SMEs ACCESS TO FINANCE AND THE VALUE OF SUPPLIER FINANCING

Cristina Martínez-Solaa, Pedro J. García-Teruelb,*, Pedro Martínez-Solanoc

a University of Alicante, Faculty of Economics and Business Sciences, Dpt. Financial Economics and Accounting, San Vicente del Rapeig, 03690 Alicante (SPAIN), tel.: +34 965903400 (Ext. 3151), fax: +34 965903621, email: mcristina.martinez@ua.es b University of Murcia, Faculty of Economics and Business, Dpt. Management and Finance, Campus Universitario de Espinardo, 30100-Murcia (SPAIN), tel: +34 868 887828, fax:+34 868 887537, email: pjteruel@um.es c University of Murcia, Faculty of Economics and Business, Dpt. Management and Finance, Campus Universitario de Espinardo, 30100-Murcia (SPAIN), tel: +34 868 883747, fax:+34 868 887537, email: pmsolano@um.es *Corresponding author

ABSTRACT

This article examines the relationship between supplier financing and small- and medium-sized firms' value as well as the variation in the marginal value of supplier financing that arises from differences in access to financial markets and internal financing. We employ a sample of Spanish small- and medium-sized enterprises from the period 1998-2014. The results show a positive relationship between supplier financing and firm value. Furthermore, the findings reveal that the marginal value of supplier financing declines with leverage, short-term financial debt and cash flow, whereas it increases with financial costs. Also, the results show a higher marginal value of accounts payable during the financial crisis period when bank credit is reduced, for all firms, regardless of their access to finance. These results are in agreement with the financing motive for trade credit use. Firms with better availability of financial resources (internal and external) and with a lower financial cost place less value on supplier financing.

Keywords: Trade Credit, Supplier Financing, SMEs, Firm Value. JEL Classification: G30, G31 Acknowledgements: This research is part of project ECO2013-47486-P financed by the Research Agency of the Spanish government. We also acknowledge financial support from "Fundación CajaMurcia".

Post-print version Martínez-Sola, C., García-Teruel, P. J. and Martínez-Solano, P. (2017), SMEs access

to finance and the value of supplier financing, Spanish Journal of Finance and Accounting, 46 (4), 455-483.

(doi.org/10.1080/02102412.2017.1345196)

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SMEs ACCESS TO FINANCE AND THE VALUE OF SUPPLIER FINANCING

INTRODUCTION

The financial literature has shown the difficulties that small and medium-sized

enterprises (SMEs) suffer in accessing financial markets, since their higher level of

information asymmetry makes them more vulnerable to capital market imperfections.

Thus, supplier financing helps SMEs to face capital market imperfection, and this can

positively affect firms' value in various ways. Firms can overcome financial constraints

(Schwartz, 1974), which is especially important in countries with less developed

financial markets and where trade credit is an alternative channel (Fisman and Love,

2003; Ge and Qiu, 2007). Trade credit can also be used by less creditworthy firms as a

mechanism to acquire reputation and therefore alleviate the problem of asymmetric

information and adverse selection (Antov and Atanasova, 2007; Biais and Gollier,

1997). Moreover, as trade credit is linked to business activity, it can be more flexible

than bank credit (Danielson and Scoot, 2004). Finally, apart from the financial

advantages, trade credit reduces transaction costs related to the receipt, verification and

payment of merchandise (Ferris, 1981; Smith, 1987) and the information asymmetry

between firms and their customers regarding product quality (Deloof and Jegers, 1996;

Emery and Nayar, 1998; Lee and Stowe, 1993; Long, Malitz and Ravid, 1993; Ng,

Smith and Smith, 1999; Smith, 1987). From the above, supplier financing would be

expected to positively affect firm value and this positive effect would be higher for firms

with less access to finance.

However, we should consider the potential costs of this funding source, such as

the implicit interest if there is a discount for prompt payment, and other disadvantages

associated with a company’s failing to fulfil its payment obligations, for example, late

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payment penalties, refinancing risk, deterioration in credit reputation, higher prices or

less favourable delivery dates in the future (Danielson and Scott, 2004; Ng et al., 1999;

Wilner, 2000; Wu, Rui and Wu, 2012).

The purpose of this paper is to provide empirical evidence of the effects of trade

credit received on firm value, using a sample of SMEs. To our knowledge, only one

previous study, by Hill, Kelly and Lockhart (2013), examines the relationship between

shareholder wealth and supplier financing, for a sample of listed US firms. However,

despite the importance of trade credit financing for SMEs, which constitutes a major

source of their short-term financing (Berger and Udell, 1998; Petersen and Rajan, 1997),

there is no empirical evidence for its effect on value. From our point of view, given the

significant contribution made by SMEs to global economy and the difficulties that these

firms face in accessing finance, analysing the value of trade credit financing is an

important issue.

This study seeks to contribute to the literature in the following ways. First, it

extends the research on trade credit by analysing the effect of supplier financing on firm

value and examining whether this effect depends on the access to financing. Second, we

employ a sample of SMEs, for which trade credit is especially important, given their

asymmetric information problems. Small firms face greater growth constraints and have

worse access to external finance than large firms (Beck and Demirguc-Kunt, 2006).

Third, unlike Hill et al. (2013), who studied a sample of listed US companies, we present

empirical evidence for a sample of Spanish SMEs. Civil law countries are characterised

by both weaker investor protection and less developed capital markets compared to

common law countries (La Porta, Lopez-de-Silanes, Shleifer and Vishny, 1997). Beck

and Demirgüç-Kunt (2006) argue that there is a positive relationship between financial

4

and institutional development and firms’ external financing, and this effect is higher for

smaller firms, that is, SMEs in countries with less developed financial and legal systems,

as is the case of Spain, which face higher growth and financing constraints than their

counterparts in countries with more developed institutions. Regarding the use of trade

credit in both systems, Demirgüç-Kunt and Maksimovic (2002) and Fisman and Love

(2003) state that in countries with legal systems of civil law origin there is a greater

reliance on supplier financing. Trade credit is more important compared to bank credit

in countries where legal protection of creditors is weaker, because it is easier to divert

cash than to divert illiquid inputs (Burkart and Ellingsen, 2004). Perhaps because of this,

Demirgüç-Kunt and Maksimovic (2002) show that Spanish firms are among the largest

users of trade credit. Concerning the financial system, the literature highlights the

significant role of trade credit as a source of firms' financing in countries with less

developed financial markets (Fisman and Love, 2003). Spain has a banking-oriented

financial system in which funding through financial markets is rarely used and firms

depend heavily on bank financing (Bentolila, Jansen, Jiménez and Ruano, 2015; Carbó-

Valverde, Mansilla-Fernández and Rodríguez-Fernández, 2017; Carbó-Valverde,

Rodríguez-Fernández and Udell, 2009). Carbó-Valverde, Rodríguez-Fernández and

Udell (2016) assert that, in Spain, the two main sources of external finance for SMEs

are bank loans and trade credit, unlike United States, where commercial finance

companies also provide a significant amount of SME finance. In this sense, firms in

countries with larger banking systems, like Spain, take more financing from suppliers

(Demirgüç-Kunt and Maksimovic, 2002). Therefore, a sample of Spanish SMEs

provides an interesting setting to analyse how supplier financing influences firm

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valuation and the differences in the marginal value of payables depending on the firm's

access to internal and external finance.

Our results show a positive relationship between supplier financing and SMEs’

value. Moreover, the value of supplier financing is lower in firms with better access to

alternative financing, either in the form of internally generated cash flows or debt.

Similarly, firms with less access to external finance in terms of cost have greater supplier

financing value. Finally, the positive relationship between supplier financing and SMEs’

value is stronger during the financial crisis and subsequent years, but during this period

there are no differences in the value of supplier financing depending on the firm’s access

to financing. The findings are consistent with the financial motive of trade credit. So,

the financing that suppliers provide is a valuable resource for firms, especially for SMEs

with less borrowing capacity and lower cash flow generation, as well as for all firms in

times of crisis.

The rest of the article is organised as follows: in Section 1, we review the main

theories of trade credit and the expected relationships between supplier financing and

firm value. In Section II, we describe the sample as well as the regression specification

and variables. In Section III, we present the results for the period 1998-2007, and in

Section IV, we analyse the effect of the financial crisis on supplier financing value.

Next, we provide additional robustness analysis. In the last section we draw conclusions.

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I. RELATED LITERATURE AND HYPOTHESES

A. Supplier financing and SMEs value

In this section, we analyse the effect of supplier financing on the value of SMEs.

According to the benefits and costs found in trade credit literature, the research question

we try to answer is whether there is any relationship between trade credit received from

suppliers and the value of small firms.

From a financial point of view, one of the main advantages of trade credit is that

it could help firms to overcome financial constraints (Schwartz, 1974), especially when

institutional credit is unavailable (Danielson and Scott, 2004) or prohibitively

expensive. Cassar and Holmes (2003) argue that smaller firms have less access to capital

or face higher costs than larger firms because for small firms it is more costly to reduce

information asymmetry between borrowers and lenders. In line with this, Beck and

Demirgüç-Kunt (2006) argue that small firms face higher risk premiums since they are

more opaque and have less collateral to offer. This can make supplier financing more

advantageous for SMEs than for large firms. Moreover, trade credit can be viewed as

relationship lending, that is, the lending decision and the terms of trade credit financing

are primarily based on private, soft information gathered over the course of a

relationship, which can help to address the opacity problem of SMEs (Berger and Udell,

2006).

