Internal and External Financing of Financially Constrained Firms
J. B. Chaya and Jungwon Suhb
This draft: August 2008
Abstract
Using firm-level data from thirty-five countries over the period 1998-2004, we conduct a
comprehensive investigation of the relation between financial constraints and the sensitivity of
investments to internal and external funds. Our investigation shows that, in the majority of
countries, the investments of financially constrained firms are not highly sensitive to internal
funds, which confirms the results of prior U.S. studies. Moreover, in many countries, financially
constrained firms use substantial amounts of external funds, and their investments tend to be
more sensitive to external financing than to internal financing. Our evidence is at odds with the
standard view in the financial constraint literature that financially constrained firms face
restricted access to external financing.
JEL Classification: G31, G32
Keywords: financial constraints, investment-cash flow sensitivity, external financing
a
Sungkyungkwan University, 3-53 Myungryun-dong, Jongno-gu, Seoul, Korea 110-745, phone:
+82-2-760-0484, email: [email protected]
b
Ewha Womans University, 11-1 Daehyun-dong, Seodaemun-gu, Seoul, Korea 120-750, phone:
+82-2-3277-4650, fax: +82-2-3227-2776, email: [email protected]
0
Internal and External Financing of Financially Constrained Firms
Abstract
Using firm-level data from thirty-five countries over the period 1998-2004, we conduct a
comprehensive investigation of the relation between financial constraints and the sensitivity of
investments to internal and external funds. Our investigation shows that, in the majority of
countries, the investments of financially constrained firms are not highly sensitive to internal
funds, which confirms the results of prior U.S. studies. Moreover, in many countries, financially
constrained firms use substantial amounts of external funds, and their investments tend to be
more sensitive to external financing than to internal financing. Our evidence is at odds with the
standard view in the financial constraint literature that financially constrained firms face
restricted access to external financing.
JEL Classification: G31, G32
Keywords: financial constraints, investment-cash flow sensitivity, external financing
1
I. Introduction
One of the most controversial issues in corporate finance literature is whether
investment-cash flow sensitivity is a measure of financial constraints. This issue is first raised in
the seminal study of Fazzari, Hubbard and Petersen (1988, FHP88, hereafter). They argue that
the pecking order theory predicts that financially constrained firmsi.e. firms that face high
costs of external financingwill rely heavily on internal funds and thus their investments will
display high sensitivities to the availability of internal funds.
However, several later studies of U.S. firms do not support this prediction of FHP88.
For example, Kaplan and Zingales (1997, KZ97, hereafter) and Cleary (1999, Cleary99,
hereafter) suggest that investment-cash flow sensitivity may not reflect financial constraints.
Alti (2003) also argues that FHP88’s prediction can be obtained even without the presence of
financial constraints.
In this study, we use firm-level data from thirty-five countries to investigate the
question of how investment-cash flow sensitivities vary with financial constraints. Following
prior U.S. studies, we identify financially constrained firms based on financial status variables,
such as creditworthiness, firm size and payout ratio. Firms with poor financial status are deemed
financially constrained because they may face restricted access to external financing.
Our international data allow us to conduct a comprehensive test of the relation (or lack
thereof) between investment-cash flow sensitivity and financial constraints. Our international
investigation, however, goes beyond simply checking the findings of prior U.S. studies. In
particular, unlike prior studies, we examine the extent to which financially constrained firms’
investments are sensitive to external financing. Doing so provides insight into the nature of
financially constrained firms, because, if high costs of external financing are a key driver for
financial constraints, then the sensitivities of financially constrained firms’ investments to
external funds will be relatively low, because these firms face restricted access to external
financing.
Our motivation for a multi-country investigation arises from the possibility that a
firm’s investment-cash flow sensitivity may be affected by the degree of financial development
in the country in which the firm operates. For example, advanced financial markets in the U.S.
allow firms to raise external capital with relative ease. Thus, firms with poor financial status in
the U.S. may not be substantially constrained. This could be why prior U.S. studies fail to find
high investment-cash flow sensitivities for firms with poor financial status. By contrast, in
countries without advanced financial markets, firms must rely more on internal funds.
Especially, the absence of advanced financial markets may force firms with poor financial status
to rely more heavily on internal funds than do firms with good financial status, if external
financial markets are restricted to firms with good financial status. Thus, firms with poor
2
financial status are more likely to display high investment-cash flow sensitivities in many
countries outside of the U.S. if it is true that investment-cash flow sensitivities reflect financial
constraints.
Among prior studies, Cleary (2006) and Kadapakkam, Kumar and Riddick (1998)
provide international evidence on investment-cash flow sensitivity. Our current investigation,
however, is different from these studies in at least two respects. First, while these prior studies
examine a sample of only six or seven major countries, we use more comprehensive data,
covering thirty-five countries in the world. Focusing only on firms in major countries may not
provide a proper test of FHP88’s prediction because firms in those countries are less likely to be
financially constrained due to the presence of advanced financial markets. Second and more
important, we examine the sensitivity of constrained firms’ investments, not just to internal
funds, but to external funds. This enables us to obtain additional insight into the nature of the
relation between financial constraints and investment-cash flow sensitivity.
In identifying financially constrained firms, we use proxy variables for a firm’s
financial status. Our primary proxy variable for financial status is creditworthiness score. We
estimate a firm’s creditworthiness score using a multivariate discriminant function as in
Cleary99. Firms with low creditworthiness scores (i.e., low credit-worthy firms) are considered
financially constrained. In addition, we use two more proxies for financial status: firm size and
dividend payout ratio. In the literature on financial constraints, it is a common practice to
assume that small firms are financially constrained, compared to large firms [See, for example,
Almeida and Campello (2007), Cleary (2006), Allayannis and Mozumdar (2004), Kadapakkam,
Kumar and Riddick (1998)].1 Also, in prior studies, low-dividend-payout firms are considered
financially constrained on the grounds that firms that pay out less of its income as dividends
may do so because they are financially constrained.2
Our sample covers thirty-five countries over the 1998-2004 period. In a given year for
each country, we rank sample firms by their financial status measured by creditworthiness
scores, firm size, or dividend payout ratio. Then we use the bottom and top one-thirds of firms
for analysis. The bottom one-third of firms is viewed as financially constrained, while the top
one-third as financially unconstrained. The next step is to estimate investment-cash flow
sensitivities for financially constrained and unconstrained firms. If investment-cash flow
1
Several other studies also adopt the view that small firms face restricted access to external financial
markets. For example, Gertler and Gilchrist (1994) that small firms are likely to face larger barriers to
external financing, because fixed costs associated with issuing publicly-traded bonds are more important
for small firms. Beck, Demirguc-Kunt and Maksimovic (2004) argue that, in countries without welldeveloped financial markets, small firms have restricted access to external financing relative to large
firms.
2
FHP88 point out that “if the cost of disadvantage of external finance is large, it should have the greatest
effect on firms that retain most of its income.”
3
sensitivity reflects financial constraints, we anticipate that financially constrained firms will
display high investment-cash flow sensitivities, as compared to financially unconstrained firms.
Our empirical investigation shows that, in the majority of the sample countries,
financially constrained firms do not display high investment-cash flow sensitivities, as
compared to financially unconstrained firms. This result holds across different methods of
identifying financially constrained firms. To elaborate, in many of the sample countries, lowcreditworthy, small-sized and low-dividend-paying firms do not display greater investment-cash
flow sensitivities than do high-creditworthy, large-sized and high-dividend-paying firms,
respectively. Thus, our international evidence is consistent with the prior U.S. studies of KZ97
and Cleary99, which reject the view that high investment-cash flow sensitivities signify
financial constraints.3
We explore reasons why financial constrained firms do not display high investmentcash flow sensitivities in many countries. Our primary explanation is based on the relation
between financial constraints and external financing. If high costs of external financing are a
key reason for financial constraints, then the sensitivities of financially constrained firms’
investments to external financing will be low because their reliance on external financing will
be low. However, our data show that, in the majority of our sample countries, the investments of
financially constrained firms (or those classified as such) display higher sensitivities to external
funds than to internal funds. Moreover, in many countries, financially constrained firms use
substantial amounts of external funds. These observations suggest that those firms that are
identified as financially constrained in conventional classification schemes may not, in fact, be
financially constrained, because they do not appear to have restricted access to external
financing.
We also investigate whether the lack of strong investment-cash flow sensitivities of
financially constrained firms is due to inclusion of negative cash-flow firms in our analysis.
Allayannis and Mozumdar (2004), Cleary, Povel and Raith (2005) and Hovakimian (2003)
argue that the investments of negative or low cash-flow firms may be insensitive, or even
negatively sensitive, to internal funds. Assuming that constrained firms experience a relatively
high occurrence of negative-cash flow years, the presence of negative cash-flow observations in
the dataset may result in the lack of significant investment-cash flow sensitivities for
constrained firms. Indeed, we find that, across countries, investment-cash flow sensitivity is
insignificant or even negative for firms with relatively low cash flows. However, we also find
that, in many countries, constrained firms do not display significantly greater investment-cash
3
It is worthwhile to note that some of our results deviate from these U.S. studies. For example, in the
majority of our sample countries, investment-cash flow sensitivity is not high for high creditworthy firms
and low for low creditworthy firms. Thus, a positive relationship between investment-cash flow
sensitivity and creditworthiness—as reported by KZ97 and Cleary99—is not a worldwide norm.
4
flow sensitivities than unconstrained firms, even after removal of negative-cash flow
observations. Thus, while there is international evidence that investment-cash flow sensitivities
vary with a firm’s ability to generate sufficient internal funds, the lack of significant investmentcash flow sensitivities for constrained firms is not necessarily due to inclusion of negative cashflow firms in the dataset.
Our contribution to the financial constraint literature can be described as follows. First,
using comprehensive international data, we confirm a couple of major findings of prior U.S
studies. In many countries, the investments of financially constrained firms are not highly
sensitive to internal funds, which is consistent with KZ97 and Cleary99. In addition, almost
invariably across countries, investment-cash flow sensitivities are affected by the firm’s ability
to generate positive internal funds, which corroborates the evidence of Allayannis and
Mozumdar (2004), Cleary, Povel and Raith (2005) and Hovakimian (2003).
Second and more importantly, unlike prior studies, we examine the relation between
financial constraints and external financing. This analysis provides us with additional insight
into the nature of financially constrained firms. In many countries, constrained firms’
investments are relatively more sensitive to external financing than to internal financing,
probably because those firms use substantial amounts of external funds. Thus, our international
evidence runs counter to the conventional view that financially constrained firms have restricted
access to external financing.
The current paper is organized as follows. Section 2 presents our methodology and
data. Section 3 conducts empirical analyses and discusses results. Section 4 concludes the paper.
2. Research design
2.1. Methodology
In analyzing the relation between investment-cash flow sensitivity and financial
constraints, we estimate the standard investment regression model used by key prior studies
such as FHP88, KZ97, and Cleary99.
( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + u it
(1)
where subscripts i and t denote firm and year, respectively; I represents investment in plant and
equipment; K is the beginning-of-the-year book value for net property, plant and equipment;
M/B represents the beginning-of-the year equity market-to-book ratio; CF represents cash flow
as measured by net income plus depreciation plus the change in deferred taxes; and uit is an
error term. This regression model is estimated using ordinary least squares with fixed firm and
5
year effects. In this regression model, investment-cash flow sensitivity is estimated by the
coefficient for CF/K, i.e. β CF / K .
In this study, our focus is to examine whether financially constrained firms display
relatively high investment-cash flow sensitivities, compared to financially unconstrained firms.
In identifying financially constrained and unconstrained firms, we use three proxy variables for
financial status: creditworthiness score, firm size and dividend payout ratio.
Our primary proxy for financial status is creditworthiness score (ZC), which we
estimate using a linear discriminant function à la Cleary99:
Z C = β1 ⋅ Current + β 2 ⋅ FCCov + β 3 ⋅ Slack / K + β 4 ⋅ NIM + β 5 ⋅ SGR + β 6 ⋅ Debt (2)
where Current is the current ratio; FCCov is fixed the charge coverage; Slack/K is the ratio of
slack to net fixed assets; NIM is the net income margin; and SGR is one-year sales growth rate;
and Debt is the debt ratio. Current and Slack/K are proxy variables for liquidity; FCCov and
Debt are proxy variables for leverage; NIM is a proxy variable for profitability; and SGR is a
proxy variable for growth. Definitions of these variables are provided in the Appendix.
Prior to estimating the linear discriminant function, we divide the entire firm-years in
each country into three groups, depending on whether the firm increases, decreases, or leaves
unchanged dividends per share during a given year. We choose two groups of firm-years: (i)
firm-years in which firms increase dividends and (ii) firm-years in which firms reduce dividends.
Then we use the above linear discriminant function to estimate the creditworthiness score for
each firm-year.4 We treat firm-years with low creditworthiness scores as financially constrained.
In addition to creditworthiness score, we use firm size and dividend payout ratio as
alternate proxies for financial status. When we use firm size as a proxy for financial status, the
underlying assumption is that small firms face restricted access to external financing and thus
are financially constrained. We use total assets as a measure of firm size, because assets can be
used as collateral and thus are a better indicator of a firm’s ability to raise external capital than
market capitalization or net sales. When we use dividend payout ratio as a proxy for financial
status, we treat low dividend paying firms as financially constrained. Our measure of dividend
payout ratio is the amount of cash dividends divided by operating income.
In constructing our dataset of financially constrained and unconstrained firms, we
execute the following procedure. In each year for each country, we rank firms based on a given
proxy and take the top and bottom one-thirds of firms. For each country, financially constrained
4
In this scheme, the key is the probability that a firm will increase or decrease dividends in the following
year. In essence, firms that are likely to decrease (increase) dividends in the following year are deemed
financially constrained (unconstrained) firms.
6
(unconstrained) firms are a collection of firm-year observations that belong to the bottom (top)
one-third in a given year in the distribution of a given proxy. Once the dataset of financially
constrained and unconstrained firms is constructed, we estimate investment-cash flow
sensitivities for both groups of firms on the basis of the regression model (Equation (1)).
Our study goes beyond testing whether financially constrained firms display relatively
high investment-cash flow sensitivities, as compared to financially unconstrained firms. We also
examine the extent to which the investments of financially constrained firms are sensitive to
external funds. For this analysis, we estimate the following regression model for financially
constrained firms:
( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + β EXT / K ( EXT / K ) it + u it
(3)
Our measure of external financing is EXT/K, i.e., the amount of external financing
(EXT) divided by the beginning-of-the-year book value of net fixed assets (K). EXT is defined
as the sum of the proceeds from equity issues and long-term borrowings during a given year.
