Reappearing Investment-Cash Flow Sensitivities

Niclas Andréna , Håkan Jankensgårdb

According to previous research the investment-cash flow sensitivity has fallen over time to

levels approaching zero in the 2000s, prompting some researchers to argue that the sensitivity

cannot be a reasonable measure of financing constraints. We show that differences in

sensitivities reappear if one sorts firm according to firms’ need for funding (investment rates)

and the cost of external funding (leverage ratios). High capex-high leverage firms consistently

have higher sensitivities, even throughout the 2000s.

Key words: Investment; financial constraints; investment-cash flow sensitivity; capital

expenditure

JEL code: G30, G32

a Department of Business Administration and Knut Wicksell Centre for Financial Studies,

Lund University. Address: P.O. Box 7080, 220 07 Lund, Sweden. Telephone: +46 46 222

4666. Email: niclas.andren@fek.lu.se.

b Corresponding author. Department of Business Administration and Knut Wicksell Centre

for Financial Studies, Lund University. Address: P.O. Box 7080, 220 07 Lund, Sweden.

Telephone: +46 46 222 4285. Email: hakan.jankensgard@fek.lu.se.

Acknowledgements. Jankensgård gratefully acknowledges the financial support of the Jan

Wallander and Tom Hedelius foundation and the Tore Browaldh foundation.

Recent research has documented disappearing sensitivities of corporate investment to cash

flow over time (Larkin, Ng, and Zhu, 2018; Andrén and Jankensgård, 2017; Chen and Chen,

2012; Brown and Petersen, 2009; Ağca and Mozumdar, 2008; Allayannis and Mozumdar,

2004). Chen and Chen (2012) argue that if one believes that financial constraints have not

completely disappeared, investment-cash flow sensitivity cannot be a good measure of

financial constraints, as originally posited by Fazzari, Hubbard, and Petersen (1988). The idea

of investment-cash flow sensitivity as a measure of financial constraints has also been

seriously challenged by the observation that the priors used to classify firms as either

constrained or unconstrained do not seem to work since “unconstrained” firms typically have

higher, not lower, sensitivities (Cleary, 1999; Hadlock and Pierce, 2010; Bodnaruk, Loughran,

and McDonald, 2015; Farre-Mensa and Ljungqvist, 2016; Andrén and Jankensgård, 2017).

An emerging view in the literature is that investment sensitivity to cash flow reflects

investment intensity rather than financial constraints. Moshirian, Nanda, Vadilyev, and Zhang

(2017) show that investment-cash flow sensitivities are higher in countries with higher rates

of physical investment, and that sensitivity increases in the level of asset tangibility in a cross-

section of manufacturing firms. According to their data, capital investment is not

systematically related to cash flow in firms with low levels of tangible assets. Grullon, Hund,

and Weston (2018) find that capital expenditure is highly concentrated to a small number of

“big spenders” (the firms that invest the largest amount of dollars) and that it is these firms

that display the highest sensitivity of investment to variations in cash flow. In fact, the

sensitivity shows a strong, positive, and near-monotonic relationship with the level of

investment. The largest two ventiles in terms of investment spending, for example, have

sensitivities nearly twice as large as the median spender.

The question we ask in this paper is the following: conditional on investment intensity, do

firms that appear more financially constrained have higher sensitivities of investment to cash

flow? We thereby acknowledge that there are, as pointed out by Hennessy and Whited (2007),

two dimensions to financial constraints, namely the need for funds and the cost of external

financing. A plausible explanation for the poor performance of commonly used sorting

mechanisms is that they mix up these two logically distinct dimensions. This potentially

creates a confounding effect if the firms deemed to have the lowest cost of external financing

also have a systematically different demand for it (Franzoni, 2009). For example, large firms

may have better access to cheap external financing, but they also tend to have systematically

higher investment rates (as measured by capital expenditure). Our intuition is that for firms

that are sufficiently capital intense a high cost wedge on external financing may constrain

investment in the sense originally posited by Fazzari et al (1988). Hoberg and Maksimovic

(2015) argue, and provide empirical evidence, that the existence of good investment projects

is a necessary condition for financial constraints to be binding.

We test our hypothesis using the standard set of US manufacturing firms used in the literature,

covering the period between 1988 and 2014. Our initial, need-for-funds sort on the capital

intensity axis is capital expenditure (“capex”, defined as investment in fixed assets scaled by

total assets) lagged one period. As pointed out by Koenker and Hallock (2001), sorting on the

dependent variable introduces a bias akin to that of self-selection, in which the error term of

the fitted regression function will be related to the probability of entrance into the sample. To

mitigate concerns about endogenous-selection bias we also use expected capex, which relies

on the entire sample of firms and not the individual firm’s actual investment behavior. For

robustness, we furthermore consider the ratio of tangible assets to total assets, i e, asset

tangibility (Moshirian et al, 2017) for the need-for-funds sort. This variable, lagged one

period, has a correlation with capex of around 0.39. While not perfect, this relatively low

correlation mitigates concerns that selection bias unduly affects our conclusions.

In the second, conditional sort we use indicators that capture the cost of external funding, i.e.

the cost dimension of financial constraints. Our primary measure of the cost of external

financing is leverage. It is well supported by financial theory that, other things equal, writing

financial contracts becomes more difficult the more leverage the firm already has (Myers,

1977). From a theoretical point of view, leverage arguably has the most attractive properties

in terms of capturing a cost wedge between internal and external sources of capital. Even so,

in an extension we systematically evaluate, using the methodology in Bodnaruk et al (2015)

several of the proxies for financial constraints that have been proposed in the literature, such

as size, credit rating, and the Whited and Wu index (Whited and Wu, 2006). As in Bodnaruk

et al (2015), we find that traditional proxies do a poor job of predicting corporate behavior

indicative of improvement or deterioration in external financial constraints.

The evidence provides clear support for our hypothesis. Consistent with Moshirian et al

(2017) and Grullon et al (2018) we find that investment intensity predicts investment-cash

flow sensitivity. The initial sort on capital expenditure yields an economically significant

difference between investment-intense and investment-light firm. Firms in the top-three

deciles of the capex distribution have an investment-cash flow sensitivity of around 0.14,

whereas it is 0.02 for the bottom-three deciles. Similar results emerge when the initial sort is

based on expected capex or asset tangibility. This finding explains why priors that target the

cost of external financing are liable to do a poor job: investment-cash flow sensitivities are

first and foremost a function of investment intensity. In investment-light firms, the need for

funds to finance capex will not be binding.

When we carry out the conditional sort, this time using leverage as splitting criterion, the data

shows that there are significantly higher sensitivities for firms with a high cost wedge on

external financing. As conjectured, this only holds for firms with high investment intensity

(top-three capex deciles). For investment-light firms, leverage has no power to differentiate in

terms of the level of investment sensitivity to cash flow. This makes economic and intuitive

sense: it is only when investment needs are sufficiently large that the availability and pricing

of external financing truly become an issue.

We consider the alternative story that leverage, as an endogenous choice variable, in fact

captures access to funds rather than a high cost differential between internal and external

sources of financing. This view, articulated in, e g , Adam (2009), holds that debt financing is

only granted to creditworthy borrowers, whereas constrained firms are denied this option and

therefore end up with low leverage. As pointed out by Farre-Mensa and Ljungqvist (2016),

financial constraints can also be construed in terms of capital rationing, i e, being shut out of

capital markets altogether. In response to this concern we evaluate leverage as a measure of

constraints using the tests proposed in Bodnaruk et al (2015). These tests revolve around

liquidity events related to the deterioration or improvement in external financing conditions,

such as changes in dividend payments, equity recycling, and underfunded pension plans. Our

results show that highly leveraged firms are more likely to decrease or omit a dividend

payment, less likely to initiate or increase dividends, less likely to engage in equity recycling,

and more likely to have underfunded pension plans. While some level of endogeneity can

never be ruled out completely for any corporate policy variable, we conclude that high-

leverage firms at the very least behave as if they were financially constrained.

As noted, we also evaluate existing measures of financial constraints in the literature.

Consistent with the findings of previous research, the data indicates that firms classified as

financially constrained as per these priors actually have higher, not lower, sensitivities (e g,

Farre-Mensa and Ljungqvist, 2016). The one exception to this pattern is status as dividend-

payer, which is the variable proposed by Fazzari et al (1988). The data also suggests that these

proxies for financial constraints fare no better once capital intensity is considered. When they

are used in the conditional sort the pattern is again that firms classified as unconstrained act as

if they were constrained (i e, they have higher sensitivities). Status as dividend-payer, as in

the first sort, is once more the exception in that dividend-paying firms have lower sensitivities

also conditional on being classified as investment intense.

This paper concludes that investment-cash flow sensitivities appear to be related to financial

constraints, understood in the sense of a cost wedge between internal and external sources of

funding as originally proposed by Fazzari et al (1988). However, it is important to appreciate

that this appears to hold only when investment intensity exceeds a threshold where financial

constraints become binding. The sensitivities themselves are first of all functions of

investment intensity, which captures the by far largest differential. Another takeaway from

our analysis is that leverage is rehabilitated as a measure of financial constraints. On the level

of theory, leverage has the most attractive properties in that it is well-known to make financial

contracting more difficult. While corporate policies can always be argued to be to some

degree endogenous, sorting on leverage does identify firms that act as if they are financially

constrained, in accordance with theoretical models.

2. Literature review

Fazzari et al (1988) posited that investment-cash flow sensitivity measures financial

constraints. They measured sensitivity using the neoclassical Q model augmented with

internally generated cash flow:

𝐶𝑎𝑝𝑒𝑥𝑖,𝑡 = 𝛾0 + 𝛾1𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑖,𝑡 + 𝛾2𝑄𝑗,𝑡−1 + 𝜀𝑗,𝑡 (1)

where Capex and Cash flow are physical investment and internal cash flow (adequately

normalized) and Q is the asset market-to-book ratio. Investment-cash flow sensitivity is

measured by γ1, while γ2 measures investment-Q sensitivity. Fazzari et al’s key contribution

was to rank firms on the severity of financing frictions. They found strong support for the

proposition that investment-cash flow sensitivity captures financial constraints in that

sensitivity fell monotonically with their financial constraints proxy (the dividend payout

ratio).

Fazzari et al spawned an enormous literature examining the impact of cash flow on

investment, and investment-cash flow sensitivity has become a standard metric in the

corporate finance literature. Still, it is being challenged, both conceptually and

methodologically. Kaplan and Zingales (1997) and Chen and Chen (2012), among others,

question the interpretation of sensitivity as a measure of financial constraints. Methodological

debates primarily center around the following aspects: (i) measurement errors in Q (e g,

Erickson and Whited, 2000; Almeida, Campello, and Galvao Jr, 2010; Ağca and Mozumdar,

2017); (ii) measurement of cash flow (Andrén and Jankensgård, 2017; Lewellen and

Lewellen, 2016); and (iii) choice of criterion for classifying firms on degree of financial

constraints. We focus on the last of these debates.

