financial constraint and tax aggressiveness · firms always have incentives to invest in tax...

Financial Constraint and Tax Aggressiveness

Chen Chen

The University of Auckland

[email protected]

Shufang Lai

The Chinese University of Hong Kong

[email protected]

Financial Constraint and Tax Aggressiveness

Abstract

We examine how the financial constraints of firms affect their tax avoidance policies.

By using a large sample of US public firms, we find that financial constrained firms

engage in the tax avoidance more aggressively than financial unconstrained firms.

Furthermore, we find constrained firms tend to use the saving cash from the tax

avoidance to invest more in the future. Our results hold constant across different

measures of financial constraints and different measures of tax avoidance and are robust

by partially controlling for the endogeneity problem.

1. Introduction

Starting from Shackelford and Shevlin (2001), accounting researchers show growing

interests on the determinants of tax aggressiveness. Despite the findings of prior research

document that several firm characteristics could explain the variation in firms’ tax

avoidance (Lisowsky 2010; Wilson 2009), Hanlon and Heitzman (2010) note that many

of the determinants of firms’ tax avoidance still remain unclear and call for further

exploration. In this paper, we examine a sample of firms which are experiencing financial

constrained to investigate one possible determinant of tax avoidance, firms’ financial

constraint status. In addition, we examine whether financial constrained firms use the

cash saving from the tax avoidance to mitigate their under-investment problems by

increasing their capital investment.

Firms always have incentives to invest in tax planning to lower their taxes since this

benefits their shareholders as the residual claimants (Mills 1996; Mills et al 1998).

However, the extant research on the determinants of firms’ tax avoidance neglects to

examine the more primitive issue of whether firms’ lack of money, namely their financial

constraints status, encourages them to involve in more tax avoidance behavior. We bridge

this gap by providing evidence on whether firms undertake more aggressive tax positions

when they are in the financial constrained status. By avoiding paying more taxes, the

financial constrained firms may save much operating cash to release their financial

constrained problems and the byproducts of financial constraints, namely the

under-investment problems by investing more in future.

Using a large sample of US public firms from 1986 to 2011, we regress our measures

of tax avoidance on our financial constrained dummy after controlling for the factors that

prior research suggests are associated with tax avoidance. Our results suggest that firms

which are financial constrained engage in higher levels of tax avoidance. Our results are

insensitive to how we measure the financial constraints and the tax avoidance behaviors.

Specifically, we measure a firm’s financial constraints by using KZ index as suggested by

Kaplan and Zingales (1997), the payout dummy used by Almeida et al (2004) and Denis

and Sibikov (2010) and the WW index of Whited and Wu (2006). Our finding is also

economically significant. In particular, we find that the effective tax rates of financial

constrained firms are approximately 3% to 8% lower than the financial unconstrained

firms.1 To further address the potential endogeneity problem that financial constraints

and corporate tax saving could be simultaneously determined by other factors, we

provide two robustness checks. The first one is the lead-lag analysis. In other words, we

regress the one year ahead financial constrained dummy on the current years’ tax

avoidance behavior. The other one is the change test. We find that after firms become

financial constrained, they are more aggressively engaged in tax avoidance behaviors.

We also find that among the firms being financially constrained, after they involve in

more aggressive tax savings, their under-investment problems become mitigated as they

make more capital investment in the future. These results highlight the role of tax

avoidance in helping the financially constrained firms to mitigate their potential under-

investment problems.

This study contributes to the literature in several ways. Firstly, it contributes to the

current tax research by identifying an additional determinant of firms’ tax planning

behaviors. Prior studies mainly focus on several firm characteristics such as, firms’ size,

1 Different effective tax rates yield different economic magnitudes of the effects of financial constraint on

the tax avoidances.

operation in foreign countries, profitability, greater litigation losses, ownership structure,

corporate governance structures, auditor characteristics, corporate cultures and

managerial incentives as well as their personal characteristics to do tax planning while

this study finds that under one critical financial situation, namely during their financially

constrained periods, when firms find it very costly to gain external finance, they have

more incentives to conduct tax avoidance behaviors.

Secondly, little research studies how a firm’s tax avoidance affects its future capital

investment behavior. Blouin et al. (2012) is the first paper which finds out that aggressive

tax avoidance increases firms’ capital investment and our findings add support to their

findings. In particular, we provide empirical evidence to show that the tax saving

behaviors of the financial constrained firms help the firms to reduce their under

investment problems by increasing their future capital investments, which is beneficial to

the firms. This finding enriches our understanding of the consequences of tax avoidance.

Thirdly, this study also contributes to the financial constraint literature. Prior literature

has already found that financial constrained firms tend to save cash more efficiently

(Almeida et al.2004), use self-generated cash more efficiently and invest more efficiently

(Denis and Sibikov 2010). This study explores through which channel financial

constrained firms save their cash for their future capital investments. By aggressively

involving in tax avoidance behaviors, financial constrained firms can save more cash

efficiently and they tend to use the saved cash to help them to get rid of the underinvest

problems which financial constrained firms are more likely to suffer.

The remainder of the paper is organized as follows. Section 2 presents the related literature

and develops our hypotheses. Section 3 details our research design. Section 4 discusses the

empirical results related to our two hypotheses and Section 5 examines the sensitivity of our

results to alternative tests and specifications. Section 6 concludes our paper.

2. Literature Review and Hypothesis Development

Recent growing tax research has shown interests on the determinants of tax

aggressiveness starting from Shackelford and Shevlin (2001). Several firm-level

characteristics are found to be related to the tax avoidance. For example, Gupta and

Newberry (1997) find that size, capital structure, asset mix and profitability are related to

GAAP ETRs. In addition, firms accused of using tax shelters are found to have more

foreign operations, subsidiaries in tax havens and higher prior-year effective tax rates,

greater litigation losses and less leverage (Wilson,2009; Lisowsky 2010). There are also a

growing number of studies examining the corporate governance characteristics

particularly the executives’ incentives with firms’ tax aggressiveness behaviors. Slemrod

(2004) develops the idea that shareholders select the level of tax aggressiveness by

linking tax manager compensation with effective tax rates or stock price. Desai and

Dharmapala (2006) links equity-based compensation to tax planning. Empirically, Rego

and Wilson (2011) find a positive association between option vega and measures of

corporate tax aggressiveness. Armstrong et al (2012) find evidence that the incentive

compensation of the tax director exhibits a strong negative relationship with the GAAP

effective tax rate. Robinson et al. (2010) attempt to measure tax manager incentives by

determining whether the tax department is viewed as a profit center. In addition, literature

investigates whether ownership structures, corporate culture and individual managers

influence a firm’s level of tax aggressiveness. In particular, Chen et al (2010) document

that family firms avoid fewer taxes than non-family firms because there long-term

concentrated holders have a longer horizon and may be more sensitive to the total costs

of avoidance arising from reputation effects and suspicions of diversion from minority

shareholders. Frank et al. (2009) argue and find evidence that there is a positive

relationship between aggressive financial and tax reporting which is consistent with a

generally aggressive corporate “tone and culture.” In another study, Dyreng et al. (2010)

find that top management is associated with tax planning. Other external factors are also

found to be related to firms’ tax saving behavior. In particular, Cheng et al. (2012) finds

firms increase their tax avoidance after the hedge fund intervention. Hoopes at al. (2012)

finds that the IRS audit monitoring could dampen firms’ tax avoidance behavior by

increasing their cost. McGuire et al. (2012) finds that firms purchasing tax services from

their external audit firm engage in greater tax avoidance when their external audit firm is

a tax expert.

