Corporate Growth the Role of Financial Structure

Document Sample
Corporate Growth the Role of Financial Structure Powered By Docstoc
					Corporate Growth:
the Role of Financial Structure

Silvia Giannangeli

 Working Paper Series
 n. 02 ■ May 2010
Statement of Purpose
The Working Paper series of the UniCredit & Universities Foundation is designed to disseminate and
to provide a platform for discussion of either work of the UniCredit Group economists and researchers
or outside contributors (such as the UniCredit & Universities scholars and fellows) on topics which are
of special interest to the UniCredit Group. To ensure the high quality of their content, the contributions
are subjected to an international refereeing process conducted by the Scientific Committee members
of the Foundation.
The opinions are strictly those of the authors and do in no way commit the Foundation and UniCredit

Scientific Committee
Franco Bruni (Chairman), Silvia Giannini, Tullio Jappelli, Catherine Lubochinsky, Giovanna Nicodano,
Reinhard H. Schmidt, Josef Zechner

Editorial Board
Annalisa Aleati
Giannantonio de Roni

The Working Papers are also available on our website (

                                                              WORKING PAPER SERIES N. 02 - MAY 2010   ■   1

                                      Abstract                 3

                              1. Introduction                  4

                                        2. Data                6

3. The financial structure of Italian firms:                   7
     definitions and descriptive statistics

        4. The relationship between firm                   10
           financial structure and growth

                  5. The relation between                  22
     financial structure and investments

          6. Discussion and conclusions                    24

                   WORKING PAPER SERIES N. 02 - MAY 2010   ■   2
Corporate Growth: the Role of Financial Structure

Silvia Giannangeli*

In the present paper I study the relationships between firm financial structure and growth in a large
sample of Italian firms (1998-2003). The paper expands upon existing analyses testing whether
liquidity constraints affect firm performance by considering among growth determinants also firm debt
structure. Panel regression analyses show that more liquid firms tend to grow more. However, firms do
not use their capital to expand, but rather to increase debt. I also find that firm growth is highly fragile
as it is positively correlated with non-financial liabilities and it not sustained by a long-term debt
maturity. The breakdown by industry, adopting Pavitt’s taxonomy, suggests that the growth process is
sustained by less fragile financial structure in firms operating in non traditional manufacturing sectors.
Finally, quantile regressions suggest that fast-growing firms are characterized by higher growth/cash-
flow sensitivities and heavily rely on external debt, but seem to be less bank-backed than the rest of
the sample. Results are generally confirmed when firm investment instead of employment growth is
considered. Overall, my findings suggest that the link between firms’ investment and expansion
decisions is far more complicated than postulated by standard tests of investment/cash-flow

Firm growth; Financial structure; Cash flow; Financial constraints;
Gibrat law; Quantile regressions.

JEL Codes:
L11, G30, D2

    UniCredit Group - CIB Strategy and Customer Analysis – Milan Italy

                                                                                     WORKING PAPER SERIES N. 02 - MAY 2010   ■   3
1. Introduction
In the last two decades, a rapidly growing stream of empirical research has investigated the relation
between corporate growth and financial structure. In particular, considerable attention has been paid
to the hypothesis that firms are constrained in their expansion process by the lack of appropriate
financial resources. The “financial constraints hypothesis” proposed by Fazzari, Hubbard and
Petersen (1988) states that a wedge between internal and external cost of funds exists, and that
capital market failures may limit the ability of firms to raise the financial resources they need in order to
undertake the desired level of investments. A rather common way to test this hypothesis has been
sought in the significance of the firm investment-cash flow sensitivity. The logic of the test works as
follows. If capital markets were perfect, we should not expect cash flow to be relevant for investment
and therefore for growth. Whenever firms do not have the necessary liquidity to realize their desired
investment, they may simply go “external” and get as much money as they like to sustain their planned
growth. Nevertheless, problems of asymmetric information might rise the cost of external finance and
may lead to credit rationing. If this is the case, the ability to generate cash flow becomes important for
financing investments.
Many empirical studies have focused on the impact of cash-flow on firms’ investment decisions and
growth (see, as an example, Carpenter and Petersen, 2002; Fagiolo and Luzzi, 2006; Oliveira and
Fortunato, 2006). However, a radical scepticism has been expressed about the usefulness of
investigating financing constraints through investment-cash flow sensitivity at the end of the ‘90s by
Kaplan and Zingales (1997; 2000). The argument against the interpretation of cash flow sensitivity of
investments as a proof of financing constraints is based on the scarce theoretical foundations of such
a view and, on the contrary, by sound empirical as well as theoretical reasons to expect that
investments may positively react on cash flow even in absence of any financial constraint. Indeed,
cash flow may contain information about firm investment opportunities, and therefore the relationship
between this variable and growth, or investments, may be the product of a virtuous selection process
in the market. Kaplan and Zingales (1997) observe that “[t]he most financially successful and least
constrained firms in our sample appear to rely primarily on internal cash flow to invest despite they […]
exhibit a high investment-cash flow sensitivity”. These authors point out that firms have some degrees
of freedom in choosing their preferred way to finance investments. The emphasis given by the
literature on the validation of the financial constraints hypothesis through tests on the significance of
cash-flow coefficients in investments as well as growth regression equations has diverted the
scientists’ attention from the question posed by Kaplan and Zingales (1997; 2000) about the causes of
the observed investment cash-flow sensitivity. In their work, the authors propose the conjecture that
managerial behaviour and, more generally, the rules of corporate governance have a considerable
role in explaining the observed financial strategies of firms.
In this context, a growing literature in corporate finance has stressed, in the last decades, the role
played by institutional factors such as tax and bankruptcy laws, the role of banks, the rules of

                                                                 WORKING PAPER SERIES N. 02 - MAY 2010   ■   4
corporate control, in determining the financial structures of firms (Rajan and Zingales, 1995). It is
widely accepted that the institutional factors mentioned above may moderate the relationship between
some firm specific characteristics, such as size or age, and some dimensions of the firms’ financial
structure, such as leverage or debt maturity. However, very little attention has been paid to the
hypothesis that something could be learned about firms’ financial choices by looking at the relation
among a plurality of dimensions of firm debt structure and firm market real performance. Schiantarelli
and Sembenelli (1997) study the relation between debt maturity and firm investments, profitability and
growth in UK and Italian firms. They find that positive effect of debt maturity on both variables. Some
more recent empirical papers provide mixing evidence on the association between financial structure
and corporate real performance (Michaelas et a., 1999; Honjo and Harada, 2006).
The present analysis has a few elements of novelty, investigating the relation among a wider set of
financial dimensions on one hand and firm employment growth or investments on the other. In section
2 we will discuss the main dimensions of firm capital structure that will be considered in the empirical
analysis of corporate growth. In section 3 we will describe the data and provide some descriptive
statistics about the financial structure of Italian manufacturing firms. In section 4 we will show the
results of estimating a descriptive model of corporate growth: panel regression estimation is adopted
to show the relation between firm debt, its sources and maturity structure on one side, and growth on
the other, controlling for firm heterogeneity. Quantile regression analysis allows to identify the relation
between financial variables and growth at different values of the growth distribution. Estimates are
presented for the whole sample of manufacturing firms as well as for distinct Pavitt’s technological
categories (Pavitt, 1984). Section 5 extends the analysis to the relation between firm financial
structure and investments. Section 6 summarizes the main results and concludes.

