Investment, Overhang, and Tax Policy
Mihir A. Desai
Harvard University and NBER
Austan D. Goolsbee
University of Chicago, American Bar Foundation and NBER
We thank Mark Veblen and James Zeitler for their invaluable research assistance and Alan Auerbach, Bill Brainard,
Kevin Hassett, John Leahy, George Perry, Joel Slemrod, and participants at the BPEA conference for their
comments. Dale Jorgenson was kind enough to provide tax term estimates. Desai thanks the Division of Research
at Harvard Business School for financial support.
Investment, Overhang and Tax Policy
high investment in the 1990s and abnormally low investment in the 2000s, despite several
major tax cuts intended to stimulate investment — prompts two questions that we tackle
in this paper: Did “capital overhang” contribute to the dramatic investment collapse of
the early 2000’s? and Why has fiscal policy been unable to revive investment? We use
firm level evidence to show that capital overhang – the notion that the late 1990s stock
market bubble led to excess investment and prevented a rebound – is not a meaningful
factor in explaining the fall of investment. There is little correlation between the growth
of investment during the boom and the declines of investment during the bust across
industries, asset classes or firms. Nor did firms with larger growth during the boom
experience any reductions in sensitivity to fundamentals in the 2000s. We believe the
standard investment model continues to be quite relevant for studying investment. We
then modify the tax-adjusted q model to allow for clearer identification of tax effects in
the presence of mismeasured q. This modification yields estimates that are larger and
more precisely measured suggesting that the tax-adjusted q model does a reasonable job
in explaining investment patterns. Using this q model we then investigate the effects of
the tax cuts. First, in keeping with the “new” view of dividend taxation, the evidence
suggests that dividend taxes do not influence marginal investment incentives. This
evidence indicates that the dividend tax cut, with forecasted revenue cost of more than
$100 billion from 2003-2008, would have had little, if any, impact on investment.
Second, the partial expensing of equipment provisions (revenue cost of approximately
$130 billion from 2002-2004) did have an effect on investment but were too small to
counteract the large aggregate investment declines stemming from market movements.
The results put the investment increases resulting from the tax policies of 2002-2004 at
only one to two percent.
The unusual behavior of investment in the 1990s and early 2000s—abnormally
Mihir A. Desai
Harvard Business School
Boston, MA 02163
Austan D. Goolsbee
University of Chicago Business School,
American Bar Foundation and NBER
5807 S. Woodlawn Ave
Chicago, IL 60637
The pattern of investment over the past decade has been unusual. 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. Whereas the drop
in equipment investment normally account for about 10-20 percent of the decline in GDP
during a recession, in 2001 it accounted for 120 percent.1
In the public mind, the boom and bust in investment are directly linked due to
“capital overhang.” Though not very precisely defined, this view generally holds that
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, there is little policy makers can do to
remedy the situation by subsidizing investment with tax policy, for example. Variants on
this view have been extensively espoused by private sector analysts and economists (e.g.,
Berner, 2001; Leach, 2002; Roach, 2002) and certainly has been on the minds of leading
Federal Reserve officials (e.g., Greenspan, 2002; Ferguson, 2002; Bernanke, 2003) and
researchers (e.g., French et al., 2002; Pelgrin et al., 2002; Kliesen, 2003; McCarthy ,
Regardless of whether overhang is the true explanation of the investment bust, it
is clear that the drop in investment has motivated policy makers to try to stimulate
investment through large fiscal policy changes.2 President Bush twice increased
depreciation allowances (2002 and 2003) for equipment investment and, in 2003,
significantly cut the tax rate on dividend income and modestly cut the tax rate on capital
gains income. These measures were mainly intended to reduce the tax term and stimulate
investment. The typical analysis of the investment collapse and policy response is
summarized by the Republican chairman of the Joint Economic Committee:
1 McCarthy (2003) documents the equipment declines as a share of GDP declines for all of the cycles since
1953 and shows the 2001 recession to be an extreme outlier.
2 Unlike investment behavior, this phenomenon of the 2000s is completely consistent with earlier time
periods. Cummins et al. (1994) have documented that a primary determinant of investment tax subsidies is
a drop in investment.
"Excessive and bad business investments made during the stock market
bubble have taken years to liquidate. In nine of the 10 quarters beginning
the fourth quarter of 2000, real business investment has actually 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." (Saxton, 2003).
Yet, after several years of tax cuts, investment has still not risen impressively
compared to previous recoveries. This contrast has reignited claims that tax policy
is ineffective at stimulating investment, though some make the more specific charge
that tax policy may only be impotent following a period of excessive investment.
In this paper, we attempt to examine the evidence on the two related issues of
overhang and taxes in some detail using micro data, usually at the firm level.
Specifically we address two questions: 1) did "over"-investment of the 1990s cause the
low investment of the 2000s and 2) did it make investment in the 2000s less sensitive to
prices and does this explain why tax policies, specifically the equipment expensing and
the dividend tax cuts of 2002 and 2003, seemed to be ineffective in restoring investment
to normal levels?
We begin by examining the degree to which growth in investment during the
boom was correlated with the decline in investment during the bust across different assets
and industries. There are, of course, many potential definitions of overhang or excess
investment. We will not be trying to show there was no over-optimism in product or
capital markets. Clearly equity prices rose and then fell as did investment rates. Instead,
we investigate whether the assets and industries whose investment grew the most
subsequently declined the most. We want to know if the investment boom of the 1990s
"remained" into the 2000s—whether firms behaved differently, given current
observables, because too much capital remained from the investment decisions of the
The suggestive evidence across assets, industries and firms indicates 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 then present some more specific evidence
using firm level data that investment behavior has remained just as responsive to
fundamentals/prices (as measured by Tobin’s q) regardless of how much investment
growth or equity price growth the firm had 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 the current period is unusual.
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 that says dividend tax cuts do not reduce
the tax term for marginal investments and the “traditional” view that says that such cuts
reduce the tax term and, thus, stimulate investment. The evidence from the firm level
data 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 as in Summers (1981) but with greater emphasis on the
importance of measurement error in measuring q as emphasized in Cummins et al.
(1994). The method introduced for handling these measurement error issues suggests
that tax policy (and q) is likely to have much larger effects on investment than in the
traditional literature where coefficients are very small and imply implausibly large costs
of adjustment. Even with the larger coefficients, however, we show that the depreciation
allowance changes of 2002 and 2003 changed the tax term by a relatively small amount
and imply that the overall impact in these two years (2002-2003) was an increase in
investment of only 1 to 2 percent, far too small to offset the double digit declines of the
Capital Overhang and Investment
During the 1990s, gross investment was considerably higher than normal. Non-
recession year investment from 1947:Q1 to 1995:Q2 averaged about 12.3 percent of GDP
and the highest quarterly level was 15 percent in 1984:Q3. From 1996:Q1 to 2000:Q4
this ratio averaged more than 16 percent and reached as high as 18 percent at its peak.
The distinctiveness of these investment rates holds even relative to the business cycle.
Norming investment in the peak quarter to one, Figure 1 shows that investment in the
quarters leading up to the peak in 2001:Q1 was higher than investment in previous
cycles. The popular view holds that this extra investment resulted from the excesses of
the 1990s bubble.3
With this view in mind, Figure 2 provides a counterpart to the previous figure by
showing the path of investment in the time after the trough quarter for the recovery in the
2000s relative to previous recoveries. Investment is normed to one in the trough quarter
for each series. The increase in investment in this recovery, at least through the
beginning of 2004, is notably lower than in an average recovery. The aggregate data
make it seem plausible to many observers that post-trough investment was lower
precisely because the previous investment was higher.