The foundation for investment-cash flow sensitivity as a measure of financial constraints is

that internal and external investment financing sources are not perfect substitutes. In face of

capital market frictions internal financing have a cost advantage over new debt and equity

financing, in the sense of the risk-adjusted cost of new debt and equity financing overshooting

the opportunity cost of cash flow and precautionary cash holdings (Fazzari et al, 1988).

Creditors and outside owners may face information asymmetries and the risk of adversely

pricing the financing they are providing, and they are exposed to agency problems and the

risk of firm insiders diverging from the interests of outside investors. The tax system may

favor capital gains over dividend taxation, hence giving a cost advantage to internal financing.

In the event of financial distress, claimholders will be forced to carry financial distress and

bankruptcy costs, and the objective of credit risk assessment is to estimate the likelihood of

this happening over the horizon of the financing contract so as to allow creditors to charge

borrowers for this expected loss. Since market frictions vary across firms and over time this

cost advantage varies across firms and time, suggesting that investment-cash flow sensitivity

should vary systematically across firms and time (Fazzari et al, 1988).

To be able to assess whether or not investment-cash flow sensitivity is actually measuring

financial constraints, the testing approach must fulfil three prerequisites. First, investigated

firms must be generating cash flows. Intuitively, it is not meaningful to talk about sensitivity

to cash flow unless the firm actually generates cash flows. For firms that have negative cash

flows, ICFS would not be capturing the effect of cash flow on investment. Rather, it would be

capturing the effect on investment of whatever negative cash flows symbolize. More

substantively, Allayannis and Mozumdar (2004), Bhagat, Moyen, and Suh (2005), Broussard,

Buchenroth, and Pilotte (2004), and Cleary, Povel, and Raith (2007) show strong

nonlinearities in sensitivity, or even that sensitivity turns negative, for firms that generate no

cash flow. Second, the substitutability between internal and external financing must be real.

Intuitively, it is not meaningful to talk about sensitivity to cash flow if investments are not

economically meaningful to the firm. As argued by Hoberg and Maksimovic (2015), there

must be a sufficient investment need for financial constraints to be binding. Thirdly,

identification requires a reliable proxy of financial constraints. We need a classification

scheme that reliably identifies firms that are most likely to be financially constrained. This

last aspect has proven to be problematic. The main problem is that constraints are not directly

observable, so must be inferred.

As Cleary et al (2007) point out, there is no obvious classification scheme to separate more

from less constrained and several classification schemes have been found to yield

counterintuitive investment-cash flow sensitivities. Kaplan and Zingales (1997) and Cleary

(1999) construct indices of financial health akin to credit scoring models where the most

constrained firms have the lowest sensitivity. Kadapakkam, Kumar, and Riddick (1998) and

Andrén and Jankensgård (2015) sort firms on size, finding that the smallest and presumably

most constrained firms exhibit the lowest sensitivity. Hadlock and Pierce (2010) find that

sorting on size, age, or the financial health indexes developed by Whited and Wu (2006, WW)

and Kaplan and Zingales (1997, KZ) yield similar counterintuitive sensitivities, while Grullon

et al (2018) refute the size-age (SA) index developed by Hadlock and Pierce (2010).

Bodnaruk et al (2015), Hoberg and Maksimovic (2015), and Farre-Mensa and Ljungqvist

(2016) evaluate if the behavior of firms classified as financially constrained is consistent with

actually being constrained. Theory suggests that financial constraints become more binding in

face of negative shocks, so constrained firms would be expected to curtail investments more

aggressively. Hoberg and Maksimovic (2015) find that neither the KZ index, nor the WW

index identifies firms that behave as expected in face of three different negative shocks. Farre-

Mensa and Ljungqvist (2016) show that neither the SA, KZ, or WW indexes, nor the payout

ratio or whether or not the firm has a credit rating are able to identify firms that behave as if

they were in fact constrained in face of shocks to demand for or supply of external financing.

Bodnaruk et al (2015) similarly show inconsistent behavior for firms classified using the SA,

KZ, and WW indexes, while Hennessy and Whited (2007) find that the Cleary (1999), KZ,

and WW indexes are perversely related with bankruptcy costs, with greater constraints being

related with smaller bankruptcy costs.

Hennessy and Whited (2007) suggest that there are two dimensions to financial constraints,

namely the need for external funds and the cost of external funds. We argue that a plausible

explanation for the poor performance of commonly used sorting mechanisms is that they mix

up these two logically distinct dimensions. This potentially creates a confounding effect if the

firms deemed to have the lowest cost of external financing also have a systematically different

demand for it (Franzoni, 2009). There must be a need for external financing for financial

constraints to be binding. Firms that do not invest or lack good investment opportunities, or

hold more than ample precautionary savings will not face a binding constraint, making the

constraints identification challenging. Hennessy and Whited (2007) show that the Cleary

(1999), KZ, and WW indexes are not correlated with the cost of external funds and conclude

by implication that they therefore seem better suited as proxies for the need for external funds.

In light of the weak performance of common constraints classifiers in Bodnaruk et al (2015),

Hoberg and Maksimovic (2015), and Farre-Mensa and Ljungqvist (2016), we believe a more

suitable avenue for identifying proxies for the need for external funds is recent findings that

investment sensitivity to cash flow reflects investment intensity. Grullon et al (2018) show

that sensitivity increases monotonically with capital expenditures, and the firms that invest

most in capital expenditures exhibit the highest sensitivity. This parallels the findings of

Moshirian et al (2017) that sensitivity is increasing in asset tangibility, and only investments

in firms with high tangible capital vary systematically with cash flows.

Moshirian et al (2017) hypothesize that investment-cash flow sensitivity reflects investment

intensity rather than financial constraints. We instead propose that investment intensity

captures the need for external funds dimension of financial constraints. Rather than refuting

investment-cash flow sensitivity as a measure of financial constraints, we hypothesize that

sensitivity must be conditioned on investment intensity. We propose that sensitivity is an

inadequate measure of financial constraints when financial constraints are not binding. A first

step in the evaluation of if investment-cash flow sensitivity captures financial constraints is,

hence, to separate investment-intense firms from investment-light ones. It is only in the first

group we should expect sensitivity to be material, since it is in this group we would expect a

material, identifiable need for external funds. If sensitivity actually captures financial

constraints, as originally posited by Fazzari et al, 1988, then we should be investigating the

firms that fulfill the precondition of exhibiting a potential need for external financing. The

second step in the evaluation is to sort the firms exhibiting a potential need for external

financing on the cost of external financing. This, in turn, requires a classification scheme that

identifies as constrained firms whose behavior is consistent with actually being constrained.

3. Data

3.1. Empirical model and data

Following the existing literature, we estimate investment-cash flow sensitivity using the

neoclassical Q model augmented with internally generated cash flow:

𝐶𝑎𝑝𝑒𝑥𝑖,𝑡 = 𝛼𝑖 + 𝛼𝑡 + 𝛾1𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑖,𝑡 + 𝛾2𝑄𝑗,𝑡−1 + 𝑣𝑗,𝑡 (2)

Capex is the firm’s capital expenditures normalized by beginning-of-period total assets. Cash

flow is the firm’s internal cash flow, measured as operating cash flow deflated by beginning-

of-period total assets. Qj,t-1 is a proxy for investment opportunities, calculated as total assets

minus book value of equity plus market value of common stock and divided by total assets.

Our sample is obtained from Compustat and all variables are defined using Compustat items;

variable definitions are found in Appendix 1. The model includes fixed firm (αi) and period

(αt) effects and standard errors are heteroscedasticity-consistent and clustered at the firm

level. The coefficient γ1 measures the investment-cash flow sensitivity and γ2 is the

investment-Q sensitivity.

To be consistent with previous research, the sample consists of all US manufacturing firms

(from industries with SIC codes that begin with 2 or 3) on Compustat. The sample spans the

period 1988 to 2014. To mitigate the effect of outliers, we apply the same filters as Chen and

Chen (2012): firms are required to have valid observations for all variables in Eq. 2; firms

with asset or sales growth exceeding 100% are excluded; and total assets and sales are

required to be at least $1 million. We winsorize all variables at the 1st and 99th percentiles.

We focus on firm-years with positive cash flows to control for distortions of negative cash

flows on investment-cash flow sensitivity. Cleary et al (2007) and Guariglia (2008) argue that

the opportunity cost of underinvestment may surpass the cost wedge on external financing

when cash flow is sufficiently small. Creditors may look beneficially upon increased

investments for such firms, since the expected profit from additional investment will improve

the firm’s ability to meet debt commitments and will belong to creditors in case of default.

The larger the additional cash shortfall, the greater the externally funded investment,

suggesting that investment-cash flow sensitivity turns negative at sufficiently low (negative)

cash flow levels. Further, negative cash flows may be indicative of financial distress, and

distressed firms are typically not expected to respond to cash flow fluctuations in the same

way as firms under normal conditions, not least due to contractual or other restrictions on

investment policies (Allayannis and Mozumdar, 2004; Bhagat et al, 2005). Neither are

expansion-stage firms that may invest heavily in spite of reporting negative cash flow due to

having access to dedicated financing, not least, venture capital (Bertoni, Colombo, and Croce,

2010). Consistent with these predictions, Guariglia (2008), Cleary et al (2007), Bhagat et al

(2005), Allayannis and Mozumdar (2004), and Broussard et al (2004), among others, find

insignificant or negative investment-cash flow sensitivities when cash flows are negative.

The characteristics of firm-years with negative cash flows in our sample suggests that the

arguments brought forward by Bertoni et al (2010), Guariglia (2008), Cleary et al (2007),

Bhagat et al (2005), and Allayannis and Mozumdar (2004) may carry over to our sample. Our

raw sample consists of 56,703 observations from 5,896 individual firms; 26.2% of the firm-

years have negative operating cash flows. Table 1 reports summary statistics for the full

sample as well as for firm-years with positive and negative cash flows. Firm-years with

negative cash flows are smaller and exhibit slower sales growth and lower profitability, and

they are more levered and pay lower dividends. Overall, observations with negative cash

flows seem to be associated with financial distress. On the other hand, the negative cash flow

firm-years are also younger and invest less in capex, but more in R&D. They have higher Q,

but lower tangibility and hold larger cash reserves. Sales growth is more dispersed, with a

larger fraction of firm-years with high growth. These characteristics suggest that at least some

negative cash flow firms may be in an expansion stage. Accordingly, 57% (34%) of our

observations with negative (positive) cash flows are in high-technology industries.1 Overall,

these descriptive statistics suggest that it is not straightforward to classify the negative cash

flow observations. Many of them may be firms in financial distress, but a substantial fraction

of these firms rather seems to be in an expansion stage. Most importantly, they differ

substantially from observations with positive cash flows.

