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Financial Constraint and Tax Aggressiveness
Chen Chen
The University of Auckland
Shufang Lai
The Chinese University of Hong Kong
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)
VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 66,712 59,338 61,518 38,208
Adjusted R-squared 0.13 0.04 0.05 0.14
Panel C:FC_Payout
(1) (2) (3) (4)
VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 37,937 34,466 35,422 22,240
Adjusted R-squared 0.14 0.06 0.07 0.13
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 10,008 7,800 8,437 5,327
Adjusted R-squared 0.11 0.10 0.10 0.09
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 10,287 7,044 8,037 5,380
Adjusted R-squared 0.07 0.06 0.05 0.06
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 25,945 19,156 20,722 13,441
Adjusted R-squared 0.13 0.12 0.12 0.13
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 5
Financial Constraint and Tax Avoidance: Lead-Lag Test
Panel A:FC_KZ
(1) (2) (3) (4)
VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 59,113 52,827 54,735 33,796
Adjusted R-squared 0.13 0.04 0.05 0.16
Panel B: FC_WW
(1) (2) (3) (4)
VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 58,347 52,470 54,299 33,432
Adjusted R-squared 0.13 0.04 0.05 0.15
Panel C:FC_Payout
(1) (2) (3) (4)
VARIABLES GETR i,t CETR i,t LETR i,t DTAX i,t
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)
Year Fixed Effects YES YES YES YES
Industry Fixed Effects YES YES YES YES
Observations 37,948 34,460 35,448 22,257
Adjusted R-squared 0.14 0.05 0.07 0.15
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 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
Adjusted R-squared 0.00 0.00 0.00 0.17
Panel B: ΔFC_WW
(1) (2) (3) (4)
VARIABLES ΔGETR i,t ΔCETR i,t ΔLETR i,t ΔDTAX i,t
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
Adjusted R-squared 0.00 0.00 0.00 0.19
Panel B: ΔFC_Payout
(1) (2) (3) (4)
VARIABLES ΔGETR i,t ΔCETR i,t ΔLETR i,t ΔDTAX i,t
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
Adjusted R-squared 0.00 0.00 0.00 0.12
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).