Despite of the vast literature providing evidence on the effect of firm-level

characteristics on firms’ tax planning strategies, there is little evidence showing how

firms’ specific incentives and the fundamental dynamics of their financial status may

affect its tax planning choices. In this study, we show that when firms are financial

constrained, when their operating cash flow is crucial to them due to the higher cost of

external financing, they have greater incentives to adopt aggressive tax avoidance choices

in order to generate more operating cash flows than the non-constrained firms.

Previous finance literature finds that cash holdings could be valuable and firms tend to

accumulate more cash when the external finances of funds are insufficient to satisfy

firms’ demand for capital (Kim et al.1998; Opler et al.1999; Almeida et al.2004;

Faulkender and Wang 2006). By involving in more aggressive cash saving behaviors,

financial constrained firms can save more cash internally to release their financial

constraint problems. To summarize, the following hypothesis is developed accordingly.

H1: Financial constrained firms have greater incentives to engage in aggressive tax

saving behaviors than the financial unconstrained counterparts.

Prior literature also show that financially constrained firms are more likely to forego

positive NPV projects due to limited access to external capitals (Almeida et al 2004;

Faulkender and Wang, 2006; Denis and Sibikov, 2010). Hence, ex ante, financially

constrained firms are more likely to suffer underinvestment. In addition, Fazzari et al.

(1988) argue that when external financing is more costly than internal financing, firms

tend to spend more operating cash flow on their capital investment and the sensitivity of

investment to cash flow will increase in the degree of financial constraints. If financial

constrained firms can increase their operating cash flow through aggressive tax saving

behaviors, they may further use the increased cash flows to mitigate the potential

underinvestment problem by increasing their capital investment. Thus, our second

hypothesis is developed as follows:

H2: The more aggressive firms engage in tax saving, the more capital investments they

are going to make.

3. Sample Selection and Research Design

3.1 Sample

We obtain our sample from the Compustat-CRSP merged database (CCM). The

sample covers firm-year observations between 1986 and 2011. We eliminate firm-year

observations that do not have the data necessary to calculate both the independent

variables in Equations (6) and our tax avoidance proxies. We also exclude firm-year

observations with negative pre-tax income to focus on firm-years during which tax

avoidance is likely to be a priority. As a result of these requirements, the sample used to

model firms’ decision to engage in aggressive tax avoidance contains between 22,240 and

67,730 firm-year observations by using different tax avoidance measures and different

financial constraint measures.

3.2 Measures for Financial Constraints

Following prior literature, we use the following three measures of financial constraints

to determine whether firms are belonged to financial constrained firms or not.2

The first one is the KZ index. This index comes from Kaplan and Zingales (1997). A

firm with a high KZ index is considered more financially constrained as the wedge

between its internal and external cost of funds increases. The KZ index gives positive

weight to Tobin’s Q and leverage, and negative weight to operating cash flow, cash

balances, and dividends. We construct the KZ index for each firm-year as the linear

combination

KZi,t=-1.002(CFi,t/TAi,t)-39.368(DIVi,t/TAi,t)-1.315(CAi,t/TAi,t)+3.129LEVi,t+0.283Qi,t(1)

where CFi,t/TAi,t is cash flow over lagged book assets, DIVi,t/TAi,t is cash dividends over

lagged book assets, CAi,t/TAi,t is cash balances over lagged book assets, LEVi,t is total debt

over book assets, and Qi,t is the ratio of the market-to-book value of the firm’s assets. To

reduce the effects of a few extreme values, we winsorize the components of the KZ index

at the 1st and 99th percentiles before constructing it. For every year of data, we sort all

2 These three financial constraint measures are commonly used by the current literature (Chen and Wang

2012; Denis and Sibokov 2010). Our results remain quantitatively similar when we use other measures of

financial constraints, including the investment-cash flow sensitivity of Fazzari et al.(1988), cash-cash flow

sensitivity developed by Almeida et al (2004) and bond rating measures used by Denis and Sibikov 2010.

the firms in Compustat into quintiles according to the value of their KZ indexes. Firms

with the lowest KZ index values are placed in quintile one, and firms with the highest

values in quintile five. Following Chen and Wang (2012), we consider firms in the

highest KZ quintile to be financially constrained and firms in other KZ quintiles to be

financially unconstrained.

The second one is the WW index. We measure a firm’s financial constraint using the

WW index of Whited and Wu(2006) as follows:

WWi,t=-0.091(CFi,t/TAi,t)-0.062DIVDUMi,t+0.021(LTDi,t/TAi,t)-0.044logTAi,t

+0.102INDSGi,t-0.035SGi,t. (2)

where DIVDUMi,t equals one if the firm pays cash dividends and zero otherwise; LTDi,t is

long-term debt; INDSGi,t is the firm’s three-digit industry sales growth; and SG is the

firm’s sales growth. A firm with a high WW index is considered more financially

constrained. As before, financially constrained firms are firms in the highest quintile of

WW index. Financially unconstrained firms are firms in other quintiles.

The last one is the payout ratio. Fazzari, Hubbard, and Petersen (1988) argue that

unconstrained firms are more likely to have higher payout ratios, while constrained firms

are likely to have lower payout ratios. Therefore, for each year, we assign those firms in

the bottom (top) three deciles of the annual cash payout ratio distribution to the

financially constrained (unconstrained) group. Following Almeida, Campello, and

Weisbach (2004); Denis and Sibikov (2010), payout ratio is defined as the ratio of

dividends and common stock repurchases to operating income. Observations with a

positive payout and zero or negative cash flow are assigned the highest payout ratio.

3.3 Measures of Tax Aggressiveness

Hanlon and Heitzman (2010) note that defining and measuring firm tax aggressiveness

/ tax avoidance is difficult. While there are several measures of tax aggressiveness in the

literature, we follow prior research and conduct our analysis using four different

measures of tax avoidance for our study. In general, prior research does not rely on one

single measure of tax avoidance because each measure has its limitations. Therefore, the

use of four different measures of tax avoidance allows us to examine the robustness of

their associations with our variables.

Our first measure of tax aggressiveness which comes from Dyreng et al (2008) is the

firm’s GAAP effective tax rate and calculated as:

GETRi,t=TXTi,t/PIi,t (3)

where a firm’s GAAP effective tax rate is equal to the total tax expense (TXT) divided by

firm pretax income (PI). The benefit of this measure is that it avoids overstating the

current tax expenses that arise from employee stock options until the post-FAS123R

period.

Our second measure of tax avoidance is the firm’s cash effective tax rate as measured

by Armstrong et al. (2012) and calculated as:

CETRi,t=(TXPDi,t+(TXBCOi,t+TXBCOFi,t))/PIi,t (4)

where a firm’s cash effective tax rate is equal to the sum of the taxes paid in cash (TXPD)

and the tax benefit of stock options (TXBCO/TXBCOF) divided by firm pretax income

(PI). In general Cash Effective Tax Rates (CETRi,t) reflect both temporary and permanent

book-tax differences. The benefit of this measure is that it also avoids tax accrual effects

present in the current tax expense (Hanlon and Heitzman 2010). However the CETRi,t is

limited in that it does not distinguish between real activities that are tax-favored and other

activities specifically undertaken to reduce taxes (Khurana and Moser 2009). Finally,

CETRi,t is subject to measurement error (Badertscher et al. 2009).

