Life Cycle Effects in Firm Financing Choices
Laarni Bulana
a
b
Zhipeng Yanb*
International Business School, Brandeis University
School of Management, New Jersey Institute of Technology
First Draft: October 2005
This Draft: August 2009
Abstract
We study firm financing behavior over a firm’s life-cycle. We find that the pecking order
theory describes the financing patterns of mature firms better than those of growth firms. While
our findings do not reject the pecking order theory, they, nevertheless, show that the relative
value of the firm’s growth options and debt-capacity constraints dominate the costs associated
with asymmetric information concerning assets in place.
Key Words: Life Cycle, Pecking Order, Capital Structure
JEL Codes: G32
*
Bulan: 415 South Street, MS 032, Waltham, MA 02454, lbulan@brandeis.edu, TEL: 781-736-2994; Yan
(corresponding author): University Heights, Newark, NJ 07102, zyan@njit.edu, TEL: 973-596-3260, FAX: 973596-3047. We are grateful for comments from two anonymous referees, Soku Byoun, Ben Gomes-Casseres, Jens
Hilscher, Li Jin, Blake LeBaron, Hong Li, Justin Murfin, Carol Osler, Paroma Sanyal, Mohamed Ariff and seminar
participants at Brandeis University, Cornerstone Research, PanAgora Asset Management, the Financial
Management Association 2007 annual meeting and the Midwest Finance Association 2007 annual meeting. We also
thank Jay Ritter for kindly providing his IPO data. We alone are responsible for any errors or omissions.
I. Introduction
There has been recent interest in a firm’s life cycle in the finance literature. Firm life cycle
stages are distinct and identifiable phases that result from fundamental changes in key internal
and/or external factors (Dickinson (2008)). Diamond (1991) suggests that debt financing through
intermediaries has a life cycle of its own. On the issue of dividends, DeAngelo, DeAngelo and
Stulz (2005) and Bulan, Subramanian and Tanlu (2007) have found that a firm’s propensity to
pay dividends is a function of which stage a firm is in its life cycle. In particular, they argue that
firms that pay dividends are mature firms.
Life cycle theory is particularly pertinent to firms’ financing decisions. Fama and French
(2005) point out that the profitability and growth characteristics of firms are central to their
financing decisions, since valuable growth opportunities indicate how much investment a firm
may need and profitability reflects to what extent these investment needs can be funded
internally. Life cycle stages encompass variation in a firm’s level of knowledge acquisition
(about industry structure and cost structure), level of initial investment and re-investment of
capital, and adaptability to the competitive environment (Gort and Klepper (1982)). Accordingly,
partitioning firms according to their life cycle stage predictably provides differential information
about what determines profitability and growth. Dickinson (2008) demonstrates that firm life
cycle affects profitability and growth, both cross-sectionally and over time. Anthony and Ramesh
(1992) find empirically that stock market reactions to growth and capital expenditure are
functions of the life cycle stage. In a direct study of financial life cycle of small private
businesses, Berger and Udell (1998) find that firms rely more on debt financing as firms grow
from “infancy” to “adolescence”, but use less debt as firms become “middle-aged” and “old”.
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They also report that on average, “smaller” firms have more equity, most of which comes from
principal owners; and “larger” firms have more debt through bank loans and trade credit.
This paper complements Berger and Udell’s work by investigating the financial life cycle of
public firms. In this paper, we study two major life cycle stages, namely, growth stage and
mature stage. We then focus on the pecking order theory of financing proposed by Myers (1984)
and Myers and Maljuf (1984). This theory is based on asymmetric information between investors
and firm managers. Due to the valuation discount that less-informed investors apply to newly
issued securities, firms resort to internal funds first, then debt and equity last to satisfy their
financing needs.
The empirical evidence for the pecking order theory has been mixed. Shyam-Sunder and
Myers (1999) propose a direct test of the pecking order and find strong support for the theory
among a sample of large firms. Frank and Goyal (2003) argue that the Shyam-Sunder and Myers
test rejects the pecking order for small public firms. They conclude that this finding is in contrast
to the theory since small firms are thought to suffer most from asymmetric information problems
and hence, should be the ones following the pecking order. More recent work by Lemmon and
Zender (2008) and Agca and Mozumdar (2007) have shown that the Shyam-Sunder and Myers
test does not account for a firm’s debt capacity, a constraint that is particularly binding for small
firms. Once debt capacity constraints are accounted for, they find that the pecking order
performs well even for small firms. Leary and Roberts (2008) use a different approach and
estimate a two-rung empirical model. They find the pecking order performs poorly for a broad
sample of firms.
Using a life cycle stage classification, this paper differentiates between a size effect and a
maturity effect. Although there is a positive correlation between firm size and maturity, it is
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important to make the distinction between large and mature as well as young and small. The size
effect was first documented by Frank and Goyal (2003), who find that large firms fit the pecking
order theory better than small firms. We find that this size effect exists only among firms in their
growth stage. For firms in their mature stage, this size effect is not significant. When
controlling for a firm’s debt capacity, this size effect disappears altogether for firms in either
stage, while a maturity effect remains. That is, more mature firms fit the pecking order better
than younger firms after firm size is controlled for.
Overall, we find that the pecking order theory describes the financing patterns of mature
firms better than young firms. Lemmon and Zender (2008) correctly point out that the tension in
Myers and Majluf (1984), the foundation for the pecking order, is between the costs associated
with asymmetric information concerning assets in place and the value of the firm’s growth
options relative to the value of its assets in place. Growth firms are less-established, less stable
and followed by fewer/no analysts, and hence, should suffer more from problems of information
asymmetry. However, they may also have more valuable growth opportunities relative to assets
in place compared to those of mature firms. Moreover, they also have less debt-capacity. Our
findings do not reject the pecking order theory. They, nevertheless, show that the relative value
of the firm’s growth options and debt-capacity constraints dominate the costs associated with
asymmetric information concerning assets in place. Older, more stable and highly profitable
firms with few growth opportunities and good credit histories are more suited to use internal
funds first, and then debt before equity for their financing needs.
This paper is organized as follows: Section II describes the data and sample selection. In
Section III, we present our results. Robustness checks are performed in Section IV, while Section
V concludes.
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II. Data
We construct two samples of firms according to their life cycle stage: firms in their growth
stage and firms in their mature stage. Life cycle stages are naturally linked to firm age. Age is an
important factor, but it is not the sole determinant of a firm’s life cycle stage. Firm life cycles
vary widely across industries (Black (1998)) and within the same industry. Several studies in
management and accounting show that firm life cycle is NOT a linear function of firm age and
life cycle stages are by no means necessarily connected to each other in a deterministic sequence.
In a study of variations of environment, strategy, structure and decision making methods over a
firm’s life cycle, Miller and Friesen (1984) identify five life-cycle stages: birth, growth, maturity,
revival and decline. They find that firms over lengthy periods often fail to exhibit the common
life cycle progression extending from birth to decline. That is, the maturity stage may be
followed by decline, revival, or even growth; revival may precede or follow decline and so on.
Liu (2008) studies accruals and managerial operating decisions over the firm life cycle. She
classifies firms into five life cycle stages using multivariate ranking procedures and finds that
about 16% mature firms move back to growth stages from the current year to the next.