The purpose of this regression analysis is to assess the prediction that high costs of external
financing cause firms to be financially constrained. If high costs of external financing are a key
driver for financial constraints, then financially constrained firms’ use of external funds will be
low and thus their investments’ sensitivities to external funds, i.e., β CF / K , will be low, relative
to their sensitivities to internal funds, i.e., β EXT / K .5
2.2. Data and variables
Our data comprise firms from thirty-five countries. In constructing our dataset, we use
the Worldscope database. We choose a seven-year period from 1998 through 2004.6 Our data
construction process begins with all available firms in the database during the period and then
removes firms in the financial services industry, foreign firms and ADRs.
Our study requires a number of firm-level variables in order to estimate the regression
model (Equation (1)) and the linear discriminant function (Equation (2)). The Appendix
5
Using both internal and external financings as explanatory variables in the same regression model may
be problematic because of a potential correlation between the two forms of financing. (Across countries,
the correlation between internal and external financings (CF/K and EXT/K) ranges from −0.24 to 0.45.)
To get around this problem, we also estimate investments’ sensitivity to one form of financing without the
other form in the same regression, i.e., (I/K)it=β M/B (M/B) it+β CF/K (CF/K) it+ u it and (I/K)it=β M/B (M/B)
it+β EXT/K (EXT/K) it+ u iti before comparison of β CF/K and β EXT/K. Results remain qualitatively unchanged.
6
Cleary99, a key U.S. study, also examines a seven-year period from 1988 through 1994. Note that, due
to backfilling in the Worldscope database, the number of usable firm-year observations tends to decrease
as we move backward in time.
7
provides definitions of these firm-level variables. We drop a firm-year observation if it has a
missing value in any of these variables. While we desire to include as many countries as
possible in our analysis, we drop countries that do not have a sufficient number of usable firmyear observations for analysis. In the end, our final dataset includes thirty-five countries.7 Table
1 reports the means of key financial ratios for the sample firm-year observations in each country.
In order to deal with extreme observations, we winsorize our data according to the
rules similar to those used by Cleary998: (i) assign a value of 100 percent (−100 percent) if SGR
is greater (less) than 100 percent (−100 percent); (ii) assign a value of 2 (−2) if I/K is greater
(less) than 2 (−2); (iii) assign a value of 5 (−5) if CF/K is greater (less) than 5 (−5); (iv) assign a
value of 5 (−5) if EXT/K is greater (less) than 5 (−5); (v) assign a value of 10 (-10) if M/B is
greater (less) than 10 (-10); (vi) assign a value of 10 if Current is greater than 10; (vii) assign a
value of +100 percent (−100 percent) if NIM is greater (less) than 100 percent (−100 percent);
and (viii) assign a value of 100 (−0.1) if FCCov is greater (less) than 100 (0).
3. Empirical results
3.1. Investment’s sensitivity to internal financing
The first step in our empirical investigation is to test whether financially constrained
firms display relatively high investment-cash flow sensitivities. To do so, we estimate the
regression model given in Equation (1). As described above, we use three different methods of
identifying financially constrained firms: that is, by: (i) creditworthiness score, (ii) firm size,
and (iii) payout ratio.
Table 2 reports regression results for constrained and unconstrained firms that are
identified by creditworthiness score for each of the thirty-five countries in the dataset.
Financially constrained (unconstrained) firms are firm-years in which creditworthiness scores
belong to the bottom (top) one-third of distribution in a given year over the 1998−2004 period.
Our focus here is on the magnitude and significance of the coefficient for cash flow (CF/K)—
i.e., β CF / K —which represents investment-cash flow sensitivity. In particular, we want to
determine whether the estimated coefficient for cash flow for constrained firms is greater than
7
Note that our final dataset includes every firm-year observation that has a valid value for each of the
aforementioned variables. Alternately, we also construct our dataset by including only those firms that
have a usable firm-year observation in every year during our sample period 1989-2004. We find that
results remain qualitatively unchanged when using this alternate dataset.
8
Compared to Cleary99, we use two more winsorization rules. The first is the one on external funds/next
fixed assets (EXT/K). (Cleary99 does not examine external funds.) The second is the one that imposes a
value of –10 for a market-to-book ratio (M/B) that is less than –10. This rule on highly negative marketto-book values is necessary because our international data contain relatively many firms with highly
negative market-to-book values. We find, however, that our key results remain qualitatively unchanged
without this additional winsorization rule on highly negative market-to-book firms.
8
that for unconstrained firms. To aid in the analysis, the table presents t-statistics for the
difference in investment-cash flow sensitivities between constrained and unconstrained firms.9
The results in Table 2 suggest that, in more than half the countries, the investment-cash
flow sensitivity for constrained firms is lower than that estimated for unconstrained firms. For
example, in the U.S., the β CF / K coefficient for constrained firms, −0.066, is less than that
estimated for unconstrained firms, 0.085. In many countries, the evidence that financially
constrained firms display relatively high investment-cash flow sensitivities is weak. According
to the t-statistics, the difference in investment-cash flow sensitivities between constrained and
unconstrained firms—i.e., βCFFC−βCFNFC—is positive and statistically significant in only twelve
out of the thirty-five countries under study. Thus, our international data do not provide strong
support for the hypothesis that investment-cash flow sensitivities reflect financial constraints.
Table 3 reports regression results for constrained and unconstrained firms that are
identified by firm size. For each country, financially constrained (unconstrained) firms are firmyears in which firm size belongs to the bottom (top) one-third of distribution in a given year
over the sample period. These regressions based on this alternative identification method
present similar results to those above. For example, in as many as twenty-four out of thirty-five
countries, the coefficient for cash flow for constrained firms is lower than that for constrained
firms. According to the t-statistics, there are only six countries in which the investment-cash
flow sensitivity for constrained firms is significantly greater than that for unconstrained firms.
Thus, across countries, the evidence that small firms—that are deemed financially
constrained—display relatively high investment-cash flow sensitivities is rather weak.
Finally, Table 4 reports regression results for constrained and unconstrained firms as
identified by payout ratio. For each country, financially constrained (unconstrained) firms are
firm-years in which payout ratio belongs to the bottom (top) one-third of distribution in a given
year over the sample period. Again, in many countries, evidence does not suggest that
constrained firms display relatively high investment-cash flow sensitivities. In as many as
eighteen out of thirty-five countries, the investment-cash flow sensitivity for constrained firms
is lower than that estimated for unconstrained firms. According to the t-statistic, there are only
six countries in which constrained firms display significantly greater investment-cash flow
sensitivities than unconstrained firms.
In summary, our evidence is in conflict with what we would expect if investment-cash
9
This t-statistic is calculated as (β1−β2)/(s12− s22)1/2, where β1 and β2 are coefficient estimates for cash
flows of constrained and unconstrained firms, respectively, and s1 and s2 are corresponding standard errors.
Allayannis and Mozumdar (2004) use the same t-statistic. We use additional techniques to assess the
significance of the difference in investment-cash flow sensitivities between constrained and unconstrained
groups. For example, we pool observations from both constrained and unconstrained groups in the same
regression that uses a group dummy variable as well as an interaction variable (cash flow interacted with
the group dummy). Results remain qualitatively unchanged.
9
flow sensitivity is a measure of financial constraints. In many countries, constrained firms do
not display high investment-cash flow sensitivities, as compared to unconstrained firms. This
observation holds across different methods of identifying financially constrained firms. Overall,
our international investigation presents evidence that is consistent with prior U.S. studies, such
as KZ97 and Cleary99.
3.2. Investment’s sensitivity to external financing
In this section, we investigate the question of whether the investments of financially
constrained firms are more sensitive to internal funds than to external funds. The purpose of this
investigation is to explore the reason for the lack of high investment-cash flow sensitivities of
financially constrained firms. Motivation for this investigation comes from the standard view in
the financial constraint literature. According to the standard view, high costs of external
financing are a key driver for financial constraints [see, for example, FHP88 and KZ97].
Financially constrained firms are those that face high costs of external financing. Thus, if this
standard view holds true, use of external funds by constrained firms will be small and, further,
the sensitivity of constrained firms’ investments to external funds will be low, relative to their
sensitivities to internal funds.
We estimate the regression model (Equation (3)) that estimates the sensitivities of
investments to internal and external financing— β CF / K and β EXT / K , respectively. Market-tobook ratio (M/B) is used as a control variable. The standard view in the financial constraint
literature predicts that the investments of constrained firms will be more sensitive to internal
financing than to external financing—that is, β CF / K > β EXT / K .
For each of our sample countries, Table 5 reports the results of this regression analysis
for both constrained and unconstrained firms that are identified by three different methods. The
table also reports F-statistics that test the significance of the difference in the estimated
coefficients for CF/K and EXT/K.
Overall, there is little international evidence that the investments of financially
constrained firms are more sensitive to internal funds than to external funds. For example, in
Panel A for which low-creditworthy firms are identified as financially constrained, F statistics
indicate that there are only six countries—Austria, China, France, New Zealand, Singapore and
Spain—in which the investments of constrained firms are significantly more sensitive to
internal financing than to external financing. In the majority of countries, the sensitivity of
constrained firms’ investments to internal financing is either lower, or not significantly greater
than their sensitivity to external financing.
Results in Panels B and C tell a similar story. In Panel B, for which small firms are
identified as financially constrained, there are only three countries—Austria, Portugal, and
10
Taiwan—in which the coefficient for internal financing ( β CF / K ) is significantly greater than the
coefficient for external financing ( β EXT / K ) for constrained firms. In Panel C, for which low
payout firms are identified as financially constrained, there are several countries—eleven
countries—in which the investments of constrained firms display a significantly greater
sensitivity to internal financing than to external financing. Still, in the majority of countries, the
sensitivity of constrained firms’ investments to internal financing is either lower or not
significantly greater than their sensitivity to external financing. In sum, across different methods
of identifying constrained firms, our results do not support the prediction that the investments of
constrained firms will display greater sensitivities to internal financing than to external
financing.
Then why are the investments of constrained firms not more sensitive to internal funds
than to external funds in many countries? According to the regression results presented in Table
5, the sensitivity of constrained firms’ investments to external funds—as measured by β EXT / K —
is positive and statistically significant in most of the sample countries across different methods
of identifying constrained firms. Thus, there is a possibility that constrained firms may rely on
external financing to a considerable extent. To evaluate this possibility, we compare the amount
of external financings used by constrained and unconstrained firms across sample countries.
Table 6 reports the mean and median values of external financing (EXT/K) along with
those of investments (I/K) and internal financings (CF/K) for constrained and unconstrained
firms across three different identification methods. Our interest is to evaluate whether the
amount of external financing by constrained firms is small, as compared to the corresponding
amount of external financing by unconstrained firms. The table shows that, in relatively many
countries, the mean and median values of external financing for constrained firms are greater
than the corresponding values for unconstrained firms. For example, in Panel A, for which
constrained firms are identified on the basis of creditworthiness score, there are eighteen
(sixteen) countries—including the U.S.—in which the mean (median) value of external
financing for constrained firms is greater than the corresponding value for unconstrained firms.
Similarly, in Panel B, for which constrained firms are identified on the basis of firm size, there
are twelve (eight) countries—including the U.S.—in which the mean (median) value of external
financing for constrained firms is greater than the corresponding value for unconstrained firms.
Finally, this pattern—that constrained firms use relatively large amounts of external financing—
is more pronounced in Panel C for which low-payout firms are identified as constrained. In as
many as thirty-one (twenty-nine) out of thirty-five countries, the mean (median) of external
financing for constrained firms is greater than the corresponding value for unconstrained firms.
Thus, the data suggest that, in many countries, firms identified as financially
constrained use substantial amounts of external financing. It appears that, in many countries,
11
constrained firms rely more on external financing than do unconstrained firms. This evidence is
consistent with the observation that the sensitivity of constrained firms’ investments to external
funds is positive and statistically significant in many countries. However, this evidence is at
odds with the view that financially constrained firms face restricted access to external financing.
In summary, our investigation into the sensitivity of investments to external financing
provides insight into the nature of financially constrained firms. In many countries, the
investments of constrained firms are more sensitive to external financing than to internal
financing. Further, in many countries, constrained firms use substantial amounts of external
financing. This evidence runs counter to the standard view in the literature that constrained
firms have restricted access to external financing as a result of high costs of external financing.
Our evidence suggests that the sensitivity of constrained firms’ investments to internal financing
may not be high because those firms use substantial amounts of external financing.
3.3. Impact of negative cash-flow firms on investment-cash flow sensitivities
In this section, we investigate another potential reason for the lack of strong
investment-cash flow sensitivities of constrained firms. According to Allayannis and Mozumdar
(2004), a lack of strong investment-cash flow sensitivities of constrained firms may be due to
inclusion of negative cash-flow firms in the analysis. They argue that the investments of
negative cash-flow firms may be insensitive or even negatively sensitive to internal funds.
Assuming that constrained firms experience a relatively high occurrence of negative-cash flow
years, the presence of negative cash-flow observations in the dataset may result in a lack of
significant investment-cash flow sensitivities for constrained firms. Cleary, Povel and Raith
(2005) and Hovakimian (2003) also propose that firms with negative or low cash flows may
display insignificant or negative investment-cash flow sensitivities.
We investigate whether negative or low cash-flow observations influence our inference
on the difference in investment-cash flow sensitivities between constrained and unconstrained
firms using our international data. For this investigation, we conduct two tests. The first test
examines whether investment-cash flow sensitivities vary with a firm’s ability to generate
sufficient internal funds. Specifically, we examine whether low-cash-flow firms display
significantly lower investment-cash flow sensitivities than do high-cash-flow firms. The second
test examines whether constrained firms display greater investment-cash flow sensitivities than
unconstrained firms after removing negative cash-flow observations from the dataset. If
inclusion of negative cash-flow observations in the dataset is a major reason for the lack of
strong investment-cash flow sensitivities for constrained firms, then their investment-cash flow
sensitivities will be more positive and significant after removal of negative cash-flow
observations, and thus greater than the investment-cash flow sensitivities of unconstrained firms.
12
Table 7 reports the results of regression analysis that estimates investment-cash flow
sensitivities for low-cash-flow and high-cash-flow firms. For each country, low-cash-flow
(high-cash-flow) firms are firm-years in which the amount of cash flow (CF/K) belongs to the
bottom (top) one-third of distribution in a given year over the sample period. The accompanying
t-statistics test the statistical significance of the difference in investment-cash flow sensitivities
between low-cash-flow and high-cash-flow firms.
A key observation from the table is that, in the majority of countries, investment-cash
flow sensitivity ( β CF / K ) is negative for low-cash-flow firms. There are twenty-seven countries
in which the β CF / K coefficient is negative. This negative investment-cash flow sensitivity for
low-cash-flow firms suggests that these firms continue to invest or make at least minimum
investments despite cash-flow shortfalls. In contrast, the table shows that high-cash-flow firms
display positive investment-cash flow sensitivities almost invariably across countries. There is
only one exception: Austria. A related observation is that, in the majority of countries, highcash-flow firms display significantly greater investment-cash flow sensitivities than low-cashflow firms. The t-statistics on the table show that, in as many as thirty out of thirty-five
countries, the β CF / K coefficient for low-cash-flow firms is significantly lower than that
estimated for high-cash-flow firms.
