FS IV 91 - 24
discussion papers
Capital Structure, Innovation, and Firm Size
Zoltan J. Acs Steven C. Isberg
University of Baltimore
June 1991
ISSN Nr. 0722 - 6748
Fors chungs s chwerpunkt Marktprozeß und Unter
nehmensentwicklung (IIMV) Research Unit
Market Processes and
Corporate Development (IIM)
ABSTRACT
Capital Structure, Innovation and Firm Size
This paper examines the impact of innovation-producing-firm-specific assets on capital structure in the context of corporate governance, net trade-offs, and firm size. Findings indicate that innovation is an important determinant of capital structure choice where the exact relationship depends crucially on firm size. For large firms, innovation is consistent with increasing leverage costs and/or a discretionary governance structure, and hence, is negatively related to debt. For small firms innovation is consistent with increasingly optimal leverage capacity due to a net tax effect that exceeds the impact of other leverage related costs.
ZUSAMMENFASSUNG
Kapitalstruktur, Innovation und Untemehmensgröße
Untemehmensspezifisches Vermögen, von dem innovationsfördernde Wirkungen ausgehen, ist Gegenstand dieser Studie. Untersucht wird der Einfluß, den dieses Vermögen auf die Kapitalstruktur ausübt, wobei gleichzeitig die bestehende Ka
pitalstruktur des Unternehmens, Netto-Trade-Offs und die Untemehmensgröße berücksichtigt werden. Aus den gewonnenen Ergebnissen läßt sich schließen, daß Innovation einen wichtigen Faktor in Bezug auf Entscheidungen über die Kapi
talstruktur darstellt, wobei der Einfluß der Unternehmensgröße erheblich ist. Für große
Unternehmen ist Innovation mit steigenden Leverage-Kosten und/oder einer
diskretionären Kapitalstruktur verbunden und hat so negative Auswirkungen auf die
Verschuldung. Für kleine Unternehmen steht Innovation im Einklang mit einer
Zunahme der optimalen Leverage-Kapazität, die auf einen Netto-Steuereffekt
zurückzuführen ist, der den Einfluß anderer leveragebezogener Kosten übersteigt.
I. Introduction.
Why firms choose a particular capital structure has eluded financial economists for quite some time (Myers, 1984). Recently, the 'capital structure puzzle' has been examined through the lens of transaction cost economics, where debt and equity are viewed as alternative forms of corporate governance and/or monitoring devices (Williamson, 1979; Harris and Raviv, 1990, 1991). While many agree that firms prefer internal to external financing, and debt to equity, these recent theories suggest that capital
structure choice is dictated by the firm's asset specificity.
For instance, Williamson (1988) posits that the firm's debt/equity ratio is determined by the optimal governance structure given the redeployability of the firms assets. If assets are easily redeployed between production alternatives, debt, a rules based governance structure, is optimal. However, if assets are not easily redeployed, equity, a more flexible governance structure, is appropriate.
Williamson's theory is interesting in light of alternative models of capital structure choice. In most cases, the
debt/equity decision is viewed as a net trade-off between tax advantages and other leverage related costs (Bradley, Jarrell and Kim, 1984). For example, asset specificity (uniqueness) may
affect the ex-ante cost of bankruptcy and liquidation, and hence, should be negatively related to debt in the capital structure
(Titman 1984; and Titman and Wessels 1988). On the other hand, Haugen and Senbet (1978, 1988) argue that bankruptcy costs are
2
insignificant and that liquidation is a capital budgeting decision, and hence, both are unrelated to capital structure.
The Haugen/Senbet approach can be reconciled with the Williamson model by the fact that the former allow for the existence of agency costs. It can be argued, for example, that specific
assets are consistent with higher growth opportunities, creating shareholder incentives to increase risk at the expense of
bondholders. The presence of such agency costs imply a negative relationship between asset specificity and debt.
In regard to the tax effect, Dammon and Senbet (1988) rigorously demonstrate that when the investment decision is
endogenized, the relationship between investment and debt becomes ambiguous. In their model, observed patterns of debt and
investment depend upon the difference between a substitution effect created by additional non-debt tax shields (NDTS) and an income effect created by additional cash flows to be sheltered.
With decreasing economies of scale, firm size has an impact on these two effects, and becomes a determinant of capital structure choice.
The purpose of this paper is to examine the effect of investment on capital structure in the context of corporate governance, net leverage trade-offs, and firm size, using
innovation as a proxy for the firm's investment decision (Acs and
A , ,
Audretsch, 1988; and Acs and Isberg, 1991). Product innovation is important because it represents the manifestation of a firms commitment of physical resources to develop a unique product and
bring it to market. Hence, innovation serves as a measure of the firm's investment policy. In addition, product innovations make proxies of asset specificity more complete.2 This study
presents a model that investigates the degree to which capital structure is affected by innovative output, and the extent to which large and small firms respond to different stimuli. The econometric analysis enables the testing of two hypotheses: (1) product innovation is negatively related to debt in the capital structure; and (2) that product innovation will have a disparate effect on small and large firm capital structure choice.
This study extends research on capital structure in three ways: (1) by introducing innovation as a more complete measure of new investment and asset specificity; (2) by examining how the relationship between investment, asset specificity and
capital structure varies across firm size for a broad spectrum of publicly traded companies; and (3) by demonstration of the
consistency of the results with the tax and governance approaches to capital structure. In the second section of this paper, the literature on capital structure is examined. In the third
section the innovation data is introduced, while in the fourth section the empirical model is presented. The econometric
Williamson criticizes various measures of asset specificity, including research and development
expenditures, as*being incomplete. One reason for such incompleteness is the lack of physical evidence of
asset uniqueness inherent in R&D expenditures.
Innovations, on the other hand, require the dedication of tangible assets to production. These are,
therefore, physical evidence of asset specificity.