Our third measure of tax aggressiveness, LETRi,t, is still based on Dyreng et al (2008)

which focus on the long-run cash effective tax rate and calculated as the same as CETRi,t

but we need to sum both the cash taxed paid and total pretax income over the prior

five-year period.

Our final measure of tax aggressiveness is a measure of discretionary permanent

book-tax differences as originally calculated by Frank et al (2009). This measure known

as DTAXi,t is calculated by regressing permanent book-tax differences (PERM_BTDi,t)

on nondiscretionary items that are associated with permanent book-tax differences. The

variable DTAXi,t is equal to the error term in the regression equation below:

PERM_BTDi,t= α0+α1INTANGi,t + α2UNCONi,t + α3MIi,t + α4CSTEi,t + α5CHGNOLi,t +

α6LAGPERM_BTDi,t +u i,t (5)

Where PERM_BTDi,t is defined as above, INTANGi,t is goodwill, UNCONi,t is income

reported under the equity method, MI is income attributable to minority interest, CSTE is

current state income tax expense, CHNGNOLi,t is the change in the NOL from the prior

year to the current year and LAGPERM_BTDi,t is the one-year lagged PERM_BTDi,t.

Larger positive error terms in the above regression equation indicate higher levels of

discretionary book-tax differences and therefore higher firm tax aggressiveness.

3.4 Economic Controls

We include Return on Assets (ROAi,t) to control for the underlying economic activity

of the firm. We include the standard deviation of the previous five years’ ROA

(STD_ROAi,t) to capture variability in the firm’s operations. Next we include a size proxy,

the natural logarithm of the firm’s market capitalization (SIZEi,t). Prior studies’ evidence

on the relationship between GAAP ETR and firm size is mixed. For example, Rego

(2003), Zimmerman (1983), and Omer et al. (1993) document a negative relationship

between firm size and GAAP ETR, a finding that is consistent with the “political cost”

hypothesis. However, Jacob (1996), Gupta and Newberry (1997), and Mills et al. (1998)

do not find a statistically significant relationship. We therefore include firm size but do

not predict the sign of the relationship. Leverage (LEVi,t), defined as the ratio of the

firm’s long-term debt to total assets (LT/AT), is included to capture the extent of the tax

shield of debt. Ceteris paribus, the greater the firm’s tax shield of debt, the lower the

need for incremental tax planning (Mackie-Mason, 1990). Change in Goodwill

(GOODWILLi,t), defined as the annual increase in the firm’s goodwill scaled by

beginning total assets (ΔGDWL/Beginning AT), is included to capture merger and

acquisition activity of the firm during the year. If goodwill decreases, then Change in

Goodwill is set to zero. New Investment (NEWINVESTi,t), defined in Richardson (2006)

as the sum of research and development expense, capital expenditures, and acquisitions

less the sum of sales of property and depreciation all scaled by total assets ((XRD +

CAPX + ACQ –SPPE – DPC) / Avg. AT), is included to control for the firm’s

investment activity, since investment often leads to book-tax differences because the tax

and accounting rules are different (e.g., the investment tax credit, accelerated

depreciation methods, and bonus depreciation).

Rego (2003) finds that multinational firms with more extensive foreign operations

have lower worldwide GAAP ETRs, a finding that she interprets as being consistent with

economies of scale in tax planning. We therefore include the foreign assets of the

company to control for differences in international planning opportunities. Foreign Assets

(FOREIGNASSETSi,t)are estimated using the methodology described in Oler et al. (2007),

which uses the consolidated turnover ratio and foreign segment sales to infer foreign

assets. We include an estimate of the firm’s foreign asset base to capture differential tax

rates that typically apply to firms’ foreign activities.

Finally, since more complex organizations require more executive talent (which

commands higher pay) and more opportunity to tax plan, we include two measures of the

complexity of the organization: Geographic Complexity (GHHi,t) and Industry

Complexity (IHHi,t). Following Bushman et al. (2004), these measures are revenue-based

Hirfindahl indices that capture the within-firm geographic and industry segment

concentration of the firm. Lower values of these measures suggest a relatively more

complex organization, so we predict a negative association between these measures of

complexity and the ETRi,t consistent with more opportunity for tax planning. To

summarize, the tax aggressiveness model is developed to test our H1 as follows:

TAX_AVOIDANCEi,t=α0+α1FCi,t+α2ROAi,t+α3STD_ROAi,t+α4SIZEi,t+α5LEVi,t+

α6GOODWILLi,t+α7NEWINVESTi,t+α8FOREIGNASSETSi,t+α9GHHi,t+

α10IHHi,t+YEARi,t+INDUSTRYi,t+i,t (6)

3.5 Investment Test

To alleviate the concerns of omitted correlated variable that may simultaneously

determine firm investment and tax avoidance, we firstly regress firm net capital

expenditure on variables that identified by prior literature to affect firm investments and

use the residuals as our measure of firm investments. Specifically, we estimate the

following equation cross sectionally each year for all the firm-year observations with

available data to gauge the coefficient of the following model precisely:

INVi,t+1=0+1Qi,t+2CFOi,t+3AGEi,t+4LEVi,t+5SIZEi,t+i,t+1 (7)

The variable INVi,t+1is the net capital expenditure for firm i in year t+1 calculated as the

difference between capital expenditures (CAPX) and depreciation expenses (XDP) scaled

by the beginning year book value of asset (AT). Consistent with prior literature, we use

firms’ average q, Qi,t, to measure firm marginal q, a proxy for growth opportunity

(Tobin 1969; Hayashi 1982). Firm average q is calculated as the sum of the market

value of equity (PRCC_F*CSHO) and total debt (DLC + DLTT) scaled by the beginning

year assets (AT). In addition, we include the following controlling variable in our model

following prior literature (Lang et al. 1996). CFOi,t is a firm i’s operating cash flow in

year t-1. LEVi,t is a firm i’s leverage ratio in year t-1. A firm with higher leverage is

subject to financial constraint problem as well as the debt overhang problem so that it

may not invest much. SIZEi,t is the total assets of firm i in year t-1. A firm with smaller

size tends to invest more as it is in its expansion stage. We also control for firm’s age

(AGEi,t) as the elder the firm is, the more likely it is to be in the mature or declining stage

of its business cycle. The residual i,t+1 captures the portion of

investments that cannot be explained by those firm specific factors and is the main

measure of firm investments, denoted as RINVi,t+1. To further test our H2, we tease out

the financial constrained sample and use their residual in model (7) as the dependent

variable in the following model:

RINVi,t=0+1TAX_AVOIDANCEi,t-1+YEARi,t+INDUSTRYi,t+i,t (8)

where TAX_AVOIDANCEi,t-1 is the one year ahead four different measures of tax

avoidance and we expect the more aggressive the financial constrained firms involve in

their tax avoidance behavior, the more investment they are going to make in the

following year.

