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INVESTMENT SMOOTHING WITH WORKING
CAPITAL: NEW EVIDENCE ON THE
IMPACT OF FINANCIAL CONSTRAINTS
Steven Fazzari and Bruce Petersen
Working Paper Series
Macro Economic Issues
Research Department
Federal Reserve Bank of Chicago
December, 1990 (WP-90-18)
INVESTMENT SMOOTHING WITH WORKING CAPITAL:
NEW EVIDENCE ON THE IMPACT OF FINANCIAL CONSTRAINTS
Steven Fazzari
Campus Box 1208
Washington University
One Brookings Drive
St. Louis, Missouri 63130
Bruce Petersen
Washington University and
Federal Reserve Bank of Chicago
230 S. LaSalle Street
Chicago, Illinois 60690
June 4,1990
(First Draft, March 1990)
We thank Ben Bernanke, Robert Chirinko, Charles Himmelberg, Glenn Hubbard, Anil Kashyap, and John
Keating for helpful comments on earlier versions of this paper.
Abstract:
This paper links research on both capital market imperfections and smoothing behavior to the study of
investment. Our approach emphasizes two basic aspects of firms’ production technology: (1) costs of adjusting
the fixed capital stock, and (2) the importance of working capital as a reversible input in the production process.
We argue that while financing constraints will likely prevent firms from equating the marginal returns on
investment over time, they should not prevent the firm from equating the marginal returns on different
investment inputs, net of adjustment costs, at each point in time. In particular, high adjustment costs for fixed
capital should cause financially constrained firms to smooth fixed capital investment relative to cash flow shocks
by optimally adjusting working capital.
Incorporating working capital changes in econometric investment equations, as motivated by our
smoothing model, leads to a substantially larger impact of internal finance on fixed investment than found in the
recent literature. Furthermore, the positive relationship between cash flow and investment found in a number
of recent studies might reflect shifts of investment demand rather than finance constraints. The results we find
for working capital and cash flow together, however, are inconsistent with the view that internal finance
variables proxy investment demand fluctuations. Rather, the results here provide strong evidence that finance
constraints are a key determinant of investment for a large fraction of U.S. manufacturing firms.
1
I. Introduction
Recent theoretical developments, particularly those that analyze the effects of asymmetric information,
explain why the level of internal finance may be a binding constraint on the level of investment for some firms.1
A number of empirical studies support this proposition, a finding with potentially important implications for
industrial organization and public finance, as well as macroeconomics. Empirical research has proceeded along
two lines: (1) reduced-form regressions of investment on cash flow,2 and (2) tests of financial constraints using
Euler equation methods.3 Both approaches have important limitations. The reduced-form approach has the
potential shortcoming that cash flow may proxy investment demand rather than the effects of binding financial
constraint on investment, even after including variables to control for demand effects, such as Tobin’s q.4 A
limitation of the Euler equation hypothesis tests is that they do not indicate the quantitative economic
importance of internal finance constraints. Furthermore, rejections of the Euler condition could arise because
the production technology or adjustment costs are misspecified rather than the existence of binding finance
constraints.5
This paper addresses these problems in the existing literature by emphasizing two basic aspects of
firms’ production technology: (1) costs of adjusting the capital stock, and (2) the importance of working capital
as a reversible input in the production process. Fixed capital adjustment costs are a central feature of standard
investment models. They give firms an incentive to smooth fixed capital investment for reasons similar to those
developed in the consumption smoothing and production smoothing literature. The importance of working
1 Seminal papers include Stiglitz and Weiss (1981) and Myers and Majluf (1984). For extensive references to recent research see the
survey by Gertler (1988).
2 See Meyer and Kuh (1957) and Eisner (1978) for early contributions. Recent papers include Fazzari and Athey (1987), Fazzari,
Hubbard and Petersen (1988, hereafter denoted by FHP), Gertler and Hubbard (1988), Hoshi, Kashyap and Scharfstein (1988), Devereux
and Schianterelli (1989), and Oliner and Rudebusch (1989).
3 See Whited (1988), Gilchrist (1989), Hubbard and Kashyap (1990), and Himmelberg (1990).
* Poterba (1988, p. 201) provides an excellent summary of this point. "If... measured Q provides an error-ridden indicator of firms’ true
prospects, then econometric results may find that current cash flow affects investment only because this variable, just like measured Q, is
correlated with the ’true’ marginal Q variable that firms consider in making investment decisions."
5 See, for example, the discussion of this point in Gilchrist (1989).
2
capital in production has been recognized by economists since Adam Smith, and it has been explicit in
accounting for at least four centuries.6 While it is often ignored by modern economists, the value of working
capital is quantitatively of the same order of magnitude as fixed capital.7
Our basic argument is as follows. In theory, the existence of financing constraints will likely prevent
firms from equating the marginal returns on investment over time because of fluctuations in their internal
finance or internal net worth. (This idea motivates research that tests whether the Euler condition arising from
firms’ dynamic optimization problem in perfect capital markets is satisfied.) Financial constraints, however,
should not prevent firms from equating expected marginal returns on different investment inputs, net of
adjustment costs, at each point in time. Therefore, high adjustment costs for fixed capital may cause financially
constrained firms to smooth fixed capital investment through disproportionately large changes in working
capital when cash flow fluctuates. Furthermore, while we argue that working capital is a readily reversible
investment, the marginal opportunity cost of sacrificing working capital to smooth fixed investment will depend
on the firm’s initial stock of working capital, an important component in measuring the strength of a firm’s
balance sheet.8
These ideas lead to several empirical implications for the link between investment and internal finance.
First, because working capital investment is substantial relative to fixed capital investment, one would not expect
the reduced-form impact of cash flow on fixed investment to exceed one less the share of working capital
investment, even for firms that have no access whatsoever to external finance. Also, the estimated coefficients
on cash flow in most recent reduced-form investment studies capture only the average "short-run” impact of
cash flow on fixed investment because firms can smooth fixed investment relative to short-run fluctuations in
6 Dewing (1941, p. 707) points out that the balance sheet prepared in 1571 by the Society of Mines Royal clearly divided capital into
"current" and fixed components.
In manufacturing, for example, the Statistics of Income show that the stock of working capital is more than half as large as the stock of
fixed capital.
O
° This kind of result is related to "internal net worth" arguments emphasized by Gertler and Hubbard (1988), Bemanke and Gertler
(1989), Calomiris and Hubbard (1990), and Hubbard and Kashyap (1990).
3
cash flow.9 Indeed, if financially constrained firms engage in fixed investment smoothing, they may come
sufficiently close to equating intertemporal marginal returns to prevent an Euler equation test from detecting
the presence of finance constraints. To measure the full "long-run” effect of internal finance, one must account
explicitly for investment smoothing.
Perhaps most importantly, our approach addresses the central criticism of decades of evidence on the
reduced-form link between cash flow and investment. Because changes in cash flow correlate highly with
changes in profits, cash flow effects on investment have been interpreted as proxies for profit-driven shifts in
investment demand rather than evidence of financial constraints. Our approach helps resolve this identification
problem. The "profit signal" interpretation would predict that changes in working capital, another potential
signal of future profits in reduced-form investment equations, will have a positive effect on investment and
reduce the strength of the cash flow effect. Our analysis of fixed investment smoothing with working capital
generates just the opposite predictions. Because reductions in working capital, for example, are a source of
funds to maintain smooth fixed investment when cash flow falls changes in working capital should have a
negative coefficient when included as an endogenous variable in a fixed investment regression. Therefore, the
impact of changes in working capital in the investment equation clearly distinguishes between competing
explanations for the observed internal finance - investment link.
We test our predictions with firm panel data. Most of our tests focus on a group of zero-dividend
firms. These firms are especially likely to face binding financing constraints, as indicated by FHP (1988) and
Gilchrist’s (1990) rejection of the Euler condition for zero-dividend firms. It is important to note that these
firms constitute a significant portion of the economy; even among publicly-traded companies listed by
Compustat, zero-dividend firms account for more than half of the sample in recent years.
Our empirical findings strongly support the joint hypotheses of financial constraints and fixed
investment smoothing with working capital. We present tests of investment smoothing analogous to the
production smoothing tests used by Blinder (1986). The evidence on the variability of working capital changes,
internal finance, and fixed investment are consistent with our predictions. We present investment regressions
9 Blinder (1988) emphasizes the importance of liquidity for the investment of financially constrained firms. This concern in the empirical
literature goes back at least to Meyer and Kuh (1957).
4
with and without changes in working capital, entered as an endogenous variable using instruments motivated by
the economic structure of our analysis. The regression coefficient for working capital is negative and highly
significant. Furthermore, our results indicate that previous studies substantially underestimated the economic
impact of finance constraints on fixed investment because they did not account for the dual role of changes in
working capital as a claim on scarce internal finance and a reversible asset that can be used to smooth fixed
investment.
The next section of the paper reviews firms’ incentives to smooth fixed investment and the impediments
to smoothing arising from capital market imperfections. Section III motivates our empirical tests by
considering the optimal investment problem of a firm that employs two kinds of capital with different
adjustment costs and faces a binding finance constraint. We describe the data sample in section IV and present
the empirical results in section V. The concluding section mentions some of the implications of our results for
industrial organization and macroeconomics.
