From Soft and Hard-Nosed BankersBank Lending Strategies and the Survival of
Financial Distressed Firms
Daniel Höwer ∗September 2009

Abstract
Do private banks act as hard-nosed bankers when rms get nancially distressed compared to public banks? It is widely acknowledgedthat state guarantees give public banks a competitive advantage. Public banks have the mandate to support regional economy and theirlending strategy can be seen as somehow xed nancing rms as longas economically reasonable. Private banks are free to choose theirlending strategy. They might adopt a credible strategy that liquidates
rms early in nancial distress. Firms self select according their risk
and private banks' risk portfolio improves. If so, nancially distressedrms nanced by private banks should show a higher probability ofexiting the market.
For German rms in the period 2000-2005, I nd that the probability leaving the market after nancial distress is higher for rmsnanced by private banks. Private banks seem to be less likely to audit rms in distressed situations or at least adopt stricter rules. While∗

Centre for European Economic Research (ZEW), P.O. Box 103443, 68034 Mannheim,
Germany. Phone: +49 621 1235-187, Fax: +49 621 1235-170. E-mail: hoewer@zew.de
Acknowledgments: The author would like to thank Yossi Spiegel, Georg Metzger, Helmut Fryges, and Georg Licht for helpful comments and discussions and Jürgen Moka fordata provisioning. The author is also grateful to the Deutsche Sparkassen und Giroverband and the Bundesvervband der Deutschen Volks- und Raieisenbanken for information
on bank mergers.

2
there is a broad discussion about public banks in the literature becauseof governmental inuences, the eects of dierent lending strategies areeven larger for cooperative banks.

Keywords: nancial distressed rms, public and private banks, relationshiplending

JEL Classication: G21, G33, L14

1 INTRODUCTION

1

1 Introduction
Relationship building in nancial services have many benets for lenders,borrowers, and the society at large. Young or distressed rms that are assessed to be viable receive nance, and can invest in uncertain but moreprotable investments. Banks "lean against the wind " and keep with theircustomers when there are most in need (Petersen and Rajan, 1995). They
do so because banks bind their costumers and get inside information. Debt
becomes equity-like as the relationship gets closer. That put banks in a better position to extract future surplus from protable projects. If rms getnancially distressed relationship lenders are in a better position for renegotiation. First because they posses priory information about the rms projectand second because they are specialized for such cases (Boot and Thakor,
2000).
Although relationship lending can be benecial for rms, it comes at acost. Firms are informational locked in and can not easily switch to othernancing partners that are less expensive. Low risk rms with more certain
but lower prots gain less by relationship lending (Boot and Thakor, 2000).
They ask for transaction lending related to lower nancing costs.
Public banks can be said to act as a relationship lender. Public banks havea mandate supporting regional economic development (Baas and Schrooten(2005), e.g. article 6 SpG (2005)). To do so, owners grand additional paymentthat lowers public banks' funding costs. Private banks acting in a competitivemarket with public banks. Within this environment specialization of privatebanks as transaction lenders can be a rational strategy. Private banks oer
rms a contract with lower interest but rule out any audit and renegotiationin nancial distress. Low risk rms self select into that contract and highrisk rms keep with savings banks or cooperative banks (Matthey, 2008).
In most cases discounted expected future cash ow after renegotiation ishigher than liquidation value (Matthey, 2008). However, if a bank adopts astrategy as a transaction lender but does not behave in that manner high risk
rms will free ride. Therefore, transaction lenders need to bind themselves
not to renegotiate but liquidate rms in distress. They need to build up
reputation as hard-nosed banker.

1 INTRODUCTION

2

If private banks adopt a strategy as transaction lender, the probabilityof their borrowers to leave the market in nancial distress should be higher
compared to those nanced with public or cooperative banks. The possibility
to act as a relationship lender depends on further conditions such as theconcentration in local banking markets or rms nancing structure. This
article explores whether the market exit for rms in nancial distress depends
on bank type.
In Germany, private banks acting in a competitive market with public andcooperative banks. Although public and cooperative banks are organized differently, both have similar aims (Engerer, 2006) and can perform the samefunctions (Hakenes and Schnabel, 2006). Traditionally, they build up close
relationships to their customers and adopt dierent lending strategies compared to private banks (Engerer and Schrooten, 2004). Private banks can beseen to act more as a transaction based lender. There is anecdotic evidence
that private banks act as hard-nosed bankers and are not interested on relationships with especially small and medium sized rms. Hilmar Kopper1
made an in Germany well known statement that the sum of unpaid bills offailed craft enterprisers are Peanuts compared with the banks loss duringthe "Schneider"-aaire.2 Therefore, German banking system seems a goodenvironment to test dierences in banks' lending strategy.
Next to banks' strategy rm's market exit depends its nancing behavior,internal, and external factors. The sample is restricted to the 2000 - 2005
period since important legislative changes for banks came in power by theend of that period.3 The model controls for serious selection bias concerning
1 From 1989-1997 Mr. Kopper was chairman of the Deutsche Bank AG. He made thestatement during press conference at the 24th of April 1994.
2 The Deutsche Bank was heavily involved in nancing a construction company owned
by Mr. Jürgen Schneider. The company modernized historical buildings in city centers,in particular. In order to receive nance, documents and books were manipulated. Information become public and the rm failed in 1994. Total bank debt accumulated to 5.4 bn
DEM, around 2 bn DEM nanced by the Deutsche Bank.
3 Within the transition period from 19.07.2001 to 18.07.2005 Anstaltslast andGewährträgerhaftung (additional payment of public banks' owners) for savings banks wereabolished. Because of grandfathering major change came into place in 2005. Basle II accord came into power at the end of that period via the Bundesamt für Finanzaufsicht
letters 34/2002 (minimum standards for credit business), 18/2005 (minimum standards
for risk management), and European Union guidelines 2006/48 and 2006/49.

2 RELATIONSHIP BANKING AND FIRM SURVIVAL

3

unobserved distress situation of rms survived. I will show that probabilityof market exit is higher for rms nanced by private banks.
The paper is organized as follows: The introduction is followed by areview of the literature concerning relationship lending, and rm survival.
Section 3 describes data sources and variables. Since the German threepillar banking system is somehow special a brief description of the maindierences is provided.4 In section 4 the empirical model and results arepresented and discussed. Section 5 summarizes the ndings and concludes
the paper.

2 Relationship Banking and Firm Survival
Diamond and Dybvig (1983) show that nancial intermediaries acting as delegated monitors are able to realize economies of scope and scale in obtaining
information about borrowers. They have advantages in collecting and analyzing information on investment projects compared to multiple nanciers,
and reduce costly information asymmetries. Therefore, nancial intermediaries allocate capital to protable investment projects and decide on theirliquidation if unprotable. In a world of asymmetric information complete
contracts are not feasible and there may be a need to renegotiate nancial
contracts. In such a situation long term interaction between borrowers andbanks is mutually benecial (Rajan, 1998).
Ongena and Smith (1998) dene relationship lending as the connection between a bank and customer that goes beyond the execution of simple,
anonymous, nancial transactions. Maintaining a relationship means making temporary scaries in favor of obtaining future benets. Relationshipbanking covers multiple ways of how banks obtain information about customers, including all banking services such as cash management, access tothe payment system, deposit holding, cross-selling other bank or insurance
products etc. (Degryse and van Cayseele, 2000). Without implicit contracts
that share intertemporal prots, banks need to break even period-by-period(Petersen and Rajan, 1994, p. 407) and are forced to liquidate unprotable
4 A more detailed analysis of the German nancial system and the link to corporatenance is provided in Krahnen and Schmidt (2004).