In line with the above, the extension of trade credit by firms’ suppliers could

give a positive signal to the investors about the creditworthiness of the firm, due to the

better knowledge that suppliers have about the situation of firms regarding financial

institutions (Biais and Gollier, 1997). In this sense, commercial debt subjects firms to

7

permanent assessment and control by their suppliers, who will not be willing to grant a

firm more credit unless it has good prospects. Therefore, trade credit could reduce the

agency problems associated with information asymmetry between the firm and its

lenders, which might facilitate access to bank debt (Biais and Gollier, 1997). In fact,

their model shows that trade credit can influence the lending process of banks. This

could be especially important for SMEs, which suffer a more serious asymmetric

information problem than large businesses (Ang, 1991) and, therefore, greater financing

frictions. In line with the above, Voordeckers and Steijvers (2006) argue that a

significant signalling effect of trade credit reduces the risk of lending, since trade credit

conveys private information that suppliers have about a firm to the bank, and

consequently the likelihood of presenting collaterals is lower. Their empirical evidence

reveals a possible signalling effect of trade credit, so mitigating the adverse selection

problem, as predicted by the Biais and Gollier model (1997).

In addition to these benefits, from the perspective of transaction costs trade credit

can save costs by separating the trading of goods from the exchange of money and by

making invoice payments periodically rather than through immediate payment upon

delivery of goods (Emery, 1987; Ferris, 1981; Nadiri, 1969). Another advantage of

supplier financing is the financial flexibility it brings to the firm. Trade credit helps

firms to improve their cash flows by reducing the speed of cash outflows. Besides, it

varies with firms' activity. In this sense, the value of supplier financing may be higher

in SMEs, as these firms have less access to other flexible financing sources, such as

lines of credit, because these are more expensive for private firms (Campello,

Giambona, Graham and Harvey, 2011). Also, trade credit fluctuates according to

business activity and it can be less costly for the firm to renegotiate trade credit

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payments with their suppliers than the payment terms of bank loans in the case of

temporary cash flow problems (Danielson and Scott, 2004). Lastly, trade credit can help

firms facing liquidity shocks (Boissay and Gropp, 2007; Cuñat, 2007; Wilner, 2000).

Trade credit offers other advantages apart from financing and transactional

benefits. In particular, trade credit allows customers a period of time to verify the quality

of the products before payment, thus reducing information asymmetry between sellers

and buyers (Deloof and Jegers, 1996; Emery and Nayar, 1998; Lee and Stowe, 1993;

Long et al., 1993; Ng et al., 1999; Smith, 1987), which can be very important for SMEs

given their lower bargaining power.

However, supplier financing may have an implicit cost, which depends on the

cash discount for prompt payment and the discount period (Ng et al., 1999; Wilner,

2000). Furthermore, trade credit could expose the firm to refinancing risk, since

suppliers as a spontaneous source of financing can stop providing credit at any time

during the relationship. Finally, late trade credit payments imply other potential costs

such as late payment penalties, deterioration in credit reputation, higher prices or less

favourable delivery dates in the future, so worsening the relationship with the supplier

(Danielson and Scott, 2004; Wu et al., 2012). Notwithstanding, as Hill et al. (2013) point

out, it seems that trade credit is not as expensive as previous studies suggest, because

the cash discount is not widely used (Giannetti, Burkart and Ellingsen, 2011). In a

similar context to Spain - Italy - Marotta (2005) finds that the proportion of suppliers

offering discounts is very low. What is more, according to the European Payment Guide

produced by Intrum Justitia (2013)1, the most common payment term offered in Spain

1 The European Payment Guide, published by Intrum Justitia, provides an international comparison of

the payment customs and practices of 29 European countries plus Turkey and Russia participating in the

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is 60 days, and trade credit contracts are in net terms with no cash discount. Moreover,

the cost of trade credit depends on penalties for delays. However, most companies do

not charge penalties for late payment (Marotta, 2005; Pike and Cheng, 2001; Wilner,

2000).

Because of the benefits of trade credit, which as stated above can be even more

important in the case of SMEs, and the fact that the cost of supplier financing may be

overrated, we expect a positive relationship between supplier financing and firm value.

Based on the above, we test the following hypothesis:

H1: The relationship between supplier financing and the value of SMEs is positive.

B. Access to financing and the value of trade credit

The financing theory justifies the use of trade credit because credit market

imperfections cause financial institutions to ration credit (Emery, 1984; Lewellen,

McConnell and Scott, 1980; Schwartz, 1974; Smith, 1987). Namely, firms use trade

credit because credit from financial institutions is limited. According to Meltzer (1960),

one motivation for trade credit is to alleviate customers’ financial frictions. Schwartz

(1974) focused on the financial motive for the use of trade credit, specifically on the

role of financial intermediation performed by nonfinancial firms. When credit is tight,

firms that have easier (cheaper) access to capital markets will use their borrowing

capacity to pass credit on to their customers with limited access to capital markets.

Theoretical models and empirical papers have developed financial theories to explain

survey.

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how financial market imperfections can affect the demand for trade credit (Biais and

Gollier, 1997; Burkart and Ellingsen, 2004; Fabbri and Menichini, 2010; Nilsen, 2002;

Ng et al., 1999; Petersen and Rajan, 1997). In this section, we go a step further and we

try to answer the following research question: depending on the financing motive for

the trade credit, does the access to external and internal financing influence the value of

supplier financing?

Modigliani and Miller (1958) state that if capital markets were perfect, financing

choices would be irrelevant and would not affect firm value. However, imperfections in

financial markets would affect the financing decisions of firms. The existence of

asymmetric information and opposing interests of lenders and borrowers may mean

companies are unable to obtain external funds (Stiglitz and Weiss, 1981). Because of

these difficulties in accessing finance, firms cannot always fund their positive net

present value (NPV) projects. Therefore, easier access to debt increases the likelihood

of taking on positive value-creating projects that might otherwise be forgone. Berger

and Udell (2002) state that small firms’ debt financing is mainly provided by

commercial banks and other financial institutions, as well as by suppliers. Therefore,

trade credit is one of the main sources of financing for firms with difficulties in

accessing financial markets. So, for financially constrained firms higher trade credit

increases the probability of taking on positive NPV projects (Faulkender and Wang,

2006). In the same sense, Carbó-Valverde et al. (2016) find that trade credit financing

does not explain investment in unconstrained firms, while for constrained firms supplier

financing does affect investment, and this relationship is stronger the more constrained

the firm is. So, trade credit prevents underinvestment costs and might increase firm

value, while it does not provide this benefit for firms with better access to debt.

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The lack of quality information about the SMEs makes these companies riskier

and therefore they have to resort to insider financing, bear higher financing costs or opt

for shorter-term financing alternatives (Berger and Udell, 1998; Gregory, Rutherford,

Oswald, and Gardiner, 2005). Thus, in order to analyse the value of supplier financing

we have to take into account SMEs’ access to financing. Based on the above discussion,

we test the following hypothesis:

H2: SMEs with better access to external and internal financing have lower

supplier financing value.

To conduct the analysis, the variables employed to proxy access to external funds

are long-term leverage ratio, financial costs and short-term bank debt, and for internal

financing it is cash flow.

First, we proxy firm’s access to external financial resources by long-term

leverage ratio, since the leverage of a firm measures its ability to issue debt (Guney,

Ozkan and Ozkan, 2007), and so higher leverage indicates better access to external

financing (Wu et al., 2012). Almeida and Campello (2007) distinguish between quantity

constraints and costs constraints on external funds. In Hennessy and Whited (2007) a

proxy for financing constraints is the cost of external financing. Similarly, the second

proxy for access to external funds is financial costs; a high cost of financing causes

financial constraints for firms. Therefore, the sub-hypothesis to be tested is:

H2a: The value of supplier financing is lower for more leveraged firms and for

firms that have lower finance costs.

In order to gain a better understanding of the effect of access to external financial

resources on the value of supplier financing, we also employ short-term bank debt, since

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SMEs mainly use short-term debt financing (Michaelas, Chittenden, and Poutziouris,

1999) and this can be considered the most important alternative external source of funds

for supplier financing (Deloof and Jegers, 1999). Firms use trade credit as a substitute

for short-term bank debt, especially when bank credit is unavailable (Fisman and Love,

2003; Nilsen, 2002; Petersen and Rajan, 1997; Wilner, 2000; among others). Firms with

more short-term bank debt may have less supplier financing value, since they have more

access to external funding sources to cover their short-term financial needs (Rodríguez-

Rodríguez, 2006), such as working capital financing. Because of this substitution effect,

the second sub-hypothesis is:

H2b: There is a negative relationship between short-term bank debt and the value

of supplier financing.

Finally, we focus on internal financing. Deloof and Jegers (1999) argue that

firms’ supplier financing is determined by their capital needs and by internally generated

cash flows, as predicted by Myers and Majluf (1984). In the same vein, previous studies

have found that firms generating more internal funds have more resources available, and

therefore will require less credit for their suppliers (Deloof and Jegers, 1999; Niskanen

and Niskanen, 2006; Petersen and Rajan, 1997). High cash flow SMEs can use internal

financing to finance their operations and investments rather than supplier financing. In

contrast, when internal financing is insufficient, SMEs can be more dependent on trade

credit financing. Therefore, our last sub-hypothesis is:

H2c: There is a negative relationship between internal financing (cash flow) and

the value of supplier financing.