II. INVESTMENT SMOOTHING AND CAPITAL MARKETS
II.A. Related Literature
Standard neoclassical theories (Holt, Modigliani, Muth, and Simon, 1960, for example) predict that
firms will seek to smooth production relative to sales if cost functions are sufficiently concave. As noted by
Blinder (1986), however, the empirical evidence for production smoothing is mixed.10 Consumption smoothing
is a central theme of the life cycle and permanent income models. If utility functions are time separable and
marginal utility declines with rising consumption, consumers will choose, if possible, to smooth consumption
relative to fluctuations of income. If capital markets allow consumers to freely borrow against their lifetime
wealth, generated by human as well as tangible capital, consumers face no impediments to achieving an
optimally smooth consumption profile.
10 The literature is too large to allow detailed citations here. See Ramey (1989) for a recent list of references.
5
There is an extensive empirical literature that tests the implications of the life cycle / permanent
income theories of consumption along with their implications about consumption smoothing. Recently, a key
question addressed in this research is whether capital market problems limit the extent of consumption
smoothing. The evidence summarized in Hayashi’s (1987) survey suggests that a significant fraction of
consumers are liquidity constrained. Furthermore, as Zeldes (1989) argues, even consumers that face
borrowing constraints can use changes in liquid asset stocks to smooth consumption. This point is related to the
tests of investment smoothing by financially-constrained firms pursued here.
II.B. Incentives for Investment Smoothing
The incentives for firms to smooth investment are similar to the motivation behind production and
consumption smoothing. Since the seminal work of Eisner and Strotz (1963) and Lucas (1967), the most
common explanation for investment smoothing is that marginal adjustment costs of acquiring and installing
capital rise as the rate of investment increases. Sargent (1979, p. 127) summarizes the central importance of
adjustment costs to investment theory, "the key to the theory is the assumption that there are costs associated
with adjusting the capital stock at a rapid rate per unit of time and that these costs increase rapidly with the
absolute rate of investment, so rapidly that the firm never attempts to achieve a jump in its capital stock at any
moment." For example, quadratic adjustment costs are typically used to formally derive a relationship between
Tobin’s q and investment.11
Another motivation for investment smoothing arises because firms cannot costlessly store or delay
investment projects, a problem of special importance in fast-growing, innovation-intensive industries. In such
industries, in which small, zero-dividend firms are likely to predominate, new investment opportunities arise
each period because of past innovative activity. If these opportunities are not undertaken as they arise, their
value to the firm rapidly decays because of short product life cycles and the first-mover advantage from
11 See, for example, the work of Abel (1979), Summers (1981), Hayashi (1982), and Chirinko (1987).
6
commercializing new technologies.12 With rapid obsolescence of investment opportunities, a firm’s incentive to
smooth investment, assuming project opportunities arrive at a relatively constant rate, is similar to an
individual’s incentive to smooth consumption under the standard assumptions of time separable utility functions
with declining marginal utility of consumption.13
II.C. Impediments to Investment Smoothing Arising From Financial Constraints
In perfect capital markets, firms would determine their optimal investment program from their
production technology as well as considerations arising from convex adjustment costs, perishability of
investment projects, etc. Financial factors would not prevent firms from optimal investment smoothing, and the
real capital accumulation path followed by the firm would be independent of its financial structure.
Much recent research on the functioning of capital markets, however, suggests that real investment
may not be independent of financial factors. FHP (1988) provides an extensive list of references to research on
how asymmetric information in capital markets can create financial constraints, Briefly, the arguments rest on
the distinction between "insiders” with full information about a particular firm’s investment prospects, and
"outsiders" that may correctly perceive the prospects for a population of firms, but cannot distinguish the quality
of individual firms. This information structure can lead to both adverse selection and moral hazard in markets
for external finance. In a seminal paper, Stiglitz and Weiss (1981) argue that credit rationing may emerge in
debt markets with asymmetric information because increased interest rates can lower lenders’ profits. This
result occurs if higher interest rates either cause relatively good firms to leave the applicant pool (adverse
selection), or if higher interest rates cause firms to undertake riskier projects (moral hazard). In external equity
markets, new investors may be asymmetrically informed about the true value of firms’ existing assets and new
12 Myers and Majluf (1984) recognize the lack o f storability for many kinds of investment projects. Personal computers is an example of
an industxy in which the product cycle appears to be less than two years.
13 Suppose the firm faces a new, downward-sloping marginal efficiency of investment schedule each period and that projects cannot be
stored for future periods. The firm will maximize its value by maintaining a smooth investment profile to equate its marginal returns over
time. If investment is driven by fluctuations in cash flow, the firm would sacrifice relatively high-valued projects in lean years for low
valued projects in years with higher than average cash flow.
7
investment opportunities. Myers and Majluf (1984) extend Akerlof s (1970) "markets-for-lemons" argument to
explain why firms may be forced to sell new shares at a discount, if they can sell shares at all.14
The combination of credit rationing and the "lemons premium" in external equity markets may result in
a pronounced financing hierarchy (see Myers, 1984) in which some firms find it optimal to retain all their
earnings but not to issue new equity shares. Numerous empirical studies (see footnotes 2 and 3 for a partial
list) have found evidence that at least some firms do appear to face financing constraints, in the sense that
variations in internal finance affect real investment. These constraints limit the firm’s ability to smooth fixed
investment with external funds when sources of internal finance fluctuate. But financially constrained firms will
still have an incentive to smooth fixed investment by changing the stock of reversible assets. As we will show,
this incentive has fundamental implications for how the existing empirical results should be interpreted, and it
motivates new tests that identify much larger internal finance effects than those estimated in previous research.
III. THEORETICAL FRAMEWORK AND EMPIRICAL IMPLICATIONS
III.A. Working Capital and Investment Smoothing
To motivate our empirical tests, we consider the investment problem of a firm that employs both fixed
and working capital and faces a binding finance constraint. For our purposes, the reversibility of working
capital is an important property. Thus, for a financially constrained firm, working capital plays a dual role as a
potential source of liquidity and an input into production. Because of high adjustment costs for fixed capital, we
argue that firms should smooth fixed investment with changes of working capital; that is, even if financing
constraints prevent the firm from equating marginal returns on investment over time, they should still be able to
equate the marginal returns on different kinds of capital, net of adjustment costs, at each point in time.
Working capital is typically defined as current assets less current liabilities. Current assets consist
primarily of accounts receivable, inventories, and cash and equivalents. On average, accounts receivable exceed
14 Greenwald and Stiglitz (1990, p. 34) examine adverse selection in equity markets, concluding that "asymmetric information may well
restrict equity issues to a small number of firms and an insignificant amount of funds, as it appears in practice."
8
inventories, which in turn greatly exceed cash and equivalents. Inventories include materials, work-in-process,
and finished goods. On average, materials exceed work-in-process which is greater than finished goods
inventories. Also, materials, followed by work-in-process, are more volatile than finished goods inventories
(Ramey, 1989). Therefore, finished goods inventories are typically a relatively small and stable component of
working capital.
Economists have long recognized that working capital is a distinct and important part of the firm’s
stock of capital.15 Dewing (1941, pp. 706-708), a leading writer in the field of finance during the first part of the
twentieth century provides some insights into the role and liquidity of working capital (which he calls ’
’current"
capital):
Every business consists of three elements ... the fixed capital, the current capital, and the
organization... current capital represents the goods acted upon by the permanent capital of
the business ... it comprises the raw materials being transformed into finished products by the
operations of the business, the finished goods on hand, the credit resulting from the sale of
these finished products, and the necessary money to keep the business running smoothly.
Liquidity arises from the fact that the current capital, being consumers’ goods, or material
easily transformed into consumers’ goods, or cash, commands a wider and therefore a quicker
market than producers goods.... Its economic value is direct for the simple reason that current
capital may be itself consumed or else easily converted into goods that can be consumed.
Dewing (1941, chapter 7) discusses the essential role of working capital in the production process. He also
explains why working capital is a readily reversible capital input. Furthermore, the presence of working capital
in a variety of standard "capital ratios" used to measure the financial health of firms attests to its usefulness as a
measure of liquidity.16
There is also an extensive operations research literature that explains the role of working capital in the
production function. Ramey (1989, pp. 340-341) provides an excellent summary of the arguments pertaining to
materials and work-in-progress inventories. She notes that uncertainty about materials quality and supply lines
causes firms to stockpile materials inventories to reduce the probability of a stockout that would leave workers
15 In the Wealth of Nations Smith distinguished between circulating and fixed capital. Although not precisely defined, it is clear that his
notion o f circulating capital is close to the current concept of working capital. See also Marshall (1949).
16 See, for example, the standard ratios recorded in the Value lin e database. We use working capital rather than current assets alone as
a measure o f reversible assets because current assets cannot be used to raise funds for investment in the short run if they must be used to
meet current liabilities.