4
results of the study are presented in the fifth section, followed by conclusions in section six. Findings show that innovation is an important determinant of capital structure choice, and that the exact relationship depends crucially on the firm size class.
For large firms, innovation is consistent with lower net tax effects, greater agency costs, discretionary governance, and hence, lower debt levels. For small firms, innovation is
consistent with a net tax effect that exceeds the agency costs of debt, leading to higher debt ratios.
II. Innovation, Firm Size and Capital Structure Theory
Current thinking on capital structure theory has evolved over the past three decades since Modigliani and Miller (MM, 1958), applied the standard tools of economic analysis to corporate finance and revolutionized the field. The main MM insight is that, "the average cost of capital to any firm is completely independent of its capital structure and is equal to the capitalization rate of a pure equity stream of its class"
(1958, 269-69). Hence, firms are undifferentiated with respect to capital. Expansion of the model to allow for taxes (MM, 1963)
leads to the conclusion that, due to the subsidy created by
deductibility of interest payments, the optimal capital structure for a firm is exclusively composed of debt. The difference
between this and observed reality is further explained by the existence of positive bankruptcy and liquidation costs associated with debt (Baxter, 1967; Scott, 1976; Kim, 1976; Altman, 1984,
Titman, 1984; Titman and Wessels, 1988, and others).
It is questionable, however, as to whether bankruptcy and liquidation costs are in fact relevant to the capital structure decision. It has been shown that significant bankruptcy costs can be eliminated through the inclusion of simple provisions in corporate charters and bond indentures. Moreover, liquidation is a capital budgeting decision and is not related to capital structure choice. If management attempts to liquidate in a way that does not maximize the value of all claims against the
assets, arbitrage profits arise. In this event, informal reorganization will force managers to follow the liquidation procedure that does optimize total value (Haugen and Senbet,
1978, 1988). There are two additional causes of cross sectional differences in observed capital structure behavior: agency costs that are not resolved by complex financial contracting or market forces, and differential tax effects.
In regard to tax effects, it has been argued that cross sectional differences in debt/equity ratios can be explained by investment generated NDTS (DeAngelo and Masulis, 1980). These results lead to an unambiguously negative relationship between investment and debt when the former is exogenous. When
investment is endogenized, however, there are two tax effects.
First, as before, is the creation of additional NDTS that lead to
A
a negative relationship between investment and debt. Second is an income effect, whereby investment generates additional taxable income to be sheltered, leading to a positive relationship
6
between investment and debt. The net effect of investment on capital structure is a function of the difference between the substitution and income effects, leading to ambiguity in the prediction of observed relationships between investment and debt
(Dammon and Senbet, 1988). If the substitution effect of
investment exceeds (is less than) the income effect, one would observe a negative (positive) relationship between innovation and debt. The model suggests that the magnitude of each of these effects may be related to firm size.3 This forms the basis for one of the empirical hypotheses tested in this paper.
Agency costs that affect capital structure take on a variety of forms. For instance, the choice of a firm's capital structure is dictated by information asymmetry between management and
outside investors. This provides much of the basis for the
pecking order theory of capital structure in which internal cash, followed by debt, is the preferred financing mechanism (Myers and M a j l u f , 198 4 ). Firms may forego projects for which the net
present value is positive if informational asymmetries result in an undervaluation of newly issued securities. Undervaluation may be reduced or eliminated by the dissemination of information
regarding the projects. However, such information cannot be revealed to the suppliers of capital without cost (Ross, 1977).
The D/S model includes an assumption of decreasing economies of scale. As firm size increases, the
marginal increase in cash flow per dollar invested will fall, leading to an income effect that is decreasing in firm size. Hence, the net tax effect becomes a
function of firm size.
Other agency costs of debt result from two related factors;
managerial risk incentives, and investment in growth
opportunities. Under the former, shareholder incentives to invest in risky projects that increase the value of their own residual claim at bondholder expense have been demonstrated
(Jensen and Meckling, 1976). Additional agency costs are
introduced when capital investment decisions fall prior to debt maturity. In this event, newly issued debt is essentially
secured by risky growth opportunities (Myers, 1977).4 To the degree that they can not be resolved by complex financial
contracting or other market forces, these costs are a positive function of the debt ratio, and hence, are relevant determinants of capital structure.
Agency costs are often considered a component of the more broadly defined "costs of financial distress," that are
positively related to volatility in the firm's expected cash flows. If a firm invests in risky and/or growth-oriented
projects, volatility, and hence, agency costs, increase (Bradley, Jarrell and Kim, 1984).
Regardless of their form, agency costs are considered to have an unambiguously negative impact on the amount of debt in the capital structure. Investment in product innovations may be considered as a proxy of agency costs. Since innovations are, by
_______________________________________________ u J
4 These cases are demonstrated by viewing equity as a option on the firm's assets, where the exercise price equals the fair value of the debt. In the case of risky growth, the option to make capital investments expires prior to the maturity of the debt.
8
definition, investments in new products that are untested in the market, it can be hypothesized that they are positively related to information asymmetry, risk, and volatility. The question of whether these investment-related agency and other costs of
financial distress are sufficient to offset the net tax effect remains an empirical issue, and provides further basis for the tests conducted in this study.
