- Email: [email protected]
Firm size effects in trade credit supply and demand
Accepted Manuscript
Firm size effects in trade credit supply and demand Jochen Lawrenz, Julia Oberndorfer PII: DOI: Reference:
S0378-4266(18)30114-6 10.1016/j.jbankfin.2018.05.014 JBF 5356
To appear in:
Journal of Banking and Finance
Received date: Revised date: Accepted date:
14 September 2017 19 April 2018 15 May 2018
Please cite this article as: Jochen Lawrenz, Julia Oberndorfer, Firm size effects in trade credit supply and demand, Journal of Banking and Finance (2018), doi: 10.1016/j.jbankfin.2018.05.014
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Firm size effects in trade credit supply and demand✩ Jochen Lawrenza , Julia Oberndorfera,∗ Department of Banking and Finance, University of Innsbruck, Universit¨ atsstraße 15, 6020 Innsbruck, Austria
CR IP T
a
Abstract
By investigating trade credit usage among SMEs and large companies following the macroeconomic shock of the financial crisis of 2007/08, we identify a firm size effect, which is
AN US
genuine in the sense that it cannot be entirely explained by financial constraints, external finance dependence or creditworthiness. We find that (i) SMEs, in contrast to large firms, do not display evidence for the inter-firm liquidity redistribution hypothesis. Especially large vulnerable firms did cut down trade credit provision to the detriment of small vulnerable firms. (ii) We document a general substitution effect between bank and trade credit
M
and show that it has strengthened during the crisis among large firms, but not among SMEs. (iii) We provide evidence that the shift in trade credit financing had adverse real
ED
effects on investment behaviour of SMEs.
PT
JEL classification: D22; G01; G20; G32
AC
CE
Keywords: trade credit, financial crisis, SMEs, redistribution, substitution
✩
We thank three anonymous referees for very helpful remarks and suggestions. We are grateful to Vidhan Goyal for an extensive discussion and his valuable comments at the DGF 2016 Conference in Bonn. We also thank Emilia Garcia-Appendini for her useful comments. We are grateful to Irene Riccabona for excellent research assistance on a previous draft. Further we thank Detlev Hummel, Hendrik Scholz and Martin Wallmeier for their comments at the International PhD-Seminar in Banking 2016 at the Friedrich-Alexander University Erlangen-N¨ urnberg. We particularly thank Matthias Bank, Alexander Kupfer, Michael Munsch, as well as numerous seminar and conference participants for their helpful comments. We gratefully acknowledge the data support from Creditreform AG. Julia Oberndorfer gratefully acknowledges the financial support from the dissertation grant provided by Creditreform Rating AG and Creditreform Wirtschaftsauskunftei Kubicki KG. All remaining errors are our own. ∗ Corresponding author. Tel./fax: +43 512 507 73102/ +43 512 507 73199. Email addresses: [email protected] (Jochen Lawrenz), [email protected] (Julia Oberndorfer) Preprint submitted to Journal of Banking and Finance
ACCEPTED MANUSCRIPT
1. Introduction Trade credit, besides bank debt, is one of the most important instruments of external financing for a majority of firms, in particular smaller-sized companies. However, since comprehensive datasets on small and medium-sized enterprises (SMEs) are usually difficult
CR IP T
to obtain, little is still known about trade credit policy in this market segment and about the cross-firm distributional effect. In this paper, we investigate the redistribution and substitution role of trade credit financing during the recent financial crisis with a major focus on the differential behavior between SMEs and large sized firms, to which we will refer as ’firm size effect’ for short.1 Differences with respect to financial behavior between large
AN US
companies and SMEs have been documented in several studies for reasons of organisational structure, financial expertise, priority of financial motives or institutional factors (see e.g. Beck et al., 2008; Graham and Harvey, 2001).
Considering trade credit usage, several recent papers put special emphasis on the SME
M
segment (see e.g. Mart´ınez-Sola et al., 2014; Gama and Van Auken, 2015; Carb´o-Valverde et al., 2016; McGuinness and Hogan, 2016). McMillan and Woodruff (1999) and Marotta
ED
(2005) report that small, financially constrained firms provide trade credit, though their empirical evidence is based on rather small samples of survey data. Interest in firm size
PT
effects is warranted by e.g. the results in Klapper et al. (2012) or Fabbri and Klapper (2016), who document that large companies are more likely to enjoy greater bargaining
CE
power in customer-supplier relationships with smaller companies. If a size effect existed, it would have an impact on results constructed from matched supplier-client samples, such
AC
as e.g. in Garcia-Appendini and Montoriol-Garriga (2013). The identification of supplierclient relationships introduces a mechanical size bias, as suppliers tend to be substantially smaller in asset size than their clients.2 Garcia-Appendini and Montoriol-Garriga (2013)
show that “credit flows from the most liquid firms to the most constrained ones”. However, 1
We explicitly state that we use the ’size effect’ notion in the context of trade credit usage, which is not to be confused with the size effect related to average stock returns. 2 Garcia-Appendini and Montoriol-Garriga (2013) identify supplier-client relationships through the Financial Accounting Standards FAS 14 and 131, which require public firms to report the identity of customers whose purchases represent more than 10% of the firm’ s total annual sales. Evidently, matched pairs will have a bias towards identifying small suppliers to large customers. Descriptive statistics in Garcia-Appendini and Montoriol-Garriga (2013) confirm that within the matched sample, suppliers have
1
ACCEPTED MANUSCRIPT
the size imbalance in matched pairs leaves open the possibility that the identified credit flow is not only driven by the extent of financial constraints but also by firm size and in turn motivates closer inspection of firm size effects. With our paper, we aim at identifying firm size differences in trade credit behavior
CR IP T
during the financial crisis of 2007/08 on the basis of a comprehensive German dataset.3 We contribute to the understanding of trade credit usage by identifying a significant firm size effect, which we label as genuine or self-contained, since (i) it is consistent across various definitions of firm size, (ii) it is not entirely explained by controlling for usual firm fundamentals such as financial constraints, external finance dependence, or credit-
AN US
worthiness, for which size is commonly used as proxy, and (iii) it cannot be replicated by conditioning the sample on these fundamental variables alone. Thus, we show that our results cannot easily be dismissed by pointing towards an omitted-variable bias. More specifically, our study contributes to the trade credit literature along different dimensions. We first point out that in response to the 2007/08 crisis, the aggregate net
M
supply of trade credit did decrease. By conditioning on measures of pre-crisis financial vulnerability, we can attribute the decline to a supply effect, consistent with the inter-firm
ED
liquidity redistribution explanation as outlined by Love et al. (2007) and Garcia-Appendini and Montoriol-Garriga (2013). We complement the recent empirical work, as we do not
PT
merely analyze large companies but also SMEs. To our knowledge, only very limited cross-sectional data on the distribution of firm size exist. Importantly, after controlling
CE
for different firm fundamentals we find both, the decline in net supply as well as the evidence for redistribution, to be significantly less pronounced among SMEs, suggesting
AC
that the adverse economic shock of the financial crisis was propagated along the trade credit supply channel more strongly by large firms and to the detriment of small vulnerable firms. Second, we investigate the relative importance of bank debt and trade credit and confirm a general substitution effect in line with Petersen and Rajan (1997), Nilsen (2002)
median asset values of 512 Mio US-$, while customers median asset value is 28,019 Mio US-$, i.e. roughly 50 times larger. 3 It is broadly recognized that the negative supply shock caused by the 2007/08 crisis is an exogenous event which is an ideal natural experiment to analyze the usage of trade credit. See e.g. Garcia-Appendini and Montoriol-Garriga (2013), Ivashina and Scharfstein (2010).
2
ACCEPTED MANUSCRIPT
or Choi and Kim (2005). Our paper builds on this research. Rather than concentrating on different size samples like Choi and Kim (2005), we use interaction terms and thus, are able to establish sadistically significant differences. Furthermore, we again control for various fundamental firms characteristics and apply several regression models to show,
CR IP T
that our results remain consistent. Similar to Choi and Kim (2005) we find, that the use of trade credit as a substitute for bank debt during the crisis years turns out to be stronger for large firms than for SMEs. Against the background of a significant decline in bank debt (which was even stronger among SMEs), this result suggests that in contrast to large firms, SMEs were less able to compensate declining bank financing by trade credit.
AN US
We further analyze the direction of causality in the substitution effect and find evidence that it is more likely that accounts payable adapt to a given change in bank debt rather than vice versa. Finally, we provide evidence on the impact of a shift in financing patterns on the real economy by investigating investment activity with respect to differences in firm size. Thus, our research complements the work by Carb´o-Valverde et al. (2016), who
M
analyze investment-trade credit sensitivities during the recent financial crisis among credit constrained and unconstrained SMEs. We find a positive dependence between investment
ED
and accounts payable among SMEs but not among large firms. Coupled with our previous finding that SMEs (in contrast to large firms) were not able to compensate declining bank
PT
financing through increased trade credit, this result identifies a channel by which trade credit financing affects real economic activity of SMEs.
CE
Overall, given that size is usually taken to be a proxy for other not directly observable firm characteristics, our analysis puts a special emphasis on not succumbing to an omitted
AC
variable bias and on investigating in detail the economic driver behind the size effect. To this end, we use measures of (i) being financially constrained from a regime-switching regression in line with the approach of Hu and Schiantarelli (1998), Hovakimian and Titman (2006), and Almeida and Campello (2007), (ii) external finance dependence from industry-specific data according to Rajan and Zingales (1998), and (iii) creditworthiness by using the classic approach of Altman’s Z-score (Altman, 1968). Adding these measures to our analyses, we find that neither of them can fully explain the size effect within the 3
ACCEPTED MANUSCRIPT
redistribution as well as the substitution hypothesis. The paper proceeds as follows. In Section 2 we present empirical hypotheses derived from the related literature, the empirical strategy and our dataset. Section 3 illustrates
AC
CE
PT
ED
M
AN US
CR IP T
empirical results. Finally, Section 4 concludes.
4
ACCEPTED MANUSCRIPT
2. Empirical hypotheses, strategy and data 2.1. Empirical hypotheses and related literature Motivated by the evidence in Love et al. (2007) and Garcia-Appendini and MontoriolGarriga (2013), we first want to test whether the change in net trade credit supply in
CR IP T
response to the 2007/08 crisis is significantly different between SMEs and large companies and if this size effect is genuine in the sense of not being explained by financial constraints, external finance dependence or creditworthiness. We summarize this hypothesis as:
H1 (Size effect in net supply): The decline in trade credit net supply in response to the 2007/08 financial crisis is less pronounced among SMEs as compared to large firms. This
AN US
size effect is genuine in the sense defined above.
The trade credit literature commonly distinguishes between the redistributive and the substitutive role of trade credit. On the one hand, it is intuitively straightforward to expect that firms’ willingness to extend trade credit to customers depends on their own economic
M
and financial situation. This reasoning is at the core of the redistribution explanation and goes back to Meltzer (1960). While large, liquid firms are more inclined to redistribute
ED
and supply parts of their assets via trade credit to smaller firms when the overall money supply tightens, financially constrained firms tend to shut down redistribution (see e.g.
PT
Meltzer, 1960; Calomiris et al., 1995; Love et al., 2007; Taketa and Udell, 2007; Love and Zaidi, 2010; Garcia-Appendini and Montoriol-Garriga, 2013). Due to the fact that
CE
comprehensive data on SMEs is scarce, there is little evidence in the literature so far with respect to the behavior of SMEs. McMillan and Woodruff (1999) and Marotta (2005)
AC
document that small, financially constrained firms provide trade credit to their customers, though they investigate rather small samples of survey data. A more recent contribution on a sample of Irish SMEs by McGuinness and Hogan (2016) confirms the redistribution effect but does not discuss size effects. Our second set of tests is therefore directed towards the identification of size effects in the redistributive role (supply effect) of trade credit, which we summarize as follows: H2a (Redistribution): The decrease in trade credit provision is more pronounced among firms that were more vulnerable prior to the 2007/08 financial crisis. 5
ACCEPTED MANUSCRIPT
H2b (Size effect in redistribution): Larger firms show stronger evidence for redistribution in the sense of H2a and this size effect is genuine. On the other hand, it is also economically plausible to expect that trade credit represents a substitute to bank financing. Authors like Ferris (1981) and Norrbin and Reffett
CR IP T
(1995), Petersen and Rajan (1997), Nilsen (2002), Choi and Kim (2005), or Guariglia and Mateut (2006) show that primarily small firms with limited access to capital markets substitute unavailable bank credit with increased trade credit during times of tight money. More recent support for the substitution view comes from Molina and Preve (2012) who show that predominantly smaller firms, with less market power, tend to use trade credit
AN US
as substitute for restricted bank financing during financial distress. Gama and Van Auken (2015) argue that SMEs substitute short-term bank debt with trade credit to escape expensive hold up costs charged by their single relationship bank. The evidence from this literature, however, conflicts with contributions by Klapper et al. (2012) and Murfin and Njoroge (2015), who document that the largest and most creditworthy borrowers receive
M
the most expedient trade credit covenants with the longest maturities from rather smaller suppliers. From these observations, our third set of tests will focus on size effects in the
ED
substitutive role of trade credit. More precisely, we formulate the following hypotheses: H3a (Substitution): The relative importance of trade credit versus bank debt has in-
PT
creased in response to the crisis.
H3b (Size effect in substitution): Large firms were better able to substitute bank debt
CE
with trade credit in the years following the crisis. Finally, to test whether the change in trade credit provision and usage during the
AC
financial crisis had any consequences on the real effects, we follow Carb´o-Valverde et al. (2016), who focus on investment activity. They argue that credit constrained SMEs depend on trade credit, but not on bank debt, while the intensity of dependence increased during the crisis. Besides, Murfin and Njoroge (2015) examine real effects and detect negative repercussions on small suppliers’ investment activity, when large customers extend trade payables. Considering these studies, we state hypothesis H4 as: H4 (Real effects): The decline in trade credit usage is negatively related to investment 6
ACCEPTED MANUSCRIPT
activity. 2.2. Empirical strategy 2.2.1. Variables description This section provides the description of the main economic variables used. A summary is
CR IP T
provided in Table A1 in Appendix A. Dependent variables
Our main dependent variables with respect to the supply and redistribution effect are accounts receivable divided by daily total sales (labelled as AR), accounts payable divided
AN US
by daily costs of goods sold (AP) and net trade credit divided by daily total sales (NET). Following the standard literature, we scale the variables by flow variables to account for shrinkages of economic activity that are usually linked to crises. Besides, the variables can be interpreted as the payment period offered by the firm in terms of days, as the payment
M
period used by the firm for its bought inputs and as the firm’s relative readiness to provide trade credit net of its own trade credit usage, respectively (Love et al., 2007).
ED
Considering the substitution effect, we use the ratio of short-term bank debt to the sum of short-term bank debt and accounts payable (BtoT) as dependent variable. The
PT
construction of BtoT is analogous to Sufi (2009), Campello et al. (2011) and Acharya et al. (2013). Campello et al. (2011) for instance define the ratio of bank credit line to bank
CE
credit line plus cash as dependent variable in a study which analyzes how firms managed liquidity during the financial crisis and how they substituted between bank credit lines
AC
and cash and profits. A change in BtoT captures a shift in the relative importance of short-term bank debt versus accounts payable as financing source and therefore provides evidence on the substitution between bank debt and trade credit. In further regressions, see Section 3.3, we apply accounts payable to total assets (APA ), short-term bank debt to total assets (SB) and a dummy (S) that is one in case of substitution following an increase/ decrease in short-term bank debt in the prior year as dependent variables. When measuring real effects, we use capital expenditures over lagged capital (CAPEX) and return on assets (ROA) as dependent variables. 7
ACCEPTED MANUSCRIPT
Main explanatory variables The main (explanatory) variables of interest are CRISIS and its interaction with CONS. The crisis dummy is defined as being one in case of the crisis years 2007 to 2010 and should offer valuable clues about the change in trade credit usage and provision during crisis.
CR IP T
Garcia-Appendini and Montoriol-Garriga (2013) set the crisis time from the third quarter of 2007 to the second quarter of 2008. However, we are interested in the firms’ post-crisis behavior as well. In a robustness check, we use the lagged growth rate of the three months Euribor to control whether our chosen crisis period is adequate and conforms to the central bank’s response to the ongoing recession. CONS is a generic constraint dummy, for which
AN US
we substitute either size proxies (SM E, T A, M S, SA as defined below) or other firm characteristics (F C, EF D, Z) depending on the specification. CONS should illustrate differences in trade credit usage and provision between firm size classes during the crisis and whether these differences can be attributed to fundamental firm characteristics like
M
financial constraints, external finance dependence or creditworthiness. To examine size effects carefully, we generate various alternative size measures. (i) Our
ED
main size measure (SM E) follows the legal definition of small and medium-sized enterprises on the basis of their number of employees, total assets and total sales. According to
PT
§267 of the German Commercial Code, firms are classified as SME if two of the following three criteria apply, respectively: Employees smaller or equal to 250, total assets smaller
CE
or equal to 19.25M and total sales smaller or equal to 38.5M. If a sample firm is identified as SME in more than half of its yearly observations, then we label it as SME. (ii) Alternatively, we sort our sample on total assets (TA). Mitigating endogeneity issues we
AC
use each firm’s three years pre-crisis average of total assets from 2004 to 2006, estimate the sample median and tag firms to be small if their pre-crisis asset average lies below the median (Duchin et al., 2010). (iii) In the same way, we sort our sample on market share (MS) which we determine as the ratio of a firm’s total sales to the total two-digit industry output as provided by Eurostat and which we compare to the industry median. (iv) Finally, we follow Hadlock and Pierce (2010) and use the size-age index (SA) of German
firms estimated by Klepsch and Szabo (2016). Again, we calculate the sample median of 8
ACCEPTED MANUSCRIPT
the firm’s pre-crisis average between 2004 and 2006 of the size-age index and identify a firm to be small if its pre-crisis average lies above the sample median.4 Size is often related to fundamental firm characteristics like financial constraints, external finance dependence or creditworthiness. To avoid an omitted variable bias, we
CR IP T
construct measures to control for these characteristics and to assess if the size effect is genuine, i.e. if these characteristics do not drive out its explanatory power. (i) Quantifying financial constraints (FC), we obey the argument in Carb´o-Valverde et al. (2016) that frequently used measures like the Kaplan and Zingales (1997) index, Whited and Wu (2006) index, or the payout ratio are not well suited for unlisted, small firms. We
AN US
therefore follow Almeida and Campello (2007) and Hovakimian and Titman (2006) and implement an endogenous switching regression with unknown sample selection to identify financially constrained and unconstrained firms. We use the perceived probabilities of being financially constrained and calculate the three years pre-crisis average for each firm, while we define a firm to be constrained if its average lies above the overall sample me-
M
dian and unconstrained otherwise. Details on the estimation of the endogenous switching regression are provided in Appendix B. (ii) We next build upon Garcia-Appendini and
ED
Montoriol-Garriga (2013) and consider industry-level measures of external finance dependence (EFD) as in Rajan and Zingales (1998). Using the time-invariant EFD index has
PT
the benefit that it is a strictly exogenous measure, since it is specified as the proportion of capital expenditures in excess of cash flows among European listed firms of the same
CE
two-digit industry. While a negative index denotes that the cash flow within an industry is more than sufficient to fund investments, a positive index means that external finance
AC
is necessary.5 (iii) As proxy for creditworthiness (Z), we use the classic Altman (1968)
4 The size-age index for each firm year observation is calculated according to Klepsch and Szabo (2016) as follows: SAi,t = −1.181 × log(Assetsi,t ) + 0.027 × log(Assets2i,t ) − 0.008 × Agei,t . 5 We follow Ferrando et al. (2007) and use the Thomson Financial DataStream database to download data on capital expenditures and funds from operations of listed firms from 15 European countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain and Sweden) in the period from 1980 to 2006. We sum each firm’s capital expenditures and cash flows over the years being in the sample and calculate the external finance dependence afterwards:
EF Di =
T P
t=1
Capexi,t − T P
t=1
T P
t=1
Cashf lowi,t
. Finally, to receive an industry-specific index, we calculate industry
Capexi,t
medians at the two-digit SIC-code and merge them to our two-digit NACE2-code.
9
ACCEPTED MANUSCRIPT
Z-score for non-listed firms.6 The size effect can be interpreted as genuine and consistent if (i) the same results cannot be replicated by substituting F C, EF D, and Z for CONS, or if (ii) we add the log-transformed probability of being financially constrained (F C) or the negative Z-score (Z) as additional control variables. Note, that we cannot include
CR IP T
EF D as control variable, as it is time invariant and will be absorbed by the fixed effect. Control variables
As independent control variables we use cash (CS), operating cash flow (OCF), sales growth (SG), equity (EQ), fixed assets labeled as tangible assets (TAA) and age (AGE).
AN US
To avoid an endogeneity and simultaneity bias, we lag all control variables except age by one period (t-1) and scale all control variables except sales growth and age by total assets. Furthermore, we add firm (αi ) and year fixed effects (νt ), which allows us to segregate crisis implications from pre-crisis ones, account for unobserved heterogeneity (Love et al.,
2.2.2. Empirical methodology
ED
Crisis impact
M
2007) and control for business cycle fluctuations and other life-cycle effects.
The focus of our research agenda is on the identification of a firm size effect during the
PT
financial crisis. Thus, to specify and test hypothesis H1, we first analyze the overall
CE
development of trade credit supply and usage during the crisis with respect to size:
(1)
AC
TCi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t ,
where the dependent variable TC will be replaced by either AR, AP or NET. Besides, CRISIS is the crisis dummy, CONS is the generic constraint dummy, X is the vector of control variables, α is a firm fixed effect, ν is a year fixed effect and is an error term. As CONS is time invariant, the coefficient of CONS is absorbed by the firm fixed effect. 6
The Altman Z-score for each firm year observation is calculated as follows: Zi,t = 0.717 × RetainedEarningsi,t EBIT Equity Sales + 0.847 × + 3.107 × Assetsi,t + 0.420 × Debti,ti,t + 0.998 × Assetsi,t . Assetsi,t i,t i,t Working capital is defined as current assets net short-term liabilities. W orkingCapitali,t Assetsi,t
10
ACCEPTED MANUSCRIPT
In terms of hypothesis H1, we expect coefficient β1 to be negative and the interaction term β2 to be positive when deploying size proxies for CONS and net supply for the dependent variable. Redistribution effect
CR IP T
First, to determine if the change in trade credit is due to a supply or demand effect, we build upon Love et al. (2007) and more recently Garcia-Appendini and MontoriolGarriga (2013) by employing a similar identification strategy. They disentangle supply and demand effects by interacting pre-crisis vulnerability measures with crisis year dummies.
AN US
The empirical strategy is similar to a diff-in-diff model. See Duchin et al. (2010), p. 424ff, or Garcia-Appendini and Montoriol-Garriga (2013) p. 275. Theory states that companies relying more on short-term debt (STD) in the pre-crisis period would suffer to an increased degree during crisis due to refinancing risk and thus, may cut back trade credit provision. Similar reasons account for illiquid firms with low cash ratios (CS) and internal funds like
M
operating cash flow (OCF). If the decrease in trade credit provision is stronger among vulnerable firms – either having more pre-crisis short-term debt or low cash holdings or
ED
operating cash flow – a supply effect, consistent with the inter-firm liquidity redistribution, can be confirmed. In addition, we also use long-term oriented variables such as the equity
PT
ratio (EQ) or the fraction of fixed assets (TAA). The leverage of a firm is a classical credit risk metric, and tangible fixed assets are well known to capture the extent to which assets
CE
can be pledged and thus, proxy the availability of credit (Carb´o-Valverde et al., 2016). By applying these variables we extend and complement the approach by Love et al. (2007).
AC
The estimation model for hypothesis H2a is therefore:
TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + γXi,t + νt + i,t ,
(2)
where the coefficient of interest is the interaction between the crisis dummy and the measures of average pre-crisis financial vulnerability, VULi,pre , which is the generic label for the above-mentioned vulnerability proxies (CS, OCF, STD, EQ, TAA). For H2a (i.e.
11
ACCEPTED MANUSCRIPT
the supply effect) to hold, we expect β2 to be significantly positive for CS, OCF, EQ, TAA and significantly negative for STD with respect to accounts receivable. Due to the within estimator it is impossible to consider the time invariant financial standing VULi,pre as a separate variable. The default definition is to determine VULi,pre as the firm’s average
CR IP T
values over the pre-crisis years 2004 to 2006. In robustness checks, we also measured the financial position one year prior to the financial crisis, i.e. in 2006. Qualitatively, our results remain stable. Again, this method is used to mitigate endogeneity, since changes in a firm’s financial position may be related to unobserved changes in its trade credit provision and usage as the crisis bursts (Love et al., 2007).
AN US
To investigate firm size effects and test H2b, we use the various size proxies (i.e. SME, TA, MS, and SA) and add them as interaction terms (generically labelled as CONS) in Equation (2). This specification leads among others to a triple interaction term (CRISISt × VULi,pre × CONSi,pre ), which allows to statistically verify if the redistribution effect is different across firm size. Consequently, if the coefficient of the triple interaction term
M
is of opposite sign as β2 , H2b holds. An alternative approach is to split the sample according to the size measure and run separate regressions in both sub-samples in order
ED
to allow for different levels of control variables. We find qualitatively unchanged results and therefore opt to report results from the interaction term approach as it makes the statistical
PT
significance obvious.In further robustness checks, we interact all independent variables with
CE
the size dummy (financial firm characteristic). Our results remain unchanged. Substitution effect
AC
To test the substitutive role of trade credit (H3a and H3b), our identification strategy is basically threefold. First, we regress BtoT on the crisis dummy and the various size
measures. As BtoT might be nonlinear regarding the boundaries of the ratio, we apply a nonlinear fractional logit response model with quasi-maximum likelihood first suggested by Papke and Wooldridge (1996). In order to account for fixed effects, we follow a more recent paper of Papke and Wooldridge (2008) and convert our unbalanced panel into a strongly balanced one and add for each independent variable its time mean to estimate 12
ACCEPTED MANUSCRIPT
average partial effects and to establish a fixed effects model, as recommended by Mundlak (1978). However, due to the fact that the totally balanced sample reduces observations substantially, we also account for the non-linearity by log-transforming the dependent variable and run linear fixed effects regressions on the unbalanced sample. Furthermore,
CR IP T
in order to control for a potential (mechanical) impact of accounts receivable, we follow Agostino and Trivieri (2014) and include the growth rates of the accounts receivable to sales ratio and the working capital to assets ratio as control variables.7 For H3a to hold the coefficient of the crisis dummy should be significantly negative indicating a relative shift in importance towards trade credit. Furthermore, for H3b to hold, the interaction term
AN US
between the crisis dummy and the size dummy should be significantly positive implying that smaller companies were less able to substitute the decrease in bank debt with trade credit.
In our second test, we regress AP on short-term bank debt SB (see e.g. Kestens et al.,
M
2012). The model is defined as follows:
TCi,t =αi + β1 CRISISt + β2 SBi,t + β3 (CRISISt × SBi,t ) + β4 (CRISISt × CONSi,pre )
ED
+ β5 (SBt × CONSi,pre ) + β6 (CRISISt × SBi,t × CONSi,pre ) + γXi,t + νt + i,t .
PT
(3)
If the substitution theory for trade payables was descriptive among large firms in pre-
CE
crisis times, then the coefficient of short-term bank debt (i.e. β2 ) should be negative. A significant and negative β3 coefficient would confirm that the financial crisis increased the substitutive effect among large firms. In contrast, a significant negative β5 would imply
AC
that the substitution effect is stronger among small firms during pre-crisis times, while a significant positive β6 would be evidence that small firms were less able to increase
substitution in order to countervail the decline in bank debt with trade credit during crisis and in line with H3b. To further investigate the direction of causality, we follow Carb´o7
As pointed out by an anonymous referee, a change in accounts receivable or other current assets might affect both accounts payable and short-term bank debt simultaneously due to altered financing needs and thus our negative effect during crisis might be caused by a mechanical relation. In unreported robustness checks, we form decile portfolios conditional on the within deviation of accounts receivable and current assets to control for the mechanical effect. Our results remain stable.
13
ACCEPTED MANUSCRIPT
Valverde et al. (2016) and complement this test by a specification where we consider bank debt to be the dependent and accounts payable to be the independent variable. If shortterm bank debt turns out to be significant, while accounts payable are not, this would provide evidence that the causality goes from short-term bank debt to accounts payable
CR IP T
(Carb´o-Valverde et al., 2016). However, since this approach may suffer from the fact that contemporaneous debt levels might be endogenous, we additionally use the approach by Illueca et al. (2016) and run a conditional fixed effects logistic regression where the dependent variable S is a dummy that is one if conditional on a decrease in short-term bank debt in the previous year accounts
AN US
payable increase in the current year, i.e. in case of substitution. This approach allows to determine the direction of causality by choosing either a change in bank debt or in accounts payable as the conditioning event. Real effects
M
To investigate whether the change in trade credit usage and provision affected the real economy during the financial crisis and to test H4, we consider investment-trade credit
ED
and profitability-trade credit sensitivities. We are interested whether trade credit explains investment and profitability. Usually investment is modeled by the structural Q-model.
PT
However, as Tobin’s Q cannot be constructed, we use an error correction model like Bond et al. (2003), Mizen and Vermeulen (2005), Mulier et al. (2016) and Buca and Vermeulen
CE
(2017). Although the error-correction model assumes perfect capital markets to hold, i.e. a firm’s investment decision should not be sensitive to financing sources and its financial
AC
constraint status, we add financial variables as explanatory variables – either trade credit or short-term bank debt. Our data sample mainly includes small unlisted firms, which do not have unrestricted access to capital markets and bank loans, particularly during crisis times (Carb´ o-Valverde et al., 2016). We assume the decline in trade credit usage during the crisis to be negatively related to the investment activity of small unlisted firms, as these firms highly depend on trade credit.8 For H4 to hold, we should find a positive 8
For further details on the model see Appendix C.
14
ACCEPTED MANUSCRIPT
investment-trade credit sensitivity, especially among SMEs and no effect for large firms, while the effect should be contrary with respect to short-term bank debt. Finally, we analyze how trade credit provision influenced performance during the crisis. We follow Garcia-Appendini and Montoriol-Garriga (2013) and regress return on assets on accounts
GMM estimation. 2.3. Data
CR IP T
receivable and its interaction with the crisis dummy by means of a two-step first-difference
We obtain access to the database of Creditreform AG, which provides the most extensive coverage of annual balance sheet data of active companies registered in Germany.9 The
AN US
time frame we observe lasts from 2003 to 2010.
The raw database includes 357,107 observations on 120,771 firms and is as such one of the largest samples in the trade credit literature so far. In order to mitigate misspecification, we delete observations with negative values of accounts receivable, accounts payable,
M
total assets, total sales, and so on. Moreover, we replace our main variables of interest (accounts receivable, accounts payable, short-term bank debt) with missings if they are
ED
smaller than one, as zeros in the data can mean both either missing or zero. Furthermore, we set observations to missings if accounts receivable and payable as well as net accounts
PT
receivable are outstanding for more than one year to investigate the common short-term trade credit. Companies in the banking, finance, insurance and real estate sector as well
CE
as utilities are also eliminated due to discrepancies in their balance sheets and income statements. Finally, we just keep firms where at least two consecutive observations are
AC
available. In order to alleviate the contingent effect of outliers we winsorize all descriptive and control variables at the one percentile in both tails and the trade credit measures regarding the left tail. After data cleansing and filters, we end up with an unbalanced sample containing 107,847 firm year observations on 29,113 firms. Depending on the dependent and independent variables, the number of observations in various specifications can differ. Table 1 provides summary statistics. 9 Creditreform AG is a holding company whose core competence is business intelligence services such as creditworthiness assessments and receivables management.
15
ACCEPTED MANUSCRIPT
Table 1: Summary statistics All
Large
Median
Std.Dev.
N
Mean
Median
Std.Dev.
N
Mean
Median
Std.Dev.
N
35.598 28.008 11.406 0.452 0.138 0.141 0.110 0.103 0.139 0.278 0.254 2.984 51.512
30.636 20.744 10.599 0.449 0.086 0.079 0.050 0.087 0.038 0.237 0.181 2.890 9.644
29.807 28.200 34.393 0.296 0.149 0.163 0.147 0.145 0.738 0.220 0.236 0.869 118.330
107847 107847 107847 54332 107847 54332 107847 107847 107847 107847 107847 107847 107847
35.321 29.557 9.728 0.472 0.151 0.159 0.122 0.106 0.154 0.263 0.238 2.851 8.462
29.241 20.864 8.857 0.473 0.096 0.099 0.055 0.088 0.037 0.210 0.153 2.773 3.392
31.057 31.098 36.375 0.292 0.157 0.170 0.160 0.157 0.779 0.227 0.236 0.806 25.554
70717 70717 70717 31788 70717 31788 70717 70717 70717 70717 70717 70717 70717
36.126 25.056 14.601 0.405 0.112 0.105 0.088 0.097 0.109 0.306 0.286 3.238 133.504
33.365 20.575 14.012 0.385 0.071 0.048 0.042 0.085 0.039 0.279 0.236 3.178 60.447
27.260 21.323 30.003 0.291 0.129 0.136 0.116 0.117 0.652 0.204 0.234 0.927 170.805
37130 37130 37130 19165 37130 19165 37130 37130 37130 37130 37130 37130 37130
CR IP T
AR AP NET BtoT APA SB CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE A
SME
Mean
AC
CE
PT
ED
M
AN US
Notes: This table reports summary statistics for the whole sample and for SME and large firms, separately. We refer to Table A1 in Appendix A for details on the respective variables. A measures total assets in millions of Euro. The sizing of SME and large firms is classified according to §261 of the German Commercial Code. A firm is classified as SME if two of the following three criteria apply, respectively, in more than half of its yearly observations: employees smaller or equal to 250, total assets smaller or equal to 19.25M and total sales smaller or equal to 38.5M. Superscript A means that the variable is divided by total assets. Subscript t-1 means that the variable is lagged one period.
16
ACCEPTED MANUSCRIPT
3. Empirical results 3.1. Crisis impact Before turning our attention to hypothesis H1, we note that the unconditional effect of the crisis was a significant decline in net trade credit. On average, net supply did decrease
CR IP T
by roughly 3 days. Given that the average maturity of net trade credit in our data is approximately 11 days, the effect is not only statistically significant but also economically meaningful.10 The results confirm previous findings, which have shown a general shrinkage in trade credit during the financial crisis (see Love et al., 2007; Garcia-Appendini and
AN US
Montoriol-Garriga, 2013; Kestens et al., 2012). We further check the robustness of the crisis dummy definition by replacing it with the Euribor rate as alternative measure for the timing of the crisis and find similar results.11
We now address the main focus of our research agenda – the identification of a firm size effect. Table 2 summarizes results with respect to H1, which we test by running the
M
regression model from Equation (1). Panel A contains results for the four size proxies as conditioning variables (CONS). Columns 1–4 show that the coefficient on the interaction
ED
term (i.e. β2 in (1)) is highly significantly positive. While large firms reduced net supply by roughly 4 days, which amounts to more than one third of average net supply, SMEs
PT
did only reduce N ET by approximately 2.5 days representing merely one fifth of average net supply. Results are robust across various size measures (SME, TA, MS, SA) and both
CE
statistically and economically significant. To establish that the size effect is genuine and not driven by an omitted variable bias, we include F C, EF D, and Z to the analysis in two
AC
ways: First, Panel B uses them as conditioning variable (CONS) to check if similar results can be replicated. Second, in Panel C we add F C, and Z as additional control variable, in 10 We provide further information in Table A2 in Appendix A, which shows that the decline in net supply is mainly driven by the significant decline in accounts receivable by approximately 4 days, while the (negative) change in accounts payable is insignificant. 11 In an unreported robustness check, we control for other operating expenses to total sales, as we expect bad debt expenses, which are write-offs of unpaid accounts receivable and usually recorded in the income statement under other operating expenses, to be the driver of our results. Regarding other operating expenses we assume a simultaneous interlinkage with accounts receivable, as more write-offs of bad debt during crisis lead to a coincidental decrease of accounts receivable in the same fiscal year. However, including this control variable does not change our results.
17
ACCEPTED MANUSCRIPT
order to see if the size dummy remains in size and significance. Both ways, we find that the impact of size on net supply cannot be explained by other variables. We find significant negative coefficients on interaction terms in the case of Z and EFD, but no significant effect for FC. These findings are distinctly different from the size measures, since coefficients
CR IP T
are either of opposite sign or insignificant. By adding the negative Z-score (Z) and the log-transformed probability of being financially constrained (FC) as a control variable, we still obtain significant positive β2 coefficients in Panel C.12 The difference between large firms and SMEs is even larger when controlling for financial constraints. Overall, results from Table 2 confirm a genuine firm size effect in line with H1 in the aggregate net supply
AN US
of trade credit during the crisis years. SMEs did reduce net supply by about 1 to 2 days less than large firms and this result holds even after controlling for financial constraints, external finance dependence and creditworthiness.
Table 2 furthermore splits up net supply into changes of AR (Cols. 5–8) and AP (Cols. 9–12). Results reveal that the size effect in net supply is mainly driven by accounts
M
payable. In fact, from Columns 5–8, we find the interaction term (β2 coefficient) to be insignificant, indicating that both large as well as small companies have reduced AR to
ED
a similar extent by about 4 days. On the contrary, with AP as dependent variable, we find clear evidence that only SMEs have significantly reduced accounts payable by about
PT
two days, which accounts for almost 7% of the average trade credit used, which is 28 days. As with net supply, we check if the size effect in AP survives the inclusion of other
CE
conditioning variables. Panel B shows that EF D has the wrong sign, while F C and Z deliver the same sign as size but smaller in magnitude. More importantly, using F C and
AC
Z as control variables in Panel C shows that the significance on the interaction term is not driven away, thereby confirming a genuine size effect. Overall, the evidence in this section provides support for hypothesis H1, and shows
that the genuine size effect is primarily driven by accounts payable, i.e. by the use of trade 12
The corresponding control variable is indicated in the row labelled as ’Add. covariates’. Note that we use F C (Z) as variable name, although it is not the dummy but the continuous variable. See the table caption for details. Note further, that some firms in Column 1 (4, 7) in Panel B have no data on pre-crisis years and thus, we cannot calculate probabilities of being constrained. Consequently the number of observations changes with respect to specifications in Column 1 (3, 5) in Panel C.
18
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
credit. We attempt to disentangle demand and supply effects in the next section.
19
CR IP T
ACCEPTED MANUSCRIPT
Table 2: Trade credit changes conditional on size and financial characteristics Panel A: Size as conditioning variables (CONS) NET
CRISIS × CONS Covariates Adjusted R2 N
(3) MS
(4) SA
(5) SME
(6) TA
(7) MS
(8) SA
-3.941*** (0.689) 1.565*** (0.405)
-3.586*** (0.686) 1.211*** (0.428)
-3.606*** (0.677) 1.471*** (0.450)
-3.633*** (0.676) 1.413*** (0.436)
-4.008*** (0.624) 0.021 (0.357)
-3.905*** (0.613) -0.596 (0.373)
-3.895*** (0.607) -0.193 (0.387)
Yes 0.700 107847
Yes 0.701 102992
Yes 0.700 102038
Yes 0.701 102946
Yes 0.706 107847
Yes 0.706 102992
Yes 0.706 102038
Panel B: Financial characteristics as conditioning variables (CONS) NET
CRISIS × CONS
(2) EFD
(3) Z
-3.781*** (0.743) 0.478 (0.442)
-3.640*** (0.695) -2.837*** (0.816)
Yes 0.674 67364
Yes 0.699 106732
CRISIS CRISIS × CONS
-7.155*** (1.179) 1.823*** (0.413)
(11) MS
(12) SA
-4.066*** (0.599) -0.628* (0.377)
0.286 (0.523) -2.145*** (0.330)
-0.103 (0.528) -2.404*** (0.348)
-0.169 (0.521) -1.592*** (0.369)
-0.290 (0.527) -2.541*** (0.357)
Yes 0.706 102946
Yes 0.697 107847
Yes 0.692 102992
Yes 0.692 102038
Yes 0.692 102946
AP
(5) EFD
(6) Z
(7) FC
(8) EFD
(9) Z
-0.840 (1.082) -1.484*** (0.567)
-4.153*** (0.641) -0.307 (0.390)
-3.894*** (0.615) 0.935 (0.752)
-2.799*** (0.989) -2.721*** (0.512)
-0.086 (0.572) -1.499*** (0.352)
-0.308 (0.528) 2.892*** (0.599)
-0.863 (0.841) -1.504*** (0.485)
Yes 0.675 54951
Yes 0.689 67364
Yes 0.707 106732
Yes 0.674 54951
Yes 0.668 67364
Yes 0.694 106732
Yes 0.677 54951
AR
AP
(2) SME
(3) SME
(4) SME
(5) SME
(6) SME
-2.689** (1.256) 1.267* (0.693)
-6.466*** (0.960) 0.238 (0.356)
-2.491** (1.077) -0.285 (0.599)
2.679*** (1.008) -2.224*** (0.336)
1.207 (0.919) -2.415*** (0.598)
ED
(1) SME
(10) TA
(4) FC
Panel C: Size (SME) including financial characteristics as control NET
CE
PT
Covariates Yes Yes Yes Yes Yes Yes Add. Covariates FC Z FC Z FC Z Adjusted R2 0.706 0.683 0.712 0.692 0.698 0.705 N 104186 52146 104186 52146 104186 52146 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row indicates the dependent variable. The second row of Panel A, B and C indicates the size measure or financial characteristic that is interacted with the crisis dummy in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. In Panel C CONS is equal to SME for all specifications, while the entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. Covariates are similar to those in Table A1 in Appendix A. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AC
20
Covariates Adjusted R2 N
(9) SME
AR
(1) FC
M
CRISIS
AP
(2) TA
AN US
CRISIS
AR
(1) SME
ACCEPTED MANUSCRIPT
3.2. Redistribution effect We now focus on the identification of a supply effect according to Hypothesis H2a. Table 3 summarizes the results from estimating the model in Equation (2) at the aggregate level, while Table 4 conditions on size and financial firm characteristics. As measures of
CR IP T
pre-crisis vulnerability, we include on the one hand short-term liquidity measures such as pre-crisis cash holdings (CS), operating cash flow (OCF) and short-term debt (STD) and on the other hand long-term oriented variables such as equity (EQ) and tangible fixed assets (TAA). From Panel A of Table 3, we find significantly positive coefficients Table 3: Redistribution evidence – Aggregate sample
CRISIS CRISIS × CS
-4.484*** (0.635) 6.364*** (1.518)
-8.068*** (0.918) 9.399*** (2.264)
-4.491*** (0.640)
CRISIS × OCF
M
Yes 0.822 102881
-2.636*** (0.751) 1.052 (1.818)
PT
-0.831 (0.545) 3.401*** (1.317)
CE
CRISIS × STD
Yes 0.701 91643
(8) AR
-4.617*** (0.636)
-4.490*** (1.052) 6.410*** (1.567)
-4.484*** (0.579) 6.364** (2.312)
2.080*** (0.777)
Yes 0.704 101946
Yes 0.706 102950
Yes 0.706 102992
Yes 0.708 102882
Yes 0.706 102882
(3) AP
(4) AP
(5) AP
(6) AP
(7) AP
(8) AP
-0.429 (0.568)
-0.400 (0.583)
-1.935*** (0.584)
-0.743 (0.566)
-1.286 (0.977) 3.613*** (1.318)
-0.831 (0.551) 3.401* (1.543)
ED
Yes 0.706 102882
Panel B: Accounts payable as dependent variable (1) (2) AP APA
CRISIS × OCF
-4.589*** (0.674)
(7) AR
2.982*** (0.990)
CRISIS × TAA
CRISIS × CS
-3.461*** (0.679)
(6) AR
-1.244 (0.789)
CRISIS × EQ
CRISIS
(5) AR
5.412*** (1.635)
CRISIS × STD
Covariates Adjusted R2 N
(4) AR
AN US
Panel A: Accounts receivable as dependent variable (1) (2) (3) AR ARA AR
1.229 (1.421) -0.155 (0.686)
CRISIS × EQ
4.704*** (0.823)
CRISIS × TAA
1.097 (0.818)
AC
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R2 0.692 0.857 0.682 0.691 0.692 0.692 0.694 0.692 N 102882 102876 91643 101946 102950 102992 102882 102882 Notes: This table reports firm fixed effects panel regression results. In Columns 1 to 7 standard errors are clustered at the firm level, in Column 8 they are double-clustered at the firm and year level. Column 7 includes industry-year fixed effects at the two-digit indsutry level. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + γXi,t + νt + i,t , while the overline reflects the three years pre-crisis average from 2004 to 2006 of the respective vulnerability variable. The first row indicates the dependent variable. We refer to Table A1 in Appendix A for details on the respective variables. STD is short-term debt net accounts payable to total assets. Covariates are similar to those in Table A1 in Appendix A. In each specification we exclude the covariate similar to VULi,pre . Superscript A means that the dependent variable is divided by daily total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
on the interaction terms in 7 out of 8 specifications, meaning that firms being financially less vulnerable in the years prior to the crisis reduced their trade credit provision to a significantly smaller extent. For instance, firms with a pre-crisis cash ratio close to 21
ACCEPTED MANUSCRIPT
zero reduced their accounts receivable by almost 4.5 days (Col. 1), which amounts to 13% compared to the average ratio of accounts receivable. In contrast, a one standard deviation increase in the pre-crisis cash ratio reduced the decrease by one day. Furthermore, firms with a cash ratio above 70% would have even increased overall trade credit supply during
CR IP T
crisis. Results remain robust when scaling accounts receivable by total assets instead of total sales (Col. 2) and when including industry-year fixed effects (Col. 7) or doubleclustering standard errors at the firm and year level (Col. 8). With respect to accounts payable, we do not find convincing evidence for an impact of pre-crisis vulnerability on trade credit demand during the crisis. While cash and equity might play a signalling role
AN US
for additional credit, the other variables show no evidence for a demand effect. Taken together, we find evidence consistent with the prior literature which concludes that the reduction in trade credit is due to a supply effect and consistent with the inter-firm liquidity redistribution hypothesis H2a. Vulnerable firms reduced their trade credit supply, thereby propagating the adverse economic shock of the crisis via the trade credit channel (Love
M
et al., 2007).
We next focus on size effects, and summarize our evidence on hypothesis H2b in Table
ED
4, where Panels A to C use different pre-crisis vulnerability measures. We report results for cash holdings (CS, Panel A), operating cash flow (OCF, Panel B) and short-term debt
PT
(STD , Panel C). For expositional reasons, we do not report results for the equity ratio
AC
CE
(EQ) and tangible fixed assets (TAA), which are qualitatively similar.
22
CR IP T
ACCEPTED MANUSCRIPT
Table 4: Redistribution evidence – Conditioning on size and financial characteristics Panel A: Cash holdings (CS) as pre-crisis vulnerability measure AR
CRISIS × VUL CRISIS × CONS CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
(4) TA
(5) MS
(6) FC
(7) EFD
(8) Z
(9) SME
(10) SME
-4.410*** (0.676) 6.410*** (2.448) -0.226 (0.471) 0.090 (3.084)
-7.231*** (1.002) 6.593*** (2.417) -0.022 (0.474) 0.117 (3.074)
-2.234* (1.191) -1.475 (4.370) -1.184 (0.794) 8.204 (5.100)
-4.529*** (0.658) 8.254*** (2.298) -0.521 (0.488) -2.794 (3.092)
-4.330*** (0.644) 6.072*** (1.730) -0.386 (0.500) 0.744 (3.066)
-4.804*** (0.681) 8.044*** (1.997) -0.444 (0.521) -2.373 (2.957)
-4.363*** (0.655) 6.178*** (1.613) 1.172 (1.023) -3.352 (6.231)
-2.943*** (1.023) 2.419 (2.166) -2.643*** (0.660) 0.675 (4.764)
0.436 (0.581) -1.728 (2.745) -3.120*** (0.432) 8.515*** (3.175)
2.534** (1.063) -0.911 (2.593) -3.121*** (0.436) 7.578** (3.047)
Yes – 0.706 102882
Yes FC 0.711 99528
Yes Z 0.688 49268
Yes – 0.706 102882
Yes – 0.706 101952
Yes – 0.690 67356
Yes – 0.707 101799
Yes – 0.674 54914
Yes – 0.692 102882
Yes FC 0.693 99528
(7) EFD
(8) Z
(9) SME
(10) SME
(11) SME
(12) TA
Panel B: Operating cash flow (OCF ) as pre-crisis vulnerability measure AR
CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(14) FC
(15) EFD
(16) Z
-0.247 (0.567) 1.543 (1.925) -3.227*** (0.465) 5.884** (2.708)
-0.074 (0.548) -0.861 (1.641) -2.744*** (0.485) 9.662*** (2.692)
-0.694 (0.605) 6.092*** (1.831) -1.626*** (0.472) -1.740 (2.594)
-0.548 (0.552) 2.187* (1.302) 3.347*** (0.775) -5.120 (5.086)
-0.790 (0.851) -0.649 (1.807) -2.367*** (0.625) 9.621** (4.745)
Yes Z 0.699 49268
Yes – 0.692 102882
Yes – 0.692 101952
Yes – 0.668 67356
Yes – 0.689 101799
Yes – 0.677 54914
AP
(3) SME
(4) TA
(5) MS
(6) FC
(13) MS
(14) FC
(15) EFD
(16) Z
-5.220*** (0.735) 12.160*** (2.725) 1.023* (0.554) -9.625*** (3.401)
-8.072*** (1.059) 11.819*** (2.695) 1.214** (0.555) -9.098*** (3.433)
-2.545* (1.340) 1.798 (6.626) -0.038 (1.002) -1.957 (7.394)
-4.619*** (0.691) 7.669*** (2.370) -0.284 (0.569) -3.987 (3.290)
-4.962*** (0.669) 10.308*** (1.933) 0.740 (0.586) -9.905*** (3.308)
-4.784*** (0.706) 6.841*** (2.207) -0.001 (0.585) -2.818 (3.366)
-4.387*** (0.651) 5.815*** (1.803) 0.932 (1.107) 2.347 (7.058)
-3.234*** (1.044) 4.882* (2.614) -2.035*** (0.750) -6.067 (5.610)
0.727 (0.598) -2.609 (2.070) -2.704*** (0.482) 5.661** (2.758)
2.967*** (1.092) -3.216 (2.004) -2.782*** (0.489) 5.757** (2.780)
2.853*** (1.080) -12.653*** (3.909) -3.872*** (0.876) 16.665*** (5.282)
0.106 (0.604) -0.439 (1.982) -2.940*** (0.494) 4.804* (2.840)
-0.045 (0.573) 0.355 (1.732) -1.825*** (0.523) 2.183 (2.861)
0.427 (0.621) -4.325** (1.969) -2.556*** (0.495) 10.571*** (2.886)
-0.271 (0.574) 1.050 (1.514) 4.057*** (0.893) -11.407** (5.663)
-0.648 (0.907) 0.533 (2.702) -1.359** (0.684) -2.167 (5.343)
Yes – 0.701 91643
Yes FC 0.706 88756
Yes Z 0.683 44005
Yes – 0.701 91643
Yes – 0.701 91089
Yes – 0.689 67332
Yes – 0.702 90681
Yes – 0.666 49801
Yes – 0.682 91643
Yes FC 0.684 88756
Yes Z 0.691 44005
Yes – 0.683 91643
Yes – 0.682 91089
Yes – 0.668 67332
Yes – 0.678 90681
Yes – 0.670 49801
M
CRISIS × CONS
(13) MS
1.624 (1.119) -4.460 (6.859) -3.875*** (0.750) 13.686* (7.346)
(2) SME
Panel C: Short-term debt (ST D) as pre-crisis vulnerability measure
AR
CRISIS × VUL CRISIS × CONS CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
-2.730*** (0.766) -3.125*** (1.179) -1.170 (0.730) 2.941* (1.580)
-5.519*** (1.059) -2.928*** (1.089) -0.883 (0.719) 2.803* (1.540)
Yes – 0.704 101946
Yes FC 0.709 98643
AP
(4) TA
(5) MS
(6) FC
(7) EFD
(8) Z
(9) SME
(10) SME
(11) SME
(12) TA
(13) MS
(14) FC
(15) EFD
(16) Z
-0.664 (1.410) -4.196* (2.480) -2.601** (1.238) 5.355* (2.906)
-2.928*** (0.723) -2.354** (1.004) -1.843** (0.771) 2.888* (1.621)
-3.487*** (0.698) -0.969 (0.882) 0.136 (0.819) -0.855 (1.805)
-3.167*** (0.760) -2.583** (1.106) -1.187 (0.833) 2.445 (1.654)
-3.312*** (0.696) -1.343* (0.804) 1.315 (1.515) -0.942 (3.231)
-2.960*** (1.129) 0.392 (1.478) -1.880* (1.090) -1.794 (2.301)
0.577 (0.633) -0.661 (1.074) -2.783*** (0.696) 1.471 (1.403)
3.077*** (1.070) -0.818 (1.070) -3.011*** (0.691) 1.824 (1.397)
0.879 (1.109) 0.991 (2.040) -3.475*** (1.159) 2.238 (2.418)
0.024 (0.621) -0.275 (0.877) -3.063*** (0.742) 1.413 (1.411)
0.437 (0.587) -1.382* (0.805) -2.921*** (0.770) 3.034** (1.498)
0.607 (0.722) -1.974* (1.177) -3.825*** (0.777) 4.944*** (1.535)
-0.137 (0.588) -0.332 (0.689) 6.717*** (1.226) -9.268*** (2.563)
-1.651* (0.922) 1.974** (0.948) -1.486 (1.034) -0.270 (1.899)
Yes Z 0.686 48760
Yes – 0.704 101946
Yes – 0.704 101526
Yes – 0.689 67364
Yes – 0.705 100873
Yes – 0.672 54676
Yes – 0.692 101946
Yes FC 0.693 98643
Yes Z 0.699 48760
Yes – 0.692 101946
Yes – 0.691 101526
Yes – 0.668 67364
Yes – 0.688 100873
Yes – 0.677 54676
PT
CRISIS
(1) SME
CE
Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + β3 (CRISISt × CONSi,pre ) + β4 (CRISISt × VULi,pre × CONSi,pre ) + γXi,t + νt + i,t , while the overline reflects the three years pre-crisis average from 2004 to 2006 of the respective vulnerability variable. The first row of Panel A, B and C indicates the dependent variable. The second row indicates the size measures and financial characteristics that are interacted with the crisis dummy and the vulnerability variable in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A. In each specification we exclude the covariate similar to VULi,pre . The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AC
23
CRISIS × VUL
(12) TA
(1) SME
ED
CRISIS
(11) SME
AN US
CRISIS
AP
(1) SME
ACCEPTED MANUSCRIPT
In Columns 1–8, the dependent variable is AR and the conditioning variables (CONS) are the various size measures (Cols. 1–5) and proxies for financial constraints (Cols. 6–8). Columns 9–16 are the corresponding specifications for AP as dependent variable. Columns 2 and 10 (3 and 11) are specifications which use SME as size proxy and additionally include
CR IP T
the log-transformed probability of being financially constrained FC (negative Z-score Z) as control variable (indicated by the entry in ’Add. Covariates’ in Table 4). The variable of major interest is the triple interaction term CRISIS × VUL × CONS, which indicates if small, vulnerable firms reacted differently than their large counterparts. We find that for the size measures, the triple interaction term is significant and with the expected sign
AN US
in 4 out of 6 specifications (3 VUL measures for AR and AP), allowing the conclusion that a firm size effect is indeed present. For accounts receivable, the specifications with OCF and STD yield significant results. Using Panel B (OCF), Column 1 as an example how to interpret the findings, we see that large firms with low average pre-crisis cash flow cut back AR by more than 5 days, which represents about 15% of average accounts
M
receivable, while a one standard deviation increase in pre-crisis cash flow reduced it to 10%. In contrast, small firms with low average pre-crisis cash flow shortened accounts
ED
receivable by more than 4 days, while an increase in the pre-crisis cash flow ratio did not change the level tremendously. These results indicate that the vulnerability measure did
PT
not have an impact on AR for small firms, which strongly conflicts with large firms. The same interpretation holds for STD. In contrast to CS and OCF, vulnerability increases
CE
in STD, so the interpretation of the coefficients is reversed, explaining the opposite signs. From Panel C, Column 1, we see that large firms with low average pre-crisis short-term
AC
debt reduced AR by about 2.7 days, whereas a one standard deviation increase in the pre-
crisis short-term debt ratio, would have implied a decrease of about 3.5 days. In contrast, small firms with low as well as high average pre-crisis short-term debt cut back accounts receivable by about 4 days. Importantly, by conditioning on firm characteristics such as financial constraints instead on size proxies in Columns 6–8, we find substantially different results, in particular non-significant coefficients on the triple interaction term. Furthermore, Columns 2 and 3, where FC and Z are included as control variable, respectively, 24
ACCEPTED MANUSCRIPT
yield basically the same results as the specification in Column 1, implying that the size effect is not driven away by controlling for financial constraints or creditworthiness. Taken together, the results suggest that the evidence for a supply effect is even stronger among large firms, thereby reinforcing the redistribution hypothesis H2b within this sub-
CR IP T
sample. In contrast, since conditioning on vulnerability measures does not affect the impact on AR, a supply effect among SMEs cannot be confirmed. Controlling for firm characteristics allows the conclusion that the size effect is genuine and thus supports H2b. Turning next to accounts payable, we find the triple interaction term with size proxies to be significant in Panels A (CS) and B (OCF), confirming again a size effect. However,
AN US
the closer inspection of the coefficients leads to a completely different interpretation than in the case of accounts receivable. From Panel A, Col. 9, we see insignificant coefficients for large firms (CRISIS and CRISIS×VUL), implying that among large firms there was no significant change in AP for either low or high cash firms. In contrast, small firms with low average pre-crisis cash holdings decreased accounts payable significantly more than high
M
cash firms. Considering FC (Col. 10) and Z (Col. 11) as additional control variable yields basically similar results as in Column 9, indicating that the size effect survives the inclusion
ED
of financial constraints or creditworthiness. Similar interpretations hold with respect to operating cash flow. We see insignificant coefficients on CRISIS and CRISIS × VUL. In
PT
contrast, small, low cash flow firms paid on average 2.7 days faster than large, low cash flow firms during crisis, while the large positive coefficient (5.661) on the triple interaction
CE
term implies that a one standard deviation increase in the pre-crisis operating cash flow ratio would have increased the payment term of small firms by almost 1 day. Controlling
AC
for financial constraints does not eliminate the size effect. Thus, compared to AR where the financial vulnerability has no impact among SMEs, we find the opposite pattern for AP where the vulnerability has no impact among large firms. To sum up, results from Table 4 suggest at least the following three conclusions: (i) With respect to the redistribution hypothesis, we identify a firm size effect, which is not entirely explained by either financial constraints, external finance dependence or creditworthiness. (ii) Conditioning on various size proxies, the evidence for a supply effect is 25
ACCEPTED MANUSCRIPT
stronger among large firms, while being absent for SMEs. (iii) In contrast, while the usage of trade credit (AP) turns out to be unaffected by financial vulnerability measures for large firms, SMEs’ trade credit financing does depend on their pre-crisis vulnerability and therefore suggests a demand effect. In a nutshell, we thus find that during the crisis, in
CR IP T
particular large, vulnerable firms did cut down their trade credit provision and especially small, vulnerable firms suffered most by this contraction in trade credit financing. We emphasize that the size effect occurs despite controlling for financial constraints, external finance dependence, and creditworthiness. Our results are consistent with the recent literature by Klapper et al. (2012) and Murfin and Njoroge (2015), showing that large firms
AN US
appear to exploit their market power at the detriment of smaller firms in terms of trade credit usage and conditions. 3.3. Substitution effect
We next investigate the substitutive role of trade credit during the crisis and how it varies
M
in the cross section. First, we specify the measure of debt financing to be short-term bank debt to total assets (labelled as SB). The usual narrative says that bank credit
ED
was more difficult to obtain for firms during the crisis years (Ivashina and Scharfstein, 2010). We confirm this tendency by a preliminary analysis, where we regress SB on the
PT
crisis dummy. Table 5 reports a general decline by about 6 to 7 percentage points during the crisis, in particular among small and distressed firms. Note that in specification (3)
CE
and (4), we condition on SME by additionally controlling for financial constraints FC and creditworthiness Z. While the size effect still holds with respect to financial constraints,
AC
it cannot be confirmed for the Z-score, though the sign goes into the right direction. The substitution hypothesis would argue that the decline in bank credit was (at least
partially) offset by trade credit. As outlined in Section 2.2.2, we test our hypotheses H3a
and H3b by (i) a fractional response regression using the ratio of short-term bank debt to the sum of short-term bank debt and accounts payable, BtoT, (ii) the fixed-effects panel model from Equation (3), and (iii) the conditional fixed effects logistic regression. Results of the three approaches are summarized in Tables 6–8, respectively. Panel A in Table 6 illustrates the marginal effects of the fractional response regression 26
ACCEPTED MANUSCRIPT
Table 5: Short-term bank debt
CRISIS CRISIS × CONS
(1) All
(2) SME
(3) SME
(4) SME
(5) TA
(6) MS
(7) SA
(8) FC
(9) EFD
(10) Z
-0.066*** (0.004)
-0.061*** (0.004) -0.013*** (0.003)
-0.036*** (0.009) -0.012*** (0.003)
-0.062*** (0.008) -0.004 (0.005)
-0.066*** (0.004) 0.001 (0.003)
-0.064*** (0.004) -0.010*** (0.003)
-0.066*** (0.004) -0.001 (0.003)
-0.066*** (0.005) 0.005* (0.003)
-0.066*** (0.004) -0.002 (0.005)
-0.063*** (0.007) -0.027*** (0.004)
CR IP T
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Add. Covariates – – FC Z – – – – – – Adjusted R2 0.726 0.726 0.725 0.723 0.722 0.721 0.722 0.691 0.725 0.702 N 54332 54332 49106 24816 52214 51789 52188 32166 53560 27187 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: SBi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row indicates the size measures and financial characteristics that are interacted with the crisis dummy in each regression. All means no interaction. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AN US
using maximum likelihood, while Panel B summarizes the results from a linear fixed effects model on the log-transformed dependent variable. In Panel A, the sample extends over the time period 2004–2010 to maximize the number of observations for the totally balanced sample. Overall, we find strongly significant negative coefficients on the crisis dummy.
M
The ratio BtoT declined during the crisis by about 9 to 10 percentage points (Panel A), meaning that the relative importance of bank to trade credit decreased in a statistically as
ED
well as economically significant sense as measured against the average ratio of roughly 45%. Due to a potential mechanical effect, we also include the lagged growth rate of the accounts receivable to sales ratio as well as the working capital to assets ratio to control for changes
PT
in accounts receivable or other current assets which might affect both accounts payable and
CE
short-term bank debt simultaneously. We interpret this finding as first piece of evidence supporting the substitution hypothesis. Interacting the crisis dummy with size measures, we can again identify a size effect resulting in a 1 to 2 percentage points weaker effect.
AC
In particular from the specification in Panel B, we obtain strongly significant positive coefficients on the interaction term, which means that the substitution effect during the crisis years is weaker among SMEs. Positive coefficients are strongly significant for all size measures, but are insignificant for each firm characteristic (Z, EFD and FC). Furthermore, the specification in Column 3 includes FC as control. We also add the negative Z-score as additional control variable, but only for Panel B as we would lose too many observations regarding the fractional response regression. Both specifications yield basically unchanged 27
ACCEPTED MANUSCRIPT
results. We conclude again that the size effect is genuine and not entirely explained by financial constraints. Table 6: Substitution evidence I – Fractional response results Panel A: BtoT – Fractional response regression (1) (2) All SME
Covariates Add. Covariates Pseudo R2 N
CRISIS × CONS
(6) SA
(7) FC
(8) EFD
(9) Z
-0.087*** (0.012)
-0.092*** (0.012) 0.017* (0.010)
-0.099*** (0.017) 0.016 (0.010)
-0.094*** (0.013) 0.018* (0.009)
-0.090*** (0.012) 0.009 (0.009)
-0.088*** (0.012) 0.009 (0.010)
-0.090*** (0.012) 0.016 (0.010)
-0.087*** (0.012) -0.029* (0.015)
-0.083*** (0.020) -0.031** (0.014)
Yes – 0.019 9121
Yes – 0.026 9121
Yes FC 0.025 7889
Yes – 0.021 9121
Yes – 0.025 9121
Yes – 0.021 9121
Yes – 0.023 8890
Yes – 0.019 9037
Yes – 0.027 4165
Panel B: BtoT – Linear fixed effects (1) (2) All SME CRISIS
(5) MS
-0.624*** (0.053)
-0.670*** (0.056) 0.120*** (0.035)
(3) SME
(4) SME
(5) TA
(6) MS
-0.881*** (0.099) 0.120*** (0.035)
-0.697*** (0.104) 0.119** (0.060)
-0.649*** (0.054) 0.120*** (0.032)
-0.648*** (0.054) 0.106*** (0.032)
CR IP T
CRISIS × CONS
(4) TA
(7) SA
(8) FC
(9) EFD
(10) Z
-0.639*** (0.054) 0.112*** (0.033)
-0.629*** (0.059) 0.040 (0.035)
-0.629*** (0.053) 0.006 (0.061)
-0.657*** (0.087) 0.001 (0.045)
AN US
CRISIS
(3) SME
ED
M
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Add. Covariates – – FC Z – – – – – – 2 Adjusted R 0.640 0.640 0.646 0.618 0.639 0.638 0.639 0.603 0.639 0.598 N 54332 54332 49106 24816 52214 51789 52188 32166 53560 27187 Notes: Panel A reports marginal effects of the firm fixed effects fractional logit response regression with robust standard errors clustered at the firm level in parentheses. Panel B reports firm fixed effects panel regression results of the log-transformed dependent variable with robust standard errors clustered at the firm level in parentheses. The regression model is given by: BtoTi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row of both panels indicates the size measures and financial characteristics that are interacted with the crisis dummy in each regression. All means no interaction. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. In Panel B we further add the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio as covariates. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
PT
As second piece of evidence, Table 7 (Cols. 1–9) reports results from regressing accounts payable on short-term bank debt according to Equation (3). Furthermore, in an attempt to
CE
address the direction of causality, we follow Carb´o-Valverde et al. (2016) by treating bank debt as dependent variable and regress it on accounts payable. Results are summarized in Columns 10–18. The generic label FS (financing source) in Table 7 therefore refers
AC
either to SB (Cols. 1–9) or AP (Cols. 10-18). To be precise, in order to use the same scaling variable (i.e. total assets), we use APA in this specification. Note further, that since we are interested in the simultaneous behavior, we measure the ratios for each year separately. Thus, it is time variant and we obtain an estimate on it. Consider first the specification in Column 1. We find an insignificant coefficient (0.001) on the crisis dummy, but a significant negative coefficient of -0.012 on CRISIS × CONS, which mirrors the earlier finding that SMEs did reduce accounts payable during the crisis while large 28
ACCEPTED MANUSCRIPT
firms did not. The coefficient of -0.051 on FS (i.e. SB in this specification) as well as on the interaction term FS × CONS (-0.03) are significantly negative, meaning that there is a negative relation between AP and SB, which is significantly stronger for SMEs. The latter result implies that over the whole sample period, we find a substitutive role for trade
CR IP T
credit which is stronger among SMEs. Interacting the explanatory variable with the crisis dummy (CRISIS × FS) yields also a significant negative value of -0.03, implying that the substitutive effect for large firms was more pronounced during the crisis. A one standard deviation decrease in short-term bank debt implies an increase of 0.5 percentage points of accounts payable among large firms, which amounts to an economically meaningful
AN US
change of 4% of the average accounts payable ratio. Finally, the triple interaction term is positive and significant for 4 out of 6 size specifications, suggesting that the substitution effect for small firms was less pronounced during the crisis. Including the log-transformed probability of being financially constrained (Col. 2) and the negative Z-score (Col. 3) as additional control variables produces similar results.
M
In Columns 10–18, we regress SB on FS, which is accounts payable in this specification. While the significant negative coefficients on CRISIS and FS repeat the finding of
ED
declining bank debt during the crisis and the substitution effect with AP, we find much less significant evidence for interaction terms with the crisis dummy. In particular, co-
PT
efficients on CRISIS × FS are barely significant, suggesting that during the crisis, bank debt explains accounts payable rather than vice versa. To rule out endogeneity problems
CE
that might occur with contemporaneous regressions, we provide additional evidence on the causal effect of bank credit and trade credit from a conditional fixed effects logistic
AC
regression by following Illueca et al. (2016). Results are summarized in Table 8.
29
CR IP T
ACCEPTED MANUSCRIPT
Table 7: Substitution evidence II – Regression results APA
FS CRISIS × FS CRISIS × CONS FS × CONS CRISIS × FS × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
(4) TA
(5) MS
(6) SA
(7) FC
(8) EFD
(9) Z
(10) SME
(11) SME
(12) SME
(13) TA
(14) MS
(15) SA
(16) FC
(17) EFD
(18) Z
0.001 (0.003) -0.051*** (0.008) -0.030*** (0.008) -0.012*** (0.002) -0.031*** (0.011) 0.014 (0.011)
0.022*** (0.005) -0.052*** (0.008) -0.030*** (0.008) -0.012*** (0.002) -0.032*** (0.010) 0.006 (0.011)
0.010* (0.005) -0.067*** (0.016) -0.045*** (0.015) -0.018*** (0.004) -0.039** (0.019) 0.038** (0.019)
0.001 (0.003) -0.046*** (0.006) -0.036*** (0.007) -0.016*** (0.002) -0.050*** (0.010) 0.034*** (0.011)
-0.001 (0.003) -0.061*** (0.007) -0.034*** (0.007) -0.011*** (0.002) -0.019* (0.011) 0.025** (0.011)
0.000 (0.003) -0.040*** (0.006) -0.034*** (0.006) -0.016*** (0.002) -0.063*** (0.010) 0.031*** (0.011)
-0.000 (0.003) -0.019*** (0.005) -0.026*** (0.006) -0.021*** (0.003) -0.109*** (0.012) 0.026** (0.011)
-0.003 (0.003) -0.068*** (0.005) -0.022*** (0.005) 0.019*** (0.003) 0.062*** (0.016) 0.003 (0.017)
-0.007 (0.005) -0.151*** (0.014) -0.005 (0.014) 0.012*** (0.003) 0.114*** (0.016) -0.004 (0.016)
-0.059*** (0.005) -0.215*** (0.027) -0.010 (0.015) -0.022*** (0.004) -0.027 (0.031) 0.051*** (0.019)
-0.030*** (0.009) -0.219*** (0.027) -0.017 (0.015) -0.023*** (0.004) -0.034 (0.031) 0.067*** (0.020)
-0.064*** (0.009) -0.284*** (0.055) 0.038 (0.038) -0.007 (0.007) 0.048 (0.058) -0.016 (0.041)
-0.068*** (0.005) -0.257*** (0.024) 0.027* (0.015) -0.001 (0.004) 0.041 (0.029) -0.010 (0.020)
-0.065*** (0.005) -0.227*** (0.020) 0.016 (0.011) -0.013*** (0.004) -0.005 (0.027) 0.011 (0.019)
-0.068*** (0.005) -0.231*** (0.024) 0.021 (0.014) -0.005 (0.004) 0.000 (0.029) 0.002 (0.019)
-0.066*** (0.005) -0.150*** (0.031) 0.009 (0.017) -0.004 (0.005) -0.092*** (0.035) 0.026 (0.022)
-0.068*** (0.004) -0.218*** (0.014) 0.019** (0.010) 0.003 (0.007) 0.119** (0.053) 0.013 (0.041)
-0.063*** (0.008) -0.255*** (0.021) -0.010 (0.016) -0.028*** (0.006) 0.098*** (0.036) 0.039 (0.029)
Yes – 0.842 54332
Yes FC 0.846 49106
Yes Z 0.821 24816
Yes – 0.841 52214
Yes – 0.840 51789
Yes – 0.841 52188
Yes – 0.835 32166
Yes – 0.841 53560
Yes – 0.822 27187
Yes – 0.730 54332
Yes FC 0.730 49106
Yes Z 0.728 24816
Yes – 0.726 52214
Yes – 0.725 51789
Yes – 0.726 52188
Yes – 0.693 32166
Yes – 0.729 53560
Yes – 0.706 27187
AN US
CRISIS
SB
(1) SME
30
M
Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model in Columns 1 to 9 is given by: APA i,t = αi + β1 CRISISt + β2 SBi,t + β3 (CRISISt × SBi,t ) + β4 (CRISISt × CONSi,pre ) + β5 (SBi,t × CONSi,pre ) + β6 (CRISISt × SBi,t × CONSi,pre ) + γXi,t + νt + i,t . In Columns 10 to 18 APA and SB are exchanged. In Columns 1 to 9 FS refers to SB and in Columns 10 to 18 to APA . The first row indicates the dependent variable. The second row indicates the size measures and financial characteristics that are interacted with the crisis dummy and the FS measure in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio and including the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. Superscript A means that the variable is divided by total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
Table 8: Substitution evidence III – Conditional logistic model results Panel A: Substitution indicator conditional on a change in short-term bank debt Decrease
CRISIS × CONS Covariates Pseudo R2 N
(2) SME
(3) Large
(4) All
(5) All
(6) SME
(7) Large
(8) All
2.125*** (0.261)
1.861*** (0.346)
2.523*** (0.427)
2.066*** (0.263) 1.077 (0.088)
0.655*** (0.102)
0.640* (0.148)
0.574*** (0.122)
0.691** (0.111) 0.867 (0.078)
Yes 0.050 15010
Yes 0.038 9024
Yes 0.083 5986
Yes 0.050 15010
Yes 0.040 12493
Yes 0.033 7067
Yes 0.058 5426
Yes 0.041 12493
(2) SME
(3) Large
(4) All
(5) All
(6) SME
(7) Large
(8) All
1.040 (0.214)
1.064 (0.191)
1.055 (0.146) 1.037 (0.092)
1.077 (0.153)
1.255 (0.270)
0.996 (0.191)
1.072 (0.156) 1.013 (0.082)
ED
CRISIS
Increase
(1) All
Panel B: Substitution indicator conditional on a change in accounts payable
CRISIS
1.070 (0.144)
CRISIS × CONS
PT
Decrease
(1) All
Increase
AC
CE
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Pseudo R2 0.010 0.011 0.016 0.010 0.013 0.018 0.012 0.013 N 12412 7341 5071 12412 13650 7778 5872 13650 Notes: This table reports odds ratios of conditional firm fixed effects logistic regressions with robust standard errors clustered at the firm level in parentheses. The regression model is given by: SIi,t = αi +β1 CRISISt +β2 (CRISISt ×CONSi,pre )+γXi,t +νt +i,t . The dependent variable is a dummy variable that is one in case of substitution and zero otherwise. In Columns 1 to 4 of Panel A the dependent variable is one if accounts payable increase from t-1 to t conditional on a decrease in short-term bank debt in the previous year. In Columns 5 to 8 the dependent variable is one if accounts payable decrease from t-1 to t conditional on an increase in short-term bank debt in the previous year. In Columns 1 to 4 of Panel B the dependent variable is one if short-term bank debt increases from t-1 to t conditional on a decrease in accounts payable in the previous year. In Columns 5 to 8 the dependent variable is one if short-term bank debt decreases from t-1 to t conditional on an increase in accounts payable in the previous year. Columns 1 and 5 include the whole sample without any interaction. Columns 2 and 3 and 6 and 7 are divided according to the SME dummy which is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. In Columns 4 and 8 the crisis dummy is interacted with the SME dummy. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio and including the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
ACCEPTED MANUSCRIPT
Panel A reports the estimated odds ratio for substitution conditional on a former decrease (Cols. 1–4) or increase (Cols. 5–8) in short-term bank debt. The substitution dummy S is considered to be one if the change in accounts payable is of opposite sign than the previous change in bank debt. In unreported robustness checks, we also considered the
CR IP T
change in the respective ratios, where the scale variable is total assets. Our results remain robust. We find a highly significant coefficient of 2.125 in Column 1. Thus, the odds of an increase in trade credit conditional on a prior decrease in bank credit are multiplied by 2.125, meaning that conditional on a decline in bank debt it is substantially more likely that accounts payable have increased during the crisis time compared to non-crisis times.
AN US
In contrast, conditioning on a prior increase in bank debt, the odds of a decrease in trade credit are multiplied by 0.655 (Col. 5), i.e. during crisis firms rather increased accounts payable after a rise in bank debt confirming that the afore detected substitution effect results from a prior decrease in bank debt and not a prior increase. Panel B uses the change in accounts payable as conditioning event. Across all specifications, we do not find
M
any significant results, i.e. there is no evidence that bank debt reacts on a prior change in accounts payable during crisis. Therefore we can draw the following conclusions: First,
ED
we find strong evidence that accounts payable react on a given change in bank debt rather than vice versa. Second, evidence for substitution can only be confirmed in case of a
PT
prior decline in bank debt. Finally, although the odds ratio for large firms is higher in magnitude than for SMEs (compare Cols. 2 and 3), we are not able to verify this difference
CE
in a statistical sense.
In sum, this section has shown that in general our substitution hypothesis H3a can be
AC
confirmed. Moreover, the substitutive effect of trade credit has increased during the crisis for large firms, but not for SMEs, which is in line with H3b. This leads to the conclusion
that contrary to SMEs, large firms were able to mitigate the decline in bank debt at least to some extent by trade credit. The firm size effect is not entirely explained by other financial firm characteristics. Overall, this finding is consistent with the evidence on the redistribution hypothesis, where it has been shown that SMEs had to reduce accounts payable while large firms maintained their level of trade credit. We further addressed the 31
ACCEPTED MANUSCRIPT
direction of causality in the substitution effect, and find strong support for the conclusion that trade credit accommodates to a decline in bank debt. 3.4. Real effects This final subsection provides evidence on the impact of the previously illustrated shift
CR IP T
in financing patterns on the real economy. We investigate the relations between investment behavior (CAPEX) and either accounts payable and short-term bank debt, following Carb´o-Valverde et al. (2016) by means of an error correction model (Bond et al., 2003) estimated with first-difference GMM according to Equation (10) in Appendix C. Furthermore, as in Garcia-Appendini and Montoriol-Garriga (2013), we analyze potential relations
AN US
between accounts receivable and profitability (return on assets, ROA). In line with our general research focus, we distinguish according to size.
Columns 1–8 of Table 9 refer to CAPEX as dependent variable and is either regressed on AP (Cols. 1–5) or SB (Cols. 6–8). For the entire sample (see Cols. 1 and 6), we
M
find positive significant coefficients on both financing sources, suggesting that AP as well as SB are positively linked to investment behavior. This link has not intensified during
ED
the crisis as suggested by the insignificant coefficients on the interaction terms. However, conditioning on size (see Cols. 2, 3 and 7, 8) yields a much more differentiated perspective.
PT
For SMEs, we obtain a significant positive coefficient on AP (0.026), while SB turns out to be insignificant. For large firms, the pattern is reversed. AP is insignificant, while SB
CE
carries a significant coefficient (0.034, see Col. 8). By conditioning on external finance dependence, we do not find a similar pattern, thereby confirming that the observed size
AC
effect is not driven by this firm characteristic. The empirical evidence suggests, that SMEs’ investment behavior is related to trade credit, while large firms’ investment behavior is more closely tied to bank debt. Coupled with results from our previous section, where we have shown that SMEs were forced to reduce accounts payable much more strongly than large firms, our finding suggests an indirect adverse impact of the financial crisis on real economic activity via the trade credit channel. As another measure of real effects, we investigate firms’ profitability (ROA, see Cols. 9– 11) and regress it on accounts receivables. We find strongly significant positive coefficients 32
ACCEPTED MANUSCRIPT
on AR as well as on its interaction with the crisis dummy. Firms with higher levels of accounts receivable are comparably more profitable and this effect is amplified during the crisis. A potential explanation could be that more profitable firms or firms with a higher anticipated profitability are prone to provide more trade credit and thus strengthen their
CR IP T
customer relationship. Combining this finding again with the evidence from previous sections that, in particular, financially vulnerable firms had to cut back accounts receivable suggests another channel by which shifts in trade credit provision yield economic real effects. Consequently, both the decline in trade credit usage and provision caused a negative impact on the real economy and we have no reason to reject our last hypothesis H4.
AN US
Table 9: Real effects CAPEX
CAPEX
t-1
CRISIS APK CRISIS × APK SBK
(2) SME
(3) Large
(4) EFD
-0.042** (0.018) -0.567*** (0.081) 0.022*** (0.005) 0.000 (0.004)
-0.031 (0.020) -0.746*** (0.114) 0.027*** (0.006) 0.010* (0.006)
-0.105*** (0.035) -0.378*** (0.067) 0.005 (0.004) -0.001 (0.001)
-0.063** (0.029) -0.699*** (0.117) 0.026* (0.013) -0.011 (0.016)
CRISIS × SBK
K
OCF
t-1
(lnK-lnS)t-2 ∆lnS ∆lnSt-1
0.000 (0.011) 0.005** (0.003) -0.227*** (0.068) 0.045 (0.177) 0.241*** (0.074)
A
AR
CRISIS × ARA
45847 0.00 0.90 0.11
CE
N AR(1)-test (p-value) AR(2)-test (p-value) H-test (p-value)
-0.007 (0.006) 0.006 (0.004) -0.520*** (0.130) 0.544** (0.213) 0.527*** (0.134)
PT
ROAt-1
0.005 (0.014) 0.003 (0.003) -0.198** (0.078) -0.060 (0.165) 0.207** (0.084)
0.015 (0.018) 0.005 (0.005) -0.315*** (0.101) 0.061 (0.107) 0.310*** (0.110)
ED
OCFK
29368 0.00 0.79 0.67
16479 0.00 0.78 0.18
21652 0.00 0.19 0.08
ROA
(5) No-EFD
(6) All
(7) SME
(8) Large
(9) All
(10) SME
(11) Large
-0.045** (0.018) -0.422*** (0.083) 0.014*** (0.005) -0.002 (0.002)
-0.048*** (0.018) -0.567*** (0.078)
-0.054** (0.022) -0.700*** (0.119)
-0.080*** (0.027) -0.218*** (0.078)
-0.017 (0.011)
0.010 (0.020)
-0.033*** (0.012)
0.030*** (0.008) -0.008 (0.008) 0.005 (0.008) 0.006** (0.003) -0.238*** (0.069) 0.158 (0.146) 0.271*** (0.074)
0.019 (0.013) -0.002 (0.008) 0.030 (0.021) 0.005 (0.004) -0.277*** (0.084) 0.214 (0.199) 0.330*** (0.093)
0.033** (0.015) -0.020 (0.017) -0.011 (0.009) 0.004 (0.004) -0.405*** (0.095) 0.580** (0.237) 0.405*** (0.095)
0.032*** (0.011)
0.024** (0.011)
0.091*** (0.018)
0.368*** (0.023) 0.143*** (0.050) 0.035*** (0.011)
0.300*** (0.028) 0.157** (0.062) 0.025* (0.014)
0.461*** (0.033) 0.135** (0.064) 0.044*** (0.015)
76220 0.00 0.02 0.00
47874 0.00 0.15 0.11
28346 0.00 0.25 0.00
M
K
(1) All
0.008 (0.014) 0.004* (0.002) -0.240*** (0.075) 0.190 (0.153) 0.279*** (0.083)
23566 0.00 0.39 0.31
45847 0.00 0.75 0.12
29368 0.00 0.65 0.47
16479 0.00 0.51 0.15
Notes: This table reports twostep first-difference GMM regression results with robust, heteroscedasticity consistent standard errors in parentheses. The CAPEXi,t first row indicates the dependent variable. The regression model for Columns 1 to 8 is given by an investment error correction model: = Ki,t-1 CAPEX
APK
APK
OCF
OCF
AC
i,t-1 αi + β1 Ki,t-2i,t-1 + β2 CRISISt + β3 Ki,t-1i,t + β4 (CRISISt × Ki,t-1i,t ) + β5 (lnKi,t-2 − lnSi,t-2 ) + β6 (∆lnSi,t ) + β7 (∆lnSi,t-1 ) + β8 Ki,t-1i,t + β9 Ki,t-2 + νt + i,t . CAPEXi,t is capital expenditures, Ki,t is the replacement value of capital stock, (lnKi,t-2 − lnSi,t-2 ) is the error correction parameter and ∆lnSi,t is the change in log sales. For details on the remaining variables, we refer to Table A1 in Appendix A. Instruments set for Columns 1 to 5 (6 to 8) n o
CAPEXi,t-j APi,t-j SBi,t-j OCFi,t-j , Ki,t-1-j ( Ki,t-1-j ), Ki,t-1-j , (lnK-lnS)i,t-1-j , ∆lnSi,t-j Ki,t-1-j
with j being 2 and higher; time dummies . The regression model for Columns 9 to 12 is given by:
ROAi,t = αi + β1 ROAi,t-1 + β2 CRISISt + β3 ARA i,t + β4 (CRISISt × ARA i,t ) + γXi,t + νt + i,t . Covariates are the cash ratio, sales growth, the working capital net cash ratio, the tangible assets ratio, the total debt ratio and the natural logarithm of age. The instrument set contains the dependent variable and control variables except age lagged twice and higher and time dummies. The second row indicates the subsamples used. All indicates no subsample used. SME includes a firm if it is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations, while Large includes the remaining firms. EFD includes a firm if the two-digit industry-specific EFD-index of listed firms prior to crisis lies above the sample median, while No-EFD includes the remaining firms. Superscript K means that the variable is divided by the lagged replacement value of capital. Superscript A means that the variable is divided by total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
33
ACCEPTED MANUSCRIPT
4. Conclusion We investigate the redistribution and substitution role of trade credit financing with a major focus on firm size effects. We use the negative supply shock caused by the 2007/08 crisis as exogenous event which is an ideal natural experiment to analyze the usage of trade
CR IP T
credit. For various reasons, we suspect that SMEs are different from large firms in their ability and willingness to provide and receive trade credit. Several studies have observed differences with respect to the financial behavior between large companies and SMEs, either based on organisational structure, financial expertise, priority of financial motives or institutional factors (see e.g. Beck et al., 2008; Graham and Harvey, 2001). Klapper
AN US
et al. (2012) or Fabbri and Klapper (2016) document that large companies are more likely to enjoy greater bargaining power in customer-supplier relationships with smaller companies.
We contribute to the existing trade credit literature by revealing a firm size effect with
M
respect to the inter-firm liquidity redistribution hypothesis, the substitution hypothesis and economic real effects. Our comprehensive German dataset covers both the SME
ED
segment as well as large traded firms and allows to condition on various size measures. It is common to consider size as a proxy to various other firm characteristics such as financial
PT
constraints, external finance dependence and creditworthiness. We construct proxies for each of these characteristics in order to check if size picks up one of them. In econometric
CE
terms, we check if a potential size effect does not succumb to an omitted-variable bias. Overall, we find (i) that the size effect is consistent across various size measures, (ii) that
AC
it cannot be replicated by conditioning the sample on the above-mentioned characteristics and (iii) that these characteristics do not drive out its explanatory power. In this sense, we interpret and label the size effect as being genuine or self-contained. In line with recent literature such as Love et al. (2007), Garcia-Appendini and Montoriol-
Garriga (2013), or Carb´o-Valverde et al. (2016), we find a decline in the aggregate net supply of trade credit following the crisis, which supports the redistribution hypothesis. We extend these results by showing that the lower net supply was particularly driven by a decline in accounts receivables of large, financially vulnerable firms. Small, vulnerable 34
ACCEPTED MANUSCRIPT
firms on the other hand, display a strong decline in accounts payable, suggesting that the adverse economic shock of the crisis was propagated primarily along the trade credit supply channel from large to small vulnerable firms. Consistent with the differences in accounts payable, we find a firm size effect with
CR IP T
respect to the substitution hypothesis. Although we can confirm a general substitutive role of trade credit, we find that SMEs were less able to compensate declining bank financing during the crisis by trade credit as compared to large firms. We further investigate the direction of causality in the substitution effect and find clear evidence that trade credit accommodates to a decline in bank financing rather than vice versa.
AN US
In a final step, we study the real effects of trade credit and find that accounts payable affect investment behavior of small firms while short-term bank debt does not. The opposite holds for large firms. Coupled with the fact that SMEs had to cut back accounts payable significantly more than large firms, we find an adverse impact of the crisis via the trade credit channel. Moreover, we can show that accounts receivable positively affected
Montoriol-Garriga (2013).
M
profitability during the crisis which is in line with the results of Garcia-Appendini and
ED
Overall, our findings indicate that firm size plays an important role in financing patterns during the financial crisis. SMEs are the often cited backbone of many economies
PT
and therefore of important policy relevance. Our results strongly suggest that the firm size effect is genuine and cannot entirely be explained by financial constraints, external finance
CE
dependence or creditworthiness. Analogous to Murfin and Njoroge (2015), we suspect that bargaining and market power is a potential driving force behind the size effect, but leave
AC
a rigorous empirical examination to future research.
35
ACCEPTED MANUSCRIPT
Appendices A. Appendix A Table A1: Variable definitions Formula
Description
Dependent variables
AP NET BtoT
ARi,t · 360 Si,t APi,t · 360 COGSi,t (AR-AP)i,t · 360 Si,t
Accounts receivable to daily total sales Accounts payable to daily costs of goods sold
Net trade credit: Accounts receivable minus accounts payable to daily total sales Bank debt relative to trade debt: Short-term bank debt to the sum of short-term bank debt and accounts payable
SBi,t (SB+AP)i,t APi,t Ai,t SBi,t Ai,t
APA SB
Accounts payable to total assets
AN US
AR
CR IP T
Variable
Short-term bank debt to total assets
Dummy variable that is 1 in case of substitution following an increase/ decrease in short-term bank debt in the prior year and 0 otherwise
S CAPEX ROA
CAPEXi,t Ki,t-1 EBITi,t TAi,t
Capital expenditures to lagged capital Return on assets
Main variables of interest
Dummy that is 1 during the crisis years 2007 to 2010 and 0 otherwise
M
CRISIS
Size variables (dummy is 1 for small firms and 0 otherwise) Dummy that measures a firm’s size of according to §267 of the German Commercial Code (small and medium-sized enterprises)
ED
SME TA
Dummy that measure a firm’s size according to total assets
MS
Dummy that measures market share according to firm’s total sales to industry output
PT
Dummy that measures the size-age index according to Hadlock and Pierce (2010) and Klepsch and Szabo (2016)
SA
FC EFD
AC
Z
CE
Firm fundamentals (dummy is 1 for constrained firms and 0 otherwise) Dummy that measures financial constraint by means of an endogenous switching regression according to Hovakimian and Titman (2006) and Almeida and Campello (2007) Dummy that measures external finance dependence according to Rajan and Zingales (1998) Dummy that measures creditworthiness according to the Altman (1968) Z-score
Control variables/ covariates CS
OCF EQ TAA
SG AGE
CSi,t Ai,t OCFi,t Ai,t EQi,t Ai,t TAAi,t Ai,t Si,t −Si,t-1 Si,t-1
log(Agei,t )
Cash to total assets Operating cash flow to total assets Equity to total assets Fixed assets to total assets labeled as tangible assets Sales growth Natural logarithm of age
36
ACCEPTED MANUSCRIPT
Table A2: Aggregate crisis impact on trade credit
CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE Cons
(2) ARA
-4.000*** (0.60)
-7.189*** (0.89)
3.812*** (1.16) -1.127 (0.86) -0.121 (0.15) 6.429*** (1.01) 2.347* (1.25) 0.334 (1.03) 35.008*** (2.84)
0.029 (1.87) 2.861** (1.43) 0.280 (0.19) -9.267*** (1.62) -11.087*** (1.72) -1.394 (1.64) 84.108*** (4.55)
(3) AR
7.755*** (1.17) 3.812*** (1.16) -1.127 (0.86) -0.121 (0.15) 6.429*** (1.01) 2.347* (1.25) 0.334 (1.03) 36.728*** (2.69)
(4) AP
(5) APA
-0.489 (0.52)
-2.377*** (0.73)
-2.528*** (0.93) -3.384*** (0.78) -0.199 (0.13) 1.297 (1.01) -5.716*** (1.50) -1.922* (1.01) 35.879*** (2.88)
-8.502*** (1.35) -3.009*** (1.06) 0.419*** (0.16) -3.713*** (1.13) -13.468*** (1.59) -5.062*** (1.37) 71.349*** (3.80)
(6) AP
0.947 (1.01) -2.528*** (0.93) -3.384*** (0.78) -0.199 (0.13) 1.297 (1.01) -5.716*** (1.50) -1.922* (1.01) 36.089*** (2.76)
(7) NET
(8) NETA
-3.376*** (0.68)
-4.959*** (1.03)
5.117*** (1.30) 1.701* (0.98) -0.000 (0.16) 1.014 (1.28) 4.030** (1.65) 2.589** (1.15) 4.193 (3.22)
8.590*** (2.07) 6.225*** (1.68) -0.143 (0.22) -5.015*** (1.87) 2.413 (2.02) 3.833** (1.88) 12.084** (5.23)
(9) NET
6.545*** (1.31) 5.117*** (1.30) 1.701* (0.98) -0.000 (0.16) 1.014 (1.28) 4.030** (1.65) 2.589** (1.15) 5.645* (3.07)
CR IP T
CRISIS ∆EURt-1
(1) AR
AC
CE
PT
ED
M
AN US
Adjusted R2 0.706 0.822 0.706 0.697 0.857 0.697 0.700 0.801 0.700 N 107847 107846 107847 107847 107841 107847 107847 107847 107847 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + βCRISISt + γXi,t + νt + i,t . The first row indicates the dependent variable. We refer to Table A1 in Appendix A for details on the respective variables. ∆EURt-1 is the lagged growth rate of the three months Euribor. Superscript A means that the dependent variable is divided by daily total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
37
ACCEPTED MANUSCRIPT
B. Appendix B In the paper we compare trade credit usage and provision among financially constrained and unconstrained firms. To sort firms according to their financial constraints we use an endogenous switching regression with unknown sample selection first developed by Mad-
CR IP T
dala (1986). We follow Hu and Schiantarelli (1998), Hovakimian and Titman (2006) and Almeida and Campello (2007), who applied the model to investigate cash flow sensitivities with respect to financial constraints. As the degree of financial constraints of a firm is not directly observable the sample selection is unknown. However, we suppose some firms to be more financially constrained than others, though we are not able to explicitly tag
AN US
them ex ante. The model offers regression estimation results of two investment regimes without a priori identifying firms as being financially constrained or unconstrained. While the number of regimes are known, the structural breakpoints are unknown. Supposing that there exist two different investment regimes and if investment-cash flow sensitivities
M
are a good proxy of financial constraints, it should be possible to tag two regimes with different cash flow coefficients. The investment-cash flow sensitivity should be low for the
ED
unconstrained regime, while the investment-cash flow sensitivity should be high for the constrained regime. However, if these two regimes cannot be identified, investment-cash
PT
flow sensitivity would not be a good proxy for financial constraints. Contingent on the magnitude of liquidity constraints a firm may be assigned to one of the two unobservable
CE
investment regimes (Hovakimian and Titman, 2006). The model consists of the following
AC
system of simultaneous equations:
I1 i,t = β1 Xi,t + u1 i,t
(4)
I2 i,t = β2 Xi,t + u2 i,t
(5)
y* i,t = γZi,t + i,t
(6)
Equations (4) and (5) are structural equations that reflect firms’ investment in the two investment regimes. Xi,t is a vector of exogenous covariates that influence investment. 38
ACCEPTED MANUSCRIPT
Equation (6) is the selection equation that reflects a firm’s probability of being in one or the other regime, while y* i,t is a latent variable that captures this probability. Zi,t is a vector of covariates that influence a firm’s likelihood of being constrained or not. u1 , u2 and are residuals. In the empirical specification of the switching model, the investment Ii,t = I1 i,t iff y* i,t < 0 Ii,t = I2 i,t iff y* i,t ≥ 0
CR IP T
of firm i in t is given by: (7)
β1 , β2 and γ are estimated via maximum likelihood. In doing this we suppose that
AN US
u1 , u2and are jointly normally distributed with mean vector 0 and covariance matrix 2 σ1 σ12 σ1 2 Σ = σ12 σ2 σ2 that allows for nonzero correlation between both the shocks to σ1 σ2 1 investment and to firms’ characteristics. σ2 is normalized to 1, as it is unidentified. The
likelihood function and the log-likelihood function subject to maximization are respectively
M
given by:
ED
li,t =Pr(i,t < −γZi,t | u1 i,t = I1 i,t − β1 Xi,t )Pr(u1 i,t = I1 i,t − β1 Xi,t ) + Pr(i,t ≥ −γZi,t | u2 i,t = I2 i,t − β2 Xi,t )Pr(u2 i,t = I2 i,t − β2 Xi,t ),
CE
(
PT
! ! " #) −γZi,t − σσ22 u2 i,t −γZi,t − σσ12 u1 i,t 1 2 r r φ(u1 i,t , σ1 ) + 1 − Φ φ(u2 i,t , σ2 ) , ln L = ln Φ 2 2 σ σ 1 2 i=1 1 − σ2 1 − σ2 N X
1
2
(8)
AC
where φ(.) and Φ(.) are the normal density and the cumulative distribution functions, respectively.13 To solve the model, we have to assign the selection vector Z considering firm characteristics that influence a firm’s likelihood of being financially constrained or unconstrained. This procedure provides the possibility to control for various parameters that simultaneously determine the regime a particular firm is likely to be part of, while common sample splits are based on one or two parameters. We follow Almeida and 13 Additional details on the approach can be found in Maddala (1986), Hu and Schiantarelli (1998) and Hovakimian and Titman (2006).
39
ACCEPTED MANUSCRIPT
Campello (2007) and use the following selection variables: the natural logarithm of total assets (Ai,t-1 ), the natural logarithm of age (AGEi,t-1 ), the ratio of short-term debt to total assets (STDi,t-1 ) , the ratio of equity to total debt (LEVi,t-1 ), the cash ratio (CSi,t-1 ), the ratio of interest payments to EBIT (ICi,t-1 ) and the tangible assets ratio (TAAi,t-1 ). We
CR IP T
lag all these variables by one period. The structural equations are estimated by an error correction model (Bond et al., 2003) as defined in Section 2.2.2. We additionally estimate the structural equations for accounts receivable, as we are interested in firms’ financial constraints with respect to trade credit.14 Table A3 reports regression results.15 While Panel A depicts the structural equation results, Panel B shows the selection equation
AN US
results. From Panel A one can see that investment-cash flow sensitivity seems to be a good proxy for financial constraints, as the coefficients in regime R2 (constrained regime) are much higher than in regime R1 (unconstrained regime). From Panel B one can see that the chosen selection parameters are quite equal considering investment and accounts receivable. The dependent variable is one if a firm belongs to regime R1 and zero otherwise.
M
While larger and older firms as well as firms with more tangible assets are more likely to be financially unconstrained, firms with more short-term debt and a higher cash ratio
ED
are more likely to be financially constrained. We also compare the probabilities of being financially constrained between the investment model and the trade credit model. Since
PT
they are highly correlated, we use the probabilities of the trade credit model to sort the
AC
CE
firms according to their financial constraints as described in the paper.
14
For further details see Section 2.2.2 and Table A1 in the Appendix. The regressions are estimated with the user-written Stata package “switchr” provided by Zimmerman (1998), which maximizes the likelihood function through the EM algorithm of Dempster et al. (1977). 15
40
ACCEPTED MANUSCRIPT
Table A3: Endogenous switching regression Panel A: Structural regressions CAPEX
OCF
K
OCF
t-1
(lnK-lnS)t-2 ∆lnS ∆lnSt-1
(2) R2
-0.008*** (0.001) 0.001*** (0.000) 0.000** (0.000) -0.082*** (0.001) 0.065*** (0.001) 0.090*** (0.001)
-0.317*** (0.020) 0.063*** (0.010) 0.015*** (0.003) -0.981*** (0.063) 0.630*** (0.043) 0.859*** (0.054)
CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE R2 N
0.341 71152
(3) R1
(4) R2 At-1 AGEt-1 STDt-1 LEVt-1 CSt-1 ICt-1
-0.308*** (0.113) -0.754*** (0.090) 0.012 (0.013) -0.088 (0.101) 0.672*** (0.127) 0.342*** (0.101)
4.394*** (1.342) -2.083* (1.096) -0.154 (0.185) 8.312*** (1.261) 3.437** (1.601) 0.515 (1.253)
0.057 102049
0.012
0.289
TAAt-1
(1) CAPEX
(2) AR
0.040*** (0.001) 0.041*** (0.003) -0.116*** (0.009) 0.000*** (0.000) -0.107*** (0.019) -0.001 (0.002) 0.498*** (0.012)
0.029*** (0.001) 0.041*** (0.002) -0.108*** (0.005) -0.000 (0.000) -0.009 (0.010) -0.003*** (0.001) 0.341*** (0.008)
0.112 71152 0.000
0.119 102049 0.000
CR IP T
K
(1) R1
R2 N Model p-value (LRT)
AN US
CAPEXK t-1
Panel B: Endogenous selection regressions AR
AC
CE
PT
ED
M
Notes: This table reports results from the endogenous switching regression model with robust standard errors clustered at the firm level in parentheses. Panel A reports the structural equation results with firm and year fixed effects. The first row indicates the dependent variable and the respective regime. Panel B reports the selection equation results with year fixed effects. The first row indicates the dependent variable of the structural equation. The dependent variable of the selection equation is a dummy that is one if a firm is assigned into regime R1 and zero if it is assigned into regime R2, while firms in R1 are classified as financially unconstrained and in R2 as financially constrained. Superscript K means that the variable is divided by the lagged replacement value of capital. For further details on the respective variables, we refer to the paper and Table A1 in Appendix A. *significant at 10%, **significant at 5%, ***significant at 1%.
41
ACCEPTED MANUSCRIPT
C. Appendix C As most firms in our sample are small unlisted firms, we are unable to construct Tobin’s Q. In order to appraise company investment and investment-trade credit sensitivities we use an error-correction model according to Bond et al. (2003), Mizen and Vermeulen (2005),
CR IP T
Mulier et al. (2016) and Buca and Vermeulen (2017). The error-correction model was firstly launched by Bean (1981) and later applied by Bond et al. (2003). The basic idea of a flexible error-correction model is to combine a long-term equilibrium relation of the capital stock with short-term investment dynamics by means of a regression model (Buca and Vermeulen, 2017). For a profit-maximizing firm with constant returns to scale, a CES
AN US
production function and without adjustment costs, the capital stock can be expressed as a log-transformed function of output and the cost of capital:
(9)
M
ki,t = ai + yi,t − σji,t ,
where ki,t is the natural logarithm of the desired capital stock of firm i in year t, yi,t is the
ED
natural logarithm of output, ji,t is the natural logarithm of the real user cost of capital, σ is the elasticity of capital regarding the real user cost and ai is a firm specific intercept
PT
(Bond et al., 2003).
To consider a slow adjustment process of the current capital stock to the desired capital
CE
stock, we apply a dynamic regression model. Thereby, we expect the capital stock to follow an ADL(2,2) model. According to Bean (1981) Equation 9 can then be rewritten in errorcorrection form, while capital expenditures over the lagged capital stock less depreciation
AC
can be used as proxy for the change in capital stock ∆ki,t ∼
CAPEXi,t Ki,t-1
− δi and the natural
logarithm of sales as proxy for the natural logarithm of output yi,t ∼ lnSi,t . The error-correction model assumes perfect capital markets to hold, i.e. a firm’s in-
vestment decision does not depend on its financial constraint status and thus investment should not be sensitive to financial variables. However, our data sample mainly includes small unlisted firms with restricted access to capital markets particularly during crisis times. Consequently, adding cash flow (OCFi,t ) and accounts payable (APi,t ) or short42
ACCEPTED MANUSCRIPT
term bank debt (SBi,t ) to the model should investigate the role of financial variables and control for investment-trade credit and investment-bank debt sensitivities and thus, capital market frictions (Fazzari et al., 1988). The error-correction model can then be specified
CR IP T
as following:
CAPEXi,t-1 APi,t APi,t CAPEXi,t =αi + β1 + β2 CRISISt + β3 + β4 (CRISISt × ) Ki,t-1 Ki,t-2 Ki,t-1 Ki,t-1 + β5 (lnKi,t-2 − lnSi,t-2 ) + β6 (∆lnSi,t ) + β7 (∆lnSi,t-1 ) OCFi,t OCFi,t-1 + β9 + νt + i,t , Ki,t-1 Ki,t-2
AN US
+ β8
(10)
where CAPEXi,t is calculated as the sum of depreciation in year t and the change in tangible fixed assets from year t-1 to year t. The replacement value of the capital stock is calculated as Ki,t = Ki,t−1 × (1 − δ) + CAPEXi,t , while the starting value of K is measured by the starting value of tangible fixed assets. The depreciation rate δ is assumed to be
M
constant at 4%.16 (lnKi,t-2 − lnSi,t-2 ) is the error correction parameter that gauges the long run equilibrium between capital and its objective value measured by sales. ∆lnSi,t is
ED
the change in log sales.17 Controlling for real effects of short-term bank debt on investment, we exchange accounts payable by short-term bank debt. We estimate the model
PT
by means of a two-step first-difference GMM estimator developed by Arellano and Bond (1991), while we use lagged variables in levels as instruments for the first differences of the
CE
explanatory variables. Furthermore, to control for business cycle fluctuations and other
AC
life-cycle effects, we include year fixed effects.18
16 In robustness checks, we also estimate the model with different depreciation rates. Our results remain stable. 17 See Bond et al. (2003), Mizen and Vermeulen (2005), Mulier et al. (2016) and Buca and Vermeulen (2017) for additional details on the model. 18 The first-difference GMM estimations are performed in Stata using the command ”xtabond2” by Roodman (2009).
43
ACCEPTED MANUSCRIPT
References References Acharya, V. V., Almeida, H., Campello, M., 2013. Aggregate risk and the choice between cash and lines of credit. Journal of Finance 68 (5), 2059–2116.
microdata. Small Business Economics 42 (1), 131–151.
CR IP T
Agostino, M., Trivieri, F., 2014. Does trade credit play a signalling role? Some evidence from SMEs
Almeida, H., Campello, M., 2007. Financial constraints, asset tangibility, and corporate investment. Review of Financial Studies 20 (5), 1429–1460.
Altman, E. I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (4), 589–609.
AN US
Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58 (2), 277–297. Bean, C. R., 1981. An econometric model of manufacturing investment in the UK. Economic Journal 91 (361), 106–121.
Beck, T., Demirg¨ uc¸-Kunt, A., Maksimovic, V., 2008. Financing patterns around the world: Are small firms
M
different? Journal of Financial Economics 89 (3), 467–487.
Bond, S., Elston, J. A., Mairesse, J., Mulkay, B., 2003. Financial factors and investment in Belgium, France, Germany, and the United Kingdom: A comparison using company panel data. Review of Economics
ED
and Statistics 85 (1), 153–165.
Buca, A., Vermeulen, P., 2017. Corporate investment and bank-dependent borrowers during the recent financial crisis. Journal of Banking & Finance 78, 164–180.
PT
Calomiris, C. W., Himmelberg, C. P., Wachtel, P., 1995. Commercial paper, corporate finance, and the business cycle: A microeconomic perspective. Carnegie-Rochester Conference Series on Public Policy
CE
42, 203–250.
Campello, M., Giambona, E., Graham, J. R., Harvey, C. R., 2011. Liquidity management and corporate investment during a financial crisis. Review of Financial Studies 24 (6), 1944–1979.
AC
Carb´ o-Valverde, S., Rodr´ıguez-Fern´ andez, F., Udell, G. F., 2016. Trade credit, the financial crisis, and SME access to finance. Journal of Money, Credit and Banking 48 (1), 113–143.
Choi, W. G., Kim, Y., 2005. Trade credit and the effect of macro-financial shocks: Evidence from U.S. panel data. Journal of Financial and Quantitative Analysis 40 (4), 897–925. Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B 39 (1), 1–38. Duchin, R., Ozbas, O., Sensoy, B. A., 2010. Costly external finance, corporate investment, and the subprime mortgage credit crisis. Journal of Financial Economics 97 (3), 418–435.
44
ACCEPTED MANUSCRIPT
Fabbri, D., Klapper, L. F., 2016. Bargaining power and trade credit. Journal of Corporate Finance 41, 66–80. Fazzari, S. M., Hubbard, R. G., Petersen, B. C., Blinder, A. S., Poterba, J. M., 1988. Financing constraints and corporate investment. Brookings Papers on Economic Activity 1, 141–206. Ferrando, A., K¨ ohler-Ulbrich, P., P´ al, R., 2007. Is the growth of euro area small and medium-sized enterprises constrained by financing barriers? Industrial Policy and Economic Reforms Papers (6).
CR IP T
Ferris, J. S., 1981. A transactions theory of trade credit use. Quarterly Journal of Economics 96 (2), 243–270.
Gama, A. P., Van Auken, H., 2015. The interdependence between trade credit and bank lending: Commitment in intermediary firm relationships. Journal of Small Business Management 53 (4), 886–904. Garcia-Appendini, E., Montoriol-Garriga, J., 2013. Firms as liquidity providers: Evidence from the 2007 -
AN US
2008 financial crisis. Journal of Financial Economics 109 (1), 272–291.
Graham, J. R., Harvey, C. R., 2001. The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics 60 (2-3), 187–243.
Guariglia, A., Mateut, S., 2006. Credit channel, trade credit channel, and inventory investment: Evidence from a panel of UK firms. Journal of Banking & Finance 30 (10), 2835–2856. Hadlock, C. J., Pierce, J. R., 2010. New evidence on measuring financial constraints: Moving beyond the
M
KZ index. Review of Financial Studies 23 (5), 1909–1940.
Hovakimian, G., Titman, S., 2006. Corporate investment with financial constraints: Sensitivity of invest-
ED
ment to funds from voluntary asset sales. Journal of Money, Credit and Banking 38 (2), 357–374. Hu, X., Schiantarelli, F., 1998. Investment and capital market imperfections: A switching regression approach using U.S. firm panel data. Review of Economics and Statistics 80 (3), 466–479.
PT
Illueca, M., Norden, L., van Kampen, S., 2016. Substitution effects in private debt: Evidence from SMEs. SSRN Working Paper, 1–51.
CE
Ivashina, V., Scharfstein, D., 2010. Bank lending during the financial crisis of 2008. Journal of Financial Economics 97 (3), 319–338. Kaplan, S. N., Zingales, L., 1997. Do investment-cash flow sensitivities provide useful measures of financing
AC
constraints? Quarterly Journal of Economics 112 (1), 169–215. Kestens, K., Van Cauwenberge, P., Bauwhede, H. V., 2012. Trade credit and company performance during the 2008 financial crisis. Accounting & Finance 52 (4), 1125–1151.
Klapper, L., Laeven, L., Rajan, R., 2012. Trade credit contracts. Review of Financial Studies 25 (3), 838–867. Klepsch, C., Szabo, T., 2016. Financial constraints of private SMEs in Germany: Measurement and consequences on corporate policies. SSRN Working Paper, 1–41. Love, I., Preve, L. A., Sarria-Allende, V., 2007. Trade credit and bank credit: Evidence from recent financial crises. Journal of Financial Economics 83 (2), 453–469.
45
ACCEPTED MANUSCRIPT
Love, I., Zaidi, R., 2010. Trade credit, bank credit and financial crisis. International Review of Finance 10 (1), 125–147. Maddala, G. S., 1986. Disequilibrium, self-selection, and switching models. Handbook of Econometrics III, 1634–1688. Marotta, G., 2005. When do trade credit discounts matter? Evidence from Italian firm-level data. Applied Economics 37 (4), 403–416.
Small Business Economics 42 (3), 561–577.
CR IP T
Mart´ınez-Sola, C., Garc´ıa-Teruel, P. J., Mart´ınez-Solano, P., 2014. Trade credit and SME profitability.
McGuinness, G., Hogan, T., 2016. Bank credit and trade credit: Evidence from SMEs over the financial crisis. International Small Business Journal 34 (4), 412–445.
McMillan, J., Woodruff, C., 1999. Interfirm relationships and informal credit. Quarterly Journal of Eco-
AN US
nomics 114 (4), 1285–1320.
Meltzer, A. H., 1960. Mercantile credit, monetary policy, and size of firms. Review of Economics and Statistics 42 (4), 429–437.
Mizen, P., Vermeulen, P., 2005. Corporate investment and cash flow sensitivity: What drives the relationship? ECB Working Papers (485).
Molina, C. A., Preve, L. A., 2012. An empirical analysis of the effect of financial distress on trade credit.
M
Financial Management 41 (1), 187–205.
Mulier, K., Schoors, K., Merlevede, B., 2016. Investment-cash flow sensitivity and financial constraints:
ED
Evidence from unquoted European SMEs. Journal of Banking & Finance 73, 182–197. Mundlak, Y., 1978. On the pooling of time series and cross section data. Econometrica 46 (1), 69–85. Murfin, J., Njoroge, K., 2015. The implicit costs of trade credit borrowing by large firms. Review of
PT
Financial Studies 28 (1), 112–145.
Nilsen, J. H., 2002. Trade credit and the bank lending channel. Journal of Money, Credit and Banking
CE
34 (1), 226–253.
Norrbin, S. C., Reffett, K. L., 1995. Trade credit in a monetary economy. Journal of Monetary Economics 35 (3), 413–430.
AC
Papke, L. E., Wooldridge, J. M., 1996. Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics 11 (6), 619–632.
Papke, L. E., Wooldridge, J. M., 2008. Panel data methods for fractional response variables with an application to test pass rates. Journal of Econometrics 145 (1-2), 121–133.
Petersen, M. A., Rajan, R. G., 1997. Trade credit: Theories and evidence. Review of Financial Studies 10 (3), 661–691. Rajan, R. G., Zingales, L., 1998. Financial dependence and growth. American Economic Review 88 (3), 559–586. Roodman, D., 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. Stata
46
ACCEPTED MANUSCRIPT
Journal 9 (1), 86–136. Sufi, A., 2009. Bank lines of credit in corporate finance: An empirical analysis. Review of Financial Studies 22 (3), 1057–1088. Taketa, K., Udell, G. F., 2007. Lending channels and financial shocks: The case of small and medium-sized enterprise trade credit and the Japanese banking crisis. Monetary and Economic Studies 25 (2), 1–44. Whited, T. M., Wu, G., 2006. Financial constraints risk. Review of Financial Studies 19 (2), 531–559.
AC
CE
PT
ED
M
AN US
CR IP T
Zimmerman, F., 1998. Switching regressions. Stata Technical Bulletin (45), 30–33.
47
Firm size effects in trade credit supply and demand Jochen Lawrenz, Julia Oberndorfer PII: DOI: Reference:
S0378-4266(18)30114-6 10.1016/j.jbankfin.2018.05.014 JBF 5356
To appear in:
Journal of Banking and Finance
Received date: Revised date: Accepted date:
14 September 2017 19 April 2018 15 May 2018
Please cite this article as: Jochen Lawrenz, Julia Oberndorfer, Firm size effects in trade credit supply and demand, Journal of Banking and Finance (2018), doi: 10.1016/j.jbankfin.2018.05.014
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Firm size effects in trade credit supply and demand✩ Jochen Lawrenza , Julia Oberndorfera,∗ Department of Banking and Finance, University of Innsbruck, Universit¨ atsstraße 15, 6020 Innsbruck, Austria
CR IP T
a
Abstract
By investigating trade credit usage among SMEs and large companies following the macroeconomic shock of the financial crisis of 2007/08, we identify a firm size effect, which is
AN US
genuine in the sense that it cannot be entirely explained by financial constraints, external finance dependence or creditworthiness. We find that (i) SMEs, in contrast to large firms, do not display evidence for the inter-firm liquidity redistribution hypothesis. Especially large vulnerable firms did cut down trade credit provision to the detriment of small vulnerable firms. (ii) We document a general substitution effect between bank and trade credit
M
and show that it has strengthened during the crisis among large firms, but not among SMEs. (iii) We provide evidence that the shift in trade credit financing had adverse real
ED
effects on investment behaviour of SMEs.
PT
JEL classification: D22; G01; G20; G32
AC
CE
Keywords: trade credit, financial crisis, SMEs, redistribution, substitution
✩
We thank three anonymous referees for very helpful remarks and suggestions. We are grateful to Vidhan Goyal for an extensive discussion and his valuable comments at the DGF 2016 Conference in Bonn. We also thank Emilia Garcia-Appendini for her useful comments. We are grateful to Irene Riccabona for excellent research assistance on a previous draft. Further we thank Detlev Hummel, Hendrik Scholz and Martin Wallmeier for their comments at the International PhD-Seminar in Banking 2016 at the Friedrich-Alexander University Erlangen-N¨ urnberg. We particularly thank Matthias Bank, Alexander Kupfer, Michael Munsch, as well as numerous seminar and conference participants for their helpful comments. We gratefully acknowledge the data support from Creditreform AG. Julia Oberndorfer gratefully acknowledges the financial support from the dissertation grant provided by Creditreform Rating AG and Creditreform Wirtschaftsauskunftei Kubicki KG. All remaining errors are our own. ∗ Corresponding author. Tel./fax: +43 512 507 73102/ +43 512 507 73199. Email addresses: [email protected] (Jochen Lawrenz), [email protected] (Julia Oberndorfer) Preprint submitted to Journal of Banking and Finance
ACCEPTED MANUSCRIPT
1. Introduction Trade credit, besides bank debt, is one of the most important instruments of external financing for a majority of firms, in particular smaller-sized companies. However, since comprehensive datasets on small and medium-sized enterprises (SMEs) are usually difficult
CR IP T
to obtain, little is still known about trade credit policy in this market segment and about the cross-firm distributional effect. In this paper, we investigate the redistribution and substitution role of trade credit financing during the recent financial crisis with a major focus on the differential behavior between SMEs and large sized firms, to which we will refer as ’firm size effect’ for short.1 Differences with respect to financial behavior between large
AN US
companies and SMEs have been documented in several studies for reasons of organisational structure, financial expertise, priority of financial motives or institutional factors (see e.g. Beck et al., 2008; Graham and Harvey, 2001).
Considering trade credit usage, several recent papers put special emphasis on the SME
M
segment (see e.g. Mart´ınez-Sola et al., 2014; Gama and Van Auken, 2015; Carb´o-Valverde et al., 2016; McGuinness and Hogan, 2016). McMillan and Woodruff (1999) and Marotta
ED
(2005) report that small, financially constrained firms provide trade credit, though their empirical evidence is based on rather small samples of survey data. Interest in firm size
PT
effects is warranted by e.g. the results in Klapper et al. (2012) or Fabbri and Klapper (2016), who document that large companies are more likely to enjoy greater bargaining
CE
power in customer-supplier relationships with smaller companies. If a size effect existed, it would have an impact on results constructed from matched supplier-client samples, such
AC
as e.g. in Garcia-Appendini and Montoriol-Garriga (2013). The identification of supplierclient relationships introduces a mechanical size bias, as suppliers tend to be substantially smaller in asset size than their clients.2 Garcia-Appendini and Montoriol-Garriga (2013)
show that “credit flows from the most liquid firms to the most constrained ones”. However, 1
We explicitly state that we use the ’size effect’ notion in the context of trade credit usage, which is not to be confused with the size effect related to average stock returns. 2 Garcia-Appendini and Montoriol-Garriga (2013) identify supplier-client relationships through the Financial Accounting Standards FAS 14 and 131, which require public firms to report the identity of customers whose purchases represent more than 10% of the firm’ s total annual sales. Evidently, matched pairs will have a bias towards identifying small suppliers to large customers. Descriptive statistics in Garcia-Appendini and Montoriol-Garriga (2013) confirm that within the matched sample, suppliers have
1
ACCEPTED MANUSCRIPT
the size imbalance in matched pairs leaves open the possibility that the identified credit flow is not only driven by the extent of financial constraints but also by firm size and in turn motivates closer inspection of firm size effects. With our paper, we aim at identifying firm size differences in trade credit behavior
CR IP T
during the financial crisis of 2007/08 on the basis of a comprehensive German dataset.3 We contribute to the understanding of trade credit usage by identifying a significant firm size effect, which we label as genuine or self-contained, since (i) it is consistent across various definitions of firm size, (ii) it is not entirely explained by controlling for usual firm fundamentals such as financial constraints, external finance dependence, or credit-
AN US
worthiness, for which size is commonly used as proxy, and (iii) it cannot be replicated by conditioning the sample on these fundamental variables alone. Thus, we show that our results cannot easily be dismissed by pointing towards an omitted-variable bias. More specifically, our study contributes to the trade credit literature along different dimensions. We first point out that in response to the 2007/08 crisis, the aggregate net
M
supply of trade credit did decrease. By conditioning on measures of pre-crisis financial vulnerability, we can attribute the decline to a supply effect, consistent with the inter-firm
ED
liquidity redistribution explanation as outlined by Love et al. (2007) and Garcia-Appendini and Montoriol-Garriga (2013). We complement the recent empirical work, as we do not
PT
merely analyze large companies but also SMEs. To our knowledge, only very limited cross-sectional data on the distribution of firm size exist. Importantly, after controlling
CE
for different firm fundamentals we find both, the decline in net supply as well as the evidence for redistribution, to be significantly less pronounced among SMEs, suggesting
AC
that the adverse economic shock of the financial crisis was propagated along the trade credit supply channel more strongly by large firms and to the detriment of small vulnerable firms. Second, we investigate the relative importance of bank debt and trade credit and confirm a general substitution effect in line with Petersen and Rajan (1997), Nilsen (2002)
median asset values of 512 Mio US-$, while customers median asset value is 28,019 Mio US-$, i.e. roughly 50 times larger. 3 It is broadly recognized that the negative supply shock caused by the 2007/08 crisis is an exogenous event which is an ideal natural experiment to analyze the usage of trade credit. See e.g. Garcia-Appendini and Montoriol-Garriga (2013), Ivashina and Scharfstein (2010).
2
ACCEPTED MANUSCRIPT
or Choi and Kim (2005). Our paper builds on this research. Rather than concentrating on different size samples like Choi and Kim (2005), we use interaction terms and thus, are able to establish sadistically significant differences. Furthermore, we again control for various fundamental firms characteristics and apply several regression models to show,
CR IP T
that our results remain consistent. Similar to Choi and Kim (2005) we find, that the use of trade credit as a substitute for bank debt during the crisis years turns out to be stronger for large firms than for SMEs. Against the background of a significant decline in bank debt (which was even stronger among SMEs), this result suggests that in contrast to large firms, SMEs were less able to compensate declining bank financing by trade credit.
AN US
We further analyze the direction of causality in the substitution effect and find evidence that it is more likely that accounts payable adapt to a given change in bank debt rather than vice versa. Finally, we provide evidence on the impact of a shift in financing patterns on the real economy by investigating investment activity with respect to differences in firm size. Thus, our research complements the work by Carb´o-Valverde et al. (2016), who
M
analyze investment-trade credit sensitivities during the recent financial crisis among credit constrained and unconstrained SMEs. We find a positive dependence between investment
ED
and accounts payable among SMEs but not among large firms. Coupled with our previous finding that SMEs (in contrast to large firms) were not able to compensate declining bank
PT
financing through increased trade credit, this result identifies a channel by which trade credit financing affects real economic activity of SMEs.
CE
Overall, given that size is usually taken to be a proxy for other not directly observable firm characteristics, our analysis puts a special emphasis on not succumbing to an omitted
AC
variable bias and on investigating in detail the economic driver behind the size effect. To this end, we use measures of (i) being financially constrained from a regime-switching regression in line with the approach of Hu and Schiantarelli (1998), Hovakimian and Titman (2006), and Almeida and Campello (2007), (ii) external finance dependence from industry-specific data according to Rajan and Zingales (1998), and (iii) creditworthiness by using the classic approach of Altman’s Z-score (Altman, 1968). Adding these measures to our analyses, we find that neither of them can fully explain the size effect within the 3
ACCEPTED MANUSCRIPT
redistribution as well as the substitution hypothesis. The paper proceeds as follows. In Section 2 we present empirical hypotheses derived from the related literature, the empirical strategy and our dataset. Section 3 illustrates
AC
CE
PT
ED
M
AN US
CR IP T
empirical results. Finally, Section 4 concludes.
4
ACCEPTED MANUSCRIPT
2. Empirical hypotheses, strategy and data 2.1. Empirical hypotheses and related literature Motivated by the evidence in Love et al. (2007) and Garcia-Appendini and MontoriolGarriga (2013), we first want to test whether the change in net trade credit supply in
CR IP T
response to the 2007/08 crisis is significantly different between SMEs and large companies and if this size effect is genuine in the sense of not being explained by financial constraints, external finance dependence or creditworthiness. We summarize this hypothesis as:
H1 (Size effect in net supply): The decline in trade credit net supply in response to the 2007/08 financial crisis is less pronounced among SMEs as compared to large firms. This
AN US
size effect is genuine in the sense defined above.
The trade credit literature commonly distinguishes between the redistributive and the substitutive role of trade credit. On the one hand, it is intuitively straightforward to expect that firms’ willingness to extend trade credit to customers depends on their own economic
M
and financial situation. This reasoning is at the core of the redistribution explanation and goes back to Meltzer (1960). While large, liquid firms are more inclined to redistribute
ED
and supply parts of their assets via trade credit to smaller firms when the overall money supply tightens, financially constrained firms tend to shut down redistribution (see e.g.
PT
Meltzer, 1960; Calomiris et al., 1995; Love et al., 2007; Taketa and Udell, 2007; Love and Zaidi, 2010; Garcia-Appendini and Montoriol-Garriga, 2013). Due to the fact that
CE
comprehensive data on SMEs is scarce, there is little evidence in the literature so far with respect to the behavior of SMEs. McMillan and Woodruff (1999) and Marotta (2005)
AC
document that small, financially constrained firms provide trade credit to their customers, though they investigate rather small samples of survey data. A more recent contribution on a sample of Irish SMEs by McGuinness and Hogan (2016) confirms the redistribution effect but does not discuss size effects. Our second set of tests is therefore directed towards the identification of size effects in the redistributive role (supply effect) of trade credit, which we summarize as follows: H2a (Redistribution): The decrease in trade credit provision is more pronounced among firms that were more vulnerable prior to the 2007/08 financial crisis. 5
ACCEPTED MANUSCRIPT
H2b (Size effect in redistribution): Larger firms show stronger evidence for redistribution in the sense of H2a and this size effect is genuine. On the other hand, it is also economically plausible to expect that trade credit represents a substitute to bank financing. Authors like Ferris (1981) and Norrbin and Reffett
CR IP T
(1995), Petersen and Rajan (1997), Nilsen (2002), Choi and Kim (2005), or Guariglia and Mateut (2006) show that primarily small firms with limited access to capital markets substitute unavailable bank credit with increased trade credit during times of tight money. More recent support for the substitution view comes from Molina and Preve (2012) who show that predominantly smaller firms, with less market power, tend to use trade credit
AN US
as substitute for restricted bank financing during financial distress. Gama and Van Auken (2015) argue that SMEs substitute short-term bank debt with trade credit to escape expensive hold up costs charged by their single relationship bank. The evidence from this literature, however, conflicts with contributions by Klapper et al. (2012) and Murfin and Njoroge (2015), who document that the largest and most creditworthy borrowers receive
M
the most expedient trade credit covenants with the longest maturities from rather smaller suppliers. From these observations, our third set of tests will focus on size effects in the
ED
substitutive role of trade credit. More precisely, we formulate the following hypotheses: H3a (Substitution): The relative importance of trade credit versus bank debt has in-
PT
creased in response to the crisis.
H3b (Size effect in substitution): Large firms were better able to substitute bank debt
CE
with trade credit in the years following the crisis. Finally, to test whether the change in trade credit provision and usage during the
AC
financial crisis had any consequences on the real effects, we follow Carb´o-Valverde et al. (2016), who focus on investment activity. They argue that credit constrained SMEs depend on trade credit, but not on bank debt, while the intensity of dependence increased during the crisis. Besides, Murfin and Njoroge (2015) examine real effects and detect negative repercussions on small suppliers’ investment activity, when large customers extend trade payables. Considering these studies, we state hypothesis H4 as: H4 (Real effects): The decline in trade credit usage is negatively related to investment 6
ACCEPTED MANUSCRIPT
activity. 2.2. Empirical strategy 2.2.1. Variables description This section provides the description of the main economic variables used. A summary is
CR IP T
provided in Table A1 in Appendix A. Dependent variables
Our main dependent variables with respect to the supply and redistribution effect are accounts receivable divided by daily total sales (labelled as AR), accounts payable divided
AN US
by daily costs of goods sold (AP) and net trade credit divided by daily total sales (NET). Following the standard literature, we scale the variables by flow variables to account for shrinkages of economic activity that are usually linked to crises. Besides, the variables can be interpreted as the payment period offered by the firm in terms of days, as the payment
M
period used by the firm for its bought inputs and as the firm’s relative readiness to provide trade credit net of its own trade credit usage, respectively (Love et al., 2007).
ED
Considering the substitution effect, we use the ratio of short-term bank debt to the sum of short-term bank debt and accounts payable (BtoT) as dependent variable. The
PT
construction of BtoT is analogous to Sufi (2009), Campello et al. (2011) and Acharya et al. (2013). Campello et al. (2011) for instance define the ratio of bank credit line to bank
CE
credit line plus cash as dependent variable in a study which analyzes how firms managed liquidity during the financial crisis and how they substituted between bank credit lines
AC
and cash and profits. A change in BtoT captures a shift in the relative importance of short-term bank debt versus accounts payable as financing source and therefore provides evidence on the substitution between bank debt and trade credit. In further regressions, see Section 3.3, we apply accounts payable to total assets (APA ), short-term bank debt to total assets (SB) and a dummy (S) that is one in case of substitution following an increase/ decrease in short-term bank debt in the prior year as dependent variables. When measuring real effects, we use capital expenditures over lagged capital (CAPEX) and return on assets (ROA) as dependent variables. 7
ACCEPTED MANUSCRIPT
Main explanatory variables The main (explanatory) variables of interest are CRISIS and its interaction with CONS. The crisis dummy is defined as being one in case of the crisis years 2007 to 2010 and should offer valuable clues about the change in trade credit usage and provision during crisis.
CR IP T
Garcia-Appendini and Montoriol-Garriga (2013) set the crisis time from the third quarter of 2007 to the second quarter of 2008. However, we are interested in the firms’ post-crisis behavior as well. In a robustness check, we use the lagged growth rate of the three months Euribor to control whether our chosen crisis period is adequate and conforms to the central bank’s response to the ongoing recession. CONS is a generic constraint dummy, for which
AN US
we substitute either size proxies (SM E, T A, M S, SA as defined below) or other firm characteristics (F C, EF D, Z) depending on the specification. CONS should illustrate differences in trade credit usage and provision between firm size classes during the crisis and whether these differences can be attributed to fundamental firm characteristics like
M
financial constraints, external finance dependence or creditworthiness. To examine size effects carefully, we generate various alternative size measures. (i) Our
ED
main size measure (SM E) follows the legal definition of small and medium-sized enterprises on the basis of their number of employees, total assets and total sales. According to
PT
§267 of the German Commercial Code, firms are classified as SME if two of the following three criteria apply, respectively: Employees smaller or equal to 250, total assets smaller
CE
or equal to 19.25M and total sales smaller or equal to 38.5M. If a sample firm is identified as SME in more than half of its yearly observations, then we label it as SME. (ii) Alternatively, we sort our sample on total assets (TA). Mitigating endogeneity issues we
AC
use each firm’s three years pre-crisis average of total assets from 2004 to 2006, estimate the sample median and tag firms to be small if their pre-crisis asset average lies below the median (Duchin et al., 2010). (iii) In the same way, we sort our sample on market share (MS) which we determine as the ratio of a firm’s total sales to the total two-digit industry output as provided by Eurostat and which we compare to the industry median. (iv) Finally, we follow Hadlock and Pierce (2010) and use the size-age index (SA) of German
firms estimated by Klepsch and Szabo (2016). Again, we calculate the sample median of 8
ACCEPTED MANUSCRIPT
the firm’s pre-crisis average between 2004 and 2006 of the size-age index and identify a firm to be small if its pre-crisis average lies above the sample median.4 Size is often related to fundamental firm characteristics like financial constraints, external finance dependence or creditworthiness. To avoid an omitted variable bias, we
CR IP T
construct measures to control for these characteristics and to assess if the size effect is genuine, i.e. if these characteristics do not drive out its explanatory power. (i) Quantifying financial constraints (FC), we obey the argument in Carb´o-Valverde et al. (2016) that frequently used measures like the Kaplan and Zingales (1997) index, Whited and Wu (2006) index, or the payout ratio are not well suited for unlisted, small firms. We
AN US
therefore follow Almeida and Campello (2007) and Hovakimian and Titman (2006) and implement an endogenous switching regression with unknown sample selection to identify financially constrained and unconstrained firms. We use the perceived probabilities of being financially constrained and calculate the three years pre-crisis average for each firm, while we define a firm to be constrained if its average lies above the overall sample me-
M
dian and unconstrained otherwise. Details on the estimation of the endogenous switching regression are provided in Appendix B. (ii) We next build upon Garcia-Appendini and
ED
Montoriol-Garriga (2013) and consider industry-level measures of external finance dependence (EFD) as in Rajan and Zingales (1998). Using the time-invariant EFD index has
PT
the benefit that it is a strictly exogenous measure, since it is specified as the proportion of capital expenditures in excess of cash flows among European listed firms of the same
CE
two-digit industry. While a negative index denotes that the cash flow within an industry is more than sufficient to fund investments, a positive index means that external finance
AC
is necessary.5 (iii) As proxy for creditworthiness (Z), we use the classic Altman (1968)
4 The size-age index for each firm year observation is calculated according to Klepsch and Szabo (2016) as follows: SAi,t = −1.181 × log(Assetsi,t ) + 0.027 × log(Assets2i,t ) − 0.008 × Agei,t . 5 We follow Ferrando et al. (2007) and use the Thomson Financial DataStream database to download data on capital expenditures and funds from operations of listed firms from 15 European countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain and Sweden) in the period from 1980 to 2006. We sum each firm’s capital expenditures and cash flows over the years being in the sample and calculate the external finance dependence afterwards:
EF Di =
T P
t=1
Capexi,t − T P
t=1
T P
t=1
Cashf lowi,t
. Finally, to receive an industry-specific index, we calculate industry
Capexi,t
medians at the two-digit SIC-code and merge them to our two-digit NACE2-code.
9
ACCEPTED MANUSCRIPT
Z-score for non-listed firms.6 The size effect can be interpreted as genuine and consistent if (i) the same results cannot be replicated by substituting F C, EF D, and Z for CONS, or if (ii) we add the log-transformed probability of being financially constrained (F C) or the negative Z-score (Z) as additional control variables. Note, that we cannot include
CR IP T
EF D as control variable, as it is time invariant and will be absorbed by the fixed effect. Control variables
As independent control variables we use cash (CS), operating cash flow (OCF), sales growth (SG), equity (EQ), fixed assets labeled as tangible assets (TAA) and age (AGE).
AN US
To avoid an endogeneity and simultaneity bias, we lag all control variables except age by one period (t-1) and scale all control variables except sales growth and age by total assets. Furthermore, we add firm (αi ) and year fixed effects (νt ), which allows us to segregate crisis implications from pre-crisis ones, account for unobserved heterogeneity (Love et al.,
2.2.2. Empirical methodology
ED
Crisis impact
M
2007) and control for business cycle fluctuations and other life-cycle effects.
The focus of our research agenda is on the identification of a firm size effect during the
PT
financial crisis. Thus, to specify and test hypothesis H1, we first analyze the overall
CE
development of trade credit supply and usage during the crisis with respect to size:
(1)
AC
TCi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t ,
where the dependent variable TC will be replaced by either AR, AP or NET. Besides, CRISIS is the crisis dummy, CONS is the generic constraint dummy, X is the vector of control variables, α is a firm fixed effect, ν is a year fixed effect and is an error term. As CONS is time invariant, the coefficient of CONS is absorbed by the firm fixed effect. 6
The Altman Z-score for each firm year observation is calculated as follows: Zi,t = 0.717 × RetainedEarningsi,t EBIT Equity Sales + 0.847 × + 3.107 × Assetsi,t + 0.420 × Debti,ti,t + 0.998 × Assetsi,t . Assetsi,t i,t i,t Working capital is defined as current assets net short-term liabilities. W orkingCapitali,t Assetsi,t
10
ACCEPTED MANUSCRIPT
In terms of hypothesis H1, we expect coefficient β1 to be negative and the interaction term β2 to be positive when deploying size proxies for CONS and net supply for the dependent variable. Redistribution effect
CR IP T
First, to determine if the change in trade credit is due to a supply or demand effect, we build upon Love et al. (2007) and more recently Garcia-Appendini and MontoriolGarriga (2013) by employing a similar identification strategy. They disentangle supply and demand effects by interacting pre-crisis vulnerability measures with crisis year dummies.
AN US
The empirical strategy is similar to a diff-in-diff model. See Duchin et al. (2010), p. 424ff, or Garcia-Appendini and Montoriol-Garriga (2013) p. 275. Theory states that companies relying more on short-term debt (STD) in the pre-crisis period would suffer to an increased degree during crisis due to refinancing risk and thus, may cut back trade credit provision. Similar reasons account for illiquid firms with low cash ratios (CS) and internal funds like
M
operating cash flow (OCF). If the decrease in trade credit provision is stronger among vulnerable firms – either having more pre-crisis short-term debt or low cash holdings or
ED
operating cash flow – a supply effect, consistent with the inter-firm liquidity redistribution, can be confirmed. In addition, we also use long-term oriented variables such as the equity
PT
ratio (EQ) or the fraction of fixed assets (TAA). The leverage of a firm is a classical credit risk metric, and tangible fixed assets are well known to capture the extent to which assets
CE
can be pledged and thus, proxy the availability of credit (Carb´o-Valverde et al., 2016). By applying these variables we extend and complement the approach by Love et al. (2007).
AC
The estimation model for hypothesis H2a is therefore:
TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + γXi,t + νt + i,t ,
(2)
where the coefficient of interest is the interaction between the crisis dummy and the measures of average pre-crisis financial vulnerability, VULi,pre , which is the generic label for the above-mentioned vulnerability proxies (CS, OCF, STD, EQ, TAA). For H2a (i.e.
11
ACCEPTED MANUSCRIPT
the supply effect) to hold, we expect β2 to be significantly positive for CS, OCF, EQ, TAA and significantly negative for STD with respect to accounts receivable. Due to the within estimator it is impossible to consider the time invariant financial standing VULi,pre as a separate variable. The default definition is to determine VULi,pre as the firm’s average
CR IP T
values over the pre-crisis years 2004 to 2006. In robustness checks, we also measured the financial position one year prior to the financial crisis, i.e. in 2006. Qualitatively, our results remain stable. Again, this method is used to mitigate endogeneity, since changes in a firm’s financial position may be related to unobserved changes in its trade credit provision and usage as the crisis bursts (Love et al., 2007).
AN US
To investigate firm size effects and test H2b, we use the various size proxies (i.e. SME, TA, MS, and SA) and add them as interaction terms (generically labelled as CONS) in Equation (2). This specification leads among others to a triple interaction term (CRISISt × VULi,pre × CONSi,pre ), which allows to statistically verify if the redistribution effect is different across firm size. Consequently, if the coefficient of the triple interaction term
M
is of opposite sign as β2 , H2b holds. An alternative approach is to split the sample according to the size measure and run separate regressions in both sub-samples in order
ED
to allow for different levels of control variables. We find qualitatively unchanged results and therefore opt to report results from the interaction term approach as it makes the statistical
PT
significance obvious.In further robustness checks, we interact all independent variables with
CE
the size dummy (financial firm characteristic). Our results remain unchanged. Substitution effect
AC
To test the substitutive role of trade credit (H3a and H3b), our identification strategy is basically threefold. First, we regress BtoT on the crisis dummy and the various size
measures. As BtoT might be nonlinear regarding the boundaries of the ratio, we apply a nonlinear fractional logit response model with quasi-maximum likelihood first suggested by Papke and Wooldridge (1996). In order to account for fixed effects, we follow a more recent paper of Papke and Wooldridge (2008) and convert our unbalanced panel into a strongly balanced one and add for each independent variable its time mean to estimate 12
ACCEPTED MANUSCRIPT
average partial effects and to establish a fixed effects model, as recommended by Mundlak (1978). However, due to the fact that the totally balanced sample reduces observations substantially, we also account for the non-linearity by log-transforming the dependent variable and run linear fixed effects regressions on the unbalanced sample. Furthermore,
CR IP T
in order to control for a potential (mechanical) impact of accounts receivable, we follow Agostino and Trivieri (2014) and include the growth rates of the accounts receivable to sales ratio and the working capital to assets ratio as control variables.7 For H3a to hold the coefficient of the crisis dummy should be significantly negative indicating a relative shift in importance towards trade credit. Furthermore, for H3b to hold, the interaction term
AN US
between the crisis dummy and the size dummy should be significantly positive implying that smaller companies were less able to substitute the decrease in bank debt with trade credit.
In our second test, we regress AP on short-term bank debt SB (see e.g. Kestens et al.,
M
2012). The model is defined as follows:
TCi,t =αi + β1 CRISISt + β2 SBi,t + β3 (CRISISt × SBi,t ) + β4 (CRISISt × CONSi,pre )
ED
+ β5 (SBt × CONSi,pre ) + β6 (CRISISt × SBi,t × CONSi,pre ) + γXi,t + νt + i,t .
PT
(3)
If the substitution theory for trade payables was descriptive among large firms in pre-
CE
crisis times, then the coefficient of short-term bank debt (i.e. β2 ) should be negative. A significant and negative β3 coefficient would confirm that the financial crisis increased the substitutive effect among large firms. In contrast, a significant negative β5 would imply
AC
that the substitution effect is stronger among small firms during pre-crisis times, while a significant positive β6 would be evidence that small firms were less able to increase
substitution in order to countervail the decline in bank debt with trade credit during crisis and in line with H3b. To further investigate the direction of causality, we follow Carb´o7
As pointed out by an anonymous referee, a change in accounts receivable or other current assets might affect both accounts payable and short-term bank debt simultaneously due to altered financing needs and thus our negative effect during crisis might be caused by a mechanical relation. In unreported robustness checks, we form decile portfolios conditional on the within deviation of accounts receivable and current assets to control for the mechanical effect. Our results remain stable.
13
ACCEPTED MANUSCRIPT
Valverde et al. (2016) and complement this test by a specification where we consider bank debt to be the dependent and accounts payable to be the independent variable. If shortterm bank debt turns out to be significant, while accounts payable are not, this would provide evidence that the causality goes from short-term bank debt to accounts payable
CR IP T
(Carb´o-Valverde et al., 2016). However, since this approach may suffer from the fact that contemporaneous debt levels might be endogenous, we additionally use the approach by Illueca et al. (2016) and run a conditional fixed effects logistic regression where the dependent variable S is a dummy that is one if conditional on a decrease in short-term bank debt in the previous year accounts
AN US
payable increase in the current year, i.e. in case of substitution. This approach allows to determine the direction of causality by choosing either a change in bank debt or in accounts payable as the conditioning event. Real effects
M
To investigate whether the change in trade credit usage and provision affected the real economy during the financial crisis and to test H4, we consider investment-trade credit
ED
and profitability-trade credit sensitivities. We are interested whether trade credit explains investment and profitability. Usually investment is modeled by the structural Q-model.
PT
However, as Tobin’s Q cannot be constructed, we use an error correction model like Bond et al. (2003), Mizen and Vermeulen (2005), Mulier et al. (2016) and Buca and Vermeulen
CE
(2017). Although the error-correction model assumes perfect capital markets to hold, i.e. a firm’s investment decision should not be sensitive to financing sources and its financial
AC
constraint status, we add financial variables as explanatory variables – either trade credit or short-term bank debt. Our data sample mainly includes small unlisted firms, which do not have unrestricted access to capital markets and bank loans, particularly during crisis times (Carb´ o-Valverde et al., 2016). We assume the decline in trade credit usage during the crisis to be negatively related to the investment activity of small unlisted firms, as these firms highly depend on trade credit.8 For H4 to hold, we should find a positive 8
For further details on the model see Appendix C.
14
ACCEPTED MANUSCRIPT
investment-trade credit sensitivity, especially among SMEs and no effect for large firms, while the effect should be contrary with respect to short-term bank debt. Finally, we analyze how trade credit provision influenced performance during the crisis. We follow Garcia-Appendini and Montoriol-Garriga (2013) and regress return on assets on accounts
GMM estimation. 2.3. Data
CR IP T
receivable and its interaction with the crisis dummy by means of a two-step first-difference
We obtain access to the database of Creditreform AG, which provides the most extensive coverage of annual balance sheet data of active companies registered in Germany.9 The
AN US
time frame we observe lasts from 2003 to 2010.
The raw database includes 357,107 observations on 120,771 firms and is as such one of the largest samples in the trade credit literature so far. In order to mitigate misspecification, we delete observations with negative values of accounts receivable, accounts payable,
M
total assets, total sales, and so on. Moreover, we replace our main variables of interest (accounts receivable, accounts payable, short-term bank debt) with missings if they are
ED
smaller than one, as zeros in the data can mean both either missing or zero. Furthermore, we set observations to missings if accounts receivable and payable as well as net accounts
PT
receivable are outstanding for more than one year to investigate the common short-term trade credit. Companies in the banking, finance, insurance and real estate sector as well
CE
as utilities are also eliminated due to discrepancies in their balance sheets and income statements. Finally, we just keep firms where at least two consecutive observations are
AC
available. In order to alleviate the contingent effect of outliers we winsorize all descriptive and control variables at the one percentile in both tails and the trade credit measures regarding the left tail. After data cleansing and filters, we end up with an unbalanced sample containing 107,847 firm year observations on 29,113 firms. Depending on the dependent and independent variables, the number of observations in various specifications can differ. Table 1 provides summary statistics. 9 Creditreform AG is a holding company whose core competence is business intelligence services such as creditworthiness assessments and receivables management.
15
ACCEPTED MANUSCRIPT
Table 1: Summary statistics All
Large
Median
Std.Dev.
N
Mean
Median
Std.Dev.
N
Mean
Median
Std.Dev.
N
35.598 28.008 11.406 0.452 0.138 0.141 0.110 0.103 0.139 0.278 0.254 2.984 51.512
30.636 20.744 10.599 0.449 0.086 0.079 0.050 0.087 0.038 0.237 0.181 2.890 9.644
29.807 28.200 34.393 0.296 0.149 0.163 0.147 0.145 0.738 0.220 0.236 0.869 118.330
107847 107847 107847 54332 107847 54332 107847 107847 107847 107847 107847 107847 107847
35.321 29.557 9.728 0.472 0.151 0.159 0.122 0.106 0.154 0.263 0.238 2.851 8.462
29.241 20.864 8.857 0.473 0.096 0.099 0.055 0.088 0.037 0.210 0.153 2.773 3.392
31.057 31.098 36.375 0.292 0.157 0.170 0.160 0.157 0.779 0.227 0.236 0.806 25.554
70717 70717 70717 31788 70717 31788 70717 70717 70717 70717 70717 70717 70717
36.126 25.056 14.601 0.405 0.112 0.105 0.088 0.097 0.109 0.306 0.286 3.238 133.504
33.365 20.575 14.012 0.385 0.071 0.048 0.042 0.085 0.039 0.279 0.236 3.178 60.447
27.260 21.323 30.003 0.291 0.129 0.136 0.116 0.117 0.652 0.204 0.234 0.927 170.805
37130 37130 37130 19165 37130 19165 37130 37130 37130 37130 37130 37130 37130
CR IP T
AR AP NET BtoT APA SB CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE A
SME
Mean
AC
CE
PT
ED
M
AN US
Notes: This table reports summary statistics for the whole sample and for SME and large firms, separately. We refer to Table A1 in Appendix A for details on the respective variables. A measures total assets in millions of Euro. The sizing of SME and large firms is classified according to §261 of the German Commercial Code. A firm is classified as SME if two of the following three criteria apply, respectively, in more than half of its yearly observations: employees smaller or equal to 250, total assets smaller or equal to 19.25M and total sales smaller or equal to 38.5M. Superscript A means that the variable is divided by total assets. Subscript t-1 means that the variable is lagged one period.
16
ACCEPTED MANUSCRIPT
3. Empirical results 3.1. Crisis impact Before turning our attention to hypothesis H1, we note that the unconditional effect of the crisis was a significant decline in net trade credit. On average, net supply did decrease
CR IP T
by roughly 3 days. Given that the average maturity of net trade credit in our data is approximately 11 days, the effect is not only statistically significant but also economically meaningful.10 The results confirm previous findings, which have shown a general shrinkage in trade credit during the financial crisis (see Love et al., 2007; Garcia-Appendini and
AN US
Montoriol-Garriga, 2013; Kestens et al., 2012). We further check the robustness of the crisis dummy definition by replacing it with the Euribor rate as alternative measure for the timing of the crisis and find similar results.11
We now address the main focus of our research agenda – the identification of a firm size effect. Table 2 summarizes results with respect to H1, which we test by running the
M
regression model from Equation (1). Panel A contains results for the four size proxies as conditioning variables (CONS). Columns 1–4 show that the coefficient on the interaction
ED
term (i.e. β2 in (1)) is highly significantly positive. While large firms reduced net supply by roughly 4 days, which amounts to more than one third of average net supply, SMEs
PT
did only reduce N ET by approximately 2.5 days representing merely one fifth of average net supply. Results are robust across various size measures (SME, TA, MS, SA) and both
CE
statistically and economically significant. To establish that the size effect is genuine and not driven by an omitted variable bias, we include F C, EF D, and Z to the analysis in two
AC
ways: First, Panel B uses them as conditioning variable (CONS) to check if similar results can be replicated. Second, in Panel C we add F C, and Z as additional control variable, in 10 We provide further information in Table A2 in Appendix A, which shows that the decline in net supply is mainly driven by the significant decline in accounts receivable by approximately 4 days, while the (negative) change in accounts payable is insignificant. 11 In an unreported robustness check, we control for other operating expenses to total sales, as we expect bad debt expenses, which are write-offs of unpaid accounts receivable and usually recorded in the income statement under other operating expenses, to be the driver of our results. Regarding other operating expenses we assume a simultaneous interlinkage with accounts receivable, as more write-offs of bad debt during crisis lead to a coincidental decrease of accounts receivable in the same fiscal year. However, including this control variable does not change our results.
17
ACCEPTED MANUSCRIPT
order to see if the size dummy remains in size and significance. Both ways, we find that the impact of size on net supply cannot be explained by other variables. We find significant negative coefficients on interaction terms in the case of Z and EFD, but no significant effect for FC. These findings are distinctly different from the size measures, since coefficients
CR IP T
are either of opposite sign or insignificant. By adding the negative Z-score (Z) and the log-transformed probability of being financially constrained (FC) as a control variable, we still obtain significant positive β2 coefficients in Panel C.12 The difference between large firms and SMEs is even larger when controlling for financial constraints. Overall, results from Table 2 confirm a genuine firm size effect in line with H1 in the aggregate net supply
AN US
of trade credit during the crisis years. SMEs did reduce net supply by about 1 to 2 days less than large firms and this result holds even after controlling for financial constraints, external finance dependence and creditworthiness.
Table 2 furthermore splits up net supply into changes of AR (Cols. 5–8) and AP (Cols. 9–12). Results reveal that the size effect in net supply is mainly driven by accounts
M
payable. In fact, from Columns 5–8, we find the interaction term (β2 coefficient) to be insignificant, indicating that both large as well as small companies have reduced AR to
ED
a similar extent by about 4 days. On the contrary, with AP as dependent variable, we find clear evidence that only SMEs have significantly reduced accounts payable by about
PT
two days, which accounts for almost 7% of the average trade credit used, which is 28 days. As with net supply, we check if the size effect in AP survives the inclusion of other
CE
conditioning variables. Panel B shows that EF D has the wrong sign, while F C and Z deliver the same sign as size but smaller in magnitude. More importantly, using F C and
AC
Z as control variables in Panel C shows that the significance on the interaction term is not driven away, thereby confirming a genuine size effect. Overall, the evidence in this section provides support for hypothesis H1, and shows
that the genuine size effect is primarily driven by accounts payable, i.e. by the use of trade 12
The corresponding control variable is indicated in the row labelled as ’Add. covariates’. Note that we use F C (Z) as variable name, although it is not the dummy but the continuous variable. See the table caption for details. Note further, that some firms in Column 1 (4, 7) in Panel B have no data on pre-crisis years and thus, we cannot calculate probabilities of being constrained. Consequently the number of observations changes with respect to specifications in Column 1 (3, 5) in Panel C.
18
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
credit. We attempt to disentangle demand and supply effects in the next section.
19
CR IP T
ACCEPTED MANUSCRIPT
Table 2: Trade credit changes conditional on size and financial characteristics Panel A: Size as conditioning variables (CONS) NET
CRISIS × CONS Covariates Adjusted R2 N
(3) MS
(4) SA
(5) SME
(6) TA
(7) MS
(8) SA
-3.941*** (0.689) 1.565*** (0.405)
-3.586*** (0.686) 1.211*** (0.428)
-3.606*** (0.677) 1.471*** (0.450)
-3.633*** (0.676) 1.413*** (0.436)
-4.008*** (0.624) 0.021 (0.357)
-3.905*** (0.613) -0.596 (0.373)
-3.895*** (0.607) -0.193 (0.387)
Yes 0.700 107847
Yes 0.701 102992
Yes 0.700 102038
Yes 0.701 102946
Yes 0.706 107847
Yes 0.706 102992
Yes 0.706 102038
Panel B: Financial characteristics as conditioning variables (CONS) NET
CRISIS × CONS
(2) EFD
(3) Z
-3.781*** (0.743) 0.478 (0.442)
-3.640*** (0.695) -2.837*** (0.816)
Yes 0.674 67364
Yes 0.699 106732
CRISIS CRISIS × CONS
-7.155*** (1.179) 1.823*** (0.413)
(11) MS
(12) SA
-4.066*** (0.599) -0.628* (0.377)
0.286 (0.523) -2.145*** (0.330)
-0.103 (0.528) -2.404*** (0.348)
-0.169 (0.521) -1.592*** (0.369)
-0.290 (0.527) -2.541*** (0.357)
Yes 0.706 102946
Yes 0.697 107847
Yes 0.692 102992
Yes 0.692 102038
Yes 0.692 102946
AP
(5) EFD
(6) Z
(7) FC
(8) EFD
(9) Z
-0.840 (1.082) -1.484*** (0.567)
-4.153*** (0.641) -0.307 (0.390)
-3.894*** (0.615) 0.935 (0.752)
-2.799*** (0.989) -2.721*** (0.512)
-0.086 (0.572) -1.499*** (0.352)
-0.308 (0.528) 2.892*** (0.599)
-0.863 (0.841) -1.504*** (0.485)
Yes 0.675 54951
Yes 0.689 67364
Yes 0.707 106732
Yes 0.674 54951
Yes 0.668 67364
Yes 0.694 106732
Yes 0.677 54951
AR
AP
(2) SME
(3) SME
(4) SME
(5) SME
(6) SME
-2.689** (1.256) 1.267* (0.693)
-6.466*** (0.960) 0.238 (0.356)
-2.491** (1.077) -0.285 (0.599)
2.679*** (1.008) -2.224*** (0.336)
1.207 (0.919) -2.415*** (0.598)
ED
(1) SME
(10) TA
(4) FC
Panel C: Size (SME) including financial characteristics as control NET
CE
PT
Covariates Yes Yes Yes Yes Yes Yes Add. Covariates FC Z FC Z FC Z Adjusted R2 0.706 0.683 0.712 0.692 0.698 0.705 N 104186 52146 104186 52146 104186 52146 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row indicates the dependent variable. The second row of Panel A, B and C indicates the size measure or financial characteristic that is interacted with the crisis dummy in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. In Panel C CONS is equal to SME for all specifications, while the entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. Covariates are similar to those in Table A1 in Appendix A. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AC
20
Covariates Adjusted R2 N
(9) SME
AR
(1) FC
M
CRISIS
AP
(2) TA
AN US
CRISIS
AR
(1) SME
ACCEPTED MANUSCRIPT
3.2. Redistribution effect We now focus on the identification of a supply effect according to Hypothesis H2a. Table 3 summarizes the results from estimating the model in Equation (2) at the aggregate level, while Table 4 conditions on size and financial firm characteristics. As measures of
CR IP T
pre-crisis vulnerability, we include on the one hand short-term liquidity measures such as pre-crisis cash holdings (CS), operating cash flow (OCF) and short-term debt (STD) and on the other hand long-term oriented variables such as equity (EQ) and tangible fixed assets (TAA). From Panel A of Table 3, we find significantly positive coefficients Table 3: Redistribution evidence – Aggregate sample
CRISIS CRISIS × CS
-4.484*** (0.635) 6.364*** (1.518)
-8.068*** (0.918) 9.399*** (2.264)
-4.491*** (0.640)
CRISIS × OCF
M
Yes 0.822 102881
-2.636*** (0.751) 1.052 (1.818)
PT
-0.831 (0.545) 3.401*** (1.317)
CE
CRISIS × STD
Yes 0.701 91643
(8) AR
-4.617*** (0.636)
-4.490*** (1.052) 6.410*** (1.567)
-4.484*** (0.579) 6.364** (2.312)
2.080*** (0.777)
Yes 0.704 101946
Yes 0.706 102950
Yes 0.706 102992
Yes 0.708 102882
Yes 0.706 102882
(3) AP
(4) AP
(5) AP
(6) AP
(7) AP
(8) AP
-0.429 (0.568)
-0.400 (0.583)
-1.935*** (0.584)
-0.743 (0.566)
-1.286 (0.977) 3.613*** (1.318)
-0.831 (0.551) 3.401* (1.543)
ED
Yes 0.706 102882
Panel B: Accounts payable as dependent variable (1) (2) AP APA
CRISIS × OCF
-4.589*** (0.674)
(7) AR
2.982*** (0.990)
CRISIS × TAA
CRISIS × CS
-3.461*** (0.679)
(6) AR
-1.244 (0.789)
CRISIS × EQ
CRISIS
(5) AR
5.412*** (1.635)
CRISIS × STD
Covariates Adjusted R2 N
(4) AR
AN US
Panel A: Accounts receivable as dependent variable (1) (2) (3) AR ARA AR
1.229 (1.421) -0.155 (0.686)
CRISIS × EQ
4.704*** (0.823)
CRISIS × TAA
1.097 (0.818)
AC
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R2 0.692 0.857 0.682 0.691 0.692 0.692 0.694 0.692 N 102882 102876 91643 101946 102950 102992 102882 102882 Notes: This table reports firm fixed effects panel regression results. In Columns 1 to 7 standard errors are clustered at the firm level, in Column 8 they are double-clustered at the firm and year level. Column 7 includes industry-year fixed effects at the two-digit indsutry level. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + γXi,t + νt + i,t , while the overline reflects the three years pre-crisis average from 2004 to 2006 of the respective vulnerability variable. The first row indicates the dependent variable. We refer to Table A1 in Appendix A for details on the respective variables. STD is short-term debt net accounts payable to total assets. Covariates are similar to those in Table A1 in Appendix A. In each specification we exclude the covariate similar to VULi,pre . Superscript A means that the dependent variable is divided by daily total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
on the interaction terms in 7 out of 8 specifications, meaning that firms being financially less vulnerable in the years prior to the crisis reduced their trade credit provision to a significantly smaller extent. For instance, firms with a pre-crisis cash ratio close to 21
ACCEPTED MANUSCRIPT
zero reduced their accounts receivable by almost 4.5 days (Col. 1), which amounts to 13% compared to the average ratio of accounts receivable. In contrast, a one standard deviation increase in the pre-crisis cash ratio reduced the decrease by one day. Furthermore, firms with a cash ratio above 70% would have even increased overall trade credit supply during
CR IP T
crisis. Results remain robust when scaling accounts receivable by total assets instead of total sales (Col. 2) and when including industry-year fixed effects (Col. 7) or doubleclustering standard errors at the firm and year level (Col. 8). With respect to accounts payable, we do not find convincing evidence for an impact of pre-crisis vulnerability on trade credit demand during the crisis. While cash and equity might play a signalling role
AN US
for additional credit, the other variables show no evidence for a demand effect. Taken together, we find evidence consistent with the prior literature which concludes that the reduction in trade credit is due to a supply effect and consistent with the inter-firm liquidity redistribution hypothesis H2a. Vulnerable firms reduced their trade credit supply, thereby propagating the adverse economic shock of the crisis via the trade credit channel (Love
M
et al., 2007).
We next focus on size effects, and summarize our evidence on hypothesis H2b in Table
ED
4, where Panels A to C use different pre-crisis vulnerability measures. We report results for cash holdings (CS, Panel A), operating cash flow (OCF, Panel B) and short-term debt
PT
(STD , Panel C). For expositional reasons, we do not report results for the equity ratio
AC
CE
(EQ) and tangible fixed assets (TAA), which are qualitatively similar.
22
CR IP T
ACCEPTED MANUSCRIPT
Table 4: Redistribution evidence – Conditioning on size and financial characteristics Panel A: Cash holdings (CS) as pre-crisis vulnerability measure AR
CRISIS × VUL CRISIS × CONS CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
(4) TA
(5) MS
(6) FC
(7) EFD
(8) Z
(9) SME
(10) SME
-4.410*** (0.676) 6.410*** (2.448) -0.226 (0.471) 0.090 (3.084)
-7.231*** (1.002) 6.593*** (2.417) -0.022 (0.474) 0.117 (3.074)
-2.234* (1.191) -1.475 (4.370) -1.184 (0.794) 8.204 (5.100)
-4.529*** (0.658) 8.254*** (2.298) -0.521 (0.488) -2.794 (3.092)
-4.330*** (0.644) 6.072*** (1.730) -0.386 (0.500) 0.744 (3.066)
-4.804*** (0.681) 8.044*** (1.997) -0.444 (0.521) -2.373 (2.957)
-4.363*** (0.655) 6.178*** (1.613) 1.172 (1.023) -3.352 (6.231)
-2.943*** (1.023) 2.419 (2.166) -2.643*** (0.660) 0.675 (4.764)
0.436 (0.581) -1.728 (2.745) -3.120*** (0.432) 8.515*** (3.175)
2.534** (1.063) -0.911 (2.593) -3.121*** (0.436) 7.578** (3.047)
Yes – 0.706 102882
Yes FC 0.711 99528
Yes Z 0.688 49268
Yes – 0.706 102882
Yes – 0.706 101952
Yes – 0.690 67356
Yes – 0.707 101799
Yes – 0.674 54914
Yes – 0.692 102882
Yes FC 0.693 99528
(7) EFD
(8) Z
(9) SME
(10) SME
(11) SME
(12) TA
Panel B: Operating cash flow (OCF ) as pre-crisis vulnerability measure AR
CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(14) FC
(15) EFD
(16) Z
-0.247 (0.567) 1.543 (1.925) -3.227*** (0.465) 5.884** (2.708)
-0.074 (0.548) -0.861 (1.641) -2.744*** (0.485) 9.662*** (2.692)
-0.694 (0.605) 6.092*** (1.831) -1.626*** (0.472) -1.740 (2.594)
-0.548 (0.552) 2.187* (1.302) 3.347*** (0.775) -5.120 (5.086)
-0.790 (0.851) -0.649 (1.807) -2.367*** (0.625) 9.621** (4.745)
Yes Z 0.699 49268
Yes – 0.692 102882
Yes – 0.692 101952
Yes – 0.668 67356
Yes – 0.689 101799
Yes – 0.677 54914
AP
(3) SME
(4) TA
(5) MS
(6) FC
(13) MS
(14) FC
(15) EFD
(16) Z
-5.220*** (0.735) 12.160*** (2.725) 1.023* (0.554) -9.625*** (3.401)
-8.072*** (1.059) 11.819*** (2.695) 1.214** (0.555) -9.098*** (3.433)
-2.545* (1.340) 1.798 (6.626) -0.038 (1.002) -1.957 (7.394)
-4.619*** (0.691) 7.669*** (2.370) -0.284 (0.569) -3.987 (3.290)
-4.962*** (0.669) 10.308*** (1.933) 0.740 (0.586) -9.905*** (3.308)
-4.784*** (0.706) 6.841*** (2.207) -0.001 (0.585) -2.818 (3.366)
-4.387*** (0.651) 5.815*** (1.803) 0.932 (1.107) 2.347 (7.058)
-3.234*** (1.044) 4.882* (2.614) -2.035*** (0.750) -6.067 (5.610)
0.727 (0.598) -2.609 (2.070) -2.704*** (0.482) 5.661** (2.758)
2.967*** (1.092) -3.216 (2.004) -2.782*** (0.489) 5.757** (2.780)
2.853*** (1.080) -12.653*** (3.909) -3.872*** (0.876) 16.665*** (5.282)
0.106 (0.604) -0.439 (1.982) -2.940*** (0.494) 4.804* (2.840)
-0.045 (0.573) 0.355 (1.732) -1.825*** (0.523) 2.183 (2.861)
0.427 (0.621) -4.325** (1.969) -2.556*** (0.495) 10.571*** (2.886)
-0.271 (0.574) 1.050 (1.514) 4.057*** (0.893) -11.407** (5.663)
-0.648 (0.907) 0.533 (2.702) -1.359** (0.684) -2.167 (5.343)
Yes – 0.701 91643
Yes FC 0.706 88756
Yes Z 0.683 44005
Yes – 0.701 91643
Yes – 0.701 91089
Yes – 0.689 67332
Yes – 0.702 90681
Yes – 0.666 49801
Yes – 0.682 91643
Yes FC 0.684 88756
Yes Z 0.691 44005
Yes – 0.683 91643
Yes – 0.682 91089
Yes – 0.668 67332
Yes – 0.678 90681
Yes – 0.670 49801
M
CRISIS × CONS
(13) MS
1.624 (1.119) -4.460 (6.859) -3.875*** (0.750) 13.686* (7.346)
(2) SME
Panel C: Short-term debt (ST D) as pre-crisis vulnerability measure
AR
CRISIS × VUL CRISIS × CONS CRISIS × VUL × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
-2.730*** (0.766) -3.125*** (1.179) -1.170 (0.730) 2.941* (1.580)
-5.519*** (1.059) -2.928*** (1.089) -0.883 (0.719) 2.803* (1.540)
Yes – 0.704 101946
Yes FC 0.709 98643
AP
(4) TA
(5) MS
(6) FC
(7) EFD
(8) Z
(9) SME
(10) SME
(11) SME
(12) TA
(13) MS
(14) FC
(15) EFD
(16) Z
-0.664 (1.410) -4.196* (2.480) -2.601** (1.238) 5.355* (2.906)
-2.928*** (0.723) -2.354** (1.004) -1.843** (0.771) 2.888* (1.621)
-3.487*** (0.698) -0.969 (0.882) 0.136 (0.819) -0.855 (1.805)
-3.167*** (0.760) -2.583** (1.106) -1.187 (0.833) 2.445 (1.654)
-3.312*** (0.696) -1.343* (0.804) 1.315 (1.515) -0.942 (3.231)
-2.960*** (1.129) 0.392 (1.478) -1.880* (1.090) -1.794 (2.301)
0.577 (0.633) -0.661 (1.074) -2.783*** (0.696) 1.471 (1.403)
3.077*** (1.070) -0.818 (1.070) -3.011*** (0.691) 1.824 (1.397)
0.879 (1.109) 0.991 (2.040) -3.475*** (1.159) 2.238 (2.418)
0.024 (0.621) -0.275 (0.877) -3.063*** (0.742) 1.413 (1.411)
0.437 (0.587) -1.382* (0.805) -2.921*** (0.770) 3.034** (1.498)
0.607 (0.722) -1.974* (1.177) -3.825*** (0.777) 4.944*** (1.535)
-0.137 (0.588) -0.332 (0.689) 6.717*** (1.226) -9.268*** (2.563)
-1.651* (0.922) 1.974** (0.948) -1.486 (1.034) -0.270 (1.899)
Yes Z 0.686 48760
Yes – 0.704 101946
Yes – 0.704 101526
Yes – 0.689 67364
Yes – 0.705 100873
Yes – 0.672 54676
Yes – 0.692 101946
Yes FC 0.693 98643
Yes Z 0.699 48760
Yes – 0.692 101946
Yes – 0.691 101526
Yes – 0.668 67364
Yes – 0.688 100873
Yes – 0.677 54676
PT
CRISIS
(1) SME
CE
Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + β1 CRISISt + β2 (CRISISt × VULi,pre ) + β3 (CRISISt × CONSi,pre ) + β4 (CRISISt × VULi,pre × CONSi,pre ) + γXi,t + νt + i,t , while the overline reflects the three years pre-crisis average from 2004 to 2006 of the respective vulnerability variable. The first row of Panel A, B and C indicates the dependent variable. The second row indicates the size measures and financial characteristics that are interacted with the crisis dummy and the vulnerability variable in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A. In each specification we exclude the covariate similar to VULi,pre . The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AC
23
CRISIS × VUL
(12) TA
(1) SME
ED
CRISIS
(11) SME
AN US
CRISIS
AP
(1) SME
ACCEPTED MANUSCRIPT
In Columns 1–8, the dependent variable is AR and the conditioning variables (CONS) are the various size measures (Cols. 1–5) and proxies for financial constraints (Cols. 6–8). Columns 9–16 are the corresponding specifications for AP as dependent variable. Columns 2 and 10 (3 and 11) are specifications which use SME as size proxy and additionally include
CR IP T
the log-transformed probability of being financially constrained FC (negative Z-score Z) as control variable (indicated by the entry in ’Add. Covariates’ in Table 4). The variable of major interest is the triple interaction term CRISIS × VUL × CONS, which indicates if small, vulnerable firms reacted differently than their large counterparts. We find that for the size measures, the triple interaction term is significant and with the expected sign
AN US
in 4 out of 6 specifications (3 VUL measures for AR and AP), allowing the conclusion that a firm size effect is indeed present. For accounts receivable, the specifications with OCF and STD yield significant results. Using Panel B (OCF), Column 1 as an example how to interpret the findings, we see that large firms with low average pre-crisis cash flow cut back AR by more than 5 days, which represents about 15% of average accounts
M
receivable, while a one standard deviation increase in pre-crisis cash flow reduced it to 10%. In contrast, small firms with low average pre-crisis cash flow shortened accounts
ED
receivable by more than 4 days, while an increase in the pre-crisis cash flow ratio did not change the level tremendously. These results indicate that the vulnerability measure did
PT
not have an impact on AR for small firms, which strongly conflicts with large firms. The same interpretation holds for STD. In contrast to CS and OCF, vulnerability increases
CE
in STD, so the interpretation of the coefficients is reversed, explaining the opposite signs. From Panel C, Column 1, we see that large firms with low average pre-crisis short-term
AC
debt reduced AR by about 2.7 days, whereas a one standard deviation increase in the pre-
crisis short-term debt ratio, would have implied a decrease of about 3.5 days. In contrast, small firms with low as well as high average pre-crisis short-term debt cut back accounts receivable by about 4 days. Importantly, by conditioning on firm characteristics such as financial constraints instead on size proxies in Columns 6–8, we find substantially different results, in particular non-significant coefficients on the triple interaction term. Furthermore, Columns 2 and 3, where FC and Z are included as control variable, respectively, 24
ACCEPTED MANUSCRIPT
yield basically the same results as the specification in Column 1, implying that the size effect is not driven away by controlling for financial constraints or creditworthiness. Taken together, the results suggest that the evidence for a supply effect is even stronger among large firms, thereby reinforcing the redistribution hypothesis H2b within this sub-
CR IP T
sample. In contrast, since conditioning on vulnerability measures does not affect the impact on AR, a supply effect among SMEs cannot be confirmed. Controlling for firm characteristics allows the conclusion that the size effect is genuine and thus supports H2b. Turning next to accounts payable, we find the triple interaction term with size proxies to be significant in Panels A (CS) and B (OCF), confirming again a size effect. However,
AN US
the closer inspection of the coefficients leads to a completely different interpretation than in the case of accounts receivable. From Panel A, Col. 9, we see insignificant coefficients for large firms (CRISIS and CRISIS×VUL), implying that among large firms there was no significant change in AP for either low or high cash firms. In contrast, small firms with low average pre-crisis cash holdings decreased accounts payable significantly more than high
M
cash firms. Considering FC (Col. 10) and Z (Col. 11) as additional control variable yields basically similar results as in Column 9, indicating that the size effect survives the inclusion
ED
of financial constraints or creditworthiness. Similar interpretations hold with respect to operating cash flow. We see insignificant coefficients on CRISIS and CRISIS × VUL. In
PT
contrast, small, low cash flow firms paid on average 2.7 days faster than large, low cash flow firms during crisis, while the large positive coefficient (5.661) on the triple interaction
CE
term implies that a one standard deviation increase in the pre-crisis operating cash flow ratio would have increased the payment term of small firms by almost 1 day. Controlling
AC
for financial constraints does not eliminate the size effect. Thus, compared to AR where the financial vulnerability has no impact among SMEs, we find the opposite pattern for AP where the vulnerability has no impact among large firms. To sum up, results from Table 4 suggest at least the following three conclusions: (i) With respect to the redistribution hypothesis, we identify a firm size effect, which is not entirely explained by either financial constraints, external finance dependence or creditworthiness. (ii) Conditioning on various size proxies, the evidence for a supply effect is 25
ACCEPTED MANUSCRIPT
stronger among large firms, while being absent for SMEs. (iii) In contrast, while the usage of trade credit (AP) turns out to be unaffected by financial vulnerability measures for large firms, SMEs’ trade credit financing does depend on their pre-crisis vulnerability and therefore suggests a demand effect. In a nutshell, we thus find that during the crisis, in
CR IP T
particular large, vulnerable firms did cut down their trade credit provision and especially small, vulnerable firms suffered most by this contraction in trade credit financing. We emphasize that the size effect occurs despite controlling for financial constraints, external finance dependence, and creditworthiness. Our results are consistent with the recent literature by Klapper et al. (2012) and Murfin and Njoroge (2015), showing that large firms
AN US
appear to exploit their market power at the detriment of smaller firms in terms of trade credit usage and conditions. 3.3. Substitution effect
We next investigate the substitutive role of trade credit during the crisis and how it varies
M
in the cross section. First, we specify the measure of debt financing to be short-term bank debt to total assets (labelled as SB). The usual narrative says that bank credit
ED
was more difficult to obtain for firms during the crisis years (Ivashina and Scharfstein, 2010). We confirm this tendency by a preliminary analysis, where we regress SB on the
PT
crisis dummy. Table 5 reports a general decline by about 6 to 7 percentage points during the crisis, in particular among small and distressed firms. Note that in specification (3)
CE
and (4), we condition on SME by additionally controlling for financial constraints FC and creditworthiness Z. While the size effect still holds with respect to financial constraints,
AC
it cannot be confirmed for the Z-score, though the sign goes into the right direction. The substitution hypothesis would argue that the decline in bank credit was (at least
partially) offset by trade credit. As outlined in Section 2.2.2, we test our hypotheses H3a
and H3b by (i) a fractional response regression using the ratio of short-term bank debt to the sum of short-term bank debt and accounts payable, BtoT, (ii) the fixed-effects panel model from Equation (3), and (iii) the conditional fixed effects logistic regression. Results of the three approaches are summarized in Tables 6–8, respectively. Panel A in Table 6 illustrates the marginal effects of the fractional response regression 26
ACCEPTED MANUSCRIPT
Table 5: Short-term bank debt
CRISIS CRISIS × CONS
(1) All
(2) SME
(3) SME
(4) SME
(5) TA
(6) MS
(7) SA
(8) FC
(9) EFD
(10) Z
-0.066*** (0.004)
-0.061*** (0.004) -0.013*** (0.003)
-0.036*** (0.009) -0.012*** (0.003)
-0.062*** (0.008) -0.004 (0.005)
-0.066*** (0.004) 0.001 (0.003)
-0.064*** (0.004) -0.010*** (0.003)
-0.066*** (0.004) -0.001 (0.003)
-0.066*** (0.005) 0.005* (0.003)
-0.066*** (0.004) -0.002 (0.005)
-0.063*** (0.007) -0.027*** (0.004)
CR IP T
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Add. Covariates – – FC Z – – – – – – Adjusted R2 0.726 0.726 0.725 0.723 0.722 0.721 0.722 0.691 0.725 0.702 N 54332 54332 49106 24816 52214 51789 52188 32166 53560 27187 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: SBi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row indicates the size measures and financial characteristics that are interacted with the crisis dummy in each regression. All means no interaction. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
AN US
using maximum likelihood, while Panel B summarizes the results from a linear fixed effects model on the log-transformed dependent variable. In Panel A, the sample extends over the time period 2004–2010 to maximize the number of observations for the totally balanced sample. Overall, we find strongly significant negative coefficients on the crisis dummy.
M
The ratio BtoT declined during the crisis by about 9 to 10 percentage points (Panel A), meaning that the relative importance of bank to trade credit decreased in a statistically as
ED
well as economically significant sense as measured against the average ratio of roughly 45%. Due to a potential mechanical effect, we also include the lagged growth rate of the accounts receivable to sales ratio as well as the working capital to assets ratio to control for changes
PT
in accounts receivable or other current assets which might affect both accounts payable and
CE
short-term bank debt simultaneously. We interpret this finding as first piece of evidence supporting the substitution hypothesis. Interacting the crisis dummy with size measures, we can again identify a size effect resulting in a 1 to 2 percentage points weaker effect.
AC
In particular from the specification in Panel B, we obtain strongly significant positive coefficients on the interaction term, which means that the substitution effect during the crisis years is weaker among SMEs. Positive coefficients are strongly significant for all size measures, but are insignificant for each firm characteristic (Z, EFD and FC). Furthermore, the specification in Column 3 includes FC as control. We also add the negative Z-score as additional control variable, but only for Panel B as we would lose too many observations regarding the fractional response regression. Both specifications yield basically unchanged 27
ACCEPTED MANUSCRIPT
results. We conclude again that the size effect is genuine and not entirely explained by financial constraints. Table 6: Substitution evidence I – Fractional response results Panel A: BtoT – Fractional response regression (1) (2) All SME
Covariates Add. Covariates Pseudo R2 N
CRISIS × CONS
(6) SA
(7) FC
(8) EFD
(9) Z
-0.087*** (0.012)
-0.092*** (0.012) 0.017* (0.010)
-0.099*** (0.017) 0.016 (0.010)
-0.094*** (0.013) 0.018* (0.009)
-0.090*** (0.012) 0.009 (0.009)
-0.088*** (0.012) 0.009 (0.010)
-0.090*** (0.012) 0.016 (0.010)
-0.087*** (0.012) -0.029* (0.015)
-0.083*** (0.020) -0.031** (0.014)
Yes – 0.019 9121
Yes – 0.026 9121
Yes FC 0.025 7889
Yes – 0.021 9121
Yes – 0.025 9121
Yes – 0.021 9121
Yes – 0.023 8890
Yes – 0.019 9037
Yes – 0.027 4165
Panel B: BtoT – Linear fixed effects (1) (2) All SME CRISIS
(5) MS
-0.624*** (0.053)
-0.670*** (0.056) 0.120*** (0.035)
(3) SME
(4) SME
(5) TA
(6) MS
-0.881*** (0.099) 0.120*** (0.035)
-0.697*** (0.104) 0.119** (0.060)
-0.649*** (0.054) 0.120*** (0.032)
-0.648*** (0.054) 0.106*** (0.032)
CR IP T
CRISIS × CONS
(4) TA
(7) SA
(8) FC
(9) EFD
(10) Z
-0.639*** (0.054) 0.112*** (0.033)
-0.629*** (0.059) 0.040 (0.035)
-0.629*** (0.053) 0.006 (0.061)
-0.657*** (0.087) 0.001 (0.045)
AN US
CRISIS
(3) SME
ED
M
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Add. Covariates – – FC Z – – – – – – 2 Adjusted R 0.640 0.640 0.646 0.618 0.639 0.638 0.639 0.603 0.639 0.598 N 54332 54332 49106 24816 52214 51789 52188 32166 53560 27187 Notes: Panel A reports marginal effects of the firm fixed effects fractional logit response regression with robust standard errors clustered at the firm level in parentheses. Panel B reports firm fixed effects panel regression results of the log-transformed dependent variable with robust standard errors clustered at the firm level in parentheses. The regression model is given by: BtoTi,t = αi + β1 CRISISt + β2 (CRISISt × CONSi,pre ) + γXi,t + νt + i,t . The first row of both panels indicates the size measures and financial characteristics that are interacted with the crisis dummy in each regression. All means no interaction. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. In Panel B we further add the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio as covariates. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
PT
As second piece of evidence, Table 7 (Cols. 1–9) reports results from regressing accounts payable on short-term bank debt according to Equation (3). Furthermore, in an attempt to
CE
address the direction of causality, we follow Carb´o-Valverde et al. (2016) by treating bank debt as dependent variable and regress it on accounts payable. Results are summarized in Columns 10–18. The generic label FS (financing source) in Table 7 therefore refers
AC
either to SB (Cols. 1–9) or AP (Cols. 10-18). To be precise, in order to use the same scaling variable (i.e. total assets), we use APA in this specification. Note further, that since we are interested in the simultaneous behavior, we measure the ratios for each year separately. Thus, it is time variant and we obtain an estimate on it. Consider first the specification in Column 1. We find an insignificant coefficient (0.001) on the crisis dummy, but a significant negative coefficient of -0.012 on CRISIS × CONS, which mirrors the earlier finding that SMEs did reduce accounts payable during the crisis while large 28
ACCEPTED MANUSCRIPT
firms did not. The coefficient of -0.051 on FS (i.e. SB in this specification) as well as on the interaction term FS × CONS (-0.03) are significantly negative, meaning that there is a negative relation between AP and SB, which is significantly stronger for SMEs. The latter result implies that over the whole sample period, we find a substitutive role for trade
CR IP T
credit which is stronger among SMEs. Interacting the explanatory variable with the crisis dummy (CRISIS × FS) yields also a significant negative value of -0.03, implying that the substitutive effect for large firms was more pronounced during the crisis. A one standard deviation decrease in short-term bank debt implies an increase of 0.5 percentage points of accounts payable among large firms, which amounts to an economically meaningful
AN US
change of 4% of the average accounts payable ratio. Finally, the triple interaction term is positive and significant for 4 out of 6 size specifications, suggesting that the substitution effect for small firms was less pronounced during the crisis. Including the log-transformed probability of being financially constrained (Col. 2) and the negative Z-score (Col. 3) as additional control variables produces similar results.
M
In Columns 10–18, we regress SB on FS, which is accounts payable in this specification. While the significant negative coefficients on CRISIS and FS repeat the finding of
ED
declining bank debt during the crisis and the substitution effect with AP, we find much less significant evidence for interaction terms with the crisis dummy. In particular, co-
PT
efficients on CRISIS × FS are barely significant, suggesting that during the crisis, bank debt explains accounts payable rather than vice versa. To rule out endogeneity problems
CE
that might occur with contemporaneous regressions, we provide additional evidence on the causal effect of bank credit and trade credit from a conditional fixed effects logistic
AC
regression by following Illueca et al. (2016). Results are summarized in Table 8.
29
CR IP T
ACCEPTED MANUSCRIPT
Table 7: Substitution evidence II – Regression results APA
FS CRISIS × FS CRISIS × CONS FS × CONS CRISIS × FS × CONS Covariates Add. Covariates Adjusted R2 N
(2) SME
(3) SME
(4) TA
(5) MS
(6) SA
(7) FC
(8) EFD
(9) Z
(10) SME
(11) SME
(12) SME
(13) TA
(14) MS
(15) SA
(16) FC
(17) EFD
(18) Z
0.001 (0.003) -0.051*** (0.008) -0.030*** (0.008) -0.012*** (0.002) -0.031*** (0.011) 0.014 (0.011)
0.022*** (0.005) -0.052*** (0.008) -0.030*** (0.008) -0.012*** (0.002) -0.032*** (0.010) 0.006 (0.011)
0.010* (0.005) -0.067*** (0.016) -0.045*** (0.015) -0.018*** (0.004) -0.039** (0.019) 0.038** (0.019)
0.001 (0.003) -0.046*** (0.006) -0.036*** (0.007) -0.016*** (0.002) -0.050*** (0.010) 0.034*** (0.011)
-0.001 (0.003) -0.061*** (0.007) -0.034*** (0.007) -0.011*** (0.002) -0.019* (0.011) 0.025** (0.011)
0.000 (0.003) -0.040*** (0.006) -0.034*** (0.006) -0.016*** (0.002) -0.063*** (0.010) 0.031*** (0.011)
-0.000 (0.003) -0.019*** (0.005) -0.026*** (0.006) -0.021*** (0.003) -0.109*** (0.012) 0.026** (0.011)
-0.003 (0.003) -0.068*** (0.005) -0.022*** (0.005) 0.019*** (0.003) 0.062*** (0.016) 0.003 (0.017)
-0.007 (0.005) -0.151*** (0.014) -0.005 (0.014) 0.012*** (0.003) 0.114*** (0.016) -0.004 (0.016)
-0.059*** (0.005) -0.215*** (0.027) -0.010 (0.015) -0.022*** (0.004) -0.027 (0.031) 0.051*** (0.019)
-0.030*** (0.009) -0.219*** (0.027) -0.017 (0.015) -0.023*** (0.004) -0.034 (0.031) 0.067*** (0.020)
-0.064*** (0.009) -0.284*** (0.055) 0.038 (0.038) -0.007 (0.007) 0.048 (0.058) -0.016 (0.041)
-0.068*** (0.005) -0.257*** (0.024) 0.027* (0.015) -0.001 (0.004) 0.041 (0.029) -0.010 (0.020)
-0.065*** (0.005) -0.227*** (0.020) 0.016 (0.011) -0.013*** (0.004) -0.005 (0.027) 0.011 (0.019)
-0.068*** (0.005) -0.231*** (0.024) 0.021 (0.014) -0.005 (0.004) 0.000 (0.029) 0.002 (0.019)
-0.066*** (0.005) -0.150*** (0.031) 0.009 (0.017) -0.004 (0.005) -0.092*** (0.035) 0.026 (0.022)
-0.068*** (0.004) -0.218*** (0.014) 0.019** (0.010) 0.003 (0.007) 0.119** (0.053) 0.013 (0.041)
-0.063*** (0.008) -0.255*** (0.021) -0.010 (0.016) -0.028*** (0.006) 0.098*** (0.036) 0.039 (0.029)
Yes – 0.842 54332
Yes FC 0.846 49106
Yes Z 0.821 24816
Yes – 0.841 52214
Yes – 0.840 51789
Yes – 0.841 52188
Yes – 0.835 32166
Yes – 0.841 53560
Yes – 0.822 27187
Yes – 0.730 54332
Yes FC 0.730 49106
Yes Z 0.728 24816
Yes – 0.726 52214
Yes – 0.725 51789
Yes – 0.726 52188
Yes – 0.693 32166
Yes – 0.729 53560
Yes – 0.706 27187
AN US
CRISIS
SB
(1) SME
30
M
Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model in Columns 1 to 9 is given by: APA i,t = αi + β1 CRISISt + β2 SBi,t + β3 (CRISISt × SBi,t ) + β4 (CRISISt × CONSi,pre ) + β5 (SBi,t × CONSi,pre ) + β6 (CRISISt × SBi,t × CONSi,pre ) + γXi,t + νt + i,t . In Columns 10 to 18 APA and SB are exchanged. In Columns 1 to 9 FS refers to SB and in Columns 10 to 18 to APA . The first row indicates the dependent variable. The second row indicates the size measures and financial characteristics that are interacted with the crisis dummy and the FS measure in each regression. SME is a dummy that is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. TA is a dummy that is one if the firm’s three years pre-crisis average of total assets lies below the three years pre-crisis sample median. MS is a dummy that is one if the firm’s three years pre-crisis average of market share lies below the three years pre-crisis sample median. SA is a dummy if the firm’s three years pre-crisis average of the size-age index lies above the three years pre-crisis sample median. FC is a dummy variable that is one if the firm’s three years pre-crisis average of the probability of being financially constrained lies above the three years pre-crisis sample median. EFD is a continuous variable and measures the two-digit industry-specific EFD-index of listed firms prior to crisis. Z is a dummy variable that is one if the firm’s three years pre-crisis average of the Z-score lies below the three years pre-crisis sample median. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio and including the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio. The entry in ’Add. Covariates’ indicates the additional covariate that we include in the regression. Note that in this case FC and Z are not dummy but continuous variables and are measured by the natural logarithm of the probability of being financially constrained and the negative Z-score, respectively. Superscript A means that the variable is divided by total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
Table 8: Substitution evidence III – Conditional logistic model results Panel A: Substitution indicator conditional on a change in short-term bank debt Decrease
CRISIS × CONS Covariates Pseudo R2 N
(2) SME
(3) Large
(4) All
(5) All
(6) SME
(7) Large
(8) All
2.125*** (0.261)
1.861*** (0.346)
2.523*** (0.427)
2.066*** (0.263) 1.077 (0.088)
0.655*** (0.102)
0.640* (0.148)
0.574*** (0.122)
0.691** (0.111) 0.867 (0.078)
Yes 0.050 15010
Yes 0.038 9024
Yes 0.083 5986
Yes 0.050 15010
Yes 0.040 12493
Yes 0.033 7067
Yes 0.058 5426
Yes 0.041 12493
(2) SME
(3) Large
(4) All
(5) All
(6) SME
(7) Large
(8) All
1.040 (0.214)
1.064 (0.191)
1.055 (0.146) 1.037 (0.092)
1.077 (0.153)
1.255 (0.270)
0.996 (0.191)
1.072 (0.156) 1.013 (0.082)
ED
CRISIS
Increase
(1) All
Panel B: Substitution indicator conditional on a change in accounts payable
CRISIS
1.070 (0.144)
CRISIS × CONS
PT
Decrease
(1) All
Increase
AC
CE
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Pseudo R2 0.010 0.011 0.016 0.010 0.013 0.018 0.012 0.013 N 12412 7341 5071 12412 13650 7778 5872 13650 Notes: This table reports odds ratios of conditional firm fixed effects logistic regressions with robust standard errors clustered at the firm level in parentheses. The regression model is given by: SIi,t = αi +β1 CRISISt +β2 (CRISISt ×CONSi,pre )+γXi,t +νt +i,t . The dependent variable is a dummy variable that is one in case of substitution and zero otherwise. In Columns 1 to 4 of Panel A the dependent variable is one if accounts payable increase from t-1 to t conditional on a decrease in short-term bank debt in the previous year. In Columns 5 to 8 the dependent variable is one if accounts payable decrease from t-1 to t conditional on an increase in short-term bank debt in the previous year. In Columns 1 to 4 of Panel B the dependent variable is one if short-term bank debt increases from t-1 to t conditional on a decrease in accounts payable in the previous year. In Columns 5 to 8 the dependent variable is one if short-term bank debt decreases from t-1 to t conditional on an increase in accounts payable in the previous year. Columns 1 and 5 include the whole sample without any interaction. Columns 2 and 3 and 6 and 7 are divided according to the SME dummy which is one if the sample firm is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations. In Columns 4 and 8 the crisis dummy is interacted with the SME dummy. Covariates are similar to those in Table A1 in Appendix A exclusive of the equity ratio and including the lagged growth rate of the accounts receivable to sales ratio and the working capital to assets ratio. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
ACCEPTED MANUSCRIPT
Panel A reports the estimated odds ratio for substitution conditional on a former decrease (Cols. 1–4) or increase (Cols. 5–8) in short-term bank debt. The substitution dummy S is considered to be one if the change in accounts payable is of opposite sign than the previous change in bank debt. In unreported robustness checks, we also considered the
CR IP T
change in the respective ratios, where the scale variable is total assets. Our results remain robust. We find a highly significant coefficient of 2.125 in Column 1. Thus, the odds of an increase in trade credit conditional on a prior decrease in bank credit are multiplied by 2.125, meaning that conditional on a decline in bank debt it is substantially more likely that accounts payable have increased during the crisis time compared to non-crisis times.
AN US
In contrast, conditioning on a prior increase in bank debt, the odds of a decrease in trade credit are multiplied by 0.655 (Col. 5), i.e. during crisis firms rather increased accounts payable after a rise in bank debt confirming that the afore detected substitution effect results from a prior decrease in bank debt and not a prior increase. Panel B uses the change in accounts payable as conditioning event. Across all specifications, we do not find
M
any significant results, i.e. there is no evidence that bank debt reacts on a prior change in accounts payable during crisis. Therefore we can draw the following conclusions: First,
ED
we find strong evidence that accounts payable react on a given change in bank debt rather than vice versa. Second, evidence for substitution can only be confirmed in case of a
PT
prior decline in bank debt. Finally, although the odds ratio for large firms is higher in magnitude than for SMEs (compare Cols. 2 and 3), we are not able to verify this difference
CE
in a statistical sense.
In sum, this section has shown that in general our substitution hypothesis H3a can be
AC
confirmed. Moreover, the substitutive effect of trade credit has increased during the crisis for large firms, but not for SMEs, which is in line with H3b. This leads to the conclusion
that contrary to SMEs, large firms were able to mitigate the decline in bank debt at least to some extent by trade credit. The firm size effect is not entirely explained by other financial firm characteristics. Overall, this finding is consistent with the evidence on the redistribution hypothesis, where it has been shown that SMEs had to reduce accounts payable while large firms maintained their level of trade credit. We further addressed the 31
ACCEPTED MANUSCRIPT
direction of causality in the substitution effect, and find strong support for the conclusion that trade credit accommodates to a decline in bank debt. 3.4. Real effects This final subsection provides evidence on the impact of the previously illustrated shift
CR IP T
in financing patterns on the real economy. We investigate the relations between investment behavior (CAPEX) and either accounts payable and short-term bank debt, following Carb´o-Valverde et al. (2016) by means of an error correction model (Bond et al., 2003) estimated with first-difference GMM according to Equation (10) in Appendix C. Furthermore, as in Garcia-Appendini and Montoriol-Garriga (2013), we analyze potential relations
AN US
between accounts receivable and profitability (return on assets, ROA). In line with our general research focus, we distinguish according to size.
Columns 1–8 of Table 9 refer to CAPEX as dependent variable and is either regressed on AP (Cols. 1–5) or SB (Cols. 6–8). For the entire sample (see Cols. 1 and 6), we
M
find positive significant coefficients on both financing sources, suggesting that AP as well as SB are positively linked to investment behavior. This link has not intensified during
ED
the crisis as suggested by the insignificant coefficients on the interaction terms. However, conditioning on size (see Cols. 2, 3 and 7, 8) yields a much more differentiated perspective.
PT
For SMEs, we obtain a significant positive coefficient on AP (0.026), while SB turns out to be insignificant. For large firms, the pattern is reversed. AP is insignificant, while SB
CE
carries a significant coefficient (0.034, see Col. 8). By conditioning on external finance dependence, we do not find a similar pattern, thereby confirming that the observed size
AC
effect is not driven by this firm characteristic. The empirical evidence suggests, that SMEs’ investment behavior is related to trade credit, while large firms’ investment behavior is more closely tied to bank debt. Coupled with results from our previous section, where we have shown that SMEs were forced to reduce accounts payable much more strongly than large firms, our finding suggests an indirect adverse impact of the financial crisis on real economic activity via the trade credit channel. As another measure of real effects, we investigate firms’ profitability (ROA, see Cols. 9– 11) and regress it on accounts receivables. We find strongly significant positive coefficients 32
ACCEPTED MANUSCRIPT
on AR as well as on its interaction with the crisis dummy. Firms with higher levels of accounts receivable are comparably more profitable and this effect is amplified during the crisis. A potential explanation could be that more profitable firms or firms with a higher anticipated profitability are prone to provide more trade credit and thus strengthen their
CR IP T
customer relationship. Combining this finding again with the evidence from previous sections that, in particular, financially vulnerable firms had to cut back accounts receivable suggests another channel by which shifts in trade credit provision yield economic real effects. Consequently, both the decline in trade credit usage and provision caused a negative impact on the real economy and we have no reason to reject our last hypothesis H4.
AN US
Table 9: Real effects CAPEX
CAPEX
t-1
CRISIS APK CRISIS × APK SBK
(2) SME
(3) Large
(4) EFD
-0.042** (0.018) -0.567*** (0.081) 0.022*** (0.005) 0.000 (0.004)
-0.031 (0.020) -0.746*** (0.114) 0.027*** (0.006) 0.010* (0.006)
-0.105*** (0.035) -0.378*** (0.067) 0.005 (0.004) -0.001 (0.001)
-0.063** (0.029) -0.699*** (0.117) 0.026* (0.013) -0.011 (0.016)
CRISIS × SBK
K
OCF
t-1
(lnK-lnS)t-2 ∆lnS ∆lnSt-1
0.000 (0.011) 0.005** (0.003) -0.227*** (0.068) 0.045 (0.177) 0.241*** (0.074)
A
AR
CRISIS × ARA
45847 0.00 0.90 0.11
CE
N AR(1)-test (p-value) AR(2)-test (p-value) H-test (p-value)
-0.007 (0.006) 0.006 (0.004) -0.520*** (0.130) 0.544** (0.213) 0.527*** (0.134)
PT
ROAt-1
0.005 (0.014) 0.003 (0.003) -0.198** (0.078) -0.060 (0.165) 0.207** (0.084)
0.015 (0.018) 0.005 (0.005) -0.315*** (0.101) 0.061 (0.107) 0.310*** (0.110)
ED
OCFK
29368 0.00 0.79 0.67
16479 0.00 0.78 0.18
21652 0.00 0.19 0.08
ROA
(5) No-EFD
(6) All
(7) SME
(8) Large
(9) All
(10) SME
(11) Large
-0.045** (0.018) -0.422*** (0.083) 0.014*** (0.005) -0.002 (0.002)
-0.048*** (0.018) -0.567*** (0.078)
-0.054** (0.022) -0.700*** (0.119)
-0.080*** (0.027) -0.218*** (0.078)
-0.017 (0.011)
0.010 (0.020)
-0.033*** (0.012)
0.030*** (0.008) -0.008 (0.008) 0.005 (0.008) 0.006** (0.003) -0.238*** (0.069) 0.158 (0.146) 0.271*** (0.074)
0.019 (0.013) -0.002 (0.008) 0.030 (0.021) 0.005 (0.004) -0.277*** (0.084) 0.214 (0.199) 0.330*** (0.093)
0.033** (0.015) -0.020 (0.017) -0.011 (0.009) 0.004 (0.004) -0.405*** (0.095) 0.580** (0.237) 0.405*** (0.095)
0.032*** (0.011)
0.024** (0.011)
0.091*** (0.018)
0.368*** (0.023) 0.143*** (0.050) 0.035*** (0.011)
0.300*** (0.028) 0.157** (0.062) 0.025* (0.014)
0.461*** (0.033) 0.135** (0.064) 0.044*** (0.015)
76220 0.00 0.02 0.00
47874 0.00 0.15 0.11
28346 0.00 0.25 0.00
M
K
(1) All
0.008 (0.014) 0.004* (0.002) -0.240*** (0.075) 0.190 (0.153) 0.279*** (0.083)
23566 0.00 0.39 0.31
45847 0.00 0.75 0.12
29368 0.00 0.65 0.47
16479 0.00 0.51 0.15
Notes: This table reports twostep first-difference GMM regression results with robust, heteroscedasticity consistent standard errors in parentheses. The CAPEXi,t first row indicates the dependent variable. The regression model for Columns 1 to 8 is given by an investment error correction model: = Ki,t-1 CAPEX
APK
APK
OCF
OCF
AC
i,t-1 αi + β1 Ki,t-2i,t-1 + β2 CRISISt + β3 Ki,t-1i,t + β4 (CRISISt × Ki,t-1i,t ) + β5 (lnKi,t-2 − lnSi,t-2 ) + β6 (∆lnSi,t ) + β7 (∆lnSi,t-1 ) + β8 Ki,t-1i,t + β9 Ki,t-2 + νt + i,t . CAPEXi,t is capital expenditures, Ki,t is the replacement value of capital stock, (lnKi,t-2 − lnSi,t-2 ) is the error correction parameter and ∆lnSi,t is the change in log sales. For details on the remaining variables, we refer to Table A1 in Appendix A. Instruments set for Columns 1 to 5 (6 to 8) n o
CAPEXi,t-j APi,t-j SBi,t-j OCFi,t-j , Ki,t-1-j ( Ki,t-1-j ), Ki,t-1-j , (lnK-lnS)i,t-1-j , ∆lnSi,t-j Ki,t-1-j
with j being 2 and higher; time dummies . The regression model for Columns 9 to 12 is given by:
ROAi,t = αi + β1 ROAi,t-1 + β2 CRISISt + β3 ARA i,t + β4 (CRISISt × ARA i,t ) + γXi,t + νt + i,t . Covariates are the cash ratio, sales growth, the working capital net cash ratio, the tangible assets ratio, the total debt ratio and the natural logarithm of age. The instrument set contains the dependent variable and control variables except age lagged twice and higher and time dummies. The second row indicates the subsamples used. All indicates no subsample used. SME includes a firm if it is labeled as SME according to §261 of the German Commercial Code in more than half of its yearly observations, while Large includes the remaining firms. EFD includes a firm if the two-digit industry-specific EFD-index of listed firms prior to crisis lies above the sample median, while No-EFD includes the remaining firms. Superscript K means that the variable is divided by the lagged replacement value of capital. Superscript A means that the variable is divided by total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
33
ACCEPTED MANUSCRIPT
4. Conclusion We investigate the redistribution and substitution role of trade credit financing with a major focus on firm size effects. We use the negative supply shock caused by the 2007/08 crisis as exogenous event which is an ideal natural experiment to analyze the usage of trade
CR IP T
credit. For various reasons, we suspect that SMEs are different from large firms in their ability and willingness to provide and receive trade credit. Several studies have observed differences with respect to the financial behavior between large companies and SMEs, either based on organisational structure, financial expertise, priority of financial motives or institutional factors (see e.g. Beck et al., 2008; Graham and Harvey, 2001). Klapper
AN US
et al. (2012) or Fabbri and Klapper (2016) document that large companies are more likely to enjoy greater bargaining power in customer-supplier relationships with smaller companies.