                                                              WORKING PAPER SERIES N. 02 - MAY 2010   ■   5
2. Data
We employ balance sheets data collected by Centrale dei Bilanci from 1998 to 2003. In order to
discard from the analysis all the phenomena related with self-employment, we remove firms with less
than two employees1. In order to avoid attrition , we use a closed panel of firms that are continuously
operating over the period 1998-2003.
We drop from, the dataset firms that exhibited a yearly growth rate of employees lower than −200% or
larger than 200% in any of the observed years, in order to weaken the problems that misreported data
may introduce in the analysis2. Moreover, we removed firms for which the share of trade debt over
total debt was less than the 1st percentile or above the 99th percentile. As it will be made clear in the
following, trade debt is more subject than other magnitudes to assume extreme values since it is
calculated as the residual amount once equity, financial debt and other provisions are removed from
the overall liabilities of the firm, and therefore it is affected by the problems of misreported data in any
of these variables. The number of available observations in the balanced panel is 9315 per year.
Table 1 summarizes the industry composition of the final sample.

Table 1. Industry composition of the sample
                     NACE code             Industry                                                                of firms
                        15                 Food and beverages                                                       1029
                        16                 Tobacco                                                                     1
                        17                 Textiles                                                                  820
                        18                 Wearing apparel and dressing                                              273
                        19                 Tanning                                                                   368
                        20                 Wood products                                                             210
                        21                 Pulp and paper                                                            256
                        22                 Publishing and printing                                                   232
                        23                 Coke petroleum and nuclear fuels                                           33
                        24                 Chemicals                                                                 551
                        25                 Rubber and plastic                                                        602
                        26                 Other non-metallic mineral products                                       548
                        27                 Basic metals                                                              394
                        28                 Fabricated metal products                                                1098
                        29                 Machinery and equipment                                                  1351
                        30                 Office machinery and computers                                             21
                        31                 Electrical machinery                                                      347
                        32                 Radio and TV                                                              123
                        33                 Medical precision and optical instruments                                 210
                        34                 Motor vehiclestrailers and semi-trailers                                  194
                        35                 Other transport equipment                                                  90
                        36                 Furniture                                                                 564
                                           Total                                                                    9315
    Bottazzi et al. (2006) show that firms with one employee radically differ from firms with two or more employees in terms of production structure.
    As a result, 150 firms are removed from the sample.

                                                                                         WORKING PAPER SERIES N. 02 - MAY 2010                   ■      6
3. The financial structure of Italian firms: definitions
and descriptive statistics
There are many ways of measuring capital structure, each measure having its pros and cons and,
ultimately, its usefulness will depend on the purpose of the investigation. In first place, we consider
cash flow as a flow measure of firm internal liquidity. Firms’ cash flow has been calculated by Centrale
dei Bilanci, on the basis of detailed information on different flow items3. Since cash flow is highly
correlated with any measure of firm size (the correlation coefficient between cash flow and value
added, sales or employment equals, respectively, 0.83, 0.60 and 0.54) the ratio between cash flow
and sales (SCF) is used throughout the analysis.
In addition, we employ the ratio between equity and firm total assets (EQ) as a stock measure of firm
propensity toward self-financing or, conversely, reliance on external debt. Firms’ equity is mainly
composed by share capital and retained earnings. Hence, the ratio between equity and assets is a
proxy for the importance of a firm own resources in financing investments.
In order to account for different dimension of the firm debt structure we build several indicators.
Indeed, the liability side of a firm’s balance sheet collects very heterogeneous items, being one firm’s
“debt” composed by short and long term liabilities, as well as financial and non financial debt. More
precisely, firms liabilities are made up by the sum of accounts receivable (the so-called “trade debt”),
tax debt, bank debt, bonds, provisions related with pensions or other social obligations as well as
other financial resources such as loans from firms belonging to the same group or other minor sources
of debt. In this paper we explicitly consider two types of financial resources: trade debt and financial
debt. While financial debt has been the object of a large recent literature in law and economics, and its
determinants both in terms of leverage and maturity structure have been investigated at length, very
little attention has been paid to other sources of finance and, among them, trade debt. Trade debt is
generated through the ordinary process of transformation and production carried on by the firm, and is
mainly composed by debts toward suppliers or, more generally, toward agents with which the firm
establishes trade contacts (debts toward controlled or related firms are also included). Conversely,
financial debt is not linked with the “nature of doing business”, but responds to specific financing
strategies by firm management, and is mainly composed by bank debt, bonds as well as firm shares
owned by other companies of the same group.
We define a firm “total debt” to be equal to the sum of all liabilities item except from equity. The relative
importance of financial debt, FD, is measured by the ratio between financial debt (defined as the sum
of debts toward credit institutions, bonds and other financial debts) and total debt. Similarly, the
importance of trade debt, TD, is measured by the ratio between trade debt (defined as the residual
amount obtained subtracting from total debt all financial items and other non financial provisions) and
total debt. In so doing, the only significant liability item not explicitly included in the analysis (along with

 The cash flow variables shows a correlation larger than 0.90 with a simpler proxy obtained by summing up firms net profits, depreciation costs
and the “Trattamento di Fine Rapporto” (the so-called “TFR”).

                                                                                     WORKING PAPER SERIES N. 02 - MAY 2010                  ■     7
minor sources of debt) is the so-called “Trattamento di Fine Rapporto”, or “TFR”, i.e. a fund where
firms are legally forced to set aside provisions for their employees and will be used to “compensate”
employees at the end of the job contract. These provisions actually represent a “debt” of the firm
toward its employees, but their magnitude does not respond to any specific (either financial or
commercial) strategy, but just mirrors the age distribution of employees as well as their turnover. For
this reason, legally required social (and tax) provisions are omitted in the analysis4.
Another interesting aspect of firm debt structure is the choice of financing sources and in particular the extent
to which a firm is dependent upon bank debt. The share of bank debt of a firm can be either high or low
depending on a number of factors both from the demand and supply side. A firm may choose to have a low
(or even zero) degree of indebtedness with banks or may be limited by the supply side, especially if the firm
has a low risk ranking. The relation between the amount of bank debt with firm growth in interesting in that it
reveals the role played by banks into the dynamics of manufacturing firms. The relative importance of bank
debt in firm financial debt is isolated and captured by the share of bank over total financial debt (BD).
Finally, we analyze the relation between the maturity structure of debt and firm growth. In spite of a rich
literature studying the determinants of debt maturity, very little is known on the link between such dimension of
firm financial structure and growth. Firm debt maturity is measured by the share of short-term over total
financial debt (SFD)5. Table 2 summarizes the definition of the financial indicators discussed so far.
Table .3 reports the mean value and the dispersion (measured through the variation coefficient) of the
distribution of SCF, EQ, FD, BD and SFD in different years of the sample period. The evolution over time of
the financial indicators shows that the share of financial and bank debt, as well as the share of short term
debt, are rather stable, while a weak upward trend characterizes the evolution of firms’ equity to asset ratio.

