Capital Structure and Innovation - Causality and Determinants

8/9/2019 Capital Structure and Innovation - Causality and Determinants

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Capital Structure and Innovation:

Causality and Determinants

Eleonora Bartoloni

Warwick Business School CV4 7AL Coventry, UK and ISTAT, National Institute ofStatistics, Via Porlezza 12, 20123 Milano, Italy. E-mail: [email protected].

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Abstract

It is widely recognized that a firms financial behaviour is the result ofa complex mix of conditions, both internal and external to the firm;these may affect its investment decisions and its growth opportuni-ties. This paper offers a twofold contribution to the empirical debateon the financing of innovation. First of all, it provides a comprehen-sive descriptions of possible simultaneous patterns which may affect afirms relevant dimensions, namely innovation inputs, innovation out-put, leverage and profitability. By using a Granger-Causality frame-work we will show that a firms leverage does not cause innovation out-put, as proxied by a measure of a firms successful innovation, while itis rather caused by successful innovation and a firms operating prof-itability. The second contribution is an original investigation of the

determinants of a firms capital structure based on a panel of Italianfirms which links the third Community Innovation Survey with an ad-ministrative data source providing economic and financial informationcollected from balance sheets and income statements referring to theperiod 1996-2003. This paper provides support for the pecking or-der theory as our firms are less indebted when operating profitabilityincreases, but the use of external funding increases with their innova-tive effort. We also find support for the existence of credit constrainswhich seem to affect small innovative firms when compared with largerenterprises.

JEL Classifications: G32, O32, L25

Keywords: Capital Structure Innovation Profitability Community In-novation Survey Panel data

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1 Introduction

The financing of an innovative project may take years before produc-ing economic returns, and a firm wanting to exploit increased opportunitiesthrough innovation may not have the internal resources needed to cover theentire cost of such an investment. However, in an imperfect world dom-inated by asymmetric information, bankruptcy risks and agency conflicts,external financing may be highly costly; thus, a firms investment behaviourmight be constrained in terms of the availability and cost of finance. Thisis theoretically explained by considering the high risk inherent in complexinnovative projects which may exacerbate informational frictions betweeninsiders (managers or entrepreneurs) and outsiders (investors). Within this

framework, a consistent part of the literature has investigated the relation-ships between financial factors and a firms investment decisions (Fazzari,Hubbard and Petersen, 1988; Hubbard, 1998). It has been argued that fi-nancial constraints should affect R&D investments more severely because ofthe high degree of uncertainty characterizing innovation output. There isalso evidence that such constraints may have different impacts dependingon firm-specific characteristics such as size and age, or institutional factors(Hall, 2002).

Much of the empirical work on the relationship between a firms financingand innovation is based on the traditional framework developed in order toanalyze capital investment decisions, and thus assumes that the direction of

causality runs from finance to innovation. This interpretation might also bereinforced by the generally recognized strategic relevance of forms of seedfunding, such as venture capital, in stimulating technological progress.

However, there is room to believe that the opposite may be the case,given that when innovative projects are able to open up opportunities, therecould be a demand for specific financial instruments which then affect afirms capital structure. The possible implications of this opposite interpre-tation could be of great interest when empirical investigations specificallyfocus on the role of equity financing, in particular venture capital.

Support for the reverse causation hypothesis, i.e. that innovation Grangercauses financial decisions, has been found at both country and sectoral levels

(Ueda and Hirukawa, 2003; Geronikolaou and Papachristou, 2008). How-ever, further investigation is fundamental, in order to better clarify the in-ner nature of the relationship between financing choice and innovation. Inparticular, firm-level investigation and the use of different proxies for bothfinancial behaviour and innovation may provide a clearer-cut picture of thepossible causality.

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Empirical evidence for the determinants of capital structure is extensive

(see Harris and Raviv, 1991, for a comprehensive review). One importantimplication from this strand of empirical literature is the argument in favourof the pecking order hierarchy of financing sources proposed by Myers andMajluf (1984). The pecking order theory of capital structure suggests thatfirms preferred option for financing new investments is internal resources,provided that an adequate flow of retained earnings is available. When theamount of internally-generated funds is not sufficient and external resourcesare required, firms prefer debt financing, which is less costly; equity will onlybe used as a last resort.

The role of firm- and industry-specific characteristics in determining afirms leverage has been much investigated (Bradley et al., 1984; Titman and

Wessel, 1988), whereas there is relatively little empirical evidence regardingthe role of a firms innovative behaviour. The empirical models developedwithin this analytical framework predict that leverage decreases with prof-itability (Hovakimian et al., 2001; Aghion et al., 2004; Heyman et al., 2008;Magri, 2009) and with alternative forms of internal resources (Colombo andGrilli, 2007). However, the effect of innovation remains ambiguous.

In a world dominated by pecking order access to finance, firms shouldinitially finance innovation by internally-generated cash flow and then byexternal funds (first debt and then equity). However, empirical results sofar available do not reveal a clear-cut picture.

A study focusing on UK industrial firms over the period 1990-2002

(Aghion et al., 2004) provides controversial results. Although firms per-forming R&D tend to use more debt than firms without R&D activities,the use of debt declines with the size of the innovative effort and the mostR&D-intensive firms tend to issue equity, thus suggesting a possible non-linear relationship between innovation and debt finance.

Interestingly, as regards the role played by external equity as an alter-native to debt financing, Schafer et al. (2004) show that German high-techfirms are more likely to make use of equity than debt when financing an in-novative project, thus supporting the hypothesis of possible credit rationing.In addition, private equity, such as that provided by venture capitalists, isnot focused on the higher risk stage or novel projects, preference being ac-

corded to firms with a consolidated innovative history; this is captured intheir model by a regular R&D dummy.

As far as the Italian context is concerned, access to finance by Italiantechnology-based small firms (TBSFs) seems to be dominated by the peckingorder hierarchy in accordance with Giudici and Paleari (2000), although inanother study of Italian start-ups in manufacturing and services, Colombo

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and Grilli (2007), while finding support for a financing hierarchy, also provide

evidence of possible credit constraints, as the amount of credit received byTBSFs is not sufficient to cover investment requirements.

Indeed, problems affecting the financing of innovative firms may be evenmore severe in the case of smaller and younger firms, amongst which thedominance of the pecking order hierarchy may be more difficult to ver-ify. These firms may have less internal resources for financing innovativeprojects. They may be affected more than large firms by asymmetric in-formation, due to a low reputation or the difficulties faced by outsiders inevaluating their default history. Moreover, small and young firms are likelyto incur higher bankruptcy costs due to their shortage of physical assetsto be used as collateral. Accordingly, some empirical investigations of the

Italian manufacturing sector support the view that financial rationing mayaffect small innovative firms more than medium-large (Ughetto, 2008) orsmall non-innovative firms (Magri, 2009).

