Capital Structure and Firm Efficiency

We rely onLeibenstein’s framework and more recent theoretical work on production frontiersand performance measurement to establish the link between productive efficiency and as a summary

Capital Structure and Firm Efficiency

Dimitris Margaritis and Maria Psillaki∗

Abstract: This paper investigates the relationship between firm efficiency and leverage We

consider both the effect of leverage on firm performance as well as the reverse causalityrelationship In particular, we address the following questions: Does higher leverage lead

to better firm performance? Does efficiency exert a significant effect on leverage over andabove that of traditional financial measures of capital structure? Is the effect of efficiency onleverage similar across different capital structures? What is the signalling role of efficiency tocreditors or investors? Using a sample of 12,240 New Zealand firms we find evidence supportingthe theoretical predictions of the Jensen and Meckling (1976) agency cost model Efficiencymeasured as the distance from the industry’s ‘best practice’ production frontier is positivelyrelated to leverage over the entire range of observed data The frontier is constructed using thenon-parametric Data Envelopment Analysis (DEA) method Using quantile regression analysis

we show that the reverse causality effect of efficiency on leverage is positive at low to mid-leveragelevels and negative at high leverage ratios Firm size also has a non-monotonic effect on leverage:negative at low debt ratios and positive at mid to high debt ratios The effect of tangibles andprofitability on leverage is positive while intangibles and other assets are negatively related toleverage

Keywords: capital structure, agency costs, firm efficiency, DEA

1 INTRODUCTION

In an influential paper Leibenstein (1966) showed how different principal-agent tives, inadequate motivation and incomplete contracts become sources of (technical)inefficiency measured by the discrepancy between maximum potential output and thefirm’s actual output He termed this failure to attain the production or technologicalfrontier as X-inefficiency

objec-Leibenstein’s work fits well with recent theories that emphasize the disciplinary role

of leverage in agency conflicts and the importance of contracting and information costs

in the determination of the firm’s capital structure policy (see Jensen and Meckling,

∗The authors are respectively Professor in the Department of Finance, AUT, Auckland, New Zealand and Associate Professor, University of Nice-Sophia Antipolis and GREDEG-CNRS, Valbonne, France They acknowledge financial support from the New Zealand Foundation for Research, Science and Technology They are grateful to the editor of this journal and an anonymous referee for their valuable comments They would also like to thank Gary Feng and Boram Lee for excellent research assistance (Paper received December 2005, revised version accepted May 2007 Online publication August 2007)

Address for correspondence: Dimitris Margaritis, Department of Finance, Faculty of Business, AUT, Private

Bag 92006, Auckland 1020, New Zealand.

e-mail: dmargaritis@aut.ac.nz

C

1976; Myers, 1977; Myers and Majluf, 1984; Harris and Raviv, 1990; and Walsh and Ryan,1997) While theoretical work on capital structure has seen remarkable progress sincethe seminal Modigliani and Miller (1958) contribution, the practical applications ofcapital structure theories are far from satisfying (see Rajan and Zingales, 1995; Ross

et al., 2005; and Beattie et al., 2006)

The main problem applied researchers face is that several of the variables which arehypothesized to affect capital structure are not directly observable or are difficult tomeasure (see Barclay and Smith, 2001) Likewise, Berger and Bonaccorsi di Patti (2006)argue that the lack of conclusive evidence in support of the agency cost hypothesis may

in part be attributed to the difficulties researchers face in obtaining a measure of firmperformance that is closely related to the theoretical definition of agency costs.The first objective of this paper is to consider explicitly the role of productionand cost decisions in determining the extent of firm leverage To do that we willneed to reconstruct the black box details of production technology which have beentraditionally left out in modern finance representations of the firm We rely onLeibenstein’s framework and more recent theoretical work on production frontiersand performance measurement to establish the link between productive efficiency and

as a summary measure of incomplete contracts, different principal-agent objectives andinadequate motivation or more generally for the types of market imperfections thatare often used as reasons to explain why firm performance may have an effect onequilibrium capital structure We then proceed to analyze the effects of efficiency on

capital structure using two competing hypotheses Under the efficiency-risk hypothesis,

more efficient firms may choose higher debt to equity ratios because higher efficiencyreduces the expected costs of bankruptcy and financial distress On the other hand,

under the franchise-value hypothesis, more efficient firms may choose lower debt to equity

ratios to protect the economic rents derived from higher efficiency from the possibility

of liquidation (Berger and Bonaccorsi di Patti, 2006)