4. Results

4.1. Disappearing investment-cash flow sensitivities?

Ağca and Mozumdar (2008), Allayannis and Mozumdar (2004), Andrén and Jankensgård

(2017), Brown and Petersen, 2009, Chen and Chen (2012), and Moshirian et al (2017), among

others, suggest that investment-cash flow sensitivities have been disappearing over time. This

is confirmed by the solid line in Figure 1. The line shows estimates of γ1’s from rolling three-

year-window panel regressions of Eq. 2 on the full sample (incl both positive and negative

cash flow firm-years) and spanning the sample period, 1988-2014. Estimated sensitivities

decrease from levels around 0.08 in the early years of the period to 0.04-0.05 during the

2000s. These sensitivities stand in stark contrast to sensitivities around 0.6 reported for

constrained firms by Fazzari et al (1988) for the 1970s. In contrast to Chen and Chen (2012),

sensitivities in Figure 1 remain significantly positive throughout the sample period, even

during the global financial crisis (2007-2009).

1 Following Kile and Phillips (2009) we classify as high-technology firms those that belong to sic codes 283

(drugs), 357 (computer and office equipment), 366 (communication equipment), 367 (electronic components and

accessories), 382 (laboratory, optic, measure, control instruments), and 384 (surgical, medical, dental

instruments).

Our conclusions change if we control for the distortions introduced by blending firm-years

with positive and negative cash flows. The dashed and dotted lines in Figure 1 show estimates

of γ1’s for firm-years with positive (dashed) and negative (dotted) cash flows. First,

sensitivities are substantially higher for the positive cash flow subsample than the full sample.

Second, with the exception of a peak in 1993-95, sensitivities for positive (negative) cash

flow firm-years fluctuate around 0.13 (0) rather than exhibit a clear trend. The main

impression for positive cash flow firm-years is a reduction in volatility rather than level of

sensitivity. This suggests that the disappearing investment-cash flow phenomenon main not

be as ubiquitous as previous research have us believe.2

4.2. Investment-cash flow sensitivity and the need for funding

We propose that to be able to evaluate if investment-cash flow sensitivity measures financial

constraints we must separate between the need for and the cost of external funding. In this

section, we begin by evaluating how the need for funding influences sensitivity. We

emphasize that all models from here onwards are estimated on firm-years with positive cash

flows, unless otherwise noticed.

Since our focus is investments in physical assets – capital expenditures – we need a proxy for

the need for funding for this type of investment, which is a function of the firm’s need for

investing and its investment opportunities. Inspired by the results of Moshirian et al (2017)

and Grullon et al (2018), we measure need for funding by investment intensity. Specifically,

2 One explanation, but certainly not an exhaustive one, is changes in the fraction of firms reporting negative cash

flows. The fraction grew from around 20% in the early 1990s to 30% by the end of the decade, but has fallen

since.

we use the previous year’s capex (the ratio of capital expenditures to beginning-of-period total

assets) as a proxy for this year’s investment and funding need. Each year we sort firms on

previous year’s capex , and we classify firms in the top (bottom) three deciles of the previous

year’s capex distribution as having a high (low) funding need. Our expectation is that firms

with a high funding need will exhibit a substantially higher investment-cash flow sensitivity

than the firms with a low funding need. Table 2 reports the results.

The results in the base models (Columns 1 and 2) are in line with our expectations. Firms with

high investment intensity exhibit a sensitivity of 0.14. This is markedly (and significantly)

higher than the sensitivity of 0.02 of the firms with low investment intensity. Investments in

firms with high investment intensity are sizably more sensitive to fluctuations in cash flow.

We do not interpret the difference in sensitivity as an indication of differences in financial

constraints per se, nor do we interpret it as suggesting that sensitivity is a proxy for

investment intensity. Rather, the difference in sensitivity tells us that firms with high

investment intensity have a material, identifiable need for funds, whereas firms with low

investment intensity do not. This interpretation is supported by the estimates in Columns 3

and 4, where we augment the base models with size (log total assets), tangibility, leverage,

cash holdings, and net issuance of long-term debt and equity. We add leverage cash holdings,

and debt and equity issuance to control for alternative sources of financing. We add size and

tangibility as additional proxies for demand for capital investments; in addition, they

influence capital structure decisions so may approximate for demand for debt (excluding them

does not influence the results). Results for investment-cash flow sensitivity remain

unchanged, with firms with high investment intensity exhibiting substantially greater

sensitivity than do firms with low investment intensity. Instead turning our attention to

estimated sensitivities on alternative financing sources, we see similar differences in

sensitivities. Firms with high investment intensity exhibit significantly larger investment

sensitivity to changes in cash holdings, net issuance of long-term debt, and net issuance of

equity. Just as for investment sensitivity to cash flow, sensitivities to alternative financing

sources are several times larger for high investment intensity firms. This supports our

interpretation of investment intensity as an indicator of funding need.

Our results verify that it is indeed important to consider need for funding when estimating

sensitivity. It also verifies the findings of Moshirian et al (2017), who show that sensitivity

varies monotonically with asset tangibility. Our results are also consistent with Grullon et al

(2018), despite the fact that Grullon et al sort on investment spending whereas we sort on

investment intensity, and big spenders are not necessarily investment-intensive.3 We rather

see investment spending as a proxy for size.4

In Figure 1, we report estimates of γ1’s from rolling three-year-window panel regressions of

Eq. 2 for firms sorted on funding need. In addition to the firms with high and low investment

intensity we also include the four intermediate deciles (referred to as medium investment

intensity). The high funding need group exhibits the same hump in sensitivities in the early

1990s as does the overall sample of firm-years with positive cash flows. Beyond that

sensitivities seem to fluctuate around a stable mean of approximately 0.19. The other two

groups exhibit lower and declining sensitivities. The downward trend in sensitivity is

particularly marked for the medium investment intensity group.

3 The average simple correlation between investment spending (log capital expenditures) and investment

intensity (capex) calculated for each yearly cross-section is 0.33.

4 The average simple correlation between log capital expenditures and log total assets calculated for each yearly

cross-section is 0.94.

We address the concern that Q may be measured with error by employing Lewellen and

Lewellen’s (2016) IV estimator, where Q is instrumented by contemporaneous, lagged, and

squared cash flow and the first four lags of stock returns. Inserting instrumented Q in Eq. 2

does not influence our results for investment-cash flow sensitivity (results in Columns 5-6).

Rather than influencing estimated investment-cash flow sensitivities, correcting for

measurement error in Q alters the investment-Q sensitivities, which increase three- to fourfold

relative the base models. In Columns 7-8 we augment the measurement error correction

models with size, tangibility, leverage, cash holdings, and net debt and equity issuance (they

are also included in the first-stage regression to control for their correlation with Q). Again,

results remain robust. In Columns 9-10, finally, we report average slopes from annual cross-

sectional regressions. Estimating annual regressions rather than fixed-effect regressions

allows sensitivities to vary over time and corrects for both time-series and cross-sectional

dependence in the data. Again, results remain unchanged.

As pointed out by Hu and Schiantarelli (1998) and Koenker and Hallock (2001), sorting on

the dependent variable introduces endogeneity, or what Hu and Schiantarelli (1998) refers to

as endogenous selection. Not least, firms may underinvest in face of financially constraints,

which reduces investment intensity. We sort on the first lag of capex rather than the

dependent variable per se, but if capex is persistent we may still face endogenous selection

bias. Persistence is a mixed blessing, of course, since high persistence is a requirement for last

year’s capex to be a good proxy for next year’s investment and funding need. We evaluate

persistence by estimating the following model using the Anderson-Hsiao estimator:

𝐶𝑎𝑝𝑒𝑥𝑖,𝑡 = 𝛼𝑖 + 𝛼𝑡 + 𝛽𝐶𝑎𝑝𝑒𝑥𝑖,𝑡−1 + 𝑣𝑗,𝑡 (3)

The estimated persistence parameter, β, is 0.376 (t = 20.3), which suggests modest persistence

(the half-life of shocks to capex is 8.5 months).5

To mitigate concerns about endogenous selection bias, we complement capex as a proxy for

funding need with each year’s expected capex. We estimate expected capex in two ways. The

first model is based on Eq. 2 augmented with size, tangibility, leverage, cash holdings, and net

issuance of long-term debt and equity. To avoid interfering with the investment-cash flow

sensitivity testing we will do in the next step, we lag cash flow and net debt and equity issues

(instead using contemporaneous cash flow and net debt and equity issues is inconsequential

for our results):

𝐶𝑎𝑝𝑒𝑥𝑖,𝑡 = 𝛼𝑖 + 𝛼𝑡 + 𝛽1𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑖,𝑡−1 + 𝛽2𝑄𝑖,𝑡−1 + 𝛽3𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡−1 +

𝛽4𝐶𝑎𝑠ℎ ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑡−1 + 𝛽5𝑆𝑖𝑧𝑒𝑖,𝑡−1 + 𝛽6𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡−1 + 𝛽7𝑁𝑒𝑡 𝑑𝑒𝑏𝑡 𝑖𝑠𝑠𝑢𝑒𝑠𝑖,𝑡−1 +

𝛽8𝑁𝑒𝑡 𝑒𝑞𝑢𝑖𝑡𝑦 𝑖𝑠𝑠𝑢𝑒𝑠𝑖,𝑡−1 + 𝑣𝑗,𝑡 (4)

where Leverage and Cash holdings are the ratios of total debt and cash and marketable

securities to total assets, Size is the log of total assets, Tangibility is the ratio of property,

plant, and equipment to total assets, Net debt issues is the ratio of (long-term debt issuance –

long-term debt reductions) to beginning-of-period total assets, and Net equity issues is the

ratio of (sale – purchase of common and preferred stock) to beginning-of-period total assets.

Our second estimate of expected capex follows Richardson (2006):

5 Half life is calculated as ln(0.5)/ln(0.376).

𝐶𝑎𝑝𝑒𝑥𝑖,𝑡 = 𝛼𝑖𝑛𝑑 + 𝛼𝑡 + 𝛽1𝑄𝑖,𝑡−1 + 𝛽2𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡−1 + 𝛽3𝐶𝑎𝑠ℎ ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑡−1 + 𝛽4𝐴𝑔𝑒𝑖,𝑡 +

𝛽5𝑆𝑖𝑧𝑒𝑖,𝑡−1 + 𝛽6𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡−1 + 𝛽7𝐶𝑎𝑝𝑒𝑥𝑖,𝑡−1 + 𝑣𝑗,𝑡 (5)

where Age is the log of the number of years the firm has been listed on CRSP, Return is the

stock return for the year prior to the investment year, and αind are industry (2-digit SIC)

indicators. Both models are estimated using the full panel. Expected capex are given by the

predicted level of capex.