2 RELATIONSHIP BANKING AND FIRM SURVIVAL

4

investments. That is refereed to as transaction lending. A rms' probability to survive a situation of nancial distress is therefore aected by thebanks' lending strategy.

Bank's Lending Strategy Banks benet from long term relationshipstrategy, as intertemporal transfers in loan pricing is value-enhancing. Banks
learn about the type of the entrepreneur and receive inside information (Petersen and Rajan, 1994; Fama, 1985) while its importance depends on thelength of the duration (Ongena and Smith, 1998). Inside debt or equity like
nance of relationship lending is underlined by the banks ability to either
refuse future lending or to accommodate the rm with new loans, conditionalon actions taken by the rm during and after the distress period. Therefore,banks are able to exert control over the rms' asset management (Ongena
and Smith, 1998). Trading partners are willing to accept below cost products,
while there is an implicit contract to recover initial losses by future surpluses(Ongena and Smith, 1998). Relationship lenders face a higher liquidation
value of provided securities (Diamond and Rajan, 2001a,b) and are therefore
in a better position when it comes to renegotiation of debt contracts (Bootand Thakor, 2000).
A bank's lending strategy might be driven by its type, where public banks
act as relationship lenders and private banks do not (Baas and Schrooten,2005). It is often argued that competition in banking is distorted by state
guarantees for public banks reducing its funding costs. Public banks have amandate to provide nance and nancial services to the people, companies,
and local authorities within the business district supporting regional economic development (e.g. article 6 SpG (2005)). Public banks should keep nancing investment projects as long economically justiable (Matthey, 2008).
In other words, public banks strategy space is xed to those of a relationshiplender. Private banks are not restricted to a certain lending policy. Matthey(2008) argues that banks compete in repayment rather than interest streams
only. Private banks can oer transaction based lending that rule out renegotiation in nancial distress in order to attract low risk rms. The liquidation
threat needs to be credible and the private bank act as a hard-nosed banker
in order to prevent high risk rms from free riding. Financial distressed rms

2 RELATIONSHIP BANKING AND FIRM SURVIVAL

5nanced by private banks should show a higher probability of market exit
than those nanced by public banks. High risk rms would self select to public banks. In order to test whether dierences in the probability of market
exit of nancially distressed rms exists between bank types further aspects
need to be considered.
Banks need to invest in sector specication in order to generate expertiseand to add value. Since banks' specication cost is independent from borrowers quality, but rent is a decreasing function of with borrower's quality,
costs exceed the marginal benet at a suciently high quality level (Boot andThakor, 2000, p. 692). Bank's possibility to act as a relationship lender isinuenced by banking market competition (Ongena and Smith, 1998; Sharpe,
1990; Rajan, 1992). A monopolistic bank can charge low payments during
the period of uncertainty and extract future surpluses. In a competitivebanking environment rms with improved quality seek nance from competitors and switch the bank. The incumbent can no longer hold equity likeclaims and need to break even period-by-period (Petersen and Rajan, 1995).
Bank's investment in sector specication critically depends on competition
within the banking market and from the capital market. Since interbank
competition aects bank's prots from relationship and transaction lending
asymmetrically Boot and Thakor (2000) conclude that relationship lending
becomes more important but add less value to each loan.

Firms nancing behavior Relationship building in lending adds value toborrowers in several dimensions. First, it signals a rms payment conditionand credit risk to outside debt holders (Boot, 2000; Fama, 1985). Therefore, the two audience signaling problem is reduced and duplication of theevaluation process avoided (Boot, 2000). Second, relationship lending eects
nancing conditions directly and allows for more exibility and discretionin project nancing (Boot, 2000). Third, young rms gain easier access tonance and banks accept higher risk not asking for full collateral (Petersen
and Rajan, 1995). Fourth, banks are more likely to audit and restructure
debt of rms in nancial distress. Banks provide an insurance in case of
sudden and temporary diculties of their relationship customer (Elsas andKrahnen, 1998).

2 RELATIONSHIP BANKING AND FIRM SURVIVAL

6

A close relationship adds more value to low and medium quality rms or
projects compared to transaction lending (Boot and Thakor, 2000), especiallysince such a relationship does not come without cost. Information monopolyof relationship banks possess a potential risk on borrowers due to ex post bargaining power (Elsas and Krahnen, 1998; Mayer, 1988; Rajan, 1992). Firms
can mitigate the hold up problem by multiple sources of nance such asother nancial intermediaries or trade credit. With an increasing number oflenders and decreasing share of nancing by a single institution rms facea higher probability of being rationed and it becomes harder to coordinate
renegotiation(Thakor, 1996). Asymmetric nancing, where the largest shareof nance is kept by a single institution and the rest is split over few others,
leaves the main bank as the relationship lender and provides sucient information to other institutions to reduce potential switching costs (Elsas et al.,2004).5

Further determinants of rms exit However, rm's probability of amarket exit is not only related to its nancing behavior. Literature on rmsurvival identies internal (rm-specic) and external factors related to therm's business environment (Manjón-Antolín and Arauzzo-Carod, 2008) inuencing a rm's probability leaving the market. One stylized fact about
entry quoted by Geroski (1995) states that age and size are positively related
to the probability of rm survival. Related to age, rms face liability of "newness" (Freeman et al., 1983), "adolescence" (Fichman and Levinthal, 1991),
or "senescence" (Hannan, 1998). Recently established rms need time tosettle and invest, learn about their ability, and draw from the initial capital.
Therefore, failure risk increases over time and reaches a peak. Older rmsmight be ill-suited for changes in competitive environment (Esteve-Pérez andManez-Castillejo, 2008). Many studies show inverse u-shaped or other nonlinear age eects on rm survival. The rms probability to survive should
be positively related to size for several reasons. Small rms face liability of"smallness" (Freeman et al., 1983). Larger rms are better able to diversify
and are less prone to shocks in single markets. Those may also be in a bet5 Empirical evidence conrms multiple but few bank relations for German rms (Elsas,

2005; Neuberger et al., 2006).

3 DATA7

ter position acquiring capital and labor (Esteve-Pérez and Manez-Castillejo,2008). Dierences in size also reect dierences in eciency. Larger rms are
more likely to produce on their industry minimum ecient scale (Audretsch
and Mahmood, 1995) and dier in organizational form and managerial ability(Esteve-Pérez and Manez-Castillejo, 2008). Empirical studies nd a decreasing, non-linear size eect on rms failure probability (Esteve-Pérez et al.,
2004; Strotmann, 2007).
Harho et al. (1998) introduce the rm's choice of liability rules to survival models because the legal form reects systematic dierences in the modeof ownership transfer and the entrepreneurs' assessment of projects riskiness. There are also dierences for information disclosure and bankruptcy
law. External factors represent specic conditions of the rm's business environment, such as industry and business cycle, inuencing a rm's survival
function. Industries dier in life cycle, entry and exit rates, industry specicshocks, or capital intensity (Geroski, 1995; Audretsch and Mahmood, 1995;
Audretsch, 1995; Harho et al., 1998; Agarwal and Grot, 2002; Esteve-Pérez
and Manez-Castillejo, 2008).

3 DataIn order to nd evidence for dierent lending strategies of dierent banktypes (private, public, and cooperative banks), the probability of exit ofnancially distressed rms is estimated. As described in the previous section
relationship lending is inuenced not only by the banks' type but by nancing
behavior. In addition, rms characteristics are used to control for internaland external factors inuencing the rm's probability of market exit. The
empirical analysis is based on the Mannheim Enterprise Panel (MUP) and abank panel. First, a brief description of the German banking system is given.
Second, data sources and the data generating process are described, followed
by denitions and descriptive statistics of variables at hand.

