Download Investment Opportunities, Free-Cash-Flow, and the Market Values of

Investment Opportunities, Free-Cash-Flow, and the
Market Values of Foreign and Domestic Cash Holdings
Richard T. Thakor∗
First Draft: April 2008
This Draft: September 2013
Abstract
It has been suggested that firms with foreign operations stockpile large amounts of cash,
primarily in their foreign subsidiaries, because bringing the cash home involves paying
a repatriation tax on foreign income. This implies that the stock market should value
foreign-held cash less than domestically-held cash. But this effect may be moderated
by the impact of investment opportunities abroad that would provide an outlet for the
foreign cash and affect how the market values it. This paper empirically examines the
difference between the market values of on-balance-sheet cash held domestically and
that held abroad by US firms, and the impact of the interaction of the repatriation tax
and investment opportunities on this difference. The results show that shareholders
assign a higher value to cash held abroad than to cash held domestically, and that
the marginal value of foreign-held cash is substantially higher than that of domestic
cash for US firms with better foreign investment opportunities. This suggests that the
effect of the differential investment opportunities for foreign and domestic cash swamps
the repatriation-tax disadvantage of foreign cash. As further evidence, this paper also
examines the effect of the exogenous shock provided by the tax repatriation holiday
in 2004, and finds that firms with better investment opportunities abroad experienced
lower abnormal returns following the passage of that legislation.
JEL Classification Numbers: G31, G32, H26
Keywords: Cash holdings, Value of cash, Foreign cash, Repatriation tax, Investment opportunities, Free-cash-flow
∗
MIT Sloan School of Management. E-mail: [email protected]. I would like to thank Bruce Petersen,
Mike Faulkender, Sudipto Bhattacharya, Antoinette Schoar, Stew Myers, Becky Lester, and seminar participants at MIT for all of their helpful comments. I would also like to thank Rong Wang and John Graham for
their help in providing data. I am grateful to the London School of Economics and Creditflux for financial
support from the Creditflux-Cairn Capital Prize, which was awarded to a previous version of this paper. I
alone am responsible for all errors.
1
Introduction
Firms in the US hold large amounts of cash on their balance sheets and these amounts
have been steadily increasing in recent years. For example, Bates, Kahle, and Stulz (2007)
document that the average cash-to-assets ratio for corporations has more than doubled from
10.5% in 1980 to 24% in 2004. And the average firm included in COMPUSTAT held nearly
$285 million in cash on its balance sheet in 2005, increasing to more than $1 billion by the
end of 2009.1 For many firms, a substantial portfion of the cash is held in foreign subsidiaries
(e.g. Drucker (2011)). Why firms hold so much cash and how the market values this cash
are interesting questions both theoretically and empirically. In particular, cross-sectional
differences in the motivations for holding cash, the locations of the cash holdings, and the
market valuation of this cash can be illuminating. This paper focuses on one aspect of
these differences by empirically examining how the market valuation of cash held in foreign
subsidiaries relative to that held domestically varies in the cross-section of firms and what
might account for this variation.
In Modigliani and Miller’s (1958) perfect (and frictionless) capital market with no taxes,
firms are indifferent between holding a dollar of cash on the balance sheet and paying it
out, since the value implications of both choices are identical. However, the introduction
of frictions of various sorts, including taxes, can open up a wedge between the net benefit
of keeping cash on the balance sheet and the net benefit of distributing it to shareholders.
Various papers have focused on these frictions to both explain why firms sometimes hold
large amounts of cash on their balance sheets and the market’s valuation of on-balance sheet
cash, with the valuation being related to the reason for holding the cash. The justifications
offered for holding cash fall into two groups: (i) self-serving behavior by managers that
leads to shareholder value destruction (inefficient cash hoarding), and (ii) shareholder-value
maximizing behavior that optimally trades off the costs and benefits of holding cash (efficient
internal capital markets).
1
Defined in COMPUSTAT as cash and short-term investments.
1
In the first group is Jensen’s (1986) free-cash-flow hypothesis that hoarding cash simply provides managers discretionary resources that can be inefficiently deployed for private
benefits. Clearly, these problems will be less severe when corporate governance is better,
so the market will value on-balance-sheet cash higher when governance is better, which in
turns creates a link between corporate governance and cash holdings. Consistent with this,
Dittmar, Mahrt-Smith, and Servaes (2003) and Harford, Mansi, and Maxwell (2008) find
that firms with weaker corporate governance have smaller cash reserves.
In the second group are papers that have focused on precautionary motives for holding
cash (e.g. McLean (2011), Miller and Orr (1966), and Mulligan (1997)) that can be theoretically justified on the basis of the benefits of building internal capital markets (e.g. Stein
(1997), and Thakor (1990)) to exploit future real options to invest, in the face of adverse selection (Myers and Majluf (1984)) and other frictions. These benefits are likely to be greater
for firms that have greater growth opportunities and riskier cash flows. Evidence consistent
with this appears in Opler, Pinkowitz, Stulz, and Williamson (1999).2
Given these two types of justifications for holding cash, the market value of $1 of cash
on the balance sheet can be more or less than $1, depending on the types of firms in the
sample. With inefficient cash hoarding, we would expect that the market value of $1 of onbalance-sheet cash is less than $1. With efficient capital markets, if investments are infinitely
divisible, we would expect the market value of $1 of on-balance-sheet cash to be driven to
$1. But if the firm has real options on lumpy investments, we would expect the market
value of on-balance-sheet cash to exceed $1. Empirical estimates of the market value of
on-balance-sheet cash suggest that both forces may be at work. Pinkowitz and Williamson
(2007) estimate that a marginal dollar of cash for the average firm is valued at about $1.20,
whereas Faulkender and Wang (2006) use a different methodology and find that the marginal
2
Along these lines, Acharya, Almeida and Campello (2007) develop a theoretical model in which cash is
not merely “negative debt”. They show that while cash allows financially-constrained firms to hedge future
investment against negative shocks to income, reducing current debt is more effective in increasing income
in future high-cash-flow states. Thus, financially-constrained firms prefer higher cash to lower debt if their
hedging needs are high, but lower debt to higher cash if their hedging needs are low.
2
value of cash for the average firm is $0.94; the value is less than $1 as a result of taxes (such as
dividend taxes) and agency costs. Dittmar and Mahrt-Smith (2007) extend the methodology
of Faulkender and Wang (2006) to look at the effects of corporate governance on the value
of cash holdings. They find that poorly-governed firms have a lower marginal value of cash:
$1.00 of cash in a poorly-governed firm is worth between $0.42 and $0.88, whereas it is twice
that value in a well-governed firm. And Liu and Mauer (2011) show that an increase in CEO
risk-taking reduces the value of cash to shareholders.
While most of this literature has focused on the role of agency problems and informational
frictions in explaining cash holdings, Foley, Hartzell, Titman, and Twite (2007) provide an
explanation based on taxes. They argue that partly as a result of the tax penalty for
repatriating earnings back to the U.S., there is a tax-based incentive for companies to hold
cash abroad since doing so reduces/delays the payment of the repatriation tax on foreign
income.3 Therefore, the repatriation tax argument implies that a dollar of foreign-held cash
should be worth less than a dollar of domestically-held cash, since the foreign-held cash is
essentially sitting inefficiently “trapped” abroad in firms that want to avoid the repatriation
tax.
Other more recent paper provide additional evidence consistent with this argument.
Blouin, Krull, and Robinson (2012) find that a higher proportion of permanently reinvested
earnings (PRE) is held in the form of cash in low tax jurisdictions relative to high tax jurisdictions. Graham, Hanlon, and Shevlin (2011) provide survey evidence indicating that
executives often consider avoiding taxes an important determinant of whether to repatriate
foreign earnings. And a working paper by Edwards, Kravet, and Wilson (2012) argues that
3
This tax is equal to the difference between the foreign taxes already paid on earnings and the taxes
that would be due if the earnings were taxed at the U.S. rate. As an example, consider $100 earned by
a US multinational company in a foreign country with a tax-rate of 10%. If the US tax-rate is 35%, then
repatriating the earnings leaves the company with $65 to use, whereas keeping it abroad leaves the company
with $90 to use. Thus, the company may have an incentive to keep the cash abroad in order to avoid/delay
the additional $25 loss associated with the repatriation tax. A recent Bloomberg article suggests that U.S.
firms may have more than $1 trillion of cash sitting offshore (Drucker (2011)). There are various strategies
to avoid paying the repatriation tax, such as legal loopholes and transfer pricing, although Foley et al (2007)
suggest that these strategies may be cumbersome and costly for firms to use.
3
because cash is trapped abroad due to the repatriation tax, firms are more likely to use the
cash to undertake value-destroying acquisitions.
However, as explained above, there are other mediating variables as well that affect the
value of foreign cash – free-cash-flow problems and growth opportunities. Foreign cash may
be worth more than domestic cash if growth opportunities abroad are better than at home, so
there is a greater need for internal capital markets in foreign operations. These higher growth
opportunities abroad may also mean that free-cash-flow problems (e.g. Jensen (1986)) are
less severe in foreign subsidiaries than domestically.4 For example, Doukas (1995) shows that
U.S. firms that bid for foreign firms experience higher abnormal returns when they have a
higher q, and that the returns are inversely related to free cash flow for low-q bidders but not
for high-q bidders. And while Blouin et. al. (2012) argue that firms hold a higher proportion
of PRE in cash due to tax incentives, they also recognize that a number of firms appear to
hold high levels of cash in low tax jurisdictions, suggesting that growth plays a larger role
than taxes for such firms.
These considerations lead to the following questions that I empirically address in this
paper: First, what is the value of foreign cash relative to domestic cash? Second, what
is the impact of investment opportunities on this relative valuation? The methodology of
Faulkender and Wang (2006) is extended in order to address the first question and estimate
the value of a dollar of foreign cash and a dollar of domestic cash for firms. The results
indicate that, for the average firm, $1.00 of cash held abroad is valued at $1.06, while $1.00
of cash held domestically is valued (statistically) significantly lower at $0.93. The higher value
of foreign cash relative to domestic cash suggests that factors other than the repatriation
tax are impacting the valuation of foreign cash.
The analysis then proceeds to address the second question and examine whether growth
opportunities abroad explain why foreign cash is valued higher than domestic cash. An
4
The idea is that managers will view the cost of engaging in wasteful activities as being higher when such
activities use up cash that has a greater demand for financing investments and growth. Thus, they would
be less likely to engage in such waste.
4
extension of the methodology of Dittmar and Mahrt-Smith (2007) is utilized for this purpose.
The results indicate that, on average, among the set of firms with high levels of foreign
operations, those with better investment opportunities have higher marginal values of cash:
$1.42 and $1.30 respectively for $1 of foreign and domestic cash. By contrast, for firms with
poor investment opportunities, the marginal values of foreign and domestic cash are $0.92
and $0.80, respectively. This suggests that the effect of foreign investment opportunities
dominates that of the repatriation tax in contributing to the higher valuation of foreign cash
relative to domestic cash. These findings are consistent with the notion that free-cash-flow
problems are worse in domestic operations than in foreign operations of profitable investment
opportunities at home.