Of course, these aggregate patterns do not establish any underlying connection
between the rise and the fall. To test for a causal relationship between the rise and
decline, we believe it is critical to disaggregate the investment data. Most academic work
looking at overhang has not disaggregated the data or has done so at a very broad level,
emphasizing that this reversal is concentrated in information technology investment.4 It
is clear that the exuberance of the 1990s was not shared equally in all sectors. Industries
such as telecommunications or the internet, experienced huge increases in the 1990s in a
way that railroads or mining, say, did not. We believe that overhang, the idea that there
was excess capital remaining at the end of the boom, as it were, is inherently an industry
or firm level phenomenon, so we must look at data at that level rather than at the
An additional reason to look at the micro 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 could simply sell the machines without any loss. For the popular view to
make sense, then, one needs to have either irreversibility of investment (which leads to a
rather different model as in, for example, Abel and Eberly (2002)) or some other type of
3 Tevlin and Whelan (2003) argue that, empirically, much of the increase in gross investment can be
explained by the falling prices of computers and their higher depreciation rates.
4 McCarthy (2001, 2004) are exceptions.
adjustment costs on disinvestment.5 The work of Shapiro and Ramey (2001) has
documented that, in some industries, there can be a sizable wedge between the purchase
and sale price of capital goods. The evidence in Goolsbee and Gross (2000) is also
consistent with that view. These types of irreversibilities are likely to be firm or asset
specific rather than applying to all types of investment in all sectors homogenously.
Fortunately, micro data on investment are available at the industry, asset type and firm
level and 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 industry level investment. Rather than
rely on the fairly aggregated categories of the BEA fixed asset data we turn to the Annual
Capital Expenditure Survey (ACES) of the United States Census. The ACES provides a
greater level of industry disaggregation than is available elsewhere. The survey samples
approximately 60,000 companies in more than 100 industries organized by the 1997
North American Industry Classification System (NAICS). We narrow this down to 81
non-overlapping industries at approximately the three-digit NAICS level.6
The ACES only provides measures of gross investment and does not estimate the
capital stock for these industries. Consequently, we cannot scale investment by lagged
capital as in traditional empirical work on investment. Instead, we simply investigate the
change in total investment both for equipment alone (Table 1) and for equipment
combined with structures (Table 2). Empirical models of investment have struggled with
explanations for structures investment and it is not known whether this 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 to some other
factor.7 Since we cannot readily isolate equipment from structures investment in the firm
level data employed below, we have to assume that equipment and overall investment
behave the same way. Given that by the 2000s, equipment accounted for something like
5 The adjustment costs could be firm-level adjustment costs or might be external in the sense that the
supply of capital goods in a particular industry is upward sloping as in Goolsbee (2000, 2001).
6 Prior to 1997, SIC codes were employed and the matching of NAICS to SIC codes enables comparison
over the entire period.
7 See, for example, the discussion in Auerbach and Hassett (1992) who discuss the problems with
estimating structures investment. Since structures are so long-lived, long-term expectations may be
especially important here and our contemporaneous tax term measures may be particularly bad.
80 percent of total investment, this may not be too problematic 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
overhang from the 1990s is a key factor determining investment in the 2000s. If
overhang is quantitatively important, we might expect to find that industries where
investment grew substantially in the 1990s would be the ones to see investment fall in the
2000s. Figure 3 plots the change in log investment from 1994 to 1999 relative to this
change for 2000 to 2002. The sizes of bubbles in Figure 3 correspond to the relative size
of investment in 2000. Several notable aggregate facts are apparent from Figure 3. First,
there does not appear to be a strong negative relationship in the within-period change in
investment for the 1994-1999 and 2000-2002 periods. Industries which saw large
increases in investment during the 1990s do not appear to systematically be the same
industries which had large declines. Of course, some industries that had large increases
in investment from 1994 to 1999 had large decreases from 2000 to 2002 but there is
limited evidence of any systematic relationship between these changes.
To test this more formally, we provide a cross-sectional regression of the change
in log investment in an industry from 2000 to 2002 (the period widely viewed as the
"collapse") on the change in log investment from 1994 to 1999 in that same industry,
estimating the equation:
,2002 ,2000,1999 ,1994
ln( ) ln(
) ln( ) ln(
This test would show no evidence of reversion, of course, if all industries boomed and
then busted together equally since that would simply go into the constant term. Given
that the growth of the 1990s was not likely to be constant across industries, this equation
provides a useful estimation strategy.
The top panel of Table 1 presents the results of estimating equation (1) by OLS
and the bottom panel provides results employing median regressions to ensure that the
results in the top panel do not purely reflect the role of large outliers. Column 1 presents
the results from the basic overhang specification. The OLS and median regressions
provide almost identical coefficients that are negative but very small and not significantly
different from zero. To give a sense of the magnitude, increasing one standard deviation
in the investment rate for the 1994-1999 period (0.53) (changing it from the median of
0.38 to about the 90th percentile) would imply investment only 2.7 percent lower over the
2000-2002 period. This is less than 1/12th of a standard deviation. This evidence of
overhang is modest, at best.
Given the serious decline of manufacturing in this recession, and given that old-
line manufacturing was not typically associated with the internet boom, we further
investigate this sector separately. To do so, we restrict the sample to the 23
manufacturing industries for the regressions provided in column 2 of the two panels. For
these industries, the evidence seems more pronounced. In both the OLS and the median
regression, there is a large and significant negative coefficient on investment in the
1990s. In the median regression, a one standard deviation increase in the 1994 to 1999
investment rate among manufacturing industries (of 0.32) would correspond with almost
22 percent lower investment in the 2000-2002 period which corresponds roughly to the
mean drop in investment (-0.26) and is equal to about 2/3 of the standard deviation of
those changes. Even if one believed this larger effect were evidence of overhang (as
opposed to something cyclical), it should be noted that manufacturing industries
constituted only about 22 percent of total equipment investment and 18 percent of total
investment in 2002 according to the ACES.8 Consequently, evidence of mean reversion
for manufacturing can only have a limited influence on the aggregate collapse of
The common explanation for capital overhang is that funds raised from the capital
market during the bubble encouraged the excess investment, particularly during the 1997-
1999 period. Indeed, the broadly disaggregated analysis in McCarthy (2003), which uses
a tax term type analysis, suggests that there was no capital overhang at all until 1998,
even in the high-tech investment goods sector (computers and communications
equipment). In columns 3 and 4, we separately consider period from 1994 to 1997 and
from 1997 to 1999 in order to isolate the effects of the so-called bubble period and to
potentially take account of underlying growth trends in different industries that might
8 This is also consistent with the evidence cited by Bernanke (2003).
mask investment reversion. Again, there is little evidence of reversion across industries
and there are larger negative coefficients in manufacturing. The later period, typically
associated with the overhang explanation, has a smaller coefficient than the earlier
period, though the standard errors are not small enough to reject 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 provided in Table 1 with little evidence of
reversion generally and 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. As with Figure 3, it is useful to consider first the aggregate facts
with respect to changes in investment by type of asset. We do this in Figure 4, showing
the change in log investment from 1997 to 1999 and the change from 2000 to 2002. All
data here are drawn from Tables 5.5.6. and 5.4.6 of the Bureau of Economic Analysis'
NIPA tables and are disaggregated into general categories of equipment and structures.
As with Figure 3, Figure 4 shows no clear pattern that assets whose investment went
significantly upward in the 1990s had investment that went down significantly in the
2000s. Even in information technology, communications equipment investment dropped
substantially in the 2000-2002 period but investment in computers actually rose from
We perform our basic regression (equation (1)) at the asset level instead of the
industry level. Using the BEA data, we have 25 different categories of equipment and an
additional 9 categories of structures.9 As in Table 1, the two panels of Table 3
correspond to OLS and median regressions.10 Asset types which had the largest increases
in investment from 1994 to 1999 show a small negative coefficient that is insignificant.