There is a risk of overfitting expected capex, so we use two additional proxies for need for

funding: industry capex and tangibility. Industry capex is contemporaneous average capex for

the firm’s industry defined as two-digit SIC, while tangibility is the firm’s beginning-of-

period ratio of property, plant, and equipment to total assets. Neither expected capex, nor

industry capex should suffer from endogenous selection bias. A firm that has the

characteristics of a high-investment-intensity firm or is active in an investment-intensive

industry, but underinvests relative other firms (expected capex) or industry peers (industry

capex) due to financial constraints would be correctly identified as belonging to the

investment intensive class. Tangibility captures capital intensity rather than investment

intensity. It may suffer from endogenous selection bias, but possibly less so than lagged

capex, since it reflects historical capex over a longer historical period.

Results using alternative proxies for need for funding are given in Table 3. Columns 1-2 (3-4)

present results for classifications based on expected capex estimated using Eq. 4 (Eq. 5),

while Columns 5-6 (7-8) contain results for classifying firms on industry capex (tangibility).

All results are in line with what we find when classifying on last year’s capex. No matter how

we measure need for funding, high need firms exhibit sizably larger investment-cash flow

sensitivity. This comes as no surprise, considering that the different classification schemes are

closely related. In Table 4, we report the fractions of firms classified as having high or low

funding need according to our main classification scheme (last year’s capex) that are also

classified as having high or low funding need according to the other classification schemes.

The fraction of congruent classifications, where a classification scheme classifies a particular

firm-year in the same way (high or low funding need according to both schemes) as the main

classification scheme ranges from 0.473 for industry capex to 0.848 for expected capex

estimated using Eq. 5. In contrast, the fraction of incongruent classifications, where the two

classification schemes give contradictory classifications (high (low) funding need according

to one scheme, low (high) according to the other) ranges from 0.260 for industry capex to

0.002 for expected capex estimated using Eq. 5. The high degree of congruent classifications

among, in particular, capex and expected capex suggests that we should expected similar

results for estimated sensitivities.

4.3. The need for funding and traditional financial constraints classifiers

To put the results using investment intensity classification in perspective, we in Table 5

present results of estimations of Eq. 2 for subsets of firms partitioned according to eight other

classification schemes proposed in the literature. Schemes 1 to 7 are commonly used in the

literature, whereas 8 is a recent concatenation of several preexisting classification schemes.

These schemes have been developed as financial constraints classifiers, not as measures of

funding need. Our intention with evaluating them is to assess to what extent they are

overlapping with investment intensity and, in the extension, draw conclusions on if there is

validity in our hypothesis that common classifiers mix up the need for and cost of funds

financial constraints dimensions.

In scheme 1, we follow Almeida et al (2004) and in every year rank firms based on their

payout ratio and assign to the financially constrained (unconstrained) group those firms that in

the previous year were in the bottom (top) three deciles of the annual payout distribution. The

payout ratio is calculated as dividends and share repurchases divided by operating income.

The intuition is that firms that distribute more funds to owners have internally generated cash

flow available to distribute rather than invest. The first two columns of Table 4 show that this

classification yields the expected result (for a financial constraints classification), with low-

payout firms exhibiting greater investment-cash flow sensitivity. However, the dispersion in

estimated sensitivities is small.

In scheme 2 we sort on beginning-of-period size, measured by log total assets. Again, we

partition firms annually into unconstrained (top three deciles) and constrained (bottom three

deciles) classes. Size is a commonly used proxy both for credit worthiness and for lower

adverse selection, but also for agency problems. In spite of this, and in spite of only including

positive-CF observations, but consistent with the findings of Kadapakkam et al (1998) and

Andrén and Jankensgård (2015) we in Columns 3 and 4 find the largest firms to exhibit the

greatest investment-cash flow sensitivity. Dispersion in estimated sensitivities is almost as

large as for the investment intensity classifications. Scheme 3 is instead based on age, again

with annual partitioning into unconstrained (top three deciles) and constrained (bottom three

deciles). Similar to size, age is a proxy for credit worthiness and lower adverse selection.

Again, results are counterintuitive, with similar sensitivities for constrained and unconstrained

firms. In scheme 4 we alternatively sort firms into those with a credit rating from Standard &

Poor’s. Following Almeida et al (2004), we classify as constrained those firm-years that never

had their debt rated during our sample period and that report positive debt. Financially

unconstrained firms are those with a public debt rating. The intuition is that a credit rating

improves transparency and opens up access to the public debt markets. However, again results

are counterintuitive with higher investment-cash flow sensitivity for firms with rated debt.

This result is consistent with Farre-Mensa and Ljungqvist’s (2016) conclusion that the

presence of a credit rating is not a reliable indicator of financial constraints.

Scheme 5 follows the logic of Cleary (1999) and Cleary et al (2007); we refer the reader to

these papers for more details on model estimation. We use logistic regression to construct a Z

index that estimates the likelihood that a firm will increase rather than decrease dividend

payments in the subsequent year. The predictor variables used for the logistic regression are

the current ratio, leverage, interest coverage, operating margin, sales growth, return on assets,

and financial slack. We re-estimate the prediction model annually and partition firms into

unconstrained (top three deciles) and constrained (bottom three deciles) ones based on last

year’s predicted Z index. The Z index are strongly correlated with the operating margin and

return on assets; high Z index firms are in essence profitable firms. In contrast to Cleary

(1999) and Cleary et al (2007), we find that constrained low Z index firms exhibit the highest

sensitivities, and with fairly substantial dispersion.

Scheme 6 uses the Whited and Wu (2006, WW) index. The WW index is a multidimensional

measure of financial strength that weighs in cash flow generation, whether the firm pays

dividends or not, leverage, size, and industry and firm sales-growth rates. As in the previous

schemes, we partition firms annually into the top (constrained) and bottom (unconstrained)

three deciles based on last year’s WW score. Again, our results are counterintuitive, with

firms with low WW-index values exhibiting the greatest sensitivity. The WW index is

strongly correlated with size (ρ = -0.96 with log assets), so it is not surprising that it yields the

same ranking as scheme 2 (and result in a dispersion that is comparable to that for size).

Scheme 7 instead uses the SA-index developed by Hadlock and Pierce (2010). The SA-index

is calculated as (-0.737×Size) + (0.043×Size squared) – (0.040×Age), where size is the log of

inflation-adjusted (to 2004) total assets (capped at 4.5 billion) and age is the number of years

the firm has been on CRSP with non-missing stock price (capped at 37 years). We partition

annually into top (constrained) and bottom (unconstrained) three deciles based on previous

year’s SA score. Again, results are counterintuitive with firms with low SA-index values

exhibiting the greatest sensitivity. This result is unsurprising given that the SA-index is highly

correlated with size (ρ = -0.94 with log assets) and with the WW-index (ρ = 0.93).

Scheme 8, finally, uses the ASCL index developed by Mulier, Schoors, and Merlevede

(2016). In the ASCL index a firm is assigned an annual score of 0 or 1 on age (= 1 if age is

below industry median), size (= 1 if two-year average size is below industry median), cash

flow (= 1 if two-year average cash flow is below industry median), and leverage (= 1 if two-

year average long-term debt to total assets is above industry median). The ASCL index is

equal to the sum of these four scores. We define industry using two-digit SIC codes. We

assign to the low (high) constraints group firms with an ASCL index in the previous year

below (above) two. The ASCL index works well for Mulier’s et al sample of unquoted

European firms. It works less well on quoted US firms; firms with low and high ASCL scores

exhibit similar investment-cash flow sensitivities.

To summarize, only the payout ratio and the Z index yield sensitivities that are consistent with

the schemes’ classification of financial constraints. Remaining schemes give sensitivities that

are inconsistent with what the schemes would have us believe if sensitivities were measuring

financial constraints. To evaluate the classification schemes further, we in Table 6 present

descriptive statistics for them. Certain corporate characteristics are shared by firms classified

as unconstrained across the different schemes. The schemes identify as unconstrained large,

profitable, and rated firms with high payout ratios and (with two exceptions) high Q and

tangibility and limited cash holdings and R&D expenses. Results are more dispersed for other

characteristics. Notably and surprisingly, in half or more of the schemes constrained firms

generate significantly greater cash flow, are less levered, and have higher interest coverage

than unconstrained firms. This can be compared to Kaplan and Zingales (1997), Hadlock and

Pierce (2010), and Hoberg and Maksimovic (2015) who use textual analysis on financial

filings to classify firms into financial-constraints categories. These studies suggest that

constrained firms tend to have lower cash flow, more debt, and higher Q values. Hadlock and

Pierce (2010) and Hoberg and Maksimovic (2015) add that constrained firms tend to be

smaller and younger. None of the classification schemes exhibits all of these five

characteristics (lower cash flow, higher debt, higher Q, smaller, and younger). Four of the

sorts exhibit four of these characteristics; sorting on dividend payouts and the ASCL and Z

indexes fails on Q, whereas sorting on age fails on cash flow.

A revealing exercise is to focus on characteristics that separate classes with high estimated

investment-cash flow sensitivity from those with low sensitivity. The high-sensitivity classes

typically (with concordance in at least six of eight schemes) comprise firms that are large, old,

profitable, rated, and with high tangibility. These are characteristics that may relate to need

for funding, but they are difficult to reconcile with high costs of external financing. High-

sensitivity classes are also characterized by high leverage, low interest coverage, high

payouts, and small R&D expenses, cash holdings, and cash flows. With the exception, these

all seem reasonable as traits of firms facing high costs of external financing. Notably, we do

not see any patterns for capex, Q, and sales growth.

In Table 6, we also present descriptive statistics for firms sorted on two measures of

investment intensity – capex and expected capex estimated using Eq. 4. We showed

previously that firms with high investment intensity exhibit high investment-cash flow

sensitivity. Comparing the high investment intensity classes with the characteristics of classes

with high sensitivity according to the other eight classification schemes reveal some

concordance. High investment intensity firms are also large, profitable, rated, have high

tangibility and payouts, and small R&D expenses and cash holdings. But they are not old, nor

are they highly leveraged or have low interest coverage. The high investment intensity firms

exhibit several patterns that are reasonable for investment-intense firms, not least being

characterized by high capex, Q, and tangibility firms. These are characteristics they share with

only two classification schemes, namely size and the WW index.

To further evaluate the overlap between traditional classification schemes and investment

intensity, we in Table 7 report the fraction of congruent classifications with our main need for

funds sort. Greater overlap means larger fractions on one diagonal and low on the other. To

exemplify, 45.7% (38.9%) of the low (high) investment intensity firms are classified as

financially constrained (unconstrained) according to the WW index, while only 17.3%

(23.3%) of the low (high) investment-intensity firms are classified as unconstrained

(constrained). This means we have a congruence ratio of (0.457+0.389)/(0.173+0.233) = 1.99.