Thus, our evidence suggests that firms may display different levels investment-cash
flow sensitivities, depending on the availability of internal funds. In particular, in the majority of
countries, low-cash-flow firms display negative investment-cash flow sensitivities. This finding
is consistent with Allayannis and Mozumdar (2004), Cleary, Povel and Raith (2005) and
Hovakimian (2003).
Our next test examines whether the presence of negative cash-flow observations in the
analysis may be a reason that constrained firms do not display greater investment-cash flow
sensitivities than unconstrained firms. To examine this question, we compare investment-cash
flow sensitivities for constrained and unconstrained firms, after removing negative cash-flow
observations.
Table 8 reports the results of the regression analysis that estimates investment-cash
flow sensitivities for three different methods of identifying constrained firms, after removing
negative cash-flow observations. The table shows that, in many countries, the evidence that
constrained firms display relatively high investment-cash flow sensitivities is still weak. In
Panel A, for which low-creditworthy firms are identified as constrained, there are only ten
countries in which the β CF / K coefficient for constrained firms is significantly greater than for
unconstrained firms according to the t-statistics. In many countries—twelve—the β CF / K
coefficient for constrained firms is significantly lower than that for unconstrained firms, even
after removal of negative cash-flow observations.
13
Similar results are found in Panels B and C. In Panel B for which small firms are
identified as constrained, there are less than half the countries—ten—in which the β CF / K
coefficient for constrained firms is significantly greater than for unconstrained firms. In Panel C,
for which low payout firms are identified as constrained, there are eleven countries—in which
constrained firms display significantly greater investment-cash flow sensitivities than
unconstrained firms. Overall, across different methods of identifying constrained firms, our
earlier finding that constrained firms do not display significantly greater investment-cash flow
sensitivities than unconstrained firms remains unchanged even after removal of negative-cash
flow observations.
In summary, our investigation shows evidence that, across countries, investment-cash
flow sensitivities are affected by a firm’s ability to generate sufficient internal funds. Firms tend
to display low or even negative investment-cash flow sensitivities if internal funds are negative
or low. These results are consistent with Allayannis and Mozumdar (2004), Cleary, Povel and
Raith (2005), and Hovakimian (2003). On the other hand, there is little international evidence to
suggest that the lack of strong investment-cash flow sensitivities for constrained firms is due to
inclusion of negative cash-flow observations in the dataset. In many countries, constrained firms
do not display greater investment-cash flow sensitivities than unconstrained firms, even after
removal of negative cash-flow observations.
4. Concluding remarks
Our analysis of comprehensive international data yields several important findings on
the relation between investments and financing. First, we find that, in many countries, the
investments of financially constrained firms are not highly sensitive to internal funds, as
compared to the investments of financially unconstrained firms. This finding is consistent with
the view of prior U.S. studies, such as KZ97 and Cleary99, that investment-cash flow
sensitivities do not reflect financial constraints.
Second, we find that, in many countries, the investments of financially constrained
firms are more sensitive to external funds than to internal financing, probably because those
firms use substantial amounts of external funds. Overall, financially constrained firms (or those
classified as such) do not appear to face restricted access to external financing.10 Our evidence
suggests that the sensitivity of constrained firms’ investments to internal financing may not be
high because those firms use substantial amounts of external financing.
10
The nature of our sample firms suggests that firms classified as financially constrained are not truly
constrained and may raise external capital without incurring high costs. Firms examined in our study—as
well as those examined in prior U.S. studiesare listed on major stock exchanges in the world. Given
that these firms satisfy the rigorous requirements expected of listed companies, external financing may
not be prohibitively expensive for such firms.
14
Third, there is strong evidence that, across countries, investment-cash flow sensitivities
vary with a firm’s ability to generate sufficient internal funds. Our evidence supports the
prediction of Allayannis and Mozumdar (2004), Cleary, Povel and Raith (2005, and Hovakimian
(2003) that investments of negative or low cash-flow firms may be insensitive or negatively
sensitive to internal funds. However, in many countries, constrained firms do not display
relatively high investment-cash flow sensitivities, even after removal of negative-cash flow
observations.
15
References
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16
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17
Appendix: Description of Variables
Cash flow (CF)
net income + depreciation and/or amortization expense + change in deferred taxes
Investment (I)
net capital expenditures
Market-to-book (M/B)
market value of common equity / book value of common equity
External financing (EXT)
equity issue + long-term borrowing
Net fixed assets (K)
net property, plant and equipment
Dividend payout ratio (PAYR)
cash dividends paid / operating income
Current ratio (Current)
current assets / current liabilities
Debt ratio (Debt)
(current portion of long-term debt + long-term debt) / total assets
Fixed charge coverage ratio (FCCov)
earnings before interest and taxes / (interest expense + preferred dividend payments × (1 / (1
– tax rate)
Net income
net income before extraordinary operations ± extraordinary items and discontinued
operations
Net income margin (NIM)
net income / net sales * 100
Net sales growth (SGR)
(net salest – net salest-1) / net salest-1
Slack (Slack)
Cash + short-term investments + (0.50 × inventory) + (0.70 × account receivable) – short
term loans
US Dollar Total Assets (USDTA)
Total assets expressed in U.S. dollar values using the end-of-the-year exchange rate
18
Table 1 Means of selected financial ratios for sample countries
The table reports the means of selected financial ratios for our sample of firm-year observations in each country over 1998-2004. US dollar total assets (USDTA)
are presented in millions of U.S. dollars.
Country
nobs
I/K
M/B
CF/K
EXT/K
USDTA
CURRENT
DEBT
FCCOV
NIM
SGR
SLACK/K
Argentina
Australia
Austria
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Greece
HongKong
India
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
137
1895
143
432
1604
355
1257
376
424
1729
1473
61
2495
1318
954
206
666
6432
1021
2681
381
582
207
353
397
116
1661
607
0.141
0.307
0.258
0.207
0.286
0.153
0.225
0.256
0.372
0.431
0.337
0.373
0.201
0.207
0.162
0.232
0.273
0.141
0.157
0.127
0.123
0.293
0.193
0.381
0.127
0.182
0.190
0.339
1.193
2.180
1.379
1.104
2.221
1.278
2.896
2.143
2.359
2.450
2.328
3.258
1.368
2.066
1.085
2.603
2.314
1.525
0.670
1.252
1.203
3.218
2.148
2.215
0.813
2.457
1.495
2.076
0.327
0.321
0.216
0.421
0.202
0.267
0.301
0.275
0.364
0.712
0.402
0.919
0.161
0.387
0.231
0.523
0.482
0.259
0.141
0.179
0.197
0.589
0.484
0.432
0.202
0.111
0.199
0.826
0.187
1.011
0.203
0.358
0.614
0.443
0.902
0.250
0.372
0.727
0.444
0.361
0.632
0.215
0.203
0.602
0.405
0.195
0.330
0.230
0.233
0.512
0.445
0.590
0.197
1.208
0.290
0.295
1224
598
1473
2318
1396
816
448
470
1813
3535
4172
754
672
581
284
1098
4346
3100
672
283
1749
3621
287
1417
356
1501
400
830
1.583
1.876
1.692
1.503
2.039
2.079
1.435
1.990
2.030
1.543
2.149
1.526
2.106
1.592
1.662
1.709
1.660
1.679
1.709
1.904
2.003
1.514
1.879
1.970
1.499
1.038
1.823
1.650
0.229
0.186
0.172
0.198
0.219
0.176
0.114
0.167
0.178
0.174
0.131
0.174
0.156
0.220
0.310
0.234
0.156
0.156
0.243
0.148
0.202
0.171
0.246
0.247
0.189
0.229
0.157
0.116
6.329
13.247
10.782
3.021
10.954
13.298
11.976
11.783
14.432
12.164
11.543
16.277
16.178
16.579
9.867
15.164
10.358
23.390
7.159
15.118
10.089
12.209
14.934
11.620
4.592
4.666
16.316
15.782
-5.314
-6.908
2.805
4.492
-4.269
10.330
4.004
1.867
1.969
0.799
-3.940
5.890
-5.368
5.689
-1.741
1.864
-0.215
1.103
-3.870
-2.367
1.824
2.343
7.466
0.386
-9.636
4.858
-2.119
6.561
16.100
15.316
9.062
19.689
17.197
11.077
18.146
7.179
11.108
10.485
6.606
19.625
6.012
12.413
16.873
14.349
8.637
2.774
5.199
7.164
11.872
6.775
9.306
12.425
8.381
11.153
8.099
16.702
1.487
4.578
1.880
6.117
2.477
0.703
2.944
7.482
5.232
6.796
5.784
1.863
5.323
1.198
2.937
2.342
6.431
4.053
2.434
2.702
1.021
3.347
1.947
5.682
11.778
1.303
2.953
5.675
19
Country
nobs
I/K
M/B
CF/K
EXT/K
USDTA
CURRENT
DEBT
FCCOV
NIM
SGR
SLACK/K
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
179
619
580
2026
1372
195
5233
18758
0.255
0.327
0.214
0.203
0.142
0.586
0.306
0.319
2.630
3.066
2.525
1.703
1.142
3.375
2.533
2.423
0.528
0.246
0.350
0.361
0.293
0.934
0.269
0.135
0.229
0.775
0.327
0.256
0.210
0.399
0.763
1.061
2802
1603
2942
686
229
1158
1316
2171
1.505
2.181
2.226
1.831
1.809
1.677
1.631
2.378
0.137
0.146
0.187
0.145
0.227
0.114
0.150
0.269
15.191
13.654
11.983
17.082
19.276
10.549
13.837
12.211
7.859
-4.765
1.724
1.371
1.973
3.578
-6.702
-10.438
13.520
12.718
7.163
14.990
9.954
53.814
13.593
12.570
2.280
7.612
2.540
2.556
1.608
2.176
10.178
7.468
20
Table 2 Investment-cash flow sensitivity for financially constrained firms identified by
creditworthiness
The table reports the results of the regression analysis (equation (1)) for financially constrained and
unconstrained firms for each country over the period 1998-2004. The regression model
is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + uit , where investment-cash flow sensitivity is the
coefficient estimate for cash flows (CF/K). ‘FC’ and ‘NFC’ indicate financially constrained and
unconstrained firms, respectively. In each country, financially constrained (unconstrained) firms are
those firm-years in which creditworthiness score belongs to the top (bottom) one-third of the distribution
in a given year. The regression model is estimated using fixed firm and year effects. The number of firmyear observations used for this regression may differ between constrained and unconstrained groups
because firms that have only one valid firm-year are removed before we run the regression for each
group. The numbers in parentheses are OLS t values. The numbers in square brackets are t(β CFFC
−βCFNFC), i.e., t-statistic for the difference in the coefficient estimates for CF/K between constrained and
unconstrained firms. *, ** and *** indicate two-tailed significance at the 10%, 5% and 1% levels,
respectively.
Country
Group
M/B
Argentina
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
0.125
0.019
(0.959)
(1.276)
0.031
0.011
(3.674)
(1.104)
Australia
Austria
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
R2
nobs
0.753
0.941
35
36
0.828
0.668
557
552
0.968
0.930
41
39
0.867
0.687
121
131
0.685
0.832
492
465
0.582
0.835
101
105
0.723
0.764
361
358
*
0.694
0.780
107
116
***
0.841
0.777
115
114
0.845
0.812
483
498
0.800
0.723
419
415
CF/K
0.160
-0.157
***
0.070
0.014
0.068
0.094
(1.458)
(1.768)
0.057
0.002
(4.353)
(0.128)
***
0.039
0.024
(4.796)
(1.919)
***
*
-0.051
0.190
0.014
0.075
(0.354)
(1.787)
*
-0.387
0.824
0.003
0.013
(0.441)
(1.619)
0.021
0.045
(0.760)
(2.701)
***
0.036
0.040
0.064
0.072
(2.979)
(2.480)
***
**
0.205
0.023
-0.004
0.015
(-0.317)
(1.763)
*
0.119
0.116
0.034
0.029
(3.094)
(3.530)
***
***
0.097
0.023
*
0.165
0.561
0.020
0.326
0.074
0.067
21
(0.817)
(-2.847)
[1.555]
(3.343)
(1.167)
[2.300]
(8.582)
(2.875)
[-2.018]
(1.192)
(4.475)
[-4.083]
(-2.638)
(7.080)
[-7.285]
(-1.879)
(10.503)
[-5.498]
(2.871)
(3.258)
[0.234]
(1.922)
(1.269)
[-0.118]
(4.026)
(0.589)
[2.827]
(5.415)
(6.252)
[0.124]
(4.435)
(1.466)
[2.784]
**
*
***
**
***
***
**
***
***
***
***
***
*
***
***
***
***
***
***
***
***
***
Country
Group
M/B
Greece
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
0.008
0.011
(2.473)
(3.016)
-0.008
-0.004
(-1.208)
(-0.415)
0.021
0.011
(1.940)
(1.522)
0.004
0.004
(0.573)
(0.529)
0.043
0.073
0.012
-0.016
(0.926)
(-0.752)
0.108
0.257
-0.024
-0.019
(-1.779)
(-0.838)
*
0.034
0.128
0.004
0.027
(2.623)
(6.659)
***
***
-0.036
0.017
0.031
-0.036
(2.375)
(-2.524)
**
**
0.048
0.376
-0.001
0.008
(-0.247)
(0.773)
0.011
0.030
(1.302)
(1.977)
0.015
0.005
(1.484)
(0.516)
-0.049
0.088
(-2.363)
(2.359)
0.070
-0.009
(1.416)
(-0.420)
0.014
0.036
(1.914)
(1.245)
0.020
-0.057
(0.327)
(-1.550)
0.008
0.017
(0.943)
(1.992)
-0.003
0.006
(-0.341)
(0.347)
0.149
0.154
-0.012
(-0.398)
0.672
HongKong
India
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
Spain
R2
nobs
***
***
***
***
***
0.680
0.781
1571
1542
0.635
0.666
711
709
***
***
***
**
***
0.747
0.840
392
379
0.709
0.627
272
282
*
***
*
**
0.746
0.914
62
65
0.675
0.762
202
202
0.736
0.766
1903
1969
0.608
0.841
304
288
0.556
0.639
811
788
0.703
0.941
107
111
0.757
0.833
171
178
0.913
0.836
56
65
0.822
0.834
97
94
0.688
0.676
119
120
0.866
0.862
31
34
0.809
0.679
498
492
***
***
***
0.777
0.879
***
0.907
179
168
347
51
CF/K
**
***
-0.094
0.092
0.058
0.076
*
-0.072
0.188
0.039
0.152
*
-0.008
0.318
0.056
0.127
**
**
0.644
-0.010
0.167
0.044
*
-0.002
-0.013
0.340
0.152
**
22
0.412
0.017
(-9.837)
(7.837)
[-12.290]
(3.286)
(6.361)
[-0.850]
(-3.593)
(6.494)
[-7.385]
(2.599)
(3.029)
[-1.033]
(1.837)
(3.344)
[-1.537]
(1.997)
(1.320)
[-0.953]
(-3.344)
(1.729)
[-3.640]
(2.580)
(12.540)
[-9.313]
(2.809)
(7.115)
[-4.437]
(-0.315)
(11.540)
[-8.794]
(2.233)
(2.809)
[-1.376]
(6.238)
(-0.316)
[6.065]
(3.010)
(1.480)
[1.958]
(-0.138)
(-0.181)
[0.143]
(1.061)
(1.603)
[0.564]
(9.885)
(0.988)
[8.775]
(5.200)
(6.666)
[-0.129]
(3.565)
***
*
***
**
***
***
***
***
***
***
***
**
***
*
***
***
***
**
***
Country
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Group
M/B
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
0.036
CF/K
(1.974)
*
***
***
0.124
0.037
0.066
(3.327)
(3.803)
0.137
-0.012
-0.000
0.012
(-0.040)
(1.121)
0.035
0.012
(3.347)
(0.867)
-0.015
0.015
(-1.114)
(3.535)
0.001
-0.021
(0.018)
(-0.223)
0.008
0.011
(2.473)
(3.016)
**
***
-0.094
0.092
0.010
0.013
(6.315)
(5.994)
***
***
-0.066
0.085
0.100
0.088
***
***
0.092
0.162
0.189
0.032
0.374
0.073
23
(0.913)
[2.358]
(4.367)
(-0.460)
[3.659]
(4.941)
(3.914)
[0.404]
(3.913)
(5.291)
[-1.803]
(4.304)
(1.447)
[3.193]
(2.720)
(0.401)
[1.317]
(-9.837)
(7.837)
[-12.290]
(-12.215)
(16.961)
[-20.480]
**
***
***
***
***
***
***
**
***
***
**
*
***
***
***
***
***
***
R2
nobs
0.830
58
0.874
0.764
178
187
0.849
0.838
149
165
0.741
0.750
553
567
0.750
0.608
416
432
0.902
0.784
42
44
0.680
0.781
1571
1542
0.635
0.794
5648
5605
Table 3 Investment-cash flow sensitivity for financially constrained firms identified by
firm size
The table reports the results of the regression analysis (equation (1)) for financially constrained and
unconstrained firms for each country over the period 1998-2004. The regression model
is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + uit , where investment-cash flow sensitivity is the
coefficient estimate for cash flows (CF/K). ‘FC’ and ‘NFC’ indicate financially constrained and
unconstrained firms, respectively. In each country, financially constrained (unconstrained) firms are
those firm-years in which firm size—measured by total assets—belongs to the top (bottom) one-third of
the distribution in a given year. The regression model is estimated using fixed firm and year effects. The
number of firm-year observations used for this regression may differ between constrained and
unconstrained groups because firms that have only one valid firm-year are removed before we run the
regression for each group. The numbers in parentheses are OLS t values. The numbers in square
brackets are t(β CFFC −βCFNFC), i.e., t-statistic for the difference in the coefficient estimates for CF/K
between constrained and unconstrained firms. *, ** and *** indicate two-tailed significance at the 10%,
5% and 1% levels, respectively.