9 The categories of structures employed by the BEA changes over the period. As a consequence, the figure
employs data after 1997 and has 25 categories of equipment and 22 categories of structures while the
regressions employ data prior to 1997 and have 25 categories of equipment and 9 categories of structures.
10 Weighting by the initial capital stock in these regressions provided very similar results.
This is equally true in the median regressions with similar magnitudes. In the top of
column 1, an asset type whose log investment grew by one standard deviation more (.36)
than the median asset from 1994 to 1999 corresponded to a drop in log investment from
2000 to 2002 of about would have investment reduced by about 1/6th of a standard
deviation compared to the median firm.
Column 2 repeats this analysis but splits the investment into the early and late
periods of the boom, 1994-1997 and 1997-1999. Here, while the coefficients are noisy,
the results are not consistent with the typical overhang story. If anything the coefficients
are larger in absolute value terms for the earlier period relative to the later period.
Indeed, some of the point estimates in the later period are greater than zero suggesting
that assets whose real investment grew most in the 1990s grew even more in the 2000s.
The irrational exuberance hypothesis would say just the opposite. Clearly in both cases,
we are not controlling for anything but merely noting the absence of a strong negative
correlation. Using the Compustat 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 from the Compustat research file
from 1962-2003. In Figure 5, we plot the average investment rate (defined as capital
expenditure divided by the beginning of period net capital stock) for manufacturing
firms, for non-manufacturing firms and for firms involved in information businesses.
Information businesses are defined as those in NAICS codes 334 (Computer
Manufacturing) and 51 (Information) and this grouping is one we return to later since the
irrational exuberance was viewed as being most extreme there. The micro data provide
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 for the rest of the economy but in some ways the magnitude of
the firm-level sample make it an overwhelmingly important component of aggregate
investment on its own. Our calculations suggest that the aggregate capital expenditures
in Compustat constituted 85 to 90 percent of private, non-residential investment in the
U.S. for most of the last 25 years.11 Our sample in 2003 does not include all firms since
some share of firms have yet to have their reports put coded by Compustat at the time of
our analysis. Nonetheless, the sample in 2003 is still large (more than 80% of 2002’s
sample) and provides a perspective not afforded by the industry level or asset level data
given their earlier cutoffs.
We begin with the general evidence that parallels the previous results in
examining the change in investment rates during the bust on the change in investment
rates during the boom but with the advantage that in the firm level data we can truly
compute the change in the investment rate because we have the capital stock for each
firm. Our modified regression equation is, then,
In the data, I is capital expenditure at the firm level and it is scaled by lagged capital. In
the Data Appendix we describe how we compute the capital stock for each firm following
Salinger and Summers (1984) and Cummins, Hassett, and Hubbard (1994). I
The top panel gives the OLS results and the bottom panel gives the median
regression results. Column 1 of the top panel of Table 4 provides the results for
specifications that emphasize the relationship between changes in investment rates over
the boom from 1994 to 1999 and the change in investment rates over the bust from 2000
to 2002. Given that a firm must exist in 1994, 1999, 2000, and 2002 to appear in the
sample for this regression, the sample size is somewhat restricted compared to the full
universe of firms in the data. These results again show a very small negative correlation
in the changes in investment rates. The median percentage change in the capital stock
from 2000 to 2002 was -0.3 percent. Here, the coefficients are tiny. The magnitude on
the lagged investment change variable indicates that a firm whose increase in investment
rates during the boom was one standard deviation (about .65) above the median firm's
11 One important shortcoming of the Compustat data (and common to virtually all empirical work that uses
it to study investment) is the inability to separately isolate domestic versus international expenditures or the
degree to which q measures worldwide investment opportunities rather than domestic investment
saw their investment fall about .02 or only about 1/35th of a standard deviation. Column
1 of the bottom panel of Table 4 repeats this specification but controlling for outliers by
using a median regression (which is particularly important when using firm data) and the
coefficient is even smaller. A firm whose investment grew one standard deviation above
the median during the boom would have investment fall only about 1/70th of a standard
Columns 2 of both panels in Table 4 include the growth rate of capital from an
earlier period, 1989 to 1993, as an additional control in order to account for firms whose
size is trending upward, for example. This did not much change the general results
showing a very small negative impact relative to trend. Finally, in column 3 we also
include the percentage change in real sales for the firm as an additional control to take
account the fact that firms might be growing or shrinking over the time period and this
could be driving the investment results (recall the large coefficients on manufacturing
investment in the industry level data). Here, higher sales growth is correlated with higher
growth in investment but the evidence on reversion is even a bit more modest than the
The suggestive evidence, then, provides very limited support for the view that
firms, assets types, and industries that had major increases in their investment in the
1990s experienced major drops in the 2000s. This seems to suggest that overhang may
not be the dominant factor influencing investment in the period. A more precise test is
available by relating overhang to the sensitivity of investment to fundamentals at the firm
2.5. Evidence on Overhang and the Sensitivity of Investment
The suggestive evidence provided in Tables 1 through 4 does not match the
standard notion of overhang. Using the firm level data, though, we can further examine
whether firms are less responsive to changes in tax adjusted q in the 2000s if they had big
significant valuation increases in the 1990s. If, in fact, firms experiencing large changes
in market value featured a distinct response to tax-adjusted q in the 2000s, this could help
explain why taxes have not seemed to have a major impact in investment. A fuller
discussion of the details of our tax-adjusted measure of q and the model underlying it is
provided in the next section and in Appendix A. In those sections, we provide a fuller
discussion of the measurement issues and predictions of that model but we include this
analysis here to fully address the overhang phenomena. Our basic estimating equation
will add an interaction term to the standard investment on q relationship.
We investigate the relevance of two different measures of overhang in the
1990s—one based on equity values and one based on capital expansion. In Table 5, we
employ the lagged change in q as a measure of the degree to which overhang is
operative.12 We create a variable which is the change in q that took place in the period 3
to 7 years before the current year and only for the time period 2000-2003.13 So in the
year 2002, for example, this variable would be the change in the firm's q that took place
from 1995 to 1999. Previous to 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. 14 We will first proxy for
overhang by looking at firms that had major increases in their equity values.
This yields an investment equation of
( /I K)()(/)
t itit it ititit
QQ Cash K
t it q
is the change in q from period t-7 to t-3 but only in the period from
2000-2003. The results from this equation estimated on all firms are presented in column
(1). They show that there is no significant difference in the investment-q relationship in
the 2000s for firms that had the larger run-ups in their stock prices in the 1990s. Indeed
the point estimate is actually positive, though small. In column 2, we exclude the firm
dummies so we are explicitly comparing across firms rather than within a given firm and
the results on the interaction term are very similar—positive and not significant.
Column 3 returns to the specification with firm dummies and restricts attention
only to information businesses. There is, again, no evidence that that big increases in
12 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 so we opted for q instead.
13 Other lags, such as the change in q from five years ago to two years ago yield similar results.
14 In a previous draft of this paper, we also examine 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) and we found virtually no evidence that it did.
equity values have reduced the sensitivity of investment to fundamentals in the 2000s.
The point estimate on the interaction term is insignificant and greater than zero. Column
4 repeats the analysis but for manufacturing and, again, there is nothing notable. Finally,
in column 5, we investigate 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.
In the next table we repeat this exercise but use the lagged percentage change in
capital for the firm during the 1990s period as the measure of overhang. The advantage
of the lagged change in q measure is that it picks up more directly the influence of asset
price bubbles, which are typically underlying much of the popular explanation of
overhang. The lagged percentage change in the capital stock, as used here, however, is a
more direct measure of capital accumulation.
