capital structure, debt structure & local financial
TRANSCRIPT
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Capital structure, debt-maturity structure and local financial development:
an empirical analysis in Italy
Elvira-Tiziana La Rocca°, Maurizio La Rocca∗
1. Object ...............................................................................................................................................2 2. Capital structure, debt-maturity and local financial development ...................................................3 3. Italian financial features and local financial development indicator ...............................................7 4. Empirical analysis ..........................................................................................................................10
4.1 Data and sample .......................................................................................................................10 4.2 Variables and hypotesys...........................................................................................................11
5. Empirical results ............................................................................................................................16 5.1 Descriptive statistics ................................................................................................................16 5.2 Econometric models and evidences .........................................................................................20
6. Conclusion .....................................................................................................................................24 7. Bibliography...................................................................................................................................24 Keywords: Capital structure; Leverage; Debt maturity structure; Local Financial Development; Financial reputation. EFM Code: 140 Capital structure
° PhD student in Bank and Finance – University of Calabria, Faculty of Economics, Dip. O.O.A.P. – Italy - Email: [email protected] ∗ PhD student in Business Economics and Management – University of Catania, Faculty of Economics, Dip. Business Economics and Management - Email: [email protected] Cell. +39 333 3452372 Fax +39 0984 32633
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1. Object Some factors related to the characteristics of the firm as well as other related to the
institutional environment, as the characteristics of the financial markets and the legal environment,
have a relevant influence on the corporate financing choices.
Most of the existing empirical studies focus on the importance of firm characteristics,
analyzing corporate financing choices within a country or in a cross-countries analysis (for a survey
still comprehensive see Harris-Raviv 1991).
A relatively new and important strand of literature has focused on how institutional factors
affect capital and maturity structure choices (Rajan-Zingales 1995, Demirguc-Kunt-Maksimovic
1996, 1998 and 2001, Booth et al 2001, and Giannetti 2003, Titman et al 2003). Indeed, an
interesting question is related on the major use of debt or equity and on the features of the kind of
debt used according to the different level of the financial system development. The prevalent
research, examining firms that face different institutional environments, is based on a cross-country
study (Titman et al 2003).
In this paper, to assess the relevance of the financial system it has been followed the same
approach of Zingales et al (2003). Rather than studying the effect of financial development across
countries (see the survey by Levine 1997), we study the effect of local financial development within
a single country: Italy.
It is examined both capital structure1 and debt maturity choices in Italy, where it is supposed
to be present a different level of financial development among the regions in which the country is
shared. The present research tries to relate the work of Zingales et al (2003) and their indicator of
financial development at local level with capital structure and debt maturity choices, moving
beyond the basic debt/equity choice to more detailed aspects of the firm’s financing decisions and,
more specifically, analyzing the joint determinant of leverage and debt maturity, as in Barclay et al
(2003), according to the presence of different institutional characteristics.
There are a lot of descriptive analysis in Italy about the differences between the south and
the north of the country; this paper tries to see if there is a different financial behavior between
firms operating in different Italian regions. In particular, the degree of financial system
development can be an important feature that determines the firm’s capital and maturity structure. If
we find out that the Zingales et al indicator is relevant and significant it means that the national
financial development will continue to matter also in a country with a high level of integration with
the international financial markets.
1 To refer to capital structure can have many facets in terms of leverage, maturity, priority, convertibility, and covenants. In this paper the capital structure choice is related to the debt/equity choice.
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Jointly with this question it is also interesting to see the role of the financial reputation a
firm granted in the financial market. According to the different goodness of the financial ratio, the
reliability in the payment with suppliers and banks and the capability to respect the financial
commitment, a firm gains a financial reputation that can facilitate the opportunities to obtain
financial resources in quantities and qualities needed. Also other interesting variables are used to
study the determinant of the capital structure and maturity structure of firm’s debt.
In general, we find that the level of local financial development, related to where a firm is
located, has a great effect on leverage, similar to industry affiliation and other firm-specific
characteristics. Less relevant is its impact on debt maturity. Moreover, institutional factors are as
important as firm-specific characteristics, such as profitability and size.
The paper is arranged as it follow. The second paragraph explains the relation among capital
structure, debt maturity and local financial leverage. The third paragraph describes the
characteristics of the Italian financial system and the local financial development indicator used in
the analysis. The forth paragraph explains the sample, the data and the variables used. The
following paragraph shows the descriptive statistics and the empirical results
2. Capital structure, debt-maturity and local financial development The relevance of capital structure on corporate governance and firm value is, as well known,
a controversial topic in the academia and also in the business community. Many important financial
researcher have restarted to write paper on capital structure and maturity structure (Fama, Myers
and Titman among others), highlighting a renewed interest for the benefits and costs of the firm’s
financial choices.
Exhibit 1 – Benefits and costs of debt financing. BENEFITS OF DEBT COSTS OF DEBT
Tax benefit Cost of financial distress Managerial discipline Agency cost Reduction of asymmetric information Lack of flexibility
The stilized facts about capital structure, synthesized in exhibit 1, show that the optimal mix
between debt and equity is influenced by many factors that raises benefits and costs. The firms that
use debt as source of finance can benefits based on tax advantages, thanks to the interest
deducibility, reduction of asymmetric information and managerial discipline. Vice versa, there are
some costs related on the use of debt based on the presence of financial distress, agency problems
and lost of financial flexibility.
In other terms, the leverage raises, jointly with the tax benefit, a set of incentives and
constrains to the managerial corporate governance that generates conflicts of interests among
managers, shareholders and debtholders, influencing the investment strategies of the firm and its
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value creation processes. In fact, the presence of these conflicts, jointly with asymmetric
information and contract incompleteness, can generate problems of under and overinvestment2.
On this direction of research is becoming important to study the capital structure of the firm
jointly with the debt-maturity choice, to understand the debt/equity choice according to the kind of
debt used in the firm (Barclay et al 2003). In fact, short term debt is normally suggested as a way to
solve problems of investment’s distortion (Myers 1977).
It is normally assumed the need of coherence between the asset and liabilities maturity
structure of the firm (Hart and Moore 1995 and Stohs and Mauer 1996). Myers 1977 was the first to
explain that the maturity of the asset has to match the maturity of the liabilities; firms with long
term asset will tend to have long debt maturity structure.
The preference for short or long debt is based on the straightforward difference between the
ex-ante and the ex-post costs of these different financing funds.