Potential conflicts of interest between owners and managers impact on the value ofthe firm For example, managers with free cash flows in mature industries with limitedprofitable growth opportunities may overinvest in their own companies or diversify

in unchartered territories both of which will negatively impact on profitability and

growth companies facing financial difficulties may be adversely affected when there areconflicts between debt and equity holders In these situations managers acting in theinterests of shareholders are likely to underinvest (see Myers, 1977) as they recognizethat most of the value created by capital investments would eventually benefit thecreditors rather than the owners The second objective of this paper is thus to assessthe extent to which leverage acts as a disciplinary device in mitigating the agency costs of

We also allow for the possibility that at high levels of leverage the agency costs of outside

1 Fried et al (2007) and F¨are et al (2007) provide an extensive review of the literature on efficiency and productivity.

2 Clearly the best strategy for adding value in these situations is to distribute the free cash flow to investors either by increasing dividends or preferably by substituting debt for equity through stock repurchases (see Barclay and Smith, 2001) There is evidence to suggest that leveraged buyouts and other leveraged recapitalisations have been associated with improvements in operational efficiencies (see Chew, 2001).

3 We thank an anonymous referee for suggesting that we incorporate the effect of leverage on efficiency.

debt may overcome those of outside equity whereby further increases in leverage lead

to an increase in total agency costs

This paper contributes to the literature in two directions: (1) by using X-efficiency

as opposed to financial variables to measure firm performance and test the predictions

of the agency cost hypothesis; (2) by examining whether the distance from the bestpractice frontier is an important determinant of capital structure We view the bestpractice frontier as a benchmark for each firm’s performance that would be realized ifagency costs were minimized As articulated by Berger and Bonaccorsi di Patti (2006),efficiency measures are closer to the theoretical definition of agency costs and havethe advantage over the more traditional measures of firm performance based onfinancial indicators in that they control for firm-specific factors outside the control

of management which are not part of agency costs More specifically, we address thefollowing questions: Does higher leverage lead to better firm performance? Doesefficiency exert a significant effect on leverage over and above that of traditionalfinancial measures? Is the effect of efficiency on leverage identical across differentcapital structures? What is the signalling role of efficiency to creditors or investors?This is to our knowledge one of the first studies to consider the association betweenproductive efficiency and leverage In a recent study Berger and Bonaccorsi di Patti(2006) examine the bi-directional relationship between capital structure and firmperformance for the US banking industry using a parametric measure of profitefficiency as an indicator of (inverse) agency costs They find evidence in support

of the agency cost hypothesis whereby the effect of the agency cost of outside equitydominates the effect of outside debt over almost the entire range of observed data Forthe reverse causality from efficiency to capital structure they report a negative effect ofefficiency on equity capital at high levels of efficiency and a positive effect at low levels

of efficiency

In a different but related context, Zavgren (1985), Keasey and Watson (1987) andBecchetti and Sierra (2003) have emphasized the importance of non-financial data aspredictors of company failures For instance, Zavgren (1985) argues that econometricmodels that solely rely on financial statement information will not predict accuratelybusiness failures Using a measure of efficiency obtained from a stochastic frontiermodel, Becchetti and Sierra (2003) find that productive inefficiency is a significantex-ante indicator of business failure while Keasey and Watson (1987) report that betterpredictions for small company failures are obtained from models using non-financialdata rather than conventional financial indicators We adopt a similar reasoning inconsidering the relationship between financial structure and X-efficiency in this paper.The reminder of the paper is organized as follows The next section details themethodology used in this study to construct the ‘best practice’ frontier and establishthe link between efficiency and capital structure Section 3 describes the variables used

in the econometric analysis of efficiency and leverage Section 4 describes the data, theempirical specification and estimation procedure and reports the empirical results.Section 5 concludes the paper

2 METHODOLOGY

(i) Firm Performance and Capital Structure

The agency cost theory is premised on the idea that the interests of the company’smanagers and its shareholders are not perfectly aligned In their seminal paper Jensen

and Meckling (1976) emphasized the importance of the agency costs of equity incorporate finance arising from the separation of ownership and control of firmswhereby managers tend to maximize their own utility rather than the value of thefirm 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 cash ratherthan investing it below the cost of capital or wasting it on organizational inefficiencies.’

