1
Robert M. Bushman
Kenan-Flagler Business School
University of North Carolina at Chapel Hill
Abbie J. Smith
Graduate School of Business, University of Chicago
X. Frank Zhang
School of Management, Yale University
First Draft: October 2004
This Draft: March 2007
Abstract
We provide strong evidence that documented patterns in investment-cash flow sensitivities across a-priori partitions based on dividend payouts, firm age and Cleary’s (1999) metric do not reflect financing frictions, but rather reflect the direct connection between capital investment and corresponding investments in non-cash working capital. We argue that the cash flow variable typically used in estimating investment-cash flow sensitivities, earnings before depreciation, really serves as a proxy for investment in non-cash working capital, rather than as a proxy for cash available to fund investment in the face of financing constraints. We document that the observed pattern in investment-cash flow sensitivities is driven by the fact that signal-to-noise ratio in this cash flow variable with respect to working capital investment varies systematically across the typical a-priori partitions used. This analysis complements and extends research focusing on measurement error in q (e.g., Erickson and Whited (2000), Alti (2003)) and on the measurement of financial constraints (e.g., Kaplan and Zingales (1997), Cleary (1999)) to explain investment-cash flow sensitivities. Our analysis reveals that estimating reduced form investment equations which measure cash flows as earnings before depreciation essentially results in capital investment being regressed on a noisy measure of working capital investment and thus does not speak directly to financing constraints .
1
We appreciate the comments of Steve Fazzari, Steve Kaplan, Anil Kashyap, Bruce Petersen, and seminar participants at Chinese University of Hong Kong, Emory University and the University of Toronto. Bushman thanks the Kenan-Flagler Business School, University of North Carolina at Chapel Hill for financial support, and
Smith appreciates support from the William Ladany Research Fund at the Graduate School of Business at the
University of Chicago.
1

Following Fazzari, Hubbard, and Petersen (1988) (hereafter FHP), a large empirical literature posits that if external financing frictions create a wedge between the cost of internal and external funds, then the capital investment decisions of financially constrained firms will be sensitive to internally generated cash flows, after controlling for investment opportunities.
Consistent with this hypothesis, empirical studies indeed document that investment-cash flow sensitivities are higher for firms a priori classified as being more financially constrained based on criteria such as dividend payout and firm age (see Hubbard (1998) for a review of the literature).
On the other hand, a number of recent papers raise serious concerns about whether investmentcash flow sensitivities reflect financing frictions, focusing on measurement error in q (e.g.,
Erickson and Whited (2000), Alti (2003)) and on the measurement of financial constraints (e.g.,
Kaplan and Zingales (1997, 2000), Cleary (1999)) to explain investment-cash flow sensitivities.
In this paper, we develop a novel argument that focuses on the underlying structure of the cash flow variable as the key to understanding investment-cash flow sensitivities. In particular, we argue that the cash flow variable typically used in estimating investment-cash flow sensitivities really serves as a proxy for investment in non-cash working capital, rather than as a proxy for cash available to fund investment in the face of financing constraints. We provide strong evidence that documented patterns in investment-cash flow sensitivities across a-priori partitions based on dividend payouts, firm age and Cleary’s (1999) measure of financial constraints do not reflect financing frictions, but rather reflect the direct connection between a firm’s capital investment and corresponding investments in non-cash working capital.
The empirical specification generally used in the literature defines firm-level cash flow as accounting earnings before depreciation ( EBD ). However, EBD is actually composed of a true
2

cash component, cash flow from operations ( CFO ), and a non-cash component (which we label working capital accruals or WCACC ) which reflects net investment in non-cash working capital such as inventories and accounts receivable.
2
This disaggregation of EBD is the key to more fully understanding the essence of investmentEBD sensitivities.
We show that the documented pattern in investmentEBD sensitivities is driven by the working capital investment component ( WCACC ), not the cash component ( CFO ). That is, the empirical patterns are really reflecting capital investment-working capital investment sensitivities, rather than investment-cash flow sensitivities! This result raises a serious challenge to the financing constraint interpretation of investmentEBD sensitivities. To the extent that fixed capital investment represents an increase in the scale of the firm, it is natural to expect a corresponding increase in non-cash working capital items such as inventories and accounts receivable.
3 This relation has nothing to do with financing constraints but rather is a manifestation of increasing scale.
The main innovation in our paper lies in our explanation for why capital investmentEBD sensitivities vary monotonically across a-priori partitions based on characteristics such as dividend payouts, firm age and Cleary’s (1999) metric. We argue that this occurs because the informativeness of the WCACC component of EBD with respect to contemporaneous investment in working capital varies systematically across these partitions. The essence of our argument is that the WCACC component of EBD consists of two aspects: investments in non-cash working capital which are positively related to growth, and random fluctuations in working capital which
2 The accounting relation is EBD = CFO + WCACC.
3 To emphasize the interpretation of WCACC as working capital investment, we replicate all the main results of the paper substituting just the change in inventory (one component of WCACC ) in place of WCACC.
The idea is that, when a company expands its manufacturing capacity and sells more products, its inventory levels naturally increase.
3

are largely independent of growth.
4
,
5
Recognizing that partitions based on dividends, age and
Cleary’s metric to a large extent partition firms based on growth, the idea is that for growing, capacity expanding firms, working capital investment is also expanding. As a result, the informativeness of WCACC with respect to working capital investment dominants the effect of the random component for such firms, and the WCACC component of EBD correlates highly with contemporaneous capital investment. On the other hand, for slow growth or steady state firms, random fluctuations dominant the working capital investment aspect of WCACC , resulting in a lower correlation between investment and EBD for these firms. We provide evidence consistent with this argument.
It is important to put our results into the context of the existing literature that argues against a financing friction interpretation of investment-cash flow sensitivities. Beginning with
Poterba (1988), a number of studies have noted that potential measurement error in q complicates the interpretation of documented investment-cash flow sensitivities. These papers include Gilchrist and Himmelberg (1995), Erickson and Whited (2000), and Alti (2003). It is most instructive contrast our paper with Alti (2003).
Alti (2003) argues that measurement error in q relative to capturing near-term investment opportunities varies systematically across firms partitioned by dividend payout, and that cash flow reflects near-term investment opportunities. As a result, cash flow should be relatively more important in explaining current investment for growth firms (relative to mature firms) because q has more measurement error relative to near-term opportunities for growth firms. Consistent
4 Zhang (2007) shows that working capital accruals, which by definition measure growth in operating non-cash working capital, are highly positively related to other growth attributes, such as growth in number of employees, growth in sales, growth in fixed assets, and growth in financing activities.
5 Such random fluctuations follow from simple timing issues such as inventory replenishment timing and cash collection timing on accounts receivable. For example, consider a steady state firm that replenishes inventory to upper threshold, S, whenever inventory levels hit lower threshold, s. This would generate timing bounces between s and S that are unrelated to growth.
4