Country
Argentina
Australia
Austria
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Group
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
M/B
-0.019
-0.002
CF/K
-0.039
0.223
(-1.205)
(-0.095)
0.024
0.044
(2.392)
(6.054)
0.026
0.010
(0.489)
(0.322)
0.076
0.010
(3.024)
(0.855)
***
0.026
0.088
0.031
0.034
(3.453)
(8.144)
***
***
-0.008
0.112
0.060
0.209
(1.454)
(3.809)
***
-0.342
0.389
0.008
-0.009
(1.459)
(-0.661)
0.002
0.023
(0.122)
(2.300)
**
0.145
0.000
0.092
0.039
(2.728)
(1.811)
***
*
0.048
0.392
0.032
0.029
(3.610)
(4.659)
***
***
0.081
0.101
0.036
(4.194)
***
0.049
**
***
0.025
0.129
0.147
0.339
0.105
0.243
24
(-1.319)
(1.727)
[-1.979]
(2.140)
(8.360)
[-5.326]
(4.548)
(2.436)
[-1.347]
(1.742)
(2.035)
[-1.354]
(-0.499)
(6.980)
[-5.214]
(-2.447)
(9.190)
[-5.010]
(5.891)
(7.074)
[-3.568]
(5.224)
(0.016)
[4.949]
(1.759)
(3.127)
[-2.678]
(5.845)
(6.484)
[-0.970]
(4.217)
*
**
**
***
***
***
**
*
*
**
*
***
***
**
***
***
***
***
***
***
***
*
***
***
***
***
***
R2
0.737
0.654
nobs
42
48
0.622
0.791
701
744
0.943
0.895
47
50
0.859
0.768
145
143
0.672
0.847
621
638
0.671
0.728
115
119
0.647
0.767
413
415
0.779
0.872
124
128
0.739
0.768
138
142
0.791
0.860
588
603
0.754
477
Country
Greece
HongKong
India
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
Group
M/B
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
-0.004
(-0.859)
0.103
0.440
(0.892)
(2.298)
-0.001
0.015
R2
nobs
0.831
499
0.864
0.932
20
23
0.579
0.750
854
892
0.760
0.803
432
444
0.599
0.604
345
351
0.739
0.900
68
70
0.660
0.684
219
224
0.714
0.856
2143
2203
0.606
0.675
332
345
0.545
0.622
926
954
0.817
0.856
137
144
0.698
0.869
206
208
0.651
0.900
70
75
0.752
0.746
117
120
0.414
0.673
171
175
***
***
***
***
***
0.926
0.920
35
36
0.650
0.713
556
574
***
***
0.789
0.854
202
205
CF/K
0.175
*
0.244
-0.146
(-0.198)
(2.448)
**
0.041
0.100
0.026
0.014
(3.646)
(1.698)
***
*
0.035
0.194
-0.010
0.009
(-1.131)
(1.752)
*
-0.004
-0.003
0.012
0.014
(1.004)
(1.703)
*
0.066
0.058
-0.021
0.029
(-1.469)
(1.569)
0.019
0.009
(5.679)
(5.843)
***
***
0.004
-0.023
0.035
-0.004
(2.446)
(-0.433)
**
0.092
0.229
0.001
0.014
(0.299)
(2.511)
**
0.026
0.087
0.045
0.019
(4.011)
(1.780)
***
*
0.152
0.036
0.032
0.003
(2.880)
(0.543)
***
0.050
0.093
0.001
0.004
(0.023)
(0.450)
0.032
0.508
-0.012
0.034
(-0.537)
(0.669)
-0.017
0.020
0.012
0.049
(0.715)
(3.024)
-0.075
0.012
(-3.871)
(1.075)
***
0.233
-0.789
0.035
0.035
(3.664)
(4.040)
***
***
0.052
0.085
0.040
0.022
(2.393)
(2.288)
**
**
0.119
0.187
0.045
0.189
***
25
-0.035
-0.064
(7.071)
[-4.608]
(1.330)
(-0.791)
[1.499]
(3.546)
(9.206)
[-3.691]
(1.759)
(6.578)
[-4.480]
(-0.244)
(-0.160)
[-0.052]
(1.368)
(2.143)
[0.139]
(2.518)
(4.043)
[-2.864]
(0.521)
(-2.828)
[2.418]
(4.552)
(10.338)
[-4.580]
(1.486)
(9.896)
[-3.133]
(7.982)
(0.991)
[2.835]
(2.474)
(3.242)
[-1.214]
(0.589)
(5.879)
[-4.679]
(-0.483)
(0.395)
[-0.599]
(-1.146)
(-4.157)
[0.865]
(4.226)
(-3.263)
[4.120]
(2.762)
(3.875)
[-1.148]
(5.548)
(8.089)
***
***
*
***
***
***
*
***
***
**
**
***
***
***
***
***
***
***
***
***
***
***
**
***
***
***
***
Country
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Group
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
M/B
R2
nobs
0.861
0.914
407
53
59
**
***
***
***
***
0.756
0.769
207
208
0.765
0.859
192
200
***
***
***
0.737
0.692
667
692
0.631
0.677
452
463
0.872
0.938
60
67
0.617
0.812
1889
1913
0.573
0.799
7735
7919
CF/K
0.044
0.096
(1.946)
(5.329)
*
***
0.231
0.399
0.011
0.050
(0.787)
(2.352)
**
0.041
0.130
-0.014
0.033
(-1.252)
(3.207)
***
0.098
0.137
0.036
0.014
(3.960)
(1.213)
-0.003
0.014
(-0.616)
(2.208)
-0.062
-0.000
(-1.239)
(-0.007)
0.012
0.007
(3.230)
(4.395)
***
***
-0.022
0.099
0.009
0.019
(6.149)
(16.369)
***
***
-0.004
0.100
***
**
0.190
0.098
0.026
-0.021
0.046
0.175
26
[-2.164]
(1.908)
(3.320)
[-0.982]
(2.491)
(5.264)
[-3.010]
(5.079)
(3.467)
[-0.893]
(7.325)
(4.479)
[2.701]
(0.930)
(-1.098)
[1.387]
(0.518)
(3.365)
[-1.257]
(-3.254)
(11.658)
[-11.144]
(-1.303)
(25.004)
[-20.238]
**
*
***
*
***
***
***
***
***
***
Table 4 Investment-cash flow sensitivity for financially constrained firms identified by
payout ratio
The table reports the results of the regression analysis (equation (1)) for financially constrained and
unconstrained firms for each country over the period 1998-2004. The regression model
is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + uit , where investment-cash flow sensitivity is the
coefficient estimate for cash flows (CF/K). ‘FC’ and ‘NFC’ indicate financially constrained and
unconstrained firms, respectively. In each country, financially constrained (unconstrained) firms are
those firm-years in which payout ratio—measured by cash dividends scaled by operating income—
belongs to the top (bottom) one-third of the distribution in a given year. The regression model is
estimated using fixed firm and year effects. The number of firm-year observations used for this
regression may differ between constrained and unconstrained groups because firms that have only one
valid firm-year are removed before we run the regression for each group. The numbers in parentheses
are OLS t values. The numbers in square brackets are t(βCFFC −βCFNFC), i.e., t-statistic for the difference
in the coefficient estimates for CF/K between constrained and unconstrained firms. *, ** and ***
indicate two-tailed significance at the 10%, 5% and 1% levels, respectively.
Country
Group
M/B
Argentina
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
0.004
0.007
(0.019)
(0.152)
0.100
0.037
(6.699)
(1.924)
Australia
Austria
Brazil
Canada
Chile
China
Denmark
Finland
France
R2
nobs
0.762
0.795
18
22
0.795
0.762
330
329
0.996
0.949
27
27
0.874
0.754
89
89
***
**
0.788
0.805
399
269
***
0.867
0.687
96
98
***
***
***
*
0.769
0.782
390
301
0.766
0.843
82
81
0.820
0.909
78
78
0.873
0.868
383
383
CF/K
0.037
0.235
***
*
-0.035 (-1.341)
0.124 (1.684)
0.118
0.175
-0.311
-0.090
0.021
0.007
(3.189)
(0.194)
***
-0.027
0.004
0.041
0.010
(4.087)
(1.452)
***
0.166
0.128
0.029
0.051
(0.428)
(1.942)
0.008
0.006
(1.463)
(0.402)
0.053
0.332
-0.021 (-0.602)
0.022 (0.852)
0.049
0.082
-0.003 (-0.067)
0.026 (1.318)
0.512
0.041
-0.004 (-0.445)
0.010 (0.798)
0.190
0.219
*
27
0.445
0.345
(0.398)
(0.878)
[-0.701]
(5.089)
(7.621)
[-1.736]
(-3.945)
(-0.130)
[-0.317]
(-0.673)
(0.057)
[-0.378]
(5.460)
(2.160)
[0.569]
(14.487)
(1.520)
[0.435]
(2.971)
(9.802)
[-7.265]
(1.774)
(1.380)
[-0.503]
(2.992)
(0.911)
[2.660]
(8.135)
(7.228)
[-0.741]
***
***
**
***
***
***
***
***
Country
Group
Germany
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
Greece
HongKong
India
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
M/B
R2
nobs
***
***
**
***
***
0.814
0.868
270
281
0.785
0.735
922
919
***
***
**
***
***
0.724
0.742
460
491
0.817
0.794
356
353
***
*
0.598
0.782
342
227
***
0.921
0.838
54
53
0.716
0.884
146
140
0.775
0.791
1509
1477
0.684
0.775
243
228
***
***
0.685
0.642
581
580
***
***
0.897
0.934
145
98
**
***
0.848
0.903
103
108
***
0.766
0.886
53
59
0.889
0.904
61
64
0.459
0.750
150
85
0.459
0.750
150
85
0.843
0.667
328
335
CF/K
0.031 (3.382)
-0.018 (-1.091)
***
0.067
0.138
0.009
0.011
**
**
0.130
0.153
(2.121)
(2.361)
-0.000 (-0.020)
-0.004 (-0.278)
0.119
0.173
0.013
0.002
(1.225)
(0.317)
0.149
0.092
0.003
0.006
(0.297)
(0.411)
0.053
0.053
0.040 (2.927)
-0.008 (-0.220)
***
0.378
0.089
0.004
0.014
(0.097)
(1.031)
0.006
0.013
(2.067)
(2.570)
**
**
0.002
0.062
0.003 (0.303)
-0.043 (-3.137)
***
0.274
0.338
***
0.182
0.159
0.001
0.060
(0.226)
(4.003)
0.048
0.026
(4.733)
(1.448)
0.001
0.023
(0.064)
(1.983)
0.087
0.036
(2.125)
(1.508)
0.083
0.501
***
*
**
-0.040 (-1.267)
0.039 (1.536)
0.262
0.315
0.115
0.086
-0.173
0.095
0.203
0.107
0.104 (2.333)
-0.056 (-1.062)
**
-0.062
-0.192
0.104 (2.333)
-0.056 (-1.062)
**
-0.062
-0.192
0.023
0.021
**
0.282
0.175
(2.488)
(0.972)
28
(2.709)
(4.639)
[-1.838]
(10.325)
(7.603)
[-0.994]
(5.393)
(7.679)
[-1.723]
(4.287)
(3.062)
[1.235]
(2.926)
(1.678)
[0.011]
(4.890)
(1.180)
[2.667]
(2.376)
(9.196)
[-6.470]
(0.137)
(6.363)
[-3.491]
(8.432)
(8.454)
[-1.233]
(11.842)
(5.680)
[0.722]
(11.298)
(5.275)
[-0.824]
(2.401)
(2.785)
[0.501]
(-3.395)
(0.683)
[-1.813]
(3.571)
(1.239)
[0.924]
(-1.012)
(-0.826)
[0.542]
(-1.012)
(-0.826)
[0.542]
(8.671)
(4.279)
***
**
***
***
***
***
***
***
**
***
***
***
Country
SouthAfrica
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Group
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
M/B
-0.014 (-1.264)
0.047 (2.897)
0.048
0.073
(1.010)
(1.204)
0.055
0.055
(2.833)
(4.976)
***
0.313
0.120
0.384
0.025
***
***
0.192
-0.030
0.006 (0.845)
-0.004 (-0.253)
0.162
0.218
0.008 (0.798)
-0.003 (-0.195)
0.298
0.274
0.003
0.047
R2
nobs
0.890
0.848
0.860
0.923
158
159
317
40
45
0.851
0.950
125
127
***
***
**
0.920
0.827
125
132
***
***
0.768
0.732
529
457
**
***
***
0.758
0.790
365
323
0.832
0.945
56
49
0.785
0.735
922
919
0.791
0.828
6730
3529
CF/K
(0.773)
(3.118)
***
-0.010 (-0.196)
-0.048 (-1.146)
0.059
0.473
-0.024
0.358
0.009
0.011
(2.121)
(2.361)
**
**
0.130
0.153
0.013
0.005
(8.020)
(2.743)
***
***
0.143
0.130
29
[2.039]
(12.681)
(3.975)
[4.965]
(1.318)
(0.207)
[1.136]
(5.739)
(-0.762)
[4.322]
(6.419)
(2.502)
[-0.621]
(7.722)
(8.457)
[0.471]
(2.422)
(8.012)
[-6.488]
(-0.321)
(3.703)
[-3.136]
(10.325)
(7.603)
[-0.994]
(30.521)
(17.479)
[1.517]
**
***
***
***
***
***
***
***
***
***
***
*
Table 5 Investment’s sensitivities to internal and external financing for financially constrained firms
The table reports the results of the regression analysis (equation (3)) for financially constrained and unconstrained firms for each country over the period 19982004. The regression model is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + β EXT / K ( EXT / K ) it + uit , where investment’s sensitivities to internal and
external financing are the coefficient estimates for cash flows (CF/K) and external financing (EXT/K), respectively. ‘FC’ and ‘NFC’ indicate financially
constrained and unconstrained firms, respectively. Financially constrained (unconstrained) firms are identified by creditworthiness score, firm size and payout
ratio in Panels A, B and C, respectively. Due to space limitation, the table reports only the coefficient estimates for CF/K and EXT/K. The regression model is
estimated using fixed firm and year effects. The number of firm-year observations used for this regression may differ between constrained and unconstrained
groups because firms that have only one valid firm-year are removed before we run the regression for each group. The numbers in parentheses are OLS t values.