The issue of optimal capital structure has recently been examined through the lens of transaction cost economics (TCE, Williamson, 1988). Under TCE, the firm's investment decision is viewed as a transaction, and debt and equity are considered to be different governance structures. Transactions are differentiated primarily by the specificity of the assets involved. Optimal capital structure under TCE involves the pairing of assets with the governance structure that minimizes ex-post costs of
incomplete contracting. Most important among these are the maladaptation costs incurred when transactions drift out of alignment:5 others include costs of re-negotiation to correct
Such costs were incurred by several airlines in the early 1980s. Deregulation called for an overhaul of labor contracts, pricing and service strategies. In cases where the airline was constrained under binding union labor contracts, interest and debt covenant requirements, managerial discretion in forming new strategies was inhibited, making it difficult to
compete.- Such ap organizational and capital structure may have been well adapted to a regulatory environment, but was maladapted to a competitive environment. As a result, costs were incurred as firms renegotiated labor contracts and restructured capital. Flexible labor contracts and equity based capital structures would have reduced such costs.
misalignments, maintenance of the mechanism for handling dispute resolution, and the cost bonding to secure commitments.
While most prior analysis of capital structure begins with an all equity firm and seeks to justify debt financing, the TCE approach assumes that debt is the preferred mechanism, and
demonstrates conditions under which debt is replaced with equity.
Debt is a rules based governance structure where management
behavior is limited by the covenants of the debt and monitored by maintenance of interest payments. Equity, on the other hand, is a discretionary governance structure that allows management more freedom of action in its decision making and provides for
monitoring by a board of directors. Williamson argues that as the degree of specificity increases, assets become less
redeployable. While the costs of both debt and equity rise with the degree of asset specificity, the costs of debt rise faster because of the imposition of restrictions by the rules based governance structure. This calls for the matching of specific
(non-redeployable) assets with the more flexible governance structure inherent in equity.
To summarize, optimal capital structure is viewed as having a variety of determinants. According to many theories, optimal capital structure is determined by the trade-off between the net tax and other leverage related costs. Under the theory of
corporate governance, optimal capital structure is considered to be a function of asset specificity. The validity of each theory is subject to empirical verification.
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III. The Data
The unprecedented data base for this study is constructed by combining three previously uncombined sources of data — the U.S.
Small Business Administration Innovation Data Base, the Business Week Survey of Company Financed R&D expenditures, and the
Standards and Poor's Compustat Annual Industrial File. While the first data source provides one of the most important and unique direct measures of innovative activity (Acs and Audretsch, 1988), the second source provides company data on R&D expenditures. The Business Week data, which incorporates 95 percent of the
company-financed R&D expenditures in the U.S. has been used in other previous studies (Soete, 1979).6 The Compustat files provide all relevant balance sheet and income statement data.
Combining these data enables us to provide one of the first examinations of how a firms innovation strategy affects
governance and therefore its capital structure. In total, 387 firms are included.
The measure of a firm's innovative activity is the number of innovations recorded in 1982 that were attributed to the firm.
The Business Week data include the company financed R&D expenditures of 735 companies. Although the Business Week sample excludes the smallest enterprises, firms which can be considered as relatively small are
included in the data. 130 firms have fewer then 500 employees, which, is the standard used by the U.S. Small Business Administration to distinguish small from large firms. The mean asset size of the whole sample in 1977 was $1,164 million with the largest being $38,453
million and the smallest $9,527 million. Slightly more than one-quarter of the sample is composed of firms with less than $100 million of sales.
The data base was created by a private firm, The Futures Group, for the U.S. Small Business Administration. An innovation is defined as "...a process that begins with an invention, proceeds with the development of the invention, and results in the
introduction of a new product, process or service to the marketplace" (Edwards and Gordon, 1984) .
Though these data are described in detail in the Acs and Audretsch (1988) study, several points should emphasized. First, the data base consists of 8,074 innovations and includes service as well as manufacturing firms. Second, based on a sub-sample of innovations from the entire data base, it has been determined that innovations recorded in 1982 were the results of inventions made, on average 4.2 years earlier (i.e., 1977). Third, unlike the measure of innovative output used by Hambrick and MacMillan
(1985), the innovation data are not weighted by sales or some other indicator of the value of the innovation.7 Finally, while the share of R&D accounted for by the largest firms slightly exceeds that of their share of sales, their share of innovations is clearly lower. While the largest 100 firms ranked by share of sales had 73.21 percent of the R&D they had only 36 percent of
7 Thus, it is conceivable that the quality or
significance of innovations is not consistent across either firm size or with respect to R&D effort.
However, using a sample of nearly five thousand of the innovations, and based upon four different significance levels, it was determined that the distribution of in innovations across the four different significance categories was virtually identical for large and small firms. That is, the quality of the innovation does not appear to vary systematically with the company size
(Acs and Audretsch, 1988).
Table 1 Long Term Debt to
For the
Common Equity, Innovation Rate, and Total Asset Size ($MM) Thirty Least Leveraged Firms in the Sample, 1982
NAME LTD/CE In./Ln(TA) (Rank) Tot.Assets (Rank)
NORTON SIMON 0.000 1.215 (41) 26.93 (381)
REEVES BROTHERS 0.000 0.185 (173) 222.51 (267)
WINNEBAGO INDUSTRIES 0.000 0.214 (163) 108.07 (330)
HAZELTINE 0.254 0.658 (92) 95.32 (337)
HEWLETT-PACKARD 0.502 6.747 (1) 3470.00 (50)
KELLOGG 0.578 0.279 (147) 1297.40 (124)
LILLY 1.132 0.248 (154) 3155.13 (56)
GENERAL DYNAMICS 1.320 0.508 ( H O ) 2639.50 (69)
EG&G w 1.457 2.143 (16) 270.39 (247)
NALCO CHEMICAL 1.458 0.326 (136) 464.87 (194)
JOHNSON & JOHNSON 1.515 2.037 (18) 4209.57 (40)
RAYTHEON 1.799 0.490 (Hl) 3510.19 (49)
UNITED INDUSTRIAL 1.980 0.201 (165) 144.69 (302)
AMP 2.168 0.143 (193) 1076.32 (139)
DIE&OLD 2.255 1.076 (48) 264.74 (249)
DIGITAL EQUIPMENT 2.395 2.530 (9) 4024.01 (42)
EASTMAN KODAK 2.459 1.834 (26) 10622.00 (13)
BRISTOL-MYERS 2.496 0.631 (96) 2756.20 (65)
BINKS MFG. 2.811 0.225 (161) 85.55 (342)
SYNTEX 2.947 0.148 (189) 859.15 (161)
PETROLITE 3.065 0.189 (172) 200.32 (273)
NASHUA 3.146 0.178 (176) 274.29 (245)
SMITHKLINE 3.415 0.880 (64) 2857.52 (61)
CTS 3.468 0.199 (166) 151.32 (298)
MINNESOTA MINING & MFG. 3.844 4.643 (3) 5514.00 (30)
SCIENTIFIC-ATLANTA 3.996 0.363 (132) 247.55 (254)
ABBOTT LABORATORIES 4.037 0.382 (128) 2566.91 (73)
MARION LABORATORIES 4.138 0.623 (98) 123.39 (320)
PITTWAY 4.184 0.177 (178) 287.07 (241)
MILLIPORE 4.265 0.721 (88) 256.80 (251)
the innovations (Acs and Audretsch, 1991, chapter 3).