9
and fixed capital idle. She also provides several reasons why firms use sizable quantities of work-in-process
inventories relative to fixed capital. If firms face large setup costs, for example, they can achieve economies of
scale by running large batch sizes. In addition, inventories allow the firm to meet fluctuations in seasonal
demand instead of paying overtime wages and maintaining fixed capital used only in peak periods. Trade credit,
in the form of accounts receivable, increases output capacity (as well as sales) for two reasons. First, trade
credit helps customers to order in large batch sizes and to store output themselves. Second, trade credit
facilitates sales to customers who themselves are liquidity constrained.17
We assume that firms produce output according to the technology:
Yt = F(Wt, Kt, Zt)
where Yt represents output, Kt is the stock of fixed capital at the beginning of t, Wt is the beginning-of-period
stock of working capital, and Zt is a vector of variable inputs that play no role in our analysis. We denote
investment in fixed capital as I and investment in working capital as AW. As in standard neoclassical models,
we assume that W and K are complementary inputs and that the first and second partial derivatives of the
technology satisfy:
(III.l)
Fw > 0, Fww < 0, Fk > 0, Fkk < 0, Fwk > 0.
For normal ranges of working capital, Fww < 0, similar to fixed capital. Unlike fixed capital, however, firms
may hold working capital in entirely liquid forms such as treasury bills.18 At high levels of working capital,
therefore, we do not rule out the possibility that Fw reaches a lower bound and Fww equals zero. This
possibility is not crucial to our argument, but it helps motivate why firms will smooth fixed investment to a
greater extent when their balance sheets are strong (W is high).
The firm maximizes the discounted value of cash flows over time subject to the condition imposed by
the production function. We assume that the costs of adjusting fixed capital are quadratic in the level of fixed
capital investment. For simplicity, we assume that there are no adjustment costs associated with working
17 For a general discussion and further references, see Kim and Srinivasen (1988, p. 116).
18
Typically, cash and equivalents constitutes a relatively small fraction of working capital. Financial analysts emphasize that working
capital, not cash on hand, is the most useful measure of a firm’s liquidity.
10
capital. While some working capital adjustment costs may exist, they are presumably small compared to those
associated with fixed capital investment. Therefore, the firm’s objective function is:
(m.2)
Max Vt =
2 0 i {Pt+j F(Kt+j,W t+j,Z t+j) - Pkt+j It+j - F V j AWt+j - (7/2)(It+j)2},
j=o
subject to
It+j = Kt+j+i - Kt+j
AWt+j = Wt+j+i - Wt+j
where P is the firm’s discount factor, Pkt and Pw are the prices of fixed and working capital, Pt is the price of
t
output, and 7 is the fixed capital adjustment cost parameter.19 All variables dated t+ 1 and later are
expectations. Following Sargent (1979, p. 340), in the absence of financing constraints, the system of first-order
conditions for an optimal solution to the problem posed in (III.2) are:
P Pt+j+i
FK(Kt+j+i,W t+j+i,Zt+j+i) - Pkt+j+
P
P Pt+j+l
(ni.3)
Fw(Kt+j+i, Wt+j+i, Zt+j+i) - Pw +
t+j
P
Pkt+j+i
Pw
t+j+i
-
7lt+j +
=
0.
P
(7lt+j+l) -
0
j = 0, ... , ®
A firm that chooses to expand its output capacity (the kind of firm we study in our empirical tests) will increase
Kt+j and Wt+j (j * 1,..., ®) to satisfy these conditions. In perfect capital markets, the limit on the firm’s speed
of expansion comes from costs of adjustment.
Now, suppose that imperfect capital markets restrict the firm’s access to finance. For the sake of
clarity, assume for the moment that firms either have no access to external capital or that it is prohibitively
expensive. (Alternatively, we could assume that because of credit rationing, all marginal finance comes from
internal sources.) Therefore,
(III.4)
CFt > Pkt It + Pwt AWt,
19 To keep the problem simple, we assume no depreciation for fixed or working capital. The discussion that follows also applies in the
more general case o f geometric depreciation.
1
1
where CFt is the firm’s internal cash flow. Because AWt can be negative, that is, investment in working capital
is reversible, It can exceed CFt without violating the internal finance constraint.20
The existence of the finance constraint may or may not affect the expansion decisions of the firm; it is
possible that cash flow is sufficient to prevent equation (III.4) from imposing a binding constraint on the sum
fixed and working capital investment. (As argued by FHP (1988), Gilchrist (1989), and Himmelberg (1990),
these firms may reveal themselves to the researcher by paying dividends.) If the financing constraint binds,
however, the firm will be unable to equate the marginal returns on its investments to zero as in equations
(III.3), and it may not be able to equate marginal returns over time. But the existence of a binding financing
constraint does not interfere with the firm’s ability to equate the marginal returns across alternative
investments, net of all adjustment costs, at each point in time. Therefore, even a financially constrained firm
should satisfy:
(III.5)
Pt Fk — Pkt + 0 Pkt - Tit + 0 (71m) = 0Pt+iFw - Pwt + j P +i = At
+i
+i
8 wt
where At, the shadow value of additional finance in period t, exceeds one.21
Equation (III.5) helps explain how a financially constrained firm optimally responds to a temporary
cash flow shock. For example, suppose the firm’s output price falls, reducing current cash flow. The firm must
decrease total investment to satisfy (111.4). Because of adjustment costs on fixed capital, however, it is unlikely
that the firm will cut I and AW proportionately. Rather the firm will adjust W more, perhaps even setting AW
negative. When the firm cuts total investment below its normal level, the marginal products of K and W both
rise, given the usual technology assumptions. In addition the marginal adjustment cost for current investment
20 While there are ways to include debt finance in the constraint, we will show later in the paper that cash flow is the dominant source of
funds the firms in our sample. In general, the predictions of the model would hold up as long as the cost of external finance at the margin
exceeds the opportunity cost o f internal funds.
21 In the solution to the dynamic optimization problem (III.2) with the constraint imposed by (III.4), At will depend on the Lagrange
multipliers on the finance constraints in both periods t and t + 1. The result that At exceeds zero is equivalent to the assumption that the
finance constraint holds with equality in period t.
There is an applied finance literature that addresses the programming problem of
how to allocate working capital when a firm faces financial constraints and variable cash flows. See Chames, Cooper and Miller (1959)
for an early example.
12
falls (7lt), while the expected marginal adjustment cost of future investment (7lt+i) rises because future
investment is expected to exceed current investment if the shock is temporary. These adjustment cost terms
reduce the change in K necessary to establish equation (III.5) for a given change in W. That is, the firm will
equate the expected marginal returns from K and W by smoothing I at the expense of AW. A symmetric
argument applies to positive cash flow shocks. We test several implications of this hypothesis later in the paper.
The model also implies that the degree of investment smoothing depends positively on the initial stock
of working capital. Because Fww < 0, when W is large relative to K the opportunity cost of foregoing a unit of
W is relatively small. If W were large enough, even growing firms may find it optimal to set AW negative in
response to a large negative cash flow shock. But if W is abnormally low relative to K, the optimal degree of
fixed investment smoothing will be lower. In this sense, the strength of a firm’s ’balance sheet” can be
’
important for the link between fixed investment and cash flow.
III.B. Empirical Predictions
This framework leads to four sets of empirical predictions for testing the effect of financing constraints
on investment. First, as we demonstrate in the next section, working capital investment is a major use of funds,
especially for the growing firms in our sample. If the typical firm requires a significant amount of working
capital relative to fixed capital per unit of output, one must account for working capital when formulating
hypotheses about the size of cash flow coefficients in reduced-form investment equations. For example, if the
production function is Cobb-Douglas with elasticities of 0.6 for fixed capital and 0.4 for working capital, then
the cash flow coefficient in a fixed investment regression should not exceed 0.6 (as opposed to unity), even for a
firm that relies entirely on internal finance for its expansion.
Second, even if firms face financial constraints, they may still exhibit lower variance in fixed investment
than in cash flow because they smooth fixed investment with changes in working capital. We predict that
<
J2(I) < <
J2(CF). If working capital is the key internal source of liquidity that firms use to smooth fixed
1
3
investment, we predict that 02(AW) > (72(CF) 22 With volatile cash flow, AW may frequently be negative, even
for growing firms. An alternative explanation for negative AW is that firms issue new shares in "bunches" to
reduce transactions costs, put the proceeds into working capital, and draw down working capital over time to
finance fixed investment. We will show later, however, that this explanation is inconsistent with the evidence
from our sample (see footnote 31).
Third, the standard "within-firm" estimator (used to control for unobservable firm-specific effects in
this literature) may underestimate the full effect of internal finance on fixed investment. This idea is explained
more fully in the next section. Basically, the within-firm estimator captures only the intra-firm relation between
fixed investment and cash flow, that may be dampened if the firm uses working capital to smooth temporary
cash flow shocks. Therefore, even if the firm is completely constrained to finance its growth with internal funds,
the regression coefficient on cash flow estimated from specifications commonly employed in the literature could
be small, depending on the initial stock of working capital. For this reason, the estimates of internal finance
effects presented in the existing literature may be interpreted as the average "short-run" or "temporary" effect;
that is, they reflect the impact of cash flow on fixed investment net of smoothing behavior.23 One way to
estimate the "long-run" effect of cash flow on fixed investment is to include (endogenous) changes in working
capital in the regression. We predict that this change in specification will increase the cash flow coefficient
compared with a specification that does not account for investment smoothing. Because a reduction of working
capital will allow an increase in fixed investment and vice-versa, we predict that the coefficient on the change in
working capital will be negative in a fixed investment regression for financially-constrained firms.