4. Descriptive Statistics and Multivariate Analyses

4.1 Descriptive Statistics and Correlation

Table 1 reports the descriptive statistics of all variables used in Equation (6). The mean

of our interested variables Financial Constraint are 0.153, 0.157 and 0.330 respectively

when we use different measures to proxy for financial constraints. The mean of FC_KZi,t

indicates that approximately 15.3% of the firm-year observations in our sample are

determined to be financial constrained firms when the KZ index is used. The mean of

FC_WWi,t indicates that approximately 15.7% of the firm-year observations in our

sample are financial constrained firms when the WW index is used. The mean of

FC_Payouti,t indicates that approximately 33% of the firm-year observations in our

sample are financial constrained firms when we use the payout ratio as a measure of

financial constraint. The means and medians of our tax avoidance measures, GETRi,t,

CETRi,t, LETRi,t and DTAXi,t are consistent with prior studies. More specifically, the

mean (median) of GETRi,t is 0.220 (0.308). The mean (median) value of CETRi,t is 0.189

(0.173). The mean (median) value of LETRi,t is 0.196 (0.184) and the mean (median) of

discretionary permanent book-tax differences (DTAXi,t) is 0.001 (0.002), which is close

to the one of Frank et al. (2009). Consistent with prior research (e.g., Chen et al. 2010;

McGuire et al.2012), the mean CETRi,t is 0.189 and is lower than that of GETRi,t. The

descriptive statistics of control variables are similar to prior studies.

Table 2 presents univariate Spearman and Pearson correlations for our sample of

firm-year observations. Our three measures of financial constraint variable are uniformly

correlated with our four tax avoidance proxies. Specifically, the FC_KZi,t, FC_WWi,t and

FC_Payouti,t are negatively and significantly correlated with GETRi,t, CETRi,t and

LETRi,t, but they are also negatively and significantly correlated with DTAXi,t. This is in

contrast to our prediction. As DTAXi,t is developed from several firm financial

characteristics, the univariate correlation could be misleading. In addition, we note that

all four measures of tax avoidance are significantly correlated. Furthermore, all control

variables are correlated with our measures of tax avoidance in a manner that is broadly

consistent with prior research.

4.2 Tests of H1 and H2

Table 3 and Table 4 present the results of our multivariate analyses. In all tables,

p-values are based on standard errors that are clustered by firm. The coefficients on the

industry and year fixed effects are not reported for the sake of brevity.

Table 3 presents our test of the association between financial constraints and tax

avoidance across each of our proxies for tax avoidance and each of our three proxies for

financial constraints. The control variables are generally associated with tax avoidance in

a manner consistent with prior research and, based on the R-Squared values, it appears

that each model has reasonable explanatory power. Note that the lower (higher) values of

GETRi,t, CETRi,t and LETRi,t (DTAXi,t) represent higher levels of tax avoidance. Panel A

shows the result when FC_KZi,t is used to proxy for financial constraints. We find a

negative and significant coefficient on FC_KZi,t (p value is less than 0.01, two-tailed)

when GETRi,t serves as our proxy for tax avoidance. The difference between financial

constrained firms and financial unconstrained firms is economically significant. For

example, the GETR of financial constrained firms is approximately 8 percents lower than

financial unconstrained firms.3 Likewise, when CETRi,t proxies for tax avoidance, the

coefficient on FC_KZi,t is negative and significant (p value is less than 0.01, two-tailed).

In addition, the coefficient on FC_KZi,t is negative and significant (p value is less than

0.01, two-tailed) when LETRi,t proxies for tax avoidance. Finally, we find that the

coefficient FC_KZi,t is insignificant (p=0.535, two-tailed) when DTAXi,t serves as our

proxy for tax avoidance. Panel B shows the result when FC_WWi,t is used to proxy for

financial constraints. We still find a negative and significant coefficient on FC_WWi,t (p

value is less than 0.01, two-tailed) when GETRi,t serves as our proxy for tax avoidance.

The difference between financial constrained firms and financial unconstrained firms is

also economically significant. Specifically, the GETRi,t of financial constrained firms is

approximately 6 percents lower than financial unconstrained firms. Likewise, we find the

coefficient on FC_WWi,t is negative and significant when CETRi,t proxies for tax

avoidance (p value is less than 0.01, two-tailed). In addition, the coefficient on FC_WWi,t

is still negative and significant (p value is less than 0.01, two-tailed) when LETRi,t

proxies for tax avoidance. Finally, we find that the coefficient FC_WWi,t is positive and

significant (p value is less than 0.01, two-tailed) when DTAXi,t serves as our proxy for

tax avoidance, which is consistent with our expectation. Panel C shows the result when

FC_Payouti,t is used to proxy for financial constraints. Consistent with the results in Panel

A and Panel B, a negative and significant coefficient on FC_Payouti,t (p value is less than

3 The economic significance of our study is much larger than the other determinants prior study find of tax

avoidance in terms of the magnitude. In a comparable study, McGuire et al 2012 finds that the firms which

hire tax-specific industry expertise reduce their tax rate by approximately 1 percent. This highlights the

importance of our findings.

0.01, two-tailed) is found when GETRi,t serves as our proxy for tax avoidance. The

difference between financial constrained firms and financial unconstrained firms is

economically significant. In particular, the GETRi,t of financial constrained firms is

approximately 10.2 percent lower than that of the financial unconstrained firms. Likewise,

we find the coefficient on FC_Payouti,t is negative and significant when CETRi,t proxies

for tax avoidance (p value is less than 0.01, two-tailed). Furthermore, the coefficient on

FC_Payouti,t is negative and significant (p value is less than 0.01, two-tailed) when

LETRi,t proxies for tax avoidance. Lastly, we find that the coefficient FC_Payouti,t is

positive but insignificant (p value is 0.267, two-tailed) when DTAXi,t serves as our proxy

for tax avoidance. In combination, these results from different measures of tax avoidance

and different measures of financial constraints suggest that the presence of financial

constraint is associated with higher levels of tax avoidance.

Table 4 presents our tests of the association between the overall abnormal capital

investment level and tax avoidance for the financial constrained samples across our four

different proxies for tax avoidance. We obtain the abnormal portion of firms’ capital

investments by getting the residuals from model (7). Panel A reports the results when

FC_KZi,t is used to determine whether a firm is financial constrained. Consistent with the

benefits of tax saving perspective for financial constrained firms stated in our H2, we find

a negative and significant association between the tax item GETRi,t-1 and current

abnormal capital investment RINVi,t (p is less than 0.01, two-tailed). The negative and

significant coefficient indicates that the less tax paid by the financial constrained firms

the more investments they are going to make in the future. Likewise, we find a negative

and significant association between the tax item CETRi,t-1 and current abnormal capital

investment RINVi,t (p is less than 0.01, two-tailed). In addition, we find a negative and

significant association between the tax item LETRi,t-1 and current abnormal capital

investment RINVi,t (p is less than 0.01, two-tailed). However, we do not find a significant

association between DTAXi,t-1 and current abnormal capital investment RINVi,t (p=0.238,

two-tailed). Panel B reports the results when FC_WWi,t is used to determine whether a

firm is financial constrained or not. Contrary to our expectations, we fail to find a

negative and significant association between our different measures of tax avoidance and

current abnormal capital investment RINVi,t (p values are all more than 0.1 across the

four different tax avoidance measures). Panel C reports the results when FC_Payouti,t is

used to determine whether a firm is financial constrained or not. We continue to find a

negative and significant association between the GETRi,t-1 and current abnormal capital

investment RINVi,t (p value is less than 0.01, two-tailed). Similarly, we find a negative

and significant association between the CETRi,t-1 and current abnormal capital investment

RINVi,t (p value is 0.041, two-tailed). In addition, we find a negative and significant

association between LETRi,t-1 and current abnormal capital investment RINVi,t (p is less

than 0.01, two-tailed). However, opposite to our expectation, we find a significant and

negative association between DTAXi,t-1 and current abnormal capital investment RINVi,t

(p=0.048, two-tailed). In combination, our results of Table 4 suggest that the financial

constrained firms tend to use the cash saving from its previous tax avoidance to increase

its current investment level. By doing this, they can release their potential under

investment problems.

