1 Rethinking SME’ s financing decisions: a demand-side approach Anahí Briozzo * Hernán Vigier ** Abstract Corporate finance, and capital structure theory in particular, study the firm’s decisions from a normative point a view: shareholder value maximization. However, Brealey and Myers (2006) point out that “how financial decisions are made” is a question that still remains unanswered. In this paper we propose a descriptive approach to the understanding of how small and medium enterprises (SME) make their financing decisions. We identify three distinctive cases of attitude towards debt financing. Considering the owner-firm intertwinement as a fundamental characteristic of these firms, we include behavioral factors as explanatory variables for these cases. Then we compare the implications of our approach with a dataset of firms from Bahía Blanca (Argentina). JEL: G3 Introduction Researchers from the small and medium enterprises (SME hereafter) field, like Storey (1994), argue that the difference between small and large firms is not only a matter of size, and consequently specific models are required to study SME. There are several characteristics that differentiate small from large firms. First of all, management and ownership unification leads to an owner-firm intertwinement (Ang, 1992), both at economical and emotional levels. Second, the form of private equity affects the diversification possibilities and risk position of the owners. Moreover, the lack of professional management causes business problems, like planning shortsightedness. Agency costs of equity exist also in these firms, when firm control and ownership are shared among the business partners. Only a firm with a single owner-manager is free of agency costs of equity. In family firms, the inclusion of new generations is another source of agency problems. On the other hand, information asymmetry problems between small firms and external funds providers are specially strong, because of the informality and scarce information available. Last, these firms have shorter life expectancy, given that the firm may cease to exist if just one person (the owner) leaves, and the lack of firm succession planning. Capital structure theory has been built around Modigliani and Miller (1958)’s propositions of irrelevance in a context of perfect capital markets. The acknowledgment of imperfections that make relevant the capital structure, such as corporate and personal taxes, transaction costs, and information asymmetries have been the guide to later developments. However, there is still no theoretical agreement on the relevance of capital structure to firm value, which can be summed up through the confronting positions of trade off theory and the pecking order. While the first argues that costs and benefits of debt lead to an optimum value, the later suggests that capital structure is just the result of “cumulative requirements for external financing” 1 . In the specific field of SME, authors like Hamilton and Fox (1998) and Hutchinson et al (1998) propose a financing hierarchy (or pecking order) of preference for internal funds, that is based on the owners’ desire for control and flexibility. While the original proposition of the * Departamento de Ciencias de la Administración - Departamento de Economía, Universidad Nacional del Sur (UNS). CONICET. E-mail: [email protected]. ** Departamento de Economía, UNS. Universidad Provincial del Sudoeste (UPSO). E-mail: [email protected]We thank Fernando Tohmé and seminar participants at Universidad Nacional del Sur (Departamento de Economía) for helpful comments. 1 Myers (1984), p. 581.
Transcript
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Rethinking SME’ s financing decisions: a demand-side approach
Anahí Briozzo* Hernán Vigier**
Abstract
Corporate finance, and capital structure theory in particular, study the firm’s decisions
from a normative point a view: shareholder value maximization. However, Brealey and Myers (2006) point out that “how financial decisions are made” is a question that still remains unanswered. In this paper we propose a descriptive approach to the understanding of how small and medium enterprises (SME) make their financing decisions. We identify three distinctive cases of attitude towards debt financing. Considering the owner-firm intertwinement as a fundamental characteristic of these firms, we include behavioral factors as explanatory variables for these cases. Then we compare the implications of our approach with a dataset of firms from Bahía Blanca (Argentina). JEL: G3
Introduction
Researchers from the small and medium enterprises (SME hereafter) field, like Storey (1994), argue that the difference between small and large firms is not only a matter of size, and consequently specific models are required to study SME.
There are several characteristics that differentiate small from large firms. First of all, management and ownership unification leads to an owner-firm intertwinement (Ang, 1992), both at economical and emotional levels. Second, the form of private equity affects the diversification possibilities and risk position of the owners. Moreover, the lack of professional management causes business problems, like planning shortsightedness. Agency costs of equity exist also in these firms, when firm control and ownership are shared among the business partners. Only a firm with a single owner-manager is free of agency costs of equity. In family firms, the inclusion of new generations is another source of agency problems. On the other hand, information asymmetry problems between small firms and external funds providers are specially strong, because of the informality and scarce information available. Last, these firms have shorter life expectancy, given that the firm may cease to exist if just one person (the owner) leaves, and the lack of firm succession planning.
Capital structure theory has been built around Modigliani and Miller (1958)’s propositions of irrelevance in a context of perfect capital markets. The acknowledgment of imperfections that make relevant the capital structure, such as corporate and personal taxes, transaction costs, and information asymmetries have been the guide to later developments. However, there is still no theoretical agreement on the relevance of capital structure to firm value, which can be summed up through the confronting positions of trade off theory and the pecking order. While the first argues that costs and benefits of debt lead to an optimum value, the later suggests that capital structure is just the result of “cumulative requirements for external financing”1.
In the specific field of SME, authors like Hamilton and Fox (1998) and Hutchinson et al (1998) propose a financing hierarchy (or pecking order) of preference for internal funds, that is based on the owners’ desire for control and flexibility. While the original proposition of the * Departamento de Ciencias de la Administración - Departamento de Economía, Universidad Nacional del Sur (UNS). CONICET. E-mail: [email protected]. **Departamento de Economía, UNS. Universidad Provincial del Sudoeste (UPSO). E-mail: [email protected] We thank Fernando Tohmé and seminar participants at Universidad Nacional del Sur (Departamento de Economía) for helpful comments. 1 Myers (1984), p. 581.