The numbers in square brackets are F(βCF−βEXT), i.e., F-statistic for the difference in the coefficient estimates for CF/K and EXT/K. *, ** and *** indicate
significance of the F-statistic at the 10%, 5% and 1% levels, respectively.
Country
Group
CF/K
Argentina
FC
0.253
(1.284)
0.068
(1.215)
0.053
(2.661)
0.022
(1.816)
0.167
(8.806)
0.583
(2.855)
0.022
(1.283)
0.207
(3.825)
-0.061
(-3.378)
NFC
Australia
FC
NFC
Austria
FC
NFC
Brazil
FC
NFC
Canada
FC
Panel A: by creditworthiness
EXT/K
F-stat
0.288
(1.556)
0.255
(5.019)
0.085
(6.672)
0.074
(4.907)
0.039
(1.332)
-0.117
(-0.488)
-0.024
(-1.090)
0.359
(8.786)
0.107
(7.448)
nobs
CF/K
[0.023]
35
[29.161]
***
[1.666]
36
-0.039
(-1.420)
0.165
(1.449)
0.026
(2.292)
0.114
(7.489)
0.144
(4.444)
0.247
(1.883)
0.024
(1.635)
0.089
(2.093)
-0.017
(-1.081)
[8.427]
***
[14.341]
***
[4.060]
*
[2.497]
[4.079]
**
[49.637]
***
557
552
41
39
121
131
492
Panel B: by firm size
EXT/K
F-stat
0.198
(2.017)
0.471
(3.255)
0.095
(6.362)
0.051
(5.906)
0.042
(0.876)
0.154
(2.782)
0.068
(3.043)
0.084
(2.387)
0.137
(8.714)
30
nobs
CF/K
[5.394]
42
[2.401]
48
[13.642]
***
[11.116]
***
[2.949]
*
[0.363]
701
[2.675]
145
[0.008]
143
[45.894]
***
621
0.037
(0.357)
0.279
(0.803)
0.119
(5.350)
0.163
(6.836)
-0.268
(-2.336)
0.125
(0.170)
-0.029
(-0.776)
0.009
(0.122)
0.142
(4.938)
744
47
50
Panel C: by payout
EXT/K
F-stat
0.031
(0.039)
0.072
(0.227)
0.099
(4.620)
0.035
(1.710)
-0.019
(-0.519)
0.156
(0.902)
0.071
(3.173)
0.105
(1.605)
0.119
(6.473)
nobs
[0.000]
18
[0.441]
22
[0.420]
330
[12.723]
***
[3.052]
329
[0.002]
27
[5.214]
**
[1.043]
89
89
[0.391]
399
27
Country
Group
NFC
Chile
FC
NFC
China
FC
NFC
Denmark
FC
NFC
Finland
FC
NFC
France
FC
NFC
Germany
FC
NFC
Greece
FC
NFC
HongKong
FC
Panel A: by creditworthiness
CF/K
EXT/K
F-stat
0.135
0.089
[1.457]
(4.848)
(5.117)
-0.332
0.317
[8.689]
(-1.676)
(2.666)
***
0.699
0.068
[42.511]
(8.296)
(3.112)
***
0.074
0.025
[2.935]
(2.889)
(1.847)
*
0.062
0.034
[1.012]
(3.042)
(1.912)
0.022
0.137
[8.235]
(1.263)
(4.141)
***
0.081
0.254
[15.462]
(2.999)
(6.038)
***
0.196
0.210
[0.045]
(4.289)
(4.361)
-0.028
0.168
[9.734]
(-0.748)
(4.210)
***
0.106
0.046
[3.705]
(4.752)
(2.709)
*
0.118
0.052
[6.013]
(6.424)
(2.542)
**
0.068
0.120
[2.339]
(3.168)
(5.606)
0.022
0.050
[1.202]
(1.465)
(2.531)
-0.079
0.052
[144.820]
(-8.162)
(6.677)
***
0.084
0.042
[8.532]
(7.229)
(5.833)
***
0.078
0.160
[17.780]
nobs
465
101
105
361
358
107
116
115
114
483
498
419
415
1571
1542
711
CF/K
0.093
(5.792)
-0.213
(-1.450)
0.320
(6.973)
0.095
(5.410)
0.189
(5.300)
0.098
(3.380)
0.002
(0.188)
0.042
(1.639)
0.342
(3.079)
0.077
(5.679)
0.097
(6.529)
0.048
(4.247)
0.147
(5.942)
-0.045
(-0.266)
-0.221
(-1.077)
0.053
Panel B: by firm size
EXT/K
F-stat
0.044
[6.254]
(5.264)
**
0.097
[5.150]
(2.318)
**
0.101
[10.668]
(3.195)
***
0.059
[2.045]
(3.731)
0.062
[8.621]
(4.228)
***
0.201
[2.055]
(3.681)
-0.028
[2.439]
(-1.783)
0.161
[5.904]
(3.997)
**
0.270
[0.339]
(5.594)
0.068
[0.163]
(3.958)
0.054
[6.431]
(6.588)
**
0.075
[1.515]
(4.141)
0.084
[3.587]
(5.065)
*
0.631
[3.396]
(2.678)
-0.141
[0.162]
(-0.879)
0.107
[12.633]
31
nobs
638
115
119
413
415
124
128
138
142
588
603
477
499
20
23
854
CF/K
0.023
(0.346)
0.384
(11.670)
0.266
(1.257)
0.056
(3.138)
0.317
(9.489)
0.058
(2.337)
0.054
(1.164)
0.134
(0.823)
0.185
(3.989)
0.172
(7.125)
0.198
(6.376)
0.010
(0.414)
0.122
(4.368)
0.119
(9.326)
0.139
(6.864)
0.089
Panel C: by payout
EXT/K
F-stat
0.073
[0.405]
(3.289)
0.084
[37.206]
(3.594)
***
0.182
[0.140]
(3.399)
0.031
[1.518]
(2.912)
0.064
[37.935]
(3.263)
***
0.195
[6.044]
(3.701)
**
0.304
[11.224]
(5.637)
***
0.496
[2.423]
(4.716)
0.275
[3.952]
(5.013)
*
0.044
[15.255]
(2.682)
***
0.050
[12.874]
(2.445)
***
0.148
[11.859]
(6.160)
***
0.161
[0.827]
(5.377)
0.037
[22.304]
(4.003)
***
0.045
[14.166]
(3.886)
***
0.084
[0.036]
nobs
269
96
98
390
301
82
81
78
78
383
383
270
281
922
919
460
Country
Group
NFC
India
FC
NFC
Indonesia
FC
NFC
Ireland
FC
NFC
Italy
FC
NFC
Japan
FC
NFC
Korea
FC
NFC
Malaysia
FC
NFC
Panel A: by creditworthiness
CF/K
EXT/K
F-stat
(4.996)
(11.937)
***
0.070
0.073
[0.034]
(6.145)
(7.330)
-0.095
0.129
[45.199]
(-4.887)
(5.553)
***
0.171
0.078
[3.660]
(5.800)
(2.438)
*
0.054
0.140
[13.293]
(3.635)
(6.958)
***
0.069
0.248
[37.461]
(3.716)
(11.171)
***
0.095
0.011
[0.561]
(1.215)
(0.251)
0.258
0.090
[4.068]
(3.524)
(2.212)
*
0.037
0.072
[1.676]
(2.209)
(3.222)
0.133
0.044
[0.789]
(1.374)
(1.429)
-0.035
-0.043
[0.322]
(-3.294)
(-4.858)
0.016
0.092
[37.442]
(1.714)
(11.078)
***
0.042
0.039
[0.007]
(2.242)
(1.840)
0.369
0.030
[27.764]
(11.559)
(0.640)
***
0.025
0.053
[2.308]
(1.812)
(5.491)
0.109
0.134
[0.433]
(4.948)
(5.675)
nobs
709
392
379
272
282
62
65
202
202
1903
1969
304
288
811
788
CF/K
(4.856)
0.076
(6.942)
0.027
(1.381)
0.177
(6.007)
0.020
(1.465)
0.017
(1.093)
0.066
(1.357)
0.041
(1.293)
0.045
(2.481)
0.191
(4.746)
-0.002
(-0.216)
-0.026
(-3.162)
0.075
(3.651)
0.202
(8.489)
0.020
(1.171)
0.077
(8.671)
Panel B: by firm size
EXT/K
F-stat
(8.995)
***
0.062
[0.733]
(7.422)
0.197
[13.012]
(4.757)
***
0.063
[9.100]
(3.293)
***
0.212
[83.851]
(11.814)
***
0.182
[44.412]
(8.522)
***
-0.005
[1.377]
(-0.136)
0.027
[0.081]
(1.127)
0.032
[0.182]
(1.408)
0.169
[0.217]
(7.817)
0.079
[45.753]
(9.105)
***
-0.032
[0.307]
(-3.497)
0.069
[0.037]
(2.835)
0.056
[16.134]
(2.862)
***
0.069
[4.227]
(4.536)
**
0.053
[2.952]
(5.669)
*
32
nobs
892
432
444
345
351
68
70
219
224
2143
2203
332
345
926
954
CF/K
(4.114)
0.171
(7.581)
0.026
(0.660)
0.105
(3.568)
0.050
(3.214)
0.050
(1.655)
0.250
(2.779)
0.060
(0.834)
0.079
(2.284)
0.504
(9.165)
-0.010
(-0.741)
0.055
(5.995)
0.213
(6.274)
0.352
(8.891)
0.176
(11.371)
0.118
(4.412)
Panel C: by payout
EXT/K
F-stat
(5.321)
0.013
[30.152]
(0.771)
***
0.167
[5.632]
(5.848)
**
0.180
[2.148]
(4.008)
0.164
[23.331]
(9.424)
***
0.168
[5.119]
(4.022)
**
0.076
[2.407]
(2.396)
0.110
[0.296]
(2.385)
0.044
[0.556]
(1.557)
-0.022
[55.285]
(-0.565)
***
0.085
[28.503]
(8.589)
***
0.132
[24.190]
(11.017)
***
0.119
[3.327]
(4.334)
*
0.139
[11.861]
(2.624)
***
0.041
[29.471]
(2.410)
***
0.224
[5.820]
(7.571)
**
nobs
491
356
353
342
227
54
53
146
140
1509
1477
243
228
581
580
Country
Mexico
Group
FC
NFC
Netherlands
FC
NFC
NewZealand FC
NFC
Norway
FC
NFC
Philippines
FC
NFC
Portugal
FC
NFC
Singapore
FC
NFC
SouthAfrica
FC
NFC
Panel A: by creditworthiness
CF/K
EXT/K
F-stat
-0.073
0.100
[13.200]
(-2.610)
(3.937)
***
0.285
0.068
[23.021]
(9.833)
(2.739)
***
0.053
0.075
[0.405]
(2.219)
(3.230)
0.103
0.045
[1.162]
(2.248)
(2.213)
0.756
-0.132
[54.618]
(7.701)
(-3.131)
***
-0.006
0.031
[0.845]
(-0.198)
(1.056)
0.187
-0.053
[8.622]
(3.240)
(-1.208)
***
0.030
0.082
[1.220]
(1.031)
(2.533)
-0.000
-0.017
[0.372]
(-0.025)
(-0.897)
0.491
0.551
[0.850]
(3.445)
(3.956)
0.226
0.101
[0.121]
(0.691)
(1.264)
0.154
0.007
[2.210]
(1.578)
(0.272)
0.415
0.063
[54.926]
(10.025)
(2.597)
***
-0.005
0.085
[12.034]
(-0.274)
(5.344)
***
0.104
0.113
[0.042]
(3.554)
(4.054)
0.137
0.093
[1.325]
nobs
107
111
171
178
56
65
97
94
119
120
31
34
498
492
179
168
CF/K
0.136
(7.526)
-0.007
(-0.192)
0.041
(2.038)
0.083
(2.970)
0.033
(0.599)
0.531
(6.398)
-0.021
(-0.637)
-0.033
(-0.758)
-0.029
(-0.935)
-0.064
(-4.150)
0.235
(4.281)
-0.747
(-3.188)
0.042
(2.269)
0.015
(0.698)
0.100
(4.495)
0.162
Panel B: by firm size
EXT/K
F-stat
0.104
[0.875]
(4.164)
0.040
[1.072]
(2.838)
0.089
[1.692]
(3.122)
0.032
[2.831]
(3.256)
*
0.011
[0.090]
(0.214)
0.085
[26.672]
(2.489)
***
0.123
[8.540]
(3.431)
***
0.194
[13.768]
(5.671)
***
-0.090
[0.716]
(-1.461)
-0.007
[5.068]
(-0.334)
**
0.044
[8.027]
(1.101)
**
0.043
[11.513]
(1.628)
***
0.096
[3.387]
(4.554)
*
0.139
[15.583]
(8.694)
***
0.064
[0.907]
(2.567)
0.094
[3.161]
33
nobs
137
144
206
208
70
75
117
120
171
175
35
36
556
574
202
205
CF/K
0.248
(10.360)
0.300
(4.589)
0.023
(0.440)
0.074
(2.527)
-0.191
(-3.250)
0.115
(0.834)
0.205
(3.522)
0.102
(1.265)
-0.066
(-1.017)
-0.196
(-0.832)
-0.066
(-1.017)
-0.196
(-0.832)
0.234
(7.016)
0.143
(3.598)
0.256
(9.730)
0.119
Panel C: by payout
EXT/K
F-stat
0.036
[39.708]
(2.026)
***
0.032
[7.143]