Motivated by these studies, we focus on a snapshot of a firm’s history where growth and
maturity is more easily determined. We define the growth stage to be the first six-year period
after the year of the firm’s IPO and the mature stage to be a consecutive six-year period
following a dividend initiation, where a firm maintains positive dividends. Miller and Friesen
(1984) find that, on average, each stage lasts for six years. Evans (1987) defines firms six years
old or younger as young firms and firms seven years or older as old firms. Accordingly, we set
the length of each stage to be 6 years. We find similar results using 4, 8, and 10 years for the
stage length. Following standard practice, we exclude financial firms (SIC codes 6000-6999)
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and regulated utilities (SIC codes 4900-4999) and consider only firms that have securities with
CRSP share codes 10 or 11.
A. Growth Stage
Our sample is constructed from the universe of firms in the CRSP-COMPUSTAT merged
database over the 1970- 2004 period. We define the growth stage to be the first six-year period
after the year of the firm’s IPO. This definition may not necessarily apply to some firms from a
mechanical point of view. For example, Metro-Goldwyn-Mayer Inc, a movie production firm,
was founded in 1930 and went public also in 1997. It is “old” enough and mature in many
respects. However, the IPO is itself an important financing decision that a firm has to make and
in many cases, indicates a significant change in the firm’s development over its life cycle. Here,
we treat the IPO as an important turning point in a firm’s history and as the starting point of the
growth.
We use flow of funds data to test the pecking order theory. IPO dates are provided by Jay
Ritter from 1970 to 1974. IPO dates between 1975 and 1998 are obtained from Loughran and
Ritter (2004). When the IPO date is not available, we use the earliest date for which we observe a
non-zero/non-missing stock price on the CRSP monthly tapes.
B. Mature Stage
DeAngelo, DeAngelo and Stulz (2006), among others, have found that a firm’s propensity to
pay dividends is a function of which stage a firm is in its life cycle. Bulan, Subramanian and
Tanlu (2007) find that firms that initiate dividends are older and highly profitable, have fewer
growth opportunities and are less risky. Based on this body of work, we identify firms in their
mature stage by their dividend initiation history. We, first, use the entire Compustat industrial
6
annual database to find consecutive six-year periods that a firm has positive dividends. We
require that such a period should immediately follow at least a year with zero or missing
dividends. This is similar to Baker and Wurgler’s (2004) definition of dividend initiations. If a
firm issues dividends during the first six years after IPO, we exclude this firm from the sample
altogether, i.e. a firm cannot be in the growth and mature stages at the same time.
C. Firm Characteristics and Comparison of Growth and Mature Stages
We construct the following variables for our analysis: book leverage, market leverage, net
equity issued, net debt issued, financing deficit, tangibility, profitability, retained earnings-tototal equity ratio, R&D, advertising expense, and capital expenditures. We also measure a firm’s
age to be the number of years from birth, where birth is the earliest year we have a non-missing
observation from three datasets: Jay Ritter’s incorporation dataset, Compustat, and CRSP.
Following Frank and Goyal (2003), we exclude firms with missing book value of assets, firms
involved in major mergers (Compustat footnote 1 with value = AB) and a small number of firms
that reported format codes (data318) 4, 5, or 6. To reduce the impact of outliers and the most
extremely mis-recorded data, all variables are truncated at the top and bottom 0.5 percentiles.
Table 1 explains the construction of these variables in detail.
Table 2 provides descriptive statistics for each life cycle stage. The sample selection
methodology outlined above results in 3,918 growth firms and 1,876 mature firms. Some
interesting patterns emerge. On average, firms in mature stage are older, larger, and more
profitable than firms in growth stage. The median firm in the mature stage is 2 years older and
more than four times larger than the median firm in the growth stage. In terms of profitability,
the median ROA (return on asset) for mature firms is 16.42 %, while for growth firms, it is 7.28
7
%. On the other hand, growth firms experience much more rapid sales growth (twice that of
mature firms) and higher market-to-book ratios as markets attach a premium to their growth
opportunities. These differences are highly significant. The table also shows that R&D
expenditure for growth firms is much larger than for mature firms (five to six times larger), but
that capital expenditures is higher for mature firms. This is also consistent with the latter having
higher tangible assets. Overall, these patterns conform to our expectations of key firm attributes
in these two stages of a firm’s life cycle. In terms of financing characteristics, we see that
growth firms have larger financing deficits, as expected. There seems to be no difference in net
debt issued between the two cohorts, while net equity issued is larger for the growth firms. From
this simple comparison, the evidence seems to suggest growth firms rely more heavily on equity
financing rather than debt, consistent with prior work.
In table 3, we provide more detail on the financing characteristics of firms across these two
life cycle stages. Frank and Goyal (2003) and Agca and Mozumdar (2007) have both
documented a significant size effect in tests of the pecking order. We follow their strategy and
divide our sample into quintiles according to firm size. To more effectively control for firm size
across the two stages, we use growth firms to pin down size quintiles. We first sort the growth
firms into 5 equal quintiles according to their real assets at the beginning of their growth stage.
We then allocate mature firms into these growth-firm quintiles according to their real assets at
the beginning of the mature stage. Since more than half of the mature firms end up in the fifth
quintile, we further divide the fifth quintile into two equal parts: 5a and 5b. Except for 5b, the
size distributions of the same quintile for these two cohorts match up satisfactorily (We run twosample Kolmogorov-Smirnov test for the equality of the distribution function of real assets
8
between the two stages but within the same size quintile and cannot reject the equality of the
distribution function for quintile 1 to quintile 5a ).
Table 3 shows that growth firms have a much greater need for external financing, as
expected. The average financing deficit for the smallest growth firms is 33.88 % while that of
the smallest mature firms is 2.70 %. In addition, the overall trend is that in the growth stage, the
deficit decreases as firm size increases, while in the maturity stage, the deficit increases with
size. Equity makes up a larger proportion of external finance for growth firms, but this
proportion declines as the firm gets larger. In contrast, for mature firms, the reliance on debt is
greater than on equity and the use of debt tends to increase as the firm gets larger.
On firm performance, the two variables identified by Fama and French (2005) to be central in
evaluating firms’ financing decisions, growth and profitability, both show extremely different
patterns between growth firms and mature firms. The real sales growth rate is about three times
higher for growth firms than for mature firms on average. It strictly decreases with firm size for
growth firms, while it is quite stable for mature firms. We view higher sales growth as indicative
of a firm’s greater relative value of the firms’ growth options versus the costs associated with
asymmetric information. On the other hand, profitability monotonically increases with firm size
for growth firms but strictly decreases with size for mature firms. In other words, for growth
firms getting bigger means getting better in terms of profitability, while for mature firms the
opposite is true. This latter finding is largely consistent with the existing literature on the
diversification discount. Lang and Stulz (1994) and Berger & Ofek (1995) find that
diversification reduces firm value. Firms that choose to diversify are poor performers relative to
firms that do not. When firms are still in the growth stage, they usually don’t diversify. At this
stage, getting bigger means a firm is getting stronger and is more capable of surviving market
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competition. On the other hand, if a firm is in its mature stage, getting bigger is usually achieved
through mergers and acquisitions, which as documented, is usually not value-enhancing.