3 DATA8

3.1 German Banking System
The German banking system consists of three banking sectors, the public,
cooperative, and private banking sector. The structure of the German banking system is often described as the Three Pillar System (Krahnen andSchmidt, 2004; Engerer and Schrooten, 2004, e.g.). Table 2 provides information about the market share of each banking sector and Table 3 provides
a list of banks assigned to the above bank types .
Public banks are either savings banks or Landesbanks.6 Savings banks are
owned by the district or municipality and are therefore decentralized organized. Governments need to credibly promise bail-out of state-owned banks
in order to avoid bank runs and owners' liability for additional payments7
was an important characteristic of state-owned banks. Savings banks adopt
the regional principal that restrict their operating area to the area of the district or municipality by whom they are owned. By law and individual status
public banks have the mandate to provide nance and nancial services tothe people, companies, and local authorities within the business district supporting regional economic development (e.g. article 6 SpG (2005), Engerer
and Schrooten (2004)).
Members of the supervisory board of savings banks are jointly electedby administrative authorities and bank's sta, while the chair of the board
is linked to the position of the district administrator. Therefore, politicians
decide on the bank's strategy, the board of managers and individual substantial nancing cases. Landesbanken are jointly owned by savings banks
and federal states. The mission of a Landesbank is to support savings banks
and municipalities by whom they are owned and are involved in local publicbusiness development programs but are also involved in corporate nancedirectly.8
6 Bank relationships with the Deutsche Bundesbank (no.

1, Table 3) or public development banks (no. 4, Table 3, federal or state owned banks for special purposes) are notconsidered since these banks do not act as ordinary lenders within the German banking
system.
7 Gewährträgerhaftung and Anstaltslast describe an unlimited cover by the banks owners in case of distress. Additional payment was abolished in 2005.
8 See e.g. Staatsvertrag über die Bildung einer gemeinsamen Sparkassenorganisation in
Hessen und Thüringen, chapter 1 B, article 8.1, 8.3, 8.4, and 9, 2006.

3 DATA

9

Cooperative banks are owned by individuals that hold cooperative shares,
respectively. The purpose of cooperative banks is to promote the acquisition
and the business of members (Engerer, 2006). According to cooperativebank's status and mission statement these banks support their members andenhance regional development. Cooperative banks can perform the samefunctions as public banks (Hakenes and Schnabel, 2006) and their strategy
space is equally xed. Members of the supervisory board are elected by thecooperative members. Votes are often restricted to heads instead of numbersof shares hold by individuals. In case of insolvency members of a cooperativebank are called for restricted but additional.
Private banks are either commercial banks or real-estate credit institutions. mostly, these banks operate in a legal form of a public listed stockcompany or limited liability. Therefore, owners' liability is limited by theircapital contribution. Private banks take decision on commercial basis andare not restricted to a certain lending policy (Engerer, 2006).

3.2 Data Sources
The core data comes from the MUP, maintained by the Centre for European
Economic Research (ZEW). The MUP is a rm-level database collected by
Creditreform, the largest credit rating agency in Germany. Since 1999, ZEW
receives twice a year full back up of Creditreforms data-warehouse of rmlevel data and constructs the panel. The database has nearly a full coverageof rms located in Germany. The MUP is based on information, that allows
to assess a rm's credit worthiness. Firm information gets updated on an irregular basis. On average, rm information is updated every 9 months. Firm
information is collected decentralized by 120 regional Creditreform branches.
Even if the investigation procedure applies to certain standards, sources andquality may dier between Creditreform branches (Almus et al., 2000).
Due to the data generating process the MUP database is prone to sample
selection. Time between updates and quality of data may dier between
both Creditreform branches and rm characteristics. A lack of data quality
corresponding to a deterioration state of the mode of payment might lead to aserious sample selection bias. This is the case if the observation of this event

3 DATA10

is correlated with the rm's insolvency. In other words, data collector infersfrom rm insolvency noticed that this rm must have had serious nancialdiculties. The chance that a change of the mode of payment is observedfor rms that recover after a short period is low.
Due to consolidation in German banking market the number of banks reduced considerably during the sample period. Via bank branches known by
the MUP rm-level data is linked to a bank panel that reect bank mergers
between 2000 and 2005.9 The bank panel contains information on numberof branches, bank type, the bank's business district, and the number of competitors in local banking markets.10

3.3 Variable Description
3.3.1 Dependent and Restrictive Variables
The empirical model estimates the probability of nancially distressed rmsin period t to exit the market within a period up to t + n (while n=0,1,...,4;
where n equals 6 months). Due to important changes in banking regulationthat came into power in 2005, the sample is restricted to a randomized 10 per
cent sub-sample of the total population of economically active, non-nancial
institutions that became nancially distressed in the period between 2000
and 2005. The years 2006 and 2007 are left for the identication of market
exit in successive sample periods. Table 5 describes variables used in themodel.

Identication of nancially distressed rms

Identication of nancial distress is crucial for the assignment into the sample. Banks gain private
information about customers while observing their payment behavior (Ongena and Smith, 1998, p. 6) and are therefore able to detect situations ofnancial distress. In general, such sensitive information is rarely available
for research. As in Kaiser (2001) nancial distress is identied if the rm's
9 Information on bank mergers was provided by: Deutscher Sparkassen und Giroverband

for the savings banks; Bundesverband der Volks- und Raieisenbanken for cooperativebanks; BankScope for private banks.
10 Local banking markets are dened by administrative districts, so called Landkreise or
kreisfreie Städte. A bank is assigned to a district if at least one branch is operated.

3 DATA

11

mode of payment status deteriorate, based on information provided in the
MUP. Unlike Kaiser (2001) the characteristics on rms' mode of payment are
categorized in ve groups11 . A decline in the mode of payment status serves
as an indicator for nancial distress. Firms are selected according to thisrestriction variable. However, a change from the rst to the second category
is not considered as nancial distress. The overall sample consists of 556,595
rms of which 67,550 rms have become nancial distressed. Because rmscan face several episodes of nancial distress over time a total of 86,564 cases
are observed within the relevant period. Due to data limitation for certain
variables 67,048 cases are used for the empirical analysis. For robustness
checks a change in the rm's creditworthiness, assessed by Creditreform, isused as an indicator of nancial distress.

Identication of rms market exit Firms exits serve as a dependent
variable. Insolvencies are known since any insolvency proceeding is to makepublic. Voluntarily exit is considered if recognized by Creditreform. In somecases exit information was adjusted in order to make rm observations consistent. Table 4 presents the number of nancial distressed rms, the numberof rms having left the market within a two-year-period after facing a nancial distress in the given year in absolute terms and relative to the numberof rms nanced by the bank type, respectively. Comparison of column 4, 7,
and 10 shows that rms nanced by private banks exit the market more often
after nancial distress. On average, for 1.89 % of observations per sample
period nancial distress is observed. In 39.3 % of those cases rms exit themarket within four successive sample periods.

3.3.2 Independent Variables
As derived in Section 2, it is assumed that exit of distressed rms depends
on the rms banking and nance behavior as well as internal and external
11 First, payments without complain where all payments are within time for payment

allowed. Second, payments without complain but made later than the date of payment
agreed. Third, payments made where the term of credit exceeds more than 30 days orrms have got reminded several times. Fourth, serious delay in payment, e.g. payment is
not made within three month, and fth, no payments made due to insolvency.