Table 2. Financial indicators adopted in the analysis
                                                    Variable                                       Construction
                                                                                                      Cash Flow
                                                                                                     Total Assets
                                                                                                   Financial Debt
                                                                                                      Total Debt
                                                                                                      Trade Debt
                                                                                                      Total Debt
                                                                                                      Bank Debt
                                                                                                   Financial Debt
                                                                                           Short-term Financial Debt
                                                                                                   Financial Debt

    It is true, indeed, that TFR provisions represent a relevant and cheap source of finance for firms: they amount, on average, to around 9% of total debt.
    Short-term financial debt is composed by short-term bank loans and other types of short-term financial resources.

                                                                                                   WORKING PAPER SERIES N. 02 - MAY 2010                       ■   8
Descriptive statistics confirm some well known features of Italian industrial system. On average, debt
accounts for more than twice as much as equity in the financing of firm assets. Equity amounts on
average to less than 30% of total assets. International comparisons show that Italian firms have
considerably lower equity-to-assets ratios than firms from other European countries (database BACH,
European Commission).
Second, a very large share of firms’ debt is non financial: on average, around 65% percent of total
debt is composed by trade debt and social provisions. Third, Italian firms are largely dependent on
bank credit, the share of bank debt in firms total financial debts amounting, on average, to 77%.

Table 3. Mean and variation coefficient of financial variables in 1998, 2000 and 2002
                                      Mean                     Variation Coefficient
             Variable     1998        2000        2002       1998      2000      2002
               SCF        0.069       0.071       0.068      0.922     0.955    1.136
               EQ         0.238       0.249       0.262      0.609     0.609    0.621
                TD        0.134       0.120       0.072      2.690     2.996    5.600
                FD        0.412       0.399       0.401      0.462     0.478    0.490
               BD         0.785       0.797       0.786      0.374     0.366    0.392
               SFD        0.674       0.683       0.684      0.435     0.423    0.428

Finally, Table 3 shows that the maturity structure of firm financial debt is largely shifted toward short
term liabilities. Although a deep investigation of this issue is beyond the scope of our analysis, it
seems that possible problems of un-balancedness of the debt structure may affect some Italian firms,
inducing long term investments to be financed through short term debt. More generally, a short debt
maturity may imply some constraints to the ability of a firm to invest and growth.

                                                             WORKING PAPER SERIES N. 02 - MAY 2010   ■   9
4. The relationship between firm financial structure
and growth
In the vein of the post-Gibrat literature, we investigate the relation between firm financial structure and
employment growth by estimating an “augmented” Gibrat-like regression, where the financial
indicators discussed in Section 3 are included among regressors.
Table 4 shows the correlation matrix among the selected financial variables. Due to the high
correlation between the share of trade debt (TD) and the equity-to-assets ratio (EQ), these two
variables will not be included in the regression simultaneously.

Table 4. Correlation matrix among variable

                              log(EMPi,t-1) log(AGEi,t-1) SCFi,t-1 EQi,t-1 TDi,t-1 FDi,t-1 BDi,t-1 SFDi,t-1
          log(EMPi,t-1)            1.000
          log(AGEi,t-1)            0.150                 1.000
             SCFi,t-1              0.154                 0.023           1.000
              EQi,t-1              0.118                 0.133           0.441 1.000
               TDi,t-1            -0.136                -0.134           -0.370 -0.835 1.000
               FDi,t-1            -0.032                -0.035           -0.149 -0.369 -0.073 1.000
              BDi,t-1             -0.076                 0.010           -0.140 -0.181 0.158 0.084 1.000
             SFDi,t-1             -0.117                -0.060           -0.197 -0.184 0.159 0.026 0.229 1.000

The final model includes a quadratic term on firm size in order to capture possible non-linearities in the size-
growth relationship, time dummies to get rid of the trend components (Dtime), sectoral dummies, defined as the
first two digit in ATECO classification (Dsector) and firm localization (by adopting a set of dummy variables, Dloc,
corresponding to geographical macro areas North–East, North–West, Center and South of Italy).
In order to account for possible delayed effects, the model contains lagged values of cash flow. Hence
the saturated models have the following expression:
G R O W T H i,t = β 1 log(E M Pi,t-1 ) + β 2 log 2 (E M Pi,t-1 ) + β 3 log(A G E i,t-1 ) + β 4 log 2 (A G E i,t-1 ) +
                     + β 5 S C Fi,t-1 + β 6 S C Fi,t-2 + β 7 S C Fi,t-3 + β 8 E Q i,t-1 (or T D i,t-1 ) + β 9 FD i,t-1 + β 1 0 B D i,t-1 (1)
                     + β 11S FD i,t-1 + β 12 D tim e + β 13 D secto r + β 14 D lo c + v i,t

where the growth rate of employees has been computed as:

                                                                        EMPi,t − EMPi,t-1
                                                 GROWTH i,t =

Final model specifications have been selected starting from the saturated model, by dropping
variables whose effect is found not to be statically significant on the basis of a Wald test6.

    Model selection has been performed using pooled OLS estimates of the saturated model.

                                                                                     WORKING PAPER SERIES N. 02 - MAY 2010                 ■ 10
4.1. Panel regression analysis
Table 5 reports the pooled OLS, random and fixed effects estimation results of the final regression
model, that takes the following form:

GROWTHi,t = β1 log(EMPi,t-1 ) + β 2 log 2 (EMPi,t-1 ) + β 3 log(AGE i,t-1 ) + β 4SCFi,t-1 +
                       + β 5SCFi,t-2 + β 6 EQi,t-1 (or TDi,t-1 ) + β 7 FDi,t-1 + β8 BDi,t-1 + β 9SFDi,t-1 + (2)
                       + β10 D time + β11Dsector + β12 Dloc + vi,t

where the error term vi,t may contain both unobservable individual effects, ( ci ), and

idiosyncratic error, ( ui,t ), that is:                     vi,t = ci + ui,t . Pooled OLS estimation is motivated by the
weaker exogeneity assumptions made on the idiosyncratic error term: both random and
fixed effects estimation make the strong exogeneity assumption that the unobservable
component              ui,t is in each period uncorrelated with explanatory variables in each other
period. However, pooled OLS turn out to be inefficient if the error term in equation 2 does
contain unobserved individual components. Indeed, Breusch and Pagan test statistic
calculated after random effects estimation does reject the hypothesis of absence of
individual unobserved effects. Both random and fixed effects account for the presence of                                 ci
in the model. Although Hausman test suggests that fixed effects estimation has to be
preferred, random effect results are also reported. Indeed, fixed effect estimation may lead
to imprecise estimates due to the low variations over time of the book “stock” variables EQ,
FD, BD and SFD (Woolridge, 2002) 7.

    The first-order autocorrelation of these variables is higher than 0.90.

                                                                                WORKING PAPER SERIES N. 02 - MAY 2010   ■ 11
Table 5. Panel regression results. Dependent variable: GROWTH it.