Congruence ratios for other schemes range from 1.05 for age to 1.75 for size. To put them in

perspective, the same ratios calculated for alternative investment intensity proxies in Table 5

range from 1.82 for industry capex to 424 for expected capex calculated using Eq. 5. Four

traditional schemes perform at par with industry capex: size and the ASCL, SA, and WW

indexes, suggesting that one explanation for why at least these indexes may not work as

financial constraints proxies is that they capture need for funds rather than cost of external

financing.

5. Investment-cash flow sensitivity and the cost of external financing

The previous section shows that it is important to consider need for funding when estimating

sensitivity. Sorting firm-years on investment intensity allows us to sort out firms where

financial constraints may be binding. It is among these firms we would expect financial

constraints to play out. In this section, we classify firms on proxies for financial constraints

conditional on exhibiting high need for funding.

5.1. Identifying financial constraints

As Cleary et al (2007) point out, there is no obvious classification scheme to separate more

from less constrained, and studies show that commonly used classification schemes are not

reliable measures of financial constraints (e g, Farre-Mensa and Ljungqvist, 2016; Bodnaruk

et al, 2015; Hoberg and Maksimovic,2015). We hence approach the identification of

classification scheme more inductively and evaluate several classification schemes on their

ability to predict corporate behavior indicative of changes in external financial constraints.

We evaluate the eight schemes covered in the previous section. In addition, we include two

measures of a firm’s financial position and credit worthiness, namely leverage and interest

coverage. A weaker financial position may be informative on the cost of, in particular,

external debt financing, both by approximating the compensation charged by creditors for

expected loss and by measuring adverse selection. Empirically, financial distress and adverse

selection are difficult to separate, as they tend to have complementary effects on the cost of

borrowing. For example, adverse selection can lead to a decline in the average

creditworthiness of the pool of firms willing to borrow, as more creditworthy firms are driven

out of the market by larger credit spreads. Consequently, bond prices are generally more

information sensitive when the issuer is closer to default (e g, Hite and Warga, 1997). Whited

(1992) use an investment Euler equation to show both leverage and interest coverage to be

related to the shadow cost of external finance.

We employ the tests suggested by Bodnaruk et al (2015) to evaluate if different classification

schemes predict subsequent corporate behavior indicative of improvement or deterioration in

external financial constraints, what Bodnaruk et al (2015) refer to as liquidity events. We

consider dividend omissions, dividend increases, dividend decreases, equity recycling, and

underfunded pension plans. Our dependent variables in these tests are defined as follows:

Dividend omission: a dummy variable set to 1 if the firm stops paying dividends. Only

firms that paid dividends in the prior year are assigned a value. Stock markets typically

respond negatively to dividend reductions and the market penalty increases with the size

of the dividend reduction (Christie, 1994). Accordingly, we expect that the willingness

to pay the cost of dividend omissions would be directly related to the degree of financial

constraints. In line with this a number of studies, beginning with Fazzari et al (1988)

argue that that the propensity to pay dividends is inversely related with financial

constraints.

Dividend initiation: a dummy variable set to 1 if the firm begins paying dividends. Only

firms that paid no dividends in the prior year are assigned a value. Starting with Lintner

(1956) the dividend policy literature argues that dividend policies are conservative and

firms smooth dividend payments over time. Hence, beginning to pay dividends is a

long-term commitment and reversal of the policy carries a market penalty. Accordingly,

the financial constraints literature argues that firms would pay out dividends only when

cash flows exceed their investment needs. Consistent with this, Officer (2011) shows

that firms with poor investment opportunities and ample cash flow benefit from higher

dividend initiation announcement returns.

DPS decrease: a dummy variable set to 1 if the firm decreases its dividend per share

(DPS) relative the previous fiscal year. Only firms that paid dividends in the prior year

are assigned a value.

DPS increase: a dummy variable set to 1 if the firm increases its dividend per share

(DPS) relative the previous fiscal year. Only firms that paid dividends in the prior year

are assigned a value.

Underfunded pension plan: a dummy variable set to 1 if the firm has an underfunded

pension plan (projected pension benefit obligations (PBPRO) greater than pension plan

assets (PPLAO)). Only firms that report pension obligations and pension plan assets are

assigned a value. Rauh (2006) shows that financially constrained firms may face greater

difficulties to fund pension obligations, and mandatory contributions to defined benefit

pension plans deprive firms of funds required to finance investments.

Equity recycling: the ratio of cash dividends plus purchases of common and preferred

stock to the sale of common and preferred stock and normalized by beginning-of-period

total assets. As pointed out by Farre-Mensa and Ljungqvist (2016), the cost of

simultaneously raising and paying out equity should be an increasing function of the

degree of financial constraints, and any net benefits of such equity recycling would be

expected to be smaller for financially constrained firms.

We estimate logit models for dividend omission and initiation, dividend decrease and

increase, and underfunded pension plan and use ordinary least squares for equity recycling.

To measure financial constraints we in every year rank firms based on the eight traditional

classification schemes outlined in the previous section. We also classify firms on leverage

(calculated as the ratio of total debt to total assets) and interest coverage (measured by the

EBITDA to interest expense ratio). For leverage, we assign to the financially constrained

(unconstrained) class those firms that in the previous year were in the bottom (top) three

deciles of the annual leverage distribution. For interest coverage, we classify the bottom (top)

three deciles of the previous year’s interest coverage distribution as constrained

(unconstrained). We define constrained and unconstrained dummy variables set to 1 for firms

in the financially constrained and unconstrained groups. Following Bodnaruk et al (2015), we

in all regressions include as control variables the natural logarithm of market capitalization,

Q, the excess prior year buy-and-hold return, period fixed effects, and Fama and French

(1997) industry dummies. Including fixed effects in a nonlinear (logit) model leads to biased

results, but Greene (2004) shows that the bias is small for panels with more than 10 time

periods.

To clarify the employed model specifications, Panel A of Table 8 present full results for

leverage, whereas Panel B presents compressed results on differences between firms

classified as constrained vs unconstrained. Beginning with the results for leverage in Panel A,

in all tests financially constrained firms deviate significantly from unconstrained ones. Firms

with leverage in the top-three (constrained) deciles are significantly more likely to omit or

decrease dividends or have an underfunded pension plan than firms in the bottom-three

(unconstrained) deciles. They are also significantly less likely to initiate or increase dividend

payments or recycle equity. The underfunded-pension-plan model excludes the lagged

pension plan dummy, but results are robust to including it. The equity-recycling model

excludes firm fixed effects, but results are robust to including them. As an alternative, we in

unreported regressions replace the constrained and unconstrained dummy variables with

leverage lagged one period. Consistent with the results for the dummy variables, leverage

significantly increases (decreases) the likelihood of omitting or reducing dividends or having

an underfunded pension plan (initiating or increasing dividends). Higher leverage also has a

significant negative impact of equity recycling.

In Panel B we rerun all tests for classification schemes 1-8 and interest coverage. Interest

coverage classifies all liquidity events correctly, while schemes 1-8 do not. The best-

performing sort is the payout ratio. It fails to generate the correct classification for dividend

omissions, initiations, and decreases, but this is because 97% of the firm-years classified as

financially constrained pay no dividends. Consequently, there are very few dividend

omissions and decreases among these and even fewer dividend initiations. In fact, the model

for dividend initiations cannot be estimated, since the unconstrained-firm dummy perfectly

predicts dividend initiations. Further, there are no instances of dividend omissions among the

firm-years classified as unconstrained. Three of the other sorts also perform relatively well.

The ASCL index fails to yield the correct classification for dividend initiations and dividend

decreases, while the age sort in addition fails on equity recycling and the Z index fails on

dividend omissions and dividend increases. The size and rating sorts do not classify any

liquidity event correctly, while the WW and SA indexes only classify dividend increases

correctly.

All commonly used schemes fail to correctly predict behavior in face of corporate liquidity

events whereas leverage and interest coverage predicts behavior correctly for all liquidity

events. This suggests that leverage and interest coverage capture financial constraints better

than do the other eight classification schemes. To better appreciate the differences between

leverage and interest coverage on the one hand and the other eight schemes on the other, we

in Table 9 present the same descriptive statistics for leverage and interest coverage as we did

previously for the other schemes. The two measures of financial position share a number of

characteristics that are logically associated with financial constraints. Firm-years classified as

financially constrained (high leverage and low interest coverage) are characterized by high

leverage, low interest coverage and dividend payouts, smaller cash flows and cash holdings,

and weaker profitability. All these characteristics indicate a more strained financial position

with reduced creditworthiness and less internal wealth to spend on investments. Constrained

firms also invest less, both on capital expenditures and R&D, and exhibit lower growth rates.

It is revealing to compare these nine characteristics to the other eight classification schemes.

The ASCL and Z indexes share all characteristics. This is not surprising for the Z index, since

it was developed as a measure of creditworthiness (Cleary, 1999). It is less intuitive for the

ASCL index, since it sorts on age, size, and cash flow, in addition to leverage. Five of the

schemes – size, age, being rated, and the WW and SA indexes – share only a minority of

characteristics with leverage and coverage. Again, this may not be surprising, considering that

these schemes were originally proposed to capture financial constraints caused by information

asymmetry.

The constrained firms according to leverage and interest coverage exhibit low Q ratios, which

may be less apparent to relate to being financially constrained. Lower Q is reasonable for

financially distressed firms, but not necessarily for constrained firms (Hoberg and

Maksimovic, 2015). Rather, as constrained firms underinvest, the marginal investment would

be expected to be more profitable than for unconstrained peers. Leverage and coverage

provide opposing characteristics on age and size, two variables that are typically suggested to

be indicative of information asymmetry problems. Whereas constrained firms according to

leverage are larger and older, they are younger and smaller when classified on coverage.

5.2. Investment-cash flow sensitivity for financially constrained firms

In this section, we estimate investment-cash flow sensitivities for firms sorted on need for

funding and cost of external financing. We first sort firms on investment intensity. Then we

undertake a second sorting within the high- and low-investment-intensity classes on leverage

and interest coverage. If sensitivity captures financial constraints, we expect this to

identifiable among the firms with high funding need. In contrast, since financial constraints

are not binding among firms with low funding need, we do not expect any pattern in the

sorting on proxies for the cost of external financing. Results are presented in Table 10; to

conserve space we only report estimated investment-cash flow sensitivities.

Results when using leverage (Panel A, Columns 1-2) and interest coverage (Panel B, Columns

1-2) as the contingent sort are in line with our expectations. Among firm with high investment

intensity, those that are financially constrained exhibit sensitivities of 0.20-0.22. This is

markedly (and significantly) higher than the sensitivities of 0.06-0.08 of unconstrained

investment-heavy firms. In contrast, sensitivities are insignificant for both constrained and

unconstrained investment-light firms. We interpret this as strong support for Fazzari et al’s

(1988) proposition that investment-cash flow sensitivity measures financial constraints.