As additional evidence, I examine the effect of an exogenous shock following the enactment of the American Jobs Creation Act of 2004, which provided a one-time tax holiday for
firms repatriating cash. The results indicate that firms with poor investment opportunities
abroad experienced significant positive abnormal returns following the enactment of the bill,
while firms with good investment opportunities abroad did not experience any significant
abnormal returns. This is consistent with the hypothesis that firms with good overseas
investments are holding cash primarily to fund those investments, so the repatriation tax
holiday was of little consequence for them.
The rest of the paper is organized as follows. Section 2 explains the baseline empirical
methodology used in the paper. Section 3 describes the data set and summary statistics.
Section 4 includes the main empirical results and discusses their implication. Section 5
examines additional evidence using an event study methodology to examine the effect of the
American Jobs Creation Act of 2004. Section 6 concludes.
5
2
Empirical Methodology
This section describes the empirical methodology used in the analysis. The empirical methodology consists of three main steps. First, the split between foreign-held and domestically-held
cash is determined for each firm in the sample. Second, the market values of foreign and domestic cash are estimated. Third, there is an empirical examination of whether the difference
in the market values of foreign and domestic cash can be explained by growth opportunity
differences.
2.1
Estimating the Amounts of Foreign and Domestic Cash
In order to estimate the marginal values of foreign-held and domestically-held cash, levels
of both variables at the firm level must be obtained. However, while a measure of the level
of overall cash for firms is available in COMPUSTAT, separate measures of the cash held
abroad and cash held domestically are not readily available and therefore must be estimated.5
While one would ideally like to have access to the split between domestic and foreign cash,
there is reason to believe that estimating this split is the appropriate way to proceed. This is
because the question of interest is how the market values domestic versus foreign cash, and
since investors themselves also lack access to the actual split of total cash between foreign
and domestic cash, they need to rely on estimates as well, just as is done in this paper.6
The methodology of Foley et al. (2007) is employed in order to obtain estimates of the
5
Foley et al. (2007) obtain a direct measurement of foreign cash using data from the Bureau of Economic
Analysis (BEA) for alternate specifications of their model. However, these are proprietary data which are not
readily available, either to investors or to me. In addition, yearly data are needed for the following analysis,
and the BEA data are only available at five-year intervals. It appears, therefore, that for the purposes of
this paper, it may not be appropriate to use the BEA data.
6
As a test of robustness, results are presented subsequently in the paper using two alternate proxies of
the level of cash in firms.
6
amounts of domestic and foreign cash held by firms. The following OLS specification is run:7
Cash
NA
= β0 + β1
i,t
R&D
TA
+ β2
CAP EX
TA
+ β3 (Lev)i,t + β4 (CF Std Dev)i,t
BV E
+β5 (Div P mt)i,t + β6 (ln (T A))i,t + β7
M V E i,t
F oreign Income
Domestic Income
+ β9
+β8
TA
TA
i,t
i,t
i,t
i,t
+β10 (T ax Burden)i,t + εi,t .
(1)
All observations consist of firm-years: the value of the variable for firm i at year t.
Several independent variables are scaled by total assets, TA, in order to normalize for firm
size. Cash is defined as cash and short-term investments. NA represents net assets, and
is defined as Total Assets less cash.8 CAPEX is capital expenditures. R&D represents
research and development expenditures, and is set to zero if R&D expenditures is missing
in COMPUSTAT. Lev is market leverage, the ratio of total debt to the sum of total debt
and the market value of equity, which is defined as the firm’s year-end closing stock price
multiplied by the number of shares outstanding. CF Std Dev represents cash flow standard
deviation, defined as the standard deviation of the firm’s earnings before interest, taxes, and
depreciation as a percentage of total assets.9 Div Pmt is a dummy variable that takes a value
of 1 if the firm paid a cash dividend in the year, and a value of 0 otherwise. ln(T A) is the
natural log of the firm’s total assets, in millions of dollars.
BV E
MV E
is the ratio of the reported
book value of the firm’s equity divided by the market value of the firm’s equity. Domestic
Income and Foreign Income represent domestic income and foreign income, respectively, and
7
Qualitatively similar results are obtained by running a lagged rather than a contemporaneous specification.
8
This follows Opler et al. (1999) and Foley et al. (2007). Using Net Assets in the denominator of the
dependent variable facilitates interpretation of the results, because a change in cash would only affect the
numerator. While Opler et al. (1999) and Foley et al. (2007) use the natural logarithm of cash to net
assets as the dependent variable, this paper uses a winsorized ratio of cash to net assets in order to feed
into the second stage regression. However, according to Foley et al. (2007), their results are robust to this
specification.
9
Foley et al (2007) use operating income standard deviation instead of cash flow standard deviation
because of limitations on the duration of their sample. Using cash flow standard deviation follows Opler et
al (1999).
7
are both pre-tax earnings. Foreign Income is included as a proxy to measure the magnitude
of the repatriation tax effect, as firms that have more foreign income tend to hold more cash
in order to avoid the repatriation tax, all else being equal. The inclusion of Domestic Income
controls for the possibility that a delay between when earnings are received and used will
create a positive relationship between cash holdings and income. Tax Burden is a measure
of the taxes a firm would face if it chose to repatriate all of its foreign income. It is defined
as:
T ax Burden =
max(U ST axRate × F oreignP reT axEarnings − F oreignT axesP aid, 0)
.
TA
(2)
USTaxRate is the marginal tax rate after interest expense, following Graham (1996).10 The
overall Tax Burden variable reflects the repatriation tax faced by firms to repatriate their
foreign earnings back to the US. It is equal to the difference between the foreign taxes already
paid on earnings and the taxes that would be due if the foreign earnings were taxed at the
US tax rate.
While all the variables in equation (1) affect the general level of cash for each firm-year,
Foreign Income and Tax Burden are the only two variables which specifically affect foreign
cash. Since the dependent variable in the regression measures the overall level of cash for
the firm, the coefficient for
F oreign Income
TA
in the regression can be interpreted as the estimated
incremental increase in foreign cash associated with an increase in foreign income, all other
things equal. However, the foreign income variable only accounts for the contribution to
foreign cash from foreign earnings. It does not account for the foreign cash that is being
held in various locations; the amount of cash held may be affected by the location because
tax rates are location-specific. In order to defer paying the costs of repatriation, firms have
an incentive to store cash away in low-tax jurisdictions, and the specific tax rates of these
10
The tax rate data were generously provided by John Graham. From Graham (1996), the marginal tax
rate is calculated as the present value of current and expected future taxes paid on an additional dollar of
income earned today.
8
jurisdictions affect the amount of cash held (since the repatriation tax is based on the amount
of foreign taxes paid). Therefore, the Tax Burden variable is included to account for these
issues, as an estimate of the amount of cash that has not been brought back to the US. The
use of these two variables to determine foreign cash is also supported by Foley et al (2007),
who run a regression explaining foreign cash levels and show that Foreign Income and Tax
Burden are the two most important variables in terms of magnitude and significance.
By combining these two variables from equation (1), an estimate of the amount foreign
cash is obtained:11
"
Estimate (F oreign Cash)i,t = T Ai,t β9
F oreign Income
TA
#
+ β10 (T ax Burden)i,t . (3)
i,t
Since a firm’s total cash is made up of foreign and domestic cash, the estimate of the amount
domestic cash is total cash minus the estimate of foreign cash from (3) above:
"
Estimate (Domestic Cash)i,t = T Ai,t
Cash
TA
− Estimate
i,t
F oreign Cash
TA
#
.
(4)
i,t
Negative estimates of Foreign Cash and Domestic Cash are truncated at zero.
2.2
Estimating the Market Values of Foreign and Domestic Cash
After obtaining these estimates of domestic and foreign cash, the methodology used by
Faulkender and Wang (2006) is used in order to estimate the marginal values to shareholders
of cash held domestically and cash held abroad. The following OLS regression is run, in which
11
In (3), Tax Burden reflects the tax burden measurement scaled by total assets, which follows from (2)
9
stock returns over the fiscal year are regressed on changes in various firm characteristics:
(4Cash)i,t
(4Earnings)i,t
(4N A)i,t
(Ri,t − Rbi,t ) = λ0 + λ1
+ λ2
+ λ3
M V Ei,t−1
M V Ei,t−1
M V Ei,t−1
(4Int)i,t
(4Div)i,t
(4R&D)i,t
+ λ5
+ λ6
+λ4
M V Ei,t−1
M V Ei,t−1
M V Ei,t−1
(Cash)i,t−1
(N F )i,t
+ λ8 (Lev)i,t + λ9
+λ7
M V Ei,t−1
M V Ei,t−1
(4Cash)i,t
+λ10 (Lev)i,t ×
M V Ei,t−1
(4Cash)i,t
(F oreign Cash)i,t−1
×
+λ11
M V Ei,t−1
M V Ei,t−1
(Domestic Cash)i,t−1
(4Cash)i,t
+λ12
×
+ εi,t .
(5)
M V Ei,t−1
M V Ei,t−1
The overall intuition of (5) is to capture the impact of a marginal dollar of cash on the
unexpected stock return of the firm, holding constant all firm characteristics that may be
correlated with cash. All observations are firm-years, and represent the observation of the
variable at firm i in year t. 4X represents the 1-year change: Xt − Xt−1 . The dependent
variable is Excess Stock Return, and is defined as the stock’s return over a given year (Ri,t )
minus the stock’s benchmark portfolio return (Rbi,t ) over the same year. Benchmark portfolio
returns are calculated for 25 Fama and French (1998) portfolios, constructed based on size
and book-to-market value. Returns are calculated by compounding monthly returns over
each year. Each stock is then matched at the beginning of each year to one of the portfolios
based on its size and book-to-market. With the exception of leverage, all independent
variables are standardized for size by the lagged market value of equity. The independent
variables include the change and level of cash holdings for the firm, and other controls
that may be correlated with changes in cash and firm value, including changes in a firm’s
financing policy, profitability, and investment policy. The financing variables include Cash,
Int (which represents interest expense), Div (which represents cash dividends), Lev (market
leverage), and NF (net financing, defined as total equity issuance minus repurchases plus
debt issuance minus debt redemption). The profitability variables include Earnings (defined
10
as earnings before interest and extraordinary items). And the investment variables include
R&D (research and development expenditures) and NA (net assets).
In addition to these variables, there are three variables with interaction terms. The first
i
h
, is the interaction of market leverage with the change in cash
variable, (Lev) × (4Cash)
MV E
for the firm; this is included in order to estimate the effect of leverage on the marginal value of
h
i
Cash)
(4Cash)
cash for different levels of cash holdings. The second variable, (F oreign
×
,
MV E
MV E
is the interaction of the previous period’s level of foreign-held cash with the change in cash
for the firm; this is included in order to estimate the effect that different levels of foreign cash
h
i
Cash)
(4Cash)
holdings have on the value of cash. Similarly, the third variable, (Domestic
×
,
MV E
MV E
is the interaction of the previous period’s level of domestically-held cash with the change
in cash for the firm. These two terms replace the single aggregated cash variable used by
Faulkender and Wang (2006). They note that since independent variables are scaled by
the lagged market value of equity, and since the stock return in the dependent variable is
the spread (M V Et − M V Et−1 ) divided by M V Et−1 , the estimated coefficients can be interpreted as the marginal change associated with a change in the corresponding independent
variable. Therefore, the coefficient on the change in cash measures the dollar change in
shareholder value stemming from a one-dollar change in the amount of cash held by the
firm.12 The overall methodology is similar to a long-term event study, examining abnormal
(unexpected) returns within an event window defined to be the fiscal year.
