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 Chi-

nese industrial firms to avoid corporate income tax. We estimate the effects of compe-

tition 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 com-

petitive 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, hbcai@gsm.pku.edu.cn

†School of Economics and Finance, Faculty of Business and Economics, University of Hong Kong, Pokfulam,

Hong Kong. Contact: (852)2859-1059, qliu@hku.hk.

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 im-

portant 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 avoid-

ance 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 mar-

ket 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 regres-

sions, 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 encour-

ages 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 devia-

tion), 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’ incen-

tives 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 economi-

3

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, mixed-

ownership 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,tπi,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 avoid-

ance behavior. In this paper, as long as managers partially care about firm value, our qualita-

tive 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 PROi,t in year t according to the national

income accounting system as follows:

PROi,t = Yi,t −MEDi,t − FCi,t −WAGEi,t − CURRDi,t − V ATi,t (2)

where Yi,t is the firm’s gross output; MEDi,t measures its intermediate inputs excluding

financial charges; FCi,t is its financial charges (mainly interest payments); WAGEi,t is the

firm’s total wage bill; CURRDi,t is the amount of current depreciation, and V ATi,t is the

value added tax.

Note that PROi,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 PROi,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 PROi,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 + PROi,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

RPROi,t = di,t PROi,t + Ei,t + �i,t, (4)

where we use RPROi,t to replace π̂i,t; Ei,t = di,tηi,t + ei,t; and �i,t = di,tθi,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 ∗ Taxi,t + β3 ∗ Financei,t + β4 ∗ Firm Sizei,t+β5 ∗Xi,t + �i,t

(5)

where Competj,t measures the level of competition in industry j; Taxi,t is firm i’s tax rate in

year t; Financei,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,tηi,t +

di,tei,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 two-

digit 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 pro-

duction activities and accounting and financial information. The information reported to the

NBS should be quite reliable, because the NBS has implemented standard procedures in cal-

culating 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 informa-

tion 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 sus-

pected 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: state-

owned 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 mixed-

ownership 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 obser-

vations 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 classifi-

cation 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 (PRO) defined under the national income account system

as in Equation (2). The dataset also contains the pre-tax accounting profit reported by each

firm (RPRO). 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 pre-

tax 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 TAX 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 TAX 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,

Dforeign, DHK/TW , 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 employ-

ment 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 dif-

ference 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:

RPROi,t = (β0 + β1Compet + β2TAX + β3FINANCE + β4LNLABOR + β5RSALE

+β6DOWN +

∑i=year

βidyear +

∑j=loc

βjdloc) ∗ PROi,t + α1Compet + α2TAX

+α3FINANCE + α4LNLABOR + α5RSALE + α6DOWN +

∑i=year

αidyear

+∑

j=loc

αjdloc + �i,t (6)

where DOWN 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 vari-

ables 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 compe-

tition 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 right-

hand-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 industry-

level 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 indus-

try 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 appli-

cation procedures a firm has to go through in order to enter a four-digit industry (STEP ).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 STEP and the interaction

of STEP 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 — STEP 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 STEP in the first

column and the interaction of STEP 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 oth-

erwise 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 “Cata-

logue 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 re-

frigerator (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 POST , 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 TREAT to specify the treatment firms

in the large-sized refrigerator and the power plant equipment. The coefficient of TREAT ×

POST × Imputed Profit 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, TREAT ×POST ×Imputed Profit, 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 POST , TREAT

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 POST and TREAT 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 TREAT × POST and for the interactive term

of profit margin with imputed profit with TREAT × POST × Imputed Profit 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 STEP 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 contempo-

raneous 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:

RPROi,t = (β0 + β1Compet + β2TAX + β3FINANCE + β4LNLABOR + β5DOWN

+β6RSALE +∑

i=year

βidyear +

∑j=loc

βjdloc) ∗ PROi,t + (λ0 + λ1Compet + λ2TAX

+λ3FINANCE + λ4LNLABOR + λ5DOWN + λ6RSALE +

∑i=year

λidyear

+∑

j=loc

λjdloc) ∗ PROi,t−1 + α1Compet + α2TAX + α3FINANCE

+α4LNLABOR + α5DOWN + α6RSALE +

∑i=year

αidyear +

∑j=loc

αjdloc + �i,t (7)

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 market-

oriented 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 (DINVi,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 (DCURRDi,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 man-

agerial 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 manage-

rial 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: depre-

ciation 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 coun-

tries, 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 equa-

tions 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 Fi-

nance 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 au-

thor’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 re-

ported 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 cen-

tral 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 classifi-

cation. 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/2006-

9/0/530239.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 bor-

rowing 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 us-

ing 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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