Although the profitability of mature firms declines with increasing size, their profitability levels
are still higher than those of growth firms. Under the pecking order theory, higher profitability
implies that firms will have more internal funds available for financing.
In sum, growth firms’ financing characteristics are quite different from mature firms even
after controlling for firm size. We expect that the degree of information asymmetry and the
relative value of growth options are functions of both firm size and life-cycle stage. Therefore,
we can come to much richer conclusions in tests of the pecking order by accounting for both
these factors.
III. Empirical Tests
A. Testing the Pecking Order Theory – A Quadratic Specification
In this section, we adopt a test of the pecking order theory proposed by Lemmon and
Zender (2008) given by the following:
ΔDebtit = b0 + b1. Deficitit + b2. Deficitit2 + εit
(1)
Where ΔDebt is net debt issued and Deficit is the financing deficit, i.e. uses of funds minus
internal sources of funds, (both scaled by total assets). This deficit is financed with debt and/or
equity. If firms follow the pecking order, changes in debt should track changes in the deficit one-forone. Hence, the expected coefficient on the deficit is 1 (Shyam-Sunder and Myers, 1999). The
quadratic specification is used to account for binding debt capacity constraints. If firms are financing
their deficit with debt first and issue equity only when they reach their debt capacities, then net debt
issued is a concave function of the deficit (as shown by Chirinko and Singha, 2000) and the
10
coefficient on the squared deficit term would be negative. If firms are issuing equity first and if debt
is the residual source of financing, then this relationship should be convex and the coefficient on the
squared deficit term would be positive. If debt and equity are issued in fixed proportions, the deficit
would have no effect on net debt issued.
We use pooled ordinary least squares estimation with heteroskedasticity-consistent (robust)
standard errors adjusted for clustering by firm. We report the total effect of the deficit, or the debtdeficit sensitivity, as the percent change in net debt issued per one percent change in the financing
deficit, evaluated at the sample mean. In unreported robustness tests, we have included year effects
as well as used firm fixed effects estimation: the results are unchanged and are very similar to those
reported here.
We estimate equation (1) for each size quintile in each life cycle stage. The results are
presented in Table 4. Across all quintiles and stages, the coefficient on deficit is positive and
negative, while the coefficient on deficit-squared is negative and significant except for the
smallest quintile of mature firms. This indicates that on average, net debt issued is a concave
function of the deficit.
We find evidence of the size effect documented by Frank and Goyal (2003) – but only
among firms in the growth stage. For growth firms, the total effect of the deficit and R-squares
are increasing in firm size. The smallest quintile has a total effect of 20.80% and an R-square of
0.171 while the largest quintile (5b) has a total effect of 74.01% and an R-square of 0.72. Thus,
the pecking order performs “best” for the largest firms in the growth stage.
In the mature stage, the story is quite different. We do not find evidence of this
monotonic size effect at all. The smallest quintile has the highest R-square (0.771), while the
largest quintile has the second highest R-square (0.671). The total effect ranges from 64.57% to
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79.05% and they are not significantly different from each other at the 10 % level. Thus, once a
firm has reached maturity, the size effect ceases to exist.
In comparing firms across life cycle stages, we find that the total effect is significantly
higher for mature firms as a group and within each size quintile, with the exception of quintile
5b. For instance, growth firms in quintile 1 have roughly the same size as mature firms in
quintile 1. But, their debt-deficit sensitivity is only 20.8% compared to 77.85% of mature firms
in the same size quintile.
In sum, we find the size effect is significant for growth firms, i.e. the total effect of the
deficit is increasing in size. While for mature firms, the total effect of the deficit is similar across
all size quintiles. Comparing across stages, we find the maturity effect remains, i.e. the total
effect of the deficit is significantly larger for mature firms than for growth firms, with the
exception of quintile 5b. From these findings we infer that mature firms more closely exhibit
financing behavior consistent with the pecking order.
B. Controlling for Bond Ratings
Thus far we have shown that in tests of the pecking order, there is a maturity effect that
dominates the size effect. We have also seen that for mature firms, firm size is not necessarily
monotonically related to debt capacity constraints. Lemmon and Zender (2008) and Agca and
Mozumdar (2007) identify firms with access to public debt markets to further control for debt
capacity. The argument is firms with bond ratings have access to low cost debt and this is a good
indicator of larger debt capacities. Lemmon and Zender use a predicted probability that a firm
will have a bond rating as a measure of the presence or absence of debt-capacity constraints.
They show that firms with a high predicted probability of having bond rating are larger, older,
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and more profitable and have lower market-to-book ratios, consistent with the characteristics of
not only the mature firms in our sample but also the largest growth firms. Thus to a certain
extent, our slice of the data presents in a very intuitive way a natural progression in terms of
access to public debt. Mature firms are more established and have longer credit histories. The
largest growth firms are the oldest firms in their cohort and are quite similar to mature firms in
many respects. These are precisely the type of firms we expect to have access to the public debt
markets.
In Table 5 we repeat our regressions across life cycle stages for the high and low
predicted bond ratings cohorts (panel A and panel B, respectively). The sample size is smaller
because of data constraints. If a high probability of having rated debt is indicative of larger debt
capacities and being less constrained, then the results in panel A confirm this. The quadratic
model performs well for both growth and mature, and small and large firms. The R-squares are
high and the total debt-deficit sensitivities are also high. For mature firms, the coefficients on the
squared deficit term are insignificant. This is evidence that mature firms with high predicted
bond ratings do not face binding debt capacity constraints. On the other hand, growth firms have
a significant negative coefficient for the deficit squared indicative of binding debt capacity
constraints. Thus, although these firms are likely to have access the public debt markets, they
have larger financing deficits due to their larger demand for external finance. Hence they resort
to issuing equity more often. When we look at firms across size quintiles but within the same
life-cycle stage, we find that there is no size effect at either stage. That is, large firms may not fit
the pecking order theory better than small firm. However, the maturity effect, though weakened
(compared with results in Table 4), still exists. Controlling for size, most mature firms fit the
theory better than growth firms.
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Panel B presents the regressions for firms in the low predicted bond ratings cohort
(constrained sample). In contrast to panel A, The results here are more dramatically different
between growth and mature firms. We observe low R-squares and low debt-deficit sensitivities
for growth firms. The negative coefficient on the squared deficit term indicates that debt capacity
constraints are binding for these growth firms. In stark contrast, mature firms have high debtdeficit sensitivities and high R-squares.
The results for mature firms in the low predict bond ratings cohort are very similar to
those in the high predicted bond ratings cohort. This shows that a low likelihood of having a
bond rating is not necessarily an indication that the firm is debt-constrained. More specifically,
mature firms are less likely to be constrained by their debt capacities, whether or not they have a
bond rating. There are two possible reasons for this: one, their need for external finance is not
that great and hence they rarely reach their debt capacities; or two, even in the presence of large
financial deficits, mature firms are also likely to have larger debt capacities because of their
credit quality. Firm maturity is essentially a substitute for access to the public debt markets, i.e.
among firms that are less likely to issue public debt (either due to firm choice or due to supplyside factors) mature firms still have access to a low cost of debt capital. Evidence from existing
work supports this view. For example, Diamond (1989) shows how a good reputation mitigates
the adverse selection problem between borrowers and lenders. Thus, mature firms who are more
established and have longer credit histories are able to obtain better loan rates compared to their
younger firm counterparts. Petersen and Rajan (1995) present evidence of higher absolute
borrowing costs for younger firms compared to those of older firms, regardless of the
competitive structure of the lending market.
