecom
[42]Instruments
[44]Electric Power
[45]Gas Production
[46]Tap Water
# of four
digit
Industry
2
2
2
14
10
16
20
13
3
21
3
11
10
5
5
5
17
3
32
7
7
9
10
31
4
18
24
33
46
25
28
21
23
14
6
1
2
# of
Firms
(N)
3709
121
1100
1248
1915
11885
4923
3360
268
16299
9616
4626
3714
2170
5902
4203
2578
1414
14495
4023
1065
2262
8890
16806
4568
3543
10881
13847
7920
8771
8403
8269
4108
3417
5114
381
2470
Logarithm
of # of
firms
8.162
4.737
6.881
7.067
7.551
9.373
8.498
8.119
5.567
9.652
9.153
8.403
8.179
7.641
8.666
8.335
7.829
7.211
9.561
8.289
6.944
7.693
9.059
9.720
8.363
8.110
9.275
9.484
8.951
9.054
7.630
9.009
8.294
8.041
8.538
5.921
7.811
Profit
Margin
Herfindahl
Index
0.051
0.320
0.085
0.110
0.052
0.031
0.057
0.072
0.112
0.033
0.049
0.036
0.040
0.047
0.048
0.091
0.045
0.010
0.049
0.095
0.033
0.041
0.049
0.049
0.051
0.044
0.047
0.053
0.051
0.054
0.017
0.045
0.058
0.051
0.080
0.017
0.027
0.013
0.086
0.018
0.018
0.010
0.002
0.004
0.009
0.037
0.001
0.002
0.003
0.006
0.005
0.005
0.003
0.004
0.023
0.003
0.005
0.019
0.010
0.002
0.001
0.012
0.006
0.001
0.002
0.003
0.011
0.013
0.007
0.008
0.009
0.013
0.032
0.009
Market
Share by
Top 4 Firms
0.152
0.448
0.206
0.210
0.136
0.066
0.082
0.140
0.309
0.046
0.055
0.059
0.102
0.077
0.095
0.055
0.074
0.191
0.088
0.101
0.197
0.138
0.044
0.029
0.155
0.098
0.039
0.061
0.065
0.164
0.149
0.119
0.126
0.125
0.172
0.283
0.125
Notes: The sample contains 514,394 firm-year observations over the 6 year period of 2000 to 2005.
Numbers in square brackets are the two-digit industry codes assigned by the National Bureau of Statistics
of China. N is the number of the above-scale firms in a given two-digit industry in the period from 2000 to
2005 (we only report the integer). Profit margin (PMARGIN) is the total before-tax profits divided by total
sales in an industry. The Herfindahl index is defined as the sum of squares of the market shares (by sales)
of all firms in an industry. The variable in the last column is calculated as the total market share (by sales)
accounted for by the four largest firms in an industry.
40
Table 2
Summary Statistics of Key Variables
Variable
Competition measures
Mean
Median
Std. Dev.
Min.
Max.
by two-digit industry
Herfindahl Index
Market Share by the Top Four Firms
Logarithm of the Number of Firms
Profit Margin
0.0049
0.0874
9.0026
0.0512
0.0030
0.0720
9.1816
0.0487
0.0054
0.0488
0.7396
0.0203
0.0005
0.0165
4.4067
0.0071
0.1402
0.5868
10.0896
0.3847
0.0284
0.2107
6.7270
0.0524
0.0147
0.1729
6.7811
0.0487
0.0436
0.1417
1.2032
0.0316
0.0015
0.0373
0.0000
0.0009
1.0000
1.0000
9.3264
0.5824
0.0515
0.1431
0.0916
0.0320
0.0786
0.0270
0.0922
0.2561
0.2383
-0.1457
-0.2847
-0.7189
0.7492
1.8408
1.3413
0.9942
0.0156
500.9
5.4672
0.2501
0.2554
0.1463
0.2166
0.1477
0.1307
163.4
0.9871
0.0093
214
5.3660
0.2949
0
0
0
0
0
35.7
0.2438
0.0143
1792.4
1.0529
0.1314
0.4361
0.3534
0.4119
0.3548
0.3371
1269.9
0.4395
0.0005
30
3.4012
0.0004
0.0000
0.0000
0.0000
0.0000
0.0000
10.0
3.5228
0.0866
161654
11.9932
0.7729
1.0000
1.0000
1.0000
1.0000
1.0000
456559.3
by four-digit industry
Herfindahl Index
Market Share by Top Four firms
Logarithm of the Number of Firms
Profit Margin
Other Variables
Reported Profit (RPRO)
Imputed Profit (PRO)
Reported Profit – Imputed Profit
(GAP)
Sales / Output
Access to Credit
Number of Employees
Log of # of Employees
Effective Tax Rate
Private Firms Dummy (Dprivate)
Collective Firms Dummy (Dcollective)
Mixed Firm Dummy (Dmixed)
HK and TW Firms Dummy (DHK/TW)
Foreign Firms Dummy (Dforeign)
Total Assets (TA) (RMB million)
Notes: The sample contains 514,394 firm-year observations over the 6 year period of 2000 to 2005.
