EARNINGS QUALITY AND EUROPEAN COMPANIES’ ACCESS TO CREDIT Word count: 21.718

Sarah Fischinger Student number: 01609236

Supervisor: Prof. dr. Heidi Vander Bauwhede

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics Academic year: 2016 - 2017

EARNINGS QUALITY AND EUROPEAN COMPANIES’ ACCESS TO CREDIT Word count: 21.718

Sarah Fischinger Student number: 01609236

Supervisor: Prof. dr. Heidi Vander Bauwhede

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics Academic year: 2016 - 2017

I

CONFIDENTIALITY AGREEMENT

PERMISSION

I declare that the content of this Master’s Dissertation may be consulted and/or

reproduced, provided that the source is referenced.

Sarah Fischinger

II

PREFACE

This dissertation represents the conclusion of my Master’s degree in Business

Economics with a major in corporate finance at Ghent University. Therefore, I would

like to use this occasion to express my gratitude towards several people who supported

me during my academic career.

First, I want to thank my supervisor, Prof. dr. Heidi Vander Bauwhede, and Sebastiaan

Laloo for the opportunity to contribute to research in a field of my personal interest, for

their continuous guidance and extensive feedback during the interim assessment.

Secondly, a big thank you goes to my friends and fellow students who made my studies

an extraordinarily exciting experience. Finally, I want to acknowledge the unconditional

love and support I received from my family during my studies. You made me a person

I can be proud of and always helped me to reach my goals – I will forever be grateful

to have you in my life.

As Frederick Buechner said, “You can kiss your family and friends good-bye and put

miles between you, but at the same time you carry them with you in your heart, your

mind, your stomach, because you do not just live in a world but a world lives in you.”

Gent, 6 June 2017

III

TABLE OF CONTENTS

List of abbreviations .................................................................................................... V

List of figures and tables ............................................................................................ VI

1. Introduction .......................................................................................................... 1

2. Literature study and hypotheses development ..................................................... 4

2.1. Asymmetric information ................................................................................. 4

2.2. Access to credit ............................................................................................. 6

2.3. Earnings quality ............................................................................................. 7

2.3.1. Informational value of reported earnings ................................................. 8

2.3.2. Earnings management ............................................................................ 9

2.4. Relationship between access to credit and earnings quality ....................... 10

2.4.1. Firm type ............................................................................................... 11

2.4.2. Institutional context ............................................................................... 13

3. Research design ................................................................................................ 15

3.1. Variable measurement ................................................................................ 16

3.1.1. Credit access ........................................................................................ 16

3.1.2. Earnings quality .................................................................................... 17

3.1.3. Institutional context ............................................................................... 19

3.1.4. Control variables ................................................................................... 20

4. Data collection ................................................................................................... 23

5. Empirical results ................................................................................................. 25

5.1. Descriptive statistics .................................................................................... 25

5.2. Earnings quality and firms’ access to credit ................................................. 33

5.3. Moderating effect of firm type ...................................................................... 38

5.4. Moderating effect of institutional context ..................................................... 41

5.5. Robustness check ....................................................................................... 44

IV

6. Conclusion ......................................................................................................... 50

7. Limitations and avenues for future research ...................................................... 52

References ............................................................................................................... VII

Appendices .............................................................................................................. XVI

V

LIST OF ABBREVIATIONS

AC – Access to credit

AQ – Accruals quality

ASCL – Age-Size-Cash Flow-Leverage Index

CA – Current assets

CF – Cash flow

CFO – Cash flow from operations

CL – Current liabilities

DEPR – Depreciation and amortization

EQ – Earnings quality

EU – European Union

FASB – Financial Accounting Standards Board

FRQ – Financial reporting quality

IFRS – International Financial Reporting Standards

KZ – Kaplan and Zingales Index

NPV – Net present value

OLS – Ordinary least squares

PPE – Property, plants and equipment

R&D – Research and development

SA – Size-Age Index

SMEs – Small and medium sized companies

STD – Short term debt

TA – Total accruals

TCA – Total current accruals

UK – United Kingdom

US – United States of America

VIF – Variance inflation factor

WW – Whited and Wu Index

VI

LIST OF FIGURES AND TABLES

Table 1: Variable measurement ................................................................................ 22

Table 2: Sample breakdown by industry ................................................................... 25

Table 3: Sample breakdown by country ................................................................... 26

Table 4: Descriptive statistics ................................................................................... 30

Table 5: Correlation matrix ....................................................................................... 32

Table 6: Access to credit – Fama McBeth (1973) regressions ................................. 35

Table 7: Access to credit – Conditional fixed effects logit model .............................. 37

Table 8: Moderating effect of firm type ..................................................................... 40

Table 9: Moderating effect of institutional context ..................................................... 43

Table 10: Robustness check – SA Index .................................................................. 47

Table 11: Robustness check – country subsample .................................................. 49

1

1. INTRODUCTION

Giving firms the opportunity to exploit positive NPV projects which add shareholder

value has a crucial influence on an economy’s development and well-being. Debt

financing is a commonly used tool for firms to invest in growth opportunities and to fund

their strategic and operational business. Overall access to credit and the reduction of

financial constraints is therefore a worldwide issue to prevent firm underinvestment

(Fazzari, Hubbard, Petersen, Blinder & Poterba, 1988; Hennessy & Whited, 2007).

Firms can face difficulties in accessing external finance due to the informational wedge

between corporate insiders and outside stakeholders. If a creditor cannot accurately

verify that a company has profitable projects available (adverse selection issue) or that

granted loans will not be used for alternative purposes (moral hazard issue), potential

borrowers can be blocked from the credit market due to asymmetric information

(Berger & Udell 2002).

In this context, it is especially interesting to study if high earnings quality, i.e. more

precise information regarding future cash flows, can help companies to mitigate

asymmetric information problems and therefore ease their access to credit. Previous

academic research highlighted the influence of asymmetric information on credit

availability by showing that small, private companies suffer more from financing

obstacles due to informational opaqueness (Berger & Udell, 1998; Hovakimian, Opler

& Titman, 2001) and that relationship banking can help to mitigate these information

asymmetry issues to ease a firm’s credit access (Bharath, Dahiya, Saunders &

Srinivasan, 2007; Berger, 1999; Boot, 2000). Additionally, extant studies about

financial reporting quality (FRQ) indicated the relevance of accurate earnings for loan

contracting by demonstrating that borrowers with high earnings quality benefit from

lower cost of debt (Francis, LaFond, Olsson & Schipper, 2005; Vander Bauwhede, De

Meyere & Van Cauwenberge, 2015) and less stringent debt maturity and collateral

conditions (Bharath, Sunder & Sunder, 2008). On the other hand, evaluating the direct

influence of earnings quality on access to debt financing is a relationship less studied.

García-Teruel, Martínez-Solano & Sánchez-Ballesta (2014) in Spain and Ding, Liu &

Wu (2016) in China have just recently investigated this topic.

2

Based on this previous research, the goal of this study is to identify whether the

reduction of information asymmetries between borrowers and lenders through

accurate accounting information helps to ease European companies’ overall access to

credit.

Additionally, this study aims to shed further light on the association between access to

credit and earnings quality, by identifying potential moderating factors that influence

this predicted relationship. Extant literature established that informational asymmetries

are especially severe for small, private firms (Berger & Udell, 1998; Hovakimian et al.,

2001) and that private companies tend to report on average lower quality financial

statements than public firms (Ball & Shivakumar, 2005). In this regard, it is in the

interest of this study to investigate whether differences in firm type influence the

relationship between access to credit and earnings quality (AC-EQ). As extant

research also highlighted the importance of the quality of legal systems and the

enforceability of laws on the credit supply within an economy (Haselmann, Pistor & Vig,

2010; Moro, Maresch & Ferrando, 2016), this study will further analyse whether

differences in institutional context across Europe have an impact on the AC-EQ relation.

How differences in firm type and institutional context will affect the association between

credit access and earnings quality is a topic which was, to the best of my knowledge,

not yet covered by academic literature. The European setting represents a unique

opportunity to research this relationship for several reasons. On the one hand,

accounting standards within the EU are not determined by the listing status of a

company and thus the same set of accounting rules apply to private firms with limited

liability and publicly listed incorporations. On the other hand, the quality of legal

systems and credit institutions differ substantially across Europe (Burgstahler, Hail &

Leuz, 2006).

This study contributes to academic literature by presenting evidence that the reduction

of creditors’ information risk exposure through high earnings quality, i.e. better

prediction of future repayment capacity, eases European firms’ access to credit. The

results further indicate that the relationship between access to credit and earnings

quality does not vary with firm type. Moreover, this study’s findings specify that

differences in the institutional context of a country have a moderating effect on the

relationship between credit access and earnings quality.

3

High earnings quality is especially able to mitigate financial obstacles for firms which

operate in European countries with weak protection of creditor and property rights.

The economic relevance of these findings is highly significant, as it provides firms with

an instrument to enhance their access to external finance. The evidence presented in

this study can add to current methods used by policymakers within Europe to support

financially constrained firms, which is an especially urgent topic after the credit supply

shocks following the recent financial crisis.

The remainder of this study is organized as follows. In section 2, existing academic

literature is discussed and hypotheses are developed based on this background.

Section 3 describes the research methodology and variable measurement. In section

4, the sampling process and data are presented. Section 5 reports primary empirical

results and robustness tests. Section 6 concludes. In section 7, potential limitations of

this study are identified and avenues for future research are highlighted.

4

2. LITERATURE STUDY AND HYPOTHESES DEVELOPMENT

In this section, existing theory on asymmetric information, credit access and earnings

quality is summarized and extended to account for the potential monitoring effects of

firm type and institutional context on the AC-EQ relationship. Hypotheses are

developed from this theoretical discussion and prior empirical evidence is reviewed.

2.1. Asymmetric information

Financing decisions and their influence on capital structure have been in central focus

of research over the last decades. The underlying basis builds the concept of

Modigliani & Miller (1958), which proposes that firm value is unaffected by debt-equity

choices in a perfect capital market. Capital structure decisions’ relevance increases

however under the appearance of market frictions such as transaction costs and taxes

(Myers, 1984), agency costs and benefits (Jensen & Meckling, 1976), financial distress

and bankruptcy costs (Titman, 1984) or information asymmetry (Myers & Majluf, 1984).

According to the static trade-off theory, firms with higher leverage will be less likely to

obtain external access to debt as potential bankruptcy costs increase (Harris & Raviv,

1991). Traditional corporate finance models therefore suggest that firms choose

optimal capital structures by trading off tax and agency benefits of debt financing

against financial distress costs (Hovakimian et al., 2001). Pecking order theory on the

other hand suggests that the cost of finance increases with information asymmetry.

This leaves internal funding as the preferred financing method, valuing debt over equity

if external finance is required (Donaldson, 1961).

Under the influence of the above stated market frictions, financing decisions can

occasionally not be exercised as planned and firms can face the problem of financial

constraints. A company is financially constrained if it cannot find sufficient external

finance to realize its value adding investments (Behr, Norden & Noth, 2013; Denis &

Sibilkov, 2010). The lack of financial resources to fund these profitable projects and

explore investment opportunities can lead to significant adverse effects on companies’

growth on a worldwide basis (Ayyagari, Demirgüç-Kunt & Maksimovic, 2008).

5

Prior literature suggests that credit rationing appears due to asymmetric information

problems (Greenwald, Stiglitz & Weiss, 1984; Myers & Majluf, 1984;) and agency

problems (Grossman & Hart, 1982; Hart & Moore, 1994; Jensen, 1986; Jensen &

Meckling, 1976).

According to one of most often used principles in economics, market equilibrium is

established when demand and supply meet. This equilibrium condition is held through

clearing price and quantity in a market. However, credit rationing is an existing

phenomenon in which a disequilibrium occurs. Stiglitz & Weiss (1981) explain this

circumstance by showing that the interest rate a bank charges affects the riskiness of

a loan pool through imperfect information problems. The core problem lies in the fact

that creditors cannot observe the risk type of a firm, which is a classical adverse

selection issue. Companies with risky projects generally have a low chance of success,

i.e. repayment of the loan, but if they are successful then their return is very high. Firms

with safe projects on the other hand have high chances of success, i.e. repayment of

the loan, but their returns in case of success are not extraordinarily high. As businesses

will only enter in credit contracts if the cost of debt is below the required return level of

their investment project, safe firms will not accept high interest rates. This condition

will lead to adverse selection from a certain interest rate onwards, where safe firms

drop out of the loan portfolio. At this point, banks’ profits will no longer increase with

the interest rate and it is thus rational for credit institutions to not set the bank optimal

interest rate above a specific level. Although firms with a positive demand for external

finance at a higher rate exist, they are denied access to credit. This leads to a credit

rationed market under the impact of adverse selection.

Holstroem & Tirole (1997) further demonstrated that financial obstacles can also occur

due to moral hazard. The problem is defined by the fact that firm success depends on

the effort an entrepreneur is willing to invest in his business. However, a borrower may

promise to put high effort into his business, but could change his behaviour after the

loan was granted. As entrepreneurial effort cannot be observed by credit institutions,

lenders require collateral to make sure that the entrepreneur covers part of the risk

himself and thus will be motivated to exert high effort. However, some high effort

entrepreneurs will not be able to meet these collateral requirements and will therefore

be denied the access to credit under the influence of moral hazard.