The coefficients of the foreign and domestic cash interaction variables (λ11 and λ12 above),
in addition to the coefficients of the other variables interacted with the change in cash,
provide estimates of the marginal value to shareholders of foreign-held and domesticallyheld cash, respectively, for different levels of cash holdings. Therefore, following (5) above:
Value(F oreign Cash) = γ1 + γ10 Mean(Lev) + γ11 Mean
12
F oreign Cash
MV E
,
(6)
According to Faulkender and Wang (2006), the marginal value of cash to shareholders is a function of the
estimated coefficient on the change in cash, and the interactions with the level of cash holdings and leverage.
Therefore, following from (5), the marginal value of cash is: Value(Cash) = γ1 + γ9 Mean(Leverage) +
γ10 Mean(Cash) where the above represent the average values of the variables for the sample.
11
and
Value(Domestic Cash) = γ1 + γ10 Mean(Lev) + γ12 Mean
Domestic Cash
MV E
.
(7)
Thus, the value of either foreign-held or domestically-held cash for the average firm is
given by the marginal value of $1 of cash not accounting for leverage effects or levels of cash
holdings (λ1 above) added to the marginal value of $1 cash accounting for levels of leverage
and cash holdings (which, for the average firm, is calculated using the mean values of Lev,
Foreign Cash scaled by the market value of equity, and Domestic Cash scaled by the market
value of equity for the sample).
As discussed earlier, if firms are stockpiling cash abroad in order to avoid or delay payment
of the repatriation tax (and the cash is thus inefficiently “trapped” abroad), then we would
expect foreign cash to be worth less than domestic cash. However, if factors other than taxes,
such as valuable growth opportunities abroad, are the main driver behind firms’ decision to
hold cash abroad and these result in smaller free-cash-flow problems abroad, then we would
expect foreign cash to be worth more than domestic cash. These two effects pull against
each other and lead to the following Hypothesis:
Hypothesis 1: The values of foreign and domestic cash may be significantly different from one another. If the repatriation-tax effect is dominant, the value
of foreign cash will be lower than the value of domestic cash. If the foreigninvestment-opportunities effect is dominant, the value of foreign cash will be
higher than the value of domestic cash.
2.3
Calculating the Effect of Investment Opportunities on the
Value of Cash
In order to further investigate the foreign-growth-opportunities argument implicitly in Hypothesis 1, the final step is to examine whether the difference between the shareholder
12
valuation of cash held abroad and cash held domestically is indeed driven by the difference
between foreign and domestic reinvestment opportunities.
To see how relative reinvestment/growth opportunities may affect the valuation difference
between foreign and domestic cash, note that most U.S. companies pursue foreign markets
because these markets offer higher growth opportunities than domestic operations in the
U.S.13 Examples are numerous. The emerging markets in Brazil, Russia, India, and China
are all attractive foreign destinations for U.S. companies, and all these are high-growth
markets. Moreover, even in developed countries like those in Europe, growth potential may
be higher as the market for the firm’s products may be less saturated than in the U.S. Higher
growth opportunities also mean that the demand for investment is higher, which further
implies that internally-generated cash in foreign operations will have a higher shadow price
for being used for purposes other than reinvestment compared to internally-generated cash in
domestic operations.14 This higher shadow price will tend to lead to a smaller free-cash-flow
problem (e.g. Jensen (1986)) in foreign subsidiaries than in domestic operations as managers
in foreign subsidiaries will perceive wasteful expenditures on things such as perquisites (e.g.
Jensen and Meckling (1976)) and pet projects to be relatively more costly. Conditional on
growth opportunities abroad being sufficiently high, U.S. corporations would be able to use
up all of their foreign cash in investments outside the U.S. and there would be little that is
repatriated to the U.S. If this is true, the repatriation tax would not be significant and its
impact on the valuation of foreign cash would be relatively small.
Moreover, foreign cash in firms with good growth opportunities should be valued significantly higher than foreign cash in firms with poor growth opportunities, since the latter
are more likely to be adversely affected by the repatriation tax burden and free-cash-flow
problems stemming from a relatively low shadow price of cash. This further implies that
13
Note that the sample in this paper consists only of firms that are incorporated in the U.S.
In part due to the repatriation tax, many foreign companies keep foreign and domestic cash in separate
buckets when it comes to allocating it for investments. For example, Emerson Electric, a multinational firm
with $21 billion in sales, repatriates very little of its foreign cash back to the US. It consolidates this cash
in special subsidiaries in Switzerland and Ireland and uses it to finance various investments abroad. Its
investments in the U.S. are financed mainly with cash generated domestically.
14
13
foreign cash for firms with poor investment opportunities should be valued at a discount to
$1, while foreign cash for firms with good investment opportunities should be valued at a
premium to $1, particularly since real options to invest in the future are likely to involve
lumpy investments, so the firm cannot invest all the way up to the point at which the NPV
of the investment is zero, i.e. these will be options to invest in positive NPV projects. To
summarize, this discussion leads to the following hypothesis:
Hypothesis 2: Firms with good investment opportunities abroad should have
substantially higher values of foreign cash than firms with poor investment opportunities abroad. In addition, foreign cash for firms with good investment
opportunities abroad should be worth more than $1, while foreign cash for firms
with poor investment opportunities abroad should be worth less than $1.
The tests will focus on firms that derive a relatively high fraction of their income from abroad
because the hypothesized effects on the value of cash are expected to be more pronounced
for these firms.15 To test these predictions, this paper uses an extension of the methodology
used by Dittmar and Mahrt-Smith (2007).16 Thus, the main regression specification below
15
As explained below, extra terms are added in the regression to examine whether good or poor investment
opportunities abroad are driving the differential value of cash. So while all firms, including those with a low
fraction of foreign income, are included in the regression, the tests focus on whether it is the firms with high
levels of foreign operations that are experiencing the largest differences between the values of foreign and
domestic cash. That is, the dummy variable that I focus on in my tests is the one associated with the firms
that have high foreign operations.
16
This methodology builds on the regression specification of Faulkender and Wang (2006) and examines
the effect of differences in corporate governance on the value of cash by including an additional regression
term which interacts corporate governance with the change in cash for each firm-year.
14
is run:
(4Cash)i,t
(4Earnings)i,t
(4N A)i,t
(Ri,t − Rbi,t ) = τ0 + τ1
+ τ2
+ τ3
M V Ei,t−1
M V Ei,t−1
M V Ei,t−1
(4Int)
(4Div)i,t
(4R&D)i,t
+ τ5
+ τ6
+τ4
M V Ei,t−1
M V Ei,t−1
M V Ei,t−1
(Cash)i,t−1
(N F )i,t
+ τ8 (Lev)i,t + τ9
+τ7
M V Ei,t−1
M V Ei,t−1
(4Cash)i,t
+τ10 (Lev)i,t ×
M V Ei,t−1
(4Cash)i,t
(F oreign Cash)i,t−1
×
+τ11
M V Ei,t−1
M V Ei,t−1
(Domestic Cash)i,t−1
(4Cash)i,t
+τ12
×
M V Ei,t−1
M V Ei,t−1
(4Cash)i,t
+τ13 (High F or Ops)i,t × (Good Inv Opp)i,t ×
M V Ei,t−1
(4Cash)i,t
+ εi,t .
+τ14 (High F or Ops)i,t × (P oor Inv Opp)i,t ×
M V Ei,t−1
(8)
The variables are defined in the same way as in regression (5), except for the last two
terms. High For Ops is a dummy variable that indicates whether a firm has a high level
of foreign operations. It takes a value of 1 if a firm-year is in the top half (of all the firms
reporting foreign income) in terms of foreign operations as a percentage of total operations
(defined as
F oreign Income
),
T otal Income
and a value of 0 otherwise. Using top and bottom terciles or
quartiles leads to similar results. Good Inv Opp is a dummy variable that indicates whether
a firm has good investment opportunities. It takes a value of 1 if a firm-year is in the top
half of the sample in terms of Tobin’s q (q-ratio). Poor Inv Opp is defined in a similar way
– it indicates whether a firm has poor investment opportunities, and takes a value of 1 if
a firm-year is in the bottom half of the sample in terms of q-ratio, and 0 otherwise. The
use of the q-ratio as an indicator of investment opportunities for firms follows from Tobin
and Brainard (1968, 1977), and various other papers (e.g. Lang and Litzenberger (1988))
that relate Tobin’s q to overinvestment. The calculation of the q-ratio in this paper follows
15
the procedure of Lindenberg and Ross (1981) and the approximation by Chung and Pruitt
(1994). It is defined as:
q ratio =
M V E + P ref erred Stock + Debt
.
TA
(9)
MVE, Debt, and TA are defined above. Preferred Stock is the liquidating value of the firm’s
outstanding preferred stock.
By defining the dummy variables this way and interacting them, the impact of foreign
investment opportunities on the value of cash can be determined. For example, for firms with
good investment opportunities and high levels of foreign operations, τ13 will represent the
contribution to the value of cash stemming from the good foreign investment opportunities,
while the τ14 term will be 0. For firms with poor investment opportunities and high levels
of foreign operations, the τ13 term will be 0, and τ14 will represent the deduction from the
value of cash stemming from the poor foreign investment opportunities.17 Therefore, in a
manner similar to (6) and (7) above, by focusing on the respective coefficients in (8), one
can examine the marginal value of cash held abroad and cash held domestically for U.S.
firms with different levels of foreign investment opportunities, and thus infer whether foreign
investment opportunities are driving the difference between the cash values:18
Value(F oreign Cash | good investment opportunities abroad) =
F oreign Cash
,
τ1 + τ13 + τ10 M ean(Lev) + τ11 M ean
MV E
(10)
17
For firms with high levels of foreign operations, it is assumed that most of the investment opportunities
will be abroad. This is a necessary assumption because it is not possible with the current data to separately
measure Tobin’s q for investment opportunities in foreign subsidiaries.
18
An alternative approach to interacting the two dummy variables would be to conditionally run the
regression for only firms with high foreign operations – running separate regressions for the firms with high
foreign operations and either good or poor investment opportunities. This is also in line with Faulkender and
Wang (2006), who consider separate results for financially constrained vs. unconstrained firms. Similarly, one
could also define the dummy variables using terciles or quartiles to denote high values. These approaches are
considered separately for the regression in untabulated results, and the results are robust to these alternate
specifications.
16
Value(Domestic Cash | good investment opportunities abroad) =
Domestic Cash
,
τ1 + τ13 + τ10 M ean(Lev) + τ12 M ean
MV E
(11)
Value(F oreign Cash | poor investment opportunities abroad) =
F oreign Cash
τ1 + τ14 + τ10 M ean(Lev) + τ11 M ean
,
MV E
(12)
and
Value(Domestic Cash | poor investment opportunities abroad) =
Domestic Cash
τ1 + τ14 + τ10 M ean(Lev) + τ12 M ean
.
MV E
(13)
Equations (10)-(13) above are interpreted in a similar way to equations (6) and (7).