In addition, financial institutional factors may play a crucial role in shap-ing the innovation path, due to the strong relationship between Italian indus-try and the banking system. While there is evidence that the developmentof local banks may affect a firms innovative process and can reduce finan-cial constraints faced by small firms investing in fixed capital (Benfratelloet al., 2008), it should be borne in mind that the use of stock and ven-ture capital markets by Italian firms is still relatively limited compared toother industrialized countries, and significant differences across European

countries emerge (Bottazzi and De Rin, 2002). Also, a shortage of venturecapital for innovative SMEs has been indicated by the European Commis-sion1 as one of the main factors hindering innovation in countries such asItaly, thus raising important policy implications. Nevertheless, empiricalevidence on the role of venture capital in Europe is not conclusive.

This paper offers a twofold contribution to the empirical debate on thefinancing of innovation. First of all, it provides a comprehensive descriptionsof possible simultaneous patterns which may affect a firms relevant dimen-sions, namely innovation inputs, innovation output, leverage and profitabil-ity. By using a Granger-Causality framework we will show that a firmsleverage does not cause innovation output, as proxied by a measure of a

firms successful innovation, while it is rather caused by successful innova-tion and a firms operating profitability.

The second contribution is an original investigation of the determinants

1Innopolicy Trend Chart, Country report 2008, European Commission, Enterprise Di-rectorate - General

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of a firms capital structure based on a panel dataset which links the third

Community Innovation Survey with an administrative data source providingeconomic and financial information collected from balance sheets and incomestatements referring to the period 1996-2003.

This paper provides support for the pecking order theory as our firms areless indebted when operating profitability increases, but the use of externalfunding increases with their innovative effort. We also find support for theexistence of credit constrains which seem to affect small innovative firms.

The paper proceeds as follow. A description of the data is presentedin Section Two, together with the presentation of our final panel of firms.The adoption of the debt to assets ratio as a measure for a firms capitalstructure is also motivated. In section 3 causality patterns of our relevant

variables are explored, while in section 4 we present our empirical modelfor a firms capital structure and discuss the econometric results. Section 5provides a summary of the main findings and concludes the paper.

2 Data description

The main source of information is represented by a panel dataset whichlinks the third Community Innovation Survey (CIS3) with an administrativedata source providing economic and financial information collected frombalance sheets and income statements referring to the period 1996-2003 for

those firms included in the CIS3 sample of respondents. Our final sampleis represented by a balanced panel of 2,591 industrial firms, correspondingto 20,728 non-missing observations (see Table 1). The firms sectoral andgeographical distribution is described in Appendix A, where a comparisonwith the CIS3 sample of respondents is provided. Descriptive statistics ofthe relevant accounting ratios are provided in Appendix B.

In order to derive additional information about the firms innovative sta-tus during the entire time span, the CIS3 sample of respondents was mergedwith the CIS2 and CIS4 samples of respondents covering, respectively, the

periods 1994-96 and 2002-04. Combining three CIS waves inevitably leadsto a considerable drop in the number of firms, as CIS3 and CIS4 samplesinclude the services sector, whereas the CIS2 sample covered exclusively theindustrial sector. As we intend to work with the three-wave panel, our at-tention has to be limited to the industrial sector, which corresponds to 9,197firms. Beyond this, CIS information for the period 1994-96 and 2002-04 is

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not available for every firm in the panel. The variable reflecting innovative

status is thus available for the entire period for a small subset of the firmsin the CIS3 sample of respondents. Limiting analysis to this subset of firmsmay seriously affect the robustness of results. It was thus decided to es-timate the innovative status of the missing observations. This is pursuedusing a Multiple Imputation approach (Rubin, 1987)2.

The adoption of MI has enabled us to retain an adequate number of ob-servations, which is larger than that obtained with a brute linkage betweenthe three CIS waves, and it is also suitable for econometric investigation oflagged effects, dynamic and causality patterns, which would otherwise notbe possible. In fact, it is well known among CIS users that the possibilityof deriving longitudinal information is heavily conditioned by the timing of

data. Most of the qualitative information, including a firms innovative sta-tus, is defined over a three-year time span, and quantitative variables, suchas those describing innovative effort, are available for the last year. Whenan attempt is made to link two or more CIS surveys, one may end up withan insufficient number of observations for performing reliable econometricinvestigations.

In contrast to a prediction derived from a single imputation, MI, whichis based on an appropriate number of random draw imputations, allows oneto take into consideration uncertainty about the true value of the missinginformation to be imputed. In this work, we have applied a multiple logis-tic imputation, which is appropriate as our target variable is dichotomous.

In the RHS of the imputation model we included a set of covariates (firmsize, firm profitability, firm R&D and patent propensity, geographical char-

2The Multiple Imputation Approach to the estimation of missing values for the innova-tion variable is focused in a specific chapter of my PhD thesis. Further details are availableon request.

Table 1: The process of selecting the firms

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acteristics, sectoral innovative intensity) which, according to the economic

literature, may represent important determinants of a firms innovativeness.This procedure has allowed us to set up an imputation method which iscrucially focused on economic theory and empirical literature on the deter-minants of a firms innovation. 3 Also note that a previous study (Bartoloniand Baussola, 2001) based on a cross-section of Italian manufacturing firmswhich also responded to the CIS2 survey, set up a well-specified logisticmodel for technology adoption by using a set of explanatory variables whichincluded the variables used in the present study. In that study the explana-tory variables reflected the typical adoption mechanism summarized by therank and epidemic effects as suggested by Karshenas and Stoneman (1993).A detailed table providing descriptive statistics for both the reduced and

the full CIS3 sample of firms is reported in Appendix A.A firms leverage is defined as the percentage ratio of total debt to total

assets. Following the relevant empirical literature, this can be considered ameasure of the extent to which a firm uses borrowing instead of equity in or-der to finance its activity. It is worth recalling that our leverage index doesnot allow us to distinguish between different categories of debt accordingto the maturity structure or the typology of lender (banks or other finan-cial institutions). However, we believe this indicator could be adequate fordescribing the financial choices of our panel of firms. In fact, one should con-sider the strong relationships which have traditionally characterised Italianindustry and the bank system and therefore the possible great relevance of

bank debt within our panel, as also confirmed by the Bank of Italys data.We also have to stress that our data do not contain information on the

possible role of external equity-holders as opposed to both debt financingand self-financing. However, it should be borne in mind that the use of thestock market and the venture capital market by Italian firms is still relativelylow compared to other industrialized countries; thus, self-financing is muchmore relevant with respect to external equity financing in the definition ofthe equity aggregate.

3It is also worth underlining that this procedure has determined an overall good pre-diction of the innovation variable according with the standard measures used for logisticregressions (Percent concordant computed with the SAS Logistic procedure does never fall

below the value of 70.8%). This procedure also meets the methodological requirementsstressed by the MI literature (Schafer, 1997; Rubin, 1996), i.e. the need of using a rich setof covariates in the imputation model, particularly those which represent a specific focusof the research.