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C. Supplier financing value and the financial crisis

In this section, we investigate the relationship between supplier financing and

firm value in a period of global financial crisis and the following and ongoing recession

in the Spanish economy. The aim is to compare the value of trade credit in a period of

economic stability versus the subsequent situation of financial and liquidity constraints

for the majority of Spanish SMEs. Previous studies have analysed the shock in the

supply of credit during the recent financial crisis (Akbar, Rehman and Ormrod, 2013;

Bentolila et al. 2015; Duchin, Ozbas and Sensoy, 2010, among others). This negative

shock has been more accentuated in the case of short term financing, such as short term

bank debt and trade credit (Akbar et al., 2013).

On the other hand, as we stated previously, small and medium-sized firms suffer

more severe credit restrictions and have fewer sources of financing available. Therefore,

these firms are particularly vulnerable to a reduction in credit supply (Bentolilla et al.,

2015). The evidence shows that SMEs suffered from a significant credit crunch during

the recent crisis (Carbó et al., 2016). Likewise, Duchin et al. (2010) argue that for

financially constrained firms the effects of credit supply shocks are more severe, due to

their problems of information asymmetry and greater financing frictions. To this must

be added the fact that Spanish SMEs are very bank-dependent, and they faced an

important reduction in the credit received from banks during the crisis (Bentolila et al.,

2015).

According to this, we test the following hypothesis:

H3: The value of supplier financing was higher during the period 2008-2014 than in

the pre-crisis period

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II. RESEARCH DESIGN

A. Sample

The sample consists of a panel of 7952 non-financial Spanish SMEs with 44,216

firm-year observations over the period 1998-2014. The information used in this study

was obtained from the SABI database (System of Iberian Financial Statement Analysis),

made by Bureau Van Dijk. We selected Spanish SMEs according to the requirements

established by the European Commission recommendation 2003/361/CE of 6 May

2003: fewer than 250 employees, turnover of less than €50 million or less than €43

million in total assets. Then, we applied a series of filters, such as non-missing data on

the variables of the model, total assets different from total liabilities and equity, negative

financial expenses, or ratio of debt to assets higher than one. Finally, to minimise the

effect of outliers, we eliminated 1% of the extreme values for all variables employed in

the analysis.

B. Regression specification and variables

Our model is primarily based on the valuation method of Fama and French

(1998) so as to be able to study the influence of debt and dividends on firm value.

Extending their valuation regression, like Pinkowitz, Stulz, and Williamson (2006),

Dittmar and Mahrt-Smith (2007) and Drobetz, Grüninger, and Hirschvogl (2010) do

when studying the shareholders' value of cash, we examine whether a change in

accounts payable leads to a change in firm value. Pinkowitz et al. (2006) modify Fama

and French’s model to estimate the value of cash. Following this procedure, in order to

15

estimate the relation between value and trade credit received, we include the change in

accounts payable. So the baseline equation to test the marginal value of accounts

payable is:

Vit = α + β1dPAYit + β2dPAYit+1 + β3dAit + β4dAit+1 + β5Eit + β6dEit + β7dEit+1 + β8RDit

+ β9dRDit + β10dRDit+1 + β11Iit + β12dIit + β13dIit+1 + β14dVit+1 + λt+ Is + ηi+ εit (1)

where Vit is the proxy for firm value, which is calculated as the book value of assets

minus book value of equity plus a proxy for the market value of equity. Since we are

studying unlisted firms, we measure the value of equity as net profit plus depreciation

over the average return on equity of the industry. Specifically, the market value of equity

is calculated considering that the shareholders' cash flow follows a perpetual rent (no

growth) which is discounted by the average return on equity of the industry. PAYit

corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and

taxes (EBIT), as we do not have information about R&D expenses, we proxy RDit as the

positive annual increase in intangible assets and Iit is interest expense. Note that dXit is

compact notation for the 1-year change, Xit - Xit-1. Likewise, dXit+1 is the change in the

level of X from year t to year t+1, Xit+1 - Xit. The increase in payables may change

expectations about future growth; for this reason, the Fama-French model includes lead

variables (dXit+1) to take into account expectations. All variables are scaled by the book

value of total assets, Ait. λt and Is are time and industry dummy variables, respectively,

which are included in the model to account for time trends and time-invariant industry

heterogeneity. ηi is the unobservable heterogeneity and εit is the error term.

Next, in order to test the influence of SMEs access to financing on the value of

the trade credit received, we estimate the following Model (2), where we include

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interaction variables between the change in payables and the dummy variables created

to measure the availability of financial resources.

Vit = α + β1dPAYit + β2dPAYit+1 + β3 [dPAYit×DUMMYit] + β4DUMMYit + β5dAit +

β6dAit+1 + β7Eit + β8dEit + β9dEit+1 + β10RDit + β11dRDit + β12dRDit+1 + β13Iit + β14dIit +

β15dIit+1 + β16dVit+1 + λt+ Is+ ηi+ εit (2)

When DUMMYit takes value 1, β1+β3 accounts for the effect of accounts payable on

firm value. Otherwise, when DUMMYit takes value 0, the interaction variable

(dPAYit×DUMMYit) is 0, and β1 accounts for the effect. So, the interaction variable

captures the difference in the value of supplier financing between groups of firms,

depending on their access to external and internal financing. Moreover, like Dittmar and

Mahrt-Smith (2007), we include the DUMMYit variable on its own because if there is

endogeneity, it is expected that this relationship will be shown in the dummy variable

instead of in the interaction with the change in payables.

Following the literature, we employ several proxies of availability of financial

resources. Regarding proxies for measuring the access to external funds, long-term

leverage ratio (DLTLEV) is defined as the book value of long-term debt divided by the

book value of total assets. So, DLTLEV will take value one if firm long-term leverage

is greater than or equal to the industry median, and zero otherwise. The variable

measuring the cost of external financing is DFCOST, which equals one when the firm’s

financial cost, calculated as the ratio of financial expenses to total debt minus accounts

payable, exceeds or equals its industry median, and zero otherwise. Next, to see whether

substituting commercial credit for bank credit influences the relationship between trade

credit and firm value, we employ the DSTDEBT dummy variable, which takes value

one if the ratio of short-term bank debt to the book value of total assets is greater than

17

or equal to the industry median, and zero otherwise. Finally, as proxy for the capacity

of firms to generate internal resources, we employ cash flow (DCFLOW), measured as

net income plus depreciation over book value of total assets. Therefore, DCFLOW will

take value one if firm cash flow is greater than or equal to the industry median cash

flow, and zero otherwise.

III. EMPIRICAL RESULTS FOR THE PERIOD 1998-2007

A. Descriptive statistics

Table 1 describes the characteristics of the sample firms. It contains the

descriptive statistics of the variables employed in the study. The mean annual change in

accounts payable as a percentage of firm’s total assets is 1.15%. Also, the average ratio

of accounts payable to total assets (PAY) is 25.09%. This value is higher than in

Giannetti et al. (2011) for their sample of US small firms (20%) and supports a more

intensive use of trade credit in civil law countries than in common law countries

(Demirgüç-Kunt and Maksimovic, 2002). In turn, this value is also higher than publicly

traded companies because of the greater importance of trade credit financing for SMEs.

Giannetti (2003) states that the average balance sheet of a listed company (in eight

European countries, including Spain) seems to have less trade credit than an unlisted

firm2.

The mean long-term leverage represents 9.80% of the assets of our sample of

Spanish SMEs. Moreover, the mean short-term bank debt of the sample firms is 16.06%;

2 Although this study focuses mainly on private companies (listed companies are a very small fraction of the companies in the sample), she makes a comparison between the balance sheets of listed and unlisted companies.

18

and accounts payable, which is the most important item in current liabilities, is 25.09%.

The economic importance of trade credit can be justified by the benefits of this source

of funding, outlined in the previous section.

Insert Table 1 here

B. Supplier financing value

Following the literature, we estimate regressions using the Fama and MacBeth

(1973) method. This technique produces standard errors robust to correlation between

firms at a moment in time, that is, clustering along a simple dimension. Additionally, in

order to give robustness to the results, we use clustered standard errors. This method

calculates standard errors that are robust to simultaneous correlations between firms and

time periods. Clustering on both firm and time leads to significantly more accurate

inference in finance panels (Thompson, 2011).

The results of Model 1 are presented in Table 2. The variable dPAYit measures

the effect of trade credit financing on firm value, specifically, the value of an additional

euro of financing received from suppliers. In the first column, we estimate model 1 using

Fama-MacBeth, while in the second column our estimation method is clustered standard

errors. The findings show that firm value is positively and significantly related to an

additional euro of accounts payable. Estimates indicate that an additional €1 of accounts

payable increases firm value by €0.1340 (column 1) or €0.1130 (column 2). The

magnitude of the coefficients is comparable to Hill et al. (2013). Using a different

valuation approach, they find that the market values an additional $1 of trade payables

at $0.15. The positive and significant coefficient of accounts payable (dPAYit) indicates

19

that there is a positive relationship between financing by accounts payable and firm

value. The findings are consistent with the aforementioned benefits of trade credit

financing, such as mitigating financing frictions and adverse selection problems (Biais

and Gollier, 1997; Schwartz, 1974), transaction cost saving (Ferris, 1981), financial

flexibility (Danielson and Scoot, 2004), verifying product quality before paying (Long

et al.,1993; Smith, 1987), and these advantages outweigh the implicit interest (if there

is discount for prompt payment), refinancing risk and other potential disadvantages

associated with accounts payable.

