Capital Struture Equity Ownership and Firm Performence

Journal of Banking & Finance 34 (2010) 621–632

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Journal of Banking & Finance

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Capital structure, equity ownership and firm performance

Dimitris Margaritis a,*, Maria Psillaki b

a Department of Finance, Faculty of Business, AUT, Auckland 1020, New Zealandb Department of Economics, University of Piraeus, Piraeus 18534, Greece

a r t i c l e i n f o a b s t r a c t

Article history:Received 24 April 2008Accepted 28 August 2009Available online 1 September 2009

JEL classification:D24G32

Keywords:Capital structureAgency costsFirm efficiencyOwnership structureDEA

0378-4266/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.jbankfin.2009.08.023

* Corresponding author. Tel.: +64 9 921 9999; fax:E-mail addresses: [email protected] (D. Marg

Psillaki).

This paper investigates the relationship between capital structure, ownership structure and firm perfor-mance using a sample of French manufacturing firms. We employ non-parametric data envelopmentanalysis (DEA) methods to empirically construct the industry’s ‘best practice’ frontier and measure firmefficiency as the distance from that frontier. Using these performance measures we examine if more effi-cient firms choose more or less debt in their capital structure. We summarize the contrasting effects ofefficiency on capital structure in terms of two competing hypotheses: the efficiency-risk and franchise-value hypotheses. Using quantile regressions we test the effect of efficiency on leverage and thus theempirical validity of the two competing hypotheses across different capital structure choices. We alsotest the direct relationship from leverage to efficiency stipulated by the Jensen and Meckling (1976)agency cost model. Throughout this analysis we consider the role of ownership structure and type on cap-ital structure and firm performance.

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1. Introduction

This paper uses an estimate of a firm’s production efficiency toassess the role of leverage in addressing agency conflicts within afirm. More specifically, we first assess the direct effect of leverageon firm performance as stipulated by the Jensen and Meckling(1976) agency cost model. Second, we investigate if firm efficiencyhas an effect on capital structure and whether this effect is similaror not across different capital structure choices. Throughout theseanalyses we consider explicitly the role of equity ownership onboth capital structure and firm performance.

Corporate financing decisions are quite complex processes andexisting theories can at best explain only certain facets of thediversity and complexity of financing choices. By demonstratinghow competing hypotheses may dominate each other at differentsegments of the relevant data distribution we reconcile some ofthe empirical irregularities reported in prior studies thereby cau-tioning the standard practice of drawing inferences on capitalstructure choices based on conditional mean estimates. By usingproductive efficiency as opposed to financial performance indica-tors as our measure of (inverse) agency costs we are able to carry

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+64 9 921 9940.aritis), [email protected] (M.

out tests of the agency theory that are not confounded by factorsthat may not be related to agency costs.

Our methodological approach is underpinned by Leibenstein(1966) who showed how different principal-agent objectives, inad-equate motivation and incomplete contracts become sources of(technical) inefficiency measured by the discrepancy betweenmaximum potential output and the firm’s actual output. He termedthis failure to attain the production or technological frontier as X-inefficiency. Based on this we model technology and measure per-formance by employing a directional distance function approachand interpret the technological frontier as a benchmark for eachfirm’s performance that would be realized if agency costs wereminimized.1 We then proceed to assess the extent to which leverageacts as a disciplinary device in mitigating the agency costs of outsideownership and thereby contributes to an improvement on firm per-formance. To properly assess the disciplinary role of leverage inagency conflicts we control for the effect of ownership structureand ownership type on firm performance. We also allow for the pos-sibility that at high levels of leverage the agency costs of outside

1 As we explain in Section 3, the directional distance function gives the maximumproportional expansion of output(s) and contraction of inputs that is feasible for agiven technology thereby yielding a measure of firm efficiency relative to bestpractice. The directional distance function has a dual association with the profitfunction and thus it provides a useful performance companion when profitability isthe overall goal of the firm.

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622 D. Margaritis, M. Psillaki / Journal of Banking & Finance 34 (2010) 621–632

debt may overcome those of outside equity whereby further in-creases in debt can lead to an increase in total agency costs.

We turn next to analyze the effects of efficiency on capitalstructure using two competing hypotheses. Under the efficiency-risk hypothesis, more efficient firms may choose higher debt toequity ratios because higher efficiency reduces the expected costsof bankruptcy and financial distress. On the other hand, under thefranchise-value hypothesis, more efficient firms may choose lowerdebt to equity ratios to protect the economic rents derived fromhigher efficiency from the possibility of liquidation (Demsetz,1973; Berger and Bonaccorsi di Patti, 2006).

Thus our paper contributes to the literature in four directions:(1) using X-inefficiency as opposed to financial indicators as ameasure of firm performance to test the predictions of the agencycost hypothesis; (2) showing that X-inefficiency as a proxy for(inverse) agency costs is an important determinant of capital struc-ture choices; (3) demonstrating how competing hypotheses maydominate each other at different segments of the leverage distribu-tion; and (4) providing new empirical evidence on the relationshipbetween ownership structure, capital structure and firm efficiency.2

This is to our knowledge one of the first studies to consider theassociation between productive efficiency, ownership structureand leverage. In a recent study Berger and Bonaccorsi di Patti(2006) examined the bi-directional relationship between capitalstructure and firm performance for the US banking industry usinga parametric measure of profit efficiency as an indicator of (in-verse) agency costs while Margaritis and Psillaki (2007) investi-gated a similar relationship for a sample of New Zealand smalland medium sized enterprises using a technical efficiency measurederived from a non-parametric Shephard (1970) distance function.In this paper we use a directional distance function approach on asample of French firms from three different manufacturing indus-tries to address the following questions3: Does higher leverage leadto better firm performance? Would different ownership structureshave an effect on firm performance? Does efficiency exert a signifi-cant effect on leverage over and above that of traditional financialmeasures? Are the effects of efficiency and the other determinantsof corporate financing decisions similar across different capitalstructures? To what extent our results are driven by certain typesof owners – e.g., family vs. non-family firms?

The reminder of the paper is organized as follows. The next sec-tion discusses the relationship between firm performance, capitaland ownership structure. Section 3 outlines the methodology usedin this study to construct measures of firm’s efficiency. Section 4 de-scribes the empirical model used to analyze the relationship be-tween efficiency, leverage and ownership. Section 5 describes thedata and reports the empirical results. Section 6 concludes the paper.

2. Firm performance, capital structure and ownership

Conflicts of interest between owners-managers and outsideshareholders as well as those between controlling and minority

2 Most studies focus on analyzing the financial structure-performance relationshipfor large firms in the US and UK. These findings may not be representative forcountries with different legal and institutional settings (see La Porta et al., 1998).There is relatively little evidence for Continental Europe where the legal environmentand the nature of ownership and control are different, ownership concentration ishigher and family ownership and control are more dominant compared to US/UK (seeFaccio and Lang, 2002).

3 Civil law systems provide less investor and creditor protection than common lawsystems and among the civil-law systems the French system provides the leastprotection (see La Porta et al., 1998). In addition, de Jong et al. (2008) report thatcreditor right protection has a significant effect on capital structure. As legalstructures with little investor and creditor protection tend to exacerbate informationasymmetries and contracting costs, a study focusing on French firms presents someinteresting features for the purposes of our investigation.

shareholders lie at the heart of the corporate governance literature(Berle and Means, 1932; Jensen and Meckling, 1976; Shleifer andVishny, 1986). While there is a relatively large literature on the ef-fects of ownership on firm performance (see for example, Morcket al., 1988; McConnell and Servaes, 1990; Himmelberg et al.,1999), the relationship between ownership structure and capitalstructure remains largely unexplored.4 On the other hand, avoluminous literature is devoted to capital structure and its effectson corporate performance – see the surveys by Harris and Raviv(1991) and Myers (2001). An emerging consensus that comes out ofthe corporate governance literature (see Mahrt-Smith, 2005) is thatthe interactions between capital structure and ownership structureimpact on firm values. Yet theoretical arguments alone cannotunequivocally predict these relationships (see Morck et al., 1988)and the empirical evidence that we have often appears to be contra-dictory. In part these conflicting results arise from difficulties empir-ical researchers face in obtaining direct measures of the magnitude ofagency costs that are not confounded by factors that are beyond thecontrol of management (Berger and Bonaccorsi di Patti, 2006). Inthe remainder of this section we briefly review the literature in thisarea focusing on the main hypotheses of interest for this study.