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3 Leverage, innovation and profitability: simul-

taneity patterns

The primary interest of this study is to analyse the determinants of afirms capital structure, with particular focus on its propensity to innovateand profitability. However, as the review of the empirical literature suggests,there is room to believe that causality may be not one-sided as the dynamicrelationships between our focus variables may be crucially affected by si-multaneity patterns. Thus, we first performed a causality analysis basedon the Granger-Causality approach with reference to our panel of industrialfirms. Our aim is to identify possible simultaneity patterns, enabling us thento move on to the main focus of our analysis. We use a firms debt ratio

(LEVERAGE), given by the percentage ratio of total debt to total assets,as a proxy for its financial leverage, and the percentage ratio of operatingmargins to total sales (ROS) as a proxy for its operating profitability; thesevariables are derived from balance sheet information. We also use a di-chotomous variable (INN) as a proxy for a firms successful innovation; thisvariable is based on the Community Innovation Survey and assumes a valueof one if the firm has introduced technological innovation (product and/orprocess innovation) and zero otherwise. We analyse the following pairs ofrelationships:

Relationship 1: Leverage and Innovation - Does leverage cause innova-tion or does innovation cause leverage?

Relationship 2: Leverage and Profitability - Does leverage cause prof-itability or does profitability cause leverage?

3.1 Bivariate causality test: methodology and empirical model

We proceed by applying standard bi-directional Granger-causality tests

(Granger, 1969; Sims, 1972) to the issue of the relationships between debtratio and profitability, innovation and profitability and debt ratio and prof-itability. We use a Vector Autoregressive Representation similar to thatoriginally proposed by Holtz-Eakin et al. (1988).

Given two variablesyitand xit, we consider the following time-stationaryVAR model in order to test whether xit Granger-causes yit:

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yit=K

k=1

kyi,tk+K

k=1

kxi,tk+ uit (1)

where: i= 1, , Nfirms (N=2,591) in years t = 1, , T (T=8), K isthe number of lags and uit = i+t+vitis a two-way error component model.vit is a serially uncorrelated error term and i and t are firm- and time-specific effects. Thus, we allow for the presence of unobserved heterogeneitycaptured by firm-specific effects, but we impose the strong assumption ofhomogeneity in the response to changes in the exogenous variable (fixedcoefficients). Similarly, we specify an identical model to that in Equation

(1) in order to test whether yit Granger-causes xit:

xit=K

k=1

kxi,tk+K

k=1

kyi,tk+ uit (2)

Finally, we test for causality by specifying a test for the joint hypothesisH0 = 1 = = k = 0. If the test is rejected, variableXwill Granger-cause variable Y. Similarly, variable Y Granger-causes variable X if the

joint hypothesis H0= 1= = k = 0 is rejected.

In the traditional Granger-causality context the choice of the optimal lag-length is important, as it is well-known that lag selection may significantlyaffect the results. In our case, due to the short time dimension of our panelof firms we decided to adopt an empirical selection method: starting from amaximum ofK= 4, we tested for different lag lengths. Results for one tothree lags are reported, although two lags seems to be a reasonable selection4.

We adopt the GMM framework when the kind of relationship to betested is linear. More specifically, we use a GMM-sys estimator (Arellanoand Bover, 1995; Blundell and Bond, 1998) for the specification both in firstdifferences and in levels. This approach combines a larger set of instrumentswith respect to a traditional IV estimator or a standard first difference

GMM estimator and can therefore result in greater efficiency, provided theassumptions of white noise error and lack of correlation between the setof instruments and the error term are valid. We present in the tables ofresults the Sargan statistic as a test for the validity of the over-identifying

4Bearing in mind the short time span of our panel, we considered the significance offurther lags in order to select the appropriate lag structure.

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restrictions of the GMM estimations and two other tests for first- and second-

order serial correlation (m1 and m2).Unfortunately, the conventional Granger-causality methodology has to

be modified in our case, as one of the variables under investigation (INN) isdichotomous. This means that a dynamic binary model has to be used whentesting for causality which runs from the other two (continuous) variables toinnovation. With reference to the first shortcoming we decided to adopt arandom effect estimator. Although it rests on more restrictive assumptionscompared to a fixed effect estimator, it nevertheless allows us to overcomethe incidental parameter problem in a non-linear context. As for the problemof initial conditions, it is quite well established that ignoring the correlationbetween the initial state of each unit and the firm-specific heterogeneity

term, when in fact it is present, may determine problems of consistency in theML parameter estimates (Anderson and Hsiao, 1982; Nerlove and Balestra,1996). However, attempts described in the empirical literature available sofar typically make strong assumptions about the possible functional formin order to model initial conditions as endogenous, and so the possibility ofderiving misleading results is not completely avoided5. We decided to avoidthe initial conditions problem, for two reasons. Firstly, at this stage our focusis on possible mutual interdependence between selected pairs of variables.We do not intend to estimate a full model for innovation, which would requirethe inclusion of other possible determinants and control variables. In thiscontext, we believe that the need to model initial conditions is in fact less

stringent. Secondly, the statistical techniques typically proposed in empiricalapplications (Heckman, 1981; Wooldridge, 2005; Stewart, 2007) rely on pre-sample information for modelling initial observations. As information on oursample of firms starts in the first year of the panel, we should remove one yearfrom our time series, thus reducing the reference period for the likelihoodestimates to a seven year time-span. This further loss of information couldaffect the power of our causality tests.

Nevertheless, in order to provide more reliable results with respect tothe relationships involving the innovation variable, we provide additionalcausality tests by using an alternative proxy for innovation: the accounting

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Kazemi and Crouchley (2006) provide an historical review of the properties of mod-elling initial conditions, and also develop an empirical application for a panel of economicgrowth data, by using a variety of model specifications. They find that even though ignor-ing initial conditions results in an upward bias of the state dependence and a downwardbias in the coefficients of the explanatory variables, models based on different assumptionsregarding initial conditions provide rather different estimation results.

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values of intangible assets (INT ASS)6. We are interested in testing for this

different measure of innovativeness for several reasons: firstly, because it isan input rather than an output measure of innovation, in that this variableincludes goodwill, intellectual property rights, patents, trademarks, R&Dinvestments, website domain names and, typically, long-term investmentswhich may describe a firms innovative effort; secondly, because it is a con-tinuous variable, thus enabling us to test for the sensitivity of our causalitytests to changes in the estimation techniques (GMM instead of ML esti-mations); finally, because information on intangible assets derives from anadministrative data source, in contrast with our INN variable which derivesfrom a statistical survey; thus, we can also check for the sensitivity of ourresults to the use of different sources of information.

3.2 Empirical results

Relationship 1: Leverage and Innovation

Our results indicate that a firms debt ratio is significantly Granger-caused by innovation (see Table 2, test 1), by considering first innovationas the right-hand-side variable. The Wald statistics of non-causation arestrongly rejected in the different specifications, even though the impact ofinnovation with lags higher than one is found to be no longer significant.The negative coefficients of the INN variable lagged one year indicate that

introducing successful innovation has a negative impact on a firms futureleverage. Although limited to a very short time span, this result seems quiterobust, as tests for residual autocorrelation and the validity of the instru-ments used are both satisfactory. We performed an additional causality test(test 2) by using INT ASS as an explanatory (continuous) variable. Thecausal relationship is confirmed as being strong and the negative sign of theone-lag coefficient reveals that having invested in intangible assets, includingR&D and other innovative efforts, again decreases a firms leverage.