By adding the coefficient of the interaction terms between 4Cash, High For Ops, and
either Good Inv Opp or Poor Inv Opp, the marginal value of foreign and domestic cash
can be estimated specifically for firms with good or poor investment opportunities abroad.
Equation (10) gives the marginal value of foreign cash, accounting for a firm’s amount of
market leverage and scaled foreign cash holdings, for a firm with a high degree of foreign
operations and good investment opportunities. Thus, it serves as a proxy for what the
value of foreign cash is for a firm with good investment opportunities abroad. τ1 gives the
marginal value of cash for a firm, holding constant the amounts of leverage and cash that
the firm holds (ie τ1 would give the marginal value of cash for a firm with no leverage, no
cash holdings, and low levels of foreign operations). Adding in the coefficients τ10 and τ11
multiplied by the mean values of Lev and scaled Foreign Cash, respectively, for the sample
17
gives the value of foreign cash for the average firm in the sample, accounting for the levels
of leverage and foreign cash held. And adding τ13 gives the portion of the marginal value
of cash specifically contributed by a firm’s good foreign investment opportunities. Similarly,
equation (11) calculates the value of domestic cash for the average firm with good investment
opportunities abroad. Equations (12) and (13) calculate the value of foreign and domestic
cash for the average firm with poor investment opportunities abroad, with τ14 representing
the portion of the marginal value of cash specifically affected by a firm’s good poor investment
opportunities.
3
Data and Summary Statistics
The data in this paper are taken from the CRSP/COMPUSTAT merged database, available
through the Wharton Research Data Service (WRDS). The sample includes all U.S. firms
incorporated in the U.S. with total assets exceeding $100 million, excluding all financial firms
and utilities (SIC codes between 6000 and 6999, and between 4900 and 4999, respectively).19
Financial firms are excluded because they are required to meet certain statutory cash-asset
reserve requirements, and they also hold substantial amounts of marketable securities that are
included in cash. Utilities are excluded because their cash holdings are subject to regulatory
supervision in many cases. Firms that are incorporated outside of the U.S. are excluded
from the sample because the U.S. repatriation tax does not apply to them. The final sample
includes 10,899 firm-year observations for a total of 1,643 firms.
Firms which report no foreign income or foreign taxes are excluded from the sample.
The sample period ranges from 1980 to 2006. All data are converted to real 2006 dollar
values using the Consumer Price Index (CPI). In all regressions, the independent variables
are winsorized at the 1% level in order to reduce the impact of outliers.20 The marginal
19
This sample restriction of firms with total assets exceeding $100 million is to focus on comparably larger
firms that are more likely to have foreign operations and hold cash abroad.
20
The exceptions to this are any dummy variables and ln(T A) from regression (1), which already accounts
for outliers in size.
18
tax rate data is estimated following the method of Graham (1996). Firm-years for which
marginal tax rate data are not available are dropped from the regressions. The benchmark
portfolio returns are taken from Kenneth French’s website. The sample period ends in 2006
to exclude the potential effect of the sub-prime crisis which began in late 2007.
Table 1 includes the summary statistics for the variables in regression (1), which is used
to estimate the amounts of foreign and domestic cash. The table indicates that firms in
the sample held on average 12.11% of their net assets in the form of cash (an actual cash
amount of $951.77 million on average). This is striking because it is roughly comparable
in magnitude to the average annual capital expenditure, which is 7.32% of total assets (an
actual amount of $643.16 million). Domestic Income is about 8.09% of total assets for the
average firm (an actual amount of $710.82 million), and Foreign Income is about 3.70% of
total assets for the average firm (an actual amount of $325.10 million). Roughly half of the
firms in the sample do not report any foreign income.
[Insert Table 1 here]
Table 2 includes the summary statistics for regression (8), which is used to estimate the
value of foreign and domestic cash. Excess Stock Return over the sample period is slightly
negative, on average. The level of cash holdings is on average 11.77% of the market value of
equity for firms. Lev indicates that, over the sample period, firms on average have 24.03%
debt in their capital structures. Foreign Operations indicates that the average firm in the
sample derives roughly 36.76% of its total income from its foreign operations. The numbers
for Foreign Cash, however, indicate that in general the distribution of foreign cash holdings
is skewed – a few firms hold large amounts of foreign cash, as a percentage of the market
value of equity of the firm. This may also be because not all of the firms with foreign cash
are being picked up due to noise in the estimation of the level of foreign cash, and also since
firms are not required to report foreign income.
[Insert Table 2 here]
19
4
Main Results
In this section, the results of the empirical tests are discussed using the approach described in
Section 2. Section 4.1 details the estimation of the amounts of foreign and domestic cash for
firms. Section 4.2 estimates the market values of foreign and domestic cash for the average
firm. Section 4.3 examines whether differences in the quality of investment opportunities
explains the difference between the values of foreign and domestic cash. Section 4.4 provides
robustness checks, by estimating the results using alternative proxies for the levels of foreign
and domestic cash. Section 4.5 conducts an event study which exploits an exogenous change
in the tax code, in order to lend further support for the results.
4.1
Estimating the Amounts of Foreign and Domestic Cash
Table 3 contains the results of regression (1), which are used to estimate the amounts of
foreign and domestic cash. The regression results are similar to the results obtained by
Foley et al. (2007). The Adjusted R2 of 0.33 is the same as the Foley et al. (2007)
regression R2 of 0.33. R&D/T A, CAP EX/T A, Lev, CF Std Dev, Div Pmt, ln(T A), and
BV E/M V E all share the same signs as the original regression, and are significant. Tax
Burden is significant and has a positive sign, and this corroborates the results of Foley et al.
(2007) – an increase in the repatriation tax burden implies an increase in cash holdings for
firms. Domestic Income/T A has the same sign as in the original regression, but is significant
at the 0.01 level in this regression as opposed to insignificant in the original regression.
[Insert Table 3 here]
The coefficient of F oreign Income/T A is significant at the 0.10 level, and has a negative
sign, which means that an increase in foreign income will decrease overall firm cash holdings.
While this finding is at odds with the results of Foley et al. (2007), it is in line with
the investment-opportunities-abroad Hypothesis posited in this paper. If a firm’s foreign
operations are experiencing high growth, the firm will have to make sizable reinvestments
20
to sustain this growth, and these reinvestments may well exceed the contemporaneous cash
flows being generated abroad. This would not only lead to all of the foreign cash being
reinvested, but also some domestic cash being siphoned off to finance the reinvestment,
leading to the relationship documented here. This is an economically plausible explanation
since an important reason why many US firms expand internationally is to pursue higher
growth prospects abroad.21
The regression coefficients on and Tax Burden, β9 and β10 , respectively, are used in
equation (3) to obtain an estimate of the amount of foreign cash. The amount of domestic
cash is obtained by deducting the estimated amount of foreign cash from total cash, as shown
in equation (4).
4.2
Estimating the Market Values of Foreign and Domestic Cash:
Testing Hypothesis 1
Using these estimates, the marginal values of foreign and domestic cash are estimated from
regression (5). The results of this regression are included in Table 4. Overall, the regression
results are similar to those in Faulkender and Wang (2006). The Adjusted R2 is 0.17,
which is slightly lower than 0.20 found by Faulkender and Wang (2006). The coefficients
for (4Cash)t , (4Earnings)t , (4N A)t , (4Int)t , (4Div)t , (Cash)t−1 , (Lev)t , and (Lev)t ×
4(Cash)t are all significant, have the same sign, and are of a comparable magnitude to the
Faulkender and Wang (2006) regression. Compared to Faulkender and Wang (2006), the
(N F )t coefficient has the opposite sign and is insignificant at the 0.10 level, and (4R&D)t
is also insignificant and has a much smaller coefficient. Differences related to these two
variables may be due to the difference between the samples in this regression and the original
regression. In their original regression, Faulkender and Wang (2006) have a sample size of
more than 80,000 observations, while this regression has approximately 10,900 because of
21
Since the sample in this paper is larger than that in Foley et al. (2007), it is possible that Foley et al.
(2007) had a sample which excluded some of the high-growth firms in the current sample.
21
the need to drop observations due to missing data while running regression (1) to estimate
the levels of cash. In addition, this regression includes data up to and including 2006, while
Faulkender and Wang (2006) include data only up to 2001; thus, events after 2001 may have
had an effect on the regression results. These differences notwithstanding, these insignificant
variables are included as controls, and are not central to the interpretation of the results.
[Insert Table 4 here]
The coefficient for (∆Cash)t × (Domestic Cash)t−1 is negative and significant, as expected. As explained above, since the stock return is the excess equity return divided by
the lagged market value of equity and the independent variables are scaled by the lagged
market value of equity, the estimated coefficients can be interpreted as the marginal value
associated with a change in the corresponding independent variable. The original regression
indicates a negative coefficient for the interaction of the change in cash and lagged holdings
of total cash, indicating that as firms’ cash positions improve, the marginal value of that
cash declines, which is economically intuitive. The negative coefficient for the interaction of
the change in cash and lagged holdings of domestic cash concurs with the original findings,
and indicates that this declining marginal value of cash holds for domestic cash as well. This
is the case because cash-starved firms are more likely than others to raise external funds
that may be costly, and thus would receive a greater benefit from having more internal funds
(cash). However, the coefficient for (∆Cash)t × (F oreign Cash)t−1 is positive and insignificant. This is somewhat surprising, but it may be because there are two forces pulling in
opposite directions. On the one hand, the higher the amount of foreign cash, the lower is its
marginal value, as I have argued in the case of domestic cash. On the other hand, a higher
amount of foreign cash may also signify higher growth opportunities abroad, which would
imply a higher marginal value of cash. This could lead to a positive but insignificant coefficient as these two opposing forces partially negate each other. In addition, as can be seen
from the summary statistics, the amount of foreign cash held is driven by a small number of
22
firms. This might be a reflection of missing data, since firms are not required to report their
foreign operations. This may also be a factor leading to the insignificant coefficient.
With these coefficient values, the marginal value of cash held abroad and cash held domestically can now be estimated and examined for the average firm. Plugging the coefficient
values above into equations (6) and (7) gives a marginal value of cash held abroad of $1.06
and a marginal value of cash held domestically of $0.93. Thus, cash held abroad is valued at
a premium to $1, and cash held domestically is valued at a discount to $1. These numbers
generally fall in line with the results obtained by Faulkender and Wang (2006), who find that
the marginal value of overall cash is approximately $0.94 for the average firm. The results
also show that the average marginal value of foreign cash is statistically significantly higher
than the average marginal value of domestic cash.22 Moreover, these results are also robust
to an alternate estimate of the amounts of foreign and domestic cash, which is detailed in
Section 4.4.
Overall, the results provide support for the prediction in Hypothesis 1 – the values of
foreign and domestic cash for the average firm are significantly different from one another.