2.1. Firm performance and capital structure

The agency cost theory is premised on the idea that the inter-ests of the company’s managers and its shareholders are not per-fectly aligned. In their seminal paper Jensen and Meckling (1976)emphasized the importance of the agency costs of equity arisingfrom the separation of ownership and control of firms wherebymanagers tend to maximize their own utility rather than the va-lue of the firm. These conflicts may occur in situations wheremanagers have incentives to take excessive risks as part of riskshifting investment strategies. This leads us to Jensen’s (1986)‘‘free cash flow theory” where as stated by Jensen (1986, p. 323)‘‘the problem is how to motivate managers to disgorge the cashrather than investing it below the cost of capital or wasting iton organizational inefficiencies.” Thus high debt ratios may beused as a disciplinary device to reduce managerial cash flowwaste through the threat of liquidation (Grossman and Hart,1982) or through pressure to generate cash flows to service debt(Jensen, 1986). In these situations, debt will have a positive effecton the value of the firm.

Agency costs can also exist from conflicts between debt andequity investors. These conflicts arise when there is a risk of de-fault. The risk of default may create what Myers (1977) referredto as an ‘‘underinvestment” or ‘‘debt overhang” problem. In thiscase, debt will have a negative effect on the value of the firm.Building on Myers (1977) and Jensen (1986), Stulz (1990) developsa model in which debt financing is shown to mitigate overinvest-ment problems but aggravate the underinvestment problem. Themodel predicts that debt can have both a positive and a negativeeffect on firm performance and presumably both effects are pres-ent in all firms.5 We allow for the presence of both effects in theempirical specification of the agency cost model.

However we expect the impact of leverage to be negative over-all. We summarize this in terms of our first testable hypothesis.

4 Recent international studies in this area include Brailsford et al. (2002) forAustralian firms, Short et al. (2002) for UK firms, and King and Santor (2008) forCanadian firms.

5 A common element in the models of Myers, Jensen and Stulz is the focus on thelink between the firm’s investment opportunity set and the effects of debt on thevalue of the firm. Thus a reasonable conjecture will be that for firms with few growthopportunities the positive effect of debt on firm performance will be more dominantwhereas the opposite effect will apply for firms with high growth opportunities (seeMcConnell and Servaes, 1995).

6 McConnell and Servaes (1995) indicate that the relation between ownershipstructure and firm performance may differ between low- and high-growth firms.Their conjecture is that ownership is likely to be more important for low-growth thanfor high-growth firms.

D. Margaritis, M. Psillaki / Journal of Banking & Finance 34 (2010) 621–632 623

According to the agency cost hypothesis (H1) higher leverage is ex-pected to lower agency costs, reduce inefficiency and thereby leadto an improvement in firm’s performance.

2.2. Reverse causality from firm performance to capital structure

But firm performance may also affect the choice of capital struc-ture. Berger and Bonaccorsi di Patti (2006) stipulate that more effi-cient firms are more likely to earn a higher return for a givencapital structure, and that higher returns can act as a buffer againstportfolio risk so that more efficient firms are in a better position tosubstitute equity for debt in their capital structure. Hence underthe efficiency-risk hypothesis (H2), more efficient firms choose high-er leverage ratios because higher efficiency is expected to lowerthe costs of bankruptcy and financial distress. In essence, the effi-ciency-risk hypothesis is a spin-off of the trade-off theory of capitalstructure whereby differences in efficiency, all else equal, enablefirms to fine tune their optimal capital structure.

It is also possible that firms which expect to sustain high effi-ciency rates into the future will choose lower debt to equity ratiosin an attempt to guard the economic rents or franchise value gen-erated by these efficiencies from the threat of liquidation (seeDemsetz, 1973; Berger and Bonaccorsi di Patti, 2006). Thus in addi-tion to a equity for debt substitution effect, the relationship be-tween efficiency and capital structure may also be characterizedby the presence of an income effect. Under the franchise-valuehypothesis (H2a) more efficient firms tend to hold extra equity cap-ital and therefore, all else equal, choose lower leverage ratios toprotect their future income or franchise value.

Thus the efficiency-risk hypothesis (H2) and the franchise-valuehypothesis (H2a) yield opposite predictions regarding the likelyeffects of firm efficiency on the choice of capital structure. Althoughwe cannot identify the separate substitution and income effectsour empirical analysis is able to determine which effect dominatesthe other across the spectrum of different capital structure choices.

2.3. Ownership structure and the agency costs of debt and equity

The relationship between ownership structure and firm perfor-mance dates back to Berle and Means (1932) who argued thatwidely held corporations in the US, in which ownership of capitalis dispersed among small shareholders and control is concen-trated in the hands of insiders tend to underperform. Followingfrom this, Jensen and Meckling (1976) develop more formallythe classical owner-manager agency problem. They advocate thatmanagerial share-ownership may reduce managerial incentives toconsume perquisites, expropriate shareholders’ wealth or to en-gage in other sub-optimal activities and thus helps in aligningthe interests of managers and shareholders which in turn lowersagency costs. Along similar lines, Shleifer and Vishny (1986) showthat large external equity holders can mitigate agency conflictsbecause of their strong incentives to monitor and disciplinemanagement.

In contrast Demsetz (1983) and Fama and Jensen (1983) pointout that a rise in insider share-ownership stakes may also be asso-ciated with adverse ‘entrenchment’ effects that can lead to an in-crease in managerial opportunism at the expense of outsideinvestors. Whether firm value would be maximized in the presenceof large controlling shareholders depends on the entrenchment ef-fect (Claessens et al., 2002; Villalonga and Amit, 2006; Dow andMcGuire, 2009). Several studies document either a direct (e.g.,Shleifer and Vishny, 1986; Claessens et al., 2002; Hu and Zhou,2008) or a non-monotonic (e.g., Morck et al., 1988; McConnelland Servaes, 1995; Davies et al., 2005) relationship between own-ership structure and firm performance while others (e.g., Demsetzand Lehn, 1985; Himmelberg et al., 1999; Demsetz and Villalonga,

2001) find no relation between ownership concentration and firmperformance.6

Family firms are a special class of large shareholders with un-ique incentive structures. For example, concerns over family andbusiness reputation and firm survival would tend to mitigate theagency costs of outside debt and outside equity (Demsetz andLehn, 1985; Anderson et al., 2003) although controlling familyshareholders may still expropriate minority shareholders (Claes-sens et al., 2002; Villalonga and Amit, 2006). Several studies (e.g.,Anderson and Reeb, 2003a; Villalonga and Amit, 2006; Maury,2006; King and Santor, 2008) report that family firms especiallythose with large personal owners tend to outperform non-familyfirms. In addition, the empirical findings of Maury (2006) suggestthat large controlling family ownership in Western Europe appearsto benefit rather than harm minority shareholders. Thus we expectthat the net effect of family ownership on firm performance will bepositive.

Large institutional investors may not, on the other hand, haveincentives to monitor management (Villalonga and Amit, 2006)and they may even coerce with management (McConnell and Serv-aes, 1990; Claessens et al., 2002; Cornett et al., 2007). In addition,Shleifer and Vishny (1986) and La Porta et al. (2002) argue thatequity concentration is more likely to have a positive effect on firmperformance in situations where control by large equity holdersmay act as a substitute for legal protection in countries with weakinvestor protection and less developed capital markets where theyalso classify Continental Europe.

We summarize the contrasting ownership effects of incentivealignment and entrenchment on firm performance in terms of twocompeting hypotheses. Under the ‘convergence-of interest hypothe-sis’ (H3) more concentrated ownership should have a positive ef-fect on firm performance. And under the ownership entrenchmenthypothesis (H3a) the effect of ownership concentration on firm per-formance is expected to be negative.

The presence of ownership entrenchment and incentive align-ment effects also has implications for the firm’s capital structurechoice. We assess these effects empirically. As external blockhold-ers have strong incentives to reduce managerial opportunism theymay prefer to use debt as a governance mechanism to controlmanagement’s consumption of perquisites (Grossman and Hart,1982). In that case firms with large external blockholdings arelikely to have higher debt ratios at least up to the point wherethe risk of bankruptcy may induce them to lower debt. Familyfirms may also use higher debt levels to the extent that they areperceived to be less risky by debtholders (Anderson et al., 2003).On the other hand the relation between leverage and insidershare-ownership may be negative in situations where managerialblockholders choose lower debt to protect their non-diversifiablehuman capital and wealth invested in the firm (Friend and Lang,1988). Brailsford et al. (2002) report a non-linear relationship be-tween managerial share-ownership and leverage. At low levels ofmanagerial ownership, agency conflicts necessitate the use ofmore debt but as managers become entrenched at high levels ofmanagerial ownership they seek to reduce their risks and theyuse less debt. Anderson and Reeb (2003b) find that insider owner-ship by managers or families has no effect on leverage while Kingand Santor (2008) report that both family firms and firms con-trolled by financial institutions carry more debt in their capitalstructure.