3 DATA12

factors. Table 5 denes variables and Table 6 presents descriptive statistics
of independent variables for nancially distressed rms.
In the overall panel for 23.8 % of the rms private banks, for 48.5 % publicbanks and for 27.7 % cooperative banks are noticed as the main bank.12
Taken 12 banks active in a local banking market on average concentration isconsiderably high.
As renegotiation depends on the number and the shares of creditors themodel captures two types of information. First, the Number of Bank Relations controls for diversity of bank nance. Unfortunately, the share of
nance according to the banks is not known. In cases where two or morebranches observed belong to the same bank number of branches is reduced.
Number of bank relationships are found lower compared to other studies
concerning Germany.13 . Second, there is no direct information about tradecredit. However, if credit between rms is suciently large and especially insituations of distress, creditors commission debt collectors. Information on
Creditreform mandate to collect debt from a particular rm is therefore used
as a proxy for suciently large outstanding non-bank debt captured by thedummy variable VC Debt Collection. It is assumed that banks collateralize
any residential, commercial, or mixed property owned by the rm. A quarterof all rms can use Real Estate for bank securitization. only few rms face
more than one episode of nancial distress covered by the variable History
of Distress.
In line with other studies on rm survival I include several variables
grouped as internal factors. Almost a third of all rms are listed in public
Business Register while most rms are not managed by a Management Teambut a single entrepreneur. Successful negotiation with claim holders mightalso be inuenced by personal ability that is captured by entrepreneurs' educational background. For 12 % of all rms its known that the highest levelof education within the management team is Master Craftsman 14 and for
12 Presented gures are restricted to the rst bank observation per rm in the sample.

Dierences to the gures presented in Table 2 are because cases rather than volume isconsidered. From comparison one can infer that private banks nance larger rms withhigher credit volume.
13 Ongena and Smith (2001) nd that the number of relationships is 8 on average, while
the median is 5. Also Elsas and Krahnen (1998) report a median of 5 relations to banks.
14 A master certicate represents a higher degree of business qualication awarded either

3 DATA13

another 12 % is Academic. Early studies on rm survival (Audretsch and
Mahmood, 1995; Mata et al., 1995; Honjo, 2000) used initial start-up size. In
contrast Mata et al. (1995) and Esteve-Pérez and Manez-Castillejo (2008) argue that current size is a better failure predictor. Therefore, Size contains thelog of the number of employees. Their polynomial control for non-linearity.
Almost two third of the rms are younger than 13 years and less than 10 %are exists than 50 or more years.
Industry dummies capture dierences in business structure and industry
cycles.15 Almost a third of all nancially distressed rms are located in East
Germany.

3.3.3 Variables of Selection Equation
Selection equation consists of variables explaining the probability that nancial distress is observed. Branch Quality captures dierences in investigation
quality. The variable is the common factor of the following indicator variableson the level of Creditreform branches per sample periode: First, the numberof new rms observed related to the total number in stock. Second, time
elapsed between rm foundation and rst observation. Third, share of rmswith unknown date of foundation. Fourth, share of rms obliged to registerwith unknown date of business registration. Fifth, share of rms with unknown number of employees and last, number of rms where the branch was
asked information related to the total number of rms in stock. The rst andlatter have a positive eect on each branches' data quality while all others
are negative. The constructed variable has an inuence on the probability
that nancial distress is observed but it should not correlate with the error
term of the main equation. For this reason Branch Quality is imposed as an
exclusion restriction (Cameron and Trivedi, 2009). All other variables usedin the selection equation are on the individual rm level and reect whethersucient rm related information should be available. These are dened inTable 5.
by the chamber of industry and commerce or the chamber of crafts.
15 Firms allocation to industry dummies is based on German business classication codefrom 1993.

4 EMPIRICAL ANALYSIS

14

4 Empirical Analysis
4.1 Econometric ModelI now turn to the econometric model. In order to analyze how dierences
in market exit probabilities of nancially distressed rms are related to thetype of their bank, I estimate a probit model on rm survival. The modelis closely related to that by Van de Ven and Van Praag (1981) which isslightly adjusted for my purpose. As noted in the previous Section, I canidentify rms facing nancial distress in the data. Unfortunately, I can notobserve whether a bank applies audit to distinguish between viable and nonviable rms or if a renegotiation has taken place. However, I can distinguishbetween those rms that survive and those who have left the market. From
this nding one can infer that surviving rms successfully renegotiate debt
contracts. Everything else equal, the probability a distressed rm to survive
should dier between nanciers that adopt auditing and renegotiate debt if
possible and those who have liquidated immediately or at least have appliedstricter rules. The probability of market exit can be specied as:

Mi = α0 Xi + ²1i(1)where M is a dummy variable that equals 0 if the rm survives and 1 if itexits the market. The conditional probability for rm i, given Xi is observed
is given byµ0

P (Mi = 1|Xi ) = P (α Xi + ε1i ≥ 0|Xi ) = H

α0 Xi
σ1

¶
.

(2)

The conditional expected value of Mi , E(Mi |Xi ) = H(α0 Xi ) falls within the[0,1] interval and can be interpreted as the probability that a rm faces asituation of nancial distress, given values of exogenous variables, exit themarket. As already noticed in the previous chapter a situation of nancial
distress is potentially observed selectively. The data generating process may
be analyzed by the means of probit analysis. The error terms in both probit
estimations might contain some common omitted variables, i.e. ρ(ε1i , ε2i ) 6=
0. In this case α̂ on the basis of only partly observed nancial distress4 EMPIRICAL ANALYSIS

15

yields inconsistent estimates. I correct for the potential bias as suggested
by Heckman (1979) and applied for dichotomous variables by Van de Ven
and Van Praag (1981). The regression function of the subsample of partly
observed distressed rms can be written as:

E(Mi |Xi , Di∗ ≥ 0) = α0 Xi + E(ε1i |Xi , Di∗ ≥ 0).

(3)

Under the assumption that ε1i and ε2i are bivariate standard normal distributed with correlation ρ it follows thatE(ε1i |Xi , Di∗ ≥ 0) = ρλi(4)h(Ai )
λi = H(−Aand Ai = −[βX],
i)

(5)

with
where h represents the pdf and H the cdf. The regression function can be
rewritten as

Mi = α0 Xi + ρλ̂i + ε1i ,(6)where Mi captures rm exit, and Xi consists of variables grouped as Banking
and Finance, Internal Factors, and External Factors. The vector λ is notknown but can be consistently estimated as λ̂ based on the estimates of β̂
of the probit Selection Equation explaining whether or not the observation
belongs to the observed sub-sample.

4.2 Estimation Results and Discussion
Estimation Results In this section, I present the results of the crosssectional regression of the market exit on the set of explanatory variables, aspresented above. The model is estimated for the period of nancial distressand up to 4 successive periods in order to take a delay in the borrowers' andlenders' decision making process, as well as a delay in observation of rm's
market exit into account. Robust clustered standard errors are used due torms with multiple episodes of nancial distress during the sample period.
Table 7 reports the estimates of the bivariate selection equation. The

4 EMPIRICAL ANALYSIS

16

correlation coecient ρ̂ is found to be dierent from zero at a one per centlevel of signicance. Estimates obtained from a normal probit model are
likely to be inecient. Branch Quality is found to be positive, suggestingthat branches with high investigation quality are more likely to observe rmsin nancial distress, as expected. Both variables, Investigation and Debt
Collection are found to be positive, as expected, indicating that it is more
likely to observe nancial distress the faster detailed information is available.
Dummy variables indicating missing information about age and industry arefound to be negative signicant. Surprisingly, Business Register is found tobe negative. This result can be interpreted in the way that the eect that
registered rms are less likely to run into diculties outweighs the higher
probability that this event is observed. Theory predicts that situations ofnancial distress occur more often to rms nanced by savings or cooperativebanks compared to private banks due to self selection according to individual
risk. Corresponding coecients are therefore expected to be positive. An
explanation of the negative sign might be that it is more likely to observenancial distress during the market exit. If, as theory predicts rms nanced
by private banks leave the market more often the probability to observe thosecases is higher and the sign for savings and cooperative banks turns negative.
I now turn to the rm survival equation. Table 1 provide marginal eectsof selected variables. Table 8 shows the models coecients and Table 9
the full list of marginal eects. Empirical ndings support the hypotheses
that lending strategies for nancial distressed rms dier across bank types.
More than a quarter of the dierence in survival probability presented inTable 4 in the descriptive statistics can be assigned to the bank type. A
rm's probability to survive the fourth period after nancial distress is 1.3
percentage points higher if nanced by Public Banks compared to the basis
group of Private Banks.

4 EMPIRICAL ANALYSIS

17

Table 1: Marginal Eects

VARIABLES

(1)

(2)

(3)

(4)

(5)

exit texit t + 1

exit t + 2

exit t + 3

exit t + 4-0.004(0.005)-0.015***(0.006)-0.042(0.039)-0.008**(0.004)0.0002(0.006)0.001(0.004)-0.046***(0.005)-0.010*(0.005)-0.017***(0.006)-0.068*(0.040)-0.007(0.004)0.002(0.006)0.011***(0.003)-0.042***(0.005)-0.013**(0.005)-0.020***(0.006)-0.054(0.042)-0.006(0.004)0.007(0.006)0.018***(0.004)-0.039***(0.006)-0.013**(0.006)-0.022***(0.006)-0.083**(0.042)-0.006(0.004)0.010(0.006)0.024***(0.004)-0.042***(0.006)

0.268***(0.006)-0.055***(0.008)-0.040***(0.007)-0.030***(0.007)0.055**(0.005)
4,723,708

0.247***(0.006)-0.060***(0.009)-0.045***(0.007)-0.034***(0.007)0.052***(0.005)
4,723,707

0.225***(0.006)-0.065***(0.009)-0.047***(0.008)-0.039***(0.008)0.053***(0.006)
4,723,706

Banking and FinancePublic Banks
Cooperative
Banks
BMC
No. of bankrelations
Debt
Collection
History of
distress
Real Estate-0.002-0.005-0.013**(0.005)-0.011(0.037)-0.008**(0.004)-0.006(0.006)-0.014***(0.004)-0.047***(0.005)Internal and External Factors
Business
RegisterManagement
Team
MasterCraftsmanAcademic
East
Germany
No. of Obs.

0.316***(0.006)-0.043***(0.008)-0.038***(0.007)-0.027***(0.007)0.054***(0.005)
4,723,708

0.291***(0.006)-0.051***(0.008)-0.036***(0.007)-0.026***(0.007)0.057***(0.005)
4,723,708

Selected Variables, Selection Equation included, robust clustered
standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1

4 EMPIRICAL ANALYSIS

18

The eects are even stronger for cooperative banks than for public banks.
This might be for two reasons. First, cooperative banks show the lowest
risk portfolio. Since cooperative banks are traditionally involved in nancing
small rms they are well diversied. Unity of ownership (member of a cooperative society) and customer relation might be a further explanation of thiseect. Although individual owner`s inuence is limited in cooperative banks
due to one head one voice members that themselves might be entrepreneurs
are seated in the banks committee deciding on investment guidelines.
However, it remains unclear whether this eect is due to a transaction
based strategy of private banks and relationship lending by public and cooperative banks. If hypotheses stated by Matthey (2008) are valid and privatebanks adopt a hard-nosed lending strategy in order to attract low risk rms,
public banks credit portfolio would need to show higher risk compared to
private banks. In contrast to Paul et al. (2003), who estimated banks' portfolio risk based on self reported risk proles by entrepreneurs, this can notsupported by the data at hand. Private banks seem to nance more riskier
rms. From Figure 1 one can infer that credit portfolio of public and cooperative banks is rst and from Figure 2 that it is second order stochastic
dominant compared to private banks' credit portfolio in terms of risk.16 If
one assumes that private banks nance larger rms, the exposure at risk ofprivate banks credit portfolio is even higher.
Banks active in concentrated markets are better able skimming future prots
and oering relationship lending. As the number of banks active in a district
decreases and concentration is therefore higher, rm's probability exiting themarket decreases (See variable BMC ). This nding is in line with hypotheses
stated by Ongena and Smith (1998); Sharpe (1990); Rajan (1992). However,signicance is weak and varies across periods, since there is low variation inconcentration across districts. The measurement could also be improved bytaking the bank's credit volume in corporate nance into account.
There are further interesting ndings, concerning rm's nancing behavior and internal factors inuencing debt renegotiation as the underlying pro16 Results of estimates of the cumulative distribution and kerneldensity using the overallrating are similar the same.

4 EMPIRICAL ANALYSIS

19

cess of rms exit probability. I nd only low and mostly insignicant inuenceof the number of bank relations on rm survival. Firms or entrepreneurs thatare able to oer Real Estate property for securitization show a lower probability exiting the market. The same is observed for rms that are managed byat least on person that hold a master certicate or university degree. Either
educated managers have are better able to renegotiate debt or education signals better future prospects and therefore lower risk. A larger management
team can better combine dierent abilities that are crucial for business. In
addition, debt holders have a better position to execute their claims against
a number of debtors in case of a market exit. Probability of market exit
reduces as the number of managers increase.
There is a dynamic eect for rm's History of Distress. In the period ofnancial distress the eect is negative and signicant, while the sign changes
in period t + 1 and is signicant from period t + 3 ongoing. Financiers knowthat the rm has managed several crises and are not willing to shut down
immediately. Over time rms can present a concept how to deal with thecrises. Financiers' on their part assess whether the rm is viable. Since it islikely that the rm is enfeebled after a series of nancial distress this might
not be the case and the rm might exit.
Further ndings of internal and external factors of rm survival are inline with the literature, while two ndings are worth to mention. First, age
is found to have a non-linear negative eect on market exit. This nding
is consistent with the literature on rm survival (Audretsch and Mahmood,1995). With increasing rm age probability surviving nancial crises increases substantially. The highest age class is an exception. Firms that age
50 years or older show a higher probability exiting the market compared toyoung rms. This might reect diculties in transferring business to anothergeneration. Second, even ten to fteen years after reunication rms locatedin Eastern Germany are more likely to exit the market. This reects still
existing regional dierences in product and banking markets due to German
reunication.

Robustness Checks, Discussion, and Limitations Data available, as
well as, the corresponding empirical model have some limitations that need to4 EMPIRICAL ANALYSIS

20

be considered for the interpretation of the results. Coecients of Business
Register are found to be positive and highly signicant in all periods andmarginal eects are considerably high. These ndings are consistent withthose presented by Harho et al. (1998) who argue that rms choose theirlegal form according to risk. More riskier rms tend to choose limited liability
forms. However, as time after nancial distress goes by the eect reduces
indicating that it takes longer to observe voluntarily market exit of rms not
publicly registered. Therefore, sample selection problems might still exist.
Market exits due to insolvencies might be over represented. Voluntary
market exit is considered if related characteristics implemented by Creditreform can be observed only. A proper assumption would be that rms whichwere not investigated or information on which was not updated for a longtime have closed. However, this would not solve the problem. Accordingly,
observations of deteriorating mode of payment are rare for those rms.
In addition to rms exiting market after nancial distress the panel also
contains observations on rms that fail without deteriorating mode of payment. Three cases can be considered. First, the deteriorating state is simply
not observed. Selection equation incorporated in the model should mitigate
possible selection bias. Second, a rm's status already refers to the worst
case. For this reason further deteriorating is not possible. An institutional
problem is given by German bankruptcy legislation for indebted rms. Thoseneed to le for bankruptcy, while bankruptcy is for balance sheet rather thansolvency reasons. Logically, deteriorating status of mode of payment is notobserved.
In addition, banks' lending strategy and rms' nancial distress and exit
might be endogenous. If the hypotheses of self selection of rms according
their risk to a certain bank type stated by Matthey (2008) is valid, probabilityof distress is higher for rms nanced by public and cooperative banks, respectively. The question arise whether bank dummies are endogenous. Thisshould not be the case for the model presented here. The model is restricted
to nancial distressed rms only. In most cases the rm's choice of nancing
partner is made a long time ago. Further, the rms viability does not depend on that choice. For robustness check deteriorating state of rm's credit
rating is used as dependent variable. Information concerning credit rating5 CONCLUSION

21

is produced by Creditreform indicating the rms credit worthiness that isa part of the overall rating. Results remain stable and are provided by theauthor on request.

5 Conclusion
With this paper I have explored whether dierences in banks' lending strategies exist. Firm's that build up a strong relationship to their main nancing
partner might nd it easier to renegotiate debt if nancial distressed. Public and cooperative banks have a mandate supporting regional economy andare traditionally involved in corporate nance. Those are said to act as relationship lenders. Theoretically, it might be rational for private banks tocompete in debt repayment and act as a hard-nosed banker. In order toovercome the public banks' funding cost advantage private banks seek to attract low risk rms by threatening early liquidation of nancially distressed
Private banks need to rule out renegotiation credibly. Otherwise high risk
rms would free ride and private banks would no longer be able to compete
protably. Therefore, rms nanced by public or cooperative banks shouldhave a higher probability to survive nancial distress.
For German rms in the period between 2000 and 2005, I nd that theprobability of exiting the market after nancial distress is higher for rms nanced by private banks compared to those nanced by public banks. Eects
are even larger for rms nanced by cooperative banks. It remains unclear,whether this is because private banks act as hard-nosed bankers and are lesslikely to audit nancial distressed rms or at least adopt stricter rules. Private banks may also take on riskier borrowers in the rst place, indicatedby a riskier credit portfolio. Firm's bank choice seems crucial for a better
understanding of this eect and is left for further research.

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A APPENDIX27

A Appendix
Table 2: Market Share of Banking Sectors in Germany, 2000-2005
Market Share in:Total Assets Corporate Finance

Private Banks

thereunder:
Real Estate Credit Institution
Commercial Banks

Public Banks

thereunder:
Savings Banks
Landesbank

Cooperative Banks
Others39.8 %

44.0 %

12.8 %
27.0 %

13.6 %
30.4

40.1 %

35.3 %

22.4 %
17.7 %

15.3 %
20.0 %

12.4 %
7.7 %Source: Bundesverband deutscher Banken e.V. (2009)12.1 %
12.1 %

Central Bank.
In general, legal form of limited liability; some are publicly
listed companies; including branches of foreign banks.
Clearing houses for savings banks. Banks are jointly owned
by savings banks and the Länder.17
Public development banks are governmentally owned andoperate either on Länder or federal level.18
Publicly listed company.
Owned by area municipalities.
Owned by members of the cooperative society.
Publicly listed company.

Deutsche Bundesbank
Real Estate and Commercial Banks
Landesbanken

Development Banks

Commerzbank AG
Savings Banks
Cooperative Banks
Deutsche Bank AG

1.
2.

4.

5.
6.
7.
8.

Private banks
Public Banks
Cooperative Banks
Private Banks

excluded

Public Banks

excludedPrivate Banks

Bank Type17 Following institutions are classied as Landesbanken: HSH Nordbank (before 2003: Hamburgische Landebank, Landesbank Schleswig
Holstein), Norddeutsche Landesbank Girozentrale (NORD/LB), Bremer Landesbank, Landesbank Berlin (LBB), Westdeutsche Landesbank (WestLB), Landesbank Hessen-Thüringen Girozentrale (Helaba), Landeskreditkasse Kassel, Landesbank Sachsen (merged with
LBBW in 2008), Landesbank Rheinland-Pfalz Girozentrale (LRP; merged with LBBW in 2008), Landesbank Saar (SaarLB; since 2002
mainly owned by BayernLB), Landesbank Baden- Württemberg/Baden Württembergische Bank (LBBW), Bayerische Landesbank (BayernLB), Deutsche Kreditbank, DekaBank, Deutsche WertpapierService Bank AG, NLB FinanzIT, LBS
18 Following institutions are classied as Public Development Banks: Landestreuhandbank Rheinland-Pfalz, LfA Förderbank Bayern, L-Bank, Investitionsbank Berlin, Investitionsbank des Landes Brandenburg, Bremer Aufbau-Bank GmbH, Hamburgische Wohnungsbaukreditanstalt, LTH-Bank für Infrastruktur, Investitionsbank Hessen, Landesförderinstitut Mecklenburg-Vorpommern, N-Bank,
Investitions- und Strukturbank Rheinland-Pfalz (ISB) GmbH, Sächsische Aufbaubank, Investitionsbank Sachsen-Anhalt, Investitionsbank Schleswig-Holstein, Thüringer Aufbaubank, Kreditanstalt für Wiederaufbau (KfW), Deutsche Ausgleichsbank (DtA, merged with
KfW in 2003)

3.

Description

No. Bank or Bank Group

Table 3: Description of Bank Types

A APPENDIX
28

Exit
896
1,076
1,292
1,319
1,515
1,295

%
39.1
42.9
44.2
43.5
44.4
43.2

17,169 7,393 43.1

Distress
2,294
2,510
2,924
3,029
3,413
2,999

Exit
1,304
1,498
1,718
2,307
2,679
2,594

%
34.8
35.1
32.5
39.9
40.4
41.4

Distress
2,152
2,305
2,918
3,248
3,833
3,431

%
34.2
35.9
38.7
38.1
39.3
39.3

6,789 38.0

Exit
737
828
1,128
1,238
1,508
1,350

Cooperative Banks

31,992 12,440 38.9 17,887

Distress
3,746
4,263
5,289
5,789
6,634
6,271

Savings Banks

Exit
2,937
3,402
4,138
4,864
5,702
5,239

Total

schaftliche Zentralbank), Deutsche Apotheker und Ärztebank eG

20 Following institutions are classied as central banks for cooperative banks: DZ Bank AG, WGZ Bank AG (Westdeutsche Genossen-

19 Dresdner Bank AG merged with Commerzbank AG in 2008.

%
35.9
37.5
37.2
40.3
41.1
41.2

67,048 26,622 39.7

Distress
8,192
9,078
11,131
12,066
13,88012,701

Table 4: Distress, market exit and type of main nancing partner

Private Banks

Note: Distress is dened as a deteriorating mode of payment status in the years 2000 to 2005. Market
exit is considered until two years after distress.
Source: Author's own calculation based on the MUP (ZEW).

Total

2000
2001
2002
2003
2004
2005

Year

Private Banks
Cooperative Banks

Publicly listed company.19
Clearing houses for cooperative banks.
owned by cooperative banks.20

9. Dresdner Bank AG
10. Central Bank for Cooperative Banks
Banks jointly

Bank Type

Description

No. Bank or Bank GroupA APPENDIX29

A APPENDIX

30
Table 5: Description of Variables

Variable

Description

Exp.
Sign

Banking & Finance
Private Banks
Public Banks
Cooperative
Banks
Banking Market
Concentration
No. of Bank relations
Debt Collection
Real Estate
History of Distress= 1 if a private bank is the main nancing partner= 1 if a public bank is the main nancing partner= 1 if a cooperative bank is the main nancing partner
1= no. of banks activein the district+-= number of bank relations?

= 1 if CREDITREFORM is commissioned to collectdebt from the particular rm= 1 if either residential, industrial or mixed property
is ownedP=of situation of distress within the sample period+=1 if companies need to be registered due to legal
form= log of number of employees= squared log of number of employees= log of number of employees raised to the power of3= 1 if rm age is between 0 and 2= 1 if rm age is between 3 and 6= 1 if rm age is between 7 and 12= 1 if rm age is between 13 and 19= 1 if rm age is between 20 and 49= 1 if rm age is 50 or older= 1 if rm age is missing

-

?

+

Internal Factors
Business Register
Size
Size2
Size3
Age 1
Age 2
Age 3
Age 4
Age 5
Age 6
Age M

Control
Control
Control
Control
Control
Control
Control
Control
Control
ControlA APPENDIX

31

Variable

Description

Exp.
Sign

Management
Team
Management
Team M
Master Craftsman= number of persons belonging to the managementboard= number of persons belonging to the management
board unknown= Entrepreneur with the highest education within
the team holds a master certicate received from thechamber of industries and commerce or the chamberof crafts
Entrepreneur with the highest education within theteam holds an university degree (either a German
diploma, degree of doctor, or professorship)-

Academic

Control--

External Factors∆ local Insolvencies
East Germany
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Industry 9
Industry 10
Industry 11
Industry 12
Industry M
Year= the insolvency in a particular district compared toprevious year
= 1 if rm is located in East Germany= 1 Cutting Edge Technology= 1 if High Technology= 1 if Manufacturing= 1 if Technology-intensive Services= 1 if Consultancy= 1 if Industry-related Services= 1 if Consumer Services= 1 if Energy and Mining= 1 if Construction= 1 if Retail and Wholesale= 1 if Transportation= 1 if Postal and IT Services= 1 if Industry code is unknown= 1 if year is respective year

Selection Equation
Branch Quality= Research Quality of Creditreform branches per period++
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
ControlA APPENDIX

32

Variable

Description

Investigation= 1 if Creditreform investigated the rm within that
period=1 if companies need to be registered due to legalform= 1 if Creditreform is commissioned to collect debt
from the particular rm= 1 if Industry code is unknown= 1 if date of foundation is unknown= 1 if Number of employees is unknown= 1 if a public bank is the main nancing partner= 1 if a cooperative bank is the main nancing partner= 1 if information on banking partner is missing

Business Register
Debt Collection
Industry M
Age M
Employee M
Public Banks
Cooperative
Banks
Bank M

Exp.
Sign

Table 6: Descriptive Statistics
Variable

Obs.

Mean

SD

Min

Max

4,723,716
4,723,716
4,723,716
4,723,716
4,723,716
4,723,716-0.004
0.119
0.236
0.022
0.156
0.136

1.095
0.324
0.425
0.145
0.363
0.343

-9.405
0
0
0
0
0

1.741
1
1
1
1
1

67,04867,04867,04867,04867,04867,048

0.477
0.267
0.080
1.281
0.113
1.295

0.410
0.442
0.055
0.594
0.317
0.596

0
0
0.006
1
0
1

1
1
1
6
1
6

Selection Equation
Branch Quality
Investigation
Business Register
Debt Collection
Industry M
Age M

Main Equation
Banking & FinancePublic Banks
Cooperative Banks
BMC
No. of bank relations
Debt Collection
History of distress

A APPENDIX

33

Variable

Obs.

Mean

SD

Min

Max

Real Estate

67,048

0.248

0.432

0

1

67,04867,04867,04867,04867,04867,04867,04859,436
67,04867,04867,048

0.270
8.389
0.263
0.293
0.163
0.126
0.092
1.064
0.114
0.120
0.115

0.444
92.446
0.440
0.455
0.369
0.332
0.289
0.286
0.317
0.325
0.320

0
1
0
0
0
0
0
1
0
0
0

1
15,241
1
1
1
1
1
9
1
1
1

67,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,04867,048

0.004
0.008
0.078
0.041
0.035
0.041
0.192
0.191
0.062
0.004
0.054
0.301
9.205
0.122
0.135
0.180
0.207
0.189

0.063
0.091
0.268
0.198
0.185
0.197
0.395
0.393
0.240
0.060
0.225
0.459
27.816
0.328
0.342
0.384
0.405
0.392

0
0
0
0
0
0
0
0
0
0
0
0
-85.7143
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1
1
1
360
1
1
1
1
1

Internal Factors
Business Register
Size
Age 2
Age 3
Age 4
Age 5
Age 6
Management TeamManagement Team M
Master Craftsman
Academic

External Factors
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 9
Industry 11
Industry 12
Industry M
East Germany∆ local Insolvencies
Year 2000
Year 2001
Year 2003
Year 2004
Year 2005

A APPENDIX

34

Table 7: Model Results - Part 1 Selection Equation
Selection Equation
VARIABLES
Branch Quality
Investigation
Business Register
Debt Collection
Industry M
Age M
Constant

ρ̂

(1)

(2)

(3)

(4)

(5)

MoP

MoP

MoP

MoP

MoP

-0.036***(0.001)0.455***(0.004)-0.048***(0.004)0.741***(0.006)-0.321***(0.007)-0.348***(0.008)-2.243***(0.003)-0.036***(0.001)0.457***(0.004)-0.048***(0.004)0.741***(0.0063)-0.321***(0.007)-0.341***(0.0083)-2.244***(0.003)-0.036***(0.001)0.459***(0.004)-0.048***(0.004)0.741***(0.006)-0.322***(0.007)-0.336***(0.008)-2.244***(0.003)-0.036***(0.0014)0.460***(0.0040-0.048***(0.004)0.741***(0.006)-0.322***(0.007)-0.333***(0.008)-2.245***(0.003)-0.036***(0.001)
0.461***(0.004)-0.048***(0.004)0.741***(0.006)-0.322***(0.007)-0.329***(0.008)-2.245***(0.003)0.740***(0.035)0.635***(0.032)0.562***(0.031)0.525***(0.030)0.472***(0.030)Robust clustered (rm) standard errors in parentheses; *** p < 0.01,** p < 0.05, * p < 0.1

A APPENDIX

35

Table 8: Model Results - Part 2 Main Equation
Main Equation

(1)

(2)

(3)

(4)

(5)

VARIABLES

exit texit t + 1

exit t + 2

exit t + 3

exit t + 4-0.005(0.011)-0.030**(0.013)-0.025(0.086)-0.019**(0.009)0.391***(0.020)-0.033***(0.009)-0.112***(0.012)-0.009(0.012)-0.036***(0.014)-0.099(0.093)-0.0198**(0.010)0.364***(0.020)0.004(0.009)-0.112***(0.013)-0.023*(0.013)-0.041***(0.014)-0.159*(0.095)-0.0155(0.0100)
0.337***(0.021)0.026***(0.009)-0.103***(0.013)-0.031**(0.013)-0.046***(0.015)-0.124(0.098)-0.0132(0.010)0.330***(0.021)
0.042***(0.009)-0.093***(0.014)-0.032**(0.013)-0.053***(0.015)-0.196**(0.100)-0.0142(0.010)0.311***(0.021)
0.056***(0.010)-0.101***(0.014)