In addition, foreign cash is valued higher than domestic cash, indicating that the effect of the
repatriation tax is more than offset by the greater reinvestment opportunities and smaller
free-cash-flow problems in foreign operations than in domestic operations. There are two
possible reasons for this result. First, the repatriation tax may have a rather small influence
on the value of foreign cash because, as suggested by Foley et. al. (2007), strategies to avoid
or indefinitely defer this tax may not be too costly. Moreover, the effects of the tax break from
the American Jobs Creation Act of 2004 may also be playing a role. As Albring, Dzuranin,
and Mills (2005) point out, this tax break was sizable. Second, as explained by Faulkender
and Petersen (2012), if a firm is able to finance domestic investments from internal funds or a
capital market with relatively small frictions, then it does not have an incentive to repatriate
foreign cash as long as the free-cash-flow problems in holding the cash abroad are not too
22
In a one and two-sided paired t-test between means rejects the null hypothesis of no difference between
the values of foreign and domestic cash at the 0.01% level.
23
large. Section 2.3 discussed how more profitable reinvestment opportunities abroad could
increase the shadow price of cash and thereby deter wasteful deployment of cash. But there
may also be an additional reason, related to career concerns, that may lead to smaller freecash-flow problems abroad than domestically. U.S. companies tend to send relatively young
(high-potential) managers abroad as expatriates to lead foreign subsidiaries. As argued
by Chevalier and Ellison (1999), younger managers may have stronger career concerns, so
free-cash-flow problems abroad are likely to be smaller.
4.3
Determining if Differences in Investment Opportunities Drive
the Difference in the Values of Foreign and Domestic Cash:
Testing Hypothesis 2
To examine whether differences in investment opportunities are driving the higher value of
cash held abroad versus cash held domestically, regression (8) is estimated. The results
are presented in Table 5. All coefficients from regression (5) remain mostly unchanged in
terms of magnitude, sign, and significance. The coefficient of (∆Cash)t × (High F or Ops)t ×
(Good Inv Opp)t is positive and significant at the 5% level, and the coefficient of (∆Cash)t ×
(High F or Ops)t × (P oor Inv Opp)t is negative, although insignificant. The signs of the two
coefficients indicate that a firm with good foreign investment opportunities will have a higher
marginal value of cash relative to a firm with poor investment opportunities, which is in line
with what was expected. To obtain the marginal values of cash for firms with good foreign
investment opportunities and poor foreign investment opportunities, the coefficient values of
regression (8) are plugged into (10)-(13). For firms with high levels of foreign operations and
good investment opportunities, the marginal values of foreign and domestic cash are $1.42
and $1.30, respectively. For firms with high levels of foreign operations and poor investment
opportunities, the marginal values of foreign-held and domestically-held cash are $0.92 and
$0.80, respectively.
24
[Insert Table 5 here]
Overall, the results provide strong support for Hypothesis 2: firms use their cash held
abroad to finance investment opportunities, and firms with better growth opportunities
abroad have higher values of domestic and foreign cash. In addition, the results indicate that
cash held domestically and abroad are both valued at a premium to $1.00 in high-growth
firms. This suggests that, for these firms, the repatriation tax disadvantage of holding cash
abroad is swamped by the valuable investment opportunities of these firms. However, for
firms with overall poor growth opportunities, cash held domestically and abroad are both
valued at a discount to $1.00. Moreover, the value of foreign cash for firms with poor
investment opportunities abroad ($0.92) is substantially lower than the value of foreign cash
for firms with good investment opportunities abroad ($1.42), likely reflecting the potential
impact of the repatriation tax burden as well as free-cash-flow problems.
4.4
Robustness and Alternate Measures of Cash
As the results in sub-sections 4.1 to 4.3 are based upon an estimation of the amounts of
foreign-held and domestically-held cash that firms carry, a concern is whether the results are
robust to alternative estimations of these cash amounts. An alternative method is presented
to proxy for the amounts of cash, and the values of foreign and domestic cash are calculated
using these estimates.
The estimation method is to use coefficient estimates from Foley et al. (2007) in their
foreign cash regression, which uses BEA data on levels of foreign cash, to create estimates of
foreign and domestic cash for firms. Specifically, Foley et al. (2007) run the following OLS
25
specification:
ln
F oreign Cash
NA
i,t
Domestic Income
TA
= γ0 + γ1 (Eff Repat T ax Rate)i,t + γ2
i,t
F oreign Income
+γ3
+ γ4 (ln (T A))i,t + γ5 (Div P mt)i,t
TA
i,t
R&D
BV E
+ γ7 (Std Dev Op Income)i,t + γ8
+γ6
M V E i,t
T A i,t
CAP EX
+γ9
+ γ10 (Lev)i,t + εi,t .
(14)
TA
i,t
The variables are defined analogously to regression (1). Eff Repat Tax Rate is a firm’s
effective repatriation tax rate, and is defined as the maximum of zero and the difference
between a firm’s weighted foreign tax rate and its marginal effective tax rate. The sample
period
Using the coefficient estimates for (14) from Foley et al. (2007), and rearranging (14)
yields the following estimates for foreign and domestic cash:23
"
0
Est(F oreign Cash)i,t = (N A)i,t
exp −5.608 + 8.802(Eff Repat T ax)i,t
F oreign Income
−1.576
+ 14.088
TA
i,t
i,t
BV E
+0.038(ln(T A))i,t − 0.020(Div P mt)i,t + 0.167
M V E i,t
R&D
CAP EX
− 2.139
+0.875 (CF Std Dev)i,t + 4.382
T A i,t
TA
i,t
#
Domestic Income
TA
−1.140 (Lev)i,t
(15)
and
0
0
Est (Domestic Cash)i,t = (Cash)i,t − (F oreign Cash)i,t .
23
(16)
Foley et al (2007) use operating income standard deviation instead of cash flow standard deviation
because of limitations on the duration of their sample. In (15), cash flow standard deviation is used. This
facilitates calculations, and follows Opler et al (1999).
26
Negative estimates of cash are truncated at zero. In (16), Eff Repat Tax is a proxy for Eff
Repat Tax Rate, since the latter is measured using BEA data for the exact foreign tax that
firms face. It is defined as:
(Eff Repat T ax)i,t =
n
max (U ST axRate)i,t ×
F oreign Income
TA
i,t
o
− (F oreignT axesP aid)i,t , 0
(T A)i,t
(17)
Summary statistics for (15), (16), and (17) when applied to the data are included in Table
6. The sample period runs from 1980-2006, which is not too different from the sample period
of 1982-2004 in Foley et al (2007).24 The mean and standard deviation of these estimates of
foreign and domestic cash are close to the estimates obtained earlier in the paper.
[Insert Table 6 here]
Column (1) of Table 7 includes regression results for equation (5) for estimating the
values of foreign and domestic cash. The magnitudes and signs of the regression coefficients
are very similar to those obtained using the original estimates of foreign and domestic cash.
Overall, the results indicate that for the average firm a dollar of cash held domestically
is valued at $0.95, and a dollar of cash held abroad is valued at $1.11. These numbers
are very close to those obtained in Section 4.2 (domestic cash valued at $0.93 and foreign
cash valued at $1.06), and once again indicate that the value of foreign cash is valued at a
premium to $1, and is significantly higher than the value of domestic cash, which is valued
at a discount to $1. Column (2) of Table 7 includes regression results for equation (8). The
magnitudes and signs of the regression coefficients are also very similar to those obtained in
Section 4.3. The results indicate that for firms with high levels of foreign operations and
good investment opportunities, the marginal values of foreign and domestic cash are $1.43
and $1.29, respectively. For firms with high levels of foreign operations and poor investment
opportunities, the marginal values of foreign-held and domestically-held cash are $0.95 and
24
With the caveat being that the data in this paper also include post-repatriation-tax-holiday data, whereas
Foley et al’s (2007) analysis does not.
27
.
$0.81, respectively. These results are similar to those obtained earlier, and once again provide
strong support for the hypotheses developed in Section 2.3. Thus, the results seem robust
to an alternative estimation of the amount of cash held by firms.
[Insert Table 7 here]
5
Additional Evidence from the American Jobs Creation Act of 2004
As a further test of the hypothesis that investment opportunities are a key driver of the
results documented here, an exogenous change in the tax code can be exploited to run an
additional test. In 2004, Congress passed the American Jobs Creation Act which, among
other things, provided a one-time break in the repatriation tax for multinational firms. Thus,
any firms that had stockpiled cash abroad because of the repatriation tax had an opportunity
to bring the cash back to the U.S. without incurring the penalty of the tax. According to
Albring et al. (2005), this created an opportunity for significant tax savings for multinational
firms. If investment opportunities are indeed playing a significant role in the results, then the
tax holiday should affect firms differently depending on their investment opportunities. In
particular, firms with poor investment opportunities abroad are likely to be stockpiling cash
abroad to avoid the repatriation tax rather than to reinvest the cash abroad, so these firms
should experience a significant positive effect from the passage of the tax holiday. However,
firms with good investment opportunities abroad are holding cash abroad mainly in order
to invest in projects and to a lesser extent to avoid the repatriation tax, so the tax break
should not have a significant effect for these firms.
28
5.1
Event Study Methodology and Results
In order to test this prediction, an event study methodology is used to examine the cumulative
abnormal returns (CARs) of firms around when the American Jobs Creation Act of 2004
was passed.
The CARs are calculated following Campbell, Lo, and MacKinlay (1997) using both the
market model and the Fama and French (1993) three-factor model. Denote event time by
τ . In this case, τ = 0 represents June 17th – the day that the Act was passed in the U.S.
House of Representatives.25 The market model can be represented as
Ri,τ = αi + βi Rm,τ + εi,τ ,
(18)
where Ri,τ is the return of stock i at time τ , and Rm,τ is the market return at time τ (proxied
by the value-weighted . The Fama-French three-factor model can be represented as
Ri,τ = αF F,i + βm,i Rm,τ + βhml,i Rhml,τ + βsmb,i Rsmb,τ + i,τ ,
(19)
where Rhml and Rsmb are the returns of the HML and SMB portfolios, respectively. For each
security i, regressions (18) and (19) are estimated in order to produce parameter estimates
for the regression coefficients. Denoting the parameter estimates via OLS from (18) and (19)
by α̂ and β̂, the estimated abnormal returns of security i at time τ for the market model
and the Fama-French model are:
m
ARi,τ
= Ri,τ − α̂i − β̂i Rm,τ ,
25
(20)
At the time, the Republican party controlled both the House and the Senate. Thus the passing of the
legislation through the House likely resolved a good deal of uncertainty about the prospects of the bill, as
the Senate was likely to follow suit (apart from disagreements surrounding reconciliation of the House and
Senate versions of the bill). That being said, any remaining uncertainty about the passage of the bill, as
well as which firms would be most affected, was likely to be resolved over a period of time rather than over
one or two days, as is explained below.
29
and
FF
ARi,τ
= Ri,τ − α̂F F,i − β̂m,i Rm,τ − β̂hml,i Rhml,τ − β̂smb,i Rsmb,τ .
(21)
Using the individual firm abnormal returns, average abnormal returns for each event date
FF
τ (denoted by ARm
τ and ARτ ) are calculated separately for firms with for firms with good
investment opportunities abroad (high degrees of foreign operations and good investment
opportunities) and for firms with poor investment opportunities abroad (high degrees of
foreign operations but poor investment opportunities). As before, a firm is defined as having
a high degree of foreign operations if it is in the top half of firms in the sample reporting
F oreign Income
TA
as of the end of the previous year (here: 2003), and a firm is defined as having
good investment opportunities if it is in the top half of firms in the sample for Tobin’s q,
defined in (9), as of the end of 2003.26 Finally, average cumulative abnormal returns (CARs)
between two dates τ1 and τ2 are defined as the sum of the average abnormal returns between
those dates:
CARτ1 ,τ2 ≡
τ2
X
ARτ ,
(22)
τ =τ1
where (22) is calculated separately for firms with good investment opportunities abroad and
for firms with poor investment opportunities abroad.