8 In the empirical part of the paper efficiency is measured as 1=ð1þ DT Þwhere DT isthe value of the directional distance function (see the Appendix). This has theadvantage of restricting the efficiency measures in the 0–1 range and hence facilitates


3. Benchmarking firm performance

In this section we explain how we benchmark firm perfor-mance. More technical details are given in the appendix. We relyon duality theory and the use of distance functions. Directional dis-tance functions are alternative representations of production tech-nology which readily model multiple input and multiple outputtechnological relationships. They measure the maximum propor-tional expansion in outputs and contraction in inputs that firmswould be able to achieve by eliminating all technical inefficiencyrelative to the performance of their best performing peers. Theyare the primal measures; their dual measures are the more familiarvalue functions such as profit, cost and revenue. We interpret theseinefficiencies to be the result of contracting costs, managerial slackor oversight. They differ from allocative inefficiencies which aredue to the choice of a non-optimal mix of inputs and outputs.7

We estimate the production technology and derive firm effi-ciency measures using data envelopment analysis (DEA) which isa non-parametric technique that employs linear programmingmethods to construct a piecewise linear representation of the fron-tier technology. DEA is a deterministic enveloping technique andhence it is not subject to standard econometric problems. Thereare no regression parameters to estimate, there is no functionalform, no error term and no distinction between dependent andindependent variables. Hence there are no endogeneity problemsto handle. In addition, by modelling technology using a directionaldistance function we allow firms to optimize in both input and out-put directions rather than in a single direction as for example is thecase with the more traditional Shephard type distance functions.

DEA is purely a data driven technique. It operates by fitting thetightest cone enveloping the data. At each segment of the piece-wise frontier it identifies a peer group of efficient firms for eachindividual firm being evaluated. As it makes no accommodationfor noise it envelopes the data set fully rather than ‘nearly’ theway for example a stochastic frontier does (see Fried et al.,2008). While it may be more sensitive to the presence of outliers,DEA has several advantages over parametric stochastic frontiermethods; e.g., it avoids functional form misspecification problemsand the effects of endogeneity or incorrect error term distributionassumptions that can impact significantly on efficiency measures.More importantly, in our context DEA provides efficiency measures(performance index numbers) that are not subject to the endoge-neity bias problems that arise when stochastic frontier estimatesof firm inefficiency are used as explanatory variables in secondstage regressions.

A firm’s ability to achieve best practice relative to its peers willbe compromised in situations where it is forced to forego valuableinvestment opportunities, participate in uneconomic activities thatsustain growth at the expense of profitability or being subject toother organizational inefficiencies. Following Leibenstein (1966)we use technical or X-inefficiency as a proxy for the (inverse)agency costs arising from conflicts between debt holders and equi-ty holders or from different principal-agent objectives. These con-flicts will give rise to resource misallocations and potential outputwill be sacrificed. The magnitude of agency costs will vary fromfirm to firm (see Jensen and Meckling, 1976) and thus individualfirms with similar technologies can be benchmarked against theirbest performing peers. As in Berger and Bonaccorsi di Patti(2006) we view these best practice firms as those which minimizethe agency costs of outside equity and outside debt.

7 Duality theory provides the basis for the decomposition of profit efficiency intoallocative efficiency and the technical efficiency component captured by thedirectional distance function and described in terms of input and output quantities(see Färe et al., 2008). However, we cannot measure profit efficiency since we do nothave data on input and output prices.

4. The empirical model

We use a two equation cross-section model to test the agencycost hypotheses (H1) and (H3/H3a) and the reverse causalityhypotheses (H2 and H2a).

4.1. Firm performance

The regression equation for the firm performance model is gi-ven by:

EFFi;t ¼ a0 þ a1LEVi;t�l þ a2LEV2i;t�l þ a3Z1i;t�l þ ui;t ; ð1Þ

where EFF is the firm’s efficiency measure8; LEV is the debt to totalassets ratio; Z1 is a vector of control variables; u is a stochastic errorterm; and as we explain in Section 5 we allow for lagged effects inthe specification of the empirical model.

According to the agency cost hypothesis the effect of leverage(LEV) on efficiency should be positive. However, the possibility ex-ists that at sufficiently high leverage levels, the effect of leverageon efficiency may be negative.9 The quadratic specification in (1)is consistent with the possibility that the relationship between lever-age and efficiency may not be monotonic, viz. it may switch from po-sitive to negative at higher leverage. Leverage will have a negativeeffect on efficiency for values of LEV < �a1/2a2. A sufficient conditionfor the inverse U-shaped relationship between leverage and effi-ciency to hold is that a2 < 0.

The variables included in Z1 control for firm characteristics.More specifically, we assume that profitability, size, asset struc-ture, growth opportunities, ownership structure and ownershiptype are likely to influence firm efficiency.

Profitability (PR) is measured by the ratio of profits (EBIT) to totalassets. In general we expect a positive effect of (past) profitabilityon efficiency. More profitable firms are generally better managedand thus are expected to be more efficient.

Firm size (SIZE) is measured by the natural log of the firm’ssales. The effect of this variable on efficiency is likely to be positiveas larger firms are expected to use better technology, be morediversified and better managed. Larger firms may also enjoy econ-omies of scale in monitoring top management (Himmelberg et al.,1999). But larger firms may suffer from hierarchical managerialinefficiencies and also incur larger monitoring costs (see William-son, 1967). Thus as in Himmelberg et al. (1999) we allow for non-linearities in the effect of firm size on performance by including thesquare of the natural log of sales in the efficiency equation.

Tangibility (TANG) is measured as the ratio of fixed tangible as-sets divided by the total assets of the firm. Tangibles are easilymonitored and provide good collateral and thus they tend to mit-igate agency conflicts (Himmelberg et al., 1999). As in Himmelberget al. (1999) we allow for nonlinearities in the effect of asset struc-ture on firm performance by including the square of the tangiblesto assets ratio.

Intangibility (INTG) is measured by the ratio of intangible assetsto the firm’s equity. This variable may be considered as an indica-tor of future growth opportunities (see Titman and Wessels, 1988)but its effect on firm performance is generally ambiguous

comparisons with more conventional – e.g., Shephard type efficiency measures wherea fully efficient firm has a score of 1.

9 Highly levered firms may face financing constraints that will prevent them fromadjusting their capital to labor ratios and thus attain more efficient productionallocations (see Spaliara, 2009). Debt financing may also have a negative effect on firmperformance for firms with plentiful growth opportunities (see Myers, 1977; Jensen,1986; McConnell and Servaes, 1995).


especially if these opportunities are the result of excessive risk-taking behavior given the size of equity (see Myers, 1977).

Sales growth (GROWTH) – this variable can also serve as a proxyfor growth prospects and investment opportunities. It is likely tohave a positive effect on firm performance (see Claessens et al.,2002; Maury, 2006; King and Santor, 2008).

We consider both the effects of ownership concentration andownership type on firm performance. We measure ownership con-centration (OWNC) by the percentage of shares held by those clas-sified as large shareholders. We allow the effect of ownership tovary in a piecewise linear form (see Morck et al., 1988) acrossdifferent segments of ownership concentration by introducingdummy variables (Independence Indicators) defined over three dif-ferent ranges of ownership holdings: low concentration (OWN1)with no owner holding more than a 25% stake in the company;intermediate concentration (OWN2) with the largest share-holder(s) holding between 25% and 50%; and high concentration(OWN3) representing equity holdings in excess of 50%. Hence thepiecewise ownership structure variable (OWNER) used in theempirical model is the product of ‘OWNC’ times ‘OWN’.

To the extent that large or block owners are more capable ofmonitoring and aligning management to their objectives owner-ship concentration would be expected to have a positive effect offirm performance (see Jensen and Meckling, 1976; Shleifer andVishny, 1986; Jiraporn and Gleason, 2007). But increased owner-ship share may adversely affect performance because it raises thefirm’s cost of capital due to decreased market liquidity or de-creased diversification opportunities (Fama and Jensen, 1983).Morck et al. (1988) argue that concentrated ownership may beassociated with a negative (entrenchment) effect on firm perfor-mance where the overall effect on firm value may be positive atlow concentration but negative at high concentration levels. Theyalso suggest that the relationship between ownership structureand firm performance is likely to vary across industries. These pre-dictions are corroborated by McConnell and Servaes (1995) whoreport that ownership has a positive effect on performance forlow growth firms but an insignificant albeit positive effect for highgrowth firms. Demsetz (1983) on the other hand argues thatalthough different types of ownership may intensify agency prob-lems, they also generate compensating advantages so that overallownership structure should not have any significant effect on firmperformance. This view is supported by the empirical findings ofDemsetz and Lehn (1985) and Demsetz and Villalonga (2001).