with Alti (2003), we show that not only does cash flow reflect near-term investment opportunities, it directly embeds near-term investment in working capital ( WCACC )! However, unlike Alti (2003), our explanation does not require that measurement error in q vary systematically across partitions. Instead, we argue that extent to which EBD ( WCACC ) reflects working capital investment relative to random fluctuations in working capital varies systematically across the common a-priori partitions. We only require that measurement error in q allows space for a direct measure of near-term working capital investment ( WCACC ) to load.
Finally, it is also instructive to compare our results with those of Kaplan and Zingales
(1997, 2000) and Cleary (1999).
6
While using different approaches, these papers attempt to directly measure financing constraints, and using these measures, find that investment-cash flow sensitivities are actually higher for financially unconstrained firms than for financially constrained firms.
7 In our paper, we replicate Cleary’s result using the Z
FC
measure he developed.
8
As we show, high Z
FC
represents growing firms while low Z
FC
represents low growth firms. The connection between FHP and Cleary then lies in the fact that both empirical designs partition firms on the extent of capacity expansion. Thus, investmentEBD sensitivities are higher for high Z
FC
firms (“unconstrained”) relative to low Z
FC
firms (“constrained”) because for growing firms, EBD ( WCACC ) is a good measure of such working capital investment and correlates highly with capital investment, while for slow growth firms, the opposite holds.
The rest of the paper is organized as follows. Section 2 lays our the empirical design.
Section 3 presents our main empirical analyses of investment-cash flow sensitivities. In section
6 Fazzari, Hubbard, and Petersen (2000) vigorously dispute both the theoretical claims of Kaplan and Zingales (1997 and 2000) and their empirical conclusions.
7 See also Moyen (2004) for a related approach.
8 The premise of Z
FC
as a proxy for financial constraint in Cleary (1999) is that firms who cut dividends are more likely to be financial constrained. Cleary (1999), estimates Z
FC
using discriminant analysis, classifying firms into dividend cut, no change, and dividend increase groups based on beginning-of-period variables that are chosen to proxy for firm liquidity, leverage, profitability, and growth.
5

4 we verify the robustness of our results to alternative empirical specifications, while section 5 concludes.
Our point of departure is the basic panel regression equation used in the literature
9
:
I t
/ K t

1
 
0
 
1 q t

1
 
2
EBD t
/ K t

1
 e t
, (1) where I t is capital investment in period t , K t-1
is capital stock at the beginning of the period, q t-1
is average q , and EBD t is the cash flow variable commonly used in the literature, measured as earnings before extraordinary items plus depreciation.
The innovation in our design follows from the recognition that EBD is not a pure measure of current, internally generated cash flows, as it also reflects a range of non-cash elements dictated by fundamental accounting principles. Using basic accounting identities, EBD can be disaggregated as
EBD = E + DEPEXP
= (ACCRUALS + CFO) + DEPEXP (2)
= (WCACC – DEPEXP +CFO) + DEPEXP
= WCACC + CFO. where E is earnings before extraordinary items, ACCRUALS is total accruals (the difference between accounting net income and cash flow from operations), WCACC is working capital accruals (primarily changes in both inventory and accounts receivable minus changes in accounts payable), CFO is cash flow from operations, and DEPEXP is depreciation expense.
9 Our main empirical analysis uses a Fama-MacBeth fixed effects approach. Specifically, we first subtract the time series average for each variable and each firm, and then estimate a series of cross-sectional regressions with the demeaned variables. Finally, we use the time series standard deviation of the estimated coefficients to compute fixed-effect Fama-MacBeth t-statistics (see Rajan et al. 2000). In section 6, we show that all results in the paper are robust to instead running specifications that include firm and year dummies in equation (1), as well as to a battery of other robustness tests.
6

Our empirical strategy then is to substitute CFO and WCACC for EBD in (1) above, and examine the pattern in both investmentCFO sensitivities and investmentWCACC sensitivities across partitions based on dividend payouts, firm age, and Cleary’s Z
FC
measure of financial constraints.
To illustrate the structure of EBD , the Appendix includes a statement of cash flows for
Columbia Sportswear Co. This statement is typical for U.S. public companies. The first section of the statement reconciles accounting net income ( E ) to cash flow from operations ( CFO ). The statement details the necessary adjustments to subtract items that increase net income but do not provide cash in the current period, to add back items that decrease net income but do not use cash in the current period, and to adjust for items that impact cash flow but not net income.
Thus, the process adds back depreciation (a non-cash expense) to earnings and adjusts for changes in working capital accounts as well as other non-cash revenues and expenses. Changes in non-cash working capital accounts are the main difference between cash flows from operations and earnings plus depreciation ( EBD ). As illustrated in the Appendix, working capital changes typically include changes in accounts receivables, changes in liabilities, changes in inventories, and changes in other operating activities.
10
Accrual accounting by its nature smoothes earnings, recognizing higher (lower) earnings than cash flows during periods of growth (decline). As a result, the difference between earnings and cash flows ( ACCRUALS ) is a function of firms’ business stage. Analogous to changes in the number of employees and other growth attributes, WCACC is sensitive to firms’ growth stage.
10 As a second illustration, consider a firm that buys inventory in response to an impending increase in demand, pays cash for it, but does not sell it by the end of the period. Note that CFO is lowered because the firm paid out cash and
WCACC is higher because inventory increased. In this example, while actual cash is reduced, profits are not because unsold inventory is considered an asset, not an expense. Inventory only impacts profits in the period when it is sold.
Summing the two components creates accrual accounting net income, and involves adding together cash flows
( CFO ) with investment in inventory ( WCACC ). A similar argument goes through when we allow for other working capital items like receivables and accounts payable.
7

During expansions, investment in fixed assets is naturally accompanied by investments in working capital to support growth.
11 What we clarify in our paper is that these investments in inventory and receivables are included in the cash measure, EBD ! Because capital expenditures and changes in working capital are two sides of the same growth phenomenon, the investment-
EBD sensitivity is driven by the positive relationship between capital investment and WCACC .
In the introduction we argued that capital investmentEBD sensitivities vary monotonically across a-priori partitions based on characteristics such as dividend payouts, firm age and Cleary’s (1999) metric because the informativeness of the
WCACC component of EBD with respect to contemporaneous investment in working capital varies systematically across these partitions. To clearly illustrate our argument for why the correlation between investment and WCACC varies across a priori partitions, consider the following simple example. Assume:
1.
Working capital investment, WCI = a*I , where I is capital investment. That is, working capital investment is proportional to capital investment. Let VAR ( I )
 
I
2
; and
2.
WCACC = WCI +

, where

is independent random fluctuation in WCACC due to timing issues. Let VAR (

)
 

2
and assume that


is the same for all firms.
Given these assumptions, it is straightforward to show that

( I , WCACC )


I
*
Cov
( a
2
( I
 2
,
I a

*

I
2

)
)
1 / 2

( a
2  2
I a


I
 2

)
1 / 2
. (3)
As we document below (table 3), dividend payout, firm age and Cleary’s (1999) metric are all significantly correlated with firm growth. In terms of our example then, our goal is to explain
11 Fazzari et al. (2000) in their reply to Kaplan and Zingales (1997 and 2000), construct a measure of “total investment” by adding the changes in both inventory and accounts receivable to capital expenditures.
8

why
 
( I , WCACC )

Growth

0 . Allowing

I
to potentially be a function of growth we write this partial derivative as
 
( I , WCACC )