Imputed profit is the imputed corporate profit from the national income account divided by total assets; and
reported profit is the reported accounting profit scaled by total assets. Access to credit is the total amount of
financial charges divided by total assets. Effective tax rate is income tax divided by its pre-tax profit, and is
set to zero for loss-making firms. TA is total assets in million RMB. Other variables are self-explanatory.
All monetary variables are in 2000 constant price.
41
Table 3
OLS Regressions of the Reported Profits, 2000-2005
Competition variables measured at the four-digit level
Independent variables
Imputed Profit
(1)
(2)
0.2022
(0.0006)
-0.0406
(0.0052)
Herfindahl Index
Herf. Index ×
Imputed Profit
Profit Margin
Herfindahl
Index
(3)
0.2096
(0.0019)
-0.0035
(0.0042)
0.1514
(0.0128)
Profit
Margin
(4)
0.1586
(0.0020)
Herfindahl
Index
(5)
-0.0465
(0.0052)
-0.0008
(0.0041)
0.1399
(0.0125)
0.2865
(0.0057)
1.2610
(0.0188)
Profit Margin ×
Imputed Profit
Profit
Margin
(6)
-0.1106
(0.0052)
Log of #
of Firms
(7)
0.0158
(0.0059)
0.0005
(0.0002)
-0.0094
(0.0005)
Log # of Firms × Imputed
Profit
Market Share by 4 Firms
-0.0019
(0.0055)
0.0539
(0.0039)
Market Share by 4 Firms ×
Imputed Profit
Access to Credit × Imp. Profit
Log Labor × Imp. Profit
Sales/Output × Imp. Profit
Dprivate × Imputed Profit
Dcollective × Imputed Profit
Dmixed × Imputed Profit
DHK/TW × Imputed Profit
Dforeign × Imputed Profit
Tax Rate
Access to Credit
Log # of Employees
Sales / Output (RSALE)
-0.0548
(0.0053)
0.2654
(0.0055)
1.3091
(0.0183)
Log # of Firms
Tax Rate × Imp. Profit
Market Share
by Top 4
Firms
(8)
-0.0572
(0.0035)
0.4633
(0.0325)
0.0113
(0.0006)
0.1966
(0.0028)
-0.0107
(0.0027)
0.0176
(0.0028)
0.0011
(0.0027)
-0.0252
(0.0029)
0.0252
(0.0029)
0.1228
(0.0012)
-0.2130
(0.0124)
-0.0011
(0.0002)
0.0211
(0.0006)
-0.0576
(0.0035)
0.4644
(0.0325)
0.0113
(0.0006)
0.1966
(0.0028)
-0.0099
(0.0027)
0.0186
(0.0028)
0.0015
(0.0027)
-0.0242
(0.0029)
0.0256
(0.0029)
0.1229
(0.0012)
-0.2118
(0.0124)
-0.0011
(0.0002)
0.0211
(0.0007)
42
-0.0602
(0.0035)
0.5950
(0.0321)
0.0112
(0.0006)
0.2034
(0.0027)
-0.0035
(0.0026)
0.0201
(0.0027)
0.0059
(0.0027)
-0.0176
(0.0028)
0.0299
(0.0029)
0.1189
(0.0012)
-0.1956
(0.0122)
-0.0004
(0.0002)
0.0220
(0.0006)
-0.0572
(0.0035)
0.4717
(0.0325)
0.0118
(0.0006)
0.1977
(0.0028)
-0.0104
(0.0027)
0.0180
(0.0028)
0.0004
(0.0027)
-0.0250
(0.0028)
0.0244
(0.0029)
0.1228
(0.0012)
-0.2120
(0.0124)
-0.0011
(0.0002)
0.0211
(0.0006)
-0.0574
(0.0035)
0.4677
(0.0325)
0.0114
(0.0006)
0.1964
(0.0028)
-0.0092
(0.0027)
0.0192
(0.0028)
0.0020
(0.0027)
-0.0233
(0.0029)
0.0261
(0.0029)
0.1229
(0.0012)
-0.2122
(0.0124)
-0.0011
(0.0002)
0.0212
(0.0007)
Table 3