Exhibit 2 – Different ex-ante and ex-post costs of short and long term debt. EX-ANTE EX-POST SHORT-TERM DEBT Low transactional cost Cost of renegotiation LONG-TERM DEBT High transactional cost Opportunity cost
The exhibit 2 points out how short term debt is subscribed without a time-expensive
transaction compared to long term debt but to renew this debt can be costly while, vice versa, to
borrow money for a long time can cause opportunity costs because of the difficulty to get back the
financial resources, moving to others investments in case of better opportunities. It means that ex-
post short term debt reduces asymmetric information while long term debt can cause opportunistic
behaviours gearing on the presence of asymmetric information.
A relatively recent strand of literature (Berglof and von Thadden 1993, Diamond 1991 and
1993 and Rajan 1992, Barclay and Smith 1995 and other researches by World Bank –
http://econ.worldbank.org) has been studying the forces which determine the maturity structure of
firm’s debt and what are the properties of the long-term financing, focusing on the governance
influence of the different kind of debt used. Especially Barclay and Smith (1995) highlighted that
short-term debt can positively influence the creation of value in the company. In particular large
firms with high growth opportunities tend to have more short term debt.
2 Myers (1977) and Jensen and Meckling (1976) showed an important cost of debt determined by the conflicts of interest among shareholder, manager and debtholder. In particular, manager, whose responsibility is to the shareholders, will select investment that maximize shareholder value rather than total corporate value. It raises a problem of underinvestment if the manager avoids safe positive net present value projects, with benefits for the debtholders and no wealth increases (and in certain case with a reduction of value) for shareholders. Vice versa, it raises a problems of overinvestment if the manager accepts risky negative net present value projects, with benefits for the shareholders and a reduction in the value of the debt. For more details see Grinblatt and Titman (2001).
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The exhibit 3 points out the most important benefits of the two kind of debt (Caprio and
Demirguç-Kunt (1997).
Exhibit 3 – Benefits of short and long term debt. BENEFITS OF SHORT-TERM DEBT BENEFITS OF LONG-TERM DEBT
Reduction of asymmetric information Keep manager/entrepreneur in control Signaling growth opportunities Prevent expropriate of value by creditors Managerial discipline Lower interest rate Prevent underinvestment and overinvestment Prevent short-terminism
The short-term debt has the advantages to limit the period during which an opportunistic
firm can exploit its creditors without being in default reducing the expropriation of creditors’ value
by managers and permitting the loans to be repriced or terminated to reflect new information, it
provides financial flexibility signalling future growth opportunities (Barclay and Smith 1995,
Barclay et al 2003), it gives owner/managers strong incentives to avoid bad outcomes and wasteful
activities, disciplining their behaviours (managerial discipline - Jensen 1986, Diamond 1991). It
reduces underinvestment problems, increasing the efficiency, allowing positive NPV project to be
realized (Myers 1977, Barclay et al 2003), uneconomic project to be terminated and quickly
responding to environment’s changes (Rajan 1992, Ofek 1993).
In particular short term debt can avoid underinvestment problems because if the debt
mature, and give back money, before the growth opportunities is exercised, that is before the
investment decision is taken, the firm, by now unlevered, will be able to set up the new project
without opportunistic behaviours (Myers 1977, Barclay et al 2003). On this way it is possible to get
rid of underinvestment problems because the short term debt offers a continuous renegotiation, in
which the firm moves from a levered to an unlevered capital structure. Moreover, short term debt
solve overinvestment problems because short term debt is less sensible to corporate risk (returns’
volatility) than the long term debt and leaves more control to the creditors on the managerial
activities, avoiding risk-shifting problems (Barnea et al 1980).
On the other side, long-term debt protects the firm from liquidation by imperfectly informed
creditors leaving the control of the company to the manager/entrepreneur, prevents opportunistic
creditors from using the threat of liquidation to expropriate the profits of healthy firms, has a lower
interest rate and avoids short-terminism (shortening of investment horizon).
The optimal mix short/long term debt is based on macroeconomic factors (limiting the
supply of long term debt), institutional factors specific to financial sector and firm’s characteristics
(Caprio and Demirguç-Kunt 1997)3. A strand of research (Demirguç-Kunt and Maksimovic 1996,
3 The credit quality (credit rating), the relevance of growth opportunities, the profitability of the projects, the perceived accuracy of the financial information, the firm’s size and the age of the company are notorious important firm-specific factors.
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La Porta et al 1998, Rajan and Zingales 2003, Titman et al 2003) is working on the relation
between law and finance and between the role of institutional factors, as the type of financial
system (market-based or bank-based), the legal system (civil law or common law), the level of
development of the financial markets, and so on. About the institutional factors, an important role
refers to the level of financial system development and the size and efficiency of the banking or
financial market sector. In fact, the financing decisions of the firm may be strongly influenced by
the role of the financial system, both financial intermediaries and stock markets.
It is interesting to see in which way a developed financial system influences the firm
debt/equity choice and its debt maturity structure. The role of financial institutions and their
interactions with financial firm’s decision is a key research question. Many studies have pointed out
that institutional differences are important in understanding determinant of firm’s financial choice
(Rajan and Zingales 1995, Demirguç-Kunt and Maksimovic 1995).
According to the different features of the institutional factors is possible that the benefits
(costs) of the debt and the advantages of the short (long) term debt have different weights.
Recent financial theory (Titman et al 2003, Barclay et al 2003) argues that the principal
source of the wedge, influencing capital structure and debt maturity choices, may be due to
asymmetric information and cost of contracting between firms and potential providers of external
finance; this wedge might be particularly high in presence of poorly developed financial system4.
A developed financial system can facilitate firm’s ability to gain access to external finance
to use for investment, providing cheaper long term finance to worthy firms (Zingales et al 2003). It
seems quite relevant to observe if differences in the use of debt or equity can be explained by
institutional factors, referring to financial system. In particular small and medium size firms, that
have limited access to alternative source of financing due to information costs, seem very sensible
to the degree of development and efficiency of the financial system.
A developed and efficient financial system can reduce the role of short term debt as
corporate governance device, or better, as tools to face problems of asymmetric information and
contract incompleteness5. May be with a well developed financial system it is not necessary the use
of short term debt to avoid underinvestment problems because the efficiency of the market helps to 4 The relative efficiency of different financial systems and its impact on corporate performance and managerial decision has become a research topic of great importance (e.g. Rajan-Zingales 2000 and 2003, Allen-Gale 2000 and Boot-Thakor 1995). The degree of efficiency of the organization of the financial system, the interdependent universe of financial markets, financial institutions, and financial instruments of all sorts in a given place at a given time (Rajan-Zingales 2001) has a large potential impact on the firm efficiency, performance and financial decisions. It appears that firms can raise finance more easily as the financial system develops because physical collateral becomes less important, while intangible assets and future cash flows can be financed. As the financial system develops it should be able to easily appreciate the goodness of the firm’s projects and of the managerial behaviors (Rajan-Zingales 1998). 5 For example, less developed is the financial system and more the banks will want to use short term credit as a way to control borrowers (Diamond, 1991).