We contribute to the existing trade credit literature by revealing a firm size effect with
M
respect to the inter-firm liquidity redistribution hypothesis, the substitution hypothesis and economic real effects. Our comprehensive German dataset covers both the SME
ED
segment as well as large traded firms and allows to condition on various size measures. It is common to consider size as a proxy to various other firm characteristics such as financial
PT
constraints, external finance dependence and creditworthiness. We construct proxies for each of these characteristics in order to check if size picks up one of them. In econometric
CE
terms, we check if a potential size effect does not succumb to an omitted-variable bias. Overall, we find (i) that the size effect is consistent across various size measures, (ii) that
AC
it cannot be replicated by conditioning the sample on the above-mentioned characteristics and (iii) that these characteristics do not drive out its explanatory power. In this sense, we interpret and label the size effect as being genuine or self-contained. In line with recent literature such as Love et al. (2007), Garcia-Appendini and Montoriol-
Garriga (2013), or Carb´o-Valverde et al. (2016), we find a decline in the aggregate net supply of trade credit following the crisis, which supports the redistribution hypothesis. We extend these results by showing that the lower net supply was particularly driven by a decline in accounts receivables of large, financially vulnerable firms. Small, vulnerable 34
ACCEPTED MANUSCRIPT
firms on the other hand, display a strong decline in accounts payable, suggesting that the adverse economic shock of the crisis was propagated primarily along the trade credit supply channel from large to small vulnerable firms. Consistent with the differences in accounts payable, we find a firm size effect with
CR IP T
respect to the substitution hypothesis. Although we can confirm a general substitutive role of trade credit, we find that SMEs were less able to compensate declining bank financing during the crisis by trade credit as compared to large firms. We further investigate the direction of causality in the substitution effect and find clear evidence that trade credit accommodates to a decline in bank financing rather than vice versa.
AN US
In a final step, we study the real effects of trade credit and find that accounts payable affect investment behavior of small firms while short-term bank debt does not. The opposite holds for large firms. Coupled with the fact that SMEs had to cut back accounts payable significantly more than large firms, we find an adverse impact of the crisis via the trade credit channel. Moreover, we can show that accounts receivable positively affected
Montoriol-Garriga (2013).
M
profitability during the crisis which is in line with the results of Garcia-Appendini and
ED
Overall, our findings indicate that firm size plays an important role in financing patterns during the financial crisis. SMEs are the often cited backbone of many economies
PT
and therefore of important policy relevance. Our results strongly suggest that the firm size effect is genuine and cannot entirely be explained by financial constraints, external finance
CE
dependence or creditworthiness. Analogous to Murfin and Njoroge (2015), we suspect that bargaining and market power is a potential driving force behind the size effect, but leave
AC
a rigorous empirical examination to future research.
35
ACCEPTED MANUSCRIPT
Appendices A. Appendix A Table A1: Variable definitions Formula
Description
Dependent variables
AP NET BtoT
ARi,t · 360 Si,t APi,t · 360 COGSi,t (AR-AP)i,t · 360 Si,t
Accounts receivable to daily total sales Accounts payable to daily costs of goods sold
Net trade credit: Accounts receivable minus accounts payable to daily total sales Bank debt relative to trade debt: Short-term bank debt to the sum of short-term bank debt and accounts payable
SBi,t (SB+AP)i,t APi,t Ai,t SBi,t Ai,t
APA SB
Accounts payable to total assets
AN US
AR
CR IP T
Variable
Short-term bank debt to total assets
Dummy variable that is 1 in case of substitution following an increase/ decrease in short-term bank debt in the prior year and 0 otherwise
S CAPEX ROA
CAPEXi,t Ki,t-1 EBITi,t TAi,t
Capital expenditures to lagged capital Return on assets
Main variables of interest
Dummy that is 1 during the crisis years 2007 to 2010 and 0 otherwise
M
CRISIS
Size variables (dummy is 1 for small firms and 0 otherwise) Dummy that measures a firm’s size of according to §267 of the German Commercial Code (small and medium-sized enterprises)
ED
SME TA
Dummy that measure a firm’s size according to total assets
MS
Dummy that measures market share according to firm’s total sales to industry output
PT
Dummy that measures the size-age index according to Hadlock and Pierce (2010) and Klepsch and Szabo (2016)
SA
FC EFD
AC
Z
CE
Firm fundamentals (dummy is 1 for constrained firms and 0 otherwise) Dummy that measures financial constraint by means of an endogenous switching regression according to Hovakimian and Titman (2006) and Almeida and Campello (2007) Dummy that measures external finance dependence according to Rajan and Zingales (1998) Dummy that measures creditworthiness according to the Altman (1968) Z-score
Control variables/ covariates CS
OCF EQ TAA
SG AGE
CSi,t Ai,t OCFi,t Ai,t EQi,t Ai,t TAAi,t Ai,t Si,t −Si,t-1 Si,t-1
log(Agei,t )
Cash to total assets Operating cash flow to total assets Equity to total assets Fixed assets to total assets labeled as tangible assets Sales growth Natural logarithm of age
36
ACCEPTED MANUSCRIPT
Table A2: Aggregate crisis impact on trade credit
CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE Cons
(2) ARA
-4.000*** (0.60)
-7.189*** (0.89)
3.812*** (1.16) -1.127 (0.86) -0.121 (0.15) 6.429*** (1.01) 2.347* (1.25) 0.334 (1.03) 35.008*** (2.84)
0.029 (1.87) 2.861** (1.43) 0.280 (0.19) -9.267*** (1.62) -11.087*** (1.72) -1.394 (1.64) 84.108*** (4.55)
(3) AR
7.755*** (1.17) 3.812*** (1.16) -1.127 (0.86) -0.121 (0.15) 6.429*** (1.01) 2.347* (1.25) 0.334 (1.03) 36.728*** (2.69)
(4) AP
(5) APA
-0.489 (0.52)
-2.377*** (0.73)
-2.528*** (0.93) -3.384*** (0.78) -0.199 (0.13) 1.297 (1.01) -5.716*** (1.50) -1.922* (1.01) 35.879*** (2.88)
-8.502*** (1.35) -3.009*** (1.06) 0.419*** (0.16) -3.713*** (1.13) -13.468*** (1.59) -5.062*** (1.37) 71.349*** (3.80)
(6) AP
0.947 (1.01) -2.528*** (0.93) -3.384*** (0.78) -0.199 (0.13) 1.297 (1.01) -5.716*** (1.50) -1.922* (1.01) 36.089*** (2.76)
(7) NET
(8) NETA
-3.376*** (0.68)
-4.959*** (1.03)
5.117*** (1.30) 1.701* (0.98) -0.000 (0.16) 1.014 (1.28) 4.030** (1.65) 2.589** (1.15) 4.193 (3.22)
8.590*** (2.07) 6.225*** (1.68) -0.143 (0.22) -5.015*** (1.87) 2.413 (2.02) 3.833** (1.88) 12.084** (5.23)
(9) NET
6.545*** (1.31) 5.117*** (1.30) 1.701* (0.98) -0.000 (0.16) 1.014 (1.28) 4.030** (1.65) 2.589** (1.15) 5.645* (3.07)
CR IP T
CRISIS ∆EURt-1
(1) AR
AC
CE
PT
ED
M
AN US
Adjusted R2 0.706 0.822 0.706 0.697 0.857 0.697 0.700 0.801 0.700 N 107847 107846 107847 107847 107841 107847 107847 107847 107847 Notes: This table reports firm fixed effects panel regression results with robust standard errors clustered at the firm level in parentheses. The regression model is given by: TCi,t = αi + βCRISISt + γXi,t + νt + i,t . The first row indicates the dependent variable. We refer to Table A1 in Appendix A for details on the respective variables. ∆EURt-1 is the lagged growth rate of the three months Euribor. Superscript A means that the dependent variable is divided by daily total assets. All specifications include year fixed effects. *significant at 10%, **significant at 5%, ***significant at 1%.
37
ACCEPTED MANUSCRIPT
B. Appendix B In the paper we compare trade credit usage and provision among financially constrained and unconstrained firms. To sort firms according to their financial constraints we use an endogenous switching regression with unknown sample selection first developed by Mad-
CR IP T
dala (1986). We follow Hu and Schiantarelli (1998), Hovakimian and Titman (2006) and Almeida and Campello (2007), who applied the model to investigate cash flow sensitivities with respect to financial constraints. As the degree of financial constraints of a firm is not directly observable the sample selection is unknown. However, we suppose some firms to be more financially constrained than others, though we are not able to explicitly tag
AN US
them ex ante. The model offers regression estimation results of two investment regimes without a priori identifying firms as being financially constrained or unconstrained. While the number of regimes are known, the structural breakpoints are unknown. Supposing that there exist two different investment regimes and if investment-cash flow sensitivities
M
are a good proxy of financial constraints, it should be possible to tag two regimes with different cash flow coefficients. The investment-cash flow sensitivity should be low for the
ED
unconstrained regime, while the investment-cash flow sensitivity should be high for the constrained regime. However, if these two regimes cannot be identified, investment-cash
PT
flow sensitivity would not be a good proxy for financial constraints. Contingent on the magnitude of liquidity constraints a firm may be assigned to one of the two unobservable
CE
investment regimes (Hovakimian and Titman, 2006). The model consists of the following
AC
system of simultaneous equations:
I1 i,t = β1 Xi,t + u1 i,t
(4)
I2 i,t = β2 Xi,t + u2 i,t
(5)
y* i,t = γZi,t + i,t
(6)
Equations (4) and (5) are structural equations that reflect firms’ investment in the two investment regimes. Xi,t is a vector of exogenous covariates that influence investment. 38
ACCEPTED MANUSCRIPT
Equation (6) is the selection equation that reflects a firm’s probability of being in one or the other regime, while y* i,t is a latent variable that captures this probability. Zi,t is a vector of covariates that influence a firm’s likelihood of being constrained or not. u1 , u2 and are residuals. In the empirical specification of the switching model, the investment Ii,t = I1 i,t iff y* i,t < 0 Ii,t = I2 i,t iff y* i,t ≥ 0
CR IP T
of firm i in t is given by: (7)
β1 , β2 and γ are estimated via maximum likelihood. In doing this we suppose that
AN US
u1 , u2and are jointly normally distributed with mean vector 0 and covariance matrix 2 σ1 σ12 σ1 2 Σ = σ12 σ2 σ2 that allows for nonzero correlation between both the shocks to σ1 σ2 1 investment and to firms’ characteristics. σ2 is normalized to 1, as it is unidentified. The
likelihood function and the log-likelihood function subject to maximization are respectively
M
given by:
ED
li,t =Pr(i,t < −γZi,t | u1 i,t = I1 i,t − β1 Xi,t )Pr(u1 i,t = I1 i,t − β1 Xi,t ) + Pr(i,t ≥ −γZi,t | u2 i,t = I2 i,t − β2 Xi,t )Pr(u2 i,t = I2 i,t − β2 Xi,t ),
CE
(
PT
! ! " #) −γZi,t − σσ22 u2 i,t −γZi,t − σσ12 u1 i,t 1 2 r r φ(u1 i,t , σ1 ) + 1 − Φ φ(u2 i,t , σ2 ) , ln L = ln Φ 2 2 σ σ 1 2 i=1 1 − σ2 1 − σ2 N X
1
2
(8)
AC
where φ(.) and Φ(.) are the normal density and the cumulative distribution functions, respectively.13 To solve the model, we have to assign the selection vector Z considering firm characteristics that influence a firm’s likelihood of being financially constrained or unconstrained. This procedure provides the possibility to control for various parameters that simultaneously determine the regime a particular firm is likely to be part of, while common sample splits are based on one or two parameters. We follow Almeida and 13 Additional details on the approach can be found in Maddala (1986), Hu and Schiantarelli (1998) and Hovakimian and Titman (2006).
39
ACCEPTED MANUSCRIPT
Campello (2007) and use the following selection variables: the natural logarithm of total assets (Ai,t-1 ), the natural logarithm of age (AGEi,t-1 ), the ratio of short-term debt to total assets (STDi,t-1 ) , the ratio of equity to total debt (LEVi,t-1 ), the cash ratio (CSi,t-1 ), the ratio of interest payments to EBIT (ICi,t-1 ) and the tangible assets ratio (TAAi,t-1 ). We
CR IP T
lag all these variables by one period. The structural equations are estimated by an error correction model (Bond et al., 2003) as defined in Section 2.2.2. We additionally estimate the structural equations for accounts receivable, as we are interested in firms’ financial constraints with respect to trade credit.14 Table A3 reports regression results.15 While Panel A depicts the structural equation results, Panel B shows the selection equation
AN US
results. From Panel A one can see that investment-cash flow sensitivity seems to be a good proxy for financial constraints, as the coefficients in regime R2 (constrained regime) are much higher than in regime R1 (unconstrained regime). From Panel B one can see that the chosen selection parameters are quite equal considering investment and accounts receivable. The dependent variable is one if a firm belongs to regime R1 and zero otherwise.
M
While larger and older firms as well as firms with more tangible assets are more likely to be financially unconstrained, firms with more short-term debt and a higher cash ratio
ED
are more likely to be financially constrained. We also compare the probabilities of being financially constrained between the investment model and the trade credit model. Since
PT
they are highly correlated, we use the probabilities of the trade credit model to sort the
AC
CE
firms according to their financial constraints as described in the paper.
14
For further details see Section 2.2.2 and Table A1 in the Appendix. The regressions are estimated with the user-written Stata package “switchr” provided by Zimmerman (1998), which maximizes the likelihood function through the EM algorithm of Dempster et al. (1977). 15
40
ACCEPTED MANUSCRIPT
Table A3: Endogenous switching regression Panel A: Structural regressions CAPEX
OCF
K
OCF
t-1
(lnK-lnS)t-2 ∆lnS ∆lnSt-1
(2) R2
-0.008*** (0.001) 0.001*** (0.000) 0.000** (0.000) -0.082*** (0.001) 0.065*** (0.001) 0.090*** (0.001)
-0.317*** (0.020) 0.063*** (0.010) 0.015*** (0.003) -0.981*** (0.063) 0.630*** (0.043) 0.859*** (0.054)
CSt-1 OCFt-1 SGt-1 EQt-1 TAAt-1 AGE R2 N
0.341 71152
(3) R1
(4) R2 At-1 AGEt-1 STDt-1 LEVt-1 CSt-1 ICt-1
-0.308*** (0.113) -0.754*** (0.090) 0.012 (0.013) -0.088 (0.101) 0.672*** (0.127) 0.342*** (0.101)
4.394*** (1.342) -2.083* (1.096) -0.154 (0.185) 8.312*** (1.261) 3.437** (1.601) 0.515 (1.253)
0.057 102049
0.012
0.289
TAAt-1
(1) CAPEX
(2) AR
0.040*** (0.001) 0.041*** (0.003) -0.116*** (0.009) 0.000*** (0.000) -0.107*** (0.019) -0.001 (0.002) 0.498*** (0.012)
0.029*** (0.001) 0.041*** (0.002) -0.108*** (0.005) -0.000 (0.000) -0.009 (0.010) -0.003*** (0.001) 0.341*** (0.008)
0.112 71152 0.000
0.119 102049 0.000
CR IP T
K
(1) R1
R2 N Model p-value (LRT)
AN US
CAPEXK t-1
Panel B: Endogenous selection regressions AR
AC
CE
PT
ED
M
Notes: This table reports results from the endogenous switching regression model with robust standard errors clustered at the firm level in parentheses. Panel A reports the structural equation results with firm and year fixed effects. The first row indicates the dependent variable and the respective regime. Panel B reports the selection equation results with year fixed effects. The first row indicates the dependent variable of the structural equation. The dependent variable of the selection equation is a dummy that is one if a firm is assigned into regime R1 and zero if it is assigned into regime R2, while firms in R1 are classified as financially unconstrained and in R2 as financially constrained. Superscript K means that the variable is divided by the lagged replacement value of capital. For further details on the respective variables, we refer to the paper and Table A1 in Appendix A. *significant at 10%, **significant at 5%, ***significant at 1%.
41
ACCEPTED MANUSCRIPT
C. Appendix C As most firms in our sample are small unlisted firms, we are unable to construct Tobin’s Q. In order to appraise company investment and investment-trade credit sensitivities we use an error-correction model according to Bond et al. (2003), Mizen and Vermeulen (2005),
CR IP T
Mulier et al. (2016) and Buca and Vermeulen (2017). The error-correction model was firstly launched by Bean (1981) and later applied by Bond et al. (2003). The basic idea of a flexible error-correction model is to combine a long-term equilibrium relation of the capital stock with short-term investment dynamics by means of a regression model (Buca and Vermeulen, 2017). For a profit-maximizing firm with constant returns to scale, a CES
AN US
production function and without adjustment costs, the capital stock can be expressed as a log-transformed function of output and the cost of capital:
(9)
M
ki,t = ai + yi,t − σji,t ,
where ki,t is the natural logarithm of the desired capital stock of firm i in year t, yi,t is the
ED
natural logarithm of output, ji,t is the natural logarithm of the real user cost of capital, σ is the elasticity of capital regarding the real user cost and ai is a firm specific intercept
PT
(Bond et al., 2003).
To consider a slow adjustment process of the current capital stock to the desired capital
CE
stock, we apply a dynamic regression model. Thereby, we expect the capital stock to follow an ADL(2,2) model. According to Bean (1981) Equation 9 can then be rewritten in errorcorrection form, while capital expenditures over the lagged capital stock less depreciation
AC
can be used as proxy for the change in capital stock ∆ki,t ∼
CAPEXi,t Ki,t-1
− δi and the natural
logarithm of sales as proxy for the natural logarithm of output yi,t ∼ lnSi,t . The error-correction model assumes perfect capital markets to hold, i.e. a firm’s in-
vestment decision does not depend on its financial constraint status and thus investment should not be sensitive to financial variables. However, our data sample mainly includes small unlisted firms with restricted access to capital markets particularly during crisis times. Consequently, adding cash flow (OCFi,t ) and accounts payable (APi,t ) or short42
ACCEPTED MANUSCRIPT
term bank debt (SBi,t ) to the model should investigate the role of financial variables and control for investment-trade credit and investment-bank debt sensitivities and thus, capital market frictions (Fazzari et al., 1988). The error-correction model can then be specified
CR IP T
as following:
CAPEXi,t-1 APi,t APi,t CAPEXi,t =αi + β1 + β2 CRISISt + β3 + β4 (CRISISt × ) Ki,t-1 Ki,t-2 Ki,t-1 Ki,t-1 + β5 (lnKi,t-2 − lnSi,t-2 ) + β6 (∆lnSi,t ) + β7 (∆lnSi,t-1 ) OCFi,t OCFi,t-1 + β9 + νt + i,t , Ki,t-1 Ki,t-2
AN US
+ β8
(10)
where CAPEXi,t is calculated as the sum of depreciation in year t and the change in tangible fixed assets from year t-1 to year t. The replacement value of the capital stock is calculated as Ki,t = Ki,t−1 × (1 − δ) + CAPEXi,t , while the starting value of K is measured by the starting value of tangible fixed assets. The depreciation rate δ is assumed to be
M
constant at 4%.16 (lnKi,t-2 − lnSi,t-2 ) is the error correction parameter that gauges the long run equilibrium between capital and its objective value measured by sales. ∆lnSi,t is
ED
the change in log sales.17 Controlling for real effects of short-term bank debt on investment, we exchange accounts payable by short-term bank debt. We estimate the model
PT
by means of a two-step first-difference GMM estimator developed by Arellano and Bond (1991), while we use lagged variables in levels as instruments for the first differences of the
CE
explanatory variables. Furthermore, to control for business cycle fluctuations and other
AC
life-cycle effects, we include year fixed effects.18
16 In robustness checks, we also estimate the model with different depreciation rates. Our results remain stable. 17 See Bond et al. (2003), Mizen and Vermeulen (2005), Mulier et al. (2016) and Buca and Vermeulen (2017) for additional details on the model. 18 The first-difference GMM estimations are performed in Stata using the command ”xtabond2” by Roodman (2009).
43
ACCEPTED MANUSCRIPT
References References Acharya, V. V., Almeida, H., Campello, M., 2013. Aggregate risk and the choice between cash and lines of credit. Journal of Finance 68 (5), 2059–2116.
microdata. Small Business Economics 42 (1), 131–151.
CR IP T
Agostino, M., Trivieri, F., 2014. Does trade credit play a signalling role? Some evidence from SMEs
Almeida, H., Campello, M., 2007. Financial constraints, asset tangibility, and corporate investment. Review of Financial Studies 20 (5), 1429–1460.
Altman, E. I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (4), 589–609.
AN US
Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58 (2), 277–297. Bean, C. R., 1981. An econometric model of manufacturing investment in the UK. Economic Journal 91 (361), 106–121.
Beck, T., Demirg¨ uc¸-Kunt, A., Maksimovic, V., 2008. Financing patterns around the world: Are small firms
M
different? Journal of Financial Economics 89 (3), 467–487.
Bond, S., Elston, J. A., Mairesse, J., Mulkay, B., 2003. Financial factors and investment in Belgium, France, Germany, and the United Kingdom: A comparison using company panel data. Review of Economics
ED
and Statistics 85 (1), 153–165.
Buca, A., Vermeulen, P., 2017. Corporate investment and bank-dependent borrowers during the recent financial crisis. Journal of Banking & Finance 78, 164–180.
PT
Calomiris, C. W., Himmelberg, C. P., Wachtel, P., 1995. Commercial paper, corporate finance, and the business cycle: A microeconomic perspective. Carnegie-Rochester Conference Series on Public Policy
CE
42, 203–250.
Campello, M., Giambona, E., Graham, J. R., Harvey, C. R., 2011. Liquidity management and corporate investment during a financial crisis. Review of Financial Studies 24 (6), 1944–1979.
AC
Carb´ o-Valverde, S., Rodr´ıguez-Fern´ andez, F., Udell, G. F., 2016. Trade credit, the financial crisis, and SME access to finance. Journal of Money, Credit and Banking 48 (1), 113–143.
Choi, W. G., Kim, Y., 2005. Trade credit and the effect of macro-financial shocks: Evidence from U.S. panel data. Journal of Financial and Quantitative Analysis 40 (4), 897–925. Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B 39 (1), 1–38. Duchin, R., Ozbas, O., Sensoy, B. A., 2010. Costly external finance, corporate investment, and the subprime mortgage credit crisis. Journal of Financial Economics 97 (3), 418–435.
44
ACCEPTED MANUSCRIPT
Fabbri, D., Klapper, L. F., 2016. Bargaining power and trade credit. Journal of Corporate Finance 41, 66–80. Fazzari, S. M., Hubbard, R. G., Petersen, B. C., Blinder, A. S., Poterba, J. M., 1988. Financing constraints and corporate investment. Brookings Papers on Economic Activity 1, 141–206. Ferrando, A., K¨ ohler-Ulbrich, P., P´ al, R., 2007. Is the growth of euro area small and medium-sized enterprises constrained by financing barriers? Industrial Policy and Economic Reforms Papers (6).
CR IP T
Ferris, J. S., 1981. A transactions theory of trade credit use. Quarterly Journal of Economics 96 (2), 243–270.
Gama, A. P., Van Auken, H., 2015. The interdependence between trade credit and bank lending: Commitment in intermediary firm relationships. Journal of Small Business Management 53 (4), 886–904. Garcia-Appendini, E., Montoriol-Garriga, J., 2013. Firms as liquidity providers: Evidence from the 2007 -
AN US
2008 financial crisis. Journal of Financial Economics 109 (1), 272–291.
Graham, J. R., Harvey, C. R., 2001. The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics 60 (2-3), 187–243.
Guariglia, A., Mateut, S., 2006. Credit channel, trade credit channel, and inventory investment: Evidence from a panel of UK firms. Journal of Banking & Finance 30 (10), 2835–2856. Hadlock, C. J., Pierce, J. R., 2010. New evidence on measuring financial constraints: Moving beyond the
M
KZ index. Review of Financial Studies 23 (5), 1909–1940.
Hovakimian, G., Titman, S., 2006. Corporate investment with financial constraints: Sensitivity of invest-
ED
ment to funds from voluntary asset sales. Journal of Money, Credit and Banking 38 (2), 357–374. Hu, X., Schiantarelli, F., 1998. Investment and capital market imperfections: A switching regression approach using U.S. firm panel data. Review of Economics and Statistics 80 (3), 466–479.
PT
Illueca, M., Norden, L., van Kampen, S., 2016. Substitution effects in private debt: Evidence from SMEs. SSRN Working Paper, 1–51.
CE
Ivashina, V., Scharfstein, D., 2010. Bank lending during the financial crisis of 2008. Journal of Financial Economics 97 (3), 319–338. Kaplan, S. N., Zingales, L., 1997. Do investment-cash flow sensitivities provide useful measures of financing
AC
constraints? Quarterly Journal of Economics 112 (1), 169–215. Kestens, K., Van Cauwenberge, P., Bauwhede, H. V., 2012. Trade credit and company performance during the 2008 financial crisis. Accounting & Finance 52 (4), 1125–1151.
Klapper, L., Laeven, L., Rajan, R., 2012. Trade credit contracts. Review of Financial Studies 25 (3), 838–867. Klepsch, C., Szabo, T., 2016. Financial constraints of private SMEs in Germany: Measurement and consequences on corporate policies. SSRN Working Paper, 1–41. Love, I., Preve, L. A., Sarria-Allende, V., 2007. Trade credit and bank credit: Evidence from recent financial crises. Journal of Financial Economics 83 (2), 453–469.
45
ACCEPTED MANUSCRIPT
Love, I., Zaidi, R., 2010. Trade credit, bank credit and financial crisis. International Review of Finance 10 (1), 125–147. Maddala, G. S., 1986. Disequilibrium, self-selection, and switching models. Handbook of Econometrics III, 1634–1688. Marotta, G., 2005. When do trade credit discounts matter? Evidence from Italian firm-level data. Applied Economics 37 (4), 403–416.
Small Business Economics 42 (3), 561–577.
CR IP T
Mart´ınez-Sola, C., Garc´ıa-Teruel, P. J., Mart´ınez-Solano, P., 2014. Trade credit and SME profitability.
McGuinness, G., Hogan, T., 2016. Bank credit and trade credit: Evidence from SMEs over the financial crisis. International Small Business Journal 34 (4), 412–445.
McMillan, J., Woodruff, C., 1999. Interfirm relationships and informal credit. Quarterly Journal of Eco-
AN US
nomics 114 (4), 1285–1320.
Meltzer, A. H., 1960. Mercantile credit, monetary policy, and size of firms. Review of Economics and Statistics 42 (4), 429–437.
Mizen, P., Vermeulen, P., 2005. Corporate investment and cash flow sensitivity: What drives the relationship? ECB Working Papers (485).
Molina, C. A., Preve, L. A., 2012. An empirical analysis of the effect of financial distress on trade credit.
M
Financial Management 41 (1), 187–205.
Mulier, K., Schoors, K., Merlevede, B., 2016. Investment-cash flow sensitivity and financial constraints:
ED
Evidence from unquoted European SMEs. Journal of Banking & Finance 73, 182–197. Mundlak, Y., 1978. On the pooling of time series and cross section data. Econometrica 46 (1), 69–85. Murfin, J., Njoroge, K., 2015. The implicit costs of trade credit borrowing by large firms. Review of
PT
Financial Studies 28 (1), 112–145.
Nilsen, J. H., 2002. Trade credit and the bank lending channel. Journal of Money, Credit and Banking
CE
34 (1), 226–253.
Norrbin, S. C., Reffett, K. L., 1995. Trade credit in a monetary economy. Journal of Monetary Economics 35 (3), 413–430.
AC
Papke, L. E., Wooldridge, J. M., 1996. Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics 11 (6), 619–632.
Papke, L. E., Wooldridge, J. M., 2008. Panel data methods for fractional response variables with an application to test pass rates. Journal of Econometrics 145 (1-2), 121–133.
Petersen, M. A., Rajan, R. G., 1997. Trade credit: Theories and evidence. Review of Financial Studies 10 (3), 661–691. Rajan, R. G., Zingales, L., 1998. Financial dependence and growth. American Economic Review 88 (3), 559–586. Roodman, D., 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. Stata
46
ACCEPTED MANUSCRIPT
Journal 9 (1), 86–136. Sufi, A., 2009. Bank lines of credit in corporate finance: An empirical analysis. Review of Financial Studies 22 (3), 1057–1088. Taketa, K., Udell, G. F., 2007. Lending channels and financial shocks: The case of small and medium-sized enterprise trade credit and the Japanese banking crisis. Monetary and Economic Studies 25 (2), 1–44. Whited, T. M., Wu, G., 2006. Financial constraints risk. Review of Financial Studies 19 (2), 531–559.
AC
CE
PT
ED
M
AN US
CR IP T
Zimmerman, F., 1998. Switching regressions. Stata Technical Bulletin (45), 30–33.
47