Continued
Dprivate
0.0603
(0.0002)
0.0294
(0.0007)
0.0319
(0.0007)
0.0191
(0.0006)
0.0187
(0.0007)
0.0280
(0.0007)
0.0075
(0.0015)
No
Yes
Dcollective
Dmixed
DHK/TW
Dforeign
Constant
Year, location
dummies and their
interactions with
PRO
Observations
Adjusted R-squared
514,394
0.2019
514,394
0.2422
0.0525
(0.0005)
Yes
514,394
0.2136
0.0392
(0.0006)
0.0294
(0.0007)
0.0318
(0.0007)
0.0191
(0.0006)
0.0187
(0.0007)
0.0280
(0.0007)
0.0075
(0.0015)
Yes
Yes
514,394
0.2272
514,394
0.2505
0.0307
(0.0007)
0.0329
(0.0007)
0.0181
(0.0006)
0.0194
(0.0007)
0.0277
(0.0007)
-0.0096
(0.0015)
Yes
514,394
0.2735
0.0294
(0.0007)
0.0319
(0.0007)
0.0191
(0.0006)
0.0187
(0.0007)
0.0280
(0.0007)
0.0046
(0.0018)
Yes
514,394
0.2510
0.0294
(0.0007)
0.0318
(0.0007)
0.0190
(0.0006)
0.0187
(0.0007)
0.0280
(0.0007)
0.0080
(0.0015)
Yes
514,394
0.2506
Notes: The dependent variable is the reported profit scaled by total assets. Controls also include year
dummies, location dummies, and their interactions with the imputed profit. Robust standard errors
adjusting for industry clusters are reported in parentheses under estimated parameters.
43
Table 4
Effect of Competition on Reported Profits: the IV Regressions
Imputed Profit
Profit margin
Profit Margin × Imputed profit
Tax rate × Imputed profit
Access to Credit × Imputed Profit
Log Labor × Imputed profit
Sales/Output × Imputed Profit
OLS
(1)
-0.1106
(0.0052)
0.2654
(0.0055)
1.3091
(0.0183)
-0.0602
(0.0035)
0.5950
(0.0321)
0.0112
(0.0006)
0.2034
(0.0027)
Weak identification test - CraggDonald Statistic
(critical value)
Observations
514,394
Competition
variable
instrumented
(2)
0.0686
(0.0256)
0.4641
(0.0966)
1.9409
(0.3911)
-0.0751
(0.0049)
0.6027
(0.0494)
0.0035
(0.0010)
0.1809
(0.0042)
Imputed profit
instrumented
(3)
-0.0443
(0.0269)
-0.0155
(0.0108)
1.8177
(0.0561)
-0.0261
(0.0114)
0.7571
(0.1326)
0.0062
(0.0028)
0.1791
(0.0155)
Both
instrumented
(4)
-0.0441
(0.010)
0.0612
(0.0127)
1.1391
(0.1981)
-0.0813
(0.0292)
0.2544
(0.0603)
0.0064
(0.0018)
0.0311
(0.0062)
21.94
(7.03)
541.11
(na)
11.45
(na)
514,394
514,394
514,394
Notes: The table presents the second stage regression results of the IV regressions when the industry profit
margin is used as the measure of competition (using other competition measures yields largely similar
results). The dependent variable is the reported profits. Covariates included in each regression are the
competition measure (i.e., profit margin), the effective tax rate, the measure of access to credit, log of
employees, the ratio of sales to total output, five ownership dummies (the state-owned enterprises are taken
as the benchmark), year dummies, location dummies, and their interactions with the imputed profit.
