1

Small and Medium Size Enterprise Financing: a note on

some of the empirical implications of a pecking order

by

ROBERT WATSONDepartment of Accounting & Finance,

University of Glasgow,Glasgow G12 8LE

&

NICK WILSONLeeds University Business School,

University of Leeds,Leeds LS2 9JT

AcknowledgementBoth authors contributed equally to this paper.

Address for correspondence: R. Watson, Department of Accounting & Finance,University of Glasgow, Glasgow G12 8LE, UKE-mail: R.Watson@ACCFIN.GLA.AC.UK

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Small and Medium Size Enterprise Financing: a note onsome of the empirical implications of a pecking order

ABSTRACT

Asymmetric information models predict a “pecking order” which reflects a combination ofowner-manager preferences and external capital supply constraints whenever insiders knowmore about the true value of the firm’s prospects than outsiders. The pecking order results inretained earnings being the most preferred source of finance, then debt and finally the issue ofnew shares to outsiders. Using a sample of 629 UK SMEs over the five-year period from1990 to 1995 we find evidence consistent with a pecking order in which retained equity ispreferred over debt. As expected, the evidence of a pecking order was particularly strong inrespect of the closely-held firms in our sample.

Key Words: Asymmetric information, pecking order, capital structure, SME finance

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Small and Medium Size Enterprise Financing: a note onsome of the empirical implications of a pecking order

INTRODUCTION

It is now widely recognised that information asymmetries can give rise to significant financial

contracting and agency costs with the potential to influence the riskiness and size (and not, as

in the Modigliani and Miller (1958 and 1963) framework, merely the distribution) of a

company’s future cash-flows. Essentially, the financial claims underpinning any particular

capital structure are seen as capable of influencing corporate value via the impact upon

managerial incentives and therefore operational and financial decision making (see Brennan,

1995 and Harris and Raviv, 1991, for reviews). Capital structure models, such as Myers

(1984), focus on information asymmetries between insiders (i.e., managers, who are assumed

to always act in current shareholder interests) and outsiders, primarily potential new debt and

equity suppliers. Insider’s superior knowledge regarding the “true” worth of the business and

the additional contracting costs associated with credibly signalling this proprietary

information to outsiders reduces both the demand for and supply of external debt and equity

finance. The demand from owner-managers will be adversely affected because the higher

perceived risks and monitoring costs will lead potential external suppliers to demand an

‘unreasonably high’ premium on any funds advanced. By making all forms of external

finance, particularly new share issues, appear excessively expensive, the Myers (1984) and

Myers and Majluf (1984) models predict the existence of a ‘pecking order’ whereby internal

cash flows (i.e., retained earnings) are the preferred form of financing new investments. Of

course, in the absence of cost-effective risk assessment, monitoring and intervention

technology, external suppliers of debt finance may also be reluctant to finance some forms of

risky investments if they are unable to raise interest rates sufficiently without encountering

severe “adverse selection” problems (Stiglitz and Weiss, 1981). Thus, financial pecking

orders may also arise due to external debt finance being subject to rationing and other supply

constraints.

This suggests that, in practice, the interaction of owner-manager and external suppliers

preferences can be expected to produce firm-level financial structure decisions consistent with

a “pecking order” whereby internally-generated sources of finance will be preferred over

external sources. In this paper we empirically test some of the implications of the pecking

order model using a sample of UK small and medium size enterprises (SMEs). One of the

main empirical implications of the pecking order models is that any pecking order will be

positively related to both the commonality of interests between current shareholders and

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corporate decision makers (managers) and the degree of information asymmetry between

insiders and outsiders. In order to investigate these issues empirically, our analyses of the

pecking order hypotheses includes separate estimates and comparisons based on a partitioning

of the sample into closely-held (i.e., owner-managed) and other companies.1

The remainder of the paper is structured as follows. The next section examines the existing

theoretical and empirical literature with respect to SME capital structure decisions. This is

followed by a section which details our main empirical hypotheses and the empirical model

used in the analyses. The fourth section describes the sample and data and the fifth section

presents the main empirical findings of the paper. The final section summarises the paper,

examines the implications of the findings and suggests some further areas of research into the

topic.

CAPITAL STRUCTURE THEORY AND EMPIRICAL RESEARCH

The Modigliani and Miller (1958 and 1963) capital structure model shows that in a perfect

and complete market setting, how the firm is financed will have no effect upon its value.

What determines firm value is the size and riskiness of the cash-flows arising from its

investment and operating activities. As these cashflows are deemed to be independent of the

financing decision, with the exception of tax effects, capital structure will therefore be

irrelevant for determining firm value. In the intervening period, there have been many

attempts to show that the MM result is itself irrelevant to understanding the capital structure

choices of actual firms operating in realistic market settings. The risk characteristics of

different types of debt and the nature of the firms assets, the increasing financial

distress/failure costs and agency-induced value losses at high debt levels have all been

suggested as creating firm-specific optimal capital structure (see chapter 13 of Grinblatt and

Titman, 1998, for a review). Thus, many of the early post MM models of optimal capital

structure, because they modified the MM propositions to reflect increasing agency costs and

bankruptcy/distress costs at higher debt levels, are often referred to as (static) “trade-off”

models (Jensen and Meckling, 1976).