Growth

 
( I , WCACC )
 
I d

I dGrowth
. (4)
Using (3), it is easy to show that the first term in (4),
 
( I , WCACC )
 
I
, is positive. Thus, the overall sign of the total derivative is determined by the sign of d

I dGrowth
. As we show empirically below (table 4), the standard deviations of investment, EBD and WCACC all increase across the growth partitions, implying that d

I dGrowth

0 . In essence, as growth increases,
WCACC becomes more informative with respect to working capital investment as

I
increases while

 is constant and, therefore, is more positively correlated to capital expenditure ( I ). It is our contention that this argument explains observed investmentEBD sensitivities.
We turn now to our empirical analysis.
In section 3.1 we describe our sample and present descriptive statistics. In section 3.2 we implement our main analyses of investment-cash flow sensitivities. In table 2, we split the cash flow variable used in the literature, EBD , into its two components, CFO and WCACC , and analyze these two components separately. In table 3, 4 & 5, we analyze the relation between capital investment and EBD , CFO, WCACC, and

INV across three commonly used partitioning variables: dividend payout ratio, firm age, and Z
FC
, the financial constraint index from Cleary
(1999). Finally in table 6, we attempt to distinguish our argument from Alti (2003).
3.1 Sample and descriptive statistics
9

Our sample selection is similar to that of Gilchrist and Himmelberg (1995), Almeida et al. (2004), and Almeida and Campello (2004). We consider the universe of manufacturing firms
(2000<=SIC<=3999) and a sample period that spans from 1971 to 2003. We delete the following observations.
(1) Firm-years with beginning PP&E less than $5 million (in 1982 dollars) in order to avoid the small denominator problem.
(2) Firm-years with asset growth exceeding 100% in order to avoid large M&A transactions and seasoned equity offers.
(3) Firms-years with negative q or with q in excess of 10 to reduce measurement error.
Additionally, following Bond and Meghir (1994) and Almeida and Campello (2004), we do not require that firms have no-missing observations throughout the sample period. Instead, we only require that firms have at least five consecutive years’ data in the sample period in order to address survivorship bias.
Following the literature, investment ( I ) is measured as capital expenditures.
12
Tobin’s q is average Tobin’s q at the beginning of the period measured as the market value of assets divided by the book value of assets. EBD is earnings before extraordinary items plus depreciation.
Working capital accruals ( WCACC ) are defined as changes in current assets excluding the cash balance, minus changes in current liability excluding debt and taxes payable. Cash flow from operations ( CFO ) equals earnings minus working capital accruals and depreciation expense (see table 1 for a detailed description of how all variables are measured).
13
12 In section 6, we try alternative measures of investment and implement a range of other robustness checks.
13 We measure WCACC using what is called the balance sheet method (i.e., WCACC t
is measured as (
– (

CA -

Cash )

CL -

STD -

TP ), where

CA = change in current assets (data4),

Cash = change in cash and cash equivalents
(data1),

CL = change in current liabilities (data5),

STD = change in short-term debt (data34), and

TP = change in tax payable (data71). A more direct method uses the cash flow statement, but this data is available only from
1989 forward. In section 6, we verify that our results are not an artifact of using the balance sheet method.
10

The premise of Z
FC
as a proxy for financial constraint in Cleary (1999) is that firms who cut dividends are more likely to be financial constrained. Following Cleary (1999), we use discriminant analysis and classify firms into dividend cut, no change, and dividend increase groups based on the following beginning-of-period variables that are chosen to proxy for firm liquidity, leverage, profitability, and growth: current ratio ( Current ), debt ratio ( Debt ), fixed charge coverage ( FCCov ), net income margin ( NI% ), sales growth ( SalesGrowth ), and slack/net fixed assets ( SLACK/K ). Z
FC
is estimated using the following model (see Cleary (1999) for more detail):
Z
FC
 
1
Current
 
 
2
FCCov
5
SalesGrowt h
 
6
 
3
SLACK
Debt
/ K
 
4
NI %
(5)
Table 1 provides descriptive statistics for our sample and describes precisely how all variables are measured. Panel A shows that our sample firms on average invest 23.8% of beginning capital. All variables exhibit significant variation, where the variables EBD , CFO,
WCACC, and

INV (all scaled by K t-1
) all range from large positive to large negative values.
Table 1, panel B reports a correlation matrix. Focusing on Pearson correlations (results from Spearman correlations are qualitatively similar), the investment variable, I t
/K t-1
, exhibits correlations of .27 or higher with all variables except for CFO (

= .06). All variables are correlated with q at greater than .17 except for CFO (

= .05), and with sales growth ( SGR ) at greater than .23 except CFO (

= -.04). Also note that CFO and WCACC are negatively correlated at -.4. This large negative correlation is well documented in the accounting literature
(see e.g., Dechow (1994) and Dechow, Kothari and Watts (1998)), and results from the smoothing processes inherent to accrual accounting systems. Interesting for our study is that, despite the large negative correlation between WCACC and CFO , both WCACC and

INV (a
11

component of WCACC ), are highly correlated with fixed investment ((

= .27 and .32 respectively) while CFO is only correlated with investment at a level of .06.
3.2 Empirical results of investment-cash flow sensitivities analyses
We begin our analysis of investment-cash flow sensitivities in table 2, where we examine the relation between investment and EBD , CFO , WCACC and

INV , after controlling for q but before considering any a-priori partitioning of firms. All regressions are based on the Fama-
MacBeth fixed effect approach, and t-statistics in parenthesis are fixed-effect Fama-MacBeth t statistics. Specifically, we first subtract the time series average for each variable and each firm.
Then, we estimate a series of cross-sectional regressions with the demeaned variables. Finally, we use the time series standard deviation of the estimated coefficients to compute t-statistics (see
Rajan et al. (2000)).
14
Table 2, column 1 documents the well-known positive and significant investment-cash flow sensitivity, with a coefficient on EBD of .146 and a t-statistic of 11.56. In sharp contrast, column 2 substitutes CFO for EBD and documents no relation between fixed investment and
CFO , while column 3 reveals a strong, positive relation between investment and WCACC
(t=24.23). Our main premise is that WCACC represents direct investment in growth and so is naturally correlated with other investments in growth like capital expenditures. To highlight our interpretation of WCACC as investment in non-cash working capital, we run the regression with change in inventory (

INV ), a component of WCACC , in place of WCACC . Column 4 shows that

INV loads positive and significant with a coefficient of .16 and a t-statistic of 34.05!
A priori partitions: dividend payouts, firm age and Z
FC
14 In section 4, we show that all results are robust to alternative specifications.
12