                                    Model 1                                      Model 2

                     Pooled        Random           Fixed          Pooled        Random          Fixed
                      OLS          Effects         Effects          OLS          Effects        Effects

log(EMPi,t-1)         -0.051 ***      -0.054 ***    -0.788 ***      -0.050 ***     -0.053 ***    -0.788 ***
                     (0.006)         (0.004)       (0.064)         (0.006)        (0.006)       (0.064)
log2(EMPi,t-1)         0.004 ***       0.005 ***     0.024 ***       0.004 ***      0.004 ***     0.023 ***
                     (0.001)         (0.001)       (0.008)         (0.001)        (0.001)       (0.008)
log(AGEi,t-1)         -0.015 ***      -0.015 ***     0.076 ***      -0.014 ***     -0.014 ***     0.076 ***
                     (0.001)         (0.001)       (0.019)         (0.001)        (0.002)       (0.019)
SCFi,t-1               0.198 ***       0.197 ***     0.141 ***       0.207 ***      0.206 ***     0.148 ***
                     (0.029)         (0.016)       (0.033)         (0.030)        (0.032)       (0.033)
SCFi,t-2               0.063 ***       0.064 ***     0.076 ***       0.081 ***      0.081 ***     0.091 ***
                     (0.020)         (0.017)       (0.027)         (0.020)        (0.020)       (0.027)
EQi,t-1               -0.037 ***      -0.036 ***    -0.004
                     (0.008)         (0.007)       (0.021)
TDi,t-1                                                              0.024 ***      0.024 ***     0.017 **
                                                                   (0.003)        (0.003)       (0.007)
FDi,t-1               -0.028 ***      -0.028 ***    -0.047 ***      -0.013 ***     -0.013 ***    -0.040 ***
                     (0.005)         (0.005)       (0.012)         (0.005)        (0.005)       (0.012)
BDi,t-1                0.012 ***       0.012 ***     0.024 ***       0.011 ***      0.011 ***     0.023 ***
                     (0.003)         (0.003)       (0.006)         (0.003)        (0.003)       (0.006)
SFDi,t-1              -0.003          -0.003        -0.008 *        -0.003         -0.002        -0.008
                     (0.003)         (0.003)       (0.005)         (0.003)        (0.003)       (0.005)
Dtime                    Yes            Yes           Yes              Yes           Yes           Yes
Dsector                  Yes            Yes                            Yes           Yes
Dloc                    Yes             Yes                           Yes            Yes
Number of obs.        37260           37260         37260           37260          37260         37260
F test                 22.98 ***                   161.99 ***        24.21 ***                  163.51 ***
Wald test                          1222.65 ***                                   2745.89 ***
Note: All estimation procedures account for heteroskedasticity at the firm level and autocorrelation of
the error term.

The estimation results of Model 1 and Model 2, that differ due to the exclusion of either EQ or TD from
the regression, are very similar. We therefore analyze the estimation results jointly.
First, we find a negative relationship between firm size and growth. The relation is not monotonic.
Rather, the negative relation tends to vanish as the size increases (the coefficient of the quadratic
term log2(EMPi,t-1) is significantly different from zero in all the estimated specifications). Second, we
don’t find very consistent results on the relationship between firms’ age and growth. While the results

                                                                 WORKING PAPER SERIES N. 02 - MAY 2010   ■ 12
obtained through the pooled and fixed effect estimations point out a negative relation, the within, fixed
effect estimation detects a positive one.
More interesting for our analysis, we find that the amount of cash flow is positively correlated with firm
growth. The positive and significant relationship detected in the data by all estimation procedures
might not mirror actual liquidity constraints to firm growth.
It is, indeed, true that firms that grow more are those endowed with larger cash flows (both
contemporaneous and lagged), but it could well be the case that the causal relation runs the other way
round than postulated by standard tests on the presence of financial constraints. The evidence of a
positive relation between cash flow and growth can be better interpreted when accounting for other
effects, captured through capital structure “stock” variables. Interesting enough, the estimated
coefficient of equity-to-assets ratio is negative in the pooled and random effects estimations,
suggesting that firms that grow more are less reliant on self financing, and rise more external funds,
relative to their assets, than low growth firms do. This result runs against the predictions of models
based on asymmetric information and agency costs, that would suggest higher costs of external
finance for “good” and high-growth firms. The puzzling negative relation between firms own-capital
based financial solidity and growth, although not supported by the within, fixed effect estimation,
suggests that firms that decide to grow do so through the creation of new debt.
It is interesting to notice that the share of financial debt (FD) is negatively related with growth. This
result confirms that non financial debt, such as firms’ provisions for pensions and other social
obligations, as well as trade debt, give firms a valuable buffer of resources for firm growth. In
particular, the negative sign of the coefficient of FD highlights the relevant correlation between the
“cycle” of current liabilities and firms’ expansion. Indeed, as indicated by the results on Model 2, the
share of trade debt is positively and significantly associated with firm growth.
An interesting result of the empirical analysis is the positive relation between the share of bank debt
(BD) and growth. Consistently with the results on EQ and SCF, firms grow through bank debt, possibly
using the amount of liquidity as a guarantee of firm solidity.
Almost no role is found to be played by debt maturity structure on firm growth: if any, the relation
between firms’ debt maturity and growth is negative.
These combined results suggest that firms do not use their equity capital to finance their expansion.
Rather, firms that decide to grow do so by creating new debt. Our results also emphasize that the
growth profile of Italian Manufacturing firms is, on average, highly fragile: on one hand, it is positively
correlated with the increase of non financial liabilities, on the other hand, it is not sustained by a long-
term debt maturity.

                                                                 WORKING PAPER SERIES N. 02 - MAY 2010   ■ 13
4.2. Quantile regression analysis
Panel regression analysis estimates the relation between the mean value of the dependent variable
(firm growth) and variations in the explanatory variables. It is possible, however, that marginal effects
of changes in some of the variables in (2) are not equal across the whole distribution of firm growth. In
other words, the estimated coefficients in Table 3.5 may be a poor estimate of the relation between
some of the explanatory variables and firm growth, at different quantiles of its distribution. Quantile
regression, introduced by Koenker and Bassett (1978), is a useful way to overcome this problem, by
providing estimates of the regression coefficients at different quantiles of the dependent variable.
Quantile regression amounts to estimating the following equation:
                                                         yi = xi ' βτ + uτ i (3)

For the τ quantile of the distribution of y. The distribution of the error term uτi is left unspecified and the only
assumption made is Quantτ (uτi | xi) = 0, which allows to write the conditional quantiles of y as a function of

explanatory variables and parameters only: Quantτ(yi | xi) = xi’βτ . The estimate                   βτ   of parameters in 3 is found

by minimizing with respect to β the quantity:           ∑ uτ h
                                                                 i i   , where the function hi is defined as:

                                                     hi =     {2τ −τ )
                                                                           if uτ i > 0

The estimate of the τth conditional quantile is therefore given by                        Quantτ ( yi | xi ) = xi ' β τ . Quantile’s
coefficient     βτ k   can be interpreted as the partial derivative of the conditional quantile of y with respect

                                                    δ Quantτ ( yi | xi )
to one of the kth explanatory variable,
                                                         δ xk

This derivative quantifies the marginal change in the τth conditional quantile due to marginal change in
the kth element of x (Buchinsky, 1998). For applications of quantile regression in the medical and
economic literature see, respectively. Abrevaya (2001) and Coad and Rao (2006).
Table 6 and Table 7 report, respectively, the results of quantile estimation of Model 1 and Model 2. A
sequence of quantile regressions was estimated for the 0.5, 0.25, 0.50, 0.75 and 0.95 quantiles of the
growth rate distribution and tests for equality of coefficients across quantiles were performed .
The estimation results are interesting: first of all, we find that the relation between cash flow and
growth is not the same across the whole distribution of growth rates. In particular, the cash flow
sensitivity of growth is significantly different for firms growing less or growing more than the median

  Stata command sqreg was used to perform quantile regression and standard errors were calculated using the bootstrapping method suggested
by Gould (1997), with 100 repetitions.