These models suggest 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 agency and

financial distress costs associated with high debt levels. The trade-off models have intuitive

appeal because they confirm casual observations of actual corporate policies, namely, as the

vast majority of firms have some debt, it is clear that that for most firms both no debt and all-

debt cannot be value-maximising, while a “moderate” amount of debt is probably optimal.

Empirically then, as each firm will have an optimal “target” debt ratio which maximises its

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value, in the absence of changes in the firms’ business risk characteristics, empirically the

“best” predictor of time t capital structure will be the t-1 capital structure, i.e.,

Debt/Total Assetst = α + β(Debt/Total Assetst-1 ) + ut (1)

where α = 0 and β = 1.

Generally however, despite their intuitive appeal, the empirical testing of some of the more

interesting implications of the trade-off models has been disappointing since their explanatory

power is typically low and the results in respect of specific propositions, such as the tax rate

relationship, are not always supportive. Miller (1977), also declared himself to be

unimpressed by these types of model. Indeed, he dismissed them as a recipe for a “horse and

rabbit stew, namely one horse and one rabbit” because the estimated financial distress/failure

costs were simply far too small to account for the significant trade-off with corporate tax

gains implied by these models.

Nevertheless, in the words of Brennan (1995), the theoretical focus had undoubtedly shifted

“from exploring the valuation of alternative ways of allocating given cash flow streams to

exploring the implications for the cash flow stream itself of the allocation mechanism used to

distribute it.” Empirical studies have continued to be published which are based on the idea of

some increasing cost component which is associated with high levels of debt to counter the

corporate tax shield benefits of debt, e.g., the amount of debt a firm carries is related to

business risk characteristics, the nature of the assets the firm invests in and the firm’s tax rate

(see Jordan et al (1998) for a recent example using UK SME data). More recently, empirical

researchers have tended to focus on what causes changes in a firm’s capital structure over

time. In first-difference form, which overcomes many of the problems associated with

unobservable firm-level fixed effects, the dependent variable is normally some measure of the

change in capital structure over the relevant time period and the right-hand-side variables

usually consist of factors which capture changes in firm-level risk over the same period.

Dhawan (1997), is a good recent example of this empirical approach, which also reflects the

contribution of finance theorists to the continued relaxation of some of the ceteris paribus

assumptions of the Modigliani and Miller model. The new theories of capital structure have

tended to emphasize the effect that the structure of financial claims has upon the incentives

and behaviour of the decision makers that determine the income stream. Myers (1984) and

Myers and Majluf (1984) developed a “pecking order” theory which was derived by assuming

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a commonality of interests between current shareholders and managers (insiders) but

asymmetric information and therefore heterogeneous expectations between insiders and

potential new investors (outsiders). The basic idea here is that, due to information

asymmetries, outsiders know less about the firms’ prospects than the owner-manager. The

owner-managers will attempt to maximise their (i.e., the current shareholders) value, not the

value of new investors. Thus, if the firm has good investment prospects, the owner-manager

will not want to issue new shares because some of the benefits of the investment projects will

have to be shared with the new shareholders.2 The conclusion is that retaining internally

generated earnings will be the preferred option, followed by raising debt finance for any

additional funding needs, since this ensures that the increased value from the future

investment projects accrue only to the existing shareholders.

If, however, prospects are poor, the owner-manager will wish to issue new equity since this

would benefit existing shareholders. Apart from the case of new and rapidly growing firms,

new share issues are, therefore, seen by outside investors as a signal of poor prospects. As a

result, share prices tend to decline when new share issues are announced, which makes new

issues an extremely expensive method of obtaining additional equity finance. It is worth

noting that both the Myers (1984) and Myers and Majluf (1984) models assume that the

decision makers (i.e., the corporate board) always act in current shareholders’ interests. Such

an assumption is uncontentious in the context of an owner-managed or closely held firm. In

the case of widely-held firms, however, where managers have discretionary decision making

powers and where governance and incentive mechanisms may not succeed wholly in aligning

shareholder and manager interests, managers may exploit their information advantage to the

detriment of the shareholders.

Another issue that the pecking order model does not address is that capital structure choices

are themselves typically constrained by information asymmetries and other market

“imperfections” which influence the supply-side, i.e., availability and costs of different types

of financial arrangement (Stiglitz and Weiss, 1981). The inability of potential lenders to

reliably distinguish between good and bad credit risks and the “optimistic” bias typically

associated with owner-manager assessments of their projects, exposes them to the danger of

“over-investment”, that is, investing in firms which subsequently default or otherwise

generate a negative net present value return to the lender (De Meza and Webb, 1990).