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financing hierarchy results from the undesirable signaling effect of new equity issues, this argument can be considered as a demand side explanation.
In our research we continue this line of investigation, and propose a new approach to SME financing. This approach is based on demand factors that are complementary to traditional variables like size, growth, profitability, and taxes, among others. The originality of this proposition lies in the consideration of personal and behavioral aspects, and the characterization of the problem under study: we focus on the financing decisions, instead of studying the observed capital structure. Moreover, we obtained a dataset of SME data with information on variables with no previous records in Argentina, such as personal costs of bankruptcy, owner-manager goals for her business, and experience with debt at personal level.
Our conclusions lead to the reconsideration of the under-leverage problem of SME, which traditionally has been studied from a credit rationing perspective (a supply-side approach). Moreover, financial aid policies generally are designed following this market failure notion. However, if demand-side variables also play a role in SME’s financing problem, improving SME access to finance would solve only one part of the problem.
The plan of the paper is as follows: in Section I we briefly review the main theories on SME financing. In Section II we discuss the three cases of attitude towards debt financing, and propose the hypothesis of the new approach to explain them. Section III describes the methodology, while Section IV shows the empirical results. Finally, we present the conclusions in Section V.
I. The firm’s capital structure2: some background
“The modern theory of capital structure began with the celebrated paper of Modigliani and Miller (1958). They (MM) pointed the direction that such theories must take by showing under what conditions capital structure is irrelevant”3. The irrelevance holds for perfect capital markets, which means: no frictions, perfect competition in product and securities markets, information efficiency, and that agents are perfectly rational and search to maximize their utility. The acknowledgment of imperfections that make relevant the capital structure, such as corporate and personal taxes, transaction costs, and information asymmetries have been the guide to later developments. In this section we briefly describe three approaches, focusing on SME financing: trade off, pecking order, and credit rationing.
Trade off The trade off theory predicts a target optimal structure, as a result of balancing what
Copeland et al (2004) name as equilibrium effects: permanent influences which effect is industry-wide, such as taxes, bankruptcy costs, and agency problems.
Prasad et al (1997) propose another point of view for this approach, as they study the effect of operating and financial risk in systematic risk. If investors cannot fully diversify their portfolios, because of capital markets imperfections, they will value the control of systematic risk in a stock, and managers will have an incentive to do so. Moreover, in a SME the owner-manager generally has a large portion, if not all, of her personal wealth invested in her firm, and controlling systematic risk impacts directly on her personal wealth. Using Mandelker and
2 The financial structure of a firm includes the whole right side of the balance sheet. This includes operating liabilities, which are obligations created in the course of ordinary business operations, such as trade credit, wages, and taxes. On the other hand, the capital structure refers to equity and financial debts. 3 Harris and Raviv (1991), p. 297.
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Rhee´s (1984) decomposition into financial leverage and business risk4, Prasad et al (1997) conclude that, if the systematic risk of the firm rises beyond acceptable because of operating factors, managers should seek to balance it reducing the financial leverage.
Applying the trade off theory to small firms rises some questions. Fist of all, the definition of an optimal target: maximizing the firm’s value, or equivalent to this, if operating cash flows are unaffected, minimizing the cost of capital. These particular objectives are difficult to measure in small firms, and may not be the only one, nor the main, goal of the owner-manager. Moreover, the main advantage of debt, the tax shield, can be especially complex to assess in those small firms where business income is taxed as personal income.
Pecking order The pecking order differentiates from the trade off in its conclusion of a hierarchy in the
financing choices (first internal funding, then debt, last external equity), instead of the existence of an optimal structure. Different arguments explain this result:
- Flexibility: managers have high discretion regarding the use of this funds.
- Transaction costs: external financing implies emission and administrative costs that are absent in internal financing.
- Information asymmetries: Insiders (managers and owner-managers) know better than outsiders the current situation and future prospects of the firm. By using internal funds, managers avoid to share information about the expected return and investment opportunities. Myers and Majluf (1984), and Myers (1984), study the signaling effect of equity, stating that investors may see new equity as bad news: equity is overpriced.
Chittenden et al (1996) state that issuing external equity may be particularly costly for SME, because of the relatively fixed costs of initial public offerings, the small firm effect on the cost of equity, and the potential loss of control by the original owner-managers. Zoppa and McMahon (2002) describe a SME pecking order, where the first choice is internal equity, including the additional time the owner-manager spends in the firm for a salary below market remuneration. In second place, the firm uses short-term debt, including trade credit and personal loans. Then, long-term debt is included, possibly beginning with loans from the owners, family and friends. New equity comes last, through original owners or relatives in first place, and finally through new partners.
Berger and Udell (1998) explain the small firm’s financial structure using a financial growth cycle, “(…) in which financial needs and options change as the business grows, gains further experience, and becomes less informationally opaque.”5 Firms face higher information asymmetries during the infant stage (first two years), when the main sources of funds are the entrepreneur, her friends and relatives, trade credit, and angel investors. Credit from financial institutions, first short-term and later long-term, becomes available when the firm reaches size and age large enough to count with historical accounting records, that should show a certain level of tangible assets. If the firm continues to grow, it may gain access to the capital markets. Access to financial institutions can be granted in the earlier stages through personal guarantees by the owners. This sequence can be seen as a dynamic view of the pecking order, where the strength of information asymmetries decreases as the firm gains experience.