Finally, the addition of AW to the standard investment equation used in this literature can go a long
way toward resolving a long-standing debate about the correct interpretation of the cash flow effect on
investment. The view that competes with the financial constraint interpretation is that cash flow simply captures
new information about the profitability of fixed investment not reflected by the other variables in the regression.
In this case, however, one would expect AW to either have no effect on fixed investment, or, because it may also
22 These results also clearly imply that o^AW ) > a2® , which may arise even in the absence o f financial constraints because o f lower
adjustment costs for working capital. See Chirinko (1990) for further discussion.
23 Himmelberg and Petersen (1989) make similar arguments for R&D expenditures.
14
include some information about future profits from fixed capital, AW may even have a positive coefficient.
Furthermore, for similar reasons, including AW would probably reduce the cash flow coefficient. These
predictions are the opposite of what our financial constraint - investment smoothing model predicts. Therefore,
our econometric evidence provides a direct way of distinguishing between these competing explanations for the
observed positive relation between cash flow and investment.
IV. Data Sample
Our data are taken from the Value Line database of financial market, balance sheet, and income
statement information for a panel of U.S. manufacturing firms (two-digit SIC codes between 20 and 39).
Variable definitions are given in the data appendix. We are interested in the behavior of firms that are
especially likely to face binding financial constraints at the margin, therefore we focus on firms that pay
essentially no dividends. The logic of this selection criterion is straightforward. In a world of perfect capital
markets, dividend behavior should reveal nothing about investment behavior, However, when firms face credit
rationing and high lemons premia for new equity finance, firms that exhaust all internal finance are likely to
face a binding finance constraint.24 Gilchrist (1989) has shown that the Euler equation that would hold in
perfect capital markets is rejected for zero-dividend firms, providing formal statistical support for this selection
criterion.
The choice of the time period for our analysis reconciles two competing objectives. We would like to
use as many years as possible to increase the time-series variation of the data and provide the most efficient
estimates. If we use too long of a panel, however, the characteristics of the firms in the sample may change. In
particular, we are concerned with the maturation of firms that are initially financially constrained. The data
collection procedure used by Value Line helps address the maturation problem. Firms are added to the sample
when they become of sufficient interest to Value Line’s customers to justify the cost of maintaining their data.
24 T o maintain comparability to earlier research, the sample and dividend selection criterion we used are the same as in FHP (1988).
Firms are chosen for the sample if they have dividend to income ratios below ten percent for at least ten o f the fifteen years between 1970
and 1984. See FHP for further details.
15
But at the time a firm is added to the database, its historical financial data is also added, going back as far as
possible. Therefore, the database contains information on firms before they were of sufficient size and interest
to be included in the Value Line sample. Most of the firms in our sample had not been added to the database
before 1980 (see FHP, 1988 for further details). These firms also paid virtually no dividends during this period,
while many of these firms began to pay some dividends in the 1980s. For these reasons, we have reported
results for the decade of the 1970s.25
The key summary statistics for our sample of 48 firms are given in Table 1. Because of skewed
distributions for these variables, we have reported both mean and median values. The firms are relatively small.
The median values of their fixed and working capital stocks in 1970, the beginning of our sample, was $19.3
million and $14.1 million, respectively (in 1982 dollars). On average, these firms grew rapidly over the sample
period. The annualized (real) growth rate of median fixed capital was 10.2 percent while median working
capital grew at an 11.3 percent rate. The large size of working capital relative to fixed capital is consistent with
our view that working capital is a key input in the production process. The nearly equal growth rates of the two
inputs indicate an approximately homogeneous production technology. The data also clearly show that changes
in working capital constitute a major portion of total investment.
The second part of Table 1 provides data on the sources of funds for these firms. Internally generated
cash flow is the primary source of funds, accounting for over 71 percent of total funds. Debt contributed 17
percent of total funds, with 12 percent coming from new share issues. The average new share issue figure is
misleading, however, because it is skewed by a small number of large issues. The median value of new share
issues is virtually zero. Even the 75th percentile is less than 5 percent of total sources of funds. The typical firm
in our sample obtains the great majority of its funds internally while using a modest amount of debt. In a world
of credit rationing, these numbers are consistent with the view that these firms rely almost completely on
internal finance at the margin.
25 We considered the results from a variety of other sample periods. For samples as short as five years, the results were more variable,
but qualitatively consistent with those reported below. The results for the full FHP sample of 1970-84 were quite strong, and consistent
with the results reported here. The results also did not change substantially if the selection criterion for zero-dividend firms was based on
1970 through 1979 only.
16
V. EMPIRICAL EVIDENCE
VA. Variance Tests of Investment Smoothing
One test of investment smoothing for financially constrained firms is analogous to the tests of
production smoothing presented by Blinder (1986) based on the identity:
(V.l)
Yt = St + (INVt - INVt-i)
where Yt is output in year t, St is sales in t, and INVt is the stock of inventories at the end of period t. To test
for production smoothing relative to sales, one checks whether the variance of production is less than the
variance of sales. Furthermore, because inventories constitute the buffer stock that allows smoothing, the
change in inventories should have a negative covariance with sales.
To develop the analogous case for investment smoothing under financial constraints, suppose that firms
only obtain funds at the margin from internal sources, and that these funds are used exclusively to finance fixed
or working capital accumulation. Then, as explained in section III, the sources and uses of funds relation can be
written as:
(V.2)
It = CFt - AWt
where It is fixed investment in year t, CFt is internal cash flow in t, and AWt is the change in the stock of
working capital during period t.26 Equation (V.2) is similar to the production smoothing identity (V.l). Firms
use the stock of working capital to smooth investment relative to variations in cash flow. Note that the existence
of financial constraints arising from imperfect capital markets is crucial to the logic of this argument. A firm
that can obtain external finance on essentially the same terms as its opportunity cost of internal funds may want
to smooth investment relative to cash flow, but it need not use working capital to accomplish such smoothing.
In practice, the firms in our sample have obtained limited amounts of external finance and may have
other uses for their funds besides the accumulation of fixed and working capital. Therefore, equation (V.2) will
only approximate the linkages between investment, cash flow, and changes in liquidity. Nevertheless, this
Cash flow is defined as after-tax income plus non-cash expenses, chiefly depreciation and amortization. A s mentioned before, working
capital is defined by Value Line as the difference between current assets and current liabilities.
17
analogy motivates some simple tests of the joint hypotheses of financial constraints and investment smoothing.
As a first step, we compare the variance of investment to the variance of cash flow. These statistics are reported
in Table 2. We have scaled all the variables by the stock of fixed capital to control for changes in the size of the
firms. So that the variance and covariance measures are not affected by differences in the ratios across firms,
the statistics in Table 2 are computed after subtracting the firm mean from each observation. The statistics
provide some evidence of investment smoothing relative to cash flow. The variance of the cash flow-to-capital
ratio is about 42 percent higher than the variance of the investment-to-capital ratio. Furthermore, the
correlation between the change in working capital and cash flow is strongly positive. When cash flow is low,
working capital is reduced, consistent with the hypothesis that working capital functions as a buffer stock.
Finally, the variance of the change in working capital, that is, the variance of working capital investment, is
almost 4 times greater than the variance of fixed investment, again consistent with the hypothesis that firms use
working capital as a source of liquidity to smooth fixed investment.
As mentioned previously, it is possible that our sample firms smooth investment through external
finance, either new debt or equity. But aside from a few outliers, the value of new equity issues is very small.
These firms appear to have some access to debt, but as Table 2 shows, the correlation of changes in debt with
cash flow is positive . Therefore, new debt tends to reinforce rather than offset cash flow fluctuations, on
average, consistent with credit rationing at the margin.
Finally, the empirical distribution of AW/K provides evidence that working capital is used for
smoothing. As the summary statistics in Table 1 show, the firms in our sample grew very quickly, in terms of
both output and capital. Yet, as the last row of Table 2 shows, the change in working capital was negative in
over 21 percent of our observations. In contrast, cash flow was negative in less than 6 percent of the
observations. If these fast-growing firms were able to easily obtain external finance, it is hard to imagine why
they would reduce the absolute level of working capital so often. On the other hand, frequent negative changes
in working capital would be expected if firms use this relatively liquid asset to smooth fixed capital investment.
18
V.R. Working Capital and Smoothing Effects in the Investment Equation
Many recent studies have examined the effect of internal finance on fixed investment use an equation
similar to:
(V.3)
(I/K)jt - A i(qjt) + A2(CF/K)jt + Aj + At + ujt.
