Why Did the Investment-Cash Flow Sensitivity Decline

Why Did the Investment-Cash Flow Sensitivity Decline
Over Time? A Productive Capital Structure Perspective
Zhen Wang and Chu Zhang∗
May 2014
∗ The
Shanghai University of Finance and Economics (e-mail: wang.zhen@mail.shufe.edu.cn) and
The Hong Kong University of Science and Technology (e-mail: czhang@ust.hk), respectively.
Why Did the Investment-Cash Flow Sensitivity Decline
Over Time? A Productive Capital Structure Perspective
Abstract
We propose an explanation for why corporate investment can be sensitive to cash flow and
why the sensitivity declined over time. It extends the notion that investment is sensitive to
current cash flow because current cash flow contains information about future cash flow. The
predictability of future cash flow depends on the productive factor structure, represented by the
fraction of tangible productive capital in total productive assets, which in turn is determined
by its productivity. New-economy firms operate with relatively more intangible capital, face
more intensive competition, and have cash flows which have less predictive power for their future
values. As the new-economy firms become more dominant in quantity and old-economy firms also
adapt to new-economy environment, the investment-cash flow sensitivity declines. The empirical
results support our explanation of the sensitivity as reflections of future cash flow predictability,
rather than indication of financial constraints.
1.
Introduction
The mainstream economic theory of corporate investment under the perfect market assumptions
postulates that investment is determined by the marginal productivity of the capital, popularly
known as the Q-theory. In empirical work, the marginal Q is unobservable and the average Q is
unable to explain the observed corporate investment activities. Instead, investment is found to
be related to cash flow of the firms. The investment-cash flow sensitivity is initially proposed as
indicative of the existence of financial constraints, a form of market imperfection, as financially
constrained firms must rely on their cash flow for new investment. An alternative explanation
is that cash flow variation explains that of investment because current cash flow predicts future
cash flow and investment is made in pursuit of future cash flow, consistent with the Q theory in
general. What is more interesting is that, while the debate has not been settled, the investmentcash flow sensitivity documented in the literature in the late 1980s declined over time. By the
new millennium, it almost disappeared. This declining pattern of investment-cash flow sensitivity
is also a puzzle to financial economists.
In this paper, we propose an explanation of why the investment-cash flow sensitivity has
declined. The explanation extends the notion that current cash flow explains investment because
it predicts future cash flow to incorporate the role of the productive capital structure, by which we
mean the mix of the productive capital: tangible capital and intangible. Over the last fifty years
or so, the US economy has experienced large technological transformations, from more traditional
industries to more high-tech oriented industries. These transformations are accompanied by the
increase in industrial products, more complicated production processes, and more competitive
environment for the firms. On one hand, the production processes rely more on intangible capital,
especially for new firms in new industries. On the other hand, cash flow becomes riskier and less
predictable. As the current cash flow contains less information about future cash flow, investment
becomes less dependent on the current cash flow.
Four sets of empirical results confirm the simple intuition outlined above. The first set of
results comes from basic descriptive statistics of the firm characteristics. During the sample
1
period from 1972 to 2011, the number of manufacturing firms listed in the major US exchanges
fluctuated mostly because of the changes in the NASDAQ listed firms. The average physical
investment as a fraction of total assets declined half of its value over the sample period. The
average cash flow as the percentage of total assets declined even more, most caused by the
newly listed high-tech firms. The average fraction of tangible capital in total productive capital
steadily declined, indicating the change in the productive capital structure for the US firms over
the sample period.
The second set of results is the main results of the paper. We show that, the investments-cash
flow sensitivity is absent if the cross-product term of cash flow and the fraction of tangible capital
in the total productive capital is controlled. In other words, the so-called investment-cash flow
sensitivity is in fact subsumed by the sensitivity of investment to the combination of cash flow
and (the share of) tangible capital. The investment-cash flow sensitivity depends positively on
the level of tangible capital. The investment of a firm with low tangible capital share is not
positively sensitive to cash flow at all. Only firms with high tangible capital share have positive
investment-cash flow sensitivity. Over time, the sensitivity of investment to the combination of
cash flow and tangible capital share declines. As a result, the investment-cash flow sensitivity also
declines. We verify that, among many variables that can potentially explain the investment-cash
flow sensitivity, the share of tangible capital is the only variable that does the job.
The third set of results explains the phenomena documented in the first two sets of the
results by the notion of the productivity of the tangible capital. The share of tangible capital in
total productive capital is determined by its productivity. We measure such productivity using
a simple model with the Cobb-Douglas type of production function at the firm level both for
panels of firms and for firms individually. We show that the productivity of tangible capital
declined over time. The share of tangible capital in total productive capital declined because of
that.
We verify our hypothesis using sub-samples of firms along several dimensions. We define neweconomy firms and old-economy firms by whether or not they are high-tech firms and whether
2
they are listed on NASDAQ/AMEX. Compared with old-economy firms, the new-economy firms
have both smaller investment-cash flow sensitivity and smaller investment-cash flow-tangible
capital share sensitivity than old-economy firms. They have lower productivity of tangible capital
and lower predictability of sales and cash flows.
We divide the entire sample according to their productivity of tangible capital, estimated
individually over four ten-year periods. Consistent with our hypothesis, the firms with high
productivity of tangible capital tend to have high investment-cash flow sensitivity and high
investment-cash flow-tangible capital share sensitivity. The firms with higher productivity of
tangible capital tend to have higher sales and cash flow predictability than lower tangible capital
productivity firms. Finally, the productivity of tangible capital declines over time and so do the
investment-cash flow and investment-cash flow-tangible capital share sensitivities.
We also examine two balanced panels of firms, which are fixed sets of firms that exist in
the first half of the sample period and in the second half of the sample period. The investment
cash-flow sensitivity for these balanced-panel firms also declines over time. This phenomenon
has made some researchers to disbelieve that the declining investment cash-flow sensitivity can
be attributed to the changing firm composition in the market. We show, however, that the
share of the tangible capital in the total productive capital has reduced for these balanced panel
firms and, moreover, the productivity of their tangible capital also declined, consistent with our
hypothesis.
Our fourth set of results concerns an alternative explanation for the role of tangible capital
share in explaining investment-cash flow sensitivity. Since tangible capital can be pledged as
collateral in obtaining debt financing, the explanatory power of the tangible capital for investment
can be potentially interpreted as evidence of financial constraints as well. To test whether this
is the case, we use four criteria from the literature to separate firms into financially constrained
firms and non-constrained firms. We find that the investment-cash flow-tangible capital share
sensitivity is higher for non-constrained firms in all the four sets of tests. This evidence clearly
indicates that the explanatory power of tangible capital for investment does not come from the
3
pledgibility.
The intended contribution of this paper is to shed light on the issue of investment-cash
flow sensitivity. The issue of why investment is sensitive to cash flow has been debated in the
literature over two decades and the disappearance of the sensitivity remains as a puzzle. Our
results provide evidence supporting the explanation that the sensitivity is a result of cash flow
predictability, rather than indication of financial constraints.
The rest of the paper is organized as follows. Section 2 briefly reviews the literature on
investment-cash flow sensitivity. In Section 3 we propose our hypothesis of why the sensitivity
has declined and what implications the hypothesis has. We also briefly describe our empirical
models and estimation methods there. Section 4 explains the data and sample selection, reports
descriptive statistics, and describes related background information. Section 5 presents the
main results of cash flow predictability and investments’ sensitivity to both cash flows and to
the combination of cash flow and tangible capital. Section 6 presents results regarding the
explanatory power of tangible capital in relation to financial constraints. Section 7 concludes.
2.
2.1.
Literature Review
Investment-Cash Flow Sensitivity
The neoclassic microeconomic theory derives corporate investment as the solution to a value
maximization problem faced by firms with a production function exhibiting constant return
to scale and adjustment costs. A related theory put forward by Tobin (1969) states that firm’s
investment rate is a function of Q, the ratio of the market value of additional unit of capital to its
replacement cost. Hayashi (1982) unifies the two theories. The Modigliani-Miller theorem under
the perfect market assumption implies that corporate investment decisions are independent of
financing decisions, such as internal liquidity, capital structure, and dividend policy. Myers and
Majluf (1984) and Stiglitz and Weiss (1981) postulate that internal funds are much less costly
than external funds because of asymmetric information between firm managers and outside
4
investors. The empirical evidence on this is mixed.
In an influential paper, Fazzari, Hubbard, and Petersen (1988) argue that financing constraints affect corporate investment. Let Inv, and CF, be the scaled investment and cash flow
during a period, respectively, and MB be the market-to-book asset ratio, a measure of average Q. By dividing firms into three classes based on dividend payout ratio, they find that the
investment-cash flow sensitivity, the a2 in the following regression,1
Invit = a0 + a1 MBi,t−1 + a2 CFit + εit ,
(1)
is high for low-dividend firms than for high dividend firms, while a1 is economically insignificant
at all. In their analysis, low dividend payout is a proxy for financing constraints. As such, the
investment-cash flow sensitivity, a2 in the regression model, measures the degree of financial constraints and corporate investment is affected by financing constraints for financially constrained
firms.
Kaplan and Zingales (1997) question the interpretation that high investment-cash flow sensitivity is evidence that financing constraints affect investment. They build a simple model
illustrating what is needed for financial constraints to have effect on investment and how this is
different from the simple regression. In their empirical work, they extract from firms’ annual report quantitative and qualitative information about whether the firms are financially constrained.
Only a small fraction of the low dividend firms have reported financing difficulty. On the other
hand, a large fraction of firms that are not financially constrained according to the classification
by Kaplan and Zingales show a large a2 in the investment-cash flow regression. Thus, whether a
large a2 is indicative of financial constraint is called into question. Later exchanges between the
two sets of authors do not resolve the debate. Cleary (1999) designs a sorting scheme based on
firm characteristics and find evidence in support of the findings of Kaplan and Zingales (1999).
In Claery’s results, financially constrained firms have smaller investment-cash flow sensitivity.
1
Fazzari, Hubbard, and Petersen (1988) define Q as the sum of the value of equity and debt less the value
of inventories, divided by the replacement cost of the capital stock, adjusted for corporate and personal tax
consideration. In subsequent analysis in this literature, the most researchers use the market-to-book asset ratio
as the average Q.
5
While the debate on whether investment-cash flow sensitivity measures financial constraints
continues, researchers turn to the question of why such sensitivity exists if it is not due to financial
constraints. The answer is also related to the question of why Tobin’s Q fails to explain firms’
investment behavior. Pertoba (1988) suggests the possibility that cash flow may capture the
errors in poorly measured Tobin’s Q.2 Alti (2003) builds a neoclassical model without financial
constraint to quantify the effect of cash flow on investment when Q is poorly measured. The
calibration and simulation results show that investment is sensitive to cash flow and the sensitivity
is higher for young, small, high growth, and low dividend payout firms. Tobin’s Q are more
poorly measured for these firms as the Q captures more long-term growth, rather than shortterm growth, which has effect on current investment. Gomes (2001) presents a model with similar
conclusions. Moyen (2004) considers two models, one in which there is no financial constraint
and the other in which there is. In the data simulated from both models, investment-cash flow
sensitivity is observed. This means that both explanations are plausible and that the debate
between the two schools remains unresolved.3
2.2.