Fama and French (2002) point that under the pecking order hypotheses, firms have no incentive to issue debt if they still have internal funds to finance their investments. These
4 Business risk is generally defined as the risk of the firm without financial leverage. Business risk depends both on the sensibility of the firm’s revenues to the business cycle and on the firm’s operating leverage. (Ross, Westerfield and Jaffe 2003, p 317). 5 Berger and Udell (1998), p. 622.
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behavior does not apply to all firms, including the small ones. Moreover, it assumes that firms will use debt if some attractive investment opportunities remain. A special case among SME, those that do not use debt even if they pass up attractive investments as a consequence, is left unexplained.
Credit rationing Stiglitz and Weiss (1981) demonstrate that information asymmetries may affect the
supply of bank credit causing credit rationing. When a bank grants credit, both interest rate and credit risk matter. If information asymmetries exist, the interest rate a bank charges affects credit risk in two ways: first, an adverse selection effect, sorting potential debtors, and second, an incentive effect, influencing debtors’ actions. The bank determines the interest rate that maximizes its loan portfolio expected return. If at that rate there is excess demand, the bank rations credit instead of increasing the interest rate, because this would attract riskier borrowers (affecting negatively the expected return). Besides, a higher interest rate would provide firms an incentive to take riskier projects (asset substitution problem).
In the context of credit rationing, Petersen and Rajan (1994) point out that changes in the firm leverage may be caused by effects on the demand and supply of funds. If financial institutions limit the amount of credit they give, firms will have to resource to more expensive sources of funds once the cheaper sources have been exhausted, given that the return on investment exceeds the cost of these funds. In this case, an under investment problem may arise. In Petersen and Rajan (1994)’s model, the expensive substitute of bank debt is trade credit. They consider that variables such as firm age and size, duration of the longest relation with creditors, and creditors concentration, may capture the characteristics of lending relationships. All these variables should relate positively with debt, as the lending relationship reduces the information asymmetries.
As SME have restricted access to capital markets, due to high fixed costs or legal form limitations, the effect of credit rationing is expected to be particularly strong for these firms.
II. The three cases of attitude towards debt financing: the new approach
The traditional corporate finance paradigm, which the preview theories belong to, is based on the assumption that agents are perfectly rational and pursue utility maximization. In particular, this means that rational players update their beliefs following Bayes’ law, and behave maximizing Savage’s notion of subjective expected utility. Behavioral finance, “(…) analyzes what happens when we relax one, or both, of the two tenets that underlie individual rationality”6.
In this research we are specially interested in the contributions of cognitive psychology to the recognition of biases in people’s beliefs and preferences. Regarding how people form beliefs, some outstanding characteristics are overconfidence, belief perseverance (Gilovich, 1997), and optimism. On the subject of preferences, we highlight the qualities of loss and uncertainty aversion (Kahneman and Tversky,1979).
Research in behavioural corporate finance can be classified in two distinct approaches: irrational managers, and irrational investors. Optimistic managers imply a pecking order: they avoid issuing equity, because the consider it is under valuated (Baker et al, 2004). These behavioral costs are internal to the firm, and are caused by managers’ cognitive imperfections and emotional influences (Shefrin, 1999). A solution often proposed by the literature, in order to achieve value maximization, is to align (irrational) managers’ incentives with (rational) investors’ interests.
Small firms have received little attention from behavioural finance theories. Nevertheless, we believe this is a potentially rich field for its application, given the owner-firm
6 Barberis and Thaler (2003), p. 1053.
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intertwinement characteristic of these firms. But special models are required, as shareholders and managers most frequently are the same people.
The financing decision is generally studied through its observable result: the capital structure. However, Brealey and Myers (2006) point out that “how financial decisions are made” is a question that still remains unanswered. In this paper we propose a descriptive approach to the understanding of how small and medium enterprises (SME) make their financing decisions. Taking into consideration the demand side motivation of financing decisions, we identify three typical cases among SME owner-managers:
Case 1: Leverage is considered to be advantageous under certain conditions, and they choose to use debt even when internal funds are available.
Case 2: Leverage is considered to be disadvantageous compared to internal sources, and they choose to use always internal funds first. If internal financing is exhausted and attractive investments remain, they use debt so not to loose the investment opportunity. In the same way, as soon as internal funds become available they choose to cancel debt before maturity.
Case 3: Leverage is considered to be highly disadvantageous, and they will not take debt even if by doing so it they pass up an attractive investment.
Traditional theories like the trade off and pecking order can explain the first two demand side cases. Under trade-off arguments, a firm can choose to use debt when internal funds are still available (case 1) if firm value is expected to rise with this decision. For the pecking order, internal funds are always the first choice (case 2). Here, we consider that both explanations can coexist, and not necessary are mutually exclusive views. The third case results more difficult to associate with the traditional arguments, and it can be even considered an irrational choice. Passing up an investment has a cost, its net present value, and a classic principle in corporate finance states that any project with positive net present value should be undertaken. Is this an irrational decision then? We think that it is not, for the owner-manager of the small firm is considering not only (her estimation of) the net present value, but other factors such as business and personal characteristics, to take the decision.
On the supply side, we identify two extreme cases: those that face no credit rationing, and thus get all the financing they demand, and those that have absolutely no access to debt under the conditions they consider acceptable. Active supply side rationing applies to firms in cases one and two, while it can not be observed in firms in the third case.