We control for the effect of ownership type by dividing ultimateowners into three groups: (1) firms owned by families or relatedindividuals (Family); (2) firms owned by financial institutions –banks, mutual funds, investment and insurance companies (Finan-cial); and firms with other types of ownership (Other). The latter isa generic category that includes non-financial companies holdingshares in other companies (cross-holding structures). Small firmsare more likely to be family controlled and their owners are likelyto be involved in the management of the company while financialcompanies are more likely to be widely held with no ownerinvolvement in the company’s management. Since family owner-ship reduces the classic owner-manager conflict, agency theorywould predict a positive effect of family ownership on firm perfor-mance (Morck et al., 1988; Anderson and Reeb, 2003a; Villalongaand Amit, 2006). This effect may be offset in situations where fam-ily managed firms forego the opportunity to hire professional man-agers that may be able to run the business more efficiently.Similarly, while there may be positive effects of cross-ownershipties on firm performance (e.g., technology transfers, vertical ties),such structures also entail offsetting entrenchment effects, increas-ing hierarchical group structure monitoring costs (see Williamson,1967) or other costs associated with inefficient businessdiversification.

4.2. The leverage model

The capital structure equation relates the debt to assets ratio toour measure of efficiency as well as to a number of other factorsthat have commonly been identified in the literature to be corre-lated with leverage (see Harris and Raviv, 1991; Myers, 2001).The leverage equation is given by:

LEVi;t ¼ b0 þ b1EFFi;t�l þ b2Z2i;t�l þ v i;t ; ð2Þ

where Z2 is a vector of factors other than efficiency (EFF) that corre-late with leverage; v is a stochastic error term and again we allowfor lagged effects in the empirical specification of the model. Underthe efficiency-risk hypothesis, efficiency has a positive effect on lever-age, i.e. b1 > 0; whereas under the franchise-value hypothesis, the ef-fect of efficiency on leverage is negative, i.e. b1 < 0. We use quantileregression analysis to examine the capital structure choices of dif-ferent subsets of firms in terms of these two conditional hypothe-ses. This is in line with Myers (2001) who emphasized that thereis no universal theory but several useful conditional theoriesdescribing the firm’s debt–equity choice. These different theorieswill depend on which economic aspect and firm characteristic wefocus on.

The variables included in Z2 control for firm characteristics thatare likely to influence the choice of capital structure (see Harrisand Raviv, 1991; Rajan and Zingales, 1995). They are the same vari-ables used in the agency cost model such as profitability, size, assetstructure, growth opportunities, and ownership structure and type.

There are conflicting theoretical predictions on the effects ofprofitability on leverage (see Harris and Raviv, 1991; Rajan andZingales, 1995; Booth et al., 2001). Myers (1984) predicts a nega-tive relationship because he argues firms will prefer to financenew investments with internal funds rather than debt. Accordingto his pecking order theory firms financing choices follow a hierar-chy in which internal cash flows (retained earnings) are preferredover external funds, and debt is preferred over equity financing.Thus more profitable firms are more likely to finance their growthby retained earnings whereas less profitable firms will use moredebt financing. Most empirical studies report a negative relation-ship between profitability and leverage although this associationmay be complicated by the presence of strong investment opportu-nities (see Booth et al., 2001).

The effect of size on leverage is expected to be positive. As lar-ger firms are more diversified and tend to fail less often than smal-ler ones, we would expect that they have better access to creditand are able to sustain more debt (Friend and Lang, 1988).

The tangibility of the firm’s assets can serve as a proxy for theagency costs of debt and the costs of financial distress (Myers,1977; Booth et al., 2001). Firms with more tangible assets havein general greater ability to secure debt as these assets can be usedas collateral (Jensen and Meckling, 1976). Thus asset tangibility isexpected to have a positive effect on leverage (Titman and Wessels,1988). Arguably the use of collateral as a device to lower agencycosts associated with debt may play an even more important rolein countries like France where creditor protection is relativelyweak in comparison to other developed countries (see La Portaet al., 1998).

The degree of asset intangibility measured by the ratio of intan-gible assets to total assets can serve as both a proxy for growthopportunities and as a source of collateral. Growth opportunitiesare generally associated with an increase in the agency costs ofdebt and are thus expected to have a negative effect on leverage(Myers, 1977). To the extent that intangibles may be perceivedby lenders as providing some form of security they will have a po-sitive effect on leverage. The overall effect of intangibles on lever-age is likely to be negative; especially for firms that have greateropportunities to expropriate bondholder wealth by substituting


safer assets for riskier assets (see Booth et al., 2001; Anderson et al.,2003).

Sales growth may be considered as another indicator of futuregrowth opportunities. Low growth firms will have less opportuni-ties to substitute low risk for high risk (high return) investments;hence they should incur lower agency costs of debt and shouldbe able to carry more debt in their capital structure. High growthfirms on the other hand may face a more intense debt overhangproblem of the type described by Jensen and Meckling (1976)and Myers (1977). But if recontracting costs are kept low theunderinvestment incentives are much smaller (see Booth et al.,2001). And if growth opportunities are viewed as an indicator ofa successful business the effect of growth on leverage may be po-sitive. This effect may be reinforced by owners of smaller privateespecially closely held companies who are fearful to lose controlor are unable to issue new equity and thus opt to fund growthopportunities with leverage (see Giannetti, 2003).

Ownership structure may have a positive or a negative effect onthe amount of debt held in the firm’s capital structure. Firms whereshareholders rights are weak are expected to carry more debt intheir capital structure as these firms are expected to incur higheragency costs (Jiraporn and Gleason, 2007). Because of their long-term commitments to the firm, family owned firms have strongerincentives to mitigate agency conflicts with debt claimants and asa result they may face lower costs of debt financing (Andersonet al., 2003). Thus family firms may carry more debt in their capitalstructure. On the other hand diversified shareholders have incen-tives to expropriate debtholder wealth by investing in risky pro-jects (Jensen and Meckling, 1976) in which case we would expectdebt holders to require a higher return. Similarly, when leverageis high the risk of bankruptcy increases which may then inducefirms to lower debt. For example, an increase in insider ownershipmay push firms to reduce leverage for fear of bankruptcy or loosingcontrol to banks (Friend and Lang, 1988). Firms owned by financialinstitutions may have better access to finance but financial institu-tions also tend to be risk averse in their investment strategy.

10 We collect data for firms with at least five employees and an annual turnovergreater than EUR 200,000. The majority of these firms are small (defined as those with5–50 employees), followed by medium-sized firms (those with 51–500 employees).In particular, 60% of the firms in chemicals are small, 32% are medium-sized and 8%are large firms (more than 500 employees). In computers and R&D, 85% of the firmsare small, 13% are medium and 2% are large. In textiles, 75% of the firms are small, 23%are medium and 2% are large.

11 As in Claessens et al. (2002) and Anderson et al. (2003) we do not separate familyownership from family management and we do not expect this distinction to beimportant in smaller unlisted firms. Faccio and Lang (2002) report that about two-thirds of French family controlled firms have top managers from the controllingfamily and this ratio is expected to be much higher for unlisted firms. Similarly, we donot expect a significant wedge between ownership and control for the type of firmswe consider and hence we do not control for mechanisms that may be used toenhance control. Such mechanisms (e.g., cross-holdings, dual class shares) are rare inFrance (see Faccio and Lang, 2002) or are used infrequently (e.g., pyramidalstructures) especially by smaller family firms.

5. Empirical results

In this section we provide answers to the questions of Section 1.As stated in the introduction we are interested in examining howcapital structure choices affect firm performance as well as the re-verse relationship between efficiency and leverage. More precisely,we want to examine if leverage has a positive effect on efficiencyand whether the reverse effect of efficiency on leverage is similaracross the spectrum of different capital structures. We are alsointerested in assessing empirically the effects of ownership struc-ture on capital structure and on firm performance.

As explained in Section 3, we measure firm efficiency using thedirectional distance function. We choose to estimate the direc-tional distance function using deterministic non-parametric fron-tier methods (DEA). The DEA model is constructed using a singleoutput (value-added) and two inputs (capital and labor) technol-ogy. The labor input is measured by the total number of full-timeequivalent employees and working proprietors whereas capital ismeasured by the firm’s fixed tangible assets. We set the elementsof the directional vector (g) equal to the sample averages of the in-put and output variables (see the Appendix).