We next consider three partitioning variables that have been used in the literature to classify firms as financially constrained: dividend payout ratio, firm age, and Cleary’s (1999) financial constraint index, Z
FC
. There has been considerable debate over the extent to which these variables actually sort firms based on financial constraints. While FHP and others have argued that dividend payout and firm age (among others) capture financing constraints, Kaplan and Zingales (1997) and Cleary (1999) construct alternative measures and arrive at the conclusion that investment-cash flow sensitivities are actually higher for unconstrained firms.
15
However, our concern in this paper is not with what makes them different, but with what significant source of commonality exists across these different sorting variables. As we document in table 3, all three partitioning variables are significantly correlated with firm growth.
The dividend payout ratio and firm age are negatively related to asset growth, sales growth and earnings growth, while Cleary’s Z
FC
is positively and significantly related to all three growth measures. We argue in this paper that it is differences in growth characteristics of firms across a priori partitions that underpin documented patterns in investmentEBD sensitivities, not differences in financial constraints. To bolster this argument, note that given that dividend payout and firm age are negatively correlated with growth, while Z
FC
is positively correlated, it goes a long way towards explaining why FHP argues investment-cash flow sensitivities are increasing in how financial constrained firms are, while Cleary (19899) argues that these sensitivities are decreasing in financial constraints. The commonality is that investment-cash flow sensitivities increase with the growth characteristics of firms.
Now that we have documented that the three partitions are fundamentally correlated with growth, we next offer empirical evidence to support our theory that the informativeness of the
15 Our formal analysis focuses on Cleary’s Z
FC
variable as it is implementable using a large data sample, while
Kaplan and Zingales (1997) construct a small sample measure based on in-depth analysis of firms’ financial reports.
13

WCACC component of EBD with respect to contemporaneous investment in working capital varies systematically with growth. First, in table 4, panel A we document that the standard deviations of capital investment, EBD , and WCACC decrease across dividend payouts and firm age partitions (which are negatively related to growth) and increase across Z
FC
partitions (which are positively related to growth).
16
This pattern is consistent with the example we presented in section 2 above. Recall that in that example, we assumed that working capital investment, WCI
= a*I , where I is capital investment, VAR ( I )
 
I
2 , and that WCACC = WCI +

, where

is independent random fluctuation in WCACC due to timing issues. We then showed that
 
( I , WCACC )

Growth

0 when d

I dGrowth

0 . Table 4, panel A documents that the data supports this latter condition.
Then in table 4, panel B, we present Pearson correlations between capital investment and
EBD , CFO , WCACC and

INV across partitions based on dividend payout, firm age and Z
FC
. As illustrated in the example, we see that the correlations between investment and WCACC (

INV,
EBD ) decrease across three groups based on dividend payouts and firm age and increasing across three groups based on Z
FC
A similar pattern was documented in FHP, table 2, with respect to dividend payout partitions. However, no consistent pattern emerges for the correlation between investment and CFO across the various partitioning variables. Taken together, the evidence is consistent with our argument that as growth increases, WCACC becomes more informative with respect to working capital investment, thus providing an intuitive explanation for observed investmentEBD that has nothing to do with financing constraints.
Next, we formally test the investment-cash flow models by controlling for q . Table 5 presents multivariate regressions. It consists of four panels, one panel for each of the four
16 Again, a similar pattern was documented in FHP, table 2, with respect to dividend payout partitiuons.
14

variables, EBD , CFO , WCACC and

INV . In each panel, we analyze the relations of investment with one of these four variables across partitions based on dividend payout ratio, firm age, and
Z
FC
.
Table 5, panel A replicates the main result of the extant literature, showing that the sensitivity between investment and EBD varies systematically across partitions based on the a priori financial constraint variables. For dividend payout ratio partitions, the sensitivity coefficient decreases fairly monotonically from .205 in the bottom quartile to .149 in the top quartile. For firm age, the bottom through third quartiles have roughly the same sensitivity
(.146, .148 and .151 respectively), while the top quartile at .139 is lower. For Z
FC
, the sensitivity increases monotonically from .075 in the bottom quartile to .226 in the top quartile. Panel B reveals that when we substitute CFO for EBD , the systematic relations documented in panel A disappear. The sensitivity coefficients on CFO are orders of magnitude smaller than those on
EBD in panel A, and the systematic pattern across financial constraint partitions is no longer discernable.
In contrast, in table 5, panel C, when we use WCACC , the systematic patterns in panel A for EBD reappear. InvestmentWCACC sensitivities vary monotonically across partitions based on all three financial constraint variables. For dividend payout ratio and firm age partitions, the investmentWCACC sensitivity decreases monotonically from the bottom quartile to the top quartile, while for Z
FC
partitions the ordering is reversed. Finally, in panel D we find that investment-

INV sensitivities display relations qualitatively similar to those displayed by investmentWCACC sensitivities in panel C.
Tables 1 through 5 show that previously documented relations between capital investment and cash flow actually represent relations between investment and WCACC , but not
15

between investment and CFO . The evidence in tables 3 through 5 provides evidence that the non-linear relation between investment-cash flow sensitivity and proxies for “financial constraints” widely documented in the literature is really capturing the relation between working capital investment and firm growth. More specifically, the proxies used to capture financial constraints proxy for growth, and WCACC represents a direct investment in growth and so is correlated with other investments in growth like capital expenditures.
Finally, we conclude this section by contrasting our explanation for investment-cash flow sensitivities with that offered by Alti (2003). Alti (2003) argues that measurement error in q relative to capturing near-term investment opportunities varies systematically across firms partitioned by dividend payout, and that cash flow reflects near-term investment opportunities.
As a result, cash flow should be relatively more important in explaining current investment for growth firms (relative to mature firms) because q has more measurement error relative to nearterm opportunities for growth firms. In contrast, we argue that extent to which EBD ( WCACC ) reflects working capital investment relative to random fluctuations in working capital varies systematically across the common a-priori partitions. Consistent with Alti (2003), we show that not only does cash flow reflect near-term investment opportunities, it directly embeds near-term investment in working capital ( WCACC ).
However, unlike Alti (2003), our explanation does not require that measurement error in q vary systematically across partitions. We only require that measurement error in q allows space for a direct measure of near-term working capital investment ( WCACC ) to load. In table 6, we assess Alti’s claims about systematic measurement errors in q as the driving force behind investment-cash flow sensitivity results by examining Pearson correlations between q and investment variables measured at different time horizons. For each a priori partitioning variable,
16

the correlation between q and investment is roughly the same regardless of time horizon. That is, contrary to Alti’s claim that measurement error in q relative to near term investment opportunities is relatively higher for low dividend (young) firms, the horizon does not seem to matter for the relation between q and investment across partitions. Note that, regardless of horizon, q is more highly correlated with I/K for low dividend (young) firms than for high dividend (mature firms).
4.1 The standard firm-year fixed effect model
As discussed above, our empirical tests are based on a Fama-MacBeth fixed effect approach. To alleviate concerns that our results are not robust to the typical panel data methodology used in the previous literature, we present evidence based on alternative specifications.
In table 7, we replicate table 3 using the standard panel technique used in the prior literature:
I t
/ K t

1
 
0
 
1 q t

1
 
2
EBD t
/ K t

1

FIRMDUMMY

YEARDUMMY
 e t
(6)
Using equation (6), Panels A, B and C of table 7 document the relation between investment and
CFO, WCACC, and

INV , respectively, across partitions based on dividend payouts, firm age and Z
CF
. Supporting the results in table 3, table 7 documents a generally monotonic, non-linear relation between investment and both WCACC and