Outside investors’ fears regarding possible over-investment can be expected to raise the cost

of capital in sectors where it is particularly difficult to distinguish between good and bad

credit risks. As Akerlof (1970) has argued, in the extreme, information sensitive markets may

collapse altogether in the absence of credible commitments/signalling mechanisms. In the

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case of corporate credit markets, whereby SME owner-managers are unable to convincingly

signal their inside information to potential new investors, systematic under-investment (i.e.,

credit rationing whereby some firms with positive net present value projects are unable to

obtain finance) may result. Thus, empirically a firm’s capital structure is likely to reflect

trade-offs between external financing constraints and owner-manager preferences. Once

made, capital structure decisions are important determinants of firm value through their

impact on the incentive structures, managerial behaviour and financial risk exposure of the

enterprise (Keasey and Watson, 1995). Hence, unlike in the (symmetrically distributed

information) Modigliani and Miller model, in which capital structure decisions are only of

“second-order” distributional and/or tax-related importance, in an asymmetric information

framework capital structure decisions may have far reaching economic consequences.

To date, the evidence regarding the relative explanatory power of the static trade-off and

pecking order models is somewhat weak, not least because the trade-off model will often fail

to be empirically rejected even when false (see Shyam-Sunder and Myers, 1999). Helwege

and Liang (1996), using a US sample of (post) IPO firms found limited evidence of a pecking

order, though arguably the asymmetric information problem that gives rise to pecking order

preferences may be less severe in the case of post IPO firms. However, a study by Griner and

Gordon (1995), also based on US data, examined the alternative empirical predictions of the

pecking order and managerial (self-interest) hypotheses and uncovered evidence in favour of

the pecking order. Jordan et al (1998), using a capital structure levels model on data from,

what appeared to be, a fairly representative sample UK SME’s, also reported that they had

found evidence consistent with a pecking order.

HYPOTHESES AND EMPIRICAL MODELLING

The static trade-off models that focus on the determinants of optimal capital structure usually

make the simplifying assumption that there are only two types of finance: equity and debt. In

empirical work, such as the recent Jordan et al (1998) study, this allows for the construction

of a dependent variable such as the debt to equity ratio or the ratio of debt to total assets.

With the pecking order model, finer distinctions between different sources of finance are

required to test predictions such as that retained earnings are preferred to new share issues

(even though both are classed as equity). Thus, the full range of pecking order predictions are

difficult to test using a single financial leverage dependent variable. Moreover, as Shyam-

Sunder and Myers (1999) have argued, most empirical specifications incorporate a partial

adjustment mechanism which means that, even if firms behave in a pure pecking order

manner, it is still extremely difficult to empirically reject the static trade-off model as a valid

description of firms’ capital structure policies.

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In order to mitigate these problems, in this paper we undertake two forms of analysis. First,

we analyse the actual one-period changes in each major category of financing. Based on the

static trade-off model predictions and each firm’s actual growth rate over the period, these

actual changes are further decomposed into their “expected” and “unexpected” components.

For example, in the case of total debt, the actual change (as a proportion of total assets) can be

decomposed into its expected and unexpected components as follows:

Actual change = Expected change in total debt + Unexpected change in total debt

in total debt

(Dt-Dt-1)/TAt-1 = (g x Dt-1/TAt-1 ) + ((Dt-Dt-1)/TAt-1 - g x Dt-1/TAt-1) (2)

.

where g = (TAt-TAt-1)/TAt-1

If all of the average “expected” changes in financing derived from the static trade-off model

are statistically indistinguishable from the actual changes (or, equivalently, the average

“unexpected” change for each category of financing = 0), then this will be evidence which is

consistent with the static trade-off model, but inconsistent with the pecking order model.

However, if:

(a) the average “unexpected” change in retained earning is statistically greater than zero,

(b) the average “unexpected” change in debt is statistically less than the “unexpected”

change in retained earnings and

(c) the average “unexpected” change in new share issues to outsiders is statistically less than

the average “unexpected” change in debt,

then this will provide statistical evidence consistent with the pecking order model but

inconsistent with the static trade-off model.

The second set of tests involve regression analyses whereby the dependent variable consists

of the actual growth rate of each firm in each period and the independent variables consist of

the relative changes in each of the different category of financing used. By focusing on how

the changes in each type of financing impacts upon total financing we are able to investigate

whether the pattern of estimated coefficients are consistent with a pecking order. We begin

from the accounting balance sheet identity, which equates the total assets of the enterprise

(the uses of funds) with different types of financial claims used to fund these assets (the

sources of funds), i.e.,

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Total Assets (TAt) = Equity (Et) + Debt (Dt) + Other Liabilities (OLt) (3)

Initially, we focus on changes in the relative proportions of debt and equity. If changes in

other liabilities (OL) for each firm are assumed to randomly fluctuate around its average

growth rate, then the following empirical model can be estimated:

(TA i t – TA i t-1)/TA i t-1 = ∑α i + β1(E i t-E i t-1)/TA i t-1 + β2(D i t-D i t-1)/TA i t-1 + u i t (4)

where ∑α i = a vector of fixed effects representing firm i’s average growth in (OL i t-OL i t-

1)/TA i t-1, and β1 = β2 if the target capital structure model is correct, i.e., the proportionate

change in the requirement of finance is exactly matched by the same proportionate changes in

equity and debt.