Table 8 below gives the average abnormal returns and cumulative average abnormal
returns, calculated using both the market model and the Fama-French model, for firms with
good and poor investment opportunities abroad. The returns are shown for a window of 15
days surrounding June 17th (τ = 0), which was the day the Act passed in the U.S. House.
The reason for focusing on this interval is that the bill was introduced to Congress and the
public on June 4th, a few days before it won passage in the House. During this period
(and even after the bill passed the House), we would expect to see a gradual revelation
26
Because these variables are only reported on a yearly basis, the values as of the previous fiscal year-end
(i.e. 2003) are used. As before, for firms with high levels of foreign operations, the assumption is that most
of the investment opportunities will be abroad. This is because it is not possible with the data to separately
measure investment opportunities that are specifically based abroad. The results are also robust to defining
high foreign operations or good investment opportunities as being in the upper tercile or quartile of the
sample rather than the upper half.
30
of information regarding how likely the bill was to be passed, as well as an evolution of
the market’s assessment of the foreign cash positions and investment opportunities of the
affected firms. If this were the case, we would expect to see a steady positive increase in the
abnormal returns for firms with poor foreign investment opportunities (rather than a large
discontinuous jump over just one or two days), but flat/insignificant abnormal returns for
firms with good foreign investment opportunities. The sample represents a total of 4,867
returns total of 157 firms – 97 firms with good investment opportunities abroad, and 60 firms
with poor investment opportunities abroad.
[Insert Table 8 Here]
Figure 1 below plots the CARs as a function of event time for firms with good foreign
investment opportunities, and for firms with poor foreign investment opportunities. The top
graph does this with CARs calculated using the market model, and the bottom graph does
this with CARs calculated using the Fama-French model.
[Insert Figure 1 Here]
As Figure 1 indicates, firms with good investment opportunities abroad did not seem to
experience any significant abnormal returns either immediately before or after the American
Jobs Creation Act was passed in the House – the dashed line for CARs seems to hover around
0. By contrast, firms with poor investment opportunities abroad seemed to experience
positive cumulative abnormal returns before the Act was passed (i.e. around the time when
it was introduced), and the CARs steadily increase around and past the date when the
bill was passed. This seems to support the interpretation that firms with poor investment
opportunities abroad are stockpiling cash abroad to avoid the repatriation tax rather than
to reinvest the cash abroad, so thus these firms experience a significant positive effect from
the passage of the tax holiday. However, firms with good investment opportunities abroad
are holding cash abroad mainly in order to invest in projects and to a lesser extent to avoid
the repatriation tax, so the tax break does not have a significant effect for these firms.
31
A formal statistical test buttresses the graphical evidence. Following Campbell, Lo, and
MacKinlay (1997), under the null hypothesis that the expectation of the abnormal returns
is zero, we can draw inferences about the CARs using the fact that CARτ1 ,τ2 ∼ N 0, σ̄τ21 ,τ2 ,
where σ̄τ21 ,τ2 is the variance of CARτ1 ,τ2 . We can then test the null hypothesis using
CARτ1 ,τ2 a
J1 = ∼ N (0, 1),
ˆτ21 ,τ2 1/2
σ̄
(23)
ˆτ21 ,τ2 is the sample variance of CARτ1 ,τ2 . When using CARs calculated through the
where σ̄
Fama-French three-factor model, applying (23) to firms with good foreign investment opportunities yields J1 = 0.71, and applying it to firms with poor foreign investment opportunities
yields J1 = 3.29.27 These results provide statistical evidence that firms with poor investment
opportunities abroad experienced positive and significant abnormal returns as a result of the
American Jobs Creation Act, but firms with good investment opportunities abroad did not
experience any significant abnormal returns, indicating that investment opportunities are a
significant driver of multinational firms’ decision to hold cash abroad.28
5.2
Robustness of Event Study Results
As noted by Kolari and Pynnönen (2010) and others, when event dates are clustered, resulting in cross-sectional correlation of abnormal returns, standard errors can be severely biased
in terms of over-rejecting the null hypothesis of zero average abnormal returns. Since there is
only one common event date of interest amongst the firms in the sample, this cross-sectional
correlation is a concern in this setting. As an alternative robustness check in order to account
for this potential bias, the portfolio method following Jaffe (1974) is used to conduct the
event study.
27
The significance of the results remains unchanged when using the market model to calculate abnormal
returns.
28
These results are also consistent with Blouin and Krull (2009), who find that firms that repatriated cash
under the American Jobs Creation Act of 2004 had poorer investment opportunities and higher free cash
flows than non-repatriating firms.
32
Specifically, firm returns are aggregated into equal-weighted portfolios for each date in
event time, and the event study analysis is applied to the portfolios. Thus, the following
model is estimated over all trading days in 2004:
Rp,τ = αp + βm,p Rm,τ + (βhml,p Rhml + βsmb,p Rsmb ) + φp Iτ + εp,τ
(24)
In (24), p represents the equal weighted portfolio of all firms in the sample, which is considered for each trading day τ in 2004. Firms which reported both domestic and foreign income
as of the end of 2003 are considered. Rp,τ is the equal-weighted average of returns for all
firms in portfolio p on day τ . Rm,τ is the market return on date τ , which is the return of the
value-weighted CRSP market index. Rhml and Rsmb are the returns on the Fama and French
(1993) value and size portfolios, and are included as additional factors for robustness. Iτ is
a dummy variable which takes a value of 1 if the date τ falls in the event window, which
is between τ = −15 and τ = 15, and a value of 0 otherwise.29 The regression (24) is run
separately by forming portfolios either for firms with good investment opportunities abroad
(high degrees of foreign operations and good investment opportunities) or for firms with
poor investment opportunities abroad (high degrees of foreign operations but poor investment opportunities), as defined earlier. In specification (24), the coefficient on the dummy
variable, φp , thus corresponds to the average abnormal returns surrounding the enactment
of the bill. Based on the previous discussion, it is predicted that φp will be positive and
significant for firms with poor investment opportunities abroad, but insignificant for firms
with good investment opportunities abroad.
[Insert Table 9 here]
The results of regression (24) are included in Table 9. The specifications in columns (1)
and (2) form the portfolios using only firms with poor investment opportunities abroad, and
29
Thus this event window captures the period in time from when the bill was introduced and when it
passed the House, during which time information about the probability of passage and the foreign cash
holdings of firms was likely slowly revealed to the market.
33
the specifications in columns (3) and (4) forms the portfolios using only firms with good
investment opportunities abroad. The results indicated that the passage of the American
Jobs Creation Act of 2004 resulted in positive and significant average abnormal returns of
0.2% for firms with poor investment opportunities abroad. However, for firms with good
investment opportunities abroad, the enactment of the bill resulted in comparatively smaller
average abnormal returns that are not significantly different from zero. Moreover, the results
are robust to using the Fama and French (1993) factors in addition to the market factor in
calculating the abnormal returns. These results are consistent with those in Section 5.1, and
support the prediction that firms with good investment opportunities abroad experienced a
negligible stock price reaction following enactment of the bill, while firms with poor investment opportunities abroad experienced a significant positive stock price reaction following
the enactment of the bill.
6
Conclusion
This paper examines the average marginal values of cash held domestically in the U.S. and
cash held abroad, and the relative roles of the repatriation tax and investment opportunities
in driving the difference in the values of foreign and domestic cash. The two main results
are as follows. First, cash held abroad is valued more highly by shareholders than cash held
domestically – the marginal value of cash held abroad is $1.07 on average, and the marginal
value of cash held domestically is $0.94 on average. Second, investment opportunities significantly impact the value of cash. For firms with high growth opportunities, the values of
cash held domestically and cash held abroad exceed $1, whereas for firms with low growth
opportunities, the values of cash held domestically and that held abroad are less than $1.
The results indicate that investment opportunities are an important reason for why firms
are stockpiling cash abroad, and that for many firms (and on average), it is likely to be a
more important reason than the repatriation tax. Additional evidence from the change in
34
the tax code provided by the enactment of the American Jobs Creation Act of 2004 provides
further support for these results.
There is a caveat about one limitation of the analysis. The Faulkender and Wang (2006)
approach does not deal with the issue that there is an endogeneity problem that arises from
the fact that more profitable firms tend to generate more cash, so it may not be that cash
drives the value but also that value determines how much cash the firm holds. Although
market-based measures of value are forward looking, the fact that they may be correlated
with contemporaneous profitability makes the interpretation of the regression coefficients
tricky.
The focus of this paper has been on how the determinants of the demand for cash within
firms – as represented by investment opportunities that firms have and the taxes in the
domains in which they operate – are correlated with how investors value the cash these firms
hold on their balance sheets. An obvious factor that is not accounted for is the supply side.
One can imagine that the market value of on-balance-sheet cash is also affected by the cost
and availability of liquidity for firms through, say, the commercial paper market and bank
lines of credit. It would be interesting to include these factors in future research.
35
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39
Table 1: Summary Statistics of Data used in Regression (1)
This table provides summary statistics of data used in regression (1), which is used to
estimate the amounts of foreign and domestic cash. Mean, standard deviation, and quartile
values are provided for each variable. Indicated variables are scaled by total assets, TA, in
order to normalize for firm size. NA is net assets, defined as total assets less cash. Cash/N A
is cash and short-term investments as a percentage of net assets. R&D/TA is the amount
of research and development expenses scaled by total assets for each firm, and is set to zero
if R&D expenditures is missing in COMPUSTAT. CAPEX/TA is capital expenditures as
a percentage of total assets for each firm. Lev is defined as the ratio of total debt to the
sum of total debt and the market value of equity. CF Std Dev is defined as the standard
deviation of the firm’s earnings before interest, taxes, and depreciation as a percentage of
total assets. Div Pmt is a dummy variable that takes a value of 1 if the firm paid a dividend
in the year, and a value of 0 otherwise. ln(T A) is the natural log of total assets, in millions
of dollars. BV E/M V E is the ratio of the reported book value of the firm’s equity, divided
by the market value of the firm’s equity, which is defined as defined as the firm’s year-end
closing stock price multiplied by the number of shares outstanding. Both Domestic Income
and Foreign Income are pre-tax earnings, and are included as a percentage of total assets.
The overall Tax Burden variable reflects the repatriation tax faced by firms to repatriate
their foreign earnings back to the U.S. It is defined as a percentage of total assets of the
firm. All variables except for Div Pmt and ln(T A) have been winsorized at the 1% level.