(0.593)
***
0.101
[1.151]
(3.366)
0.116
[0.783]
(3.400)
-0.035
[7.153]
(-0.633)
**
0.046
[0.246]
(1.354)
-0.009
[9.120]
(-0.266)
***
0.255
[1.416]
(2.604)
0.010
[0.654]
(0.202)
0.017
[0.658]
(0.178)
0.010
[0.654]
(0.202)
0.017
[0.658]
(0.178)
0.082
[11.978]
(4.179)
***
0.110
[0.460]
(4.753)
0.107
[11.414]
(4.395)
***
0.019
[1.467]
nobs
145
98
103
108
53
59
61
64
150
85
150
85
328
335
158
159
Country
Group
Spain
FC
NFC
Sweden
FC
NFC
Switzerland
FC
NFC
Taiwan
FC
NFC
Thailand
FC
NFC
Turkey
FC
NFC
UK
FC
NFC
US
FC
Panel A: by creditworthiness
CF/K
EXT/K
F-stat
(6.107)
(3.617)
0.682
-0.018
[8.074]
(3.419)
(-0.177)
***
0.126
-0.018
[0.882]
(0.911)
(-0.296)
0.142
0.118
[0.377]
(4.834)
(4.146)
-0.009
0.020
[0.806]
(-0.353)
(0.886)
0.080
0.065
[0.120]
(3.657)
(2.141)
0.097
0.053
[2.543]
(4.391)
(2.704)
0.083
0.116
[1.334]
(3.746)
(6.910)
0.134
0.186
[1.553]
(4.593)
(7.014)
0.183
0.156
[0.268]
(4.362)
(5.343)
0.017
0.021
[0.009]
(0.680)
(1.212)
0.377
0.193
[0.469]
(2.720)
(0.820)
-0.219
0.642
[11.418]
(-1.514)
(4.231)
***
-0.079
0.052
[144.820]
(-8.162)
(6.677)
***
0.084
0.042
[8.532]
(7.229)
(5.833)
***
-0.049
0.076
[415.270]
(-9.312)
(18.168)
***
nobs
51
58
178
187
149
165
553
567
416
432
42
44
1571
1542
5648
CF/K
(6.977)
0.232
(1.873)
0.412
(3.353)
0.043
(2.644)
0.057
(2.074)
0.090
(5.080)
0.143
(3.839)
0.177
(6.786)
0.097
(4.591)
-0.005
(-0.180)
-0.042
(-2.158)
0.043
(0.477)
0.182
(3.246)
-0.015
(-2.213)
0.085
(10.070)
0.005
(1.711)
Panel B: by firm size
EXT/K
F-stat
(3.825)
*
0.011
[1.403]
(0.073)
-0.026
[10.291]
(-0.625)
***
0.047
[0.035]
(2.250)
0.150
[3.106]
(4.723)
*
0.139
[2.076]
(5.075)
0.062
[4.187]
(4.444)
**
0.059
[12.265]
(3.339)
***
0.120
[0.643]
(6.435)
0.077
[5.220]
(4.635)
**
0.061
[12.751]
(3.750)
***
0.035
[0.002]
(0.252)
-0.024
[4.436]
(-0.380)
**
0.076
[87.306]
(9.566)
***
0.036
[23.212]
(8.176)
***
0.089
[313.517]
(22.479)
***
34
nobs
53
59
207
208
192
200
667
692
452
463
60
67
1889
1913
7735
CF/K
(3.910)
0.521
(1.935)
-0.060
(-0.506)
0.176
(5.237)
-0.016
(-0.394)
0.161
(6.324)
0.172
(1.932)
0.274
(7.581)
0.267
(8.200)
0.042
(1.715)
0.456
(7.615)
-0.058
(-0.828)
0.360
(3.582)
0.119
(9.326)
0.139
(6.864)
0.129
(27.330)
Panel C: by payout
EXT/K
F-stat
(0.253)
-0.329
[6.653]
(-2.347)
**
0.229
[2.670]
(2.298)
0.093
[1.918]
(2.187)
0.019
[0.819]
(0.878)
0.002
[20.645]
(0.079)
***
0.057
[1.273]
(1.880)
0.149
[8.451]
(7.264)
***
0.058
[15.933]
(1.582)
***
0.043
[0.002]
(3.134)
0.067
[22.966]
(1.489)
***
0.277
[4.768]
(2.269)
**
-0.007
[7.129]
(-0.089)
**
0.037
[22.304]
(4.003)
***
0.045
[14.166]
(3.886)
***
0.045
[176.947]
(13.824)
***
nobs
40
45
125
127
125
132
529
457
365
323
56
49
922
919
6730
Country
Group
NFC
Panel A: by creditworthiness
CF/K
EXT/K
F-stat
0.071
0.069
[0.126]
(14.577)
(16.201)
nobs
5605
CF/K
0.089
(22.577)
Panel B: by firm size
EXT/K
F-stat
0.048
[70.818]
(19.611)
***
35
nobs
7919
CF/K
0.126
(17.012)
Panel C: by payout
EXT/K
F-stat
0.018
[156.400]
(4.524)
***
nobs
3529
Table 6 Mean and median values for internal and external financing for financially constrained firms
The table reports the mean and median values for the amounts of investments (I/K), internal financing (CF/K) and external financing (EXT/K) for financially
constrained and unconstrained firms for each country over the period 1998-2004. ‘FC’ and ‘NFC’ indicate financially constrained and unconstrained firms,
respectively. Financially constrained (unconstrained) firms are identified on the basis of creditworthiness score, firm size and payout ratio in Panels A, B and C,
respectively. The numbers in square brackets are medians.
Country
Group
Argentina
FC
NFC
Australia
FC
NFC
Austria
FC
NFC
Brazil
FC
NFC
Canada
FC
NFC
Chile
FC
Panel A: by creditworthiness
I/K
CF/K
EXT/K
nobs
0.122
0.358
[0.088] [0.190]
0.138
0.016
[0.066] [0.013]
0.333
0.793
[0.182] [0.345]
0.349
-0.407
[0.185] [-0.042]
0.032
0.300
[0.205] [0.208]
0.266
0.329
[0.194] [0.281]
0.214
0.671
[0.183] [0.418]
0.212
0.282
[0.138] [0.304]
0.280
-0.384
[0.137] [0.058]
0.360
0.678
[0.253] [0.359]
0.264
0.131
[0.096] [0.274]
0.154
[0.000]
0.282
[0.006]
0.782
[0.224]
1.536
[0.534]
0.375
[0.042]
0.125
[0.060]
0.432
[0.096]
0.353
[0.172]
0.915
[0.194]
0.488
[0.153]
0.179
[0.072]
44
45
630
631
45
47
143
143
533
535
115
I/K
Panel B: by firm size
CF/K
EXT/K nobs
0.141
0.416
[0.080] [0.153]
0.140
0.276
[0.099] [0.218]
0.397
-0.733
[0.175] [-0.161]
0.243
0.583
[0.160] [0.275]
0.460
0.280
[0.324] [0.253]
0.194
0.198
[0.160] [0.216]
0.211
0.291
[0.183] [0.372]
0.186
0.413
[0.137] [0.301]
0.460
-0.473
[0.239] [0.063]
0.204
0.337
[0.151] [0.212]
0.156
0.289
[0.104] [0.267]
36
0.265
[0.000]
0.120
[0.019]
1.508
[0.420]
0.912
[0.326]
0.414
[0.105]
0.154
[0.063]
0.288
[0.065]
0.492
[0.228]
1.187
[0.242]
0.413
[0.128]
0.353
[0.162]
45
49
758
759
49
50
146
150
656
659
119
I/K
0.141
[0.088]
0.163
[0.102]
0.313
[0.154]
0.303
[0.176]
0.400
[0.172]
0.265
[0.210]
0.166
[0.139]
0.232
[0.188]
0.388
[0.260]
0.200
[0.136]
0.161
[0.091]
Panel C: by payout
CF/K
EXT/K
0.478
[0.165]
0.601
[0.349]
0.767
[0.308]
0.984
[0.431]
0.518
[0.192]
0.325
[0.288]
0.376
[0.254]
0.533
[0.436]
0.638
[0.362]
0.495
[0.245]
0.292
[0.181]
0.233
[0.000]
0.170
[0.003]
0.803
[0.236]
0.750
[0.219]
0.393
[0.114]
0.117
[0.063]
0.348
[0.137]
0.254
[0.106]
0.663
[0.171]
0.284
[0.083]
0.586
[0.290]
nobs
26
28
379
381
29
32
99
101
413
279
104
Country
China
Denmark
Finland
France
Germany
Greece
HongKong
India
Panel A: by creditworthiness
Group
I/K
CF/K
EXT/K
nobs
NFC
0.189
0.306
0.767
119
[0.104] [0.224]
[0.503]
FC
0.193
0.307
0.859
416
[0.112] [0.210]
[0.172]
NFC
0.248
0.261
0.920
419
[0.164] [0.172]
[0.492]
FC
0.221
0.281
0.435
122
[0.157] [0.221]
[0.091]
NFC
0.297
0.190
0.165
126
[0.196] [0.327]
[0.000]
0.838
0.230
139
FC
0.315
[0.209] [0.457]
[0.031]
NFC
0.427
-0.303
0.592
142
[0.117]
[0.239] [0.146]
FC
0.526
1.101
1.009
574
[0.329] [0.660]
[0.213]
NFC
0.391
0.340
0.630
576
[0.258] [0.250]
[0.164]
0.737
0.548
490
FC
0.383
[0.243] [0.400]
[0.112]
0.334
-0.009
0.473
491
NFC
[0.215] [0.106]
[0.055]
FC
0.304
0.859
0.155
19
[0.140] [0.480]
[0.015]
NFC
0.425
0.634
0.443
19
[0.259]
[0.302] [0.263]
FC
0.236
0.070
0.629
829
[0.103] [0.075]
[0.110]
0.176
0.247
0.608
832
NFC
[0.089] [0.155]
[0.101]
FC
0.133
0.095
0.199
437
Panel B: by firm size
I/K
CF/K
EXT/K nobs
0.165
0.286
0.440
121
[0.101] [0.177] [0.208]
0.170
0.174
0.598
433
[0.082] [0.157] [0.008]
0.264
0.371
1.141
434
[0.183] [0.250] [0.619]
0.275
0.140
0.250
127
[0.168] [0.209] [0.000]
0.244
0.322
0.361
129
[0.184] [0.285] [0.045]
0.497
0.157
0.615
141
[0.273] [0.434] [0.117]
0.244
0.417
0.188
144
[0.169] [0.224] [0.041]
0.521
0.552
0.860
610
[0.316] [0.444] [0.137]
0.306
0.631
0.566
612
[0.254] [0.362] [0.154]
0.401
0.180
0.484
512
[0.237] [0.256] [0.015]
0.287
0.531
0.346
515
[0.227] [0.324] [0.113]
0.409
0.580
0.327
24
[0.333] [0.324] [0.096]
0.301
0.504
0.332
26
[0.213] [0.266] [0.217]
0.241
-0.317
0.702
908
[0.085] [-0.040] [0.083]
0.168
0.347
0.590
910
[0.080] [0.160] [0.185]
0.237
0.514
0.154
453
37
I/K
0.129
[0.100]
0.180
[0.109]
0.264
[0.178]
0.333
[0.208]
0.247
[0.188]
0.431
[0.253]
0.293
[0.192]
0.467
[0.294]
0.357
[0.267]
0.368
[0.224]
0.355
[0.245]
0.213
[0.175]
0.454
[0.257]
0.223
[0.085]
0.203
[0.128]
0.184
Panel C: by payout
CF/K
EXT/K
0.327
0.304
[0.271]
[0.107]
0.286
0.845
[0.166]
[0.303]
0.381
0.797
[0.280]
[0.256]
0.587
0.468
[0.341]
[0.072]
0.481
0.119
[0.316]
[0.005]
0.797
0.353
[0.354]
[0.136]
0.529
0.248
[0.308]
[0.050]
1.193
1.003
[0.523]
[0.287]
0.927
0.394
[0.531]
[0.104]
0.691
0.448
[0.330]
[0.096]
0.777
0.321
[0.441]
[0.050]
0.480
0.163
[0.254]
[0.079]
0.989
0.241
[0.395]
[0.107]
0.410
0.678
[0.154]
[0.207]
0.616
0.361
[0.394]
[0.044]
0.286
0.244
nobs
106
439
367
90
91
92
94
421
422
314
316
16
19
520
522
380
Country
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Panel A: by creditworthiness
Group
I/K
CF/K
EXT/K
nobs
[0.084] [0.089]
[0.093]
NFC
0.301
0.717
0.199
438
[0.210] [0.551]
[0.028]
FC
0.143
0.300
0.216
316
[0.091] [0.190]
[0.000]
NFC
0.150
0.091
0.154
317
[0.058] [0.060]