                                                                                  WORKING PAPER SERIES N. 02 - MAY 2010               ■ 14
firm in the sample9. Firms growing more than the median value (50th percentile) show a significantly
larger sensitivity to cash-flow. This result is consistent with different, but opposites stories: on one
hand, one could interpret the result by saying that firms with higher growth opportunities are also
riskier from an external investors’ viewpoint, and therefore they may incur in credit rationing with
higher probability than low growth firms. This will force high growth firms to use their internal cash flow
in order to finance new investments. On the other hand, the result can be interpreted as a support to
the view that cash flow contains information about the investments, profit and growth opportunities of a
firm: detecting a positive relation between growth and cash flow is therefore not a symptom of the
presence of financial constraints to firm decision to expand but, rather, a signal that a virtuous
selection mechanism is at play in the market.

Table 6. Quantile regression results. Model 1. Numbers in italics represent the value taken by
the dependent variable at each of the quantiles shown in table.

                                                                  Model 1
                                          5%                   25%           50%                            75%                  95%
                                       (-0.161)             (-0.037)       (0.000)                         (0.067)              (0.250)
         log(EMPi,t-1)                 0.083 ***              0.007         0.003 **                      -0.056 **            -0.275 ***
                                     (0.016)               (0.044)        (0.001)                        (0.004)              (0.036)
         log2(EMPi,t-1)               -0.008 ***            -0.001 *       -0.001 ***                      0.004 ***            0.025 ***
                                     (0.002)              (0.0004)      (0.0001)                         (0.001)              (0.004)
         log(AGEi,t-1)                 0.004                -0.006 ***     -0.009 ***                     -0.020 ***           -0.045 ***
                                     (0.004)               (0.001)        (0.001)                        (0.001)              (0.006)
         SCFi,t-1                      0.198 ***              0.167 ***     0.145 ***                      0.230 ***            0.297 ***
                                     (0.027)               (0.010)        (0.010)                        (0.027)              (0.068)
         SCFi,t-2                      0.092 ***              0.025 **      0.022 *                        0.035                0.055
                                     (0.025)               (0.012)        (0.012)                        (0.027)              (0.062)
         EQi,t-1                       0.048 ***            -0.007         -0.022 ***                     -0.066 ***           -0.161 ***
                                     (0.016)               (0.006)        (0.003)                        (0.006)              (0.023)
         FDi,t-1                      -0.059 ***            -0.029 **      -0.012 ***                     -0.022 ***           -0.020
                                     (0.013)               (0.004)        (0.002)                        (0.003)              (0.014)
         BDi,t-1                       0.015 **               0.010 ***     0.008 ***                      0.014 ***            0.011
                                     (0.006)               (0.002)        (0.001)                        (0.002)              (0.008)
         SFDi,t-1                     -0.023 ***            -0.005 **      -0.002                         -0.003                0.014
                                     (0.005)               (0.002)        (0.001)                        (0.002)              (0.010)
         Dtime                           Yes                    Yes           Yes                            Yes                  Yes
         Dsector                         Yes                    Yes           Yes                            Yes                  Yes
         Dloc                            Yes                    Yes           Yes                            Yes                  Yes
         Observations                 37260                  37260         37260                          37260                37260
         Pseudo R2                     0.039                  0.017         0.009                          0.034                0.067

  F tests fail to reject the null hypothesis that the SCF coefficient at the 5% or 25% percentile are equal to the 50% percentile at conventional
significance levels (respectively, F test values are equal to 1.31 and 2.82). Similarly, no significant difference is found between coefficients at the
75% and 90% percentile.

                                                                                         WORKING PAPER SERIES N. 02 - MAY 2010                     ■ 15
Second, the coefficient on EQ is not constant across quantiles of the growth rates distribution: in the
case of equity-to-assets ratio, the coefficient at any of the growth rate quantiles are found to be
statistically different from any other quantile. In particular, the relationship between firms’ propensity
toward self finance and growth is positive and significant for firms in the 5th percentile, meaning that
firms that are more reliant on own funds are those that downsize. As the growth rate increases, firms
are found to use more and more debt.
As for the type of debt the firm is using, we find that (Table 3.6) the negative relation already detected
on the whole sample between the share of financial debt and growth is significant at all but the 95th
percentiles. A partially different result if shown in Table 3.7, where the share of trade debt is included
in the model: in this case FD coefficient is significant only for the lowest part of the support of the
growth rates distribution10. Although delivering partially different results, the discrepancy between
Model 1 and Model 2 is not radical, both models being consistent with the fact that for firms that are
growing less (actually, shrinking), the increase in the amount of financial debt, relative to other types of
debt, is associated with a significant decrease in the growth rate11. The share of trade debt (Table 3.7)
is instead found to have a positive relation with growth, except from the lowest quantiles of the growth
rates distribution. Interesting enough, the relation between the importance of trade debts and growth
strengthen as firms' rate of growth increase12.

     Tests for equality of coefficients at 5th and 25th percentiles suggest that coefficients are statistically different.
     In Model 1 F test reject the hypothesis that the coefficients of FD at 5th and 25th percentiles are equal, while this is not the case for upper quantiles.
     F tests reject the hypotheses of equality between coefficients across all quantiles, except when comparing the 25th and 50th ones (F test p-value: 0.143).

                                                                                                   WORKING PAPER SERIES N. 02 - MAY 2010                          ■ 16
Table 7. Quantile regression results. Model 2. Numbers in italics represent the value taken by
the dependent variable at each of the quantiles shown in table.

                                                  Model 2
                                5%             25%             50%             75%            95%
                             (-0.161)        (-0.037)         (0.000)         (0.067)        (0.250)
     log(EMPi,t-1)             0.082 ***        0.007 **         0.003 *       -0.055 **      -0.273 ***
                             (0.012)          (0.003)          (0.002)        (0.005)        (0.023)
     log2(EMPi,t-1)           -0.008 ***       -0.001 ***       -0.001 ***     0.004 ***      0.025 ***
                             (0.001)        (0.0003)         (0.0001)         (0.001)        (0.002)
     log(AGEi,t-1)             0.006 *         -0.005 ***       -0.008 ***     -0.019 ***     -0.043 ***
                             (0.003)          (0.001)          (0.001)        (0.001)        (0.006)
     SCFi,t-1                  0.222 ***        0.174 ***        0.159 ***      0.231 ***      0.295 ***
                             (0.054)          (0.017)          (0.013)        (0.024)        (0.047)
     SCFi,t-2                  0.119 **         0.047 **         0.028 **       0.053 *        0.056
                             (0.047)          (0.020)          (0.009)        (0.029)        (0.051)
     TDi,t-1                   0.002            0.013 ***       0.015 ***      0.031 ***      0.058 ***
                             (0.006)          (0.002)          (0.001)        (0.002)        (0.005)
     FDi,t-1                  -0.069 ***       -0.027 ***       -0.003         -0.001          0.027
                             (0.011)          (0.004)          (0.002)        (0.002)        (0.020)
     BDi,t-1                   0.014            0.009 ***        0.006 ***      0.013 ***      0.006
                             (0.008)          (0.002)          (0.001)        (0.002)        (0.010)
     SFDi,t-1                 -0.027 ***       -0.006 **        -0.002         -0.001          0.017 *
                             (0.008)          (0.002)          (0.002)        (0.002)        (0.009)
     Dtime                       Yes              Yes              Yes            Yes           Yes
     Dsector                     Yes              Yes              Yes            Yes           Yes
     Dloc                        Yes              Yes              Yes            Yes           Yes
     Observations             37260            37260            37260          37260          37260
     Pseudo R2                 0.038            0.018           0.010            .036         0.069