If, however, the pecking order model, which assumes that retained earnings are preferred to

debt, is valid, then (ignoring new equity issues for the moment) it is to be expected that the

coefficient on the change in equity variable (β1) will be greater than the coefficient on the

change in debt variable (β2), i.e., β1 > β2.

Of course, the pecking order model also assumes that taking on more debt will normally be

preferable to issuing new equity to outsiders since the latter will be expensive and will lead to

a dilution of ownership. In order to include this into the model, we can define the change in

equity to be as follows:

Eit = Eit-1 + Pit – Divit + NEit, (5)

where NEit = net new equity issued over the period, Pit = profit available for distribution to

shareholders, Divit = dividend payments and, therefore, Pit – Divit = retained profit for the

period.

Substituting (Pit – Divit) + NEit into equation (4) gives the following:

(TAit – TAit-1)/TAit-1 = ∑αi + β1((Pit - Divit )/TAit-1 ) + β2(NEit/TAit-1 )

+ β3(Dit-Dit-1)/TAit-1 + uit (6)

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where, if a pecking order exists with retained equity, then debt and finally new equity to

outsiders as the preferred ranking of finance, β1 > β3 > β2.

In Table 2, we present panel data empirical results from estimating equation (6) that are

consistent with the pecking order model. In order to further test some of the implications of

the pecking order model, we partition the sample into high and low information asymmetry

and managerial ownership groups, i.e., closely-held and other firms respectively. The pecking

order pattern of the coefficients should be more apparent in the case of the closely-held (high

information asymmetry and managerial ownership) group than in the case of the other firms

group (i.e., the relatively low information asymmetry and managerial ownership firms). Also

included in Table 2 are the separately estimated regressions for each group, plus an

augmented version of equation (6) which includes an additional interaction term to measure

the differential slope coefficient on the retained earnings variable in respect of the high

information asymmetry firms. If the estimated coefficient on this interaction term is

significantly positive then this will indicate that the closely-held firms have a significantly

greater preference for using retained earnings over other sources of finance.

The next stage of the analysis focuses on the composition of debt, since not all types of debt

may be equally preferred. The total change in debt finance can be decomposed into categories

that reflect its costs, availability and owner-manager preferences. Four categories of debt are

considered: hire purchase liabilities, long term debt, short term debt (including overdrafts)

and intra-group debt balances. Intra-group balances may be governed by external factors

such as head office credit policies that are unrelated to information asymmetries. Hire

purchase liabilities are essentially a form of fixed term finance tied to a specific asset

purchase in which the legal title to the asset remains with the lender until the final instalment

has been made. Thus during the term of the loan, the finance is “secured” on the basis of the

second-hand value of the asset. This may be a significant source of finance for very small

owner-managed firms that are either already highly geared and/or have insufficient cash or

collateral to finance the purchase of “general purpose” items in which there is a well-

organised second-hand market, e.g., vehicles. Due to the relatively higher information

requirements and greater monitoring costs associated with long term finance, it is reasonable

to assume a likely preference amongst owner-managers for short-term rather than long term

debt finance. Thus, equation (6) can be further augmented as follows:

(TAit–TAit-1)/TAit-1 = ∑αi + β1((Pit-Divit)/TAit-1)) + β2(NEit/TAit-1) + β3(GRPit-GRPit-1)/TAit-1

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+ β4(HPit-HPit-1)/TAit-1 + β5(SDit-SDit-1)/TAit-1 + β6(LDit-LDit-1)/TAit-1 + uit (7)

where (GRPit - GRPit-1)/TAit-1, (HPit-HPit-1)/TAit-1, (SDit-SDit-1)/TAit-1 and (LDit-LDit-1)/TAit-1

are respectively the change in intra-group balances, hire purchase, short term debt and long

term debt.

Empirical estimates of the model described by equation (7) are presented in Table 3. As with

the previous table, additional estimates of the model are presented which include an

interaction term to measure the differential slope coefficient on the closely-held firms’

retained equity variable, plus separately estimated models for the closely-held firms and the

other firms samples.

Our data also allows an exploration of the relationships between gross operating profits,

directors fees, corporate taxes and dividends. Given that firms have a preference for retained

earnings, how do they “manage” their capital structures, what discretion do they have in

regard to dividends and/or directors fees? Non-listed, smaller, owner-managed firms clearly

have more discretion over these matters (and have greater incentives to exercise their

discretion) than listed and widely-held firms where outside investors may rely upon/expect a

certain level of dividends each year and where directors remuneration is determined via

remuneration committees and related to external managerial labour market factors (see

Ezzamel and Watson, 1998).

So, for example, if the profit before directors remuneration is defined as, πit = Pit + DRit, this

will represent the total funds available for reinvestment from internal sources and the

“discretionary” outflows will be (Divit + DRit) and, therefore, the actual amounts reinvested

(i.e., the retained earnings at time t) will be:

πit - (Divit + DRit.).