Previous studies have used different measures of capital structure. In this study, three measures of capital structure are tested in an attempt to capture the effect of innovation on short term, long term, and total indebtedness. The measures are obtained by taking the book values of short term debt, long term debt, and total debt divided by the total market value of common equity.8 While data availability precludes the use of market value data for debt, its' book and market values are found to be highly correlated (Bowman, (1980). In addition, there is no reason to assume that differences between market and book values of debt are related cross-sectionally to variables used to
explain capital structure (Titman and Wessels, 1988).
Selected data for the thirty least leveraged firms are
introduced in Table 1. Innovations are normalized by the log of total assets, converting the innovations variable into the firm's innovation rate. This provides a measure of the concentration of a firm's investment in innovative products, that serves as a
better proxy for asset specificity. Although data for only the least leveraged firms are reported here, the debt-to-equity ratios are broadly distributed. The mean long term debt to equity ratio for 1982 is 46.37%, with a standard deviation of 56.14%.
8 Short term debt does not include non-debt current liabilities, and is defined as that which matures in one year or less. Long term debt includes capitalized leases, and is defined as that which matures in one year or more.
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categories are reported. The distribution of innovations is apparently skewed, with a small number of firms making numerous innovations, and most firms contributing fewer than three
innovations. In fact, of 387 firms included in our sample the mean number of innovations was 3.22 (un-normalized), with about one third of the firms contributing zero innovations.
For each of the three debt measures reported in Table 2, there exists a monotonic, inverse relationship between the
debt/equity (D/E) ratio and average innovation rate. A "f'-test indicates that the differences in mean D/E ratios between the least and most innovative firms (i.e., categories 0 and 2) are significant in the cases of long term and total debt. This is consistent the corporate governance theory of capital structure.
It also implies that the trade-off between the net tax effect and other leverage costs is negative, leading to the use of
additional equity. While no evidence regarding a potential size effect is provided, it is interesting to note that innovation and asset size do appear to be positively correlated. This is
consistent with the findings of other studies (Acs and Audretsch, 1990).
When the sample is independently ranked into three
additional categories based on total assets, as in Table 3, a size effect begins to take shape. For firms in the medium and
In Table 2 the sample is ranked into three categories based on the 1982 innovation rate. Statistics regarding total assets, innovation rates, and capital structure for each of the
Table 3
Mean Short Term, Long Term, and Total Debt to Common Equity,
Innovations, and Total Assets ($MM) by Size and Innovation Class for 1982.
(standard deviation in parentheses)
Size Variable INNOV=0 INNOV=1 INNOV=2 T-test*
LTD/CE 51.780 45.047 55.410 -0.4070
(59.681) (76.717) (61.530)
STD/CE 16.895 9.775 18.706 -0.2172
(29.804) (12.738) (32.286)
SMALL TD/CE 72.174 57.825 74.116 -0.0787
(73.247) (89.898) (82.740)
INNOV. RATE 0.000 0.215 0.879
(0.000) (0.036) (0.817) TOTAL ASSETS 113.170 135.320 125.230
(61.100) (63.200) (57.600)
LTD/CE 40.548 36.831 27.574 2.4267
(51.264) (34.594) (25.626)
STD/CE 11.572 14.815 6.901 2.7615
(26.192) (45.046) (7.932)
MEDIUM TD/CE 55.556 51.646 34.475 3.0391
(61.569) (51.817) (31.735)
INNOV. RATE 0.000 0.221 1.261
(0.000) (0.080) (0.915) TOTAL ASSETS 563.810 570.050 654.220
(281.600) (288.100) (312.400)
LTD/CE 73.160 58.313 36.044 2.0160
(82.881) (51.270) (48.585)
STD/CE 10.331 11.695 10.929 -0.8902
(22.874) (18.101) (21.980)
LARGE TD/CE 83.491 70.009 46.973 2.5701
•» (99.560) (64.278) (66.991)
INNOV. RATE 0.000 0.199 1.381
(0.000) (0.081) (1.090) TOTAL ASSETS 3857.490 3725.490 6834.930
(4840.600) (3028.800) (10321.400) Ho: Mean D/CEjnnov=0 = Mean D/CEjnnov=2
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large size categories, the degree of leverage in the capital structure declines as the innovation rate increases. For
example, in 1982, large, non-innovative firms show long term D/E ratios averaging 73.16%, while the large, most innovative firms average 36.04%. With the exception of the short term measure, differences in D/E ratios between the most and least innovative firms are significant at the five percent level.