6

Following these concepts, information asymmetries represent a main source of

external financing obstacles. Adverse selection and moral hazard lead to rationing in

the credit market, which consequently leaves a group of companies financially

constrained.

2.2. Access to credit

Bank loans represent one of the major sources of financing for companies across the

globe, whereby firms especially rely on external finance to execute their investment

and innovation activities (Harhoff & Körting, 1998). Following financial constraints

literature, major determinants of access to external finance are firm size, age, leverage,

dividends, sales growth and cash flow (Mulier, Schoors & Merlevede, 2016; Lamont,

Polk & Saaá-Requejo, 2001; Whited & Wu, 2006). However, not only firm level

determinants are important to highlight, but also country level attributes can impact

credit availability. Business and credit cycle as well as the quality of financial

institutions and law enforcement need to be considered when analysing loan

accessibility (Bai, Lu & Tao, 2006; Qian & Strahan, 2007; Santos & Winton, 2008).

Especially a company’s cash flow is of high interest for creditors, as it represents a

firms’ ability to fulfil future repayment and interest obligations. The high importance of

repayment capacity in the loan contracting process of credit institutions was already

highlighted by previous academic research (Barth, Cram & Nelson, 2001; Ding et al.,

2016; Minnis, 2011). Lenders need to assess the default risk of a potential loan

candidate and are therefore interested in evaluating a company’s future cash flows

(Vander Bauwhede et al., 2015). External debt providers are assessing the information

risk of a company by examining how precisely future cash flows can be estimated.

Firms that suffer from asymmetric information problems consequently inherit higher

borrower risk and will thus have a more restricted access to credit (García-Teruel et

al., 2014).

Anticipating the issue that asymmetric information and moral hazard lead to credit

constraints for some companies within an economy, it is necessary to mention that

financial constraints negatively impact companies’ development. Insufficient access to

credit restricts firm growth and results in reduced R&D spending (Ayyagari et al., 2008;

Harhoff & Körting, 1998).

7

The high practical significance of financial constraints was demonstrated by Campello,

Graham & Harvey (2010), who reported that around 86% of chief financial officers

questioned in their sample reported that they had to forgo profitable investment

opportunities during the financial crisis in 2008. Problematic is that more than half of

the executive managers had to cancel or postpone their projects and had to make deep

cuts in tech spending and employment budgets. The relative importance of financial

constraints in an economy was further demonstrated by Chodorow-Reich (2014), who

showed that lender health and financial constraints have significant negative

implications on employment levels. This study highlighted that companies with

connections to unhealthy creditors prior to the financial crisis had a lower chance to

obtain debt financing after Lehman Brothers went bankrupt. Those firms also faced

higher interest rates and needed to decrease their employment rates more than

customers of healthier banks. Musso & Schiavo (2008) further identified, for French

manufacturing firms, that more financially constrained companies are less likely to

survive and consequently have to exit the market.

To avoid financial constraints and their consequences, it is important for companies to

understand the underlying determinants of access to credit and find possibilities to

reduce information asymmetry levels with their lenders.

2.3. Earnings quality

The Statement of Financial Accounting Concepts No. 1 (SFAC No. 1) by the Financial

Accounting Standards Board (FASB) defines earnings quality as follows:

“Higher quality earnings provide more information about the features of a firm’s

financial performance that are relevant to a specific decision made by a specific

decision-maker” (Dechow, Ge, & Schrand, 2010, p. 344).

If the specific decision-maker is a credit institution facing the decision to enter a loan

contract with a third party, earnings quality can represent a crucial information resource.

Therefore, the next sections will discuss the informational value of reported earnings

and reasons why accounting data can be of low quality.

8

2.3.1. Informational value of reported earnings

Reported earning numbers contain vital information for institutional lenders in order to

predict the future repayment capacity of an enterprise. This was shown by several

previous scholars (Barth et al., 2001; Dechow 1994; Dechow, Kothari & Watts, 1998),

which suggest that current earnings are a better indicator for future cash flows than

realized cash flows of the accounting period. This relationship especially holds when

firms face crucial changes in working capital, investment and finance decisions. Under

such circumstances current cash flows are especially faced with matching and timing

issues and can accordingly predict future performance less accurately (Dechow, 1994).

Accruals accounting is used to mitigate these problems by influencing the timing of

cash flow recognition in earnings. Following this evidence, current earnings are a

preferred indicator for future firm performance and predict future operating cash flows

more precisely. This can have important implications for a company’s loan applications,

as future cash flows will determine the repayment capacity a firm can offer (Ding et al.,

2016; Vander Bauwhede et al., 2015). High quality accounting statements can help to

reduce information asymmetry problems, as more precise earnings give lenders the

opportunity to evaluate repayment capacity more accurately (García-Teruel et al.,

2014). Consequently, earnings information is considered an important input factor in

a credit institution’s risk assessment process. Several previous studies already

investigated the potential benefits of high earnings quality. Vander Bauwhede et al.

(2015) argued that high earnings quality can reduce SMEs’ exposure to information

asymmetries and found evidence that their cost of debt can be decreased by reporting

low accruals-cash flow discrepancies. Bharath et al. (2008) highlighted that both

interest rates and non-price related terms of loan contracts, like maturity or collateral

obligations, are more favourable for borrowers with enhanced financial reporting

quality.

The importance of earnings statements for creditors can further be demonstrated by

the research of Ball, Robin & Sadka (2008), who found evidence that accounting

information is more heavily oriented on satisfying debt markets’ reporting demands in

contrast to equity markets. Additional support for the value content of earnings figures

can be obtained from looking at an investor’s point of view. Francis, Schipper & Vincent

(2003) and Liu, Nissim & Thomas (2002) provided empirical evidence that investors

value earnings information more than other performance measures.

9

This is in line with the conclusions of Graham, Harvey & Rajgopal (2005), who found

that managers also perceive earnings as the key information provider for investors and

external analysts.

2.3.2. Earnings management

Current earnings are only a good indicator for future cash flows and therefore a firm’s

repayment capacity if the accounting figures are representing the underlying business

reality of a company (Vander Bauwhede et al., 2015). Consequently, only firms with

high earnings quality will be able to reduce information asymmetries with their lenders.

The quality of financial statements is influenced by stakeholders’ information demand

for contractual agreements and corporate insiders’ opportunities to perform earnings

management. Managers of public companies have incentives to manage earnings to

fulfil analyst expectations (Bartov et al., 2002) and to avoid the reporting of losses or

earnings decreases (Burgstahler & Dichev, 1997). Additional motivation to adjust

reported financials is given when management compensation is share-based or parts

of salaries are paid in stocks (Cheng & Warfield, 2005). Private firms’ managers, on

the other hand, can find incentives for earnings management to avoid breaking debt

covenants or to increase bonuses based on reported earnings (Givoly, Hayn & Katz,

2010). Another interesting field in literature proposes that creditors, to some extent,

demand earnings management from companies, in the form of reporting smooth

earnings. In their study of European private firms, Gassen & Fülbier (2015)

demonstrated that companies with higher leverage tend to report smoother earnings

and that this relationship is even more pronounced when debt-contracting works

inefficiently on the country level.

Since accounting standards leave room for flexibility and interpretation, corporate

insiders can use earnings management to follow their personal interests. However,

personal judgement of managers and measurement assumptions can also be used to

improve the financial statements’ reflection of the underlying business reality

(Burgstahler et al., 2006). Vander Bauwhede et al. (2015) stated that overall earnings

quality represents to which degree financial reporting information is free from noise

introduced by accounting standards, measurement assumptions and earnings

management.

10

Former inquiries follow the assumption that increasing transparency mitigates firms’

information risk and can therefore help to reduce credit market constraints (Ding et al.,

2016; García-Teruel et al., 2014). In this context, high earnings quality can help to

reduce asymmetric information between lending and borrowing party in a credit

contract. More accurate earnings information enables lenders to make more precise

predictions of a company’s future cash flows, which determine its loan repayment

capacity. Likewise, higher quality earnings enable creditors to forecast future cash

flows in a more profound manner and thus can assess a firm’s default probability with

less uncertainty. This leads to the conclusion that high quality financial reports ease a

credit institution’s risk assessment process and can therefore increase a firm’s

likelihood of obtaining a loan. Therefore, it can be expected to find evidence for the

following hypothesis:

H1: High financial reporting quality is positively associated with European

companies’ access to credit

2.4. Relationship between access to credit and earnings quality

Previous academic research has already documented that earnings quality can

influence a company’s access to credit. García-Teruel et al. (2014) found evidence to

support this argument for private Spanish firms. Ding et al. (2016) confirm these results

for Chinese privately held companies. However, these studies are restricted by the fact

that they focus only on private firms and limit their research to one specific country.

The goal of this study is to investigate the relationship between credit access and

earnings quality even further. As brought forward by Hope (2015), it is interesting to

include public and private firms in the same study to compare both groups. Specifically,

this study wants to examine whether differences in firm type will influence the AC-EQ

relationship. Additionally, the European setting presents an interesting opportunity to

analyse whether the association between financial reporting quality and credit

availability differs with regard to institutional context. In this light, the following two

subsections will discuss previous literature in these fields and accordingly develop

hypotheses.

11

2.4.1. Firm type

Previous academic research in accounting focused extensively on publicly disclosed

financial reports and its informational quality (Ball & Shivakumar, 2005; Francis,

LaFond, Olsson & Schipper, 2004; Givoly et al., 2010). Less is known about the

financial reporting quality of private firms, because missing disclosure regulations

make it harder for outsiders to obtain relevant data. Ball & Shivakumar (2005) also

argued that missing market based proxies for earnings quality like the association with

share prices or returns are reasons for lacking research in this area. That extant

literature on unlisted companies is rather limited due to data accessibility and not

because of their inferior economic importance was brought forward by Hope (2015). In

fact, private firms contribute substantially to economic growth around the world. Their

workforce is about four times greater than the one of public companies, they generate

four times higher total revenues and hold around twice as many assets as listed firms

(Berzins, Bøhren & Rydland, 2008). These findings are representative for the majority

of countries worldwide (Hope, 2015). Furthermore, investigating private firms’ access

to credit is especially important, because their survivorship in difficult times like the

recent financial crisis can strongly depend on external funding possibilities. Evidence

regarding this topic was presented by Gertler & Gilchrist (1994), who showed that

smaller private firms are forced to cut back their economic activities faster and more

severely than larger firms when confronted with macroeconomic shocks. The

importance of bank debt during a crisis period was also discussed by Bolton & Freixas

(2000), who highlighted that companies rely heavily on investment funding by banks,

because these lenders help them to overcome times of financial distress.

Extant literature suggests that the degree to which firms are impacted by asymmetric

information differs with their characteristics (Berger & Udell, 1998; Hovakimian et al.,

2001). Large companies are found to attract outside financing easier compared to

SMEs, as they are less informationally opaque, their cash flows tend to be less volatile

and their bankruptcy costs are lower (Hovakimian et al., 2001). These findings are in

line with previous literature within the financial constraints domain, which presented

evidence that private firms’ access to external finance is more constrained than it is for

public firms (Moro, Fink & Kautonen, 2014). Berger & Udell (1998) were one of the first

to recognize that a liability of being comparatively small and privately held exists, which

negatively impacts private firms’ chances to receive access to credit.

12

Further, it is more likely for private firms that difficulties in the lender-borrower

relationship arise due to agency conflicts and the lack of tangible assets, collateral or

human capital (Berger, Klapper & Udell, 2001).

That a shortage of informational resources available to credit institutions represents a

problem for privately held firms was also demonstrated by Collis, Jarvis & Skerratt

(2004). They observed that small UK companies are willing to voluntarily audit their

financial reports in order to improve information quality and establish beneficial agency

relationships with their lenders. Allee & Yohn (2009) found similar evidence by showing

that privately held firms in the US can improve their likelihood of obtaining external

finance by having audited financial reports. Similarly, they found that private

companies with accrual based financial statements can borrow at more favourable

conditions, leading to lower overall cost of debt financing. In this context, high reporting

quality represents an avenue for private firms to increase their access to external debt.

However, a study by Ball & Shivakumar (2005), conducted in the United Kingdom,

found that the overall financial reporting quality of private firms is on average lower

than it is for public firms. In their setting, quality was defined as the usefulness of the

report information to stakeholders like investors, analysts, mangers or financial

institutions. Therefore, it can be argued that private firms have an ex-ante

disadvantage compared to public firms regarding credit access due to their lower

earnings quality, resulting in higher informational opaqueness.

Private firms do not have to disclose their financial data or provide investors with

annual reports, resulting in difficulties for banks to estimate their future cash flows and

thus repayment capacity. If banks face the issue of lower quality earnings for private

firms, those companies within this classification that can offer high financial reporting

quality should have an informational advantage in loan negotiations. Institutional

lenders have in general less information available about private firms compared to

public ones, since they are less regulated and observed by authorities, financial

analysts, rating agencies or the media (Ding et al., 2016). Since information resources

regarding private firms are more scarce than those of public firms, banks will have to

rely more heavily on financial reporting information in that case. As high earnings

quality eases lenders assessment of future repayment capacity and firm risk, accurate

accounting information from informationally opaque private firms should be especially

valuable for creditors.