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avoid opportunistic behaviours. Moreover, it could be interesting to see if small and medium size
firms obtain less long term debt with less developed financial system
Previous studies highlighted the presence of systematic differences in the capital and debt-
maturity structure claims issued by firms in different countries (Demirguç-Kunt and Maksimovic
1996a and Demirguç-Kunt and Maksimovic 1996b, Titman et al 2003). But it is interesting to know
if also inside a country, differences in the financial structure of a firm are explained by differences
in the degree of financial development at local level.
In particular, in Italy, financial intermediaries have the advantages to have economies of
scale in obtaining information, have greater incentives to use the collected information on the firm
to discipline the borrowers and exert corporate control.
For all these reasons the object of this paper, following the approach of Zingales et al
(2003), is to analyze how the local financial development and others institutional differences,
jointly with other factors at firm-level, affect the above mentioned financing decisions of the Italian
firms. To study the effects on financial choices of differences in local financial development within
an integrated financial system, as the Italian one, gives an important chance to highlight the role of
the institutional contest in some of the most important managerial financial decisions. The paper
tries to shed some light on the use of debt by firm-specific characteristic, as profitability, size,
tangibility and ownership structure, and by factors related to the institutional contest, as the degree
of local development of the financial system, and the financial reputation.
3. Italian financial features and local financial development indicator
Driving research into the Italian economy is important to take into account the characteristics
of this system. Italian industrial structure and financial markets have several distinct features. The
industrial structure of Italy includes a large number of small and medium sized firms (more than
90%), few of the Italian firms trade publicly. The proliferation of small scale enterprises has often
been pointed to as one of the reasons for Italy’s economic success. The limited type of external
funds available to Italian firms makes them prone to financing constraints.
Capital markets in Italy are also relatively undeveloped compared not only to the US, but to
some extent, other large European countries as well. Italy has a bank-oriented financial system.
Bank debt is by far the most important source of outside funds for Italian firms. Bank loans
are the largest net source of external finance. Non-bank sources of debt, other than trade credit, are
sparse. Very few companies in Italy have publicly traded corporate debt. Furthermore, the
development of institutional investors, such as investment funds, merchant banks, and especially
pension funds, is at an early stage.
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The Italian banking system until very recently was not allowed to hold equity in firms and
was mostly state-owned and heavily regulated, which limited its effectiveness6.
Like other Continental European countries, the Italian stock market is not an important
source of finance in Italy. Very few Italian companies trade publicly, even companies that are quite
large (e.g., Ferrero, Fininvest, Barilla). In 2002 only 295 firms traded on the stock exchange in
Milan (222 in 1993 and 141 in 1980) and the total capitalized value of firms was 457,993 million
Euros, approximately 36,6% of GDP7.
One more feature characterizes the Italian economy. In most of the case the Italian model of
corporate governance is quite far from that one proposed by Berle and Means; there is not a
separation between ownership and control. Instead, the ownership of most Italian firms, even large
firms, is tightly held and based on family business model. The ownership of Italian firms is tightly
concentrated. In a comprehensive study La Porta et al (1999) find that ownership in publicly traded
Italian companies is highly concentrated within a single family and controlling families participate
in top management8. The tight concentration of ownership has its pluses and minuses; it may serve
as an additional constraint on firm growth and an additional factor that influences financing
decisions.
Ownership is even more concentrated among non-listed companies. This concentration, as a
way to answer to the relative lack of protection on minority shareholders received by the Italian
securities law, has been suggested as a constraint on growth.
The desire to maintain family control represents an additional constraint on Italian firms’
growth. Who is in charge with a majority of the control power (high share of equity) doesn’t want
to give up the control of the company; this motivation can constrain the financial resources
available for the company. To grow may require significant levels of outside finance that results in
reduced family control.
According to these industrial and financial features in Italy, our research, following the same
strand of research in some recent papers by Demirguc-Kunt-Maksimovic (1996, 1998 and 2001),
Booth et al (2001) and Giannetti (2003), emphasizes the influence of the financial system as
6 Because the Bank of Italy could and did restrict entry (e.g., imposing geographical boundaries to individual banks operations; restricting new branches) competition in the banking sector was limited. In addition, the market for banking services was segmented. Some banks, Aziende di Credito Ordinario (Commercial banks) could directly provide only short-term credit, while medium and long term credit was granted through independent branches of the commercial banks and through Special Credit Institutions. 7 Pagano, Panetta, and Zingales (1998), using a data set highly representative of the Italian economy, state that during the period 1982–1992 there were only 76 new listings of non-financial companies on the Milan Stock Exchange. In contrast, there were over 3,000 new listings in the US during a similar time period. Since few firms trade publicly and no large institutional investors exist, the market for corporate control is also poorly developed. 8 Using 1993 data for Italian firms, Bianchi et al. (1999) find that the largest shareholder owned 51.4% of the firm, on average. In nearly 30% of listed firms, the largest shareholder was an “individual” (a family).
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institutional factor. Instead of examines leverage and debt maturity across countries this paper goes
through differences in the degree of local financial development within a country, according to the
research of Zingales et al (2003).
The services the financial sector provides are an important catalyst of economic growth
(Rajan and Zingales 1998). The importance of the role financial markets and institutions play in an
economy is undisputed and fairly well documented elsewhere (see Levine 1997 for example). A
well developed financial system does a better job of ensuring that value-adding projects get funded,
influencing the firm’s growth. What is questioned is how it influences the financing decision of the
firms.
To understand if an improved development of the financial system get better conditions for
the use of debt or equity, influencing the debt maturity structure we investigate the role of the
differences in financial development among the 19 Italian regions (the Italian regions are 20 but
Valle d’Aosta is very small and is not gather in the Zingales et al analysis).
To measure the local financial development we use the new indicator calculated by Zingales
et al (2003), that is perfect to represent this phenomenon.
One of the main roles of the financial system is to transfer funds from agents with a surplus
of resources to agents whose investment opportunities exceed their current resources. Equating the
development of a financial system with its degree of efficiency in performing this task, it is possible
to measure its development by estimating how well funds are transferred. A good indicator would
be the ease with which individuals in need of external funds can access them and the premium they
have to pay for these funds. The right indicator to measure the level of local financial development
should capture the ease with which any entrepreneur or company with a sound project can obtain
finance and the confidence with which investors anticipate an adequate return. Moreover, a
developed financial sector should gauge, subdivide, and spread difficult risks, letting them rest
where they can best be bear. Finally, it should do all this at low cost (Rajan-Zingales 2002). In
practice, it is quite difficult to observe and compute all these things.