In other words complete contracts cannot be written A higher level of leverage may beused as a disciplinary device to reduce managerial cash flow waste through the threat

of liquidation (Grossman and Hart, 1982) or through pressure to generate cash flows

to service debt (Jensen, 1986)

Agency costs can also exist from conflicts between debt and equity investors.These conflicts arise when there is a risk of default The risk of default may createwhat Myers (1977) referred to as an ‘underinvestment’ or ‘debt overhang’ problem.Alternatively, it could lead to increased risk-taking activity as managers acting on theirshareholders’ behalf have incentives to take excessive risks as part of risk shiftinginvestment strategies (see Jensen and Meckling, 1976) Whereas the agency costs

of outside equity underpin a positive relationship between firm performance andleverage, the agency costs of outside debt result in a negative effect of leverage onfirm performance as highly leveraged firms especially those on the brink of default aremore likely to pass up profitable investment opportunities or shift to riskier operatingstrategies

Thus a firm’s ability to achieve best practice relative to its peers will be compromised

in situations where it is forced to forego valuable investment opportunities, participate

in uneconomic activities that sustain growth at the expense of profitability or is subject

to other organizational inefficiencies Following Leibenstein (1966) we use technical orX-inefficiency as a proxy for the (inverse) agency costs arising from conflicts betweendebt holders and equity holders or from different principal-agent objectives Theseconflicts will give rise to a discrepancy between a firm’s potential and actual output

so that individual firms with similar technologies can be benchmarked against theirbest performing peers As in Berger and Bonaccorsi di Patti (2006) we view these bestpractice firms as those which minimize the agency costs of outside equity and outsidedebt

In line with Jensen and Meckling (1976) we expect the effect of leverage on agencycosts to be negative overall We do however, allow in our model specification for thepossibility that this effect may be reversed at the point where the expected costs offinancial distress outweight any gains achieved through the use of debt rather than

higher leverage is expected to lower agency costs, reduce inefficiency and thereby lead

to an improvement in firm’s performance with the proviso that the direction of thisrelationship may switch at a point where the disciplinary effects of further increases inleverage become untenable

But firm performance may also affect the choice of capital structure As Bergerand Bonaccorsi di Patti (2006) pointed out, regressions of firm performance onleverage may confound the effects of capital structure on performance with the reverserelationship from performance on capital structure This reverse causality effect was

in essence a feature of theories considering how agency costs (Jensen and Meckling,1976; Myers, 1977; and Harris and Raviv, 1990); corporate control issues (Harris andRaviv, 1988); and in particular, asymmetric information (Myers and Majluf, 1984; and

Myers, 1984) and taxation (DeAngelo and Masulis, 1980; and Bradley et al., 1984) arelikely to affect the value of the firm.

Although corporate capital structure theory lacks consensus, it is arguably the casethat financial distress costs, signaling and information costs all play some role indetermining a firm’s capital structure (see Barclay et al., 1995; and Myers, 2001) As

in Berger and Bonaccorsi di Patti (2006) we put forward two reasons explaining whythere may be a relationship between capital structure and firm performance Under the

efficiency-risk hypothesis (H2), more efficient firms choose higher leverage ratios becausehigher efficiency is expected to lower the costs of bankruptcy and financial distress.Berger and Bonaccorsi di Patti (2006) note that more efficient firms are more likely toearn a higher return for a given capital structure, and that higher returns can act as

a buffer against portfolio risk so that more efficient firms are in a better position

to substitute equity for debt in their capital structure In effect, the efficiency-riskhypothesis is a spin-off of the trade-off theory of capital structure whereby differences

in efficiency, other things constant, enable firms to alter their optimal capital structure

to guard the economic rents or franchise value generated by these efficiencies fromthe threat of liquidation (see Demsetz et al., 1996; and Berger and Bonaccorsi di Patti,2006) Thus in addition to the substitution effect, the relationship between efficiencyand capital structure may also be characterized by the presence of an income effect

more efficient firms tend to hold extra equity capital and therefore, all else equal,choose lower leverage ratios to protect their future income or franchise value

opposite predictions regarding the likely effects of firm efficiency on its choice ofcapital structure Although we cannot identify the separate substitution and incomeeffects, our empirical analysis is able to determine which effect dominates the otheracross the spectrum of different capital structure choices