Investments in constrained firms with high funding need are sizably more sensitive to

fluctuations in cash flow, whereas financial constraints cannot discriminate among firms that

exhibit limited funding need and, as a result, are not exposed to the risk of facing binding

financial constraints.

We verify the robustness of these results in several ways. In Columns 3-4, we add size (log

total assets), tangibility, leverage, cash holdings, and net issuance of long-term debt and

equity as additional controls, with no influence on our results. As further support for the two-

way sort on need for funding and cost of external financing, sensitivities to debt and equity

issues also vary systematically across both the need for funding and cost of external financing

dimension. Sensitivities to both debt and equity issues are highest (smallest) for constrained

(unconstrained) firms with high (low) funding need. Estimated sensitivities to net debt

(equity) issues are 0.19 (0.32) for constrained firms with high funding need when using

leverage as the second, financial constraints sort and 0.30 (0.29) when sorting on interest

coverage. In contrast, sensitivities to net debt and equity issues for unconstrained firms with

low funding need instead lie in the range 0.04-0.12. In Columns 5-6 we replace Q by

Lewellen and Lewellen’s (2016) instrumented Q, while still including the same controls as in

Columns 3-4. Again, results remain robust. In unreported tests, we in addition estimate

sensitivities annually, with no impact on our results. In Columns 7-8 we replace capex with

predicted capex estimated using Eq 4 as proxy for need for funding. Again, results remain

robust. In unreported results we alternatively replace capex with predicted capex estimated

using Eq. 5, industry capex, and tangibility. Again, results remain the same.

In Panel C, we instead use classification schemes 1-8 for the second sort. Only three sorts

yield the expected results with firms classified as financially constrained exhibiting higher

sensitivity: classification on the payout ratio, age, and the Z index. What is interesting about

these the schemes is that they exhibit the lowest degree of overlap with the investment

intensity sorts in Table 7, so they may be the indexes that mix the need for and cost of

financing dimensions of financial constraints the least. All other sorts yield inconsistent

results, with firms classified as unconstrained exhibiting the same or higher sensitivity as

constrained firms. Since our previous assessment of corporate responses showed that none of

these schemes measures financial constraints adequately, we rather refute the schemes than

the investment-cash flow sensitivities. The differences in results between the dual sorting

using leverage and coverage, on the one hand, and these five sorts on the other support our

hypothesis that common classifiers mix up the need for and cost of financing dimensions of

financial constraints, especially when considering that they exhibit a fair amount of overlap

with investment intensity.

To further elaborate on what it is different sorts actually measure, we in Table 11 estimate

overlaps with dual sorts on leverage and interest coverage for high-investment-intensity firms.

Unsurprisingly, the overlap is sizable between the leverage and interest coverage sorts.

However, the overlap is just as large for the Z index, and the ASCL index also exhibits a fair

degree of overlap. In contrast, there is little overlap between the payout and age sorts on the

one hand and the leverage and coverage sorts on the other. Neither of these schemes overlap

with investment intensity either.

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Appendix 1: Variable definitions

Variable Definition (Compustat mnemonics in parenthesis)

Capex The ratio of capital expenditures (capx) to beginning-of-period

Assets

Cash flow The ratio of operating cash flow (oancf) to beginning-of-period

Assets

Q Total assets minus book value of equity (seq) plus market value of

common stock (prcc_f × csho) and divided by Assets

Sales Total sales revenues

Assets Book value of total assets (at)

Age Current year minus year of first observation on CRSP

Tangibility The ratio of property, plant, and equipment to Assets

Sales growth Annual growth rate of Sales

Return on assets Ratio of earnings before interest, depreciation, and amortization

(EBITDA) to beginning-of-period Assets

R&D intensity Ratio of R&D expenses to Sales with missing values for R&D

expenses set to zero

Leverage Ratio of total (short-term and long-term) debt to Assets

Interest coverage Ratio of EBITDA to interest expense

Fraction rated Fraction of firms that had their debt rated by Standard and Poor’s

at some point over the sample period

Dividend payout ratio Ratio of dividends paid to net income

Cash holdings Ratio of cash and marketable securities to Assets

Debt issues net Ratio of (long-term debt issuance – long-term debt reductions) to

beginning-of-period Assets

Equity issues net Ratio of (sale – purchase of common and preferred stock) to

beginning-of-period Assets

Table 1. Descriptive Statistics

Variable All observations Positive cash flow Negative cash flow

Mean Median Mean Median Mean Median

Observations 56,703 41,835 14,868

Capex 0.052 0.037 0.057*** 0.041 0.040 0.024

Cash flow 0.036 0.071 0.115*** 0.100 -0.184 -0.098

Q 1.965 1.442 1.788 1.415 2.463*** 1.560

Sales (mn) 2,069 166 2,698*** 321 301 25

Assets (mn) 2,802 157 3,661*** 302 386 36

Age 19.250 15.000 17.314*** 13.000 11.307 8.000

Tangibility 0.241 0.205 0.262*** 0.230 0.184 0.136

Sales growth 0.037 0.059 0.061*** 0.066 -0.030 0.019

Return on assets -0.023 0.035 0.053*** 0.056 -0.233 -0.154

R&D intensity 0.175 0.021 0.047 0.015 0.537*** 0.081

Leverage 0.230 0.188 0.218 0.192 0.263*** 0.173

Interest coverage 3.472 1.276 20.805*** 2.626 -49.400 -5.991

Fraction rated 0.255 0.000 0.314*** 0.000 0.077 0.000

Dividend payout ratio 0.139 0.000 0.181*** 0.097 0.020 0.000

Cash holdings 0.177 0.098 0.150 0.088 0.252*** 0.149

The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3) between

1988 and 2014 with asset or sales growth below 100% and total assets and sales in excess

of $1 million; all variables winsorized at the 1st and 99th percentiles. Variable definitions

are found in Appendix 1.

Table 2. Investment-cash flow sensitivity and need for funding

Base models Robustness checks

High

capex

(1)

Low

capex

(2)

High

capex

(3)

Low

capex

(4)

High

capex

(5)

Low

capex

(6)

High

capex

(7)

Low

capex

(8)

High

capex

(9)

Low

capex

(10)

Constant 0.055*** 0.019*** 0.347*** 0.098*** -0.014 0.008** 0.001 0.063* 0.073*** 0.022***

(16.7) (15.6) (5.99) (4.78) (-1.47) (2.42) (0.01) (1.86) (10.3) (8.65)

Cash flow 0.141*** 0.021*** 0.145*** 0.021** 0.133*** 0.016** 0.140*** 0.015* 0.204*** 0.034

(8.32) (2.72) (8.18) (2.40) (8.47) (2.09) (7.80) (1.75) (3.51) (1.09)

Q-1 0.012*** 0.003*** 0.008*** 0.002*** 0.046*** 0.008*** 0.038*** 0.007*** -0.001 0.000

(9.18) (4.11) (5.85) (2.76) (9.45) (3.90) (7.67) (2.78) (-0.22) (0.01)

Assets-1 -0.014*** -0.004*** 0.000 -0.002

(-4.93) (-3.64) (0.06) (-1.47)

Tangibility-1 -0.024 -0.031** -0.010 -0.035*

(-1.36) (-2.18) (-0.57) (-1.81)

Leverage-1 -0.036*** -0.008* -0.031** -0.002

(-3.33) (-1.91) (-2.25) (-0.42)

Cash holdings-1 0.061*** -0.003 0.027 -0.008

(4.08) (-0.63) (1.59) (1.56)

Debt issues net 0.197*** 0.033*** 0.181*** 0.044***

(9.95) (3.33) (8.26) (3.14)

Equity issues net 0.175*** 0.049** 0.161*** 0.036***

(6.82) (2.52) (5.40) (3.28)

N 10,725 9,018 9,262 7,882 8,135 7,188 6,686 5,959

Adj R2 0.396 0.391 0.490 0.421 0.388 0.267 0.486 0.294

The table reports estimates from panel regressions of capex on cash flow and beginning-of-period Q for firms sorted on previous year’s

capex (Columns 1 and 2). We assign to the high (low) funding need class firms in the top (bottom) three deciles of the previous year’s capex

distribution. Columns 3-4 augment the base models in Columns 1-2 with log assets, tangibility, leverage, cash holdings, and net issues of

long-term debt and equity. Columns 5-8 as similar to Columns 1-4, except that we instrument Q by lagged and squared cash flow and the

past four years’ stock returns. Columns 9-10 report average slopes from annual cross-sectional regressions. Variable definitions are found in

Appendix 1. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3) between 1988 and 2014. All models include

firm and period fixed effects. Standard errors are heteroscedasticity-consistent and clustered at the firm level.

Table 3. Investment-cash flow sensitivity and alternative proxies for need for funding

Expected capex

(Eq. 4)

Expected capex

(Eq. 5)

Industry capex Tangibilityt-1

High

capex

(1)

Low

capex

(2)

High

capex

(3)

Low

capex

(4)

High

capex

(5)

Low

capex

(6)

High

capex

(7)

Low

capex

(8)

Constant 0.047*** 0.018*** 0.057*** 0.019*** 0.038*** 0.027*** 0.034*** 0.023***

(12.3) (20.1) (17.7) (11.7) (16.0) (15.3) (8.20) (17.1)

Cash flow 0.164*** 0.011** 0.122*** 0.018*** 0.107*** 0.037*** 0.151*** 0.022***

(9.12) (2.57) (7.37) (2.68) (7.88) (5.37) (8.24) (3.18)

Q-1 0.014*** 0.003*** 0.009*** 0.004*** 0.010*** 0.008*** 0.020*** 0.004***

(9.80) (5.27) (8.67) (3.18) (9.78) (9.09) (9.29) (7.21)

N 10,469 10,469 10,525 10,525 14,614 14,669 10,591 9,173

Adj R2 0.267 0.248 0.385 0.295 0.465 0.462 0.425 0.511

The table reports estimates from panel regressions of capex on cash flow and beginning-of-period Q for firms sorted on expected capex

estimated using Eq. 4 (Columns 1 and 2) and Eq. 5 (Columns 3-4), industry capex (Columns 5-6), and beginning-of-period tangibility

(Columns 7-8). We assign to the high (low) funding need class firms in the top (bottom) three deciles of the distribution of each of the

classification schemes. Variable definitions are found in Appendix 1. The sample consists of US manufacturing firms (SIC codes that begin

with 2 or 3) between 1988 and 2014. All models include firm and period fixed effects. Standard errors are heteroscedasticity-consistent and

clustered at the firm level.