Zingales et al (2003) used the Italian Survey of Households Income and Wealth that asked
households whether they have been denied credit or have been discouraged from applying. Hence, it
contains information on individuals’ access to credit, containing precise information on the location
of the respondents. Than they estimate a linear probability model of the likelihood a household is
shut off from the credit market and use this conditional probability of being rejected as a measure of
financial underdevelopment.
Thus, controlling for individual characteristics, it is possible to obtain a local indicator of
how more likely an individual is to obtain credit in one area of the country, rather than in a different
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one. It means that this indicator measures how easy it is for an individual to borrow at a local level.
Moreover, it takes also in account the level of judicial inefficiency and the level of social capital.
One could object that such indicator of financial development is not very useful in so much
as it measures a local condition of the credit market. If individuals and firms can tap markets other
than the local one, local market conditions become irrelevant. There is a growing literature,
however, documenting that distance matters in the provisions of funds, especially for small firms.
Petersen and Rajan (1995), for instance, documents the importance of distance in the provision of
bank credit to small firms and especially for country, as Italy, where very high are the problems of
asymetric information.
4. Empirical analysis
4.1 Data and sample To analyze the joint determinant of leverage and debt maturity according to the local
financial development, this paragraph describes the sample employed in this study, the variables
used and the related relations between them.
The source of our firm-level data is AIDA (Analisi Informatizzata Delle Aziende) database
collected by Bureau Van Dijk. In the selection of the sample we include firms that exhibit a value
for the financial reputation index and the independence indicator, two indicator that are provided in
the Aida data base. The sample employed in this study was stratified according to financial
reputation index and independence indicator (that are both included in Aida data bank) and also
according to location; it constitutes a statistically significant representation of the Italian
manufacturing industry. Hence, the database includes 4609 Italian manufacturing firms. It is a
cross-section analysis that covers 4.609 non-financial firms, using data for the 2000 or averaged
from the 1994 to the 2000 to compute some variables. We include only operating firms and not
companies interested by bankruptcy. The sample firms represent 2.6% of the number of firms
contained in the AIDA dataset and 5,4% of the revenue of it. We select firms in 31 industries to be
analyzed in this study.
Two dependent variables are used in the empirical model.
Firms raise investment funds in a number of ways (borrowing from banks or other financial
institutions or issuing various kinds of debt, preferred stock, warrants and common equity). The mix
of these different sources is the capital structure of the firm. As a proxy of the capital structure in
this paper is used the financial (or interest bearing) leverage calculated by the ratio of long-term and
short-term interest bearing (financial) debt divided by financial debt plus equity. Because of many
empirical paper used, making a mistake, a ratio of total liabilities (interest bearing debt plus trade
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debt) on total asset to proxy capital structure we use this variable as well to compare the results with
the financial leverage.
Interesting as the leverage is the analysis of the debt maturity structure of the firm that is
related to the fact that the debt can have different length of time to be paid off. We measure debt
maturity as the fraction of the firm’s total interest bearing debt that matures in more than one year;
it means the ratio of long-term financial debt over total financial debt. We use also a variable
measuring the maturity structure of the total debt (trade and financial debt) to compare the results
with the financial debt maturity.
4.2 Variables and hypothesis According with the goal of this paper and the related literature exhibit 4 synthesizes the
variables used in the empirical model and the expected sign for the regressors. It follows an
explanation of these variables.
Exhibit 4 – Variables used in the empirical model and expected relation between the capital and maturity structure and the explanatory variables. Variables LEVERAGE DEBT MATURITY Local financial development indicator (Zingales et al 2003) + + Amount of Deposits/GDP + - Number of bank Branches for 10.000 habitants + + Financial Reputation Index (Novescore) +/- +/- Independence Indicator - - Size + + Tangibility + Roa +/- Asset maturity + NoDebt Tax Shield (NDTS) - Leverage + Debt Maturity +
Local financial development. - Zingales et al (2003) assess the relevance of the local
financial development for the firm growth; this paper tries to shed some light on the role of the local
financial development in the financial decision of the firm. In particular, its influences in the capital
and maturity structure choice.
It is hypotised a positive role of the local financial development indicator that it means that
in a bank-based system like the Italian one the firm financial decision are influenced by the location
in regions with different level of financial development. Moving in regions financially better
developed increases the availability, at a good price, of credit for the firms. The banks are better
able to value the quality of the firm’s project providing credit with the same maturity of the
investment.
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In presence of low financial development the asymmetric information are high and, as a
consequence, the probability to have access to external resources at a good price is low. Increasing
the financial development a banks should be able to convey a substantial amount of information on
the firm and provide financial resources at a good price.
The maturity structure should be also influences by the improvement of the local financial
development. As highlighted by Barclay and Smith (1995) financial system developed and
regulated are able to provide long-term debt, reducing potential problem of asymmetric information.
The banks should be able to provide long-term credit (short-term credit is a way to protect from
moral hazard problems).
For these reason it is supposed a positive relation between local financial development and
capital and maturity structure.
If it ends up to see a lack of significance and relevance of this variables it could means that
the integration with well-developed international financial markets, and the availability to raise
funds in other international markets, can substitute the role of the local financial market.
Banking sector – Titman et al (2003) evidenced that higher levels of funds available to
institutional investors are associated with lower leverage and the use of long-term debt, suggesting
their impact in both equity and debt markets. And so a high amount of deposit should provide more
funds available to finance firm’s investment. In contrast, banks, because of their monitoring
capabilities, may have a comparative advantage holding short-term debt. Hence, one might expect
to see firms being financed with more short-term debt in regions where the banking sector has
access to more funds.
We use the ratio of deposits (commercial banks plus savings banks) to GDP reached in each
region as a measure of the development of the banking sector. One shortcoming is that this measure
captures only the liability side of banks, ignoring differences in the composition of the banks’
assets. Another shortcoming is that this measure cannot indicate if banks operate as a cartel,
forming a closed shop to new industrial entrants.
Jointly with the previous measure the model take care about the different number of “bank’s
branches” for 10.000 habitants in each region. According with Petersen and Rajan (2002) if a bank
is very close to the customers and has many “branches” it is able to do a better job in providing
capital to finance investment’s projects.
Ownership structure (Independence Indicator) – The governance of a firm, and so its
financial decision, is strictly influenced by the related ownership structure. In Jensen and
Meckling’s (1976) analysis, separation of ownership and control increases the agency costs of
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equity and forces firms to choose debt to finance their investments. As the concentration of insider
ownership increases, agency costs of equity decrease and agency costs of debt increase, due to the
risk-incentive problem in which equityholders, after the issuance of debt, may benefit from “going
for broke”, i.e., invest in very risky projects with very high returns9.
