Investment, overhang, and tax policy.
Desai, Mihir A. ; Goolsbee, Austan D.
THE PAST DECADE HAS seen an unusual pattern of investment. The boom
of the 1990s generated unusually high investment rates, particularly in
equipment, and the bust of the 2000s witnessed an unusually large
decline in investment. A drop in equipment investment normally accounts
for about 10 to 20 percent of the decline in GDP during a recession; in
the 2001 recession, however, it accounted for 120 percent. (1)
In the public mind, the recent boom and bust in investment are
directly linked due to "capital overhang." Although the term
is not very precisely defined, this view generally holds that excess
investment in the 1990s, fueled by an asset price bubble, left
corporations with excess capital stocks, and therefore no demand for
investment, during the 2000s. The popular view also holds that these
conditions will continue until normal economic growth eliminates the
overhang and, consequently, that there is little policymakers can do to
remedy the situation, by subsidizing investment with tax policy, for
example. Variants on this view have been espoused by private sector
analysts and economists, (2) and the notion of a capital overhang has
certainly been on the minds of leading Federal Reserve officials and
researchers. (3)
Whether or not a capital overhang is the true explanation of the
investment bust, it is clear that the drop in investment has motivated
policymakers to try to stimulate investment through ambitious fiscal
policy changes. (4) Under President George W. Bush, depreciation
allowances for equipment investment have been increased twice, in 2002
and 2003, and in 2003 the tax rate on dividend income was cut sharply
and that on capital gains income more modestly. These measures were
mainly intended to increase after-tax returns and stimulate investment.
The typical analysis of the investment collapse and policy response is
summarized by the Republican chairman of the Joint Economic Committee:
Excessive and bad business investments made during the stock market
bubble have taken years to liquidate. In nine of the 10 quarters
beginning with the fourth quarter of 2000, real business investment
has declined. Fortunately, recent tax legislation signed into law
in 2003 should promote business investment by increasing the
after-tax returns from investing in capital assets and alleviating
financing constraints among small and medium-size firms. (5)
Yet, after several years of tax cuts, investment has still not
risen impressively compared with previous recoveries. This contrast has
reignited claims that tax policy is ineffective at stimulating
investment, although some make the more specific charge that tax policy
is impotent when it follows a period of excessive investment.
This paper examines the evidence on the two related issues of
capital overhang and taxes using data at the industry, the asset, and
especially the firm level. Specifically, we address two questions:
first, did "over"-investment in the 1990s cause the low
investment of the 2000s, and, second, did investment in the 2000s become
less sensitive to prices, and does this explain why tax policies,
specifically the equipment expensing and the dividend tax cuts of 2002
and 2003, seem to have been ineffective in restoring investment to
normal levels?
We begin by examining the degree to which growth in investment
during the boom was correlated with a decline in investment during the
bust across different assets and industries. There are, of course, many
possible definitions of overhang or excess investment. We will not try
to show that there was no overoptimism in product or capital markets.
Clearly equity prices rose substantially and then fell, as did
investment rates. Instead we investigate whether investment grew the
most in those assets and industries in which it subsequently declined
the most. We want to know if any aftereffects of the investment boom of
the 1990s persisted into the 2000s--whether firms behaved differently
because too much capital remained from the investment decisions of the
1990s.
The evidence across assets, industries, and firms suggests that,
contrary to the popular view, there is little correlation between the
investment boom of the 1990s and the investment bust of the 2000s. We
also present some more specific evidence, using firm-level data, that
investment behavior has remained just as responsive to the fundamentals
(as measured by Tobin's q) regardless of how much a firm's
investment grew or how much its market value rose in the 1990s.
Essentially, we find that the explanatory power of the standard
empirical model of investment has not deteriorated in the 2000s, despite
the common perception that it has.
We then use that standard model to consider the impact of tax cuts.
To estimate the impact of the dividend tax reduction, we revisit an
enduring debate in public finance between the "new" view of
dividend taxation, which says that dividend tax cuts do not reduce the
cost of capital for marginal investments, and the
"traditional" view, which says that such cuts do reduce the
marginal cost of capital and thus stimulate investment. The evidence at
the firm level strongly supports the new view and suggests that the
dividend tax reductions enacted in 2003 had little or no effect on
investment.
Finally, to estimate the impact of the changes in depreciation
allowances, we estimate a tax-adjusted q model similar to that of
Lawrence Summers, (6) but with greater emphasis on the importance of
error in the measurement of q, as emphasized by Jason Cummins, Kevin
Hassett, and Glenn Hubbard. (7) The method introduced for handling these
measurement error issues yields results that suggest that both tax
policy and q are likely to have much larger effects on investment than
found in the traditional literature, where coefficients are very small
and imply implausibly large costs of adjustment. Even with the more
reasonable adjustment costs, however, we show that the depreciation
allowance changes of 2002 and 2003 changed the tax term by a relatively
small amount: the estimated overall impact in these two years was an
increase in investment of only 1 to 2 percent, far too small to offset
the double-digit declines of the early 2000s.
Capital Overhang and Investment
Real investment was considerably higher than normal during the late
1990s. When recession years are excluded, investment from 1947:1 to
1995:2 averaged about 12 percent of GDP; the highest quarterly level was
15 percent in 1984:3. From 1996:1 to 2000:4, in contrast, this ratio
averaged more than 16 percent, and it reached 18 percent at its peak.
The distinctiveness of these investment rates holds even relative to the
business cycle. Figure 1 shows that investment in the quarters leading
up to the 2001 peak was higher than it had been during comparable
periods in previous cycles. The popular view holds that this extra
investment resulted from the excesses of the 1990s bubble. (8)
[FIGURE 1 OMITTED]
With this view in mind, figure 2 provides a counterpart to figure
1, showing the path of investment in the period after the trough quarter
for the recovery that began in late 2001 and for the average of previous
recoveries. The increase in investment in the current recovery, at least
through the beginning of 2004, is notably smaller than in the average
recovery. Taken together, these trends make it plausible to many
observers that investment after the most recent trough was lower than in
previous cycles precisely because investment in previous years had been
higher.
[FIGURE 2 OMITTED]
Of course, these aggregate patterns do not establish any underlying
connection between the rise and the fall. To test for a causal
relationship, we believe, it is critical to disaggregate the investment
data. Most academic work looking at the capital overhang has not done
so, or has done so at a very broad level, emphasizing that the reversal
in investment has been concentrated in information technology
investment. (9) It is clear that the exuberance of the 1990s was not
shared equally in all sectors. Industries such as telecommunications
experienced huge increases in the 1990s in a way that railroads or
mining, say, did not. We believe that any overhang, in the sense of
excess capital remaining at the end of the boom, is inherently an
industry- or firm-level phenomenon, which requires that we look at data
at that level.
An additional reason to look at the industry- and firm-level data
is that investment theory typically begins with the premise that there
is a perfectly functioning secondary market for capital goods and a flat
supply curve for capital. In such a world, firms with an overhang of
unused capital equipment can simply sell it without incurring any loss.
For the popular view to make sense, then, either investment must be in
some way irreversible (which leads to a rather different model), (10) or
there must be some other type of adjustment cost associated with
disinvestment. (11) Matthew Shapiro and Valerie Ramey have documented
that, in some industries, a sizable wedge can develop between the
purchase price of capital goods and the sale price. (12) These types of
irreversibilities are likely to be firm or asset specific rather than
applying to all types of investment in all sectors homogeneously.
Fortunately, data on investment are available at the industry, asset
type, and firm level, and, as we will show, the evidence at all three
levels of disaggregation is generally the same.
Evidence at the Industry Level
We begin with the evidence on changes in investment at the industry
level. Rather than rely on the more aggregated fixed asset data
available from the Bureau of Economic Analysis (BEA), we turn to the
Annual Capital Expenditure Survey (ACES) of the U.S. Census Bureau,
which provides a finer industry disaggregation than is available
elsewhere. The survey samples approximately 46,000 companies in more
than 100 industries, categorized according to the 1997 North American
Industry Classification System (NAICS). We narrow this categorization
down to eighty-one nonoverlapping industries at approximately the
three-digit NAICS level. (13)
The ACES provides measures of gross investment only and does not
estimate industries' capital stock. Consequently, we cannot scale
investment by lagged capital as is done in traditional empirical work on
investment. Instead we simply investigate the change in total investment
both for equipment alone and for equipment and structures combined.
Empirical models of investment have struggled to explain the behavior of
investment in structures, and it is not known whether this problem is
due to mismeasurement in the tax term, unobservable factors in
structures markets (such as liquidity and financing issues relating to
the supply side of the market), or some other factor. (14) Since we
cannot readily isolate equipment investment from structures investment
in the firm-level data employed below, we have to assume that equipment
investment and overall investment behave in the same way. Given that, by
the 2000s, equipment accounted for something like 80 percent of
nonresidential investment, this may not be too heroic an assumption, but
the results in these areas will allow us to check the results in a
circumstance where we have both sets of data.
Our goal with these data is to look for general evidence supporting
the view that a capital overhang from the 1990s is a key factor
determining investment in the 2000s. If overhang is quantitatively
important, one might expect to find that those industries in which
investment has fallen in the 2000s are the same as those in which
investment grew substantially in the 1990s. To test this relationship
formally, we performed a cross-sectional regression of the change in log
investment by industry from 2000 to 2002 (the period widely viewed as
the "collapse") on the change in log investment from 1994 to
1999 in the same industry, estimating the following equation:
(1) ln([I.sub.i, 2002]) - ln([I.sub.i, 2000]) = [alpha] +
[beta][ln([I.sub.i, 1999]) - ln([I.sub.i, 1994])] + [[epsilon].sub.i].
This test would show no evidence of reversion, of course, if all
industries boomed and then busted together equally, since any such
effect would simply appear in the constant term. Given that the
investment growth of the 1990s was not likely to have been identical
across industries, this equation provides a useful estimation strategy.
Table 1 presents, in the top panel, the results of estimating
equation 1 by ordinary least squares (OLS) and, in the bottom panel,
results for the same specifications employing median regressions, to
ensure that the results in the top panel do not purely reflect the role
of large outliers. Column 1-1 reports the results from the basic
overhang specification. The OLS and median regressions produce
coefficients that are negative but very small and not significantly
different from zero. To give a sense of the magnitude of this effect, a
1-standard-deviation change in the investment rate from 1994 to 1999
(0.53, or from the median of 0.38 to about the 85th percentile) is
associated with only a 2.9 percent lower level of investment (less than
one-twelfth of a standard deviation) from 2000 to 2002. This is modest
evidence of an overhang, at best.
Given the serious decline of manufacturing in the most recent
recession, and given that old-line manufacturing was not typically
involved in the Internet boom, we further investigate the manufacturing
sector separately. In column 1-2, which restricts the sample to the
twenty-three manufacturing industries, the evidence for an overhang
seems more pronounced. In both the OLS and the median regressions, there
is a large and significant negative coefficient on the change in
investment from 1994 to 1999. In the median regression, a
1-standard-deviation increase in the investment rate among manufacturing
industries (a log value of 0.32) in 1994-99 corresponds to an almost 22
percent lower investment rate in 2000-02, which is equal to about
two-thirds of the standard deviation of those changes. If one takes this
larger effect as evidence of overhang (as opposed to a cyclical
phenomenon), however, it should be noted that manufacturing industries
accounted for only about 22 percent of total equipment investment and 18
percent of total investment in 2002, according to the ACES. (15)
Consequently, on the present evidence, mean reversion for manufacturing
can explain only a limited part of the aggregate collapse of investment.
The common explanation for capital overhang is that an abundance of
funds raised in the capital market during the bubble encouraged the
excess investment, particularly during the 1997-99 period. Indeed, the
broadly disaggregated, cost of capital-type analysis done by Jonathan
McCarthy suggests that there was no capital overhang at all until 1998,
even in the high-technology investment goods sector (computers and
communications equipment). (16) In columns 1-3 and 1-4, therefore, we
consider separately the periods from 1994 to 1997 and from 1997 to 1999
in order to isolate the effects of the bubble period and to take account
of underlying growth trends in different industries that might mask
investment reversion. Again there is little evidence of reversion across
all industries, and there are larger negative coefficients in
manufacturing. The later period, in which the overhang is alleged to
have occurred, has a smaller coefficient than the earlier period,
although the standard errors are not small enough to reject the
hypothesis that they are equal. Rather than supporting the intuition of
a bubble-induced capital overhang, this consideration of the two
subperiods suggests some underlying, more secular mechanism associated
with the continuing decline in U.S. manufacturing.
Table 2 considers the behavior of both equipment and structures
investment. The results are qualitatively similar to those in table 1 in
that they show little evidence of reversion, either generally or in
manufacturing, featuring the dynamics discussed earlier.
Evidence at the Asset Level
Next we consider the general evidence on investment by type of
investment good rather than by industry. We did this by testing our
basic regression model (equation 1) using data by asset category instead
of by industry. Using only the BEA data available for the whole period
1994-2002, we have twenty-five different categories of equipment and an
additional nine categories of structures. (17) As in tables 1 and 2, the
two panels of table 3 report both OLS and median regressions. (Weighting
by the initial capital stock in these regressions provides very similar
results, which we do not report here.) Those asset types that had the
largest increases in investment from 1994 to 1999 are not systematically
those that had the largest drop in investment from 2000 to 2002: the
regressions show a small and insignificant negative coefficient. This is
equally true in the OLS and the median regression, and the coefficients
have similar magnitudes. In the top panel, the estimate in column 3-1
indicates that an asset type whose log investment grew by 1 standard
deviation (a log value of 0.36) more than the median asset from 1994 to
1999 would be expected to have a drop in log investment from 2000 to
2002 about one-sixth of a standard deviation larger than that of the
median firm.
Column 3-2 repeats this analysis but splits the data into the early
and late periods of the boom, 1994-97 and 1997-99, respectively. Here,
although the coefficient estimates are imprecise, they are not
consistent with the typical overhang story. If anything, the
coefficients are again larger in absolute value in the earlier period
than in the later period. Indeed, both of the point estimates in the
later period are greater than zero, suggesting that those assets whose
real investment grew most in the 1990s saw even greater investment
growth in the 2000s. The irrational exuberance hypothesis would say just
the opposite. Of course, in both cases the regressions do not control
for anything but merely indicate the absence of a strong negative
correlation. Using the firm-level data, we can further investigate these
phenomena at the firm level, with better controls for observables
related to investment opportunities.
Evidence at the Firm Level
Our firm-level sample includes all companies that appear in the
Compustat research file from 1962 to 2003. Figure 3 plots the average
investment rate (defined as capital expenditure divided by the
beginning-of-period net capital stock) for manufacturing firms, for
nonmanufacturing firms, and for firms involved in information
businesses. Information businesses are defined as those in NAICS
categories 334 (computer manufacturing) and 51 (information); this
grouping is one we return to later, because the irrational exuberance of
the late 1990s is commonly viewed as having been most extreme there.
These data reveal the same pattern as the aggregate data: investment
rates rose dramatically in the 1990s and then fell dramatically in the
2000s. We cannot say how representative the universe of publicly traded
firms is of the rest of the economy, but in some ways the sheer
magnitude of the firm-level sample makes it an overwhelmingly important
component of aggregate investment on its own. Our calculations suggest
that aggregate capital expenditure in the firms in the Compustat data
constituted 85 to 90 percent of private, nonresidential investment in
the United States for most of the last twenty-five years. (18) Our
sample in 2003 does not include all firms, since some share of firms had
yet to have their reports coded by Compustat at the time of our
analysis. Nonetheless, the sample is large (more than 80 percent of the
2002 sample) and provides a perspective not afforded by the industry-or
asset-level data, given their earlier cutoffs.
[FIGURE 3 OMITTED]
We begin with the general evidence that parallels the previous
results in examining the changes in investment rates during the bust and
the subsequent boom, but with the advantage that, in the firm-level
data, we can compute the change in the investment rate because we have
capital stock data for each firm. Our modified regression equation,
then, is where I is capital expenditure at the firm level and is scaled
by the lagged capital stock K. (19)
As before, the top panel of table 4 presents the OLS results and
the bottom panel the median regression results. Column 4-1 reports
results for a specification that emphasizes the relationship between the
change in investment rates over the 1994-99 boom and that over the
2000-02 bust. Given that a firm must have existed in 1994, 1999, 2000,
and 2002 to appear in the sample for this regression, the sample size is
somewhat smaller than the full universe of firms. These results again
show a very small and insignificant negative correlation in the changes
in investment rates. The median percentage change in the capital stock
from 2000 to 2002 was -0.3 percent. The estimated coefficients are
indeed tiny compared with the median firm. The coefficient on the lagged
investment change variable indicates that a firm whose increase in
investment rate during the boom was 1 standard deviation (about 0.65)
above that of the median firm would have seen its investment fall by
about 0.02, or only about one-thirty-fifth of a standard deviation,
during the bust. The bottom panel of table 4 repeats this specification
but controls for outliers by using a median regression (particularly
important when firm data are used), and here the coefficient is even
smaller but statistically significant. A firm whose investment grew by 1
standard deviation more than the median during the boom would have seen
its investment fall during the bust by only about one-seventieth of a
standard deviation more than the median firm.
The regressions reported in column 4-2 of both panels in table 4
split the 1994-99 period into two parts as an additional control, to
account for firms whose size is trending upward. Inclusion of this
split, however, produced little change in the general result of a very
small negative impact relative to trend. Finally, the regressions in
column 4-3 include the percentage change in the firm's real sales
as an additional control, to further take into account the fact that
firms might be growing or shrinking over the period in a way that drives
the investment results. (Recall the large coefficients on manufacturing
investment in the industry-level data.) In this regression, sales growth
is positively correlated with growth in investment, but the evidence on
reversion is even a bit more modest than in the first two columns.
The evidence thus far, then, provides very limited support for the
view that firms, asset types, and industries that had major increases in
their investment in the 1990s experienced major drops in investment in
the 2000s. This seems to suggest that overhang was not the dominant
factor influencing investment in the later period. A more precise test
is available, however, by relating overhang to the sensitivity of
investment to fundamentals at the firm level.