The reverse causality relationship, i.e. considering innovation as causedby leverage, does not hold (Table 3). Results from the Wald test stronglyreject the hypothesis of joint significance of the LEVERAGE coefficient in

all the lag specifications adopted. Conversely, the test performed using ouralternative (input) measure of innovation tells a rather different story, asvariable LEVERAGE is now strongly causal, at least in the short run. Thus

6Although innovation inputs such as R&D expenditures are covered by the CIS survey,this information is not available for the entire period under analysis and thus is not suitablefor causality tests.

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Table 2: Does Innovation cause Leverage?

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Table 3: Does Leverage cause Innovation?

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Table 4: Does Profitability cause Leverage?

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Table 5: Does Leverage cause Profitability?

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the results of these bi-directional causality tests suggest that innovation

is causal when it is used as an output (INN). In contrast, our tests providestrong evidence of dynamic feedback effects between innovation and leveragewhen innovation is used as an input (INT ASS).

Relationship 2: Leverage and Profitability

Results reported in Table 4 show quite clearly that a firms profitability,as measured by the return on sales (ROS) index, does cause leverage. Thisinterpretation is strongly confirmed by the Wald statistics associated withthe entire set of lag specifications. In accordance with the pecking ordertheory, the effect of profitability on leverage is negative and persists over

time. The sign of the coefficients remains significantly negative even aftertwo years in all the dynamic specifications presented.

The reverse relationship is not fully confirmed by the empirical tests,as causality may be observed only in the one-lag specification (Table 5).Possible simultaneity patterns between LEVERAGE and profitability arelimited to the short run, whereas in a longer time span, here necessarilylimited to the two- and three-lag specifications, the only confirmed causalrelationship runs in the opposite direction.

Overall, our results seems to support the view that a firm which inno-vates successfully can account on a flow of internal resources which may bereinvested in new opportunities (Relationship 1 ). This suggestion is also re-

inforced by the evidence that leverage is negatively associated with a firmsprofitability (Relationship 2). Thus, one can argue, on the basis of thesecausality tests, that innovation may enhances a firms ability to self-finance,provided that an adequate flow of profits is generated. The different picturewhich emerges when an input measure of innovation is adopted and when welook at the bi-directional link with the debt ratio variable, is evidence of thepossible two-way relationship characterising a firms financial choice and itsengagement in long-term innovative investments, better captured by an in-put measure. Conversely, no causal effect from a firms leverage is registeredwhen we adopt the dichotomous INN variable (an output measure), whichis thus probably less appropriate for capturing any effect deriving from a

firms financial structure within the short time span of our panel.

4 The leverage equation

The causality analysis presented in the previous Section indicates that afirms capital structure is driven by both innovation and profitability. These

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- the effect on a firms financial structure of its geographical location

as proxied by the usual set of area dummies. We expect that firmslocated in the northern regions (traditionally characterised by highergrowth rates) should be able to access external financing under betterconditions and thus have higher levels of leverage;

- the possible role played by the institutional context characterising thelending market in the industry sector. This is proxied using the realannual growth rate of bank lending to industrial firms. The LEND-ING GR variable may be considered an indicator of credit accessibility,which is crucially linked to the economic cycle. Bank lending sloweddown considerably as of the late nineties (see Figure 1) as a first signal

of the early 2000s economic recession. We expect that an increase inthe growth of bank lending may boost firms investment decisions and,through this route, affect their leverage levels positively.

Figure 1: Real growth rate of bank lending to industrial firms (percentagechange on the previous year)

Our estimations highlight some important results regarding the role ofa firms performance in terms of innovation and profitability. However, inother cases, results are more uncertain. More specifically, a firms prof-itability exerts a negative and relevant impact on leverage decisions. Thecoefficient of variable ROS is large and quite stable across the different spec-

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ifications, thus signalling that a higher level of operating profitability is as-

sociated with a lower debt ratio. The view that more profitable firms aremore reliant on internal resources with respect to external financing is inline with the theoretical predictions of the pecking order mechanism and,also, with results of the bivariate causality tests provided in the previoussection. In addition, it should be noted that in all the specifications theinterest rate payments-to-sales ratio (IR, a variable reflecting the positionof a firm with respect to external financial resources) presents the expectedpositive sign, as higher debt intensity is likely to be associated with a higherinterest burden.

The effect of past innovation (L.INN) in model 1 is not significant ac-cording with the conventional significance levels . However, when we take

industry structure (model 2) into account by including industry dummies,the significance of the INN coefficient turns out to be noticeably improved.Our results confirms the negative association between variable INN andLEVERAGE which also emerged in the causality analysis, thus support-ing the view that more innovative firms tend to be more reliant on internalresources, possibly due to high uncertainty associated with innovative in-vestments rendering external financing more costly.

Intangible asset intensity, as proxied by the INTANG TA ratio, may beconsidered an alternative (continuous) proxy of a firms innovative effort,as R&D expenditures are recorded in this balance sheet item. In addition,following the literature on moral hazard and collateral, it may be considered

an indicator of the role of immaterial versus physical assets in determininga firms capital structure. The effect of this variable on leverage is notclearly determined within the empirical debate. Following the literatureon collateral, the expected sign is negative, as the higher the immaterialcomponent of a firms capital structure, the higher one would expect thebankruptcy risk associated with external finance to be. When this variable isassumed as a proxy for the intensity of a firms innovative effort, the expectedsign should be positive in accordance with the pecking order argument,as firms need external financing when innovation efforts are particularlycostly and internal funds are not sufficient to cover the entire investment.Our results show a positive and significant effect of lagged intangible asset

intensity on leverage, thus supporting the view that this variable shouldbe considered a proxy for the pecking order mechanism, rather than anindicator of a firms bankruptcy risk.

This positive effect on leverage is not in contrast with the negative effectshown by the INN variable. In fact, these two variables capture differentpatterns: dichotomous INN is a proxy of a firms innovation success, while

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Table 6: Leverage equation: static model

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continuous INTANG TA measures the size of the innovation effort. There-

fore, although it is correct to expect that a firm which has successfullyinnovated might have generated internal resources in order to finance futureactivities, the flow of internal revenues may be insufficient when the size ofthe investment projects increases, thus making the use of external financingnecessary.

As for the role of SIZE and AGE, these are traditionally considered asproxies for a firms probability of default. The relationship between lever-age and different proxies for a firms reputation or quality may be difficultto assess. In general, the agency costs of debt are expected to be higherfor those firms with a lower reputation, such as those which are smalleror less established, and, therefore, a positive relationship between a firms

debt ratio and a measure for credit worthiness such as size or age should beexpected, according to this interpretation. However, empirical evidence isnot conclusive. Aghion et al. (2004) found a positive relationship betweensize and the debt ratio, while according to Magri (2009) the effects of bothage and size are almost irrelevant in determining a firms financial lever-age. Heyman et al. (2008) found a negative relationship between the debtratio and size measured by total assets in logarithm terms, while Titmanand Wessels (1988), emphasising the role of debt maturity, found that therelationship between size and a measure of short-term debt is negative, thusimplying that small firms tend to use significantly more short-term financingwith respect to large firms.

