how do banking relationships affect financial constraints ... · jel classification: g21; g32. 3 ....

1

How do banking relationships affect financial

constraints of SMEs?

Ludovic VIGNERON, Ph.D.

Associate Professor

Valenciennes University, France

[email protected]

Ramzi BENKRAIEM, Ph.D.

Associate Professor,

Audencia Nantes School of Management, France

[email protected]

Anthony MILOUDI, Ph.D.

Professor

La Rochelle Business School & CRIEF University of Poitiers, France

[email protected]

mailto:[email protected]



2

How do banking relationships affect financial

constraints of SMEs?

Abstract: We examine the impact of the closeness of banking relationships on financial constraint

problems of SMEs in the French context. To do so, we use the cash-flow sensitivity of cash method

elaborated by Almeida et al (2004). Through a unique sample of 1 145 bank-firm relationships observed

during the 2003-2012 period, we find evidence that single bank firms and firms engaged with a

decentralized main bank are less constrained than other firms, but that those which meet those two criteria

simultaneously are not. Firms with a decentralized main bank and less than three banks appear however

less constrained. KZ and WW index analyses performed as robustness checks underline the importance of

the decentralized organizational structure of the main bank in reducing financial constraints of SMEs. The

analysis of trade credit use also provides results in line with the hypothesis of a less important financial

constraint problem for SMEs in close banking relationship. Finally we find evidence that the impact of the

closeness of banking relationships on cash-flow sensitivity of cash is more important after the recent

financial crisis.

Keywords: Financial Constraints; Banking Relationships; Cash Holding; Cash Flow Sensitivity; SMEs.

JEL Classification: G21; G32.

3

1. INTRODUCTION

The difficulties of access to external finance affecting SMEs are pointed out by both

financial economists (Beck and Demirguc-Kunt, 2006; Aghion et al., 2007) and policy makers

(Biggs, 2002) as an obstacle for economic growth1. Informational opacity and related asymmetric

information problems make financial contracting particularly problematic for this category of

firms (Berger and Udell, 1998; Gregory et al., 2005). As a consequence, external funds providers

like banks usually prefer to ration their supply of funds to SMEs in order to avoid the risk

associated with bad investments (Stiglitz and Weiss, 1981; Williamson, 1987; Ang, 1991, 1992).

In such a context of financial constraints, investment choices and funding can no longer be

considered as independent. As Fazzari et al. (1988) report in their influential paper, firms

investment spending varies with the availability of internal funds rather than with the availability

of positive net present value projects. These findings have been supported by various studies

carried out in different countries (Carpenter and Petersen, 2002; Becchetti and Trovato, 2002).

Financial intermediaries and more specifically banks have developed technologies to deal

with asymmetric information problems (Berger and Udell, 2006). Relationship lending is one of

these technologies and it appears to be particularly adapted for SMEs funding. Its mechanism is

based on implicit long term contracts in which banks and firms agree to work together more or

less exclusively in the long run (Sharpe, 1990; Rajan, 1992). This creates incentives for the firm

to share information with its bank and for the bank to invest in monitoring (for an extensive

review of this mechanism see Boot (2000)). Previous empirical studies report evidence of a less

important credit rationing problem for more opaque SMEs when they benefit from a relationship

lending. Degryse and Ongena (2008) have extensively listed this research. Nevertheless, even if

these studies have shown that relationship lending allows a better access to credit for SMEs, they

do not say much about their effect on global financial constraints which they have to face.

In this paper, we examine the effect of relationship lending on financial constraints

affecting SMEs. To do so, we use the metrics developed by Almeida et al. (2004), the cash-flows

sensitivity of cash. Through a new unique sample of 1 145 bank relationships involving French

SMEs over the 2003-2012 period, we test whether or not firms which benefit from relationship

1 Even if the goal of all SMEs not necessarily to grow indefinitely (Hurst and Pugsley, 2011; Hamelin, 2012) the lack of funds can be an obstacle for them to reach their optimal size in which they are the more efficient.

4

lending contracts appear less constrained than those that do not. We report evidence, firstly, that

SMEs have difficulties to access external funds, secondly, that single bank relationship and the

fact for a SME to work with a decentralized main bank reduce their cash-flows sensitivity of

cash. Nevertheless, simultaneously these two criteria have no significant impact on firm’s

financial constraint problems. Moreover, in spite of the positive effect of close bank relationship,

SMEs engaged in this type of relationship have a higher level cash holding than the others.

Another interesting result is that relationship lending appears to reduce SMEs financial

constraints mainly after the beginning of the financial crisis of 2007. To assess the robustness of

these results, we use alternative methods to measure financial constraints. The analysis of the

effect of the closeness of firm bank relationships on KZ index and WW index provides evidence

that SMEs with decentralized main banks are less constrained than the others. Analysis of trade

credit use supports the hypothesis that close bank relationships reduce SME financial constraints.

Overall these findings point out the importance of banking relationships to deal with

financial constraint problems in the specific context of SMEs. However they raise some new

questions. First, it appears that working with a single decentralized bank does not allow SMEs to

reduce their cash-flows sensitivity of cash. Some competition appears to be needed to create

incentives for the bank to provide relationship lending. SMEs with less than three banks (one or

two) and a decentralized main bank have a lower cash-flows sensitivity of cash. Second, if close

bank relationships providing relationship lending reduce financial constraint problems for SMEs,

we can ask why they have to keep larger levels of cash holding comparatively to those that arm’s

length relationships with their banks. The main bank could impose this higher level of cash as a

type of guaranty or could exploit their informational advantage through this canal by imposing

SMEs to contract more debt than really needed taking into consideration their investment

opportunities.

The remainder of the paper proceeds as follows. Section 2 presents the literature dealing

with financial constraint problems and banking relationships for SMEs. This work helps to

formulate our different hypotheses. Section 3 exposes the sample characteristics and the

methodology. Section 4 displays and discusses the results of our empirical investigations. Section

5 reports the robustness analyses and section 6 serves as a conclusion.

5

2. LITERATURE AND HYPOTHESIS DEVELOPMENT

2.1. Financial constraints in the context of SMEs

Following Fazzari et al. (1988), numerous empirical studies have investigated the question

of the effect of financial constraints on various dimensions of corporations’ financial policy (see

Hubbard (1998) for a review), as well as the question of the different market frictions which

shape the financial constraints (internal capital market efficiency (Shin and Stulz, 1998; Hadlock,

1998), intragroup financing (Hoshi at al., 1991) or managerial characteristics (Bertrand and

Shoar, 2001; Malmendier and Tate, 2005 among others). This research initially focused on

sensitivity of investment to cash flows as a metric for financial constraints but this

methodological option has generated an important debate. The starting point of the controversy is

Kaplan and Zingales (1997) which in a theoretical analysis question the fact that cash flows

sensitivity of investments to cash flows increase monotonically with financial constraints2 and in

a related empirical study using an alternative measure of financial constraints report that more

constrained firms face a higher cash flows sensitivity of their investments. To remedy the

weaknesses of the method, new metrics of financial constraints was developed during the past

decades. Most of them are built as an index: KZ Index (Lamont et al., 2001) , WW Index (Whited

and Wu, 2006), AZ Index (Hadlock and Pierce, 2010) , to quote only the most frequently used.

None of them appears to actually dominate the others.

Beyond this methodological debate, theoretical analysis associates firms’ financial

constraints, and the related difference in cost between internal and external finance, with

asymmetric information problems (Myers and Majluf, 1984; Greenwald et al., 1984) and agency

problems (Jensen and Meckling, 1976; Hart and Moore, 1995). These problems increase the cost

of external funding in order to compensate funds providers for valuation and expropriation risks

and can also generate funds rationing situations. SMEs are particularly affected by these

difficulties. Their small size affects the quantity and the quality of information available about

their investment projects as well as about the collateral they can pledge. They are perceived as

opaque. The relatively limited size of their project limits the scale on which the fixed cost of

screening and monitoring can be recovered (Devraux and Schiantarelli, 1990; Beck et al., 2005).

Moreover, they are frequently associated with more specific and less numerous fixed assets than 2 See Caggasse (2007) for a recent discussion of this literature.

6

large firms which reduce their possibility to provide acceptable collaterals. This limits the access

to external debt and, thus, makes the investment more sensitive to cash flows (Almeida and

Cappello, 2007). Many studies report that SMEs’ investments and the consecutive growth are

more sensitive to inside finance than those of large firms (Westhead and Storey, 1997; Cress and

Olofsson, 1997; Audresch and Elston, 2002). These firms’ specific difficulties are doubled by

institutional configuration which only offers SMEs not fully developed financial markets.

For SMEs banks appear to be the main source of external funds (Berger and Udell, 1998;

Beck et al., 2008). Consequently most of the studies on SMEs financial constraints are focused

on credit rationing problems. They use two alternative empirical strategies. The first based on this

survey consists of asking firms if they actually applied for credit and whether they were turned

down or not (Berger and Udell, 1995; Cole, 1998; Berkowitz and White 2004; and Berger,

Cerqueiro, and Penas 2011). The second focuses on firms’ use of costly funding alternatives

relatively to bank credit like trade credit (Petersen and Rajan, 1994; 1995) or leasing (Sharpe and

Nguyen, 1995; Slotty, 2009). In both cases these studies report that credit rationing problems are

more important for more opaque firms which are the smallest and the youngest ones. With this

regard, Carbro-Valverde et al. (2008) report evidence that investment sensitivity to bank loans is

more important for firms considered as not restricted in their access to loans than for the others

and that investment sensitivity to trade credit is more important for the firms that are restricted in

their access to loans than for the other ones. This result is particularly interesting. It highlights the

fact that financial constraints must be considered as a whole. If one source of funding like bank

credit is limited another one can be used as a substitute in order to avoid losing positive net

present value investment opportunities.