Insert Table 2 here

Next, we examine how the value of supplier financing differs between firms with

greater access to external and internal financing and firms with less access to finance.

The benefits attributable to trade credit might differ based on the firm’s access to

external and internal finance and, therefore, the value of supplier financing could be

different. Specifically, to empirically contrast the influence of the financing motive of

trade credit on the value of supplier financing we estimate Model 2. The objective is to

determine whether there is a value discount or premium of trade credit for firms with

high leverage, high financial costs high short-term debt, and firms with greater internal

cash flow. The results are presented in Tables 3 and 4.

In Table 3, we present the effect of long-term debt and financial costs on the

value of accounts payable. Thus, we have estimated Model 2 by including the

interactions of dPAYit with DLTLEVit (columns 1 and 2 Table 3) and with DFCOSTit

(column 3 and 4) in order to classify firms into two subgroups according to both their

20

long-term leverage ratio and their financial costs ratio, respectively. The interaction

coefficient represents the premium or discount associated with the value of trade credit

received depending on firms’ access to financing in terms of availability and cost. It is

expected that the need for additional funding of high-long-term leveraged firms will be

lower, and therefore the value of accounts payable will be lower. The results confirm a

negative relationship between the value of payables and the availability of long-term

debt. Specifically, estimating by Fama and MacBeth (1973) methodology in column 1

of Table 3, the coefficient for the interaction variable DLTLEVit×dPAYit is negative and

statistically significant indicating a discount in the marginal value of trade credit of

0.2770 for firms with greater long-term debt than the industry median (DLTLEV=1),

compared to firms with lower levels of long-term debt. Similar results are obtained by

clustered standard errors (column 2 of Table 3). In this case, for firms with better access

to long-term debt the supplier value is 0.0906 lower than for firms with worse access.

With regard to the financial cost, we find a positive relationship between financial costs

and supplier financing value. The coefficient of the interaction variable

DFCOSTit×dPAYit is positive and statistically significant (columns 3 and 4 of Table 3),

it implies a value premium of accounts payable of 0.1088 by Fama-MacBeth estimation

(or 0.0966 if the estimation method is clustered standard errors) for firms with higher

financial cost (DFCOST=1). That is to say, supplier financing is more valuable for

financially constrained firms in terms of cost than for firms with lower cost of financing.

Another explanation could be that when the cost of debt is high, an increase in supplier

financing (sometimes free financing) will decrease the cost of capital, which in turn

increases the value of trade credit.

Insert table 3 here

21

Regarding the third proxy for measuring the access to external funds, in columns

1 and 2 of Table 4 we present the value of supplier financing depending on firms' access

to short-term bank debt. The results show a negative and statistically significant

coefficient of variable DSTDEBTit×dPAYit, for both Fama-MacBeth (column 1 of

Table 4) and clustered standard errors (column 2 of Table 4) estimations, which

indicates that the marginal value of accounts payable is 0.1490 (0.1318 when the

estimation method is clustered standard errors) lower for SMEs with better access to

short-term financing (short term bank debt ratio greater than or equal to the industry

median; DSTDEBT=1) than for firms with worse access. This indicates that better

access to external financing, in the form of short-term debt, reduces the value of supplier

financing, as short-term debt could be a substitute for trade credit. The findings support

a line of research that finds that firms with little access to bank credit depend more on

trade credit from suppliers (Carbó-Valverde et al., 2016; Fisman and Love, 2003;

Nilsen, 2002; Ogawa, Sterken and Tokutsu, 2013; Petersen and Rajan, 1997; among

others). Moreover, the results are consistent with the substitution hypothesis, since for

firms with lower levels of short-term bank debt, the financing that suppliers provide has

a greater effect on SMEs value.

Results for the variables used to proxy access to external fund are consistent with

those of Faulkender and Wang (2006) on the value of cash holdings, and may indicate

that trade credit increases the probability of taking positive NPV projects for firms with

more difficulties in accessing the capital market. Indeed, firms with greater financial

constraints are more reliant on trade credit to finance their investment projects (Carbó-

Valverde et al., 2016). Therefore, SMEs with better access to external financing are less

dependent on supplier financing and this is reflected in a lower value of this financing

22

source. In contrast, trade credit helps SMEs with difficulties in accessing finance trade

credit to prevent underinvestment.

Finally, we focus on cash flow as the proxy for firm internal financing.

Particularly, the effect of payables on value may differ from the firm cash flow,

especially in the case of SMEs, which rely heavily on internal resources (Sogorb-Mira,

2005). In this sense, García-Teruel and Martínez-Solano (2010) and Petersen and Rajan

(1997) find that firms that generate more resources internally (cash flow) reduce their

demand for financing from their suppliers. Consistent with this, the interaction variable

DCFLOWit×dPAYit presents negative and significant coefficients in columns 3 and 4 of

Table 4, in quantitative terms, supplier financing value for cash flow rich firms

(DCFLOW=1) is 0.2940 lower than for firms with lower cash flow ratios (or 0.2329

when the estimation is carried out with clustered standard errors). Otherwise, results

indicate that when internal finance is insufficient, Spanish small firms seem to be more

dependent on supplier financing, as a higher value of trade credit is found. For SMEs,

which face greater financing frictions, trade credit represents a crucial financing source,

which helps to prevent underinvestment when firms' cash flows are not sufficient.

Insert table 4 here

In short, the results show that firms' long-term leverage, short-term financial debt

and cash flow moderate the impact of accounts payable on firm value. However, firms’

financial costs reinforce the accounts payable-firm value relationship. Specifically, we

obtain that better access to financial markets and internal financing reduces the value of

accounts payable. Using a different approach, Hill et al. (2013) reach the same

conclusion, since their results indicate a value premium for payables held by financially

constrained firms.

23

IV. THE EFFECT OF THE FINANCIAL CRISIS ON SUPPLIER

FINANCING VALUE

After studying the impact of trade credit financing on firm value for the period

1998-2007, it was found that access to financing affects the value of accounts payable.

Accordingly, and taking into account that the financial crisis of 2008 and the subsequent

recession provoked a credit supply shock, in this section we examine if, as expected, the

value of supplier financing was higher during the period 2008-20143.

In Table 5, we estimate model 1 using Fama-MacBeth (column 1) and clustered

standard errors (column 2) to analyse the effect of the financial crisis on the value of

accounts payable. The results show a positive relationship found between supplier

financing and SMEs value, and this positive effect is stronger during the period 2008-

2014. Effectively, the coefficient of the variable dPAYit for the period 2008-2014 is

higher (0.1864 and 0.1833 for Fama-MacBeth and cluster estimations, respectively)

than in the pre-crisis period 1998 to 2007 (0.1340 and 0.1130 for Fama-MacBeth and

cluster, respectively; see Table 2). This is consistent with the main result of the paper;

worse access to finance increases the value of the financing received by suppliers.

Moreover, during the crisis the substitution effect between bank credit and trade credit

could be higher, since SMEs have fewer sources of financing available, and trade credit

is sometimes the only alternative to bank debt. This effect is shown in the higher

coefficient of payables variable during the 2008-2014 period.

Insert table 5 here

3 Descriptive statistics for period 2008-2014 in Appendix.

24

The finding shows the important role of trade credit financing for SMEs during

the financial crisis given the financial constraints in terms of availability and cost of

credit of these firms, due to their greater vulnerability. The negative shock to the supply

of external finance, together with the presence of financing restrictions, makes supplier

financing a more valuable source of financing for Spanish SMEs.

Next, we analyse whether there are differences in the value of supplier financing

according to SMEs’ access to finance during financial crisis and the subsequent

recession. Financial crisis provoked a credit supply shock which affected all constrained

and unconstrained small and medium-sized firms. Actually, according to Bentolila et al.

(2015), Spanish SMEs have been particularly affected by the credit restrictions during

the crisis. In order to do that, we estimated model 2 for the crisis period. Moreover,

according to previous literature, we classified firms by their access to financing, and

controlling for a demand effect, since this could be higher during crisis periods. More

concretely, following García-Appendini and Montoriol-Garriga (2013) and Illueca

Muñoz, Lars and Stefan (2016), we create the dummy variable DEFDit, which takes the

value 1 when the company demands financing because its investment is greater than the

cash flows generated by the company, and 0 otherwise. Then we multiply the proxies of

the capacity of access to external and internal financing with external finance demand

dummy (DEFDit) and the increase in accounts payable (dPAYit). Overall, the results

presented in Tables 6 and 7 show that during the crisis and the subsequent years (period

2008-2014) the access to financing does not seem to affect the value of trade credit

financing since only two of eight regressions present a significant coefficient.