13

Following this concept, high earnings quality can be a more important tool to reduce

asymmetric information issues and therefore obtaining credit for private firms. This

argumentation can be summarized in the following hypothesis:

H2: The relationship between high earnings quality and access to credit is

more pronounced for private firms

2.4.2. Institutional context

Legal and institutional differences determine the conditions and availability of bank

credit across the world. Next to the firm specific factors that influence loan decisions,

credit institutions must also anticipate the question whether their claims are protected

in the case of bankruptcy. The degree to which their rights are protected will influence

the type and conditions of loans creditors are willing to grant to companies. In countries

with high quality legal systems and strong creditor protection rights, banks can manage

borrower risk more effectively and have a higher likelihood to recover the loan amount

in a default event (BAE & Goyal, 2009). Consequently, banks will offer increased

lending ex-ante and enhance firms access to credit (La Porta, Lopez-de-Silanes,

Shleifer & Vishny, 1997).

If a firm’s credit quality is declining, it is necessary for a lender to reassess the agreed

interest rates, raise collateral obligations or reduce loan maturities. The expenses

associated with these activities can be described as re-contracting costs, which

increase when lender rights are poorly enforced. Due to the fact that it will take longer

to repossess collateral in case of bankruptcy if property laws are characterized by poor

enforcement, creditors recovery rates will be substantially lower (BAE & Goyal, 2009).

A high degree of creditor protection will consequently lead to an increase in loan supply,

leaving less firms financially constrained (Haselmann et al., 2010). This can be

especially interesting for private companies, as Haselmann & Wachtel (2010) indicated.

Their study demonstrated that in countries with higher quality legal systems and

regulations, a higher proportion of total bank lending is allocated to firms with high

levels of information asymmetries such as small and medium sized enterprises. Not

only the availability of credit is enhanced through strong legal systems, but also the

conditions under which loans are granted.

14

Qian & Strahan (2007) found evidence that in countries with stronger developed

creditor protection rights, bank debt has longer maturities and will be available at lower

interest rates. Their results indicated that firms who operate in countries with stronger

creditor protection rights can also benefit from more favourable loan terms. This leads

to the assumption that not only the quality of creditor protection rights, but also the

overall enforceability of credit contracts will impact banks’ loan contracting decisions.

This is in line with the findings of Moro et al. (2016), who state that the probability of

accessing credit is up to 40% higher in countries with stronger legal systems. Following

this concept, it can be argued that in countries with poor creditor protection rights

companies which face high information asymmetry degrees will more likely be

financially constrained.

Ding et al. (2016) showed in their paper that high financial reporting quality has a

different influence on financial constraints across provinces in China with different

levels of economic development. This concept can also be applied to Europe, where

credit institutions in countries with weaker creditor and property protection rights might

rely more heavily on accurate accounting information to mitigate adverse selection and

moral hazard issues. It can be argued that earnings quality matters more in countries

where creditor claims are less protected, because information asymmetry problems

are more severe for financial institutions who face little legal protection and therefore

higher losses given default. In this case, banks will attach higher value to accurate

financial reporting information to lower their risk exposure. Consequently, firms who

are able to reduce information asymmetry problems through high earnings quality

should be able to benefit from better credit access. This leads to the formulation of the

following hypothesis:

H3: The relationship between high earnings quality and access to credit is more

pronounced in European countries with weak legal systems

15

3. RESEARCH DESIGN

In order to test whether the earnings quality of European public and private companies

is related to their overall access to credit, regression (1) is estimated. In this equation,

i and t represent firms and years. Where appropriate, variables are scaled by total

assets for standardization (Vanacker & Manigart, 2010). Furthermore, they are

calculated using book values (Meuleman & De Maeseneire, 2012). An overview of the

variable definitions can be found in table 1.

(1) Credit Accessi,t = β0 + β1 * AQi,t + β2 * Firm Typei,t + β3 * Sizei,t + β4 * Agei,t + β5 *

Liquidityi,t + β6 * Tangibilityi,t + β7 * Sales Growthi,t + β8 * Performancei,t + β9 * Leveragei,t

+ βindustry * Industry Control + βcountry * Country Control + εi,t

Additionally, the goal of this study is to identify whether the relationship between

earnings quality and credit access differs across public and private European

companies. The European setting is interesting for a comparison between public and

private firms, because accounting standards within the European Union are not

determined by the listing status of a company. The regulations for both types of

companies are similar within each country, because the same set of accounting rules

apply to private firms with limited liability and public listed incorporations (Burgstahler

et al., 2006). To investigate the moderating effect of firm type on the AC-EQ association,

an interaction term between the variables AQ and Firm Type is included in the model

specification. Consequently, regression (2) is exercised as indicated below.

(2) Credit Accessi,t = β0 + β1 * AQi,t + β2 * Firm Typei,t + β3 * AQ*Firm Typei,t + β4 * Sizei,t +

β5 * Agei,t + β6 * Liquidityi,t + β7 * Tangibilityi,t + β8 * Sales Growthi,t + β9 * Performancei,t +

β10 * Leveragei,t + βindustry * Industry Control + βcountry * Country Control + εi,t

Hypothesis 3 tries to test whether differences in creditor and property rights protection

have a moderating effect on earnings quality’s relationship with access to credit. In this

context, it should be stated that European institutions favour EU wide accounting

harmonization and already support this progress for many years (Van Hulle, 2004).

Nevertheless, especially the quality of legal systems and credit institutions differs

substantially across Europe (Burgstahler et al., 2006). This presents an opportunity to

exploit the impact of such differences, by estimating regression (3), (4) and (5).

16

Interaction terms between the earnings quality metric (AQ) and the proxies for the

degree of legal protection of creditors and property rights as well as the joint effects of

both are added to the initial model specification (1). In line with extant research about

the impact of institutional context on earnings management and earnings smoothness

within Europe (Burgstahler et al., 2006; Gassen & Fülbier, 2015), industry controls are

included in the regression specifications.

(3) Credit Accessi,t = β0 + β1 * AQi,t + β2 * Creditor Protectioni,t + β3 * AQ*Creditor

Protectioni,t + β4 * Firm Typei,t + β5 * Sizei,t + β6 * Agei,t + β7 * Liquidityi,t + β8 * Tangibilityi,t

+ β9 * Sales Growthi,t + β10 * Performancei,t + β11 * Leveragei,t + βindustry * Industry Control

+ εi,t

(4) Credit Accessi,t = β0 + β1 * AQi,t + β2 * Property Rightsi,t + β3 * AQ*Property Rightsi,t +

β4 * Firm Typei,t + β5 * Sizei,t + β6 * Agei,t + β7 * Liquidityi,t + β8 * Tangibilityi,t + β9 * Sales

Growthi,t + β10 * Performancei,t + β11 * Leveragei,t + βindustry * Industry Control + εi,t

(5) Credit Accessi,t = β0 + β1 * AQi,t + β2 * Institutional Contexti,t + β3 * AQ*Institutional

Contexti,t + β4 * Firm Typei,t + β5 * Sizei,t + β6 * Agei,t + β7 * Liquidityi,t + β8 * Tangibilityi,t +

β9 * Sales Growthi,t + β10 * Performancei,t + β11 * Leveragei,t + βindustry * Industry Control +

εi,t

All variables get winsorized at 1% and 99% to control for outlier effects. This is not

applied to the variable Age, as there is little doubt about the accuracy of firm

incorporation dates (Vander Bauwhede et al., 2015).

3.1. Variable measurement

3.1.1. Credit access

The dependent variable in this study is access to credit. Previous academic research

on credit access often relies on direct information obtained from mangers or business

owners through surveys (Bai et al., 2006; Hersch, Kemme & Netter, 1997). Since the

goal of this study is to obtain empirical evidence for private and public firms across

Europe, a quantitative measure of credit availability is preferred.

17

Previous studies that investigated the relationship between earnings attributes and

accessibility of credit use the total amount of debt a firm holds in proportion to their

total assets (Ding et al., 2016; García-Teruel et al., 2014). In line with this approach in

academic research, this variable measurement will be used for Credit Access1, the

first proxy for credit access.

A second proxy for access to credit, Credit Access2, is introduced by following the

suggested method of Meuleman & De Maeseneire (2012). If a company shows a net

increase in outstanding financial debt which exceeds 5% of total assets, a dummy

variable is coded as 1 and 0 otherwise. The 5% benchmark is introduced following

prior literature, to assure that the study focuses on relatively meaningful funding events

(De Haan & Hinloopen, 2003; Meuleman & De Maeseneire, 2012; Vanacker &

Manigart, 2007).

3.1.2. Earnings quality

The independent variable used in this study is earnings quality. Since real company

performance is unobservable, different proxies for earnings quality have been

developed by prior research (see Dechow, Ge & Schrand, 2010 for a literature

overview). Francis et al. (2004) argued that comparing all accounting and market

based earnings quality (EQ) measurements, the effects of EQ can be best identified

by using accrual quality (AQ). The usefulness of this methodology was shown by

various studies (Francis et al., 2004; Francis et al., 2005; Vander Bauwhede et al.,

2015) and has become the accepted model in accounting research to capture

discretion according to Dechow et al. (2010).

Following these arguments, this study uses the accrual quality approach, developed

by Dechow & Dichev (2002) to measure the variable AQ. The concept of AQ shows

the degree to which accruals are error-prone with regard to measurement and

estimations. Residuals from accrual-based models show the components of accruals

that are due to management discretion or estimation errors, which reduce the

adequacy of the information content financial statements can offer. The original model

by Dechow & Dichev (2002) explains total current accruals (TCA) as a function of

current, past and future operational cash flows.

18

On this basis, Francis et al. (2005) further developed the model by adding the change

in sales revenues and gross value of PPE (property, plant and equipment). This

adjustment was made to include expectations about current accruals into the

estimating model, as proposed by McNichols (2002). The variables are scaled by total

assets to guarantee comparability across firm size and to reduce possible

heteroskedasticity concerns (Vanacker & Manigart, 2010).

(6) TCAi,t = βo + β1 * CFOi,t-1 + β2 * CFOi,t + β3 * CFOi,t+1 + β4 * ΔSalesi,t +

β5 * PPEi,t + ɛi,t

Equation (6) is estimated for each industry group per country in year t. The annual

cross-sectional estimations of this model will generate firm- and year-specific residuals,

which form the desired accruals quality metric. Accordingly, the variable AQ shows the

degree to which accruals and earnings are free from any form of error and hence can

be used to map future cash flows in earnings (Dechow & Dichev, 2002; Francis et al.,

2005). The unexplained residual of this model (ɛi,t) represents accruals that are not

related to prior, current or future cash flows, changes in turnover or property, plant and

equipment. Consequently, the larger the absolute value of remaining residuals, the

more likely it is that higher accrual estimation errors are involved (Dechow & Dichev

2002; Francis et al., 2005). Thus, taking the additive inverse of the unexplained portion

of the variation in TCA will ease the interpretation of the variable AQ, leading to the

result that higher residuals represent higher earnings quality (Vander Bauwhede et al.

2015).

The letters i and t within the regression equation specify firms and years. TCAi,t

represent total current accruals as the change in non-cash working capital from year t-

1 to year t. CFOi,t-1, CFOi,t and CFOi,t+1 are the cash flows from operations realized in

year t-1, t and t+1, respectively. ΔSalesi,t is the change in turnover in year t relative to

year t–1. PPEi,t represents the value of property, plant and equipment.

In line with prior literature, total current accruals are calculated as TCAi,t = (ΔCAi,t -

ΔCashi,t) - (ΔCLi,t - ΔSTDi,t), where ΔCAi,t is the change in current assets, ΔCashi,t is

the change in cash and cash equivalents, ΔCLi,t is the change in current liabilities and

ΔSTDi,t is the change in short term financial debt or current portion of long term debt

(Francis et al., 2005).

19

Following this concept, total current accruals are calculated as the change in non-cash

current assets minus the change in current non-interest bearing liabilities.

The explanatory variable cash flow from operations (CFO) in this model is also

calculated in line with previous studies (Francis et al., 2005; García-Teruel et al., 2014),

by subtracting total accruals from bottom line net income. Following Francis et al.

(2005), total accruals are calculated as TAi,t = (ΔCAi,t - ΔCLi,t - ΔCashi,t + ΔSTDi,t -

DEPRi,t), where ΔCAi,t is firm i’s change in current assets between year t-1 and t, ΔCLi,t

is firm i’s change in current liabilities between year t-1 and t, ΔCashi,t is firm i’s change

in cash and cash equivalents between year t-1 and t, ΔSTDi,t is firm i’s change in short

term debt between year t-1 and t, and DEPRi,t is firm i’s depreciation and amortization

expense in year t. As used by Chen et al. (2011), total accruals are measured as the

change in non-cash current assets minus the change in current non-interest bearing

liabilities minus depreciation and amortization expense for firm i in year t.