We can replace the retained earnings variable (Pit-Divit)/TAit-1 used in the previous two tables

with the above three variables (divided by TAit-1) that together constitute retained earnings. A

significant improvement in the explanatory power of the empirical estimates vis-à-vis Table 2

is to be expected if these discretionary payments across the firms in the sample are of any

systematic importance in explaining their relative need for new finance. The positive

coefficient on the profit before directors fees variable (πit) should also be larger in absolute

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size than the (expected) negative coefficients on the dividend (Divit) and directors fees

variables (DRit.).

These results, because they confirm the main findings discussed later in the paper, are not

reported in detail but are available from the authors upon request. In addition, the following

forms of partitioning, reflecting other possible causes of any one-period changes in capital

structure, have also been undertaken, but not reported since these results did not materially

alter the conclusions to be drawn from the reported results:

• the firm being debt constrained, i.e., firms with prior period high debt/Equity ratios can be

expected to use relatively more retained equity and other sources of equity than low D/E

ratio firms.

• firms with unexpectedly high earnings (and/or unexpectedly low Dividends) relative to

last year, since these may prefer retained earnings over debt.

• firms experiencing positive growth, since the pecking order model coefficients may only

apply to growing firms as firms experiencing negative growth may have different

preferences.

• young firms, age less than 10 years old, since information asymmetries may be expected

to decrease with time

• small firms, less than 50 employees, may be expected to have different pecking order

preferences irrespective of ownership concentration.

THE SAMPLE AND DATA

Data for this study is from 655 firms that responded to a mail survey on trade credit practice

conducted in late 1994, constituting a useable response rate of 18%3. The survey collected

detailed data on the firm itself; its ownership and organisational structure, products, markets,

supplier relationships, and credit management issues. The firms mailed were a randomly

selected stratified sample of manufacturing companies originally drawn from the UK FAME

database and all had single digit SIC Codes 2,3 or 4. Details of the survey instrument can be

obtained from the authors. This was then matched with the financial and behavioural data

contained on a full credit reference report, provided by Dun & Bradstreet International. The

sample reduces to 626 if we include only those firms for which we have more than one year

of financial data.

Section A of Table 1 shows the number of observations per year and the (almost-equal) split

between closely-held businesses (i.e., those in which the directors collectively own a majority

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stake) and other firms. It is to be expected that the pecking order models will have greater

explanatory power in respect of the closely-held firms than for the other firms.

Section B of Table 1 indicates that the mean total asset size of the closely-held firms, at

slightly less than £4m, is less than 40% that of the other firms (£10.3m). In terms of the

relative proportions of different types of financing, Section B indicates that though both

groups of firms have approximately the same proportions of equity finance (share capital plus

retained profits), the closely-held firms have a smaller proportion of issued share capital and a

correspondingly higher proportion of retained earnings vis-à-vis the other firms. In terms of

the non-equity forms of finance, it is also clear that the closely-held firms make greater use of

short-term debt (e.g., overdrafts) and other liabilities (e.g., trade credit, provisions for future

tax, other accrued charges and directors loans, unpaid fees, etc.), whilst the other firms obtain

proportionately more of their funds from other group company members.

In Section C of Table 1, the profit and loss account variables are expressed in terms of their

percentage of annual sales. For both the closely-held and other firms, the total appropriations

of profit in the form of directors fees and dividends is about 5% of sales. In the case of the

other firm sample, the average appropriations of profit as directors fees and dividends are

approximately equal. However, as should be expected for the closely-held firms, directors fee

payments are some 3.5 times the size of dividend payments.

Section D of Table 1 provides a summary of the variables used in the empirical models in

which all items are expressed in terms of the annual balance sheet opening total assets figures.

As can be seen from the table, the average percentage change in total assets over the five year

period from 1989-90 to 1993-4, is a little over 3.6%, slightly higher at 4% in the case of the

closely-held firms. Retained profits at 2.41% represents approximately 60% of the additional

finance for the growth in total assets in respect of the closely-held firms whilst in the case of

the other firms retained profits account for only one-third of the additional finance.

THE EMPIRICAL RESULTS

In section E of Table 1, the actual changes in retained earnings, total debt and new share

issues are each decomposed into their “expected” and “unexpected” changes. It will be

recalled from equation (1) that the static trade-off model implies that all of the “unexpected”

changes will be statistically indistinguishable from zero. The pecking order model predicts

that the average unexpected retained earnings will be positive, the average unexpected debt

either negative or significantly less positive than the average level of unexpected retained

earnings, and the average unexpected new share issues significantly less than the average

unexpected debt. Moreover, these differences in unexpected changes will be most apparent in

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the case of the closely-held firms, due to the predicted greater responsiveness to owner-

manager preferences, greater information asymmetries between insiders and outsiders and the

greater costs associated with external financing characteristic of these firms.