INV across partitions, but no such relation between CFO and investment.
In table 8, we extend the analysis of table 7 to include additional proxies for investment opportunities. In particular, to equation (7), we add q t
, q t-2
, and q t-3
. In addition, we run specifications adding the median analyst forecast of long-term growth and the median analysts
17

forecast of year t+1
’s earnings scaled by assets per share in year t . The basic relations documented in tables 3 and 7 continue to hold in the specifications of table 8.
4.2. Measurement error in WCACC
Consistent with a vast accounting literature, we estimate working capital accruals from the balance sheet because the statement of cash flows is only available after 1989. Hribar and
Collins (2002) show that the balance-sheet-based accrual measure suffers a measurement error problem, mainly due to mergers and acquisitions. Namely, changes in working capital accounts due to mergers and acquisitions are not a component of earnings and, therefore, should not be included in accruals. To address the measurement issue, we conduct the following three robustness checks, and the tenor of the paper is unchanged (Results are available from the authors upon request).
(1) Because WCACC is based on the balance sheet approach, we measure capital expenditure using the balance sheet approach as well. Namely, capital expenditure is measured as changes in net property, plant, and equipment plus depreciation expense.
(2) Because the measurement error is mainly from mergers and acquisitions, we exclude any firm-year observations where sales from mergers and acquisitions account for 5% of total sales or above.
(3) We measure both cash flow from operations and working capital accruals from statement of cash flows. We conduct this analysis using the post-1989 sample period due to the availability of the statement of cash flow.
18

Following Fazzari, Hubbard, and Petersen (1988), a large empirical literature examines whether external financing frictions impact the investment decisions of firms. In this paper, we critique the primary empirical design used in the literature and present evidence that the documented investment-cash flow sensitivities are not likely related to financing constraints. The distinguished feature of our approach is to focus on the underlying structure of the primary cash flow measure used in the literature to proxy for change in net worth, exploiting the fact that it can be written as cash flow from operations ( CFO ) plus working capital accruals ( WCACC ).
WCACC primarily capture changes in working capital accounts (e.g., inventory, accounts receivable, and accounts payable), a term often called investment in working capital in the accounting literature. We show that previously documented relations between capital investment and cash flow actually represent relations between investment and WCACC , but not between investment and CFO . A positive correlation between capital investment and WCACC naturally follows because WCACC represents a direct investment in growth and so is correlated with other investments in growth like capital expenditures.
We also provide evidence that the non-linear relation between investment-cash flow sensitivity and proxies for “financial constraints” widely documented in the literature is really capturing the relation between firm growth and investments in fixed assets and working capital.
More specifically, the proxies used to capture financial constraints proxy for growth, and the investment information contained in WCACC relative to random fluctuations due to timing issues
(the signal to noise ratio) increases with growth. That is, changes in working capital accounts are more likely to represent investment in working capital for capacity-expanding firms than for steady-state firms, for which WCACC may simply capture random fluctuations in working
19

capital accounts due to timing issues. As a result, the correlation between capital investment and
WCACC is higher for high-growth firms than for low-growth firms.
Our novel approach easily explains seemingly contradictory evidence on the investmentcash flow sensitivity documented in the literature (Fazzari et al. (1988), Kaplan and Zingales
(1997), and Cleary (1999)). The contradictory evidence is due to the fact that proxies for financial constraint in the prior literature are related to growth in opposite ways. Namely, firm age and dividend payout ratios are negatively related to growth, while Z
FC
is positively related to growth. Our approach also explains why, in prior literature, cash flows have a significant coefficient even for financially unconstrained firms (e.g., Fazzari et al. (1988)).
20

References
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Almeida, Heitor, Murillo Campello, and Michael Weisbach. 2004. The cash flow sensitivity of cash. Journal of Finance 59, 1777-1804.
Alti, Aydogan. 2003. How sensitive is investment to cash flow when financing is frictionless?
Journal of Finance 58: 707-722.
Bond, Stephen and Costas Meghir. 1994. Dynamic investment models and the firm’s financial policy. Review of Economic Studies 61, 197-222.
Carpenter, Robert E., Steven M. Fazzari, and Bruce C. Petersen, ‘‘Inventory Investment,
Internal-Finance Fluctuations, and the Business Cycle,’’ Brookings Papers on Economic
Activity (1994:2), 75–138.
Cleary, Sean. 1999. The relationship between firm investment and financial status. Journal of
Finance , 673-692.
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Dechow, P., S. Kothari, and R. Watts. 1998. The relation between earnings and cash flows.
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Erickson, Timothy and Toni Whited. 2003. Investment-cash flow sensitivity and proxy quality thresholds. Working Paper. Bureau of Labor Statistics and University of Wisconsin.
Fama, Eugene and Kenneth French. 2001. Disappearing dividends: changing firm characteristics or lower propensity to pay. Journal of Financial economics 60, 3-43.
Fazzari, Steven, R. Glenn Hubbard, and Bruce Petersen. 1988. Financing constraints and corporate investment. Brookings Papers on Economic Activities , 141-195.
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705.
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Kaplan, Steven and Luigi Zingales. 2000. Investment-cash flow sensitivities are not valid measures of financing constraints. Quarterly Journal of Economics , 707-712.
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22

Table 1 Descriptive statistics
Panel A: Descriptive statistics
Variable Mean Std Min Q1 Median Q3 Max
I t
/K t-1 q t-1
EBD t
/K t-1
CFO t
/K t-1
0.238
1.447
0.302
0.275
0.190
0.915
0.499
0.528
0.014
0.551
-1.996
-2.018
0.117
0.905
0.155
0.104
0.188
1.151
0.294
0.264
0.292
1.626
0.479
0.472
1.104
5.890
1.987
2.135
WCACC t
/K t-1

INV t
/K t-1
0.027
0.024
0.390
0.271
-1.394
-0.937
-0.101
-0.063
0.013
0.007
0.147
0.100
1.553
1.106
SGR t
0.057 0.222 -0.534 -0.051 0.042 0.142 0.976
Panel B. Correlation Matrix (Pearson correlations are shown above the diagonal with Spearman below)
I t
/K t-1 q t-1
EBD t
/K t-1
CFO t
/K t-1
WCACC t
/K t-1

INV t
/K t-1
SGR t
I t
/K t-1 q t-1
EBD t
/K t-1
CFO t
/K t-1
WCACC t
/K t-1

INV t
/K t-1
1
0.337
0.445
0.163
0.268
0.321
0.337
1
0.347
0.123
0.234
0.229
0.282
0.191
1
0.582
0.339
0.330
0.056
0.051
0.667
1
-0.412
-0.246
0.273
0.177
0.366
-0.404
1
0.688
0.315
0.174
0.321
-0.252
0.723
1
SGR t
0.319 0.262 0.377 -0.023 0.448 0.490
I t
is capital expenditure (data128). K t-1
is beginning capital stock (data8). q t-1
is average q at the beginning of the period measured as the market value of assets divided by the book value of assets ((data6+data25*data199-data60data74)/data6). EBD t
is cash flow as measured as earnings before extraordinary items (data18) plus depreciation
(data14). WCACC t
is working capital accruals measured as ( change in current assets (data4),

CA -

Cash ) – (

CL -

STD -

TP ), where

CA =

Cash = change in cash and cash equivalents (data1),

CL = change in current liabilities (data5),

STD = change in short-term debt (data34), and

TP = change in tax payable (data71).