In fact, a negative relationship between LEVERAGE and a measure of afirms reputation is explained on theoretical grounds by the possibly higheragency costs of equity (and long-term debt) with respect the cost of (short-term) debt which may characterise firms with less reputation (Smith andWarner, 1979; Pettit and Singer, 1985). Thus, in accordance with thesepredictions regarding the determinants of debt maturity, we would expectthat small and less established firms prefer to borrow in the short termrather than issuing new equity or even long-term debt. Unfortunately, ourdata do not allow us to determine the maturity structure of a firms debt, sowe cannot provide evidence on this particular aspect of the empirical debate.

The effect of firm SIZE is negative and significant, when using the spec-

ification in model 2, although the size of the coefficient remains quite small.A possible negative relationship between LEVERAGE and factors which areconnected with a firms reputation may be verified on empirical grounds. Ithas been argued that problems connected with a firms possible default maybe reduced by borrowing short-term (Diamond, 1991). Moreover, in an in-dustrial system based on strong relationships with banks, as in Italy, it may

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be easier for a firm with a moderate reputation8 to renegotiate its debt than

in a system based on non-bank lenders (Hoshi et al., 1990).Although, given the nature of our data, a direct investigation is not

possible, we think that the potential role of short-term bank borrowing couldexplain the negative relationship observed between debt ratio and SIZE (orAGE) in our panel of firms, thus supporting the view that smaller or youngerfirms are likely to be more reliant on (short-term) debt.

Giving the relevance of this aspect, we have also tried to further investi-gate the role of firm size by dividing our sample into three size classes9. Thesignificance of the split has been tested for by computing the Chow test onthe three groups separately.

The most important result observed for the split sample is the effect

of innovation on leverage: having introduced innovation negatively affectsa firms debt ratio in the group of small firms but not in the other twogroups, where the effect of past innovation on the debt ratio is not significant.It could be useful to analyse this result in association with the effect ofour indicator of credit accessibility (LENDING GR). In general, the effecton LEVERAGE is positive, indicating that the more favourable the creditsupply, the higher the level of external financing. However, this positiverelationship is not verified for small firms, thus indicating that changes inlending conditions do not much affect their financial structure. These resultsseem to indicate possible difficulties in accessing external financing for smallinnovative firms, which are more likely to rely to a greater extent on internal

finance even when credit accessibility is wider.Some differences can also be observed between medium and large firms.

For example, the impact of credit accessibility on LEVERAGE is significantin the group of medium firms, whereas it is not significant for the largefirms. This difference in behaviour is possibly explained on the groundsthat large firms take advantage of a preferred relationship with the (bank)system compared to the medium firms. In fact our results show that thefinancial structure of large industrial firms is not sensitive to change in theshort-term availability of bank credit, whereas it is for medium firms.

8It is worth stressing the fact that our data set is based on a balanced panel of firmswhich have operated continuously in the industrial sector during our time span. Thischaracteristic makes it plausible to assume that our firms are, on average, of relativelygood quality.

9Small firms are those with no more than 50 employees; medium firms are those withbetween 50 and 500 employees, and large firms have more than 500 employees. Groupsare determined on the basis of the average size during the period 1996-2003. Results donot change when allowing the firms to enter and exit the three size classes.

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Other evidence which may corroborate the hypothesis of a preferred

relationship between large firms and the credit market is to be found in thedifferent role played a firms reputation as proxied by variable AGE: havinga long history does not matter in explaining a firms leverage for largefirms, whereas it does for small and medium firms. However, the sign ofthe relationship is negative, thus indicating the possible higher relevance ofshort-term financing for less-established firms within the groups of small andmedium firms10.

Finally, it is worth noting the increase in the explanatory power whenmoving from model 1 to model 2, where regional and industrial dummies areincluded. The effect of localisation on a firms leverage is as we expected:firms located in the southern regions are generally less leveraged with respect

to their counterparts in the central-northern regions. It is worth noting thatthe dummy associated with the northwest regions shows a negative coeffi-cient with respect to the northeast, which is the reference dummy. This couldbe due to the high dynamism which has characterised the north-eastern re-gions during our time span, and which may have determined favourableaccess to external financing for those firms in the northeast. The situationof favourable growth in north-eastern industry may explain the higher debtratio of the firms in our panel compared to the northwest of Italy.

4.2 Further investigation: long-run patterns

In this section we provide further evidence confirming the robustness ofthe previous results. We estimate an additional model specification based ontime averages of our key variables (Table 7). Although we are aware that ourtime horizon is relatively short (and, thus, any conclusion about the long-run behaviour of the observed relationships must be derived with caution),the intuitive meaning of this additional test is that when the estimatedrelationships can be confirmed by using a time averaged specification, wecan reasonably assume that they should hold in the long run as well.

Furthermore, the use of a cross-sectional approach is particularly suitedto our purpose, as it allows us to recover additional information from theCommunity Innovation Survey which is not provided on a longitudinal basis

and thus cannot be used within a panel approach. Conversely, by using a10We achieve the same conclusion by using another specification (not reported) where

the variable SIZE is included in the set of regressions by size class, as an additional testfor the role of a firms reputation in explaining LEVERAGE. We found that the coefficientof SIZE is negative and significant for the groups of medium firms, whereas it is not forthe group of large firms.

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cross-sectional approach, we can plausibly consider some firm-specific vari-

ables which were collected with the CIS3 survey as additional determinantsof a firms leverage11. More specifically, we here estimate an OLS specifica-tion for a firms leverage which takes into consideration alternative measuresof its innovation. Thus, the following variables are in turn tested for:

- INN 1996: this is the usual dichotomous innovation variable referringto the beginning of our time span. It is intended to capture the effectof early successful innovation on the average debt ratio.

- PREV INN: this is a new dummy variable which assumes value onewhen the firm introduced at least one technological innovation in at

least four years of the entire period and zero otherwise. It is designedto capture the effect of frequent successful innovation.

- R&D CIS3: this is a dummy variable derived from the CIS3 survey.It is an input indicator of innovation and assumes value one if the firmperformed systematic R&D activities during the period 1998-2000 andzero otherwise. It is intended to capture the effect of non-occasionalR&D activities on a firms debt-ratio.

- INNSALES CIS3: this is a continuous measure of output innovation.It is given by the share of total turnover due to products introducedduring the period 1998-2000 which are new to the reference market.

A firms group membership (GP CIS3) is also added to the list of regres-sors, as it is reasonable to suppose that being part of an industrial groupmay generate synergies, both financial and operative, within the group, thusreducing the need for external financing.

In addition, the role of financial constraints to innovation in shaping afirms financial structure is another interesting aspect we want to test for.

As we have stressed previously, theories on corporate leverage do notfocus directly on innovation. In addition, the lack of a clear-cut definitionof financial constraints has made it difficult to find a broad measure of suchconstraints. The conventional way to investigate empirically whether fi-

nancing constraints matter for innovative activity is based on the standardmethodology for the identification of excessive investment sensitivity to cash

11In fact, it is worth recalling that information collected with the CIS3 survey refersto the three-year period 1998-2000, which falls in the middle of our panel. The implicithypothesis is that this additional information at the firm level can reasonably be assumedto be time invariant during our time span.