Again, Panel B reports no size effect at either stage, but a strong maturity effect.
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C. Controlling for conventional factors
A competing theory of explaining capital structure is the trade-off theory, which
originated from the seminal paper of Modigliani and Miller (1958) – firms weigh the benefits of
debt tax shields against the costs of financial distress. In addition, Jensen and Mecking (1976)
argue that not only does having debt mitigate free cash flow problems; it also creates agency
costs between debt holders and equity holders. The trade-off between these costs and benefits of
debt is evaluated by maximizing firm value, resulting in a target leverage ratio.
In contrast, the pecking order theory predicts there is no leverage target; costly debt at
high leverage ratios is equivalent to the notion of debt capacity. The trade-off theory predicts that
more profitable firms should have higher leverages because of the discipline of debt payment and
benefits from larger tax shields. Fama and French (2002) also claim that, controlling for the
profitability of assets in place, the trade-off theory predicts firms with more investment
opportunities have less leverage because (i) they have stronger incentives to avoid
underinvestment that can arise from stockholder-bondholder agency problems, and (ii) they have
less need for the discipline of debt payments.
To test the competing capital structure theories over firms’ life cycle stages, we include
proxies for the costs and benefits of debt in the regressions. Rajan and Zingales (1995) identified
four main factors that have consistently been found to explain leverage in prior work. We do the
same here and estimate the following equation:
ΔDebtit = b0 + b1. Deficitit + b2. Deficitit2 + b3. ΔTangibility it + b4. ΔMarket-to-Book it + b5.
ΔLog Salesit + b6. ΔProfitabilityit + indi + yt + it
15
(2)
where Δ represents change over the annual period t, tangibility is the ratio of net property
plant and equipment to total assets, market to book is the ratio of a firm’s market value of equity
to its book equity, log sales (size) is the logarithm of total real sales revenue, profitability is the
return on assets, ind and y are industry and year dummies respectively.
The regression results are presented in Table 6. Overall, results are generally consistent
with those reported in earlier literature. The coefficients on asset tangibility, growth opportunity,
size and profitability are mostly positive, negative, positive and negative, respectively, with the
exception of the coefficients on size and profitability at maturity stage.
Looking across life cycle stages, we find tangible assets matters more for growth firms as
a whole. Most of their value is derived from growth and intangibles; hence these firms need
collateral for their loans. With respect to profitability, we find that its negative impact is greater
for growth firms and not significant for mature firms.
Table 6 shows that even though the effects of most conventional variables are still
significant, the additional explaining power added by the conventional variables is not
significant, compared to the results in Table 4. The deficit and deficit-squared are consistently
significant, indicating the deficit plays the dominant role in explaining new debt issues when
used in competition with the conventional factors. This is consistent with Agca and Mozumber
(2007). Once again, size effect only exists among growth firms and maturity effect remains
across size quintiles except for firms in quintile 5b.
D. Maturity Effect vs. Size Effect
In Table 7, we document the relative importance of maturity versus firm size. Using
young firms in the first asset quintile as a benchmark, we can identify two effects (size effect and
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maturity effect) relative to the smallest firms in the growth stage. This is achieved in the
following model:
ΔDebtit = b0 + b1. Deficitit + b1Sq. Deficitit2 + b2. Deficitit *Quintile2+ b3. Deficitit *Quintile3
+ b4.Deficitit *Quintile4+ b5a. Deficitit *Quintile5a + b5b. Deficitit *Quintile5b
+ C1.Deficitit*Maturity*Quintile1 + C2. Deficitit*Maturity*Quintile2
+ C3.Deficitit*Maturity*Quintile3 + C4. Deficitit*Maturity*Quintile4
+ C5a.Deficitit*Maturity*Quintile5a+ C5b. Deficitit*Maturity*Quintile5b +εit
(3)
Here, quintilei is a dummy variable which equals one when a firm is in the ith quintile and zero
otherwise, for i=1, 2, 3, 4, 5a, and 5b. Maturity is a dummy variable which equals one when a
firm is in the mature stage and zero otherwise.
For example, a growth firm in the first quintile has a debt-deficit sensitivity of b1 +
2*b1Sq*mean(deficit). For a growth firm in the third quintile, its sensitivity is b1 +
2*b1Sq*mean(deficit) + b3. Since both firms are in the growth stage, there is no maturity effect.
The difference between the two sensitivities is b3. Therefore, b3 is a size effect. For a mature
firm in the third quintile, the debt-deficit sensitivity is b1 + 2*b1Sq*mean(deficit) + b3 + c3. The
difference between the sensitivities of a mature firm in the third quintile and a growth firm in the
first quintile is b3 + c3, where b3 is the size effect and c3 is the maturity effect.
Panel B of Table 7 reports the outcome of the above regression and the relative
importance of the maturity effect. We find that the maturity effect dominates the size effect in
the first four quintiles: only for the largest mature firms is the size effect more dominant.
Therefore, sorting firms into different size groups conceals other factors that cannot be explained
simply by the difference in firm size. Our findings suggest that a firm’s life cycle stage is
arguably a more significant characteristic that explains differences in firm behavior. This is not
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only true for financing behavior, as we have just documented, but it is also true for firm
strategies, structure, and decision making methods, as shown by Miller and Friesen (1984).
IV. Robustness Tests
To ensure that our results are being driven by life cycle stages and not simply by our
sample selection criteria, we define the growth and mature stages in alternative ways. First, for
growth firms, we limit our original sample to firms with high industry-adjusted growth rates. We
first construct industry adjusted growth rates, for each firm in each year, as the firm’s sales
growth minus the industry median sales growth rate. The industry medians are computed at the
most precise SIC level for which there is a minimum of three firms, by using all firms in
Compustat’s industrial annual database. We use industry adjusted sales growth to smooth out
any macroeconomic impacts on an industry. Next, for each firm we get the median industryadjusted growth rate over its life span and the mean industry-adjusted growth rate over the first
six year period after IPO. Finally, we define a firm to be in its growth stage when its mean
industry-adjusted growth rate in the first six years post-IPO is greater than the median industryadjusted growth rate over its life span.
Second, we use the ratio of retained earnings to total equity (RE/TE) as a proxy for firm
maturity. De Angelo, De Angelo and Stulz (2005) argue that the earned/contributed capital mix
is a logical proxy for a firm’s life cycle stage because it measures the extent to which a firm is
reliant on internal or external capital. Firms with low RE/TE tend to be in the capital infusion
stage, whereas firms with high RE/TE tend to be more mature with ample cumulative profits that
make them largely self-financing. Moreover, they find that the firm’s earned/contributed capital
mix is significantly positively correlated with a firm’s propensity to pay dividends, which is our
18
main criterion in identifying firm maturity. Thus, we get the median value of RE/TE for each
year from the universe of Compustat industrial firms. We then define a six-year period in which
a firm’s RE/TE is greater than the median value of RE/TE as the maturity stage for the firm. If a
firm has more than one such period, we require these two or more periods should be at least 10
years apart from each other. Note that according to this definition, a mature firm can be either a
dividend payer or a non-dividend payer, since a firm that has reached maturity need not be
paying dividends. Using this criterion, only 21.3 percent of firm-year observations have positive
dividends if we assume the maturity stage lasts for six years.