Finally, some differences are found in the relation between firm growth and the share of bank debt at
different quantiles of the growth rate distribution: the most interesting result is that at the 95th percentile
of the growth rates distribution there is no association between growth and bank debt. As for the
maturity structure of debt, it is interesting to notice that shrinking firms are those for which the more the
term structure of debt is short, the less they grow. No significant relation between the share of short-
term financial debt, relative to total, and growth is found at upper percentiles.

                                                                WORKING PAPER SERIES N. 02 - MAY 2010      ■ 17
4.3. Analysis by Pavitt’s categories
The results described in Section 4.1 and 4.2 suggest that Italian manufacturing growing firms tend to
use debt in order to finance their investments. However, holding fixed the total amount of debt, an
increase in the share of financial debt translates into a slowdown in growth, especially for firms that
exhibit low (often negative) growth rates. In turn, this is consistent with the fact that growth is
associated to an increase in the share of trade debts over the total amount of debt and the relation is
significantly stronger as growth rates increase. This may mean that growing firms tend to finance their
expansion by simply not paying the cost of growing, i.e. by creating debt toward suppliers. Although
not a surprising news, this circumstance highlight the very rudimental financial structure of Italian firms
and the weak bases on which they build their investments pattern.
We now turn to the analysis of the impact of firms financial structure on growth by taking into account
the technological characteristics and the nature of the innovative process in industrial sectors where
firms operate. We do so by adopting the Pavitt’s taxonomy that distinguishes between “supplier
dominated” (or, following a nomenclature that is often adopted in the Italian debate “traditional”)
sectors, “scale-intensive” sectors, “specialized suppliers” and “science-based” sectors (Pavitt, 1984).
Figure 1 shows the estimated probability density of SCF, EQ, TD, FD, BD and SFD disaggregated by
Pavitt’s category in year 200013.
It is quite evident from the probability densities that some of the variables’ distributions show a bi- or
even multi-modality. This is the case for the share of bank debt (BD) and the share of short-term
financial debt (SFD): both of them present a mode in 1 (all the financial debt being, respectively, bank
debt or short-term) and another peak around zero. The differences among Pavitt’s categories in the
distribution of financial variables is not sizeable in the case of cash-flow (SCF) and equity-to-assets
ratio (EQ). Firms operating in the “specialized suppliers” industrial sectors are characterized by a debt
structure where trade debt is more important (and, correspondingly, financial debt is less) as
compared with other firms. More interestingly, there are considerable differences in the share of bank
debt owned by firms categorized in different technological classes: the share of bank debt held by
firms operating in “science-based” sectors is smaller than that in any other class. Similarly, the share
of short-term financial debt is lower for these firms.
These facts are consistent with the existence of alternative financial sources for firms in high-tech
sectors, different from traditional bank credit, like for instance venture capital. Firms in the “traditional”
sectors are those where the importance of bank debt relative to total financial debt is the highest and
where the maturity structure of debt is the shortest.

     The estimated probability densities do not differ across years.

                                                                       WORKING PAPER SERIES N. 02 - MAY 2010   ■ 18
                                  SCF                                                                           EQ




        -.5                       0                                 .5                  -.2        0       .2        .4             .6   .8

                           Suppl.                    Scale                                              Trad.        Scale
                           Spec.                     Science                                            Spec         Science

                                      FD                                                                        TD





              0       .2     .4                 .6             .8        1         -3             -2            -1              0        1

                           Trad.                 Scale                                                  Trad          Scale
                           Spec.                 Science                                                Spec.         Science

                                      BD                                                                       SFD






                  0                        .5                            1              0         .2      .4         .6             .8   1

                           Trad.                 Scale                                                  Trad.         Scale
                           Spec.                 Science                                                Spec.         Science

Figure 1 Kernel estimates of the probability density of SCF, EQ, FD, TD, BD and SFD by Pavitt’s
taxonomy. Note: “Trad” stands for “traditional sectors”, “Scale” for “scale-intensive” sectors, “Spec.” for
“specialized suppliers” and “Science” for “Science-based” sectors. Empirical densities are estimated
adopting Epanechnikov kernel with bandwidth h = 0.9(min {σ , IQR /1.34} N −1/ 5 , as suggested by

Silverman (1986).

                                                                                            WORKING PAPER SERIES N. 02 - MAY 2010        ■ 19
Table 8 shows the estimation results of Model 1 and Model 2 disaggregated by Pavitt’s category14. We
find that growth is positively related with firms’ cash flow in all industrial sectors, although in the
“specialized suppliers” the effect appears to be lagged by one year. Interesting enough, the negative
relation between equity-to-assets ratio and growth detected on the whole sample is found to apply
only in the “traditional” industries, while in scale intensive sectors this relation is found to be positive,
although weak. In science-based or specialized suppliers industries the relation between equity ratio
and growth is not significant.
Although the negative association between FD and growth is found to hold in all sectors, it is worth
noting that the claim that firms’ growth is associated with the creation of new trade debts no longer
applies in scale intensive and specialized suppliers sectors, while the evidence on science-based
industries is very weak.
Finally, an increase in the importance of bank debt over total financial debts is not found to be
associated with growth in all sectors: in science-based industries no significant relation between the
share of bank debts and growth is at play.
Summing up, these results highlight a considerable inter-sectoral heterogeneity in the financial
structure of firms and in its impact on firm growth. Different from firms in the traditional sectors (for
which the results closely mirror what was found on the whole manufacturing), the growth process of
firms in scale intensive industries is associated with an increase in the equity-to-assets ratio, while in
the other industries there is no significant relation among this ratio and firms’ growth rates. Moreover,
the positive dependence between growth and the share of trade debt is limited to the traditional
sectors although a much weaker relation is found also in science-based sectors. These combined
results are consistent with the hypothesis that the growth pattern of firms outside the traditional
sectors is not as fragile as suggested by the results on the whole sample. A final remark: the role of
bank debt is important for firm growth in all sectors but science-based industries, suggesting that
either demand or supply factors prevent high-tech firms to finance their investments through this type
of debt.

   Fixed effect estimation results are reported. Results using pooled Ordinary Lest Squares or random effects panel estimation are not significantly
different. Results are available upon request.