Prior research suggests that there are two ways in which accruals quality from the

above stated equation (6) can be calculated. This involves using the standard deviation

of residuals on the one hand and calculating the absolute value of residuals on the

other hand (Dechow & Dichev, 2002). The first method is generally preferred, because

consistently large residuals do not necessarily represent low earnings quality (Vander

Bauwhede et al., 2015). Nevertheless, scholars like Vander Bauwhede et al. (2015)

and Francis et al. (2004) show that their results are robust to using both methods.

Using the standard deviation approach involves crucial data requirements and its

computationally intensiveness would reduce the sample of this study to a three years’

period (2013-2015). Therefore, the absolute value of residuals approach is preferred

to obtain the AQ variable.

3.1.3. Institutional context

To assess whether the institutional context of a country influences the relationship

between earnings quality and access to credit, three additional variables are

introduced. Firstly, the quality of creditor protection rights can be proxied by using the

‘Strength of Legal Rights Index’ as indicated by the World Bank. This index represents

the level to which collateral and bankruptcy laws protect the rights of the parties

involved in a credit contract and will thus represent the Creditor Protection variable.

20

Ranging from 0 to 12, the World Bank index gives higher scores to countries with

greater legal quality and protection mechanisms (Moro et al., 2016). The databank is

available online on the World Bank’s website. To ease the interpretation, the ‘Strenghts

of Legal Rights Index’ is multiplied by (-1), so that a high score equals weak creditor

protection rights.

Secondly, as suggested by BAE & Goyal (2009), the ‘Property Rights Index’ provided

by the Heritage Foundation will be used to proxy the ability of individuals to accumulate

private property and the degree to which property rights are protected by the legal

environment within a country. Further, it includes how efficiently those laws are

enforced by the government. It will therefore be used to measure the variable Property

Rights. A high score indicates that the legal protection of property within a country is

certain. Thus, the more certain the legal protection of property, the higher a country’s

score. The metric is part of the Index of Economic Freedom, which is calculated by the

Heritage Foundation/Wall Street Journal since 1995. The data can be obtained online

via the Heritage Foundation’s homepage. Again, to simplify the result interpretation,

the ‘Property Rights Index’ is multiplied by (-1), so that higher index scores represent

weaker property right protection laws in a country.

Lastly, a total score for the overall institutional context within a country is introduced by

combining both ‘Strengths of legal rights Index’ and ‘Property rights Index’. The

importance of this metric was demonstrated by BAE & Goyal (2009), who argued that

the laws and enforcement of property and creditor right protection can have joint effects

on bank lending. Thus, the combined variable Institutional Context is introduced, which

embodies creditor protection and property rights proxies.

3.1.4. Control variables

To control for other factors that can have an impact on the availability of credit for a

company, a number of control variables are included in different model specifications.

First, we control for the need of external credit by measuring the amount of internal

finance available within the firm. This is necessary, because following the pecking

order theory (Donaldson, 1961) internal funds are the preferred financing method over

bank debt. Thus, liquidity (cash & CE/total assets) is included as a control variable (De

Haan & Hinloopen, 2003).

21

The higher the liquidity ratio, the less external debt firms will need to fund their

operations since more cash is available internally. Further, financial performance is

included by coding 1 if a company’s net income is positive and 0 otherwise, predicting

that high performing firms have more internal funds available to finance investments

(Ding et al., 2016). However, high performing firms could generate higher cash flows,

which are an important determinant of a company’s repayment capacity (Mulier et al.,

2016). As high repayment capacity will increase a firm’s likelihood to obtain external

finance, the relationship between performance and credit access can arguably also be

positive. Another factor that needs to be considered is firm leverage (total debt/total

assets), as highly indebted firms are less likely to have access to additional credit due

to increasing bankruptcy costs (Harris & Raviv, 1991). Additionally, to include

information asymmetry issues, firm size (ln of total assets), age (number of years since

inception) and firm type (dummy variable takes 1 for private firms – 0 otherwise) are

included as control variables, since larger and well-established firms tend to be less

informationally opaque and enjoy easier access to credit (Hovakimian et al., 2001;

Vander Bauwhede et al., 2015). More mature firms might also be able to profit from

closer bank-borrower relationships, which can support the availability of credit

(Niskanen & Niskanen, 2010). Asset tangibility (total fixed assets/total assets) is also

included in the model, because firms which can offer more collateral as securities in

loan agreements face less credit constraints (Vander Bauwhede et al., 2015). High

growth firms are often classified as riskier and inherit increased agency problems,

therefore sales growth is used as an additional control variable (García-Teruel,

Martínez-Solano & Sánchez-Ballesta, 2010). To control for industry and country effects,

n-1 dummy variables are introduced to the model. A summary of all variable definitions

is presented in table 1.

22

Table 1 Variable measurement

Variable name Variable definition Exp. sign

Dependent variables

Credit Access1 Total debt / total assets

Credit Access2 Dummy variable is coded as 1 if increase in total debt

exceeds 5% of total assets – 0 otherwise

Independent variables

Earnings Quality (EQ) Accruals quality (AQ), measured as the absolute value of

residuals obtained through the modified DD model * (-1)

+

Firm Type Dummy variable is coded as 1 if firm type is private – 0

otherwise

-

Creditor Protection Strength of legal rights index provided by the World Bank

* (-1)

-

Property Rights Property rights index provided by the Heritage

Foundation * (-1)

-

Institutional Context (Strengths of legal rights index + property rights index) *

(-1)

-

Control variables

Liquidity Cash & cash equivalents / total assets -

Size ln(total assets) +

Age number of years since inception +

Tangibility Total fixed assets / total assets +

Sales Growth (Salest – salest-1) / salest-1 -

Performance Dummy variable is coded as 1 if net income is positive –

0 otherwise

+/-

Leverage Total debt / total assets -

Industry control Dummy variable is coded as 1 if the firm belongs to the

NACE two-digit code industry of interest – 0 otherwise

?

Country control Dummy variable is coded as 1 if the firm is registered in

the country of interest – 0 otherwise

?

Table 1: Variable measurement

23

4. DATA COLLECTION

This study is based on financial information of private and public firms obtained from

the April 2017 version of the Orbis Europe database, provided by Bureau van Dijk. This

resource contains a multitude of financial statements from various national information

collectors across Europe. One of the main benefits of using this database is the

availability of data for privately held corporations, which allows a comparison with this

economically crucial set of firms that is relatively understudied by academic literature.

The European setting gives a unique possibility to investigate the relationship between

earnings quality and credit availability. Generally, the accounting regulation applied for

companies within the European Union is not determined by their listing status.

Therefore, private firms with limited liability are largely exposed to the same accounting

standards as publicly traded companies (Burgstahler et al., 2006). Thus, accounting

requirements can be held constant across the two firm characteristics within a specific

country. Additionally, there are essential differences in the quality of legal systems

across the EU (Burgstahler et al., 2006), which supports the desired investigation of

institutional context’s impact on the AC-EQ relationship.

The sample consists of the EU15 countries, which includes all members of the

European Union prior to the accession of ten candidate countries during 2004. This

includes Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,

Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. The

focus on EU15 member states is frequently seen in academic literature (Barros,

Ferreira & Williams, 2007; Berger, 2007; Valverde, Humphrey & del Paso, 2007). All

firm-year observations between 2007–2016 of public limited companies and private

limited companies (or their national equivalents) that fulfil the data requirements for

variable measurement are included in the sample (Burgstahler et al., 2006). Thus,

observations without balance sheet data are deleted (Dechow & Dichev, 2002). In line

with previous research, financial institutions, insurance companies and other financial

holdings, public administrative institutions and non-profit organizations are excluded

from the sample (Gassen & Fülbier, 2015; Minnis, 2011; Vander Bauwhede et al. 2015).

As a second step, subsidiaries of listed companies are deleted from the sample,

because their access to credit can be influenced by the holding organisation

(Burgstahler et al., 2006).

24

Further, firms with less than ten employees are excluded from the sample to ensure

the exclusion of microenterprises and companies specifically founded for non-

economic reasons (Vanacker & Manigart, 2010). Additionally, observations with total

assets below EUR 100.000 are excluded from the sample base (Gassen & Fülbier,

2015). Since the hypotheses tests in this study are partly based on measures that are

estimated on industry levels, the requirement of at least 10 firm-level observations for

a two digit NACE group is set (Burgstahler et al., 2006; Gassen & Fülbier, 2015). Up

to this point, the dataset contains 47.150 observations, from 1.791 public and 2.924

private firms over a period of 10 years (2007-2016).

The measurement of the AQ proxy entails severe financial data requirements, as non-

missing information over multiple years is necessary to compute non-cash working

capital, CFO, sales and property, plant and equipment. Specifically, three consecutive

years of data are needed to compute the AQ measure, due to the leads and lags

implied by the Dechow & Dichew (2002) model and because the calculation of CFO

demands information on the previous year. This results in a reduction of the final

dataset to a period between 2009 and 2015. Consequently, the sample consists of

33.005 observations by a total of 4.715 European companies.

25

5. EMPIRICAL RESULTS

The following section summarizes descriptive statistics and reports the regression

results regarding the relationship between access to credit and earnings quality.

Additionally, robustness tests are performed.

5.1. Descriptive statistics

First, table 2 presents a sample breakdown by industry, to show the cross-sectional

structure of the sample. The industries with the highest representation are

manufacturing (31%), wholesale (22%) and construction (10%). The applied industry

classification in this study is in line with the statistical classification of economic

activities in the European community, using two digit NACE codes. To ease the

analysis of the industry composition, table 2 is reported according to the NACE Rev. 2

(2008) classification.

Table 2 Sample breakdown by industry

NACE Rev. 2 Frequency Percent

A – Agriculture, forestry and fishing (NACE codes 01 – 03) 810 1,72

B – Mining and quarrying (NACE codes 05 – 09) 150 0,32

C – Manufacturing (NACE codes 10 – 33) 14.650 31,07

D – Electricity, gas steam and air conditioning supply (NACE code 35) 280 0,59

E – Water supply, sewerage, waste management and remediation

activities (NACE codes 36 – 39)

100 0,21

F – Construction (NACE codes 41 – 43) 4.910 10,41

G – Wholesale and retail trade, repair of motor vehicles and motor

cycles (NACE codes 45 – 47)

10.290 21,82

H – Transportation and storage (NACE codes 49 – 53) 3.980 8,44

I – Accommodation and food service activities (NACE codes 55 – 56) 1.580 3,35

J – Information and communication (NACE codes 58 – 63) 1.970 4,18

L – Real estate activities (NACE code 68) 1.070 2,27

M – Professional, scientific and technical activities (NACE codes 69 –

75)

3.340 7,08

N – Administrative and support service activities (NACE codes 77 – 82) 1.540 3,27

P – Education (NACE code 85) 550 1,17

Q – Human health and social work activities (NACE codes 86 – 88) 530 1,12

R – Arts, entertainment and recreation (NACE codes 90 – 93) 1.150 2,44

S – Other service activities (NACE codes 94 – 96) 250 0,53

Total 47.150 100

Table 2: Sample breakdown by industry

26

Next, table 3 summarizes the distribution of observations over the EU15 countries.

Sweden (34%), the United Kingdom (21%), Italy (10%) and France (10%) account for

the biggest part of the sample. The German representation in the sample is with 5%

relatively low, although the country is one of the biggest economies within the EU15.

This is due to the fact that data from Orbis Europe/Amadeus shows a significant

coverage bias, which was already indicated by previous studies (Gassen & Fülbier,

2015; Peek, Cuijpers & Buijink, 2010). The databanks receive financial data in

accordance with local disclosure rules and regulations. Differences in these

compliance requirements across countries contribute to the coverage bias (Gassen &

Fülbier, 2015). Therefore, these shortcomings in the sampling process lead to the

result that the sample in this study cannot be regarded as perfectly descriptive for the

unobservable population of European companies.

Table 3 Sample breakdown by country

Country Frequency Percent

Austria 120 0,25

Belgium 3.550 7,53

Germany 1.450 3,08

Denmark 600 1,27

Spain 2.180 4,62

Finland 2.540 5,39

France 4.620 9,8

United Kingdom 9.750 20,68

Greece 1.000 2,12

Ireland 170 0,36

Italy 4.670 9,9

Luxembourg 40 0,08

Netherlands 230 0,49

Portugal 210 0,45

Sweden 16.020 33,98

Total 47.150 100

Table 3: Sample breakdown by country

27

Table 4 presents an overview of detailed summary statistics for the main variables in

this study. The mean of the Credit Access1 variable is 22% and is thus slightly lower

than the values reported by Burgstahler et al. (2006), who reported an average

leverage of 29% for European private and public firms. Public European companies

show a mean debt to assets ratio of 20%, compared to 23% for private European firms.

This is a first indication that private European companies do not face credit constraints

under the aspect of debt ratios, as they seem to hold higher amounts of interest bearing

liabilities than public companies. The average private European firm has lower

leverage than Spanish small and medium sized companies, which report average debt

ratios of around 28% (García-Teruel et al., 2014). As also indicated by a leverage close

to 27% for Belgian SMEs reported by Vander Bauwhede et al. (2015), especially SMEs

seem to rely heavily on debt financing. In comparison to US firms, debt ratios seem to

be slightly lower in this European sample, as Bharath et al. (2008) report a mean

leverage of 26% for publicly listed firms compared to 20% in this sample.