Time-series Trend of the Investment-Cash Flow Sensitivity
While the debate about the correct interpretation of the investment-cash flow sensitivity continues, an interesting development is that this sensitivity has declined over time dramatically. While
in the 1960s, the sensitivity stays around 0.4, by the 2000s, it drops to near zero. Allayannis
and Mozumdar (2004) document that the sensitivity declined over the 1977-1996 period. They
found the decline is more obvious for financially constrained firms. Investment is not sensitive
to cash flow when cash flow is negative. Agca and Mozumdar (2008) examine the decline of
the investment-cash flow sensitivity in relation to the reduction of the market imperfection and
claim that the decline is associated with increasing aggregate institutional fund flows, institu2
There is a large literature on the measurement errors in Tobin’s Q, which are a potential problem for why
Tobin’s Q fails to explain investment. See Ericson and Whited (2000) and the references there.
3
Almeida and Campello (2001) consider the credit constraints on the investment-cash flow sensitivity, Povel
and Raith (2001) discuss the effect of asymmetric information, and Dasgupta and Sengupta (2007) discuss the
issue in a multi-period framework. The latter two studies assume unobservability of investment and both find
that the relation between investment and cash flow is not monotonic.
6
tional ownership, analyst following, anti-takeover amendments and with the existence of a bond
rating. The contribution of the changes in these five capital market factors in explanation of
the change in the investment-cash flow sensitivity is rather small, however. When the interactive terms of these factors with cash flow are added to the investment-cash flow regressions, the
sensitivity measures reduce marginally and the goodness-of-fit measures increase only slightly.
Brown and Petersen (2009) also ask the question of why the investment-cash flow sensitivity
declined so sharply over time. They attribute it to the changing composition of investment from
physical investment towards more R & D investment and the rising importance of public equity
as a funding source. In their view, it is the combination of the decline of physical investment
itself and the relaxing of financial constraint that makes the investment-cash flow sensitivity to
decline. Chen and Chen (2012) make a good observation that the investment-cash flow sensitivity
disappeared even during the 2007–2009 financial crisis in which financial constraints are strongly
binding. Therefore, the sensitivity cannot be due to financial constraints. They report that the
decline of the investment-cash flow sensitivity is very robust and cannot be reconciled by many
explanations proposed by previous studies. For example, the decline in the sensitivity occurs for
small and large firms, young and old firms, firms with negative and positive cash flows, firms
with and without credit ratings, and firms with different corporate governance practice and with
different market power. Not only the physical investment, but also R & D investment have their
cash flow sensitivity declining over time. While measurement error in Tobin’s Q is ultimately
the reason for the investment-cash flow sensitivity to exist in the first place, the reason for its
decline remains, by and large, a puzzle.
3.
3.1.
Hypothesis, Implications and Empirical Methodology
Hypothesis
The decline in the investment-cash flow sensitivity over time provides an opportunity for researchers to find out why it existed in the earlier years. Our hypothesis is based on the notion
of productive capital structure. The productive capital structure refers to the mix of the pro7
ductive assets: tangible capital assets and intangible capital assets.4 The main idea is that, over
time, the product markets have evolved, and along with this, the production technologies have
changed, the productive capital structures have tilted more to intangible productive capital, and
the environment firms operate in has become more competitive. The predictability of future
cash flow in the later years of the sample is reduced. This causes the corporate investment less
traceable from the current cash flow.
The US economy in the past fifty years has experienced tremendous changes. Traditional
industries declined in their importance, making way for new industries. In the early years of
the sample period, old-economy firms dominate, producing more or less standardized products.
Since 1960s, new-economy firms emerge, producing consumer electronics, medical equipments
and health products, computers and software, mobile phones, etc. These new products are made
possible through enormous efforts in research and development activities. As more new-economy
firms get listed in exchanges, the overall productive capital structure changed. Tangible capital
now plays a smaller role in production, while knowledge-based intangible capital becomes more
essential in the economic growth. In fact, not only new-economy firms are conducting research
and development, some of the old-economy firms also engage developing newer products and
changing their productive capital structure in order to gain market shares from their competitors.5
Associated with new products and new technologies is the competition among firms. Whether
a product or a firm can survive not only depends on the absolute quality and cost structure of
the product, but also depends on the relative advantage to the competitors. While this is also
true to old-economy products and firms, it is more relevant for new-economy products and firms,
as research and development involve higher degrees of uncertainty, products’ life-span is much
shorter, and consumers’ tastes keep changing. As a result, many firms, especially those smaller,
newer ones, experience difficulty in making profits, even though they may have good business
4
It is to be distinguished with the financial capital structure, which refers to the mix of various types of
financial assets firms issue to raise funds: equity, debt, and hybrid. The tangible capital assets are also to be
distinguished with non-productive tangible assets such as inventories and cash holdings.
5
A case in point is Nike, an athletic footwear and apparel maker, which officially belongs to a traditional
industry, but has developed all kinds of high-tech gadgets, related to sports and health, and is rightfully called a
high-tech company in a Bloomberg Businessweek article by Brustein (2013).
8
plans and have high market valuations.
Figure 1 plots the number of firms that are classified as high-tech firms and are listed in the
major exchanges, respectively. These plots show that it is the high-tech firms and firms that are
listed on NASDAQ that enter and exit the sample of the listed firms.
Figure 1 here
We hypothesize that the pattern in the time-series of the investment-cash flow sensitivity is the
reflection of the changes in the cash flow predictability and the role productive capital structure
plays. In the early years of the sample, the economy is dominated by old-economy firms, future
cash flow can be predicted by current cash flow and the productive capital structure is heavily
tilted towards tangible capital, as the output is mainly generated from the tangible capital. In
the later years of the sample, however, the product market has changed. Many new firms that
produce new products do not rely on tangible capital as much as the old-economy firms do. Even
for some old-economy firms the productivity of tangible capital declines. As such, the physical
investment rate declines, causing the share of tangible capital to decline.
In standard macroeconomics, a firm employs multiple productive factors, such as capital,
labor, land, etc., to produce. The most popular type of production function is of the CobbDouglas type with constant return to scale. For our purpose, let
1
2
Salesit = Ait TCbi,t−1
ICbi,t−1
,
(2)
where Salesit is the firm sales or total revenue, TCi,t−1 is the tangible capital, ICi,t−1 is intangible
capital, and Ait captures the productivity shock and other productive factors. The marginal
product of tangible capital is positively related to b1 , other things being equal.6 While a dynamic model is beyond the scope of this paper, it is not difficult to understand the logic behind
an extended Q theory in which there are multiple productive factors including both tangible
capital and intangible capital and the rate of investment/employment of each productive factor
6
In a static model with perfect competition, b1 also measures the share of income to the owner of the tangible
capital.
9
is determined by its marginal Q. As the marginal product of tangible capital relative to other
productive factors varies across firms and over time the physical investment rate will also vary.
As a result, the share of tangible capital in total productive capital contains information about
the marginal product of tangible capital. As argued by other researchers, investment may vary
with cash flow because cash flow can provide information about marginal Q. What we add to this
is that the link between investment and cash flow also depends on the share of tangible capital
because it contains information about the marginal Q with respect to tangible capital.
3.2.
Implications
While our hypothesis is intuitive, testing it is not an easy task. The difficulty lies in the unobservability of the productivity of tangible and intangible capital. This is deeply rooted in the
difficulty of measuring the marginal Q. In addition, intangible capital itself is difficult to measure.
We proceed our test of the implications of the hypothesis with these difficulties in mind.
The implications from our hypothesis are stated in terms of the following regression equations.
First, we extend the standard investment regressions as follows,
Invit = a0 + a1 MBi,t−1 + a2 CFit + a3 CFit TCSi,t−1 + a0 xi,t−1 CFit + εit ,
(3)
where TCSi,t−1 is the tangible capital share of firm i at the end of year t − 1, xi,t is a vector
of other variables, which can potentially provide alternative explanations for why investmentcash flow sensitivity exists, and a the corresponding coefficient vector. The identity of xit will
be specified later. When the models are estimated over different subperiods, our hypothesis has
certain implications in terms of the parameters of the regression models. As documented in many
studies cited in the literature review, when the model is estimated without interactive terms, a2
declined over time. Under our hypothesis, when the model is estimated with the cross-product
term CFit TCSi,t−1 its coefficient a3 should be positive and significant, while the significance of
a2 in early years should be weakened. In addition, if the hypothesis is right, the reason a2 is
reduced over time is that a3 is reduced over time.
10
To trace the marginal product of tangible capital, we consider the log version of (2).
ln Salesit = b0 + b1 ln TCi,t−1 + b2 ln ICi,t−1 + ηit ,
(4)
where b0 = E ln Ait and ηit = ln Ait − b0 . Under our hypothesis, the parameter b1 declines over
time, indicating less importance of tangible capital in the aggregate production process. The
model can also be estimated for individual firms with sufficient data. One direct implication
is that firms with high b1 should also have high investment-cash flow sensitivity. The declining
investment-cash flow sensitivity pattern should be most evident in the subsample of high b1 .
Finally, we can examine the autoregression model of cash flow. As have been observed by
other researchers, both the autoregressive coefficient of cash flow and the goodness-of-fit of the
autoregressive model has declined over time when the model is estimated over subperiods.
CFit = c0 + c1 CFi,t−1 + ξit .
(5)
The autoregressive model has been used by Chen and Chen (2012) to argue that cash flow as proxy
for the future profitability stands the best chance to explain the investment-cash flow sensitivity.
Chang et al (2014) contain further results using the AR(1) model for cash flow predictability. By
classifying firms into old-economy firms and new-economy firms, we examine the autoregressive
model for these two classes of firms separately. Under our hypothesis, the old-economy firms
have higher c1 coefficient and higher goodness-of-fit of the model than new-economy firms. In
addition, both the c1 coefficient and the goodness-of-fit decline over time.
We also examine the autoregressive model for firms with high, medium, and low parameter of
marginal product of tangible capital separately. Under our hypothesis, the higher b1 firms should
have higher c1 coefficient and higher goodness-of-fit of the model than lower b1 firms. Similarly,
both the c1 coefficient and the goodness-of-fit decline over time.
3.3.
Empirical Methodology
Following the literature, we estimate the investment regressions over five-year subperiods and
track the coefficients over different subperiods. The models will be estimated with fixed firm
11
effects. As such, the estimated coefficients reflect the variation in the left-hand side variable in
response to the time-series variation to the right-hand side variables, with the constant (over time)
cross-sectional variations of the right-hand-side variables being controlled. We implement this by
subtracting the mean of each variable in the entire sample period before running regressions. The
goodness-of-fit measure, the adjusted R2 , measures the time-series variation in the dependent
variable on the left-hand side of the regression equation explained by the explanatory variables
on the right-hand side.
4.
4.1.
Data and Descriptive Statistics
Data, Variable Construction and Sample Selection
We construct our main sample based on the manufacturing firms (SIC code from 2000 to 3999) in
the COMPUSTAT annual file from 1972 to 2011. The starting point of the sample corresponds
to the time when data on NASDAQ firms become available. We define the contemporaneous
investment (Inv) as the capital expenditure (COMPUSTAT item, CAPX) of a firm-year (i, t),
scaled by the total assets (COMPUSTAT item, AT) at the beginning of the year. The contemporaneous cash flow (CF) is the sum of the income before extraordinary item (COMPUSTAT item,
IB) and the depreciation (COMPUSTAT item, DP) over the beginning-of-the-year total assets.