Trying to develop a more complete understanding of the small firm financing decision, we propose a new approach, taking into consideration a fundamental characteristic of these firms: the owner-firm intertwinement. This approach is complementary and not a substitute for the traditional theories. Our idea is to integrate some disperse contributions by other authors, and to propose some new factors that we expect to be related with the firm’s financial structure. We classify the arguments into two groups: the managerial view, which takes into consideration the impact of the personal characteristics of the owner-managers and the way they run their organizations, and life cycle approaches, where the focus relays in the changing characteristics through time of the firm and its owner-managers.
The managerial view includes the following variables:
- Business goals of the owner, which can vary from traditional financial objectives, such as to increase the value of the firm, or sales growth, to more family-oriented goals, like to provide family with business careers, to pass onto the next generation, or to improve lifestyle. Carland et al (1984) suggest there may be differences in risk propensities of founders who primarily focus on profit and growth, owners of small business who focus on more personal goals and family income, and corporate managers.
- Attitude toward debt financing and previous debt experiences, personal and for the firm, as frequently there are no clear limits between the owner personal finances, and
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the firm’s finance. We expect that experience with debt at the personal level will reduce uncertainty aversion (a demand effect). In addition, this experience can soften the information asymmetries, thus improving access to debt (supply effect).
Both variables are proposed by Romano et al (2000), for family firms. Here we extend this to small firms in general, as well as relate previous debt experiences with changes in uncertainty aversion and information asymmetries. The following variables are an original contribution of this paper:
- Professionalization of management, particularly in the field of economic sciences, which we expect to be related with the diversification of financing sources. There are numerous non traditional sources that are often unknown to SME, just because of lack of information, or perhaps absence of interest. We also include here the use of formal planning methods7, which are expected to reduce informational opacity8.
- Personal costs of bankruptcy are a consequence of the usual owner-firm intertwinement present in SME. These costs include the socio-economic and emotional consequences that the firm’s bankruptcy implies for the owner, even with limited liability. We consider them as a result of the lack of diversification of the owner’s human capital, and the emotional bond that the owner has with the firm, especially in a family business. For partnerships with no limited liability, or the case of Sole Proprietorship, the legal consequences are larger, and can lead to bankruptcy at the personal level.
On the other hand, we propose two life cycles approaches, which describe the changing features of firms and their owner- managers through time. The life cycle of the owner-manager argues that the owner – manager’s risk and uncertainty aversion and goals will evolve during her lifetime, as her objectives change from pursuing profit and growth to focusing in more personal goals and family income. As Ang (1992) points out, small firms have shorter expected life, which depends highly on the founders permanency in the firm, and the succession plans. When the owner is preparing her succession, long-term planning may be neglected, affecting the term choice in financing decisions. The financial growth cycle of the firm (described by Berger and Udell, 1998) and the life cycle of the owner-manager are expected to be connected with each other, sometimes with opposite effects. For example, as the firm and its owner grow older, information asymmetries decrease grating easier access to debt (a supply side effect), while the owner’s risk aversion and personal costs of bankruptcy increase with age, and thus desires to use less leverage (demand side effect).
Finally, we believe that the life cycle of the family firm may also affect the financing choice. Following Gallo (1998), we recognize three distinctive stages in the family firm: the founder-owner, the second generation (brothers and sisters as partners), and the third generation (cousins and relatives as stockholders). The first generation owners are expected to be entrepreneurial and prone to risk taking (Ang, 1991), characteristics not necessarily transferable to later generations. As new people join the ownership of the firm, agency costs of equity and personal costs of bankruptcy increase9. Family businesses may use less debt that non-family businesses because of aversion to financial risk, and the owner’s fear of losing freedom to dictate business policies (Gallo et al, 2004).
The life cycle approaches we propose can be seen as a dynamic view of the managerial variables. To illustrate this idea, suppose the following demand function for external funds (financial liabilities) for an individual firm:
D (t, E, Ct, Ot, I, F, R, X)
7 This variable is included in Romano et al (2000). 8 This could carry an endogeneity problem, as formal planning may be a consequence of using debt. 9 If the whole family income depends on the firm, its bankruptcy implies losing the mean of subsistence, reputation, and lifestyle for all the family members.
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Where:
t: Owner – manager’ age.
E: Previous debt experiences at personal level (experience ranges from null to higher degree).
C(t): Emotional costs of bankruptcy (ranges from null to higher degree).
O(t): Pursuing business objectives (profits, sales or value maximization) as opposite of personal goals (to provide family with business careers, to pass onto the next generation, or to improve lifestyle). The emphasis on business objectives ranges from null to higher degree.
I: Interest rate for the particular firm.
F: Net cash flow deficit, it represents the requirements for external financing. This depends on firm profitability and age, its production function, dividend policy, size, growth, and business risk.
R: Other characteristics, like current capital structure, legal form, and taxes.
X: Variables that are external to the firm, such as the term structure of interest rates, and expected inflation.
This demand function can be described through the following derivates:
0DC∂
<∂
, when the emotional costs of bankruptcy rise, the demand for financing would
decrease.
0DO∂
>∂
, because following a business goal more intensively would lead to increasing needs
for financing.
