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.
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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