Furthermore, we try various combinations of different stage lengths and different
definitions of growth and/or maturity stages. For instance, we use high-sales-growth firms only
for the growth stage and firms with high RE/TE only for the mature stage and assume the length
of each stage is 6 years. Our main conclusions still hold with various combinations of growth
and mature stage definitions and stage lengths – the size effect only exists in the growth stage
and mature firms fit the pecking order better than growth firms.
Lastly, to further explore the pecking order theory within the framework of firms’ life
cycles, we estimate the Leary and Roberts (2008) two-rung empirical model for firms in each
quintile at each life cycle stage. We again find no size effect for firms in the mature stage and
find such an effect among firms in the growth stage with regards to debt-equity issuance
decisions. We find a maturity effect, though weaker than we obtain from using the Lemmon and
Zender model in the previous sections, when pitting firms in their growth stage against those in
their mature stage.
V. Conclusion
19
In this paper, we classify firms into two life cycle stages, namely growth and maturity, and
test the pecking order theory of financing proposed by Myers (1984) and Myers and Maljuf
(1984). Under Lemmon and Zender (2008) empirical framework, we identify two effects: a size
effect and a maturity effect. The size effect is such that the pecking order theory better explains
the financing decisions of firms as they increase in size. The maturity effect is such that mature
firms financing decisions are better explained by the pecking order theory compared to younger
growth firms. We find that this size effect exists only among firms in their growth stage. For
firms in their mature stage, this size effect is not significant. When controlling for a firm’s debt
capacity, this size effect disappears altogether, while the maturity effect remains.
Overall, we find that the pecking order theory describes the financing patterns of mature
firms better than that of younger growth firms. Older and more mature firms are more closely
followed by analysts and are better known to investors, and hence, should suffer less from
problems of information asymmetry. For example, a good reputation (such as a long credit
history) mitigates the adverse selection problem between borrowers and lenders. Thus, mature
firms are able to obtain better loan rates compared to their younger firm counterparts (Diamond
(1989)). On the other hand, young and growth firms have abundant growth options that are larger
in value relative to assets in place compared to those of mature firms. Moreover, growth firms
also have less debt capacity. Hence, our findings do not reject the pecking order theory. They,
nevertheless, show that the relative value of the firm’s growth options and debt-capacity
constraint dominate the costs associated with asymmetric information concerning assets in place.
20
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21
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Asymmetry”, working paper, Cornell University.
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pages 229-252, November
22
Table 1: Key Variable Definitions
Variable:
Preferred Stock
Book Equity
Market Equity
Market-to-Book Ratio
Book Debt
Book Leverage
Market Leverage
Tobin’s Q
ΔEquityt
ΔDebtt
Deficitt
Tangibility
Profitability
Log Sales
Retained Earnings-toTotal Equity Ratio
R&D
Advertising Expense
Capital Expenditures
Dividends
Definitions and COMPUSTAT annual data item in parenthesis:
= Liquidating value(10), if available, else Redemption Value (56)
if available, else Carrying Value (130)
= Total Assets (6) – Liabilities (181) + Balance Sheet Deferred
Taxes and Investment Tax Credit (35), if available – Preferred
Stock
= Stock price (199) times Shares Outstanding (25)
= Market Equity/Book Equity
= Total Assets (6) – Book Equity
= Book Debt/ Total Assets (6)
= Book Debt/(Total Assets (6) – Book Equity + Market Equity)
= (Market Equity + Total Assets (6) – Common Equity (60))/
Total Assets (6)
= [Sale of Common and Preferred Stock (108) at t – Purchase of
Common and Preferred Stock (115) at t]/(Total Assets at t-1)
= [Long-term Debt Issuance (111) at t – Long-term Debt
Reduction (114) at t]/(Total Assets at t-1)
=ΔEquityt +ΔDebtt
= Net Property, Plant and Equipment (8) / Total Assets(6)
= Earnings Before Interest, Tax and Depreciation (13) / Total
Assets(6)
= Natural log of Sales (12), deflated by the Consumer Price Index
= Retained Earnings (36)/Common Equity(60)
= Research and Development Expense (46) /Total Assets (6)
= Advertising Expense (45)/ Total Assets(6)
= Capital Expenditures (128)/ Total Assets (6)
= Dividend Per Share (26)
23
Table 2: Summary Statistics - Life Cycle Stages
This table reports summary statistics by life cycle stage, namely growth and maturity. The sample period is from 19712004. Financial firms, utilities and firms with missing assets are excluded. Variables are scaled by total assets unless
otherwise noted. For variable definitions, please refer to the appendix. Means, medians and standard deviations of key
variables across stages are obtained in two steps. Step 1: Calculate the mean value of a variable in each stage for each firm.
Step 2: Calculate the mean, median and standard deviation of the variable mean across all the firms for each stage. The
difference in the number of firms used in the calculations is due to missing values. The t-test of the difference between
means of firms in growth and maturity stages (a) and the Wilcoxon rank-sum test of the difference between medians of
firms in growth and maturity stages (b) are reported. + significant at 10% level; * significant at 5% level; ** significant at
1% level
Stage
Age (years)
Real Assets (in millions,
constant 1992 Dollars)
Real Sales (in million,
constant 1992 Dollars)
Annual real sale growth
rate (%)
Market to Book ratio
Return on Assets (%)
Tangible Assets (%)
Capital expenditure (%)
R&D (%)
Advertising expense (%)
Dividend per share ($)
Retained earnings to total
equity ratio (%)
Market Leverage (%)
Book Leverage (%)
Δdebt (%)
Δequity (%)
Deficit (%)
Meana
Growth
Maturity
Medianb
Growth Maturity
Standard Deviation
Growth Maturity
Number of Firms
Growth Maturity
12.34**
15.65
7.50
9.50
13.85
18.28
3918
1876
177.59**
1342.30
38.56**
168.89
664.61
3973.26
3918
1876
182.31**
1120.85
37.64**
211.28
701.10
2845.80
3895
1875
41.87**
2.53**
-0.20**
27.17**
10.91
1.54
17.56
36.62
20.66**
1.78**
7.28**
20.27**
8.36
1.22
16.42
31.31
72.96
2.19
25.55
21.44
15.91
1.10
7.85
22.28
3887
3895
3915
3917
1875
1780
1874
1876
7.99*
11.47**
3.70
0.00**
8.42
2.51
3.72
0.52
5.85**
6.62**
1.97
0.00**
6.97
1.07
2.01
0.26
6.97
15.17
5.21
0.00
5.90
3.67
5.76
1.02
3905
2636
1976
3917
1866
1029
914
1876
-82.46**
58.88
5.06**
63.66
330.08
50.48
3917
1821
33.59**
45.61*
4.50**
14.18**
19.32**
40.85
46.79
3.16
1.67
4.73
30.24**
44.89*
0.83
3.99**
9.17**
41.07
47.09
1.56
0.11
2.48
22.13
20.89
9.57
25.50
29.82
20.91
18.27
6.03
5.31
8.82
3895
3894
3907
3844
3828
1780
1873
1856
1865
1842
24
Table 3: Key Summary Statistics - Life Cycle Stage and Size Quintiles
This table reports summary statistics by life cycle stage and size (asset) quintile. The sample period is from 1971-2004.