Evidence on Overhang and the Sensitivity of Investment
The firm-level data allow us to further examine whether firms have
been less responsive to changes in tax-adjusted q in the 2000s if they
had significant valuation increases in the 1990s. If, in fact, firms
experiencing large changes in market value exhibit a different response
than other firms to tax-adjusted q in the 2000s, this could help explain
why taxes have not seemed to have a major impact on investment. The next
section and appendix B discuss in more detail our tax-adjusted measure
of q and the model underlying it. There we provide a fuller discussion
of the measurement issues and the predictions of that model, but we
include this analysis here in order to fully address the overhang
phenomenon. Our basic estimating equation will add an interaction term
to the standard equation relating investment to q.
We investigate the relevance of two different measures of overhang
in the 1990s: one based on equity values and one based on capital
expansion. Table 5 reports results of regressions using the lagged
change in q as a measure of the degree to which overhang is operative.
(20) We create the variable
[[DELTA].sup.t-7.sub.t-3][q.sup.2000+.sub.it], which is the change in q
observed in the period three to seven years before the current year and
only for the time period 2000-03. (21) So, in 2002 for example, this
variable would be the change in the firm's q from 1995 to 1999.
Before the 2000s this variable is always zero. One view of the overhang
hypothesis is that investment for firms with large capital overhangs
from the 1990s should be less sensitive to fundamentals or tax rates.
(22)
This yields the following investment equation:
(3) [(I/K).sub.it] = [[alpha].sub.i] + [[gamma].sub.t] +
[[beta].sub.1][Q.sub.it] + [delta][Q.sub.it]]
([[DELTA].sup.t-7.sub.t-3][q.sup.2000+.sub.it]) +
[[beta].sub.2][(cash/K).sub.it] + [[member of].sub.it]].
Here [[alpha].sub.i] and [[gamma].sub.t] are firm and year dummies,
respectively, and Q is tax-adjusted q, as defined below. Column 5-1 of
table 5 presents the results from estimating this equation over all
firms. It shows that there is no significant difference in the
investment-q relationship in the 2000s for firms that had larger run-ups
in their stock prices in the 1990s. Indeed, the point estimate is
actually positive, although small. Column 5-2 excludes the firm dummies,
so that we are explicitly comparing results across firms rather than
within a given firm. The result on the interaction term is very similar
to that in column 5-1: positive and not significant.
Column 5-3 returns to the specification with firm dummies but
restricts the sample to firms in information businesses; these are the
firms most closely associated with the technology bubble. There is again
no evidence that large increases in equity values in the 1990s have
reduced the sensitivity of investment to the fundamentals in the 2000s.
The point estimate on the interaction term is insignificant, although
this time slightly less than zero. Column 5-4 repeats the analysis, this
time for manufacturing firms only, and again there is nothing notable.
Finally, column 5-5 investigates whether the relationship changed any
differently in the 2000s than it did in earlier periods that followed
asset price increases. The evidence suggests that it did not.
Table 6 repeats the exercise reported in table 5 but uses the
lagged percentage change in capital for the firm during the 1990s as the
measure of overhang. The advantage of the lagged change in q as the
overhang measure in table 5 is that it picks up more directly the
influence of asset price bubbles, which typically underlie the popular
explanation of overhang. The lagged percentage change in the capital
stock as used in table 6, in contrast, is a more direct measure of
capital accumulation.
Table 6 reports estimates of the following equation:
(4) [(I/K).sub.it] = [[alpha].sub.i] + [[gamma].sub.t] +
[[beta].sub.1][Q.sub.it] + [delta][Q.sub.it]]
(%[[DELTA].sup.t-7.sub.t-3][K.sup.2000+.sub.it]) +
[[beta].sub.2][(cash/K).sub.it] + [[member of].sub.it]].
where %[[DELTA].sup.t-7.sub.t-3][K.sup.2000+.sub.it] is the
percentage change in the net capital stock of the firm between time t -
3 and time t - 7 for 2000-03 (in other words, the change in the capital
stock during the mid-1990s).
Estimating this equation for the entire sample of firms, as
reported in column 6-1, does show a significant negative coefficient on
the interacted Q term, indicating that firms that had larger
accumulations of capital in the 1990s did, indeed, show less sensitivity
to the fundamentals in their investment behavior in the 2000s. Although
the direction is consistent with the overhang view, the magnitude is
extremely small. To see this, consider that the highest mean value of
lagged capital growth was 1.37 in 2002 (with a median value of past
growth of 0.41). This value predicts that the coefficient on Q would
fall by only 0.0037 (and only 0.0011 for the median). When we explicitly
compare across firms by dropping the firm dummies (column 6-2), the
point estimate becomes positive. Column 6-3 restricts the sample to
information businesses as before; the coefficient on lagged capital
growth, although slightly negative, is similarly modest. Column 6-4
repeats the analysis for manufacturing firms only and again finds
similar results. Column 6-5 demonstrates that, with this measure of
overhang, there is normally a small negative impact of lagged capital
growth on current investment rates, even in the period before the 2000s.
The difference in the coefficient between the 2000s and the pre-2000s
period is only about 0.0017.
Taken together, the results in this section provide little evidence
that capital overhang has played a key role in investment behavior in
the 2000s. Low investment during the bust is not correlated strongly
with excessive investment in the boom. Similarly, the sensitivity of
investment in the 2000s to the fundamentals is not markedly different
for firms overall or for those firms usually at the heart of the
overhang view. In other words, the standard firm-level model using
tax-adjusted q has not become noticeably worse at explaining investment.
Accordingly, in the following section we use this model to analyze the
impact of taxes.
q Theory, Investment Incentives, and Dividend Taxes: Theory and
Empirics
As a prelude to using the tax-adjusted q model to study the impact
of the Bush tax cuts, it is useful to consider the aggregate movements
in q over the whole of the period that our firm sample covers,
1962-2003, in thinking about the root determinants of the behavior of
aggregate investment in the 1990s and 2000s. Figure 4 plots average q
for the corporate sector as a whole, measured as the total market value
of all publicly traded firms as computed by CRSP (Center for Research in
Security Prices) divided by the total stock of corporate capital as
computed by the BEA in its fixed reproducible tangible wealth series.
The series shows a historic rise in q in the mid-1990s and an
unprecedentedly steep fall in the 2000s. (23) Previous studies have
found only very small coefficients on tax-adjusted q, and so this rise
and fall in q might not imply much in the way of aggregate investment
changes. We estimate the investment relationship, however, within a
slightly different framework than is typical, to overcome possibly
conflating measurement issues. It should be clear that, if the true
coefficient on q for investment is not 0.02 but closer to 1, as argued
below, the investment collapse is eminently comprehensible within the
conventional framework. Clearly, within a standard q model of
investment, an equity price bubble can still drive investment up and
then down through movements in q. Such an account of the investment
experience of the 1990s and 2000s is distinct from the intuition of a
lingering overhang from the 1990s.
[FIGURE 4 OMITTED]
Of course, as critics have frequently pointed out, the fundamental
question is why the coefficients on q in investment regressions are
typically so low, implying extremely large adjustment costs. One of the
key potential problems discussed in the literature has been the
importance of measurement error in q. Marginal q is the variable of
interest, but the data can provide only average q. At least some of the
existing literature has argued that measurement error is at the root of
the relatively weak empirical performance of traditional investment
models. (24) This issue of measurement error is particularly important
for thinking about the impact of taxation, as we demonstrate below.
If, in fact, an empirical implementation of the q model provides
more reasonable coefficients through an alternative strategy of dealing
with these measurement issues, then these estimates can serve as the
foundation for analyses of the true marginal costs of adjustment and the
impact of the various tax policy changes enacted during the Bush
administration (the changes in depreciation allowances as well as the
changes in dividend and capital gains taxes). The rest of this section
undertakes such an analysis.
Tax-Adjusted q Theory, Dividend Taxes, and the Marginal Source of
Funds
To use the q model to analyze the impact of taxes, particularly
dividend taxes, we revisit the techniques for incorporating taxes into
such models developed by Summers and by James Poterba. (25) A crucial
issue in determining the impact of dividend taxes in this framework is
what the marginal source of funds for firms' investments is.
Briefly, if the marginal source of funds is retained earnings, then
dividend taxes will have no impact on marginal investment incentives.
But if the marginal source of funds is new equity, then dividend taxes
will influence investment. This distinction is the subject of an
enduring debate in public finance between the "new" and the
"traditional" view of dividend taxation.
Appendix B works through the implications of the two views in some
detail and provides alternative estimating equations. The investment
model typically estimated in the literature follows the traditional
view. In this view dividend taxes influence investment by, essentially,
double-taxing corporate income. Assuming quadratic adjustment costs,
this view generates the following investment-q relationship:
(5) [I.sub.t]/[K.sub.t-1] = [mu] + (1/[phi])([q.sub.t]/1 - [tau] -
1 - [GAMMA]/1 - [tau]),
where I is investment, K is the firm's capital stock, [phi] is
the adjustment cost parameter, and [mu] represents an average investment
rate. Under this assumption, net new equity finances investment, so that
investment is determined by the point at which shareholders are
indifferent between holding a dollar inside or holding it outside the
firm. In a world without other taxes, the firm stops investing once q =
1. Of course, if investment is heavily subsidized (that is, if [GAMMA]
> [tau]), firms may even continue investing with q < 1, but the
general idea is the same. Changes in dividend taxes will influence
equity values and investment incentives by influencing the relative
preference of investors to hold their money inside rather than outside
the firm.
If, however, retained earnings are the marginal source of finance,
the traditional investment-q relationship of equation 5 will not hold.
In this case (again assuming quadratic adjustment costs), the
relationship will follow
(6) [I.sub.t]/[K.sub.t-1] = [mu] + (1/[phi]){[[q.sub.t](1 - c/1 -
[theta])/1 - [tau]] - (1 - [GAMMA]/1 - [tau])},
where [theta] is the tax rate on dividends is and c is the
accrual-equivalent tax rate on capital gains.
Equation 6 corresponds to the "new" (or "trapped
equity" or "tax capitalization") view of the role of
dividend taxation. In this view dividend taxes do not influence the tax
term for marginal investments. (26) Instead they are fully capitalized
into existing share prices. In other words, changes in dividend taxes
serve solely as a penalty or windfall on existing firm values. To see
the intuition behind this, consider a firm that uses retained earnings
at the margin to finance investment, with dividends determined as a
residual. In this model dividends are the only means of distributing
earnings to shareholders. In this setting, given that retained earnings
are the marginal source of financing, investment is determined by the
point at which shareholders are indifferent between receiving a dollar
today as a dividend, with value 1 - [theta], and having the dollar
reinvested, yielding (1 - c)q. Accordingly, the firm will stop investing
at the point q = 1 - [theta]/1 - c < 1 in a world of no other taxes,
rather than at q = 1 as in the traditional case.
What is counterintuitive about the new view, as expressed in
equation 6, is that although it argues that dividend taxes do not
influence investment, the dividend tax rate appears in the investment
equation. In contrast, equation 5, which exemplifies the traditional
view, does not include a dividend tax term but corresponds to the view
that dividend taxes do influence investment. The intuition for this is
simply that, when dividend taxes get capitalized into share values, they
influence q. This effect needs to be removed from the investment
equation under the new view, because this part of q has no impact on
investment. Alternatively, under the traditional view embodied in
equation 5, a permanent dividend tax cut raises the value of q above 1,
and this encourages further investment, just as any other increase in q
does.
There is an old and contentious debate within public finance over
which of these views is the more accurate. Proponents of the traditional
view cite evidence on the effects of dividend tax rates on corporate
dividend payout policy. (27) Furthermore, dividends seem more stable
than the new view implies, and other means of distributing profits to
shareholders, such as share repurchases, have become increasingly
important. Proponents of the new view note that new equity issuances are
still quite rare for most companies and that firms pay dividends even
though dividends are tax disadvantaged. The arguments in this debate are
considerably more involved than we can describe here. (28)
Fundamentally, however, which view is more accurate is primarily an
empirical matter. Surprisingly, except for the work on aggregate
investment in the United Kingdom by Poterba and Summers, (29) there have
been no direct attempts to test between the two views using investment
data.
Poterba and Summers use thirty years of annual data from the United
Kingdom to test between equations 5 and 6; their results support the
traditional view. Although Alan Auerbach and Hassett have been critical
of these findings for, among other things, failing to account for other
macroeconomic and tax changes occurring at the same time, (30) these
estimates are still the only direct empirical tests of how dividend
taxes affect investment. Oddly, no one has extended the methods of
Poterba and Summers to firm-level data, where it is possible to control
for many aggregate factors. Nor has anyone ever applied their method to
the United States, perhaps because, until the 2003 tax cut, U.S.
dividend taxes did not change in isolation from other changes, but
rather varied only through changes in personal income tax rates. Instead
the empirical work testing the new versus the traditional view has
adopted the indirect method of examining the relationship between
dividend taxes and dividend payments (or the valuation of dividend
payments by investors). (31)
The recent large changes in dividend taxes, however, make such an
analysis possible. Indeed, testing between these two views (and making
the required detour into the public finance debate) is critical for
evaluating the impact of the Bush tax cuts on investment. If the
marginal source of funds turns out to be retained earnings, the dividend
tax cut will have little or no impact on marginal incentives to invest.
Before explicitly testing between the two views, however, we lay out the
basic tax-adjusted q model and illustrate why we believe measurement
error is a primary reason that such models have implied high adjustment
costs and have performed so poorly in the past.
Empirical Implementation of the Q Model
In computing q empirically, we use the historical and current
Compustat database, which provides data on a panel of firms from 1962
through 2003. (32) For some firms we also match this sample to the
earnings estimates provided by I/B/E/S (Institutional Brokers'
Estimate System). Estimates of the tax term at the asset level are
derived from data provided generously by Dale Jorgenson. (33) As
described in more detail in appendix A, we follow Cummins, Hassett, and
Hubbard and Robert Chirinko, Steven Fazzari, and Andrew Meyer in using
the BEA's capital flows table for 1997 to calculate the share of
investment in each industry for each asset type. (34) With that
weighting, we calculate the weighted-average tax term in each year for
each four-digit industry in the Compustat data. Average marginal tax
rates on dividend and capital gains income (on an accrual basis) are
taken from Poterba. (35) Further discussion of the variable construction
and the sources of data is provided in appendix A.
The measurement of q and, in turn, Q, hinges on constructing a
measure of the ratio of a firm's market value to its book value.
The corporate finance literature and the public finance literature have
diverged somewhat in their measurement of this ratio, and we consider
both alternatives in the results that follow. The corporate finance
literature, as exemplified in the 1997 paper by Steven Kaplan and Luigi
Zingales, (36) employs data from Compustat stat to derive a measure of q
as BV Assets + (MV Equity - BV Equity)/BV Assets,
where BV stands for book value and MV for market value, and all
values are taken from public financial records. (37) In contrast, the
public finance literature has emphasized the derivation by Salinger and
Summers, (38) which constructs q as MV Equity + MV Debt/MV Assets, where
debt and equity values are taken from financial reports, but the market
value of assets is imputed using perpetual inventory methods and
valuations of inventory as discussed in the data appendix to Cummins,
Hassett, and Hubbard. (39) Implicitly, this formulation takes the market
value of debt to be its book value.
As with any firm-level analysis employing Compustat data to study
investment, rules for considering extreme observations must be employed.
Following studies such as that by Simon Gilchrist and Charles
Himmelberg, we truncate our measures of q (and of investment and cash
flow as a share of the capital stock) at the 1st and the 99th
percentile. (40) Investment rates and cash flow rates are taken as the
ratio of capital expenditure and operating cash flow before
depreciation, respectively, to the capital stock.
Table 7 reports the results of estimating q models in our firm
sample, under both of the two alternative definitions of q, as well as
Q, that is, q adjusted for taxes, ([q.sub.t] / 1 - [tau] - 1 - [GAMMA] /
1 - [tau]). We postpone discussion ofthe relevance of dividend taxes,
and so our estimating equation is equation 5 above, with and without
consideration of taxes. Columns 7-1 and 7-4 of table 7 contrast the
performance of the corporate finance and public finance measures of q
without consideration of tax factors. Both coefficients are significant
and positive, but the coefficient on the corporate finance q is much
larger. Inspection of the public finance qs indicates that extreme
values make up a large fraction of the sample and may contribute to this
pattern. Comparison of columns 7-2 and 7-5 provides a similar result,
with significantly larger coefficients on the corporate finance-based
measure of Q. Nonetheless, the coefficients reflect the common
difficulty in this literature, which is that these small coefficients
translate into extremely high adjustment cost parameters (the inverse of
the measured coefficient). Inclusion of both q and Q, in the
specifications reported in columns 7-3 and 7-6, results in a similar
pattern, but does indicate that tax-adjusted q outperforms q in
explaining investment. This finding parallels the finding by Summers of
the relevance of tax adjustments in improving the estimation of the q
model. (41)
Given the relative performance of q and Q in the results reported
in table 7, it is useful to consider separately the terms that make up Q
to better understand the sources of the relatively small coefficients on
Q. As discussed above, Q = [q.sub.t] / 1 - [tau] - 1 - [GAMMA] / 1 -
[tau], and so the specifications in table 7 can naturally be recast to
consider the separate effects for these two terms. Splitting Q in this
manner has the advantage of allowing us to consider the role of
measurement error in biasing the estimates previously obtained. More
specifically, Cummins, Hassett, and Hubbard argue that mismeasurement of
q means that using the estimated coefficients from standard investment
regressions can dramatically understate the impact of investment taxes.