For small firms a different pattern emerges. Rather than declining as innovation increases, the degree of debt increases slightly for innovative firms. While significance tests result in rejection of the hypotheses of differences in the D/E levels, it is interesting to note that the mean D/E ratios for small innovating firms are greater than those of small, non-innovating firms. These results suggest that both firm size and the
innovation rate affect capital structure. There is an inverse relationship between D/E and innovation for all but the smallest firms, where the relationship may be direct.
IV. The Empirical Methodology
Prior to examining a more sophisticated model, a simple reduced form equation is estimated for the three measures of capital structure. Its' purpose is to replicate results of
earlier studies, as well as establish the appropriateness of the data base. The model is as follows:
D/CEit = a0 + ß 1 (INNOV)ifc + ß2 (R&D/S)it + ß3 (ASSETS)it
+ ß4 (OPM)it + e it (1)
where: INNOV = the ratio of total innovations to the log of total assets, R&D/S = the ratio of research and
development expenses to sales, ASSETS = the size of the firm measured by
the log of total assets, OPM = the operating profit margin
e = a randomly distributed error term,
R&D is considered a measure of uniqueness (Titman and Wessels, 1988), agency costs (Myers, 1977) and non-debt tax shields (Bradley, Jarrell, and Kim, 1984). In the context of asset specificity, R&D measures investment in the process of identifying and developing unique products. Since R&D generally requires specialized labor, it is directly associated with asset uniqueness. In terms of agency costs, R&D can be viewed as an option on future investment, whereby its findings lead to a decision regarding capital investment. If this point precedes the maturity of associated debt financing, an agency cost is created (Myers, 1977). Since R&D expenditures are completely deductible, they serve as non-debt tax shields. In each of these interpretations, the predicted relationship between R&D and debt is negative.
It is possible that agency costs can be offset by
diversification on the part of the firm (Warner, 1977).
Diversification reduces volatility in expected cash flows, and hence, increases debt capacity. As such, it would be expected that larger firms face lower debt costs due to their greater diversification opportunities. Hence, the log of asset size is included in the equation as a measure of debt capacity, and its sign is predicted to be positive.
Operating profit margin (OPM) is included in the model as a proxy for the firm's position in relation to the pecking order theory of capital structure. If firms prefer to finance with internal rather than external funds, the sign on OPM should be negative.
Like R&D, the innovation rate provides a measure of a
variety of firm characteristics. First, it serves as a measure of asset specificity, as previously discussed. Second, since investment in innovative products is hypothesized to be more risky, it also serves as a proxy for agency costs due to managerial risk incentives and volatility. Third, ex-ante
information regarding innovative products is, by nature, little known. Therefore, innovation can serve as a proxy for
information asymmetry. In each of these three cases, the
predicted relationship between innovation and debt is negative.
If innovation provides a measure of a firm's capital investment, predictions regarding the net tax effect may be muddled. As already indicated, the net tax effect of investment
k A
depends crucially on the difference between the income and 18
substitution effects. Hence, there is an ambiguous expectation regarding the sign. This ambiguity may be eliminated by further controlling for the impact of firm size. To accomplish this, the model will be estimated first for the sample as a whole, and
again for each of two distinct sub-samples. The first sub-sample consists of firms from the small size category, while the second combines the firms in the medium and large categories (hereafter referred to as "large firms"). The sign of the innovation
coefficient is expected to be unambiguously negative under the theory of corporate governance. Differences in signs and
significance across size categories would indicate that the impact of tax effects and other leverage related costs are a function of firm size.
Capital structure choice is viewed by most firms as a long term strategic decision. Fischer, et.al (1990) argue, however, that despite the long term nature of the capital structure
decision, capital market friction may cause temporary deviations from the optimal debt/equity ratio. With this in mind, it is hypothesized that inclusion of lagged measures of capital
structure would increase the power of a model explaining current capital structure. To test this hypothesis, the model in
equation (1) is re-estimated with lagged capital structure included as an independent variable.
In modeling capital structure choice, it is important to recognize that investment and financing are two distinct
decisions that are made simultaneously. Both Dammon and Senbet
20
(1990) and Williamson (1988) argue in favor of the endogenization of the investment and capital structure decisions. In prior
literature, however, investment proxies are generally included as right hand side (RHS) variables. Under these circumstances,
ordinary least squares estimates of coefficients on various capital structure determinants may be inconsistent due to correlation of the RHS variables with the error term. In addition, the coefficients are inefficient to the degree that information used to estimate capital structure choice parameters is incomplete.
To account for the endogeneity of investment in the process of capital structure choice, the empirical model is re-specified as a recursive simultaneous system, as follows:
INNOVit = aa + ßal (R&D/S) it_5 + ßa2(ASSETS)it_5
+ ß a3 ( ° PM) i t - 5 + ß a4 (D /C E ) i t _ 5 + e a i t (2)
D/CEit = ab + ßbl(INNOV)it + ßb2 (R&D/S) it
+ ßb3 (ASSETS) it + ßb4(OPM)it + ebit (3)
The D/E equation (i.e., (3)) in the simultaneous model is identical to the simple regression model, equation (2) describes the determinants of innovation. Lagged terms are included to capture the fact that innovations appearing in 1982 originate with inventions made, on average, in 1977. While it may appear that this specification attempts to model a complete "innovation
cycle,” it is designed under the assumption that innovating
behavior characterizes a firm for more than one period, and thus, is permanent rather than transitory.
A variety of theoretical and empirical findings indicate that innovative output is related to innovative input (Jaffe, 1986). Among these are results showing innovation to be a positive function of R&D (Acs and Audretsch, 1988; Acs and
Isberg, 1991). Therefore, R&D is included in the equation (2), and its coefficient is expected to be positive. Prior findings also indicate that innovative activity is positively related to firm size, and thus, the log of total assets (ASSETSit_5) is included in the model as a size control with the same predicted result. Lagged OPM is included to account for the relationship between innovation and the availability of internal financing.