The market-to-book ratio (MB) of a firm is the ratio of the market value of total assets over the
book value of total assets. The market value of total assets is the market capitalization (COMPUSTAT items, CSHO*PRCC F), plus total assets, minus common equity (COMPUSTAT item,
CEQ), minus deferred taxes (COMPUSTAT item, TXDB). To make our results comparable to
those in the literature we require that each firm-year in our sample have relevant data to compute
investment, cash flow and market-to-book ratio. To be consistent with Chen and Chen (2012),
we exclude firm-years for which we cannot calculate the lagged cash flow. Following Almeida,
Campello and Weisbach (2004), we eliminate firm-years for which the sales growth or the assets
growth exceeds 100 percent to avoid structural changes in the business of the firms. To ameliorate the effects from the outliers, for each firm-year we require that the net capital (net property,
12
plant and equipment), book assets and sales in the previous year be equal or greater than $1
million. Furthermore, all variables, when used in the regressions, are winsorized at one percent
level in both tails of the distribution for each year.
In our paper, tangible capital is the net property, plant and equipment (COMPUSTAT item,
PPENT). Intangible capital is the COMPUSTAT intangible assets (COMPUSTAT item, INTAN). We define the tangible capital share (TCS) as the ratio of tangible capital over the
total productive capital (tangible capital and intangible capital). This ratio measure the extent
to which a firm relies on tangible capital in production. In accordance with our intention of
measuring the mix between tangible and intangible productive capital, this definition excludes
non-productive assets. The measure is not perfect, however. It has been reported elsewhere that
some newly listed, small firms do not report their research and development, and corresponding,
their intangible capital is underestimated by the accounting data.
We classify firms that are non-high-tech and listed on NYSE as old-economy firms and firms
that are high-tech and listed on NASDAQ/AMEX as new-economy firms. The classification
is non-exhaustive, as many firms are unclassified. Following Chen and Chen (2012) a firm is
regarded as a high-tech firm if its three-digit SIC code is 283, 357, 366, 367, 382, or 384. We
acknowledge that this classification of new and old-economy firms is a crude and oversimplified
one. We note that such a simple classification would not work in our advantage to illustrate our
point.
We construct two balanced-panel samples of firms. The first sample consists of manufacturing
firms that exist during the 1972-1991 subperiod, and the second sample consists of manufacturing
firms that exist during the 1992-2011 subperiod. Very few (less than 60) manufacturing firms
exist through the entire sample period of 1972-2011, so they are not examined. Through these
balanced panel samples, we show how firm characteristics change over time, even for the same
set of the firms.
13
4.2.
Descriptive Statistics
Table 1 reports the descriptive statistics of a few key variables used in this paper. Panel A
contains those for the full sample. During the sample period from 1972 to 2011, the number
of manufacturing firms listed in the major US exchanges is quite stable until 1991, increased
towards 2000 and then declined after the so-called internet bubble. By 2011, the number of
manufacturing firms is smaller than that at the beginning of the sample. The average physical
investments as a fraction of total assets declined from roughly 8% in the beginning of the sample
period to roughly 4% by the end of the sample period. The market-to-book asset ratio is higher
in the later years of the sample than in the earlier years, indicating that more growth firms are
present in the sample in the later years. The average cash flows as the average fraction of fixed
assets in total assets sharply declined from more than 10% to less than 2%. In the meantime,
the average fraction of tangible capital also declined, from almost 100% to slightly above 60%.
Another salient feature from the descriptive statistics is that cash flow becomes much left skewed
in the later years of the sample. While the average declines sharply, the median declines only
modestly. Naturally, as cash flow becomes more skewed, the standard deviation increases. The
distribution of tangible capital is not very skewed, but its standard deviation roughly increases
over time.
Table 1 here
Panels B and C of the table show the same descriptive statistics for the new-economy firms.
The number of new-economy firms increases toward 2000 and then declines modestly. On the
contrary, the number of old-economy firms declines overall. The new-economy firms tend to
have higher market-to-book ratio, as most of these new economy firms are expected to grow in
their cash flow. The competition, however, renders the cash flow to be low. The average cash
flow is in fact negative, while the cash flow of the old-economy firms is rather higher and more
symmetrically distributed with smaller standard deviation. The mean and median fraction of
tangible capital for the new-economy firms are no less than those of old-economy firms. This is
14
likely due to the data problem mentioned earlier.
Panels D and E of the table report the same descriptive statistics for the two balanced panel
samples. For the first panel of 210 firms that exist in 1972-1991, the change in the mean and
median of the firm characteristics is not obvious, expect for the subperiod of 1987-1991 during
which the average tangible capital declines and the standard deviation increases. For the second
panel of 269 firms that exist in 1992-2011, the change in the descriptive statistics of physical
investment, cash flow and tangible capital is evident. The purpose of showing the descriptive
statistics of the firm characteristics for these balanced panels is the following. Chen and Chen
(2012) dismiss the possibility that investment-cash flow sensitivity declines on average because
the composition of firms in the sample has changed over time. The reasoning is based on the
finding that the sensitivity for the balanced panel firms also declines. We argue here that although
the firms in the balanced panels are the same firms in the name, their firm characteristics have
changed and, more importantly, their products, technology and entire productive processes have
changed. It is these changes, rather than the name of the firm, that matter for the sensitivity of
their investment to cash flow.
5.
5.1.
The Declining Investment-Cash Flow Sensitivity
The Full-sample Results
We examine the investment regressions (1) and (3). The results are reported in Panel A of
Table 2. The slope coefficients, a1 , of the market-to-book ratio, MBi,t−1 , in all regressions are
statistically significant throughout the entire sample, but economically insignificant, around 0.01,
compared with the theoretical value of one under the simplest model with constant return-toscale production function and without adjustment cost in the Q-theory. Since there is a large
literature on the measurement errors of Q and it is not the focus of the current paper, we will
not discuss the coefficient of the market-to-book ratio in the remaining of the paper, but we
keep MBi,t−1 in all the investment regressions as a control variable. For equation (1), the slope
15
coefficient, a2 , of cash flow is significantly positive in each of the five-year subperiod, but the
magnitude steadily declines since the second five-year subperiod. Both t-ratio and R2 in the last
five-year subperiod are substantially reduced in the later subperiods.
Table 2 here
For regression model (3) with added cross-product term of beginning of period tangible capital
and cash flow, we find two very important results. First, the slope coefficient of the linear term
of cash flow, a2 , is insignificant for each of the subperiods, even for the early subperiods, after
controlling for the cross-product term. However the slope coefficient, a3 , of the cross-product
term itself is significantly positive in each of the subperiods. This result implies that the well
documented positive investment-cash flow sensitivity is driven by the effect of tangible capital,
which is consistent with our hypothesis. Specifically, cash flow has a positive effect on capital
investment. The ratio of tangible capital measures the persistence of future profitability, as we
hypothesize and verify later. Thus firms with higher tangible capital invest more when they have
marginal cash flows, displaying a higher investment-cash flow sensitivity.
Second, the slop coefficient of the cross-product term of tangible capital and cash flow shows
a pattern of declining over time, though not monotonically. This pattern clearly shows that
the declining trend in the investment-cash flow sensitivity documented in Brown and Petersen
(2009) and Chen and Chen (2012) simply reflects the fact that the effect of tangible capital on
the investment-cash flow sensitivity weakens over time. As we have mentioned, the nature of
U.S. firms has changed profoundly over time. They rely more on new technologies and face more
fierce competitions than before. As more intangible capital-intensive firms enter the sample,
tangible capital plays less essential role and is no long a good measure of the persistence of
future profitability. Therefore it does not predict the investment-cash flow sensitivity as well as
it did in the earlier years of the sample.
Panel B of Table 2 reports the sales regressions for the full sample. It shows that the productivity of tangible capital, represented by b1 , is strongly significant in all subperiods, but declining
16
over time, though not monotonically. The productivity of tangible capital, represented by b2 , is
not always significant in all subperiods, and has no particular pattern over time. Panel C of Table
2 reports the cash flow autoregressions for the full sample. It shows that both the autoregressive
coefficient, represented by c1 , is strongly significant in all subperiods, but declining over time.
Chen and Chen (2012) show the declining pattern of c1 graphically. The result is reported here
for completeness purpose only. The significantly positive c1 is the basis for the argument that
investment-cash flow sensitivity is supported by the Q-theory. The declining pattern in the cash
flow persistence c1 is also broadly consistent with the declining pattern in investment-cash flow
sensitivity. A closer link will be established in the subsequent subsection by examining subsamples classified by measures related to tangible capital productivity, after we consider alternative
explanations.
5.2.
The Full-sample Results: Other Alternative Explanations
Studies in the literature have documented that the many characteristics of U.S. listed firms have
evolved over the decades in addition to tangible capital. These characteristics may affect both
the capital investments and the investment-cash flow sensitivity. Our parsimonious specifications
above do not include these firm characteristics. We examine these characteristics and see whether
our previous results are robust to the addition of certain relevant variables.
Bates, Kahle and Stulz (2009) find that the average cash holdings (cash-to-assets ratio) of U.S.
firms have more than doubles from 1980 to 2006. If the investments of financially constrained
firms truly rely on internal cash flows, higher level of cash holdings as internal funds will definitely
reduce the investment-cash flow sensitivity. Omission of the cash holdings in the regression will
bias the estimated coefficient of tangible capital.
Lemmon, Liu, Mao and Nini (2014) mention that the asset backed securitizations (ABS), as
a rising source of financing, typically moves the account receivables from the firm that initiates
it to a separate special purpose entity. This might lead to a declining trend in the account
receivables of U.S. firms. Gao (2012) argue that the prevalent adoption of Just-in-Time (JIT)
17
techniques lowers the incentives of firms to hold inventories. These technqiues together results
in a declining trend in the level of working capital of U.S. firms.
7
Bates, Kahle and Stulz
(2009) regard working capital as a liquid asset, and a substitute for cash holding. Therefore
if financial constraints matter for investment, working capital should have a negative effect on
investment-cash flow sensitivity.
A large number of papers devote to study how firm level cash flow volatility affects the
corporate investments. Minton and Schand (1999) find that firms with higher level of cash flow
volatilities are associated with lower level of capital investments. They argue that firms with
more volatile cash flows are more likely to experience funds shortfalls, in which case they tend to
forgo investment projects. In our sample the five-year median of the firm level cash flow volatility
more than doubles from 1972 to 2011, which might also introduce a declining trend in both the
capital investment and the investment-cash flow sensitivity.8
From the perspective of R&D expenses, Brown and Petersen (2009) explain why investmentcash flow sensitivity declines over time. They argue that U.S. firms become more technology
intensive and spend more internal cash flows on R&D expenses. Fewer cash flows are used on
capital investment, thereby causing lower investment-cash flow sensitivity.
Chen and Chen (2012) argue that one potential reason why investment-cash flow sensitivity
declines over time might be that firms in general becomes less constrained over time. Since firm
size is a reliable measure of financial constraints (Hadlock and Pierce (2010)) we test how it
changes the investment-cash flow sensitivity over time in this section.