Variable
Cash/N A
R&D/T A
CAP EX/T A
Lev
CF Std Dev
Div Pmt
ln(T A)
BV E/M V E
Domestic Income/T A
F oreign Income/T A
Tax Burden
Mean
0.1211
0.0156
0.0732
0.2913
0.0335
0.0479
7.7663
0.6480
0.0809
0.0370
0.0013
SD
0.2747
0.0346
0.0545
0.2463
0.0343
0.4336
1.9963
0.4711
0.0776
0.0357
0.0033
40
p25
0.0068
0.0000
0.0315
0.0799
0.0066
0.0000
6.0599
0.3146
0.0204
0.0072
0.0000
Median
0.0257
0.0000
0.0587
0.2356
0.0250
1.0000
7.3728
0.5338
0.0505
0.0352
0.0000
p75
0.1096
0.0130
0.1084
0.4621
0.0479
1.0000
9.3955
0.8808
0.1331
0.0609
0.0002
Table 2: Summary Statistics of Data used in Regression (8)
This table provides summary statistics of data used in regression (8). Mean, standard deviation, and
quartile values are provided for each variable. Excess Stock Return is defined as the stock’s return
over a given year minus the stock’s benchmark portfolio over the same year. With the exception
of Mkt Leverage, all independent variables are standardized for size by the lagged market value of
equity. 4X represents 1-year change: Xt − Xt−1 . Cash is defined as cash holdings plus marketable
securities. Earnings is earnings before interest and extraordinary items. NA is net assets, defined
as total assets excluding cash. R&D represents research and development expenditures for the
firm. Dividends are total dividends, and Interest is interest expense, as reported by the firm. Mkt
Leverage is the ratio of total debt to the sum of total debt and the market value of equity. NF
is net financing, defined as total equity issuance minus repurchases plus debt issuance minus debt
redemption. F oreign Casht−1 is the lagged estimate of foreign cash. Domestic Casht−1 is the
lagged estimate of domestic cash. High For Ops is a dummy variable which takes a value of 1 if
a firm-year is in the top half of the sample reporting foreign income in terms of amount of foreign
operations as a percentage of total operations, and a value of 0 otherwise. Good Inv Opp is a
dummy variable which takes a value of 1 if a firm-year is in the top half of the sample in terms
of Tobin’s q (q-ratio). Poor Inv Opp is a dummy variable which takes a value of 1 if a firm-year
is in the bottom half of the sample in terms of q-ratio, and 0 otherwise. The q-ratio is defined as
the market value of equity (defined as the product of its share price and the number of common
shares outstanding) plus preferred stock (the liquidating value of the firm’s outstanding preferred
stock) plus debt (the value of short-term liabilities net of short-term assets, plus the book value of
long term debt), all divided by the book value of the total assets of the firm. Foreign Operations is
defined as F oreign Income/T A. The q-ratio and Foreign Operations are used for the calculation
of the dummy variables, but are not directly included in the regression. All variables have been
winsorized at the 1% level.
Variable
Excess Stock Return
(4Cash)t
(4Earnings)t
(4N A)t
(4R&D)t
(4Interest)t
(4Dividends)t
(Cash)t−1
(M kt Leverage)t
(N F )t
(F oreign Cash)t−1
(Domestic Cash)t−1
(High F or Ops)t
(Good Inv Opp)t
(P oor Inv Opp)t
(q-ratio)t
(F oreign Operations)t
Mean
-0.0063
0.0089
0.0064
0.0232
0.0009
0.0000
0.0004
0.1177
0.2403
0.0154
0.0061
0.1128
0.4471
0.5592
0.4408
1.2331
0.3676
SD
0.4269
0.0970
0.1427
0.3750
0.0121
0.0209
0.0068
0.1490
0.2092
0.1685
0.0183
0.1503
0.4972
0.4965
0.4965
1.1426
0.3574
41
p25
-0.2641
-0.0171
-0.0237
-0.0541
-0.0001
-0.0029
-0.0002
0.0257
0.0738
-0.0370
0.0000
0.0219
0.0000
0.0000
0.0000
0.5215
0.0475
Median
-0.0480
0.0015
0.0056
0.0205
0.0000
-0.0000
0.0000
0.0649
0.1905
-0.0017
0.0000
0.0601
0.0000
1.0000
0.0000
0.8813
0.2504
p75
0.1862
0.0267
0.0319
0.1079
0.0024
0.0031
0.0009
0.1464
0.3564
0.0380
0.0003
0.1394
1.0000
1.0000
1.0000
1.5105
0.6254
Table 3: Results of Regression (1): Estimating the Amounts of Foreign and
Domestic Cash
This table presents the results of regression (1), which is used to calculate foreign and domestic cash levels.
The dependent variable is Cash/N A. NA is net assets, defined as total assets less cash. Indicated variables
are scaled by total assets, TA, in order to normalize for firm size. R&D/TA is research and development
expenses scaled by total assets for each firm, and is set to zero if R&D expenditures is missing in COMPUSTAT. CAPEX/TA is capital expenditures as a percentage of total assets for each firm. Lev is defined as
the ratio of total debt to the sum of total debt and the market value of equity. CF Std Dev is defined as
the standard deviation of the firm’s earnings before interest, taxes, and depreciation as a percentage of total
assets. Div Pmt is a dummy variable that takes a value of 1 if the firm paid a dividend in the year, and a
value of 0 otherwise. ln(T A) is the natural log of total assets, in millions of dollars. BV E/M V E is the ratio
of the book value of the firm’s equity to the market value of the firm’s equity, which is defined as defined as
the firm’s year-end closing stock price multiplied by the number of shares outstanding. Both Domestic Income and Foreign Income are pre-tax earnings, and are included as a percentage of total assets. The overall
Tax Burden variable reflects the repatriation tax faced by firms to repatriate their foreign earnings back to
the U.S. It is defined as a percentage of total assets of the firm. All variables are winsorized at the 1% level,
with the exception of Div Pmt and ln(T A). Specification includes year and industry fixed effects. Robust
standard errors are in parentheses. Standard errors are corrected for correlation across observations of a
given firm (White (1980)). *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.
Dependent Variable: Cash/N A
Independent Variables
R&D/T A
Coefficients
1.780***
(0.156)
-0.720***
(0.083)
-0.282***
(0.025)
1.363***
(0.190)
-0.080***
(0.010)
-0.017***
(0.004)
-0.021**
(0.010)
0.227***
(0.082)
-0.216*
(0.121)
5.470***
(1.429)
0.485***
(0.067)
CAP EX/T A
Lev
CF Std Dev
Div Pmt
ln(T A)
BV E/M V E
Domestic Income/T A
F oreign Income/T A
Tax Burden
Intercept
Observations
Number of Firms
Adjusted R2
15,660
2,059
0.33
42
Table 4: Results of Regression (5): Market Value of Foreign and Domestic Cash
This table presents the results of regression (5). The dependent variable is Excess Stock Return, defined
as the return of a stock’s benchmark portfolio over a given year subtracted from the return of that stock
during the same year: Rbi,t is the benchmark portfolio return of a stock over a given year, and Ri,t is the
return of that same stock over the year. All variables except for Lev and Excess Stock Return are scaled by
the lagged market value of equity (M V Ei,t−1 ). ∆X represents 1-year change: Xt − Xt−1 . Cash is defined
as cash holdings plus marketable securities. Earnings is earnings before interest and extraordinary items.
NA represents net assets, and is defined as total assets excluding cash. R&D are research and development
expenditures for the firm. Div is cash dividends, and Int is interest expense, as reported by the firm. Lev is
the ratio of total debt to the sum of total debt and the market value of equity. NF is net financing, defined
as total equity issuance minus repurchases plus debt issuance minus debt redemption. (F oreign Cash)t−1
is the lagged estimate of foreign cash. (Domestic Cash)t−1 is the lagged estimate of domestic cash. Robust
standard errors are in parentheses. Standard errors are corrected for correlation across observations of a
given firm (White (1980)). *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.
Dependent Variable: Excess Stock Return (Ri,t − Rbi,t )
Independent Variables
Coefficients
(4Cash)t
1.361***
(0.138)
(4Earnings)t
0.614***
(0.044)
(4N A)t
0.133***
(0.022)
(4R&D)t
0.369
(0.494)
(4Int)t
-2.079***
(0.340)
(4Div)t
2.049***
(0.737)
(Cash)t−1
0.341***
(0.040)
(Lev)t
-0.397***
(0.024)
(N F )t
-0.011
(0.045)
(Lev)t × (4Cash)t
-1.316***
(0.303)
(4Cash)t × (F oreign Cash)t−1
3.832
(6.259)
(4Cash)t × (Domestic Cash)t−1
-0.933***
(0.364)
Intercept
0.031***
(0.007)
Observations
Number of Firms
Adjusted R2
10,899
1,643
0.17
43
Table 5: Results of Regression (8): Investment Opportunities and the Values of
Cash
This table presents the results of regression (8). The dependent variable is Excess Stock Return, defined as the return of a stock’s
benchmark portfolio over a given year subtracted from the return of that stock during the same year: Rbi,t is the benchmark
portfolio return of a stock over a given year, and Ri,t is the return of that stock over the same year. All variables except
for Lev and Excess Stock Return are scaled by the lagged market value of equity (M V Ei,t−1 ). ∆X represents 1-year change:
Xt − Xt−1 . Cash is defined as cash holdings plus marketable securities. Earnings is earnings before interest and extraordinary
items. NA is net assets, defined as total assets excluding cash. R&D represents research and development expenditures for the
firm. Div is cash dividends, and Int is interest expense, as reported by the firm. Lev is the ratio of total debt to the sum of
total debt and the market value of equity. NF is net financing, defined as total equity issuance minus repurchases plus debt
issuance minus debt redemption. (F oreign Cash)t−1 is the lagged estimate of foreign cash. (Domestic Cash)t−1 is the lagged
estimate of domestic cash. High For Ops is a dummy variable which takes a value of 1 if a firm-year is in the top half of the
sample in terms of amount of foreign operations as a percentage of total operations, and a value of 0 otherwise. Good Inv Opp
is a dummy variable which takes a value of 1 if a firm-year is in the top half of the sample in terms of Tobin’s q (q-ratio). Poor
Inv Opp is a dummy variable which takes a value of 1 if a firm-year is in the bottom half of the sample in terms of q-ratio, and
0 otherwise. Robust standard errors are in parentheses. Standard errors are corrected for correlation across observations of a
given firm (White (1980)). *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.
Dependent Variable: Excess Stock Return (Ri,t − Rbi,t )
Independent Variables
Coefficients
(4Cash)t
1.252***
(0.157)
0.612***
(0.044)
0.131***
(0.021)
0.341
(0.494)
-2.067***
(0.340)
1.990***
(0.732)
0.343***
(0.039)
-0.397***
(0.024)
-0.010
(0.045)
-1.066***
(0.333)
2.830
(6.221)
-0.885**
(0.366)
0.405**
(0.178)
-0.094
(0.139)
0.031
(0.007)
(4Earnings)t
(4N A)t
(4R&D)t
(4Int)t
(4Div)t
(Cash)t−1
(Lev)t
(N F )t
(Lev)t × (4Cash)t
(4Cash)t × (F oreign Cash)t−1
(4Cash)t × (Domestic Cash)t−1
∆(Cash)t × (High F or Ops)t × (Good Inv Opp)t
∆(Cash)t × (High F or Ops)t × (P oor Inv Opp)t
Intercept
Observations
Number of Firms
Adjusted R2
44
10,893
1,642
0.17
Table 6: Summary Statistics of Alternative Measures of Foreign and Domestic
Cash, and the Effective Repatriation Tax Rate
This table provides summary statistics of data defined in (15), (16), and (17). Mean, standard deviation,
and quartile values are provided for each variable. Eff Repat Tax is defined
Income
− (F oreignT axesP aid)i,t , 0 / (T A)i,t , and is a proxy
as max (U ST axRate)i,t × F oreign
TA
i,t
0
for a firm’s effective repatriation tax rate. (F oreign Cash)t−1 is the lagged estimate of foreign cash defined using coefficient estimates from the Foley et al (2007) regression results.