As can be seen from section E of the table, the overall sample unexpected changes in retained

earnings, though positive, is not statistically significantly so at conventional levels of

confidence. However, as predicted by the pecking order model, for the sub-sample

consisting of closely-held firms, the average positive unexpected retained earnings is

statistically significant at 1% levels. The unexpected change in debt is statistically

significantly negative as predicted by the pecking order model at 5% for the full sample and

for the other firms, though not for the closely-held sub-sample. As regards new share issues,

though the overall sample average is statistically significantly positive at the 5% level of

confidence, it is clear from the sub-sample results that only in the case of the other firms is the

average positive unexpected new share issues of any statistical importance. T-tests for the

equality of means between the closely-held and other firms conducted on the three

unexpected changes in finance variables show that with the exception of the debt component,

the differences between the two sub-samples are statistically significant at 1% levels of

confidence. On the basis of these results, it seems safe to conclude that the pecking order

model provides a more empirically plausible explanation of the changes in financing than the

static trade-off model, particularly, as expected, in relation to the closely-held firm sub-

sample.

We now turn to the regression estimates detailing the relative influence of each of the

financing components in relation to overall financial changes. Table Two presents the

simplest models in which we distinguish between retained earnings and changes in share

capital and total debt. The first two columns, labelled Models 1 and 2, are estimated using all

observations whilst the final two columns, closely-held and other firms, have been estimated

using only the respective sub-samples. In all 4 models, however, the pattern of coefficients

are the same in terms of relative size: the retained profits coefficient is largest, followed by

the change in total debt coefficient and, finally, the change in share capital coefficient being

in all cases the smallest. Model 2 includes an additional interaction term to measure the

differential coefficient on the retained profits variable for the closely-held firms and, as

expected, this is significantly positive. The separately estimated sub-sample models, unlike

model 2 which allows only the coefficient on the retained profits variable to vary, allows all

of the coefficients in the model to differ across the two groups of firms. As can be seen from

the table, all of the estimated coefficients for the closely-held firms are higher than those

estimated on the other firms sample. The separately estimated model is also able to explain a

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significantly higher proportion of the variance in the dependent variable for the closely-held

firms than is the case for the other firms sample model4.

The models estimated in Table 2 do not distinguish between different types of debt. In Table

3 we distinguish between four types of debt: hire purchase, debts owing to group members,

long-term debt and short-term debt. As can be seen from the table, the inclusion into the

models of these additional variables produces stronger results in terms of the adjusted R2’s,

and in terms of the relative size and statistical significance of all the (new and old)

independent variable coefficients. The pattern of coefficients in all cases confirm the

previous results but also suggest that in the case of the closely-held firms there is also a

stronger preference for HP debt than for other types of debt, but that all types of debt are

preferred over the issuing of new share capital. The greatly increased explanatory power of

all of the empirical estimates vis-à-vis Table 2 suggests that there may be a pecking order

within debt finance, particularly in regard to the other firms sub-sample since these firms

appear to have a greater variety of choices of finance open to them than the closely-held

firms. It is also evident that for many closely-held firms there is a greater preference for

additional hire purchase finance than for other types of debt.

As indicated earlier, it is possible to replace the retained profit variable with the 3 variables

which together constitute the retained profits measure: profit before directors fees, directors

fees and dividend payments. If, as expected, closely-held firms have both greater

opportunities and incentives to retain profits in the business then we would expect to find that

the model including the separate components of retained earnings would provide higher

explanatory power than the model which included only the retained profits variable. Also, the

(negative) coefficients on the directors’ fees and dividend variables should be smaller (in

absolute terms) than the (positive) total profit coefficient. The estimate for the closely-held

firms, not reported here, is indeed able to explain a significantly higher proportion of the

variance in the dependent variable vis-à-vis the Table 2 estimates. Moreover, the (negative)

coefficients on the directors’ fees and dividend variables are, as expected, smaller than that of

the total profit variable. These findings were, however, also equally applicable in respect of

the empirical results of the other firms. It seems, therefore, that irrespective of the level of

insider ownership all SMEs have the necessary incentives and opportunities to vary their

profit distribution policies in a manner which appears to increase the explanatory power of the

pecking order model.5

When we further partition both samples by age, we find some evidence that younger firms

have a stronger preference for financing via retained earnings and short-term debt. This latter

16

finding, though entirely consistent with the pecking order explanation may, however, be more

to do with supply-side constraints than owner-manager preferences. Finally, as can be seen

from tables 1 and 2, as expected, the firm-level fixed effects are only of any statistical

significance in respect of the other firms sample. Indeed, it is the explanatory power of the

fixed effects that largely accounts for the greater explanatory power of the other firms model

relative to that of the closely-held firms. We interpret this finding as being consistent with the

pecking order model as it confirms that the changes in other liabilities (mainly trade credit

and unpaid tax balances) for the closely-held firms are relatively less stable than these of the

other firms. Though, once again, this finding could equally reflect supply-side constraints.

CONCLUDING REMARKS

The paper has made a preliminary attempt at empirically testing the pecking order model

implication that when SMEs require additional finance the use of retained earnings will be

preferred over debt and that debt will be preferred over new share issues to outsiders. We

examined the relative size of the estimated coefficients on the various equity and debt

variables in a regression model that attempted to explain how the annual changes in firm

assets had been financed. The pattern of coefficients was found to be consistent with the

pecking order model predictions and this was found to be particularly strong in relation to the

closely-held firms where information asymmetries and a commonality of interests between

shareholders and managers, and therefore the suggested pecking order preferences, would be

most apparent.