Insider ownership is found to be inversely related with debt usage. Low levels of insider
ownership are positively correlated with levels of debt; such a relationship is inverted when
ownership is highly concentrated in the hands of the managers/entrepreneurs.
The ownership of Italian firms is tightly concentrated, even more concentrated among non-
listed companies. Who is in charge with a majority of the control power (high level of equity share)
doesn’t want to give up the control of the company; this motivation can constrain the financial
resources available for the firm because to grow may require significant levels of outside finance.
The model contains a variables dealing with the ownership structure of the firm according
with the weight of the shares of the shareholders. AIDA has created an independence indicator to
signify the degree of independence of a company (the management) with regard to its shareholders.
This Independence Indicator is assigned to each company and gets value equal to A, B and C,
translated in 1, 2 and 3, according to the controlling power the shareholders can exert over a
company. This independence indicator is equal to 3 for any company with shareholders none of
which having more than 24.9% of ownership share. The logic behind these qualifiers is that the
probability of having an ownership that influences the management is low and, vice versa, the
company's degree of independence is more certain. The independence indicator equal to 2 is
attached to any company with a shareholder none of which with an ownership percentage over
49.9%, but having one or more shareholders with an ownership percentage above 24.9%. The
independence indicator equal to 1 is allocated to any company with a shareholder with an
ownership over 49.9%. In this case the shareholder is able to exert a high control power on the
company.
Financial Reputation - Agency problems of asset substitution or debt overhang can be
solved thanks to the presence of a “good reputation” (Diamond 1989).
A positive reputation indicates that an organization is highly esteemed, worthy or
meritorious; it implies a good name and high regard (Webster’s Third New International Dictionary,
1961). A firm reputation is an intangible asset that may serve as signal of the firm quality (Grant
1991). Reputation has three different features. It may be function of the quality of the goods sold in
9 If the investment fails, because of limited liability, lenders bear the consequences. If bondholders correctly anticipate this behaviour, the cost of the incentive to invest in risky project, i.e., the agency cost of debt, is eventually borne by the equityholders (Harris-Raviv, 1992).
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the product market, of the effectiveness of the management that gain reputation in the labor market
and may depend on the financial performance.
Creditors concerned about adverse selection may ration credit, finance only a fraction of
assets and operations, claim high collateral, or shorten the maturity of their loans (Hirshleifer and
Thankor 1992).
Without a good reputation the cost of debt is too high and the banks finance firms with
short-term debt that is able to prevent against opportunistic behaviors. On this way there should be a
negative relation between leverage and reputation and a positive relation between debt-maturity
structure and reputation.
The presence of asymmetric information lets the debt becomes an expensive form of
financing. The risk to exploit asymmetric information to obtain better financial terms and
expropriate value can be very high. The consequence is obvious: faced with a typical asymmetric
information problem, banks will not provide funds to the firm. A positive reputation reduces
asymmetric information problems and agency problems. For this reason it is assumed a positive
relation between leverage and reputation, because a positive financial reputation lets the problems
of informational asymmetries be eliminated or at least substantially reduced. The presence of a
positive financial reputation should also let become available to the firm mote long term debt.
The firm’s financial reputation is measured through the financial reputation index, called
Novescore, provide in the Aida database by Novcredit (www.novcredit.it). This indicator ranks the
firm from 0 to 100 (in the empirical model the reputation index is divided by 100 to let it varies
from 0 to 1) observing more than 400.000 financial position and according to the reliability of the
firm in the payments and in the financial transactions with the stakeholder. It a kind of financial
credibility index.
Roa - Most tax and agency-cost models of capital structure predict that leverage will be
increasing in profitability. Jensen (1986), for example, argues that high leverage is a valuable
mechanism that commits profitable firms to distribute their free cash flow. Profitable firms
generally also have high-expected marginal tax rates and high tax benefits of debt. Theoretically
and empirically, the opposite relation often has been found. In the “Pecking Order” theory of
financial choices, investment is financed first with internal funds, primarily retained earnings; then
by new issues of debt and finally with new issues of equity (Myers 1984). It follows that a more
profitable firm should more likely substitute debt for internal funds. For example, Titman and
Wessels (1988) and Fama and French (2002) document a negative relation between leverage and
profitability. Thus, without making a strong prediction on the sign of the relation, we include
15
profitability, which is given as the ratio of “earnings before interests and taxes” (EBIT) over total
assets, as a control in our leverage regression.
Non-debt tax shields (NDTS) - DeAngelo and Masulis (1980) argue that firms that are able
to reduce taxes using different way than the deducibility of interest will employ lower debt in their
capital structure. Tax shield substitutes for debt, such as depreciation, should therefore lessen the
tax advantage of debt. The non-debt tax shields considered in this study are the Depreciation of
physical asset and the Depreciation of intangible assets both divided by total assets. Both should be
inversely related to firm leverage.
Tangibility - The agency costs of debt due to the possibility of moral hazard on the part of
borrowers increases when firms cannot collateralise their debt (Jensen and Meckling, 1976). Hence,
lenders will require more favourable terms and firms may choose equity instead. To mitigate this
problem, a large percentage of firms’ assets can be used as collateral. Tangible assets provide better
collateral for loans and thus are associated with higher leverage. We measure asset tangibility as the
ratio of property, plant, and equipment to total book assets.
Asset maturity - Previous studies also suggest that firms match the maturity of their assets
with the maturity of their liabilities. Thus, firms with long-lived assets are expected to have more
long-term debt. We measure the firm’s asset maturity net property, plant, and equipment divided by
depreciation and amortization.
Size - Several previous studies have found firm size to be an important determinant of both
the leverage and debt maturity (Barcley et al 1995). Large firms tend to have more collateralizable
assets and more stable cash flows. Thus, firm size typically is inversely related to the probability of
default, which suggests that large firms would be expected to carry more debt. Diamond (1993) also
argues that large established firms have better reputations in the debt markets, which also allows
them to carry more debt. An inverse correlation is also found between firm size and cost of debt,
due to the better diversification of risk that large firm can enjoy.
Barclay and Smith (1995) document that debt maturity generally increases with firm size.
The positive relation between debt maturity and firm size may be related to the large fixed costs of
public debt issues; there is scale economies associated with public debt issues. Since smaller firms
are not able to take advantage of these scale economies, they often opt for bank debt; for this reason
they have more short-term debt.