                                                                                      WORKING PAPER SERIES N. 02 - MAY 2010                   ■ 20
           Table 8. Fixed effect panel regression results by Pavitt’s category

                                           Model 1                                                         Model 2

                                   Scale         Specialized      Science                          Scale         Specialized       Science
  Variable       Traditional                                                     Traditional
                                 intensive        suppliers        based                         intensive        suppliers         based
log(EMPi,t-1)     -0.739   ***    -0.826   ***    -0.815   ***    -0.871   ***    -0.739   ***    -0.826   ***    -0.815   ***     -0.869      ***
                 (0.033)         (0.038)         (0.058)         (0.093)         (0.033)         (0.038)         (0.058)          (0.094)
log2(EMPi,t-1)     0.020   ***     0.027   ***     0.016   **      0.048   ***     0.020   ***     0.027   ***     0.016   **       0.048      ***
                 (0.004)         (0.005)         (0.007)         (0.011)         (0.004)         (0.005)         (0.007)          (0.011)
log(AGEi,t-1)      0.091   ***     0.066   **      0.057           0.070           0.091   ***     0.067   **      0.057            0.068
                 (0.020)         (0.027)         (0.037)         (0.061)         (0.020)         (0.027)         (0.037)          (0.061)
SCFi,t-1           0.194   ***     0.117   ***     0.041           0.144   **      0.196   ***     0.137   ***     0.047            0.151      **
                 (0.028)         (0.037)         (0.040)         (0.073)         (0.027)         (0.036)         (0.040)          (0.071)
SCFi,t-2           0.087   ***     0.049           0.132   **      0.200   ***     0.092   ***     0.059           0.147   ***      0.212      ***
                 (0.026)         (0.038)         (0.056)         (0.062)         (0.026)         (0.038)         (0.056)          (0.062)
EQi,t-1           -0.044   *       0.065   **      0.016          -0.061
                 (0.024)         (0.032)         (0.041)         (0.062)
TDi,t-1                                                                            0.026   ***    -0.002           0.015            0.037      *
                                                                                 (0.008)         (0.010)         (0.013)          (0.020)
FDi,t-1       -0.048       ***    -0.022          -0.073   ***    -0.093   **     -0.030   **     -0.035   *      -0.069   ***     -0.070      *
             (0.014)             (0.019)         (0.024)         (0.039)         (0.014)         (0.019)         (0.024)          (0.036)
BDi,t-1        0.015       *       0.027   **      0.038   ***     0.030           0.014   *       0.027   **      0.038   ***      0.029
             (0.008)             (0.011)         (0.013)         (0.021)         (0.008)         (0.011)         (0.013)          (0.021)
SFDi,t-1      -0.013       *      -0.002          -0.011           0.001          -0.013   *      -0.002          -0.011           -0.001
             (0.007)             (0.009)         (0.007)         (0.020)         (0.007)         (0.009)         (0.007)          (0.020)
Dtime            Yes                 Yes             Yes             Yes             Yes             Yes             Yes              Yes
Dsector          Yes                 Yes             Yes             Yes             Yes             Yes             Yes              Yes
Dloc             Yes                 Yes             Yes             Yes             Yes             Yes             Yes              Yes
Observations 18904                10852             5868            1636          18904           10852             5868             1636
R2                 0.01            0.01            0.01            0.01            0.01            0.01            0.01                 0.01
F test           435.01    ***   286.85    ***   179.13    ***    36.21    ***   435.94    ***   286.37    ***   179.27    ***          36.5   ***

                                                                                 WORKING PAPER SERIES N. 02 - MAY 2010           ■ 21
5. The relation between financial structure and
Let us now turn to the last piece of analysis, by studying the relation between firms’ financial structure
and investments. It is possible indeed that the fluctuations in the financial items that compose the
liabilities structure of a firm do not have sizeable impact on firms’ growth process as measured in
previous section, and that firms’ employment growth is not an appropriate indicator to capture any
effect of financial variables on firms’ real performance. We therefore focus on a “intermediate”
indicator, i.e. investment rate, that is in principle more likely to respond to variations in the financial
structure of the firm. We do so by estimating equation (2), where firms investment rate in tangible
assets15 is included as the dependent variable instead of employment growth.
The regression equation is therefore the following:

                 INVi,t = β1 log(EMPi,t-1 ) + β 2 log 2 (EMPi,t-1 ) + β 3 log(AGE i,t-1 ) + β 4SCFi,t-1 +
                                    + β 5SCFi,t-2 + β 6 EQi,t-1 + β 7 FDi,t-1 + β8 BDi,t-1 + β 9SFDi,t-1 +                     (3
                                    + β10 D time + β11Dsector + β12 Dloc + vi,t
where investment rate is defined as:

                                            fixed tangible assetsi,t − fixed tangible assetsi,t-1
                                INVi,t =
                                                               fixed tangible assetsi,t

Results reported in Table 9 do not differ significantly from those shown in Table 5 as for the sign and
significance of coefficients on cash-flow, equity-to-assets ratio, the share of financial and trade debt.

     We disregard investments in immaterial assets due to the noisy measurement of the highly heterogeneous components of this type of assets.

                                                                                      WORKING PAPER SERIES N. 02 - MAY 2010                ■ 22
Table 9. Panel regression results on investment rate. Fixed effect estimation.
                                      Model 1                                         Model 2
                     Pooled          Random            Fixed           Pooled        Random            Fixed
                      OLS            Effects          Effects           OLS          Effects          Effects
  log(EMPi,t-1)     -0.041            -0.041          -0.015          -0.041          -0.041          -0.017
                   (0.032)           (0.014)         (0.246)         (0.032)         (0.031)         (0.247)
  log2(EMPi,t-1)     0.004             0.004          -0.019           0.004           0.003          -0.019
                   (0.003)           (0.003)         (0.027)         (0.003)         (0.003)         (0.027)
  log(AGEi,t-1)     -0.032     ***    -0.032   ***    -0.087          -0.032   ***    -0.032   ***    -0.098
                   (0.005)           (0.005)         (0.072)         (0.005)         (0.005)         (0.072)
  SCFi,t-1           0.185     ***     0.177   **     -0.063           0.177   ***     0.188   ***    -0.129   *
                   (0.055)           (0.054)         (0.071)         (0.054)         (0.054)         (0.072)
  SCFi,t-2           0.127     **      0.129   ***     0.036           0.129   **      0.126   **      0.006
                   (0.051)           (0.051)         (0.074)         (0.051)         (0.053)         (0.072)
  EQi,t-1           -0.121     ***    -0.117   ***    -0.481   ***
                   (0.023)           (0.022)         (0.095)
  TDi,t-1                                                              0.046   ***     0.045   ***     0.132   ***
                                                                     (0.008)         (0.007)         (0.024)
  FDi,t-1           -0.092     ***    -0.088   ***    -0.302   ***    -0.092   ***    -0.047   ***    -0.155   ***
                   (0.017)           (0.015)         (0.017)         (0.015)         (0.015)         (0.041)
  BDi,t-1            0.006             0.006          -0.006           0.006           0.006          -0.008
                   (0.012)           (0.010)         (0.028)         (0.012)         (0.010)         (0.028)
  SFDi,t-1           0.004             0.004           0.021           0.005           0.005           0.021
                   (0.011)           (0.010)         (0.024)         (0.011)         (0.010)         (0.025)
  Dtime                Yes               Yes             Yes             Yes             Yes             Yes
  Dsector                Yes            Yes             Yes             Yes             Yes             Yes
  Dloc                Yes              Yes             Yes              Yes            Yes             Yes
  Observations      24748            24748           24748            24748          24748           24748
  F test             19.70     ***                    50.56    ***     24.21   ***                    51.02    ***
  Wald test                          749.69    ***                                   720.41    ***

Firms’ investments are financed through debt. However, as already discussed, it seems that
investments are supported by a particularly rudimental and fragile financial structure, with investments
being positively correlated with the growth of non-financial debt items. Differently from the results on
firms’ employment growth, the share of bank debt over total financial debt held by the firm is not found
to affect investments.