The effect of financial slack, and its miss financial constraint, on investments is well

established but the precise mechanism involved in the phenomenon remains unclear. Hence direct

study of firms’ investments behavior can be misleading if the objective of the investigation is to

understand how it shapes financial constraint. Almeida et al. (2004) propose a flexible solution

derivate from the classical approach of Fazzari et al (1988). Instead of considering cash flows

sensitivity of investments, they consider cash flows sensitivity of cash. The economic thinking

beyond this is that a firm’s demand for liquidity is driven by their anticipation of financial

constraints. They are hoarding cash today in order to avoid future loss of positive net present

value projects caused by difficulties accessing external funding. This practice is costly. It reduces

7

the availability of funds dedicated to current projects. Consequently financially constrained firms

have to determine optimal cash holding policy to balance profitability of current projects and

future ones. For unconstraint firms the choice of a level of cash holding does not depend on a

tradeoff between current and future investments. It results from this that the cash flow sensitivity

of cash can be used as an indicator of financial constraints.

Extending this reasoning, we adapt Almeida et al. (2004) methodology to the context of

SMEs which mostly include unlisted firms. This last category of firms is considered as opaque.

So for them, asymmetric information problems limit the access to external funds and must

generate financial constraints. As a result, we can establish the following hypothesis (H1).

H1: SMEs are financially constrained.

We expect to find a positive correlation between variation of cash and cash flows, and more

specifically a positive sign of the cash flow sensitivity of cash, over our sample of SMEs.

2.2. Banking relationships

Extensive literature has shown that close bank firm relationships can improve SMEs access

to credit by allowing the use of relationship lending (Boot, 2000). Since Petersen and Rajan

(1994)’s seminal paper which reports on a sample of US SMEs using NSSBF database that credit

availability increases with the duration of relationship and decreases with the number of banks

that the firm works with, empirical evidence has accumulated. In many countries, studies have

reported the same kind of results: Harhoff and Korting (1998), in Germany; Angelini et al.

(1998), in Italy; De Bodt et al. (2005), in Belgium; Dietsch and Golitin-Boubakari (2002) and

Ziane (2004) in France… For a more detailed survey see Degryse and Ongena (2008). This

particular contractual configuration helps the firm’s main bank to efficiently deal with

asymmetric information problems. For opaque SMEs, it allows the bank involved to build

informational advantage. By financing its clients at an early stage, the bank collects information

that competing banks do not have. This discourages those last to offer credits to the bank client

firm because of asymmetric information problems and resulting winner curse risk. The main bank

can consequently benefit of monopoly conditions during the firm’s lock-in period which allow it

to be paid back for its initial investment in information (Sharpe, 1990; Rajan, 1992). From the

borrower’s point of view, relationship lending encourages information sharing with its main bank

8

because its will to build an informational monopoly appear as a guaranty of confidentiality

(Battacharya and Chiesa, 1995).

As a result, opaque firms, such as SMEs, which benefit from close banking relationships,

must be less financially constrained than those which do not have this chance. Previous studies

based on sensitivity of investment to cash flows do not provide sufficient evidence to evaluate

this hypothesis. Most of them focus on big firms and present mixed evidence. They can be

classified following the measure of the relationship intensity they use. The first group considers

the presence of the bank as shareholder or on the board of directors as a mark of the closeness of

the relationship. Folhin (1998), for German firms on historical data covering the period 1900-

1913, finds no evidence that close bank relationships relax financial constraints. Carcia-Marco

and Ocana (1999), in Spain, find the opposite. More recent studies differentiate single bank firms

from multibank firms or debt market funding. Houston and James (2001), in US, do not observe

lower financial constraints for bank dependent firms relatively to firms which issue market debt

except for the biggest investments. Fuss and Vermeulen (2008), in Belgium, report that firms

which work only with one bank can more easily obtain extra credit in order to avoid investment

cuts in case of adverse shock. Cinquegana et al (2012), in Italy, use another indicator of the

closeness of relationship, its length. They establish that small firms can relax dependence of

investment to internal finance by obtaining credit from their long term main bank.

As we previously discussed by focusing directly on investments in order to deal with

measures of financial constraints, these studies are exposed to methodological difficulties. They

fail to clearly identify the canal from which market imperfections, like information asymmetries,

generate limited access to external finance and affect firms’ investment policy independently

from corporate governance configuration that can affect both funding and investment choice:

conservatism for family firms, manager entrenchment or empire building behavior… In order to

avoid these debates and to assess the effect for a SME of close bank relationship on its financial

constraint problems, we focus more directly on firm’s funding policy using Almeida et al (2004)

methodology. We test the following hypothesis (H2):

H2: Firms engaged in close banking relationships are less financially constrained.

We expect to find a lower cash flows sensitivity of cash for SMEs which benefit from

relationship lending than those which do not. By reducing asymmetric information problem at the

firm bank level, this contractual configuration increase firm’s access to credit which limit its

9

global financial constraint problem. In this context, it seems less frequent that SMEs have to

reduce their cash holding in order to avoid missing a value creating investment opportunity.

To be more precise in our investigations, we use two proxies of the closeness of banking

relationship and the related supply of relationship lending to SMEs.

First, we consider the number of banks that the firm works with. Elsas (2005), in a study in

which he investigates the determinants of the assessment by a German bank to be a firm’s

Hausbank, its main bank, reports that the number of banks the firm work with is, with their share

of borrower debt financing, the most important determinant. The duration of the relationship

appears here as an irrelevant factor. For firms with no informational problems, large transparent

firms, a higher number of banks increase the access to credit by multiplying the number of its

potential suppliers. It mechanically reduces corporate financial constraint problems. For an

opaque firm, like SMEs are, the relationship appears to be the opposite. Competition discourages

banks to invest in close relationship and to offer relationship lending. It makes it more difficult

for banks to build informational monopolies. As a result debt contracts are negotiated in an arm’s

length mod (Rajan, 1992). Asymmetric information problems are in this context almost untreated

and financial constraints must be high. Detragliache et al. (2000) shows however that, in context

of banks fragility, it can be optimal for opaque firms even to have a pool of banks. This gives

firms an assurance that its efforts realized to communicate information about its perspectives to

their external financing partners would not be lost in case of bank default. In the French context,

such unpleasant events are rare. So we formulate the following hypothesis (H3a) about the link

between the number of banks the firm works with and its financial constraint problems.

H3a: SMEs working with only one bank are less financially constrained.

We expect to find a lower cash flows sensitivity of cash for single bank relationship SMEs

than for multibank ones. They probably benefit more from relationship lending which improves

their access to credit and makes their cash holding less related to investment opportunities or

external shocks.

Second, we consider the organizational structure of the firm’s main bank as proxy of the

use of relationship lending. Stein (2002) shows that decentralized banks are more efficient than

centralized ones in supplying this type of credit. The key element for credit decision is the

information that the bank’s loan officer collects through is interaction with the firm manager.

This information is of two natures: hard and soft. Hard information is quantitative and easily

10

ascertainable. It’s mainly accounting information. Soft information is qualitative. It’s composed

of opinions and other elements which are not formally defined (Petersen, 2004). The transmission

of hard information through bank organization hierarchical levels is cheap but the transmission of

soft information is very costly. Decision based on soft information cannot be evaluated fairly by

bank management. Consequently loan officers have negative incentives to collect and use it to

attribute credit in centralized banks. This soft information is the base of relationship lending. It is

more frequently supplied by decentralized banks. Empirical studies provide evidence in line with

these theoretical assertions. Cole et al (2004) reports that large banks use more standardized

processes to analyse SMEs credit requests whereas small banks use more subjective elements in

their decision process. Scott and Dunkelberg (2005), Scott (2006) and Uchida et al (2006) notice

that bank loan officer’s rotation reduces credit availability for opaque firms. Berger et al. (2001)

in Argentina, and Hajj Chehade and Vigneron (2008), in the French context, provide evidence

that centralized banks more frequently ration opaque firms than decentralized ones. Resulting

from these developments, we can propose the following hypothesis (H3b).

H3b: SMEs which works with a decentralized bank are less financially constrained.

We expect to find a lower cash flows sensitivity of cash for SMEs when the main bank is

decentralized. The latter more probably offers relationship lending implicit contracts which

improves firm access to credit and reduces financial constraints.

3. DATA AND METHODOLOGY:

3.1. Sample description

In order to conduct our investigations, we combine two databases: the DIANE3 database,

which provides both accounting information and other financial information, and the Kompass

Europe4 database, which provides information in particular about the different banks that the

firms work with. At first, we identify the firms for which the name of the main bank is available

in DIANE for year 2010. We obtain 6 908 firms. Then, in Kompass Europe, we collect the

identity of the different banks which finance those firms. We obtain a sample composed of 1 145

firms for which all this information is available. Finally, we use European Commission’s criteria

3 Edited by Bureau Van Dick. 4 Edited by Kompass Group.

11

to identify SMEs among them. We only retain firms which, on average over the period 2003-

2012, employ between 10 and 250 peoples, and which, on average over the same period, had a

turnover of between 2 and 50 million Euros or have total assets included between 2 and 43

million Euros. The final sample contains 1 084 firms that we observe over the period 2003-2012.

This provides us with an unbalanced panel composed of 10 148 observations.

Table 1 displays some descriptive statistics about those firms. One notices that the sample

includes a wide range of SMEs’ size. The smallest ones have just 10 employees and the largest

ones are sufficiently large to be listed. Nine firms of the sample are listed companies.

