Competition and Corporate Tax Avoidance: Evidence from Chinese Industrial Firms

Transcription

1 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 Contact: (86) , School of Economics and Finance, Faculty of Business and Economics, University of Hong Kong, Pokfulam, Hong Kong. Contact: (852) , 1

2 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 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 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

3 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

4 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 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 economi- 3

5 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

6 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 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

7 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 In 2001, the total revenue from corporate income tax was 263 billion RMB, and in 2005 it increased to 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

8 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 π i,t be firm i s true accounting profit in year t. We postulate that it reports a profit of ˆπ i,t = d i,t π i,t + e i,t + ζ i,t (1) where ˆπ i,t is the profit firm i reports, d i,t < 1 and e i,t 0 are two parameters, and ζ i,t is a mean zero error term. This means that firms under-report their profits. Clearly, the amount of profit under-reported, π i,t ˆπ i,t, is decreasing in d i,t and e i,t. In other words, when d i,t and e i,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 d i,t and e i,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

9 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 π i,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 RO i,t in year t according to the national income accounting system as follows: P RO i,t = Y i,t MED i,t F C i,t W AGE i,t CURRD i,t V AT i,t (2) where Y i,t is the firm s gross output; MED i,t measures its intermediate inputs excluding financial charges; F C i,t is its financial charges (mainly interest payments); W AGE i,t is the firm s total wage bill; CURRD i,t is the amount of current depreciation, and V AT i,t is the value added tax. Note that P RO i,t defined here, as an imputed profit, can legitimately differ from firm i s true accounting profit π i,t. A main reason is the differences in the revenue and expense 8

10 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 RO i,t and π i,t. For these reasons, this imputed profit is at best a very noisy proxy for π i,t. In fact, a key challenge for our empirical strategy is to effectively address the measurement errors in P RO i,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: π i,t = η i,t + P RO i,t + θ i,t (3) where η i,t is an unknown parameter, and θ i,t is a mean zero error term. The unobservable η i,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 η i,t : it can be positive or negative. By substituting Equation (3) into Equation (1), we derive RP RO i,t = d i,t P RO i,t + E i,t + ɛ i,t, (4) where we use RP RO i,t to replace ˆπ i,t ; E i,t = d i,t η i,t + e i,t ; and ɛ i,t = d i,t θ i,t + ζ i,t. Furthermore, we propose the following econometric specification for d i,t (note that j denotes the industry of firm i): d i,t = β 0 + β 1 Compet j,t + β 2 T ax i,t + β 3 F inance i,t + β 4 F irm Size i,t +β 5 X i,t + ɛ i,t (5) where Compet j,t measures the level of competition in industry j; T ax i,t is firm i s tax rate in year t; F inance i,t is a measure of how easily firm i can access capital market; and X i,t is a 9

11 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 β 1 < 0, i.e., a firm s incentives to engage in tax avoidance are positively correlated with the degree of product market competition. Hypothesis 2 β 2 < 0, i.e., a firm s incentives to engage in tax avoidance are positively correlated with its tax rate. Hypothesis 3 β 3 > 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 β 4 > 0, i.e., larger firms have weaker incentives to engage in tax avoidance. We have a specification for E i,t, which is similar to that for d i,t as in Equation (5). However, since we cannot determine the sign of η i,t in Equation (3) a priori, and since E i,t = d i,t η i,t + d i,t e i,t, our model has no prediction about how E i,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 E i,t. 10

12 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 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

13 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 and that of the imputed profit is during We define a variable GAP to denote the difference between the two profit measures. The variable GAP has a sizable mean of , but a much smaller median of

14 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

15 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 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 and 0.745, respectively. Industrial average profit margin is negatively correlated with the logarithm of firm number ( ), and positively correlated with both the Herfindahl index and the concentration ratio (the correlation coefficients are 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

16 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

17 sample mean of 1.56% and a standard deviation of 1.43%. We construct six dummy variables to represent a firm s ownership status: D SOE, D private, D foreign, D HK/T W, D mixed, and D collective. 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 and standard deviation is (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 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

18 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 RO i,t = (β 0 + β 1 Compet + β 2 T AX + β 3 F INANCE + β 4 LNLABOR + β 5 RSALE +β 6 D OW N + β i d year + β j d loc ) P RO i,t + α 1 Compet + α 2 T AX i=year j=loc +α 3 F INANCE + α 4 LNLABOR + α 5 RSALE + α 6 D OW N + α i d year + j=loc i=year α j d loc + ɛ i,t (6) where D OW N is a set of five ownership dummy variables with the SOE dummy taken as the benchmark; d year is a set of year dummies; and d loc is a set of location dummies. Including d year and d loc 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 β 1. If it is negative and significant, then firms in more competitive industries tend to report less profit (Hypothesis 1). Our other hypotheses say that β 2 < 0, β 3 > 0 and β 4 > 0. Note that we include all control variables and dummy variables in the constant term in Equation (6) corresponding to E i,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

19 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 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, β 1 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 β 1 is precisely estimated with a standard error ranging from to , 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

20 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 and 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

21 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 β 1 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

22 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 β 1 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

23 (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 (0.042) in 2002 to (0.03) in 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 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

24 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 rofit 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 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 rofit, 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

25 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 rofit 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 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

26 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 RO i,t = (β 0 + β 1 Compet + β 2 T AX + β 3 F INANCE + β 4 LNLABOR + β 5 D OW N +β 6 RSALE + β i d year + β j d loc ) P RO i,t + (λ 0 + λ 1 Compet + λ 2 T AX i=year j=loc +λ 3 F INANCE + λ 4 LNLABOR + λ 5 D OW N + λ 6 RSALE + λ i d year + j=loc i=year λ j d loc ) P RO i,t 1 + α 1 Compet + α 2 T AX + α 3 F INANCE +α 4 LNLABOR + α 5 D OW N + α 6 RSALE + α i d year + α j d loc + ɛ i,t (7) i=year 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 β k + λ k for k = 0, 1, 2, 3, 4, 6. As shown in Panel B, β 1 + λ 1 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

27 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 (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

28 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 (DINV i,t ); the change of current liabilities (liquid liabilities in the NBS database) scaled by total assets (DCL i,t ); the ratio of depreciation in the current year to total assets (DCURRD i,t ); the ratio of sale charges (including advertisement fees, packaging fees, delivery fees, etc) to total sales (SC i,t ); and the ratio of administrative charges over total sales (ADC i,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

29 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

30 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 , , , and 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

31 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

32 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

33 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 ) 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

34 2005). 6 The classification of corporate income tax has changed over time since 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 Current Situations and Suggestions for Improvement of Corporate Income Tax Collection, by Yushen Li and Xin Li, on webpage 9 On the internet there is a widely circulated article without source titled 60 Methods to Evade Corporate Taxes, see, for example, In one case that involved 39 firms in three provinces in 2004, firms used fake receipts of recycling materials to evade taxes totaling 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 10 Article 33 of Regulations on National Economic Census states that The use of materials 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

35 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 (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

36 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

37 References Banerjee, A. and Esther Duflo, E. (2004). Do firms want to borrow more? testing credit constraints using a directed lending program, MIT Working Paper. Bollinger, C. R. and Chandra, A. (2005). Iatrogenic specification error: a cautionary tale of cleaning data, Journal of Labor Economics, 23 (2) (April), pp Bound, J, Jaeger, D. A., and Baker, R.M. (1995). Probelms with instrumental variables estimation when the correlation between the instruments and the endogenous variables is weak, Journal of American Statistical Association, 90 (June), pp Cai, H. and Liu, Q. (2007). Competition and corporate tax avoidance: evidence from Chinese industrial firms, Working Paper, Peking University and University of Hong Kong. Chen, K. P. and Chu, C. Y. (2005). Internal control and external manipulation: a model of corporate income tax evasion, Rand Journal of Economics, 36(1) (Spring), pp Cragg, J.G. and Donald, S.G. (1993). Testing identifiability and specification in instrumental variable models, Econometric Theory, 9(1) (Feburary), pp Crocker, K. and Slemrod, J. (2005). of Public Economics, 89(9-10) (September), pp Corporate tax evasion with agency costs, Journal Cui, J. (2005). Qiye Shuodeshui Shiwu Chaozhuo (Corporate Income Tax Practice), Beijing: Jinji Guanli Publisher. Cummins, J. G. and Nyman, I. (2005). The dark side of competitive pressure, Rand Journal of Economics, 36(2)(Summer), pp

38 Dechow, P., Kothari, S.P. and Watts, R. L. (1998). The relation between earnings and cash flows, Journal of Accounting and Economics, 25 (2) (May), pp Desai, M. (2003). The divergence between book income and tax income, in (James Poterba, ed.,) Tax Policy and the Economy (17), pp , Boston: MIT Press. Desai, M. (2005). The degradation of reported corporate profits, Journal of Economic Perspectives, 19 (1) (Winter), pp Desai, M. and Dharmapala, D. (2006). Corporate tax avoidance and high powered incentives, Journal of Financial Economics 79 (1) (January), pp Desai, M., Dyck A., and Zingales, L. (2007) Theft and taxes, Journal of Financial Economics, 84(3) (June), pp Desai, M., Foley F. and Hines, J.R. (2006). The demand for tax haven operations, Journal of Public Economics, 90(3)(Feburary), pp Feldman, N. and Slemrod, J. (2007). Estimating tax noncomplicance with evidence from unaudited tax returns, The Economic Journal, 117 (March), Fisman, R. and Wei, S.J. (2004). Tax rates and tax evasion: evidence from missing imports in China, Journal of Political Economy, 112(2), Holz, C. A. (2006). No razor s edge: reexamining Alwyn Young s evidence for increasing inter-provincial trade barriers in China, Working Paper, HKUST. Kuijs, L., Mako W., and Zhang, C. (2005). World Bank Policy Note, World Bank s Beijing Office. SOE dividends: how much and to whom? 37

39 Rajan, R. and Zingales, L. (1998). Financial dependence and growth, American Economic Review, 88 (3) (June), pp Shi, Y.P. (2006). Essays on Regulations and Government Decentralization in China, Ph.D dissertation, Peking University. Sivadasan, J. and Slemrod, J. (2006). Tax law changes, income shifting and measured wage inequality: evidence from India, NBER Working Paper Shleifer, A. (2004). Does competition destroy ethical behavior? American Economic Review Papers and Proceedings, 94 (2) (May), pp Slemrod, J. (2004). Economics of corporate tax selfishness, National Tax Journal, 57(4) (December), pp Slemrod, J. and Yitzhaki, S. (2002). Tax avoidance, evasion and administration, (in Auerbach, A. J. and Feldstein, M.,eds.) Handbook of Public Economics, Vol. 3, pp , Amsterdam: North-Holland. Staiger, D. and Stock, J. (1997). Instrumental variables regression with weak instrumetns, Econometrica, 65(3) (May), pp Stock, J., Wright, J., and Yogo, M. (2002). A survey of weak instruments and weak identification in generatlized method of moments, Journal of Business and Economic Statistics, 20 (4) (October), pp Stock, J. and Yogo, M. (2005). Testing for weak instruments in linear IV regression, (in Andrews D.,and Stock, J. eds.), Identification and Inference for Econometric Models: Essays 38

40 in Honor of Thomas Rothenberg, pp , Cambridge: Cambridge University Press. National Statistical Bureau of China (2006) China Statistical Yearbook, Beijing: China Statistics. Wu, J. (2003). Contemporal Chinese Economic Reforms, Shanghai: Shanghai Far-East Publisher. Young, A. (2000) The razor s edge: distortions and incremental reform in the People s Republic of China, Quarterly Journal of Economics, 115(4)(November), pp

41 Table 1 Competition Intensity across the Two-Digit Industries in China ( ) Two-Digit Industry # of four digit Industry # of Firms (N) Logarithm of # of firms Profit Margin Herfindahl Index Notes: The sample contains 514,394 firm-year observations over the 6 year period of 2000 to 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. Market Share by Top 4 Firms [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

42 Table 2 Summary Statistics of Key Variables Variable Mean Median Std. Dev. Min. Max. Competition measures by two-digit industry Herfindahl Index Market Share by the Top Four Firms Logarithm of the Number of Firms Profit Margin 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 (D private ) Collective Firms Dummy (D collective ) Mixed Firm Dummy (D mixed ) HK and TW Firms Dummy (D HK/TW ) Foreign Firms Dummy (D foreign ) Total Assets (TA) (RMB million) Notes: The sample contains 514,394 firm-year observations over the 6 year period of 2000 to 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

43 Table 3 OLS Regressions of the Reported Profits, Competition variables measured at the four-digit level Independent variables (1) (2) Herfindahl Index (3) Profit Margin (4) Herfindahl Index (5) Profit Margin (6) Log of # of Firms (7) Market Share by Top 4 Firms (8) Imputed Profit (0.0006) (0.0052) (0.0019) (0.0020) (0.0052) (0.0052) (0.0059) (0.0053) Herfindahl Index (0.0042) (0.0041) Herf. Index Imputed Profit (0.0128) (0.0125) Profit Margin (0.0057) (0.0055) Profit Margin Imputed Profit (0.0188) (0.0183) Log # of Firms (0.0002) Log # of Firms Imputed Profit (0.0005) Market Share by 4 Firms (0.0055) Market Share by 4 Firms Imputed Profit (0.0039) Tax Rate Imp. Profit (0.0035) (0.0035) (0.0035) (0.0035) (0.0035) Access to Credit Imp. Profit (0.0325) (0.0325) (0.0321) (0.0325) (0.0325) Log Labor Imp. Profit (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) Sales/Output Imp. Profit (0.0028) (0.0028) (0.0027) (0.0028) (0.0028) D private Imputed Profit (0.0027) (0.0027) (0.0026) (0.0027) (0.0027) D collective Imputed Profit (0.0028) (0.0028) (0.0027) (0.0028) (0.0028) D mixed Imputed Profit (0.0027) (0.0027) (0.0027) (0.0027) (0.0027) D HK/TW Imputed Profit (0.0029) (0.0029) (0.0028) (0.0028) (0.0029) D foreign Imputed Profit (0.0029) (0.0029) (0.0029) (0.0029) (0.0029) Tax Rate (0.0012) (0.0012) (0.0012) (0.0012) (0.0012) Access to Credit (0.0124) (0.0124) (0.0122) (0.0124) (0.0124) Log # of Employees (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) Sales / Output (RSALE) (0.0006) (0.0007) (0.0006) (0.0006) (0.0007) 42

44 Table 3 Continued D private (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) D collective (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) D mixed (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) D HK/TW (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) D foreign (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) Constant (0.0002) (0.0015) (0.0005) (0.0006) (0.0015) (0.0015) (0.0018) (0.0015) Year, location dummies and their interactions with PRO No Yes Yes Yes Yes Yes Yes Yes Observations 514, , , , , , , ,394 Adjusted R-squared 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

45 Table 4 Effect of Competition on Reported Profits: the IV Regressions Competition variable instrumented (2) Imputed profit instrumented (3) Both instrumented (4) OLS (1) Imputed Profit (0.0052) (0.0256) (0.0269) (0.010) Profit margin (0.0055) (0.0966) (0.0108) (0.0127) Profit Margin Imputed profit (0.0183) (0.3911) (0.0561) (0.1981) Tax rate Imputed profit (0.0035) (0.0049) (0.0114) (0.0292) Access to Credit Imputed Profit (0.0321) (0.0494) (0.1326) (0.0603) Log Labor Imputed profit (0.0006) (0.0010) (0.0028) (0.0018) Sales/Output Imputed Profit (0.0027) (0.0042) (0.0155) (0.0062) Weak identification test - Cragg- Donald Statistic (critical value) (7.03) (na) (na) Observations 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

46 Table 5 Summary of First Stage Regression Results for Regressions in Table 4 Model (2) Model (3) Model (4) Dependent Variables Dependent Variables Dependent Variables Profit Margin Profit Margin Imputed Profit Imputed Profit Profit Margin Imputed Profit (0.002) Imputed Profit Average imputed profit (Av. PRO) (0.044) (0.079) STEP (0.002) (0.009) (0.001) STEP Av. PRO (0.004) STEP Imputed Profit (0.008) (0.003) Profit Margin AV. PRO (0.121) (0.007) Profit Margin (0.007) (0.000) (0.011) Profit Margin Imputed Profit (0.004) (0.001) (0.000) F-statistic, test that instruments jointly equal zero Adjusted R 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

47 Table 6 A Natural Experiment: Large-sized Refrigerator, Power Plant Equipment Industries, and Comparable Industries Refrigerator vs. Electrical Fan (N = 969) Power Plant Equipment vs. E. Generator (N=2,201) OLS (1) OLS (2) IV (3) OLS (4) OLS (5) IV (6) Imputed Profit (0.1153) (0.1824) (0.1223) (0.0541) (0.0763) (0.0764) POST (0.0068) (0.0078) (0.0112) (0.0041) (0.0062) (0.0071) POST Imputed Profit (0.0249) (0.0265) (0.0419) (0.0221) (0.0235) (0.0263) TREAT (0.0172) (0.0076) (0.0102) (0.0041) (0.0050) (0.0050) TREAT Imputed Profit (0.0345) (0.0241) (0.0324) (0.0225) (0.0172) (0.0185) TREAT POST (0.0051) (0.0213) TREAT POST Imputed Profit (0.0396) (0.0269) Profit Margin (0.5149) (1.6121) (0.1302) (0.1768) Profit Margin Imputed Profit (0.4671) (0.8781) (0.3071) (0.3977) Tax rate Imp. Profit (0.0623) (0.0633) (0.0671) (0.0411) (0.0482) (0.0479) Credit Imp. Prof (0.6832) (0.6871) (0.6971) (0.4516) (0.4805) (0.4782) Log Labor Imp. Prof (0.0103) (0.0104) (0.0105) (0.0189) (0.0089) (0.0090) Sales/Output Imp. Prof (0.0633) (0.0628) (0.0648) (0.0319) (0.0396) (0.0394) Other controls Yes Yes Yes Yes Yes Yes Adj. R-squared Weak identification test (Cragg-Donald Statistic) na na na na First stage regressions for models (3) and (6) Model (3) dependent variables Model (6) dependent variables PMARGIN PMARGIN Imputed profit PMARGIN PMARGIN Imputed Profit TREAT POST (0.0009) (0.0002) (0.0137) (0.0004) TREAT POST Imputed Profit (0.0032) (0.0010) (0.0048) (0.0014) F-statistic, test that instruments jointly equal zero Adjusted R

48 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 Among the industries restricted in 1995, large-sized refrigerator (4063), power plant equipment (4011) were permitted in 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 They are Electrical Fans (4064) and Electric Generator (4012) respectively. TREAT is a dummy variables specifying whether a firm was permitted in 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

49 Table 7 Robustness Checks Alternative Model Specifications Competition Measures by the Four-Digit Codes Herfindahl Index Profit Margin Log # of firms Share by top 4 firms Panel A: Adding the lagged imputed profit (PRO t-1 ) to the baseline model (N = 400,210) Imputed Profit (0.012) (0.012) (0.0088) (0.0121) Lagged Imputed Profit (0.0017) (0.0017) (0.0017) (0.0017) Competition (0.0079) (0.0103) (0.0004) (0.0025) Competition Imputed profit (0.0284) (0.0213) (0.0014) (0.0092) Adjusted R-squared Panel B: Interacting both imputed profit and lagged imputed profit with controls (N = 400,210) Imputed Profit (0.0097) (0.0091) (0.0094) (0.0088) Competition Imputed Profit (0.0024) (0.0452) (0.0021) (0.0071) Tax rate Imputed Profit (0.0041) (0.0039) (0.0037) (0.0047) Access to Credit Imputed Profit (0.0771) (0.0714) (0.0823) (0.0927) Sales/Output Imputed Profit (0.0074) (0.0077) (0.0082) (0.0071) Log Labor Imputed Profit (0.0011) (0.0011) (0.0012) (0.0013) Adjusted R-squared Panel C: Dependent variable = average reported profit (N=46,211) Imputed Profit (0.0061) (0.0084) (0.0104) (0.0085) Competition Imputed Profit (0.0288) (0.0401) (0.0009) (0.0084) Adjusted R-squared 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

50 Table 8 The OLS Estimates of Reported Profit Equations Controlling for Additional Variables Results from Table 3 Results with additional Variables Covariates Herfindahl Index (1) Profit Margin (2) Herfindahl Index (3) Profit Margin (4) Herfindahl Index (0.0041) (0.0082) Herfindahl Index Imputed Profit ( ) (0.0282) Profit Margin (0.0055) (0.0103) Profit Margin Imputed Profit ( ) (0.0464) DINV, DCL, RCURRD, SC, ADC No No Yes Yes and their interactions with the imputed profit included? Adj. R-squared 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

51 Gap between imputed profits and reported profits 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