If innovation is directly related to information asymmetry and agency costs, the pecking order theory implies that firms will prefer internal financing. As a result, a positive sign is
expected on the OPM coefficient. The expectation concerning the sign on the lagged debt/equity coefficient in the innovation model is ambiguous, but is expected to be related to the size class of the firm.
A three stage least squares (3SLS) model is used to estimate the coefficients of the system. The problem of incomplete
information calls for at least a two-stage least squares approach to increase the efficiency of the parameter estimates. The
three-stage approach additionally controls for cross-equation
Table 4 Descriptive Characteristics of
Variable Mean
Innovations (82) 3.227
Innov./ L n (Total Assets) (82) 0.452
STD/CE (82) (%) 12.783
LTD/CE (82) (%) 46.374
TD/CE (82) (%) 59.157
STD/CE' (77) (%) 13.178
LTD/CE (77) (%) 53.902
TD/CE (77) (%) 67.080
R&D/Sales (82) (%) 2.942 R&D/Sales (77) (%) 2.160 Operating Profit Margin (82) (%) 11.089 Operating Profit Margin (77) (%) 13.670 Ln(Total Assets) (82) 6.334 Ln(Total Assets) (77) 5.819
the Regression Sample
Std. Dev. Minimum Maximum
6.132 0.000 55.000
0.798 0.000 6.747
26.493 0.000 244.538
56.141 0.000 361.630
70.372 0.330 492.904
30.846 0.000 320.813
93.775 0.000 1111.320 113.910 0.395 1264.020
2.818 0.000 16.641
2.119 0.000 9.882
8.567 -40.260 54.530
10.039 -10.790 89.910
1.581 2.794 11.040
1.537 2.254 10.557
V. Empirical Results
Using the ratio of total, long term, and short term debt to total equity in 1982 as the dependent variable, the OLS cross- section regression results for 387 firms are shown in Table 5.
In general, these findings are consistent with those of the
capital structure literature. For example, in column (1), using total debt to common equity (TD/CE) as the dependent variable, the emergence of a negative and statistically significant
coefficient for R&D/Sales is consistent with expectations and matches the findings of both Bradley, Jarrell and Kim (1984), and Titman and Wessels (1988). The negative and statistically
significant coefficient on OPM is consistent with the pecking order theory, as found by Titman and Wessels (1988). The
negative sign on the innovation coefficient is consistent with the corporate governance hypothesis of capital structure, using innovation as a proxy for asset specificity. The coefficient, however, is not significantly different from zero. This may be due to ambiguity resulting from a size relationship in the trade
off between tax and other leverage related effects.
When lagged D/CE is included in the equation, neither the sign nor significance of any of the other coefficients change
(i.e., column (2)). The lagged debt term is positive and
correlation of the error terms, yielding parameter estimates that are both consistent and efficient. Descriptive characteristics of the regression sample are included in Table 4.
24
statistically significant, implying that current capital
structure is closely related to its prior characteristics. This supports the hypothesis that the capital structure of the firm is stable over time, as advanced by Fischer, et.al. (1989). These results are robust for different specifications of capital
structure, as can be seen in columns (3)-(6).
Because of the potential size related ambiguity, the results of the debt/equity equations are re-estimated for the two
separate size classes. Results for the larger firms are
presented in Table 6. The important difference between these and the estimates for the uncut sample can be seen in column (1), where the innovation coefficient is negative and significant.
This is consistent with the hypothesis that the net investment related tax benefits are exceeded by leverage related costs as well as the governance approach to capital structure, in which specific assets are financed with equity. The positive and significant sign on the size coefficient is consistent with the hypothesis that larger firms are more diversified, and hence, use more debt. The sign and significance on the R&D/Sales and OPM coefficients are preserved.
When lagged D/CE is added to the equation, as in column (2), innovation becomes insignificant, indicating the presence of
collinearity or simultaneity between the two variables. The pattern for the long term ciebt equations in columns (3) and (4) is the same. Columns (5) and (6) imply that the most important determinant of short term debt for large firms is operating
Table 5
Regression Results (OLS) for Short Term, Long Term and Total Debt to Common Equity Equations for All Firms in 1982
(t-statistics below coefficients)
Variable
(1) TD/CE
(2) TD/CE
(3) LTD/CE
(4) LTD/CE
(5) STD/CE
(6) STD/CE Intercept 95.824 48.008 65.342 33.476 30.481 17.005
5.871 3.604 5.071 3.090 4.663 2.828
INNOV -1.944 -1.197 -3.505 -2.799 1.561 1.571
-0.411 -0.321 -0.939 -0.917 0.825 0.931 R&D/S -6.701 -3.927 -5.135 -2.868 -1.574 -1.186 -4.899* —3.578* —4.751* -3.181* -2.87* — 2.419*
Ln(Assets) 0.954 2.805 2.143 2.897 -1.189 -0.207
0.386 1.437 1.099 1.816* -1.202 -0.233
OPM -1.872 -1.869 -1.356 -1.433 -0.515 -0.439
-3.872* -4.909* -3.554* -4.597* -2.664* -2.554*
STD/CE -5 0.391
9.036*
LTD/CE -5 0.407
12.479*
TD/CE -5 0.424
13.816*
Sample Size 387 387 387 387 387 387
A d j . R-square 0.045 0.2417 0.113 0.408 0.118 0.4526 F-Statistic 4.719 21.084 11.048 44.281 11.568 53.808 Significant at the .05 level
26
Table 6
Regression Results (OLS) for Short Term, Long Term and Total Debt to Common Equity Equations for Large Firms in 1982