INV t
is inventory accruals, measured as changes in inventory. CFO t
is cash flows from operations measured as earnings before extraordinary items minus accruals, where accruals are equal to working capital accruals minus depreciation.
SGR t
is sales growth. The sample includes all manufacturing firms from 1971 to 2003. All variables are winsorized at 1% and 99%.
1
0.312
0.232
0.282
-0.039
0.400
0.433
23

Table 2 Regressions of investment on Tobin’s q and cash flows
Regression Models
Intercept q t-1
EBD t
/K t-1
1
-0.001
(-0.33)
0.057
(16.37)
0.146
(11.56)
2
0.000
(0.08)
0.079
(23.20)
3
0.000
(0.02)
0.069
(22.98)
4
0.000
(0.06)
0.068
(23.11)
CFO t
/K t-1
-0.002
(-0.47)
WCACC t
/K t-1
0.091
(24.23)

INV t
/K t-1
0.163
(34.05)
Adj. R 2 0.156 0.087 0.128 0.151

INV t
/K t-1
is the inventory component of working capital accruals. Other variables are as defined in Table 1. The regressions are based on the Fama-MacBeth approach, and t-statistics in parenthesis are fixed-effect Fama-MacBeth t-statistics (see Rajan et al. 2000). Specifically, we first subtract the time series average for each variable and each firm. Then, we estimate a series of cross-sectional regressions with the demeaned variables. Finally, we use the time series standard deviation of the estimated coefficients to compute t-statistics.
24

Table 3 Pearson correlations between “proxies” for financial constraints and growth
Dividend Payout Ratio Firm Age
The financial constraint index Z
FC in Cleary
(1999)
Assets growth -0.170** -0.064** 0.164**
Sales growth -0.183** -0.114** 0.151**
Earnings growth -0.111** -0.001 0.189**
** significant at 0.01% level.
Asset growth is the growth in total assets (data6) from year t-1 to t. Sales growth is the growth in sales (data12) from year t-1 to t. Earnings growth is the growth in earnings before extraordinary items (data18) from year t-1 to t, where earnings in year t-1 has to be positive. Other variables are as defined in Table 1.
25

Table 4 Explaining correlations between investment, CFO, EBD, and WCACC
Panel A: The standard deviation of investment and cash flow variables across partitions
The standard deviation of
I t
/K t-1
EBD t
/K t-1
WCACC t
/K t-1
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Three groups based on dividend payout ratio (negatively correlated with growth)
0.228 0.402 0.438
0.154
0.136
0.311
0.306
0.302
0.279
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Three groups based on firm age (negatively correlated with growth)
0.233 0.572 0.448
0.178 0.442 0.379
0.119 0.320 0.289
Three groups based on financial constraint index Z
FC
(Cleary
1999, positively correlated with growth)
0.186 0.342 0.309
0.161
0.195
0.274
0.512
0.320
0.456
26

Table 4, Panel B: Pearson Correlations
Pearson correlation between I t
/K t-1
and
EBD t
/K t-1
CFO t
/K t-1
WCACC t
/K t-1
∆ INV t
/K t-1
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Group 1 (low)
Group 2 (Medium)
Group 3 ( Top)
Four quartiles based on dividend payout ratio (negatively correlated with growth)
0.492 0.075 0.349 0.355
0.435
0.371
0.356
0.458
0.153
0.171
0.009
0.140
0.258
0.188
0.272
0.262
Four quartiles based on firm age (negatively correlated with growth)
0.300 0.041 0.303 0.340
0.275
0.335
0.090
0.166
0.244
0.189
Four quartiles based on financial constraint index Z
FC
(Cleary
1999, positively correlated with growth)
0.092 -0.086 0.201 0.271
0.295
0.326
0.288
0.260
0.313
0.361
Each year we partition firms into three equal-size groups based on dividend payout ratio, firm age, or Z
FC
.
∆
INV t
/K t-
1 is the inventory component of working capital accruals (changes in inventory scaled by beginning capital). Other variables are as defined in Table 1.
27

Panel A: I t
/ K t

1
Table 5 Regressions across financial constraint quartiles
 
0
 
1 q t

1
 
2
EBD t
/ K t

1
 e t
Intercept q t-1
EBD t
/K t-1
Adj. R 2
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Partitioned by Dividend Payout Ratio (negatively related to growth)
-0.007
(-1.91)
-0.001
(-0.20)
-0.002
(-0.53)
-0.010
(-2.29)
0.065
(9.89)
0.047
(11.69)
0.035
(8.72)
0.027
(8.75)
0.205
(12.16)
0.215
(12.49)
0.191
(11.92)
0.149
(14.15)
Partitioned by Firm Age (negatively related to growth)
0.202
0.190
0.162
0.110
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.016
(3.95)
-0.005
(-1.19)
-0.008
(-1.72)
-0.007
(-1.40)
0.076
(14.38)
0.057
(11.50)
0.054
(14.37)
0.026
(8.77)
0.146
(8.71)
0.148
(9.48)
0.151
(12.44)
0.139
(10.81)
0.192
0.156
0.144
0.124
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
-0.017
(-3.87)
-0.003
(-0.77)
-0.003
(-0.62)
0.003
(0.60)
0.079
(13.92)
0.070
(12.65)
0.055
(8.51)
0.038
(12.11)
0.075
(7.67)
0.174
(10.62)
0.204
(11.55)
0.226
(15.81)
0.106
0.145
0.174
0.208
28

Table 5, Panel B: I t
/ K t

1
 
0
 
1 q t

1
 
2
CFO t
/ K t

1
 e t
Intercept q t-1
CFO t
/K t-1
Adj. R 2
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Partitioned by Dividend Payout Ratio (negatively related to growth)
0.006
(1.24)
0.014
(2.40)
0.005
(1.00)
-0.011
(-2.20)
0.098
(16.48)
0.082
(18.93)
0.063
(14.61)
0.045
(14.29)
-0.018
(-2.57)
0.013
(2.10)
0.007
(0.82)
0.011
(1.60)
0.121
0.092
0.078
0.048
Partitioned by Firm Age (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.021
(4.72)
-0.005
(-0.93)
-0.008
(-1.38)
-0.007
(-1.17)
0.101
(24.57)
0.081
(16.46)
0.077
(18.11)
0.041
(13.78)
-0.006
(-1.19)
-0.001
(-0.23)
0.005
(0.93)
0.011
(1.65)
0.131
0.085
0.071
0.056
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
-0.027
(-6.31)
-0.003
(-0.50)
0.007
(1.15)
0.023
(3.57)
0.090
(16.68)
0.100
(18.46)
0.084
(13.51)
0.068
(15.70)
-0.017
(-3.15)
-0.012
(-2.26)
-0.011
(-1.66)
0.031
(3.42)
0.081
0.084
0.094
0.106
29