In this subsection we test how the five firm characteristics, cash holding (CH), working capital
(WC), cash flow volatility (CV), R&D expenses (RD) and firm size (SZ) affect the investments
and investment-cash flow sensitivity compared to the tangible capital does.
7
In our sample the average ratio of receivables over total assets decreases from 0.22 for the first five-year period
(1972-1976) to 0.15 for the last five-year period (2007-2011). The average ratio of inventories over total assets
declines from 0.28 for the first five-year period (1972-1976) to 0.15 for the last five-year period (2007-2011).
8
For a firm at year t, its cash flow volatility is defined as the standard deviation of the ratio of cash flow over
total assets from year t − 4 to year t. The average of the firm level cash flow volatility for the first five-year period
(1972-1976) is 0.04, while that for the last five-year period (2007-2011) is 0.13.
18
In Table 3 we test how the cross product term of the five firm characteristics and the tangible
capital affect the coefficients of cash flows in the investment regressions. We first add the cross
product term of the five firm characteristics into the investment regression (1). We find that
higher cash holdings, more working capitals, larger firms, higher R&D expenses and higher cash
flow volatility are associated with lower investment-cash flow sensitivities, which is consistent
with the standard predictions. More importantly, we find that even though the cross product
terms of the five variables are added into the investment regression (1), the coefficients of the
cash flows are still statistically significant and show a declining pattern over time. But when we
include the cross product term of tangible capital into the investment regressions, the coefficients
of cash flows immediately become statistically insignificant. These results clearly show that the
tangible capital has a strong prediction of the investment-cash flow sensitivity.
Table 3 here
5.3.
The Old-economy and New-economy Firms
The results of the investment regressions for the new-economy firms and the old-economy firms in
Table 4 exhibit the same pattern as in those in Table 2, but they also show difference across oldand new-economy firms. First, the investment-cash flow sensitivity parameter, a2 , is smaller for
the new-economy firms than for the old-economy firms. This naturally leads to the implication
that, as the new-economy firms increase in number while the old-economy firms decrease in
number, the investment-cash flow sensitivity for the full sample declines over time. Second,
for both old-economy firms and new-economy firms the slope coefficients of the linear term of
cash flow are are insignificant after the cross-product term is introduced into the investment
regression. However, the slope coefficient, a3 , of the cross-product term of cash flow and tangible
capital is not always significant for the new-economy firms in all subperiods, while it is significant
for the old-economy firms in all subperiods with the significance increasing over time. And the
magnitude of the coefficient of product term remains strong over time for old-economy firms. For
all regressions, the goodness-of-fit is greater for the old-economy firms than for the new-economy
19
firms.
Table 4 here
Overall, the results for the old-economy firms and the new-economy firms in Table 4 provide
further evidence to our hypothesis that investment-cash flow sensitivity in the early years is
mainly due to cash flow’s predictive power for its future value. The results also indicate that the
declining investment-cash flow sensitivity has something to do with the increasing importance of
the new-economy firms which have less predictable future cash flow as we will show later. The
results show important contribution of tangible capital in explaining the variation in corporate
investment.
5.4.
Tangible Capital Productivity
In order to gain further insight about how investment-cash flow sensitivity depends on tangible
capital productivity, we estimate the sales regression individually to obtain estimates of b1 and
b2 for each individual firm. The entire sample period is divided into four ten-year subperiods
and the sale regressions are run for each firm within a ten-year subperiod. Firms that have less
than five observations with a ten-year period are dropped. The number of firms whose b1 and
b2 coefficients can be estimated for a subperiod is much reduced. The estimation errors can be
high due to small sample issues.
Within each ten-year subperiod, these firms are classified into high (top 30%), medium (middle 40%) and low (bottom 30%) b1 categories. Panel A of reports the mean (trimmed at 1%
on both sides) and median of the estimated b1 s, b2 s and the goodness-of-fit R2 s for the overall
sample and for the high, medium, low categories for each ten-year subperiods. Across the three
categories, the estimated b1 are quite different, from less than zero to greater than one, while
typical macroeconomic models would assume it to be between zero and one. There is a downward
trend in the mean and median of estimated b1 for all three categories.
20
There is a tendency that a high b1 is associated with a high R2 of the sales regression. The
down trend is also seen in the R2 , though the correspondence is not perfect. On the other hand,
the estimated productivity of intangible capital b2 is small in magnitude and shows no particular
pattern with the productivity of tangible capital, except for the last ten-year subperiods in which
b2 tend to larger for low b1 firms.
Panel B of Table 6 shows the investment regression for the high, medium, and low TC
productivity firms separately. It is clear that high TC productivity firms tend to have larger
coefficient of the cross-product term CFit TCSi,t−1 . than medium and low TC productivity firms.
As expected, as the tangible capital productivity declines over time, the investment-cash flowtangible capital share sensitivity declines over time. Panel C of Table 6 shows that high TC
productivity firms tend to have higher cash flow persistence and higher goodness-of-fit of the
autoregressive model than medium and low TC productivity firms. There are a rough pattern
of declines in these measures. These results lend support to the hypothesis we propose in this
paper.
5.5.
Balanced Panel Firms
This subsection deals with firms that exist through the 1972-1991 period or through the 19922011 period. Chen and Chen (2012) find that balanced panel firms also show a declining pattern
of the investment-cash flow sensitivity over time, therefore, they doubt the decline in sensitivity
in the full sample can be due to changing composition of the firms in the sample, as the pattern
of declining investment-cash flow sensitivity is also observed for the balanced panel of firms which
are unchanged in the sample. In Table 1 we report that, even though the firms remain the same
in balanced panel, their share of tangible capital declines over time on average. Therefore, they
should not be regarded as the same firms from economic point of view.
Table 7 reports the investment regressions for the two balanced panels. In Panel A, for firms
that exist through the 1972-1991 period, patterns similar to the results in Table 2 show up.
The investment-cash flow sensitivity a2 declines over subperiods when the cross-product term is
21
absent. It becomes insignificant when the cross-product term is added. The coefficient of the
cross-product term a3 declines over subperiods, which explains why a2 estimated without the
cross-product term declines.
The results in Panel B for firms that exist through the 1992-2011 period are less clear. As
the firms in Panel A, the investment-cash flow sensitivity a2 declines over subperiods when the
cross-product term is absent. However, when the cross-product term is added into regression,
neither a2 nor a3 is significant for the 1992-1996 and 1997-2001 subperiods. For the 2007-2011
subperiod, a2 turns significantly negative, while a3 becomes large and very significant, making it
difficult to understand.
Table 7 here
In Table 8, we repeat what we do in Table 6 for balanced panels. In Panel A we see that these
firms also have cross-sectional difference in the tangible capital productivity. The more interesting
observation is that, while the firms remain unchanged, their tangible capital productivity declines
from the first ten-year subperiod to the second ten-year subperiod.
Table 8 here
In panels B and C, the results show that high tangible capital productivity firms tend to have
their investment-cash flow-tangible capital share sensitivity and cash flow persistence declining
over time, although the patterns are less clear for medium and low tangible capital productivity
firms. Since the most salient features of the investment-cash flow sensitivity are driven by high
tangible capital productivity firms, this result, by and large, explains what the investment-cash
flow sensitivity also declined for balanced panel firms.
6.
Tangible Capital and Financial Constraints
The tangible capital share might impose a positive effect on the investment-cash flow sensitivity
of U.S. firms through two different channels: (A) the information content channel, and (B) the
22
credit multiplier channel. Up to now we have argued that the tangible capital share impacts the
cash flow sensitivity of investment through the first channel: firms with higher tangible capital
share exhibit larger investment-cash flow sensitivity because this ratio is a good proxy for the
persistence of the profitability (cash flow) of U.S. firms. However from a different perspective,
Almeida and Campello (2007) propose the second channel which claims that asset tangibility
has a positive effect on the cash flow sensitivity of investment when firms face costly external
financing. Specifically, it assumes that financially constrained firms will invest more when they
have more cash flows. The tangibility of the assets strengthens this positive relation between
investment and cash flow, because with more tangible assets as collateral constrained firms are
able to borrow more and therefore invest more. To the extent that the property, plant and
equipment are important tangible assets, our empirical results are also consistent with Almeida
and Campello (2007).
To distinguish the information content channel and the credit multiplier channel, we test how
the impact of the tangible capital on the investment-cash flow sensitivity differs for financially
constrained and unconstrained firms. The two channels have opposite predictions on how the
tangible capital share affects the investment-cash flow sensitivity of financially constrained and
unconstrained firms differently. If the information content channel works, a marginal cash flow
of a firm with higher tangible capital share should be more persistent in the future compared
to that of a firm with lower tangible capital share. The high tangible capital firm will invest
more and display higher investment-cash flow sensitivity. Furthermore this positive effect of
tangible capital on the cash flow sensitivity of investment should be much stronger for financially
unconstrained firms. Unconstrained firms are able to invest as much as possible to fully capture
the investment opportunities when their marginal cash flows are more profitable (when the
tangible capital share is higher), while constrained firms might not be able to do so due to their
potential limits in obtaining enough funds. However as was mentioned in Almeida and Campello
(2007), the credit multiplier channel dictates that the tangible capital share has a significant
positive impact on the investment-cash flow sensitivity only for financially constrained firms. If
the credit multiplier channel works, only the constrained firms have positive investment-cash flow
23
sensitivity. With more tangible capital, constrained firms can borrow more and therefore invest
more, leading to higher investment-cash flow sensitivity. The investments of unconstrained firms
are not sensitive to the status of liquidity (cash flows), therefore the tangible capital share should
have no effect on the investment-cash flow sensitivity of unconstrained firms. To empirically test
the information content channel and the credit multiplier channel, we estimate regressions (3)
for financially constrained and unconstrained firms separately. If the tangible capital affects the
investment-cash flow sensitivity through the information content channel, we should find a higher
coefficient of the interactive term, b3 , for financially unconstrained firms. However if the credit
multiplier channel works, unconstrained firms should have a lower b3 .
We adopt four alternative schemes to define financially constrained and unconstrained firms.
The first scheme is the Whited and Wu index (WW-index). This index is initially constructed in
Whited and Wu (2006) based on GMM estimation of an investment Euler equation to measure
the firm level financial constraint. It is a linear combination of six variables, cash flow, dividend
dummy, firm size, leverage, firm sales growth and industry sales growth. In each year we sort the
firms according to their WW-index. Firms that are ranked at the top (bottom) three deciles are
defined as financially constrained (unconstrained). The results are reported in Panel A of Table
9.
Table 9 here
The second scheme is to use dividend dummy to separate firms into constrained and unconstrained ones. This variable equals one for a firm-year if the firm pays out either common stock
dividends or preferred stock dividends in that specific year, and zero otherwise. A firm is defined
as financially unconstrained (constrained) in a specific year if the dividend dummy is one (zero)
at that year. The result based on this classification are reported in Panel B.
The third scheme is to use firm size to separate firms into constrained and unconstrained
ones. This variable is defined as the logarithm of the book assets. In each year we sort firms
according to the firm size. Firms in the top (bottom) three deciles are defined as financially
24
unconstrained (constrained). The regression results are reported in Panel C.
The last scheme is to use bond rating dummy to separate firms into constrained and unconstrained ones. The bond rating dummy equals one for a firm-year if the firm is assigned a bond
rating by Standard & Poor in that year. Otherwise this variable is zero. A firm is defined as
financially unconstrained (constrained) in a specific year if the bond rating dummy is one (zero).