                                                                     WORKING PAPER SERIES N. 02 - MAY 2010         ■ 23
6. Discussion and conclusions
This paper is an attempt to extend the analysis of the links between firm financial structure and
performance, beyond the traditional tests on financial constraints based on estimated investment
cash-flow sensitivities. In particular, the purpose of the analysis is to shed some light on several
aspects that obtained very little attention in the literature, namely the relation between the liability
structure, the sources of debt and the debt maturity on one side and firm growth on the other.
Our results suggest that Italian manufacturing firms, on average, do not use their own capital to
finance their expansion. Rather, firms that decide to grow do so by creating new debt. Our results also
emphasize that the growth profile of Italian manufacturing firms is, on average, highly fragile: on one
hand, it is positively correlated with the increase of non financial liabilities, on the other hand, it is not
sustained by a long-term debt maturity. The relation among financial variables and growth is not
constant across the distribution of growth rates: firms that grow more are characterized by higher
growth cash-flow sensitivities and heavily rely on external debt, especially non financial debt. The very
rudimental and fragile characterization of Italian manufacturing firms that emerges from the aggregate
estimation hides a considerable inter-sectoral heterogeneity. As a general result from the sectoral
disaggregation, we find that the growth pattern of firms outside the traditional sectors is not as fragile
as suggested by the results on the whole sample.
The results leave the door open to many possible and different interpretations. Although it is very
difficult to identify the channels through which the inter-sectoral differences in the competitive and
technological environments impact on the demand (and supply) of debt and ultimately on the firms’
financial structure-performance relationship, a conjecture might be proposed. In sectors dominated by
low innovation and technological competition, firm growth can be conditioned by the costs of growth,
hereby including coordination costs and the fear of dependence on external investors. The prevalence
of short-term debt, as well as the role played by non financial debt in allowing firm growth confirm this
evidence. This is less the case for more technologically advanced industries, where firms use a
plurality of financial sources in order to sustain their investments, and where - with the exception of
science-based sectors - the role of bank debt is crucial.
A last remark is in order: all the relationships detected through the empirical analysis can not be
interpreted as causal ones. Each single dimension of firm financial structure is undoubtedly dependent
upon the real side of firm activities, and vice versa. Equations (2) and (3) should thus be conceived as
part of a structural model where all financial and real variables are endogenously determined. This
reverse causality among financial indicators and firm growth prevent us to give our results any causal
interpretation and rather imposes to accept them as descriptive evidence, calling for future research
both at an empirical and theoretical level.

                                                                WORKING PAPER SERIES N. 02 - MAY 2010    ■ 24
Abrevaya, J., 2001, The effects of demographics and maternal behavior on the distribution of birth
outcomes, Empirical Economics, 26, 247-257.
Bottazzi, G., Secchi, A, and F. Tamagni, 2006, Financial Fragility and Growth Dynamics of Italian
Business Firms, LEM Working Paper 2006/07 Sant’Anna School of Advanced Studies, Pisa, Italy.
Buchinsky, M., 1998. Recent Advances in Quantile Regression Models: A Practical Guide for
Empirical Research, Journal of Human Resources, 33, 1, 88-126.
Carpenter, R. E. and B. C. Petersen, 2002, Is the growth of small firms constrained by internal
finance?, The Review of Economics and Statistics, 84(2), 298-309.
Coad, A. and R. Rao, 2006, Innovation and Firm Growth in High-Tech Sectors: A Quantile Regression
Approach, Sant’Anna School of Advanced Studies, LEMWorking Paper 2006/18.
Fagiolo, G. and A. Luzzi, 2006, Do liquidity constraints matter in explaining firm size and growth?
Some evidence form the Italian manufacturing industry, Industrial and Corporate Change, 15(1), 1-39.
Fazzari, S. M., R. G. Hubbard and B. C. Petersen, 1988, financial constraints and corporate
investment, Brooking Papers on Economic Activity, 1, 141-206.
Gibrat, R., 1931, Les Inegalités Economiques, Librairie du Recueil Sirey, Paris.
Gould, W. W., 1997, sg70: Quantile regression with bootstrapped standard errors, Stata Technical
Bullettin, 9, 19-21.
Hall, B., 1992, Investment and research and development at the firm level: does the source of
financing matter?, NBER Working Paper 4096.
Honjo, Y., Harada, N., 2006, SME policy, financial structure and firms growth: evidence from Japan,
Small Business Economics, 27, 289-300.
Kaplan, S. N. and L. Zingales, 1997, Do investment-cash flow sensitivities provide useful measures of
financing constraints?, The Quarterly Journal of Economics, 169-225.
Kaplan, S. N. and L. Zingales, 2000, Investment-cash flow sensitivities are not valid measures of
financing constraints, The Quarterly Journal of Economics, 707-712.
Koenker, R. and G. Bassett, 1978, Regression quintiles, Econometrica, 46, 1, 33-50. Koenker, R. and
K. F. Hallock, 2001, Quantile regression, Journal of Economic Perspectives, 15, 4, 143-156.
Michaelas, N., F. Chittenden and P. Poutziouris, Financial policy and capital structure choice in the U.
K: SMEs: Empirical evidence from company panel data, Small Business Economics, 12, 113-130.
Oliveira, B. and A. Fortunato, 2006, Firm growth and liquidity constraints. A dynamic analysis, Small
Business Economics, 27(2), 139-156.
Pavitt K., 1984, Sectoral patterns of technical change: towards a taxonomy and a theory, Research
Policy, 13, 343-373.
Rajan, R. G. and L. Zingales, 1995, What do we know about capital structure? Some evidence from
international data, The Journal of Finance, 1421-1460.

                                                            WORKING PAPER SERIES N. 02 - MAY 2010   ■ 25
Silverman, B. W. (1986) Density Estimation for Statistics and Data Analysis. London: Chapman and
Schiantarelli, F. and A. Sembenelli, 1997, The maturity structure of debt. Determinants and Effects on
firms’ performance: evidence from the United Kingdom and Italy, The World Bank Policy Research
Working Paper 1699.
Schiantarelli, F., 1996, Financial constraints and investment: methodological issues and international
evidence, Oxford Review of Economic Policy, 12, 70-89.
Woolridge, J. M., 2002, Econometric analysis of cross section and panel data, Cambridge: The MIT

                                                           WORKING PAPER SERIES N. 02 - MAY 2010   ■ 26
UniCredit & Universities
Knight of Labor Ugo Foscolo Foundation
Via Broletto, 16
20121 Milan

Giannantonio De Roni – Secretary General
Tel. +39 02 8862 8039
Annalisa Aleati - Scientific Responsible
Tel. +39 02 8862 2668

Info at:

                                                WORKING PAPER SERIES N. 02 - MAY 2010   ■ 27

Shared By: