bart by pengxuebo

VIEWS: 0 PAGES: 26

									Title: Trade Credit and Small Distressed Firms



Abstract: We analyze trade credit in a sample of small distressed firms that are restructured under the
Belgian procedure of court-supervised reorganization (Chapter 11 in the U.S). During the pre-
bankruptcy period, firms rely on trade credit to finance a lack of internally generated cash. This is
specifically true if banks contract their lending during the pre-bankruptcy period. Trade creditors lend
more if firms are more transparent, and if their board members are less involved with previous
bankruptcies. Suppliers may also lend to our sample firms because they have a long-run interest in
their survival since our sample firms indeed succeed in getting a plan voted by their creditors and
confirmed by the court.

Keywords: trade credit; small distressed firms; court-supervised reorganization; bankruptcy

JEL: G30, G33, L26




                                                                                                      1
1.       Introduction

We analyze the role of trade creditors in small distressed firms that have some prospects of corporate
rescue. We specifically test whether suppliers act as liquidity providers in the period before filing a
petition for court-supervised reorganization (hereafter the pre-bankruptcy period) in response to a
shortfall in internally generated cash and a contraction of the bank debt. We find that entrepreneurs
demand more trade credit during the pre-bankruptcy period to finance their loss-making business, and
suppliers are willing to provide this credit.

As in Peterson and Rajan (1997), the firm’s ability to generate cash internally strongly affects the
firm’s demand for trade credit1. In line with Peterson and Rajan, it could be argued that cash flow
precedes trade credit in the pecking order. Related, Cunat (2007) finds that trade credit is more
prevalent when firms experience a liquidity shock that may threaten their survival. In his study, a firm
is defined to have a liquidity shock if the total amount of cash and bank deposits scaled by assets drops
by more than 10% (that is 10% of the assets of the firm) or if dividends are cut. Our finding that cash
flow precedes trade credit in the financing order is robust to controlling for a distressed firm’s access
to bank debt and government debt2. Since Franks and Sussman (2005) find that bank debt is
substituted by trade credit in the running up to bankruptcy, we specifically analyze the impact of bank
behavior on the trade credit levels at the start of the reorganization procedure. We find that if banks
contract their lending during the pre-bankruptcy period, distressed firms rely on trade credit to finance
a shortfall in cash flow, while this is not the case under a bank debt expansion. This suggests that
entrepreneurs use trade credit to fill liquidity gaps created by both a lack of internal profit generation
and the contraction of bank debt. This pre-bankruptcy dynamics strongly contributes to the high trade
credit percentage of 40.05% observed in our sample firms.

Several trade credit theories explain why suppliers may want to extend credit to distressed companies
in the first place. Debt enforcement theories suggest that trade creditors may have an advantage in the
enforcement of due trade credit. Several papers (see e.g. Santos and Longhofer 2003; Frank and
Maksimovic 2004) argue that suppliers’ access to distribution channels gives them an advantage in
liquidating intermediate goods in case of default. Cunat (2007) suggests that suppliers are more able to
enforce debt repayment than banks because they can credibly threaten to cut the supply of
intermediate goods to their debtors. Equity-stake theories put forward that trade creditors may have a
long-term interest in the survival of their customer, which encourages them to contribute to their
rescue. Cunat (2007) argues that trade creditors have an equity-like stake in a customer’s business as
long as the value of their relationship is higher than the cost of helping in the survival of the distressed
firm by providing additional trade credit or by extending the maturity of existing trade credit. The
trade creditor’s implicit equity stake makes them act as liquidity providers, supporting their customers

1
  Peterson and Rajan (1997) find that each additional dollar of profits lowers the firm’s demand for trade credit
by 23 cents.
2
  We also control for unpaid taxes and social contributions, which account for a substantial part of the unsecured
debt in our Belgian court-supervised reorganization cases. For Chapter-11 firms and especially for small ones,
Baird et al. (2007) equally show that unpaid sales taxes and payroll taxes are sizable. They argue that
entrepreneurs of small distressed firms have fewer incentives to transfer sales taxes and payroll taxes in the
running up to bankruptcy.


                                                                                                                2
whenever they face temporary liquidity shocks. Wilner’s model (2000), where suppliers give larger
concessions than banks in the case of debt renegotiation, is also based on the existence of a trade
creditor’s stake in the survival of their customer. Huyghebaert et al. (2007) investigate the choice
between bank debt and trade credit in Belgian start-ups. They suggest that these start-ups rely more on
trade credit than bank debt, because banks follow a strict liquidation policy, whereas a supplier’s
implicit equity stake results in more willingness to renegotiate the outstanding debt or grant additional
debt. Information advantage theories suggest that suppliers may have an information advantage over
financial institutions in assessing their customers’ creditworthiness, which allows them to play an
active role in financing distressed firms. Specifically trade creditors may get information faster and at
lower cost compared to banks because it is obtained in the normal course of business3.

Peterson and Rajan (1997) argue that these debt enforcement theories and the equity-stake theory
explain why suppliers are still willing to lend to constrained small firms4. They find evidence
suggesting that firms use more trade credit when credit from financial institutions is limited or
unavailable. Franks and Sussman’s analysis (2005) reveals some related findings. In a subsample of
distressed small to medium sized UK firms that eventually are liquidated, they find that banks rarely
expand their credit, while trade creditors are more willing to provide. For every pound sterling that the
bank has withdrawn, the trade creditors have put in on average £0.50. Franks and Sussman rely on the
existence of a trade creditors’ equity-like stake in a distressed company to explain their findings5. In
our sample we can assume that an implicit equity-like exists for all firms, since they all obtain a
creditor-confirmed going-concern plan under Belgian court-supervised reorganization (Chapter 11 in
the U.S.). We cannot test directly the debt enforcement theory, but we control for its effects by
including industry dummies. The information advantage theory is supported since trade creditors lend
more if firms are more transparent and if their board members are less involved with previous
bankruptcies.

This paper is organized as follows. Section 2 discusses the data and gives an overview of the debt
composition at the start of the court-supervised procedure. Section 3 formulates the hypothesis and
presents the empirical approach, while results are discussed in section 4. Section 5 concludes.


2.       Data
2.1.     Data sources and sampling procedure
Our hand-collected dataset consists of the complete judicial records of distressed firms that eventually
obtain confirmed reorganization plans under court-supervised reorganization in Belgium.
Approximately 306 plans were confirmed between January 1, 1998 and June 30, 2004 with one of the


3
  If trade creditors have private information about a firm’s creditworthiness, Biais and Gollier (1997) show how
firms can rely on trade credit to convey their private information to the bank, and eventually get additional bank
finance.
4
  Peterson and Rajan (1997) give a fairly complete overview of all existing trade credit theories. Example given,
trade credit can also be used as a tool for price discrimination because it changes the effective price of goods.
5
  Franks and Sussman (2005) however argue that trade creditors might be simply unaware of the financial
difficulties of their customers, which results in ill-informed suppliers lending to distressed companies.


                                                                                                                3
23 regional Belgian Bankruptcy Courts. Our sample is restricted to all confirmed reorganization plans
submitted to 17 of those Bankruptcy Courts. This amounts to 190 reorganization plans or 62% of the
population of confirmed plans. Corporations and sole proprietorships submitted respectively 125 and
65 plans (125+65 = 190). Blocks of closely related corporations jointly submitted five out of those 125
plans6. The dataset is complemented with pre-bankruptcy financial statement data from the Graydon-
database and the Belfirst DVD’s, which are delivered by the private data vendors Graydon Belgium
and Bureau van Dijk respectively.

We analyze a sample of small distressed corporations that submitted a going concern plan. We exclude
corporations with total assets exceeding € 5.000.000, which leaves a sample of 107 small corporations.
We additionally exclude an incorporated soccer club and one liquidation scheme among the small
corporations7. After these restrictions we retain 105 small corporations in the sample. Our dataset is
complemented with financial statement data prior to petition filing for bankruptcy-reorganization. To
ensure a sufficiently high quality of the financial statement data, we do not include corporations for
which the time period between the financial statement date and the filing date for bankruptcy-
reorganization is longer then 18 months. This removes another 14 corporations, resulting in a sample
of 91 corporations.

Since the court jointly appraises the cases of closely related corporations, the data on the financial
statements should be aggregated. Simple data aggregation is not recommended though, because of
intra-group transactions and consolidated accounts are not available. Plans submitted by closely
related corporations are therefore excluded from the sample of corporations resulting in final sample
of 89 corporations8.


2.2.     Sample firms

Table 1: Firm characteristics sorted by legal form.
                                             N        Mean      Median    Std. Dev.    Min        Max
Public Limited Liability Corporation
Pre-bankruptcy total Assets (€ 1000)         45       1472       1069       1365        90        4942
Employees (No.)                              45       10.22        6         10          1         37
Liabilities (€ 1000)                         45       1343       1104       1142        103       4873
Liabilities/pre-bankruptcy assets            45      1.1395     0.9788     0.5650     0.3793     3.0206
Private Limited Companies
Pre-bankruptcy total Assets (€ 1000)         41       509        235         620        21        3015
Employees (No.)                              41       5.05         2        7.15         0         28
Liabilities (€ 1000)                         41       463        311         481       18.54      1848
Liabilities/pre-bankruptcy assets            41      1.1080     0.9742     0.4975     0.3665     2.6315

The corporations differ by legal form. 45 corporations are non-quoted public limited liability
corporations (Société Anonyme), 41 are private limited companies (Société Privée à Responsabilité
Limitée), and 3 incorporated firms have another legal status. Table 1 gives summary statistics sorted


6
  Five blocks of incorporated firms file jointly a plan. Those blocks respectively consists of 9, 4, 2, 2, and 2
corporations. 139 corporations (120+9+4+2+2+2) are subsequently involved with the 125 plans.
7
  Three large corporations confirmed a liquidation scheme, but are already excluded.
8
  Three groups were already removed before because total group assets were larger than € 5.000.000.


                                                                                                              4
by legal form. Total liabilities are measured at the initiation of the procedure, i.e. 6 to 9 months before
plan confirmation. The public limited liability corporations are clearly larger than the private limited
companies. Our sample firms are less underwater compared to those in Bris et al. (2006), likely
because we use a sample of confirmed plans like in Baird et al. (2007).

2.3.       The debt structure at the moment of initiation of the procedure

Table 2: Debt composition of sample firms as reported in the confirmed reorganization plans.

Panel A shows the debt structure of our 89 small corporations at the start of the procedure. In panel B, the
sample firms are restricted to 68 firms with (secured and unsecured) bank debt. Panel C provides data on loan
securities. These securities provide a contractual liquidation right contingent upon default. A fixed charge is a
security in real estate. A floating charge is a security on mainly working capital.
Panel A: Debt structure at the initiation of the procedure
                                   Mean       Median        St. Dev.   Min      Max      Number of plans
                                                                                         with specific debt
Secured bank debt                  0.2582     0.2288        0.2508     0.0000   0.8521   58 out of 89
Unsecured bank debt                0.0238     0.0000        0.0839     0.0000   0.4777   10 out of 89
Trade credit                       0.4005     0.3647        0.2385     0.0482   1.0000   89 out of 89
Tax & Social Contributions         0.2485     0.1837        0.2185     0.0000   0.9028   86 out of 89
Owner-Directors                    0.0691     0.0000        0.1602     0.0000   0.6753   24 out of 89
Panel B: Debt structure of bank-financed firms at the initiation of the procedure
                                   Mean       Median       St. Dev.    Min      Max      Number of plans
                                                                                         with specific debt
Secured bank debt                  0.3380     0.3276       0.2351      0.0000   0.8521   58 out of 68
Unsecured bank debt                0.0311     0.0000       0.0949      0.0000   0.4777   10 out of 68
Trade credit                       0.3620     0.3365       0.2028      0.0482   0.9726   68 out of 68
Tax & Social Contributions         0.2053     0.1708       0.1649      0.0000   0.6297   66 out of 68
Owner-Directors                    0.0636     0.0000       0.1507      0.0000   0.6753   19 out of 68
Panel C: Collateral rights
Number of bank-financed firms
with . . .
Both a Fixed and floating charge      36
Only a Fixed charge                   1
Only a Floating charge                21
No security                           10
Total bank-financed firms          68 (36 + 1 + 21 + 10)

Personal guarantee (in addition       13
to other securities)


Panel A of table 2 shows that bank debt and trade credit are the main sources of finance in our sample
of 89 small corporations and that the latter source dominates the former on average9 . Trade credit
amounts on average to 40% of total debt in our sample10. Only 58 of the 89 distressed sample firms

9
  Creditors benefiting from retention of title clauses are most likely trade creditors, and their claims are therefore
included in the trade credit. Due employee wages are incorporated in the trade credit because bankruptcy
documents do not allow to distinguish them from trade claims. Social security contributions regarding the
employee wages are included in the government debt. Clearly, the continuation decision of distressed firms
critically depends on the employees, which typically results in paying out wages (but without transferring social
contributions to the administration). Fisher & Martel (1994) report that only 23% of Canadian plans involve
some wage claims; wage claims to total liabilities amounts to 0.35% in their sample study.
10
   Cunat (2007) reports a ratio of trade credit to total debt of 35% for a sample of small US firms (data-input
from the NSSBF). For the U.K., he finds a percentage of 41% for a sample of medium and large corporations
(data-input from FAME). See Rajan and Zingales (1995) for an analysis of the capital structure for a sample of
large listed companies of the G-7 countries. Their accounts payable to assets amounts approx. 15 %.


                                                                                                                    5
have secured bank financing at all at (the moment of) procedure initiation. Due taxes and social claims
also constitute a considerable debt mass, while junior-subordinated owner/director debt11 is not a
frequent source of finance. Panel B shows that trade creditors remain the main providers of external
funds even for the 68 cases with bank debt (both secured and unsecured). Remarkably, in both panel A
and B distressed firms rely heavily on tax and social contributions as a source of finance (more than
20% in both panels). Unpaid government claims are omnipresent and seem larger in Belgium than in
other countries like the U.S. or Canada. Bris et al. state that median Chapter 11 tax claims are zero in
their sample12. Their ratio of tax claims on total liabilities depends on the filing district. The ratio
averages 14% in New York, while it is only 3% in Arizona. Using a sample of confirmed plans, Baird
et al. (2007) report a percentage of 7,3%. Unpaid Canadian government claims average only a few
percentages (Fisher & Martel, 1994, 1995).

Panel C of table 2 reports that almost 90% of bank debt is covered by a fixed and/or floating charge.
These securities provide a contractual liquidation right contingent on default. A fixed charge is a
security in real estate. A floating charge is a security on machinery and working capital such as
receivables and inventory. This high degree of collateralization is comparable to other European
countries (see Davydenko and Franks for the U.K., France and Germany, 2008). In our sample,
multiple bank situations occur in only 16 of 89 cases13, implying that securities are often concentrated
in the hands of a single bank.


3.       Hypotheses and empirical approach

Peterson and Rajan (1997) argue that the demand of trade credit may be driven by arguments from the
pecking order theory (Myers 1984). The classical pecking order theory predicts a sequence for
financing decisions: firms finance new investments first with internal funds, then with safe debt, then
risky debt and finally with outside equity. Adverse selection costs (due to information asymmetries)
and transaction costs of issuing risky debt and equity securities induce this hierarchy. Internal funds
have no adverse selection problem, while both debt and equity require an adverse selection risk
premium. Debt demands a lower risk premium than equity. The nature of the financing sequence of
the pecking order theory results in a minimization of the adverse selection premiums and transaction
costs14. Specifically Peterson and Rajan argue that internal funds precede trade credit in the pecking
order. They find that each additional dollar of profits lowers the demand for trade credit by 23 cents15.




11
   Owner/director debt includes credit provided by group companies.
12
   Tax claims include social contributions in the U.S. Government debt in our study refers to both tax and social
contributions.
13
   2, 3 and 4 banks are involved with respectively 12, 3 and 1 corporations.
14
   The empirical literature on the pecking order theory especially focuses on the financing decisions of public
quoted American firms; we refer to Helwege and Liang (1996), Shyam-Sunder and Myers (1999), and Frank and
Goyal (2003) in this respect. See Barclay et al. (2006) and Manigart and Van Acker (2009) on capital structure
and high growth ventures.
15
   Peterson and Rajan (1997) appropriately argue that cash flow rather than profits is the correct variable to test
the pecking order theory.


                                                                                                                 6
This result is found in a sample of small firms, of which are 90% owner-managed. Outside equity is
however not issued by these small firms, implying that the model of Myers and Majluf (1984) based
on informational asymmetries between existing and prospective shareholders is not appropriate to
explain the found pecking order sequence. The trade creditor’s equity-like stake and other specific
trade credit theories (see introduction) may however explain why trade creditors are willing to finance
a shortfall in cash flow. We formulate our first hypothesis as follows:

Hypothesis 1.: Cash flow precedes trade credit in the pecking order for small distressed firms.

The term ‘pecking order’ in hypothesis 1 refers to the financing sequence implied by specific trade
credit theories rather than by the model of Myers and Majluf (1984). In order to test hypothesis 1, we
estimate reduced form models in the spirit of Peterson and Rajan (1997) that link the use of trade
credit to both demand and supply factors. Specifically we estimate


(TCt / TAt −1 )i = b0 + b1Ci ,t −1 + b2 BDi ,t −1 + b3GDi ,t −1 + b4 STCi,t + b5 Z i,t −1 + ε i               (1)


The subscript t-1 refers to the last annual account prior to filing bankruptcy (pre-bankruptcy), t refers
to the moment of initiation of the procedure, and the subscript i refers to the firm. The vector C is a
vector of proxies for the firm’s ability to generate cash through its operations. Peterson and Rajan
(1997) find that a firm’s ability to generate cash internally (vector C) decreases its demand for trade
credit, and this after controlling for the firm’s access to credit from financial institutions. We will
therefore define various proxies to control for the firm’s access to bank debt (vector BD) in the
empirical sections. Baird et al (2007) argue that small firms also rely on unpaid taxes to finance their
business operations in the running-up to Chapter 11. Since the access to government debt may
therefore also affect the demand for trade credit, we include a proxy for unpaid government debt
(vector GD) in the analysis. Last we also control for the supply of trade credit (vector STC), by
including measures of the quality of the management and the transparency of the firm that are proxies
for the information advantage theories of trade credit. Z is a vector of control variables including
sector dummies. If b1<0, we cannot reject hypothesis 1.

Special attention will be devoted to the impact of a contraction or expansion of bank debt during the
pre-bankruptcy period on the provision of trade credit. Rodríguez-Rodríguez (2006) suggests that bank
debt and trade credit may be substitutes and that firms experiencing short term bank finance
constraints may be more likely to use trade credit. If banks contract their lending in the running up to
bankruptcy filing, the bank debt capacity of the distressed firm has been reached16. Specifically under


16
   We refer to Lemmon and Zender (2007) for an empirical study of the classical pecking order theory of Myers
(1984) after controlling for a firm’s debt capacity. They argue that the debt capacity of a distressed firm is
reached if the costs of financial distress curtail further debt issues. According to Lemmon and Zender (2007), the
combination of debt capacity and the pecking order theory suggests that the costs of adverse selection are
dominant for “low to moderate” leverage levels but that tradeoff-like forces become primary motivators of
financing decisions at “high” levels of leverage. Those so-called tradeoff-like forces refer to the tradeoff theory
(that competes with the pecking order theory) to explain the financing decisions of firms in modern corporate
finance literature. The tradeoff theory of capital structure predicts that firms choose their mix of debt and equity


                                                                                                                    7
a bank debt contraction, an additional liquidity constraint due to a shortfall in internally generated cash
might heavily increase the use of trade credit. In the opposite case of a pre-bankruptcy bank debt
expansion, the distressed firm’s bank debt capacity is not fully exhausted as banks are still willing to
provide additional credit. This bank debt expansion improves the distressed firm’s liquidity position,
and entrepreneurs are expected to resort less on trade credit to finance a shortfall in internal funds.
Hence our second hypothesis:

Hypothesis 2: Small distressed firms with low internal cash flows rely more on trade credit it their
bank contracts lending in the pre-bankruptcy period.

In order to test hypothesis 2, we will estimate a simplified benchmark version of (1) for several
subsamples. First we distinguish between a subsample with a bank debt contraction and a bank debt
expansion. Later we distinguish between large and small bank debt contractions. We consider two data
sources for our bank debt contraction and expansion variables. Hypothesis 2 cannot be rejected if |b1 |
is larger in the subsample with bank debt contraction than in the subsample with bank debt expansion
and is further emphasized if |b1| is larger in the subsample with the largest bank debt contraction


4.       Results

4.1.     Does cash flow precedes trade credit in the pecking order?

In table 3 below, we provide estimates of (1). Trade credit is measured at the initiation of the
bankruptcy procedure (data from the judicial files), and is normalized by pre-bankruptcy assets. To
verify hypothesis 1, we introduce a number of financial variables. If we find that trade credit is
negatively related to the internal money flows, the net demand effect of the pecking order formulated
in hypothesis 1 dominates. In specification 1 we introduce profitability (net profits17/assets). The
negative coefficient confirms more heavily distressed firms demand more trade credit. In specification
2 we substitute cash flow (cash flow/assets) for profitability as a measure of internal money flow
generation. Again we find a strongly significant negative relation, supporting the earlier conclusion
that more distressed firms demand more trade credit. The estimated cash flow coefficient of -0.3175
implies that each additional euro of loss in cash flow increases the demand for trade credit by around
32 cents (similar for net profits/assets in specification 1). This cash flow variable is introduced in most
specifications and the results are very robust. Hypothesis 1 cannot be rejected: trade credit is higher in
the “pecking order” than internally generated cash in our sample of small distressed firms.

Aspects of both economic and financial distress drive the variables net profits/assets and cash
flow/assets. Earning before interests, taxes, depreciation and amortization (EBITDA) is commonly

to balance the benefits and costs of debt. Tax benefits of borrowed money and the control of free cash flow
problems are argued to increase the use of debt, while the costs of financial distress and conflicts between debt
holders and equity provides firms with incentives to limit their debt financing. A value-maximizing firm equates
benefit and costs at the margin. At high levels of leverage, the costs of financial distress typically curtail further
debt issues. Lemmon and Zender (2007) argue that the use of debt capacity makes it more difficult to distinguish
between the pecking order theory and the tradeoff theory.
17
   Net profit before taxes.


                                                                                                                    8
used as a proxy to investigate the degree of economic distress, as it does not reflect differences in debt
structure (see Hotchkiss 1995). This operational cash flow variable is scaled by assets and introduced
in specification 3. Its estimate is 1.71 standard deviations away from zero and the explanatory power
of the model drops to some extent, suggesting that both economic and financial distress, rather than
only the former type of distress, drive the demand for trade credit in the pre-bankruptcy period.


In specification 4 to 11 we control for the access to bank debt. Peterson & Rajan (1997) include
proxies for a firm’s credit availability and the relationships with financial institutions in their trade
credit models. Firms with large unused lines of credit and with strong bank lending relationships
demand less trade credit. The relationship with financial institutions has however no effect on the
supply of trade credit. The validity of the pecking order hypothesis might critically depend on the
distressed firm’s access to bank financing, especially in the bank-based European credit system. The
results however show that the coefficient for cash flow in table 3 is consistently negative across
specifications 4 to 11. Our finding that b1 < 0 is therefore very robust to the inclusion of proxies for
the access of bank debt. All proxies for the access to bank debt show up with a negative sign,
suggesting that bank debt and trade credit are substitutes.

Specification 4 controls for the distressed firm’s access to bank debt financing by including the
variable pre-bankruptcy bank debt (bank debt scaled by assets). Specification 4 also controls for
industry effects18. Cunat (2007) finds that trade credit use is higher in firms with low levels of
collateralizable assets as firms with more land and fixed assets have more access to other financing
sources. He finds that the economic effect of his collateral variable measured as the book value of land
and fixed assets to total assets is particularly strong. In our sample, we find that bank debt is heavily
collateralized (see section 3). We show in table 1 of appendix A that the ratio of the book value of land
and buildings to total assets is a very good predictor of outstanding bank debt. Specification 5
therefore introduces the variable Land and buildings on total assets as a proxy for a firm’s access to
bank financing. A significant negative coefficient is found with a relatively large coefficient. The
difference between the first and third quartiles of our collateral variable is 0.2874 (0.31 in Cunat),
which implies a reduction of trade credit of 10.36% (4.03% in Cunat). Specification 6 additionally
controls for industry effects. Specification 7 and 8 are two-stage least squares regressions with the
variable Bank debt at procedure initiation/assets as instrumented variable. The instrumental variables
are discussed in appendix A, and include the (1) Land and buildings/assets and (2) a proxy for the
variability in the business returns19 (firms with stable cash flows have more access to bank debt) and
(3) the dummy variable Debt personally guaranteed. The coefficient of the variable Bank debt at
procedure initiation/assets is marginally significant in specification 7, and becomes insignificant after
controlling for industry effects in specification 8. One euro of additional bank debt at procedure

18
   In the remaining specifications, we will often control for industry effects in main specifications. Industry
dummies are defined as follows: wholesale (23 cases), retail (15 cases), manufacturing (13 cases), hotels and
restaurants (9 cases), construction (8 cases), and other industries (21 cases). Other industries are the omitted
category. We find that manufacturing firms have significantly lower levels of trade credit.
19
   We use the industry’s variation in profit margin as proxy for the variability in the business returns. The
industry’s variation in profit margin consists in the industry average of the standard deviation of the operating
profit margin over the last 3 fiscal years prior to petition filing. This variable is based on variation in profit
margin within businesses over time (i.e. non cross-sectional).


                                                                                                                9
initiation results in an expected reduction of trade credit use of around 35 and 28 cents in respectively
specification 7 and 8.

The impact of the access to government debt as ‘informal’ financing mechanism for distressed firms
(see Baird et al 2007) is controlled in specifications 9 to 11. Table 1 of appendix B shows that the
variable Payroll costs/assets is a very good predictor of the unpaid government debt level at the
moment of initiation of the bankruptcy-reorganization procedure, since overdue taxes and social
contributions on salaries are the origin of the unpaid government debt. In specifications 9 to 11 we
find a large and negative coefficient for Payroll costs/Assets, indicating that trade credit and overdue
government debt are substitutes. After controlling for a distressed firm’s access to both government
debt and bank debt, we find that each additional euro of loss in cash flows increases the demand for
trade credit by even more than 30 cents.

Trade creditors do not supply trade credit blindly to any firm that demands it. In all specifications we
include the log of firm age and a dummy for Public Limited liability Corporation. Both variables are a
proxy of the transparency of the firms. We indeed find that Public limited Liability Corporations get
more trade credit from their suppliers, but older firms do not. In specifications 10 to 11 we enrich the
supplier’s information set with information about the management of the distressed firm. We tracked
down the involvement of members of the executive board in earlier bankruptcies in Belgium (Previous
bankruptcies) and the experience of the executive board of the distressed firm on the boards of other
Belgian firms (Management experience). The former variable counts earlier bankruptcies in which the
board of directors has been involved as a director20, while the latter counts all positions on boards ever
held by members of the distressed firm’s board, excluding earlier bankruptcies. We find that suppliers
extend less trade credit to firms whose managers have more bankruptcies on their slate. Suppliers may
also tend to extend more trade credit to firms with a more experienced management team, although
this result is not significant.

All specifications include a control for firm age. Larger firms have less trade credit in all
specifications. It could be that larger firms demand less trade credit because they have more access to
other financing sources (due to limited information asymmetries) or because they have less growth
opportunities, as suggested by Peterson & Rajan (1997). Hart & Moore (1994) and Diamond (1991)
present rationales for firms matching the maturity of assets and liabilities. Indeed, our control variable
for current assets (current assets excluding cash/assets) is clearly related to trade credit levels,
although its significance is specification sensitive. In specifications 5 to 11, the variable Current assets
excl. cash/assets is replaced by two main components of current assets: Inventories/assets and
Receivables/assets. In this way we avoid the simultaneous introduction of variables that are highly
correlated (Current assets excl. cash/assets and Land and buildings/assets). The variables
Inventories/assets and Receivables/assets are not significant and the latter has even a negative sign21.

20
   If a firm goes bankrupt two years after management dismissal, we consider the dismissed manager responsible
and count it as an involvement in a previous bankruptcy. In Belgium, from a legal point of view, replaced
managers even remain responsible for three years after their discharge.
21
   This suggests that the positive coefficient on current assets excl. cash / assets found in specifications 1 to 4 is
largely driven by the collateral variable (likely idem in the study of Peterson and Rajan).


                                                                                                                  10
Table 3: Determinants of Trade credit/Total assets.
The dependent variable Trade Credit/Total Assets is the firm’s trade credit at the start of the procedure scaled by pre-bankruptcy assets. Apart from the variables Bank debt at
procedure initiation/assets, L(Firm Age), D-Public Limited Liability Corporation, Previous Bankruptcies, Management Experience, all variables are directly obtained from the
latest annual account prior to the filing for bankruptcy-reorganization. The numerator of the variable Bank debt at procedure initiation / assets is obtained from the judicial
records (i.e. at the moment of procedure initiation). The variable Land and buildings/assets is used as collateral proxy (like in Cunat 2007) and determines the firm’s access to
external bank financing. The variable Bank debt at procedure initiation/assets is instrumented using a simplified bank debt model described in appendix A (specification 6 of
table 1 of appendix A). The variable Payroll costs/assets is used as proxy for unpaid taxes and social contributions (see appendix B for a simple model of government debt).
The variables Previous Bankruptcies and Management Experience respectively amount to the number of earlier bankruptcies (of other Belgian firms) in which the board of
directors has been involved, and their number of past and current management positions in the board of other Belgian firms. The values in brackets are the robust t-statistics: *
/ ** / *** significant at 10% / 5% / 1%.
                                             Spec. 1     Spec. 2      Spec. 3    Spec. 4     Spec. 5     Spec. 6     Spec. 7      Spec. 8      Spec. 9      Spec. 10     Spec. 11
Internal money flow generation
Net profits / assets                         -0.3255
                                             [-2.16]**
Cash flow / assets                                       -0.3175                 -0.2820     -0.3085     -0.2664     -0.3228      -0.2740      -0.3825      -0.3733      -0.3733
                                                         [-2.63]***              [-2.24]**   [-2.61]**   [-2.19]**   [-3.04]***   [-2.53]**    [-3.59]***   [-3.82]***   [-3.82]***
EBITDA / assets                                                       -0.2625
                                                                      [-1.71]*
Access to external funds
Bank debt / total assets                                                         0.0138
                                                                                 [0.10]
Land and buildings / total assets                                                            -0.3603     -0.2920                               -0.3886      -0.3685      -0.3685
(collateral)                                                                                 [-2.20]**   [-1.57]                               [-2.43]**    [-2.52]*     [-2.52]*
Bank debt at procedure initiation / assets                                                                           -0.3471      -0.2830
                                                                                                                     [-1.66]*     [-1.02]
                                                                                                                     (instrum.)   (instrum.)
Payroll costs / assets                                                                                                                         -0.3143      -0.3498      -0.3498
                                                                                                                                               [-2.39]**    [-2.97]***   [-2.97]***
Supply of trade credit
D-Public Limited Liability Corporation       0.1255      0.1223       0.1507     0.1640      0.1337      0.1756      0.1365       0.1837       0.1257       0.0876       0.0876
                                             [1.44]      [1.40]       [1.76]*    [1.87]*     [1.56]      [2.05]**    [1.56]       [2.19]**     [1.47]       [1.13]       [1.13]
L(Firm Age)                                  -0.0224     -0.0368      -0.0136    -0.0623     -0.0129     -0.0372     -0.0391      -0.0566      -0.0212      0.0001       0.0001
                                             [-0.52]     [-0.89]      [-0.29]    [-1.30]     [-0.34]     [-0.85]     [-0.98]      [-1.25]      [-0.56]      [0.00]       [0.00]
Previous Bankruptcies                                                                                                                                       -0.0786      -0.0786
                                                                                                                                                            [-2.80]***   [-2.80]***
Management Experience                                                                                                                                       0.0149       0.0149
                                                                                                                                                            [1.23]       [1.23]




                                                                                                                                                                                      11
Continuation table 3                Spec. 1      Spec. 2      Spec. 3      Spec. 4      Spec. 5      Spec. 6     Spec. 7      Spec. 8      Spec. 9     Spec. 10     Spec. 11
Controls
L(Total Assets)                     -0.0987      -0.0864      -0.1135      -0.0806      -0.0747      -0.0739     -0.0825      -0.0817      -0.0698     -0.0856      -0.0856
                                    [-3.21]***   [-3.28]***   [-2.90]***   [-2.74]***   [-2.64]***   [-2.51]**   [-2.80]***   [-2.93]***   [-2.45]**   [-2.90]***   [-2.90]***
Current assets excl. cash/ assets   0.2655       0.1971       0.2358       0.1488
                                    [2.24]**     [1.66]       [1.98]*      [1.38]
Inventories / assets                                                                    0.0938       0.0521      0.1699       0.1091       0.0963      0.0583       0.0583
                                                                                        [0.42]       [0.19]      [0.75]       [0.42]       [0.44]      [0.28]       [0.28]
Receivables / assets                                                                    -0.1810      -0.2139     -0.1241      -0.1950      -0.0780     -0.0775      -0.0775
                                                                                        [-0.89]      [-0.91]     [-0.74]      [-0.91]      [-0.38]     [-0.39]      [-0.39]

Industry dummies                    NO           NO           NO           YES          NO           YES         NO           YES          NO          NO           YES
Intercept                           0.8204       0.8286       0.9550       0.9871       0.8947       1.0389      1.0160       1.1392       0.9454      1.0259       1.0259
                                    [4.59]***    [5.08]***    [4.44]***    [5.55]***    [6.24]***    [6.67]***   [6.57]***    [6.54]***    [6.43]***   [6.79]***    [6.79]***

No. of observations                 89           89           89           89           89           89          89           89           89          89           89
R²                                  0.2777       0.3138       0.2414       0.3762       0.3326       0.3916      0.2954       0.3724       0.3821      0.4315       0.4315
                                                                                                                 (Centered    (Centered
                                                                                                                 R²)          R²)




                                                                                                                                                                                 12
4.2.     The effect of pre-bankruptcy bank behavior

Hypothesis 2 states that distressed firms rely more on trade credit to finance a shortfall in cash
flow if bank debt contracts than if bank debt expands. We test this by estimating a simplified
benchmark specification of (1) for various subsamples of pre-bankruptcy bank debt contraction
and expansion. The sample split estimations presented in table 4 are based on the difference
between the average pre-bankruptcy bank debt, calculated using the annual accounts three years
prior to filing, and the bank debt reported in the confirmed plan. Banks reduced their credit in 47
cases, while additional credit was provided in 36 cases22. In panel A of table 4 we report estimates
for the subsample with pre-bankruptcy bank debt contraction (left three columns) and pre-
bankruptcy bank debt expansion (right three columns). We find that trade credit is negatively and
significantly related to the internal money flow generation for the 47-case subsample with bank
debt contraction, where there is no such relation in the subsample with bank debt expansion.
Panel B distinguishes between large and small bank debt contractions, using the median
contraction during the 3-year pre-bankruptcy period as cut-off point. The size of the contractions
is calculated as the difference between the volume of debt reported in the reorganization plan and
the average pre-bankruptcy bank debt level reported in the accounts, scaled by the latter bank
debt level. The median contraction amounts -42.80%. Under large bank debt contractions
entrepreneurs rely more heavily on trade credit to finance a shortfall in cash, while the results do
not point in this direction under small bank debt contractions. Hypothesis 2 cannot be rejected.

The sample split estimations presented in table 5 rely on unique data on the bank debt flows
during a 12-month pre-bankruptcy period, obtained by intermediation of the Central Corporate
Credit Register of the National Bank of Belgium. These data were only available for 51 firms (out
of 61 cases with bank debt)23. Banks contract their lending during a 12-month pre-bankruptcy
period in 37 cases, while they expand it in 14 cases. In panel A of table 5 we report estimates for
the subsample with pre-bankruptcy bank debt contraction (left three columns) and pre-bankruptcy
bank debt expansion (right three columns). Trade credit is negatively related to the internal
money flow variables in the subsample of debt contraction, though not always significantly, while
there is again no negative relation in the subsample with bank debt expansions. In panel B, the
median pre-bankruptcy contraction24 (-22,8%) is employed as cut-off point to define a subsample
with large contractions (19 out of 37) and one with small contractions (18 out of 37). Again
results indicate that large bank debt contractions push entrepreneurs to heavily rely on trade credit
to finance a shortfall in cash, while there is no such evidence if the bank debt contraction is small.
Again hypothesis 2 cannot be rejected.

22
   There are 6 cases without a change in the bank debt during the pre-bankruptcy period.
23
   Data are obtained by intermediation of the Central Corporate Credit Register of the National Bank of
Belgium. Missing cases are largely due to small credits (< € 25.000), which are not reported in the register.
24
   The flows are calculated as the difference between the bank debt at procedure initiation and the bank debt
12 months before filing for bankruptcy-reorganization, scaled by this latter bank debt level.



                                                                                                         13
Table 4: The effect of pre-bankruptcy bank behavior on the levels of trade credit (sample split 1).
The dependent variable Trade Credit/Total Assets is the firm’s trade credit at the start of the procedure
scaled by pre-bankruptcy assets. To define the subsamples in panel A, we rely on a comparison between the
average pre-bankruptcy bank debt, as reported in the accounting data three years prior to petition filing, and
the bank debt reported in the reorganization plans. Banks reduced their credit for 47 cases, while additional
credit was provided for 36 cases. Panel B defines subsamples with large and small bank debt contractions
during the 3-year pre-bankruptcy period. The median contraction is used as cut-off point to define a
subsample with large contractions (24 cases) and one with small contractions (23 cases). The values in
brackets are the robust t-statistics:* / ** / *** significant at 10% / 5% / 1%.
Panel A                             Bank debt contraction               Bank debt expansion
                              Spec. 1      Spec. 2      Spec. 3     Spec. 1      Spec. 2      Spec. 3
Internal funds
EBITDA / assets               -0.4177                               0.0059
                              [-2.49]**                             [0.05]
Net profits / assets                       -0.3467                               -0.0168
                                           [-2.00]**                             [-0.15]
Cash flow / assets                                      -0.3016                               -0.0171
                                                        [-1.71]*                              [-0.14]
Controls
L(total assets)               -0.0826      -0.0777      -0.0720     -0.0514      -0.0505      -0.0507
                              [-2.23]**    [-2.11]**    [-2.03]**   [-2.20]**    [-2.13]**    [-2.18]**
Land and buildings / assets   -0.4063      -0.3952      -0.4120     -0.3104      -0.3075      -0.3074
(bank debt collateral)        [-2.18]**    [-2.04]**    [-2.21]**   [-2.33]**    [-2.34]**    [-2.32]**


Intercept                     1.0467       0.9600       0.9548      0.7489       0.7388       0.7408
                              [4.20]***    [3.92]***    [4.04]***   [4.79]***    [4.43]***    [4.62]***
Number of obs.                47           47           47          36           36           36
R-squared                     0.2800       0.2316       0.2258      0.2188       0.2192       0.2191
Prob > F                      0.0037***    0.0051***    0.0097***   0.0011***    0.0010***    0.0009***


Panel B                       Large bank debt contraction           Small bank debt contraciton
                              Spec. 1      Spec. 2      Spec. 3     Spec. 1      Spec. 2      Spec. 3
Internal funds
EBITDA / assets               -0.5029                               -0.0093
                              [-2.81]**                             [-0.04]
Net profits / assets                       -0.4399                               0.0058
                                           [-2.40]**                             [0.02]
Cash flow / assets                                      -0.3783                               0.0025
                                                        [-1.92]*                              [0.01]
Controls
L(total assets)               -0.0734      -0.0782      -0.0683     -0.0893      -0.0886      -0.0887
                              [-1.17]      [-1.28]      [-1.18]**   [-1.37]      [-1.55]      [-1.51]
Land and buildings / assets   0.0878       0.0062       0.0565      -0.4338      -0.4308      -0.4309
(bank debt collateral)        [0.29]       [0.02]       [0.19]      [-1.42]      [-1.49]      [-1.57]


Intercept                     0.9457       0.9052       0.8808      1.1226       1.1176       1.1174
                              [2.38]**     [2.31]**     [2.39]**    [2.17]**     [2.23]**     [2.27]**
Number of obs.                24           24           24          23           23           23
R-squared                     0.3101       0.2233       0.2085      0.1587       0.1587       0.1586
Prob > F                      0.0297**     0.0598*      0.1959      0.4437       0.1289       0.2430




                                                                                                          14
Table 5: The effect of pre-bankruptcy bank behavior on the levels of trade credit (sample split 2).
The dependent variable Trade Credit/Total Assets is the firm’s trade credit at the start of the procedure
scaled by pre-bankruptcy assets. To define the subsamples in panel A, we rely on unique data on the bank
debt flows during a 12-month pre-bankruptcy period, which are obtained by intermediation of the National
Bank of Belgium, and were available for 51 firms (out of 68 cases with bank debt). Banks reduced their
credit for 37 cases, while additional credit was provided for 14 cases. Panel B defines subsamples with
large and small bank debt contractions during the 12-month pre-bankruptcy period. The median contraction
is used as cut-off point to define a subsample with large contractions (19 cases) and one with small
contractions (18 cases). The values in brackets are the robust t-statistics:* / ** / *** significant at 10% / 5%
/ 1%.
Panel A                          Bank debt contraction                Bank debt expansion
                              Spec. 1      Spec. 2      Spec. 3      Spec. 1      Spec. 2      Spec. 3
Internal funds
EBITDA / assets               -0.3419                                0.2799
                              [-1.63]                                [0.91]
Net profits / assets                       -0.2395                                0.2088
                                           [-1.52]                                [0.71]
Cash flow / assets                                      -0.3369                                0.2860
                                                        [-2.12]**                              [0.84]
Controls
L(total assets)               -0.0938      -0.0793      -0.0829      -0.0738      -0.0593      -0.0719
                              [-3.78]***   [-3.75]***   [-3.97]***   [-0.86]      [-0.74]      [-0.83]
Land and buildings / assets   -0.1318      -0.1485      -0.1390      -0.5854      -0.5685      -05653
(bank debt collateral)        [-1.24]      [-1.37]      [-1.31]      [-1.23]      [-1.20]      [-1.22]


Intercept                     0.9945       0.8684       0.8921       1.0240       0.9497       1.0286
                              [5.42]***    [5.58]***    [5.78]***    [1.49]       [1.40]***    [1.44]
Number of obs.                37           37           37           14           14           14
R-squared                     0.3913       0.3186       0.4448       0.1484       0.1339       0.1501
Prob > F                      0.0067***    0.0042***    0.0014***    0.5619       0.4806       0.5591


Panel B                            Large bank debt contraction            Small bank debt contraction
                              Spec. 1      Spec. 2      Spec. 3      Spec. 1      Spec. 2      Spec. 3
Internal funds
EBITDA / assets               -0.6543                                0.0487
                              [-3.93]***                             [0.39]
Net profits / assets                       -0.5743                                -0.0299
                                           [-1.98]*                               [-0.36]
Cash flow / assets                                      -0.5219                                -0.0147
                                                        [-3.71]***                             [-0.16]
Controls
L(total assets)               -0.1003      -0.0588      -0.0702      -0.1036      -0.1014      -0.1020
                              [-3.55]***   [-1.88]*     [-3.02]***   [-2.43]**    [-2.35]**    [-2.37]**
Land and buildings / assets   0.0297       -0.0930      -0.0303      -0.1806      -0.1744      -0.1783
(bank debt collateral)        [0.19]       [-0.45]      [-0.15]      [-1.51]      [-1.52]      [-1.54]


Intercept                     1.0433       0.7154       0.8026       1.0685       1.0401       1.0497
                              [5.78]***    [3.36]***    [5.34]***    [3.28]***    [3.11]***    [3.16]***
Number of obs.                19           19           19           18           18           18
R-squared                     0.6860       0.4473       0.6833       0.4325       0.4312       0.4297
Prob > F                      0.0054***    0.1299       0.0075***    0.0200**     0.0874*      0.0662*




                                                                                                            15
5.      Conclusions

Small distressed firms demand more trade credit during the pre-bankruptcy period to compensate
a shortfall in internally generated cash. This is especially the case if banks contract their lending
during the pre-bankruptcy period. Bank debt and government debt are found to be substitutes for
trace credit: firms with higher bank debt and government debt capacity demand less trade credit.
Trade creditors may be willing to play an active role in financing distressed firms because they
are well-informed. We indeed find that trade creditors do not supply credit blindly to distressed
firms demanding it. Trade creditors are more eager to supply trace credit to Public Limited
Liability Corporations which are likely to be more transparent. They are also less eager to supply
trade credit to entrepreneurs that were involved in earlier bankruptcies (in other boards than the
distressed sample firms’), suggesting that the reputation of the debtor does play an important role.
Suppliers might also want to extend trade credit to our sample firms, because all sample firms had
their going-concern plan confirmed under court-supervised reorganization. For these plans, the
business activity is expected to continue, and the going concern value can be split between the
entrepreneur and the creditors. Cunat (2007) shows that if there is a divisible surplus between the
suppliers and the entrepreneur, the trade creditors may act as liquidity providers, insuring against
liquidity shocks that could endanger the survival of their customer relationships.




                                                                                                  16
Appendix A.

Bank debt financing: a simple model.

Financial contracts critically depend on the liquidation value of the pledged assets (see e.g. Hart
& Moore 1994, 1998; Berglöf & Von Thadden 1994). The creditor’s willingness to provide credit
increases with the pool of collateralizable assets. Less-specialized assets are preferred as
collateral to avoid ‘fire sales’ because of illiquid markets upon liquidation (Shleifer & Vichny,
2002). Berger et al. (1996) find that less specialized assets results in more liquidation option
value per dollar of book value. Land and buildings are typically considered as very redeployable
assets because of their less-specialized nature25.

We expect that more bank debt is attracted when firms have more land and buildings on their
balance sheets. In specification 1 of table 1 of this appendix, we regress the book value of land
and buildings on the bank’s loan size (both variables scaled by pre-bankruptcy assets). The book
value of real estate is reported in the pre-bankruptcy accounts and the outstanding bank debt is
measured at the moment of imitation of the bankruptcy-reorganization procedure. We control for
pre-bankruptcy assets. The empirical findings of specification 1 are in line with our expectations.
Specification 2 shows that Machinery and equipment/Assets has no effect on the outstanding
bank debt.

Creditors are reluctant to provide credit when a distressed firm’s profit and cash flow realization
is highly uncertain (see e.g. Lemmon & Zender 2007). After all, creditors bear the full risk if the
distressed business ultimately fails, and they need to share the potential business surplus value
with the shareholders. Specification 3 controls for uncertainty by introducing the variation in the
industry’s profit margin26 and the industry attrition rate. The latter rate is the proportion of small
businesses within a particular industry that file a petition for bankruptcy-liquidation each year
(see Morrison 2007). We also introduce EBITDA/assets in specification 3. We find that firms
with more variation in the industry’s profit margin significantly attract less bank debt.

Asymmetric information reduces the willingness of creditors to provide credit. Firm age is used
as a measure of the informational transparency of a firm, whereby older firms are expected to be
more transparent. Specification 4 introduces the logarithmic value of the age of a firm, i.e. the
variable L(firm age). We surprisingly find that older firms have lower levels of bank debt.
Specification 5 shows that firms older than 20 years have less bank financing, while young firms
(< 5 years) do not suffer from informational asymmetries.


25
  Ronen & Sorter (1972) classify land and buildings as less specialized than other fixed assets.
26
  The industry’s variation in profit margin consists in the industry average of the standard deviation of the
operating profit margin over the last 3 fiscal years. This variable is based on variation in profit margin
within businesses over time (i.e. non cross-sectional – based on 3-digit Nace codes).


                                                                                                         17
Specification 6 adds the dummy variable D-Debt personally guaranteed that amounts 1 if the
entrepreneur provided a personal guarantee to the bank (13 out of 89 cases). This dummy variable
is significantly positive as expected. Specification 6 is used to instrument the level of bank debt at
the moment of procedure initiation (scaled by pre-bankruptcy assets) in our trade credit model of
section 4.1.

Table 1 of appendix A
The dependent variable is bank debt at the moment of initiation of the procedure scaled by total assets. The
independent variables Land and buildings/assets, Machinery and equipment/assets, total assets,
EBITDA/assets are obtained from the latest pre-bankruptcy fiscal accounts prior to petition filing. The
industry’s variation in profit margin consists in the industry average of the standard deviation of the
operating profit margin over the last 3 fiscal years. This variable is based on the variation in profit margin
within businesses over time (i.e. non cross-sectional). The variable Industry attrition rate is the proportion
of small businesses within a particular industry that file a petition for bankruptcy-liquidation each year. The
dummy D-Debt personally guaranteed amounts one if the entrepreneur provided a personal guarantee to the
bank, and zero otherwise. The values in brackets are the robust t-statistics: * / ** / *** significant at 10% /
5% / 1%.
                                            Spec. 1     Spec. 2     Spec. 3     Spec. 4     Spec. 5      Spec. 6
Book value of land and buildings / assets   0.5168      0.5182      0.5137      0.5308      0.5363       0.3840
                                            [3.89]***   [3.89]***   [3.62]***   [4.41]***   [4.52]***    [2.35]**
Book value machinery and equipment /                    0.0812
assets                                                  [0.54]
Industry variation in profit margin                                 -0.0200     -0.0236     -0.0296      -0.0186
                                                                    [-1.72]*    [-2.28]**   [-2.73]***   [-2.35]**
Industry Attrition Rate                                             0.0405
                                                                    [0.55]
EBITDA / assets                                                     -0.0485
                                                                    [-0.42]
L(Firm age)                                                                     -0.0587
                                                                                [-1.99]**
D - Old firm (>20 years)                                                                    -0.2109
                                                                                            [-2.61]**
D - Young firm (< 5 years)                                                                  0.0294
                                                                                            [0.47]
D-Debt personally guaranteed                                                                             0.2115
                                                                                                         [1.67]*
Controls
L(assets)                                   -0.0024     -0.0016     -0.0175     -0.0074     -0.0140
                                            [-0.13]     [-0.08]     [-0.79]     [-0.33]     [-0.67]

Intercept                                   0.2383      0.2271      0.3737      0.5070      0.4674       0.3041
                                            [1.85]*     [1.70]*     [1.72]*     [2.71]***   [2.47]**     [5.08]***

No. of observations                         89          89          89          89          89           89
R-squared                                   0.1430      0.1443      0.1860      0.2034      0.2327       0.2338




                                                                                                                     18
Appendix B.

A model of government debt.

Baird et al. (2007) show that unpaid taxes are sizable for small Chapter-11 cases. They argue that
owner-managers of small distressed businesses typically invade trust funds meant to meet tax
obligations in order to escape financial distress, in the hope that their business is facing only a
temporarily lack of cash flow and they can replace the money before it is missed.

Although small firms in trouble owe little in the way of income tax (due to their limited profit
generation), their obligations to the tax and social security administration are substantial. First,
employers are required to withhold taxes from the employee income; i.e. withholding tax on the
employee’s personal income. Second, social security contributions related to the salaries of the
employees need to be paid. These contributions consist of two types: the employee’s and the
employer’s contribution to the social security of the employees. The previous contribution
requires that employers withhold around 13% from the gross employee income, while for the
latter contribution, they have to pay at least 35% of the gross employee salaries for the social
security of their employees. Third, value added taxes (sales taxes in the U.S.) need to be
transferred to the taxation authorities. The withholding tax (the 1st) and the contributions to the
social security (the 2nd) are related to the salaries of the employees, and account for slightly more
than 55% of the unpaid government debt. Value added taxes account for 37%27.

Firms that pay a higher payroll and add more value are ceteris paribus expected to pay more taxes
and contributions to the government. Given this higher flow of tax payments, these firms have a
higher access to government debt in times of distress since they can always finance themselves by
not paying what they owe the government. Specification 1 shows a strongly significant estimate
for payroll costs/assets (obtained from the last annual account prior to filing for court-supervised
reorganization) to estimate the due government debt. This strongly suggests that firms with
considerable payroll have more access to government debt by not transferring payroll taxes and
contributions to the authorities. In part, the variable Payroll costs/assets also controls for unpaid
value added taxes, because payroll costs contribute to the firm’s added value to the extent that
they are incorporated in the price of the goods and services delivered by the distressed firm (so,
the more payroll, the more added value, and the more payable VAT). Specifically for labor-
intensive firms, the generation of added value heavily depends on the size of the payroll costs.
Specification 2 introduces the contribution of the employer for the social security of its
employees (scaled by assets). Accounting data on this type of social security contribution, which
is at least 35% of the gross employee income, is separately reported in the annual accounts. We


27
  These statistics are drawn from our dataset on the confirmed reorganization plans. The remaining taxes
and social contributions are around 8%; i.e. customs duties, excise taxes, municipal taxes etc.


                                                                                                     19
find a positive and significant estimate that explains as follows: € 1 of additional employer’s
contribution increases the supply of government debt by € 1.47. The coefficient larger than one
suggests that the other social contributions (the 13%-employee contribution) and payroll
withholding taxes, which are both much related to the 35%-employer’s contribution, are also not
transferred to the authorities.

Table 1 of appendix B
The dependent variable is government debt at the moment of initiation of the procedure scaled by total
assets. All explanatory variables are obtained from the latest pre-bankruptcy fiscal accounts prior to petition
filing. The values in brackets are the robust t-statistics: * / ** / *** significant at 10% / 5% / 1%.
                                                                    Spec. 1      Spec. 2
Payroll costs/assets                                                0.4257
                                                                    [4.04]***

Employer’s contribution for social security of employees / assets                1.4700
                                                                                 [5.13]***
L(total assets)                                                     -0.0969      -0.0969
                                                                    [-5.23]***   [-5.21]***

Intercept                                                           0.7501       0.7619
                                                                    [5.66]***    [5.88]***
Number of obs.                                                      89           89
R-squared                                                           0.3815       0.4138
Prob > F                                                            0.0000***    0.0000***




                                                                                                           20
Appendix C: summary statistics
                                                                Mean      Median    Std. Dev.
Debt composition variables (debt reported in confirmed plans)
Trade credit / assets                                           0.4628    0.3919    0.3938

Internal money flow generation
Net profits / assets                                            -0.2364   -0.1008   0.3714
Cash flow / assets                                              -0.2036   -0.0443   0.4613
EBITDA / assets                                                 -0.0711   0.0220    0.3479

Access to external funds
Pre-bankruptcy bank debt / total assets                         0.3055    0.2593    0.2534
Land and buildings / total assets (collateral proxy)            0.1463    0         0.2177
Bank debt at procedure initiation / assets                      0.2990    0.2407    0.2942
Payroll costs /assets                                           0.3029    0.2380    0.3061

Supply of trade credit
D-Public Limited Liability Corporation                          0.5056    1         0.5028
L(Firm Age)                                                     2.1005    2.1963    0.8386
Previous Bankruptcies28                                         0.5618    0         1.0220
Management Experience29                                         3.4831    2         4.8433

Controls
L(Total Assets)                                                 6.1229    6.3333    1.3698
Current assets excl. cash/ assets                               0.5789    0.5999    0.2737
Inventories / assets                                            0.1934    0.1520    0.1941
Receivables / assets                                            0.2404    0.2161    0.2166




28
     Maximum is 5
29
     Maximum is 25


                                                                                                21
Appendix D: correlation matrix of variables used in table 4

              S1           S2           S3           S4         S5         S6          S7          S8       S9       S10       S11       S12       S13       S14       S15       S16
S1       1,0000      -0,3625      -0,4522      -0,2768     -0,1053     -0,3166    -0,1554     -0,0578   0,0395    -0,1344   -0,1770   0,0592    -0,3257   0,2265    0,0372    -0,0941
S2      -0,3625       1,0000       0,8619       0,8869     0,0338      0,1385     -0,0222     -0,3175   -0,1778   -0,0270   0,0523    -0,0976   0,0763    -0,0116   0,1420    0,1433
S3      -0,4522       0,8619       1,0000       0,7256     0,1373      0,1534      0,0767     -0,3248   -0,1046   -0,0742   0,0191    -0,0452   0,1606    -0,1567   0,1111    0,0788
S4      -0,2768       0,8869       0,7256       1,0000     0,0431      0,1046     -0,0283     -0,3103   -0,2019   -0,0268   0,0185    -0,1082   -0,0129   -0,0725   0,0667    0,0417
S5      -0,1053       0,0338       0,1373       0,0431     1,0000      0,3624      0,5270     -0,2724   0,1133    0,0492    0,0343    0,0437    0,2677    -0,3051   -0,0730   -0,1830
S6      -0,3166       0,1385       0,1534       0,1046     0,3624      1,0000      0,3780     -0,1737   0,1545    0,2045    0,0205    0,0161    0,3866    -0,5253   -0,1741   -0,2585
S7      -0,1554      -0,0222       0,0767      -0,0283     0,5270      0,3780      1,0000     -0,0966   0,0899    -0,0689   -0,0787   -0,0817   0,1365    -0,2320   -0,0100   -0,1343
S8      -0,0578      -0,3175      -0,3248      -0,3103     -0,2724     -0,1737    -0,0966      1,0000   0,0360    -0,0018   -0,0200   0,1536    -0,0348   0,0479    -0,0343   0,2122
S9       0,0395      -0,1778      -0,1046      -0,2019     0,1133      0,1545      0,0899      0,0360   1,0000    0,0610    0,0601    0,4725    0,4994    -0,0043   0,0392    0,0863
S10     -0,1344      -0,0270      -0,0742      -0,0268     0,0492      0,2045     -0,0689     -0,0018   0,0610    1,0000    0,0069    -0,1301   0,3387    0,0634    0,0038    0,1569
S11     -0,1770       0,0523       0,0191       0,0185     0,0343      0,0205     -0,0787     -0,0200   0,0601    0,0069    1,0000    0,2200    0,0310    0,0884    -0,1099   0,0119
S12      0,0592      -0,0976      -0,0452      -0,1082     0,0437      0,0161     -0,0817      0,1536   0,4725    -0,1301   0,2200    1,0000    0,3510    0,0071    0,0067    0,0622
S13     -0,3257       0,0763       0,1606      -0,0129     0,2677      0,3866      0,1365     -0,0348   0,4994    0,3387    0,0310    0,3510    1,0000    -0,1226   0,0615    0,0972
S14      0,2265      -0,0116      -0,1567      -0,0725     -0,3051     -0,5253    -0,2320      0,0479   -0,0043   0,0634    0,0884    0,0071    -0,1226   1,0000    0,4833    0,4486
S15      0,0372       0,1420       0,1111       0,0667     -0,0730     -0,1741    -0,0100     -0,0343   0,0392    0,0038    -0,1099   0,0067    0,0615    0,4833    1,0000    -0,0587
S16     -0,0941       0,1433       0,0788       0,0417     -0,1830     -0,2585    -0,1343      0,2122   0,0863    0,1569    0,0119    0,0622    0,0972    0,4486    -0,0587   1,0000


S1: Trade credit / assets (dependent variable)        S9: D-Public Limited Liability Corporation
S2: Net profits / assets                              S10: L(Firm Age)
S3: Cash flow / assets                                S11: Previous Bankruptcies
S4 : EBITDA / assets                                  S12: Management Experience
S5: Pre-bankruptcy bank debt / total assets           S13: L(Total Assets)
S6: Land and buildings / total assets (collateral)    S14: Current assets excl. cash/ assets
S7 : Bank debt at procedure initiation / assets       S15: Inventories / assets
S8: Payroll costs/assets                              S16: Receivables / assets




                                                                                                                                                                                  22
Acknowledgments

The authors appreciate helpful comments and suggestions from Cynthia Van Hulle, Sophie
Manigart, Michel Tison, Armin Schwienbacher, Abe de Jong, Oscar Couwenberg, Joshua
Goodman, Peter Coussement and participants from the 2008 Conference on Empirical Legal
Studies at Cornell Law School, the 2008 Corporate Finance Day at Erasmus University
Rotterdam, and the 2008 Conference on Bankruptcy and Distress Resolution at Ghent University.
A preliminary version of this research was incorporated in our working paper Court-supervised
Restructuring: Pre-bankruptcy Dynamics, Debt Structure and Debt Rescheduling. The financial
support of the Fund for Scientific Research Flanders is gratefully acknowledged.




                                                                                          23
References

Baird, D.G., Bris, A., Zhu, N. (2007). The Dynamics of Large and Small Chapter 11 Cases: An
Empirical Study. Yale ICF Working Paper No. 05-29.

Barclay, M.J., Smith Jr., C.W., Morellec, E. (2006). On the Debt Capacity of Growth Options.
Journal of Business, 79(1), 37-59.

Berger, P.G., Ofek, E., and Swary, I. (1996). Investor Valuation of the Abandonment Option.
Journal of Financial Economics, 42(2), 257-287.

Cunat, V. (2007). Trade Credit: Suppliers as Debt Collectors and Insurance Providers. Review of
Financial Studies, 20(2), 491-527.

Berglof, E., Van Thadden, E. (1994). Short-Term vs Long-Term Interests: Capital Structure with
Multiple Investors. The Quarterly Journal of Economics, 109(4), 1055-1084.

Biais, B., Collier, C. (1997). Trade Credit and Credit Rationing. Review of Financial Studies,
10(4), 903–937.

Bris, A., Welch, I., Zhu, N. (2006). The Costs of Bankruptcy: Chapter 7 Liquidation versus
Chapter11 Reorganization. Journal of Finance, 61(3), 1253-1303.

Davydenko, S., Franks, J. (2008). Do Bankruptcy Codes Matter? A Study of Defaults in France,
Germany and the UK. Journal of Finance, 63(2), 565-608.

Diamond, D. (1991). Debt Maturity Structure an Liquidity Risk. The Quarterly Journal of
Economics, 106(3), 709-738.

Fisher, T.C.G., Martel, J. (1994). Will the Bankruptcy Reform Work? An Empirical Analysis of
Financial Reorganization in Canada. Canadian Public Policy, 20(3), 265-277.

Fisher, T.C.G., Martel, J. (1995). The Creditor’s Financial Reorganization Decision: New
Evidence form Canadian Data. Journal of Law Economic and Organization, 11(1), 112-126.

Frank, M. Z., Maksimovic, V. (2005), Trade Credit, Collateral, and Adverse Selection. Working
paper University of Maryland




                                                                                            24
Frank, M.Z., Goyal, V.K. (2003). Testing the Pecking Order Theory of Capital Structure. Journal
of Financial Economics,67(2) , 217-248.

Franks, J.R., Sussman, O. (2005). Financial Distress and Bank Restructuring of Small to Medium
Size UK Companies. The Review of Finance, 9(1), 65–96.


Hart, O., Moore, J. (1994). A Theory of Debt Based on the Inalienability of Human Capital. The
Quarterly Journal of Economics, 109(4), 841-879

Hart, O., Moore, J. (1998). Default and Renegotiation: A Dynamic Model of Debt. The Quarterly
Journal of Economics, 113(1), 1-41.

Helwege, J., Liang, N. (1996). Is there a Pecking Order? Evidence from a Panel of IPO Firms.
Journal of Financial Economics, 40(3), 429-458.

Hotchkiss, E.S. (1995). Postbankruptcy Performance and Management Turnover. Journal of
Finance, 50(1), 3-21.

Huyghebaert, N., Van de Gucht, L., Van Hulle, C. (2007). The Choice between Bank Debt and
Trade Credit in Business Start-ups. Small Business Economics, 29(4), 435-452

Lemmon, M.L., Zender, J.F. (2007). Debt Capacity and Tests of Capital Structure Theories.
Working Paper, University of Colorado and University of Washington.

Manigart, S., Vanacker, T.R. (2009). Pecking Order and Debt Capacity Considerations for High-
growth Companies Seeking Financing. Small Business Economics, forthcoming.

Myers, S.C. (1977). Determinants of Corporate Borrowing. Journal of Financial Economics,
5(2), 147-175.

Myers, S. C. (1984). The Capital Structure Puzzle. Journal of Finance, 39(3), 575-592.

Myers, S.C., Majluf, N.S., (1984). Corporate Financing and Investment Decisions when Firms
have Information that Investors do not have. Journal of Financial Economics, 13(2), 187-221.

Peterson, M.A., Rajan, R.G., (1997). Trade Credit: Theories and Evidence. Review of Financial
Studies, 10(3), 661-691.




                                                                                            25
Rajan, R., Zingales, L. (1995). What Do We Know about Capital Structure? Some Evidence from
International data?. Journal of Finance, 50(5), 1421-1460.

Rodríguez-Rodríguez, O.M. (2006). Trade Credit in Small and Medium Size Firms: an
Application of the System Estimator with Panel Data. Small Business Economics, 27, 103-126.

Ronen, J., Sorter, G.H. (1972). Relevant Accounting. Journal of Business, 46(2), 258-282.

Santos, J.A.C., Longhofer, S.D. (2003). The Paradox of Priority. Financial Management, 32(1),
69-82.

Shleifer A., Vishny R. (1992). Liquidation Values and Debt Capacity: A Market Equilibrium
Approach. Journal of Finance, 47(4), 1343-1366.

Shyam-Sunder, L., Myers, S.C. (1999). Testing Static Tradeoff against Pecking Order Models of
Capital Structure. Journal of Financial Economics, 21(2), 219-244.

Wilner, B.S. (2000). The Exploitation of Relationships in Financial Distress: the Case of Trade
Credit. Journal of Finance, 55(1), 153-178.




                                                                                            26

								
To top