Table 5, Panel C: I t
/ K t

1
 
0
 
1 q t

1
 
2
WCACC t
/ K t

1
 e t
Intercept q t-1
WCACC t
/K t-1
Adj. R 2
Partitioned by Dividend Payout Ratio (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.001
(0.27)
0.011
(2.04)
0.003
(0.74)
-0.009
(-1.99)
0.082
(14.28)
0.074
(18.29)
0.058
(13.80)
0.042
(14.65)
0.106
(18.83)
0.088
(13.78)
0.079
(9.08)
0.074
(11.55)
0.170
0.123
0.104
0.072
Partitioned by Firm Age (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.018
(4.59)
-0.005
(-0.96)
-0.007
(-1.30)
-0.007
(-1.22)
0.087
(20.78)
0.070
(15.91)
0.070
(17.77)
0.039
(15.04)
0.100
(19.70)
0.098
(14.32)
0.081
(16.24)
0.058
(9.30)
0.171
0.129
0.104
0.075
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
-0.022
(-5.37)
-0.004
(-0.77)
0.004
(0.65)
0.021
(3.52)
0.081
(15.85)
0.088
(17.00)
0.072
(12.77)
0.061
(14.29)
0.064
(13.29)
0.082
(16.52)
0.099
(12.83)
0.116
(12.96)
0.106
0.116
0.137
0.143
30

Table 5, Panel D: I t
/ K t

1
 
0
 
1 q t

1
 
2

INV t
/ K t

1
 e t
Intercept q t-1

INV t
/K t-1
Adj. R 2
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Partitioned by Dividend Payout Ratio (negatively related to growth)
0.002
(0.42)
0.010
(1.93)
0.004
(0.75)
-0.008
(-1.81)
0.083
(15.09)
0.073
(18.21)
0.057
(14.10)
0.040
(14.16)
0.168
(21.62)
0.151
(15.19)
0.141
(16.46)
0.154
(15.40)
0.185
0.140
0.120
0.103
Partitioned by Firm Age (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.018
(4.50)
-0.004
(-0.92)
-0.007
(-1.24)
-0.006
(-1.13)
0.086
(20.97)
0.069
(16.75)
0.069
(17.30)
0.038
(14.39)
0.171
(22.99)
0.168
(18.05)
0.146
(19.06)
0.133
(15.74)
0.190
0.151
0.127
0.110
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
-0.019
(-5.08)
-0.004
(-0.80)
0.003
(0.61)
0.079
(16.77)
0.086
(17.08)
0.072
(12.85)
0.127
(18.22)
0.139
(17.09)
0.176
(18.68)
0.129
0.133
0.159
Top quartile (Q4)
0.019
(3.25)
0.059
(14.32)
0.230
(18.91)
0.174
Each year we partition firms into four quartiles based on dividend payout ratio, firm age, or Z
FC
.
Dividend payout ratio is dividend and stock repurchase divided by earnings before interest and tax. Firm age is the number of years since first time covered by Compustat. Z
FC is the financial constraint index in Cleary (1999). Other variables are as defined in Table 1. The regressions are based on the Fama-MacBeth approach, and t-statistics in parenthesis are fixed-effect Fama-MacBeth t-statistics. Specifically, we first subtract the time series average for each variable and each firm. Then, we estimate a series of cross-sectional regressions with the demeaned variables. Finally, we use the time series standard deviation of the estimated coefficients to compute t-statistics.
31

Table 6 Pearson correlations between q and investment variables at different horizons
Pearson correlation between Q t-1
and
I t
/ K t

1 j
2 

0
I t
 j j
3 

1
I t
 j
K t

1
K t

1
Four quartiles based on dividend payout ratio (negatively correlated with growth)
Bottom quartile (Q1)
0.446 0.460 0.419
Second quartile (Q2)
0.332 0.344 0.301
Third quartile (Q3)
0.284 0.290 0.259
Top quartile (Q4)
0.207 0.240 0.222
Bottom quartile (Q1)
Four quartiles based on firm age (negatively correlated with growth)
0.408 0.420 0.387
Second quartile (Q2)
0.314 0.321 0.284
Third quartile (Q3)
0.273 0.291 0.269
Top quartile (Q4)
Bottom quartile (Q1)
0.222 0.233 0.210
Four quartiles based on financial constraint index Z
FC
(Cleary
1999, positively correlated with growth)
0.322 0.365 0.339
Second quartile (Q2)
0.325 0.311 0.268
Third quartile (Q3)
0.287 0.281 0.250
Top quartile (Q4)
0.296 0.277 0.241
32

Table 7 Robustness:
Regressions across financial constraint quartiles using standard panel data technique
Panel A: I t
/ K t

1
 
0
 
1 q t

1
 
2
CFO t
/ K t

1

FIRMDUMMY

YEARDUMMY
 e t
Intercept q t-1
CFO t
/K t-1
Adj. R 2
Partitioned by Dividend Payout Ratio (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.109
(28.45)
0.155
(44.43)
0.145
(51.23)
0.152
(56.63)
0.112
(51.25)
0.067
(31.62)
0.046
(26.48)
0.028
(17.62)
-0.002
(-0.47)
0.034
(8.07)
0.039
(9.90)
0.037
(10.63)
Partitioned by Firm Age (negatively related to growth)
0.199
0.118
0.091
0.055
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.131
(34.81)
0.144
(44.80)
0.140
(49.16)
0.146
(71.11)
0.096
(49.82)
0.064
(35.92)
0.054
(29.94)
0.027
(21.41)
0.020
(6.27)
0.023
(7.70)
0.027
(8.66)
0.039
(13.88)
0.171
0.103
0.082
0.066
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.107
(32.39)
0.105
(29.97)
0.140
(40.43)
0.170
(45.53)
0.074
(34.86)
0.096
(38.09)
0.070
(31.41)
0.050
(30.37)
-0.003
(-1.27)
-0.003
(-0.78)
0.006
(1.41)
0.043
(9.49)
0.106
0.123
0.080
0.095
33

Table 7, Panel B: I t
/ K t

1
 
0
 
1 q t

1
 
2
WCACC t
/ K t

1

FIRMDUMMY

YEARDUMMY
 e t
Intercept q t-1
WCACC t
/K t-1
Adj. R 2
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Partitioned by Dividend Payout Ratio (negatively related to growth)
0.122
(33.26)
0.165
(49.98)
0.157
(58.64)
0.165
(63.80)
0.094
(43.19)
0.062
(29.77)
0.045
(26.73)
0.029
(19.60)
0.135
(30.12)
0.105
(21.36)
0.096
(19.60)
0.079
(17.07)
0.261
0.153
0.116
0.071
Partitioned by Firm Age (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.146
(40.49)
0.156
(50.84)
0.153
(55.76)
0.157
(78.59)
0.083
(43.66)
0.058
(32.76)
0.050
(28.32)
0.029
(23.50)
0.125
(28.79)
0.103
(25.07)
0.085
(21.98)
0.064
(18.00)
0.222
0.144
0.111
0.076
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
0.116
(36.60)
0.115
(34.67)
0.151
(47.56)
0.187
(56.53)
0.071
(34.76)
0.084
(33.60)
0.057
(26.20)
0.043
(27.38)
0.063
(18.36)
0.102
(24.20)
0.139
(29.19)
0.181
(32.93)
0.131
0.156
0.144
0.167
34