The equation we estimate in column 1 of Table 6 is
( / I K) (%)(/)
t itititit it it
QQK Cash K
is the percentage change in the net capital stock of the firm between
time t-3 and time t-7 for the years 2000-2003 (i.e., just the change in the capital stock
during the mid-1990s).
Estimating this equation for the entire sample of firms, as reported in column 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 fundamentals in the 2000s. While the direction is consistent with the
overhang view, however, the magnitude is extremely small. To see this, note that the
highest mean value of lagged capital growth was in 2002 at 1.37 (with a median value of
past growth of 0.41). This value predicts that the coefficient on Q falls by only .003 (and
only .001 for the median). When we explicitly compare across firms by dropping the
firm dummies as in column 2, the point estimate becomes positive. Column 3 restricts
attention to the information businesses that are most closely associated with the
technology bubble. The coefficient on lagged capital growth is similarly modest.
Column 4 repeats the analysis for manufacturing and again finds similar results with
almost identical magnitudes. Column 5 demonstrates that using 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 .017.
Taken together, the results in this section do not provide much evidence in favor
of capital overhang playing a key role for investment in the 2000s. Investment
experiences during the bust are not correlated strongly with excessive investment in the
1990s. Similarly, the sensitivity of investment in the 2000s to 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, we use this model to analyze
the impact of taxes in the following section.
3. The 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 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. In Figure 6, we plot a measure of average
q for the corporate sector as a whole using the total market value of all publicly traded
firms as computed by CRSP scaled by the total stock of corporate capital as computed by
the BEA Fixed Reproducible Tangible Wealth series. The series shows a historic rise in
q in the mid 1990s and an unprecedented fall in q in the 2000s.15 Previous studies of the
magnitude of coefficients on tax-adjusted q have yielded only very small coefficients so
this rise and fall of q might not imply much in the way of aggregate investment changes.
We estimate the investment relationship, however, in a slightly different framework than
is typical in order to overcome possibly conflating measurement issues. It should be
clear that if the true coefficient on q for investment is not .02 but closer to 1, as argued
below, the investment collapse is eminently comprehensible in 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
15 A plot using the average firm q’s in our sample yields a similar picture.
investment experience of the 1990s and 2000s is distinct from the intuition of a lingering
overhang from the 1990s, discussed above.
Of course, the fundamental question, as critics have frequently pointed out, 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 an average q. At least some of the existing work has argued
that measurement error is at the root of the relatively weak empirical performance of
traditional investment models (e.g., Cummins, et al., 1994; Bond and Cummins, 2000;
Goolsbee, 2001). 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 by the Bush
administration (the changes in depreciation allowances as well as the changes in dividend
capital gains taxes). The following sections undertake such an analysis.
3.1. 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 model for incorporating taxes into such models along the lines of Summers
(1981) and Poterba and Summers (1983, 1985). A crucial issue in determining the
impact of dividend taxes in this framework is what the marginal source of funds for firms'
investments is. In short, if the marginal source of funds is retained earnings, then
dividend taxes have no impact on marginal investment incentives. If the marginal source
of funds is new equity, then dividend taxes do influence investment. This distinction is
the subject of an enduring debate in public finance between the "new" view and the
"traditional" view of dividend taxation.
Appendix A 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. Under this view dividend taxes influence
investment by, essentially, double taxing corporate income. Assuming quadratic
adjustment costs, this view generates an investment-q relationship of
where I is investment, K is the firm’s capital stock, φ is the adjustment cost parameter,
and µ represents an average investment rate. τ is the corporate tax rate and Γ is the
standard measure of the tax treatment of investment including tax depreciation
allowances, investment tax credits, and so on. Under this assumption, net new equity
finances investment so investment is determined by the indifference of shareholders
between holding a dollar inside or outside the firm. In a world without other taxes, the
firm stops investing once
1q = . Of course, if investment is heavily subsidized (i.e., Γ>
τ), 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 have their money inside versus outside the firm.
If, however, retained earnings are the marginal source of finance, then the
traditional investment-q relationship of equation (5) will not hold. In this case (again
assuming quadratic adjustment costs), the relationship will follow
where the tax rate on dividends is θ and the accrual-equivalent tax rate on capital gains is
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. Instead they are fully capitalized into
existing share prices. In other words, dividend taxes serve solely as a tax or windfall on
existing firm values. To see the intuition for this, consider a firm where retained earnings
are employed on the margin to finance investments and dividends are determined as a
residual. In this model, dividends are the only means to distribute earnings to
shareholders. In this setting, given that retained earnings are the marginal source of
financing, investment is determined by the indifference of shareholders between
receiving a dollar today as a dividend with value 1 θ
− and having the dollar reinvested
1 c q
. Accordingly, investment will stop investing at the point
in a world of no other investment taxes, rather than q=1 in the traditional case.
The counterintuitive part of equation (6) is the fact that the new view argues that
dividend taxes do not influence investment but the dividend tax rate appears in the
investment equation. In contrast, equation (5), that exemplifies the traditional view, does
not include a dividend tax term but corresponds to a 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 as dividend taxes don’t affect marginal
investments. Alternatively, under the traditional view embodied in equation (5), a
permanent dividend tax cut raises the value of q above one 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 most accurate. Proponents of the traditional view cite evidence on the effects of
dividend tax rates on corporate dividend payout policy.16 Furthermore, dividends seem
more stable than implied by the new view 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 despite being tax disadvantaged. The arguments in this debate
are considerably more involved than we can describe here and fuller assessments of both
views can be found in Auerbach and Hassett (2003), Carroll, Hassett, and Mackie (2003),
and Poterba and Summers (1985). Fundamentally, though, determining which view is
more accurate is a primarily empirical matter. Surprisingly, with the exception of the
16 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 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.
work on aggregate investment in the U.K. by Poterba and Summers (1983, 1985), there
are no direct attempts to test between the two views using investment data directly.
Poterba and Summers (1983, 1985) test between equations (5) and (6) using 27
years of annual data in the U.K. and their results support the traditional view. Although
Auerbach (2002) and Auerbach and Hassett (2004) have been critical of these findings
for, among other things, failing to account for other macroeconomic and tax changes
occurring at the same time, these estimates are the still 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 U.S., likely because dividend taxes have
not changed here in isolation until the 2003 tax cut. Previously, dividend taxes have only
varied through changes in personal income tax rates. Instead, the empirical work testing
the new versus traditional view has adopted the indirect method of examining the
relationship of dividend taxes and dividend payments (or the valuation of dividend
payments by investors).17
The recent large changes in dividend taxes, however, allow us to conduct such an
analysis. Testing between these two views, and the required detour into public finance, 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, then the dividend tax cut will have little
or no impact on the 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 performed so poorly in the past.
Empirical Implementation of the Q model
17 See, for example, Bernheim and Wantz (1995), Poterba (2004), Chetty and Saez (2004), Poterba and
Summers (1985) as well as the opposing evidence in Bolster and Janjigan (1991), Blouin et al. (2004) and
Julio and Ikenberry (2004).
In computing q empirically, we use the historic and current Compustat database
that provides a panel of firms from 1961 through 2003.18 For some firms, we will also
match this sample to the earnings estimates provided by the IBES data. Estimates of the
tax term at the asset level are derived from data provided, generously, by Dale
Jorgenson.19 As described in more detail in the data appendix, we follow Cummins et al.