0.661***(0.016)-0.009(0.019)0.058***(0.012)-0.011***(0.002)-0.045**(0.019)-0.148***(0.019)-0.207***(0.021)

0.633***(0.016)0.0247(0.021)0.049***(0.013)-0.011***(0.002)-0.101***(0.020)-0.239***(0.021)-0.299***(0.023)0.594***(0.016)
0.053**(0.021)
0.041***(0.013)-0.011***(0.002)-0.123***(0.021)-0.287***(0.022)-0.350***(0.024)0.549***(0.015)0.076***(0.021)0.030**(0.013)-0.010***(0.002)-0.131***(0.021)-0.315***(0.022)-0.382***(0.024)0.507***(0.015)0.104***(0.022)0.015(0.013)-0.007***(0.002)-0.138***(0.021)-0.337***(0.022)-0.407***(0.024)

Banking & FinancePublicBanks
Cooperative
Banks
BMC
No. of bankrelations
Debt Collection
History of
distress
Real Estate

Internal Factors
Business
Register
Size
Size2
Size3
Age 2
Age 3
Age 4

A APPENDIX

36

Main Equation

(1)

(2)

(3)

(4)

(5)

VARIABLES

exit texit t+1

exit t+2

exit t+3

exit t+4

Age 5-0.188***(0.023)0.483***(0.029)-0.100***(0.0181)-0.224***(0.026)-0.090***(0.016)-0.065***(0.016)-0.298***(0.024)0.378***(0.029)-0.121***(0.019)-0.245***(0.028)-0.089***(0.017)-0.062***(0.017)-0.356***(0.025)0.313***(0.029)-0.132***(0.020)-0.257***(0.029)-0.097***(0.018)-0.074***(0.018)-0.391***(0.026)
0.266***(0.029)-0.142***(0.020)-0.275***(0.029)-0.109***(0.018)-0.083***(0.018)-0.416***(0.026)
0.234***(0.029)-0.153***(0.021)-0.282***(0.030)-0.115***(0.019)-0.095***(0.019)Age 6Management
Team
Management
Team M
MasterCraftsmanAcademic

External Factors
Industry
East
Germany∆ localInsolvencies
Year
Constant

Yes***
0.124***(0.011)0.0006***(0.0002)
Yes***-2.078***(0.055)

Yes***
0.134***(0.012)0.0008***(0.0002)
Yes***-1.776***(0.061)

Yes***
0.129***(0.013)0.0006***(0.0002)
Yes***-1.542***(0.065)

Yes***
0.123***(0.013)0.0005***(0.0002)
Yes***-1.365***(0.066)

Yes***
0.127***(0.013)0.0005***(0.0002)
Yes***-1.166***(0.069)No. of Obs.

4,723,716

4,723,704

4,723,687

4,723,677

4,723,662

Robust clustered (rm) standard errors in parentheses; *** p < 0.01,** p < 0.05, * p < 0.1

A APPENDIX

37
Table 9: Marginal Eects

VARIABLES

(1)

(2)

(3)

(4)

(5)

exit texit t + 1

exit t + 2

exit t + 3

exit t + 4-0.002(0.005)-0.013**(0.005)-0.011(0.037)-0.008**(0.004)-0.006(0.006)-0.014***(0.004)-0.047***(0.005)-0.004(0.005)-0.015***(0.006)-0.042(0.039)-0.008**(0.004)0.0002(0.006)0.001(0.004)-0.046***(0.005)-0.010*(0.005)-0.017***(0.006)-0.068*(0.040)-0.007(0.004)0.002(0.006)0.011***(0.003)-0.042***(0.005)-0.013**(0.005)-0.020***(0.006)-0.054(0.042)-0.006(0.004)0.007(0.006)0.018***(0.004)-0.039***(0.006)-0.013**(0.006)-0.022***(0.006)-0.083**(0.042)-0.006(0.004)0.010(0.006)0.024***(0.004)-0.042***(0.006)

0.316***(0.006)-0.004(0.008)0.025***(0.005)-0.005***(0.0008)-0.019**(0.008)-0.061**(0.008)-0.084***(0.008)0.291***(0.006)0.010(0.009)0.021***(0.005)-0.005***(0.0008)-0.042***(0.008)-0.096***(0.008)-0.116***(0.008)0.268***(0.006)0.022**(0.009)0.017***(0.005)-0.004***(0.0008)-0.051***(0.008)-0.115***(0.008)-0.136***(0.008)0.247***(0.006)0.032***(0.009)0.013**(0.005)-0.004***(0.0008)-0.055***(0.009)-0.128***(0.008)-0.150***(0.008)0.225***(0.006)
0.043***(0.009)0.007(0.005)-0.003***(0.0008)-0.057***(0.009)-0.137***(0.008)-0.160***(0.009)Banking & FinancePublic Banks
Cooperative
Banks
BMC
No. ofbank relations
Debt
Collection
History of
distress
Real Estate

Internal Factors
Business
Register
Size
Size2
Size3
Age 2
Age 3
Age 4

A APPENDIX

38
(1)

(2)

(3)

(4)

(5)

VARIABLES

exit texit t+1

exit t+2

exit t+3

exit t+4

Age 5-0.076***(0.009)0.219***(0.012)-0.043***(0.008)-0.092***(0.010)-0.038***(0.007)-0.027***(0.007)-0.116***(0.009)0.164***(0.012)-0.051***(0.008)-0.099***(0.010)-0.036***(0.007)-0.026***(0.007)-0.138***(0.009)0.134***(0.012)-0.055***(0.008)-0.104***(0.011)-0.040***(0.007)-0.030***(0.007)-0.154***(0.009)0.114***(0.012)-0.060***(0.009)-0.112***(0.011)-0.045***(0.007)-0.034***(0.007)-0.164***(0.009)0.099***(0.012)-0.065***(0.009)-0.115***(0.012)-0.047***(0.008)-0.039***(0.008)Age 6Management
Team
Management
Team M
MasterCraftsmanAcademic

External Factors
Industry
East
Germany∆ localInsolvenciesYear

Yes***
0.054***(0.005)0.0003***(7.3e-5)
Yes***

Yes***
0.057***(0.005)0.0003***(7.5e-5)
Yes***

Yes***
0.055**(0.005)0.0002***(7.4e-5)
Yes***

Yes***
0.052***(0.005)0.0002***(7.4e-5)
Yes***

Yes***
0.053***(0.006)0.0002***(7.5e-5)
Yes***

No. of Obs.

4,723,708

4,723,708

4,723,708

4,723,707

4,723,706*** p < 0.01, ** p < 0.05, * p < 0.1

A APPENDIX

39

1

2

Credit Rating
3
4

5

6

Figure 1: Risk Portfolio related to Bank Type (2005) - Cumulative Distribution

0

.2

.4
.6
Fraction of the data
Private Banks
Cooperative Banks.8

1

Public Banks

Note: Credit Rating Score 1 indicate low risk and 6 high risk. Calculation is based on
the number of rms nanced by each bank type where a credit risk score was assigned by
Creditreform in August 2005.
Source: Author's own calculation based on the MUP (ZEW)

.05

.1

density
.15

.2

.25

Figure 2: Kerneldensity of Bank Types Credit Risk (2005)

0

2

4
Credit Rating Scores
Private Banks
Cooperative Banks

6

8

Public Banks

Epanechnikov kernel function, bwidth=1

Note: Credit Rating Score 1 indicate low risk and 6 high risk. Calculation is based on
the number of rms nanced by each bank type where a credit risk score was assigned by
Creditreform in August 2005.
Source: Author's own calculation based on the MUP (ZEW)