0dDdt
< , where a direct effect and two different indirect effects are acting. First, the direct
effect of higher risk and uncertainty aversion with age. Second, age rises the emotional costs
of bankruptcy: 0Ct
∂>
∂. Finally, younger owners would tend to follow business goals, while
older owners would rather focus on family and succession: 0Ot
∂<
∂. Mathematically:
0 0 0 0 0
0dD D D C D Odt t C t O t
< < > > <
∂ ∂ ∂ ∂ ∂= + ⋅ + ⋅ <∂ ∂ ∂ ∂ ∂
0DE∂
>∂
, given that higher debt experience at the personal level would increase the demand
for financing.
0DI
∂<
∂, if the interest rate rises, the demand decreases.
0DF∂
>∂
, higher deficit increases the demand.
On the other hand, the supply of external funding can be described, following Salloum and Vigier (1997), as a scaled function, as shown in graph 1. Each line of credit a bank offers is represented by a “package” of different conditions. In the supply function, each level of the interest rate is a result of a package design.
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Graph 1: Supply and demand for external funding
Case A: No credit rationing There is no unsatisfied demand for externalfinancing.
Case B: Partial rationing The firm obtains QE financing. If it could access the lower rate, it would demand Q*. The difference is partial rationing.
Case c: Complete rationing The firm demands zero financing. If it could access the lower rate, it would demand Q*.
Case d: Self - exclusion The demand function is completely inelastic,the firm does not require external funds atany interest rate. This represents case 3 ofattitude we described previously.
S
i
D
QE = Q* $
D
QE Q*
S
i
$
D
Q*
S
i
$
S
i
D
0 $
9
Considering the demand and supply functions, it could exist:
- No rationing equilibrium (case A).
- Credit rationing (partial or complete): the firm obtains lower financing that it would get if it could access a lower rate (case B), or demands no funds at the given interest rates (case C). Here the problem may be caused by two situations: information asymmetries distort the evaluation the bank does of the firm’s risk, or even if this is not the case, over-confident and optimistic owner-managers sub-valuate the firm real risk, and they assign a lower cost of capital.
- Self- exclusion: the firm does not demand external funds (case D). Under this new approach, we recognize multiple goals, not only shareholder value
maximization. This implies that the trade off proposition can be seen as a complement to other theories, and not as an universal explanation.
Moreover, in our formulation the net cash flow deficit does not equal directly to the demand for external funds, but other variables also play a role. We think in a dynamic system: suppose a firm has a deficit of $100, and given the interest rates, age of the owner, emotional costs, and others, it would demand $60 of external financial funds. The difference could be covered by owner’s funds, or if not, the dividend and investment policies could be re-formulated until an equilibrium is reached.
The managerial view and life cycle approaches propose a demand-side explanation for SME’s financing decisions. Under this view, firms with similar “objective” features, like size, age, asset structure, and access to financial debt, could have different financing choices, if their “subjective” characteristics differ.
Now we can rethink the three cases of attitude into this framework, and propose an explanation through variables such as the owner-manager’s age and goals, the status as a family firm, emotional costs of bankruptcy, professionalization of management, and experience with debt at the personal level.
Case 3 firms will have older owners, who have higher risk aversion and are less willing to undertake financial leverage. The lack of experience with debt at personal level will also affect uncertainty aversion. We also expect them to focus more on personal goals, as they would be less willing to pass up positive net present value projects if they pursued business goals intently.
We expect that firms belonging to cases 2 and 3 will be predominantly family firms, as their dislike for debt could be explained by aversion to financial risk, and the owner’s fear of losing freedom to dictate business policies. We also expect them to have higher emotional costs of bankruptcy, because of long term human capital invested in the firm, and a stronger emotional bond. In addition, aversion to debt could be explained by inadequate evaluation of the financing choices and consequences, caused by low professionalization of management.
Information asymmetries may also have a demand effect, beyond adverse selection. Firms from case 2, less willing to use financial debt that those from case 1, could be reflecting the previous experience of the owner in the financial markets, where they find credit rationing, or higher than expected interest rates. This would be a case of belief perseverance. As younger and smaller firms are prone to have more information problems, we expect them to have higher chances to belong to case 2, instead to case 1.
We sum up the expected effects of the variables we propose on the three cases of attitude in table 1.
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Case 1 Case 2 Case 3
Age of the owner Younger Younger Older
Family firm Scarcely family firms Predominantly family firms.
Predominantly family firms.
Professionalization of management High Low Low
Owner’s objectives for the business
Business oriented goals: sales growth or enhancing firm
value.
Business oriented goals: sales growth or enhancing firm
value.
Personal goals such as to employ family members, to pass
onto next generation or to improve lifestyle.
Personal costs of bankruptcy Low High High
Attitude towards debt
Positive. Perhaps with experience at the personal level.
Positive. Perhaps with experience at the personal level.
Negative, or without experience at the
personal level.
Information asymmetries Low High Not relevant
Table 1: The three cases of debt financing under the new approach
III. Methodology
The three cases of attitude can be represented through a qualitative nominal variable, which can take three values:
1, if firm belongs to case 1
2, if firm belongs to case 2
3, if firm belongs to case 3
Y
=
We use the Multinomial Logit Model (MNLM) to model the proposed relations , which can be written as10:
10 Extracted from Long (1997).
11
2
2
1Pr( 1 )1 exp( )
exp( )Pr( ) for m>11 exp( )
i i J
i jj
i mi i J
i jj
y xx
xy m xx
β
β
β
=
=
= =+
= =+
∑
∑
Where y is the depend variable, with J nominal outcomes, and Pr( )y m= Χ is the probability of observing outcome m given X. Pr(.) is a function of the linear combination
mβΧ , where βm differs for each outcome.