(1994) and Chirinko et al. (1999) by using the BEA's Capital Flow Table (of 1997) to
create the share of investment in each industy for each of the asset types. With that
weighting, we create the weighted average tax term in each year for each four digit
industry in Compustat. Average marginal tax rates on dividend and capital gains income
(on an accrual basis) are taken from Poterba (2004). More discussion of the variable
construction and the sources of data is provided in the Data Appendix.
The measurement of q and, in turn Q, hinges on constructing a measure of the
ratio of market value to book value of the firm. The corporate finance and public finance
literature have diverged somewhat in their measurement of this ratio and we consider the
two alternatives in the results that follow. Specifically, the corporate finance literature, as
in Kaplan and Zingales (1997), employs data from Compustat to derive a measure of q as
BV Assets + MV Equity - BVEquity
for market value and all values are taken from public financial records.20 Implicitly, this
where BV stands for book value and MV stands
formulation takes the market value of debt to be its book value. In contrast, the public
finance literature has emphasized the derivation in Salinger and Summers (1984) (and as
implemented in Cummins, Hassett and Hubbard (1994)) which constructs q as
MV Equity + MVDebt
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 the inventory as discussed in the data appendix to Cummins, Hassett and Hubbard
18 Due to reporting conventions, the 2003 sample is somewhat smaller than the sample from the fuller years
as discussed above.
19 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.
20 This numerator is also sometimes adjusted for deferred taxes.
As with any firm-level analysis employing Compustat data to study investment,
rules for considering extreme observations must be employed. We follow studies such as
Gilchrist and Himmelberg (1998) and winsorize our measures of q (and also of
investment and cash flow as a share of the capital stock), dropping observations below
the first or above the 99th percentile. Investment rates and cash flow rates are taken as
the ratio of capital expenditures and operating cash flow before depreciation and then
scaled by the capital stock.
3.2.2. q-model Results
Table 7 provides the results for estimating q models in our firm sample examines
both the two alternative definitions of q, as well as the difference between q and Q (i.e.,
). We will postpone discussion of the relevance of dividend taxes and so
our estimating equation is (5) above with and without consideration of taxes. Columns 1
and 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 significantly larger. Inspection
of the public finance q’s indicate that extreme values comprise a large fraction of the
sample and may contribute to this pattern. Comparison of columns 2 and 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 provided in columns 3 and 6 indicate a similar pattern and that tax-
adjusted q outperforms q in explaining investment. This finding parallels the finding in
Summers (1981) of the relevance of tax-adjustments in improving the estimation of the q
Given the relative performance of q and Q in the results provided in Table 7, it is
useful to separately consider the terms that comprise Q to better understand the sources of
the relatively small coefficients on Q. As discussed above,
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 considering the role of
measurement error in biasing the estimates previously obtained. More specifically,
Cummins, Hassett and Hubbard (1994) argue that mismeasurement of q means that using
the estimated coefficients from a standard investment regressions can dramatically
understate the impact of investment taxes. They emphasize large tax reforms as being
times when the tax part of Q is not mismeasured and use them as the basis for comparing
actual investment to projected investment. The specifications provided in Table 7 take a
simpler approach 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
term should provide a better estimate of the true coefficient if q were
Table 8 presents the results from splitting Q into its component parts.
Specifically, the specification in column 1 replaces the term Q with q scaled by one
minus the corporate tax rate and terms for the equipment tax term and the structures tax
term. It is difficult to measuring a firm’s relative investment in equipment and structures
so we simply include both costs of capital as separate regressors. Given the traditional
difficulties in understanding the dynamics of structures incentives (see Auerbach and
Hassett, 1991), and the fact that equipment in the last decade has accounted for
something like 80 percent of total investment, we expect the equipment tax term to be
much better estimated. Controls for internal cash flow are included as well.
The key result from this table is that while 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 one in absolute value.22 Column 2 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. Columns 3 and 4 consider two alternative robust
21 This assumes the measurement errors are not correlated in the two series. We tried the same regressions
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 so we are not as concerned about this issue.
22 We also tried including lagged q and tax term terms but this did not have any impact on the results.
checks for these results that are modification 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 column 3, we approximate
the size of these shields as described in the data appendix and add the value of these tax
shields to the value of the firm in Q. This does not change the estimated results
significantly. We found this to be true for all of our major results in the paper and since
computing the allowances means reducing the sample by more than 30,000, we will
exclude them from the results that follow. Similarly, the model presented above follows
most of the literature and assumes away any issues regarding debt financing. In column
4, we incorporate the share of the firm’s financing that comes from debt following
Summers (1981) and the results are again similar.
The large coefficient on the tax term terms is worth dwelling on. First, the model
predicts that it should be the same magnitude as the tax-adjusted q (but of the opposite
sign). Here instead, it is 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 one and, consequently, corresponds to more
realistic estimates of adjustment costs. Restricting attention to major tax reforms (as in
Cummins, Hassett and Hubbard, 1994) yields similar estimates.
To give some further evidence on the role of q mismeasurement as being the
reason for the small coefficient on the q term, we also modify the empirical strategy of
Bond and Cummins (2002), within the framework of Table 8. Specifically, their intuition
is that earnings estimates from equity analysts as provided in the IBES database are a part
of q that is based only on fundamentals. 23 Rather than use them to create and alternative
q measure, though, we will use them as instruments for q.24 Employing the earnings
estimates comes at considerable cost given the shorter time frame covered by the IBES
database (1983-2003) and the seriously restricted number of firms covered through time.
Nonetheless, column 5 provides the results for this estimation. Several points are worth
noting. First, as indicated by the ten-fold increase in the coefficient on tax-adjusted q,
23 Cummins et al. (forthcoming) also looks at investment equations including analysts' earnings estimates.
24 To be precise, we use the earnings estimates divided by (1-τ) as instruments for q/(1- τ).
mismeasurement of q seems to be important. Second, the coefficient the equipment tax
term rises significantly as well.
Given the considerably smaller panel that provide these IV results, we rely on the
coefficients on the equipment tax term term provided in column 1 as the best estimate of
the true coefficient from a tax-adjusted q model. This analysis suggests that that the q
model suggests that the true adjustment costs for investment are of plausible size and so
we will 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 only to manufacturing. We do
not want to do this since manufacturing makes up 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,
coefficients on the relevant tax term terms are not significantly different from each other
in the two subsamples suggesting that the model performs similarly well in both settings.
The Impact of Tax Cuts in the 2000s
The Bush administration made two major changes to the tax code to reduce the
tax term. One, in 2003, reduced the top capital gains tax rate to 15 percent (from 20) and
reduced the tax rate of most dividends from the personal ordinary income tax rate (of
38.6 percent) to the capital gains tax rate. Two, it substantially accelerated depreciation.
In 2002, depreciation allowances for virtually all types of equipment investment
increased as firms gained the right to immediately expense 30 percent of their purchases.
In 2003 depreciation allowances increased again as the immediate expensing increased to
50 percent. Each of these needs to be treated differently under the Q model.
4.1. Dividend taxes
While implementation of the dividend tax reduction was somewhat complex,
essentially the maximum rate on dividends for individuals fell 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 (Hederman, 2004; Kudlow, 2004). The Joint Committee on Taxation (2003a)
estimated that the dividend tax cut would cost more than $100 billion from 2003 to 2008.
Given this cost, it is useful to assess its impact. If the “new” view is correct, then
dividend taxes would have little or no impact on the tax term.
We, therefore, test between the two views in Table 10. In the specification
provided in column 1, we consider the relevance of dividend taxes by considering the
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 column (1), we use all years of our firm data. The measure
of q that is interacted with the
term has a positive coefficient and is highly
significant. The one that is not interacted with the
term is insignificant and
actually has a negative point estimate. They are significantly different from one another,
as well. In other words, although we cannot directly observe the marginal source of
funds for these firms, we can see that their investment behavior is consistent with them
treating retained earnings as the marginal source (so the new view is the correct view).