(DomesticCash)t−1 is the lagged estimate of domestic cash, defined as total cash less the
0
0
estimate of foreign cash. (F oreign Cash)t−1 and (Domestic Cash)t−1 have been standardized for size by the lagged market value of equity. All variables have been winsorized at the
1% level.
Variable
(Eff Repat T ax)t
0
(F oreign Cash)t−1
0
(DomesticCash)t−1
Mean
0.0117
0.0063
0.1116
SD
0.0420
0.0084
0.1577
45
p25
0.0000
0.0012
0.0196
Median
0.0000
0.0038
0.0571
p75
0.0000
0.0075
0.1365
Table 7: Results of Regressions (5) and (8) Using Alternate Measures of Cash
Column (1) includes results for regression (5), and column (2) presents the results of regression (8) using the alternate measures
of foreign and domestic cash defined by (16) and (17).The dependent variable is Excess Stock Return, defined as the return
of a stock’s benchmark portfolio over a given year subtracted from the return of that stock during the same year: Rbi,t is
the benchmark portfolio return of a stock over a given year, and Ri,t is the return of that stock over the same year. All
variables except for Lev and Excess Stock Return are scaled by the lagged market value of equity (M V Ei,t−1 ). ∆X represents
1-year change: Xt − Xt−1 . Cash is defined as cash holdings plus marketable securities. Earnings is earnings before interest
and extraordinary items. NA is net assets, defined as total assets excluding cash. R&D represents research and development
expenditures for the firm. Div is cash dividends, and Int is interest expense, as reported by the firm. Lev is the ratio of total debt
to the sum of total debt and the market value of equity. NF is net financing, defined as total equity issuance minus repurchases
0
0
plus debt issuance minus debt redemption. (F oreign Cash)t−1 is the lagged estimate of foreign cash. (Domestic Cash)t−1 is
the lagged estimate of domestic cash. High For Ops is a dummy variable which takes a value of 1 if a firm-year is in the top half
of the sample in terms of amount of foreign operations as a percentage of total operations, and a value of 0 otherwise. Good Inv
Opp is a dummy variable which takes a value of 1 if a firm-year is in the top half of the sample in terms of Tobin’s q (q-ratio).
Poor Inv Opp is a dummy variable which takes a value of 1 if a firm-year is in the bottom half of the sample in terms of q-ratio,
and 0 otherwise. Robust standard errors are in parentheses. Standard errors are corrected for correlation across observations
of a given firm (White (1980)). *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.
Dependent Variable: Excess Stock Return (Ri,t − Rbi,t )
Independent Variables
(1)
(4Cash)t
1.354***
(0.148)
(4Earnings)t
0.610***
(0.044)
(4N A)t
0.134***
(0.021)
(4R&D)t
0.397
(0.494)
(4Int)t
-2.030***
(0.337)
(4Div)t
2.038***
(0.743)
(Cash)t−1
0.336***
(0.040)
(Lev)t
-0.400***
(0.024)
(N F )t
-0.016
(0.045)
(Lev)t × (4Cash)t
-1.274***
(0.303)
0
(4Cash)t × (F oreign Cash)t−1
9.929
(13.665)
0
(4Cash)t × (Domestic Cash)t−1
-0.874***
(0.288)
∆(Cash)t × (High F or Ops)t × (Good Inv Opp)t
0.032***
(0.007)
(2)
1.263***
(0.161)
0.607***
(0.044)
0.132***
(0.021)
0.371
(0.494)
2.019***
(0.337)
1.981***
(0.738)
0.338***
(0.040)
-0.400***
(0.024)
-0.015
(0.045)
-1.029***
(0.332)
6.900
(14.179)
-0.834***
(0.290)
0.369**
(0.179)
-0.109
(0.144)
0.032***
(0.007)
10,849
1,640
0.17
10,843
1,639
0.17
∆(Cash)t × (High F or Ops)t × (P oor Inv Opp)t
Intercept
Observations
Number of Firms
Adjusted R2
46
Table 8: Abnormal Returns Surrounding Passage of the American Jobs Creation
Act of 2004
This table displays abnormal returns and CARs for firms with good and poor investment
opportunities abroad. Results are given separately using the market model and using the
Fama and French (1993) factors. τ = 0 represents June 17th, when the Act won passage in
the U.S. House of Representatives. The sample covers represents a total of 4,867 returns for
157 firms, comprised of 97 firms with good investment opportunities abroad, and 60 firms
with poor investment opportunities abroad.
Event Time τ
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Market
Good Foreign
Invest. Opps.
AR
CAR
0.0004 0.0004
0.0013 0.0017
-0.0059 -0.0042
0.0027 -0.0015
0.0026 0.0012
-0.0092 -0.0080
-0.0050 -0.0130
0.0014 -0.0116
-0.0015 -0.0132
0.0023 -0.0109
-0.0010 -0.0119
0.0003 -0.0116
-0.0019 -0.0135
0.0048 -0.0087
0.0014 -0.0073
-0.0064 -0.0137
-0.0027 -0.0164
0.0019 -0.0144
0.0062 -0.0082
0.0035 -0.0047
-0.0021 -0.0068
0.0112 0.0044
-0.0031 0.0013
0.0056 0.0069
0.0009 0.0078
-0.0050 0.0027
-0.0044 -0.0017
-0.0118 -0.0134
-0.0012 -0.0146
0.0008 -0.0138
0.0020 -0.0117
Model
Poor Foreign
Inv. Opps.
AR
CAR
0.0039 0.0039
-0.0032 0.0006
-0.0005 0.0001
0.0041 0.0042
0.0072 0.0114
-0.0018 0.0096
-0.0047 0.0048
-0.0004 0.0044
-0.0009 0.0035
0.0006 0.0041
0.0091 0.0132
-0.0022 0.0110
-0.0009 0.0100
0.0027 0.0127
0.0023 0.0150
0.0063 0.0213
-0.0027 0.0186
0.0032 0.0219
0.0015 0.0234
0.0041 0.0275
0.0005 0.0280
0.0059 0.0338
0.0008 0.0347
0.0064 0.0411
0.0054 0.0465
-0.0001 0.0464
0.0046 0.0510
0.0041 0.0551
-0.0021 0.0530
-0.0033 0.0497
0.0007 0.0504
47
Fama-French Model
Good Foreign
Poor Foreign
Inv. Opps.
Inv. Opps.
AR
CAR
AR
CAR
0.0017 0.0017 0.0035 0.0035
0.0012 0.0030 -0.0034 0.0002
-0.0022 0.0008 0.0027 0.0029
0.0034 0.0042 0.0039 0.0068
-0.0022 0.0019 0.0040 0.0108
-0.0066 -0.0047 0.0009 0.0117
-0.0011 -0.0058 -0.0009 0.0108
0.0010 -0.0048 -0.0010 0.0097
0.0036 -0.0012 0.0001 0.0098
0.0036 0.0024 0.0020 0.0118
-0.0007 0.0017 0.0096 0.0214
0.0050 0.0068 -0.0012 0.0202
-0.0025 0.0043 0.0017 0.0219
0.0005 0.0048 -0.0019 0.0200
0.0005 0.0053 0.0013 0.0213
-0.0060 -0.0007 0.0056 0.0269
0.0002 -0.0005 -0.0019 0.0251
-0.0003 -0.0008 0.0014 0.0265
0.0056 0.0048 0.0011 0.0275
0.0032 0.0081 0.0022 0.0297
-0.0037 0.0044 -0.0003 0.0294
0.0009 0.0053 -0.0017 0.0277
-0.0043 0.0010 0.0013 0.0290
0.0021 0.0031 0.0040 0.0330
0.0026 0.0057 0.0049 0.0379
-0.0027 0.0030 -0.0012 0.0367
-0.0054 -0.0025 0.0018 0.0385
-0.0050 -0.0075 0.0036 0.0421
0.0024 -0.0052 -0.0005 0.0415
0.0065 0.0013 -0.0008 0.0408
0.0015 0.0028 0.0009 0.0417
Table 9: Results of Regression (24): Abnormal Returns from the American Jobs
Creation Act
This table presents the results of regression (24). The dependent variable, Rp,τ , is the equalweighted average of returns for portfolio p as of event time τ . Rm,τ is the market return on date τ ,
which is the return of the value-weighted CRSP market index. Rhml and Rsmb are the returns from
the Fama and French (1993) value and size portfolios. Iτ is a dummy variable which takes a value
of 1 if the date τ falls in the event window, which is τ = −15 to 15, and a value of 0 otherwise.
Specifications (1) and (2) form the portfolio using only firms with poor investment opportunities
Income
abroad, defined as firms that are in the upper half of the sample in terms of F oreign
, but are
TA
in the lower half of the sample in terms of q-ratio. Specifications (3) and (4) include only firms with
good investment opportunities abroad, defined as firms that are in the upper half of the sample in
Income
terms of F oreign
, and are also in the upper half of the sample in terms of q-ratio. Robust
TA
standard errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1%
level, respectively.
Portfolio:
Iτ
Rm,τ
(1) Poor
Investment
Opportunities
Abroad
0.002***
(0.001)
1.284***
(0.042)
Rhml
Rsmb
Constant
0.0004
(0.0003)
Observations
Adjusted R2
252
0.78
Dependent Variable: Rp,τ
(2) Poor
(3) Good
Investment
Investment
Opportunities
Opportunities
Abroad
Abroad
0.002***
-0.0002
(0.0006)
(0.0004)
1.080***
1.505***
(0.045)
(0.049)
0.334***
(0.071)
0.586***
(0.050)
0.0003
-0.0002
(0.0003)
(0.0004)
252
0.84
252
0.81
48
(4) Good
Investment
Opportunities
Abroad
0.0001
(0.0007)
1.139***
(0.040)
-0.272***
(0.063)
0.834***
(0.055)
-0.0002
(0.0002)
252
0.90
Figure 1: Plot of CARs Surrounding Enactment of American Jobs Creation Act
These graphs plot the average cumulative abnormal returns (CARs) for firms with good
foreign investment opportunities (blue dashed line) and firms with poor foreign investment
opportunities (solid red line). The top graph displays CARs calculated using the market
model, while the bottom displays CARs calculated using the Fama-French factors.
.0 2
-.0 2
-.0 1
0
.0 1
CARs
.0 3
.0 4
.0 5
.0 6
CARs Calculated Using Market Model
-15 -14 -13 -12 -11 -10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12 13
14
15
10
11
12 13
14
15
Event Time
Good Foreign Inv. Opps.
Poor Foreign Inv. Opps.
.0 2
.0 1
0
-.0 1
CARs
.0 3
.0 4
CARs Calculated Using Fama-French Factors
-15 -14 -13 -12 -11 -10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
Event Time
Good Foreign Inv. Opps.
Poor Foreign Inv. Opps.
49