Consequently firms in the sample are on average bigger than those included in different version

of NSSBF database and those included in previous studies that focus on relationship lending in

the French context (Refait, 2003; Ziane, 2004). They are also older with mean age of 30 years5.

The sample contains both very poorly performing firms and profitable ones. However the mean

operating income over total asset ratio is 10.49% and the mean net income over equity ratio is

4.64%. Some firms present net negative equity value. They constitute merely 1.65% of the

observations. Every SME in the sample has debt. The mean debt ratio is 59%. More than half of

the firms are in the manufacturing industry sector.

[Please insert Table 1 nearby]

Table 2 describes the banking relationships developed by the SMEs of the sample. The

average number of banks that the firms work with is 1.9, the median is 2. These values are

relatively low compared to the 11 and 7 reported by Ongena and Smith (2000) for French very

large firms. However they are close to those reported by Refait (2003) and Ziane (2004)6 for

SMEs. The most important number of banks that a firms work with is here 7. 44.68% of the firms

have single bank relationship. 75.35% borrow from less than 3 banks. We do not find statistical

evidence that firms with centralized main bank work with a higher number of banks, nevertheless

we notice that 68.39% of firms with a decentralized main bank have only one bank against

31.61% for those with centralized main bank.

5 Mean age in sample NSSBF used in Petersen and Rajan (1994) is 13.9 years old and the mean age in Ziane (2004)’s sample is 19 years old. 6 3.9 and 3 for the first study and 2.3 and 2 for the second one.

12


3.2. Test specifications and variable descriptions

Following Almeida et al. (2004), we should expect to find a strong relation between cash

flows and variation in cash holdings for constrained firms. To test our hypothesis 1, we estimate

the following model named after the equation below:

∆𝑐𝑐𝑐𝑐𝑐𝑐ℎ ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑖𝑖,𝑡𝑡 = 𝛼𝛼1 + 𝛼𝛼 2𝐶𝐶𝑐𝑐𝑐𝑐ℎ 𝐹𝐹𝑜𝑜𝑜𝑜𝐹𝐹𝑐𝑐𝑖𝑖,𝑡𝑡 + 𝛼𝛼3 𝐼𝐼𝑜𝑜𝐼𝐼𝐼𝐼𝑐𝑐𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝐼𝐼 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝐼𝐼𝑐𝑐𝑖𝑖,𝑡𝑡

+𝛼𝛼4 𝑆𝑆𝑜𝑜𝑆𝑆𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡

(1)

The variation of the cash holding over total asset ratio between t-1 and t for the enterprise i

is explain by a set of factors. The first one, our variable of interest, is firm’s cash flows over the

same period of time computed as the difference between its earnings before extraordinary items

and taxes and the dividend that it paid. 𝛼𝛼 2 is the cash flows sensitivity of cash measure that we

expect to be positive and lower for the group of firms which more probably benefit from

relationship lending, single bank firms and those with a decentralized bank as main bank. The

second factor is firm’s investment opportunities. They are an important control variable in the

analysis. Considering them, it is highly unlikely that a positive cash flows sensitivity of cash

would be associated with a simple relationship between cash policies and investment

opportunities existing even without market frictions. In classic Almeida et al. (2004)

configuration, investment opportunities are measured by firm’s Tobin Q. In our specific context

of French SMEs which are mainly unlisted, this option is not available. As an alternative, we use

the capital expenditures over total sales ratio. Firms for which this ratio is higher have more

investment opportunities than the others in as much as they dedicate a more important fraction of

their turnover to the acquisition of new production capacities. We expect 𝛼𝛼3 to be positive. Some

more important investment opportunities would be related with higher positive variations of cash

holding. Cash policy should be influenced by the attractiveness of investment opportunities

especially for constrained firms. The third factor is the firm’s size measured as the natural log of

its total assets. It is considered to deal with the classical argument of economy of scale in cash

management. We do not expect a specific sign for 𝛼𝛼 4 .

13

Equation (1) is estimated using OLS regression including firm fixed effects. Standard

deviations of coefficients are obtained using the “sandwich” (or Hubert-White)

variance/covariance estimator.

In order to test our hypothesis 2 and its different variations (H2a; H2b), we modify equation

(1) by introducing interaction variables between cash flows and the different variables indicating

the closeness of the relationship. We obtain the equation (2).

∆𝑐𝑐𝑐𝑐𝑐𝑐ℎ ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑖𝑖,𝑡𝑡 = 𝛼𝛼1 + 𝛼𝛼2 𝐶𝐶𝑐𝑐𝑐𝑐ℎ𝐹𝐹𝑜𝑜𝑜𝑜𝐹𝐹𝑐𝑐𝑖𝑖,𝑡𝑡 + 𝛼𝛼3 𝑅𝑅𝐼𝐼𝑜𝑜𝑐𝑐𝐼𝐼𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐ℎ𝑜𝑜𝑜𝑜 𝐵𝐵𝑐𝑐𝑜𝑜𝐵𝐵𝑜𝑜𝑜𝑜𝑜𝑜 𝑖𝑖

+𝛼𝛼4 𝑐𝑐𝑐𝑐𝑐𝑐ℎ 𝑓𝑓𝑜𝑜𝑜𝑜𝐹𝐹𝑖𝑖,𝑡𝑡 × 𝑅𝑅𝐼𝐼𝑜𝑜𝑐𝑐𝐼𝐼𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐ℎ𝑜𝑜𝑜𝑜 𝐵𝐵𝑐𝑐𝑜𝑜𝐵𝐵𝑜𝑜𝑜𝑜𝑜𝑜 𝑖𝑖

+ 𝛼𝛼5 𝐼𝐼𝑜𝑜𝐼𝐼𝐼𝐼𝑐𝑐𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝐼𝐼 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝐼𝐼𝑐𝑐𝑖𝑖,𝑡𝑡 + 𝛼𝛼6 𝑆𝑆𝑜𝑜𝑆𝑆𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡

(2)

Relationship Banking is a dummy variable which takes the value one if the firm works with

only one bank and that this bank is its main one, has a decentralized organizational structure.

SMEs fulfilling these two criteria have closer relationships with their banks and then more

probably benefit from relationship lending. This particular contractual configuration allows them

to reduce their financial constraint problems through an easier access to bank credit.

Consequently, we expect to find a negative sign for the coefficient 𝛼𝛼4 related to the interaction

variable. The cash flows sensitivity of cash must be less important for firms engaged in

relationship lending as assumed in the hypothesis 2. To test the first variation (H2a), we

substitute the variable One Bank to Relationship Banking in equation (2). It’s a new dummy

variable taking the value one if the SME works with a single bank. We use it as another proxy of

the use lending relationship. Theory shows that competition associated with multibank

configuration discourages each bank to investment in information about its clients’ hidden

quality. So, in this context, it’s hard for a bank to constitute the informational advantage which

allows it to provide relationship lending. For single bank firms this difficulty does not exist

(Rajan, 1992). We also expect to find a negative sign for the coefficient of the interaction

variable built on this dummy one. To test the second variation of the hypothesis 2 (H2b), we

substitute the variable Decentralized main bank to Relationship Banking in equation (2). It’s

another dummy variable taking the value one if the SME’s main bank is classified as

decentralized. As Stein (2002) shows these structures are more adapted to use soft information

and consequently can provide relationship lending more efficiently. Following Hajj Chehade and

14

Vigneron (2008), we classify a bank as decentralized if it is a local one, a mutualistic one or a

saving and loans company. We expect to find a negative coefficient for the interaction variable

built on this last proxy of the use of relationship lending.

Equation (2) and its different variations are estimated through maximum likelihood

regression considering firms’ random effects. We use this econometric specification because

bank relationship characteristics do not vary across time. They are collected in 2010 and

considered as fixed for the entire study period.

We report in appendix 1 a synthetic definition for each variable, the expected signs of their

relationship with variation of cash holding level and some descriptive statistics. A quick bivariate

analysis shows that variations in cash holding are not statistical different between firms engaged

in close bank relationship, those with a single bank with a decentralized organizational structure,

and those which are not. However cash-flows, investment opportunities and size appear to be

significantly more important for firms in close bank relationships.

4. EMPIRICAL FINDINGS

Table 3 presents summary statistics on the level of cash holding maintained by the SMEs of

the sample. It considers different groups following characteristics of their bank relationships.

Firms which more probably benefit from relationship lending, those working with a single

decentralized bank, appointed as involved in relationship banking, and more generally the firms

with only one bank, hold on average 12-13% of their total assets in cash. Firms which have

multibank relationships retain a less important fraction of their assets in cash (on average 11%).

Tests on mean and median reject the equality between the groups. Main bank organization yields

substantially different results. SMEs engaged with a decentralized main bank, those which more

probably benefit from relationship lending, hold less cash than those working with a centralized

bank. The firsts holds on average 11.6% and the second 12.7%. The difference of mean between

these two groups is statistically significant but not the difference of median. It appears that firms

working banks in monopoly position have to accumulate more cash reserves even if the banks are

decentralized ones which can more efficiently provide lending relationship. This cash could be

imposed by the banks to reduce their risk exposure or simply be the consequence of anticipation

by the firms of future difficulties to access credit. However, when competition is possible, firms

15

with decentralized main banks could benefit from lending relationships and reduce their cash

holding to allocate their resources in a more productive way.


Table 4 presents the estimation of the equation (1) for the total sample and within each of

the above partitions. A total of seven regressions were carried out. Overall, firms display positive

and significant cash-flows sensibility of cash. The estimate sensitivity is 0.297 for total sample.

This figure is higher than the 0.062 reported by Almeida et al. (2004). The seminal paper focused

on American listed firms over a very long period 1971-2000 and use Tobin’s Q in order to

consider investment opportunities. We do not have such history, moreover we focus on smaller

firms with more informational problems and then with more difficulties to access external funds.

This can explain the difference and the more important coefficient. Be that as it may, the result is

in line with the hypothesis 1. SMEs are financially constrained. Cash-flows sensitivity of cash

also appears lower for firms which more probably benefit from relationship lending than for the

others. SMEs engaged with only one bank, or those with a decentralized bank as the main bank

and those which present simultaneously those two characteristics, present sensitivity coefficient

between 0.25 and 0.28 following the considered subsample. The others firms present cash-flows

sensitivity of cash between 0.30 and 0.32. This difference is in line with the hypothesis 2 and its

variations (H2a, H2b). Relationship lending appears to reduce SMEs financial constraint

problems.


Table 5 reports the results obtained by fitting the equation (2) to the data. The estimations

are performed via maximum of likelihood and consider firms random effects. Three models are

estimated, one for each indicator of the closeness of firms-banks relationship. Each specification

includes a dummy variable taking the value 1 if the type of relationship that the firm maintains

with its banks can supply it with relationship lending and an interaction variable built on this

dummy variable and the Cash-flows variable. This allows testing hypothesis 2 more precisely and

its variations (H2a, H2b). Coefficients associated with interaction variables are all negative but

16

only those dealing individually with single bank relationships and decentralized main banks are

significant. Their values are included between -0.081 and -0.083. This result partially confirms

previous conclusions about the impact of relationships lending on SMEs financial constraint

problems. Firms engaged in single bank relationship and firms with a decentralized main bank

have a lower cash-flows sensitivity of cash than the others. The joint variable, relationship

banking, is however not significant. The fact of meeting the two criteria simultaneously does not

reduce SMEs financial constraints. This result leads to two comments. First, centralized banks, if

they are the firm’s only bank, can also reduce financial constraints maybe through relationship

lending. Second, decentralized banks, if they are the firm’s only bank, do not necessarily provide

firm relationship lending. In a certain way, competition can engage them to provide this kind of

funding. We also notice that the effect of the closeness of the firm bank relationship on variation

of cash holding level is driven by cash-flows sensitivity. The coefficients of the different

dummies variables built on the type of relationship that the firms maintain with their banks are

not statistically different from zero. All the coefficients for the other regressors are statistically

significant and attract the expected signs.

[

Please insert Table 5 nearby]

5. ROBUSTNESS CHECKS

5.1. Subsample tests and alternative specifications

We subject our estimates to a number of robustness checks in order to address potential

concerns about sample effects, specifications effects and others potential estimations issues. The

first type of robustness checks, that we perform, is to consider regressions based on equation (2)

on different subsamples in which we exclude observations that can potentially influence the

results. The results are reported in table 6. The first category of exclusion involves firms with

important financial difficulties and those with negative equities accounting value. Such distress

can discourage external funds because of the high probability of default associated with it. The

second category involves firms which operate in the manufacturing industry sector. The latter

represents about 51% of the sample. Some specific effects of the sector can influence the results.

The third category involves the firms which have access to the bond market. Those firms are

17

considered less opaque than the other SMEs. For them close bank relationships appear to be a

less important factor to deal with funding needs. The interaction variables between bank

relationships’ characteristics and cash-flows follow the same pattern as in the general estimation

of equation (2). The only exception is the single bank effect on cash-flows sensitivity of cash

which is not significant for firms working in other sectors than the manufacturing sector.

Coefficient of interaction variable built on decentralized main bank is also higher for this

category of firms 0.155. It appears that industry and more specially belonging or not to the

manufacturing sector affects the way that the bank relationship influence firms’ financial

constraint problems.


Table 7 displays estimations of the same model but using alternative measure of the

closeness of the bank firm relationship. We extend the maximum number of banks that the SMEs

work with to be considered as in relationship lending configuration to two banks. We also

consider an alternative classification for main bank organizational structure. We consider only

mutual banks as decentralized. Like in previous analyses, we built joint variables based on these

new classifications. Relationship banking 2 indicates the group of firms which has one or two

banks and a decentralized main bank. Relationship banking 3 indicates the group of firms which

works with a single mutual bank. Relationship banking 4 indicates the group of firms working

with one or two bank and for which the main bank is a mutual bank. The estimates provide

evidence that the fact for SMEs to work with a maximum of two banks allows them to reduce

their financial constraints. In this context decentralized main banks appear to act as relationship

lending providers. The coefficient associated with Relationship banking 2 is negative and

statistically significant. It’s also the case for the interaction variable associated with mutual bank

as main bank but not Relationship banking 3 and 4 which are not statistically significant different

from 0.


18

Table 8 reports more complete specifications for equation (2). Initially to deal with

potential missing variable problems, we include additional control variables related theoretically

with cash holding variations: the evolution over the accounting period of the firm working capital

over total asset ratio and the evolution over the same period of the short term debt over total asset

ratio. Opler et al. (1999) show that working capital can be used as a substitute for cash. For

Fazzari and Petersen (1993), it may complete the firm’s available pool of financial resources.

Short term is frequently analyses as negative cash. Firms can use it to build cash reserves. Second

to deal with any potential endogeneity problems associated with simultaneity of investment and

financing decisions, we use instrumental variable approach. The set of instruments, that we use,

is one lag of the annual variation of sales and the two lags of the natural logarithm of the

following variables: firm’s PPE (proprietaries, plants and equipment), working capital, and short

term debt. This choice is in line with Almeida et al. (2004) and Fazzari and Petersen (1993)

which consider that the investment on a specific class of assets must depend on the initial stock of

that asset because of the decreasing current value associate with the level of stock. Once again the

estimations provide evidence that SMEs working with a decentralized main bank are less

financially constrained than the others. The fact that a firm is engaged with a single bank

produces the same effect but with a lower intensity. These results are in line with previous ones.


5.2. Alternative measures of financial constraints

In order to assess, once again, the robustness of our conclusions, we subject our analysis of

the effects of bank firm relationship characteristics on firms’ access to external funds to

alternative measures of financial constraints. Instead of using cash flow sensitivity of cash, we

use more metrics the KZ index proposed by Kaplan and Zingales (1997), the WW index

proposed by Whited and Wu (2006) and the firm use of trade credit (Petersen and Rajan, 1994).

These new tests are based on a new specification. We regress the different proxies of financial

constraints on bank relationship characteristics, single bank relationship, decentralized banks as

main banks and the cross of these two criteria, and a set of control variables. We expect to find

19

negative coefficients for these variables. The detail of regression specification is given by

equation (3).

𝐹𝐹𝑜𝑜𝑜𝑜𝑐𝑐𝑜𝑜𝑐𝑐𝑜𝑜𝑐𝑐𝑜𝑜 𝑐𝑐𝑜𝑜𝑜𝑜𝑐𝑐𝐼𝐼𝑜𝑜𝑐𝑐𝑜𝑜𝑜𝑜𝐼𝐼𝑐𝑐𝑖𝑖,𝑡𝑡 = 𝛼𝛼1 + 𝛼𝛼2𝐵𝐵𝑐𝑐𝑜𝑜𝐵𝐵 𝑜𝑜𝐼𝐼𝑜𝑜𝑐𝑐𝐼𝐼𝑜𝑜𝑜𝑜𝑜𝑜𝑐𝑐ℎ𝑜𝑜𝑜𝑜 𝑐𝑐ℎ𝑐𝑐𝑜𝑜𝑐𝑐𝑐𝑐.𝑖𝑖+ 𝛼𝛼3𝐶𝐶𝑐𝑐𝑐𝑐ℎ − 𝑓𝑓𝑜𝑜𝑜𝑜𝐹𝐹𝑐𝑐𝑖𝑖,𝑡𝑡

+𝛼𝛼4𝑆𝑆𝑜𝑜𝑆𝑆𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛼𝛼5𝑇𝑇𝑐𝑐𝑜𝑜𝑜𝑜𝑜𝑜𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜𝐼𝐼𝑇𝑇𝑖𝑖,𝑡𝑡 + 𝛼𝛼6𝐴𝐴𝑜𝑜𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛼𝛼7𝐼𝐼𝑜𝑜𝐼𝐼𝐼𝐼𝑐𝑐𝐼𝐼.𝑂𝑂𝑜𝑜𝑜𝑜𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡

(3)

Cash-flows, Size and Investment opportunity are measured in the same way as in general

specification. Tangibility is the proprietary, plant and equipment over total assets ratio. Age is the

natural logarithm of a firm’s age. These elements are introduced to control for other factors that

can improve the access to external funding (Size, Tangibility, Age) and for the needs of this type

of funding (Cash-flows, Investment opportunities).

We adapt the KZ formula in order to fit it unlisted SMEs. We substitute the variable

Investment opportunities to the Tobin’s Q of firm to consider the last factor. This gives us the

equation (4).

𝐾𝐾𝐾𝐾𝑖𝑖,𝑡𝑡 = −1.001909 × 𝐶𝐶𝑐𝑐𝑐𝑐ℎ − 𝑓𝑓𝑜𝑜𝑜𝑜𝐹𝐹𝑐𝑐𝑖𝑖,𝑡𝑡 + 3.139193 × 𝐿𝐿𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝑐𝑐𝑜𝑜𝐼𝐼𝑖𝑖,𝑡𝑡 − 39.36780

× 𝐷𝐷𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜 𝑜𝑜𝑐𝑐𝐼𝐼𝐼𝐼 − 1.314759 × 𝐶𝐶𝑐𝑐𝑐𝑐ℎ ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑖𝑖,𝑡𝑡 + 0.2826389

× 𝐼𝐼𝑜𝑜𝐼𝐼𝐼𝐼𝑐𝑐𝐼𝐼.𝑂𝑂𝑜𝑜𝑜𝑜𝑖𝑖,𝑡𝑡

(4)

The leverage is the financial debt over total assets ratio. The dividend rate the dividend over

total assets ratio.

For the WW index, we use the classical formula given by the author in their seminal paper

that I remain in equation (5).

𝑊𝑊𝑊𝑊𝑖𝑖,𝑡𝑡 = −0.091 × 𝐶𝐶𝑐𝑐𝑐𝑐ℎ − 𝑓𝑓𝑜𝑜𝑜𝑜𝐹𝐹𝑐𝑐𝑖𝑖,𝑡𝑡 − 0.062 × 𝑃𝑃𝑜𝑜𝑐𝑐.𝐷𝐷𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜𝐼𝐼𝑜𝑜𝑜𝑜.𝑖𝑖,𝑡𝑡+ 0.021 × 𝐿𝐿𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜𝑐𝑐𝑜𝑜𝐼𝐼𝑖𝑖,𝑡𝑡− 0.044 × 𝑆𝑆𝑜𝑜𝑆𝑆𝐼𝐼𝑖𝑖,𝑡𝑡 + 0.102 × 𝐼𝐼𝑜𝑜𝑜𝑜. 𝐼𝐼𝑐𝑐𝑜𝑜. 𝑐𝑐𝑐𝑐𝑜𝑜𝐼𝐼𝑐𝑐𝑖𝑖,𝑡𝑡 − 0.035 × 𝑉𝑉𝑐𝑐𝑜𝑜. 𝑐𝑐𝑐𝑐𝑜𝑜𝐼𝐼𝑐𝑐𝑖𝑖,𝑡𝑡

(5)

Pos. dividend is a dummy variable taking the value 1 if the firm has paid dividend. Ind.

Var. sales variable is the mean growth of sales in the firm’s sector computed based on the revised

NAF2 two digits classification. The Var. sales variable is just the firm’s growth of sales.

20

For KZ and WW index regressions, the estimations are run on the subsamples of the most

constrained firms defined as the higher tercile of these indicators. It is for this category of firms

that the effect of a close firm bank relationship must be the more important. In the particular

context of the trade credit analysis, we include to the regression a set of dummies variable

controlling for firms’ industry sectors which is an important determinant of the use of this type of

funding.

Table 9 presents results of these different estimations considering firm random effect and

obtained using GLS. Reported standard deviations are computed through Hubert-White robust

methodology. KZ and WW index regressions analysis provide evidence that a decentralized main

bank allows SMEs to reduce their financial constraints. The fact of working with only one bank

has similar effects only if this one is a decentralized one. The analysis of trade credit which is

assimilated to a substitute to bank credit produces slightly different results. In this context all the

coefficients associated with variable related to the closeness of bank firm relationship are

significant and with the expected negative signs. To work with a single bank, to have a

decentralized main bank and to meet these two criteria simultaneously reduces the use of trade

credit. The difference with KZ and WW index analysis can be explained by the fact that these

variables consider global financial constraints and trade credit more difficult to access bank

credit. Single bank firms can use trade credit to reduce the lack of external funding and this leads

them to be relatively similar to the other firms in term of financial constraints.


5.3. The recent (2007) financial crisis effect

The final performed robustness check considers the impact of the financial crisis which

began in 2007 by splitting the observations in two parts: before and after the first related

disclosures accounting in 2008. The events have greatly affected banks which have reduced their

funding to the economy in order to rebuild their books capital. As a result, SMEs’ financial

constraint problems have to be more important after the beginning of the financial crisis and

during the resulting economic crisis which have followed. In this context, the closeness of bank

relationship and the possibility to access to relationship lending associated with this could be

more important for a SME after the beginning of the crisis. Banks engaged in long term

21

relationships with opaque SMEs must prefer to cut funding to their other customers to preserve

the value of their past investment in information. Hence, we expect the impact of the type of bank

relationships to have a more important impact on firms’ cash-flows sensitivity of cash after the

beginning of the crisis than before.

Table 10 displays the estimations of the equation (2) for the period before 2008 and the

period after. Specifications and estimation method are the same as those used in table 5.

Consequently, two elements results from the analysis. First, financial constraint problems appear

to be more important after the beginning of the crisis than before. The value coefficient

associated with the cash-flows sensitivity of cash is display between 0.347 and 0.434 after 2008

depending on the proxy used to consider bank relationship characteristics. This same coefficient

before 2008 took a value between 0.269 and 0.278. This report is in line with hypothesis 1, SMEs

are financially constrained, and the hypothesis that the crisis increase the difficulties to access

external funding. Second, the nature of the firm-bank relationship only has a significant impact

on firm’s cash-flows sensitivity of cash after 2008. This effect follows the same pattern as those

reported in main results but with higher value for the coefficient of interaction variables. The

impact single bank relationship on financial constraints is -0.171 instead of -0.081 in the initial

regression and the impact of having a decentralized main bank is -0.187 instead of -0.083. The

beneficial effect of the closeness of bank firm relationships for SMEs is twice more important if

we only consider the crisis period.


6. CONCLUSION

Because of their informational opacity, SMEs suffer from difficulties to access external

funds. Reducing asymmetric information problems, relationship lending can allow them to

overcome at least partially these difficulties. The aim of this paper is to test whether close bank

firm relationships reduce SMEs financial constraint problems through relationship lending. To

proceed, we adapt the methodology of measure of financial constraints proposed by Almeida et al

(2004) on a new and unique sample of 1 145 French SMEs followed over the 2003-12 period. We

estimate the firms’ cash-flows sensitivity of cash over two groups: firms which more probably

benefit from relationship lending and those which do not. We consider the difference in cash-

flows sensitivity of cash between firms engaged with a single bank and multibank firms. We also

22

consider the difference between firms for which the main bank have a decentralized

organizational structure and firms working with a centralized main bank. We report evidence in

line with the theory that close bank firm relationships help SMEs to reduce financial constraint

problems. Single bank firms and firms with a decentralized main bank appear to be less

constrained. However firms which meet those two criteria simultaneously do not appear less

constrained than the others. We perform different robustness checks considering subsamples,

alternative financial constraint measures, and alternative measure of the closeness of bank firm

relationships. We also focus on the context of the financial crisis. It provides some interesting

elements. First, single bank relationship effect is linked to manufacturing firms. Second, KZ and

WW index studies underline the importance of a decentralized main bank to reduce financial

constraint problems. Third, we report evidence that the impact of the closeness of the bank firm

relationship is more important after the beginning of the crisis.

These results leave issues to future research. First, we report that working with a single

decentralized bank does not reduce financial constraint problems but working with one or two

banks and simultaneously to have a decentralized main bank reduces financial constraints.

Limited competition appears to be a condition to encourage the main bank to provide lending

relationship implicit contracts. In this context, how is firms monitoring organized? Can an

informational advantage be built and exploited by the main bank? Second, we also report

evidence that firms with close bank relationships, even if they appear to have a less important

cash-flows sensitivity of cash, maintain a higher level of cash holding than the others. Do the

banks impose a suboptimal reserve of liquidity in order to reduce their risk exposure or do they

allow SMEs to reach optimal cash level by improving their access to bank credit?

23

REFERENCES

Aghion, P., Fally, T. and Scarpetta S. (2007). Credit constraints as a barrier to the entry and post-

entry growth of firms. Economic Policy. 22, 731-779.

Almeida, H., Campello, M. and Weisbach, M. (2004). The cash-flows sensitivity of cash. Journal

of Finance. 59, 1777-1804.

Almeida, H. and Campello, M. (2007). Financial constraints, asset tangibility, and corporate

investment. Review of Financial Studies. 20, 1429-1460.

Ang, J. (1991). Small business uniqueness and the theory of financial management. Journal of

Small Business Finance. 1, 1-13.

Ang, J. (1992). On the theory of finance for private held firms. Journal of Small Business

Finance. 1, 185-203.

Angelini, P., Di Salvo and Ferri, G. (1998). Availability and cost of credit for small businesses:

customer relationships and credit cooperatives. Journal of Banking and Finance. 22, 925-

954.

Audrestch, D. and Elston, J. (2002). Does firm size matter? Evidence on the impact of liquidity

constraints on firm investment behavior in Germany. International Journal of Industrial

Organization. 20, 1-17.

Becchetti, L. and Trovatto, G. (2002). The determinant of growth for small and medium size

firms: the role of availability of external finance. Small Business Economics, 19, 291-306.

Beck, T. and Demirgüç-Kunt, A. (2006). Small and medium-size enterprises: access to finance as

a growth constraint. Journal of Banking and Finance. 30, 2931-2943.

Beck, T., Demirgüç-Kunt, A. and Masimovic, V. (2005). Financial and legal constraints to

growth: does firm size matter?. Journal of Finance. 60, 137-177.

Beck, T., Dermirgüç-Kunt, A. and Maksimovic, V. (2008). Financing pattern around the world:

are small firms different?. Journal of Financial Economics. 89, 467-487.

Berger, A. and Udell, G. (1995). Relationship lending and lines of credit in small firm finance.

Journal of Business. 68, 351-381.

Berger, A. and Udell, G. (1998). The economics of small business finance: the role of private

equity and debt markets in financial growth. Journal of Banking and Finance. 22, 613-673.

24

Berger, A. and Udell, G. (2006). A more complete conceptual framework for SME finance.

Journal of Banking and Finance. 30, 2945-2966.

Berger, A., Cerquiero, G. and Penas, M. (2011). Does debtor protection really protect debtors?

Evidence from the small business credit market. Journal of banking and Finance. 35, 1843-

1857.

Berger, A., Klapper, L. and Udell, G. (2001). The ability of banks to lend to informationally

opaque small businesses. Journal of Banking and Finance. 25, 2127-2167.

Berkowitz, J. and White, M. (2004). Bankruptcy and small firm’s access to credit. Rand Journal

of Economics. 35, 69-84.

Bertrand, M. and Schoar, A. (2003). Managing with style: the effect of managers on firm

policies. Quarterly Journal of Economics. 118, 1169-1208.

Bhattacharya, S. and Chiesa, G. (1995). Propriety information, financial intermediation, and

research incentives. Journal of Financial Intermediation. 4, 328-357.

Biggs, T. (2002). Is small beautiful and worthy of subsidy? Literature review. IFC Mimeo.

Boot, A. (2000). Relationship banking: what do we know?. Journal of Financial Intermediation.

9, 7-25.

Caggese, A. (2007). Test of financing constraints on firm investment using variable capital.

Journal of Financial Economics. 86, 683-723.

Carbo-Valverde, S., Rodriguez-Fernandez, F. and Udell, G. (2008). Bank lending, financing

constraints SME investment. Proceedings, Federal Reserve Bank of Chicago. May, 262-

279.

Carpenter, R. and Petersen B. (2002). Is the growth of small firms constrained by internal

finance?. Review of Economics and Statistics. 84, 298-309.

Cinquegrana, G. and Sarno, D. (2012). Financial constraints and relationship lending in the

growth of Italian SMEs. MRPA Paper 39825. University Library of Munich. Germany.

Cole, R. (1998). The importance of relationships to availability of credit. Journal of Banking and

Finance. 22, 959-977.

Cole, R., Goldberg, L. and White, L. (2004). Cookie cutter vs. character: the micro structure of

small business lending by large and small banks. Journal of Financial and Quantitative

Analysis. 39, 227-251.

25

Cressy, R. and Olofsson, C. (1997). European SME financing: an overview. Small Business

Economics. 9, 87-96.

De Bodt, E. and Lobez, F. (2005). Credit rationing, customer relationship and the number of

banks: an empirical analysis. European Financial Management. 11, 195-228.

Degryse, H. and Ongena, S. (2008). Competition and regulation in the banking sector: a review

of the empirical evidence on the sources of bank rents. In Thakor A. and Boot A. (eds.),

Handbook of Financial Intermediation and Banking (Elsevier), 483-554.

Detragiache, E., Garella, P. and Guiso, L. (2000). Multiple versus single banking relationships:

Theory and evidence. Journal of Finance. 55, 1133-1161.

Devreux, M. and Schiantarelli, F. (1990). Investment financial factors and cash flow: evidence

from UK panel data. In Hubbard R. (ed.). Asymmetric information corporate finance and

investment. University of Chicago Press, 279-306.

Dietsch, M. and Golitin-Boukari, V. (2002). L’évolution des relations banques-entreprises dans

les années 1990. Bulletin de la Commission bancaire. 27, 74-107.

Elsas, E. (2005). Empirical determinants of relationship lending. Journal of Financial

Intermediation. 14, 32-57.

Fazzari, S., Hubbard, R. and Petersen, B. (1988). Financing constraints and corporate investment.

Brooking Papers on Economic Activity. 1, 141-206.

Folhin, C. (1998). Relationship banking, liquidity, and investment in the German

industrialization. Journal of Finance. 5, 1737-1758.

Fuss, C. and Vermeulen, P. (2008). The response of firms’ investment and financing to adverse

cash-flow shocks: the role of bank relationship in Belgium. Review of Business and

Economics. 1, 5-34.

Garcia-Marco, T. and Ocana, C. (1999). The effect of bank monitoring on the investment

behavior of Spanish firms. Journal of Banking and Finance. 23, 1579-1603.

Greenwald, B., Stiglitz, J. and Weiss, A. (1984). Informational imperfection in the capital market

and macroeconomic fluctuation. American Economic Review. 74, 194-199.

Gregory, B., Rutherford, M., Oswald, S. and Gardiner, L. (2005). An empirical investigation of

the growth cycle theory of small firm financing. Journal of Small Business Management.

43, 382-392.

26

Hadlock, C. (1998). Ownership, liquidity, and investment, Rand Journal of Economics. 29, 487-

508.

Hadlock, C. and Pierce, J. (2010). New evidence on measuring financial constraints: moving

beyond the KZ index. Review of Financial Studies. 23, 1909-1940.

Hajj Chehade, H. and Vigneron, L. (2008). SMEs choice of main bank and organizational

structure: the role of soft information and of credit rationing. Bankers, Markets & Investors.

102, 33-44.

Hamelin, A. (2012), Influence of family ownership on small business growth: Evidence from

French SMEs. Small Businesses Economics, forthcoming available online.

Harhoff, D. and Korting, T. (1998). Lending relationships in Germany – Empirical evidence from

survey data. Journal of Banking and Finance. 22, 1317-1353.

Hart, O. and Moore, J. (1994). A theory of debt based on the inalienability of human capital.

Quarterly Journal of Economics. 109, 841-879.

Hoshi, T., Kashyap, A. and Scharfstein, D. (1991). Corporate structure, liquidity, and investment:

evidence from Japanese industrial groups. Quarterly Journal of Economics. 106, 33-60.

Houston, J. and James, M. (2001). Do relationships have limits? Banking relationships, financial

constraints, and investment. Journal of Business. 74, 347-374.

Hubbard, R. (1998). Capital market imperfection and investment. Journal of Economic

Literature. 36, 193-227.

Hurst, E. and Pugsley, B. (2011). What do small businesses do?. Brookings Papers on Economic

Activity, Economic Study Program, The Brooking Institution. 43, 73-142.

Jensen, M. and Meckling, W. (1976). Theory of the firm: management behavior, agency costs

and ownership structure. Journal of Financial Economics. 3, 305-360.

Kaplan, S. and Zingales, L. (1997). Do investment-cash flow sensitivities provide useful

measures of financing constraints?. Quarterly Journal of Economics. 112, 169-215.

Lamont, O., Polk, C. and Saa-Requejo, J. (2001). Financial constraints and stock returns. Review

of Financial Studies. 14, 529-554.

Malmendier, U. and Tate, G. (2005). CEO overconfidence and corporate investment. Journal of

Finance. 60, 2661-2700.

Myers, S. and Majluf, N. (1984). Corporate financing and investment decisions: when firms have

information that investors do not have. Journal of Financial Economics. 13, 187-221.

27

Petersen M. (2004). Information: Hard and Soft. Mimeo, Kellogg School of Management,

Northwestern University.

Petersen, M. and Rajan, R. (1994). The benefit of lending relationships: evidence from small

business data. Journal of Finance. 49, 3-37.

Petersen, M. and Rajan, R. (1995). The effect of credit market competition on lending

relationships. Quarterly Journal of Economics. 110, 407-443.

Rajan, R. (1992). Insiders and outsiders: the choice between informed and arm’s length debt.

Journal of Finance. 47, 1397-1400.

Refait, C. (2003). La multibancarité des entreprises: choix du nombre de banques et choix du

nombre de banques principales. Revue Economique. 54. 649-662.

Scott, J. (2006). Loan officer turnover and credit availability for small firms. Journal of Small

Business Management. 44, 544-562.

Scott, J. and Dunkelberg, W. (2005). Why do small firms change banks?. Working Paper Fox

School of Business and Management. Temple University.

Sharpe, S. (1990). Asymmetric information, bank lending and implicit contracts: a stylized model

of customer relationship. Journal of Finance. 45, 1069-1087.

Sharpe, S. and Nguyen, H. (1995). Capital market imperfections and the incentive to lease.

Journal of Financial Economics. 39, 271-294.

Shin, H. and Stulz, R. (1998). Are internal capital markets efficient?. Quarterly Journal of

Economics. 113, 531-552.

Slotty, C. (2009). Financial constraints and the decision to lease – evidence from German SME.

Working paper Goethe University Frankfurt.

Stein, J. (2002). Information production and capital allocation: decentralized versus hierarchical

firms. Journal of Finance. 57, 1891-1921.

Stiglitz, J. and Weiss, A. (1981). Credit rationing in markets with imperfect information.

American Economic Review. 71, 393-410.

Uchida, H., Udell, G. and Yamori, N. (2006). Loan officers and relationship lending. REITI

Discussion paper N°06E029.

Westhead, P. and Storey, D. (1997). Financial constraints on the growth of high technology small

firms in the United Kingdown. Applied Financial Economy. 7, 197-201.

28

Whited, T. and Wu, G. (2006). Financial constraints risk. Review of Financial Studies. 19, 531-

559.

Williamson, S. (1987). Costly monitoring, loan contracts, and equilibrium credit rationing.

Quarterly Journal of Economics. 102, 135-146.

Ziane, Y. (2004). Nombre de banques et relations de crédit : une approche empirique. Revue

Economique. 55, 419-428.

29

Table 1: Sample Description

Panel A. Firms Characteristics

Mean Standard

Deviation

Median Minimum Maximum

Total assets (in Euros) 14 801 110 317 961 800 3 162 000 299 000 14 900 000 000

Total sales (in Euros) 10 624 670 17 742 190 5 210 000 58 000 405 366 000

Nb. employees 44.26 38.56 32 10 249

Age 29.81 11.79 21 1 113

Debt over total asset (in percent) 59.01 21.36 59.66 0.57 383.95

Net income over total asset (in percent) 4.64 8.87 4.25 -217.05 53.94

Earnings before extraordinary items and

depreciations over total asset (in percent) 10.49 9.54 9.86 -107.9 81.52

Panel B. Industry Sectors Repartition

Sectors Number of firms Percent of firms

Manufacturing 554 51%

Trade 249 23%

Building 105 10%

Transport 57 5%

Engineering services 43 4%

Management services 26 2%

I.T. and telecoms 25 2%

Others 25 2%

Total 1 084 100%

30

Table 2: Firms Banks Pool Characteristics

Panel A. Contingency table of the number of banks of firms and of the organizational structure of their main bank

Number of banks Total

Percent 1 2 3 4 5 6 7

Centralized

Main Bank

158

14.86

98

9.22

52

4.89

17

1.60

7

0.66

3

0.28

1

0.09

336

31.61

Decentralized

Main Bank

317

29.82

228

21.45

120

11.29

47

4.42

12

1.13

3

0.28

0

0

727

68.39

Total

Percent

475

44.68

326

30.67

172

16.18

64

6.02

19

1.79

6

0.56

1

0.09

1 063

100

Chi2

p-value

5.210

0.517

Panel B. Comparison of the number of banks between firms with centralized main bank and firms with decentralized one

Mean Median St. Dev. Min. Max. N. Obs.

Centralized

Main Bank 1.898 2 1.107 1 7 336

Decentralized

Main Bank 1.924 2 1.034 1 6 727

p-value 0.721 0.388

31

Table 3: Summary Statistics on Cash Holdings

This table displays summary statistics about cash holdings, the cash and cash equivalents over total assets

ratio, across group firms engaged or not in relationship banking. We assign the letter (A) for firms

engaged in relationship banking the ones which work with only one bank, the ones for which the main

bank is a decentralized one and the ones which meet this two criteria. Cash holdings are measured by the

ratio of firms’ cash and cash equivalents over total assets. The statistical tests performed here are

respectively Student’s test on difference in mean and Mann Whitney’s test on difference in median.

Cash Holdings Mean Median St. Dev. N. Obs.

Bank Relationship Criteria

1. Relationship Banking Indicator Relationship banking (A) 0.125 0.082 0.126 2 988 Arm’s length contracts (B) 0.118 0.073 0.129 6 970

p-value (A-B≠0) 0.013 0.008

2. Number of banks

One bank (A) 0.131 0.084 0.138 4 577

More than one bank (B) 0.112 0.070 0.121 5 571

p-value (A-B≠0) 0.000 0.000

3. Main bank organization

Decentralized main bank (A) 0.116 0.075 0.120 6 794

Centralized main bank (B) 0.127 0.076 0.144 3 164

p-value (A-B≠0) 0.000 0.601

32

Table 4: Baseline Regression Model

This table displays results for OLS estimations of the baseline regression model (equation (1)) considering

firms fixed effects. The dependent variable is the variation of the ratio cash and cash equivalent over total

assets. The independent variables are Cash-Flows, the earnings before extraordinary items and

depreciation over total assets ratio, Investment opportunities, the capital expenditure over total sales ratio,

and Size, the natural log of total assets. Standard deviations estimations are obtained using the “sandwich”

(or Hubert-White) variance/covariance robust estimator. They are reported in brackets.

Independent Variables

Dependent Variable

∆ 𝐶𝐶𝑐𝑐𝑐𝑐ℎ𝐻𝐻𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜

Constant Cash-Flows Investment

opportunities

Size R2

Nb. Obs.

1. Total sample -0.209*** 0.294*** -0.0006 0.023*** 0.063

(0.034) (0.031) (0.0006) (0.004) 9 059

2. Relationship Banking

Indicator

Relationship banking (A) -0.270***

(0.064)

0.261***

(0.043)

-0.128***

(0.036)

0.031***

(0.007)

0.058

2 671 Arm’s length contracts (B) -0.195***

(0.042)

0.309***

(0.040)

-0.0005

(0.0005)

0.021***

(0.004)

0.073

6 218

3. Number of banks

One bank (A) -0.227***

(0.049)

0.255***

(0.041)

-0.004***

(0.0005)

0.025***

(0.005)

0.049

4 092

More than one bank (B) -0.202***

(0.048)

0.329***

(0.046)

-0.0000

(0.0001)

0.021***

(0.005)

0.081

4 967

4. Main bank organization

Decentralized main bank (A) -0.233***

(0.038)

0.287***

(0.036)

-0.006

(0.005)

0.026***

(0.004)

0.062

6 067

Centralized main bank (B) -0.169**

(0.067)

0.312***

(0.060)

-0.0004

(0.0005)

0.017**

(0.007)

0.071

2 822 * indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.

33

Table 5: Interaction Variables Regression Model

This table displays results for ML estimations of the equation (2) considering firms random effects. The

dependent variable is still the variation of the ratio cash and cash equivalent over total assets and the

independent variables are the same than those use in table 4 plus dummies built on firms banking

relationships characteristics and interaction variables built with those dummies and the variable Cash-

flows. The first dummy takes the value 1 if the firm is engaged in a single bank relationship with a

decentralized bank. The second dummy takes the value 1 if the firm works with only one bank. The third

one takes the value 1 if the firm has a decentralized main bank. Standard deviations estimations are

reported in brackets.

(1) (2) (3)

Constant -0.029**

(0.014)

-0.030**

(0.014)

-0.027

(0.014)

Cash-Flow 0.299***

(0.025)

0.323***

(0.029)

0.340***

(0.034)

Relationship banking 0.0003

(0.004)

Cash-Flow × Relationship

banking

-0.035

(0.048)

One bank 0.002

(0.003)

Cash-Flow × One bank -0.081*

(0.042)

Decentralized main bank 0.0005

(0.004)

Cash-Flow ×

Decentralized main bank

-0.083**

(0.044)

Investment opportunities 0.003***

(0.000)

0.003***

(0.000)

0.003***

(0.000)

Size 0.002*

(0.001)

0.002*

(0.001)

0.002*

(0.001)

Wald Chi2 196.85*** 201.27*** 201.32***

Log likelihood 4628 4781 4630

Nb. Obs. 8 889 9 059 8 889 * indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.

34

Table 6: Robustness Checks on Subsamples

This table displays estimations of the equation (2) using the same method and the same variables than those used in table 5 but on different subsample. The first subsample excludes

firms with negative equities. The second subsample excludes firms work in manufacturing industry sector. The third subsample excludes firms which have between 2003 and 2012

at least once issue bonds.

Without Negative Equities Without Manufacturing firms Without Market debt issuers

(1) (2) (3) (1) (2) (3) (1) (2) (3)

Constant -0.046***

(0.017)

-0.046***

(0.016)

-0.043**

(0.017)

-0.038

(0.025)

-0.040*

(0.024)

-0.033

(0.026)

-0.025*

(0.014)

-0.027*

(0.014)

-0.023

(0.015)

Cash-Flow 0.354***

(0.028)

0.381***

(0.032)

0.392***

(0.039)

0.342***

(0.045)

0.357***

(0.053)

0.410***

(0.060)

0.306***

(0.025)

0.332***

(0.029)

0.341***

(0.035)


(0.005)

0.001

(0.008)

0.000

(0.004)


banking

-0.038

(0.056)

-0.086

(0.091)

-0.043

(0.049)

One bank 0.002

(0.004)

0.002

(0.007)

0.002

(0.004)

Cash-Flow × One bank -0.096**

(0.048)

-0.092

(0.076)

-0.090**

(0.043)


(0.005)

0.0008

(0.007)

0.000

0.004

Cash-Flow ×


-0.082*

(0.050)

-0.155*

(0.080)

-0.077*

(0.045)


(0.000)

0.003***

(0.000)

0.003***

(0.000)

0.002***

(0.000)

0.002***

(0.000)

0.002***

(0.000)

0.029***

(0.002)

0.029***

(0.002)

0.029***

(0.002)

Size 0.004**

(0.001)

0.004**

(0.001)

0.003**

(0.001)

0.003

(0.002)

0.003

(0.002)

0.003

(0.002)

0.002

(0.001)

0.002

(0.001)

0.001

(0.001)

Wald Chi2 214.60*** 219.22*** 218.73*** 77.82*** 78.93*** 82.19*** 373.55*** 380.00*** 377.34***

Log likelihood 4490 4642 4492 950 1032 952 4544 4697 4545

Nb. Obs. 8 743 8 913 8 743 4325 4 453 4 325 8 697 8 867 8 697

* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.

35

Table 7: Robustness checks with alternative relationships classification This table displays estimations of the equation (2) with new bank relationship characteristics variables: the fact for SME to work or not with less

than three banks and the fact for it to have a decentralized main bank. The other elements are the same than in table 5. Relationship banking 2 is a

dummy variable taking the value 1 if the firm works with less than three banks and have a decentralized main bank. Relationship banking 3 is also

a dummy variable but taking the value 1 if the firm works with a single bank and that this bank is a mutual bank. Relationship banking 4 is still a

dummy variable but taking the value 1 if the firm works with less than three banks and has a mutual bank as main bank.