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flow variations. Within this tradition, a substantial body of empirical lit-

erature has demonstrated that small firms are more likely to be financiallyconstrained compared to larger firms (Hall, 2002; Ughetto, 2008). The hy-pothesis that small innovative firms are constrained in accessing external fi-nance seems to be supported even when more recent methodologies, adoptedas alternatives to the standard cash flow analysis, make use of innovationsurvey data (see, for example, Canepa and Stoneman (2007) who used datafrom the second and third UK CIS). However, there is a lack of empiricalevidence concerning Italian firms.

We have derived a qualitative variable intended to capture a firms sub-jective perception of the presence of financial constraints to innovation fromthe CIS3 questionnaire. Thus, a dichotomous variable (FIN CONST CIS3)

assuming value one when the firm considers relevant the presence of at leastone economic obstacle to technological innovation12 and zero otherwise isincluded in our OLS leverage specification.

The first relevant result is that a negative relationship between inno-vation and LEVERAGE is confirmed when using both the INN 1996 andthe PREV INN variables: in general firms which introduced a technologicalinnovation at the beginning of the period or on a frequent basis tend, onaverage, to use more internal finance. Results by size class confirm that in-novation is mainly financed with internal resources by small firms, whereasthis relationship is not found significant for the groups of medium and largefirms. Unfortunately, variable R&D CIS3, indicating whether the firm has

undertaken systematic R&D activity, does not give any useful indication;in fact it is highly insignificant. This result is however in part as expected,due to the well-known scarcity of Italian firms conducting R&D activity instructural laboratories (less than one quarter of our sample).

Another interesting result linking a firms innovation and leverage is thepositive impact of the variable INNSALES CIS3, measuring the share ofa firms total sales due to innovative products. This is a measure of theeconomic return following the introduction of a product innovation, as afirm is expected to show a positive value of this variable when an innovativeproduct has been successfully introduced for the first time on the referencemarket. In addition, this could also be considered an indirect measure of

the innovative effort connected with the commercialisation of a product in-novation, as the higher the share of total sales which can be related to newproducts, the more strategic a firms innovation activity should be and, in

12The list of obstacles to innovation includes: excessive perceived economic risks, in-novation costs too high and lack of source of finance.

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Table 7: Leverage equation: OLS estimations

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the same way, the higher the need for financial resources required to develop

an innovative project should also be. According to this interpretation, itis not surprising to note that the relationship with our leverage indicatoris positive, indicating that a higher impact in terms of revenues generatedfrom the development and introduction of a new product is likely to be as-sociated with the need for a higher level of external finance. This positiverelationship holds both for small and large firms, although the coefficientsize is higher for large firms, the latter also revealing a higher share of totalturnover due to innovative products. Unfortunately, the significance of therelationship is uncertain for the group of medium firms.

The positive association of INNSALES CIS3 with a firms leverage re-minds us of the positive association emerging when the innovative effort is

measured by using the share of intangible assets to total assets, which isa measure of input. This evidence, together with the confirmed negativerelationship between a firms profitability and its debt ratio, represent quitea strong confirmation of the possible non-linear relationship linking innova-tion to the external finance requirement and a further proof of the peckingorder hypothesis governing access to financial resources.

The inclusion of the dummy variable indicating whether a firm belongs toan industrial group, as an additional leverage determinant based on the CIS3survey, confirms our expectations, as the coefficient is negative, indicatingthat firms forming part of an industrial group use more internal resources,probably due to the positive synergies within the group generating greater

availability of self-financing. OLS regressions by firm size indicate that thenegative relationship between group membership and a firms debt ratiois statistically significant for the group of medium firms, whereas it is notverified for small and large firms.

Finally, the effect of financial constraints to innovation on leverage, asproxied by the FIN CONST CIS3 dummy, is analysed. In general, the ob-served positive relationship with a firms leverage is somewhat counterintu-itive; one would expect that in the presence of financial constraints a firm islikely to use more internal resources, thus suggesting a possible negative re-lationship. However, our empirical model has not been designed to providea specific test for the sensitivity of a firms leverage to financial constraints,

so the presence of a few inconsistencies cannot be considered conclusive. Infact, our dummy variable refers to the firms subjective perception of itsparticular financial position, so a positive coefficient indicates that perceiv-ing ones firm to be financially constrained is a consequence of being highlyleveraged and thus reflects a fragile financial structure as proxied by a highdebt ratio.

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Rather, the possible role of financial constraints to innovation is sug-

gested by a complementary interpretation of the regression results, withparticular reference to results by firm size. In fact:

i) while in general innovation is negatively related to a firms leverage(implying that the firm should use more internal resources than debt whenintroducing an innovation), results by firm size indicate that the coefficientof the INN variable is negative and significant only in the group of smallfirms (see the panel data model in Table 6 and the cross sectional model inTable 7). This result is confirmed even when we use a model specification(not shown here) including only variable INN as regressor. These results areconsistent with the view that financial constraints do not affect innovationper se, but only innovation carried out by small innovative firms as opposed

to medium and large firms.ii) with reference to our qualitative indicator of financial constraints to

innovation (FIN CONSTR CIS3), the results by firm size shown in Table7 (corresponding to the OLS specification) indicate that the coefficient ofthe financial constraints dummy is significantly positive for the medium andlarge firms, thus suggesting that for them a high level of indebtedness mayrepresent an obstacle to financing future innovative projects with externalfunds, whereas it is not significant for small firms, which are more likely torely on internal resources to finance innovation (as suggested by the negativeassociation between INN and the level of leverage). This result is confirmedeven when we replace the dichotomous INN variable in the leverage model

with a continuous measure of innovation output (INN SALES CIS3).We interpret these results as evidence of the fact that within the Ital-

ian manufacturing sector small innovative firms may face higher financialconstraints compared to medium and large firms.

iii) when focusing specifically on small firms, results show that the sensi-tivity of leverage to a firms profitability is higher in the group of innovativefirms with respect to non-innovative firms; indeed the coefficient of the ROSvariable is higher, in absolute value, for the innovative group, here definedas firms which have frequently innovated during our time span 13.

On the basis of this additional result one might expect that small innova-tive firms are probably more willing to rely on internal resources because of

the higher cost of capital associated with risky innovative activities, whichmay discourages them from relying on debt.

13A higher coefficient associated with the ROS variable in the group of small innovativewith respect the group of small non-innovative firms is also confirmed when using the paneldata specification corresponding to model 3) (not shown here for the sake of brevity).

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Table 8: Leverage equation: dynamic model

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restrictions is clearly unsatisfactory, thus casting doubts on the validity of

the instruments used for the difference equation. In the second model (model5), the standard system specification, which uses both lagged levels for thedifference equation and lagged differences for the level equation, is presented(Arellano and Bover, 1995; Blundell and Bond, 1998). Here the laggedleverage presents a much higher coefficient; however, the use of both thelagged levels of the debt ratio for the difference equation and the laggeddifferences for the level equation continues to be inappropriate, according tothe Sargan statistic. We suspect that the high persistency of our leverageindicator causes lagged levels of debt ratio to be poor instruments for thedifference equation. Thus we have tried to selectively reduce the number ofmoment conditions. In the adjustedsystem equation presented in model 6,

we use the second lag of both debt and total asset variables as instrumentsfor the difference equation and the differences of these variables in the levelequation. Both variables enter the leverage definition, the former beingthe numerator and the latter the denominator. This choice seem to beappropriate, as suggested by the higher p-values of the Sargan testassociatedwith this specification.

