Has the Funding Mix of German Firms Changed After Stricter Bank Regulation?
Linda Dastorya,d, Dorothea Schäferb,c, Andreas Stephanc,d,∗
aRoyal Institute of Technology, Stockholm, Sweden
bDIW Berlin, Germany; JIBS, Sweden; and CERBE, Italy; dschaefer@diw.de
cJönköping International Business School, Sweden
dThe Ratio Institute, Stockholm, Sweden
Abstract
Using a panel of about 6,000 firms from the Mannheim innovation survey, we study whether stricter bank regulations have changed the funding mix of firms.
In particular, we shed light on the question of whether the importance of bank fi- nancing for tangible investments or innovation expenditures has decreased since stricter regulations were imposed. The results of the multivariate probit models show that the likelihood of using bank loans for financing tangible investments or innovation expenditures has not changed following stricter bank regulations.
Thus, banks did not, on average, reduce their lending to firms for tangible invest- ments or innovation expenditures due to stricter regulatory requirements.
Keywords: funding mix, innovation expenditures, investment, bank loans, bank regulation
JEL codes:D22, D21, D24, O31, O32
∗Corresponding author
Email address: andreas.stephan@ju.se (Andreas Stephan)
1. Introduction
Many scholars consider regulatory failure to be responsible for the events leading to Lehman’s insolvency in September 2008 (Acharya et al., 2012). Ac- cordingly, in the aftermath of Lehman’s failure, the demand for firmer and stricter capital regulations rose sharply. In December 2010, the Basel Committee on Bank- ing Supervision introduced new regulations, generally referred to as Basel III.
Basically, Basel III tightens the definition of bank capital, defines higher liquidity requirements and increases minimum capital ratios (Cosimano & Hakura,2011).
While the benefits of higher capital requirements are rather clear in terms of leverage and lowering the risk of bank defaults, there is less consensus regarding its disadvantages. One major concern is that higher capital requirements will increase the overall cost of capital, resulting in higher lending rates,1aggravating financial restrictions and thereby mitigating overall economic activity (Baker &
Wurgler,2015).2
Tighter financial constraints are expected to affect small and medium-sized firms more than larger ones, and also to affect firms engaged in R&d and innova- tion activity more than firms that do not perform R&d. Raising external funding tends to be more difficult for smaller companies that, on average, are younger, have less collateral, carry higher operational risks and are more likely to fail. In addition, these firms are less able to accumulate internal cash flow for funding their investment projects. In comparison, older and larger firms potentially own more sizable assets and may also benefit from long-established borrower-lender relationships, which reduce information asymmetries (Petersen & Rajan, 1995;
Berger & Udell,2002). The lack of external funding has been acknowledged as a significant obstacle to SMEs’ growth (Mina et al.,2013).
Furthermore, raising external funding for innovation-related expenditures3 may be more restricted in comparison to tangible investment expenditures. The intangible nature of innovation projects and the unknown economic outcome of innovation activities restrict access to external funding. Thus, banks are more likely to finance tangible assets that can be seized in the case of default (Hall &
Lerner,2010).
1See Admati et al.(2013) for a detailed discussion regarding increased capital requirements and capital costs.
2See, for instance, Cummins et al. (1994);Philippon(2009);Gilchrist et al.(2013) for further discussion and evidence on how the cost of capital affects real investments.
3Innovation is defined as new or significantly improved products, services and / or / in-house processes. Expenditures related to innovation activities include wages & salaries, development costs, investments, distribution and product-launching costs.
Previous literature on how adverse shocks to bank capital may impact lend- ing shows that as a direct response to stricter capital regulations banks tend to decrease their supply of credit and/or increase lending rates.4 However, stud- ies on firms’ responses to stricter bank regulations from a micro perspective are rather scarce, as previous research has generally focused on real economic activ- ity and GDP growth.5 Giebel & Kraft(2017) show that during the financial crisis (when credit was limited), innovative firms and firms who used external fund- ing reduced their investment expenditures more than non-innovative firms did.
Moreover, among innovative firms, those who used external funding in terms of bank loans decreased their investments to a greater extent.
The objective of this paper is to explore whether firms have had to change the funding of tangible investments or innovation expenditures in response to Basel III. Specifically, we examine whether firms have altered their funding mix and use bank loans less frequently post-Basel III. In addition, we investigate whether SMEs differ from large firms in their reactions to the regime changes in the bank- ing sector.
Funding restrictions may require firms to forgo profitable investment oppor- tunities, and increased capital requirements could affect their capital accumula- tion, technology adoption and competitive conduct.
The empirical analysis uses the Mannheim innovation panel. Germany is in- teresting as a case, as it is one of Europe’s largest economies, and its SMEs are considered to be the backbone of the economy. Our results show that the likeli- hood of using bank loans as a funding source has not changed for tangible and in- novation investments since the imposition of stricter bank regulations. However, the probability of using other external funding sources, such as mezzanine capital and overdrafts, has decreased. On the other hand, subsidies have increased due to programs that were implemented after the financial crisis. Strong evidence is found that medium-sized firms use more bank loans than both smaller and larger firms. Furthermore, productivity has not changed in light of stricter bank regu- lations. Firms that have used mezzanine capital or subsides exhibit significantly lower productivity afterwards. The overall impact of other funding sources on productivity is rather limited.
The rest of the paper is organized as follows. Section 2 provides the theoretical background, and Section 3 contains the data and model specifications. Next, Sec-
4See for examplePeek & Rosengren(1997);Bernanke et al.(1991);Hancock & Wilcox(1998);
Kashyap et al.(2010);Baker & Wurgler(2015).
5See, for example,Peek & Rosengren(2000);Berrospide & Edge(2009)
tion 4 presents the estimation results. Finally, Section 5 provides the discussion and conclusion.
2. Background
A firm has essentially two available sources for investment expenditures: in- ternal and external funding. Internal funding originates from retained earnings, while external funding consists of various debt contracts such as bank loans or equity. In an imperfect capital market, information asymmetries and taxes related to capital structure matter. When suppliers of capital have less information about the quality of an asset or a security, they are forced to charge a risk premium reflecting the information asymmetry of an investment project. This premium creates a gap between the cost of internal and external funding. Firms face a hi- erarchy of financial funding sources in which funds with lower cost will be used first. Thus, internal cash flow is preferred over debt, and debt is preferred over equity (Meyer & Kuh, 1957; Myers & Majluf, 1984). Generally, this ranking is referred to as the pecking order theory. Despite extensive empirical research in- vestigating the explanatory power of the pecking order theory, no consensus has been reached among scholars yet.Shyam-Sunder & Myers(1999) find strong sup- port for the pecking order theory for continuously-traded, mature firms during the time period 1971-1989. However, using the same methodology with a more comprehensive data set, Frank & Goyal (2003) find that the pecking order the- ory has weak explanatory power. On the other hand,Agca & Mozumdar(2007);
Bulan & Yan (2009); Lemmon & Zender (2010); De Jong et al. (2011) find sup- port for the pecking order theory. The majority of the literature investigating the pecking order theory is based on large, listed U.S. firms. Studies using European firm data are more scarce.Gaud et al.(2007) use data from 13 European countries and conclude that that the pecking order theory is not an appropriate descrip- tion of capital structure policies in European firms. On the other hand,Mateev et al.(2013) present results for Central and Eastern European firms which provide great support for the pecking order theory.
Empirical research on SME funding indicates that SMEs’ main source of fi- nancing is the entrepreneurs’ private wealth and retained earnings (Ou & Haynes, 2006; Vos et al., 2007; Ughetto, 2008). Furthermore, SMEs’ capital structures are not static but tend to change over their life cycles. Younger firms tend to prefer internal funds as a main source of finance; however, as they grow older, access to external financing becomes easier. This change in access is linked to the growth of firms’ assets, which can be used as collateral. Collateralizable assets may decrease
the degree of information asymmetry (Berger & Udell, 1998, 2006). In principle, innovation funding follows the same pattern as SME funding. Firms tend to use internal funds over external funds when financing innovation projects (e.g.,Hall, 1989, 1992; Himmelberg & Petersen, 1994; Bougheas et al., 2003; Czarnitzki &
Hottenrott,2011).
Furthermore, in Europe, external funding tends to be bank based to a large extent. In Europe, the ratio of bond funding to total corporate credit funding is less than 50 percent, while, in the U.S., it is about 70 percent. Thus, European firms are highly dependent on bank funding (Franke & Krahnen, 2016). More- over, banks prepare for stricter capital regulation. This process causes banks to reduce their lending. Accordingly, the banking sector is then expected to shrink substantially. This shrinkage requires other financial intermediaries and insti- tutions to supply the credit needs of the economyWehinger(2012). Considering these facts, it is assumed that stricter capital requirements on bank loans will have an effect on firms’ funding mixes, as firms may have to shift from bank funding to other funding sources. In fact, stricter capital requirements could make eq- uity and other sources of funding from outside the banking sector less expensive relative to bank loans and thus cause a rearrangement of the funding mix of a firm. In this case, the effect could be stronger for firms with more capital de- mand or riskier investment projects. Thus, one can assume that for innovation investments, this affect will be amplified for firms with high innovation capabil- ity. Innovation capability refers to a firm’s ability to generate and launch new products or processes.6
Furthermore, restricted access to funding may have a negative impact on a firm’s productivity. Any funding constraint that could lead to a firm reducing its profitable investments may also lead to hampering completion and capital invest- ment, which, in turn, may have an adverse effect on optimal resourse allocation Heil(2017).
3. Empirical approach 3.1. Econometric model
We assume that the likelihood that firm i uses funding source k, denoted ( f sk), for tangible investments or for innovation expenditure at time t, is a function of several firm characteristics, e.g., firm size, innovation capability (IC), and a number of further controls, e.g., industry, firm age, geographical location and a
6SeeHottenrott & Peters(2012) for a detailed discussion.
company’s legal form. We further assume that the probability depends on bank regulation, in particular with regard to the likelihood of obtaining a bank loan.
We also expect that regulation influences the likelihood of using other funding sources, because regulation affects access to and cost of bank loans vis a vis other instruments. Thus, equation (1) with dependent variable fs ∈ [0, 1], is specified as
prob(fskit =1) = f(PostRegut, ICit, sizeclassit, controlsit) +εikt (1) where k=1, . . . , K denotes the various funding sources. Given k funding sources, we can define a system of k equations, where each equation comprises a probit model. Accordingly, we allow that error terms εiktare correlated across the equa- tions which is possible when using a multivariate probit model for estimating the K equations. We employ Roodman’s (2011) conditional mixed process (CMP) procedure written for STATA to estimate the K equations (1). One key advan- tages of the CMP proecedure is that it is a seemingly unrelated regression (SUR) estimator that allows us to estimate several equations simultaneously where the error terms are allowed to be correlated across equations. In our context, the modelling of financing choices allowing for correlations between equations re- flects the fact that there might be unobserved determinants of funding decisions that are captured in this model. The coefficient of interest is the one in front of the PostRegu-variable. It indicates how a firm’s menu of funding instruments changed in response to the introduction of stricter bank regulation.
3.2. Data
We use the 2007 and 2014 waves of the Mannheim innovation panel7to ana- lyze how the mix of funding sources has changed after the introduction of Basel III. The questions asked in the survey take into account a firm’s investment be- haviour of the past three years. Thus, the first wave covers the run-up period to Basel II’s full implementation which occurred at the end of 2006. The second wave represents the years 2011-2013. Since Basel III was introduced in 2010, the survey responses are expected to reveal how firms adjusted in response to both the attitude of banks towards the new regulation and the ongoing crisis in the banking sector.
Table1 presents the definitions of the variables used in the empirical model.
Small firms are firms with less than 50 employees and medium sized have less
7This panel is part of the Community Innovation Survey (CIS) and available at the Centre for European Economic Research (ZEW).
than 250 employees according to the SME classification of the European Com- mission.
Table 1: Variable definitions
Variable name Definition
fsk ∈ {0, 1}, firm uses funding source k for tangible or intangible investment
PostRegu ∈ {0, 1}, indicates period 2011-2013 after intro- duction of stricter bank regulation, reference cat- egory period 2004-2006
sizeclass a firm’s size class defined by number of employ- ees: 1 = 0−49, 2 = 50−250, 3 = 251 or more employees, reference category size class 1
industry 21 industry dummies according to NACE 2-digit code
fambus ∈ {0,1}, indicates whether firm is a family busi- ness
young ∈{0,1}, indicates whether firm is less than 3 years old
hum cap IC-indicator: share of employees with university degree (%)
R&D cont IC-indicator: ∈{0,1}, firm performs R&D contin- uously
R&D occ IC-indicator: ∈ {0,1}, firm performs R&D occa- sionally
East ∈{0,1}, firm is located in East Germany
trade partner ∈ {0,1}, firm has legal form of partnership, own- ers fully liable
limited liability ∈ {0,1}, firm has legal form of limited liability company, German GmbH
listed corp ∈{0,1}, firm has legal form of listed corporation, German AG
Two survey questions are used to define fsk (Table1). The first question asks firms to state which financial sources they use for tangible investment and the second question asks for the financial sources the firm uses for innovation expen- diture.
Table 2 presents the definition of the response options available for the first survey question. Firms who responded that they did not use any of the financial sources are excluded from the data set.
Table 2: Definition of funding sources
Funding source
variable fsk Definition
internal cash On-going business operation (profit/surplus, re- serves)
equity New equity, admission of new shareholders, par- ticipation of other enterprises
mezzanine Shareholders’ loans, dormant equities, participa- tion certificates
bonds Issue of bonds and debt obligations overdraft Earmarked bank credit
public loans Publicly subsidized loan programs subsidy Public allowance/grant
factoring Factoring, leasing and supplier credit line
Following previous empirical literature, a set of control variables are employed (e.g., Schäfer et al., 2017). Innovation capability, IC, is a firm’s ability to create and implement innovation. We use two proxies for capturing this ability, the share of employees with a university degree and the frequency of engaging in R&D. We assume that the higher a firm’s innovation capability, the higher the de- mand for innovation funding.8 We also control for industry and firm type where firm type refers to the legal form of a company. Firms in different industries may be more or less dependent on a certain type of funding source. For example, the real estate industry is more dependent on debt funding rather than equity fund- ing. Moreover, different firm types have access to different sources of funding as, for example, public equity and the bond market is only available for listed firms. Firms located in East Germany have traditionally been granted subsidies, therefore, we include a dummy variable indicating the geographical location of a firm. Firm age is controlled for as a dummy variable indicating whether a firm is younger or older than three years.
Tables 3 and 4 present statistics for firms’ use of different funding sources
8SeeHottenrott & Peters(2012) for a detailed discussion.
for tangible and innovation investments by year and size respectively. Internal funding (cash flow) s the most common source of funding for both tangible and innovation investments. Bank loan is the second most important source for fund- ing of tangible investments. Notice the significant increase in the use of subsides for innovation investments, which now has become the second most used fund- ing source, after the introduction of stricter bank regulation. The likelihood of using bank loan as a funding source is greater for tangible investments than for innovation funding. This is in line with our expectation that, in general, banks are more reluctant to finance innovation expenditures because of the inherent risk.
Table 3: Fraction of firms using funding source k for tangible investment by size and year (%)
firm size class 1: <50 2: 50-249 3: ≥250 All All year 2006 2013 2006 2013 2006 2013 2006 2013 internal cash 77.7 75.0 86.4 84.9 93.4 92.3 83.7 80.8
equity 7.1 5.2 7.5 3.9 7.0 7.0 7.2 5.1
mezzanine 14.6 11.5 13.6 10.8 11.6 9.4 13.7 10.9
bond 0.2 0.3 0.3 0.3 1.6 2.8 0.5 0.7
bank loan 36.4 34.3 37.2 35.7 22.8 25.4 33.8 33.3 overdraft 29.2 28.0 24.6 23.5 19.4 19.0 25.7 25.2 public loan 12.4 12.1 14.1 16.1 9.7 12.2 12.4 13.3 subsidy 9.9 12.6 18.4 18.5 13.8 14.2 13.4 14.7
factoring — 17.8 — 19.7 — 21.5 — 19.0
#Obs 3,383 3,319
Table 4: Fraction of firms using funding source k for innovation expenditure by size and year (%)
firm size class 1: <50 2: 50-249 3: ≥250 All All year 2006 2013 2006 2013 2006 2013 2006 2013 internal cash 88.1 80.8 92.6 84.4 95.8 92.1 91.9 84.8
equity 8.2 4.1 5.4 3.8 5.2 4.1 6.4 4.0
mezzanine 13.7 10.0 10.2 5.8 6.6 7.9 10.4 8.1
bond 0.3 0.2 0.5 0.2 1.2 1.8 0.6 0.6
bank loan 12.2 11.9 14.5 15.3 10.4 13.0 12.4 13.3 overdraft 20.6 16.5 20.6 14.5 14.6 11.8 18.8 14.7
public loan 7.1 7.0 7.6 10.7 8.3 9.5 7.6 8.9
subsidy 13.4 30.7 15.9 28.8 15.8 24.0 14.9 28.4
factoring — 6.1 — 7.7 — 7.7 — 7.0
#Obs 1,747 1,563
4. Estimation results
Table5presents the estimation results for tangible investments as described in Equation (1). The insignificant PostRegu coefficient for cash, bank loan, overdraft and public loan indicates that there has been no change in the probability of the use of these sources post stricter bank regulation. However, the negative and sig- nificant coefficient for equity and mezzanine funding implies that the likelihood of using these funding sources has decreased post stricter bank regulation. In contrast, the positive subsidy coefficient indicates an increase in the probability of using this funding source after the introduction of stricter regulation.
Furthermore, the larger the firm, the greater the probability of using internal funding (cash flow). The probability of using equity funding decreases as firms become larger. Medium sized firms (size class=2) have the greatest probability of using bank loan as a funding source. The probability of using overdraft as funding source decreases as firms become larger. Medium sized firms also have a greater probability of using subsidies than the smaller and larger firms. Usage of public loans is less common among large firms compared to medium sized and smaller ones, while using subsidies is more common for medium and large firms.
Table6presents the estimation results for innovation expenditures. The neg- ative and significant coefficients for cash, equity, mezzanine and overdraft in- dicate that there is a lower probability of using these sources of funding post stricter bank regulation. The insignificant coefficients for bank loan and public loan (loan from a public bank, usually with better conditions compared to com- mercial banks) imply that there has been no change in the likelihood of their use.
However, a significant increase in the use of subsidies is observed post stricter bank regulation.
The larger the firm, the greater the probability of using internal funding. Note, that this result is only significant for the largest firms. The use of equity funding and mezzanine capital decreases as firms become larger. The probability of using bank loans and subsidies is highest among medium sized firms (size class=2). The largest firms have the lowest probability of using overdraft as a funding source.
However, the importance of public loans increases as firms become larger.
Table 5: Multivariate probit model: funding sources for tangible investment, see Eq. (1)
internal mezza- bank over public
cash equity nine loan draft subsidy loan
PostRegu=1 -0.017 -0.242*** -0.146*** -0.025 -0.055 0.135** 0.015 [0.050] [0.069] [0.054] [0.044] [0.045] [0.055] [0.052]
sizeclass= 2 0.305*** -0.036 -0.071 0.098** -0.108** 0.402*** 0.091 [0.056] [0.074] [0.059] [0.048] [0.050] [0.062] [0.057]
sizeclass=3 0.649*** -0.006 -0.134* -0.083 -0.287*** 0.327*** -0.139*
[0.086] [0.094] [0.079] [0.065] [0.069] [0.085] [0.081]
r&d cont 0.146** 0.208** -0.016 -0.242*** -0.011 0.414*** 0.101 [0.071] [0.083] [0.070] [0.060] [0.061] [0.072] [0.071]
r&d occa 0.269*** 0.092 -0.034 -0.229*** 0.041 0.232*** 0.011 [0.079] [0.096] [0.078] [0.065] [0.066] [0.083] [0.078]
hum cap 0.005*** 0.002 0.001 -0.006*** -0.003** 0.001 -0.001 [0.001] [0.002] [0.001] [0.001] [0.001] [0.002] [0.002]
East=1 -0.134*** 0.137** -0.01 0.161*** -0.134*** 1.038*** 0.075 [0.052] [0.067] [0.056] [0.045] [0.048] [0.057] [0.054]
fambus=1 -0.160*** 0.044 0.134** 0.262*** 0.249*** -0.150*** 0.054 [0.055] [0.069] [0.057] [0.046] [0.048] [0.058] [0.056]
young=1 -0.014 0.577*** 0.614*** -0.247* -0.061 -0.174 0.067 [0.146] [0.144] [0.128] [0.132] [0.132] [0.170] [0.152]
trade partner 0.281*** -0.038 0.603*** -0.256*** -0.157** -0.252** -0.137 [0.085] [0.119] [0.106] [0.076] [0.078] [0.104] [0.090]
limited liability 0.282*** 0.031 0.556*** -0.262*** -0.224*** -0.159* -0.185**
[0.068] [0.099] [0.094] [0.063] [0.064] [0.083] [0.075]
listed corp 0.377** 0.400** 0.271 -0.342*** -0.302** -0.102 -0.370**
[0.170] [0.159] [0.180] [0.131] [0.138] [0.159] [0.171]
Constant 0.669*** -1.656*** -1.966*** 0.078 -0.464*** -1.658*** -0.721***
[0.132] [0.175] [0.165] [0.110] [0.118] [0.150] [0.127]
#Obs: 4284, df=223, chi2=1517.2, p-value=0.000
Notes: Standard errors in brackets, * p<0.10 ** p<0.05 *** p<0.01. For variable definitions, see Table1.
Reference categories: PostRegu=period 2004-2006, size class =1 (1-49 employees), r&d=no, company type=1 (sole proprietorship).
Table 6: Multivariate probit model: funding sources for innovation expenditure, see Eq. (1)
internal mezza- bank over public
cash equity nine loan draft subsidy loan
PostRegu=1 -0.362*** -0.416*** -0.176** 0.037 -0.306*** 0.546*** 0.032 [0.083] [0.114] [0.088] [0.079] [0.075] [0.072] [0.088]
sizeclass= 2 0.082 -0.217* -0.189** 0.242*** 0.036 0.180** 0.177*
[0.093] [0.118] [0.095] [0.089] [0.081] [0.087] [0.102]
sizeclass= 3 0.400*** -0.281** -0.340*** 0.16 -0.169* 0.115 0.288**
[0.128] [0.139] [0.119] [0.108] [0.100] [0.106] [0.120]
r&d cont 0.136 0.426*** 0.031 -0.234** 0.204** 0.646*** 0.307***
[0.104] [0.133] [0.103] [0.096] [0.090] [0.101] [0.115]
r&d occa 0.198* 0.071 -0.201* -0.044 0.114 0.283** 0.305**
[0.115] [0.150] [0.116] [0.100] [0.095] [0.112] [0.123]
human cap 0.003 0.002 0.001 0.001 -0.003 0.005** 0.002
[0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002]
East=1 -0.169* 0.148 -0.033 0.122 0.059 0.822*** 0.214**
[0.087] [0.104] [0.088] [0.081] [0.075] [0.077] [0.092]
fambus=1 0.103 -0.216** 0.127 0.256*** 0.280*** -0.106 0.199**
[0.087] [0.104] [0.088] [0.082] [0.075] [0.076] [0.092]
young=1 0.068 0.691*** 0.516*** -0.112 -0.159 -0.117 -0.146 [0.236] [0.197] [0.182] [0.223] [0.199] [0.217] [0.258]
trade partner 0.284* -0.109 0.709*** -0.473*** -0.468*** 0.06 -0.181 [0.165] [0.212] [0.215] [0.151] [0.144] [0.166] [0.182]
limited liability 0.243* -0.093 0.592*** -0.421*** -0.357*** -0.02 -0.202 [0.140] [0.184] [0.203] [0.132] [0.127] [0.149] [0.165]
listed corp 0.22 0.326 0.15 -0.324 -0.388* 0.345* -0.518*
[0.229] [0.237] [0.309] [0.211] [0.202] [0.205] [0.275]
Constant 1.026*** -2.050*** -1.906*** -0.583** -0.606** -2.015*** -1.539***
[0.294] [0.478] [0.309] [0.240] [0.244] [0.289] [0.298]
#Obs: 2,149, df= 219, chi2=751.1, p-value=0.000
Notes: see previous Table.
5. Conclusions
This study investigates whether the funding mix of firms has changed be- tween period 2004-2006 and 2011-2013 due to stricter bank regulation. The econo- metric results show that the likelihood of firms using bank loans as a funding source has changed neither for tangible investments nor for innovation expendi- tures after stricter bank regulation. Overall, however, significant changes of the funding mix can be observed. First, we find that in period 2011-2013, firms use less internal cash for innovation expenditures, relying instead on subsidies from governmental programs. This is because of the launch of several governmen- tal programs in Germany after the financial crisis. Moreover, when compared to 2004-2006, the use of equity or mezzanine capital as a funding source has de- creased in the period 2011-2013.
The results also show that the funding mix depends not only on firm size and firm age, but also on company type. While sole traders mainly rely on bank loans, other firm types are more likely to use internal cash to finance their tangible in- vestment or innovation expenditures. Medium sized firms tend to use more bank loans and also public loans compared to smaller firms. Young firms mainly rely on equity and mezzanine capital. Family firms use bank loans or overdraft credits more frequently than non-family firms for financing their innovation expenditure implying that they most likely have funding gaps.
Our analysis shows that stricter regulatory requirements on banks has not had a detrimental impact on the likelihood that firms will use bank loans as a financing source. This holds for both tangible investment and for innovation expenditures.
References
Acharya, V., Drechsler, I., & Schnabl, P. (2012). A tale of two overhangs: The nexus of financial sector and sovereign credit risks. Banque de France Financial Stability Review, 16.
Admati, A. R., DeMarzo, P. M., Hellwig, M. F., & Pfleiderer, P. C. (2013). Fallacies, irrelevant facts, and myths in the discussion of capital regulation: Why bank equity is not socially expensive. Rock center for corporate governance, 161.
Agca, S., & Mozumdar, A. (2007). Firm size, debt capacity, and corporate financ- ing choices, .
Baker, M., & Wurgler, J. (2015). Do strict capital requirements raise the cost of cap- ital? Bank regulation, capital structure, and the low-risk anomaly. The American Economic Review, 105, 315–320.
Berger, A. N., & Udell, G. F. (1998). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking & Finance, 22, 613–673.
Berger, A. N., & Udell, G. F. (2002). Small business credit availability and relation- ship lending: The importance of bank organisational structure. The Economic Journal, 112.
Berger, A. N., & Udell, G. F. (2006). A more complete conceptual framework for sme finance. Journal of Banking & Finance, 30, 2945–2966.
Bernanke, B. S., Lown, C. S., & Friedman, B. M. (1991). The credit crunch. Brook- ings papers on economic activity, 1991, 205–247.
Berrospide, J., & Edge, R. (2009). Linkages between the financial and real sectors:
Bank credit and capital over the crisis. board of governors of the federal reserve system. Unpublished manuscript, .
Bougheas, S., Görg, H., & Strobl, E. (2003). Is r & d financially constrained? theory and evidence from irish manufacturing. Review of Industrial Organization, 22, 159–174.
Bulan, L., & Yan, Z. (2009). The pecking order theory and the firm’s life cycle.
Banking and Finance Letters, 1, 129.
Cosimano, T. F., & Hakura, D. S. (2011). Bank Behavior in Response to Basel III: A Cross-Country Analysis. IMF Working Papers 11/119 International Monetary Fund. URL:https://ideas.repec.org/p/imf/imfwpa/11-119.html.
Cummins, J. G., Hassett, K. A., Hubbard, R. G., Hall, R. E., & Caballero, R. J.
(1994). A reconsideration of investment behavior using tax reforms as natural experiments. Brookings papers on economic activity, 1994, 1–74.
Czarnitzki, D., & Hottenrott, H. (2011). R&d investment and financing constraints of small and medium-sized firms. Small Business Economics, 36, 65–83.
De Jong, A., Verbeek, M., & Verwijmeren, P. (2011). Firms’ debt–equity decisions when the static tradeoff theory and the pecking order theory disagree. Journal of Banking & Finance, 35, 1303–1314.
Frank, M. Z., & Goyal, V. K. (2003). Testing the pecking order theory of capital structure. Journal of financial economics, 67, 217–248.
Franke, G., & Krahnen, J. P. (2016). SME funding without banks? On the interplay of banks and markets. Technical Report White Paper Series.
Gaud, P., Hoesli, M., & Bender, A. (2007). Debt-equity choice in europe. Interna- tional Review of Financial Analysis, 16, 201–222.
Giebel, M., & Kraft, K. (2017). External financing constraints and firm’s innova- tive activities during the financial crisis, .
Gilchrist, S., Sim, J. W., & Zakrajšek, E. (2013). Misallocation and financial market frictions: Some direct evidence from the dispersion in borrowing costs. Review of Economic Dynamics, 16, 159–176.
Hall, B. H. (1989). The impact of corporate restructuring on industrial research and development. Technical Report National Bureau of Economic Research.
Hall, B. H. (1992). Investment and research and development at the firm level: does the source of financing matter?. Technical Report National bureau of economic research.
Hall, B. H., & Lerner, J. (2010). The financing of r&d and innovation. Handbook of the Economics of Innovation, 1, 609–639.
Hancock, D., & Wilcox, J. A. (1998). The “credit crunch” and the availability of credit to small business. Journal of Banking & Finance, 22, 983–1014.
Heil, M. (2017). Finance and productivity, .
Himmelberg, C. P., & Petersen, B. C. (1994). R & d and internal finance: A panel study of small firms in high-tech industries. The Review of Economics and Statis- tics, (pp. 38–51).
Hottenrott, H., & Peters, B. (2012). Innovative capability and financing constraints for innovation: More money, more innovation? Review of Economics and Statis- tics, 94, 1126–1142.
Kashyap, A. K., Stein, J. C., & Hanson, S. (2010). An analysis of the impact of
‘substantially heightened’capital requirements on large financial institutions.