Table 4. Overlap between alternative measures of need for funding

Low

Expected

capex (Eq. 4)

High

Expected

capex (Eq. 4)

Low

Expected

capex (Eq. 5)

High

Expected

capex (Eq. 5)

Low

Industry

capex

High

Industry

capex

Low

Tangibilityt-1

High

Tangibilityt-1

Low Capext-1 0.634 0.063 0.811 0.003 0.496 0.270 0.559 0.118

High Capext-1 0.027 0.684 0.001 0.885 0.249 0.450 0.074 0.573

The table reports the fractions of firms classified as High/Low capext-1 that are also classified as High/Low expected capex, industry capex,

and tangibility. To exemplify the interpretation of the fractions, out of the firms that are classified as having high investment intensity

according to last year’s capex and that are also classified according to beginning-of-period tangibility, 57.3% are classified as having high

funding need based on beginning-of-period tangibility. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3)

between 1988 and 2014. Variable definitions are found in Appendix 1.

Table 5. Investment-cash flow sensitivity using traditional classification schemes

High pay-

out ratio

(NFC)

Low pay-

out ratio

(FC)

Large

(NFC)

Small

(FC)

Old

(NFC)

Young

(FC)

Rated

(NFC)

Not rated

(FC)

Constant 0.035*** 0.032*** 0.031*** 0.036*** 0.032*** 0.037*** 0.029*** 0.032***

(15.7) (10.9) (12.5) (12.8) (15.7) (15.9) (14.3) (15.2)

Cash flow 0.058*** 0.078*** 0.123*** 0.046*** 0.076*** 0.077*** 0.110*** 0.064***

(4.80) (4.97) (8.10) (3.91) (8.27) (6.11) (9.10) (7.15)

Q 0.006*** 0.010*** 0.007*** 0.008*** 0.009*** 0.009*** 0.008*** 0.011***

(6.30) (6.87) (7.52) (5.89) (7.07) (9.30) (9.20) (9.33)

N 10,945 10,617 11,454 8,555 12,840 11,503 11,914 16,519

Adj R2 0.495 0.435 0.566 0.396 0.457 0.532 0.552 0.421

High Z-

score

(NFC)

Low Z-

score

(FC)

Low WW

index

(NFC)

High WW

index

(FC)

Low SA

index

(NFC)

High SA

index

(FC)

Low ASCL

index

(NFC)

High ASCL

index

(FC)

Constant 0.043*** 0.026*** 0.034*** 0.034*** 0.031*** 0.036*** 0.033*** 0.023***

(13.0) (5.56) (14.9) (11.5) (13.0) (11.8) (21.8) (5.51)

Cash flow 0.045*** 0.103*** 0.106*** 0.046*** 0.109*** 0.041*** 0.072*** 0.067***

(2.70) (4.98) (7.95) (3.86) (7.58) (3.25) (8.55) (4.00)

Q 0.008*** 0.011*** 0.007*** 0.008*** 0.008*** 0.009*** 0.007*** 0.014***

(7.91) (3.68) (7.66) (5.27) (8.14) (5.96) (11.2) (5.69)

N 9,864 7,573 11,661 8,117 10,328 7,529 18,820 7,347

Adj R2 0.574 0.396 0.577 0.387 0.568 0.406 0.485 0.455

The table reports estimates from panel regressions of capex on cash flow and beginning-of-period Q for firm-years classified

according to eight different schemes. Scheme 1 is the previous year’s payout ratio; scheme 2 is beginning-of-period total assets;

scheme 3 is age, scheme 4 is whether or not the firm had a credit rating from Standard & Poor’s; scheme 5 is the Cleary (1999) Z

index; scheme 6 is the Whited and Wu (2006) index; scheme 7 is the Hadlock and Pierce (2010) SA index; and scheme 8 is the

Mulier et al (2017) ASCL index. In schemes 1-5 we assign to the high (low) financial constraints class firms in the bottom (top)

three deciles of the relevant distribution, whereas we in schemes 6-8 assign to top (bottom) three deciles to the high (low)

financial constraints class. The abbreviation NFC (FC) refers to not financially constrained (financially constrained). Variable

definitions are found in Appendix 1. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3) between

1988 and 2014. All models include firm and period fixed effects. Standard errors are heteroscedasticity-consistent and clustered

at the firm level.

Table 6. Descriptive statistics for traditional classification schemes

High

payout

Low

payout

Large

Small

Old

Young

Rated

Not rated

High Z-

score

Low Z-

score

Capex 0.055 0.056* 0.059*** 0.056 0.054 0.064*** 0.054 0.057*** 0.068*** 0.050

Cash flow 0.128*** 0.109 0.114 0.124*** 0.112 0.120*** 0.105 0.110*** 0.151*** 0.092

Q 1.939*** 1.704 1.839*** 1.766 1.662 1.922*** 1.741*** 1.650 2.233*** 1.466

Assets (mn) 6,566*** 1,002 11,751*** 53 6,034*** 2,292 9,938*** 768 5,606*** 2,969

Age 26.516*** 13.705 28.198*** 15.324 36.935*** 4.282 28.445*** 16.492 21.374*** 17.285

Tangibility 0.282*** 0.256 0.311*** 0.231 0.279*** 0.258 0.300*** 0.263 0.269 0.299***

Sales growth 0.045 0.059* 0.052 0.052 0.041 0.092*** 0.053 0.056*** 0.075*** 0.030

Return on assets 0.082*** 0.041 0.068*** 0.062 0.065** 0.063 0.063*** 0.042 0.127*** 0.009

R&D intensity 0.038 0.051*** 0.037 0.044*** 0.029 0.055*** 0.031 0.043*** 0.042*** 0.039

Leverage 0.204 0.249*** 0.272*** 0.168 0.216 0.220** 0.341*** 0.206 0.155 0.371***

Interest coverage 26.676*** 16.098 11.035 32.204*** 16.699 21.809*** 5.944 15.720*** 49.129*** 6.007

Fraction rated 0.462*** 0.213 0.775*** 0.003 0.433*** 0.284 1.000*** 0.000 0.359 0.394***

Dividend payout ratio 0.390*** 0.037 0.318*** 0.111 0.295*** 0.128 0.277*** 0.137 0.243*** 0.111

Cash holdings 0.138 0.162*** 0.100 0.187*** 0.110 0.174*** 0.088 0.136*** 0.150*** 0.116

Low WW

index

High WW

index

Low SA

index

High SA

index

Low ASCL

index

High

ASCL

index

High

Capex

Low

Capex

High

Expected

capex Eq.

4

Low

Expected

capex Eq.

4

Capex 0.059*** 0.053 0.058 0.057 0.061*** 0.050 0.098*** 0.026 0.096*** 0.024

Cash flow 0.119 0.117 0.118 0.126*** 0.139*** 0.096 0.137*** 0.103 0.143*** 0.099

Q 1.854*** 1.703 1.834 1.845 2.088*** 1.507 1.932*** 1.639 2.005** 1.573

Assets (mn) 11,284*** 64 10,588*** 73 6,340*** 1,425 5,465*** 2,442 5,077*** 3.278

Age 28.979*** 14.595 32.629*** 11.985 29.271*** 10.898 19.095 18.860 19.022 21.205***

Tangibility 0.313*** 0.229 0.301*** 0.224 0.268 0.273*** 0.379*** 0.171 0.389*** 0.154

Sales growth 0.050 0.048 0.049 0.062*** 0.060*** 0.051 0.077*** 0.032 0.080*** 0.023

Return on assets 0.075*** 0.049 0.072*** 0.065 0.094*** 0.036 0.068*** 0.062 0.069*** 0.058

R&D intensity 0.032 0.049*** 0.037 0.051*** 0.048*** 0.036 0.038 0.045*** 0.038 0.048***

Leverage 0.252*** 0.192 0.244*** 0.154 0.153 0.286*** 0.216 0.235*** 0.203 0.224***

Interest coverage 13.532 24.773*** 12.019 36.526*** 39.483*** 7.746 24.636 22.968 30.248*** 20.567

Fraction rated 0.718*** 0.020 0.717*** 0.009 0.414*** 0.240 0.361*** 0.282 0.318*** 0.285

Dividend payout ratio 0.358*** 0.059 0.323*** 0.088 0.296*** 0.095 0.209*** 0.177 0.207*** 0.177

Cash holdings 0.097 0.185*** 0.104 0.202*** 0.159*** 0.119 0.123 0.177*** 0.132 0.174***

The table reports descriptives for firm-years classified according to nine different schemes. Scheme 1 is the previous year’s payout ratio; scheme

2 is beginning-of-period total assets; scheme 3 is age, scheme 4 is whether or not the firm had a credit rating from Standard & Poor’s; scheme 5

is the Cleary (1999) Z index; scheme 6 is the Whited and Wu (2006) index; scheme 7 is the Hadlock and Pierce (2010) SA index; and scheme 8 is

the Mulier et al (2016) ASCL index. The final two schemes are previous year’s investment intensity, measured as the ratio of capital expenditures

to beginning-of-period total assets and previous year’s expected capex, estimated using Eq. 4. In schemes 1-5 we assign to the high (low)

financial constraints class firms in the bottom (top) three deciles of the relevant distribution, whereas we in schemes 6-8 assign to top (bottom)

three deciles to the high (low) financial constraints class. The abbreviation NFC (FC) refers to not financially constrained (financially constrained).

Variable definitions are found in Appendix 1. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3) between 1988

and 2014.

Table 7. Overlap between traditional classification schemes and investment intensity

High payout

(NFC)

Low payout

(FC)

Large

(NFC)

Small

(FC)

Old

(NFC)

Young

(FC)

Rated

(NFC)

Not rated

(FC)

Low Capext-1 0.246 0.524 0.196 0.443 0.294 0.322 0.258 0.461

High Capext-1 0.296 0.357 0.335 0.249 0.312 0.370 0.350 0.419

High Z-score

(NFC)

Low Z-score

(FC)

Low WW

index

(NFC)

High WW

index

(FC)

Low SA index

(NFC)

High SA

index

(FC)

Low ASCL

index

(NFC)

High ASCL

index

(FC)

Low Capext-1 0.262 0.366 0.173 0.457 0.193 0.436 0.228 0.389

High Capext-1 0.385 0.212 0.350 0.233 0.330 0.257 0.417 0.230

The table reports the fractions of firms classified as High/Low capext-1 that are classified as financially constrained (FC) or not financially

constrained (NFC) according to eight different classification schemes. Scheme 1 is the previous year’s payout ratio; scheme 2 is beginning-

of-period total assets; scheme 3 is age, scheme 4 is whether or not the firm had a credit rating from Standard & Poor’s; scheme 5 is the

Cleary (1999) Z index; scheme 6 is the Whited and Wu (2006) index; scheme 7 is the Hadlock and Pierce (2010) SA index; and scheme 8 is

the Mulier et al (2016) ASCL index. To exemplify the interpretation of the fractions, out of the firms that are classified as having high

investment intensity according to last year’s capex and that are also classified according to scheme 1, 29.6% are classified as NFC according

to scheme 1. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3) between 1988 and 2014. Variable definitions

are found in Appendix 1.