[0.000]
0.255
0.602
66
FC
0.187
[0.148] [0.228]
[0.094]
NFC
0.294
0.888
0.777
69
[0.062]
[0.225] [0.567]
FC
0.266
-0.056
0.468
220
[0.157] [0.151]
[0.000]
0.295
0.807
0.452
222
NFC
[0.213] [0.408]
[0.066]
0.044
0.213
2141
FC
0.101
[0.076] [0.076]
[0.106]
NFC
0.200
0.552
0.217
2144
[0.140] [0.302]
[0.007]
FC
0.146
-0.092
0.474
339
[0.046] [0.065]
[0.014]
NFC
0.173
0.313
0.212
340
[0.099] [0.217]
[0.040]
0.027
0.303
891
FC
0.085
[0.035] [0.021]
[0.038]
0.160
0.359
0.156
894
NFC
[0.091] [0.228]
[0.005]
FC
0.085
-0.028
0.361
124
[0.051] [0.069]
[0.075]
NFC
0.164
0.415
0.180
126
[0.113] [0.267]
[0.002]
Panel B: by firm size
I/K
CF/K
EXT/K nobs
[0.148] [0.364] [0.012]
0.188
0.326
0.235
455
[0.118] [0.177] [0.096]
0.187
0.271
0.196
352
[0.090] [0.159] [0.000]
0.149
0.257
0.198
353
[0.073] [0.136] [0.000]
0.193
0.502
0.402
69
[0.139] [0.316] [0.012]
0.215
0.400
0.690
70
[0.167] [0.344] [0.226]
0.296
0.286
0.504
226
[0.159] [0.292] [0.000]
0.236
0.570
0.406
229
[0.169] [0.286] [0.087]
0.169
0.380
0.255
2224
[0.099] [0.185] [0.032]
0.133
0.202
0.164
2226
[0.111] [0.157] [0.077]
0.221
0.136
0.497
352
[0.085] [0.141] [0.005]
0.123
0.194
0.293
353
[0.063] [0.149] [0.103]
0.121
0.111
0.143
972
[0.056] [0.111] [0.002]
0.122
0.262
0.310
974
[0.064] [0.115] [0.035]
0.143
0.134
0.208
142
[0.077] [0.179] [0.003]
0.106
0.227
0.200
144
[0.093] [0.188] [0.084]
38
I/K
[0.091]
0.196
[0.150]
0.164
[0.089]
0.238
[0.166]
0.309
[0.232]
0.212
[0.156]
0.276
[0.163]
0.288
[0.209]
0.183
[0.124]
0.123
[0.095]
0.105
[0.046]
0.174
[0.100]
0.122
[0.053]
0.149
[0.092]
0.141
[0.077]
0.132
[0.105]
Panel C: by payout
CF/K
EXT/K
[0.130]
[0.080]
0.520
0.137
[0.401]
[0.026]
0.165
0.254
[0.109]
[0.000]
0.596
0.197
[0.384]
[0.000]
0.643
0.744
[0.371]
[0.186]
0.630
0.810
[0.407]
[0.118]
0.554
0.462
[0.251]
[0.089]
0.832
0.243
[0.479]
[0.002]
0.471
0.279
[0.239]
[0.084]
0.240
0.118
[0.152]
[0.029]
0.230
0.292
[0.121]
[0.066]
0.364
0.158
[0.250]
[0.024]
0.203
0.231
[0.102]
[0.025]
0.365
0.160
[0.221]
[0.007]
0.242
0.333
[0.155]
[0.046]
0.302
0.093
[0.243]
[0.001]
nobs
381
365
253
56
59
161
162
1738
1741
272
256
638
641
150
104
Country
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
Spain
Panel A: by creditworthiness
Group
I/K
CF/K
EXT/K
nobs
FC
0.300
0.425
0.645
191
[0.190] [0.225]
[0.040]
NFC
0.288
0.670
0.561
195
[0.212] [0.386]
[0.058]
FC
0.195
0.360
0.206
68
[0.137] [0.237]
[0.023]
NFC
0.244
0.832
0.910
68
[0.237]
[0.171] [0.437]
FC
0.455
0.612
0.756
115
[0.296] [0.221]
[0.267]
0.325
0.408
0.483
117
NFC
[0.220] [0.104]
[0.052]
FC
0.087
0.297
0.264
130
[0.030] [0.033]
[0.000]
NFC
0.178
0.157
0.144
132
[0.000]
[0.062] [0.157]
FC
0.189
0.010
0.785
36
[0.142] [0.128]
[0.293]
0.185
0.213
1.808
38
NFC
[0.144] [0.167]
[1.161]
0.164
0.185
552
FC
0.150
[0.075] [0.136]
[0.048]
NFC
0.234
0.229
0.456
553
[0.122] [0.157]
[0.023]
FC
0.281
0.705
0.311
200
[0.199] [0.348]
[0.033]
NFC
0.434
1.088
0.421
201
[0.330] [0.598]
[0.036]
0.347
0.127
57
FC
0.229
[0.183] [0.326]
[0.033]
NFC
0.278
0.920
0.326
60
Panel B: by firm size
I/K
CF/K
EXT/K nobs
0.344
0.502
0.660
208
[0.221] [0.325] [0.010]
0.257
0.547
0.630
210
[0.203] [0.377] [0.129]
0.289
0.877
0.512
73
[0.183] [0.525] [0.026]
0.136
0.219
0.326
77
[0.077] [0.141] [0.138]
0.472
0.387
0.784
121
[0.263] [0.150] [0.055]
0.298
0.142
0.465
122
[0.186] [0.146] [0.181]
0.142
0.162
0.140
177
[0.036] [0.058] [0.000]
0.181
0.250
0.354
178
[0.092] [0.157] [0.042]
0.190
0.086
1.076
36
[0.152] [0.170] [0.251]
0.155
0.185
0.921
38
[0.121] [0.153] [0.629]
0.227
0.103
0.273
585
[0.102] [0.121] [0.006]
0.162
0.267
0.282
588
[0.090] [0.135] [0.055]
0.357
1.008
0.347
212
[0.270] [0.566] [0.016]
0.323
0.691
0.368
213
[0.247] [0.369] [0.079]
0.252
0.330
0.099
57
[0.209] [0.326] [0.011]
0.234
0.369
0.339
60
39
I/K
0.306
[0.193]
0.261
[0.209]
0.179
[0.105]
0.224
[0.191]
0.422
[0.303]
0.314
[0.186]
0.155
[0.057]
0.201
[0.138]
0.178
[0.092]
0.285
[0.233]
0.216
[0.105]
0.178
[0.092]
0.410
[0.299]
0.316
[0.268]
0.302
[0.182]
0.254
Panel C: by payout
CF/K
EXT/K
0.806
0.532
[0.323]
[0.091]
0.623
0.243
[0.418]
[0.019]
0.453
0.443
[0.232]
[0.097]
0.672
0.345
[0.471]
[0.027]
1.148
1.092
[0.284]
[0.279]
0.504
0.199
[0.221]
[0.041]
0.424
0.189
[0.139]
[0.000]
0.368
0.255
[0.250]
[0.014]
0.277
1.685
[0.133]
[1.132]
0.127
1.293
[0.148]
[0.576]
0.411
0.377
[0.191]
[0.071]
0.502
0.207
[0.242]
[0.012]
0.873
0.482
[0.428]
[0.060]
1.028
0.146
[0.535]
[0.016]
0.377
0.212
[0.300]
[0.065]
0.918
0.236
nobs
111
116
60
62
72
73
157
95
18
19
377
380
170
173
47
51
Country
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Panel A: by creditworthiness
Group
I/K
CF/K
EXT/K
nobs
[0.225] [0.362]
[0.124]
FC
0.347
1.191
0.605
204
[0.227] [0.610]
[0.098]
NFC
0.337
-1.066
1.240
206
[0.203] [-0.035]
[0.109]
FC
0.265
0.227
0.441
190
[0.166] [0.276]
[0.065]
0.218
0.552
0.376
195
NFC
[0.150] [0.369]
[0.049]
FC
0.175
0.396
0.227
673
[0.095] [0.210]
[0.000]
NFC
0.256
0.356
0.326
676
[0.132] [0.194]
[0.092]
0.483
0.111
454
FC
0.180
[0.113] [0.356]
[0.000]
0.113
0.160
0.282
458
NFC
[0.044] [0.106]
[0.014]
FC
0.740
1.561
0.346
62
[0.660] [1.310]
[0.122]
NFC
0.518
0.349
0.493
65
[0.079]
[0.242] [0.437]
FC
0.287
-0.954
0.979
1741
[0.152] [-0.069]
[0.083]
0.380
1.240
0.884
1744
NFC
[0.253] [0.680]
[0.079]
-1.175
1.504
6252
FC
0.313
[0.157] [-0.204]
[0.461]
NFC
0.394
1.112
0.873
6253
[0.273] [0.691]
[0.192]
Panel B: by firm size
I/K
CF/K
EXT/K
[0.174] [0.306] [0.116]
0.405
-0.366
1.251
[0.294] [0.233] [0.227]
0.316
0.595
0.490
[0.187] [0.348] [0.093]
0.247
-0.122
0.338
[0.130] [0.159] [0.015]
0.199
0.472
0.388
[0.167] [0.323] [0.090]
0.222
0.524
0.252
[0.096] [0.252] [0.000]
0.221
0.352
0.250
[0.131] [0.192] [0.092]
0.109
0.196
0.098
[0.059] [0.200] [0.000]
0.163
0.381
0.356
[0.078] [0.180] [0.025]
0.579
1.048
0.689
[0.311] [0.812] [0.078]
0.510
0.846
0.428
[0.445] [0.616] [0.190]
0.392
-0.685
1.195
[0.195] [-0.013] [0.052]
0.250
0.671
0.551
[0.186] [0.354] [0.080]
0.384
-1.019
1.465
[0.187] [-0.249] [0.291]
0.288
0.690
0.864
[0.200] [0.402] [0.240]
40
nobs
211
213
199
203
704
705
467
470
68
71
1937
1939
8034
8035
I/K
[0.225]
0.387
[0.267]
0.290
[0.207]
0.263
[0.170]
0.188
[0.139]
0.210
[0.106]
0.219
[0.122]
0.109
[0.051]
0.192
[0.120]
0.545
[0.377]
0.742
[0.685]
0.348
[0.229]
0.258
[0.172]
0.360
[0.248]
0.215
[0.168]
Panel C: by payout
CF/K
EXT/K
[0.304]
[0.108]
1.080
0.622
[0.547]
[0.184]
0.680
0.366
[0.376]
[0.086]
0.520
0.513
[0.246]
[0.094]
0.473
0.274
[0.289]
[0.033]
0.284
0.279
[0.154]
[0.055]
0.707
0.222
[0.362]
[0.000]
0.212
0.209
[0.153]
[0.001]
0.535
0.144
[0.328]
[0.000]
0.599
0.552
[0.510]
[0.182]
1.436
0.406
[1.062]
[0.030]
1.216
0.783
[0.569]
[0.069]
0.671
0.383
[0.367]
[0.019]
1.139
0.959
[0.610]
[0.249]
0.665
0.459
[0.422]
[0.122]
nobs
138
140
146
149
598
515
402
358
67
59
1008
1009
6774
3613
Table 7 Investment-cash flow sensitivity for low cash flow firms
The table reports the results of the regression analysis (equation (1)) for low and high cash-flow firms for
each
country
over
the
period
1998-2004.
The
regression
model
is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + uit , where investment-cash flow sensitivity is the
coefficient estimate for cash flows (CF/K). ‘L-CF’ and ‘H-CF’ indicate low and high cash-flow firms,
respectively. In each country, low (high) cash-flow firms are those firm-years in which the amount of cash
flows belongs to the top (bottom) one-third of the distribution in a given year. The regression model is
estimated using fixed firm and year effects. The number of firm-year observations used for this regression
may differ between constrained and unconstrained groups because firms that have only one valid firmyear are removed before we run the regression for each group. The numbers in parentheses are OLS t
values. The numbers in square brackets are t(βCFFC −βCFNFC), i.e., t-statistic for the difference in the
coefficient estimates for CF/K between constrained and unconstrained firms. *, ** and *** indicate twotailed significance at the 10%, 5% and 1% levels, respectively.