Financial firms, utilities and firms with missing assets are excluded. Variables are scaled by total assets unless otherwise
noted. For variable definitions, please refer to the appendix. The quintiles are obtained as follows: First, firms in the growth
stage are allocated into 5 equal quintiles according to their real assets at the beginning of the stage. The range of real assets in
each quintile in the growth stage determines the corresponding firms in each quintile in the mature stage. The fifth quintile is
divided further into two parts: 5a and 5b.
Mean Key Firm Characteristics by Life Cycle Stage and Size Quintile
Growth Stage
Quintile Quintile
1
2
Age (years)
Sales growth rate(%)
Market to Book ratio
Return on Assets (%)
Tangible Assets (%)
R&D(%)
Market Leverage (%)
Book Leverage (%)
Δdebt (%)
Δequity (%)
Deficit (%)
Number of firms
6.65
69.86
4.03
-18.17
25.15
15.02
24.49
42.60
4.25
28.21
33.88
783
Quintile
3
8.79
50.04
2.41
-3.28
27.97
11.70
33.28
46.13
4.26
16.01
20.78
784
12.06
34.65
2.15
2.69
24.98
13.38
32.17
41.82
3.32
12.27
16.02
784
Quintile Quintile
1
2
Quintile
3
Quintile Quintile Quintile
4
5a
5b
14.66
29.57
2.16
6.50
25.80
10.66
33.22
42.22
4.29
8.99
13.62
784
16.93
28.14
2.07
11.15
29.98
7.19
38.30
48.70
5.22
6.89
12.52
392
22.16
23.76
1.77
11.37
33.99
2.92
51.22
62.34
7.55
3.32
11.24
391
All firms
12.34
41.87
2.53
-0.20
27.17
11.47
33.59
45.61
4.50
14.18
19.32
3918
Maturity Stage
Quintile Quintile Quintile
4
5a
5b
All firms
Age (years)
7.82
9.83
10.98
14.75
17.81
18.51
15.65
Sales growth rate(%)
11.08
10.00
10.46
12.13
12.67
9.96
10.91
Market to Book ratio
2.25
1.91
1.51
1.46
1.50
1.49
1.54
Return on Assets (%)
25.62
20.60
19.63
18.31
17.67
15.29
17.56
Tangible Assets (%)
27.88
23.51
33.31
33.73
35.31
42.28
36.62
R&D(%)
3.45
3.89
2.78
2.32
2.18
2.21
2.51
Market Leverage (%)
19.76
28.53
35.58
40.47
41.27
46.19
40.85
Book Leverage (%)
26.66
36.12
38.22
43.58
46.92
54.79
46.79
Δdebt (%)
3.34
2.14
2.18
3.40
3.46
3.13
3.16
Δequity (%)
0.55
1.88
1.67
1.98
1.70
1.54
1.67
Deficit (%)
2.70
4.01
4.91
5.34
5.16
4.45
4.73
Number of firms
44
150
269
365
300
745
1873
Note: We perform the Wilcoxon-Mann-Whitney test for the equality of means between the two stages but within the
same size quintile. Bold font denotes significance at the 10% level or better. We also use a two-sample independent
t-test with separate variances and obtain similar results.
25
Table 4. Tests of the Pecking Order over Life Cycle Stages
Equation: Δdebtit = b0 + b1. Deficitit + b2. Deficitit2 + εit., where, Δdebtit refers to new debt issued in period t scaled by total assets at the beginning of period t (assett-1). Deficitit refers to
the financing deficit in period t scaled by total assets at the beginning of period t. The sample period is from 1971-2004. The quintiles are obtained as follows: First, firms in the growth
stage are allocated into 5 equal quintiles according to their real assets at the beginning of the stage. The range of real assets in each quintile in the growth stage determines the
corresponding firms in each quintile in the mature stage. The fifth quintile is divided further into two parts: 5a and 5b. The total effect of the deficit is the percent change in net debt
issued per one percent change in the deficit (evaluated at the mean value of the deficit in each quintile)
Growth Stage
Deficit
Deficit2
Constant
Debt-deficit
sensitivity
Maturity Stage
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
0.237**
[0.012]
-0.045**
[0.004]
-0.009**
[0.003]
0.370**
[0.012]
-0.082**
[0.004]
-0.009**
[0.003]
0.364**
[0.012]
-0.086**
[0.005]
-0.004
[0.002]
0.457**
[0.013]
-0.081**
[0.005]
-0.007**
[0.003]
0.605**
[0.017]
-0.142**
[0.007]
-0.004
[0.003]
0.765**
[0.015]
-0.114**
[0.007]
0.001
[0.003]
0.391**
[0.005]
-0.087**
[0.002]
-0.006**
[0.001]
0.779**
[0.059]
-0.015
[0.107]
0.001
[0.003]
0.767**
[0.029]
-0.516**
[0.033]
0.007*
[0.003]
0.652**
[0.018]
-0.064**
[0.011]
0.004+
[0.002]
0.717**
[0.016]
-0.182**
[0.014]
0.001
[0.002]
0.744**
[0.016]
-0.135**
[0.008]
0.001
[0.002]
0.805**
[0.010]
-0.168**
[0.005]
0.001
[0.001]
0.721**
[0.007]
-0.137**
[0.004]
0.002*
[0.001]
20.80%
33.67%
33.89%
43.60%
57.11%
74.01%
35.90%
77.85%
72.81%
64.57%
69.77%
73.01%
79.05%
70.84%
1602
0.639
3696
0.671
9777
0.619
Observations
Adjusted R2
4103
4123
3941
3879
1938
1837
19821
247
822
1468
1942
0.171
0.27
0.263
0.374
0.457
0.72
0.281
0.771
0.51
0.641
0.595
Note: Robust standard errors clustered by firm are reported in brackets. +, *, ** significant at 10%, 5%, and 1% level respectively.