Table 1 (Panel A) gives the descriptive statistics of the firms inthe sample for 2005. The data comprises samples of French firmsfrom two traditional manufacturing industries (textiles and chem-icals) and a growth industry (computers and related activities andR&D). We collect data from 2002 to 2005 to allow for sufficientlagged dynamic structure to resolve the identification and endoge-neity problems in the empirical specification of the cross-section

model. We limit the data to all firms that report non-negative val-ues for all inputs and output. The source of the data is the Dianedatabase compiled by the Bureau van Dijk.10 On average firms inthe chemicals industry are much larger and more capital intensivethan firms in the computers and textiles industries. Firms in thecomputers and R&D industry have higher intangibles to assets ratiosand carry on average more debt in their capital structure. Profitabil-ity appears to be much higher on average in the textiles industry butits distribution is highly skewed – note that the median chemicalsfirm is more profitable than the median textiles firm. Firms inthe computers industry appear to be closer on average to the techno-logical frontier compared to those in the chemicals and textilesindustries. We do not find any significant differences in efficiencyperformance over time (i.e. from 2002 to 2005). There appears tobe a slight improvement in performance for firms in the chemicalsindustry and a slight decline on average for firms in the textilesindustry.

Panel B of Table 1 shows that family ownership is highest in thetextiles industry and comparatively low in the chemicals industry.While ownership is quite concentrated across all industries, firmsin the computers and R&D industry appear to have the least con-centrated ownership structure. This observation is consistent withthe predictions of the Mahrt-Smith (2005) model. For growth firmswhere long-term project discovery and development investmentsare more important than short-term projects, ownership is likelyto be more dispersed as managers are motivated to protect theselong-term rents.

Table 2 shows how efficiency, profitability and leverage varyacross family vs. non-family firms; and across firms with dispersed(<25%) vs. more concentrated (>25%) ownership.11 We find thatfamily firms are much more efficient and more profitable thannon-family firms. These differences are statistically significant onaverage across all industries. We also find that family firms in thecomputers and R&D and textiles industries carry less debt in theircapital structure than non-family firms and these differences are sta-tistically significant on average. This is an interesting finding sincefamily firms in both industries are also shown to be more efficient.As Anderson et al. (2003) point out family monitoring and controlmay result in better operating performance for family firms and thismay also mitigate the owner-manager agency conflicts and hencethe need to use debt as a disciplinary device. There is no evidenceof statistically different debt ratios between family and non-familyfirms in chemicals.

Table 2 shows that the net effect of ownership concentration onefficiency varies across industries. We find that efficiency is higheron average for firms with more concentrated ownership in com-puters and R&D and for firms with more dispersed ownership inchemicals. These differences in efficiency between higher and low-er concentration are statistically significant on average. On the

Table 1Descriptive statistics. The table reports the 2005 sample statistics for inputs, output, efficiency measures and firm characteristics for the three industries (Chemicals, Computersand R&D, and Textiles). In Panel A, output (Y) is value-added; labor (L) is the number of employees; tangibles (K) is fixed tangible assets; Y/L is labor productivity; K/L is capitalintensity; efficiency is measured as 1/(1 + distance function value) and is reported for 2003–2005; profit is earnings before interest and taxes; PR is the profit to assets ratio; LEV isthe debt to assets ratio; INTG is the intangibles to total assets ratio; TANG is the tangibles to equity ratio; growth is the percentage change in sales revenue; OWNC is theproportion of largest shareholder(s) equity ownership. In Panel B all variables are 0/1 dummy variables, OWN1 denotes low, less than 25% ownership concentration; OWN2denotes intermediate, greater than 25% but less than 50% ownership concentration; OWN3 denotes high, greater than 50% ownership concentration; family, financial and otherare ownership type variables.

Chemicals Computers and R&D Textiles

Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median

Panel A: summary statisticsOutput (Y) 17855.81 63789.21 2363.00 4413.77 21226.04 997.00 2568.00 6491.76 976.00Labor (L) 166.24 467.06 37.00 55.95 235.44 15.00 51.09 94.16 23.00Tangibles (K) 10101.60 37225.20 719.00 341.01 2888.03 35.00 711.32 2186.38 137.50Y/L 94.89 209.82 65.05 78.78 130.61 64.14 51.43 36.71 41.95K/L 47.76 123.25 20.07 7.11 83.32 2.06 11.95 25.70 5.42Efficiency_05 0.82 0.24 0.94 0.87 0.17 0.93 0.78 0.22 0.86Efficiency_04 0.81 0.24 0.92 0.86 0.16 0.92 0.79 0.20 0.86Efficiency_03 0.81 0.23 0.91 0.87 0.16 0.93 0.80 0.19 0.87Revenue 69714.61 258236.50 8338.50 8245.26 35689.20 1816.00 9283.36 21864.07 3129.00Profit 5973.86 51290.61 213.00 395.53 5338.94 75.00 432.97 3192.63 71.00Total assets 66863.22 416953.10 5621.00 7134.45 44318.08 1092.00 6407.74 19825.20 1879.50Total debt 33239.00 191354.70 2928.50 4175.51 20492.68 679.00 3271.51 8747.22 867.50Intangibles 3771.61 38017.68 38.50 643.29 5595.82 11.00 252.55 1299.80 15.00PR 0.07 0.13 0.06 0.08 0.26 0.05 0.15 0.05 0.05LEV 0.58 0.26 0.56 0.69 0.42 0.65 0.56 0.30 0.54INTG 0.04 0.10 0.01 0.08 0.14 0.01 0.05 0.10 0.01TANG 0.70 3.58 0.32 0.23 3.88 0.08 0.37 2.36 0.15Growth 0.07 0.30 0.05 0.14 0.34 0.09 0.02 0.30 0.00OWNC 0.76 0.27 0.90 0.65 0.30 0.52 0.68 0.27 0.51

OWN1 OWN2 OWN3 OWN1 OWN2 OWN3 OWN1 OWN2 OWN3

Panel B: ownership concentration tabulation by ownership type (%)3.87 24.16 71.97 11.83 23.63 64.54 4.92 21.90 72.21

Of which:Family 1.94 18.86 13.89 4.22 15.48 35.58 2.80 17.74 40.21Financial 0.42 0.76 8.00 2.65 2.27 4.92 1.14 0.59 9.18Other 1.51 4.50 50.18 4.96 5.88 24.04 0.98 3.57 22.82

Obs. 1188 3253 1705


other hand there is no statistical evidence of such differences inefficiency for firms in the textiles industry.

We turn next to empirically assess the relationship betweenleverage and efficiency controlling for the effect of ownershipand other firm characteristics. The simultaneous equation systemgiven by (1) and (2) above requires adequate structure to be prop-erly identified. An obvious way to deal with the identificationproblem is by imposing relevant restrictions on the structural sys-tem. Undoubtedly the task of both properly identifying the systemof equations for efficiency and leverage and ensuring that the con-ditioning variables entering these two equations are indeed exog-enous is fraught with difficulty.

We have dealt with the identification and endogeneity issues inthe following way. Arguably both the effect of leverage on effi-ciency and the reverse effect from efficiency on leverage are notexpected to be instantaneous. Time lags are also likely to prevailwhen considering the effect of other conditioning variables on effi-ciency and leverage. For example, the pecking order theory statesthat it is past not current profitability that is envisaged to havean effect on leverage.12

An explicitly account of the dynamics in the relationship be-tween efficiency and leverage would thus help solve the identifica-

12 Given the stability of ownership patterns that we observe in our sample we treatownership as an exogenous variable rather than the endogenous outcome of‘competitive selection’ as advocated by Demsetz (1983). This is contrary to theresults of Demsetz and Lehn (1985), Himmelberg et al. (1999) and Demsetz andVillalonga (2001) for large publicly traded US firms but consistent with the stableownership structures of Continental European firms and in particular smaller unlistedcompanies that comprise the bulk of the firms in our sample.

tion problem while rendering a structure that is more robust tosimultaneity bias problems. Based on this we have proceeded toestimate the agency cost and leverage equations using both staticand dynamic model specifications.13 We have estimated structuralforms of these equations using instrumental variables techniquesand their dynamic or reduced form specifications using OLS andquantile regressions. The results we obtained from different modelsor estimation techniques appear to be quite robust, particularly inrelation to assessing the predictions of the agency cost and efficiencyhypotheses. We only report the results obtained from estimating dy-namic models for both the efficiency and leverage equations. Theregressors in these equations are predetermined (lagged endogenousor exogenous) variables thereby circumventing simultaneity prob-lems. Parsimonious forms of these equations were obtained byapplying a standard general to specific methodology starting withmodels that used variables with up to three year lags. We usetwo-year average to mitigate the effects of year to year fluctuationsin some of these variables.