5. Sensitivity Analysis

There is always a concern of endogeneity in the current accounting research. Our paper

may also suffer from the same endogeneity problem as other factors that may determine

the firms’ financial constraint statuses and the firms’ tax avoidance behavior

simultaneously which our model (6) fails to control. To further address the possible

endogeneity problem in our study, we conduct two additional tests. The first one is the

lead-lag analysis and the second one is the change test. The detail research design and the

results are discussed as follows:

5.1 Lead-Lag Analysis

To shed light on the causal relationship between firms’ financial constraints and their

tax avoidance behavior, the following regression is used.

TAX_AVOIDANCEi,t=α0+α1FCi,t-1+α2ROA,t+α3STD_ROAi,t+α4SIZEi,t+

α5LEVi,t+α6GOODWILLi,t+α7NEWINVESTi,t+α8FOREIGNASSETSi,t+

α9GHHi,t +α10IHHi,t +YEARi,t+INDUSTRYi,t+i,t (8)

Table 5 shows the results when we use the one year ahead financial constraint dummy

in the regression model (8) for all different proxies for financial constraints and proxies

for tax avoidance. As shown in Table 5, our results become even stronger after we use the

one year ahead financial contrainted dummies. The coefficients of our financial

constrained dummies are all significantly correlated at 1% level with different tax

avoidance measures in expected direction as stated in H1.

5.2 Change Analysis

To further address the potential endogeneity problem, we use the change model rather

than the level model to conduct our robustness check. In particular, we expect one firm to

be more aggressive in conducting tax avoidance behaviours after it becomes financial

constrained while previously it is a financially unconstrained firm as it may cost the firm

some time to change its tax policy.4 To test this expectation, the following model is used.

ΔTAX_AVOIDANCEi,t=α0+α1ΔFCi,t-1+α2ΔROAi,t+α3ΔSTD_ROAi,t+α4ΔSIZEi,t+

α5ΔLEVi,t+α6GOODWILLi,t+α7ΔNEWINVESTi,t+α8ΔFOREIGNASSETSi,t+

α9ΔGHHi,t+α10ΔIHHi,t +i,t (9)

Table 6 exhibits the results for Model (9) across different measures of financial

constraints and different measures of tax avoidance. Panel A reports the results when KZ

index is used to determine financial constraint. Among the four different measures of tax

avoidance, the change of financial constraints are negatively (positively) correlated with

ΔLETRi,t and ΔDTAXi,t . These results are consistent with our expectation. Panel B reports

the results when WW index is used to determine financial constraint. Likewise, we find

the change of financial constraints negatively (positively) correlated with ΔLETRi,t and

ΔDTAXi,t. Panel C reports the results when the payout ratio is used to determine financial

constraints. Three out of our four different tax avoidance measures yields expected

significant results. In combination, the results of change test generally support our H1

and partially address the potential endogeneity problem.

6. Conclusion

This paper studies the impacts of firms’ financial constrained conditions on firms’ tax

avoidance behavior. Using a large sample of US public firms, we find that the financial

constrained firms are more aggressive in conducting tax avoidance behavior than the

4 Our results remain quantitatively constant when we use the current year change of financial constraint

status.

financial unconstrained firms. In particular, they report 3% to 8% lower tax rates than

their financial unconstrained peers. In addition, we find that the financial constrained

firms tend to use the cash saving from previous tax avoidance behaviors to invest more in

the future. Our results are even stronger when we use the one year ahead financial

constrained dummies and our results generally hold constant when we conduct the

change test.

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Appendix A

Variable Definitions Variable Definition Source and data codes

Tax variables

GETRi,t The GAAP effective tax rate for the

year defined as total income-tax

expense scaled by pre-tax income.

Compustat TXT/PI

CETRi,t The cash effective tax rate for the

year defined as the sum of total

income taxes paid scaled by pre-tax

income.

Compustat TXPD/PI

LETRi,t The long-run cash effective tax rate

defined as the mean of previous five

years' CETR.

Compustat

DTAXi,t Discretionary permanent differences

DTAX is the residual of the following

regression: PERMDIFFit = α0

+α1INTANGit +α2 UNCONit +α3

MIit +α4 CSTEit +α5ΔNOLit+ α6

LAGPERMit + εit as defined in

Frank et al.(2009)

Compustat

Financial constraint variables

KZi,t An index comes from Kaplan and

Zingales (1997), KZ

= –1.002(CF/TA) –

39.368(DIV/TA) – 1.315(CA/TA) +

3.139LEV + 0.283Q, a firm with a

high KZ index is considered more

financially constrained as the wedge

between its internal and external cost

of funds increases.

Compustat

FC_KZi,t Dummy variable, each year sort KZ

into quintiles, financial constraint

firms are in the highest quintile, if KZ

belongs to the top20% then FC_KZ=1

else FC_KZ=0

Compustat

WWi,t Following Whited and Wu (2006),

WW = –0.091(CF/TA) –

0.062DIVDUM + 0.021(LTD/TA) –

0.044logTA+ 0.102INDSG –

0.035SG, a firm with a high WW

index is considered more financially

constrained.

Compustat

FC_WWi,t Dummy variable, each year sort WW

into quintiles, financial constraint

firms are in the highest quintile, if

WW belongs to the top20% then

FC_WW=1 else FC_WW=0

Compustat

Payouti,t Payout ratio Compustat

(DVC+PRSTKC)/EBIT

FC_Payouti,t Dummy variable, if Payout is equal or

less than the bottom 20%, then

FC_Payout=1; if Payout is equal or

greater than top 30%, then

FC_Payout=0.

Compustat

Control variables

ROAi,t Net income (or loss) scaled by

beginning total assets

Compustat NIt/ATt-1

STDROAi,t Standard deviation of Return on

Assets over the previous five fiscal

years

Compustat Std

dev(NIt/ATt-1)

SIZEi,t Natural log of firm's market capital Compustat

Log(CSHO*PRCC_F)

LEVi,t Long-term debt over total assets Compustat LT/AT

GOODWILL i,t The annual change in goodwill if

greater than 0; otherwise 0

Compustat

(DGDWL/Avg AT)

NEWINVEST i,t The annual investment as described in

Richardson (2006) and defined as

Research and Development expense

plus Capital Expenditures plus

Acquisitions minus Sale of Property

minus Depreciation all scaled by

average total assets

Compustat

((XRD+CAPX+ACQ-SP

PE-DPC)/Avg. AT)

FOREIGNASSET i,t Estimated foreign assets using the

methodology described in Oler et

al.(2007) over Total Assets

Compustat

GHHi,t Estimated as the revenue-based

Hirfindahl-Hirschman indices

calculated as the sum of the squares

of each geographic segment’s sales as

a percentage of the total firm sales as

described in Bushman et al.(2004)

Compustat

IHHi,t Estimated as the revenue-based

Hirfindahl-Hirschman indices

calculated as the sum of the squares

of each industry segment’s sales as a

percentage of the total firm sales as

described in Bushman et al.(2004)

Compustat

Investment variables:

INVi,t+1 Investment defined as the sum of

R&D expenditure and capital

expenditure, scaled by beginning total

assets.