The variable Ijt represents plant and equipment investment for firm j at time t. The tax-adjusted measure of
Tobin’s q at the beginning of year t (q jt) controls for changes in investment demand.27 Investment and cash
flow are scaled by the firm’s beginning-of-period capital stock. The intercept coefficients, Aj and At allow for
firm-specific effects (a "fixed effects" model) and year effects; U is a random error term.
jt
It is likely that the individual firm effect (Aj) is correlated with cash flow, perhaps because of
differences in managerial ability or the kind of capital the firm uses. With panel data, the standard practice in
this literature has been to control for unobservable fixed effects by subtracting the firm-specific mean of each
variable from each observation before running regressions. This "within-firm" estimator sweeps out the
influences of differences in average levels of the regressors across firms. In particular, any relation between
firms’ average fixed investment and average cash flow will not affect the cash flow coefficient estimate.
As we have argued, use of this technique causes a possibly serious problem with estimating equation
(V.3) because the results may measure only the short-run effect of financing constraints on investment. If firms
smooth fixed investment around their average I/K ratio because of adjustment costs, the within-firm
relationship between fixed investment and cash flow may appear quite weak. Yet the firm could still rely almost
entirely on cash flow to finance its average level of investment, a relationship which is discarded in the effort to
control for the problem of unobservable fixed firm effects.
To account for smoothing, while still controlling for firm-specific effects, we include AW in the
regression, as suggested by the analysis in section III. Note that AW is dimensionally equivalent to cash flow,
but it can be a net use or source of funds since firms can readily choose to expand or contract the stock of
working capital. Our basic empirical investment equation is:
27 O ur definitions o f tax-adjusted q and the replacem ent value o f the capital stock m easure used throughout the paper are based on
Salinger and Sum m ers (1983). S ee the data appendix for details.
1
9
(V.4)
(I/K)jt = A i(qjt) + A2(CF/K)jt + A3(AW/K)jt + Aj + At + ujt.
We will also consider the effect of introducing other variables that affect investment demand, particularly
output or sales.
In estimating equation (V.4), we must account for the fundamental endogeneity of changes in working
capital (AWjt), a decision variable in our model from section III. This endogeneity may result in correlation
between disturbances to investment and changes in working capital. For this reason, we estimate various forms
of equation (V.4) with instrumental variables. The instruments are beginning-of-period q, cash flow, the
beginning-of-period stock of working capital divided by fixed capital, (W/K)jt, and the fixed time and firm
effects.28 The choice of the (W/K)jt instrument, which identifies equation (V.4), follows directly from the
smoothing model discussed in section III. If the firm begins the period with a low stock of working capital
relative to fixed capital, then the marginal product of working capital is large, and the firm will find it optimal to
set AW at higher than normal levels, if possible. A symmetric argument shows that a high working capital stock
will be associated with low, possibly negative AW. Because of this relationship, and the fact that the stock of
working capital is measured at the beginning of the period, it is an ideal instrument.
The first stage OLS regression from our instrumental variables procedure supports this interpretation
of the role played by the instruments:
(AW/K)jt = Aj + At + 0.0064 (q jt)
(3.5)
+ 0.883 (CF/K)jt
(12.1)
- 0.217 (W/K)jt.
(5.1)
The fixed effects are not reported and t-statistics appear below the estimated coefficients. The strong cash flow
effect and the negative coefficient on the beginning-of-period stock of working capital are completely consistent
with our predictions from section III. This equation functions much like a modified stock adjustment model for
working capital. For panel data, this instrumental variables regression also provides a relatively good fit for the
Cash flow may also have som e degree o f endogeneity because if firms reduce working capital to sm ooth fixed investm ent, current costs
rise and cash flow falls. O bserved cash flow changes may exceed exogenous variations in cash flow, therefore, biasing the cash flow
coefficient toward zero. W e find som e support for this view in that treating cash flow as endogenous increases its estim ated coefficient.
This result is not reliable, however, because it is difficult to find good instruments for the idiosyncratic variations in current cash flow that
should be o f fundam ental importance for investm ent in firms that face financial constraints.
20
endogenous change in working capital. The adjusted R2 is 0.36 in this first-stage regression with all variables
expressed as deviations from their firm means, thereby excluding the explanatory power of the firm fixed
effects.
Estimates of both (V.3) and (V.4) appear in Table 3. Equation (3.1) includes only q, cash flow, and the
fixed effects, the standard regression that many recent papers use to test for the importance of internal finance
constraints on investment. Our results are consistent with other findings of statistically and economically
significant coefficients on cash flow in a q investment equation. The estimated cash flow coefficient in equation
(3.1) is similar to those reported by previous studies. As argued in section III, however, this kind of regression
is likely to capture only the short-run impact of cash flow shocks on investment. If fixed investment smoothing
is important, it understates the full impact of internal finance for financially constrained firms. In equation (3.2)
we add the (endogenous) change in working capital to the specification of equation (3.1) and estimate the
coefficients with instrumental variables as described above. The results are striking. The change in working
capital has the expected negative coefficient and is significantly different from zero. The cash flow coefficient
nearly doubles, consistent with our previous discussion 29
These results provide stronger evidence for the importance of internal finance for investment than the
basic link between investment and cash flow, evident in equation (3.1). The positive relation between
investment and cash flow has been interpreted as evidence of misspecification of the investment equation. If
this were the case, cash flow could proxy variations in profits that are not adequately captured by q or other
variables in the equation. Results such as those reported in equation (3.1) might be viewed as an empirical
failure of standard investment demand models rather than evidence for the existence of financial constraints.30
^9 A n alternative to the com m only used fixed-effects estim ator is to estim ate the equation in first differences. O ur results with first
differences are qualitatively consistent with those reported in equation (3.2), the cash flow coefficient rises substantially w hen AW is
added to the regression and the AW coefficient is negative and quite significant. T h e size o f the coefficients is som ewhat sm aller (0.536
for CF, -0.216 for AW), T h e m ost likely explanation for this change is m easurem ent error. G rilliches and H ausm an (1986) show that the
first-difference estim ator is m ore sensitive to m easurem ent error, causing downward bias in the estim ates relative to the fixed-effect,
within-firm estim ator.
30 S ee the com m ents o f P oteib a (1988, p. 202). Jorgenson (1971) interprets the significance o f cash flow or profits in an investm ent
equation as a proxy for output effects, an issue w e take up later. A b el and Blanchard (1986) find a residual role for profits (as w ell as
output) indicating possible m is-specification in q equations. Hubbard and Kashyap (1990) also em phasize the am biguous interpretation
o f cash flow effects in em pirical investm ent equations.
21
But this misspecification interpretation is not consistent with the results in equation (3.2). Changes in working
capital are positively correlated with profits, output, and the business cycle. If the AW variable in equation (3.2)
proxied some kind of omitted "accelerator effect," its estimated coefficient would be positive and it should reduce
the effect o f cashflow when compared with the results from a specification like equation (3.1). The actual
results are just the opposite of these predictions, consistent with the joint hypotheses of investment smoothing
and the existence of financial constraints.31
The cash flow coefficient in equation (3.2) can be interpreted as the effect on fixed investment of
changing the flow of internal finance holding the stock of working capital constant. Of course, there is no
reason to assume in general that working capital will not change. Therefore, the quantitative impact of cash
flow shocks on investment depends on the particular conditions the firm faces when the shocks occur, especially
the condition of their balance sheets. If a negative cash flow shock occurs at a time when firms have strong
balance sheets and an abundance of working capital, AW will be smaller than normal, even negative, to smooth
the impact of low cash flow on investment. The same cash flow shock when the firm is illiquid, after a downturn
in the business cycle for example, will have a bigger impact on fixed investment. With low working capital the
firm’s opportunity cost of sacrificing working capital investment to smooth fixed investment will be high.32
Therefore, these results show that the link between cash flow and investment can be much more complex than it
appears in most of the existing literature.
31 A n other possible explanation for the negative coefficient on AW is that high transaction costs o f external finance, especially new share
issues, cause firms to issue them infrequently, storing the proceeds in working capital until they are needed for investm ent. This
phenom enon is unlikely to explain our results, however. First, we have shown that the firms in our sam ple make little use o f external
finance, especially new share issues. Second, the sam ple correlation between fixed investm ent and changes in working capital is positive,
not negative as the "bunching” o f new equity issues would imply. Finally, w e split the sam ple on the m edian usage o f new equity finance
and re-estim ated the regressions. T he AW coefficients remained negative and significant in both halves o f the sam ple, but the coefficient
for firms in lower half o f the new equity distribution was substantially la r g e r than for the firms that used a greater am ount o f new equity,
again inconsistent with the bunching explanation. W e thank Ben B em anke and Charles H im m elberg for helpful com m ents on this issue.
T his point is m ade by G ertler and Hubbard (1989). They find empirical support for the proposition that cash flow shocks m atter more
for investm ent during downturns o f the business cycle.