One major criticism of most previous analyses of dividend tax rates on any
economic behavior has been that dividend tax rates themselves do not change in isolation,
only when the top marginal rate on ordinary income changes (see, for example,
Auerbach, 2002). In the latter part of our sample, though, from 1997 to 2003, there are
tax changes that are fairly specific in isolating the impact of dividend and capital gains
taxes. In 1997 and again in 2003, capital gains rates fell without changing the top
marginal income tax rate and in 2003 the dividend tax rate fell without a fall in the top
marginal income tax rate. This period, then, should be particularly instructive. For this
reason (as well as 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 after 1997.
These are listed in columns 2 and 3. They both show that the new view outperforms the
traditional view but the evidence is particularly strong in the new view's favor for the
latter period. In the earlier period, the point estimates still show the same thing but the
standard errors on the two terms cannot reject that the hypothesis that they are equal. In
the latter period, we can reject that hypothesis.25 Again, the evidence in all the cases
supports the new view and implies a small or negligible impact of dividend taxes on
In keeping with the discussion in Auerbach and Hassett (2002) and others,
though, who have argued that there are some firms for whom the new view applies and
others for whom the traditional view applies, we calculate what the dividend tax would
do to the tax term if our findings were wrong and the traditional view held. Our
calculation is meant only as an approximation. Carroll, Hassett and Mackie (2003)
simulate the impact in more detail under various assumptions.
Under the traditional view, the required after-tax rate of return r* will be
( ) (
where r is the pre-tax rate of return, p is the dividend payout rate
and c and θ correspond to the capital gains and dividend tax rates. The full cost of
capital, assuming no inflation in the price of investment goods and a permanent change in
tax policy will then be
( *r) COC
With a real interest rate of 0.05, depreciation of 0.15, a payout rate of 0.5 and an
accrual tax rate on capital gains equal to one quarter the statutory rate (as per the common
assumption of the literature), reducing the dividend tax from 38.6 down to the level of the
capital gains rate in 2003 for a fully taxable investor would be the equivalent of dividing
the COC by 1.035. The equipment tax term in 2003 was about 1.031 so this would have
approximately the same magnitude effect on investment as would 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 see below in our discussion of partial expensing,
however, that changes to the tax term of that magnitude may not increase investment by
much in the short-run during this sample period.
25 We also tried using only the personal tax rates from Poterba (2004) to take out any potential bias that the
trends in corporate and non-taxable investor shares of dividends received might have on the average
marginal tax rates. This made no difference to our results and also 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 would
be absorbed in the year dummies) but instead the interaction with q.
4.2 The Impact of Partial Expensing
Although we find no impact of dividend tax cuts on investment, the other tax
incentives enacted during the early 2000s, specifically the depreciation allowance/partial
expensing changes, directly reduced the tax term under either view of the dividend tax
and should have stimulated investment. The apparent failure to do so has caused some to
argue that tax policy is not effective.
4.2.1 Magnitude of the cuts
The president signed a change in 2002 allowing for partial expensing of
equipment that was 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 of the investment is depreciated
according to the normal schedule (which allows them to write off some portion in the
first year, and so on, for the tax life of the asset). Given that a fairy 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. Cohen et al. (2002)
provide a comprehensive analysis of the 2002 change. In 2003, the law was changed
again (and again made retroactive to cover investments made the entire year) to allow for
first-year expensing of 50 percent of the investment. Although scheduled to expire at the
end of 2003, this provision was extended to 2004 and may be extended further in the
future since, at the time of this writing, many legislators and commentators are arguing it
should be made permanent.
These incentives were costly to provide, of course. The Joint Committee on
Taxation (2002, 2003a, 2003b) estimated the cost of the changes in 2002 was about $35
billion and the cost of the higher expensing in 2003 and 2004 was about $40 billion and
$53 billion. Presumably extending it indefinitely would entail similar annual costs.
To estimate the effect of changed investment incentives, we compute the implied
investment increase from the changes to depreciation allowances. We list the change in
the tax term from 2001 to 2003 averaged at the 3-digit level in the last two columns of the
Appendix Table. The first column looks at the overall change in the tax cost and the
second looks at the change in the tax term just for equipment. Unsurprisingly, 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) thus provide the largest changes in the tax term. For
industries like real estate firms or hotels, little of their investment is in equipment and
what equipment they buy tends to be computers and other short-lived assets where
immediate expensing is not as valuable.
This table shows that even these rather dramatic changes to 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 (or 0.02 after incorporation of the
equipment share). Such a change is modest compared to changes like the investment tax
credit of 1962, the restoring of the ITC in 1971 or the Reagan depreciation allowance
increases of 1981, all of which changed the overall tax term by around 0.10. Historic
changes in the tax term are provided in Figure 7. The most recent changes in investment
incentives have been modest by historical standards.
This relatively small effect stems from several factors. First, the value of
accelerations in depreciation allowances are a function of the corporate tax rate and,
given lower corporate tax rates, altering depreciation schedules has a more muted effect
now. Second, the well-documented shift of investment toward computers and toward
equipment with shorter lives has meant that accelerated depreciations provides less relief.
The average NPV of depreciation allowances for equipment investment in 2001 was
already approximately 0.9, even before the tax cuts, suggesting that even complete
expensing (i.e., raising the NPV to 1) would provide limited additional benefits. Given
the 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. Estimates from our tests suggest that these incentives do work as they are
designed. Their magnitude, however, is simply not big enough to counteract the
4.2.2 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 saddle path for investment in the standard q model
(see Abel (1981) and Summers (1981) for discussion). Auerbach (1989) outlines a
linearization that makes this particularly easy and we adopt his notation to derive the
predicted effects. If we assume a Cobb-Douglas production function with a capital share
of 1-a, a real interest rate of r, the adjustment cost parameter assuming quadratic
adjustment costs of φ (the reciprocal of the true coefficient on Q in our regressions), and
the adjustment cost modified depreciation rate for capital in the firm of $δ (whose specific
formula is listed below), Auerbach shows that for an unanticipated permanent change in
tax policy, the capital stock follows a simple partial adjustment model with
where K* is the desired capital stock. The rate of adjustment, -λ1
follows the formula
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 one minus the gross
output share of value added for each industry as reported in the disaggregated NIPA data
for the year 1998. We take the true coefficient on Q to be 1, following the results above.
We compute $δ =δ(1-φ δ/2) using our value for φ and using the industry average
depreciation rate on their equipment or their 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 us an adjustment rate per year for each firm. The average annual
adjustment rate for all firms is about 0.33 and the average value for each 3-digit industry
is listed in the appendix table (one column includes structures, the other only equipment).
We then use the Cobb-Douglas production function to derive the optimal capital stock.26
26 This implies a long-run elasticity of K with respect to the user cost of -1. Such a figure is consistent with
the empirical findings surveyed in Hassett and Hubbard (2002) or Goolsbee (2000) but larger than the
findings discussed in Chirinko (1993) or Chirinko et al (1999).
To compute the effect of these policies over the past two years, we assume the
depreciation changes were unanticipated and thought to be permanent.27 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* and actual K (where actual K is higher
than it was in 2001 because of the investment done in 2002). Averaging things for each
3-digit industry and summing over the two years we estimate the impact of the tax cuts
on investment listed in Table 11. The average increase in the period is only about 1.0 to
1.5 percent 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 changed incentives were
effective. They were just not large enough to counteract the double digit declines in
investment rates observed in the 2000s. Tax policy is effective but using changes to
depreciation allowances simply cannot have much impact when the system is already so
close to full expensing and when aggregate declines in market values (and therefore q)
are so large. The firms are asymptoting to the optimal capital stock so further years of
the policy will have smaller effects than the first two years. After 2004, the average total
increase is still less than 2 percent.