(t-statistics below coefficients)
Variable
(1) TD/CE
(2) TD/CE
(3) LTD/CE
(4) LTD/CE
(5) STD/CE
(6)
STD/CE Intercept 14.672 10.127 -3.718 —7.442 18.391 17.677
0.527 0.472 -0.173 -0.447 1.571 1.627
INNOV -10.279 -5.044 -8.461 -4.388 -1.818 -0.772
— 2.036* -1.291 -2.168* -1.445 -0.858 -0.391 R&D/S -6.691 -3.801 -5.892 -3.287 -0.801 -0.588 -3.591* -2.612* -4.087* -2.895* -1.023 -0.809 Ln (Assets) 11.083 7.113 10.481 7.281 0.602 -0.056 2.928* 2.432* 3.582* 3.195* 0.378 -0.038
OPM -1.105 1.276 -0.511 -0.685 -0.594 -0.586
-1.931 -2.894* -1.155 -1.999* -2.469* 2.625*
STD/CE -5 0.425
5.944*
LTD/CE -5 0.412
12.054*
TD/CE -5 0.424
12.183*
Sample Size 258 258 258 258 258 258
A d j . R-square 0.1277 0.482 0.146 0.489 0.028 0.162 F-Statistic 9.017 41.844 10.353 42.903 2.603 9.481 Significant at the .05 level
Table 7
Regression Results (OLS) for Short Term, Long Term and Total Debt to Common Equity Equations for Small Firms in 1982
(t-statistics below coefficients)
(1) (2) (3) (4) (5) (6)
Variable TD/CE TD/CE LTD/CE LTD/CE STD/CE STD/CE Intercept 185.955 124.981 104.01 77.902 81.945 50.246
3.587 2.637 2.431 1.913 4.116 2.953
INNOV 47.026 28.838 32.389 18.856 14.636 11.764
3.727* 2.461* 3.Ill* 1.808* 3.021* 2.941*
R&D/S -8.102 -5.645 -5.819 —4.207 -2.285 -1.646 -4.191* -3.177* -3.647* -2.701* -3.075* -2.671*
Ln (Assets) -14.331 -8.818 —2.109 -1.359 -12.222 -7.511 -1.272 -0.879 -0.227 -0.156 -2.814* -2.074*
OPM -4.555 -3.868 -3.771 -3.353 -0.784 -0.568
-4.828* -4.574* -4.842* -4.541* -2.166* -1.899*
STD/CE -5 0.336
6.709*
LTD/CE -5 0.293
3.688*
TD/CE -5 0.332
5.118*
Sample Size 129 129 129 129 129 129
A d j . R-square 0.291 0.444 0.242 0.334 0.201 0.4611 F-Statistic 10.715 16.182 8.596 10.548 6.954 17.255 Significant at the .05 level
28
profit margin, consistent with pecking order theory.
The results for the small firms are shown in Table 7. In column (1), the innovation coefficient is positive and
statistically significant. This result is inconsistent with the hypothesis of Williamson (1988), that innovative behavior is associated with greater levels of equity for all firms. The result is consistent, however, with the hypothesis that the net investment-related tax effect offsets other leverage related costs, leading to increased use of debt. The results remain robust when lagged debt/equity variables are included, and for different specifications of the dependent variable.
Table 8 presents the results of the 3SLS estimates of the innovation equation. The system is estimated separately for the small and large firm size classes using each of the three debt measures. In all six equations, the positive and statistically
significant coefficient for lagged R&D/S indicates that research and development is an important determinant of innovation. This is consistent with both theory and prior results (Acs and
Audretsch 1991; and Acs and Isberg, 1991). The positive and statistically significant coefficient for the log of assets in the large firm size class implies that innovation increases with firm size. This is also consistent with prior findings. The statistically insignificant coefficients on the log of assets for
*.
the small firms model indicates that innovation is not
differentiated by size for small firms. While the coefficient of lagged operating profit is positive and significant for small
3SLS Results for the Innovation Rate Equation for 1982 (t-statistics below coefficients)
Variable
Small Firms Large Firms
(1) (2) (3) (4) (5) (6)
Intercept -0.317 -0.439 0.074 -0.573 -0.673 -0.597 -0.601 -0.916 0.138 -1.662 -2.402 -1.636 R&D/S (77) 0.063 0.074 2.604 0.123 0.102 0.164 1.984* 2.263* 0.825 4.134* 3.723* 5.393*
OPM (77) 0.012 0.012 0.012 0.002 0.004 -0.002
2.097* 2.046* 2.107* 0.388 0.679 -0.265 Ln(Ass.) (77) 0.028 0.044 -0.012 0.164 0.180 0.141 0.258 0.447 -0.105 3.362* 3.806* 2.785*
TD/CE (77) 0.002 -0.003
3.861* -7.506*
LTD/CE (77) 0.004 -0.004
4.550* -7.837*
STD/CE (77) 0.004 -0.007
3 . Ill* -2.976*
Sample Size 129 129 129 258 258 258
S y s .W t .R - s q u . 0.304 0.259 0.278 0.301 0.301 0.160
Sys.Wt.MSE 1.307 1.192 1.313 1.540 1.687 1.083
Significant at the .05 level
Separate regressions estimated for each equation.
30
firms, it is insignificant for large firms. This suggests that internal cash is more important for small firm innovation than for large firm innovation, ceteris paribus.
The most important findings in the innovation equations regard the lagged D/E ratio. The statistically significant coefficients in both firm size classes suggest that the firm's investment and financing decisions are related. In the small- firm class, innovations are positively related to debt, while for large firms, however, the relationship between lagged D/E and innovation is negative.
These findings are further substantiated in the 3SLS
estimates of the D/E equations presented in Table 9. Examination of the innovation coefficients in the small firm equations, (1)-
(3), indicate the presence of a direct and significant
relationship between the debt/equity ratio and innovation. This confirms the suggestion that, ceteris paribus, small innovative firms use more, and not less, debt to finance firm specific assets. The results are again robust for different
specifications of the dependent variable. For large firms, the innovation coefficients are negative and statistically
significant when long term and total D/E ratios are dependents, but insignificant for the short term ratio.