Additionally, table 4 summarizes the descriptive statistics for the accruals quality

measure from Dechow & Dichew (2002) as advanced by McNichols (2002). The

median AQ metric in this sample is 0,001. Since the AQ metric was multiplied by (-1),

higher values indicate higher earnings quality. As public firms show higher average

and median AQ values than private firms, it can be argued that public firms accounting

information is of higher quality. To better compare the findings regarding accruals

quality to previous literature, a common alternative calculation of AQ is introduced by

calculating the standard deviation of the previous five years for the residuals of the DD-

model (Core, Guay & Verdi, 2008; Dechow & Dichew, 2002; Doyle, Ge & McVay, 2007).

The new variable AQ_SD is defined as the accruals quality in year t, which is calculated

as the standard deviation of firm-level residuals derived from equation (6) over the

years t−4 to t. Thus, considering the absolute value of the standard deviation of the AQ

metric, a mean and median of 0,042 and 0,033 respectively for AQ_SD is found in this

sample. Comparing these results with US listed companies, mean (median) accruals

quality as reported by Dechow & Dichew (2002) take values of 0,028 (0,020) and

Francis et al. (2005) report a mean (median) AQ of 0,044 (0,031). As higher absolute

values indicate lower overall accruals quality, earnings quality of European companies

seems to be overall lower.

28

This supports the findings of Leuz, Nanda & Wysocki (2003), who state that Anglo-

American firms undertake less earnings management than European and Asian

companies. Interestingly, the average earnings quality of private firms (0,044) again

seems to be lower than the one of public firms (0,038). This confirms the results of

Burgstahler et al. (2006), who analysed that mean and median earnings management

of publicly listed companies are significantly lower than private firms. As public firms

seem to engage in less earnings management, consequently their earnings quality is

higher. This also supports the research of Ball & Shivakumar (2005), who found that

unlisted firms generally present lower earnings quality. The overall findings are also in

line with the results of Vander Bauwhede et al. (2015), who reported an accruals quality

mean (absolute value) of 0,045 for Belgian SMEs, indicating that the European

average earnings quality is slightly higher than the one of Belgian small and medium

sized companies.

Furthermore, table 4 exhibits summary statistics of the control variables included in

various model specifications. The sample shows an average age for public (private)

companies of 59 (27) years, which is slightly older than the means of Burgstahler et al.

(2006), reporting an age of 46 and 17 for public and private European firms,

respectively. Size shows a mean of 9,5 for the total sample. In comparison, Gassen &

Fülbier (2015) report an average size of 8,0 for private European firms. The result

shows that public firms tend to have higher amounts of total assets than private firms.

Sales growth has a mean (median) of 0,06 (0,03), which is considerably lower than the

growth rates reported by American private firms. Francis et al. (2005) state a mean

(median) sales growth of 0,19 (0,12). The substantial difference in growth rates could

be due to the time frame of this sample, which includes the financial crisis during which

European companies faced rough economic conditions. In contrast, the American

study covers a time span between 1970 and 2001. Further, the descriptives for liquidity

indicate that European public firms, on average, have higher internal cash reserves

available than private European companies.

As various measurement requirements apply for variables, the number of observations

(N) within the sample can differ. The variable Credit Access2 for example is calculated

by comparing the change in financial debt to total assets, which requires data on the

previous year. This leads to the exclusion of year 2007 in the total observation number

and reduces it from 47.150 to 42.435.

29

Most importantly, the measurement of AQ_SD entails severe data requirements, as

non-missing information for eight consecutive years is needed (see Section 4: Data).

This is due to the leads and lags implied by the Dechow & Dichew (2002) model and

because the calculation of CFO demands information on the previous year. Calculating

the standard deviation of accrual residuals requires further information on the four

preceding years, which decreases the total number of available observations from

33.005 to 14.145.

Table 4 Descriptive statistics

Mean SD p10 Median p90 N

Credit Access1 total

public

private

0,218

0,199

0,229

0,286

0,273

0,292

0

0

0

0,165

0,154

0,173

0,497

0,457

0,520

47.150

17.910

29.240

Credit Access2 total

public

0,180

0,163

0,384

0,369

0

0

0

0

1

1

42.435

16.119

private 0,190 0,393 0 0 1 26.316

AQ total

public

private

6,55e-12

0,004

-0,002

0,662

0,063

0,067

-0,067

-0,057

-0,073

0,001

0,004

-0,001

0,064

0,063

0,065

33.005

12.537

20.468

AQ_SD total

public

private

0,0420

0,038

0,044

0,035

0,035

0,034

0,013

0,011

0,015

0,033

0,028

0,036

0,079

0,076

0,081

14.145

5.373

8.772

Age total

public

private

39,808

59,926

27,486

32,883

40,133

18,903

11

14

9

27

43

23

105

109

50

47.150

17.910

29.240

Size total

public

private

9,543

10,952

8,680

2,347

2,619

1,649

7,052

8,047

6,809

9,053

10,338

8,447

12,902

14,981

10,889

47.150

17.910

29.240

Liquidity total

public

private

0,124

0,125

0,122

0,145

0,148

0,142

0,003

0,006

0,002

0,073

0,075

0,071

0,322

0,318

0,324

47.150

17.910

29.240

Tangibility total

public

private

0,279

0,223

0,314

0,249

0,216

0,261

0,020

0,016

0,022

0,210

0,162

0,254

0,671

0,544

0,708

47.150

17.910

29240

Sales Growth total

public

private

0,063

0,063

0,062

0,976

0,510

1,173

-0,169

-0,165

-0,171

0,027

0,023

0,028

0,266

0,253

0,273

42.435

16.119

26.316

30

Leverage total

public

private

0,218

0,199

0,229

0,286

0,273

0,292

0

0

0

0,165

0,154

0,173

0,497

0,457

0,520

47.150

17.910

29.240

Performance total

public

private

0,822

0,816

0,825

0,383

0,387

0,379

0

0

0

1

1

1

1

1

1

47.150

17.910

29.240

Firm Type total

public

private

0,620

0

1

0,485

0

0

0

0

1

1

0

1

1

0

1

47.150

17.910

29.240

Creditor Protection total

public

private

-5,399

-4,726

-5,812

1,610

1,735

1,376

-7

-7

-7

-6

-4

-6

-2

-2

-4

47.150

17.910

29.240

Property Rights total

public

private

-82,295

-77,202

-85,413

13,786

15,523

11,543

-90

-90

-90

-90

-80

-90

-50

-50

-70

47.150

17.910

29.240

Institutional Context total

public

private

-76,895

-81,929

-91,226

12,413

16,942

12,813

-84

-97

-97

-83

-84

-96

-48

-52

-75

47.150

17.910

29.240

Credit Access1 = total debt/total assets, Credit Access2 = dummy variable takes 1 if debt increase exceeds

5% of total assets – 0 otherwise, AQ: see section 3; Research design, AQ_SD = standard deviation of AQ

over the years t−4 to t, Age = number of years since inception, Size = ln(total assets), Liquidity = cash &

CE/total assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-year percentage growth

in sales, Performance = dummy variable taking 1 if net income is positive – 0 otherwise, Firm Type = dummy

variable taking 1 if firm type is private – 0 otherwise, Creditor Protection = additive inverse of strengths of

legal rights index, Property Rights = additive inverse of property rights index, Institutional Context = additive

inverse of strengths of legal rights index + property rights index

Table 4: Descriptive statistics

Table 5 shows a Pearson correlation matrix, which can be used to obtain a first

impression of the potential relationship between the variables used in this study. The

correlation between Credit Access1 and AQ is significantly positive (0,09, p < 0,01),

which is consistent with the expectation that access to credit increases with accruals

quality. Further, the variable Firm Type is positively correlated with Credit Access1

(0,05, p < 0,01), indicating that private firms have sufficient access to credit. Both

proxies Creditor Protection and Property Rights show a negative correlation with Credit

Access1 (-0,09; -0,02), statistically significant at the 1% level. This is in line with the

prediction that access to credit is more restricted in countries with weak legal systems.

31

The variables Size, Tangibility and Performance are positively correlated with the

access to credit proxies, which is consistent with the expectations based on the

literature study. Surprisingly, firm Age shows an insignificant correlation with the first

proxy for credit access and even a negative association with the second one. This

strongly contradicts previous academic research arguing that age is one of the major

determinants of financial constraints (Mulier et al., 2016; Lamont et al., 2001; Whited

& Wu, 2006). Interestingly, old and large companies seem to have higher earnings

quality, as AQ shows a significant positive correlation with firm Age and Size.

Nevertheless, no conclusions should be drawn from the correlation matrix. Therefore,

more advanced analyses are conducted in the following section.

32

Table 5: Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

(1) Credit Access1 1,00

(2) Credit Access2 0,27*** 1,00

(3) AQ 0,09*** 0,00 1,00

(4) Firm Type 0,05*** 0,03*** -0,04*** 1,00

(5) Age 0,00 -0,02*** 0,04*** -0,48*** 1,00

(6) Size 0,08*** 0,00 0,03*** -0,47*** 0,58*** 1,000

(7) Liquidity -0,26*** -0,14*** -0,05*** -0,01* -0,03*** -0,12*** 1,00

(8) Tangibility 0,29*** 0,10*** 0,00 0,18*** -0,06*** -0,05*** -0,26*** 1,00

(9) Sales Growth -0,01 0,03*** -0,01 -0,00 -0,01 0,02*** 0,02*** -0,01** 1,00

(10) Leverage 1,00*** 0,28*** 0,09*** 0,05*** 0,00 0,08*** -0,26*** 0,29*** -0,00 1,00

(11) Performance 0,18*** 0,07*** 0,34*** -0,01* 0,01** -0,02*** -0,13*** 0,07*** -0,04*** 0,18*** 1,00

(12) Creditor

Protection

-0,09*** -0,04*** 0,04*** -0,33*** -0,10*** -0,07*** -0,01** -0,16*** -0,00 -0,09*** 0,03*** 1,00

(13) Property Rights -0,02*** -0,02*** 0,06*** -0,29*** -0,00 0,02*** -0,08*** -0,12*** 0,00 -0,02*** 0,06*** 0,87***

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level

Table 5: Correlation matrix

33

5.2. Earnings quality and firms’ access to credit

The use of longitudinal data in this study leads to the likely effect of correlated error

terms over observations, therefore the requirements of an OLS regression cannot be

met (Neter, Kutner, Nachtsheim & Wasserman, 1996). To control for these cross-

sectional correlations, time-series standard errors of the estimated coefficients are

used as proposed by Fama & McBeth (1973). Fama McBeth (1973) regressions are

preferred to fixed effect models in this study, as the earnings quality of a firm is unlikely

to fluctuate substantially over time (Vander Bauwhede et al., 2015). The regression

procedure suggested by Fama & McBeth (1973) involves the execution of year-specific

OLS regressions, whose coefficients will then be aggregated into coefficients and

standard errors across years. Thus, the obtained coefficients represent the unweighted

average of the OLS coefficients and the standard errors are measured as the standard

deviation of OLS coefficients divided by the square of the sample’s number of years

(Fama & MacBeth, 1973). This provides a control for time effects under circumstances

where fixed effects models for panel data cannot be used (Vander Bauwhede et al.,

2015).

To test Hypothesis 1, the first access to credit proxy is regressed on the explanatory

variable earnings quality as indicated in equation (1). Due to the high collinearity

between Leverage and Credit Access1, the control variable is excluded from this model.

Table 6 gives an overview of the regression results. The table shows two specifications,

a basic model and a full model. Column three reports the results of the basic model,

which includes only the relevant control variables of regression (1). The fourth column

depicts the full model, where the basic model is extended with the accruals quality

variable. Both model specifications contain industry and country controls, where the

largest industry (NACE code F42 – civil engineering) and the country with the highest

representation (Sweden) serve as base cases.

The regression results of the full model in table 6 show that the AQ metric coefficient

(0,224) is highly statistically significant at the 5% level. Adding the earnings quality

proxy to the basic model also increases the average R2 from 0,1851 to 0,1917. This

indicates that adding the AQ metric improves the explanatory power of the model.

34

This leads to the conclusion that the proxy for financial reporting quality is able to

explain an additional part of the variation in credit access in contrast to the more

established credit accessibility determinants.

The regression results further indicate that the expected relationship between earnings

quality and credit access holds, as the predicted coefficient of the AQ variable has a

positive sign and is highly statistically significant (0,224, p < 0,05). As higher values of

the AQ variable indicate higher earnings quality, an increase in AQ has a positive

impact on credit access. Consequently, the results can be interpreted as support for

Hypothesis 1. It can be stated that the positive relationship between financial reporting

quality and access to credit as indicated by Ding et al. (2016) for Chinese private firms

and García-Teruel et al. (2014) for private Spanish firms also holds for public and

private European companies.