The results also suggest that there may be a pecking order within debt types since the

explanatory power of all the estimated models increases significantly when the change in debt

is decomposed into its various components. Clearly, much more work has to be undertaken

prior to firm empirical conclusions can be drawn. Our analysis does not take into account

differences in the use of the funds, i.e., the asset side of the balance sheet, changes in

perceived risks and nor does it address the cost and availability supply-side factors which,

rather than owner-manager (demand-side) preferences, may be driving the results obtained.

17

REFERENCES

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Dhawan, R. (1997), ‘Asymmetric Information and Debt Financing: the empirical importance of size and balance sheet factors’, International Journal of the Economics of Business, Vol.4, pp. 189-202.

Ezzamel, M. and R. Watson (1998), ‘Market Comparison Earnings and the Bidding-Up of Executive Cash Compensation: evidence from the UK’, Academy of Management Journal, Vol. 41, pp. 221-231.

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Myers, S.C. (1984), ‘The Capital Structure Puzzle’, Journal of Finance, Vol. 39, pp. 575- 92.

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firms have information that investors do not have’, Journal of Financial Economics, Vol.13 pp. 187-221.

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19

Table OneDescriptive Statistics

Section A: Observations by Year and Firm Type

Year All Firms Closely-Held Other Firms1990 498 244 2541991 547 268 2791992 574 282 2921993 578 288 2901994 384 213 170Total 2581 1295 1286

Section B:Capital Structure Variables

All Firms Closely-Held Other FirmsMean (SD) Mean (SD) Mean (SD)

Total Assets (£m) 7.14 (10.86) 3.98 (4.70) 10.32 (14.65)Share Capital (%) 14.88 (24.26) 11.22 (17.35) 18.56 (29.64)Retained profits (%) 19.14 (35.80) 24.17 (28.58) 14.07 (41.23)Long-term Debt (%) 4.12 (10.10) 4.74 (8.22) 3.50 (11.65)Hire Purchase (%) 2.24 (5.30) 2.95 (6.03) 1.53 (4.33)Short-term Debt (%) 9.04 (12.72) 10.51 (12.38) 7.55 (12.88)Group Debt (%) 12.85 (20.05) 5.28 (13.42) 20.47 (29.40)Other Liabilities (%) 37.73 (20.27) 41.11 (20.48) 34.32 (19.48)

All section B variables expressed in terms of their % of the end of year balance sheettotal asset values.

Section C: Profit & Loss Account Variables

All Firms Closely-Held Other FirmsMean (SD) Mean (SD) Mean (SD)

Sales (£m) 10.44 (12.29) 6.57 (7.06) 14.11 (14.80)Profit before DF(%) 6.95 (21.06) 7.21 (14.32) 6.70 (25.86)Directors Fees (%) 3.09 (11.02) 3.75 (10.55) 2.44 (11.43)Profit after DF (%) 3.77 (19.11) 3.27 (9.57) 4.24 (11.43)Tax (%) 1.20 (2.82) 1.07 (2.17) 1.33 (3.33)Dividends (%) 1.83 (8.04) 1.04 (3.52) 2.62 (10.77)Retained profits (%) 0.61 (19.31) 1.02 (8.28) 0.22 (25.70)

All section C variables, with the exception of sales, are expressed in terms of their % ofannual turnover

20

Section D: Variables in the Empirical ModelsAll Firms Closely-Held Other FirmsMean (SD) Mean (SD) Mean (SD)

% ∆ in Total Assets 3.62 (16.50) 4.00 (16.12) 3.24 (16.88)% ∆ share capital 0.92 (7.67) 0.30 (3.04) 1.55 (10.39)% ∆ Long-term Debt -0.05 (7.19) 0.17 (6.11) -0.27 (8.13)% ∆ Short-term Debt -0.28 (10.21) -0.15 (9.39) -0.41 (10.98)% ∆ HP Debt 0.11 (3.53) 0.19 (3.49) 0.02 (3.57)% ∆ Group Debt 0.41 (12.26) 0.15 (8.00) 0.67 (15.41)Profit before DF 11.54 (16.37) 13.14 (17.13) 9.98 (15.59)Directors Fees 4.96 (9.40) 6.55 (12.11) 3.37 (4.97)Tax 2.11 (3.36) 1.99 (3.10) 2.22 (3.60)Dividends 2.46 (6.43) 1.71 (4.71) 3.22 (7.72)Retained Profits 1.73 (10.63) 2.41 (8.68) 1.07 (12.20)

All section D variables expressed in terms of their % of start-of-period total assets.

Section E: Unexpected Changes in Financing

All Firms Closely-Held Other FirmsMean (SD) Mean (SD) Mean (SD)

% unexpected ∆ inRetained Profits

t-value for equalityof means =

0.24 (10.85)

2.38***

0.76 (8.40) *** -0.27 (12.77)

% unexpected ∆ inshare capital

t-value for equalityof means =

0.56 (8.57) **

2.71***

0.11 (3.91) 1.02 (11.47)***

% unexpected ∆ inTotal Debt

t-value for equalityof means =

-0.53 (11.67) **

1.00

-0.31 (10.68) -0.76 (12.58) ***

All section E variables expressed in terms of their % of start-of-period total assets.