In general, in addition to strong persistence in the debt structure, ourestimations confirm the results based on the static specification. More specif-ically, a firms profitability exerts a negative and relevant impact on leveragedecisions even when past debt structure is taken into account. The effectof variable ROS is large and in line with the static specification. A firms

innovative intensity (INTANG TA) and interest burdens (IR) are confirmedas being significant determinants with the expected signs, even though thesize of the relative coefficients is much smaller than that based on the staticmodel.

When we move on to analysing the effect of past innovation, we ob-tain more controversial results. In our GMM estimations the coefficient oflagged INN is still negative, although not significant at conventional signif-icance levels. Nevertheless, the fixed effects model shows a negative andsignificant coefficient, thus supporting the view that more innovative firmstend to be more reliant on internal resources, possibly due to high uncer-tainty associated with innovative investments rendering external financing

more costly.The unsatisfactory results provided by the consistent estimations can

possibly be explained by the characteristics of our innovation variable, whichis dichotomous and therefore not an ideal proxy for a firms innovation effort.This problem may also be amplified by the strong explanatory power of thelagged dependant variable, which may dampen the direct effect of variable

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INN.

Our results, based on a dynamic specification, show that a firms leverageis not significantly affected by SIZE, thus confirming results based on ourstatic model: the estimated coefficient is generally either not statisticallysignificant or imprecisely estimated using a GMM system specification. Asfor a firms AGE, the effect is negative and significant, as shown by ourpreferred specifications in the GMM sys adj and the FE models.

5 Conclusions

We have investigated the determinants of a firms capital structure, pay-ing particular attention to the role played by the firms innovation and prof-itability. An administrative data source, derived from balance sheet informa-tion for a panel of 2,591 industrial firms operating in Italy during the period1996-2003, has been explored and a measure of a firms leverage selected,on the basis of which we have been able to assess the relative importance ofexternal funding (debt) with respect to internal funding as a share of totalassets.

The role of a firms adoption of innovation has been examined by usingboth a qualitative indicator, an output measure derived from the Com-munity Innovation Survey indicating whether a firm introduced or did notintroduce technological innovation during our relevant time span, and an

input measure, given by the book value of intangible assets as a propor-tion of a firms total asset value. As the dynamic relationships between ourfocus variables may be crucially affected by simultaneity patterns, we firstperformed a causality analysis based on the Granger-Causality approach.Results support the view that a firms leverage is caused by both innovationand profitability, while no causal effect is registered as running from a firmsleverage to either profitability or to our measure of innovation output.

We then concentrated on the analysis of an econometric model of a firmsleverage. Different specifications, both static and dynamic, have been testedfor, in order to estimate short- and long-run effects.

Overall our results indicate that Myers and Majlufs pecking order mech-

anism dominates access to financial resources. In particular:i) More profitable firms tend to use internal finance more, as implied bythe negative relationship linking a firms debt ratio and return on sales. Therole of a firms profitability in reducing the need for external finance char-acterises all firms, regardless of size as measured by employment, althoughlarge firms show a lower sensitivity of leverage to profit variations. This re-

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lationship is robust to different model specifications; coefficients associated

with the ROS variable are large and significant even when lagged leverage istaken into account in our dynamic specification, and this is also confirmed inthe long run, as suggested by our OLS specification based on time averages.

ii) The need for external finance increases with the innovative effort, asis shown by the fact that in all the econometric specifications, both staticand dynamic, a positive relationship is found between alternative proxies forinnovation intensity, based on measures of both innovative input (the shareof intangible assets to total assets, derived from accounting information)and innovative output (the share of a firms sales due to the introductionof a product innovation, derived from CIS data). This result, which holdsregardless of a firms size, indicates that when internal resources are not

sufficient to cover large innovative projects, debt financing is required.Unfortunately, given the characteristics of our data set, we cannot test

for other important implications of the pecking order theory regarding inparticular the trade-off between different sources of finance according to thematurity structure of debt and the possibility of issuing equity finance.

Our results also show that credit rationing issues do not seem to prevailin association with innovation per se. However, credit constraints could rep-resent a serious problem for small innovative firms, which are more likely torely on internal funds rather than debt. This relationship has been tested forby using alternative proxies capturing different timings of innovation. Giventhe relevance of internal resources for small innovative firms, we have also

shown that financial factors perceived as constraints to innovation invest-ments do not seem to affect financial structure, while they do in the groupof medium and large firms. Unfortunately, we do not have any elements todetermine whether and to what extent the presence of credit rationing mayactually constrain the size of the innovative effort in small firms. Our resultsalso show that for small innovative firms, access to debt financing could bemore difficult for small firms which are not engaged in innovation, given thehigher sensitivity of leverage to operating profitability. All these elementsseem to indicate an unfavourable situation for small innovative firms whenaccessing debt financing.

In addition, our empirical investigation provides a comprehensive de-

scription of the role played by structural factors such as sectoral character-istics and localization in affecting a firms capital structure. Evidence doesnot directly relate to a firms hierarchical selection of financial alternativesas suggested by the pecking order theory, but does give us a way to accountfor the most basic stylized facts characterizing the Italian industrial sector.

The econometric investigation at the firm level shows that capital struc-

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ture is highly persistent over time. Results from our dynamic specification

show that lagged leverage captures a consistent part of the model variability,with a large and significant coefficient in our preferred consistent specifica-tion (GMM sys adj).

The relationship between alternative measures of a firms reputation andleverage is negative due to the characteristics of our credit market, mainlydominated by the traditional banking system (limited venture capital); wesuspect that this relationship actually captures the role of short-term bor-rowing, which is less costly with respect to long-term debt or equity and isthus more likely to be used by smaller and younger firms with respect tomore established firms. This evidence, which is robust to different modelspecifications, also seems to hold in the long run, as suggested by the results

based on our cross sectional specification.Finally, we have found evidence that regional gaps significantly affect

a firms financial decisions, in that firms located in the northern regionsshow a debt ratio which is higher, on average, with respect to those in thecentre and south. Positive differentials also characterise the leverage of firmslocated in the northeast compared to the northwest.

Acknowledgements

A special thanks to Paul Stoneman for helpful comments on a earlier draft of thispaper. I would also like to thank participants at the DRUID Summer Conference2010, Imperial College Business School, London, at the Trans-Atlantic DoctoralConference 2010, London Business School and two anonymous referees for theirfruitful remarks and suggestions. Finally, Id like to acknowledge the support byGiulio Perani, director of the Innovation and R&D Statistic Unit and CaterinaViviano, director of the methodological unit at the division for statistical registersand by the Directorate of Regional Offices of the National Institute of Statistics forproviding access to the data set.

References

[1] Aghion, P., Bond, S., Klemm, A., and Marinescu, I. (2004), Technology andFinancial Structure: are Innovative Firms different?, Journal of European

Economic Association, 2, 277-288.[2] Anderson, T.W. and Hsiao, C. (1982),Formulation and Estimation of Dynamic

Models using Panel Data, Journal of Econometrics, 18, 47-82.