16
In the empirical analysis presented in this paper we control with some dummies for industry
affiliation, to be listed in the Stock Exchange and to be located in the south of Italy. In particular the
data set contains information regarding the ATECO04 industry classification of each firm, which is
used to construct 31 intercept dummies based on the classification’s first two digits. These variables
are included to capture industry-specific unobserved characteristics as discussed in Showalter
(1999). Moreover it is well-known that in Italy, from an economic, politic and social point of view,
the north is more developed than the south. In order to take into account the possibility that the
location in the south influences the financial decisions of the Italian companies a dummy (equal to 1
if a firm is located in the south and 0 otherwise) is added in the model to control this economic
differences. On this way it is possible to avoid distortions in the use of the indicator of Zingales et
al.
5. Empirical results
5.1 Descriptive statistics
We start our analysis examing the descriptive statistics of the most interesting variables to
get a basic idea about the distribution of them and about the financial behavior of the firms. Jointly
with some institutional variables as the local financial development indicator, calculated by
Zingales et al (2003), the amount of regional bank deposit on regional GDP and the number of bank
branches per 10.000 habitants, a set of control variables at firm-level, as explained previously, is
used.
The major descriptive values of the depended and explanatory variables are summarized in
exhibit 5.
17
Exhibit 5 - Descriptive statistics.
25 75
Financial Leverage 0,51 0,56 0,28 0 1 -0,33 -1,08 0,28 0,75
Financial Debt-Maturity 0,21 0,09 0,26 0 1 1,13 0,24 0 0,37
Tot.Debt / Tot.Asset 0,65 0,69 0,21 0 1 -0,42 1,26 0,52 0,8
Tot.Long Debt / Tot.Debt 0,08 0,03 0,11 0 0,93 2,26 7,03 0 0,12
LocFinDev(Zingales Indicator) 0,41 0,44 0,13 0 0,587 -1,79 2,56 0,44 0,52
Deposit/GDP 0,91 0,83 0,14 0,55 1,11 0,07 -1,68 0,79 1,05
Banks Branches 5,46 4,8 1,68 1,9 8,7 0,73 -0,43 4,8 6,5
Novescore (Reputation Index) 0,562 0,56 0,02 0,14 0,89 0,32 1,10 0,5 0,62
Indipendence Indicator 1,34 1 0,63 1 3 1,67 1,49 1 2
Roa 0,09 0,08 0,10 -1,25 1,05 -0,49 15,98 0,05 0,13
NDTS 0,04 0,04 0,04 0 0,59 3,00 22,64 0,02 0,06
Tangibility 0,39 0,4 0,20 0 0,97 0,12 -0,39 0,25 0,53
Asset Maturity 32,85 9,82 498,6 0 290 48,97 265,1 5,58 17,57
Size 10,00 9,83 1,48 1,39 17,68 0,67 1,81 9,00 10,81# firms 4609
Mean Median Std. Deviation Min Max KurtosisSkewness Percentiles
As well known in the descriptive Italian literature, Italian firms make a large use of short-
term debt. The continuous and systematic renewal of the short-term debt makes it a traditional form
of financing on the Italian firms. Comparing financial leverage and financial and trade leverage it
raises the relevance of the trade debt in the liabilities of the Italian firms. The independence
indicator shows, as waited, that the major part of the Italian firms are owned by a shareholder with
more than 50% of the shares.
To gain an overview about how capital and maturity structures differ across regions, we start
our analysis by examining, as showed in exhibit 6, about how capital and maturity structures differ
across the Italian regions considering mean, median and standard deviation of leverage (measured
as interest bearing debt divided by interest bearing debt plus equity) and maturity structure
(measured as long-term interest bearing debt over total interest bearing debt) ratios in the 19 Italian
regions ordered by degree of local financial development (Zingales et al indicator).
18
Exhibit 6 - Descriptive statistics by variables and by Zingales et al indicator of local financial development (corresponding to the Italian regions). .
Deposit GDP
Banks Branches
Mean Mean Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev.
0 19 0,640 2,400 0,510 0,28 0,20 0,21 0,70 0,17 0,09 0,09 0,50 0,82 1,368 0,67 0,055 0,05 0,028 0,02 0,434 0,25 9,549 1,61
0,027 113 0,787 4,200 0,537 0,26 0,21 0,24 0,64 0,19 0,08 0,09 0,55 0,11 1,389 0,62 0,072 0,08 0,040 0,03 0,433 0,22 9,743 1,35
0,067 297 1,106 8,200 0,469 0,31 0,21 0,26 0,65 0,21 0,08 0,13 0,54 0,10 1,290 0,59 0,086 0,11 0,043 0,06 0,336 0,24 10,497 2,01
0,165 43 0,783 5,300 0,449 0,30 0,18 0,24 0,64 0,19 0,07 0,10 0,54 0,09 1,349 0,64 0,076 0,08 0,038 0,03 0,442 0,20 10,157 1,38
0,214 50 0,695 4,700 0,629 0,26 0,18 0,21 0,66 0,18 0,09 0,11 0,54 0,11 1,300 0,50 0,095 0,12 0,054 0,05 0,506 0,21 9,925 1,53
0,248 16 0,550 2,700 0,428 0,24 0,10 0,18 0,73 0,14 0,04 0,05 0,53 0,08 1,625 0,70 0,047 0,07 0,031 0,02 0,424 0,22 9,958 1,21
0,347 11 0,682 2,500 0,418 0,21 0,27 0,22 0,69 0,16 0,09 0,10 0,58 0,13 1,091 0,29 0,117 0,07 0,052 0,03 0,498 0,18 10,548 1,53
0,359 66 0,751 3,200 0,467 0,29 0,23 0,24 0,62 0,21 0,09 0,11 0,58 0,11 1,273 0,57 0,108 0,09 0,045 0,03 0,407 0,16 10,147 1,08
0,36 326 0,828 7,400 0,549 0,28 0,20 0,27 0,67 0,21 0,08 0,13 0,56 0,09 1,451 0,70 0,085 0,08 0,035 0,03 0,403 0,18 9,564 1,20
0,374 27 0,779 1,900 0,467 0,34 0,20 0,22 0,62 0,21 0,07 0,09 0,54 0,11 1,444 0,63 0,091 0,07 0,049 0,04 0,409 0,21 9,791 1,45
0,398 50 0,761 5,400 0,538 0,29 0,24 0,25 0,63 0,19 0,08 0,09 0,53 0,07 1,460 0,73 0,081 0,06 0,041 0,03 0,492 0,18 9,717 1,29
0,41 128 0,895 4,700 0,517 0,26 0,16 0,25 0,62 0,28 0,07 0,10 0,56 0,10 1,461 0,73 0,097 0,10 0,049 0,03 0,451 0,18 10,182 1,42
0,435 1738 1,054 4,800 0,500 0,28 0,21 0,26 0,65 0,21 0,08 0,11 0,56 0,10 1,296 0,60 0,094 0,10 0,044 0,04 0,364 0,20 10,044 1,44
0,457 75 0,837 2,900 0,498 0,28 0,25 0,29 0,61 0,20 0,07 0,08 0,55 0,10 1,333 0,70 0,101 0,08 0,056 0,04 0,491 0,21 10,058 1,25
0,472 448 0,785 3,486 0,510 0,28 0,21 0,26 0,65 0,20 0,08 0,11 0,55 0,09 1,330 0,63 0,089 0,11 0,048 0,04 0,402 0,21 10,223 1,70
0,516 550 0,737 5,300 0,543 0,27 0,21 0,26 0,65 0,21 0,07 0,11 0,56 0,09 1,338 0,61 0,099 0,10 0,045 0,03 0,432 0,18 9,772 1,25
0,523 472 0,794 8,700 0,536 0,27 0,22 0,27 0,65 0,20 0,08 0,11 0,56 0,09 1,360 0,65 0,091 0,08 0,045 0,03 0,397 0,18 9,992 1,36
0,586 93 0,752 6,500 0,491 0,30 0,22 0,27 0,63 0,20 0,08 0,12 0,55 0,08 1,344 0,66 0,098 0,06 0,046 0,05 0,418 0,23 9,819 1,72
0,587 87 0,818 4,300 0,543 0,29 0,23 0,28 0,69 0,19 0,09 0,13 0,57 0,09 1,425 0,67 0,090 0,08 0,041 0,03 0,398 0,16 9,894 1,45
Total 4609 0,906 5,458 0,514 0,28 0,21 0,26 0,65 0,21 0,08 0,11 0,56 0,09 1,336 0,63 0,092 0,10 0,044 0,04 0,393 0,20 10,003 1,48
Fin LeverageZingales et al Indicator of Local Fin. Develop.