Column (1) reports the OLS results from Table 3 by the way of comparison. Column (2) uses STEP – the
number of permissions needed to establish a new firm in a four-digit industry – as an instrument for the
competition variable. Column (3) reports the second stage regression results, in which the imputed profit is
instrumented with the average imputed profit at the four-digit industry level (excluding the firm itself).
Column (4) presents the second stage regression results, in which imputed profit, competition measures and
their interactions with other variables are also instrumented. For brevity, we only report the estimates of
variables of interest. The Cragg-Donald statistics at the bottom of the table test the weak instrument
problem. The critical values are taken from Stock and Yogo (2005). Robust standard errors adjusting for
industry clusters appear in parentheses under estimated parameters.
44
Table 5
Summary of First –Stage Regression Results for Regressions in Table 4
Model (2)
Dependent Variables
Profit
Margin
Average imputed profit
(Av. PRO)
STEP
Profit Margin
× Imputed
Profit
0.016
(0.002)
0.086
(0.009)
-0.071
(0.008)
0.082
(0.003)
Model (3)
Dependent Variables
Profit Margin × AV.
PRO
Dependent Variables
Imputed
Profit
Profit Margin
× Imputed
Profit
Imputed
Profit
Profit
Margin
Profit Margin
× Imputed
Profit
1.143
(0.044)
0.019
(0.002)
0.799
(0.079)
-0.002
(0.001)
0.009
(0.004)
-0.052
(0.007)
0.007
(0.000)
-0.023
(0.011)
0.013
(0.004)
- 0.002
(0.001)
0.005
(0.000)
-0.216
(0.121)
0.913
(0.007)
STEP × Av. PRO
STEP ×Imputed Profit
Model (4)
F-statistic, test that
instruments jointly
equal zero
107.48
389.45
278.06
590.39
753.75
959.66
279.61
Adjusted R2
0.086
0.761
0.114
0.201
0.092
0.081
0.067
Notes: There are 514,394 firm-year observations. Robust standard errors adjusting for industry
clusters are reported in parentheses under estimates. Variables definitions are in Table 4.
45
Table 6
A Natural Experiment: Large-sized Refrigerator, Power Plant Equipment Industries, and
Comparable Industries
Imputed Profit
POST
POST × Imputed Profit
TREAT
TREAT ×Imputed Profit
TREAT ×POST
TREAT ×POST ×
Imputed Profit
Refrigerator vs. Electrical Fan
(N = 969)
OLS
OLS
IV
(1)
(2)
(3)
0.1354
0.1661
0.1164
(0.1153)
(0.1824)
(0.1223)
0.0072
0.0062
0.0022
(0.0068)
(0.0078)
(0.0112)
0.0565
0.0248
-0.0406
(0.0249)
(0.0265)
(0.0419)
-0.0103
-0.0052
-0.0005
(0.0172)
(0.0076)
(0.0102)
0.0978
0.0291
0.0679
(0.0345)
(0.0241)
(0.0324)
0.0034
(0.0051)
-0.1126
(0.0396)
Profit Margin
Profit Margin × Imputed
Profit
Tax rate × Imp. Profit
Credit × Imp. Prof.
Log Labor × Imp. Prof.
Sales/Output ×Imp. Prof.