This paper addresses two major questions arising from the puzzling investment
experience of the 2000s: how correlated was the equity bubble of the 1990s with the
decline in investment in the 2000s? and why didn't the major tax cuts of 2002-2003 do
more to restore investment to normal levels?
The micro data is quite clear that the popular intuition of how capital overhang
affected the investment market in the 2000s has little support in practice. The general
evidence across assets, industries and firms shows that rapid growth of investment in the
1990s had very little correlation with the investment declines in the 2000s. The evidence
27 Unanticipated is probably fairly accurate. An assumption of permanence seems reasonable as they were
announced to be temporary but from the moment they were passed many have been arguing that they be
made permanent (and indeed have already been extended to 2004). We assume permanence here in order
to significantly simplify the computation of the investment path.
further indicates that the firm level investment-q relationship has not changed noticeably
in the recent period for firms that either feature large increases in market value or
investment 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
This conventional tax adjusted q model then serves as the basis for our analysis of
the impact of the tax cuts and their seeming inefficacy. Our results show that the
dividend tax cut, while having a 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. The partial expensing provisions passed in 2002 and 2003 were not large
enough to provide much counterweight to the declines in aggregate investment. The
estimates provided in the paper suggest that tax policies contributed to an increase of the
capital stock of 1 to 2 percent.
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Firm-level data financials
Annual data for all companies in the Compustat data base, from 1950 on, are accessed through
Capital stock: Market valuation
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 expenditures. Each of
these measures is converted from current terms to real terms by dividing by the current value of
Three factors enter into the current valuation of capital stock. First, changes in prices of capital
goods held over from previous years. We sidestep this component by first deflating our measure
of PPE by the producer price index for capital equipment, to give us a real measure of capital on
hand. Second, additions to capital through investment expenditures. Third, depletion of capital
on hand through depreciation.
The firm’s current real capital stock can be thought of as the sum of the non-depreciated stocks of
all prior years plus the current level of investment. Following (Cummins, Hassett, and Hubbard,
1994), assuming a constant rate of depreciation, δ, the current capital stock is calculated as:
For example, the firm starts in time 0 with capital stock K0, but has only the non-depreciated part
of this stock (1-δ) K0 to carry on to the next year, time 1. Some of this carried-over capital is
used up in the production of period 2, leaving (1-δ)2 K0 to carry on to period 3, and so forth. By
period T, only (1-δ)T K0 is carried over from period 0. Similar reasoning explains the coefficients
of the levels of investment carried over to period T from all prior years. IT represents investment
expenditures in period T.
Given the ending levels of capital stock for all years, including the final year, and the final year’s
investment spending, all deflated by the PPI-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-
Inventories: Book to market valuation
Inventories – Total is used as a measure of the current value of inventory holdings. As in
Cummins, Hasset and Hubbard (1994), inventory levels are converted from book to LIFO market
value by adjusting carried-over inventories, the lagged book value, for year-to-year changes in
prices of finished goods. The implementation of the adjustment mechanism depends on whether
final levels of 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 Inv Inv
Essentially, under LIFO valuation rules, the ending levels of inventories include all 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, so needs no further adjustment.
On the other hand, if inventories decrease during the current year, then all current production is
sold, plus some part of carried-over inventories. Everything remaining at the end of the year is
valued at current prices
Operating income before depreciation and Operating income after depreciation were used as
measures of net income. Each was converted from nominal to real terms by dividing by PPI-
Consensus analysts’ estimates of future years’ earnings per share were taken from the I/B/E/S
summary statistics data maintained on WRDS. The variables in this file include number of
estimates, and mean, median, and standard deviation of estimates for a number of fiscal periods
(quarters or years) into the future. I/B/E/S identifies firms in its files by ticker, but also indicates
periods for which each ticker can be related to a particular firm CUSIP. Compustat identifies
firms in its files by GVKEY. We matched the I/B/E/S ticker to the Compustat GVKEY via the
CRSP/Compustat Merged data base linkage file. I/B/E/S ticker is linked to CUSIP in CRSP,
which is linked to PERMNO in CRSP, which is linked to GVKEY in Compustat. Thus,
Compustat firm-level financial data is merged with I/B/E/S firm-level analysts’ estimates. We
kept the summary estimate made during the latest month before the end of the firm’s fiscal year.
Price indices are used for two purposes: capital valuation and inventory valuation. The producer
price index for capital equipment (PPI-Capital Equipment) is used for the first, and the producer
price index for all finished goods (PPI-Finished Goods) is used for the second. Annual time
series of both indices are downloaded directly from the Bureau of Labor Statistics.
Asset Level Tax Term
The asset level tax term comes from Dale Jorgenson and 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 rate investment tax credit, the depreciation rate, as well as
the capital stock and the average corporate tax rate. We compute Γ 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 and are assumed permanent. We modify the net present values of
depreciation allowances in 2002 and 2003 to account for the partial expensing rules by
recomputing the z, as 70 percent the old z and 30 percent a z of 1 in 2002 and doing the same but
with 50-50 percentages in 2003.
Industry and Firm Level Tax Terms
To derive industry level values of the tax term for equipment and structures as well as to get the
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
3-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 and we then
merge that series to each firm-year based on its first listed NAICS code in Compustat.
Present Value of Depreciation Allowances on Past Investments
To estimate the value of A, the net present value of depreciation allowances on past investments,
we divide firms according to the weighted average depreciation rates on the types of equipment
invested in their industry. Using one over this depreciation rate as an estimate of the estimated
lifetime of capital for the firm, 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 relevant year times the
investment to capital ratio lagged that many periods. For example, if the actual depreciation
allowances for a three year lived good were .333 each year (i.e., purely straight line depreciation),
then we would say that depreciation allowances on investment from the previous investments
for current investment (time t) is not included in this measure (though it is in z). That is why in
an industry where investment lasts 3 years, there are not 3 terms in A.
. Note that the NPV of depreciation allowances
their average depreciation rate between 3 and 4, 4 years for any firm with an inverse between 4
and 5, and so on, but capped at nine years (there were a few firms with average equipment lives
of a bit over ten years.
We compute the NPV assuming an asset life of 3 years for any firm with the inverse of
whole sample. In other words, the net present value of depreciation allowances on current
investment, z, that we get from Jorgenson varies over time but we do not have the entire
depreciation schedules that each z is based on so we cannot let the calculation vary for A. We
tried many different ways of computing the A, such as different assumptions on depreciation
methods, different discount rates, and so on and found negligible impact on the regression results.
Note that our measure is an approximation because it assumes a constant tax law over the
Appendix A: Tax Adjusted q
We begin by establishing the equilibrium condition that shareholders receive their
required return, r, to hold equity which provides taxable dividends and capital gains so
rVθ Dc E VVV
The tax rate on dividends is θ and c is the accrual-equivalent tax rate on capital gains. Dt
denotes dividends paid to shareholders in period t, V is equity value, and
equity contributions 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, γ:
Given the realization based nature of capital gains taxes, γ is considered to be less than
one.28 Solving (A1) 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:
where β is the appropriate after-tax discount factor. Equation (A3) 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 equity contributions required to maintain
a proportional shareholding in the firm.
issuance are constrained to be non-negative.29 The firm capital stock, K, evolves
Firm value maximization is subject to several constraints. Dividends and equity
28 Even with similar rates on dividends and realized capital gains, γ<1 is thought to hold. Typically, the
accrual-equivalent c is usually taken as one-quarter of the statutory rate applicable to capital gains.