# FirmsROAFinancial
Debt-Maturity Tot. Debt Tot Asset
Tot Long-Term Debt/Tot.Debt NDTS Tangibility SizeNovescore Indipendence
Indicator
19
Exhibit 6 shows, as it happens effectively, that most of the Italian firms in the sample are
located in Lombardia (the industrial area around Milan).
In the model we consider industry affiliation as an initial proxy for the firm characteristics
suggested by the various “trade-off” theories. Exhibit 7 presents the mean and median leverage and
maturity structure by industry, following the Italian classification Ateco 2004.
Exhibit 7 - Descriptive statistics by the 31 Industry– leverage e debt-maturity structure.
Financial Leverage for the 31 Industry sectors
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
Mean St.Dev.
Financial Debt-Maturity structure for the 31 Industry sectors
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
0,45
Mean St.Dev.
20
Moreover it is interesting to notice, without reporting the table, that in young firms (less than
5 years old) the standard deviation of the financial leverage is very high compared with the one of
mature firms (more than 25 years old). Morel long-term debt is present in mature firms compared
with young firms (the debt-maturity structure is higher); the standard deviation is higher for young
firms compared with old and mature firms and,, in this case, is double compared with the financial
leverage variable.
5.2 Econometric models and evidences
This paragraph describes the empirical procedure applied in this study and shows the results.
First of all we examine theories of leverage and debt-maturity structure focusing on the
impact of some institutional factors, and especially on the local financial development indicator,
jointly with other firm-specific characteristics, as financial reputation, ownership structure, size etc,
that determine these policies.
The present paper tries to contribute to the understanding of corporate capital and maturity
structure decisions comparing the results of a classical OLS (ordinary least squares regression
model) with a simultaneous solution of two different regressions (two stage least squares regression
model). The two stage least squares is appropriate considering a linear relation between the capital
and maturity structure (Barclay et al 2003).
The two equations to be estimated is based on the previous analysis considers alternatively,
and then simultaneously, leverage and debt-maturity as dependent variables and then taking care
about local financial development, financial reputation and controlling for other explanatory
variables. The equations are:
Eq.A
Leverage = a1 Financial Debt-Maturity + b10 + b11 Local fin dev Indicator + b12 regional amount deposit/GDP + b13 #bank branches + b14 Reputation Index + b15 Independence Indicator + b16 Roa + b17 NDTS + b18 Tangibility + b19 Size + b110 Tax + e1
Eq.B Debt-maturity = a2 Leverage + b20 + b21 Local fin dev Indicator + b22 regional amount deposit/GDP
+ b23 #bank branches + b24 Reputation Index + b25 Independence Indicator + b26 Asset Maturity + b27 Size + e2
Three dummies are included to control for listing (firm listed in the Italian Stock Exchange
or not), for being located in the South of Italy or not and for industry affiliation.
It is used as proxy of the capital structure, alternatively, a financial leverage and a ratio of
total trade and financial debt divided by total asset, and as proxy of the maturity structure,
21
alternatively, a financial debt-maturity variable (financial long-term debt divided by total financial
debt) and a ratio of total, trade and financial, long-term debt divided by total debt.
On this way it is possible to take in account about how the use of different proxy of capital
and maturity structure can mislead the empirical analysis. To analyze the capital and maturity
structure choice needs to compute on the right way the leverage and maturity ratio. The financial
leverage considers just financial count to determine the financial debt. Instead, to compute total debt
includes trade and financial debt. The same relates to the maturity structure; the financial maturity
structure take in account only financial debt while total long-term debt considers trade and financial
debt.
The regressions performed using both the ordinary least square method and the two-stage
least squares methods include a set of firm-specific variables that capture factors that are known to
affect leverage and maturity structure (see Harris-Raviv 1991).
Exhibit 8 presents the results of the Ols regressions. The first column in exhibit 8 reports the
variables’ name, than the further columns report regression results for the financial leverage ratio
and for the case where the leverage ratio include trade credit jointly with financial debt, and then for
the financial debt-maturity and the ratio of long-term debt divided by total debt, that is the case
where trade credit is included. The results reported in the columns are very similar.
Exhibit 8 – OLS Regression results.