Compustat

(XRD+CAPX)/LagAT

Qi,t Tobin's Q Compustat

CSHO*PRCC_F/(AT-LT)

CFOi,t Operating cash flow scaled by the

average of total assets

Compustat

2*OANCF/(AT+LagAT)

AGEi,t The difference between year t and the

first year firm i appears in the

Compustat database

Compustat

LEVi,t Long-term debt over total assets Compustat LT/AT

SIZEi,t Natural log of firm's total assets Compustat Log(AT)

Table 1

Descriptive statistics Variable N Mean SD p25 Median p75

GETRi,t 67,730 0.220 0.289 0 0.308 0.379

CETRi,t 59,810 0.189 0.438 0 0.173 0.343

LETRi,t 62,104 0.196 0.404 0.003 0.184 0.341

DTAXi,t 38,689 0.001 0.133 -0.021 0.002 0.028

FC_KZi,t 67,730 0.153 0.360 0 0 0

FC_WWi,t 66,712 0.157 0.364 0 0 0

FC_Payouti,t 37,937 0.330 0.470 0 0 1

ROAi,t 67,730 -0.046 0.423 -0.031 0.035 0.081

STDROAi,t 67,730 0.154 0.520 0.024 0.051 0.116

SIZEi,t 67,730 5.244 2.544 3.386 5.258 7.130

LEVi,t 67,730 0.597 0.562 0.342 0.531 0.693

GOODWILL i,t 67,730 0.006 0.0418 0 0 0

NEWINVEST i,t 67,730 0.037 0.112 0 0 0.026

FOREIGNASSET i,t 67,730 0.173 0.278 0 0 0.277

GHHi,t 67,730 0.892 0.218 1 1 1

IHHi,t 67,730 0.809 0.267 0.556 1 1

Note: Variables are defined in Appendix A. All continuous variables are winsorized

(reset) at the 1st and 99th percentiles.

Table 2

Spearman and Pearson Correlations

Gaapetr Cashetr1 Lretr Dtax FC_kz FC_ww FC_payout ROA Sdroa Size Lev ChGDWLNewinv SforeignatGHH IHH

Gaapetr 0.293 0.217 0.027 -0.155 -0.250 -0.295 0.191 -0.145 0.251 -0.112 0.050 -0.126 -0.040 -0.025 -0.089

Cashetr1 0.455 0.482 0.011 -0.087 -0.116 -0.205 0.109 -0.079 0.134 -0.069 0.040 -0.042 -0.005 -0.020 -0.038

Lretr 0.408 0.650 0.007 -0.098 -0.146 -0.201 0.102 -0.096 0.150 -0.073 0.024 -0.056 0.003 -0.035 -0.056

Dtax -0.132 -0.024 -0.044 -0.050 -0.025 -0.060 0.337 -0.076 0.019 -0.082 0.061 -0.095 0.010 -0.017 -0.021

FC_kz -0.105 -0.140 -0.153 -0.024 0.213 0.171 -0.275 0.192 -0.166 0.493 -0.043 0.110 -0.045 0.056 0.070

FC_ww -0.226 -0.221 -0.224 -0.007 0.161 0.403 -0.388 0.277 -0.538 0.222 -0.069 0.227 -0.112 0.140 0.170

FC_payout -0.279 -0.388 -0.355 -0.038 0.139 0.367 -0.316 0.233 -0.397 0.115 -0.005 0.215 -0.023 0.095 0.148

ROA 0.174 0.328 0.233 0.132 -0.139 -0.229 -0.400 -0.514 0.262 -0.496 0.034 -0.402 0.065 -0.076 -0.096

Sdroa -0.284 -0.320 -0.369 0.024 0.121 0.287 0.379 -0.139 -0.199 0.327 -0.009 0.254 -0.048 0.065 0.092

Size 0.092 0.154 0.123 0.038 0.009 -0.382 -0.327 0.311 -0.272 -0.187 0.111 -0.058 0.284 -0.308 -0.256

Lev -0.008 -0.093 -0.073 -0.031 0.380 -0.022 -0.004 -0.301 -0.134 0.187 -0.050 0.100 -0.053 0.036 0.021

ChGDWL 0.010 0.047 0.010 0.060 -0.021 -0.050 -0.015 0.144 -0.023 0.164 -0.009 0.013 0.032 -0.043 -0.044

Newinv -0.119 -0.023 -0.049 0.019 0.003 0.056 0.048 0.104 0.119 0.063 -0.168 0.050 0.044 0.023 0.098

Sforeignat -0.151 -0.036 -0.048 0.040 0.005 -0.091 -0.017 0.037 0.040 0.350 0.027 0.097 0.230 -0.587 -0.193

GHH 0.067 0.007 0.012 -0.043 0.019 0.095 0.057 0.004 0.028 -0.319 -0.096 -0.076 -0.092 -0.634 0.563

IHH -0.024 -0.029 -0.033 -0.033 0.042 0.119 0.087 0.031 0.095 -0.238 -0.149 -0.054 0.018 -0.237 0.646 Note: All variables are defined in Appendix A. All continuous variables are winsorized (reset) at the 1st and 99th. Spearman and

Pearson of our measures of financial constraints and tax avoidance correlation coefficients are estimated based on different firm-year

observations as different measures yield different samples. But the Spearman and Pearson of the control variables and tax avoidance

correlation coefficients are estimated based on 67,730 firm-year observations. Spearman correlations are in the left triangle while

Pearson correlations are in the right triangle. Coefficients shown in bold are significant at p<=0.05 (two-tailed test).

Table 3

Financial Constraints and Tax Avoidance

Panel A:FC_KZ

(1) (2) (3) (4)

VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t

FC_KZi,t -0.081*** -0.078*** -0.082*** -0.002

(-20.14) (-12.64) (-9.58) (-0.62)

ROAi,t 0.044*** 0.063*** 0.017*** 0.160***

(13.57) (11.48) (3.60) (21.91)

STDROAi,t -0.010*** -0.010** -0.025*** 0.029***

(-4.27) (-1.96) (-5.83) (6.59)

SIZEi,t 0.029*** 0.023*** 0.025*** -0.004***

(38.52) (22.85) (16.98) (-10.82)

LEVi,t 0.015*** 0.011*** 0.009* 0.029***

(6.53) (3.04) (1.89) (6.83)

GOODWILL i,t 0.167*** 0.251*** 0.089** 0.158***

(5.79) (5.61) (2.24) (7.35)

NEWINVEST i,t -0.098*** -0.046** -0.103*** 0.037**

(-8.12) (-2.35) (-4.56) (2.00)

FOREIGNASSET i,t -0.081*** -0.034*** -0.027* -0.006*

(-11.01) (-3.07) (-1.87) (-1.78)

GHH i,t 0.011 -0.007 -0.011 -0.002

(1.12) (-0.49) (-0.56) (-0.46)

IHH i,t -0.025*** -0.005 -0.015 -0.001

(-3.66) (-0.46) (-1.05) (-0.28)

Constant 0.116 -0.155*** 0.101** -0.054***

(1.58) (-2.71) (2.00) (-4.79)

Year Fixed Effects YES YES YES YES

Industry Fixed Effects YES YES YES YES

Observations 67,730 59,810 62,104 38,689

Adjusted R-squared 0.13 0.04 0.05 0.15

Panel B:FC_WW

(1) (2) (3) (4)


FC_WWi,t -0.060*** -0.027*** -0.053*** 0.027***

(-11.35) (-3.80) (-5.56) (8.13)

ROA i,t 0.043*** 0.071*** 0.012** 0.169***

(10.67) (9.86) (2.11) (21.13)

STDROA i,t -0.013*** -0.012** -0.029*** 0.029***

(-4.29) (-2.13) (-5.62) (5.65)

SIZE i,t 0.024*** 0.022*** 0.021*** -0.002***

(28.96) (18.06) (12.59) (-5.86)

LEV i,t -0.011*** -0.017*** -0.020*** 0.026***

(-4.31) (-4.29) (-3.77) (6.79)

GOODWILL i,t 0.170*** 0.257*** 0.092** 0.165***

(5.83) (5.69) (2.28) (7.62)

NEWINVEST i,t -0.090*** -0.040* -0.093*** 0.032*

(-6.72) (-1.83) (-3.80) (1.78)

FOREIGNASSET i,t -0.083*** -0.036*** -0.029** -0.005

(-11.20) (-3.19) (-2.01) (-1.61)

GHH i,t 0.007 -0.009 -0.015 -0.000

(0.73) (-0.60) (-0.74) (-0.11)

IHH i,t -0.024*** -0.007 -0.015 -0.002

(-3.62) (-0.68) (-1.11) (-0.74)

Constant 0.139* -0.035 0.029 -0.007

(1.95) (-0.64) (0.59) (-1.12)



Observations 66,712 59,338 61,518 38,208


Panel C:FC_Payout

(1) (2) (3) (4)


FC_Payouti,t -0.102*** -0.159*** -0.114*** 0.002

(-21.50) (-21.76) (-14.35) (1.11)

ROA i,t 0.028*** 0.040*** -0.005 0.154***

(6.22) (5.32) (-0.75) (15.63)

STDROA i,t -0.014*** -0.006 -0.028*** 0.031***

(-4.51) (-1.03) (-3.73) (4.78)

SIZE i,t 0.015*** 0.008*** 0.009*** -0.003***

(15.36) (5.35) (4.79) (-5.80)

LEV i,t -0.010*** -0.023*** -0.024*** 0.035***

(-3.09) (-4.04) (-3.79) (6.93)

GOODWILL i,t 0.166*** 0.243*** 0.080 0.153***

(4.12) (3.80) (1.50) (5.46)

NEWINVEST i,t -0.105*** -0.045* -0.114*** 0.029

(-6.97) (-1.68) (-3.90) (1.19)

FOREIGNASSET i,t -0.068*** -0.016 -0.009 -0.003

(-7.82) (-1.10) (-0.53) (-0.69)

GHH i,t 0.003 -0.023 -0.020 -0.002

(0.24) (-1.26) (-0.82) (-0.53)

IHH i,t -0.014* 0.004 -0.018 -0.001

(-1.82) (0.35) (-1.19) (-0.22)

Constant 0.268*** 0.367 0.258 -0.061***

(6.83) (0.27) (0.00) (-3.20)



Observations 37,937 34,466 35,422 22,240


Note: *, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels,

respectively. All variables are defined in Appendix A. All p-values are based on

two-tailed tests (in parentheses) and are calculated based on standard errors that are

clustered by firm (Petersen 2009).

Table 4

Tax Avoidance and Capital Investment

Panel A:FC_KZ

(1) (2) (3) (4)

GETR i,t-1 -0.030***

(-5.37)

CETR i,t-1 -0.014***

(-4.21)

LETR i,t-1 -0.026***

(-5.69)

DTAX i,t-1 -0.023

(-1.18)

Constant -0.020 0.023 -0.032 -0.063

(-0.49) (0.59) (-1.05) (-0.92)



Observations 10,008 7,800 8,437 5,327


Panel B:FC_WW

(1) (2) (3) (4)

GETR i,t-1 -0.002

(-0.35)

CETR i,t-1 0.002

(0.60)

LETR i,t-1 -0.008

(-1.52)

DTAX i,t-1 0.006

(0.41)

Constant -0.060*** 0.006 -0.104*** -0.019

(-5.05) (0.12) (-6.75) (-0.46)



Observations 10,287 7,044 8,037 5,380


Panel C:FC_Payout

(1) (2) (3) (4)

GETR i,t-1 -0.014***

(-4.69)

CETR i,t-1 -0.004**

(-2.07)

LETR i,t-1 -0.011***

(-3.92)

DTAX i,t-1 -0.021**

(-2.02)

Constant -0.070** -0.118*** -0.023 -0.044*

(-1.98) (-4.77) (-0.78) (-1.66)



Observations 25,945 19,156 20,722 13,441






Table 5

Financial Constraint and Tax Avoidance: Lead-Lag Test

Panel A:FC_KZ

(1) (2) (3) (4)


FC_KZi,t-1 -0.070*** -0.066*** -0.080*** 0.020***

(-15.69) (-10.21) (-9.14) (6.14)

ROA i,t 0.046*** 0.066*** 0.019*** 0.165***

(12.42) (10.15) (3.48) (20.81)

STDROA i,t -0.010*** -0.010* -0.026*** 0.031***

(-3.35) (-1.70) (-4.93) (6.31)

SIZE i,t 0.029*** 0.023*** 0.025*** -0.004***

(36.61) (21.14) (16.04) (-9.65)

LEV i,t 0.006** -0.001 0.002 0.024***

(2.47) (-0.16) (0.44) (5.63)

GOODWILL i,t 0.194*** 0.282*** 0.096** 0.157***

(6.41) (5.90) (2.29) (7.20)

NEWINVEST i,t -0.094*** -0.049** -0.101*** 0.036*

(-6.91) (-2.21) (-4.12) (1.82)

FOREIGNASSET i,t -0.081*** -0.033*** -0.026* -0.006

(-10.38) (-2.80) (-1.69) (-1.63)

GHH i,t 0.015 -0.007 -0.012 -0.003

(1.46) (-0.43) (-0.58) (-0.71)

IHH i,t -0.025*** -0.003 -0.010 -0.001

(-3.58) (-0.28) (-0.71) (-0.25)

Constant 0.117* 0.098** 0.052 0.014

(1.73) (1.98) (.) (1.09)



Observations 59,113 52,827 54,735 33,796


Panel B: FC_WW

(1) (2) (3) (4)


FC_WWi,t-1 -0.066*** -0.024*** -0.051*** 0.046***

(-11.34) (-3.17) (-5.25) (12.54)

ROA i,t 0.041*** 0.069*** 0.012* 0.174***

(9.72) (9.04) (1.95) (20.01)

STDROA i,t -0.014*** -0.016** -0.032*** 0.027***

(-3.81) (-2.18) (-5.00) (4.87)

SIZE i,t 0.024*** 0.022*** 0.022*** -0.001***

(27.92) (17.27) (12.28) (-2.89)

LEV i,t -0.013*** -0.022*** -0.022*** 0.027***

(-4.36) (-4.86) (-3.82) (6.33)

GOODWILL i,t 0.200*** 0.280*** 0.098** 0.156***

(6.52) (5.81) (2.32) (7.11)

NEWINVEST i,t -0.081*** -0.048** -0.094*** 0.021

(-5.55) (-2.03) (-3.55) (1.10)

FOREIGNASSET i,t -0.085*** -0.036*** -0.030* -0.003

(-10.79) (-2.99) (-1.91) (-1.00)

GHH i,t 0.011 -0.008 -0.016 -0.000

(1.06) (-0.54) (-0.72) (-0.08)

IHH i,t -0.024*** -0.004 -0.011 -0.002

(-3.45) (-0.41) (-0.77) (-0.77)

Constant 0.155** -0.011 0.322*** -0.082***

(2.28) (-0.17) (3.70) (-4.70)



Observations 58,347 52,470 54,299 33,432


Panel C:FC_Payout

(1) (2) (3) (4)


FC_Payouti,t-1 -0.105*** -0.111*** -0.124*** 0.012***

(-22.01) (-15.46) (-16.05) (5.53)

ROA i,t 0.042*** 0.062*** 0.006 0.168***

(8.70) (7.38) (0.77) (16.48)

STDROA i,t -0.003 0.005 -0.016*** 0.032***

(-1.09) (0.88) (-2.59) (4.93)

SIZE i,t 0.015*** 0.012*** 0.010*** -0.003***

(15.89) (8.03) (5.14) (-5.77)

LEV i,t -0.010*** -0.024*** -0.025*** 0.036***

(-3.24) (-4.10) (-3.76) (7.27)

GOODWILL i,t 0.189*** 0.275*** 0.128** 0.160***

(4.64) (4.27) (2.48) (6.09)

NEWINVEST i,t -0.071*** -0.058** -0.104*** 0.050**

(-4.41) (-1.99) (-3.27) (1.99)

FOREIGNASSET i,t -0.070*** -0.016 -0.003 -0.005

(-7.83) (-1.20) (-0.18) (-1.21)

GHH i,t 0.005 -0.008 -0.017 -0.003

(0.44) (-0.42) (-0.69) (-0.59)

IHH i,t -0.022*** -0.006 -0.016 -0.002

(-3.00) (-0.53) (-1.04) (-0.59)

Constant 0.097* -0.029 0.237 -0.054***

(1.80) (-0.39) (0.42) (-2.88)



Observations 37,948 34,460 35,448 22,257






Table 6

Financial Constraint and Tax Avoidance: Change Test

Panel A: ΔFC_KZ

(1) (2) (3) (4)

VARIABLES ΔGETR i,t ΔCETR i,t ΔLETR i,t ΔDTAX i,t

ΔFC_KZi,t-1 0.002 -0.011 -0.006** 0.040***

(0.30) (-0.80) (-2.05) (5.83)

ΔROA i,t 0.011 0.055*** 0.001 0.294***

(1.51) (4.12) (0.56) (14.62)

ΔSTDROA i,t 0.165*** 0.116** -0.027** 0.096**

(5.40) (2.48) (-2.08) (2.23)

ΔSIZE i,t 0.019*** 0.008 0.001 0.005

(4.91) (1.12) (0.81) (1.55)

ΔLEV i,t -0.019*** -0.036*** -0.008*** -0.003

(-3.12) (-2.69) (-2.96) (-0.20)

GOODWILL i,t 0.104* 0.127 0.049** 0.125***

(1.90) (1.29) (2.15) (4.34)

ΔNEWINVEST i,t -0.023 0.013 0.020* 0.046

(-0.79) (0.19) (1.80) (0.93)

ΔFOREIGNASSET i,t -0.078** -0.036 0.007 -0.007

(-2.55) (-0.68) (0.65) (-0.49)

ΔGHH i,t -0.047 -0.043 -0.013 -0.010

(-1.46) (-0.77) (-1.20) (-0.68)

ΔIHH i,t -0.030* -0.014 -0.004 -0.000

(-1.84) (-0.46) (-0.60) (-0.05)

Constant -0.005*** -0.006*** -0.004*** 0.001

(-4.61) (-3.13) (-4.91) (0.97)

Observations 49,553 44,267 46,019 26264


Panel B: ΔFC_WW

(1) (2) (3) (4)


ΔFC_WWi,t-1 -0.008 -0.020 -0.008** 0.042***

(-0.69) (-0.93) (-2.08) (4.73)

ΔROA i,t 0.012 0.058*** -0.001 0.326***

(1.47) (4.01) (-0.34) (17.30)

ΔSTDROA i,t 0.173*** 0.145*** -0.037*** 0.150***

(5.49) (2.97) (-2.73) (4.24)

ΔSIZE i,t 0.020*** 0.010 0.002 0.004

(5.13) (1.41) (1.33) (1.25)

ΔLEV i,t -0.013** -0.038*** -0.009*** 0.002

(-1.99) (-2.77) (-3.19) (0.11)

GOODWILL i,t 0.093* 0.091 0.041* 0.119***

(1.69) (0.93) (1.85) (4.29)

ΔNEWINVEST i,t -0.020 0.025 0.021* 0.051

(-0.63) (0.34) (1.80) (1.07)

ΔFOREIGNASSET i,t -0.083*** -0.046 0.002 -0.012

(-2.76) (-0.90) (0.18) (-0.82)

ΔGHH i,t -0.056* -0.049 -0.017 -0.011

(-1.80) (-0.90) (-1.61) (-0.72)

ΔIHH i,t -0.026 -0.017 -0.005 -0.007

(-1.64) (-0.57) (-0.83) (-0.95)

Constant -0.006*** -0.005*** -0.004*** 0.001

(-5.72) (-3.08) (-5.37) (1.30)

Observations 49,970 44,893 46,608 26,479


Panel B: ΔFC_Payout

(1) (2) (3) (4)


ΔFC_Payouti,t-1 -0.015** 0.008 -0.012*** 0.007***

(-2.21) (0.47) (-3.46) (3.56)

ΔROA i,t 0.044*** 0.045*** 0.003 0.206***

(9.08) (4.30) (1.59) (13.01)

ΔSTDROA i,t 0.021 -0.033 -0.024*** 0.065**

(1.14) (-0.89) (-2.90) (2.44)

ΔSIZE i,t 0.021*** 0.003 0.001 -0.001

(7.06) (0.44) (0.79) (-0.41)

ΔLEV i,t -0.016*** -0.014 -0.002 -0.024*

(-3.14) (-1.19) (-0.87) (-1.83)

GOODWILL i,t 0.128** 0.022 0.054** 0.159***

(2.00) (0.18) (2.00) (4.02)

ΔNEWINVEST i,t -0.007 0.049 0.012 0.006

(-0.27) (0.70) (1.15) (0.20)

ΔFOREIGNASSET i,t -0.057** -0.107* -0.011 -0.017

(-2.19) (-1.95) (-1.02) (-1.38)

ΔGHH i,t -0.033 -0.055 -0.020* -0.017

(-1.16) (-0.94) (-1.76) (-1.41)

ΔIHH i,t -0.005 0.021 -0.001 -0.001

(-0.34) (0.62) (-0.22) (-0.19)

Constant -0.004*** -0.009*** -0.004*** 0.001

(-4.45) (-3.42) (-4.68) (1.19)

Observations 35,057 33,701 34,393 19,436






financial constraint and tax aggressiveness · firms always have incentives to invest in tax...

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