Table 3 reports the estimates of the firm performance equation.We report both cross-section results (Panel A) and panel estimateswith random-effects (Panel B). The panel estimates use larger sam-ples of firms-years. The results show that leverage has a significant

13 Given the limited number of time periods for which data is available we focus oncross-section rather than panel model estimates. This ensures sufficient dynamicconditioning of the firm performance and leverage equations. In addition, it wouldhave been difficult to apply quantile regression methods to panel data as quantiles ofconvolutions of random variables are highly intractable objects (see Koenker andHallock, 2001). For completeness, we present panel estimates for the firm perfor-mance model.

Table 2Distribution of efficiency, profitability and leverage by ownership type and ownership concentration. The table reports the 25th (p25), 50th (p50) and 75th (p75) percentiles of theefficiency, profit to assets ratio (profitability) and debt to assets ratio (leverage) distributions by ownership type (family vs. non-family) and by ownership concentration (lessthan 25% vs. greater than 25%). Also reported are the means and standard deviations by ownership type and concentration and the F-statistics with p-values in brackets for testingthe hypotheses of equal means across ownership types and concentration, respectively.

p25 p50 p75 Mean Std. Dev. F-statistics Obs.

Panel A: chemicalsEfficiency

Family 0.952 0.984 0.994 0.949 0.097 227.74* 412Non-family 0.603 0.850 0.963 0.751 0.257 (0.000) 776Low (<25%) concentration 0.934 0.975 0.990 0.927 0.257 10.05* 46High (>25%) concentration 0.720 0.935 0.985 0.815 0.237 (0.002) 1142

ProfitabilityFamily 0.012 0.072 0.137 0.083 0.124 9.900** 412Non-family �0.002 0.052 0.127 0.057 0.132 (0.017) 776Low (<25%) concentration 0.012 0.063 0.142 0.079 0.106 0.43 46High (>25%) concentration 0.004 0.056 0.129 0.065 0.131 (0.514) 1142

LeverageFamily 0.387 0.560 0.745 0.571 0.229 0.19 412Non-family 0.412 0.565 0.736 0.579 0.266 (0.666) 776Low (<25%) concentration 0.370 0.539 0.655 0.541 0.259 0.95 46High (>25%) concentration 0.402 0.565 0.736 0.579 0.258 (0.330) 1142

Panel B: computers and R&DEfficiency

Family 0.892 0.946 0.978 0.915 0.098 405.59* 1806Non-family 0.726 0.880 0.953 0.803 0.209 (0.000) 1447Low (<25%) concentration 0.790 0.886 0.953 0.840 0.161 6.65* 264High (>25%) concentration 0.842 0.929 0.971 0.868 0.167 (0.010) 2989

ProfitabilityFamily 0.020 0.083 0.173 0.092 0.184 75.30* 1806Non-family �0.018 0.048 0.135 0.019 0.267 (0.000) 1447Low (<25%) concentration �0.047 0.034 0.110 0.004 0.231 14.95* 264High (>25%) concentration 0.008 0.073 0.156 0.064 0.227 (0.000) 2989

LeverageFamily 0.484 0.629 0.768 0.648 0.312 36.98* 1806Non-family 0.515 0.681 0.843 0.739 0.521 (0.000) 1447Low (<25%) concentration 0.428 0.608 0.751 0.596 0.276 13.87* 264High (>25%) concentration 0.500 0.655 0.814 0.697 0.430 (0.000) 2989

Panel C: textilesEfficiency

Family 0.825 0.919 0.967 0.870 0.139 379.23* 1046Non-family 0.531 0.745 0.898 0.688 0.246 (0.000) 659Low (<25%) concentration 0.734 0.892 0.952 0.817 0.186 0.49 66High (>25%) concentration 0.705 0.875 0.954 0.799 0.208 (0.483) 1639

ProfitabilityFamily 0.014 0.064 0.132 0.078 0.210 9.45* 1046Non-family �0.002 0.045 0.113 0.049 0.129 (0.002) 659Low (<25%) concentration 0.007 0.040 0.121 0.061 0.145 0.07 66High (>25%) concentration 0.008 0.056 0.123 0.067 0.185 (0.796) 1639

LeverageFamily 0.351 0.533 0.690 0.542 0.246 6.63* 1046Non-family 0.380 0.546 0.726 0.580 0.347 (0.010) 659Low (<25%) concentration 0.354 0.556 0.698 0.548 0.267 0.06 66High (>25%) concentration 0.364 0.536 0.702 0.557 0.302 (0.810) 1639

* Indicates significance at the 1% level.** Significant at the 5% level.


effect on efficiency. This effect is positive at the mean of leveragefor each industry and it remains positive over the entire relevantrange of leverage values. Thus we find support for the agency costhypothesis (H1) that higher leverage is associated with improvedfirm performance. Based on the magnitude of estimated coeffi-cients, we observe that the effect of debt on efficiency appears tobe stronger for firms in the traditional (chemicals and textiles)industries. This finding corroborates the conjecture of McConnelland Servaes (1995), namely that debt has a fundamentally differ-ent role on performance between firms with few and those withmany growth opportunities (see footnote 5 above). The findingthat debt is more important for firm performance for industries

with less growth opportunities is also consistent with the theoret-ical predictions of Jensen (1986) and Stulz (1990) (see also Boothet al., 2001).

Profitability has a positive and significant effect on efficiency forall industries. The effect of size is mostly significant in chemicalsand is non-monotonic, i.e. positive for smaller firms but negativefor larger firms. Similarly, asset tangibility has a non-monotonic ef-fect; negative at low fixed tangibles to total assets ratios and posi-tive at high tangibles to asset ratios. An explanation for this findingis that a high proportion of hard tangible assets would reduce theextent of the firm’s growth opportunities and as a result diminishthe agency costs of managerial discretion (see Booth et al., 2001).

Table 3The firm performance model. Panel A reports least squares estimates with heteroskedasticity-consistent standard errors (SEs). The dependent variable is the firm’s efficiency scorein 2005 computed as 1/(1 + distance function value). LEV_AVG(�1) is the debt to assets average ratio for 2003 and 2004; PR_AVG(�1) is the profit to assets average ratio for 2003and 2004; LOGR(�1) is log sales revenue in 2004; TANG(�1) is the tangibles to assets ratio in 2004; INTG(�1) is the intangibles to equity ratio in 2004; GROWTH_AVG(�1) isaverage sales growth for 2003 and 2004; Owner1 = OWN1 * OWNC, Owner2 = OWN2 * OWNC, and Ownwr3 = OWN3 * OWNC capture the piecewise linear effect of ownershipstructure where OWN1 is a dummy variable that denotes low <25% ownership concentration; OWN2 denotes intermediate >25% but <50% ownership concentration; OWN3denotes high >50% ownership concentration; and OWNC is the percentage of largest shareholder equity ownership; family and financial are ownership type dummy variables.Panel B reports random-effects GLS panel regression estimates with Huber-White robust standard errors (SEs) for the period 2002–2005. The dependent variable is the firm’sefficiency score in 2005 computed as 1/(1 + distance function value). LEV_AVG(�1) is a two-year average debt to assets ratio with one period lag; PR_AVG(�1) is a two-yearaverage profit to assets ratio with one period lag; LOGR(�1) is log sales revenue with one period lag; TANG(�1) is the tangibles to assets ratio with one period lag; INTG(�1) is theintangibles to equity ratio with one period lag; GROWTH_AVG(�1) is a two-year average sales growth rate with one period lag.

Dependent variable: efficiency (EFF)

Variable Chemicals Computers and R&D Textiles

Coefficient SE Coefficient SE Coefficient SE

Panel A: cross-section estimatesLEV_AVG(�1) 0.0716* 0.0180 0.0423* 0.0100 0.0769* 0.0226LEV_AVG(�1)_Square �0.0002 0.0002 �0.0036* 0.0012 �0.0175* 0.0048PR_AVG(�1) 0.0910** 0.0386 0.0345* 0.0112 0.1101** 0.0478LOGR(�1) 0.1469* 0.0387 0.0140 0.0462 0.0654 0.0795LOGR(�1)_Square �0.0128* 0.0021 �0.0071** 0.0029 �0.0109** 0.0048TANG(�1) �0.9508* 0.0689 �1.5487* 0.1298 �1.4275* 0.0654TANG(�1)_Square 0.8516* 0.1037 2.2679* 0.3914 1.3937* 0.1160INTG(�1) �0.0015* 0.0004 0.0004 0.0004 �0.0003 0.0007Growth_AVG(�1) 0.0089 0.0116 �0.0018 0.0093 0.0022 0.0145Owner1 0.0009** 0.0004 �0.0007** 0.0003 �0.0002 0.0003Owner2 0.0004*** 0.0002 0.00003 0.0002 �0.0001 0.0002Owner3 0.0004* 0.0001 0.00004 0.0001 �0.0001 0.0001Family 0.0148** 0.0076 0.0099** 0.0049 0.0261* 0.0072Financial 0.0190 0.0164 �0.0008 0.0101 0.0087 0.0137Constant 0.6393* 0.1669 1.2221* 0.1815 1.0706* 0.3288R-squared 0.792 0.653 0.738F-statistic 261.9* 252.0* 226.9*