Insert tables 6 and 7 here

25

These results can be explained by the fact that all Spanish SMEs suffered

financial constraints during the financial crisis. Actually, the differences in the access

to financing narrowed during the crisis. For instance, the difference in means of the

variable STDEBTit between firms with better and worse access to short-term debt is

17.51% lower in the crisis period compared to the pre-crisis years. The same happens

with internal financing, the difference in means of the variable CFLOWit between high

cash flows firms and low cash flows firms is 17.57% lower in the crisis period than in

the pre-crisis period.

V. ROBUSTNESS ANALYSIS

Additionally, in this section we perform several robustness tests. First, the results

are robust when we employ as to the alternative accounts payable variable a measure

adjusted by the industry (EXCESSdPAYit), calculated following Hill et al. (2013),

namely, subtracting from our variable dPAYit the industry median increase in the ratio

of accounts payable by year. The results show that trade credit used in excess of the

industry increases its value (see columns 1 and 2 of Table 8). In the same line, although

we include industry dummies as control variables, we analyse the potential differences

in the value of trade credit received according to the type of industry. Specifically, we

create the dummy variable DINDUSTRYit, which takes value one for manufacturing

firms, and we analyse its interaction with dPAYit. We find then that the value of supplier

financing is greater for the manufacturing industry, but only statistically significant for

estimation using clustered standard errors (see column 4, Table 8). This is consistent

with Ng et al. (1999), who find that trade credit is more important for buyers who

consume the product in the process of manufacturing another product or service.

26

Insert table 8 here

Next, we analyse the effect of supplier financing on the value of firms when sales

were decreasing during the period 1998-2007. For that, we create the dummy variable

NGROWTHit which takes value one for firms with annual sales growth lower than or

equal to 0, and zero otherwise. The results for the interaction NGROWTHit×dPAYit

show that for firms whose sales are decreasing the value of supplier financing is lower

than for firms with growing sales (see columns 1 and 2, Table 9). As expected, an

increase in accounts payable for firms with declining sales may indicate that they are

paying late, or not paying, their suppliers.

Furthermore, we analyse the supplier financing value for firms jointly with

growing sales and financially constrained (in terms of availability and cost).

Specifically, we generate the dummy variable PGROWTHit, which takes value one for

firms with annual sales growth greater than 0. Moreover, constrained firms are classified

using two dummy variables, DLOWSTDEBTit, which takes value one if the ratio of

short-term bank debt to total assets is lower than the industry median and zero otherwise,

and DFCOSTit, which takes value one when the firm’s financial costs exceed or equal

its industry median and zero otherwise. The results for the interaction variables

PGROWTHit×DLOWSTDEBTit×dPAYit (see columns 3 and 4 in Table 9) and

PGROWTHit×DFCOSTit×dPAYit (see columns 5 and 6 in Table 9) show higher supplier

value for growing firms, which have greater financing needs, and which are in turn more

financially constrained than for the rest of the companies in the sample for the period

1998-2007.

Insert table 9 here

27

Likewise, we study whether sales growth and financial constraints influenced

supplier financing value during the crisis. To do this, in Table 10 we repeat the analysis

of Table 9 but for the years 2008 to 2014. The findings are in line with those obtained

for the access to finance in Tables 6 and 7, and there are no significant differences in

the value of accounts payable during the crisis because of the shock in the supply of

credit suffered by all Spanish SMEs.

Insert Table 10 here

VI. CONCLUSIONS

SMEs play a central role in the economy although market imperfections cause

firms to have difficulties in obtaining funding, meaning that the financing of SMEs is

crucial. In this regard, trade credit received constitutes a major source of short-term

financing for SMEs, especially in countries with less developed financial markets. Thus,

this paper investigates the value of supplier financing for a sample of SMEs in a country

of civil law origin - Spain. It contributes to the SME financing literature by analysing

the value of supplier financing and the differences in this value according to firms’

access to external and internal finance. Furthermore, we study the value of trade credit

financing during the recent financial crisis. The findings indicate that there is a positive

effect of supplier financing on firm value. Furthermore, the results suggest that the value

of supplier financing in small businesses is conditional on external and internal finance.

Particularly, we find that for SMEs with better access to external and internal financing

the value of trade credit financing is lower than for other small and medium sized firms.

The evidence indicates that firms with higher long-term leverage, and therefore better

28

access to alternative external financing, value credit received from suppliers less. Also,

for firms facing higher financing costs, the value of supplier financing is higher. In line

with this, SMEs which have access to short-term financial debt value accounts payable

less than firms that do not. Regarding internal financing, firms with higher cash flows

have a lower value of trade credit financing. Lastly, the results show that during the

financial crisis the value of trade credit for all SMEs (with better or worse access to

alternative finance) is higher than in the previous period. For SMEs, with poorer access

to financing, supplier financing constitutes an important alternative source of external

financing. Besides the implications for academics, this study may be interesting for

policy makers, since raising the right kind of finance is a main concern of entrepreneurs

and SMEs.

29

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37

TABLES

Table 1: Descriptive statistics 1998-2007

Variable Obs Mean Perc. 25 Median Perc. 75 Std. Dev.

Vit 33,822 1.3899 1.0011 1.2427 1.6628 0.6209

dPAYit 33,822 0.0115 -0.0221 0.0087 0.0496 0.0852

dPAYit+1 33,822 0.0165 -0.0264 0.0072 0.0512 0.0942

PAYit 33,822 0.2509 0.1202 0.2200 0.3534 0.1663

dAit 33,822 0.0631 -0.0167 0.0625 0.1471 0.1410

dAit+1 33,822 0.0789 -0.0239 0.0567 0.1574 0.1678

Eit 33,822 0.0734 0.0291 0.0600 0.1058 0.0696

dEit 33,822 0.0024 -0.0185 0.0022 0.0228 0.0466

dEit+1 33,822 0.0036 -0.0191 0.0027 0.0248 0.0496

RDit 33,822 0.0039 0.0000 0.0000 0.0012 0.0111

dRDit 33,822 0.0010 0.0000 0.0000 0.0003 0.0134

dRDit+1 33,822 0.0007 0.0000 0.0000 0.0001 0.0166

Iit 33,822 0.0145 0.0053 0.0123 0.0208 0.0117

dIit 33,822 0.0003 -0.0021 0.0000 0.0028 0.0060

dIit+1 33,822 0.0008 -0.0019 0.0002 0.0034 0.0065

dVit+1 33,822 0.0896 -0.2436 0.0614 0.3906 0.6767

LTLEVit 27,656 0.0980 0.0194 0.0633 0.1435 0.1028

FCOSTit 31,367 0.0501 0.0241 0.0383 0.0597 0.0463

STDEBTit 28,114 0.1606 0.0419 0.1311 0.2514 0.1348

CFLOWit 33,111 0.0808 0.0419 0.0710 0.1102 0.0525

The table shows descriptive statistics of the model variables: number of observations (Obs), 25 and 75 percentiles, mean, median, and standard deviation. dXit is past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. All variables are scaled by the book value of total assets Ait. Vit is the proxy for firm value, which is calculated as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. LTLEVit is long-term leverage, calculated as the ratio of long-term debt to the book value of total assets. FCOSTit is the ratio of financial expenses to total debt minus accounts payable. STDEBTit is short-term bank debt to book value of total assets. Finally, CFLOWit is net income plus depreciation over book value of total assets.

38

Table 2: Supplier financing value

(1) Fama-MacBeth

(2) Cluster

dPAYit 0.1340*** 0.1130*** 0.0060 0.0000

dPAYit+1 0.1395** 0.0948*** 0.0410 0.0000

dAit 0.0811* 0.0297 0.0710 0.1560

dAit+1 0.2906* 0.3463*** 0.0660 0.0010

Eit 4.3675*** 4.8990*** 0.0000 0.0000

dEit 0.6243*** 0.6574*** 0.0000 0.0000

dEit+1 1.8807*** 2.3136*** 0.0040 0.0000

RDit 5.6276*** 6.1523*** 0.0000 0.0000

dRDit -1.7937*** -2.2804*** 0.0010 0.0000

dRDit+1 2.0771*** 1.6343*** 0.0050 0.0000

Iit 1.6360** 1.4264** 0.0180 0.0210

dIit -2.6951** -2.7426*** 0.0100 0.0030

dIit+1 -0.4178 -1.3800** 0.3360 0.0490

dVit+1 -0.3356** -0.3596*** 0.0140 0.0000

Constant 0.7369 0.0233 0.1560 0.8470

R-squared 0.6336 0.5665 Observations 33,822 33,822

The table reports the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. Column (1) presents the estimation of model 1 using the method of Fama and MacBeth (1973). Column (2) estimates the same model using clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

39

Table 3: Access to financing and supplier financing value (I)