Coeff. VIF Coeff. VIF Coeff. VIF Coeff. VIF
(Constant) 0,994 1,018 0,211 0,066Financial Debt-Maturity 0,112*** 1,090Long-term Debt / Tot.Debt 0,320*** 1,105Financial Leverage 0,106*** 1,230Tot.Debt / Tot.Asset 0,111*** 1,379Local Fin.Development 0,108*** 1,114 0,0984*** 1,104 0,002 0,679 -0,011 1,109Regional Deposit/GDP -0,034 1,123 0,009 1,114 -0,132*** 0,000 -0,055*** 1,112Banks Branches (10.000 habit.) 0,004** 1,021 0,004*** 1,021 -0,003* 0,291 -0,001 1,023Reputation Index -0,989*** 1,279 -0,739*** 1,280 0,135*** 0,000 0,000 1,462Indipendence Indicator 0,016*** 1,017 -0,006 1,015 0,026*** 0,000 0,011*** 1,011Roa 0,068 1,394 -0,269*** 1,393NDTS -1,292*** 1,200 -0,801*** 1,193Tangibility 0,087*** 1,205 -0,020 1,166Asset Specificity 0,000 0,166 0,000*** 1,416Size 0,002 1,116 0,003* 1,127 0,023*** 0,000 0,013*** 1,099Adjusted R Square 0,195 0,302 0,050 0,086Durbin-Watson 1,933 1,906 1,942 1,955F 84,4 166,8 24,5 44,1Sig. F 0,000 0,000 0,000 0,000
Financial Leverage
Tot.Debt. Tot Asset
Financial Debt-Maturity
Long-Term Debt. Tot.Debt
22
Capital structures decisions and debt maturity structure decisions are complementary (it
means endogenous) and frequently are chosen concurrently. Many previous works did not take in
account of the simultaneous interaction.
By the fact that the debt/equity choice is jointly determined with the debt-maturity structure
arises an important form of endogeneity, typically through an equilibrium mechanism. An Ols
model applied to a single equation in a simultaneous system is generally biased and inconsistent.
Therefore, the previous Ols regression results could be misleading. A solution to the simultaneity
problem is to move from an Ols econometric method to a Two-stage least squares econometric
method.
Equation A and equation B constitute a simultaneous equation model that determines a1 and
a2 (we must assume that a1 ≠ a2 ) and, given the explanatory variables, considered as exogenous
variables. From a statistical standpoint, the key assumption concerning the explanatory (exogenous)
variables for equation A and B is that they are both uncorrelated with the error (e1 and e2). Without
going far explaining the statistical implication of the two-stage least squares (see Wooldridge 2003
for statistical implications and Barclay et al 2003 for economic and managerial implications) we
show and analyze the results applying this method (exhibit 9).
Exhibit 9 – Two-Stage Least Squares regression results.
Financial Leverage Financial Debt-Maturity(Constant) 0,979 0,259Financial Debt-Maturity -0,092Financial Leverage 0,106***Local Fin.Development (Zingales et al indicator) 0,112*** 0,0017Regional Deposit/GDP -0,054 -0,132***Banks Branches (10.000 habitants) 0,004 -0,003*Reputation Index -0,977 0,136***Indipendence Indicator 0,022 0,026***Roa 0,078NDTS -1,183*Tangibility 0,135Size 0,006 0,022***Asset Maturity 0,000Adjusted R Square 0,18 0,05F 76,5 18,6Sig. F 0,000 0,000 Leverage structure
OLS results for the control variables are generally consistent with previous studies. In the
leverage regression, the firm-size coefficients are positive but statistically insignificant. The
coefficient on Roa is positive but statistically insignificant using financial leverage as dependent
23
variable and negative and statistically significant using total debt on total assets. The coefficient on
tangibility is positive and statistically significant.
Using both Ols and 2SLS regressions reveal that leverage is significantly related to local
financial development. The regression results show that capital structure is positively related to the
financial institutional contest. The Zingales et al indicator of local financial development is
economically and statistically significant, keeping the same weight and the same amount of stars.
However, we do not find a statistically significant relation between leverage and the amount
of deposits available in the local area, used as proxy of the liquidity of the regional banking system.
The number of bank branches is statistically relevant but economically trivial.
Using the two-stage least squares method in the leverage regression the explanatory
variables appear less well behaved compared to the ols results and also to the maturity regression
(applying the two-stage least squares methods).
In this regression, all the coefficients, except one, keep the same sign losing in significance
(reputation index, Banks branches, NDTS tangibility and size lose in significance).
With the Ols econometric method the financial debt-maturity structure is significant and
shows a positive relation with the leverage. With the two stage least squares the coefficient on
financial debt maturity is negative and statistically not significant. The coefficient on financial debt
maturity flips from positive to negative. According to the work of Barclay et al (2003) the
coefficient on financial debt maturity in the leverage regression and the coefficient on leverage in
the financial debt maturity regression should have the same sign. In our two-stage regressions, they
do not.
One possible explanation for these results is that our model is misspecified. For example, the
omission of variables correlated to policy choices might bias our estimated coefficients. There
might be important exogenous determinants of leverage and maturity that are not captured within
our model or not included in our regressions.
Moreover we could have had poor instruments to identify the structural-equation
coefficients. And at last, this negative coefficient could means that, even though the simple and
conditional correlations between leverage and maturity are positive, leverage and maturity are
substitutes in addressing the under and over-investment problems. By the fact that this coefficient is
not significant means that its sign is not reliable and so we should not take in account for it.
Debt maturity structure OLS results in the debt-maturity regression showed that all of the control variables are
significant and have the expected sign. Financial leverage, local financial development, reputation
24
index, independence indicator and firm size are associated with more long-term debt. Instead the
coefficient in the local financial development and asset maturity does not show any relevance.
Using the two-stage least squares method the explanatory variables in the maturity
regression appear to be as well specified as in the Ols model. The results does not change. The
coefficients on financial leverage, liquidity of the banking system, the reputation index, the
independence indicator and the firm size are all significant and have the same sign as in the
reduced-form regressions.
The results suggest that after controlling for other factors, debt maturity structure is
statistically unrelated to the level of local financial development. The Zingales et al indicator does
not influence the maturity of the debt provided to the firm. Instead the level of liquidity in the
banking system seems to influence the use of long-term debt; in particular le level of deposits
divided by GDP is positively related, and statistically significant, to the financial debt-maturity
structure. It means that, consistent with prior evidence, debt maturity is negatively associated with
the size of the banking sector.
6. Conclusion The overall results are consistent with those in prior research and generally support existing
capital structure theories. However, in contrast to the econometric predictions the use of the two
stage least squares method does not change any results (both economically that statistically) for the
debt-maturity regression. Debt-maturity, local financial development, reputation index, ownership
structure and other firm-level variables keep the same value and significance. In particular, the
Zingales et al (2003) indicator of local financial development among the Italian regions seems to
show an important positive influence on the debt/equity choice.
Instead there are important differences (both economically that statistically) in the results
showed in the leverage regression using different economic model. Some variables that show a
good significance in the Ols model lose relevance in the two stage least squares model. The
maturity structure is influenced by the leverage but there is not a reciprocal relation. The influence
of debt maturity on leverage is trivial. The level of local financial development among the Italian
regions does not seem to influence the debt-maturity choice.
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