(42) They emphasize large tax reforms as being times when the tax part
of Q is not mismeasured, and they use these periods as the basis for
comparing actual with projected investment. The specifications provided
in table 7 take a simpler approach but in the same spirit. If
measurement error in q is a problem, splitting Q into two parts has the
advantage that the coefficient (or, more accurately, its absolute value)
on the 1 - [GAMMA] / 1 - [tau] term should provide a better estimate of
the tree coefficient on q. (43)
Table 8 presents the results from splitting Q into its component
parts. Specifically, the specification in the first column replaces Q
with q scaled by 1 minus the corporate tax rate and terms for the
equipment tax term and the structures tax term. It is difficult to
measure a firm's relative investment in equipment and structures,
and so we simply include both tax terms as separate regressors. Given
the traditional difficulties in understanding the dynamics of incentives
for investment in structures, (44) and given that equipment accounts for
approximately 80 percent of corporate investment, we expect the
equipment tax term to be much more precisely estimated. Controls for
internal cash flow are included as well.
The key result from this table is that, although the q term remains
small, the coefficient on the equipment tax term is considerably larger
than typically estimated when just using Q and is close to 1 in absolute
value. (45) The second column includes q without a tax adjustment and
indicates, as with the results in table 7, that a tax-adjusted q term
performs better than ordinary q. In this specification the coefficient
on the equipment tax term remains significant and large. The third and
fourth columns report two alternative robustness checks for these
results that are modifications to the basic tax-adjusted q model. First,
the theory does imply that the present value of tax depreciation
allowances on previously purchased investment should be included in the
value of the firm. This is frequently left out of empirical work on Q,
since it is difficult to compute. In the third column we approximate the
size of these tax shields as described in appendix A, and we add the
value of these shields to the value of the firm in Q. This does not
change the estimated results dramatically. We found this to be true for
all of the major results in the paper, and, since computing the
allowances requires reducing the sample by more than 30,000, we exclude
them from the results that follow. Similarly, the model presented above
follows most of the literature in assuming away any issues regarding
debt financing. In the fourth column we incorporate the share of the
firm's financing that comes from debt, (46) and the results are
again similar.
The large coefficients on the tax term terms are worth dwelling on.
First, the model predicts that these coefficients should be of the same
magnitude as those on tax-adjusted q, but of opposite sign. Here,
instead, the coefficients on the equipment tax term are considerably
larger. With measurement error in q, the coefficient on the tax term may
provide a more realistic estimate of the true coefficient. Such a
coefficient is considerably closer to 1 and, consequently, corresponds
to more realistic estimates of adjustment costs. Restricting attention
to years with major tax reforms yielded similar estimates. (47)
To obtain some further evidence on the role of q mismeasurement as
the reason for the small coefficient on the q term, we also modify the
empirical strategy of Stephen Bond and Cummins, (48) within the
framework of table 8. Their intuition is that earnings estimates by
equity analysts as provided in the I/B/E/S database are a part of q that
is based only on fundamentals. (49) Rather than use these estimates to
create an alternative q measure, however, we use them as instruments for
q. (50) Employing the earnings estimates comes at considerable cost,
given the shorter time frame covered by the I/B/E/S database (1983-2003)
and the severely restricted number of firms covered by I/B/E/S.
Nonetheless, we report in the fifth column of table 8 the results of
this estimation. Several points are worth noting. First, as indicated by
the tenfold increase in the coefficient on tax-adjusted q,
mismeasurement of q seems important. Second, the coefficient on the
equipment tax term rises considerably as well. Given the considerably
smaller panel for these instrumental variables results, we rely on the
coefficients on the equipment tax term term in the first column of table
8 as the best estimate of the true coefficient from a tax-adjusted q
model. This analysis suggests that the true adjustment costs for
investment are of plausible size, and so we use the model to estimate
the impact of the Bush tax cuts. Finally, it is useful to consider
whether the relevance of the q model is different in manufacturing
industries, since many previous studies have restricted their sample to
manufacturing. We prefer not to do this, since manufacturing accounts
for only a small fraction of total investment. Table 9 replicates the
analysis from the first column of table 8 and divides the sample.
Although the reduced sample sizes reduce the power of these tests, the
coefficients on the relevant tax term terms are quite similar in the two
subsamples, suggesting that the model performs similarly well in both
settings.
The Impact of Tax Cuts in the 2000s
During the George W. Bush administration two major changes have
been made to the tax code to reduce taxes on capital. First, in 2003 the
top capital gains tax rate was reduced from 20 percent to 15 percent,
and the tax rate on most dividends was reduced from the ordinary
personal income tax rate (which then had a maximum of 38.6 percent) to
the capital gains tax rate. The second change substantially accelerated
depreciation. In 2002 depreciation allowances for virtually all types of
equipment investment were increased, as firms gained the right to
immediately expense 30 percent of their purchases. In 2003 depreciation
allowances were increased again, as the fraction that could be
immediately expensed increased to 50 percent. Each of these tax changes
needs to be treated differently under the Q model.
Dividend Taxes
Although implementation of the dividend tax reduction was somewhat
complex, essentially the maximum rate on dividends for individuals was
reduced from the top rate on ordinary income (38.6 percent) to the
capital gains tax rate (maximum of 15 percent). Advocates argued that
this tax cut would reduce the tax term and stimulate business
investment. (51) The Joint Committee on Taxation estimated that the
dividend tax cut would reduce revenue by more than $100 billion from
2003 to 2008. (52) Given this high cost, it is worth assessing the
impact of the cut. If the "new" view is correct, changes in
dividend taxes have little or no impact on the cost of capital.
Table 10 reports results of our testing between the two views. In
the first column we consider the relevance of dividend taxes by
contrasting the predictions of equations 5 and 6 in one specification.
The difference is simply whether the q term is adjusted by the dividend
tax preference parameter or not. In the empirical specification of the
first column, we use all years for which we have firm data. The measure
of q that is interacted with the (1 - c / 1 - [theta]) term has a
positive coefficient and is highly significant. The measure that is not
interacted with the (1 - c / 1 - [theta]) term is insignificant and
actually has a negative coefficient. The two coefficients are
significantly different from one another as well. In other words,
although the marginal source of funds for these firms cannot be observed
directly, their investment behavior is consistent with their treating
retained earnings as the marginal source, and this implies that the new
view is the correct one.
One major criticism of most previous analyses of the impact of
dividend tax rates on investment and other economic behavior has been
that changes in the rates themselves do not occur in isolation, but
instead accompany changes in the top marginal rate on ordinary income.
(53) In the later part of our sample, however (1997-2003), the tax
changes that occurred are fairly specific in isolating the impact of
dividend and capital gains taxes. In 1997 capital gains rates fell
without any change in the top marginal income tax rate, and in 2003 both
the capital gains rate and the dividend tax rate did so. This period,
then, should be particularly instructive. For this reason (and because
firm financing decisions may have changed over time with the rise of new
equity issuances), we break the sample into the period before and the
period including and after 1997. Results for these two subsamples are
reported in the second and third columns of table 10. Both regressions
show that the new view outperforms the traditional view, but the
evidence is particularly strong for the later period. (54) Again, the
evidence in all cases supports the new view and implies a small or
negligible impact of dividend taxes on investment.
At the other extreme, in keeping with the discussion in Auerbach
and Hassett, among others, (55) who have argued that the new view may
apply for some firms and the traditional view for others, we calculate
approximately how the dividend tax change would change the cost of
capital if our findings were wrong and the traditional view held. (56)
Under the traditional view, the required after-tax rate of return
[r.sup.*] will be r/[1 - p[theta] - (1 - p)c], where r is the before-tax
rate of return, p is the dividend payout rate, and c and [theta]
correspond to the capital gains and dividend tax rates, respectively.
The full cost of capital, assuming no inflation in the price of
investment goods and a permanent change in tax policy, will then be COC
= ([r.sup.*] + [delta]) (1 - [GAMMA] / 1 - [tau]).
With a real interest rate of 5 percent, annual depreciation of 15
percent, a payout rate of 50 percent, and an accrual tax rate on capital
gains equal to one quarter the statutory rate (as is commonly assumed in
the literature), reducing the dividend tax from 38.6 percent to the
level of the capital gains rate in 2003 for a fully taxable investor
would be the equivalent of dividing the cost of capital by 1.035. The
equipment tax term in 2003 was about 1.031, so this would have an effect
on investment of approximately the same magnitude as converting the tax
code to complete and immediate expensing of all equipment investment in
2003 (since dividing the tax term by 1.035 would yield a value of
approximately 1, the same as immediate expensing). We will show in the
next subsection, however, that changes to the tax term of that magnitude
may not increase investment by much in the short run during this sample
period.
The Impact of Partial Expensing
Although we find no impact of the dividend tax cuts on investment,
the other tax incentives enacted during the early 2000s, specifically
the depreciation allowance and partial expensing changes, directly
reduced the tax term under either view of the dividend tax and should
have stimulated investment. Their apparent failure to do so has led some
to argue that tax policy is not effective.
MAGNITUDE OF THE CUTS. In 2002 President Bush signed a change in
the tax code to allow for partial expensing of equipment; this change
was made retroactive to cover all investment in 2002. In essence this
rule change broke an investment into two parts. Thirty percent of the
investment is immediately expensed. The remaining 70 percent is
depreciated according to the normal schedule (which allows the firm to
write off some portion in the first year, some portion in the second,
and so on, for the tax life of the asset). Given that a fairly large
share of the investment not being expensed already gets depreciated in
the first year, this new law heavily weighted the depreciation
allowances toward the first year. (57) In 2003 the law was changed again
(and again made retroactive to cover investments made at any time during
the year) to allow for first-year expensing of 50 percent of the
investment. Although this provision was scheduled to expire at the end
of 2003, it was extended to 2004 and may be extended further in the
future--as of this writing, many legislators and commentators are
arguing that it should be made permanent.
These incentives were costly to provide, of course. The Joint
Committee on Taxation estimated the cost of the changes in 2002 at about
$35 billion and the cost of the higher expensing in 2003 and 2004 at
about $32 billion and $53 billion, respectively. (58) Extending it
indefinitely would presumably entail similar annual costs.
To estimate the effect of the changed investment incentives, we
compute the increase in investment implied by the changes to
depreciation allowances. The last two columns of appendix table A-1
report the change in the tax term from 2001 to 2003, averaged across
industries at the three-digit NAICS level. The first column considers
the overall change in the tax cost, and the second the change in the tax
term for equipment only. Not surprisingly, the amounts differ across
industries depending on the nature of the investment goods they
purchase. Airlines, for example, invest mostly in equipment and mostly
in long-lived assets such as aircraft. Long-lived assets that qualify
for bonus depreciation receive the largest boost from allowing 50
percent immediate expensing (since they were depreciated over a longer
period before) and thus provide the largest changes in the tax term.
Firms in industries such as real estate and hotels invest little in
equipment, and what equipment they buy tends to consist of computers and
other short-lived assets for which immediate expensing is not as large
an improvement.
The table shows that even these rather dramatic changes to the
depreciation and expensing rules did not have a very large impact on the
tax term. The average change in the equipment tax term across all firms
is about 0.03 (0.02 after incorporation of the equipment share). Such a
change is modest compared with changes such as the investment tax credit
of 1962, its restoration in the early 1970s, or the increases in the
depreciation allowance in 1981, all of which changed the overall tax
term by around 0.10. Figure 5 depicts the industry-average equipment tax
term over roughly the last half century and shows that the most recent
changes in investment incentives have been modest by historical
standards.
[FIGURE 5 OMITTED]
This relatively small effect stems from several factors. First, the
value of an acceleration in depreciation allowances is a function of the
corporate tax rate: with corporate tax rates already lower than they
were in previous decades, altering depreciation schedules has a more
muted effect. Second, the well-documented shift of investment toward
computers and other equipment with shorter lives has meant that
accelerated depreciation provides less relief. The average net present
value of depreciation allowances for equipment investment in 2001 was
already approximately 90 percent of the investment value even before the
tax cuts, suggesting that even complete expensing (raising the net
present value to 1) would provide limited additional benefit. Given the
smaller magnitude of the 2002 and 2003 cuts, it is unsurprising that
such incentives could not overcome the dramatic drop in investment
induced by the remarkable drop in q over the period. Our estimates
suggest that these incentives do work as they are designed, but that
their magnitude is simply too small to counteract the aggregate trend.
HOW MUCH DID THESE INCENTIVES INCREASE INVESTMENT? To estimate the
precise impact of the tax changes on investment, we return to the
tax-adjusted q model. To use that model to simulate the impact of the
tax cuts in 2002 and 2003, we need to compute the transition path for
investment in the standard q model. (59) Auerbach outlines a
linearization that makes this particularly easy, (60) and we adopt his
notation to derive the predicted effects. Assuming a Cobb-Douglas
production function with a capital share of 1 - a, a real before-tax
interest rate of r, quadratic adjustment costs of [phi] (the reciprocal
of the true coefficient on Q in our regressions), and an adjustment
cost-modified depreciation rate for capital in the firm of [delta] (the
specific formula for which is given below), Auerbach shows that, for an
unanticipated permanent change in tax policy, the capital stock follows
a simple partial adjustment model with [K.sub.t] =
[[lambda].sub.1]([K.sup.*] - [K.sub.t]), where [K.sup.*] is the desired
capital stock. The rate of adjustment - [[lambda].sub.1] follows the
formula
(7) [[lambda].sub.1] = r - [square root of ([r.sup.2] + 4a(r +
[delta]/[phi])]/2.
To compute this adjustment rate empirically, we assume a real
interest rate of 5 percent. We compute a, the complement of the capital
share, as 1 minus the gross output share of value added for each
industry as reported in the disaggregated National Income and Product
Accounts data for 1998. We take the true coefficient on Q to be 1,
following the results above. We compute [delta] = [delta][1 -
([phi][delta])/2], using our value for [phi] and the industry-average
depreciation rate on equipment or total investment, as computed from the
weighted average by asset in the Jorgenson data using the industry
weights in the capital flow table. This gives an annual adjustment rate
for each firm. The average annual adjustment rate for all firms is about
33 percent, and the average value for each three-digit industry is given
in appendix table A-1 (for structures and equipment as well as for
equipment only). We then use the Cobb-Douglas production function to
derive the optimal capital stock. (61)
To compute the effect of these policies over the past two years, we
assume that the depreciation changes were unanticipated and thought to
be permanent. (62) We first derive the optimal capital stock and amount
of adjustment in the first year (2002). We then calculate the new
optimal capital stock for 2003 (after the second tax cut) and the amount
of adjustment based on the new gap between [K.sup.*] and actual K (where
actual K is higher than it was in 2001 because of the investment
undertaken in 2002). Averaging for each three-digit industry and summing
over the two years, we estimate the impact of the tax cuts on the
capital stock for each industry (table 11). The average increase for the
period is only about 1.0 to 1.5 percent, and so it is immediately clear
why these tax cuts have seemed to have little success in stemming the
investment declines: their short-run stimulus effect is too small. This
is not a refutation of the view that taxes matter. The tax cuts were
effective in changing incentives--they simply were not large enough to
counteract the double-digit declines in investment rates observed in the
2000s. Changes to depreciation allowances simply do not have much impact
when the system is already so close to full expensing and when aggregate
declines in market value (and therefore in q) are so large. Since firms
are moving asymptotically to the optimal capital stock, the effects of
the policy change will be smaller in later years than in the first two
years. After 2004 the average total increase will still be less than 2
percent.
Conclusion
This paper has addressed two major questions arising from the
puzzling investment experience of the 2000s thus far: First, to what
extent was the equity bubble of the 1990s correlated with the decline in
investment in the 2000s? And second, why didn't the major tax cuts
of 2002-03 do more to restore investment to normal levels?
The data at the firm, asset, and industry level do not support the
popular explanation of how capital overhang affected the investment
market in the 2000s. The general evidence shows that rapid growth of
investment in the 1990s had very little correlation with the investment
declines in the 2000s. The evidence further indicates that the
firm-level relationship between investment and q has not changed
noticeably in the recent period for firms that saw large increases in
their market value or that invested heavily in the 1990s. Instead the
rise and fall of equity prices, in the context of a conventional
tax-adjusted q model that better accounts for measurement error in
measuring marginal q, is the best explanation for the investment
experience of the recent past.
This conventional tax-adjusted q model then serves as the basis for
our analysis of the impact of the recent tax cuts and their seeming
impotence. Our results show that the dividend tax cut, despite its high
revenue cost, had minimal, if any, impact on marginal investment
incentives. The results strongly favor the "new" view of
dividend taxation, in which such taxes are capitalized into share prices
and do not affect marginal incentives. Similarly, the partial expensing
provisions passed in 2002 and 2003 were not large enough to provide much
counterweight to the declines in aggregate investment. Our estimates
suggest that tax policies contributed to an increase in the capital
stock of only 1 to 2 percent.
APPENDIX A
Data Sources and Definitions
Firm-Level Financial Data
Annual data for all companies in the Compustat database, from 1950
on, are accessed through Wharton Research Data Services (WRDS).
Market Valuation of the Capital Stock
The Compustat series "Property, Plant, and Equipment--Total
(Net)" is used as a measure of capital equipment; "Capital
Expenditures (Statement of Cash Flows)" is used as a measure of
capital expenditure. Each of these measures is converted to constant
dollars by dividing by the current value of the producer price index
(PPI) for capital goods, taken from the website of the Bureau of Labor
Statistics.
Three factors enter into the current valuation of the capital
stock. The first is changes in the prices of capital goods held over
from previous years. Our conversion to constant dollars sidesteps this
component. The second is additions to capital through investment
expenditure. The third is depletion of capital on hand through
depreciation.
The firm's current real capital stock can be thought of as the
sum of the nondepreciated stocks of all previous years plus investment
in the current year. Following Cummins, Hassett, and Hubbard (1994), and
assuming a constant rate of depreciation [delta], we calculate the
current capital stock as
[K.sub.T] = [K.sub.0][(1 - [delta]).sup.T] + [I.sub.1][(1 -
[delta]).sup.T - 1] + ... + [I.sub.T -1](1 - [delta]) + [I.sub.T].