(1) (2) (3) (4) Fama-MacBeth Cluster Fama-MacBeth Cluster

dPAYit 0.2491*** 0.1927*** 0.1563*** 0.1643*** 0.0010 0.0000 0.0080 0.0000

dPAYit+1 0.1116 0.0654** 0.1012* 0.0767*** 0.1170 0.0190 0.0540 0.0010

DLTLEVit×dPAYit -0.2770* -0.0906* 0.0960 0.0810

DLTLEVit 0.1366*** 0.1451*** 0.0000 0.0000

DFCOSTit×dPAYit 0.1088** 0.0966*** 0.0410 0.0010

DFCOSTit -0.1361*** -0.1327*** 0.0000 0.0000

dAit 0.0821 -0.0040 0.0121 -0.0513** 0.3030 0.8870 0.8170 0.0400

dAit+1 0.3072* 0.3774*** 0.3131* 0.3709*** 0.0740 0.0010 0.0660 0.0010

Eit 4.5923*** 5.0359*** 4.4350*** 4.9658*** 0.0000 0.0000 0.0000 0.0000

dEit 0.5707*** 0.5234*** 0.5522*** 0.5970*** 0.0010 0.0000 0.0000 0.0000

dEit+1 1.9872*** 2.2995*** 1.7678*** 2.2455*** 0.0010 0.0000 0.0060 0.0000

RDit 4.3184*** 4.6065*** 5.2124*** 5.6672*** 0.0000 0.0000 0.0000 0.0000

dRDit -1.3002*** -1.6899*** -1.6748*** -2.0304*** 0.0020 0.0000 0.0000 0.0000

dRDit+1 2.1026** 1.4577*** 1.9385*** 1.6135*** 0.0230 0.0000 0.0030 0.0000

Iit -0.2242 -0.3552 4.7278*** 4.4243*** 0.7430 0.5900 0.0000 0.0000

dIit -2.8136*** -2.7486*** -2.4444*** -2.5513*** 0.0040 0.0000 0.0070 0.0020

dIit+1 -1.7633** -2.6258*** -1.3777*** -2.1831*** 0.0120 0.0000 0.0090 0.0000

dVit+1 -0.3525*** -0.3607*** -0.3360** -0.3609*** 0.0080 0.0000 0.0150 0.0000

Constant 0.6144 0.0744 0.8171 0.1927* 0.1970 0.6690 0.1290 0.0800

Observations 27,656 27,656 31,367 31,367 R-squared 0.6405 0.5646 0.6439 0.5722

The table reports the impact of long-term leverage and financial costs on the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise, dXit+1is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. All independent variables are standardised by the book value of total assets. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. DLTLEVit is a dummy variable which takes value one if firm long-term leverage is greater than or equal to the industry median, and zero otherwise. DFCOSTit equals one when the firm’s financial cost exceeds or equals its industry median and zero otherwise. Columns (1) and (3) present the estimation of model 2 using the method of Fama and MacBeth (1973). Columns (2) and (4) estimate the same model using clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

40

Table 4: Access to financing and supplier financing value (II)

(1) (2) (3) (4) Fama-MacBeth Cluster Fama-MacBeth Cluster

dPAYit 0.1900*** 0.1757*** 0.2476*** 0.2095*** 0.0030 0.0000 0.0020 0.0000

dPAYit+1 0.1736 0.0802** 0.1563* 0.0832*** 0.1240 0.0360 0.0770 0.0000

DSTDEBTit×dPAYit -0.1490* -0.1318*** 0.0540 0.0080

DSTDEBTit 0.0510** 0.0293*** 0.0140 0.0000

DCFLOWit×dPAYit -0.2940*** -0.2329*** 0.0030 0.0000

DCFLOWit 0.3408*** 0.3843*** 0.0000 0.0000

dAit 0.0564* 0.0242 0.1248*** 0.0914*** 0.0590 0.4330 0.0020 0.0000

dAit+1 0.2677 0.3763*** 0.1883 0.2959*** 0.1410 0.0010 0.1500 0.0010

Eit 4.5195*** 4.9629*** 2.7244*** 3.0018*** 0.0000 0.0000 0.0000 0.0000

dEit 0.5442*** 0.5895*** 0.6007*** 0.6636*** 0.0000 0.0000 0.0000 0.0000

dEit+1 1.8513*** 2.3098*** 1.4509*** 2.0294*** 0.0050 0.0000 0.0050 0.0000

RDit 5.2220*** 5.5750*** 4.0703*** 4.4552*** 0.0000 0.0000 0.0000 0.0000

dRDit -1.5941*** -2.0151*** -1.2734*** -1.7128*** 0.0010 0.0000 0.0010 0.0000

dRDit+1 1.9540*** 1.5592*** 1.4997** 1.0642*** 0.0080 0.0000 0.0110 0.0000

Iit 0.1151 0.1021 3.8267*** 4.2718*** 0.8630 0.8730 0.0010 0.0000

dIit -2.3041** -2.2894** -2.1564** -2.0715** 0.0160 0.0130 0.0160 0.0180

dIit+1 -1.3468** -2.1211*** 1.0608 0.3442 0.0130 0.0010 0.1460 0.7370

dVit+1 -0.3388** -0.3655*** -0.2535** -0.3165*** 0.0140 0.0000 0.0220 0.0000

Constant 0.7376 -0.1713 0.6774 -0.0111 0.1670 0.2040 0.1600 0.9300

Observations 28,114 28,114 33,111 33,111 R-squared 0.6411 0.5675 0.6851 0.6223

The table reports the impact of short-term debt and cash flows on the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. All independent variables are standardised by the book value of total assets. PAYit corresponds to accounts payable. Ai tis total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. DSTDEBTit is a dummy variable which takes value one if the ratio of short-term bank debt to the book value of total assets is greater than or equal to the industry median, and zero otherwise, respectively. DCFLOWit is a dummy variable which takes value one if firm cash flow over book value of total assets is greater than or equal to the industry median, and zero otherwise. Columns (1) and (3) present the estimation of model 2 using the method of Fama and MacBeth (1973). Columns (2) and (4) estimate the same model using clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

41

Table 5: Supplier financing value during the financial crisis

(1) Fama-MacBeth

(2) Cluster

dPAYit 0.1864* 0.1833*** 0.0660 0.0000

dPAYit+1 0.1173* 0.1236* 0.0620 0.0640

dAit -0.0364 -0.0100 0.5780 0.8570

dAit+1 0.1842* 0.2079*** 0.0670 0.0000

Eit 7.1552*** 7.2177*** 0.0000 0.0000

dEit 0.5559** 0.5897*** 0.0340 0.0080

dEit+1 3.1499*** 3.2583*** 0.0090 0.0000

RDit 13.3296*** 13.5833*** 0.0000 0.0000

dRDit -6.0993*** -6.5215*** 0.0010 0.0000

dRDit+1 2.1013* 1.8680* 0.0780 0.0810

Iit 5.4239*** 5.5043*** 0.0000 0.0000

dIit -5.1344** -5.5490*** 0.0260 0.0000

dIit+1 0.8439 0.5204 0.5590 0.6610

dVit+1 -0.3684** -0.3765*** 0.0220 0.0000

Constant 0.8445*** 0.7363*** 0.0020 0.0000

R-squared 0.6269 0.6133 Observations 10,394 10,394

The table reports the value of accounts payable during the period 2008-2014. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. Column (1) presents the estimation of model 1 using the method of Fama and MacBeth (1973). Column (2) estimates the same model using clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

42

Table 6: Access to financing and supplier financing value during the financial crisis (I)

(1) Fama-MacBeth

(2) Cluster

(3) Fama-MacBeth

(4) Cluster

dPAYit 0.3028*** 0.2823*** 0.2078** 0.2054*** 0.0020 0.0000 0.0200 0.0000

dPAYit+1 0.0501 0.0519 0.0346 0.0518 0.2810 0.4420 0.5260 0.4310

DLTLEVit×DEFDit×dPAYit -0.3324 -0.3565** 0.1080 0.0340

DLTLEVit 0.1786*** 0.1813*** 0.0000 0.0000

DFCOSTit×DEFDit×dPAYit 0.3116 0.3526* 0.1160 0.0530

DFCOSTit -0.1520*** -0.1532*** 0.0000 0.0000

DEFDit -0.0625*** -0.0621*** -0.0780*** -0.0807*** 0.0040 0.0000 0.0020 0.0000

dAit 0.0780 0.0982* 0.1217 0.1418** 0.2830 0.0580 0.1500 0.0400

dAit+1 0.2094** 0.2373*** 0.2432** 0.2680*** 0.0480 0.0000 0.0180 0.0000

Eit 7.3141*** 7.3758*** 6.9764*** 7.0370*** 0.0000 0.0000 0.0000 0.0000

dEit 0.4433* 0.4542** 0.5110** 0.5282** 0.0650 0.0350 0.0410 0.0140

dEit+1 3.0398*** 3.1703*** 3.0421*** 3.1871*** 0.0080 0.0000 0.0060 0.0000

RDit 11.2643*** 11.3150*** 12.0849*** 12.2467*** 0.0000 0.0000 0.0000 0.0000

dRDit -5.4258*** -5.7182*** -5.8703*** -6.0976*** 0.0010 0.0000 0.0000 0.0000

dRDit+1 1.6895 1.3900 1.5043 1.3310 0.1780 0.2380 0.1400 0.1750

Iit 1.5183*** 1.5788** 10.0950*** 10.2311*** 0.0030 0.0120 0.0000 0.0000

dIit -3.7096** -4.2212*** -4.2090** -4.4681*** 0.0420 0.0010 0.0450 0.0010

dIit+1 -0.0815 -0.4019 -0.2266 -0.5180 0.9460 0.6800 0.8540 0.6170

dVit+1 -0.3495** -0.3643*** -0.3634** -0.3781*** 0.0190 0.0000 0.0160 0.0000

Constant 0.8500*** 0.7905*** 1.0221*** 0.8126*** 0.0030 0.0000 0.0010 0.0000

R-squared 0.6560 0.6418 0.6447 0.6316 Observations 9370 9370 10,068 10,068

The table reports the effect of sales growth on the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. DLTLEVit is a dummy variable which takes value one if firm long-term leverage is greater than or equal to the industry median, and zero otherwise. DFCOSTit equals one when the firm’s financial cost exceeds or equals its industry median and zero otherwise. DEFDit is a dummy variable which takes value one whether the external finance demand (increase in assets minus cash flow) is positive, and zero otherwise. In Columns (1) and (3) the estimation method is Fama and MacBeth (1973), and in Columns (2) and (4) clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