(ii) Benchmarking Firm Performance

In this section we develop the model that underpins the relationship between efficiencyand capital structure First, we explain how we benchmark firm performance To

do that we rely on duality theory and the use of distance functions The differencebetween maximum potential output and observed output while maintaining a givenlevel of input use, or actual and minimum potential input for given output, or somecombination of the two is attributed to technical or X-inefficiency We interpret theseinefficiencies to be the result of contracting costs, managerial slack or oversight Theydiffer from allocative inefficiencies which are due to the choice of a non-optimal mix

of inputs and outputs

Distance functions are alternative representations of production technology whichreadily model multiple input and multiple output technological relationships Theymeasure the maximum proportional expansion in outputs or contraction in inputsthat firms would be able to achieve by eliminating all technical inefficiency They arethe primal measures; their dual measures are the more familiar value functions such

as profit, cost and revenue

(a) The Production Structure

Following F¨are et al (1985 and 1994) we assume that firms employ N inputs denoted

Technology may be characterised by a technology set T , which is the set of all feasible

The technology is illustrated in Figure 1 The graph consists of the input-output (x, y) combinations that are bounded by the curved line and the x-axis The technology set is assumed to satisfy a set of reasonable axioms Here we assume that T is a closed, convex,

The Shephard output and input distance functions (see Shephard, 1970) aredefined, respectively, as:

input direction by seeking the maximum feasible contraction λ of the input vector.5In

Figure 1 the Shephard output distance function would project (x, y) due North onto

distance function would project due West If a firm is efficient the Shephard distance

Note that we only require a weak form of optimality for measuring X-efficiency,

viz., radial (or hyperbolic) optimization Revenue maximization and cost minimization

are stronger forms of optimization Profit maximization entails an even strongeroptimization requirement (see F¨are and Grosskopf, 1996: p 24) It implies costminimization given the profit maximizing choice of output(s); it also implies revenuemaximization given the optimal choice of inputs (but not the converse) In this regard,the proposed methodology is general enough to encompass a variety of behaviouralassumptions and market structures

(b) Data Envelopment Analysis (DEA)

The input or output distance functions may be estimated using linear programmingmethods In particular, Data Envelopment Analysis (DEA), a non-parametric mathe-matical programming technique can be used to construct the empirical technological

or ‘best practice’ frontier and obtain the efficiency measures as distances from thisfrontier This can be implemented as follows:

Suppose we have k observations of inputs and outputs for k firms From these we can

construct a reference technology under constant returns to scale (CRS) as:

similar set of constraints

5 Alternatively, we could employ a hyperbolic efficiency measure, defined as D h (x, y) = minfλ : (λx, y/λ)

∈ Tg This measure simultaneously contracts inputs and expands outputs proportionally It does this along

a hyperbolic path to the frontier Under constant returns to scale (CRS) the hyperbolic distance function equals the square root of the Shephard output distance function or the square root of the reciprocal of the Shephard input distance function Also under CRS technical efficiency is dual to a profitability measure (see F¨are and Grosskopf, 2004).

6 An important property of the distance functions is the homogeneity condition The input and output

distance function D i and D oare homogeneous of degree +1 in inputs and outputs, respectively Under CRS the output distance function value is the reciprocal of the input distance function.

The right-hand sides of the inequalities in (3) above represent all of the outputsand inputs that are feasible given the observed inputs and outputs that are on the

left-hand side In other words, the N +M inequality constraints restrict the technology

in that for a particular firm no more output can be produced using no less input than

a linear combination of all observed inputs and outputs The z’s are usually referred

to as intensity variables and serve to construct convex combinations of the observeddata points Alternatively, a variable returns to scale (VRS) technology which allows forincreasing, constant and decreasing returns to scale can be constructed by restricting

the z’s to add up to one In this case maximum profits can be positive, negative, or zero.

Scale efficiency, a measure of the optimum scale of operations, can be estimated asthe ratio of technical efficiency measured under CRS over technical efficiency measuredunder VRS This ratio takes values between zero and one with a value of one indicatingscale efficiency Values of less than one are either due to decreasing or increasing

3 THE EMPIRICAL MODEL

(i) The Agency Cost Model

The regression equation for this model for each firm is given by:

D i = a0+ a1L i + a2L2i + a3Z 1i + u i (5)

where D is the firm efficiency measure obtained from (4) above; L is the debt to total

According to the agency cost hypothesis the effect of leverage (L) on efficiency should

high leverage levels, the effect of leverage on efficiency may be negative The quadraticspecification in (5) is consistent with the possibility that the relationship between

leverage and efficiency may not be monotonic, viz it may switch from positive to

negative at higher leverage Leverage will have a negative effect on efficiency for values

specifically, we assume that risk, size, growth opportunities, market power and exposure

by the standard deviation of earnings before tax during the period 2000 to 2004 The

7 An estimate of allocative efficiency (AE) may be obtained under CRS by taking the ratio of observed revenue over cost and dividing it by our measure of technical efficiency (see F¨are and Grosskopf, 2004) The allocative efficiency measure may take values above or below one, with one indicating allocative efficiency.