22
A full treatment of the long-run impact of cash flow on fixed investment would require estimation of a
structural equation for the change in working capital, which is beyond the scope of this paper,33 Under some
simplifying assumptions that are consistent with our estimates, however, we can gain additional structural
information from our equation. The summary statistics in Table 1 show that the firms in our sample make little
use of new debt or equity, especially at the median values. Suppose that these firms use each dollar of cash flow
exclusively for fixed or working capital investment and that they do not have alternative sources of funds at the
margin. Using the sample median values to approximate the long-run ratio of fixed to working capital
investment would imply that about 60 percent of each dollar of cash flow would go to fixed investment, with the
remaining 40 percent allocated to working capital. We can use the estimates of equation (3,2) to test the
consistency of this interpretation. The long-run effect of changes in cash flow on working capital would be 0.40.
Therefore, equation (3.2) in table 3 predicts the long-run effect of changes in cash flow on fixed investment as
0.743 - 0.430 (0.40) = 0.571. This result is remarkably close to the mean value of the input share for fixed
capital (0.60) calculated from table 1. The results are even closer if one uses the sample means rather than
medians. This calculation demonstrates two important points. First, it shows that the long-run impact of cash
flow on fixed investment can be much larger than conventionally estimated. Second, these results are consistent
with the view that firms such as those in our sample are completely dependent on internal finance for their total
growth, including both fixed and working capital.
To further investigate this point, we defined total investment as the sum of fixed and working capital
investment. For this measure of total investment, the firm no longer has any obvious means of smoothing. We
regressed this composite investment variable on q, cash flow, and fixed effects for each firm and each year,
using ordinary least squares. We obtained a cash flow coefficient quite close to unity (1.221 with a standard
error of 0.083). If we include sales and lagged sales in the model (see the discussion in the next section) the
cash flow coefficient is 0.975 with a standard error of 0.102. These results corroborate the point that the long-
33 T h e key problem is finding an appropriate instrument to identify the working capital equation. T h e stock o f fixed capital w ould be the
obvious choice, analogous to the approach used for the fixed capital equation, But this variable is used to scale the data, m aintaining
comparability o f our results to the rest o f the literature.
23
run effect of financing constraints on total investment is much larger than the findings reported in previous
studies suggest. It appears that the effect of internal finance on total investment can be virtually dollar-fordollar.
V.C. Alternative Specifications and Robustness
The basic econometric results of this paper appear in Table 3. We have found these results to be
remarkably robust to alternative specifications and changes in estimation technique. This section summarizes
these results.
In spite of the well-developed microfoundations for the q theory of investment demand, its empirical
performance has been criticized from a variety of perspectives. One significant problem is that output or sales
variables typically have explanatory power for investment in q equations (see Abel and Blanchard, 1986, for
example). The strong effect of sales can be motivated by traditional ’’accelerator" ideas (see Eisner, 1978).
More recently, the importance of sales for investment has been explained by imperfect competition. Regardless
of the microeconomic motivation for including sales in investment equations, it is interesting to consider
whether the conclusions reached above regarding the empirical importance of investment smoothing for the
evaluation of financial constraints hold up in equations that include sales variables.
The results in Table 4 show the effect of adding contemporaneous and lagged sales-to-capital ratios to
the basic model. The estimated coefficients on the sales variables are quite significant. Including sales reduces
the quantitative effect of the internal finance variables, cash flow and the change in working capital, compared
with the basic model estimates from Table 3. This change is not surprising given the collinearity of cash flow
and sales. The coefficients still remain significant, however, both statistically and economically.
Most importantly, the addition of the change in working capital in equation (3.2) has the same
qualitative effect on the cash flow coefficient that we observed earlier in models that excluded the sales
variables: the effect of changes in working capital is negative, and the marginal impact of cash flow is
substantially larger in the regression including the change in working capital. This test provides further
evidence that the effect of working capital in these regressions is not simply a proxy for some kind of omitted
24
accelerator effect; the financial constraint interpretation holds up well when sales variables are included directly
in the model.
We also added lagged investment to the model (with and without sales) as suggested by Devereux and
Schianterelli (1989) and Gilchrist (1989). The lagged investment term was statistically significant, but the other
coefficients were virtually unchanged in these regressions. Without sales, the CF and AW coefficients were
0.709 and -0.388, respectively; with sales and lagged sales in the model these coefficients were 0.389 and -0.213.
As we discussed previously, the interpretation of cash flow coefficients in investment equations has
been clouded by the question of whether cash flow effects indicated financial constraints or signals of future
profitability not captured by other variables. The interpretation of our results for cash flow and the change in
working capital seem inconsistent with the profit signal explanation for reasons discussed above. Nevertheless,
we included end-of-period q in the regressions to capture any ’’news" about the firms' prospects that may arise
during the year. The results presented in Table 5 show that this change has very little effect on the results; the
impact of adding changes in working capital to the regression are almost the same as in the basic model. If
sales variables are included in the regression with end-of-period q, the results are virtually identical to those
presented in Table 4.
As a final test of the robustness of our results, we estimated our equations with the sample of highdividend firms from FHP (1988). Table 6 presents results for firms that pay more than 20 percent of their
income as dividends in most years (see FHP, 1988 for details). In the regressions that exclude sales variables,
cash flow has a significant impact on investment. Furthermore, the change in working capital has a negative
estimated coefficient, and adding the change in working capital to the regression increases the cash flow
coefficient, consistent with the financial constraint / investment smoothing model. As emphasized by FHP
(1988), however, the cash flow coefficient is much smaller than the corresponding effect estimated for zerodividend firms (see Table 3). The estimated coefficient on the change in working capital is also much smaller
for high-dividend firms, and its inclusion has a smaller impact on the cash flow coefficient. When the sales
variables are included, the results are even more striking. While the signs of the coefficients on the internal
finance variables are still consistent with the existence of financial constraints and investment smoothing, the
25
magnitude of the coefficients is quite small, only about one-fifth to one-sixth the values for low-dividend firms in
the corresponding specification that includes sales variables.
These results provide some evidence that the large, high-dividend firms from the FHP sample
experience financial constraints on investment spending. But, as one would expect, the size of these effects is
much smaller than for zero-dividend firms. The results in Table 6 must be interpreted with caution, however.
To explain why investment would be financially constrained at the margin for such firms, one must explain why
they cut investment rather than dividends when cash flow declines. If changes in dividends provide signals
about future profits, this kind of behavior may be optimal, but we do not pursue this question further here.
Also, while the cash flow and liquidity variables remain statistically significant in the regression with sales, their
estimated coefficients are quite small. For our purposes, however, the interesting point is that the differential
effect of internal finance and liquidity on investment between the high and low dividend samples provides
further support for the hypothesis that financial constraints and investment smoothing are at the root of the
results for the zero-dividend firms we focus on in this paper.
IV. Conclusion
The investment behavior of firms that may face financial constraints has come under close scrutiny in
many recent studies. The results strongly suggest that at least some firms face binding internal finance
constraints, especially those that pay low or zero dividends. Measuring the economic importance of financing
constraints, however, remains an unresolved issue. Moreover, it is an issue that economists should care about,
as a substantial proportion of publicly-traded companies in the United States do not pay dividends at any point
in time. Furthermore, one would expect financial constraints to be an even greater problem for the typical
private firm.
In this paper, we tackle this problem by explicitly considering two important aspects of investment
behavior that the recent literature largely ignores: firms’ incentives to smooth fixed investment because of
adjustment costs and the key role of working capital in the production process. An important property of
working capital is its reversibility. Therefore, if smoothing incentives are strong enough, firms that face binding
26
constraints on total investment may nevertheless smooth the path of fixed investment by adjusting working
capital to an extent that depends on the strength of firms’ balance sheets. This results implies that previous
attempts to estimate the impact of cash flow on fixed investment looking only at intra-firm movements of
investment and cash flow may have identified only the "short-run" impact of financing constraints.
Incorporating changes in working capital in a fixed investment equation also helps resolve the debate in
the literature about whether cash flow effects on fixed investment represent binding finance constraints or
simply proxy changes in expected profits. If the profit signal story explains the empirical role for internal
finance in investment equations, then the change in working capital, another potential signal of expected profits,
should enter with a positive sign and reduce the estimated effect of cash flow. The financial constraint /
investment smoothing explanation we propose, however, has just the opposite empirical prediction. There is no
identification problem.
Our findings strongly suggest that zero-dividend firms smooth fixed investment with working capital.
Working capital investment is much more variable than movements in cash flow, which in turn is more variable
than movements in the rate of fixed investment. More importantly, we find that the measured effect of cash
flow on fixed investment is much larger once we control for movements in working capital. Our results suggest
that changes in internal finance may have close to a dollar-for-dollar effect on the total capital investment of the
firm. In addition, the negative coefficient on changes in working capital clarifies the role of liquidity (both cash
flow and the stock of working capital) in reduced-form regressions. This result clearly supports the financial
constraint interpretation of the impact of internal finance, as opposed to the view that internal finance variables
just proxy changes in investment demand not captured by the other variables in the equation.
These results have important implications for research in both industrial organization and
macroeconomics. In industrial organization, economically large effects of financial constraints on investment
provide the foundation for claims that differential access among firms to capital markets result in barriers to
entry.34 Our results also provide support for strategic models of firm behavior based on the assumption that
some competitors must rely on internal finance for expansion. This behavior includes predatory pricing
34 S ee B ain’s (1956) original discussion o f this issue.
27
(Fudenberg and Tirole, 1986, for example) and dynamic limit pricing (Judd and Petersen, 1986). In addition,
our results support theories of conglomerate mergers based on differential access to capital markets.