43

Table 7: Access to financing and supplier financing value during the financial crisis (II)

(1) Fama-MacBeth

(2) Cluster

(3) Fama-MacBeth

(4) Cluster

dPAYit 0.2507** 0.2389*** 0.1874** 0.1650*** 0.0380 0.0000 0.0160 0.0000

dPAYit+1 0.0853 0.0898 0.0927** 0.0895 0.2040 0.2050 0.0320 0.1320

DSTDEBTit×DEFDit×dPAYit -0.1933 -0.1437 0.2520 0.3490

DSTDEBTit 0.0796*** 0.0787*** 0.0010 0.0000

DCFLOWit×DEFDit×dPAYit -0.4387 -0.4153 0.1830 0.1090

DCFLOWit 0.3162*** 0.3168*** 0.0000 0.0000

DEFDit -0.0872*** -0.0902*** -0.0085 -0.0090 0.0020 0.0000 0.4450 0.3000

dAit 0.1793* 0.2152** 0.0378 0.0588 0.0960 0.0160 0.5800 0.2930

dAit+1 0.2302** 0.2616*** 0.0826 0.1300*** 0.0140 0.0000 0.3050 0.0000

Eit 7.1349*** 7.2034*** 5.2187*** 5.2368*** 0.0000 0.0000 0.0000 0.0000

dEit 0.5602** 0.5873*** 0.5713** 0.6053*** 0.0170 0.0050 0.0240 0.0030

dEit+1 3.1905** 3.3178*** 2.5365*** 2.8830*** 0.0110 0.0000 0.0060 0.0000

RDit 11.4864*** 11.6526*** 9.9487*** 10.2539*** 0.0000 0.0000 0.0000 0.0000

dRDit -5.5760*** -6.0485*** -4.0925*** -4.4188*** 0.0080 0.0020 0.0070 0.0020

dRDit+1 1.2522 0.9433 1.8262* 1.6947** 0.3730 0.4740 0.0540 0.0370

Iit 2.3566*** 2.4881*** 8.6259*** 8.7360*** 0.0010 0.0010 0.0000 0.0000

dIit -3.9132* -4.3086*** -4.8370** -5.2157*** 0.0550 0.0010 0.0260 0.0000

dIit+1 -0.5533 -0.8305 2.2615 1.6568 0.6840 0.5120 0.1090 0.1190

dVit+1 -0.3707** -0.3805*** -0.2779** -0.3151*** 0.0190 0.0000 0.0260 0.0000

Constant 0.5632 0.0030 0.6363** 0.2717*** 0.1630 0.9870 0.0360 0.0020

R-squared 0.6229 0.6069 0.6902 0.6769 Observations 8631 8631 10,186 10,186

The table reports the effect of sales growth on the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. DSTDEBTit is a dummy variable which takes value one if the ratio of short-term bank debt to the book value of total assets is greater than or equal to the industry median, and zero otherwise, respectively. DCFLOWit is a dummy variable which takes value one if firm cash flow over book value of total assets is greater than or equal to the industry median, and zero otherwise. DEFDit is a dummy variable which takes value one whether the external finance demand (increase in assets minus cash flow) is positive, and zero otherwise. In Columns (1) and (3) the estimation method is Fama and MacBeth (1973), and in Columns (2) and (4) clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

44

Table 8: Industry effect on supplier financing value

(1) Fama-MacBeth

(2) Cluster

(3) Fama-MacBeth

(4) Cluster

EXCESSdPAYit 0.1340*** 0.1097*** 0.0060 0.0000

dPAYit+1 0.1395** 0.0942*** 0.0410 0.0000

dPAYit 0.1022** 0.0760** 0.0170 0.0240 dPAYit+1 0.1373** 0.0958*** 0.0390 0.0000 DINDUSTRYit×dPAYit 0.1146 0.1293** 0.3650 0.0140 dAit 0.0811* 0.0310 0.0808* 0.0292 0.0710 0.1900 0.0690 0.1610 dAit+1 0.2906* 0.3464*** 0.2933* 0.3463*** 0.0660 0.0010 0.0610 0.0010 Eit 4.3675*** 4.8986*** 4.3611*** 4.8991*** 0.0000 0.0000 0.0000 0.0000 dEit 0.6243*** 0.6575*** 0.6364*** 0.6554*** 0.0000 0.0000 0.0000 0.0000 dEit+1 1.8807*** 2.3138*** 1.8811*** 2.3140*** 0.0040 0.0000 0.0040 0.0000 RDit 5.6276*** 6.1511*** 5.6567*** 6.1610*** 0.0000 0.0000 0.0000 0.0000 dRDit -1.7937*** -2.2803*** -1.7892*** -2.2898*** 0.0010 0.0000 0.0010 0.0000 dRDit+1 2.0771*** 1.6344*** 2.1004*** 1.6303*** 0.0050 0.0000 0.0060 0.0000 Iit 1.6360** 1.4267** 1.6116** 1.4276** 0.0180 0.0200 0.0220 0.0210 dIit -2.6951** -2.7454** -2.6291** -2.7449*** 0.0100 0.0200 0.0100 0.0030 dIit+1 -0.4178 -1.3828** -0.4088 -1.3964** 0.3360 0.0480 0.3450 0.0480 dVit+1 -0.3356** -0.3596*** -0.3363** -0.3600*** 0.0140 0.0000 0.0130 0.0000 Constant 0.7367 0.0225 0.7352 0.0243 0.1560 0.8510 0.1560 0.8400 R-squared 0.6336 0.5665 0.6340 0.5666 Observations 33,822 33,822 33,822 33,822 The table reports the value of accounts payable adjusted by the industry (EXCESSdPAYit, columns 1 and 2) and the industry effect on the value of accounts payable (columns 3 and 4). All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. EXCESSdPAYit is calculated by subtracting the annual industry median change in payables ratio from dPAYit (change in accounts payable from year t to year t-1 to the book value of assets). Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. DINDUSTRYit is a dummy variable which takes value one for manufacturing firms, according to NACE code4, and zero otherwise. Columns 1 and 3 present the estimation of model 1 using the method of Fama and MacBeth (1973). Columns 2 and 4 estimate the same model using clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

4NACE is the European classification of economic activities. NACE is a classification derived from ISIC (International Standard Industrial Classification) to enable international comparability.

Table 9: Sales growth, financial constraints and supplier financing value

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

MacBeth Cluster Fama-

MacBeth Cluster Fama-

MacBeth Cluster

dPAYit 0.1522*** 0.1462*** 0.0079 0.0096 0.0311 0.0971** 0.0040 0.0000 0.7970 0.8400 0.7370 0.0290 dPAYit+1 0.1803 0.0884*** 0.1727 0.0695* 0.1362 0.0725*** 0.1300 0.0000 0.1600 0.0850 0.1440 0.0020 NGROWTHit×dPAYit -0.1515*** -0.1803*** 0.0090 0.0010 NGROWTHit -0.0386* -0.0628*** 0.0690 0.0000 PGROWTHit×DLOWSTDEBTit×dPAY

0.2401** 0.1898*** 0.0200 0.0030 PGROWTHit 0.0384** 0.0605*** 0.0460 0.0000 DLOWSTDEBTit -0.0544** -0.0276*** 0.0250 0.0010 PGROWTHit× DFCOSTit×dPAYit 0.2887* 0.2094*** 0.0600 0.0000 PGROWTHit 0.0315 0.0604*** 0.1940 0.0000 DFCOSTit -0.1360*** -0.1322*** 0.0000 0.0000 dAit 0.0403 -0.0067 0.0319 -0.0051 -0.0124 -0.0737** 0.2290 0.7610 0.2960 0.8860 0.7920 0.0110 dAit+1 0.2482 0.3392*** 0.2610 0.3723*** 0.2643 0.3621*** 0.1700 0.0010 0.1620 0.0010 0.1770 0.0010 Eit 4.3852*** 4.8779*** 4.4656*** 4.9408*** 4.4573*** 4.9436*** 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 dEit 0.4485*** 0.4792*** 0.4617*** 0.4174*** 0.3934*** 0.4218*** 0.0000 0.0000 0.0010 0.0000 0.0000 0.0000 dEit+1 1.8174*** 2.2717*** 1.8529*** 2.2602*** 1.6931*** 2.1999*** 0.0050 0.0000 0.0040 0.0000 0.0090 0.0000 RDit 5.7456*** 6.0914*** 5.2916*** 5.5281*** 5.2435*** 5.5598*** 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 dRDit -1.7609*** -2.2344*** -1.5645*** -2.0028*** -1.5984*** -1.9939*** 0.0000 0.0000 0.0010 0.0000 0.0000 0.0000 dRDit+1 2.1377*** 1.6307*** 2.1044** 1.5592*** 2.0258*** 1.5716*** 0.0080 0.0000 0.0170 0.0000 0.0090 0.0000 Iit 1.6034** 1.3407** -0.0902 0.1380 4.5913*** 4.1723*** 0.0180 0.0330 0.9140 0.8320 0.0000 0.0000 dIit -2.9543*** -3.1970*** -2.7725*** -2.8276*** -2.5836*** -2.9663*** 0.0050 0.0010 0.0070 0.0030 0.0060 0.0010

dIit+1 -0.3158 -1.3903** -1.1720** -2.1213*** -1.3870** -2.2943*** 0.5050 0.0380 0.0160 0.0010 0.0100 0.0000 dVit+1 -0.3307** -0.3577*** -0.3363** -0.3634*** -0.3308** -0.3584*** 0.0150 0.0000 0.0140 0.0000 0.0170 0.0000 Constant 0.7686 0.2131 0.9190 -0.0681 0.7931 0.2853* 0.1500 0.1800 0.1300 0.6200 0.1700 0.0680 Observations 33,135 33,135 27,606 27,606 30,755 30,755 R-squared 0.6382 0.5661 0.6415 0.5676 0.6483 0.5714