The foregoing results are consistent with the governance
*■ , > , ,
approach for large firms, but not for small firms. This is not surprising given institutional economists' focus on theories regarding large firms (FitzRoy and Acs, 1991). Governance
3SLS Results for Total, Long Term, and Short Term Debt Equations for 1982 (t-statistics below coefficients)
Variable
Small Firms Large Firms
(1) TD/CE
(2) LTD/CE
(3) STD/CE
(4) TD/CE
(5) LTD/CE
(6) STD/CE Intercept 137.255 77.194 45.600 -18.859 -43.151 11.497
1.839 1.487 1.244 -0.416 -1.203 0.693 INNOV (82) 185.695 106.046 109.828 -76.020 -67.025 -16.286 3.788* 3.289* 3.580* -3.190* —3.153* -1.622 R&D/S (82) -9.668 -6.172 -3.406 63.375 0.229 0.849 -2.875* -2.626* -1.969* 0.190 0.093 0.575 OPM (82) -6.150 -4.281 -2.552 -1.138 -0.596 -0.611 -2.762* -2.850* -1.876* -1.800* -1.281 —2.173*
L n (Ass.) (82) -9.209 -0.676 -6.765 18.673 18.602 2.185 -0.554 -0.059 -0.810 2.724* 3.347* 0.877*
Sample Size 129 129 129 258 258 258
Sys.Wt.R-squ. 0.304 0.259 0.278 0.301 0.301 0.160
Sys.W t .MSE 1.307 1.192 1.313 1.540 1.687 1.083
Significant at the .05 level
Separate regressions estimated for each equation
32
patterns for small firms are different because they tend to be more closely held, and therefore, subject to greater proprietary control. Given that there are generally fewer participants in the governance mechanism of small firms, structural differences in governance costs may explain why the governance model does not fit well for small firms.
In regard to leverage trade-offs, the foregoing results for small firms are consistent with the hypothesis that net
investment related to tax effects is exceed the sum of all other leverage related costs, leading to the greater use of debt by innovating firms. Conversely, for large firms, the 3SLS results are consistent with the net tax effects that are not sufficient to offset the combined costs of agency and information asymmetry, calling for the use of more equity by innovating firms. This apparent size-related net leverage effect can be explained by the presence of decreasing economies of scale, as previously
illustrated. This result may also indicate, however, that leverage costs as well as tax effects are size related.
One can argue that the cost of information asymmetry is related to both innovation and firm size. Jensen and Meckling
(1976), contend that small entrepreneurial firms will seek debt and not equity for several three reasons: First, small firms are more closely held and management is less willing to give up
equity control of the firm*. Second, small firms do not have a track record for paying dividends. Three, the entrepreneur may have a greater ability to raise debt in the informal rather than
the formal capital market (Gaston, 1989). Given this third point, small firms may have an advantage over large firms in
reducing the cost of information asymmetry because they seek debt in different capital market sectors. The costs of revealing
information in the informal market, where transactions are private and involve fewer parties, is lower than that of
revealing the same information in the public debt markets. In this case, information regarding an innovation provides tangible security for debt financing. Hence, it is not surprising to see a positive relationship between debt and small firm innovation.
Large firms, on the other hand, turn to the larger, formal, public debt markets for capital, where the arguments of Ross
(1977) and Myers and Majluf (1984) are more applicable.
The impact of risk-related agency costs on optimal capital structure may also be a function of firm size. For small firms seeking capital in less formal debt markets, it is easier to reduce and/or eliminate agency costs via contracting between the borrower and lender. Since fewer parties are involved in the financing process, covenants can be established and monitored with greater flexibility and at lower cost. For large firms involved in the formal debt markets, it is more costly to establish and provide for monitoring of such contracts. As a result, one can argue that the negative impact of risk-related agency costs is lower for femall firms. The remainder of the results of the debt/equity equations are consistent with finance theory.
34
VI. Concluding Remarks
This paper examines the effect of product innovation on capital structure choice in the context of corporate governance, net leverage trade-offs, and firm size. A simultaneous model of product innovation and capital structure is specified and
estimated with a three-stage least squares methodology to control for cross-equation correlation of error terms. The results
suggest that product innovation is an important determinant of capital structure choice, and that the exact relationship between capital structure and asset specificity depends crucially on the firm size class. For small firms, product innovation coincides with greater levels of debt financing. For large firms a
negative relationship between debt/equity and innovations is found.
The small-firm results are consistent with both the pecking order and leverage trade-off theories. Small firm innovation is positively related to internal cash flow. For small firms that utilize external financing, the net tax effect exceeds the sum of all other leverage related costs, leading to a positive
relationship between innovation and debt in the capital
structure. For large firms, the results are also consistent with the trade-off theory, however, the net tax effect does not exceed other leverage related costs. The large firm findings are also consistent with the governance approach to capital structure.
Innovative firms, whose assets are more unique, use greater
portions of equity in their capital structure.
Because of the similarity between the TCE and agency approaches to corporate finance, it is not unreasonable to conclude that structural differences in agency cost structure affect the efficiency of the corporate governance as it is
applied to the capital structure of small and large firms. It is evident from these empirical findings the TCE in more vital for large firms rather than small firms.
The findings of this study indicate that innovation is a more complete measure of asset uniqueness and firm growth
potential than R&D for large firms. Whereas R&D is intangible, innovation provides a tangible measure of asset specificity.
Future research will have to examine the extent to which the strategies of small innovative firms have been successful by comparing small innovative firms that are actively traded with those that were either the targets of takeovers or went bankrupt.
36
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Discussion Paper FS IV 91 - 24, Wissenschaftszentrum Berlin für Sozialforschung 1991.