With regard to the control variables used in this study, most of them show the expected

relationship with credit access and all of them are statistically significant. Size shows

a positive correlation with credit access (0,013, p < 0,01), which supports the

argumentation in literature that larger companies benefit from better external finance

accessibility and that firm size is one of the most reliable determinants of financing

obstacles (Beck & Demirguc-Kunt, 2006; Hovakimian et al., 2001). Surprisingly, Age

shows a very low negative coefficient (-0,001, p < 0,01). This contradicts the point of

view in academic research that older firms tend to be less informationally opaque and

thus get easier access to the loan market (Mulier et al., 2016; Whited & Wu, 2006).

This could be explained by the relationship theory, which predicts that close firm-bank

relationships can be developed by young, private companies and increase their

likelihood to obtain loans (Berger & Udell, 1995). The control variable Liquidity is

significantly and negatively related to the dependent variable (-0,329, p < 0,01),

confirming the pecking-order theory argument that internal funds are preferred over

external finance (Donaldson, 1961). Thus, European companies who are more liquid

tend to have less demand for external debt. This can also explain the negative

association between Performance and credit availability (-0,082, p < 0,01), as more

profitable firms are able to generate higher amounts of retained earnings and internal

funds to finance their investments. These results are in line with the findings of García-

Teruel et al. (2014), who stated that Spanish companies with high profitability and cash

flow from operations borrow less from banks.

35

In contrast with the prediction, Sales Growth appears to have a positive relationship

with access to credit and is statistically significant at the 10% level. This could indicate

that growing sales can represent a positive signal to credit institutions about a

company’s operational business. As this could enhance a firm’s risk assessment, the

bank could offer eased credit access.

Table 6 Access to credit – Fama McBeth (1973) regressions

Dependent variable Credit Access1

Basic model Full model

Exp. sign Coefficient p value Coefficient p value

Constant 0,148*** 0,000 0,129*** 0,000

AQ + 0,224** 0,034

Firm Type - 0,018*** 0,000 0,018*** 0,000

Size + 0,012*** 0,000 0,013*** 0,000

Age + -0,001*** 0,000 -0,001*** 0,000

Liquidity - -0,330*** 0,000 -0,329*** 0,000

Tangibility + 0,229*** 0,000 0,232*** 0,000

Sales Growth - 0,014** 0,010 0,013* 0,052

Performance +/- -0,101*** 0,000 -0,082*** 0,000

Observations 42.435 Observations 33.005

Average R2 0,1851 Average R2 0,1917

F statistic 48,58 F statistic 60,61

Prob > F 0,000 Prob > F 0,000

Credit Access1 = total debt/total assets, AQ: see section 3; Research design, Firm Type = dummy

variable taking 1 if firm type is private – 0 otherwise, Size = ln(total assets), Age = number of years since

inception, Liquidity = cash & CE/total assets, Tangibility = total fixed assets/total assets, Sales Growth

= year-over-year percentage growth in sales, Performance = dummy variable taking 1 if net income is

positive – 0 otherwise, Industry and country dummies are included in both models – the largest industry

(NACE code F42 – civil engineering) and the country with the highest representation (Sweden) serve

as base cases. The test variable of interest is highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level Table 6: Access to credit – Fama McBeth (1973) regressions

36

Next to Fama McBeth (1973) regressions, which are popular in earnings quality

research (Ball & Shivakumar, 2005; Francis et al., 2004; Vander Bauwhede et al.,

2015), a second method is considered to estimate regression (1). As the dependent

variable Credit Access1 is constructed as a ratio and is therefore bound between 0 and

1, a Tobit regression represents an appropriate econometric analysis tool. The model

used in this study follows the approach used by Li, Lou, Otto & Wittenberg Moerman

(2016), who investigated the relationship between accounting quality and debt

concentration. The Tobit model is censored at 1 and 0. It further includes n-1 year,

industry and country dummies. The additional Tobit regression results draw in essence

the same picture as the findings concluded from the Fama McBeth regression.

Accruals quality again reports a positive and highly significant correlation with access

to credit (0,193, p < 0,01), which provides additional support for Hypothesis 1. The

variable Performance shows the only difference. It seems to have a positive correlation

with the availability of external debt. This is in line with the argumentation that more

profitable firms have a higher repayment capacity and will therefore be evaluated as

less risky by lenders (Binks & Ennew, 1996). The regression findings are reported in

Appendix A, table A.1. Overall, the variance inflation factor (VIF), which is used as an

indicator of multicollinearity, is well below the accepted guideline of 10 for all the

specified regression models (Cohen, Cohen, West & Aiken, 2003).

To confirm the results stated above, regression model (1) is re-estimated by using a

different proxy for credit access. Following the argumentation of Meuleman & De

Maeseneire (2012), a proxy for credit availability should only focus on substantial

financing events. Thus, the dependent variable Credit Access2 is coded as 1 if the

increase in total debt of a company exceeds 5% of total assets in a year and 0

otherwise. As this entails a binominal outcome of the metric, a logit panel model has

to be used. In this context, the approach suggested by Meuleman & De Maeseneire

(2012) is followed. Therefore, regression (1) is re-estimated under the application of a

conditional fixed effects model. The findings from the fixed effects model are presented

in table 7. In contrast to the Fama McBeth (1973) and Tobit regressions, the findings

of the fixed effects model display a negative relation between accruals quality and

credit access (-1,277, p < 0,01). Thus, firms with higher earnings quality seem to be

engaged in a lower number of major financing events.

37

The results regarding the other control variables remain practically the same, however

the variable Firm Type cannot be analysed because it gets omitted in a fixed effects

model due to its stability over time. It has to be mentioned that the findings of a fixed

effects model should be interpreted with caution, because AQ is generally expected to

be rather stable over time (Vander Bauwhede et al., 2015). Furthermore, the

specification shows an extremely low Log Likelihood, indicating a low explanatory

power of the model. As earnings quality, firm type and institutional context are all

expected to stay generally stable over time, Fama McBeth (1973) and Tobit

regressions are preferred over using a conditional fixed effects model to test

hypotheses two and three in the following sections.

Table 7 Access to credit – Conditional fixed effects logit model

Dependent variable Credit Access2

Exp. sign Coefficient p value

AQ + -1,277*** 0,000

Firm Type - 0 (omitted)

Size + 1,420*** 0,000

Age + -0,033*** 0,000

Liquidity - -1,867*** 0,000

Tangibility + 1,530*** 0,000

Sales Growth - 0,231*** 0,000

Performance +/- -0,082*** 0,000

Observations 21.245

Log likelihood -7767

Credit Access2 = dependent variable takes 1 if increase in total debt exceeds 5% of total

assets – 0 otherwise, AQ: see section 3; Research design, Firm Type = dummy variable

taking 1 if firm type is private – 0 otherwise, Size = ln(total assets), Age = number of years

since inception, Liquidity = cash & CE/total assets, Tangibility = total fixed assets/total

assets, Sales Growth = year-over-year percentage growth in sales, Performance = dummy

variable taking 1 if net income is positive – 0 otherwise. The test variable of interest is

highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level Table 7: Access to credit – Conditional fixed effects logit model

38

5.3. Moderating effect of firm type

To further investigate the relationship between earnings quality and European

companies’ access to credit, the interaction term AQ*Firm Type is included in the

regression model. This variable should help to examine whether firm type moderates

the previously evaluated association. Thus, the interaction term should identify if

private firms can reduce their asymmetric information problems through high financial

reporting quality in a more pronounced manner than public companies. To test this

effect, regression model (2) is estimated by using Fama & McBeth (1973) regressions.

The findings are shown in table 8, where Panel A reports the regression results for a

specification without the interaction term and Panel B presents the outputs after

including AQ*Firm Type.

After the inclusion of the interaction term between accruals quality and firm type, the

overall result that higher earnings quality increases access to credit remains

unchanged. AQ still shows a statistically significant positive coefficient (0,314, p < 0,10).

However, the interaction term AQ*Firm Type indicates a negative relationship with

credit availability, but is statistically insignificant (-0,139, p > 0,10). Again, the results

are robust to using Tobit models instead of Fama McBeth (1973) regressions. When a

Tobit specification is used to test the moderating effect of firm type, the results also

report a negative and insignificant coefficient on the interaction term AQ*Firm Type (-

0,042, p > 0,10). The Tobit coefficients for regression model (2) are reported in Panel

B of Table A.1, Appendix A. Consequently, no evidence is found to support Hypothesis

2. Thus, high earnings quality does not seem to play an especially important role in

enhancing private European companies’ access to credit by mitigating asymmetric

information problems. This can be due to the fact that private firms in this sample seem

to have higher debt to total assets ratios, indicating that their access to finance is not

restricted compared to public firms when the Credit Access1 proxy in line with García-

Teruel et al. (2014) and Ding et al. (2016) is used. This could be explained by the

relationship banking theory, which suggests that small firms are able to establish closer

relationships with their lenders compared to larger companies. This proximity between

creditor and borrower can ease monitoring and screening efforts for the bank and

therefore mitigates information asymmetry issues (Boot, 2000).

39

The ‘soft’ information obtained through lenders’ on-going relationships with borrowers

may be an alternative source of information (Petersen & Rajan, 1994, 2002) relative to

earnings quality. Such ‘soft’ information, which includes a loan officer’s perception of

the potential borrower’s capability and trustworthiness, can substantially reduce

information asymmetry issues for private firms (Berger & Udell, 1995). According to

Berger (1999), the financial intermediary is able to gather additional information about

a private company beyond what is publicly available. The relevance of this theory was

further demonstrated by Bharath et al. (2007), who showed that the probability of a

relationship lender providing a future loan lies at 42%, while for a non-relationship

lender, this probability is with only 3% considerably lower. Therefore, close and

mutually beneficial relationships with their banks could be a tool of higher relevance to

reduce adverse selection issues of private firms compared to high earnings quality.

This theory could also represent a reason why European private firms do not seem to

face more restricted access to credit compared to public companies.

40

Table 8 Moderating effect of firm type – Fama McBeth (1973) regressions

Dependent Variable Credit Access1

Panel A Panel B

Exp. sign Coefficient p value Coefficient p value

Constant 0,129*** 0,000 0,046*** 0,002

AQ + 0,224** 0,034 0,314* 0,089

Firm Type - 0,018*** 0,000 0,019*** 0,000

AQ*Firm Type + -0,139 0,281

Size + 0,013*** 0,000 0,013*** 0,000

Age + -0,001*** 0,000 -0,001*** 0,000

Liquidity - -0,329*** 0,000 -0,330*** 0,000

Tangibility + 0,232*** 0,000 0,231*** 0,000

Sales Growth - 0,013* 0,052 0,013* 0,056

Performance +/- -0,082*** 0,000 0,083*** 0,000

Observations 33.005 Observations 33.005

Average R2 0,1917 Average R2 0,1930

F statistic 60,61 F statistic 75,45

Prob > F 0,000 Prob > F 0,000

Credit Access1 = total debt/total assets, AQ: see section 3; Research design, AQ*Firm Type =

interaction term between AQ and Firm Type, Firm Type = dummy variable taking 1 if firm type is private

– 0 otherwise, Size = ln(total assets), Age = number of years since inception, Liquidity = cash & CE/total

assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-year percentage growth

in sales, Performance = dummy variable taking 1 if net income is positive – 0 otherwise, Industry and

country dummies are included in both models – the largest industry (NACE code F42 – civil engineering)

and the country with the highest representation (Sweden) serve as base cases. The test variables of

interest are highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level Table 8: Moderating effect of firm type

41

5.4. Moderating effect of institutional context

To test Hypothesis 3 and evaluate the moderating effect of different institutional

contexts across European countries on the AC-EQ relationship, Fama & McBeth (1973)

regressions are applied again. In this context, the first access to credit proxy (Credit

Access1) is regressed on the predictor variable earnings quality (AQ) and the

interaction term between earnings quality and the institutional context proxies (Creditor

Protection, Property Rights and Institutional Context). Control variables as well as

industry dummies are included in the model.

First, the impact of creditor protection on the relationship between earnings quality and

access to credit within the European Union is tested by estimating regression

specification (3). The results are summarized in table 9, Panel A. The findings clarify

that the metric Creditor Protection has a negative relationship with Credit Access1 (-

0,011), which is statistically significant at the 1% level. As high scores demonstrate

that a country has weak creditor protection rights, this indicates that in European

countries with weak legal systems companies face a more restricted access to credit.

This supports the research of Haselmann et al. (2010), who argue that changes in the

legal system of a country that lead to an improvement in creditor protection laws

increases overall bank lending. In addition, the regression results indicate that creditor

protection rights have a moderating function on the association between earnings

quality and credit access, as the interaction term AQ*Creditor Protection shows a

positive sign (0,029). Thus, in countries with weak legal systems, earnings quality can

help to enhance a firm’s access to credit. However, the interaction term is not

statistically significant.

Secondly, the potential enforcing effect of weak property protection rights within

Europe on the AC-EQ relationship is investigated by estimating regression model (4).

The test outcomes are presented in table 9, Panel B. Again, the coefficient for Property

Rights is negative (-0,001, p > 0,10). As high values represent weak property rights

index scores, this result shows that firms who operate in countries with weak legal

systems face lower credit accessibility. The interaction term AQ*Property Rights on the

other hand is characterized by a positive sign (0,013, p < 0,10).

42

This demonstrates that in countries with weak property rights protection, increased

earnings quality is an especially important tool that can help to enhance a European

company’s access to credit. Thus, the positive relationship between high earnings

quality and access to credit is more pronounced in countries with weak property rights.