The MNLM can also be expressed as an odds model:
Pr( )( )
Pr( )i i
im ni i
y m xx
y n x=
Ω ==
which allows us to interpret the odds ratio:
,.( , )
( , )k m nkm n
km n
xe
xδ βδΩ Χ +
=Ω Χ
as: for a unit change ( 1δ = ) in xk , the odds of m versus n are expected to change by a factor of ,exp( )k m nβ , holding all other variables constant.
Besides the explanatory variables mentioned in section I, we also include some control variables from the traditional theories: sector and limited liability.
The operational definitions of the variables are as follows:
- Owner and firm age: both are quantitative variables.
- Family firm: following Gallo (1997) we consider a business as a family firm if ownership and control belong to members of a single family. It is a binary variable, one is assigned to family firms.
- Professionalization of management: this is a binary variable. One is assigned if the owner has an academic degree in Economics or Management sciences11.
- Experience with debt at the personal level: this is a binary variable. One is assigned if the owner – manager has used debt for personal purposes.
- Owner’s objectives for the business: this is a binary variable. One is assigned if the owner – manager states she pursues sales or value maximization.
- Personal costs of bankruptcy: this is a binary variable. One is assigned if the owner – manager considers that emotional costs of bankruptcy are higher than the economic costs.
- Size: we use two binary variables: small and medium firm. The base category are micro-firms, all measured by the standards of resolution 675/2002 and 303/2004 of Sub-secretaría de la Pequeña y Mediana Empresa y Desarrollo Regional12.
11 We also tried other specifications, like owner has an academic degree, employees have an academic degree in Economics or Management sciences, and use of formal planning methods.
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- Sector: we use three binary variables: commerce, services, and others (construction and agriculture). The base category is industry, which is expected to have a higher proportion of tangible assets that could serve as collateral, and thus have better access to debt.
- Limited liability: this is a binary variable. One is assigned if the legal form implies limited liability. This variable may capture two further effects: the tax system, as limited liability goes with a fixed profits tax rate (35%), and degree of informality, because according to regulation these firms must present financial statements.
In table 2 we show the expected effects of explanatory and control variables in the odds of belonging to one case relative to another. Each column is read as follows:
Case A-B: the effect of variable X on the odds of belonging to case A relative to B.
Variables Case 3-1
Case 3-2
Case 2-1
Professionalization of management - - Owner’s age + + Owner’s objectives - - Personal costs of bankruptcy + + Experience with debt at the personal level - - Family firm + + Size - Firm age - Sector (industry) - - Limited liability - -
Table 2: Hypothesis in terms of the odds ratios.
There is no previous dataset in Argentina that includes these variables. Therefore, to collect the data we designed an ad hoc questionnaire, which was completed through personal interview. The list of firms was provided by Subdirección Estadística de la Municipalidad de Bahía Blanca13. From 265 firms contacted between July and October 2006, we obtained a 54% response rate. Considering the complete answers we use in this paper, the dataset has 101 firms. If we replace the missing values of quantitative variables with their means, the sample has 111 firms. With this study, we developed a dataset of SME with information on variables with no previous records in Argentina, such as personal costs of bankruptcy, owner-manager goals for her business, and experience with debt at personal level.
IV. Results
In table 3 we show the descriptive statistics of the data, for the global mean values and
also per each case of attitude. For binary variables (marked with *), the shown value is percentage of the sub-sample with that characteristic. The proportion of cases 1, 2, and 3 in
12 They state the legal requirements to be considered a SME in Argentina; we show the classification in the Appendix. 13 We limited our empirical study to Bahía Blanca city because of budgeting and methodological reasons, as a representative sample of SME at national level should include at least 1,000 firms. Moreover, firms located in different regions of the country could represent different underlying populations.
13
the sample is 62.3%, 23.8%, and 13.9%, respectively. The software used for all estimations is Stata 7.
Variable Case 1 Case 2 Case 3 Mean Use of financial liabilities* 60.67% 48.38% 0% 49.5% Owner’s age 47.5 years 47.4 years 54.2 years 48.3 years Emotional bankruptcy* 25.85% 33.33% 40% 28.7% Business goal* 55.56% 42.3% 50% 49.5% Owner has E. or M. degree* 15.56% 6.45% 12.5% 13.8% Personal debt* 25.56% 22.58% 0% 22.7% Family firm* 85.56% 87.1% 87.5% 87% Size: Micro * 30% 20% 50% 33.6% Size: Small * 52.23% 70% 43.75% 54.4% Size: Medium* 17.78% 10% 6.25% 11.8% Firm age 25.63 years 24.38 years 29.4 years 24.7 years Limited liability* 65.6% 58.1% 31.25% 54.5% Sector: Services* 20% 25.8% 18.75% 18.8% Sector: Commerce* 50% 51.6% 75% 58.4% Sector: Other* 6.67% 9.6% 0% 6.9% Sector: Industry* 23.33% 13% 6.25% 15.9%
Table 3: Descriptive statistics.
The data in table 3 shows that some variables behave, a priori, as expected. For emotional bankruptcy, personal debt, family firm, limited liability, sector (industry), firm age, and size, the coincidences are complete, while partial support is found for owner’s age (case 3-1), and business goals and professionalization (case 2-1). We show in bold those variables that have statistically significant differences among group means (ANOVA for quantitative variables, Pearson chi2 for categorical ones)14.