Columns 1 to 2 of this table report the effect of sales growth on the value of accounts payable. Columns 3 to 6 report the joint effect of sales growth and financial constraints on the value of accounts payable. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. NGROWTHit is a dummy variable which takes value one whether annual sales growth is lower than or equal to 0, and zero otherwise. PGROWTHit is a dummy variable which takes value one whether annual sales growth is greater than 0, and zero otherwise. DLOWSTDEBTit is a dummy variable which takes value one if the ratio of short-term bank debt to the book value of total assets is lower than the industry median, and zero otherwise. DFCOSTit equals one when the firm’s financial cost exceeds or equals its industry median and zero otherwise. In Columns 1, 3 and 5 the estimation method is Fama and MacBeth (1973), and in Columns 2, 4 and 6 clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

46

Table 10: Sales growth, financial constraints and supplier financing value during the financial crisis

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

MacBeth Cluster Fama-

MacBeth Cluster Fama-

MacBeth Cluster

dPAYit 0.1740 0.1626** 0.0851 0.0895** 0.1187** 0.1349*** 0.2090 0.0130 0.2040 0.0130 0.0320 0.0050 dPAYit+1 0.1363* 0.1438** 0.1214 0.1341* 0.0569 0.0772 0.0590 0.0410 0.2040 0.0900 0.4010 0.2670 NGROWTHit×dPAYit -0.1165 -0.0596 0.4060 0.5930 NGROWTHit -0.0629*** -0.0578*** 0.0000 0.0000 PGROWTHit×DLOWSTDEBTit×dPAYit 0.2069 0.1859* 0.2940 0.0960 PGROWTHit 0.0652*** 0.0586*** 0.0010 0.0000 DLOWSTDEBTit -0.0713*** -0.0717*** 0.0030 0.0000 PGROWTHit× DFCOSTit×dPAYit 0.1728 0.1752 0.2860 0.1000 PGROWTHit 0.0609*** 0.0553*** 0.0000 0.0000 DFCOSTit -0.1470*** -0.1484*** 0.0000 0.0000 dAit -0.0824 -0.0530 -0.0807 -0.0415 -0.1047 -0.0834 0.2680 0.3910 0.3220 0.5640 0.1600 0.1810 dAit+1 0.1852* 0.2070*** 0.2402** 0.2669*** 0.2527** 0.2733*** 0.0630 0.0000 0.0170 0.0000 0.0160 0.0000 Eit 7.1048*** 7.1709*** 7.1884*** 7.2728*** 7.0235*** 7.0944*** 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 dEit 0.3581 0.4142* 0.3246 0.4036* 0.3089 0.3523 0.1320 0.0640 0.1350 0.0650 0.1870 0.1140 dEit+1 3.1613*** 3.2672*** 3.2915** 3.3825*** 3.1309*** 3.2423*** 0.0080 0.0000 0.0120 0.0000 0.0060 0.0000 RDit 13.3477*** 13.6567*** 11.6881*** 11.9839*** 12.4110*** 12.6645*** 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 dRDit -5.8480*** -6.2605*** -5.5933*** -6.0626*** -5.8740*** -6.1085*** 0.0010 0.0000 0.0040 0.0010 0.0000 0.0000 dRDit+1 2.2124* 2.0217* 1.3369 1.0385 1.6493 1.5405* 0.0670 0.0540 0.3110 0.3950 0.1040 0.0980 Iit 5.3612*** 5.4382*** 2.2923*** 2.3442*** 9.6524*** 9.7742*** 0.0000 0.0000 0.0020 0.0020 0.0000 0.0000 dIit -5.1511** -5.5844*** -4.0555* -4.4398*** -4.3418** -4.5307*** 0.0270 0.0000 0.0520 0.0010 0.0390 0.0010 dIit+1 0.8518 0.5590 -0.7957 -1.0085 -0.5862 -0.7781 0.5530 0.6410 0.5710 0.4400 0.6300 0.4400 dVit+1 -0.3791** -0.3859*** -0.3945** -0.3984*** -0.3827** -0.3923*** 0.0200 0.0000 0.0180 0.0000 0.0150 0.0000 Constant 0.8811*** 0.7465*** 0.7603* 0.0947 0.9095*** 0.9932*** 0.0030 0.0000 0.0580 0.6040 0.0020 0.0000 Observations 10,274 10,274 8533 8533 9950 9950 R-squared 0.6287 0.6144 0.6206 0.6036 0.6436 0.6299

Columns 1 to 2 of this table report the effect of sales growth on the value of accounts payable during the crisis. Columns 3 to 6 report the joint effect of sales growth and financial constraints on the value of accounts payable during the crisis. All variables are standardised by the book value of total assets. dXit is the past 1-year change, Xit - Xit-1. Likewise dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. The dependent variable is firm value Vit, defined as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense. NGROWTHit is a dummy variable which takes value one whether annual sales growth is lower than or equal to 0, and zero otherwise. PGROWTHit is a dummy variable which takes value one whether annual sales growth is greater than 0, and zero otherwise. DLOWSTDEBTit is a dummy variable which takes value one if the ratio of short-term bank debt to the book value of total assets is lower than the industry median, and zero otherwise. DFCOSTit equals one when the firm’s financial cost exceeds or equals its industry median and zero otherwise. In Columns 1, 3 and 5 the estimation method is Fama and MacBeth (1973), and in Columns 2, 4 and 6 clustered standard errors (Thompson, 2011). We report p-values under the coefficient estimates. Industry and time dummies are included (unreported). Significant at ***1 percent, **5 percent, and *10 percent.

47

APPENDIX

Descriptive statistics 2008-2014

Variable Obs Mean Perc. 25 Median Perc. 75 Std. Dev.

Vit 10,394 1.2413 0.9197 1.1658 1.4878 0.4709

PAYit 10,394 0.1622 0.0657 0.1331 0.2308 0.1241

dPAYit 10,394 -0.0067 -0.0283 -0.0017 0.0209 0.0596

dPAYit+1 10,394 -0.0010 -0.0241 -0.0006 0.0228 0.0557

dAit 10,394 0.0044 -0.0538 0.0107 0.0734 0.1202

dAit+1 10,394 0.0232 -0.0418 0.0170 0.0821 0.1176

Eit 10,394 0.0595 0.0264 0.0456 0.0790 0.0481

dEit 10,394 -0.0034 -0.0185 -0.0015 0.0116 0.0366

dEit+1 10,394 -0.0004 -0.0142 -0.0001 0.0130 0.0337

RDit 10,394 0.0009 0.0000 0.0000 0.0000 0.0033

dRDit 10,394 0.0001 0.0000 0.0000 0.0000 0.0036

dRDit+1 10,394 0.0001 0.0000 0.0000 0.0000 0.0038

Iit 10,394 0.0113 0.0029 0.0086 0.0171 0.0103

dIit 10,394 -0.0015 -0.0037 -0.0005 0.0013 0.0066

dIit+1 10,394 -0.0001 -0.0022 -0.0001 0.0017 0.0057

dVit+1 10,394 -0.0009 -0.1673 -0.0119 0.1550 0.3223

LTLEVit 9370 0.1026 0.0192 0.0689 0.1559 0.1034

FCOSTit 10,068 0.0350 0.0164 0.0307 0.0471 0.0266

STDEBTit 8631 0.1218 0.0238 0.0893 0.1943 0.1135

CFLOWit 10,186 0.0708 0.0378 0.0607 0.0948 0.0433

The table shows descriptive statistics of the model variables: number of observations (Obs), 25 and 75 percentiles, mean, median, and standard deviation. dXit is past 1-year change, Xit - Xit-1. Likewise, dXit+1 is the change in the level of Xi from year t to year t+1, Xit+1 - Xit. All variables are scaled by the book value of total assets Ait. Vit is the proxy for firm value, which is calculated as the book value of assets minus book value of equity plus a proxy for the market value of equity. PAYit corresponds to accounts payable. Ait is total assets, Eit is earnings before interest and taxes (EBIT), RDit is the increase in intangible assets from year t-1 to year t and Iit is interest expense.