Booth School of Business, University of Chicago, mimeo, 2.
Lemmon, M. L., & Zender, J. F. (2010). Debt capacity and tests of capital structure theories. Journal of Financial and Quantitative Analysis, 45, 1161–1187.
Mateev, M., Poutziouris, P., & Ivanov, K. (2013). On the determinants of sme cap- ital structure in central and eastern europe: A dynamic panel analysis. Research in International Business and Finance, 27, 28–51.
Meyer, J. R., & Kuh, E. (1957). The investment decision: An empirical study volume 102. Harvard University Press Cambridge, MA.
Mina, A., Lahr, H., & Hughes, A. (2013). The demand and supply of external finance for innovative firms. Industrial and Corporate Change, 22, 869–901.
Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of financial economics, 13, 187–221.
Ou, C., & Haynes, G. W. (2006). Acquisition of additional equity capital by small firms–findings from the national survey of small business finances. Small Busi- ness Economics, 27, 157–168.
Peek, J., & Rosengren, E. S. (1997). The international transmission of financial shocks: The case of japan. The American Economic Review, (pp. 495–505).
Peek, J., & Rosengren, E. S. (2000). Collateral damage: Effects of the japanese bank crisis on real activity in the united states. American Economic Review, 90, 30–45.
Petersen, M. A., & Rajan, R. G. (1995). The effect of credit market competition on lending relationships. The Quarterly Journal of Economics, 110, 407–443.
Philippon, T. (2009). The bond market’s q. The Quarterly Journal of Economics, 124, 1011–1056.
Schäfer, D., Stephan, A., & Mosquera, J. S. (2017). Family ownership: does it matter for funding and success of corporate innovations? Small Busi- ness Economics, 48, 931–951. URL:https://doi.org/10.1007/s11187-016-9813-y.
doi:10.1007/s11187-016-9813-y.
Shyam-Sunder, L., & Myers, S. C. (1999). Testing static tradeoff against pecking order models of capital structure. Journal of financial economics, 51, 219–244.
Ughetto, E. (2008). Does internal finance matter for r&d? new evidence from a panel of italian firms. Cambridge Journal of Economics, 32, 907–925.
Vos, E., Yeh, A. J.-Y., Carter, S., & Tagg, S. (2007). The happy story of small busi- ness financing. Journal of Banking & finance, 31, 2648–2672.
Wehinger, G. (2012). Bank deleveraging, the move from bank to market-based financing, and sme financing. OECD Journal: Financial Market Trends, 2012, 65–
79.
Appendix
Table A.1: Industry category by firm size class for 2013 (%)
Industry Firm size class
<50 50-249 >250 All
Mining 3.2 4.4 5.7 3.9
Food, tobacco 4.7 5.5 3.7 4.8
Textiles 4.5 3.1 1.3 3.7
Wood, paper 3.2 4.7 1.9 3.4
Chemicals 2.3 4.0 5.4 3.2
Plastics 2.7 4.3 3.0 3.1
Glass, ceramics 2.0 2.6 3.8 2.4
Metals 6.8 8.4 5.8 7.1
Machinery 4.9 8.1 7.4 6.0
Electrical equipment 2.5 6.5 9.8 4.5
Medical, instruments 1.5 3.2 6.1 2.6
Transport equipment 6.2 7.2 3.4 6.1
Furniture 6.3 4.9 3.7 5.6
Wholesale 4.2 2.8 2.8 3.7
Retail, automobile 8.1 8.7 7.5 8.2
Transport, communications 5.1 4.6 2.6 4.6
IT, telecom 3.3 2.8 10.0 4.0
Technical services 7.4 3.1 1.3 5.4
Firm-related services 7.1 1.7 2.0 5.1
Other services 4.9 4.4 8.3 5.2
n.a. 9.1 4.9 4.4 7.4
Total 100.0 100.0 100.0 100.0
Obs 3,235 1,374 702 5,311
Table A.2: Legal company forms by firm size class for 2013 (%)
Legal Firm size class
form <50 50-249 >250 All
1 27.3 9.8 9.6 20.4
2 13.6 22.2 23.8 17.2
3 57.9 65.4 53.5 59.2
4 1.3 2.6 13.1 3.2
Total 100.0 100.0 100.0 100.0 Obs 3,232 1,372 701 5,305
1=sole proprietorship, partnership 2=trade partnership, limited company 3=limited liability corporation (GmbH) 4=listed corporation (AG)