Obs. 1188 3253 1705

Panel B: panel estimates 2002–2005LEV_AVG(�1) 0.0898* 0.0218 0.0330* 0.0069 0.1144* 0.0192LEV_AVG(�1)_Square �0.0203** 0.0096 �0.0026* 0.0090 �0.0488* 0.0126PR_AVG(�1) 0.0443** 0.0199 0.0216* 0.0063 0.0637** 0.0284LOGR(�1) 0.1578* 0.0287 0.0208 0.0420 0.0443 0.0618LOGR(�1)_Square �0.0131* 0.0015 �0.0072* 0.0026 �0.0091** 0.0038TANG(�1) �0.8150* 0.0565 �1.5829* 0.1051 �0.3271* 0.0206TANG(�1)_Square 0.6847* 0.0799 2.2904* 0.3157 0.1148* 0.0124INTG(�1) �0.0003 0.0002 0.0001 0.0002 0.0003 0.0004Growth_AVG(�1) 0.0159* 0.0060 0.0179* 0.0045 0.0499* 0.0088Owner1 0.0008* 0.0003 �0.0004*** 0.0002 �0.0002 0.0003Owner2 0.0005** 0.0002 0.0001 0.0001 �0.0001 0.0002Owner3 0.0003* 0.0001 0.00001 0.0001 �0.0002 0.0001Family 0.0177** 0.0088 0.0079** 0.0037 0.0438* 0.0081Financial 0.0256*** 0.0142 0.0011 0.0082 0.0039 0.0135Constant 0.5487* 0.1287 1.1822* 0.1663 1.0280* 0.2488R-squared overall 0.802 0.694 0.688Wald Chi-square (14) 2573.48* 2655.6* 2210.19*

Obs. 2138 5530 3069

* Significant at the 1% level.** Significant at the 5% level.

*** Significant at the 10% level.

14 This finding provides partial support for the conjecture of McConnell and Servaes(1995), namely that the effect of ownership on performance should be moreimportant for low-growth rather than high-growth firms (see also footnote 6 above).


The effect of intangibles is negative and significant only for firms inthe chemicals industry while the effect of growth is insignificantacross all industries in the cross-section regressions.

Turning now to the ownership results we find that the contrast-ing effects of convergence of interest (H3) and ownership entrench-ment (H3a) vary across industries. Ownership concentration has apositive and significant effect on firm performance across differentconcentration ratios in the chemicals industry but its strengthdiminishes at higher concentration segments suggesting the pres-ence of entrenchment effects (note that differences in estimatedcoefficients are only statistically significant between low and highconcentration with an F-statistic value of 2.96). On the other handfor firms in the computers industry we find that low ownershipconcentration has a negative effect on firm performance. Higherownership concentration has an insignificant effect in this industry

suggesting the presence of offsetting entrenchment and incentivealignment effects.14 For firms in the textiles industry we find no evi-dence that ownership concentration has a significant effect on per-formance across the three concentration segments. The absence ofa statistically significant relationship between ownership concentra-tion and efficiency for textiles and in part for firms in computers andR&D appears to support the views expressed by Demsetz (1983).However we find that ownership type has a significant effect on firmperformance. The results of Table 3 show quite consistently thatfamily firms perform better on average in comparison to non-familyfirms.

Table 4The leverage (LEV) model. The table reports least squares (OLS) estimates with heteroskedasticity-consistent standard errors and simultaneous quantile regression estimates atleverage quantiles .20 (q20), .50 (q50) and .80 (q80) with bootstrap standard errors (SEs). The dependent variable (LEV) is the debt to assets ratio in 2005. Efficiency(�1) is thefirm’s efficiency score in 2004 computed as 1/(1 + distance function value); PR_avg(�1) is the profit to assets average ratio for 2003 and 2004; LogR(�1) is log sales revenue in2004; tang(�1) is the tangibles to equity ratio in 2004; intg(�1) is the intangibles to assets ratio in 2004; GR_avg(�1) is average sales growth for 2003 and 2004;Owner1 = OWN1 * OWNC, Owner2 = OWN2 * OWNC, and Ownwr3 = OWN3 * OWNC capture the piecewise linear effect of ownership structure where OWN1 is a dummy variablethat denotes low <25% ownership concentration; OWN2 denotes intermediate >25% but <50% ownership concentration; OWN3 denotes high >50% ownership concentration; andOWNC is the percentage of largest shareholder equity ownership; family and financial are ownership type dummy variables.

Coefficient SE Coefficient SE Coefficient SE Coefficient SE

OLS q20 q50 q80

Panel A: chemicals – OLS and quantile regression estimatesEfficiency(�1) 0.1840* 0.0569 0.1675** 0.0746 0.1833* 0.0738 0.2518* 0.0879PR_avg(�1) �0.848* 0.0847 �0.593* 0.1035 �0.7727* 0.0893 �0.866* 0.1061LogR(�1) 0.0169** 0.0084 0.0255** 0.0114 0.0179 0.0130 0.0212 0.0135tang(�1) 0.0071* 0.0027 0.0067 0.0178 0.0143 0.0166 0.0189** 0.0093intg(�1) �0.187* 0.0659 �0.181** 0.0845 �0.1867** 0.0947 �0.231*** 0.1219GR_avg(�1) 0.0870* 0.0289 0.1597* 0.0521 0.0753** 0.0313 0.0491 0.0428owner1 0.0004 0.0012 0.0003 0.0022 0.0012 0.0013 0.0008 0.0023owner2 0.0003 0.0006 0.0008 0.0009 0.0004 0.0007 0.0006 0.0007owner3 0.0007** 0.0003 0.0008*** 0.0005 0.0008*** 0.0004 0.0009* 0.0004Family 0.0060 0.0225 0.0065 0.0394 0.0164 0.0316 0.0057 0.0289Financial 0.0054 0.0248 �0.0149 0.0418 0.0302 0.0286 �0.0064 0.0348Const. 0.2637** 0.1243 �0.0115 0.1753 0.2287 0.1830 0.3113 0.2031R-squared 0.23 0.09 0.14 0.15

Panel B: computers and R&D – OLS and quantile regression estimatesEfficiency(�1) 0.2826* 0.0793 0.1527*** 0.0803 0.2088* 0.0692 0.1679* 0.0414PR_avg(�1) �0.897* 0.1581 �0.312* 0.0767 �0.6829* 0.0690 �0.986* 0.0758LogR(�1) 0.0076 0.0105 0.0148 0.0096 0.0200* 0.0079 0.0027 0.0076tang(�1) 0.0060 0.0043 0.0184 0.0232 0.0189 0.0196 0.0059 0.0080intg(�1) �0.0607 0.0938 0.0010 0.0771 �0.0586 0.0575 �0.0767*** 0.0451GR_avg(�1) �0.0436 0.0494 0.1326* 0.0302 0.0746* 0.0230 0.0351 0.0278owner1 0.0002 0.0011 �0.0014 0.0010 �0.0018** 0.0008 �0.0019*** 0.0011owner2 0.0016* 0.0005 �0.0002 0.0009 0.0004 0.0005 0.0010* 0.0003owner3 0.0025* 0.0004 0.0008** 0.0004 0.0008* 0.0002 0.0011* 0.0002Family �0.0154 0.0193 0.0025 0.0212 �0.0237 0.0168 �0.042** 0.0176Financial 0.0060 0.0581 �0.0330 0.0358 �0.0415** 0.0110 �0.0270 0.0347Const. 0.3081** 0.1358 0.2022 0.1251 0.3195* 0.1252 0.6488* 0.1105R-squared 0.22 0.05 0.11 0.17

Panel C: textiles – OLS and quantile regression estimatesEfficiency(�1) 0.1769* 0.0675 0.0799 0.0883 0.2506* 0.0614 0.3018* 0.0850PR_avg(�1) �0.868 * 0.1270 �0.310* 0.0849 �0.546* 0.1169 �0.773* 0.1329LogR(�1) 0.0225** 0.0092 0.0242 0.0154 0.0315* 0.0124 0.0284 0.0123tang(�1) 0.0350* 0.0074 0.0393** 0.0185 0.0659* 0.0190 0.0513* 0.0142intg(�1) 0.1812** 0.0908 0.1590 0.1603 0.2280** 0.1129 0.0946 0.1460GR_avg(�1) 0.0366 0.0443 0.1082*** 0.0593 0.1094*** 0.0584 0.0334 0.0407owner1 0.0001 0.0009 0.0003 0.0009 �0.0007 0.0015 0.0018 0.0015owner2 0.0008 0.0006 0.0007 0.0008 0.0009 0.0007 0.0008 0.0006owner3 0.0006*** 0.0003 0.0003 0.0005 0.0007* 0.0004 0.0008** 0.0003Family �0.0296 0.0194 �0.0081 0.0286 �0.0127 0.0233 �0.0293 0.0248Financial �0.0440 0.0286 �0.0416 0.0299 �0.0398 0.0265 �0.0255 0.0286Const. 0.2203*** 0.1229 0.0541 0.1816 0.0327 0.1538 0.2103 0.1579R-squared 0.21 0.06 0.11 0.16

* Significant at the 1% level.** Significant at the 5% level.