The bond rating assigned by Standard & Poor is only available since 1986. Thus when using this
scheme we drop all the firm-years before 1986. The regression results are reported in Panel D.
The results can be summarized as follows. First, we find that for both constrained and
unconstrained firms the presence of the product term of cash flow and tangible capital share
in the conventional investment-cash flow sensitivity regression significantly reduces the t-ratios
of the coefficients of cash flow. In more than half of the five-year periods the t-ratios of cash
flows become statistically insignificant and even negative after the product terms of cash flow
and tangible capital share have been included. This is similar to what we have found for the
overall sample, which means that how the investment of a firm is sensitive to the cash flow is
largely determined by the amount of tangible capital it possesses.
Second, whichever financial constraint separator is adopted, we find that unconstrained firms
always have much larger coefficients on the product of cash flow and tangible capital share
than constrained firms. This finding shows that the tangible capital share has a much stronger
impact on the investment-cash flow sensitivity of financially unconstrained firms, which suggests
that tangible capital is more likely to affect the investment-cash flow sensitivity through the
information content channel not the credit multiplier channel. We note that this does not nullify
the pledgability of tangible capital. It shows only that the pledgability of tangible capital is not
driving our results on investment-cash flow sensitivity.
Furthermore the effect of tangible capital on the investment-cash flow sensitivity declines in
general for both financially constrained and unconstrained firms. As we have mentioned U.S.
firms overall have evolved over time. They use less tangible capital in production and rely more
on new technologies nowadays. Since the nature of the production changes, the cash flows of firms
25
become noisier inherently. The role of cash flow as an indicator of a firms future profitability
weakens over time. Therefore in general the effect of tangible capital on the investment-cash flow
sensitivity diminishes for both financially constrained and unconstrained firms.
7.
Conclusions
In this paper, we propose and examine a hypothesis about why corporate investment is sensitive
to cash flow and why the sensitivity has declined. The explanation is built on the existing
explanation in the literature that current cash flow explains investment because it predicts future
cash flow. We emphasize the role of the productive capital productivity in this explanation. In
our framework, the economy has been changing from an old economy which rely more on tangible
capital to a new economy which adopt more intangible capital. The new economy firms, however,
also face more competition and their future cash flow is less predictable from the current cash
flow. As a result, investment become less explainable by current cash flow.
We provide empirical results which confirm our explanation. During the sample period, the
number of manufacturing firms listed in the major US exchanges fluctuated mostly because of
the changes in the high-tech firms listed on NASDAQ. The average cash flow declines, caused
by the newly listed high-tech firms. The average fraction of tangible capital in total productive
capital also declines, showing the change in the productive capital structure. More importantly,
the tangible capital productivity declined over time. Cash flow become less predictable. The
new-economy firms have both smaller investment-cash flow sensitivity than old-economy firms.
It is the decline in tangible capital share and tangible capital productivity in the economy that
causes the investment-cash flow sensitivity to decline.
We also provide evidence that tangible capital explains investment not because it can be
used as collateral for reducing financial constraints, as the investment-tangible capital sensitivity
is higher for non-constrained firms than for the constrained firms, using four criteria from the
literature to separate firms into financially constrained firms and non-constrained firms. We
26
contribute to the literature by answering the question of why investment-cash flow sensitivity
exists in the first place and why it declines.
27
References
Agca, S., Mozumdar, A., 2008. The impact of capital market imperfections on investment-cash
flow sensitivity. Journal of Banking and Finance 32, 207-216.
Allayannis, G., Mozumdar, A., 2004. The impact of negative cash flow and influential observations on investment-cash flow sensitivity estimates. Journal of Banking and Finance 28,
901-930.
Almeida, H., Campello, M., 2007. Financial constraints, asset tangibility, and corporate investment. Review of Financial Studies 5, 1429-1460.
Almeida, H., Campello, M., Weisbach, M., 2004. The cash flow sensitivity of cash. Journal of
Finance 59, 1777-1804.
Alti, A., 2003. How sensitive is investment to cash flow when financing is frictionless? Journal
of Finance 58, 707-722.
Bates, T., Kahle, K., Stulz, R., 2009. Why do U.S. firms hold so much more cash than they
used to? Journal of Finance 64, 1985-2021.
Brown, J., Petersen, B., 2009. Why has the investment-cash flow sensitivity declined so sharply?
Rising R&D and equity market developments. Journal of Banking and Finance 33, 971-984.
Brustein, J. 2013. Sorry, Nike, you’re a tech company now, Bloomberg Businessweek, October
15, 2013.
Chang, X., Dasgupta, S., Wong, G., and Yao, J., 2014, Cash flow sensitivities and the allocation
of internal cash flow, Working paper.
Chen, H., Chen, S., 2012. Investment-cashflow sensitivity cannot be a good measure of financial
constraints: evidence from the time series. Journal of Financial Economics 103, 393-410.
28
Cleary, S., 1999. The relationship between firm investment and financial status. Journal of
Finance 54, 673-692.
Dasgupta, S., Sengupta, K., 2007. Corporate liquidity, investment and financial constraints:
implication from a multi-period model. Journal of Financial Intermediation 16, 151-174.
Dasgupta, S., Noe. T., Wang, Z., 2011. Where did all the dollars go? The effect of cash flows on
capital and asset structure. Journal of Financial and Quantitative Analysis 46, 1259-1294.
Erickson, T., Whited, T., 2000. Measurement error and the relationship between investment
and q. Journal of Political Economy 108, 1027-1057.
Fazzari, S., Hubbard, R., Petersen, B., 1988. Financing constraints and corporate investment.
Brookings Papers on Economic Activity, 141-195.
Fazzari, S., Hubbard, R., Petersen, B., 2000. Investment-cash flow sensitivities are useful: a
comment on Kaplan and Zingales. Quarterly Journal of Economics 115, 695-705.
Gao, X., Corporate cash hoarding: the role of Just-in-Time adoption. working paper.
Gatchev, V., Pulvino, T., Tarhan, V., 2010. The interdependent and intertemporal nature of
financial decisions: an application to cash flow sensitivities. Journal of Finance 65725-763.
Gomes, J., 2001. Financing investment. American Economic Review 91, 1263-1285.
Hadlock, C., Pierce, J., 2010. New evidence on measuring financial constraints: moving beyond
the KZ index. Review of Financial Studies 23, 1909-1940.
Hayashi, F., 1982. Tobin’smarginal q and average q: a neoclassical interpretation. Econometrica
50, 213-224.
Hubbard, R., 1998. Capital market imperfections and investment. Journal of Economic Literature 36, 193-227.
29
Kaplan, S., Zingales, L., 1997. Do investment-cash flow sensitivities provide useful measures of
financing constraints? Quarterly Journal of Economics 112, 169-215.
Kaplan, S., Zingales, L., 2000, Investment-cash flow sensitivities are not useful measures of
financial constraints. Quarterly Journal of Economics 115, 707-712.
Lemmon, M., Liu, X., Mao, Q., Nini, G.,Securitization and capital structure in nonfinancial
firms: an empirical investigation. Journa of Finance, forthcoming.
Minton, B., Schrand, C., 1999, The impact of cash flow volatility on discretionary investment
and the costs of debt and equity financing. Journal of Financial Economics 54, 423-460.
Moyen, N., 2004. Investment-cash flow sensitivities: constrained versus unconstrained firms.
Journal of Finance 59, 2061-2092.
Myers, S., Majluf, N., 1984. corporate financing and investment decisions when firms have
information that investors do not have. Journal of Financial Economics 13, 187-221.
Povel, P., and M. Raith, 2004, Optimal Debt with Unobservable Investments. Rand Journal of
Economics, 35, 599C616.
Riddick, L., Whited, T., 2009. The corporate propensity to save. Journal of Finance 64,
1729-1766.
Tobin, J, 1969. A general equilibrium approach to monetary theory. Journal of Money, Credit
and Banking 1, 15-29.
30
Table 1
Descriptive statistics of the key variables
This table presents the five-year panel mean, median and standard deviation of physical investment (Inv), market-to-book ratio (MB), cash flows (CF), and beginning-of-the-year tangible
capital share (TCS) in the full sample and two balanced panels. Inv and CF are the percentage
in total assets at the beginning of the year. MB is the ratio at the beginning of the year. NF is
the average number of the firms.
Period
mean
A. Full Sample
Inv (%)
med
std
mean
MB
med
std
mean
CF (%)
med
std
mean
TCS (%)
med
std
NF
1972-2011
6.32
4.72
5.73
1.60
1.22
1.18
6.27
9.18
15.57
81.91
94.45
24.46
1612.2
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
7.37
8.58
7.52
6.62
6.58
5.84
3.92
3.87
5.74
6.87
5.91
5.19
5.08
4.37
2.88
2.65
5.93
6.67
6.25
5.63
5.69
5.37
3.72
3.96
1.21
1.09
1.36
1.47
1.79
1.99
1.96
1.88
0.92
0.95
1.12
1.20
1.39
1.45
1.51
1.47
0.92
0.49
0.74
0.88
1.25
1.63
1.37
1.26
10.59
11.31
7.86
6.82
6.90
2.59
2.19
1.76
10.42
11.66
9.47
8.73
9.60
7.71
7.04
7.15
6.64
7.77
10.48
12.53
15.53
20.49
19.72
20.43
91.71
93.14
93.55
88.19
83.85
76.73
67.08
62.06
98.40
99.74
100.00
98.55
94.05
85.89
72.73
64.89
13.37
11.88
11.79
18.25
21.42
25.63
29.32
30.98
1611.8
1602.2
1615.4
1555.6
1677.0
1871.2
1593.2
1371.2
B. Old-economy firms
1972-2011
6.54
5.37
4.85
1.38
1.17
0.77
9.92
10.22
7.86
80.39
90.95
23.59
471.5
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
7.45
8.51
7.09
6.93
6.50
6.02
4.14
4.30
6.16
7.25
6.13
6.04
5.31
5.08
3.36
3.32
5.29
5.60
4.65
4.77
4.73
4.21
3.00
3.61
1.18
1.04
1.19
1.37
1.58
1.66
1.60
1.62
0.92
0.93
1.04
1.20
1.35
1.40
1.37
1.41
0.85
0.39
0.47
0.60
0.80
0.94
0.84
0.82
10.65
11.68
9.15
9.36
9.61
9.84
9.32
8.76
10.23
11.76
9.99
10.21
10.18
10.29
9.17
9.00
5.32
6.04
7.76
8.61
9.17
8.96
7.83
9.12
92.17
92.64
92.62
85.67
80.42
71.65
62.08
57.18
97.65
97.74
98.06
94.07
87.26
75.63
63.59
55.43
12.09
11.66
11.68
18.41
21.22
24.00
26.05
27.86
604.8
561.6
482.0
400.6
461.8
484.2
404.8
372.2
C. New-economy firms
1972-2011
5.78
3.82
6.12
2.17
1.62
1.61
-0.15
6.42
22.80
79.09
94.55
27.66
412.3
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
8.05
10.50
8.91
6.45
6.59
5.50
3.51
3.22
5.87
7.98
6.58
4.75
4.89
3.77
2.37
2.04
7.03
8.40
7.85
6.01
6.13
5.65
3.72
3.64
1.45
1.41
1.85
1.71
2.29
2.62
2.40
2.24
1.09
1.24
1.53
1.31
1.71
1.88
1.90
1.73
1.13
0.69
1.02
1.14
1.66
2.11
1.64
1.52
10.87
12.34
5.16
3.69
3.10
-5.29
-4.78
-5.56
11.37
13.00
8.28
7.37
8.44
2.82
2.42
3.21
8.57
9.81
14.52
16.14
21.14
27.24
24.79
25.94
90.91
93.31
94.14
90.17
86.50
80.11
67.19
62.31
99.24
100.00
100.00
100.00
100.00
95.27
73.86
65.67
14.87
12.48
11.49
17.35
21.01
26.23
31.14
32.75
147.6
183.8
316.8
421.6
482.6
630.6
609.6
505.4
31
Table 1 (cont’d)
Period
Inv (%)
mean
med
D. Balanced panel 1
std
mean
MB
med
std
mean
CF (%)
med
std
mean
TCS (%)
med
std
NF
1972-1991
8.27
7.29
4.85
1.34
1.08
0.81
11.71
12.04
6.56
92.79
98.19
11.68
210
1972-1976
1977-1981
1982-1986
1987-1991
8.26
9.41
7.91
7.51
7.24
8.32
7.09
6.69
5.10
5.26
4.33
4.43
1.51
1.13
1.24
1.46
1.03
0.98
1.09
1.23
1.27
0.46
0.49
0.68
12.18
12.91
10.83
10.92
11.83
12.88
11.47
11.59
5.39
5.27
7.03
7.92
91.69
93.11
93.54
88.16
98.40
99.74
100.00
98.55
13.37
11.88
11.79
18.25
210
210
210
210
E. Balanced panel 2
1992-2011
5.22
4.04
4.33
1.73
1.42
1.05
10.36
10.48
8.96
72.74
78.17
24.94
269
1992-1996
1997-2001
2002-2006
2007-2011
6.95
5.85
4.22
3.87
5.57
4.72
3.32
2.92
5.37
4.22
3.33
3.32
1.69
1.84
1.75
1.65
1.40
1.44
1.44
1.42
0.95
1.32
0.99
0.88
12.21
11.27
9.68
8.27
12.11
11.33
9.65
8.97
7.62
8.37
8.56
10.53
83.83
76.73
67.07
62.06
94.05
85.89
72.73
64.89
21.42
25.63
29.32
30.98
269
269
269
269
32
Table 2
Investment, sales and cash flow regressions: full sample
This table presents the five-year panel regressions of (A) investment on cash flow and its cross-product term with
tangible capital share, (B) log sales on log tangible capital and log intangible capital, and (C) cash flow on lagged
cash flow, for the full sample
Invit
ln Salesit
CFit
= a0 + a1 MBi,t−1 + a2 CFi,t + a3 CFi,t TCSi,t−1 + εit .