Table 8. Regressions for corporate liquidity events

Panel A Dividend

omission

Dividend

initiation

Dividend

decrease

Dividend

increase

Underfunded

pension plan

Equity recycling

Constant -3.447*** -2.457*** -2.068*** -1.383*** -2.191*** 0.839***

(-6.40) (-6.24) (-10.5) (-8.19) (-8.28) (8.29)

FC (top 3 deciles on leverage) 0.551*** -0.049 0.057* -0.620*** 0.138*** -0.084***

(5.84) (-0.52) (1.66) (-19.2) (2.89) (-3.03)

NFC (bottom 3 deciles) -0.066 0.333*** -0.045 0.071** -0.201*** 0.152***

(-0.58) (3.81) (-1.16) (2.14) (-3.22) (2.98)

Log(market capitalization) -0.114*** -0.104*** 0.180*** 0.393*** 0.073*** -0.109***

(-6.30) (-6.17) (24.7) (53.5) (6.38) (-7.75)

Log(Q) -0.426*** -0.258*** -0.620*** -0.200*** 0.042 0.134**

(-3.25) (-2.92) (-14.9) (-6.11) (0.67) (2.37)

Excess returnt-1 -0.543*** 0.412*** -0.120*** 0.021 -0.018 -0.041

(-6.31) (7.14) (-4.52) (0.82) (-0.41) (-1.47)

FC≠NFC? Yes (***) Yes (***) Yes (**) Yes (***) Yes (***) Yes (***)

Industry fixed effects Yes Yes Yes Yes Yes Yes

Period fixed effects Yes Yes Yes Yes Yes Yes

No observations 32,766 32,766 32,745 32,745 16,750 22,399

McFadden R2/Adj. R2 0.061 0.041 0.065 0.161 0.354 0.012

Panel B

NFC/FC → FC≠NFC?

Dividend

omission

Dividend

initiation

Dividend

decrease

Dividend

increase

Underfunded

pension plan

Equity recycling

High/low interest coverage Yes (***) Yes (***) Yes (***) Yes (***) Yes (***) Yes (***)

High/low payout ratio No –– No Yes (***) Yes (***) Yes (***)

Large/Small No No No No No No

Old/young Yes (**) No No Yes (***) Yes (***) No

Rated/not rated No No No No No No

High Z/low Z No Yes (***) Yes (***) No Yes (***) Yes (***)

Low WW/high WW No No No Yes (***) No No

Low SA/high SA No No No Yes (***) No No

Low ASCL/high ASCL Yes (***) No No Yes (***) Yes (***) Yes (***)

The table reports

Table 9. Descriptive Statistics for alternative sorts

High

leverage

Low

leverage

Low

interest

coverage

High

interest

coverage

Capex 0.051 0.060*** 0.054 0.067***

Cash flow 0.097 0.141*** 0.088 0.153***

Q 1.556 2.165*** 1.365 2.276***

Assets ($m) 4,596*** 1,381 4,179 4,438*

Age 18.969*** 16.864 18.340 20.819***

Tangibility 0.310*** 0.205 0.322*** 0.244

Sales growth 0.039 0.072*** 0.028 0.077***

Return on assets 0.047 0.095*** -0.018 0.131***

R&D intensity 0.022 0.067*** 0.041 0.046***

Leverage 0.457*** 0.040 0.299*** 0.121

Interest coverage 1.839 84.975*** -4.257 68.533***

Fraction rated 0.567*** 0.079 0.348** 0.266

Dividend payout ratio 0.176 0.190*** 0.139 0.223***

Cash holdings 0.071 0.261*** 0.117 0.175***

Table 10. Investment-cash flow sensitivity using dual sorting on need for funding and costs of external financing

Panel A Base models Robustness checks

Low

leverage

(NFC)

High

leverage

(FC)

Low

leverage

(NFC)

High

leverage

(FC)

Low

leverage

(NFC)

High

leverage

(FC)

Low

leverage

(NFC)

High

leverage

(FC)

High capext-1 0.061** 0.200*** 0.058** 0.193*** 0.035 0.214*** 0.065** 0.188***

(2.23) (4.33) (2.10) (4.45) (1.22) (4.53) (2.10) (4.35)

Low capext-1 0.003 0.041 0.000 0.049* -0.001 0.055* -0.001 0.022

(0.16) (1.58) (0.01) (1.65) (-0.002) (1.77) (-0.19) (1.20)

Panel B Base models Robustness checks

High interest

coverage

(NFC)

Low interest

coverage

(FC)

High interest

coverage

(NFC)

Low interest

coverage

(FC)

High interest

coverage

(NFC)

Low interest

coverage

(FC)

High interest

coverage

(NFC)

Low interest

coverage

(FC)

High capext-1 0.080*** 0.222*** 0.096*** 0.236*** 0.085** 0.229*** 0.095** 0.225***

(2.61) (5.14) (2.71) (5.71) (2.04) (4.52) (2.39) (4.23)

Low capext-1 0.006 0.036 0.007 0.029 -0.008 0.025 -0.001 0.014

(0.35) (0.94) (0.34) (1.11) (-0.50) (0.81) (-0.08) (0.77)

Panel C

High payout

(NFC)

Low payout

(FC)

Large

(NFC)

Small

(FC)

Old

(NFC)

Young

(FC)

Rated

(NFC)

Not rated

(FC)

High capext-1 0.113*** 0.162*** 0.211*** 0.078*** 0.115*** 0.130*** 0.145*** 0.098***

(3.37) (3.90) (6.72) (2.83) (4.73) (3.64) (6.79) (3.81)

Low capext-1 -0.001 0.033** 0.026*** 0.019 0.015* 0.022* 0.014* 0.011

(-0.06) (2.12) (3.32) (1.00) (1.80) (1.67) (1.65) (0.82)

High Z index

(NFC)

Low Z index

(FC)

Low WW

index

(NFC)

High WW

index

(FC)

Low SA index

(NFC)

High SA

index

(FC)

Low ASCL

index

(NFC)

High ASCL

index

(FC)

High capext-1 0.113*** 0.200*** 0.176*** 0.083*** 0.193*** 0.094*** 0.124*** 0.128***

(2.91) (4.11) (6.15) (2.90) (6.84) (3.05) (5.80) (4.86)

Low capext-1 0.002 0.018 0.013* 0.029 0.016** 0.024 0.008 0.025

(0.11) (0.54) (1.85) (1.52) (2.23) (1.18) (1.01) (1.45)

Table 11. Overlap between traditional classification schemes and leverage and interest coverage conditional sorts

High

payout

(NFC)

Low

payout

(FC)

Large

(NFC)

Small

(FC)

Old

(NFC)

Young

(FC)

Rated

(NFC)

Not rated

(FC)

High Z

index

(NFC)

Low Z

index

(FC)

Low leveraget-1 0.177 0.217 0.098 0.239 0.165 0.209 0.056 0.242 0.308 0.079

High leveraget-1 0.193 0.232 0.271 0.129 0.210 0.227 0.337 0.238 0.080 0.341

High int covt-1 0.336 0.276 0.259 0.320 0.340 0.319 0.239 0.469 0.680 0.063

Low int covt-1 0.263 0.523 0.302 0.333 0.313 0.340 0.392 0.447 0.024 0.712

Low WW

index

(NFC)

High WW

index

(FC)

Low SA

index

(NFC)

High SA

index

(FC)

Low ASCL

index

(NFC)

High ASCL

index

(FC)

Low

leverage

(NFC)

High

leverage

(FC)

High int

cov

(NFC)

Low int

cov

(FC)

Low leveraget-1 0.105 0.212 0.107 0.237 0.345 0.239 0.420 0.078

High leveraget-1 0.256 0.150 0.238 0.127 0.160 0.468 0.040 0.313

High int covt-1 0.280 0.277 0.276 0.317 0.595 0.395 0.345 0.039

Low int covt-1 0.259 0.378 0.273 0.332 0.240 0.750 0.064 0.303

The table reports the fractions of high-funding-need firms classified as High/Low leveraget-1 and High/Low interest coveraget-1 (int covt-1)

that are classified as financially constrained (FC) or not financially constrained (NFC) according to ten different classification schemes. We

assign to the high-funding-need class firms in the top three deciles of the previous year’s capex distribution. These firms are further

stratified based on leverage (top (bottom) three deciles of the beginning-of year debt to total assets distribution are classified as FC (NFC))

or interest coverage (bottom (top) three deciles of the previous year’s EBITDA to interest expense distribution are classified as FC (NFC)).

Scheme 1 is the previous year’s payout ratio; scheme 2 is beginning-of-period total assets; scheme 3 is age, scheme 4 is whether or not the

firm had a credit rating from Standard & Poor’s; scheme 5 is the Cleary (1999) Z index; scheme 6 is the Whited and Wu (2006) index;

scheme 7 is the Hadlock and Pierce (2010) SA index; and scheme 8 is the Mulier et al (2016) ASCL index. To exemplify the interpretation of

the fractions, out of the high-funding-need firms that are classified as being financially constrained according to leverage and that are also

classified according to scheme 1 (payout ratio), 19.3% are classified as NFC according to scheme 1. The sample consists of US manufacturing

firms (SIC codes that begin with 2 or 3) between 1988 and 2014. Variable definitions are found in Appendix 1.

Figure 1. Investment-cash flow sensitivity across cash flows over time. Investment-cash flow

sensitivity is the estimated coefficient γ1 in Eq. 2, where capex normalized by total assets is

regressed on cash flow normalized by total assets and Qt-1. The regression is estimated on

three-year rolling window regressions spanning the period 1988-2014 for the full dataset as

well as datasets limited to firm-years with positive and negative cash flows. The sample

consists of US manufacturing firms (SIC codes that begin with 2 or 3) between 1988 and

2014.

-0,05

0

0,05

0,1

0,15

0,2

0,25

19

88

-90

19

89

-91

19

90

-92

19

91

-93

19

92

-94

19

93

-95

19

94

-96

19

95

-97

19

96

-98

19

97

-99

19

98

-00

19

99

-01

20

00

-02

20

01

-03

20

02

-04

20

03

-05

20

04

-06

20

05

-07

20

06

-08

20

07

-09

20

08

-10

20

09

-11

20

10

-12

20

11

-13

20

12

-14

All Observations Positive cash flows Negative cash flows

Figure 2. Investment-cash flow sensitivity and need for funding. Investment-cash flow

sensitivity is the estimated coefficient γ1 in Eq. 2, where capex normalized by total assets is

regressed on cash flow normalized by total assets and Qt-1. The regression is estimated on

three-year rolling window regressions spanning the period 1989-2014. We assign to the high

(low) funding need class firms in the top (bottom) three deciles of the previous year’s capex

distribution and refer to the intermediate four deciles as the medium investment intensity

class. The sample consists of US manufacturing firms (SIC codes that begin with 2 or 3)

between 1988 and 2014.