One or two banks main bank is a mutual bank One or two banks/ main

bank is a mutual bank

(1) (2) (3) (4) (5)

Constant -0.030

(0.014)

-0.041***

(0.014)

-0.030***

(0.014)

-0.030**

(0.014)

-0.031**

(0.014)

Cash-Flow 0.324***

(0.029)

0.471***

(0.040)

0.294***

(0.023)

0.311***

(0.026)

0.304***

(0.024)

Relationship banking 2 0.003

(0.004)


banking 2

-0.078*

(0.043)

One or two banks 0.011***

(0.004)

Cash-Flow × One or two

banks

-0.180***

(0.033)

Relationship banking 3 0.0006

(0.005)


banking 3

-0.059

(0.056)

Mutual banks as main bank

0.0006

(0.004)

Cash-Flow × Mutual bank

as main bank

-0.078*

(0.044)

Relationship banking 4

0.002

(0.004)


banking 4

-0.074

(0.047)


(0.000)

0.003***

(0.000)

0.003***

(0.000)

0.002***

(0.000)

0.003***

(0.000)

Size 0.002

(0.001)

0.002*

(0.001)

0.002*

(0.001)

0.002*

(0.001)

0.002*

(0.001)

Wald Chi2 199.43*** 226.73*** 198.64*** 201.85*** 199.87***

Log likelihood 4629 4794 4780 4782 4781

Nb. Obs. 8 889 9 059 9 059 9 059 9 059


36

Table 8: Robustness checks with alternative specifications

This table displays new estimations of the equation (2) including more control variables and using instrumental variable

methodology. The new control variables are Change in non-cash working capital, the variation between t-1 and t of the working

capital over total assets, and Change in short term debt, the variation between t-1 and t of the working capital over total assets.

The instruments are the one period lagged variation of sales and the two lagged period natural logarithm of the following

variables: firm’s PPE (proprietaries, plants and equipment), working capital, and short term debt.

More complete specification IV estimation

(1) (2) (3) (4) (5) (6)

Constant -0.038**

(0.016)

-0.038**

(0.015)

-0.036**

(0.016)

-0.045**

(0.017)

-0.045***

(0.017)

-0.043**

(0.018)

Cash-Flow 0.205***

(0.025)

0.227***

(0.029)

0.252***

(0.034)

0.158***

(0.028)

0.184***

(0.032)

0.218***

(0.038)


(0.004)

0.001

(0.005)


banking

-0.053

(0.048)

-0.037

(0.055)

One bank 0.002

(0.004)

0.002

(0.004)

Cash-Flow × One bank -0.080*

(0.042)

-0.074

(0.047)


(0.004)

0.002

(0.005)

Cash-Flow ×


-0.101**

(0.044)

-0.114**

(0.049)


(0.000)

0.002***

(0.000)

0.002***

(0.000)

0.011***

(0.001)

0.011***

(0.001)

0.011***

(0.001)

Size 0.003**

(0.001)

0.003**

(0.001)

0.003**

(0.001)

0.004**

(0.002)

0.004***

(0.001)

0.004**

(0.002)

Change in non-cash

working capital

-0.228***

(0.018)

-0.231***

(0.017)

-0.227***

(0.018)

-0.153***

(0.020)

-0.159***

(0.020)

-0.153***

(0.020)

Change in short term debt -0.623***

(0.020)

-0.622***

(0.020)

-0.622***

(0.020)

-0.738***

(0.024)

-0.737***

(0.023)

-0.738***

(0.024)

Wald Chi2 1090.76** 1110.43*** 1096.32*** 1275**** 1151*** 1141***

Log likelihood/R2 4866 5024 4869 0.150 0.150 0.151

Nb. Obs. 8 642 8 810 8 642 7 389 7 534 7 389


37

Table 9: Tests on others Financial Constraint Indicators This table displays estimations with GLS of equation (3) including firms’ random effects. It considers alternatively three dependent variables KZ index, WW index and the trade credit over total assets

ratio. The independent variables are the same bank relationship characteristics than in table 5, Cash-flows, the earnings before extraordinary items and depreciation over total assets ratio; Size, the natural

logarithm of firm’s total assets; Tangibility, the proprietary, plant and equation over total assets ratio; Age, the natural logarithm of firm’s age; Investment opportunities, the capital expenditure over total

sales ratio. For KZ index and WW index estimations are realized on the subsample of most constraint firms (the third tercile of these measures). For trade credit estimation industry sectors fixed effects

are added. The Hubert-White robust standard deviations are reported in brackets.

KZ Index WW Index Trade credit

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Constant -232.5***

(52.88)

-236.6***

(53.07)

-222.61***

(50.277)

518.6***

(9.969)

518.1***

(9.875)

518.7***

(10.00)

0.898***

(0.142)

0.888***

(0.140)

0.909***

(0.146)

Relationship banking -6.198***

(2.417)

-0.841*

(0.430)

-0.029***

(0.011)

One bank

1.563

(3.824)

-0.074

(0.369)

-0.026***

(0.009)


-11.94***

(4.041)

-0.964***

(0.368)

-0.019*

(0.011)

Cash-Flow 0.318

(17.74)

0.134

(17.59)

-1.143

(17.705)

0.838

(1.338)

0.946

(1.332)

0.707

(1.335)

-0.308***

(0.026)

-0.301***

(0.026)

-0.308***

(0.026)

Size

31.73***

(7.152)

31.93***

(7.145)

31.33***

(6.983)

-80.41***

(1.329)

-80.39***

(1.321)

-80.35***

(1.335)

-0.046**

(0.018)

-0.044**

(0.018)

-0.045**

(0.018)

Tangibility

-26.04*

(15.82)

-26.25*

(15.75)

-24.28

(15.49)

-1.183

(1.643)

-0.830

(1.611)

-0.962

(1.648)

-0.353***

(0.047)

-0.353***

(0.046)

-0.353***

(0.047)

Age

-4.153

(3.150)

-4.201

(3.138)

-4.263

(3.141)

-0.019

(0.369)

-0.004

(0.373)

-0.036

(0.370)

-0.053***

(0.010)

-0.055***

(0.010)

-0.053***

(0.010)


(0.581)

9.058***

(0.581)

9.044***

(0.575)

3.257***

(0.143)

3.234***

(0.145)

3.279***

(0.145)

-0.0009

(0.002)

-0.0009

(0.002)

-0.0009

(0.002)

Sectors fixed effects no no no no no no yes yes yes

Wald Chi2 793.84*** 800.98*** 822.31*** 13968*** 10028*** 12967*** 1298*** 1305*** 1292***

R2 0.333 0.332 0.336 0.961 0.960 0.961 0.271 0.266 0.269

Nb. Obs. 2 981 3 019 2 981 2 948 3 019 2 948 8 889 9 059 8 889


38

Table 10: Impact of the Financial Crisis on Financial Constraint

This table displays estimations of the equation (2) using the same technics and the same variables than in

table 5. It however split the observation into two sub periods: before and after the beginning of the

financial crisis taking for reference the accounting disclosures which occur in 2008.

Before 2008 After 2008

(1) (2) (3) (1) (2) (3)

Constant -0.010

(0.010)

-0.014

(0.010)

-0.006

(0.011)

-0.049*

(0.030)

-0.050*

(0.029)

-0.047

(0.031)

Cash-Flow 0.269***

(0.020)

0.278***

(0.022)

0.269***

(0.027)

0.347***

(0.051)

0.394***

(0.059)

0.434***

(0.070)

Relationship banking -0.005

(0.003)

0.002

(0.008)

Cash-Flow ×

Relationship banking

0.041

(0.041)

-0.107

(0.094)

One bank -0.000

(0.003)

0.003

(0.007)

Cash-Flow × One bank -0.0006

(0.034)

-0.171**

(0.084)

Decentralized main bank -0.006*

(0.003)

0.002

(0.008)

Cash-Flow ×


0.015

(0.035)

-0.187**

(0.088)

Investment opportunities -0.0001

(0.0007)

-0.0001

(0.0007)

-0.0001

(0.0007)

0.003***

(0.001)

0.003***

(0.001)

0.003***

(0.001)

Size 0.0005

(0.001)

0.0008

(0.001)

0.0003

(0.001)

0.004

(0.003)

0.004

(0.003)

0.004

(0.003)

Wald Chi2 246.09*** 248.97*** 248.35*** 70.00*** 73.06*** 73.77***

Log likelihood 4026 4961 4863 695 740 697

Nb. Obs. 4 861 4 105 4 026 3 840 3 911 3 840 * indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.

39

Appendix 1: Variables Description and Summary Statistics

This table displays synthetic definitions, expected signs of relationships, and descriptive statistics for the mains variables of the study. Mean,

Median and Standard deviation are reported both for total sample and for firms in close relationship, those working with only one bank which has a

decentralized organizational structure. Student’s t-test of difference in mean and Mann Whitney’s test of difference in median have been performed

between the subsample of firms in close relationship and the others.

Variable Description Expected

relationship

Total sample In close bank relationship

Mean Median St. dev. Mean Median St. dev.

∆ 𝐶𝐶𝑐𝑐𝑐𝑐ℎ𝐻𝐻𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 The ratio in t of the holding of

cash and marketable securities

over total assets minus the ratio

in t-1 of the holding of cash and

marketable securities over total

assets

0.010 0.001 0.144 0.009 0.001 0.075

Cash-Flow The ratio earnings before

extraordinary items and

depreciations minus dividend

over total assets

+ 0.060 0.058 0.070 0.062** 0.060*** 0.066

Investment

Opportunities

Capital expenditure over total

sales + 0.045 -0.001 2.218 0.007* -0.001 0.060

Size The natural logarithm of total

assets +/- 8.527 8.384 0.968 8.349*** 8.258*** 0.892


how do banking relationships affect financial constraints ... · jel classification: g21; g32. 3 ....

Documents