= b0 + b1 ln TCi,t−1 + b2 ln ICi,t−1 + ηit ,
= c0 + c1 CFi,t−1 + ξit
The regressions are estimated with fixed firm effects. The t-stat right to an estimate is the t-statistic clustering at
firm-year. NF is the average number of the firms. The R2 is the adjusted R2 for serially demeaned panel data.
A. Investment regressions
Period
1972-2011
MB
0.009
t-stat
20.99
CF
0.087
t-stat
27.15
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.009
0.008
0.016
0.011
0.012
0.008
0.006
0.006
7.83
4.16
9.05
8.14
12.54
12.48
11.66
8.16
0.260
0.276
0.168
0.122
0.089
0.049
0.044
0.040
16.71
18.40
15.62
14.95
13.45
10.22
10.34
8.10
1972-2011
0.009
19.61
-0.020
-3.57
0.134
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.009
0.009
0.017
0.010
0.012
0.007
0.006
0.006
7.58
4.31
9.13
7.04
11.14
10.78
11.40
8.48
-0.001
-0.050
-0.046
-0.019
0.001
0.017
-0.007
-0.031
-0.04
-0.83
-1.16
-0.80
0.06
1.87
-0.91
-3.67
0.283
0.345
0.227
0.166
0.105
0.043
0.068
0.105
33
CF*TCS
NF
1612.2
R2
0.11
1611.8
1602.2
1615.4
1555.6
1677.0
1871.2
1593.2
1371.2
0.12
0.12
0.12
0.09
0.11
0.09
0.07
0.07
16.33
1612.2
0.12
6.49
5.39
5.31
6.13
5.49
3.37
6.41
7.86
1611.8
1602.2
1615.4
1555.6
1677.0
1871.2
1593.2
1371.2
0.12
0.12
0.12
0.10
0.11
0.09
0.08
0.09
t-stat
Table 2 (cont’d)
B. Sales regressions: ln Sales
Period
ln TC
t-stat
ln IC
t-stat
NF
R2
1972-2011
0.569
47.56
0.019
4.72
902.3
0.45
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.626
0.717
0.584
0.630
0.560
0.564
0.548
0.457
30.08
28.68
18.73
20.93
25.04
24.99
26.25
18.78
0.000
-0.006
0.033
0.015
0.047
0.011
0.038
0.003
0.03
-0.60
3.15
1.64
5.75
1.37
4.55
0.32
892.6
733.6
609.2
692.8
853.8
1078.6
1227.4
1130.4
0.49
0.49
0.40
0.35
0.42
0.31
0.40
0.22
C. Cash flow autoregressions: CFit
CFi,t−1
t-stat
NF
R2
1972-2011
0.334
34.46
1612.2
0.15
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.492
0.544
0.450
0.354
0.393
0.250
0.306
0.337
29.02
31.13
25.36
15.83
18.29
13.15
16.26
17.20
1611.8
1602.2
1615.4
1555.6
1677
1871.2
1593.2
1371.2
0.27
0.30
0.21
0.14
0.17
0.10
0.13
0.16
Period
34
Table 3
Investment-cash flow sensitivity: other potential explanatory variables
This table presents the five-year panel regressions of investment on the market-to-book ratio (MB), cash flow
(CF), and the product term of CF with tangible capital share (TCS), cash holding (CH), working capital (WC),
firm size (SZ), R&D expenditure (RD), cash flow volatility (CV).
Invit
=
a0 + a1 MBi,t−1 + a2 CFit + a3 CFit TCSi,t−1 + a4 CFit CHit
+a5 CFit WCit + a6 CFit SZit + a7 CFit RDit + a8 CFit CVit + εit .
Panels A and B report the results without and with the term CFit TCSi,t−1 , respectively. The regression is
estimated with fixed firm effects. The t-stat right to an estimate is the t-statistic clustering at firm-year. NF is
the average number of the firms. The R2 is the adjusted R2 for serially demeaned panel data.
A. Investment regression without CFit TCSi,t−1
Period
1972-2011
MB
0.008
t-stat
19.86
CF
0.129
t-stat
12.98
CF*TCS
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.009
0.009
0.016
0.010
0.011
0.007
0.006
0.005
7.38
4.34
9.05
7.47
11.23
11.52
10.59
7.46
0.313
0.461
0.325
0.175
0.103
0.077
0.087
0.077
5.25
8.45
8.26
6.20
4.50
4.70
6.17
3.72
CF*WC
0.074
t-stat
5.25
CF*SZ
-0.004
t-stat
-2.60
CF*RD
-0.115
-0.024
-0.164
-0.154
-0.016
-0.031
0.020
0.098
0.093
-0.29
-2.17
-2.70
-0.38
-0.92
0.77
4.69
3.77
-0.005
-0.013
-0.008
-0.003
0.009
0.002
-0.007
-0.006
-0.64
-1.62
-1.54
-0.65
2.05
0.89
-3.11
-2.02
0.321
0.695
0.098
-0.111
-0.117
-0.122
-0.048
-0.036
35
t-stat
CF*CH
-0.052
t-stat
-4.56
0.024
-0.206
-0.219
-0.063
-0.054
-0.041
-0.040
-0.015
0.23
-2.34
-3.32
-1.44
-1.84
-1.99
-2.91
-0.82
t-stat
-6.61
CF*CV
-0.054
t-stat
-6.10
NF
1588.7
R2
0.13
1.09
2.62
0.59
-1.34
-2.60
-4.37
-2.03
-1.30
-0.636
-1.222
-0.532
-0.128
-0.093
-0.035
-0.016
-0.023
-3.23
-5.65
-4.50
-1.57
-3.01
-3.68
-1.94
-2.17
1608.6
1589.2
1599.0
1527.2
1639.6
1841.0
1566.6
1338.0
0.12
0.13
0.13
0.09
0.11
0.10
0.09
0.08
Table 3 (cont’d)
B. Investment regression with CFit TCSi,t−1
Period
1972-2011
MB
0.008
t-stat
18.40
CF
0.013
t-stat
1.14
CF*TCS
0.137
t-stat
15.95
CF*CH
-0.078
t-stat
-5.98
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.009
0.010
0.017
0.010
0.011
0.007
0.006
0.005
7.25
4.35
9.31
6.47
9.89
9.71
10.29
7.47
0.018
0.051
0.081
0.056
0.020
0.008
0.037
-0.017
0.25
0.71
1.39
1.57
0.72
0.43
2.28
-0.79
0.302
0.415
0.224
0.174
0.122
0.073
0.069
0.112
6.31
7.01
4.68
6.02
6.00
5.01
6.18
7.96
-0.037
-0.259
-0.266
-0.097
-0.067
-0.046
-0.055
-0.048
-0.35
-2.79
-3.80
-1.94
-1.89
-1.84
-3.49
-2.22
CF*WC
0.046
t-stat
2.95
CF*SZ
0.000
t-stat
-0.10
CF*RD
-0.129
t-stat
-6.58
CF*CV
-0.041
t-stat
-4.43
NF
1402.0
R2
0.14
-0.057
-0.224
-0.146
-0.083
-0.060
0.028
0.085
0.073
-0.70
-2.81
-2.26
-1.82
-1.58
0.94
3.88
2.64
0.003
-0.005
-0.002
-0.004
0.007
0.006
-0.006
0.000
0.38
-0.58
-0.31
-0.65
1.57
1.85
-2.43
0.03
0.430
0.783
0.158
-0.156
-0.145
-0.144
-0.058
-0.042
1.44
2.85
0.82
-1.69
-2.83
-4.20
-2.39
-1.36
-0.616
-1.044
-0.446
-0.145
-0.104
-0.023
-0.013
-0.016
-3.02
-4.59
-3.32
-1.74
-2.88
-2.17
-1.43
-1.42
1528.6
1419.8
1347.8
1265.2
1309.4
1524.6
1506.2
1314.2
0.13
0.14
0.13
0.10
0.12
0.11
0.10
0.11
36
Table 4
Investment-cash flow sensitivity: old-economy and new-economy firms
This table presents the five-year panel regressions of investment on cash flow and its cross-product term with
tangible capital for old-economy and new-economy firms,
Invit
= b0 + b1 MBi,t−1 + b2 CFi,t + b3 CFi,t TCSi,t−1 + εit .
The regression is estimated with fixed firm effects. The t-stat right to an estimate is the t-stat clustering at
firm-year. NF is the average number of the firms. The R2 is the adjusted R2 for serially demeaned panel data.