For example, the firm starts in period 0 with capital stock
[K.sub.0], but only the nondepreciated part of this stock, (1 -
[delta])[K.sub.0], remains to be carried over to the next period. Some
of this carried-over capital is used up in producing output in the
second period, leaving [(1 - [delta]).sup.2][K.sub.0] to be carried over
to the third period, and so forth. By period T, then, only [(1 -
[delta]).sup.T][K.sub.0] is carried over from period 0. Similar
reasoning explains the coefficients on the levels of investment carried
over to period T from all previous years. [I.sub.T] represents
investment expenditure in period T.
Given the ending levels of the capital stock for all years,
including the final year, and the final year's investment spending
(all deflated by the PPI for capital goods), we can solve for the
average rate of depreciation for each firm. This average rate of
depreciation is then applied sequentially, from the first observed year
for each firm to the last, to derive an estimated capital stock for each
firm-year observation.
Conversion of Inventory from Book to Market Valuation
The Compustat series "Inventories--Total" is used as a
measure of the current value of inventory holdings. As in Cummins,
Hassett, and Hubbard (1994), we convert inventory levels from their book
value to market value (on a last-in, first-out, or LIFO, basis) by
adjusting the lagged book value of carried-over inventories for
year-to-year changes in the prices of finished goods. How the adjustment
mechanism is implemented depends on whether final inventories increase
or decrease from one year to the next. If inventories increase, those
goods carried over from the previous year are revalued at current
prices, as is the net addition to total inventories:
[Inv.sup.m.sub.t] = [Inv.sup.m.sub.t-1]/([P.sub.t]/[P.sub.t-1]) +
[DELTA][Inv.sub.t], if [DELTA][Inv.sub.t] [greater than or equal to] 0.
Essentially, under LIFO valuation rules, the ending levels of
inventories include all goods that are carried over from the previous
year plus unsold current production. All inventories carried into the
current year remain at the end of the year and are revalued at current
prices. The net addition to inventories is already measured at current
prices and so needs no further adjustment.
On the other hand, if inventories decrease during the current year,
it is assumed that all current production has been sold as well as some
part of inventories carried over from the previous year. All goods
remaining at the end of the year are then valued at current prices:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Operating Income
The Compustat series "Operating Income before
Depreciation" and "Operating Income after Depreciation"
are used as measures of net income. Each was converted from nominal to
real terms by dividing by the PPI for finished goods, taken from the
website of the Bureau of Labor Statistics.
Analysts' Earnings Estimates
Consensus analysts' estimates of firms' earnings per
share in future years were taken from the I/B/E/S summary statistics
data maintained on WRDS. The variables in this file include the number
of estimates and the mean, median, and standard deviation of estimates
for a number of fiscal periods (quarters or years) into the future. We
merged the Compustat firm-level financial data with the I/B/E/S
firm-level analysts' estimates. We used the summary estimate made
during the latest month before the end of the firm's fiscal year.
Asset-Level Tax Term
Data for the asset-level tax term come from Dale Jorgenson of
Harvard University; his methodology is described in Jorgenson and Yun
(2001), These data provide, for each asset type, an estimate of the net
present value of depreciation allowances z, the investment tax credit
rate, and the depreciation rate, as well as the capital stock and the
average corporate tax rate. We compute [GAMMA] as ITC + tz and the full
tax term as (1 - ITC - tz)/(1 - t). The calculations are myopic in that
they do not include the impact of expected future tax changes; current
tax rates are assumed to be permanent. We modify the net present values
of depreciation allowances in 2002 and 2003, to account for the changes
in the partial expensing rules. We recomputed z, for 2002, using a 70-30
weighted average of the old z and 1; we do the same, but with 50-50
weights, for 2003.
Industry- and Firm-Level Tax Terms
To derive industry-level values of the tax term for equipment and
structures as well as to derive industry-level depreciation rates, we
use the 1997 capital flow tables of the BEA and compute the share of
equipment and structures investment by asset type for each industry at
approximately the three-digit NAICS level. We match these weights to the
Jorgenson tax term figures by year for each asset type to compute a
weighted-average tax term in each year for each industry. We then merge
that series to each firm-year based on its first listed NAICS code in
Compustat (table A-1).
Present Value of Depreciation Allowances on Past Investment
To estimate the value of A, the net present value (NPV) of
depreciation allowances on past investments, we sort firms according to
the weighted average of depreciation rates on the types of equipment in
which firms in their industry invest. Using the inverse of this average
depreciation rate as an estimate of the lifetime of the firm's
capital, we assume that all firms in the industry have a discount rate
of 10 percent and use double-declining-balance depreciation until
straight-line depreciation exceeds it, and then switch to straight-line.
We then multiply the NPV of the remaining depreciation allowances on
investment from a given year in the asset's life by the
investment-to-capital ratio lagged that many periods. For example, if
the actual depreciation allowances for a three-year-lived good costing
$1 were one-third each year (pure straight-line depreciation), then the
NPV of the allowances in the year of the investment would be
[z.sub.age=0] = (.333 + .333/1.1 + .333/[1.1.sup.2]), the NPV of the
allowances remaining one year later would be [z.sub.age=1] = (.333 +
.333/1.1), and the NPV of allowances after two years would be
[z.sub.age=2] = (.333). We would then compute the value of depreciation
on previous investments as A = t [[z.sub.age=1][(I/K).sub.t-1] +
[z.sub.age=2][(I/K).sub.t-2]] Note that the NPV of depreciation
allowances for current (time t) investment is not included in this
measure (although it is in z); hence the computation of A for an
industry whose asset life is three years has only two terms.
We compute the NPV assuming an asset life of three years for any
firm for which the inverse of its average depreciation rate is between 3
and 4, four years for any firm for which the inverse is between 4 and 5,
and so on, but with a cap at nine years (a few firms had average
equipment lives of slightly over ten years).
Note that our measure is an approximation because it assumes that
tax law remains unchanged over the whole sample period. In other words,
the NPV of depreciation allowances on current investment, z, that we get
from Jorgenson varies over time, but we do not have the entire
depreciation schedules on which each z is based, and so we cannot let
the calculation vary for A. We tried many different ways of computing A,
for example adopting different assumptions about depreciation methods,
different discount rates, and so on, and found they had negligible
impact on the regression results.
Table A-1. Estimates of Adjustments to Tax
Changes by Industry
Adjustment rate
(-[[lambda].sub.1],)
Equipment
Equipment share
NAICS and Equipment of total
code Industry structures only (percent)
111 Crop Production 0.148 0.143 91
112 Animal Production 0.154 0.147 87
113 Forestry & Logging 0.318 0.304 85
114 Fishing, Hunting, 0.272 0.262 81
& Trapping
211 Oil & Gas Extraction 0.315 0.252 21
212 Mining (except 0.300 0.286 82
Oil & Gas)
213 Support for Mining 0.379 0.371 90
221 Utilities 0.240 0.207 58
233 Building, Developing, 0.365 0.362 97
& Gen. Contracting
234 Heavy Construction 0.365 0.362 97
235 Special Trade 0.365 0.362 97
Contractors
311 Food Mfg. 0.311 0.296 84
312 Beverage & Tobacco 0.316 0.303 87
Product Mfg.
313 Textile Mills 0.335 0.324 88
314 Textile Product Mills 0.363 0.348 87
315 Apparel Mfg. 0.342 0.324 83
316 Leather & Allied 0.344 0.332 89
Product Mfg.
321 Wood Product Mfg. 0.334 0.319 84
322 Paper Mfg. 0.307 0.301 93
323 Printing & Related 0.356 0.343 88
Support
324 Petroleum & Coal 0.243 0.237 91
Products Mfg.
325 Chemical Mfg. 0.286 0.268 80
326 Plastics & Rubber 0.300 0.290 88
Products Mfg.
327 Nonmetallic Mineral 0.302 0.291 88
Product Mfg.
331 Primary Metal Mfg. 0.345 0.332 88
332 Fabricated Metal 0.339 0.328 89
Product Mfg.
333 Machinery Mfg. 0.371 0.353 85
334 Computer & Electronic 0.395 0.377 86
Product Mfg.
335 Elect. Equip., 0.327 0.312 86
Appliance,
& Component Mfg.
336 Transp. Equip. Mfg. 0.366 0.353 88
337 Furniture & Related 0.345 0.327 83
Product Mfg.
339 Miscellaneous Mfg. 0.334 0.320 86
421 Wholesale Trade, 0.402 0.383 86
Durable Goods
422 Wholesale Trade, 0.402 0.383 86
Nondurable Goods
441 Motor Vehicle & 0.397 0.320 48
Parts Dealers
442 Furniture & Home 0.397 0.320 48
Furnishings Stores
443 Electronics & 0.397 0.320 48
Appliance Stores
444 Building Mtrl, Garden 0.397 0.320 48
Supplies Dealers
445 Food & Beverage 0.397 0.320 48
Stores
446 Health & Personal 0.397 0.320 48
Care Stores
447 Gasoline Stations 0.397 0.320 48
448 Clothing & Clothing 0.397 0.320 48
Accessories Stores
451 Sporting Goods, Book, 0.397 0.320 48
& Music Stores
452 General Merchandise 0.397 0.320 48
Stores
453 Miscellaneous Store 0.397 0.320 48
Retailers
454 Nonstore Retailers 0.397 0.320 48
481 Air Transp. 0.313 0.309 96
482 Rail Transp. 0.288 0.253 59
484 Truck Transp. 0.350 0.339 90
485 Transit & Ground 0.367 0.360 94
Passenger Transp.
486 Pipeline Transp. 0.336 0.273 48
487 Scenic & Sightseeing 0.391 0.377 90
Transp.
488 Support for Transp. 0.391 0.377 90
492 Couriers & Messengers 0.402 0.397 96
493 Warehousing & Storage 0.383 0.364 84
511 Publishing Indus. 0.400 0.381 86
512 Motion Picture & Sound 0.386 0.342 67
Recording
513 Broadcasting & 0.278 0.246 65
Telecommunications
514 Info Servs. & Data 0.448 0.428 87
Processing Servs.
522 Credit Intermediation 0.347 0.311 72
& Related
523 Securities, Commodity 0.439 0.411 82
Contracts, & Other
524 Insurance Carriers 0.389 0.368 85
& Related
525 Funds, Trusts, & Other 0.419 0.260 19
Financial Vehicles
531 Real Estate 0.182 0.097 5
532 Rental & Leasing 0.227 0.218 88
Servs.
533 Lessors of Nonfinancial 0.250 0.220 68
Intangible Assets
541 Professional, Scientific, 0.404 0.385 86
& Technical Servs.
561 Administrative & 0.412 0.400 91
Support Servs.
562 Waste Management & 0.352 0.307 61
Remediation Servs.
611 Educational Servs. 0.447 0.340 41
621 Ambulatory Health 0.367 0.347 83
Care Servs.
622 Hospitals 0.409 0.363 68
623 Nursing & Residential 0.414 0.328 44
Care Facilities
624 Social Assistance 0.419 0.357 59
711 Performing Arts, 0.365 0.273 36
Spectator Sports
713 Amusement, Gambling, 0.346 0.277 43
& Recreation
721 Accommodation 0.334 0.232 20
722 Food Servs. & Drinking 0.382 0.343 69
Places
811 Repair & Maintenance 0.368 0.342 78
812 Personal & Laundry 0.386 0.353 75
Servs.
813 Religious, Grantmaking, 0.402 0.329 52
Civic, Profess.
Depreciation rate
([sigma])
Equipment
NAICS and Equipment
code Industry structures only
111 Crop Production 0.154 0.143
112 Animal Production 0.172 0.153
113 Forestry & Logging 0.164 0.145
114 Fishing, Hunting, 0.106 0.094
& Trapping
211 Oil & Gas Extraction 0.172 0.091
212 Mining (except 0.135 0.118
Oil & Gas)
213 Support for Mining 0.171 0.161
221 Utilities 0.140 0.092
233 Building, Developing, 0.187 0.182
& Gen. Contracting
234 Heavy Construction 0.187 0.182
235 Special Trade 0.187 0.182
Contractors
311 Food Mfg. 0.155 0.135
312 Beverage & Tobacco 0.161 0.144
Product Mfg.
313 Textile Mills 0.128 0.117
314 Textile Product Mills 0.161 0.144
315 Apparel Mfg. 0.153 0.132
316 Leather & Allied 0.156 0.142
Product Mfg.
321 Wood Product Mfg. 0.142 0.124
322 Paper Mfg. 0.135 0.128
323 Printing & Related 0.163 0.147
Support
324 Petroleum & Coal 0.155 0.144
Products Mfg.
325 Chemical Mfg. 0.180 0.150
326 Plastics & Rubber 0.133 0.121
Products Mfg.
327 Nonmetallic Mineral 0.154 0.139
Product Mfg.
331 Primary Metal Mfg. 0.156 0.141
332 Fabricated Metal 0.162 0.147
Product Mfg.
333 Machinery Mfg. 0.196 0.171
334 Computer & Electronic 0.212 0.186
Product Mfg.
335 Elect. Equip., Appliance, 0.166 0.147
& Component Mfg.
336 Transp. Equip. Mfg. 0.171 0.155
337 Furniture & Related 0.152 0.131
Product Mfg.
339 Miscellaneous Mfg. 0.162 0.144
421 Wholesale Trade, 0.208 0.183
Durable Goods
422 Wholesale Trade, 0.208 0.183
Nondurable Goods
441 Motor Vehicle & 0.198 0.108
Parts Dealers
442 Furniture & Home 0.198 0.108
Furnishings Stores
443 Electronics & Appliance 0.198 0.108
Stores
444 Building Mtrl, Garden 0.198 0.108
Supplies Dealers
445 Food & Beverage 0.198 0.108
Stores
446 Health & Personal 0.198 0.108
Care Stores
447 Gasoline Stations 0.198 0.108
448 Clothing & Clothing 0.198 0.108
Accessories Stores
451 Sporting Goods, Book, 0.198 0.108
& Music Stores
452 General Merchandise 0.198 0.108
Stores
453 Miscellaneous Store 0.198 0.108
Retailers
454 Nonstore Retailers 0.198 0.108
481 Air Transp. 0.124 0.121
482 Rail Transp. 0.097 0.064
484 Truck Transp. 0.184 0.169
485 Transit & Ground 0.192 0.182
Passenger Transp.
486 Pipeline Transp. 0.177 0.098
487 Scenic & Sightseeing 0.193 0.176
Transp.
488 Support for Transp. 0.193 0.176
492 Couriers & Messengers 0.209 0.202
493 Warehousing & Storage 0.180 0.156
511 Publishing Indus. 0.272 0.238
512 Motion Picture & Sound 0.196 0.140
Recording
513 Broadcasting & 0.146 0.103
Telecommunications
514 Info Servs. & Data 0.258 0.228
Processing Servs.
522 Credit Intermediation 0.276 0.206
& Related
523 Securities, Commodity 0.272 0.227
Contracts, & Other
524 Insurance Carriers 0.270 0.233
& Related
525 Funds, Trusts, & Other 0.296 0.077
Financial Vehicles
531 Real Estate 0.193 0.026
532 Rental & Leasing 0.194 0.173
Servs.
533 Lessors of Nonfinancial 0.249 0.178
Intangible Assets
541 Professional, Scientific, 0.249 0.218
& Technical Servs.
561 Administrative & 0.209 0.193
Support Servs.
562 Waste Management & 0.190 0.131
Remediation Servs.
611 Educational Servs. 0.214 0.099
621 Ambulatory Health 0.181 0.154
Care Servs.
622 Hospitals 0.166 0.119
623 Nursing & Residential 0.172 0.087
Care Facilities
624 Social Assistance 0.206 0.132
711 Performing Arts, 0.192 0.084
Spectator Sports
713 Amusement, Gambling, 0.165 0.087
& Recreation
721 Accommodation 0.160 0.052
722 Food Servs. & Drinking 0.171 0.126
Places
811 Repair & Maintenance 0.175 0.142
812 Personal & Laundry 0.199 0.155
Servs.
813 Religious, Grantmaking, 0.222 0.128
Civic, Profess.
Change in tax term,
2001-03
Equipment
NAICS and Equipment
code Industry structures only
111 Crop Production 0.028 0.031
112 Animal Production 0.024 0.029
113 Forestry & Logging 0.022 0.027
114 Fishing, Hunting, 0.033 0.043
& Trapping
211 Oil & Gas Extraction 0.007 0.033
212 Mining (except 0.023 0.030
Oil & Gas)
213 Support for Mining 0.028 0.031
221 Utilities 0.019 0.038
233 Building, Developing, 0.026 0.027
& Gen. Contracting
234 Heavy Construction 0.026 0.027
235 Special Trade 0.026 0.027
Contractors
311 Food Mfg. 0.026 0.032
312 Beverage & Tobacco 0.026 0.031
Product Mfg.
313 Textile Mills 0.029 0.034
314 Textile Product Mills 0.027 0.032
315 Apparel Mfg. 0.026 0.033
316 Leather & Allied 0.028 0.032
Product Mfg.
321 Wood Product Mfg. 0.026 0.033
322 Paper Mfg. 0.030 0.033
323 Printing & Related 0.027 0.032
Support
324 Petroleum & Coal 0.030 0.033
Products Mfg.
325 Chemical Mfg. 0.022 0.030
326 Plastics & Rubber 0.026 0.030
Products Mfg.
327 Nonmetallic Mineral 0.027 0.031
Product Mfg.
331 Primary Metal Mfg. 0.026 0.030
332 Fabricated Metal 0.024 0.029
Product Mfg.
333 Machinery Mfg. 0.023 0.029
334 Computer & Electronic 0.024 0.030
Product Mfg.
335 Elect. Equip., Appliance, 0.025 0.030
& Component Mfg.
336 Transp. Equip. Mfg. 0.024 0.029
337 Furniture & Related 0.025 0.031
Product Mfg.