*** Significant at the 10% level.


Table 4 reports the estimates of the leverage model. We reportOLS results and estimates for three leverage quantiles (0.20, 0.50and 0.80); estimates for other quantiles are available from theauthors. The results from the OLS and quantile regressions showthat the effect of efficiency on leverage is positive and significantin the low to high range of the leverage distribution supportingthe efficiency-risk hypothesis (H2): more efficient firms with rela-tively low levels of debt tend to choose higher debt ratios becausehigher efficiency lowers the expected costs of bankruptcy andfinancial distress. However there is no evidence to suggest thatthe franchise-value effect (H2a) outweighs the efficiency-risk effecteven for the most highly levered firms.

Consistent with pecking order theory, profitability has a nega-tive effect on leverage for all industries on average and also acrossdifferent capital structures. The effect of profitability appears to be

stronger for firms with higher debt. For example, the F-statistic forthe null hypothesis of no difference in profitability coefficients inthe chemicals industry is 4.74 at leverage quantiles (.20 and .80)which is significant at the 5% level. We also find that a higher pro-portion of tangible assets are more important in increasing debtcapacity for the smaller typically riskier firms in the textiles indus-try. The effect of intangible assets is negative in chemicals, positivein textiles and generally not significant for firms in computers andR&D. Growth has a positive effect on leverage on average (OLS esti-mates) for firms in chemicals as well as for low to medium lever-aged firms across all industries. The owners of these firms appearto opt for debt finance for reasons we have discussed earlier on.

We find that in general firms with more concentrated owner-ship carry more debt in their capital structure. For mid- to high-leveraged firms in the computers and R&D industry we find that


low ownership concentration has a negative effect on leverage. Fi-nally, our results show that the effect of ownership type on lever-age is generally non-significant across all industries with theexception of some firms in computers and R&D. In particular, wefind that among the highly levered firms in this industry, familyfirms opt to carry less debt in their capital structure.

6. Conclusion

This paper investigates the relationship between efficiency,leverage and ownership structure. This analysis is conducted usingdirectional distance functions to model the technology and obtainX-inefficiency measures as the distance from the efficient frontier.We interpret these measures as a proxy for the (inverse) agencycosts arising from conflicts between debt holders and equity hold-ers or from different principal-agent objectives. Using a sample ofFrench firms from low- and high-growth industries, we considerboth the effect of leverage on firm performance as well as the re-verse causality relationship while controlling for the effects ofownership structure and ownership type. We find support for thecore prediction of the Jensen and Meckling (1976) agency costhypothesis in that higher leverage is associated with improved effi-ciency over the entire range of observed data. The contrastingalignment and entrenchment agency effects of ownership concen-tration vary across industries and across concentration ratios. Wefind that while more dispersed firms face higher agency costs incomputers and R&D; the reverse is true for firms in the chemicalsindustry. Moreover, we find that family firms outperform non-fam-ily firms.

We have also investigated the reverse causality relationshipfrom efficiency to leverage in terms of two competing hypotheses:the efficiency-risk hypothesis and the franchise-value hypothesis.Using quantile regression analysis we show that the effect of effi-ciency on leverage is positive in the low to high ranges of the lever-age distribution supporting the efficiency-risk hypothesis. We alsofind that more concentrated ownership is generally associatedwith more debt in the capital structure. However, we generally findno evidence that ownership type has an effect on leverage choices.

Our methodology has gone some way in reconciling some of theempirical irregularities reported in prior studies. In particular, wehave shown how competing hypotheses may dominate each otherat different segments of the relevant data distribution thereby cau-tioning the standard practice of drawing inferences on capitalstructure choices using conditional mean (OLS) estimates. By usingproductive efficiency as opposed to financial performance indica-tors as our measure of (inverse) agency costs we have been ableto carry out tests of the agency theory without the confoundingproblems that may be associated with the more traditional finan-cial measures of firm performance. In future research it will be ofinterest to extend this analysis across different countries andacross different industries as well as examine further aspects ofownership and governance characteristics.

Acknowledgements

We are grateful to an anonymous referee and the Editor (IkeMathur) for helpful comments and suggestions. We also thankthe participants at the 2008 EFMA Conference in Athens and at aMassey University seminar for helpful comments and Chris Yungfor research assistance.

15 In the empirical section of the paper technology is restricted to a specificationthat uses two inputs (capital and labor) to produce a single output.

16 Input weak disposability means that if all inputs increase proportionally thenoutput will not decrease. Strong or free disposability on the other hand requires thatoutput does not decrease if any or all feasible inputs are increased. Disposable outputsare similarly defined.

Appendix A

This section outlines the specification of the efficiencymeasures. Following Färe and Grosskopf (2004) and Färe et al.

(2008) we assume that firms employ N inputs denoted byx ¼ ðx1; . . . ; xNÞ 2 RN

þ to produce M outputs denoted by y ¼ðy1; . . . ; yMÞ 2 RM

þ .15 Technology may be characterised by a technol-ogy set T, which is the set of all feasible input–output com-binations, i.e.

T ¼ fðx; yÞ : x can produce yg; x 2 RNþ: ðA:1Þ

The technology set is assumed to satisfy a set of reasonable axi-oms. Here we assume that T is a closed, convex, nonempty set withinputs and outputs which are either freely or weakly disposable.16

To provide a measure of efficiency we use a directional technologydistance function approach. This function completely characterizestechnology (i.e., it is equivalent to T), it is dual to the profit functionand allows for adjustment of inputs and outputs simultaneously.Thus the directional distance function entails an extremely flexibledescription of technology without restricting firms to optimize byeither increasing outputs proportionately without changing inputsor by decreasing inputs proportionally for given outputs. To defineit we need to specify a directional vector, denoted by g = (gx, gy)where gx 2 RN

þ and gy 2 RMþ . This vector determines the direction in

which technical efficiency is assessed, i.e. the path of the projectionof the observed data to the frontier of technology. The directionaltechnology distance function is defined as:

~DTðx; y; gx; gyÞ ¼ supfb : ðx� bgx; yþ bgy 2 Tg: ðA:2Þ

The directional distance function expands outputs in the direc-tion gy and contracts inputs simultaneously in the direction gx tothe frontier T. If the observed input output bundle is technicallyefficient, the value of the directional distance function would bezero. If the observed input output bundle is interior to technologyT, the distance function is greater than zero and the firm is techni-cally inefficient. In this paper we choose g ¼ ð�x; �yÞ which impliesthat output(s) may be increased by ~DTðx; y; �x; �yÞ � �y and inputs de-creased by ~DTðx; y; �x; �yÞ � �x for a firm to eliminate all technical inef-ficiency relative to its best performing peers.

The directional distance function can be estimated non-para-metrically using DEA – a mathematical programming envelopingtechnique – under a VRS (variable returns to scale) technology asfollows:

~DTðx; y; gx; gyÞ ¼max b ðA:3Þ

subject to:XK

k¼1kkxkn � xkn � bgx; n ¼ 1; . . . ;N;

XK

k¼1kkykm � ykm þ bgy; m ¼ 1; . . . ;M;

XK

k¼1kk ¼ 1; kk � 0; k ¼ 1; . . . ;K:

The intensity variables ðkkÞ form combinations of inputs andoutputs from the observed set of inputs and outputs of the firmsin the sample, one for each activity or observation (k) of data. Theseare non-negative variables whose solution value may be inter-preted as the extent to which an activity is involved in frontier pro-duction. Thus at each segment of the piecewise frontier DEAidentifies a peer group of best practice reference firms (i.e. thosewith non-zero kk) for each firm being evaluated. Each firm (k)can produce no more output using no less input than a linear com-bination of all the firms’ inputs and outputs in the sample. There-fore the technology is constructed from the data ðxk; ykÞ by forming


the tightest convex cone that includes all data hence the descrip-tive title data envelopment analysis. Constraining the intensityvariables to add up to one imposes the VRS technology.

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