Period
MB
A. Old-economy firms
t-stat
CF
t-stat
CF*TCS
t-stat
NF
R2
1972-2011
0.008
6.23
0.168
17.59
471.5
0.19
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.011
0.009
0.004
0.007
0.012
0.007
0.006
0.006
4.94
2.41
1.48
2.19
4.50
3.24
3.02
3.35
0.290
0.335
0.221
0.166
0.159
0.139
0.083
0.102
9.53
12.26
11.96
7.22
8.44
7.80
5.60
7.37
604.8
561.6
482.0
400.6
461.8
484.2
404.8
372.2
0.15
0.16
0.13
0.11
0.16
0.15
0.09
0.13
1972-2011
0.008
5.62
-0.071
-5.00
0.290
14.51
471.5
0.21
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.011
0.008
0.005
0.005
0.010
0.007
0.006
0.005
4.67
1.94
1.47
1.56
3.49
3.05
3.02
3.48
0.088
0.066
0.009
-0.029
-0.018
-0.068
-0.087
-0.095
1.30
0.80
0.09
-0.59
-0.64
-1.88
-4.05
-5.46
0.212
0.310
0.224
0.220
0.217
0.259
0.221
0.307
2.90
3.70
2.17
3.78
5.08
6.36
6.76
10.85
604.8
561.6
482.0
400.6
461.8
484.2
404.8
372.2
0.15
0.17
0.13
0.11
0.16
0.19
0.13
0.22
B. New-economy firms
1972-2011
0.0081
15.53
0.0473
12.13
412.3
0.11
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.0070
0.0131
0.0196
0.0104
0.0098
0.0066
0.0063
0.0062
2.73
2.70
6.75
4.94
8.00
9.02
9.77
6.86
0.2080
0.1918
0.1224
0.0958
0.0571
0.0205
0.0247
0.0260
5.78
6.62
6.98
7.45
6.58
3.57
4.94
4.50
147.6
183.8
316.8
421.6
482.6
630.6
609.6
505.4
0.13
0.09
0.13
0.08
0.09
0.07
0.07
0.07
1972-2011
0.008
14.70
0.006
1.05
0.053
5.69
412.3
0.12
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.008
0.012
0.021
0.010
0.010
0.006
0.006
0.006
2.98
2.41
7.52
4.12
7.22
7.88
9.50
6.84
0.036
-0.070
-0.002
-0.032
0.006
0.037
0.009
-0.003
0.32
-0.47
-0.04
-1.04
0.26
3.60
1.09
-0.33
0.190
0.283
0.133
0.153
0.067
-0.020
0.021
0.043
1.54
1.79
2.06
4.25
2.46
-1.45
1.77
2.84
147.6
183.8
316.8
421.6
482.6
630.6
609.6
505.4
0.14
0.08
0.14
0.08
0.10
0.06
0.07
0.07
37
Table 5
Tangible capital productivity: old-economy and new-economy firms
This table presents the five-year panel regressions of log sales on log tangible capital and log intangible capital
for old-economy and new-economy firms,
ln Salesit = b0 + b1 ln TCi,t−1 + b2 ln ICi,t−1 + ηit .
The regressions are estimated with fixed firm effects. The t-stat right to an estimate is the t-statistic clustering at
firm-year. NF is the average number of the firms. The R2 is the adjusted R2 for serially demeaned panel data.
Period
ln TC
A. Old-economy firms
t-stat
ln IC
t-stat
NF
R2
1972-2011
0.680
41.00
0.009
1.47
309.4
0.58
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.725
0.816
0.679
0.730
0.592
0.644
0.707
0.585
23.70
25.70
15.93
19.10
14.60
19.57
25.88
13.56
-0.013
-0.003
-0.004
0.012
0.051
-0.020
0.022
0.012
-1.07
-0.26
-0.23
0.93
3.95
-1.88
1.65
0.78
353.8
302.2
222.8
229.0
300.8
345.8
366.2
354.2
0.59
0.64
0.49
0.47
0.46
0.39
0.59
0.33
1972-2011
0.476
21.42
0.033
3.76
211.6
0.35
1972-1976
1977-1981
1982-1986
1987-1991
1992-1996
1997-2001
2002-2006
2007-2011
0.561
0.547
0.401
0.517
0.516
0.528
0.467
0.396
10.66
8.27
5.94
7.03
10.32
13.55
14.14
9.81
-0.007
-0.008
0.026
0.019
0.051
0.039
0.057
0.025
-0.26
-0.30
1.21
0.90
2.42
2.16
3.75
1.85
74.2
72.8
98.4
158.6
187.4
286.2
429.4
385.6
0.46
0.43
0.21
0.24
0.35
0.25
0.31
0.19
B. New-economy firms
38
Table 6
Tangible capital productivity: individual firms
This table presents the results from the ten-year regressions of log sale on log tangible capital and log intangible
capital for individual firms,
ln Salesit = b0i + b1i ln TCi,t−1 + b2i ln ICi,t−1 + ηit .
Table A shows the mean and median of the estimated tangible capital productivity b1i s, intangible capital productivity b2i s, and the R2 s of the individual regressions, for overall sample and three categories classified by b1i :
high (top 30%), medium (middle 40%) and low (bottom 30%). Panels B and C show the investment regressions
and cash flow autoregressions estimated for panels of high, medium, and low tangible capital productivity firms.
The t-stat right to an estimate is the t-value that clusters at firm-year. NF is the average number of the firms.
The R2 in Panels B and C are the adjusted R2 for serially demeaned panel data.
A. mean and median of coefficients and R2 s
b1i
R2
b2i
NF
TC productivity
Overall
Period
1972-2011
mean
0.37
median
0.40
mean
0.01
median
0.06
mean
0.57
median
0.71
871.3
High
High
High
High
1972-1981
1982-1991
1992-2001
2002-2011
1.59
1.31
1.25
1.20
1.31
1.08
1.04
0.99
-0.05
-0.06
-0.15
-0.17
-0.04
-0.04
-0.06
0.00
0.74
0.70
0.71
0.61
0.85
0.83
0.81
0.72
248
174
264
357
Medium
Medium
Medium
Medium
1972-1981
1982-1991
1992-2001
2002-2011
0.70
0.44
0.34
0.21
0.71
0.45
0.33
0.21
-0.10
0.08
0.02
0.17
0.03
0.05
0.08
0.12
0.76
0.62
0.54
0.44
0.86
0.78
0.67
0.52
333
234
354
478
Low
Low
Low
Low
1972-1981
1982-1991
1992-2001
2002-2011
-0.19
-0.47
-0.60
-1.02
0.02
-0.23
-0.34
-0.69
-0.60
0.10
0.18
0.44
-0.03
0.13
0.18
0.26
0.49
0.38
0.42
0.51
0.66
0.45
0.55
0.62
248
174
264
357
39
Table 6 (cont’d)
B. Investment regressions: Invit
TC productivity
High
High
High
High
Period
1972-1981
1982-1991
1992-2001
2002-2011
MB
0.006
0.011
0.003
0.005
t-stat
2.59
3.51
2.06
4.53
CF
-0.164
-0.137
-0.029
-0.035
t-stat
-2.19
-2.07
-1.16
-2.26
CF*TCS
0.477
0.337
0.174
0.132
t-stat
5.86
4.84
4.78
4.19
NF
248
174
264
357
R2
0.17
0.14
0.12
0.09
Medium
Medium
Medium
Medium
1972-1981
1982-1991
1992-2001
2002-2011
0.003
0.007
0.010
0.007
1.26
2.10
4.88
7.76
0.024
-0.054
-0.016
-0.037
0.26
-1.12
-0.58
-3.72
0.382
0.337
0.158
0.141
3.76
5.24
4.17
7.89
333
234
354
478
0.18
0.16
0.13
0.12
Low
Low
Low
Low
1972-1981
1982-1991
1992-2001
2002-2011
0.008
0.017
0.010
0.006
2.75
3.40
3.75
4.33
0.167
-0.065
-0.001
-0.055
2.29
-0.89
-0.03
-2.85
0.054
0.233
0.093
0.143
0.59
2.75
2.24
5.62
248
174
264
357
0.12
0.16
0.09
0.09
CFi,t−1
t-stat
NF
R2
C. Cash flow autoregressions: CFit
TC productivity
Period
High
High
High
High
1972-1981
1982-1991
1992-2001
2002-2011
0.606
0.464
0.302
0.423
20.58
9.64
3.40
13.82
248
174
264
357
0.38
0.23
0.12
0.23
Medium
Medium
Medium
Medium
1972-1981
1982-1991
1992-2001
2002-2011
0.585
0.461
0.381
0.305
16.78
12.74
7.30
11.96
333
234
354
478
0.36
0.24
0.16
0.14
Low
Low
Low
Low
1972-1981
1982-1991
1992-2001
2002-2011
0.475
0.330
0.403
0.281
13.27
5.57
11.23
9.65
248
174
264
357
0.25
0.10
0.20
0.15
40
Table 7
Investment-cash flow sensitivity: balanced panel firms
This table presents the five-year panel regressions of investment on cash flow, and its cross-product term with
tangible capital for two balanced panels of firms,
Invit
= b0 + b1 MBi,t−1 + b2 CFi,t + b3 CFi,t TCSi,t−1 + εit .
The regression is estimated with fixed firm effects. The t-stat right to an estimate is the t-stat clustering at
firm-year. The R2 is the adjusted R2 for serially demeaned panel data. NF is the average number of the firms.
A. Balanced panel 1
Period
MB
1972-1991
0.006
t-stat
3.57
CF
0.225
t-stat
8.80
CF*TCS
t-stat
NF
210
R2
0.15
210
210
210
210
0.19
0.11
0.12
0.11
1972-1976
1977-1981
1982-1986
1987-1991
0.004
0.005
0.003
0.012
1.89
1.15
0.93
3.28
0.375
0.305
0.211
0.145
7.07
6.58
7.09
5.00
1972-1991
0.007
3.25
-0.075
-1.19
0.327
5.13
210
0.16
1972-1976
1977-1981
1982-1986
1987-1991
0.003
0.007
0.005
0.011
1.42
1.62
1.33
2.66
0.019
-0.165
-0.074
-0.111
0.21
-1.19
-0.60
-1.11
0.403
0.487
0.277
0.279
3.48
3.57
2.38
2.76
210
210
210
210
0.21
0.13
0.11
0.13
1992-2011
0.005
3.80
0.094
7.00
269
0.19
1992-1996
1997-2001
2002-2006
2007-2011
0.010
0.002
0.004
0.005
3.85
1.03
1.66
2.86
0.177
0.111
0.074
0.057
6.53
5.57
3.17
2.80
269
269
269
269
0.13
0.11
0.06
0.09
1992-2011
0.005
3.78
-0.045
-1.69
0.180
5.44
269
0.21
1992-1996
1997-2001
2002-2006
2007-2011
0.011
0.001
0.004
0.006
3.58
0.64
1.82
3.22
0.088
0.026
-0.021
-0.076
1.31
0.36
-0.53
-2.68
0.111
0.116
0.122
0.191
1.51
1.37
2.53
4.82
269
269
269
269
0.14
0.11
0.07
0.12
B. Balanced panel 2
41
Table 8
Tangible capital productivity: balanced panel firms
This table presents the results from the ten-year regressions of log sale on log tangible capital and log intangible
capital for balanced panel firms,
ln Salesit = b0i + b1i ln TCi,t−1 + b2i ln ICi,t−1 + ηit .