8 Most of these variables are used as determinants of firm efficiency in previous studies – see for example, Becchetti and Sierra (2003) and Berger and Bonaccorsi di Patti (2006) We would also have liked to control for ownership structure but information on this variable is not available in the data provided by Statistics New Zealand.

effect of this variable on firm efficiency is expected to be negative Riskier firms tendalso to be those which are poorly organized (see Berger and Bonaccorsi di Patti, 2006).Firm Size (SIZE) is measured by the logarithm of the firm’s sales The effect of thisvariable on efficiency is likely to be positive as larger firms are expected to use bettertechnology, be more diversified and better managed A negative effect may be observed

in situations where there will be loss of control resulting from inefficient hierarchicalstructures in the management of the company (see Wiliamson, 1967)

variable may be considered as an indicator of future growth opportunities (see Titmanand Wessels, 1988; Michaelas et al., 1999; and Ozkan, 2001) We would generally expectthat companies with substantial intangible investment opportunities will tend to adoptfaster to better technology, be better managed and thereby be more efficient and moreprofitable

Market power is proxied by the concentration index (CI) representing the share

of the largest four firms in the industry We would expect the effect of this variable

on efficiency to be negative, as competition forces firms to operate more efficiently

It is however, arguable that higher concentration does not necessarily indicate lowercompetition, but rather reflects market selection and consolidation through survival

of more efficient companies (see Demsetz, 1973) In this case, higher concentrationwill have a positive effect on efficiency

Exposure to international trade (TRADE) is measured by a dummy variableindicating whether the firm belongs to the tradables or the non-tradables sector Thehypothesis here is that firms which compete against imported goods or those in theexport sector should on average be more efficient than firms which operate in thenon-tradables sector

(ii) The Leverage Model

The capital structure equation relates the debt to assets ratio to our measure ofefficiency as well as to a number of other factors that have commonly been identified

in the literature to be correlated with leverage (see Harris and Raviv, 1991; and Myers,2001) The leverage equation is given by:

L i = β0+ β1D i + β2Z 2i + v i (6)

is an error term Under the efficiency-risk hypothesis, efficiency has a positive effect on

the financing choices of different subsets of firms in terms of these two conditionalhypotheses This is in line with Myers (2001) who emphasized that there is no universaltheory but several useful conditional theories describing the firm’s debt-equity choice.These different theories will depend on which economic aspect and firm characteristic

we focus on

structure, profitability, risk and growth and for industry characteristics such as market

9 Statistics in New Zealand do not provide a break down of other assets into intangibles and investments.

power that are likely to influence the choice of capital structure (see Harris and Raviv,1991; and Rajan and Zingales, 1995) Firm size (SIZE) is measured by the logarithm ofthe firm’s sales (see Titman and Wessels, 1988; Rajan and Zingales, 1995; and Ozkan,2001) As larger firms are more diversified and tend to fail less often than smaller ones,

we would expect that size will be positively related to leverage However Rajan andZingales (1995) discuss the possibility that size may also be negatively correlated withleverage They argue that size may act as a proxy for the information outside investorshave, and that informational asymmetries are lower for large firms which implies thatlarge firms should be in a better position to issue informationally sensitive securitiessuch as equity rather than debt

Asset Tangibility (TA) is measured by the ratio of fixed tangible to total assets (seeTitman and Wessels, 1988; Rajan and Zingales, 1995; Frank and Goyal, 2003; andHall et al., 2004) The existence of asymmetric information and agency costs mayinduce lenders to require guarantees materialized in collateral (Myers, 1977; Scott,1977; and Harris and Raviv, 1990) For example, if a firm retains large investments inland, equipment and other tangible assets, it will normally face smaller funding costscompared to a firm that relies primarily on intangible assets We would thus expect thattangibility should be positively related to debt