Country
Argentina
Australia
Austria
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Greece
Group
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
M/B
0.002
-0.009
CF/K
-0.049
0.054
(0.248)
(-0.280)
0.009
0.060
(1.157)
(5.259)
-0.045
-0.000
(-0.590)
(-0.017)
0.007
0.086
(0.589)
(3.883)
***
0.044
0.120
0.024
0.034
(3.323)
(3.070)
***
***
-0.163
0.197
0.056
0.025
(1.299)
(0.797)
-0.738
0.038
0.001
0.008
(0.357)
(0.528)
0.025
0.175
0.048
0.008
(4.043)
(0.492)
***
-0.270
0.217
0.102
0.061
(4.777)
(2.364)
***
**
-0.110
0.166
0.028
-0.003
(3.404)
(-0.306)
***
-0.034
0.155
0.017
0.030
(2.732)
(2.367)
***
**
-0.088
0.168
0.006
0.006
(1.863)
(2.092)
*
**
-0.144
0.149
***
-0.092
0.125
0.207
-0.057
41
(-1.014)
(0.635)
[-1.050]
(-4.049)
(5.597)
[-6.810]
(0.596)
(-0.574)
[0.730]
(1.394)
(2.515)
[-1.339]
(-6.604)
(7.328)
[-9.864]
(-6.428)
(0.188)
[-3.364]
(1.819)
(4.958)
[-3.968]
(-5.263)
(2.926)
[-5.401]
(-2.583)
(3.454)
[-4.298]
(-1.431)
(6.866)
[-5.775]
(-4.427)
(5.712)
[-7.211]
(-13.477)
(12.897)
***
***
***
**
*
***
***
***
***
***
*
***
***
***
***
***
**
***
***
***
***
***
***
***
***
***
R2
0.823
0.761
nobs
38
38
0.672
0.807
548
571
0.888
0.984
41
40
0.732
0.780
128
133
0.720
0.815
484
470
0.788
0.695
110
111
0.779
0.773
368
362
0.770
0.848
106
115
0.745
0.823
120
128
0.716
0.880
509
522
0.716
0.796
426
425
0.678
0.786
1599
1635
Country
HongKong
India
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
Spain
Group
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
M/B
CF/K
-0.006
0.022
(-1.245)
(2.371)
**
-0.146
0.185
0.006
0.012
(0.813)
(1.668)
*
-0.026
0.171
0.004
-0.007
(0.638)
(-0.682)
-0.229
0.139
0.010
0.010
(0.890)
(0.528)
0.177
0.169
-0.012
0.000
(-0.971)
(0.001)
-0.029
0.193
0.002
0.021
(1.398)
(5.518)
0.007
0.010
(0.978)
(0.593)
-0.003
0.283
0.002
0.008
(0.661)
(0.720)
-0.035
0.169
0.002
0.054
(0.256)
(4.346)
0.012
0.022
(1.239)
(1.550)
0.085
-0.045
(2.985)
(-1.600)
***
0.053
-0.064
(1.739)
(-2.542)
*
**
0.008
0.163
0.044
0.066
(2.282)
(2.508)
**
**
-0.082
0.185
-0.013
-0.046
(-0.380)
(-1.429)
-0.002
0.059
(-0.348)
(4.167)
-0.004
-0.000
0.000
0.022
0.073
(-0.588)
(-0.007)
(0.007)
(1.445)
(3.076)
***
***
-0.190
0.102
-0.014
0.283
-0.072
0.076
0.131
0.149
0.079
0.329
***
-0.166
0.187
***
-0.062
0.189
0.186
0.226
-0.099
42
[-18.618]
(-9.441)
(10.893)
[-14.411]
(-1.203)
(5.775)
[-5.394]
(-6.761)
(4.732)
[-8.208]
(0.991)
(2.328)
[0.040]
(-1.079)
(4.903)
[-4.670]
(-16.963)
(7.510)
[-16.599]
(-0.149)
(7.477)
[-6.634]
(-1.979)
(6.734)
[-6.634]
(-0.773)
(10.222)
[-9.043]
(-1.431)
(2.329)
[-2.466]
(1.203)
(3.557)
[-0.154]
(0.139)
(3.859)
[-2.147]
(-1.879)
(2.794)
[-3.367]
(1.024)
(2.158)
[-1.459]
(-6.874)
(7.416)
[-10.110]
(-1.779)
(7.152)
[9.558]
(0.523)
(-0.682)
[0.712]
***
***
***
***
***
***
***
***
***
**
***
***
***
***
***
***
***
**
***
***
***
***
**
***
***
***
**
*
***
***
**
*
***
***
***
*
***
***
R2
nobs
0.582
0.781
751
744
0.719
0.809
398
401
0.659
0.749
281
286
0.699
0.912
60
66
0.588
0.798
202
210
0.739
0.792
1881
1954
0.649
0.729
299
296
0.580
0.652
814
804
0.712
0.942
117
110
0.692
0.792
175
183
0.793
0.886
63
63
0.831
0.878
105
104
0.484
0.689
118
118
0.865
0.906
33
34
0.598
0.774
512
499
0.820
0.848
0.837
0.806
0.869
178
185
363
50
55
Country
Group
M/B
Sweden
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
L-CF
H-CF
t-stat
0.058
0.025
(3.763)
(1.732)
***
*
-0.077
0.124
0.032
-0.003
(2.781)
(-0.349)
***
0.029
0.252
0.012
0.004
(1.392)
(0.263)
0.001
0.022
(0.260)
(2.686)
0.004
-0.009
(0.089)
(-0.114)
0.006
0.006
(1.863)
(2.092)
*
**
-0.144
0.149
0.010
0.008
(6.732)
(4.588)
***
***
-0.132
0.167
Switzerland
Taiwan
Thailand
Turkey
UK
US
CF/K
-0.029
0.235
***
-0.196
0.070
-0.126
0.242
43
(-2.561)
(4.034)
[-4.677]
(1.594)
(9.896)
[-7.123]
(-2.238)
(7.654)
[-7.919]
(-8.105)
(3.468)
[-8.443]
(-1.039)
(1.964)
[-2.128]
(-13.477)
(12.897)
[-18.618]
(-22.016)
(30.527)
[-36.821]
**
***
***
***
***
**
***
***
***
***
***
*
**
***
***
***
***
***
***
R2
nobs
0.747
0.822
180
186
0.821
0.900
162
176
0.648
0.768
615
611
0.592
0.788
414
418
0.883
0.849
45
51
0.678
0.786
1599
1635
0.658
0.807
5754
5817
Table 8 Investment-cash flow sensitivity for financially constrained firms after removing negative cash-flow firms
The table reports the results of the regression analysis (equation (1)) for financially constrained and unconstrained firms for each country over the period 19982004 after removing negative cash-flow observations. The regression model is: ( I / K ) it = β M / B ( M / B ) it + β CF / K (CF / K ) it + uit , where investment-cash flow
sensitivity is the coefficient estimate for cash flows (CF/K). ‘FC’ and ‘NFC’ indicate financially constrained and unconstrained firms, respectively. Financially
constrained (unconstrained) firms are identified by creditworthiness score, firm size and payout ratio in Panels A, B and C, respectively. Due to space limitation,
the table reports only the coefficient estimate for CF/K. The regression model is estimated using fixed firm and year effects. The number of firm-year
observations used for this regression may differ between constrained and unconstrained groups because firms that have only one valid firm-year are removed
before we run the regression for each group. The numbers in parentheses are OLS t values. The numbers in parentheses are OLS t values. The numbers in square
brackets are t(βCFFC −βCFNFC), i.e., t-statistic for the difference in the coefficient estimates for CF/K between constrained and unconstrained firms. *, ** and ***
indicate significance at the 10%, 5% and 1% levels, respectively.
Country
Argentina
Australia
Austria
Brazil
Canada
Chile
China
Group
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
Panel A: by creditworthiness
CF/K
nobs
0.094
(1.586)
26
-0.058
(-0.296)
26
[0.743]
427
0.196
(6.991)
***
0.108
(5.050)
***
406
[2.509]
***
0.243
(1.282)
37
0.498
(2.274)
**
36
[-0.880]
0.035
(1.851)
*
111
0.674
(6.161)
***
119
[-5.749]
***
388
0.270
(7.904)
***
0.258
(7.740)
***
384
[0.256]
0.021
(0.067)
94
0.870
(10.963)
***
102
[-2.594]
***
310
0.142
(3.639)
***
Panel B: by firm size
CFK
-0.098
0.235
0.198
0.222
-0.058
0.531
0.051
0.119
0.199
0.233
0.080
0.478
0.136
44
(-0.788)
(1.350)
[-1.556]
(8.315)
(8.595)
[-0.679]
(-0.299)
(2.347)
[-1.973]
(1.802)
(1.812)
[-0.942]
(7.080)
(7.456)
[-0.798]
(0.457)
(9.730)
[-2.184]
(3.517)
*
***
***
Panel C: by payout
nobs
33
35
CF/K
-0.010
0.249
468
540
0.214
0.290
**
**
*
*
42
44
-0.311
-0.079
132
131
-0.005
0.015
***
***
468
506
0.245
0.131
105
113
0.461
0.345
359
0.111
***
**
***
(-0.097)
(0.769)
[-0.761]
(7.248)
(11.622)
[-1.952]
(-3.945)
(-0.107)
[-0.309]
(-0.177)
(0.198)
[-0.249]
(6.740)
(2.179)
[1.628]
(13.545)
(1.520)
[0.505]
(4.039)
nobs
16
21
***
***
**
***
314
318
27
26
86
87
***
**
*
***
386
265
***
348
91
98
Country
Denmark
Finland
France
Germany
Greece
HongKong
India
Indonesia
Ireland
Italy
Group
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
Panel A: by creditworthiness
CF/K
nobs
0.047
(1.189)
303
[1.713]
**
86
0.380
(2.109)
**
0.229
(2.873)
***
102
[0.764]
99
0.172
(2.820)
***
0.186
(1.286)
103
[-0.088]
427
0.181
(5.684)
***
0.239
(6.365)
***
440
[-1.169]
0.210
(5.834)
***
323
0.248
(7.276)
***
327
[-0.761]
0.185
(12.555)
*** 1175
0.160
(9.476)
*** 1171
[1.086]
484
0.273
(10.650)
***
0.148
(7.515)
***
483
[3.892]
***
0.058
(1.239)
336
0.174
(6.100)
***
331
[-2.105]
**
0.041
(1.072)
212
0.267
(6.373)
***
201
[-3.992]
***
0.031
(0.179)
59
0.242
(3.197)
***
62
[-1.101]
0.033
(0.734)
165
0.250
(2.342)
**
177
[-1.873]
**
Panel B: by firm size
CFK
0.265
0.481
0.124
0.194
0.389
0.161
0.180
0.235
0.202
0.244
0.144
0.197
0.145
0.239
0.198
0.154
0.108
0.138
0.215
0.125
0.299
45
(6.690)
[-2.324]
(3.244)
(3.235)
[2.333]
(4.111)
(2.884)
[-1.360]
(6.573)
(8.867)
[-0.601]
(9.899)
(7.297)
[0.888]
(1.330)
(0.365)
[0.231]
(9.821)
(10.511)
[2.126]
(6.894)
(6.201)
[0.867]
(6.116)
(5.569)
[1.430]
(2.213)
(1.998)
[-0.625]
(2.840)
(5.678)
[-2.542]
Panel C: by payout
nobs
373
CF/K
0.469
101
109
0.401
0.078
121
127
0.512
0.041
503
525
0.216
0.217
373
404
0.248
0.182
20
20
0.171
0.164
***
***
**
***
***
548
610
0.124
0.205
380
389
0.168
0.100
***
***
*
**
*
256
273
0.284
0.052
64
66
0.339
0.074
***
***
***
183
191
0.035
0.512
***
**
***
***
**
***
***
*
***
***
***
***
(9.113)
[-6.124]
(6.487)
(1.335)
[3.803]
(2.992)
(0.911)
[2.660]
(9.087)
(7.475)
[-0.045]
(8.203)
(4.861)
[1.366]
(11.380)
(8.285)
[0.276]
(4.835)
(8.822)
[-2.353]
(4.850)
(3.140)
[1.437]
(11.336)
(1.290)
[4.906]
(4.496)
(1.140)
[2.651]
(0.318)
(8.930)
[-3.847]
***
***
***
***
***
***
***
***
nobs
287
78
80
78
78
369
376
***
***
*
***
***
258
274
***
***
***
***
***
*
***
420
456
***
***
897
897
320
334
249
186
54
52
***
***
***
134
134
Country
Japan
Korea
Malaysia
Mexico
Netherlands
NewZealand
Norway
Philippines
Portugal
Singapore
SouthAfrica
Group
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
Panel A: by creditworthiness
CF/K
nobs
-0.004
(-0.147)
1672
0.097
(6.663)
*** 1767
[-3.573]
***
0.230
(5.245)
***
234
0.403
(9.232)
***
232
[-2.802]
***
0.092
(4.593)
***
584
0.147
(5.476)
***
608
[-1.631]
*
0.118
(1.149)
94
0.332
(10.214)
***
96
[-1.990]
**
0.103
(3.197)
***
150
0.102
(2.064)
**
167
[0.016]
0.663
(6.257)
***
54
0.104
(3.262)
***
65
[5.048]
***
0.314
(3.383)
***
66
0.115
(2.059)
**
71
[1.831]
**
0.028
(1.131)
84
0.415
(1.874)
*
88
[-1.738]
**
0.273
(0.791)
27
0.434
(3.956)
***
29
[-0.445]
0.462
(9.888)
***
371
0.139
(5.052)
***
353
[5.963]
***
0.190
(6.159)
***
163
0.240
(7.887)
***
146
Panel B: by firm size
CFK
0.132
0.051
0.321
0.327
0.135
0.123
0.325
0.018
0.055
0.164
0.148
0.506
0.196
0.857
0.023
0.195
0.424
-0.769
0.280
0.206
0.197
0.158
46
(9.443)
(4.809)
[4.596]
(10.184)
(9.699)
[-0.122]
(3.768)
(11.291)
[0.318]
(9.495)
(0.323)
[4.623]
(1.782)
(5.135)
[-2.472]
(2.247)
(5.669)
[-3.235]
(4.602)
(2.422)
[-1.855]
(0.570)
(2.263)
[-1.814]
(3.652)
(-2.737)
[3.924]
(9.449)
(9.492)
[2.004]
(6.655)
(6.141)
Panel C: by payout
nobs
1912
1976
CF/K
0.134
0.179
258
276
0.305
0.356
***
***
709
745
0.219
0.162
***
122
128
0.293
0.344
179
184
0.110
0.165
69
72
0.141
0.095
88
94
0.213
0.096
125
132
-0.009
-0.193
29
31
0.306
-0.441
399
446
0.309
0.178
182
189
0.324
0.117
***
***
***
***
***
***
*
***
***
**
***
***
***
**
**
**
**
***
**
***
***
***
**
***
***
(8.530)
(10.300)
[-1.939]
(7.586)
(8.632)
[-0.872]
(11.188)
(5.627)
[1.611]
(10.420)
(3.780)
[-0.537]
(2.237)
(4.501)
[-0.898]
(2.246)
(0.683)
[0.306]
(3.627)
(1.027)
[1.069]
(-0.175)
(-0.799)
[0.741]
(8.194)
(-1.548)
[2.601]
(8.916)
(4.258)
[2.423]
(12.569)
(3.891)
***
***
**
***
***
nobs
1443
1416
215
214
***
***
*
***
***
538
543
**
***
100
104
**
52
59
***
55
58
128
93
138
79
*
***
***
***
***
***
***
12
14
307
316
159
155
Country
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Group
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
FC
NFC
t-stat
Panel A: by creditworthiness
CF/K
nobs
0.233
[9.634]
***
309
0.682
(3.489)
***
49
0.144
(0.843)
54
[2.075]
**
140
0.150
(4.403)
***
0.227
(5.871)
***
137
[-1.495]
*
0.281
(9.615)
***
134
0.155
(4.183)
***
146
[2.679]
***
457
0.131
(4.377)
***
0.523
(9.466)
***
471
[-6.246]
***
0.312
(7.015)
***
372
0.054
(2.044)
**
361
[4.990]
***
0.436
(3.072)
***
38
0.555
(1.599)
34
[-0.320]
0.185
(12.555)
*** 1175
0.160
(9.476)
*** 1171
[1.086]
0.162
(20.399)
*** 4057
0.176
(23.961)
*** 4026
[-1.312]
*
Panel B: by firm size
CFK
0.159
0.522
0.394
0.083
0.289
0.313
0.307
0.252
0.166
0.092
0.067
0.158
0.233
0.188
0.122
0.159
0.206
47
[5.137]
(2.584)
(3.212)
[0.538]
(2.829)
(9.597)
[-4.923]
(10.412)
(7.502)
[0.113]
(7.971)
(5.803)
[2.007]
(2.092)
(3.520)
[0.527]
(1.036)
(3.813)
[-0.461]
(16.258)
(11.908)
[4.213]
(29.011)
(39.520)
[-6.295]
Panel C: by payout
nobs
371
51
56
CF/K
0.137
0.502
0.042
***
***
***
***
***
158
171
0.231
-0.030
170
176
0.194
0.230
***
***
**
**
***
561
587
0.596
0.266
385
393
0.068
0.469
50
57
0.105
0.359
1356
1432
0.171
0.164
5333
5615
0.182
0.154
***
**
***
***
***
***
***
***
***
***
[4.102]
(1.318)
(0.352)
[1.152]
(7.120)
(-0.762)
[5.148]
(7.107)
(2.581)
[-0.387]
(13.081)
(8.367)
[5.940]
(2.302)
(7.796)
[-5.991]
(0.812)
(3.563)
[-1.541]
(11.380)
(8.285)
[0.276]
(34.499)
(18.635)
[2.818]
***
***
***
***
**
***
***
***
**
***
***
***
*
***
***
***
***
***
nobs
314
39
44
120
127
127
130
484
442
318
310
43
47
897
897
6537
3462