The period 1972-1991 is for Panel 1 and the period 1992-2011 is for Panel 2. Table A shows the mean and
median of the estimated tangible capital productivity b1i s, intangible capital productivity b2i s, and the R2 s of the
individual regressions. Panels B and C show the investment regressions and cash flow autoregressions estimated
for panels of high, medium, and low tangible capital productivity firms. The t-stat right to an estimate is the
t-stat clustering at firm-year. NF is the average number of the firms. The R2 in Panels B and C are the adjusted
R2 for serially demeaned panel data.
A. mean and median of coefficients and R2 s
b1i
R2
b2i
NF
TC productivity
High
High
Period
1972-1981
1982-1991
mean
1.54
1.23
median
1.33
1.04
mean
0.12
-0.05
median
-0.03
-0.04
mean
0.80
0.78
median
0.88
0.88
34
34
High
High
1992-2001
2002-2011
1.22
1.01
1.02
0.83
-0.10
-0.05
-0.05
-0.01
0.72
0.59
0.81
0.67
51
71
Medium
Medium
1972-1981
1982-1991
0.86
0.55
0.85
0.57
0.04
0.01
0.01
0.03
0.88
0.75
0.91
0.86
48
47
Medium
Medium
1992-2001
2002-2011
0.43
0.16
0.42
0.19
0.03
0.24
0.04
0.15
0.63
0.49
0.73
0.58
70
97
Low
Low
1972-1981
1982-1991
-0.01
-0.22
0.20
0.01
-0.44
0.18
-0.04
0.16
0.70
0.53
0.78
0.70
34
34
Low
Low
1992-2001
2002-2011
-0.25
-0.94
-0.11
-0.59
0.09
0.36
0.15
0.28
0.48
0.44
0.62
0.50
51
71
42
Table 8 (cont’d)
B. Investment regressions: Invit
TC productivity
High
High
Period
1972-1981
1982-1991
MB
0.009
-0.001
t-stat
2.32
-0.24
CF
-0.133
-0.040
t-stat
-0.96
-0.38
CF*TCS
0.403
0.307
t-stat
2.43
2.74
NF
34
34
R2
0.19
0.13
High
High
1992-2001
2002-2011
-0.001
0.006
-0.60
2.21
-0.088
-0.049
-1.32
-1.11
0.351
0.161
4.06
2.21
51
71
0.21
0.10
Medium
Medium
1972-1981
1982-1991
0.004
0.007
0.97
0.92
-0.150
-0.100
-0.59
-0.60
0.549
0.388
1.79
2.19
48
47
0.17
0.19
Medium
Medium
1992-2001
2002-2011
0.000
0.004
0.07
1.89
0.181
-0.061
1.08
-2.08
0.106
0.193
0.56
3.37
70
97
0.14
0.14
Low
Low
1972-1981
1982-1991
-0.001
0.007
-0.10
0.95
0.103
-0.059
0.78
-0.24
0.207
0.169
1.44
0.62
34
34
0.16
0.07
Low
Low
1992-2001
2002-2011
0.004
0.004
0.92
1.31
0.054
-0.103
0.74
-1.73
0.086
0.191
0.78
3.16
51
71
0.08
0.08
CFi,t−1
t-stat
NF
R2
C. Cash flow autoregressions: CFit
TC productivity
Period
High
High
1972-1981
1982-1991
0.795
0.659
15.62
8.21
34
34
0.62
0.37
High
High
1992-2001
2002-2011
0.431
0.546
3.79
8.51
51
71
0.21
0.31
Medium
Medium
1972-1981
1982-1991
0.779
0.607
20.50
8.66
48
47
0.63
0.34
Medium
Medium
1992-2001
2002-2011
0.506
0.312
10.08
3.23
70
97
0.30
0.12
Low
Low
1972-1981
1982-1991
0.525
0.183
6.82
0.99
34
34
0.29
0.04
Low
Low
1992-2001
2002-2011
0.186
0.409
1.44
5.37
51
71
0.06
0.24
43
Table 9
Investment-cash flow sensitivity for constrained and unconstrained firms
This table presents the five-year panel regressions of investment on cash flow, and the product term between cash
flow and tangible capital for constrained in and unconstrained firms.
Invit
= b0 + b1 MBi,t−1 + b2 CFi,t + b3 CFi,t TCi,t−1 + εit .
Firms are classified as the constrained and unconstrained by the Whited-Wu index in Panel A, firm size in Panel
B, dividend in Panel C and bond rating in Panel D. The regression is estimated with fixed firm effects. The t-stat
right to an estimate is the t-stat clustering at firm-year. NF is the average number of the firms. The R2 is the
adjusted R2 for serially demeaned panel data.
Period
MB
t-stat
CF
t-stat
CF*TCS
t-stat
NF
R2
A. Constraint by Whited-Wu index
Unconstrained firms
1972-1976
0.007
1977-1981
0.007
1982-1986
0.012
1987-1991
0.007
1992-1996
0.009
1997-2001
0.006
2002-2006
0.005
2007-2011
0.005
4.61
2.91
5.60
3.94
5.30
5.50
5.42
4.94
0.037
0.030
-0.038
-0.017
-0.032
-0.010
-0.025
-0.067
0.80
0.41
-0.72
-0.51
-1.05
-0.48
-2.01
-5.70
0.319
0.364
0.314
0.235
0.231
0.180
0.152
0.225
6.35
4.77
5.61
6.46
6.47
6.42
7.98
11.19
1029.0
1090.4
1007.6
944.8
995.4
1018.8
918.8
792.6
0.15
0.16
0.17
0.13
0.16
0.17
0.14
0.16
Constrained firms
1972-1976
0.012
1977-1981
0.011
1982-1986
0.020
1987-1991
0.011
1992-1996
0.011
1997-2001
0.006
2002-2006
0.006
2007-2011
0.005
5.56
2.54
6.65
4.85
7.51
7.92
9.17
5.57
0.005
-0.063
-0.000
-0.002
0.030
0.031
0.016
0.004
0.07
-0.65
-0.01
-0.05
1.74
3.41
1.67
0.32
0.190
0.274
0.133
0.113
0.038
-0.006
0.011
0.030
2.40
2.64
2.32
3.04
1.74
-0.45
0.87
1.94
582.8
510.2
604.2
609.4
679.0
848.0
670.4
573.2
0.10
0.08
0.10
0.08
0.09
0.05
0.06
0.05
Unconstrained firms
1972-1976
0.009
1977-1981
0.007
1982-1986
0.014
1987-1991
0.006
1992-1996
0.012
1997-2001
0.007
2002-2006
0.006
2007-2011
0.006
6.02
2.70
6.11
3.66
8.06
6.81
7.59
6.39
-0.012
-0.014
-0.104
-0.025
-0.016
0.001
-0.035
-0.046
-0.27
-0.22
-1.84
-0.91
-0.76
0.09
-3.77
-5.02
0.326
0.363
0.321
0.215
0.160
0.133
0.130
0.179
6.68
5.46
5.41
6.90
5.89
6.37
8.39
10.57
1052.6
1121.0
1061.8
1017.4
1122.4
1158.8
1031.8
890.4
0.15
0.15
0.15
0.12
0.14
0.15
0.10
0.14
Constrained firms
1972-1976
0.008
1977-1981
0.015
1982-1986
0.020
1987-1991
0.013
1992-1996
0.011
1997-2001
0.007
2002-2006
0.006
2007-2011
0.005
4.54
3.45
6.69
5.75
7.13
7.28
8.20
4.59
0.036
-0.046
0.012
-0.009
0.025
0.025
0.026
-0.008
0.52
-0.45
0.24
-0.21
1.04
2.04
2.13
-0.57
44
0.202
0.290
0.133
0.127
0.053
-0.003
0.007
0.041
2.60
2.63
2.35
2.79
1.85
-0.21
0.46
2.17
559.2
481.2
553.6
538.2
554.6
712.4
561.4
480.8
0.10
0.10
0.10
0.08
0.09
0.05
0.07
0.04
B. Constraint by size
Table 9 (cont’d)
t-stat
CF
t-stat
CF*TCS
t-stat
NF
R2
Unconstrained firms
1972-1976
0.008
1977-1981
0.007
1982-1986
0.013
1987-1991
0.008
1992-1996
0.012
1997-2001
0.008
2002-2006
0.005
2007-2011
0.007
5.75
3.05
5.12
3.91
5.26
6.78
3.71
4.90
-0.027
-0.007
-0.047
-0.032
-0.054
-0.021
-0.018
-0.108
-0.63
-0.09
-0.86
-0.84
-1.95
-1.05
-0.91
-5.50
0.342
0.331
0.279
0.219
0.193
0.091
0.119
0.198
7.30
4.45
4.90
4.81
5.11
3.51
4.76
6.40
1202.8
1249.8
1042.4
806.8
780.8
733.0
577.2
502.8
0.13
0.13
0.13
0.11
0.12
0.10
0.10
0.12
Constrained firms
1972-1976
0.011
1977-1981
0.012
1982-1986
0.020
1987-1991
0.011
1992-1996
0.011
1997-2001
0.007
2002-2006
0.007
2007-2011
0.007
4.79
2.48
7.44
5.33
9.32
8.76
10.97
8.30
0.069
-0.109
-0.024
-0.007
0.024
0.035
0.005
0.001
0.82
-0.92
-0.44
-0.22
1.30
3.46
0.57
0.06
0.153
0.363
0.174
0.138
0.070
0.018
0.043
0.057
1.65
2.88
2.91
4.09
3.15
1.25
3.86
4.75
409.0
352.4
573.0
748.8
896.2
1138.2
1016.0
868.4
0.12
0.11
0.12
0.09
0.11
0.08
0.08
0.08
Unconstrained firms
1987-1991
0.007
1992-1996
0.006
1997-2001
0.002
2002-2006
0.003
2007-2011
0.003
2.09
2.31
1.70
1.93
2.13
0.065
0.020
0.010
-0.073
-0.091
1.29
0.38
0.35
-4.26
-5.31
0.143
0.146
0.117
0.222
0.311
2.37
2.31
2.74
7.30
10.44
327.4
317.4
374.0
393.4
361.8
0.13
0.12
0.10
0.13
0.21
Constrained firms
1987-1991
0.010
1992-1996
0.012
1997-2001
0.008
2002-2006
0.007
2007-2011
0.006
6.59
10.69
10.82
11.23
7.81
-0.030
0.003
0.021
0.007
-0.012
-1.17
0.18
2.25
0.82
-1.38
0.170
0.097
0.032
0.039
0.066
5.75
4.98
2.37
3.60
4.79
1228.2
1359.6
1497.2
1199.8
1009.4
0.10
0.11
0.09
0.08
0.07
Period
MB
C. Constraint by dividend
D. Constraint by bond rating
45
Number of high tech firms
1000
500
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2005
2010
Number of NYSE/AMEX/NASDAQ firms
1000
500
0
1970
1975
1980
1985
1990
NYSE
1995
2000
AMEX
NASDAQ
Figure 1. The number of high-tech firms and NYSE/AMEX/NASDAQ listed firms
This figure shows the numbers of high-tech manufacturing firms (top panel) and manufacturing
firms listed on NYSE, AMEX and NASDAQ (bottom panel).
46