[3] Arellano, M., and Bover, O. (1995), Another look at the instrumental variableestimation of error-components models, Journal of Econometrics, 68, 29-51.

35


36/40

[4] Bartoloni, E., and Baussola, M. (2001),The Determinants of Technology Adop-

tion in Italian Manufacturing Industries, Review of Industrial Organization,19, 305-328.

[5] Benfratello, L., Schiantarelli, F., and Sembenelli A. (2010), Banks and in-novation: Microeconometric evidence on Italian firms, Journal of FinancialEconomics, 90, 197-202.

[6] Blundell, R., and Bond, S. (1998), Initial conditions and moment restrictionsin dynamic panel-data models, Journal of Econometrics, 87, 115-143.

[7] Bond, S. (2002),Dynamic Panel Data Models: A Guide to Micro Data Methodsand Practice, Cemmap Working Paper Serie n.CWP09/02, Institute for FiscalStudies, London.

[8] Bottazzi, L., and De Rin, M. (2002), Venture Capital in Europe and the Fi-nancing of Innovative Companies, Economic Policy, 17, April, 231-269.

[9] Bradley, M., Jarrell, G., and Kim, E.H. (1984),On the Existence of an OptimalCapital Structure: Theory and Evidence, Journal of Finance, 39, 857-878.

[10] Canepa, A, and Stoneman, P. (2008), Financial constraints to innovation inthe UK: evidence from CIS2 and CIS3, Oxford Economic Papers, 60, 711-730.

[11] Colombo, M.G., and Grilli, L. (2007),Funding Gaps? Access To Bank LoansBy High-Tech Start-Ups, Small Business Economics, 29, 25-46.

[12] Diamond, D.W. (1991),Debt Maturity Structure and Liquidity Risk, The Quar-terly Journal of Economics 106, 709-737.

[13] Fazzari, S.M., Hubbard, R. G., and Petersen, B.C. (1988), Financing Con-straints and Corporate Investment, Brookings Papers on Economic Activity,1, 141-205.

[14] Geronikolaou, G., and Papachristou G. (2008), Venture Capital andInnovation in Europe, Working Paper, University of Thessaloniki,http://ssrn.com/abstract=1309186.

[15] Giudici, G., and Paleari, S. (2000),The Provision of Finance to Innovation: ASurvey Conducted among Italian Technology-based Small Firms, Small Busi-ness Economics, 14, 37-53.

[16] Granger, C.W. (1969), Investigating causal relations by econometric modelsand cross-spectral methods, Econometrica 37,3, 424-38.

[17] Hall, B. (2002),The financing of research and development, Oxford Review ofEconomic Policy, 18, 35-51.

[18] Harris, M., and Raviv, A. (1991),The Theory of Capital Structure, The Journalof Finance, 46, 297-355.

36


37/40

[19] Heckman, J. (1981), The incidental parameters problem and the problem of

initial conditions in estimating a discrete time-discrete data stochastic process,in: Manski, C.F., McFadden, D. (eds.), Structural Analysis of Discrete Datawith Econometric Applications, MIT Press, Cambridge, USA. 179-195.

[20] Heyman, D., Deloof, M. and Ooghe, H. (2008) The Financial Structure ofPrivate Held Belgian Firms, Small Business Economics, 30, 301-313.

[21] Holtz-Eakin, D., Newey, W., and Rosen, H., (1988), Estimating Vector Au-toregressions with Panel Data, Econometrica 56, 1371-95.

[22] Hoshi, T., Kashyap, A, and Scharfstein, D. (1990),The role of banks in reduc-ing the costs of financial distress in Japan, Journal of Financial Economics,27, 67-88.

[23] Hovakimian, A., Opler T., and Titman S. (2001), The Debt-equity Choice,Journal of Financial and Quantitative Analysis, 36, 1-24.

[24] Hubbard, R.G. (1998), Capital market imperfections and investment, Journalof Economic Literature, 36, 193-225.

[25] Kazemi, I., and Crouchley, R. (2006), Modelling the Initial Conditions in Dy-namic Regression Models of Panel Data with Random Effects, in: Baltagi B.H. (Ed.), Panel Data Econometrics, Chapter 4, Elsevier, 91-117.

[26] Karshenas M., and Stoneman, P. (1993),Rank, Stock, Order and Epidemic Ef-fects in the Diffusion of New Process Technology, Rand Journal of Economics,24, 4, 503-9.

[27] Magri, S. (2009), The financing of small innovative firms: the Italian case,

Economics of Innovation and New Technology, 18, 2, 181-204.

[28] Myers, S.C., and Majluf N.S. (1984), Corporate financing and investment de-cisions when firms have information that investors do not have, Journal ofFinancial Economics, 13, 187-221.

[29] Nerlove, M., and Balestra, P., (1996), Formulation and Estimation of Econo-metric Models for Panel Data, in: Matyas L., and Sevestre, P. (Eds.), TheEconometrics of Panel Data: A Handbook of the Theory with Applications,Dordrecht, The Netherlands: Kluwer, 3-22.

[30] Pettit, R. and Singer R. (1985), Small Business Finance: a Research Agenda,Financial Management, Autumn, 14,3,47-60.

[31] Rubin, D.B. (1987),Multiple Imputation for Nonresponse in Surveys, J. Wiley& Sons, New York.

[32] Rubin, D.B. (1996),Multiple imputation after 18+ years, Journal of the Amer-ican Statistical Association, 91, 473-489.

[33] Schafer, J.L. (1997), Analysis of Incomplete Multivariate Data, Chapman &Hall, London.

37


38/40

[34] Sims, C. (1972), Money, Income and Causality, American Economic Review

62, 540-52.

[35] Schafer, D., Werwatz, A., and Zimmermann, V. (2004), The Determinants ofDebt and (Private) Equity Financing: The Case of Young, Innovative SMEsfrom Germany, Industry and Innovation, 11,3, 225-248.

[36] Smith, C.W., and Warner, J.B. (1979), On financial contracting: an analysisof bond covenants, Journal of Financial Economics, 7, 117-161.

[37] Stewart, M.B. (2007),The inter-related dynamics of unemployment and low-wage employment, Journal of Applied Econometrics, 22, 511-531.

[38] Titman, S., and Wessels, R. (1988), The determinants of capital structurechoice, Journal of Finance, 43, 1-19.

[39] Ueda, M., and Hirukawa, M. (2003), Venture Capital andproductivity, Working Paper, University of Wisconsin, USA.http://www.math.niu.edu/ hirukawa/papers/VCAP4.pdf

[40] Ughetto, E. (2008),Does internal finance matter for R&D? New evidence froma panel of Italian firms, Cambridge Journal of Economics, 32, 907-925.

[41] Wooldridge, J. (2005), Simple solutions to the initial conditions problem indynamic nonlinear panel data models with unobserved heterogeneity, Journalof Applied Econometrics, 20,1, 39-54.

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A CIS samples of respondents and final panel of

firms: descriptive statistics

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B Variables definition and summary statistics

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Capital Structure and Innovation - Causality and Determinants

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