From a macroeconomic perspective, fluctuations of investment account for a substantial portion of the
movements in GNP 35 It is also well known that profits are volatile over the business cycle. The link between
internal finance and investment, therefore, can help explain an important feature of output fluctuations. Our
results go further, however, because they tie the magnitude of investment fluctuations induced by cyclical
changes in internal finance to the strength of firm’s balance sheets. If firms are highly liquid, they will be able
to smooth fixed investment relative to fluctuations in cash flow without the need for costly external funds. But a
severe downturn that weakens balance sheets, or a wave of corporate restructuring that reduces liquidity, may
make investment much more susceptible to recession and declining internal finance than most estimates of
"cash flow effects” in previous studies imply. This point provides empirical support for recent models of
business cycles that emphasize fluctuations of "internal net worth" as a key factor in propagating, magnifying
and even causing cyclical fluctuations.36
Finally, these results may help explain cyclical fluctuations of inventories, long recognized as a major
part of the business cycle. Because inventories, particularly materials and work-in-process, constitute part of
working capital, our results predict that they will vary procyclically as financially-constrained firms smooth fixed
investment relative to variations in profits. Such behavior may help explain Ramey’s (1989) findings of large
"unobserved shocks" to inventory demand during recessions, which she suggests may arise from capital market
imperfections, consistent with our findings here. Furthermore, our approach implies that these demand shifts
may be explained as the endogenous outcome of investment smoothing when firms face financial constraints,
rather than exogenous "shocks."
Barro (1987) concludes that 88 percent o f the shortfall in G N P during recessions is due to declines in all categories o f investm ent
expenditures.
36 See, in particular, B em an k e and G ertler (1989) and empirical studies by G ertler and Hubbard (1988) and Hubbard and Kashyap
(1990). A lthough this idea has resurfaced in the literature only recently, its roots go back a long way. G urley and Shaw’s (1955) concept
o f "financial capacity" is related to the liquidity and balance effects w e study here. M insky (1975) em phasizes that the process o f financing
investm ent during a boom system atically weakens balance sheets, making the effects o f a later downturn m ore severe.
28
DATA APPENDIX
The data used for this study were drawn from the sample developed by FHP (1988). A brief
description follows; for further details see FHP (pp. 191-195).
As explained in the text, our primary interest is in firms that pay low dividends. All the statistics
reported in this paper, with the exception of Table 6, are based on firms in the Value Line database that paid
less than 10 percent of their income as dividends in at least 10 years from 1970-84 (class 1 firms in FHP). As
indicated in the paper, the results we obtain are quite similar if we select low dividend firms based on their
behavior over the 1970-79 period alone. Therefore, we used the FHP class 1 sample to maintain comparability
with earlier work. We excluded one firm from the FHP sample because of its unrealistic Tobin’s q values early
in the sample.
Q definition: Our Q variable is adjusted for corporate taxation. It is defined as:
Q = (1 -r )-1 {(V + B - X - N ) / K - ( l - k - 7 z ) }
where V is the market value of equity, B is the book value of total debt, X is the present value of tax deductions
from existing capital, N is an estimate of the market value of inventories, and K is an estimate of the
replacement value of the capital stock. The investment tax credit rate is denoted by k, z is the expected present
value of future depreciation deductions per dollar of investment, and T is the corporate income tax rate. A
variety of definitions of Q were used in the FHP study (including changes in the tax adjustments and estimates
of the market value of debt) with little impact on the results.
Replacement Value o f the Capital Stock: Book values of fixed capital were adjusted for depreciation and
inflation using the method developed by Salinger and Summers (1983).
Cash Flow: Cash flow is after-tax income plus all non-cash charges to income (primarily depreciation and
amortization). We do not subtract dividends from cash flow.
Working Capital: Current assets (assets expected to be converted into cash, sold, or consumed in the normal
course of business, including accounts receivable, inventories and cash and marketable securities) less current
liabilities (obligations due in a year or less).
Sales: Gross revenue less returns, discounts and allowances.
29
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31
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The Capital Structure Puzzle,”Journal o f Finance, volume 39 (May 1984), pp. 575-592.
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32
Table 1
Sample Summary Statistics
Sample
Mean
Sample
Median
Estimated Replacement Value of Fixed Capital (millions of 1982 dollars)
85.4
1970
1593
1979
193
46.4
Working Capital Stock (millions of 1982 dollars)
1970
1979
47.5
93.5
14.1
37.0
Real Sales Growth
15.7%
13.1%
Fixed Investment to Capital Ratio
0.247
0.176
Change in Working Capital to Capital Ratio
0.220
0.115
Cash Flow to Net Sources Ratio
0.715
0.686
Change in Debt to Net Sources Ratio
0.169
0.184
Value of New Share Issues to Net Sources Ratio
0.116
0.001
Dividend to Capital Ratio
0.006
0
Note: Net sources are defined as the sum of cash flow the change in debt, and the value of new equity. Mean
observations for the net sources ratios are based on sample aggregates.
Table 2
Summary Statistics on Investment Smoothing
Variance of the Cash Flow to Capital Ratio (CF/K)
0.044
Variance of the Investment to Capital Ratio (I/K)
0.031
Variance of the Change in Working Capital to Capital Ratio (AW/K)
0.123
Correlation of CF/K and AW/K
0.534
Correlation of CF/K and the Change in Debt to Capital Ratio
0.185
Proportion of Years with Negative CF
0.058
Proportion of Years with Negative AW
0.214
Note: All the statistics in Table 2 are based on within-firm calculations (deviations from firm means).
33
Table 3
Investment Equation Estimates: Basic Specification
(Dependent Variable: I/K)
Independent
Variable
Equation (3.1)
Equation (3.2)
Q
0.0046
0.0054
(5.1)
CF/K
0.382
0.743
(6.4)
-0.430
(3.4)
(4.5)
(11.2)
AW/K
Note: Equation (3.1) was estimated with ordinary least squares. Equation (3.2) was estimated with
instrumental variables as described in the text. Estimated t-statistics appear in parentheses after the coefficient
estimates. Fixed firm and time effects are not reported.
Table 4
Investment Equation Estimates: Including Sales and Lagged Sales to Capital Ratios
(Dependent Variable: I/K)
Independent
Variable
Equation (4.1)
Equation (42)
Q
0.0036
(4-5)
0.0038
(4.2)
CF/K
0.217
(5.3)
0.385
(5.5)
-0.222
(3.1)
Aw / k
S/K
0.052
(6.5)
0.057
(6.4)
S l/K
-0.034
(3.8)
-0.034
(3.5)
Note: Equation (4.1) was estimated with ordinary least squares. Equation (4.2) was estimated with
instrumental variables as described in the text. Estimated t-statistics appear in parentheses after the coefficient
estimates. Fixed firm and time effects are not reported.
34
Table 5
Investment Equation Estimates Including End-of-Period Q
(Dependent Variable: I/K)
Independent
Variable
Equation (5.1)
Equation (5.2)
Q (B.O.P)
0.005
(5.0)
0.004
(2.7)
Q (E.O.P.)
-0.002
(1.0)
0.003
(0.9)
CF/K
0.393
(11.0)
0.784
(5.9)
-0.489
(3.7)
AW/K
Note: Equation (S.l) was estimated with ordinary least squares. Equation (5.2) was estimated with
instrumental variables as described in the text. Estimated t-statistics appear in parentheses after the coefficient
estimates. Fixed firm and time effects are not reported.
Table 6
Investment Equation Estimates: High Dividend Firm Sample
(Dependent Variable: I/K)
Independent
Variable
Equation
(6.1)
Equation
(6.2)
Equation
(63)
Equation
(6.4)
Q
0.002 (6.7)
0.002 (7.7)
0.002 (6.0)
0.002 (6.4)
CF/K
0.185 (13.8)
0.299 (14.1)
0.038
0.059
(3.0
-0.051
(2.3)
AW/K
-0.180
(2.2)
(7.0)
S/K
0.030 (10.1)
0.033 (10.2)
S l/K
0.001 (0.1)
-0.002
(0.7)
Note: Equations (6.1) and (6.3) were estimated with ordinary least squares. Equations (6.2) and (6.4) were
estimated with instrumental variables as described in the text. Estimated t-statistics appear in parentheses after
the coefficient estimates. Fixed firm and time effects are not reported.
Working Papers and Staff Memoranda
The following lists papers developed in recent years by the Bank's research staff. Copies
of those materials that are currently available can be obtained by contacting the Public
Information Center (312) 322-5 111.