339 Miscellaneous Mfg. 0.024 0.029
421 Wholesale Trade, 0.024 0.029
Durable Goods
422 Wholesale Trade, 0.024 0.029
Nondurable Goods
441 Motor Vehicle & 0.013 0.029
Parts Dealers
442 Furniture & Home 0.013 0.029
Furnishings Stores
443 Electronics & Appliance 0.013 0.029
Stores
444 Building Mtrl, Garden 0.013 0.029
Supplies Dealers
445 Food & Beverage 0.013 0.029
Stores
446 Health & Personal 0.013 0.029
Care Stores
447 Gasoline Stations 0.013 0.029
448 Clothing & Clothing 0.013 0.029
Accessories Stores
451 Sporting Goods, Book, 0.013 0.029
& Music Stores
452 General Merchandise 0.013 0.029
Stores
453 Miscellaneous Store 0.013 0.029
Retailers
454 Nonstore Retailers 0.013 0.029
481 Air Transp. 0.036 0.038
482 Rail Transp. 0.020 0.034
484 Truck Transp. 0.025 0.028
485 Transit & Ground 0.028 0.030
Passenger Transp.
486 Pipeline Transp. 0.017 0.037
487 Scenic & Sightseeing 0.027 0.031
Transp.
488 Support for Transp. 0.027 0.031
492 Couriers & Messengers 0.027 0.028
493 Warehousing & Storage 0.025 0.031
511 Publishing Indus. 0.024 0.029
512 Motion Picture & Sound 0.019 0.031
Recording
513 Broadcasting & 0.032 0.041
Telecommunications
514 Info Servs. & Data 0.026 0.030
Processing Servs.
522 Credit Intermediation 0.018 0.026
& Related
523 Securities, Commodity 0.022 0.027
Contracts, & Other
524 Insurance Carriers 0.022 0.027
& Related
525 Funds, Trusts, & Other 0.005 0.026
Financial Vehicles
531 Real Estate 0.002 0.027
532 Rental & Leasing 0.026 0.030
Servs.
533 Lessors of Nonfinancial 0.020 0.030
Intangible Assets
541 Professional, Scientific, 0.025 0.028
& Technical Servs.
561 Administrative & 0.026 0.029
Support Servs.
562 Waste Management & 0.016 0.029
Remediation Servs.
611 Educational Servs. 0.010 0.029
621 Ambulatory Health 0.026 0.032
Care Servs.
622 Hospitals 0.021 0.033
623 Nursing & Residential 0.012 0.031
Care Facilities
624 Social Assistance 0.016 0.028
711 Performing Arts, 0.010 0.031
Spectator Sports
713 Amusement, Gambling, 0.010 0.027
& Recreation
721 Accommodation 0.005 0.031
722 Food Servs. & Drinking 0.018 0.028
Places
811 Repair & Maintenance 0.023 0.028
812 Personal & Laundry 0.019 0.027
Servs.
813 Religious, Grantmaking, 0.014 0.029
Civic, Profess.
Source: Authors' calculations using data from Compustat.
APPENDIX B
Tax-Adjusted q with Dividend Taxes
WE BEGIN BY establishing the equilibrium condition that
shareholders receive their required return, r, from holding equity that
provides taxable dividends and capital gains, so that
(B1) r[V.sub.t] = (1 - [theta])[D.sub.t] + (1 -
c){[E.sub.t][[V.sub.t + 1]] - [V.sub.t] - [V.sup.N.sub.t]},
where [theta] is the tax rate on dividends and c is the
accrual-equivalent tax rate on capital gains. [t]D denotes dividends
paid to shareholders in period t, V is equity value, and [V.sup.N.sub.t]
denotes equity contributions made in period t. Given that dividends and
capital gains are alternative forms of returns to shareholders, it is
useful to summarize the relative tax penalty on dividends and capital
gains with the dividend tax preference parameter [gamma]:
(B2) [gamma] = (1 - [theta])/(1 - c).
Given that capital gains taxes are paid only when the gain is
realized, [gamma] is considered to be less than 1. (63) Solving equation
B1 forward, and imposing the transversality condition that firm value
cannot be infinite in a finite period, provides a value equation for the
firm that implies
(B3) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [beta] is the appropriate after-tax discount factor. Equation
B3 corresponds to the straightforward intuition that firm value at time
0 is the present-discounted, tax-adjusted value of all future dividends,
taking into account any equity contributions required to maintain a
proportional shareholding in the firm.
Firm value maximization is subject to several constraints.
Dividends and equity issuance are constrained to be nonnegative. (64)
The firm's capital stock K evolves according to
(B4) [K.sub.t] = [K.sub.t-1] (1 - [delta]) + [I.sub.t],
where [delta] is a constant proportional rate of decay and I is
investment. The underlying cash flow identity for the firm is given by
(B5) (1 - [tau]) [F([K.sub.t] [L.sub.t]) - [w.sub.t][L.sub.t] -
C([I.sub.t], [K.sub.t-1]) [p.sub.t]] + [V.sup.N.sub.t] + [tau][A.sub.t]
= [D.sub.t] + [I.sub.t] [p.sub.t] (1 - [[GAMMA].sub.t]),
where [tau] is the statutory corporate tax rate, F(K, L) is firm
output, L is labor, w is the wage rate, C([I.sub.t] [K.sub.t-1]) is an
adjustment cost function for investment, and [tau]A captures the tax
value of depreciation allowances on previous investments. Variable p is
the price of capital goods relative to output, and [GAMMA] is a summary
measure of tax provisions that directly influence investment, such as
the tax value of depreciation allowances and investment tax credits. The
source and measurement of [GAMMA] are described in appendix A. In short,
equation B5 states that a firm's after-tax cash flow and its new
equity issuances are sources of funds, which are used for investment and
for paying dividends, and the ps ensure that all terms are properly
price adjusted. (65)
Given the expression for firm value in equation B3 and the
constraints discussed above, firm value maximization employs the
following Hamiltonian equation:
(B6) [H.sub.t] = [gamma][D.sub.t] - [V.sup.N.sub.t] -
[[lambda].sup.l.sub.t][[K.sub.t] - [K.sub.t-1](1 - [delta]) - [I.sub.t]]
- [[lambda].sup.2.sub.t][D.sub.t] - [[lambda].sup.3.sub.t]
[V.sup.N.sub.t].
In this setting, [[lambda].sup.1.sub.t], [[lambda].sup.2.sub.t],
and [[lambda].sup.3.sub.t] correspond to the shadow values of capital
goods, dividends, and negative equity issuances, respectively.
Substituting the value of dividends from the cash flow identity in
equation B5, we can rewrite equation B6 as
(B7) [H.sub.t] = ([gamma] - [[lambda].sup.2.sub.t]){(1 -
[tau])[F(K, L) - wL - C([I.sub.t], [K.sub.t-1]) [p.sub.t]] +
[V.sup.N.sub.t] + [I.sub.t][p.sub.t](1 - [GAMMA]) + [tau][A.sub.t]} -
[V.sup.N.sub.t] - [[lambda].sup.1.sub.t] [[K.sub.t] - [K.sub.t-1] (1 -
d) - [I.sub.t]] - [[lambda].sup.3.sub.t][V.sub.N.sub.t].
Differentiating this Hamiltonian provides the relevant first-order
conditions. The first-order condition for investment is provided by
(B8) -([gamma] - [[lambda].sup.2.sub.t]) (1 -
[tau])[C.sub.I][p.sub.t] - [p.sub.t](1 - [GAMMA]) +
[[lambda].sup.1.sub.t] = 0,
and the conditions for dividends and net equity issuance are
provided by
(B9) [D.sub.t] [greater than or equal to] 0; [[lambda].sup.2.sub.t]
[greater than or equal to] 0; and [D.sub.t][lambda].sup.2.sub.t] = 0
and
(B10) [V.sup.N.sub.t] [greater than or equal to] 0; ([gamma] -
[[lambda].sup.2.sub.t] - 1 [[lambda].sup.3.sub.t]) [greater than or
equal to] 0; and [V.sup.N.sub.t]([gamma] - [[lambda].sup.2.sub.t] - 1 -
[[gamma].sup.3.sub.t]) = 0, respectively.
Rearranging the investment first-order condition provided in
equation B8 provides an expression for q that corresponds to the shadow
price for capital:
(B11) ([[lambda].sup.1.sub.t]/[p.sub.t]) = [q.sub.t] = ([gamma] -
[[lambda].sup.2.sub.t])[(1 - [tau]) [C.sub.1] + (1 - [GAMMA])],
where [C.sub.I] is the marginal adjustment cost of new investment.
In order to put this in more familiar terms, we specify a conventional,
quadratic adjustment cost function:
(B12) C([I.sub.t], [K.sub.t-1]) =
([phi]/2)[[([I.sub.t]/[K.sub.t-1]) - [[mu].sup.2][K.sup.t-1],
where [phi] is the adjustment cost parameter and [mu] represents an
average investment rate. This quadratic adjustment cost function allows
us to represent equation B11 in more familiar terms. Differentiating the
cost function with respect to I and substituting it into equation B 11
yields
(B13) [I.sub.t]/[K.sub.t-1] = [mu] + (1/[phi]) ([q.sub.1]/[gamma] -
[[lambda].sup.2.sub.t] - (1 - [GAMMA])/ 1 - [tau]).
Equation B13 is the basic estimating equation commonly used in the
q-theory literature, with the slight peculiarity that [q.sub.t] is
divided by ([gamma] - [[lambda].sup.2.sub.t]). In the existing
literature and our discussion below, [[q.sub.t] - (1 - [GAMMA])/1 -
[tau]] is also referred to as Q rather than q. It will be important in
our discussion of measurement error to note that Q is actually composed
of two parts, associated with investment opportunities and taxes.
In order to consider under what conditions the additional, peculiar
term disappears, it is critical to specify the marginal source of
financing. To do so we return to the conditions B9 and B10 and consider
the alternative cases where the marginal source of financing is either
retained earnings or new equity issuance.
First, consider the case where the marginal source of finance is
new equity issuances. In this case, [[lambda].sup.3.sub.t] = 0 and,
consequently, [[lambda].sup.2.sub.t] = [gamma] - 1, as indicated by
equation B 10. In this case equation B13 becomes its more familiar
variant:
(B14) [I.sub.t]/[K.sub.t-1] = [mu] + (1/[phi])([q.sub.t]/1 - [tau]
- 1 - [GAMMA]/1 - [tau]).
Now consider the alternative case where the marginal source of
finance is retained earnings rather than new equity issuance. This
implies that dividends are positive and that [[lambda].sup.2.sub.t] = 0.
In turn, this implies that equation B 13 can be rewritten as
(B15) [I.sub.t]/[K.sub.t-1] = [mu] +
(1/[phi]){[[q.sub.t](1-c/1-[theta])/1 - [tau]] - (1 - [GAMMA]/1 -
[tau])}.
Equations B14 and B15 provide alternative q-theory specifications
for investment that incorporate different assumptions about the marginal
source of finance and, consequently, about the role of dividend taxation
in influencing investment.
Table 1. Regressions of Changes in Investment during 2000-02 on
Changes in Investment during the 1990s, Equipment Only, Using Data
by Industry (a)
Manufacturing
All industries only
Sample period 1-1 1-2
Ordinary least squares regressions
1994-99 -0.084 -0.5315
(0.0693) (0.1894)
1997-99
1994-97
No. of observations 81 23
Adjusted [R.sup.2] .018 0.273
Median regressions
1994-99 -0.0205 -0.5836
(0.0994) (0.2210)
1997-99
1994-97
No. of observations 81 23
Manufacturing
All industries only
1-3 1-4
Sample period
Ordinary least squares regressions
1994-99
1997-99 -0.0582 -0.4204
(0.0853) (0.2047)
1994-97 -0.1435 -0.7878
(0.1331) (0.2704)
No. of observations 81 23
Adjusted [R.sup.2] .022 .330
Median regressions
1994-99
1997-99 .0239 -0.5712
(0.1392) (0.2753)
1994-97 -0.1164 -0.8117
(0.2206) (0.4492)
No. of observations 81 23
Source: Authors' regressions using data trout the Annual Capital
Expenditure Survey of the U.S. Census Bureau.
(a.) The dependent variable in all regressions is the change in log
capital expenditure on equipment from 2000 to 2002; the independent
variable is the same change in log values in the same industry for
the indicated sample period. Numbers in parentheses are standard
errors.
Table 2. Regressions of Changes in Investment during 2000-02 on Changes
in Investment during the 1990s, Equipment and Structures, Using Data by
Industry (a)
Manufacturing
All industries only
Sample period 2-1 2-2
Ordinary least squares regressions
1994-99 -0.0516 -0.5426
(0.0645) (0.1690)
1997-99
1994-97
No. of observations 81 23
Adjusted [R.sup.2] 0.008 0.329
Median regressions
1994-99 0.0533 -0.6793
(0.1066) (0.2030)
1997-99
1994-97
No. of observations 81 23
Manufacturing
All industries only
Sample period 2-3 2-4
Ordinary least squares regressions
1994-99
1997-99 -0.0677 -0.4786
(-0.0871) (0.1916)
1994-97 -0.0304 -0.6663
(0.1001) (0.2395)
No. of observations 81 23
Adjusted [R.sup.2] 0.009 0.247
Median regressions
1994-99
1997-99 0.0285 -0.545
(0.1182) (0.3532)
1994-97 -0.1186 -0.6564
(0.1184) (0.3665)
No. of observations 81 23
Source: Author's regressions using data from the Annual Capital
Expenditure Survey of the U.S. Census Bureau.
(a.) The dependent variable in all regressions is the change in log
capital expenditure on equipment and structures from 2000 to
2002; the independent variable is the change in log values in the same
industry for the indicated sample period. Numbers in parentheses are
standard errors.
Table 3. Regressions of Changes in Investment during 2000-02 on Changes
in Investment during the 1990s, Using Data by Asset Type (a)
Sample period 3-1 3-2
Ordinary least squares regressions
1994-99 -0.0945
(0.0970)
1997-99 0.0388
(0.1576)
1994-97 -0.2326
(0.1611)
No. of observations (b) 34 34
Adjusted [R.sup.2] .029 .063
Median regressions
1994-99 -0.149
(0.1105)
1997-99 0.0545
(0.1270)
1994-97 -0.2407
(0.1113)
No. of observations (b) 34 34
Source: Authors' regressions using National Income and Product Accounts
data from the Bureau of Economic Analysis.
(a.) The dependent variable in all regressions is the change in log
capital expenditure from 2000 to 2002; the independent variable
is the same change in log values for the same asset type for the
indicated sample period, Numbers in parentheses are standard errors.
(b.) Sample includes twenty-five categories of capital equipment and
nine categories of structures.
Table 4. Regressions of Changes in Investment Rates during 2000-02
on Changes in Investment Rates during the 1990s, Using Data
by Firm (a)
Independent variable 4-1 4-2 4-3
Ordinary least
squares regressions
Investment rate
change, 1994-99 -0.0325 -0.0237
(0.0234) (0.0216)
Investment rate
change, 1997-99 -0.0739
(0.0376)
Investment rate
change, 1994-97 -0.0174
(0.0252)
Change in sales, 0.0364
2000-02 (b) (0.0304)
No. of observations 3,249 3,225 3,172
Adjusted [R.sub.2] .002 .005 .004
Median regressions
Investment rate
change. 1994-99 -0.0170 -0.0120
(0.0051) (0.0051)
Investment rate
change, 1997-99 -0.0351
(0.0063)
Investment rate
change, 1994-97 -0.0071
(0.0051)
Change in sales,
2000-02 (b)
0.0545
(0.0040)
No. of observations 3,249 3,225 1,798
Source: Authors' regressions using data from Compustat.
(a) The dependent variable in all regressions is the change
in the firm's investment rate, as defined in the text, from
2000 to 2002. Numbers in parentheses are standard errors.
(b) In percent.
Table 5. Regressions Testing Sensitivity of the Firm-Level Investment-Q
Relationship to Past Changes in q(a)
All All
industries industries
Independent variable 5-1 5-2
Q 0.0124 0.0221
(0.0014) (0.0013)
Q x %[DELTA]q(t - 3 to t - 7), 0.0006 0.0002
2000 or after (b) (0.0004) (0.0005)
Q x %[DELTA]q(t - 3 to t - 7),
1999 or before (c)
Ratio of cash flow to 0.0198 0.0157
capital stock (0.0031) (0.0024)
Year dummies included Yes Yes
Firm dummies included Yes No
No. of observations 69,540 69,540
Adjusted [R.sup.2] 0.403 0.039
Information
businesses Manufacturing
only only
Independent variable 5-3 5-4
Q 0.0143 0.0137
(0.0032) (0.0018)
Q x %[DELTA]q(t - 3 to t - 7), -0.0005 0.0010
2000 or after (b) (0.0011) (0.0007)
Q x %[DELTA]q(t - 3 to t - 7),
1999 or before (c)
Ratio of cash flow to 0.0069 0.0236
capital stock (0.0057) (0.0042)
Year dummies included Yes Yes
Firm dummies included Yes Yes
No. of observations 11,758 36,313
Adjusted [R.sup.2] .399 .377
All
industries
Independent variable 5-4
Q 0.0123
(0.0014)
Q x %[DELTA]q(t - 3 to t - 7), 0.0006
2000 or after (b) (0.0004)
Q x %[DELTA]q(t - 3 to t - 7), 0.0003
1999 or before (c) (0.0003)
Ratio of cash flow to 0.0198
capital stock (0.0031)
Year dummies included Yes
Firm dummies included Yes
No. of observations 69,540
Adjusted [R.sup.2] 0.403
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's investment
rate in a given year, as defined in the text. Q is tax-adjusted q,
defined as [q/(1 - [tau])1 - [(1 - [GAMMA])/(1 - [tau])], where [GAMMA]
is the standard measure of the tax treatment of investment and [tau] is
the corporate tax rate. Numbers in parentheses are standard errors
clustered at the firm level.
(b.) Q times the change in q observed over the period three to seven
years before the current year, where the current year is 2000, 2001,
2002, or 2003.
(c.) As above, where the current year is 1999 or before.