Other controls
Adj. R-squared
Weak identification test
(Cragg-Donald Statistic)
-0.1298
(0.0623)
2.3177
(0.6832)
-0.0111
(0.0103)
0.2412
(0.0633)
Yes
0.219
na
0.7204
(0.5149)
0.7449
(0.4671)
1.0669
(1.6121)
1.4136
(0.8781)
-0.1225
(0.0633)
2.5451
(0.6871)
-0.0096
(0.0104)
0.2418
(0.0628)
Yes
0.239
na
-0.0661
(0.0671)
2.2798
(0.6971)
-0.0056
(0.0105)
0.2829
(0.0648)
Yes
0.207
43.09
Power Plant Equipment vs. E. Generator
(N=2,201)
OLS
OLS
IV
(4)
(5)
(6)
-0.0411
-0.0646
-0.0651
(0.0541)
(0.0763)
(0.0764)
0.0058
-0.0038
-0.0034
(0.0041)
(0.0062)
(0.0071)
-0.0385
-0.0291
-0.0288
(0.0221)
(0.0235)
(0.0263)
-0.0977
-0.0088
-0.0087
(0.0041)
(0.0050)
(0.0050)
0.0418
-0.0111
-0.0107
(0.0225)
(0.0172)
(0.0185)
-0.0105
(0.0213)
-0.0588
(0.0269)
-0.2007
(0.0411)
1.3188
(0.4516)
-0.0213
(0.0189)
0.4228
(0.0319)
Yes
0.386
na
0.2066
(0.1302)
0.4661
(0.3071)
0.1921
(0.1768)
0.6979
(0.3977)
-0.2981
(0.0482)
0.3932
(0.4805)
-0.0198
(0.0089)
0.4241
(0.0396)
Yes
0.391
na
-0.2979
(0.0479)
0.3911
(0.4782)
-0.0197
(0.0090)
0.4241
(0.0394)
Yes
0.397
34.63
First stage regressions for models (3) and (6)
TREAT ×POST
TREAT ×POST ×
Imputed Profit
F-statistic, test that
instruments jointly equal
zero
Adjusted R2
Model (3) – dependent variables
PMARGIN
PMARGIN ×
Imputed profit
-0.0097
-0.0001
(0.0009)
(0.0002)
-0.0073
-0.0126
(0.0032)
(0.0010)
32.35
46.06
Model (6) – dependent variables
PMARGIN
PMARGIN ×Imputed
Profit
-0.0549
-0.0008
(0.0137)
(0.0004)
0.0018
-0.0696
(0.0048)
(0.0014)
48.18
53.19
0.373
0.754
0.925
46
0.955
Notes: The dependent variable is the reported profit. Profit margin and the interaction of profit margin with
the imputed profit in models (3) and (6) are instrumented with TREAT ×POST and TREAT ×POST ×
imputed profit respectively. Other controls include ownership, location dummies, and their interactions
with the imputed profit. The State Development and Planning Commission of China (SDPC) issued in 1995
its first investment catalogue, the “Catalogue Guiding Foreign Investment in Industry”, defining what
investment projects were encouraged, permitted, restricted or prohibited for foreign investors. SDPC
revised the catalogue in 2002. Among the industries restricted in 1995, large-sized refrigerator (4063),
power plant equipment (4011) were permitted in 2002. We define a time dummy variable POST which
takes the value of 1 after 2002 and 0 otherwise. For each of the two industries, we also identify a
comparable industry which is in the same three-digit industry but has been opened since 1995. They are
Electrical Fans (4064) and Electric Generator (4012) respectively. TREAT is a dummy variables specifying
whether a firm was permitted in 2002. Columns (1) and (4) report the OLS results, including treatment
firms and control firms for the two newly opened industries respectively. In models (2) – (5) and (3) – (6),
we depart slightly from the specifications in Table 4. While we use PMARGIN as the competition measure,
we also include POST, TREAT, and their interactions with imputed profit as controls. We report both the
OLS results and IV results. The bottom of the table reports the first stage regression results for the IV
regressions. Robust standard errors adjusting for sample clusters appear in parentheses.