Profitability (PR) is measured by pre-interest and pre-tax operating surplus divided

by total assets (see Titman and Wessels, 1988; and Fama and French, 2002) Thereare conflicting theoretical predictions on the effects of profitability on leverage (seeHarris and Raviv, 1991; Rajan and Zingales, 1995; Barclay and Smith, 2001; and Booth

et al., 2001) Myers (1984) and Myers and Majluf (1984) predict a negative relationshipbecause they argue that firms will prefer to finance new investments with internalfunds rather than debt According to their pecking order theory, because of signallingand asymmetric information problems, firms financing choices follow a hierarchy

in which internal cash flows (retained earnings) are preferred over external funds,and debt is preferred over equity financing Thus, they argue, we should expect anegative relationship between past profitability and leverage On the other hand,using arguments based on the trade-off and contracting cost theories we can predict apositive relation between profitability and leverage For example, the trade-off theorysuggests that the optimal capital structure for any particular firm will reflect the balance(at the margin) between the tax shield benefits of debt and the increasing agencyand financial distress costs associated with high debt levels (Jensen and Meckling,1976; Myers, 1977; and Harris and Raviv, 1990) Similarly, Jensen (1986) argues that

if the market for corporate control is effective and forces firms to pay out cash

by levering up, then there will be a positive correlation between profitability andleverage

Intangible assets (OA) can be considered as future growth opportunities (Titmanand Wessels 1988; Michaelas et al., 1999; and Ozkan, 2001) Following Myers (1977)the underinvestment problem becomes more intense for companies with more growthopportunities The latter pushes creditors to reduce their supply of funds to this type

of firms Firms with expected growth opportunities would keep low leverage in order

to avoid adverse selection and moral hazard costs associated with the financing ofnew investments with new equity capital In theory, and according to Myers’ assertion,there should be a negative relationship between debt and growth opportunities Asnoted above, we use in this study a broader measure of ‘other assets’ which includesintangibles, shares, mortgages and debentures

Risk (SV) is measured by the standard deviation of annual earnings before taxes(Castanias, 1983; and MacKie-Mason, 1990) Several authors have included a measure

of risk as a determinant of leverage (e.g., Jordan et al., 1998; Titman and Wessels, 1988;MacKie-Mason, 1990; and Booth et al., 2001) A negative relation between risk andleverage is expected from a pecking order theory perspective: firms with high volatility

on earnings try to accumulate cash to avoid underinvestment problems in the future.From an agency costs or asymmetric information theory perspective we expect a positiverelationship (see Ross, 1977; and Harris and Raviv, 1990)

Growth is measured as the annual percentage change on earnings Growth is likely

to put a strain on retained earnings and push the firm into borrowing (Michaelas et al.,1999) On the other hand, Myers (1977) argues that firms with growth potential willtend to have lower leverage He argues that growth opportunities can produce moralhazard effects and can push firms to take more risk This may explain why firms withample growth opportunities may be considered as risky and thus face difficulties inraising debt capital on favorable terms

Market power is proxied by the concentration index (CI) This index represents themarket share of the largest four firms in the industry Berger and Bonaccorsi di Patti(2006) argue that this variable captures the effect of expected future rents from localmarket power on the firm’s capital structure decision so it is expected to be positivelyassociated with leverage

4 EMPIRICAL RESULTS

In this section we provide answers to the questions in Section 1 As mentioned inthe Introduction we are interested in examining how capital structure choices affectfirm value as well as the reverse relationship between efficiency and leverage Moreprecisely, we want to examine if leverage has a positive effect on efficiency and whetherthe reverse effect of efficiency on leverage is similar across the spectrum of differentcapital structures

We use a sample of 12,240 New Zealand firms from the 2004 Annual EnterpriseSurvey New Zealand is an interesting case study for the purposes of this investigation.Small and medium enterprises (SMEs) constitute the majority of all enterprises inthe country The Ministry of Economic Development (MED) in New Zealand defines

businesses in New Zealand share the following features:

managerial expertise

SMEs also play a major role in firm dynamics Births and deaths among small firms haveincreased by 142 and 126 percent, respectively, over the last decade Thus, financialdistress and liquidation are particularly important issues for the firms in our sample.Arnold et al (2003) in a study of the New Zealand industry structure report thatNew Zealand in comparison with other developed countries has the highest industry

10 Report of the Ministry of Economic Development (2005) on SMEs in New Zealand.