Since the interaction term is positive and significant at the 10% level, it can be

interpreted as evidence to support Hypothesis 3.

Lastly, as argued by BAE & Goyal (2009), the combination of creditor and property

right protection within a country is also important to investigate, as both metrics jointly

effect bank lending. Table 9, Pancel C reports the regression results with the combined

variable Institutional Context, embodying both creditor protection and property rights

proxies. It can be highlighted that AQ shows a positive relationship with access to credit

in all model specifications. This provides further support for Hypothesis 1. Additionally,

the regression output indicates a negative and significant coefficient for the variable

Institutional Context (-0,002, p < 0,05). Thus, when both creditor and property

protection laws are weak within a country, the availability of loans is more restricted for

European companies. This is in line with the findings of BAE & Goyal (2009), who

found that a combination of strong property rights and strong creditor protection laws

results in additional decreases in loan interest rate spreads and are therefore beneficial

for firms. The found relationship between institutional context and credit access is also

similar to the conclusions of Moro et al. (2016), who stated that the probability of

accessing credit is up to 40% higher in countries with stronger legal systems.

Moreover, the interaction term AQ*Institutional Context shows a positive association

with Credit Access1 (0,011), significant at the 10% level. This again demonstrates that

the relationship between earnings quality and credit access is influenced by the

institutional context of a country. Specifically, the regression output indicates that firms

operating in environments with weak creditor and property rights protection are

especially able to increase their likelihood to obtain credit through high earnings quality.

The results could be backed by the argument that strong legal systems are associated

with less earnings management in European firms (Burgstahler et al., 2006). If

earnings quality is overall lower in countries with weak legal systems, the benefit for a

company with more precise earnings figures could be increased as banks could value

accurate information more in riskier environments.

43

As reduced asymmetric information and better prediction of future repayment capacity

decreases the risk exposure of the lender, a firm with high earnings quality will find it

easier to obtain loans. Consequently, high earnings quality will improve a company’s

access to credit and this relationship is more pronounced in countries with weak legal

systems.

Table 9 Moderating effect of institutional context – Fama McBeth (1973) regressions

Dependent Variable: Credit Access1

Panel A Panel B Panel C

Exp. sign Coeff. p value Coeff. p value Coeff. p value

Constant 0,003 0,692 0,016** 0,043 0,011 0,137

AQ + 0,392* 0,078 1,308** 0,047 1,213** 0,048

Creditor Protection - -0,011*** 0,000

AQ*Creditor Protection + 0,029 0,278

Property Rights - -0,001 0,293

AQ*Property Rights + 0,013* 0,053

Institutional Context - -0,002** 0,049

AQ*Institutional Context + 0,011* 0,056

Firm Type - 0,014*** 0,000 0,031*** 0,000 0,029*** 0,000

Size + 0,013*** 0,000 0,014*** 0,000 0,014*** 0,000

Age + -0,001*** 0,000 -0,001*** 0,000 -0,001*** 0,000

Liquidity - -0,338*** 0,000 -0,333*** 0,000 -0,334*** 0,000

Tangibility + 0,233*** 0,000 0,240*** 0,000 0,239*** 0,000

Sales Growth - 0,012** 0,043 0,012** 0,028 0,012** 0,029

Performance +/- 0,084*** 0,000 0,084*** 0,000 0,085*** 0,000

Observ. 33.005 Observ. 33.005 Observ. 33.005

Avg. R2 0,1799 Avg. R2 0,1805 Avg. R2 0,1808

F statistic 365,17 F statistic 51,81 F statistic 49,89

Prob > F 0,000 Prob > F 0,000 Prob > F 0,000

Credit Access1 = total debt/total assets, AQ: see section 3; Research design, Creditor Protection = additive inverse

of strengths of legal rights index, AQ*Creditor Protection = interaction term between AQ and Creditor Protection,

Property Rights = additive inverse of property rights index, AQ*Property Rights = interaction term between AQ and

Property Rights, Institutional Context = additive inverse of strengths of legal rights index + property rights index,

AQ*Institutional Context = interaction term between AQ and Institutional Context, Firm Type = dummy variable

taking 1 if firm type is private – 0 otherwise, Size = ln(total assets), Age = number of years since inception, Liquidity

= cash & CE/total assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-year percentage

growth in sales, Performance = dummy variable taking 1 if net income is positive – 0 otherwise. Industry dummies

are included in both models – the largest industry (NACE code F42 – civil engineering) serves as base case. The

test variables of interest are highlighted in bold letters.

* Statistically significant at the 10 % level ** Statistically significant at the 5 % level *** Statistically significant at

the 1 % level

Table 9: Moderating effect of institutional context

44

These findings are confirmed by applying Tobit regression specifications. In this model,

all three proxies for the quality of the legal system within a country, Creditor Protection,

Property Rights and Institutional Context, are significantly negatively related to credit

access (-0,012, p < 0,01; -0,001, p < 0,01; -0,001, p < 0,01). This indicates again that

in countries with weak legal protection of creditor and property rights, overall credit

availability is lower. Moreover, all three interaction terms with AQ show a statistically

significant, positive relationship with Credit Access1 (0,019, p < 0,10; 0,005, p < 0,01;

0,004, p < 0,01). The Tobit regression results further confirm that firms who are

operating in environments with weak legal systems are able to mitigate their financial

constraints through reporting high quality accounting information in a more pronounced

manner. Consequently, it can be interpreted as additional evidence for Hypothesis 3.

The regression outcomes are presented in Appendix A, Table A.2.

In summary, this study offers evidence to support the expected impact of institutional

context on the AC-EQ relationship and thus Hypothesis 3 is not rejected.

5.5. Robustness check

To confirm the robustness of the above reported results, the empirical tests are

repeated with an additional proxy for access to credit. A different approach on

estimating the availability of external debt is given in financial constraints literature

(Fazzari et al., 1988; Kaplan & Zingales, 1997; Lamont et al., 2001). If a firm is

financially constrained, it cannot find sufficient external financing to realize its value

adding investments (Behr et al., 2013; Denis & Sibilkov, 2010). Thus, measuring the

likeliness a firm will face financial constraints represents an interesting avenue to

measure credit access. Previous literature suggests various approaches to measure

financial constraints, including investment-cash flow sensitivities (Fazzari et al., 1988),

the Kaplan and Zingales Index (KZ) (Kaplan & Zingales, 1997; Lamont et al., 2001) or

the Whited and Wu Index (WW) (Whited & Wu, 2006). For a literature review of

financial constraint proxies, see Hadlock & Pierce (2010). The Kaplan and Zingales

Index, constructed following Lamont et al. (2001), is one of the most commonly used

approaches in financial constraints research (Farre-Mensa & Ljungqvist, 2016;

Hadlock & Pierce, 2010).

45

However, Hadlock & Pierce (2010) found evidence that previous measures of financial

constraints like the KZ index and the WW index lack explanatory power. They therefore

suggest the use of a simplified index, which focuses on two major constraints

determinants – firm size and age. The financial constraints metric by Hadlock & Pierce

(2010) is called Size-Age (SA) index and is calculated as follows:

(7) SA Index = (-0,737* Size) + (0,043* Size2) - (0,040*Age)

In this equation, size represents the log of the book value of total assets. The variable

age is defined as the number of years since inception. The higher the score for a

specific firm in year t, the more financially constrained that company is. The index is

calculated for each sample firm within each year and represents the variable SA Index.

The major limitation of the Size-Age index is that it was constructed by using a sample

of US listed companies between 1995 and 2004. Similarly, also KZ and WW indices

were established by a sample of US listed companies (Lamont et al., 2001; Whited &

Wu, 2006). Therefore, the financial constraints indices might not be perfectly suited to

estimate financing obstacles of private firms. Nevertheless, the indices were previously

also used in academic literature to capture the degree of financial constraints for

private companies (Behr et al., 2013; Mulier et al., 2016).

As the SA index represents a firm’s financial constraint, with higher values indicating

more restricted access to credit, the expected signs of the regression coefficients for

predictor and control variables change to the opposite. Consequently, firms with higher

AQ are expected to be less financially constrained and thus have better access to

credit. Private European companies are argued to be more informationally opaque

(Berger & Udell, 1998) and should therefore face more severe constraints. As a new

proxy to measure credit access across Europe, the SA index is regressed against

regression specification (1), to test the robustness of the results for Hypothesis 1. As

the variables Size and Age are used to calculate the SA index, the control variables

are excluded from this model.

The results show a similar picture to the previous findings and are reported in table 10,

Panel A. The variable AQ shows a negative coefficient (-0,351) with a significance level

of 5%. Again, as a high score for AQ indicates high earnings quality, better financial

reporting quality reduces the financial obstacles a European firm faces. This provides

additional support for Hypothesis 1.

46

Further, private European firms seem to be more financially constrained than their

public peers, as the coefficient for Firm Type is positive (0,889) and highly statistically

significant at the 1% level. This is in line with previous research of Berger & Udell (1998)

and Hovakimian et al. (2001). Additionally, high liquidity, tangibility and performance

are firm characteristics that mitigate financial constraints, as they all show negative

and significant coefficients in the estimated model. High leverage on the other hand

increases the financial obstacles a European company has to deal with and thus

reduces its credit accessibility. These results confirm the expected impact of the control

variables on access to credit discussed in section 3.1: Variable measurement.

Table 10, Panel B further provides the results of regression equation (2), designed to

test the moderating effect of firm type on the relationship between credit access and

earnings quality. The findings are again similar to the previous outcomes with the

dependent variable Credit Access1. Interestingly, the coefficient for the interaction term

AQ*Firm Type has a negative coefficient (-0,045). This backs the argumentation that

especially private firms can use high earnings quality as a tool to reduce their credit

constraints. Nevertheless, as in section 5.3: Impact of firm type, the coefficient is not

statistically significant and thus Hypothesis 2 is rejected.

Moreover, the robustness of the results for the moderating effect of institutional context

on the AC-EQ association is checked by estimating regression (5) with the SA Index

as dependent variable. The findings report again that higher AQ is connected with

reduced financial constraints. Also, the positive coefficient on the Firm Type variable

(0,804) is highly significant in this model specification. Most interestingly, however, is

the confirmed result that European companies located in weak legal system countries

are confronted with higher degrees of credit constraint. This can be concluded from

the positive coefficient for Institutional Context (0,005, p < 0,01), because a weak score

for creditor and property protection laws in a country is associated with higher degrees

of financial constraints. As the interaction term AQ*Institutional Context is negative (-

0,039) and statistically significant at the 1% level, it can be argued that especially firms

with high EQ in a weak legal environment are able to decrease their financing obstacles.

As a result, the robustness check is able to support the finding that higher earnings

quality can mitigate the negative effect of weak legal systems on credit access.

47

Thus, the positive relationship between earnings quality and access to credit is more

pronounced in European countries with a low degree of creditor and property rights

protection. These findings provide further support for Hypothesis 3.

Table 10 Robustness check – SA Index

Dependent variable SA Index

Panel A Panel B Panel C

Exp.

sign

Coeff.

p value

Coeff.

p value

Coeff.

p value

Constant -5,130*** 0,000 -5,130*** 0,000 -4,477*** 0,000

AQ - -0,351** 0,015 -0,374 0,177 -3,367*** 0,003

Firm Type + 0,889*** 0,000 0,889*** 0,000 0,804*** 0,000

AQ*Firm Type - -0,045 0,872

Institutional

Context

+ 0,005*** 0,000

AQ*Institutional

Context

- -0,039*** 0,007

Liquidity - -0,153*** 0,002 -0,151*** 0,002 -0,119*** 0,008

Tangibility - -0,169*** 0,000 -0,169*** 0,000 -0,286*** 0,000

Sales Growth + 0,024 0,225 0,025 0,220 0,027 0,263

Performance - -0,047** 0,025 -0,048** 0,023 -0,055** 0,035

Leverage + 0,233*** 0,000 0,235*** 0,000 0,106*** 0,000

Observ. 33.005 Observ. 33.005 Observ. 33.005

Avg. R2 0,3481 Avg. R2 0,3485 Avg. R2 0,2328

F statistic 8609,60 F statistic 6241,62 F statistic 4597,32

Prob > F 0,000 Prob > F 0,000 Prob > F 0,000

SA Index = (-0,737* Size) + (0,043* Size2) - (0,040*Age) – where Size is log(total assets) and Age is the number

of years since inception, AQ: see section 3; Research design, Firm Type = dummy variable taking 1 if firm type

is private – 0 otherwise, AQ*Firm Type = interaction term between AQ and Firm Type, Liquidity = cash &

CE/total assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-year percentage growth

in sales, Performance = dummy variable taking 1 if net income is positive – 0 otherwise, Leverage = total

debt/total assets, Industry and country dummies are included – the largest industry (NACE code F42 – civil

engineering) and the country with the highest representation (Sweden) serve as base cases. The test variables

of interest are highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level

Table 10: Robustness check – SA Index

48

The results for regression (1), (2) and (3) are further robust to using the standard

deviation approach of measuring accruals quality. AQ_SD is calculated by taking the

standard deviation of the AQ metric over years t-4 until t. As higher values for AQ_SD

should again indicate higher accruals quality, the variable is multiplied by (-1). This

measurement is most commonly used in academic research in the field of accruals

quality (Core et al., 2008; Doyle et al., 2007; Francis et al., 2005). Nevertheless, this

calculation method reduces the sample size of this study substantially, from 33.005 to

14.145 observations over the years 2013-2015. The results are reported in table B.1,

Appendix B.