*** = significant at 1%; ** significant at 5%; * significant at 10%.

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Table TwoEmpirical Estimates:Basic Model

Dependent Variable = (TAt-TAt-1)/TAt-1

All Firms All Firms Closely-Held Other FirmsModel 1 Model 2

Variable Name Coefficient Coefficient Coefficient CoefficientRetained Profit 0.568

(15.65) ***0.488(11.27) ***

0.780(12.62) ***

0.467(10.32) ***

∆ Share Capital 0.192(4.19) ***

0.187(4.08) ***

0.437(2.96) ***

0.150(3.02) ***

∆ Total Debt 0.447(17.14) ***

0.447(17.17) ***

0.608 (16.07) ***

0.330 (9.23) ***

Retained Profit xclosely-helddummy

0.260(3.37) ***

Constant 0.025(8.47) ***

0.023(7.81) ***

0.017(4.05) ***

0.027 (6.45) ***

Year dummiesSignificant?

Yes*** Yes*** Yes*** Yes***

Fixed EffectsSignificant?

Yes* Yes* No Yes***

Adjusted R2 (%) 28.4 28.8 34.7 26.4

N = 2509 2509 1237 1272

*** = significant at 1%; ** significant at 5%; * significant at 10%.

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Table ThreeEmpirical Estimates:Types of Debt Model

Dependent Variable = (TAt-TAt-1)/TAt-1

All Firms All Firms Closely-Held Other FirmsModel 1 Model 2

Variable Name Coefficient Coefficient Coefficient CoefficientRetained Profit 0.747

(22.62) *** 0.687(17.50) ***

0.906(15.42) ***

0.670 (16.62) ***

∆ Share Capital 0.469(11.00) ***

0.464 (11.00) ***

0.641 (4.65) ***

0.431(9.53) ***

∆ Short-term Debt 0.603(21.67) ***

0.604(21.72) ***

0.640(15.38) ***

0.566(14.98) ***

∆ Long-term Debt 0.629(15.60) ***

0.624(15.50) ***

0.677(9.93) ***

0.583(11.48) ***

∆ HP Debt 0.676 (8.99) ***

0.672 (8.96) ***

0.781 (7.21) ***

0.546 (5.15) ***

∆ Group Debt 0.583 (23.08) ***

0.580 (22.97) ***

0.670 (12.22) ***

0.545 (18.56) ***

Retained Profit xclosely-helddummy

0.195 (2.86) ***

Constant 0.016 (6.17) ***

0.015(5.64) ***

0.010(2.71) ***

0.018(5.00) ***

Year dummiesSignificant?

Yes*** Yes*** Yes*** Yes***

Fixed EffectsSignificant?

No No No Yes***

Adjusted R2 (%) 44.4 44.6 42.6 46.1

N = 2509 2509 1237 1272*** = significant at 1%; ** significant at 5%; * significant at 10%.

23

Endnotes 1 Closely-held is a binary variable, coded 1 when the current directors have a majorityequity stake and 0 otherwise. It would be desirable to measure ownershipconcentration as a continuous variable i.e. percentage of equity owned by currentdirectors in order to test hypothesis concerning the precise relationship betweenfunding sources and principal/agent compatibility. Moreover, there are other potentialproxies, the age of the firm, for instance, that might be used to test whetherinformational asymmetries decrease over time. Also, as size and ownershipconcentration are correlated, a number of tests for age/time and size impacts on thepecking order hypothesis were also undertaken.

2 Also, there are no issue costs associated with retained earnings.

3 The response rate was as expected given the detailed nature of the questionnairesurvey. However, a response rate of 18% requires that tests for non-response biasshould be undertaken. The comprehensive data-base from which the sample framewas drawn allowed thorough tests to be completed. Two tests were carried out. Thefirst involved tests for differences in the sample characteristics between earlyrespondents (returned questionniares within one month) and later respondents(returned > 1 month). No significant differences were found. The second test involvedt-tests on the full sample of respondents and non-respondents in four areas: size (e.g.total assets, employees, turnover); profitability (ROCE, ROTA); working capitalstructure (debtor days, creditor days) and age. There were no differences in the meansof these variables at 5% significance. Pearson’s chi square was used to test forgeographical and industry bias and differences in legal status.

4 Various experiments were conducted with variables representing both age and sizeof firm. Age was measured as a continuous variable, a quadratic function, a linearspline and a binary variable for firms less than 10 years old and used as bothadditional control variable and an alternative partitioning mechanism. The sample waspartitioned by age (less than 10 years old) and size (less than 50 employees). Apattern of results consistent with a pecking order was always maintained. In respect ofage partitions it was evident that younger firms pay lower dividends and are morereliant on short-term debt finance. The latter result, however, may be more consistentwith supply-side financial constraints than firm–level preferences.

5 Also, the lack of any significant ownership effect either within or between the twogroups of firms appears to be consistent with Griner and Gordon’s (1995) inability touncover any significant evidence in favour of the managerial (self-interest)hypothesis.