29 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.
where δ is constant proportional rate of decay and I is investment. The underlying cash
flow identity for the firm is given by:
τ F K ,L w LC(I ,K )pVτAD I pΓ
where τ is the corporate statutory tax rate, ()
F K,L is firm output, L is labor, w is the wage
C I ,K− is an adjustment cost function for investment and the τA term captures
the tax value of depreciation allowances on previous investments. p is the price of capital
goods relative to output and Γ 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 Γ is described in the data appendix below. In
short, equation (A5) states that after-tax firm cash flows plus new equity issuances are
sources of funds which are employed for investment and dividends and the p terms
ensure that all terms are properly price- adjusted.30
Given the expression for firm value in equation (A3) and the constraints discussed
above, firm value maximization employs the following Hamiltonian equation:
HγDVλ KKδIλ DλV
In this setting,
λ , λ , and λ 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 (A5), equation (A6) can be rewritten as:
Hγλτ F K,LwLC(I ,K)pVI pΓτAV
Differentiating this Hamiltonian provides the relevant first order conditions. The first
order condition for investment is provided by:
30 We abstract from debt and the presence of tax-deductible interest without loss of generality.
γλτ C ppΓλ
and the conditions for dividends and net equity issuance are provided by:
D;λ ; and D λ
V; γλλ ; and Vγλλ
≥− − −≥− − −=
Rearranging the investment first order condition provided in (A8) provides an
expression for q which corresponds to the shadow price for capital.
where CI 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:
C( I ,K)
where φ is the adjustment cost parameter and µ represents an average investment rate.
This quadratic adjustment cost function allows us to represent (A11) in more familiar
terms. Differentiating the cost function with respect to I and substituting it into (A11)
Equation (A13) is the basic estimating equation that is common in the q theory literature
with the slight peculiarity that qt is multiplied by
. In the existing literature and our
is also referred to as Q, rather than q. It will be
important in our discussion of measurement error below 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. In order to do so, we
return to the conditions in (A9) and (A10) 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,
= − , as indicated by equation
(A10). In this case, equation (A13) becomes its more familiar variant:
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
tλ = . In turn, this implies that equation (A13) is:
Equations (A14) and (A15) provide alternative q-theory specifications for investment that
incorporate different assumptions about the marginal source of finance and,
consequently, the role of dividend taxation in influencing dividend taxation.
Notes: The lines depict the behavior of investment by quarter relative to investment at the peak of business cycles for the most recent
peak (as drawn with dashed lines) and for the average behavior during previous business cycles (as drawn with the solid line).
Notes: The lines depict the behavior of investment by quarter relative to investment at the trough of business cycles for the most recent
trough (as drawn with dashed lines) and for the average behavior during previous business cycles (as drawn with the solid line).
Figure 1: Investment Relative to Peak Investment Levels -Comparing 1996-2000
to Averages of Previous Cycles
Quarters Before Peak
Figure 2: Investment Relative to Trough Investment Levels - Comparing 2001-2004
to Previous Cycles
Quarters past Trough
Figure 3: Changes in Log Investment by Industry, 1994-1999, 2000-2002
Notes: The figure depicts the change in log investment from 1994 to 1999 relative to change in log investment from 2000 to 2002, by
industry. The size of bubbles corresponds to the value of investment in 2000. The data for this figure comes from the Annual Capital
Expenditure Survey as described in the text.
Figure 4: Changes in Log Investment by Asset, 1997-1999, 2000-2002
Notes: The figure depicts the change in log investment from 1997 to 1999 relative to change in log investment from 2000 to 2002, by
asset, for equipment and structures. The size of bubbles corresponds to the value of investment in 2000. The data for this figure comes
from Table 5.5.6 for equipment and Tables 5.4.6a and 5.4.6b for the structures.
Oil & Gas extrac.
Spec. trade contractors
Text & apparel
Comm. equip. and electronic components
Transp natural gas
Change in Log Investment 2000-2002
-1-.8 -.6 -.4 -.20 .2 .4 .6 .81 1.2 1.4 1.6 1.82
Change in Log Investment 1994-1999
Engines & turbines
Other power struc
-.7 -.6 -.5 -.4 -.3 -.2 -.1
Change in Log Investment 2000-2002
-.5 -.4-.2 -.10 .1 .2 .3.4.5 .6 .7.8
Change in Log Investment 1997-1999
Notes: The figure plots the means of investment rates for firms in the manufacturing, non-manufacturing and information business sectors.
Investment rates are calculated as the ratio of capital expenditures to the lagged value of the simulated capital in the prior period.
Figure 6: Aggregate q, 1962-2002
Notes: The figure plots the ratio of the aggregate market values of all firms in the Compustat sample to the stock of corporate capital as
computed by the BEA Fixed Reproducible Tangible Wealth series.
Figure 5: Firm-Level Investment Patterns, 1962-2003
1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Manufacturing Average Investment Rate
Information Business Average Investment Rate
Non-Manufacturing Mean Investment Rate
1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Figure 7: The Tax Term for Equipment, Averaged Across Industries, 1950-2003
Notes: This figure plots the tax term averaged across three hundred NAICS categories from 1950 to 2003.
1950 195419581962 19661970 1974 197819821986 19901994 19982002
%∆ in I
1994 to 1999
%∆ in I
1997 to 1999
%∆ in I
1994 to 1997
No. of Obs.
81 2381 81
%∆ in I
1994 to 1999
%∆ in I
1997 to 1999
%∆ in I
1994 to 1997
No. of Obs.81 238123
Notes: The dependent variable in all regressions is the percentage change in capital expenditures on equipment by industry from 2000
to 2002. Data is drawn from the Annual Capital Expenditure Survey. The top panel provides OLS specifications and the bottom
panel provides median regressions. Columns 1 and 3 are for all industries and columns 2 and 4 restrict attention to manufacturing
Testing for Reversion Of Investment, Equipment Only, Industry Level Data from ACES
Dependent Variable: Percentage Change in Investment 2000-2002
Panel B: Median Regressions
Panel A: OLS
Dependent Variable: Percentage Change in Investment 2000-2002
(1)(2) (3) (4)
%∆ in I
1994 to 1999
%∆ in I
1997 to 1999
%∆ in I
1994 to 1997
No. of Obs.
81 23 8123
%∆ in I
1994 to 1999
%∆ in I
1997 to 1999
%∆ in I
1994 to 1997
No. of Obs. 8123 81 23
Notes: The dependent variable in all regressions is the percentage change in capital expenditures by industry from 2000 to 2002.
Data is drawn from the Annual Capital Expenditure Survey. The top panel provides OLS specifications and the bottom panel
provides median regressions. Columns 1 and 3 are for all industries and columns 2 and 4 restrict attention to manufacturing
Panel B: Median Regressions
Testing for Reversion of Investmtent, All Investment, Industry-Level Data from ACES
Panel A: OLS
Dependent Variable: Log Investment in 2002 - Log Investment in 2000 Download full-text
∆ Ln(Real I) from
1994 to 1999
∆ Ln(Real I) from
1997 to 1999
∆ Ln(Real I) from
1994 to 1997
No. of Obs.
∆ Ln(Real I) from
1994 to 1999
∆ Ln(Real I) from
1997 to 1999
∆ Ln(Real I) from
1994 to 1997
No. of Obs. 3434
Notes: The dependent variable in all regressions is the change in log real investment by asset type from 2000 to 2002.
Data is drawn from the BEA. The top panel provides OLS specifications and the bottom panel provides median
Panel B: Median Regressions
Panel A: OLS
Testing for Reversion in Real Investment, Asset-Level Data from BEA