Wald Test of the Equality of the Total Effect of the Deficit Between Stages and within the Same Quintile
Quintile 1
c
Quintile 2
a
Quintile 3
c
Quintile 4
c
Quintile 5a
a
Quintile 5b
N/S
Wald Test of the Equality of the Total Effect of the Deficit Between Adjacent Quintiles within the Same Life-Cycle Stage
Q1 vs. Q2
Q2 vs. Q3
Q3 vs. Q4
c
N/S
b
N/S
N/S
N/S
Note: a, b, c significant at the 10%, 5%, and 1% level respectively. N/S: not significant
Growth
Maturity
Q4 vs. Q5a
b
N/S
Q5a vs. Q5b
c
N/S
All firms
c
Table 5. Tests of the Pecking Order over Life Cycle Stages: Predicted Bond Ratings
Equation: Δdebtit = b0 + b1. Deficitit + b2. Deficitit2 + εit., where, Δdebtit refers to new debt issued in period t scaled by total assets at the beginning of period t (assett-1). Deficitit refers to
the financing deficit in period t scaled by total assets at the beginning of period t. The sample period is from 1971-2004. The quintiles are obtained as follows: First, firms in the growth
stage are allocated into 5 equal quintiles according to their real assets at the beginning of the stage. The range of real assets in each quintile in the growth stage determines the
corresponding firms in each quintile in the mature stage. The fifth quintile is divided further into two parts: 5a and 5b. The total effect of the deficit is the percent change in net debt
issued per one percent change in the deficit evaluated at the mean value of the deficit in each sub-group. Robust standard errors clustered by firm are reported in brackets. +, *, **
significant at 10%, 5%, and 1% level respectively. Panel A(B) reports results for firms with high(low) predicted bond ratings. The predicted bond rating is calculated from a logit model
of the likelihood of having rated debt according to Lemmon and Zender (2008).
Panel A: High Predicted Bond Ratings
Growth Stage
Deficit
Deficit2
Constant
Total Effect:
Deficit
Maturity Stage
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
0.617**
[0.022]
-0.048
[0.031]
-0.024**
[0.003]
0.649**
[0.023]
-0.098**
[0.024]
-0.012**
[0.003]
0.665**
[0.032]
-0.078*
[0.035]
-0.004
[0.004]
0.588**
[0.028]
-0.062*
[0.024]
0
[0.004]
0.687**
[0.036]
-0.160**
[0.030]
0.001
[0.006]
0.760**
[0.033]
-0.096**
[0.029]
0.003
[0.004]
0.667**
[0.011]
-0.088**
[0.011]
-0.007**
[0.002]
0.745**
[0.058]
-0.105
[0.111]
-0.006
[0.006]
0.643**
[0.038]
-0.14
[0.101]
-0.001
[0.003]
0.715**
[0.024]
0.044
[0.025]
0
[0.002]
0.781**
[0.022]
-0.286**
[0.033]
0.005*
[0.002]
0.758**
[0.027]
-0.09
[0.048]
0
[0.002]
0.746**
[0.013]
-0.002
[0.014]
0.003*
[0.001]
0.733**
[0.009]
-0.018
[0.012]
0.002+
[0.001]
61.28%
63.51%
65.09%
57.37%
64.64%
74.22%
65.19%
73.80%
63.55%
71.81%
75.13%
75.33%
74.58%
73.16%
739
0.787
1431
0.82
4245
0.744
Observations
Adjusted R2
1252
1252
1252
1252
626
627
6261
166
389
623
897
0.575
0.578
0.542
0.531
0.665
0.714
0.588
0.672
0.546
0.761
0.691
Note: Robust standard errors clustered by firm are reported in brackets. +, *, ** significant at 10%, 5%, and 1% level respectively.
Wald Test of the Equality of the Total Effect of the Deficit Between Stages and within the Same Quintile
Quintile 1
a
Quintile 2
N/S
Quintile 3
a
Quintile 4
c
Quintile 5a
a
Quintile 5b
N/S
Wald Test of the Equality of the Total Effect of the Deficit Between Adjacent Quintiles within the Same Life-Cycle Stage
Q1 vs. Q2
Q2 vs. Q3
Q3 vs. Q4
N/S
N/S
N/S
Growth
N/S
N/S
N/S
Maturity
Note: a, b, c significant at the 10%, 5%, and 1% level respectively. N/S: not significant
27
Q4 vs. Q5a
N/S
N/S
Q5a vs. Q5b
a
N/S
All firms
c
Panel B: Low Predicted Bond Ratings
Growth Stage
Deficit
Deficit2
Constant
Total Effect:
Deficit
Maturity Stage
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
0.258**
[0.018]
-0.123**
[0.014]
-0.017**
[0.003]
0.364**
[0.019]
-0.180**
[0.015]
-0.014**
[0.003]
0.413**
[0.022]
-0.176**
[0.019]
-0.007*
[0.003]
0.419**
[0.023]
-0.167**
[0.018]
-0.004
[0.003]
0.284**
[0.037]
-0.091**
[0.031]
0
[0.005]
0.335**
[0.043]
-0.052+
[0.031]
-0.002
[0.008]
0.346**
[0.010]
-0.127**
[0.008]
-0.009**
[0.002]
0.598**
[0.137]
0.401
[1.035]
0
[0.008]
0.628**
[0.073]
-0.248
[0.177]
0.01
[0.006]
0.632**
[0.041]
-0.269**
[0.094]
0.006+
[0.003]
0.533**
[0.035]
-0.365**
[0.040]
0.007*
[0.003]
0.853**
[0.044]
-0.545**
[0.044]
0.010*
[0.004]
0.676**
[0.028]
0.085**
[0.032]
0.006*
[0.003]
0.687**
[0.019]
-0.204**
[0.022]
0.008**
[0.002]
22.47%
31.31%
36.57%
37.55%
25.59%
30.93%
30.83%
59.25%
62.79%
62.95%
51.57%
80.74%
68.54%
67.27%
267
0.614
632
0.757
1621
0.579
Observations
Adjusted R2
1773
1774
1774
1774
887
887
8869
28
95
210
389
0.148
0.224
0.252
0.261
0.167
0.253
0.216
0.606
0.558
0.599
0.392
Note: Robust standard errors clustered by firm are reported in brackets. +, *, ** significant at 10%, 5%, and 1% level respectively.
Wald Test of the Equality of the Total Effect of the Deficit Between Stages and within the Same Quintile
Quintile 1
a
Quintile 2
b
Quintile 3
b
Quintile 4
a
Quintile 5a
c
Quintile 5b
c
Wald Test of the Equality of the Total Effect of the Deficit Between Adjacent Quintiles within the Same Life-Cycle Stage
Q1 vs. Q2
Q2 vs. Q3
Q3 vs. Q4
b
N/S
N/S
Growth
N/S
N/S
N/S
Maturity
Note: a, b, c significant at the 10%, 5%, and 1% level respectively. N/S: not significant
28
Q4 vs. Q5a
a
c
Q5a vs. Q5b
N/S
N/S
All firms
c
Table 6. Tests of the Pecking Order over Life Cycle Stages: conventional factors
Equation: ΔDebtit = b0 + b1. Deficitit + b2. Deficitit2 + b3. ΔTangibility it + b4. ΔMTB it + b5. ΔLog Salesit + b6. ΔProfitabilityit + indi + yt + it ., where, Δdebtit refers to new debt
issued in period t scaled by total assets at the beginning of period t (assett-1). Deficitit refers to the financing deficit in period t scaled by total assets at the beginning of period t.
ΔTangibility it is the change of the ratio of net property plant and equipment to total assets, ΔMTBit is the change of the ratio of a firm’s market value of equity to its book equity, ΔLog
Salesit is the change of the logarithm of total real sales revenue, ΔProfitabilityit is the change of the return on assets. indi are Fama-French 49 industries. yt is year dummy.