47
Table 7
Robustness Checks – Alternative Model Specifications
Competition Measures by the Four-Digit Codes
Herfindahl
Profit
Log # of
Share by
Index
Margin
firms
top 4 firms
Panel A: Adding the lagged imputed profit (PROt-1) to the baseline model (N = 400,210)
Imputed Profit
0.0831
0.2172
0.0009
0.0958
(0.012)
(0.012)
(0.0088)
(0.0121)
Lagged Imputed Profit
0.0621
0.0629
0.0617
0.0623
(0.0017)
(0.0017)
(0.0017)
(0.0017)
Competition
-0.0078
0.2279
0.0011
-0.0089
(0.0079)
(0.0103)
(0.0004)
(0.0025)
0.1082
2.144
-0.0129
0.0708
Competition ×Imputed profit
(0.0284)
(0.0213)
(0.0014)
(0.0092)
0.2599
0.2987
0.2609
0.2603
Adjusted R-squared
Panel B: Interacting both imputed profit and lagged imputed profit with controls (N = 400,210)
Imputed Profit
Competition × Imputed Profit
Tax rate ×Imputed Profit
Access to Credit ×Imputed Profit
Sales/Output ×Imputed Profit
Log Labor × Imputed Profit
Adjusted R-squared
-0.0388
(0.0097)
0.0901
(0.0024)
-0.0237
(0.0041)
0.8527
(0.0771)
0.2368
(0.0074)
0.0151
(0.0011)
0.2701
-0.1931
(0.0091)
2.4149
(0.0452)
-0.0332
(0.0039)
0.4221
(0.0714)
0.2601
(0.0077)
0.0158
(0.0011)
0.3105
0.0591
(0.0094)
-0.0142
(0.0021)
-0.0231
(0.0037)
0.8133
(0.0823)
0.2387
(0.0082)
0.0158
(0.0012)
0.2919
-0.0508
(0.0088)
0.0755
(0.0071)
-0.0237
(0.0047)
0.8372
(0.0927)
0.2368
(0.0071)
0.0146
(0.0013)
0.2875
0.1033
(0.0104)
-0.0132
(0.0009)
0.3143
0.0024
(0.0085)
0.0679
(0.0084)
0.3136
Panel C: Dependent variable = average reported profit (N=46,211)
Imputed Profit
Competition ×Imputed Profit
Adjusted R-squared
0.0119
(0.0061)
0.1749
(0.0288)
0.3166
-0.0733
(0.0084)
1.7132
(0.0401)
0.3435
Notes: The dependent variable is the reported profit. In Panel A, we add lagged impute profit in the
baseline model defined in Tables 3 to control for potential income smoothing over time. In Panel B, we
interact both imputed profit and lagged imputed profit with controls. We report the sums of the estimated
coefficients of Y × Imputed Profit and Y ×lagged Imputed Profit. Y in Panel B includes covariates as
specified in Table 3. We test whether the sums of estimated coefficients are significantly different from
zero and reported the standard errors under the summed estimates. Panel C reports the OLS results of
regressing average reported profit against average imputed profit. In Panels A and C, robust standard errors
adjusting for industry clusters are in parentheses.
48
Table 8
The OLS Estimates of Reported Profit Equations Controlling for Additional Variables
Results from Table 3
Covariates
Herfindahl Index
Herfindahl Index ×Imputed Profit
Herfindahl
Index
(1)
Profit Margin
(2)
-0.0008
(0.0041)
0.1399
(0.0125 )
Herfindahl
Index
(3)
Profit Margin
(4)
-0.0031
(0.0082)
0.0834
(0.0282)
Profit Margin
0.2654
(0.0055)
1.3091
(0.0183 )
Profit Margin × Imputed Profit
DINV, DCL, RCURRD, SC, ADC
and their interactions with the
imputed profit included?
Adj. R-squared
Results with additional Variables
0.2193
(0.0103)
1.8432
(0.0464)
No
No
Yes
Yes
0.2505
0.2735
0.2772
0.3063
Notes: We construct a set of five variables to capture factors that may affect the relationship between the
reported profit and the imputed profit: DINV – the change in inventories from t-1 to t scaled by total assets;
DCL – the change in current liabilities from t-1 to t scaled by total assets; RCURRD – the ratio of the
depreciation in the current year to total assets; SC – the ratio of sale charges; finally, ADC – the ratio of
administrative charges over total sales. The table reports the OLS regression results when we add the five
variables, and their interactive terms with the imputed profit. For comparison, in columns (1) and (2), we
also report the corresponding OLS results from Table 3. Robust standard errors adjusting for industry
clusters appear in parentheses under estimated coefficients.
49
Gap between imputed profits and reported profits
-.1
0
.1
.2
.3
4
6
8
Log number of firms at two-digit industry level
Fig. 1.
Imputed profits – reported profits vs. log number of firms at two-digit industry level
.
50
10