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
REG IO N A L EC O N O M IC ISSUES
Taxation of Public Utilities Sales: State Practices
and the Illinois Experience
Diane F. Siegel and William A. Testa
WP-86-1
Measuring Regional High Tech Activity with Occupational Data
WP-87-1
Alenka S. Giese and William A. Testa
Alternative Approaches to Analysis of Total Factor Productivity
at the Plant Level
Robert H. Schnorbus and Philip R. Israilevich
WP-87-2
Industrial R&D An Analysis of the Chicago Area
WP-87-3
Alenka S. Giese and William A. Testa
Metro Area Growth from 1976 to 1985: Theory and Evidence
William A. Testa
W P8-91
Unemployment Insurance: A State Economic Development Perspective
William A. Testa and Natalie A. Davila
WP-89-2
A Window of Opportunity Opens for Regional Economic Analysis:
BEA Release Gross State Product Data
WP-89-3
Alenka S. Giese
Determining Manufacturing Output for States and Regions
WP-89-4
Philip R. Israilevich and William A. Testa
The Opening of Midwest Manufacturing to Foreign Companies:
The Influx of Foreign Direct Investment
Alenka S.Giese
WP-89-5
l
Working paper series continued
A New Approach to Regional Capital Stock Estimation:
Measurement and Performance
Alenka S. Giese and Robert H. Schnorbus
WP-89-6
Why has Illinois Manufacturing Fallen Behind the Region?
William A, Testa
WP-89-7
Regional Specialization and Technology in Manufacturing
W P-89-8
Alenka S. Giese and William A. Testa
Theory and Evidence of Two Competitive Price Mechanisms for Steel
Christopher Erceg, Philip R. Israilevich and Robert H. Schnorbus
Regional Energy Costs and Business Siting Decisions:
An Illinois Perspective
David R. Allardice and William A. Testa
Manufacturing's Changeover to Services in the Great Lakes Economy
William A. Testa
WP-89-9
W P-89-10
W P-89-12
Construction of Input-Output Coefficients
with Flexible Functional Forms
Philip R. Israilevich
WP-90-1
Regional Regulatory Effects on Bank Efficiency
WP-90-4
Douglas D. Evanoffand Philip R. Israilevich
Regional Growth and Development Theory: Summary and Evaluation
Geoffrey JD . Hewings
Institutional Rigidities as Barriers to Regional Growth:
A Midwest Perspective
Michael Kendix
W P-90-5
WP-90-6
ISSUES IN FIN A N C IA L REGU LA TION
Technical Change, Regulation, and Economies of Scale for Large Commercial
Banks: An Application of a Modified Version of Shepard’s Lemma
Douglas D. Evanoff, Philip R. Israilevich and Randall C. Merris
WP-89-11
2
Working paper series continued
Reserve Account Management Behavior: Impact of the Reserve Accounting
Scheme and Carry Forward Provision
W P-89-12
Douglas D. Evanoff
Are Some Banks too Large to Fail? Myth and Reality
WP-89-14
George G. Kaufman
Variability and Stationarity of Term Premia
WP-89-16
Ramon P. De Gennaro and James T. Moser
A Model of Borrowing and Lending with Fixed and Variable Interest Rates
WP-89-17
Thomas Mondschean
Do “Vulnerable" Economies Need Deposit Insurance?: Lessons from the
U.S. Agricultural Boom and Bust of the 1920s
W P-89-18
Charles W. Calomiris
The Savings and Loan Rescue of 1989: Causes and Perspective
W P-89-23
George G. Kaufman
The Impact of Deposit Insurance on S&L Shareholders' Risk/Retum Trade-offs
WP-89-24
Elijah Brewer III
Payments System Risk Issues on a Global Economy
WP-90-12
Herbert L. Baer and Douglas D. Evanoff
M ACR O ECO N OM IC ISSUES
Back of the G-7 Pack: Public Investment and Productivity
Growth in the Group of Seven
WP-89-13
David A. Aschauer
Monetary and Non-Monetary Sources of Inflation: An Error
Correction Analysis
WP-89-15
Kenneth N. Kuttner
Trade Policy and Union Wage Dynamics
WP-89-19
Ellen R. Rissman
3
Working paper series continued
Investment Cyclicality in Manufacturing Industries
WP-89-20
Bruce C. Petersen and William A. Strauss
Labor Mobility, Unemployment and Sectoral Shifts:
Evidence from Micro Data
Prakash Loungani, Richard Rogerson and Yang-Hoon Sonn
Unit Roots in Real GNP: Do We Know, and Do We Care?
WP-89-22
W P-90-2
Lawrence /. Christiano and Martin Eichenbaum
Money Supply Announcements and the Market's Perception
of Federal Reserve Policy
WP-90-3
Steven Strongin and Vefa Tarhan
Sectoral Shifts in Interwar Britain
W P-90-7
Prakash Loungani and Mark Rush
Money, Output, and Inflation: Testing the P-Star Restrictions
WP-90-8
Kenneth N. Kuttner
Current Real Business Cycle Theories and Aggregate Labor
Market Fluctuations
WP-90-9
Lawrence J. Christiano and Martin Eichenbaum
The Output, Employment, and Interest Rate Effects of
Government Consumption
S. Rao Aiyagari, Lawrence J. Christiano and Martin Eichenbaum
WP-90-10
Money, Income, Prices and Interest Rates after the 1980s
W P-90-11
Benjamin M. Friedman and Kenneth N. Kuttner
Real Business Cycle Theory: Wisdom or Whimsy?
WP-90-13
Martin Eichenbaum
Macroeconomic Models and the Term Structure of Interest Rates
WP-90-14
Steven Strongin
4
Working paper series continued
Stock Market Dispersion and Real Economic Activity:
Evidence from Quarterly Data
WP-90-15
Prakash Loungam, Mark Rush and William Tave
Term-Structure Spreads, The Money Supply Mechanism,
and Indicators of Monetary Policy
WP-90-16
Robert D. Laurent
Another Look at the Evidence on Money-Income Causality
WP-90-17
Benjamin M. Friedman and Kenneth N. Kuttner
Investment Smoothing with Working Capital:
New Evidence on the Impact of Financial Constraints
WP-90-18
Steven Fazzari and Bruce Petersen
5
Staff Memoranda
A series of research papers in draft form prepared by members of the Research
Department and distributed to the academic community for review and comment. (Series
discontinued in December, 1988. Later works appear in working paper series).
Risks and Failures in Banking: Overview, History, and Evaluation
George /. Benston and George G. Kaufman
SM-86-1
The Equilibrium Approach to Fiscal Policy
SM-86-2
David Alan Aschauer
Banking Risk in Historical Perspective
SM-86-3
George G. Kaufman
The Impact of Market, Industry, and Interest Rate Risks
on Bank Stock Returns
SM-86-4
Elijah Brewer, III and Cheng Few Lee
Wage Growth and Sectoral Shifts: New Evidence on the
Stability of the Phillips Curve
SM-87-1
Ellen R. Rissman
Testing Stock-Adjustment Specifications and
Other Restrictions on Money Demand Equations
SM-87-2
Randall C. Merris
The Truth About Bank Runs
SM-87-3
George G. Kaufman
On The Relationship Between Standby Letters of Credit and Bank Capital
SM-87-4
Gary D. Koppenhaver and Roger Stover
Alternative Instruments for Hedging Inflation Risk in the
Banking Industry
Gary D. Koppenhaver and Cheng F . Lee
SM-87-5
The Effects of Regulation on Bank Participation in the Market
SM-87-6
Gary D. Koppenhaver
Bank Stock Valuation: Does Maturity Gap Matter?
SM-87-7
Vefa Tarhan
6
Staff Memoranda continued
Finite Horizons, Intertemporal Substitution and Fiscal Policy
SM-87-8
David Alan Aschauer
Reevaluation of the Structure-Conduct-Performance
Paradigm in Banking
SM-87-9
Douglas D. Evanoff and Diana L. Fortier
Net Private Investment and Public Expenditure in the
United States 1953-1984
SM-87-10
David Alan Aschauer
Risk and Solvency Regulation of Depository Institutions:
Past Policies and Current Options
SM-88-1
George J. Benston and George G. Kaufman
Public Spending and the Return to Capital
SM-88-2
David Aschauer
Is Government Spending Stimulative?
SM-88-3
David Aschauer
Securities Activities of Commercial Banks: The Current
Economic and Legal Environment
SM-88-4
George G. Kaufman and Larry R. Mote
A Note on the Relationship Between Bank Holding Company
Risks and Nonbank Activity
SM-88-5
Elijah Brewer, III
Duration Models: A Taxonomy
G. O. Bierwag, George G. Kaufman and Cynthia M. Latta
SM-88-6
Durations of Nondefault-Free Securities
G. 0. Bierwag and George G. Kaufman
Is Public Expenditure Productive?
SM-88-7
David Aschauer
7
Staff Memoranda continued
Commercial Bank Capacity to Pay Interest on Demand Deposits:
Evidence from Large Weekly Reporting Banks
Elijah Brewer, III and Thomas H. Mondschean
SM-88-8
Imperfect Information and the Permanent Income Hypothesis
SM-88-9
Abhijit V. Banerjee and Kenneth N. Kuttner
Does Public Capital Crowd out Private Capital?
SM-88 1
-0
David Aschauer
Imports, Trade Policy, and Union Wage Dynamics
SM-88-11
Ellen Rissman
8
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