Table 6. Regressions Testing Sensitivity of the Firm-Level
Investment-Q Relationship to Past Changes in Investment (a)
All All
industries industries
Independent variable 6-1 6-2
q 0.0124 0.0214
(0.0014) (0.0012)
Q x %[DELTA]K(t - 3 to t - 7), -0.0027 0.0008
2000 or after (b) (0.0007) (0.0004)
Q x %[DELTA]K(t - 3 to t - 7),
1999 or before (c)
Ratio of cash flow to 0.0168 0.0114
capital stock (0.0024) (0.0017)
Year dummies included Yes Yes
Firm dummies included Yes No
No. of observations 83,147 83,147
Adjusted [R.sup.2] .371 .035
Information
businesses Manufacturing
only only
Independent variable 6-3 6-4
q 0.0155 0.0117
(0.0011) (0.0017)
Q x %[DELTA]K(t - 3 to t - 7), -0.0021 -0.0023
2000 or after (b) (0.0011) (0.0009)
Q x %[DELTA]K(t - 3 to t - 7),
1999 or before (c)
Ratio of cash flow to 0.0058 0.0143
capital stock (0.0045) (0.0033)
Year dummies included Yes Yes
Firm dummies included Yes Yes
No. of observations 14,735 44,326
Adjusted [R.sup.2] .357 .337
All
industries
Independent variable 6-5
q 0.0137
(-0.0015)
Q x %[DELTA]K(t - 3 to t - 7), -0.0030
2000 or after (b) (0.0007)
Q x %[DELTA]K(t - 3 to t - 7), -0.0013
1999 or before, (0.0004)
Ratio of cash flow to 0.0168
capital stock (0.0024)
Year dummies included Yes
Firm dummies included Yes
No. of observations 83,147
Adjusted [R.sup.2] .371
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's investment
rate, as defined in the text, in a given year. Q is tax-adjusted q
as defined in table 5; K is the capital stock. Numbers in parentheses
are standard errors clustered at the firm level.
(b.) Q times the change in the capital stock observed over the period
three to seven years before the current year, where the current year is
2000, 2001, 2002, or 2003.
(c.) As above, where the current year is 1999 or before.
Table 7. Regressions Testing Sensitivity of the Firm-Level
Investment-Q Relationship to q and Tax-Adjusted q
Using Alternative Definitions (a)
Regressions using corporate finance
definition of q
Independent variable 7-1 7-2 7-3
q 0.0379 -0.1111
(0.0019) (0.0174)
Q 0.0231 0.0863
(0.0011) (0.0102)
Ratio of cash flow 0.0020 0.0006 0.0015
to capital stock (0.0015) (0.0015) (0.0016)
No. of observations 160,051 142,043 142,043
Adjusted [R.sup.2] .377 .376 .377
Regression s using public finance
definition of q
Independent variable 7-4 7-5 7-6
q 0.0007 -0.003
(0.0002) (0.0018)
Q 0.0005 0.0023
(0.0001) (0.0010)
Ratio of cash flow 0.0003 -0.0013 -0.0013
to capital stock (0.0015) (0.0015) (0.0016)
No. of observations 161,416 142,882 142,882
Adjusted [R.sup.2] .368 .367 .367
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's
investment rate, as defined in the text. in a given year. Q is
lax-adjusted q as defined in table 5. All regressions include
year and firm dummies.
Numbers in parentheses are standard errors clustered at the
firm level.
Table 8. Regressions Testing Sensitivity of the Firm-Level
Investment-Q Relationship to Components of Tax-Adjusted q (a)
Including
Independent variable Baseline ordinary q in
regression regression
q/(1 - t) 0.0231 0.0858
(0.0011) (0.0102)
q -0.1103
-0.0174
Tax term, equipment -0.8895 -0.7865
(0.3173) (0.3162)
Tax term, structures -0.0169 -0.0064
(0.0452) (0.0453)
Ratio of cash flow to
capital stock 0.0005 0.0004
(0.0015) (0.0015)
No. of observations 141,629 141,629
Adjusted [R.sup.2] .376 .377
Include controls
for past Include
depreciation controls for
Independent variable allowances (b) debt shares (c)
0.0166 0.0245
q/(1 - t) (0.0011) (0.0011)
q
-0.7078 -0.8949
Tax term, equipment (0.2870) (0.3163)
-0.0333 -0.0127
Tax term, structures (0.0408) (0.0450)
Ratio of cash flow to 0.0090 0.0002
capital stock (0.0018) (0.0015)
111,059 141,245
No. of observations .381 .378
Adjusted [R.sup.2]
Instrumental
Independent variable variables
regression (d)
q/(1 - t) 0.2120
(0.0054)
q
Tax term, equipment -2.5351
(1.0101)
Tax term, structures -0.7902
(0.2716)
Ratio of cash flow to
capital stock 0.0170
(0.0022)
No. of observations 46,154
Adjusted [R.sup.2] --
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's
investment rate, its defined in the text, in a given year. All
regressions include year and firm dummies. Numbers in parentheses
are standard errors clustered al the firm level.
(b.) Controls for the present value of tax depreciation allowances
on previously purchased investment, its described in appendix B.
(c.) Controls for the share of debt in firm financing.
(d.) Financial analysts' reported estimates of tire firm's earnings
are used as an instrument for firm q.
Table 9. Regressions Testing Sensitivity of the Firm-Level
Investment-Q Relationship to Components of Q in Manufacturing
and Nonmanufacturing Industries (a)
All Manufacturing
Independent variable industries industries only
q/(1 - t) 0.0231 0.0210
(0.0011) (0.0015)
Tax term, equipment -0.8895 -1.1545
(0.3173) (0.6943)
Tax term, structures -0.0169 -0.0035
(0.0452) (0.1762)
Ratio of cash flow to 0.0005 0.0001
capital stock (0.0015) (0.0022)
No. of observations 141,629 68,680
Adjusted [R.sup.2] .376 .336
Nonmanufacturing
Independent variable industries only
q/(1 - t) 0.0169
(0.0011)
Tax term, equipment -0.7034
(0.2853)
Tax term, structures -0.0307
(0.0405)
Ratio of cash flow to 0.0090
capital stock (0.0018)
No. of observations 111,059
Adjusted [R.sup.2] .381
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's
investment rate, as defined in the text, in a given year.
All regressions are as specified in the first column of table 8.
Numbers in parentheses are standard errors clustered at the firm
level.
Table 10. Regressions Testing Sensitivity of the Firm-Level
Investment-Q Relationship to Capital Gains and Dividend Tax Rates (a)
Sample period
Independent variable 1961-2003 1961-1996 1997-2003
q/(1 - t) -0.0158 -0.0067 -0.0652
(0.0130) (0.0154) (0.0345)
(1 - tcg)/(1 - tdiv)
x q/(1 - t) (b) 0.0312 0.0241 0.0673
(0.0105) (0.0122) (0.0291)
Tax term, equipment -0.7795 -0.6258 0.748
(0.3154) (0.3219) (2.6180)
Tax term, structures -0.0081 -0.0237 9.464
(0.0454) (0.0459) (2.7730)
Ratio of cash flow
to capital stock 0.0004 0.0134 -0.0184
(0.0015) (0.0025) (0.0023)
No. of observations 141,643 100,525 41,118
Adjusted [R.sup.2] .376 .426 .486
Source: Authors' regressions using data from Compustat.
(a.) The dependent variable in all regressions is the firm's
investment rate, as defined in the text. in a given year.
All regressions include year and firm dummies. Numbers in
parentheses are standard errors clustered at the firm level.
(b.) tcq is the accrual-equivalent tax rate on capital gains
and tdiv the tax rate on dividends.
Table 11. Estimates of Change in Capital Stock Attributable to
Tax Cuts of 2002 and 2003, by Industry
Change in capital stock,
2002-03 (a) (percent)
Using tax term Using tax term
NAICS for equipment for equipment
code Industry and structures only
481 Air Transp. 1.58 1.68
514 Information Servs. & 1.47 1.79
Data Processing Servs.
492 Couriers & Messengers 1.44 1.52
561 Administrative & 1.43 1.63
Support Servs.
213 Support Activities 1.42 1.61
for Mining
487 Scenic & Sightseeing 1.42 1.63
Transp.
488 Support Activities for 1.42 1.63
Transp.
485 Transit & Ground 1.39 1.52
Passenger Transp.
541 Professional, Scientific, & 1.32 1.58
Technical Services
233 Building, Developing, & 1.32 1.38
General Contracting
234 Heavy Construction 1.32 1.38
235 Special Trade Contractors 1.32 1.38
313 Textile Mills 1.31 1.58
314 Textile Product Mills 1.30 1.60
316 Leather & Allied 1.30 1.54
Product Mfg.
323 Printing & Related 1.30 1.57
Support Activities
322 Paper Mfg. 1.28 1.46
421 Wholesale Trade, 1.27 1.58
Durable Goods
422 Wholesale Trade, 1.27 1.58
Nondurable Goods
493 Warehousing & Storage 1.26 1.63
513 Broadcasting & 1.25 1.68
Telecommunications
511 Publishing 1.25 1.55
114 Fishing, Hunting, 1.25 1.68
& Trapping
621 Ambulatory Health 1.24 1.61
Care Servs.
331 Primary Metal Mfg. 1.22 1.43
523 Securities, Commodity 1.20 1.59
Contracts, & Other Fin.
334 Computer & Electronic 1.19 1.58
Product Mfg.
321 Wood Product Mfg. 1.18 1.53
336 Transp. Equip. Mfg. 1.18 1.46
315 Apparel Mfg. 1.18 1.56
484 Truck Transp. 1.16 1.36
312 Beverage & Tobacco 1.15 1.39
Product Mfg.
337 Furniture & Related 1.14 1.50
Product Mfg.
332 Fabricated Metal 1.12 1.35
Product Mfg.
524 Insurance Carriers & 1.12 1.41
Related Activities
333 Machinery Mfg. 1.12 1.43
327 Nonmetallic Mineral 1.11 1.35
Product Mfg.
339 Miscellaneous Mfg. 1.11 1.41
811 Repair & Maintenance 1.11 1.45
311 Food Mfg. 1.10 1.42
335 Electrical Equip., Appliance 1.09 1.37
& Component Mfg.
326 Plastics & Rubber 1.07 1.29
Products Mfg.
622 Hospitals 1.03 1.82
324 Petroleum & Coal 1.02 1.18
Products Mfg.
212 Mining (except 0.97 1.37
Oil & Gas)
812 Personal & 0.95 1.43
Laundry Servs.
113 Forestry & Logging 0.94 1.20
512 Motion Picture & 0.93 1.66
Sound Recording
722 Food Servs. & 0.88 1.48
Drinking Places
532 Rental & 0.86 1.00
Leasing Servs.
325 Chemical Mfg. 0.86 1.28
522 Credit Intermediation 0.80 1.28
& Related Activities
624 Social Assistance 0.77 1.59
482 Rail Transp. 0.73 1.39
562 Waste Management & 0.71 1.44
Remediation Servs.
486 Pipeline Transp. 0.66 1.75
813 Religious, Grantmaking, 0.65 1.60
Civic, Prof. & Similar
533 Lessors of Nonfin. 0.63 1.10
Intangible Assets
111 Crop Production 0.61 0.70
444 Building Mtrl & Garden 0.58 1.56
Equip. & Supplies
448 Clothing & Clothing 0.58 1.56
Accessories Stores
443 Electronics & 0.58 1.56
Appliance Stores
445 Food & Beverage 0.58 1.56
Stores
442 Furniture & Home 0.58 1.56
Furnishings Stores
447 Gasoline Stations 0.58 1.56
452 General Merchandise 0.58 1.56
Stores
446 Health & Personal 0.58 1.56
Care Stores
453 Miscellaneous 0.58 1.56
Store Retailers
441 Motor Vehicle & 0.58 1.56
Parts Dealers
454 Nonstore Retailers 0.58 1.56
451 Sporting Goods, Hobby, 0.58 1.56
Book, & Music Stores
221 Utilities 0.56 1.36
623 Nursing & Residential 0.56 1.74
Care Facilities
112 Animal Production 0.54 0.66
611 Educational Servs. 0.49 1.72
713 Amusement, Gambling, 0.41 1.31
& Recreation
711 Performing Arts, Spectator 0.38 1.54
Sports, & Related
211 Oil & Gas Extraction 0.26 1.48
525 Funds, Trusts, & Other 0.18 1.46
Fin. Vehicles
721 Accommodation 0.18 1.46
531 Real Estate 0.03 0.72
Source: Authors' calculations using data from Compustat as described
in appendix A.
(a.) Change in capital stock associated with changes in the expensing
provisions by industry as described in the text.
We thank Mark Veblen and James Zeitler for their invaluable
research assistance, as well as Alan Auerbach, Kevin Hassett, John
Leahy, Joel Slemrod, and participants at the Brookings Panel conference
for their comments. Dale Jorgenson was kind enough to provide estimates
of the tax term by asset. Mihir Desai thanks the Division of Research at
Harvard Business School for financial support. Austan Goolsbee thanks
the American Bar Foundation and the National Science Foundation for
financial support.
(1.) McCarthy (2003) documents the decline in equipment investment
as a share of GDP for all business cycles since 1953 and shows the 2001
recession to be an outlier.
(2.) See, for example, Berner (2001); Graeme Leach, "The
Worries of the World," GCIEye no. 1, 2002, accessed August 2004
(www.gcieurope.com/eye/GCIEye_Issue01. pdf), and Stephen Roach,
"The Costs of Bursting Bubbles," New York Times, September 22,
2002, section 4, p. 13.
(3.) See, for example, Greenspan (2002), Ferguson (2001), Bernanke
(2003), French, Klier, and Oppedahl (2002), Pelgrin, Schich, and de
Serres (2002), Kliesen (2003), Doms (2004), and McCarthy (2004).
(4.) Unlike the behavior of investment, the behavior of tax policy
in the 2000s is completely consistent with earlier time periods.
Cummins, Hassett, and Hubbard (1994) have documented that a primary
determinant of investment tax subsidies is a drop in investment.
(5.) Saxton (2003).
(6.) Summers (1981).
(7.) Cummins, Hassett. and Hubbard (1994).
(8.) Tevlin and Whelan (2003) argue that much of the increase in
gross investment can be explained empirically by falling prices of
computers and their higher depreciation rates.
(9.) Two papers by McCarthy (200l, 2004) are exceptions.
(10.) As in, for example, Abel and Eberly (2002).
(11.) The adjustment costs could be at the firm level, or they
might be external in the sense that the supply of capital goods in a
particular industry is upward sloping as in Goolsbee (1998, 2001).
(12.) Ramey and Shapiro (2001). Evidence presented by Goolsbee and
Gross (2000) is also consistent with that view.
(13.) Before 1997, Standard Industrial Classification (SIC) codes
were employed.
(14.) See, for example, Auerbach and Hassett (1992), who discuss
the problems with estimating structures investment.
(15.) This is also consistent with the evidence cited by Bernanke
(2003).
(16.) McCarthy (2003).
(17.) The categories of structures employed by the BEA change
slightly over the period.
(18.) One important shortcoming of the Compustat data (and common
to virtually all empirical work that uses these data to study
investment) is the inability to isolate domestic from international
expenditure or the degree to which q measures worldwide rather than
domestic investment opportunities.
(19.) Appendix A describes how we compute the capital stock for
each firm, following Salinger and Summers (1984) and Cummins, Hassett,
and Hubbard (1994).
(20.) We considered using the lagged change in the price-earnings
ratio as the measure of firms with overhang, but this had the obvious
problem that many firms had negative earnings.
(21.) Other lags, such as the change in q from five years ago to
two years ago, yield similar results.
(22.) In a previous draft we also examined whether having had a
large increase in K or in q during the 1990s led the level of investment
at the firm to be lower, controlling for current q (as opposed to the
increase changing the slope of the investment-q relationship). We found
virtually no evidence that it did.
(23.) A plot using the average of firm qs in our sample (not shown)
yields a similar picture.
(24.) See, for example, Cummins, Hassett, and Hubbard (1994), Bond
and Cummins (2000), and Goolsbee (2001).
(25.) Summers (1981); Poterba and Summers (1983, 1985).
(26.) This view originates in the work of King (1977), Auerbach
(1979), and Bradford (1981).
(27.) See the work of Poterba and Summers (1985), Chetty and Saez
(2004), and Poterba (2004) and the papers they cite. Auerbach (2002)
points out, however, that interpretation of the empirical evidence on
this point is complicated by the fact that temporary cuts in dividend
taxes should encourage dividend payouts, even under the new view.
(28.) Fuller assessments of both views can be found in Auerbach and
Hassett (2003), Carroll, Hassett, and Mackie (2003), and Poterba and
Summers (1985).
(29.) Poterba and Summers (1983, 1985).
(30.) Auerbach (2002); Auerbach and Hassett (2003).
(31.) See, for example, Bernheim and Wantz (1995), Poterba (2004),
Cherty and Saez (2004), and Poterba and Summers (1985), as well as the
opposing evidence in Bolster and Janjigian (1991), Blouin, Raedy, and
Shackelford (2004), and Ikenberry and Julio (2004).
(32.) Because of reporting conventions, the 2003 sample is somewhat
smaller than the sample in earlier years.
(33.) The data are described in more detail in Jorgenson and Yun
(2001). Importantly, the Jorgenson calculations do not take any future
expectations of tax changes into account. They use only the statutory
tax rules for the year in question.
(34.) Cummins, Hassett. and Hubbard (1994): Chirinko, Fazzari, and
Meyer (1999).
(35.) Poterba (2004).
(36.) Kaplan and Zingales (1997).
(37.) This numerator is also sometimes adjusted for deferred taxes.
(38.) Salinger and Summers (1984); see also Cummins, Hassen, and
Hubbard (1994).
(39.) Cummins, Hassett. and Hubbard (1994).
(40.) Gilchrist and Himmelberg (1998).
(41.) Summers (1981).
(42.) Cummins, Hassett, and Hubbard (1994).
(43.) This assumes that the measurement errors are not correlated
in the two series. We tried the same regressions in the exercise below,
but excluding the q term and including only the tax terms, and found the
coefficient on the tax term to be even slightly larger in absolute
value, and so we are not as concerned about this issue.
(44.) See Auerbach and Hassett (1992).
(45.) We also tried including lagged q and tax term terms, but this
had no impact on the results.
(46.) Following Summers (1981).
(47.) These results are similar to findings by Cummins, Hassett,
and Hubbard (1994).
(48.) Bond and Cummins (2000).
(49.) Cummins, Hassett, and Oliner (forthcoming) also look at
investment equations that include analysts' earnings estimates.
(50.) To be precise, we use the earnings estimates divided by (1 -
[tau]) as an instrument for q/(1 - [tau]).
(51.) See Hederman (2004) and Larry Kudlow, "A Capital Idea
from Microsoft," National Review Online, posted on July 23, 2004
(www.nationalreview.com/kudlow/ kudlow200407230854.asp).
(52.) Joint Committee on Taxation (2003a).
(53.) See, for example, Auerbach (2002).
(54.) We also tried using only the personal tax rates from Poterba
(2004) to take out any potential bias that the trends in corporate and
nontaxable investor shares of dividends received might have on average
marginal tax rates. This made no difference to our results and
consistently showed evidence in favor of the new view. Note that our
results are not identified by the level of the dividend tax term (which
gets absorbed in the year dummies) but instead by the interaction with
q.
(55.) Auerbach and Hassett (2002).
(56.) Carroll, Hassett, and Mackie (2003) simulate the impact in
more detail under various assumptions.
(57.) Cohen, Hassett, and Hansen (2002) provide a comprehensive
analysis of the 2002 change.
(58.) Joint Committee on Taxation (2002, 2003a, 2003b).
(59.) See Abel (1981) and Summers (1981) for discussion.
(60.) Auerbach (1989).
(61.) Following the traditional literature, we make these
calculations assuming that the elasticity of [K.sup.*] with respect to
the cost of capital is equal to -1, for a Cobb-Douglas production
function for a given level of output. Such a figure is consistent with
the empirical findings surveyed in Hassett and Hubbard (2002) but larger
than the findings discussed in Chirinko (1993) or in Chirinko, Fazzari,
and Meyer (1999). If labor were held fixed rather than output, our
calculations would be scaled by 1/[alpha], where R is the complement of
the industry capital share (averaging about .66 to .70 in our data). To
compute the total effect with varying output would require a full
general equilibrium model for all sectors with industry-specific demand
information. More details on the assumptions behind such calculations
can be found in Coen (1969) and Hall and Jorgenson (1969).
(62.) That the changes were unanticipated is probably fairly
accurate. An assumption of permanence seems reasonable, because although
the changes were announced as temporary, from the moment they were
passed many have been advocating that they be made permanent (and indeed
the changes have already been extended to 2004). We assume permanence
here because it considerably simplifies the computation of the
investment path.
(63.) Even with similar rates on dividends and realized capital
gains, [gamma] < 1 is thought to hold. Typically, the
accrual-equivalent c is usually taken as one-quarter of the statutory
rate applicable to capital gains.
(64.) As Poterba and Summers (1985) note, repurchases can be
allowed without loss of generality. However, negative new equity
issuances must be bounded by some maximum amount, an assumption
justified by the IRS's ability to characterize large, regularized
repurchases as dividends.
(65.) We abstract from debt and the presence of tax-deductible
interest without loss of generality.
Comments and Discussion
Kevin A. Hassett: This paper by Mihir Desai and Austan Goolsbee
examines the impact of recent corporate tax changes on investment
behavior and investigates the impact of a possible capital overhang on
investment. Since my fellow discussant will focus on the overhang issue,
I will concentrate my remarks on the tax and investment side of the
paper.
To summarize my conclusions: The paper is a good, state-of-the-art
econometric effort that confirms many of the findings of the recent
investment literature. The regression results are competently arrived at
and believable. However, the authors' policy discussion and their
discussion of the impact of recent tax reforms offer conclusions that do
not follow from their results. In relating their results to the impact
of current policies, the authors have favored some extreme assumptions
that are not supported by their empirical work, all aligned in a manner
to make the tax cuts seem ineffective. A more balanced assessment of the
recent impact of the tax reforms would certainly be more favorable.
A LOOK AT THE LITERATURE. The first of the recent corporate tax
changes reduced the user cost of capital by allowing firms to expense a
fraction of their capital purchases. This expensing has been
"temporary" from the outset, although there has been
significant uncertainty about whether the expensing provisions would be
allowed to expire. There is little dispute that this tax change will
lower the user cost of capital. In a recent paper in the National Tax
Journal, my coauthors and I found that this reduction would vary by
asset class, averaging about 2 to 3 percent, if the change were viewed
as permanent and if the expensing fraction were the original, lower
number. (1) If instead the provision were expected to expire, firms
would have an incentive to shift investment forward. We found that this
effect would reduce the current user cost by much more.
The second change was to lower the tax rate on dividends and
capital gains. The effect of this change on the user cost depends on the
marginal source of finance. In another recent paper, my coauthor and I
found that approximately half of all firms behave as if they use new
share issues as their marginal source of finance, (2) and approximately
half behave as if they use retained earnings. Accordingly, relying on a
third recent paper, in which my coauthors and I modeled the impact of
dividend tax law changes, (3) I find that the reduction attributable to
the recent changes would be quite large under the "old view"
of dividend taxation and much smaller under the "new view."
However, these conclusions depend crucially on unobservables. The most
important of these are the marginal tax rates on dividends and on
capital gains, which in turn depend on the nature of financial
equilibrium. If, for example, a "Miller equilibrium" describes
the world, the relevant rates are those at which the marginal investor
is just indifferent between debt and equity, not the average observed
rate. Across a range of assumptions, however, the dividend change
reduces the cost of capital by about 7 percent on average, assuming that
firms themselves are split 50-50 between the old and the new views.
More recent evidence consistent with the idea that there exist both
old and new view firms includes work by James Poterba, (4) who has found
that dividend payout responds significantly to tax changes, and by Raj
Chetty and Emmanuel Saez, (5) who found that dividend payouts increased
sharply after the recent change in the law. In a work in progress, Alan
Auerbach and I are exploring share price responses to the dividend
change and finding results that confirm the conclusions in our earlier
paper. There thus appears to be a great deal of heterogeneity in the
data, with some firms behaving according to the old view and some
according to the new view.
Whether these changes would result in a stimulus to investment
depends on the elasticity of investment with respect to the user cost.
Glenn Hubbard and I, in our chapter in the Handbook of Public Economics,
concluded that the literature is now leaning toward a relatively large
elasticity.
A number of recent papers have strengthened support for the
conclusion that neoclassical fundamentals are important, by overturning
past findings that cash flow is a much more important determinant of
investment than the user cost or Q. For example, in a forthcoming paper,
my coauthors and I find that cash flow does not matter in investment
equations once one controls for measurement error in Q. (6) Thus
liquidity constraints should not, the literature suggests, mute the
possible effects of tax cuts.
THIS PAPER IN RELATION TO THE LITERATURE. This paper provides a
very clear view of where the empirical investment literature is at the
moment. The authors find that, once one controls for measurement
problems, fundamentals such as taxes are hugely important in investment
equations. Indeed, in most of their specifications, the estimated
coefficient for the tax term is close to or exceeds 1 in absolute value.
Confirming the findings of Jason Cummins, Stephen Oliner, and myself,
(7) they also find that cash flow does not influence investment over and
above the fundamentals.
The paper breaks from the literature in only one way. Using the
same data as much of the previous literature, the authors follow an
empirical strategy much like that of Poterba and Lawrence Summers in
their seminal paper on the old and new views. (8) They reverse the
Poterba and Summers result and find that the new view cost of capital
explains investment. This departs significantly from the recent
literature that has used dividend rather than investment behavior to
assess the relative importance of the two views.
Their result favoring the new view is not very convincing, however.
For one thing, they favor the new view on the basis of parameter
estimates that are interacted with the Q variable, which the authors
themselves concede is mismeasured. The new view Q estimate is slightly
less absurd than the old view Q estimate, but both are an order of
magnitude smaller than the coefficients on the separate tax terms. If
the coefficients were the same as those on the tax terms, one might have
more confidence in the results.
In addition, there is serious doubt that Desai and Goolsbee's
approach has any power to distinguish between the two views. What mainly
distinguishes the two models is a marginal tax wedge variable that is
unobservable but approximated by an average taken from tax returns. That
should give one pause. Second, the tax wedge variable does not vary
across firms. This means that the authors have no within variation to
identify the two models, and they have very few real degrees of freedom.
The correlation between the two relevant variables must be
enormous--within a given year it is unity. Add year effects, and it is
something of a miracle that their computer did not crash. It did not
only because of the interaction with the noisy Q variable. That the two
variables enter with opposite-signed coefficients suggests that the
design matrix may be ill conditioned, and this raises red flags. This is
exactly why other recent empirical attempts to evaluate the relative
importance of the two views have not attempted to do so with investment
data.
Moreover, the tax wedge variable mostly follows a steady upward
trend during this period (figure 1). Indeed, over the period observed in
the paper, in only two years (1982 and 2003) did the variable deviate
more than 5 percent, positively or negatively, from its value in the
previous year. The explanation of the results may simply be a spurious
trend relationship.
[FIGURE 1 OMITTED]
Or maybe not. It is quite uncertain whether this average wedge is
the appropriate measure. It might be just as reasonable to use statutory
tax rates for dividends. Indeed, one of the more interesting aspects of
the recent tax changes is that the marginal dividend tax rate is no
longer one of any number of tax rates but instead is a known parameter.
We do not know what it is until the last observation. The
accrual-equivalent capital gains tax rate is also a guesstimate. Do the
results change if other, equally valid measures are used? The authors
are too confident that they have the correct measure of the crucial
variable.
Thus, where the paper agrees with the literature, it is on firm
ground. Its disagreements with the literature may just be the result of
poor design.
DID THE RECENT TAX CHANGES STIMULATE INVESTMENT? Given the
authors' finding that tax variables affect investment, one might
expect that they would conclude that the recent tax changes had a major
impact on investment. They conclude otherwise for two reasons. First,
they offer a misguided view of the recent data. Second, they greatly
understate the user cost effects of the recent tax changes, and hence
find a small impact on investment. I will respond to each of these in
turn.
The authors argue that investment has not increased after these tax
changes, but the chart they present in making this case is rather
misleading. Equipment investment surged after the dividend tax cut
(figure 2). We do not know yet whether this happened because of the cut,
but surge it did. The authors conclude that it did not respond to tax
policy only by cyclically adjusting the chart. But the current recovery
may have been slower than past recoveries for a number of reasons
unrelated to investment policy. The key question is, Did investment
policy stimulate investment, ceteris paribus? The regressions that the
authors run are not cyclically adjusted, nor should the prima facie
evidence be. The authors appear to presume that a pro-investment policy
can be judged successful only if it immediately returns investment to a
point beyond its previous cyclically adjusted peak. I can think of no
coherent rationale for such a view.
[FIGURE 2 OMITTED]
The authors understate the user cost reductions for two main
reasons. First, they accept their own estimates and assume that all
firms adhere to the new view. In doing so they neutralize most of the
user cost effect of the recent dividend and capital gains tax cuts.
Second, they assume that investors ignore the scheduled 2004 expiration
of the partial expensing provision and instead assume that it will
remain in effect. Recall that, if the provision does not expire, the
user cost effects of the combined policies sum to about 9 to 10 percent.
Given the authors' estimates, such a decline is an impressive
stimulus.
For partial expensing, the expiration is a big deal. For example,
for seven-year-lived equipment, the percentage reduction in the
Jorgensonian user cost in an expiration year (with only 30 percent
partial expensing) is about 14 percent. (9) By assuming that the measure
does not expire, the authors load the case in favor of their conclusion
that, despite the high investment elasticity, the recent tax cuts had no
effect. Using their coefficients and the more reasonable assumption that
the expensing provision is expected to expire, one could just as easily
generate, from their own model, a predicted 20 percent increase in
equipment investment this year. And investment has been increasing
sharply, supporting, casually at least, the view that policy has had the
effect that the empirical evidence would predict. Their conclusions
about the efficacy of current tax policy are likely the reverse of the
truth.
CONCLUSION. The empirical evidence that the paper presents with
regard to investment and the user cost supports the earlier literature
in numerous ways and will likely be cited often as a competent and
up-to-date estimation effort. The conclusions with regard to the new
view versus old view debate differ from those of the earlier literature,
but questions about the authors' specification suggest that the
optimal Bayesian weight on these results is fairly low. In discussing
policy, the authors' assumptions lead to an understatement of the
user cost effects. Contrary to their claims, the evidence supports the
view that the recent tax cuts likely had a significant impact on
investment.
John V. Leahy: Mihir Desai and Austan Goolsbee have written a very
interesting and provocative paper on the recent downturn in investment
spending in the United States. In fact, they have written two papers.
The first deals with one commonly attributed cause of the downturn,
namely, "capital overhang." The second investigates the
success of one of the intended solutions to this problem, namely, the
Bush tax cuts. Sticking to my comparative advantage, I will address my
comments to the paper on capital overhang.
Briefly stated, Desai and Goolsbee make two claims. First, they
claim that there is little evidence of capital overhang in the cross
section. To back this claim, they show that high investment in the 1990s
is not associated with low investment in the 2000s regardless of whether
one sorts the data by industry, by firm, or by type of capital. Second,
they claim that there is little need for the capital overhang story,
because a fundamentals story based on Tobin's q performs
adequately. Variables associated with capital overhang provide no
additional explanatory power in a standard q-theoretic model of
investment.
My main quibble with the paper is that Desai and Goolsbee never
write down an explicit model of capital overhang, without which the
concept is not clearly defined. We have no way of knowing how capital
overhang will manifest itself in their cross-sectional data, and we have
no way of knowing whether or not their tests are efficient. Is high past
investment a good measure of capital overhang at the firm level? Does
the capital overhang story imply that high past investment is negatively
correlated with current investment? A few examples will illustrate how
different theories could lead one to different conclusions regarding
these points.
In their introduction Desai and Goolsbee define capital overhang as
the "view ... that excess investment in the 1990s, fueled by an
asset price bubble, left corporations with excess capital stocks, and
therefore no demand for investment, during the 2000s." In this view
an overhang is something that is irrational and unjustified.
But there is another, more benign view of capital overhang: that
there was simply too much capital, not because of excessive investment
or a stock market bubble, but rather because of a change in firms'
view of the profitability of investment. Much happened between 1999 and
2001 that could have induced such a change: disillusion with the
information technology revolution, the decline in stock prices,
corporate scandals, Y2K, 9/11. In this view rationally optimistic firms
accumulated capital during the 1990s, received new information around
2000, and found themselves with more capital than they desired. There is
nothing here that is hard to reconcile with standard investment theory,
and no reason that a standard q-theoretic model would have any greater
difficulty dealing with this episode than it would with other cycles.
There are several ways to build such a change in perspective into a
standard model of investment. One way would be to consider a
neoclassical model of the business cycle and endow firms with a capital
stock that is above the long-run equilibrium level because of past
optimism regarding the profitability, of investment. Such a model would
be broadly consistent with the experience over the past five years. The
real interest rate, investment, and employment would all fall below
their steady-state levels, and consumption would rise above its
steady-state level.
How would the Desai-Goolsbee tests look in this neoclassical model?
Since firms do not differ in their response to aggregate news, the cross
section would be uninformative. Moreover, since the model is completely
standard, marginal q would be sufficient to explain investment. Terms
associated with overhang would not provide any additional information.
This neoclassical model is a bit of a straw man, however. One would
not expect that a shock would hit all firms equally, or that the degree
of capital overhang would be the same across firms. One might therefore
expect to see some evidence of capital overhang in the cross section.
What form might this evidence take?
One way to model firm-level differences in the desire to invest
would be to introduce "Ss dynamics" in the spirit of Giuseppe
Bertola. Ricardo Caballero, and Eduardo Engel. (1) In such a model,
idiosyncratic shocks would lead to cross-sectional differences in
firms' desire to invest, and frictions would prevent firms from
adjusting immediately to their optimal capital stock. There would then
be a gap between the marginal productivity of capital that triggers
investment and that which triggers disinvestment: To induce investment,
capital would need to be productive enough to cover both the cost of
capital and the cost of adjustment. Similarly, for disinvestment to
occur, the gains achieved by disinvesting would need to cover the
adjustment costs. Capital overhang might be interpreted as a situation
in which adjustment costs lead a firm to hold onto more capital than it
would in the absence of these frictions.
What would be the correlation between past investment and current
investment in such a model? It depends on the form of the adjustment
costs and the common trend in the shocks. If fixed costs dominate,
investment will tend to be lumpy. These lumpy investment episodes will
tend to reduce the marginal productivity of capital and make subsequent
investment less desirable. In such cases one might indeed expect to see
a negative correlation between investment in the past and investment in
the present. Russel Cooper, John Haltiwanger, and Laura Power have
shown, however, that it may be very difficult to tease this correlation
out of the data. (2) Unobserved heterogeneity in the adjustment costs
and differences in trend growth in productivity both tend to bias the
correlation in the opposite direction.
If instead the investment friction takes the form of
irreversibility or a wedge between the purchase and the sale price of
capital, the situation is entirely different. In this case past
investment is a signal that a firm had a high marginal productivity of
capital in the past. If the idiosyncratic shocks are uncorrelated with
marginal productivity, such a firm is more likely to have a high
marginal productivity of capital today as well. Absent other differences
across firms, past investment would then be positively, not negatively,
correlated with current investment.
How does capital overhang manifest itself in the irreversible
investment model? Past investment in that case is not necessarily a good
measure of capital overhang. Firms that invest are generally those with
too little capital. It is therefore the other firms that are more
naturally susceptible to overhang. A better measure might be an
indicator of the irreversibility of investment, although even here the
correlation between overhang and investment might not accord with
intuition. During a downturn, the firms that face the greatest
irreversibilities and therefore suffer from the greatest threat of
overhang may be precisely the firms that cut investment the least. The
irreversibility prevents them from disinvesting. The Desai-Goolsbee
tests are not well targeted to this theory.