Growth Stage
Deficit
Deficit2
ΔTangibility
ΔMTB
ΔLog Sales
ΔProfitability
Constant
Total Effect:
Deficit
Maturity Stage
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
All firms
0.237**
[0.013]
-0.037**
[0.004]
0.239**
[0.032]
-0.001
[0.001]
0.022**
[0.005]
-0.028*
[0.012]
-0.018**
[0.004]
0.367**
[0.012]
-0.080**
[0.005]
0.232**
[0.030]
-0.006**
[0.001]
0.012*
[0.005]
-0.022
[0.014]
-0.014**
[0.003]
0.353**
[0.012]
-0.081**
[0.005]
0.222**
[0.031]
-0.002
[0.001]
0.012*
[0.005]
-0.046**
[0.015]
-0.007**
[0.003]
0.454**
[0.013]
-0.073**
[0.006]
0.101**
[0.038]
-0.003+
[0.002]
0.007
[0.007]
-0.116**
[0.020]
-0.011**
[0.003]
0.621**
[0.019]
-0.133**
[0.008]
0.033
[0.049]
-0.006*
[0.003]
-0.035**
[0.010]
-0.026
[0.035]
-0.002
[0.004]
0.777**
[0.018]
-0.124**
[0.008]
-0.024
[0.045]
-0.009**
[0.002]
-0.019*
[0.010]
-0.066+
[0.036]
0.002
[0.003]
0.388**
[0.006]
-0.080**
[0.002]
0.160**
[0.015]
-0.003**
[0.001]
0.018**
[0.003]
-0.060**
[0.007]
-0.010**
[0.001]
0.710**
[0.087]
-0.410*
[0.159]
0.425**
[0.067]
0
[0.004]
-0.003
[0.030]
-0.032
[0.068]
0.004
[0.005]
0.681**
[0.034]
-0.443**
[0.037]
0.273**
[0.054]
-0.006+
[0.003]
0.027+
[0.015]
-0.04
[0.044]
0.004
[0.003]
0.693**
[0.021]
-0.078**
[0.012]
0.165**
[0.042]
-0.002
[0.003]
-0.015
[0.013]
0.032
[0.040]
0.005+
[0.002]
0.729**
[0.016]
-0.063**
[0.015]
0.074**
[0.026]
-0.012**
[0.003]
-0.026**
[0.009]
0.004
[0.029]
0.004*
[0.002]
0.796**
[0.015]
-0.122**
[0.007]
0.113**
[0.030]
-0.006*
[0.002]
-0.016+
[0.008]
0
[0.030]
0.003
[0.002]
0.771**
[0.010]
-0.009
[0.011]
0.018
[0.017]
-0.003*
[0.001]
-0.012*
[0.005]
-0.019
[0.019]
0.003**
[0.001]
0.742**
[0.007]
-0.099**
[0.005]
0.103**
[0.013]
-0.005**
[0.001]
-0.010*
[0.004]
0.001
[0.013]
0.003**
[0.001]
21.31%
33.46%
32.94%
43.43%
58.88%
75.01%
35.85%
68.86%
64.77%
68.51%
72.17%
78.36%
77.05%
73.26%
Observations
Adjusted R2
3550
3699
3774
3769
1884
1804
18480
127
576
1181
1669
1450
3211
8214
0.218
0.29
0.274
0.396
0.479
0.714
0.308
0.727
0.526
0.675
0.732
0.763
0.799
0.712
Note: Robust standard errors clustered by firm are reported in brackets. +, *, ** significant at 10%, 5%, and 1% level respectively.
Wald Test of the Equality of the Total Effect of the Deficit Between Stages and within the Same Quintile
Quintile 1
Quintile 2
Quintile 3
Quintile 4
Quintile 5a
Quintile 5b
All firms
c
c
c
c
c
N/S
c
Wald Test of the Equality of the Total Effect of the Deficit Between Adjacent Quintiles within the Same Life-Cycle Stage
Q1 vs. Q2
Q2 vs. Q3
Q3 vs. Q4
Q4 vs. Q5a
Q5a vs. Q5b
c
N/S
b
c
c
Growth
N/S
N/S
N/S
N/S
N/S
Maturity
Note: a, b, c significant at the 10%, 5%, and 1% level respectively. N/S: not significant
29
Table 7: Pooled Regression with Size Quintile Dummies and Life Cycle Stage Dummy
Model: ΔDebtit = b0 + b1. Deficitit + b1Sq. Deficitit2 + b2. Deficitit *Quintile2+ b3. Deficitit *Quintile3 +
b4.Deficitit *Quintile4+ b5a. Deficitit *Quintile5a + b5b. Deficitit *Quintile5b + C1.Deficitit*Maturity*Quintile1 +
C2. Deficitit*Maturity*Quintile2 + C3.Deficitit*Maturity*Quintile3 + C4. Deficitit*Maturity*Quintile4 +
C5a.Deficitit*Maturity*Quintile5a+ C5b. Deficitit*Maturity*Quintile5b +εit where, Δdebtit is new debt issued in
period t scaled by total assets at the beginning of period t (assett-1), Deficitit is the financing deficit in t scaled by
total assets at the beginning of t, Quintile i, for i=1,…5a , and 5b is a dummy variable that equals one when the firm
is in the ith size quintile and zero otherwise, Maturity is a dummy variable that equals one when the firm is in the
maturity stage and zero when the firm is in the growth stage.
Panel A: Regression Results
Regressors
Deficit
Coefficient
0.313**
B1
[0.018]
-0.076**
B1Sq
Deficit2
[0.006]
0.038+
Deficit*Quintile2
B2
[0.020]
0.03
Deficit*Quintile3
B3
[0.023]
0.131**
Deficit*Quintile4
B4
[0.028]
0.169**
Deficit*Quintile5a
b5a
[0.043]
0.382**
Deficit*Quintile5b
b5b
[0.028]
0.503**
Deficit*Maturity*Quintile1
C1
[0.085]
0.112
Deficit* Maturity *Quintile2
C2
[0.075]
0.334**
Deficit* Maturity *Quintile3
C3
[0.054]
0.189**
Deficit* Maturity *Quintile4
C4
[0.061]
0.186**
Deficit* Maturity *Quintile5a
C5a
[0.071]
-0.017
Deficit* Maturity *Quintile5b
C5b
[0.068]
29598
Observations
0.381
Adjusted R2
Note: Robust standard errors clustered by firm are reported in
brackets. +, *, ** significant at 10%, 5%, and 1% level respectively.
Panel B: Size and Maturity Effects
Size effect – growth firms relative to growth firms in the
1st size quintile
Maturity effect- mature firms relative to growth firms in
the same asset quintile
Total effect – mature firms relative to growth firms in the
1st size quintile
Relative importance of the maturity effect contribution of the maturity effect to the total effect
Quintile
1
Quintile
2
Quintile
3
Quintile
4
Quintile
5a
Quintile
5b
0
C1
=0.503
b2
=0.038
C2
=0.112
b3
=0.03
C3
=0.334
b4
=0.131
C4
=0.189
C1
=0.503
b2+C2
=0.15
b3+C3
=0.364
b4+C4
=0.32
b5a
=0.169
C5a
=0.186
b5a+C5
a
=0.355
b5b+C5b
=0.365
100%
74.67%
91.76%
59.06%
52.39%
-4.66%
b5b
=0.382
C5b
=-0.017