The different level of use of financial liabilities also results statistically significant among groups. Although this is not an explanatory variable, this result highlights an important contribution of this approach: it considers the financing decision (case of attitude) and not just the result (the observed capital structure). By doing so, we expect to discern some demand- side characteristics left behind by previous studies.
Our estimations of the MNLM are highly limited by the data. Given the small size of the sample, the results shown in this section are just a fuzzy sketch for later studies.
We estimate two models: model B differs from model A in the inclusion of size dummy variables, and the exclusion of professionalization15. In the Appendix we show the odds ratios for both estimated models, for the original and extended samples16, as well as the extensions to the econometric analysis.
We sum up the MNLM estimates in table 4, compared to the expected effects. Statistical support at 5% is marked with *, and at 1% level, with **. NS states for non statistically significant.
14 We present both analysis in the Appendix. 15 The Wald test with H0 = professionalization has no effect, cannot be rejected (p-value 0.73) 16 We use the Huber/White/sandwich variance estimators (option robust after mlogit command in Stata). Robust variances give accurate assessments of the sample-to-sample variability of the parameter estimates even when the model is misspecified.
14
Table 4: Expected versus observed results.
Owner’s age, personal debt, and information asymmetries show all the expected sign. Emotional bankruptcy also behaves as expected, but only for case 3 relative to 1, and with weaker results. Contrary to what we expected, the observed sign for business goal is positive for case 3 relative to 2 (this unexpected result also appears in Table 3). Beyond sample-size limitations, we believe that this result could as well reflect data problems for this variable.
The interpretation of the model can be extended by studying the changes in predicted probabilities. In graph 2 we present the probability of belonging to each case18, depending on owner’s age and personal debt use, holding other variables at their mean values19. Graph 2 shows the non linearity of the model. The probability of belonging to cases 1 and 3 when no personal debt is used, remains approximately constant until the age of 50 to 54 years old. Then the probability of belonging to case 3 increases strongly, while it decreases for case 1. If personal debt is used, the owner’s age has little effect on the probability of belonging to cases 1, 2 and 3.
Graph 2: Probability of belonging to each case, depending on owner’s age and personal debt.
17 Size and firm age are inverse proxies for information asymmetries. 18 Estimates for model B under the original sample. 19 The probability of belonging to case 3 if personal debt is used is zero. The probability of belonging to case 2 if personal debt is used changes less than 1% for the whole range of owner’s age (16.45% at minimum and 15.98% at maximum).
Variables Case 3-1
Observed
Case 3-2
Observed
Case 2-1
Observed
Owner’s age + +** + +** Family firm + ns + ns Professionalization - ns - ns Business goal - ns - +* Emotional bankruptcy
+ +* + ns
Personal debt - -** - -** Information asymmetries17
+ +*
0
0.25
0.5
0.75
1
24 34 44 54 64 74 84
Owner's age
Pr(G
)
G2-No personal debt G1-No personal debtG1- Personal debt G3-No personal debt
15
In graph 3 we present the predicted probability of belonging to each case, depending on firm age and emotional costs of bankruptcy, holding other variables at their mean values20. Graph 3 also shows the non linearity of the model. The probability of belonging to case 1 increases with firm age, and shifts downwards with the presence of personal costs. On the other hand, the probability of belonging to case 2 decreases with firm age, and shifts upwards with the existence of personal costs. Both differences are higher for younger firms, and decrease with firm age.
Graph 3: Probability of belonging to each case, depending on firm age and emotional costs of bankruptcy.
For the control variables, we find that:
Limited liability: the evidence shows support for a positive effect on the odds of belonging to cases 1 and 2, relative to case 3. This variable can be showing three different effects. First, limited liability per se, which is expected to reduce the bankruptcy costs for the owner-manager (thus affecting positively the attitude towards debt on one hand), and to enlarge moral hazard problems (then affecting access to debt negatively) on the other hand. Second, limited liability means a fixed profits tax rate (35%) in the Argentine taxing system, while other legal forms face a progressive scheme going from 9% to 35%. Then, firms with limited liability would have incentives to use more debt because of a higher tax shield. Finally, this may capture informality, because according to regulation these firms must present financial statements, which could cause a reduction of information asymmetries.
Sector: we find empirical support for the importance of the construction and agriculture sectors on the odds of belonging to cases 1 and 2, relative to case 3. However, we do not consider this result to be relevant, because of the reduced participation of these firms in the whole sample.
Beyond the limitations of these results, it is interesting to note that the statistical significance of owner’s age, personal debt and limited liability is robust to different specifications of the model, and also in the ANOVA and Pearson Chi2 tests we commented on earlier.
20 The probability of belonging to case 3 is zero.
IV. Conclusions In this paper we make two main contributions: we identify three cases in SME’s financing
decisions, and propose a new approach that includes some behavioral characteristics of these firms. We classify the arguments into two groups: the managerial view, which takes into consideration the impact of the personal characteristics of the owner-managers and the way they run their organizations, and life cycle approaches, where the focus relays in the changing characteristics through time of the firm and its owner-managers.
We compare the implications of this approach with data on SME from Bahía Blanca, Argentina. This dataset includes information on variables that have not been previously studied in our country. Even though our empirical results are limited by restrictions in the sample size and by the local nature of the studied population, the life cycle of the owner-manager and the managerial view appear to be promising research lines.