A common feature of most investment models is that investment is
correlated with the marginal product of capital, or, more precisely, the
present value of the marginal product of capital. For the Desai-Goolsbee
version of the capital overhang story to hold, one needs a model in
which the high- and low-marginal-productivity firms switch places in or
about 2000. Firms that had high marginal productivity in the 1990s need
to have low marginal productivity in the 2000s. This switch would
generate the negative correlation between past investment and present
investment that their data reject. Given that present values are
dominated by expectations about the future, this switch can only happen
as a result of a shock that drastically alters firms' views of the
future.
The lumpy Ss model generates this switch through a leapfrogging
effect, in which firms with too little capital accumulate capital and
surpass firms that start out with greater capital stocks. The following
model might also fit these requirements: In the 1990s firms differed in
their expected rate of productivity growth. Firms with high expected
productivity growth invested more heavily than others, hoping to cash in
on the expected future growth. For some reason, however, these
expectations were not fulfilled, and by 2000 those firms that had
expected high productivity growth found themselves with more capital
than they desired, and they cut their investment spending by a greater
amount than did other firms. Note that in this model one does not need
to take a stand on whether or not investment was excessive. The mistaken
expectations could be the result either of irrational exuberance or of
rational ignorance. One interpretation of the Desai-Goolsbee regressions
is that this last story does fully not explain the data, although it may
be part of the story if it is combined with some other story that
generates a positive correlation in investment rates.
I enjoyed reading this paper. The questions it raises are
interesting and important, and the authors' answers are
provocative. The evidence presented will prove an important contribution
to our understanding of investment behavior. Whether or not overhang is
a good term to describe recent events remains an open question.
General discussion: Several panelists raised questions about the
authors' definition and measure of the capital overhang. Olivier
Blanchard and Daniel Sichel noted that the regressions using q as an
explanatory variable cannot distinguish between two important
hypotheses: first, that firms and markets are perfectly rational, and
investment moves in step with promising fundamentals (that is, rational
forecasts of the expected present value of marginal profits), and,
second, that firms make investments on the basis of stock market
valuations that include a bubble component. In both cases, ex post, the
firms have accumulated too much capital and are now restraining
investment: in the first case because their rational forecast was in
error, and in the second because they responded to an irrational market.
Since the authors do not explore whether the surge in investment or the
rise in q in the late 1990s was justified by plausible forecasts of the
fundamentals, they cannot analyze the reasons for the overinvestment.
William Nordhaus noted further that the authors' equations
show only whether investment is surprisingly high or low relative to
market valuations. Insofar as investment in the 1990s, for example in
fiber optics, turned out to have a lower rate of return than was
expected, both the market value and the need for investment will now be
low. Since both the decrease in q and the decrease in investment are due
to the same outside factor, the residuals from regressions of investment
on q are not informative about whether earlier investment was excessive.
Robert Gordon remarked that the experience of the late 1990s and early
2000s should remind us of the difficulty of attributing causation to q.
It seems likely that both firms, in their investment decisions, and the
market were overoptimistic about the profitability of the new economy.
Ben Bernanke reminded the Panel that the empirical literature has found
very small responses to changes in q, which implies that firms would
have smoothed their investment through the bubble. He wondered whether
there were significant positive residuals to q in investment equations
in the late 1990s. Benjamin Friedman added that during the bubble
period, for the first time since World War II, there was a significant
net inflow of funds to the corporate sector from equity issuance. Kevin
Hassett noted further that this was a period when there were many
initial public share offerings, often by new, small firms accessing the
stock market for the first time.
Nordhaus suggested that changes in the dispersion of investment
across firms might provide some clues about the importance of any
capital overhang from the 1990s. The firms the authors consider belong
to very different industries, ranging from shopping centers to aircraft
engine manufacturers. Many types of capital are nontransferable across
sectors, implying upper and lower bounds on investment; the upper bounds
due to capacity limits in the investment-producing sectors, and the
lower bounds given by zero gross investment. Given industry-specific
shocks, one would expect wide dispersion in investment rates across
different industries even in normal times. If overhang were specific to
particular industries in 2000, then one should observe greater
dispersion of investment than usual. Nordhaus reported that the
dispersion in the early 2000s does not appear to be significantly
greater than during other cycles of the post-World War II period.
Christopher House observed that, if overhang were firm specific,
reflecting dispersion of optimism across firms, then even if that
dispersion in the degree increased, there would be no reason to expect
aggregate investment to have been excessive in the late 1990s. The fact
that it was supported John Leahy's view that one should look for an
explanation at the aggregate rather than at the firm level.
Turning to the discussion of the effects of dividend taxes on
investment, House noted that the authors' analysis of tax
incentives assumes that the recent tax cuts are permanent; the response
would be quite different if agents expected the cuts to expire as the
law mandates. Standard neoclassical analysis suggests that a temporary
dividend tax cut should affect the timing of dividends, with little
effect on investment. House also suggested that the Congressional Budget
Office's projection of $130 billion in lost revenue from the
changes to partial expensing is an overestimate. Extending the
CBO's projection beyond 2005 reveals that increased revenue later
on makes up for much of the short-run cost; the decline in the present
discounted value of revenue would be much smaller than reported.
Peter Orszag pointed out that changes in the user cost of capital
due to tax cuts depend on how the tax cut is financed. He suggested
that, with reasonable values for the effects of deficit-financed tax
cuts on interest rates, the user cost of capital might actually rise. In
the same spirit, William Brainard remarked that. although the supply of
saving may be elastic in the short run when resources are underutilized,
in the long run what happens depends on the elasticity of saving with
respect to after-tax rates of return. It is quite possible that
increasing the after-tax rate of return actually decreases private
saving, since defined-benefit pension plans are target savers, and most
defined-contribution plans are designed to achieve a target replacement
ratio. For such plans the income effect of rate changes dominates the
substitution effect. The same is true for life-cycle savers whose
intertemporal elasticity of consumption is less than 1, which is what
most studies suggest. He also observed that, in an open economy, the
marginal source of funds may be the saving of foreigners who are not
affected by the U.S. tax rate on dividends.
(1.) Cohen, Hansen, and Hassett (2002).
(2.) Auerbach and Hassett (2002).
(3.) Carroll, Hassett, and Mackie (2003).
(4.) Poterba (2004).
(5.) Chetty and Saez (2004).
(6.) Cummins, Hassett, and Oliner (forthcoming).
(7.) Cummins, Hassett, and Oliner (forthcoming).
(8.) Poterba and Summers (1983).
(9.) Cohen, Hansen, and Hassett (2002, table 2).
(1.) Bertola and Caballero (1990): Caballero and Engel (1999).
(2.) Cooper, Haltiwanger, and Powell (1999).
References
Abel, Andrew B. 1981. "A Dynamic Model of Investment and
Capacity Utilization." Quarterly Journal of Economics 96: 379-403.
Abel, Andrew B., and Janice C. Eberly. 2002. "Q for the Long
Run." Working paper. University of Pennsylvania and Northwestern
University (July).
Auerbach, Alan. 1979. "Wealth Maximization and the Cost of
Capital." Quarterly Journal of Economics 93, no. 3: 433-46.
--. 1989. "Tax Reform and Adjustment Costs: The Impact on
Investment and Market Value." International Economic Review 30, no.
4, 939-62.
--. 2002. "Taxation and Corporate Financial Policy." In
Handbook of Public Economics, vol. 3, edited by Alan Auerbach and Martin
Feldstein. Amsterdam: North-Holland.
Auerbach, Alan J., and Kevin A. Hassett. 1992. "Tax Policy and
Business Fixed Investment in the United States." Journal of Public
Economics 47, no. 2: 141-70.
--.2002. "Optimal Long-Run Fiscal Policy: Constraints,
Preferences and the Resolution of Uncertainty." Working Paper
W9132. Cambridge, Mass.: National Bureau of Economic Research (August).
--.2003. "On the Marginal Source of Investment Funds."
Journal of Public Economics 87, no. 1: 205-32.
Bernanke, Ben. 2003. "Will Business Investment Bounce
Back?" Remarks before the Forecasters Club, New York, April 24.
Berner, Richard. 2001. "Purging Excess--The Capacity
Overhang." Presented at the Global Economic Forum, November 9, 2001
(www.morganstanley.com/ GEFdata/digests/20011109-fri.html#anchor1
[accessed August 2004]).
Bernheim, B. Douglas, and Adam Wantz. 1995. "A Tax-Based Test
of the Dividend Signaling Hypothesis." American Economic Review 85,
no. 3: 532-51.
Bertola, Giuseppe, and Ricardo Caballero. 1990. "Kinked
Adjustment Costs and Aggregate Demand." In NBER Macroeconomics
Annual 1990, edited by Olivier J. Blanchard and Stanley Fischer. MIT
Press.
Blouin, Jennifer L., Jana S. Raedy, and Douglas A. Shackelford.
2004. "Did Dividends Increase Immediately after the 2003 Reduction
in Tax Rates?" Working Paper 10301. Cambridge, Mass.: National
Bureau of Economic Research (February).
Bolster, Paul J., and Vahan Janjigian. 1991. "Dividend Policy
and Valuation Effects of the Tax Reform Act of 1986." National Tax
Journal 44, no. 4: 511-18.
Bond, Stephen R, and Jason G. Cummins. 2000. "The Stock Market
and Investment in the New Economy: Some Tangible Facts and Intangible
Fictions." BPEA, no. 1: 61-108.
Bradford, David. 1981. "The Incidence and Allocation Effects
of a Tax on Corporate Distributions." Journal of Public Economics
15, no. 1: 1-22.
Caballero, Ricardo J., and Eduardo M. R. A. Engel. 1999.
"Explaining Investment Dynamics in U.S. Manufacturing: A
Generalized (S, s) Approach." Econometrica 67, no. 4: 783-826.
Carroll, Robert, Kevin A. Hassett, and James B. Mackie III. 2003.
"The Effect of Dividend Tax Relief on Investment Incentives."
National Tax Journal 56: 629-51.
Chetty, Raj, and Emmanuel Saez. 2004. "Do Dividend Payments
Respond to Taxes? Preliminary Evidence from the 2003 Dividend Tax
Cut." Working Paper 10572. Cambridge, Mass.: National Bureau of
Economic Research (June).
Chirinko, Robert S. 1993. "Business Fixed Investment Spending:
Modeling Strategies, Empirical Results, and Policy Implications."
Journal of Economic Literature 31, no. 4: 1875-1911.
Chirinko, Robert S., Steven M. Fazzari, and Andrew P. Meyer. 1999.
"How Responsive Is Business Capital Formation to Its User Cost? An
Exploration with Micro Data." Journal of Public Economics 74, no.
1: 53-80.
Coen, Robert M. 1969. "Tax Policy and Investment
Behavior." American Economic Review 59, no. 3: 370-79.
Cohen, Darryl S., Kevin A. Hassett, and Dorthe-Pernille Hansen.
2002. "The Effects of Temporary Partial Expensing on Investment
Incentives in the United States." National Tax Journal 55, no. 3,
457-66.
Cooper, Russel, John Haltiwanger, and Laura Power. 1999.
"Machine Replacement and the Business Cycle: Lumps and Bumps."
American Economic Review 89, no. 4: 921-46.
Cummins, Jason G., Kevin A. Hassett, and R. Glenn Hubbard. 1994.
"A Reconsideration of Investment Behavior Using Tax Reforms as
Natural Experiments." BPEA, no. 2: 1-59.
Cummins, Jason G., Kevin A. Hassett, and Stephen D. Oliner.
Forthcoming. "Investment Behavior, Observable Expectations, and
Internal Funds." American Economic Review.
Doms, Mark C. 2004. "The Boom and Bust in Information
Technology Investment." Federal Reserve Bank of San Francisco
Economic Review 2004, pp. 19-34.
Ferguson, Roger. 2001. "Reflections on the Capital Good
Overhang." Remarks to the Charlotte Economics Club, Charlotte,
N.C., July 18.
French, Eric, Thomas Klier, and David Oppedahl. 2002. "Is
There Still an Investment Overhang, and If So, Should We Worry about
It?" Federal Reserve Bank of Chicago, Chicago Fed Letter, Special
Issue 177a (May).
Gilchrist, Simon, and Charles Himmelberg. 1998. "Investment:
Fundamentals and Finance." In NBER Macroeconomics Annual 1998,
edited by Ben S. Bernanke and Julio J. Rotemberg. MIT Press.
Goolsbee, Austan. 1998. "Investment Tax Incentives, Prices,
and the Supply of Capital Goods." Quarterly Journal of Economics
113, no. 1: 121-48.
--. 2001. "The Importance of Measurement Error in the Cost of
Capital." National Tax Journal 53, no. 2: 215-28.
--. 2004. "Taxes and the Quality of Capital." Journal of
Public Economics 88, no. 3-4: 519-43.
Goolsbee, Austan, and David B. Gross. 2000. "Estimating
Adjustment Costs with Data on Heterogeneous Capital Goods."
Graduate School of Business, University of Chicago.
Greenspan, Alan. 2002. "Economic Volatility." Remarks at
the Federal Reserve Bank of Kansas City Symposium, Jackson Hole, Wyo.,
August 30 (www. federalreserve.gov/boarddocs/speeches/2002/20020830/default.htm).
Hall, Robert E., and Dale W. Jorgenson. 1969. "Tax Policy and
Investment Behavior: Reply and Further Results." American Economic
Revies 59, no: 388-401.
Hassett, Kevin A., and R. Glenn Hubbard. 2002. "Tax Policy and
Business Investment." In Handbook of Public Economics, vol. 3,
edited by Alan Auerbach and Martin Feldstein. Amsterdam: North-Holland.
Hederman, Rea S., Jr. 2004. "Tax Cuts Boost Business
Investment." Heritage Foundation Web Memo
(www.heritage.org/Research/Taxes/wm412.cfm [accessed August 2004]).
Ikenberry, David L., and Brandon R. Julio. 2004. "Reappearing
Dividends." University of Illinois College of Business Working
Paper (July).
Joint Committee on Taxation. 2002. "Estimated Revenue Effects
of the 'Job Creation and Worker Assistance Act of 2002.'"
Document JCX-13-02. Washington: U.S. Government Printing Office (March
6).
--. 2003a. "Estimated Budgetary Effects of the Conference
Agreement for H.R. 2 and the 'Jobs and Growth Tax Relief
Reconciliation Act of 2003.'" Document JCX-55-03. Washington:
U.S. Government Printing Office (May 22).
--. 2003b. "Estimates of Federal Tax Expenditures for Fiscal
Years, 2004-2008." Document JCS-8-03. Washington: U.S. Government
Printing Office (December 22).
Jorgenson, Dale, and Kun-Young Yun. 2001. Investment, Vol. 3:
Lifting the Burden: Tax Reform, the Cost of Capital, and U.S. Economic
Growth. MIT Press.
Kaplan, Steven N., and Luigi Zingales. 1997. "Do
Investment-Cash Flow Sensitivities Provide Useful Measures of Financing
Constraints?" Quarterly Journal of Economics 112, no. 1: 169-215.
King, Mervyn A. 1977. Public Policy and the Corporation. London:
Chapman and Hall.
Kliesen, Kevin L. 2003. "Was Y2K behind the Business
Investment Boom and Bust?" Federal Reserve Bank of St. Louis Review
85, no. 1: 31-42.
Kudlow, Larry. 2004. "A Capital Idea from Microsoft."
National Review Online
(www.nationalreview.com/kudlow/kudlow200407230854.asp [accessed December
2004]).
McCarthy, Jonathan. 2001. "Equipment Expenditures since 1995:
The Boom and the Bust." Federal Reserve Bank of New York Current
Issues in Economics and Finance 7, no. 9: 1-6.
--. 2003. "Capital Overhangs: Has Equipment Investment
Spending Suffered from a Hangover?" Business Economics, October,
pp. 20-27.
--. 2004. "What Investment Patterns across Equipment and
Industries Tell Us about the Recent Investment Boom and Bust."
Federal Reserve Bank of New York Current Issues in Economics and Finance
10, no. 6: 1-7.
Pelgrin, Florian, Sebastian Schich, and Alain de Serres. 2002.
"Increases in Business Investment Rates in OECD Countries in the
1990s: How Much Can Be Explained by Fundamentals?" OECD Economics
Department Working Paper 327. Paris: OECD (April 15).
Poterba, James. 2004. "Taxation and Corporate Payout
Policy." American Economic Review 94, no. 2: 171-75.
Poterba, James M., and Lawrence H. Summers. 1983. "Dividend
Taxes, Corporate Investment, and 'Q.' "Journal of Public
Economics 22: 135-67.
--. 1985. "The Economic Effects of Dividend Taxation." In
Recent Advances in Corporate Finance, edited by Edward Altman and Marti
Subrahmanyam. Homewood, Ill.: Richard D. Irwin Publishers.
Ramey, Valerie A., and Matthew D. Shapiro. 2001. "Displaced
Capital: A Study of Aerospace Plant Closings." Journal of Political
Economy 109, no. 5: 958-92.
Salinger, Michael A., and Lawrence H. Summers. 1984. "Tax
Reform and Corporate Investment: A Microeconometric Simulation
Study." Working Paper 0757. Cambridge, Mass.: National Bureau of
Economic Research.
Saxton, Jim. 2003. "Economic Repercussions of the Stock Market
Bubble: A Joint Economic Committee Study"
(www.house.gov/jec/growth/07-14-03.pdf [accessed August 2004]).
Summers, Lawrence H. 1981. "Taxation and Corporate Investment:
A q-Theory Approach." BPEA, no. 1: 67-127.
Tevlin, Stacey, and Karl Whelan. 2003. "Explaining the
Investment Boom of the 1990s." Journal of Money, Credit, and
Banking 35, no. 1: 1-22.
MIHIR A. DESAI
Harvard University
AUSTAN D. GOOLSBEE
University of Chicago