Table 7, Panel C: I t
/ K t

1
 
0
 
1 q t

1
 
2

INV t
/ K t

1

FIRMDUMMY

YEARDUMMY
 e t
Intercept q t-1

INV t
/K t-1
Adj. R 2
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
Partitioned by Dividend Payout Ratio (negatively related to growth)
0.118
(32.82)
0.163
(50.00)
0.157
(59.60)
0.168
(66.10)
0.096
(45.31)
0.062
(30.38)
0.044
(26.67)
0.027
(18.26)
0.205
(33.08)
0.165
(25.00)
0.156
(23.77)
0.161
(24.91)
0.269
0.167
0.129
0.098
Partitioned by Firm Age (negatively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
Top quartile (Q4)
0.146
(41.21)
0.156
(51.98)
0.152
(56.58)
0.159
(81.52)
0.082
(43.49)
0.056
(32.58)
0.049
(28.15)
0.027
(22.41)
0.208
(33.84)
0.179
(30.70)
0.154
(28.54)
0.139
(27.78)
0.238
0.163
0.132
0.105
Partitioned by financial constraint index Z
FC
(Cleary 1999, positively related to growth)
Bottom quartile (Q1)
Second quartile (Q2)
Third quartile (Q3)
0.122
(39.07)
0.115
(35.15)
0.147
(47.21)
0.067
(33.67)
0.083
(33.66)
0.058
(27.18)
0.131
(26.67)
0.163
(28.26)
0.213
(33.15)
0.156
0.170
0.161
Top quartile (Q4)
0.182
(56.13)
0.043
(28.03)
0.296
(38.33)
0.191
Each year we partition firms into four quartiles based on dividend payout ratio, firm age, or Z
FC
. Dividend payout ratio is dividend and stock repurchase divided by earnings before interest and tax. Firm age is the number of years since first time covered by Compustat. Z
FC
is the financial constraint index in Cleary (1999). Other variables are as defined in Table 1. All regression models include firm and year fixed effects.
35

Table 8 Robustness checks on Table 5 (dividend payout ratio) –including other proxies for investment opportunities
Coefficient Estimate of CFO
Regression Models
Baseline
Baseline, F t+1
Baseline, LG
Baseline, q t
Baseline, q t
, q t-2
Baseline, q t
, q t-2
, q t-3
Bottom DP quartile
-0.002
(-0.47)
-0.021
(-3.65)
-0.008
(-1.35)
-0.005
(-1.35)
-0.003
(4.04)
0.002
(0.53)
Second DP quartile
0.034
(8.07)
0.017
(2.92)
0.032
(5.13)
0.033
(7.73)
0.033
(7.64)
0.031
(6.96)
Third DP quartile
0.039
(9.90)
0.032
(6.26)
0.038
(7.19)
0.042
(10.38)
0.044
(10.89)
-0.010
(-2.81)
Top DP quartile
0.037
(10.63)
0.055
(11.47)
0.053
(11.13)
0.042
(12.03)
0.040
(11.31)
0.042
(11.73)
Regression Models
Baseline
Coefficient Estimate of WCACC
Second DP quartile
0.105
(21.36)
Third DP quartile
0.096
(19.60)
Top DP quartile
0.079
(17.07)
Baseline, F t+1
Baseline, LG
Baseline, q t
Baseline, q t
, q t-2
Baseline, q t
, q t-2
, q t-3
Bottom DP quartile
0.135
(30.12)
0.144
(22.11)
0.140
(19.37)
0.136
(29.93)
0.132
(28.43)
0.126
(26.21)
0.126
(17.75)
0.124
(16.84)
0.105
(21.18)
0.103
(20.41)
0.099
(19.39)
0.095
(15.31)
0.094
(14.25)
0.096
(19.55)
0.092
(18.55)
0.090
(18.06)
0.081
(12.91)
0.076
(11.52)
0.077
(16.38)
0.081
(17.33)
0.082
(17.37)
36

Regression Models
Baseline
Baseline, F t+1
Baseline, LG
Baseline, q t
Baseline, q t
, q t-2
Bottom DP quartile
0.205
(33.08)
0.230
(24.54)
0.223
(21.10)
0.204
(32.64)
0.196
(30.62)
Coefficient Estimate of

INV
Second DP quartile
0.165
(25.00)
Third DP quartile
0.156
(23.77)
0.201
(21.66)
0.201
(19.59)
0.165
(24.77)
0.164
(24.24)
0.168
(19.29)
0.172
(18.25)
0.157
(23.69)
0.151
(22.67)
Baseline, q t
, q t-2
, q t-3
0.194
(29.36)
0.157
(22.90)
0.148
(22.01)
0.160
(24.32)
The baseline model is
I t
/ K t

1
 
0
 
1 q t

1
 
2
CFO t
( WCACC ,

INV ) / K t

1

FIRMDUMMY

YEARDUMMY
Then we consider the following additional proxies for investment opportunities
F t+1
Median analyst forecast of year t+1’s earnings scaled by assets per share in year t.
LG Median analyst forecast of long-term growth. q t
Tobin’s q in year t. q t-2
Tobin’s q in year t-2. q t-3
Tobin’s q in year t-3.
Each year we partition firms into four quartiles based on dividend payout ratio ( DP ). The table only reports the coefficient estimates on CFO , WCACC , or

INV for each DP quartile.
 e t
Top DP quartile
0.161
(24.91)
0.169
(18.18)
0.164
(16.37)
0.159
(24.32)
0.161
(24.79)
37

Appendix Statement of cash flows—Columbia Sportswear Co. (COLM)
( http://finance.yahoo.com/q/cf?s=COLM&annual )
PERIOD ENDING
Net Income
31-Dec-04 31-Dec-03 31-Dec-02
138,624 120,121 102,518
Operating Activities, Cash Flows Provided By or Used In
Depreciation
Adjustments To Net Income
Changes In Accounts Receivables
Changes In Liabilities
Changes In Inventories
Changes In Other Operating Activities
Total Cash Flow From Operating Activities
Investing Activities, Cash Flows Provided By or Used In
Capital Expenditures
Investments
Other Cashflows from Investing Activities
Total Cash Flows From Investing Activities
Financing Activities, Cash Flows Provided By or Used In
Dividends Paid
Sale Purchase of Stock
Net Borrowings
Other Cash Flows from Financing Activities
Total Cash Flows From Financing Activities
Effect Of Exchange Rate Changes
Change In Cash and Cash Equivalents
18,628
4,785
(51,375)
21,445
(32,908)
(5,501)
93,698
(44,490)
245
610
(43,635)
-
(24,699)
(4,588)
-
(29,287)
5,112
$25,888
23,065
3,721
(30,825)
21,492
(16,635)
160
121,099
(17,118)
-
(29,762)
(46,880)
19,367
5,809
6,517
13,598
23,001
(2,259)
168,551
(38,023)
-
52
(37,971)
-
15,574
(20,863)
-
-
6,924
(21,980)
-
(5,289) (15,056)
985 64
$69,915 $115,588
38