To sum up, this research gives support to the idea that the difference between small and large firms is not only a matter of size, and consequently specific models are required to study SME.
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Appendix A.1- Definition of SME in Argentina
Resolutions 675/2002 and 303/2004 from Sub-secretaría de la Pequeña y Mediana Empresa y Desarrollo Regional state that a firm is considered a SME if its annual sales (without internal taxes) belong up to the following ranges (in Argentine pesos): Agriculture Industry and
Mining Commerce Services Construction
Micro $270,000 $900,000 $1,800,000 $450,000 $400,000 Small $1,800,000 $5,400,000 $10,800,000 $3,240,000 $2,500,000 Medium $10,800,000 $43,200,000 $86,400,000 $21,600,000 $20,000,000
Table 5: Definition of SME in Argentina
A.2. ANOVA and Pearson Chi2
One-way analysis of variance (ANOVA) is used for experimental data in which there is a continuous response variable and a single independent classification variable. The total variation in the response variable is explained as the sum of the variation due to the effects of the classification variable (between groups) and the variation due to random error (within groups). An F-test ratio tests the null hypothesis that all group means are equal. See Johnson and Wichern (1992) for more information. Two categorical response variables are defined to be independent if all joint probabilities equal the product of their marginal probabilities. The Pearson Chi2 statistic tests the null hypothesis of statistical independence in two-way contingency tables. When the sample size is small, alternative methods (like Fisher’s exact test) use exact small-sample distributions rather than large-sample approximations. See Agrestri (2002) for more information. A.3- Extensions to the econometric results
Odds ratios In Tables 7 and 8 we present the odds ratios for models A and B. When the odds ratio is
lower than one, we analyze the inverse relation. Statistical support at 5% is marked with *, and at 1% level, with **.
For case 2 relative to case 1, only Firm age is statistically significant in model B. This suggests that the odds of belonging to case 1 relative to case 2, are 1.03 to 1.04 times greater for each year of firm age, holding all other variables constant. For a 10-year change, the odds are 1.39 to 1.49.
The odds of belonging to case 3 relative to case 1, holding all other variables constant, are:
- 5.64 times greater for owner-managers with emotional costs of bankruptcy. - 1.14 to 1.15 times greater for each year additional year the owner has. For a 10
years change, the odds are 3.86 to 4.14 times greater.
19
Table 6: Odds ratios for Model A. ** p-value <1%, * p-value <5%.
The odds of belonging to case 1 relative to case 3, holding all other variables constant,
are:
- 9 to 16 times greater for firms with limited liability. - Infinitely times greater for firms from the construction and agriculture sectors, as well
as for firms with owners-managers with experience with debt at personal level.
The odds of belonging to case 3 relative to case 2, holding all other variables constant, are:
- 10.8 to 11.36 times greater when a business goal is pursued. - 1.14 to 1.16 times greater for each year additional year the owner has. For a 10 years
change, the odds are 3.67 to 4.65 times greater. - 1.05 to 1.06 times grater for each year of firm age, holding all other variables
constant. For a 10-year change, the odds are 1.7 to 1.8.
Finally, the odds of belonging to case 2 relative to case 3, holding all other variables constant, are:
- 9.1 times greater for firms with limited liability. - Infinitely times greater for firms from the construction and agriculture sectors, as well
as for firms with owners-managers with experience with debt at personal level.
Measures of fit21 The count R2, which is the proportion of correct predictions, is 0.703 for both models. It
can be compared to 62% of the cases that were observed as case 1 (the largest marginal). The adjusted count R2 is the proportion of correct guesses beyond the number that would be correctly guessed by choosing the largest marginal, and tells how much knowledge of the independent variables reduces the error in prediction. The models reduces the error in prediction by 21%. The LR test that all coefficients are zero shows that the null hypothesis can be rejected at 0.01 level for model A and 0.004 level for model B.
Independence of irrelevant alternatives. We use the Hausman test and Small and Hsiao test for the null hypothesis of
Independence of irrelevant alternatives. The Hausman test indicates that H0 cannot be rejected, while the Small and Hsiao test gives contradictory results depending on which case is used as base category. Nevertheless, the model was specified to involve distinct outcomes that are not substitutes for one another.
Joint significance of the explanatory variables
Table 9 shows the p-values of the Wald and LR tests22 for 0 ,21 ,31: 0k kH β β= = .
Model A Model B Variables LR test Wald LR test Wald Owner’s age 0.001** 0.005** 0.002** 0.005** Personal debt 0.007** 0.000** 0.012* 0.000** Professionalization 0.714 0.733 Emotional costs 0.075 0.096 0.101 0.121 Family firm 0.321 0.16 0.296 0.157 Business goal 0.103 0.162 0.03* 0.05* Limited liability 0.015* 0.057 0.032* 0.107 Sector: Services 0.677 0.635 0.722 0.739 Sector: Commerce 0.140 0.213 0.18 0.333 Sector: Other 0.672 0.000** 0.45 0.000** Firm age 0.116 0.098 0.066 0.052
21 All tests on the original sample. 22 For the base category (case 1) MNLM assumes ,11 0kβ = .
21
Size: Small 0.088 0.167 Size: Medium 0.841 0.792
Table 8: LR and Wald tests for the joint significance of the explanatory variables.
Support is stronger for the significance of owner’s age and use of personal debt in both models, followed by business goal in model B. Emotional costs have weaker support, only in model A.