Furthermore, it is interesting to analyse whether the results of this study are driven by

a specific country, as Sweden and the United Kingdom account for around 50% of the

European sample. To examine if the estimated relationship between earnings quality

and access to credit also holds for the non-dominant countries in this sample, the Fama

McBeth (1973) regressions are re-exercised for a subsample that only includes the

non-dominant countries, i.e. excludes Sweden and the UK. Italy, as the remaining

country with the highest sample representation, functions as base case for the included

country dummies.

The results are reported in table 11. Panel A shows the outputs of regression

specification (1). Accruals quality is positively associated with Credit Access1 (0,282,

p < 0,05). This can be interpreted as evidence that the positive AC-EQ relationship is

also present in the non-dominant countries of this sample. Again, no empirical support

can be found for Hypothesis 2, because the interaction term between AQ and Firm

Type is statistically insignificant (-0,251, p > 0,10). The variable Institutional Context

presents again a negative coefficient (-0,001), demonstrating that weak legal systems

have a negative association with credit accessibility. This result is also highly

statistically significant at the 1% level. Finally, the interaction term between AQ and

Institutional Context reports a positive coefficient (0,018, p < 0,05), which further

supports Hypothesis 3 and the fact that the AC-EQ relationship is more pronounced in

countries with weak legal systems. To summarize, the found results seem to hold for

non-dominant EU15 countries as well, demonstrating that the findings are not driven

by one particular country in this sample. Additional results for the regression

specifications with the variables Creditor Protection and Property Rights are presented

in Appendix C, table C.1 and are in line with the previous findings.

49

Table 11 Robustness check – country subsample

Dependent variable Credit Access 1

Panel A

non-dominant countries

Panel B

non-dominant countries

Panel C

non-dominant countries

Exp. sign Coefficient p value Coefficient p value Coefficient p value

Constant 0,078*** 0,000 0,077*** 0,000 0,110*** 0,000

AQ + 0,282** 0,024 0,377** 0,033 1,681** 0,038

Firm Type - -0,005 0,128 -0,003 0,258

AQ*Firm Type + -0,251 0,104

Institutional Context - -0,001*** 0,007

AQ*Institutional Context + 0,018** 0,041

Size + 0,009*** 0,000 0,009*** 0,000 0,010*** 0,003

Age + -0,000 0,610 -0,000 0,721 0,008*** 0,000

Liquidity - -0,313*** 0,000 -0,313*** 0,000 0,001*** 0,000

Tangibility + 0,152*** 0,000 0,151*** 0,000 -0,334*** 0,000

Sales Growth - 0,008 0,268 0,008 0,266 0,169*** 0,000

Performance +/- 0,045*** 0,001 0,045*** 0,001 0,008 0,164

Observations 14.996 Observations 14.966 0,047*** 0,001

Average R2 0,1752 Average R2 0,1772 Observations 14.966

F statistic 9,81 F statistic 80,61 Average R2 0,1636

Prob > F 0,004 Prob > F 0,000 F statistic 40,95

Prob > F 0,000

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level

Table 11: Robustness check – country subsample

50

6. CONCLUSION

This study extends existing literature on access to credit by analysing the relationship

between earnings quality and credit access on a balanced sample of 4.715 public and

private European companies over a seven-year period (2009-2015). Firstly, evidence

is presented that a positive relationship between high earnings quality and access to

external finance exists. Firms seem to be able to reduce their asymmetric information

exposure by reporting more accurate accounting information. More adequate financial

reporting information can ease lenders risk assessment process, as more precise

earnings enable them to better predict a firms’ future repayment capacity.

Consequently, firms with high-quality financial reporting inherit less information risk

and thus benefit from enhanced access to credit. This confirms that the results of Ding

et al. (2016) and García-Teruel et al. (2014) hold in a European setting for both public

and private firms.

Secondly, this study adds to current literature by being the first of its kind to further

investigate the relationship between access to credit and earnings quality by examining

the aspects of firm type and institutional context. The results show that the listing status

of a European company does not influence the found association. This indicates that

high quality accounting information reduces external financing constraints for both

public and private companies in the same manner. These findings may be explained

by relationship banking theory, which predicts that small, private firms are able to

establish close connections with credit institutions and can reduce their informational

opaqueness in this way (Boot, 2000; Petersen and Rajan, 1994, 2002; Berger & Udell,

1995; Bharath et al., 2007). Therefore, close and mutually beneficial relationships with

banks could be a preferred instrument to reduce adverse selection issues for private

firms relative to high quality accounting information.

Institutional context, on the other hand, seems to have a significant moderating effect

on the AC-EQ association. This study’s results highlight that the relationship between

access to credit and earnings quality is more pronounced in European countries with

weak legal systems. Thus, high earnings quality seems to be especially beneficial for

companies which operate in countries with lacking creditor and property right

protection.

51

As previous research already identified, overall earnings management tends to be

higher, i.e. earnings quality tends to be lower, in countries with weak legal systems

(Burgstahler et al., 2006). It can be argued that the benefit of reducing information

asymmetries through high-quality earnings in these riskier environments for lenders is

especially high. The economic relevance of these findings is highly significant, as the

findings demonstrate that European companies can rely on high earnings quality to

mitigate information asymmetry issues and ease their access to credit. It further could

help policymakers to formulate strategies about how financing obstacles across the

European Union can be reduced.

The concluding point of this study is that the association between access to credit and

earnings quality is a complex one, which should be interpreted under consideration of

potential moderating effects.

52

7. LIMITATIONS AND AVENUES FOR FUTURE RESEARCH

Some limitations apply to this study, which might bias the overall findings and influence

the validity of this research. Nevertheless, these limitations also point out several

interesting avenues for future academic research.

As mentioned earlier, a main limitation of this study is connected to the applied

sampling process. As Sweden (34%), the United Kingdom (21%), Italy (10%) and

France (10%) represent the majority of the sample, this study can most likely not be

regarded as a perfect descriptive for the unobservable population of European public

and private companies. This can be explained by the significant coverage bias of the

Orbis Europe and Amadeus databanks, which was already described by previous

scholars (Gassen & Fülbier, 2015; Peek et al., 2010). As the data providers receive

only publicly available financial statements in accordance with domestic disclosure

laws and regulations, differences in compliance requirements across European

countries contribute to this coverage bias (Gassen & Fülbier, 2015). Consequently, it

would be interesting for future research to cover a more extended European sample in

order to verify the presented results. In this context, it also has to be stated that the

results of this European study cannot easily be generalized to other countries. To

address this external validity issue, future academic investigations are welcome to test

the relationship between access to credit and earnings quality for various regions

across the world. As Asia is, for example, one of the fastest growing economic areas

nowadays, it would represent an interesting research setting.

Another limitation of this study represents a possible survivorship bias, since firms with

missing values over the study period are excluded from the sample in order to receive

a balanced panel of 4.715 European firms. Companies who failed to survive can be

assumed to have had restricted access to external finance due to their bad business

situation and assumable lack of financial performance. Since those firms would likely

have been denied further access to external credit regardless of their financial

reporting quality, this does not represent a crucial issue for this study.

Further attention also needs to be drawn to the use of the absolute value of accrual

residuals as a proxy for earnings quality in favour of the standard deviation approach.

53

Typically, the standard deviation measurement is preferred in extant literature,

because sizable accrual residuals do not necessarily pose a crucial threat if they are

consistently large (Francis et al., 2005). In addition, industry specific factors might

explain systematically large residuals (Vander Bauwhede et al., 2015). As the main

reason for the AQ metric choice are the severe data requirements, it might be

interesting for future research to analyse an extended time-period and thus test the

predicted associations by applying the standard deviation method. Moreover, it would

be interesting to examine if the found relationship between access to credit and

earnings quality holds when different proxies for earnings quality are used. Similarly,

instead of measuring access to credit based on quantitative data, carrying out surveys

can represent a possible way to determine the degree to which European firms face

financing obstacles (Bai et al., 2006; Hersch et al., 1997). A different approach to

measuring financial constraints could also represent the recently designed ASCL index

by Mulier et al. (2016). As their constraints metric is constructed based on a sample of

unlisted European SMEs, it provides an excellent opportunity to investigate the AC-EQ

relationship in a setting with small and medium sized companies.

Additionally, future research could examine other potential factors that can influence

the relationship between access to credit and earnings quality. As this study focuses

exclusively on creditor and property protection rights, different aspects of legal systems

and institutional context can be evaluated. For example, investigating the relationship

under the aspect of common and civil legislation can be a possible avenue for future

research. As the political influence on accounting regulations differs between common

and civil law countries (Ball, Kothari & Robin, 2000), both systems have different effects

on earnings properties. Thus, the degree in which this political influence on accounting

regulations effects the AC-EQ association represents another interesting topic for

forthcoming investigations. Another possible avenue for future research is the

introduction of new accounting standards itself. In July 2009 International Financial

Reporting Standards (IFRS) specifically for SMEs were published, but the European

Union still discusses how these standards should be implemented in its member states

(Gassen & Fülbier, 2015). As the role of International Financial Reporting Standards

and the domestic accounting regulation for SMEs differ within the European Union

(Hope, 2015) it would also be interesting for future research to examine the effects of

changing accounting legislation on credit access.

VII

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XVI

APPENDICES

Appendix A: Access to credit regressions using Tobit

Table A.1 Access to credit and moderating effect of firm type - Tobit regression

Dependent variable CreditAccess1

Panel A Panel B

Exp. sign Coefficient p value Coefficient p value

Constant 0,119*** 0,000 0,119*** 0,000

AQ + 0,193*** 0,000 0,219*** 0,000

AQ*Firm Type + -0,042 0,237

Firm Type - 0,010*** 0,006 0,010*** 0,005

Size + 0,017*** 0,000 0,016*** 0,000

Age + -0,001*** 0,000 -0,001*** 0,000

Liquidity - -0,476*** 0,000 -0,476*** 0,000

Tangibility + 0,276*** 0,000 0,276*** 0,000

Sales Growth - 0,002* 0,056 0,002* 0,056

Performance +/- 0,618*** 0,000 0,061*** 0,000

Observations 33.005 Observations 33.005

Log likelihood 619,76 Log likelihood 620,46

Credit Access1 = total debt/total assets, AQ: see section 3; Research design, AQ*Firm Type =

Interaction term between AQ and Firm Type, Firm Type = dummy variable taking 1 if firm type is

private – 0 otherwise, Size = ln(total assets), Age = number of years since inception, Liquidity =

cash & CE/Total assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-

year percentage growth in sales, Performance = dummy variable taking 1 if net income is positive

– 0 otherwise, Industry, country and year dummies are included in both models – the largest

industry (NACE code F42 – civil engineering) and the country with the highest representation

(Sweden) serve as base cases. The test variables of interest are highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level

XVII

Table A.2 Moderating effect of institutional context - Tobit regressions

Dependent Variable: Credit Access1

Panel A Panel B Panel C

Exp. sign Coeff. p value Coeff. p value Coeff. p value

Constant 0,003 0,820 -0,002 0,867 -0,005 0,668

AQ + 0,310*** 0,000 0,583*** 0,000 0,561*** 0,000

Creditor

Protection

- -0,012*** 0,000

AQ*Creditor Protection + 0,019* 0,066

Property Rights - -0,001*** 0,000

AQ*Property Rights + 0,005*** 0,000

Institutional Context - -0,001*** 0,000

AQ*Institutional Context + 0,004*** 0,000

Firm Type - 0,005 0,112 0,020*** 0,000 0,019*** 0,000

Size + 0,017*** 0,000 0,018*** 0,000 0,018*** 0,000

Age + -0,001*** 0,000 -0,001*** 0,000 -0,001*** 0,000

Liquidity - -0,479*** 0,000 -0,478*** 0,000 -0,476*** 0,000

Tangibility + 0,275*** 0,000 0,282*** 0,000 0,281*** 0,000

Sales Growth - 0,002** 0,033 0,002** 0,027 0,002** 0,027

Performance +/- 0,063*** 0,000 0,063*** 0,000 0,062*** 0,000

Observ. 33.005 Observ. 33.005 Observ. 33.005

Log likelihood 333,95 Log likelihood 244,41 Log likelihood 249,59

* Statistically significant at the 10 % level ** Statistically significant at the 5 % level *** Statistically significant at the 1 % level

XVIII

Appendix B: Robustness check with AQ_SD variable

Table B.1 Robustness check with AQ_SD variable

Dependent variable SA Index

Panel A Panel B Panel C

Exp. sign Coeff. p value Coeff. p value Coeff. p value

Constant 0,08*** 0,000 0,013*** 0,000 -4,025*** 0,000

AQ_SD - -0,846** 0,029 -0,527* 0,076 -2,893* 0,091

Firm Type + 0,868*** 0,000 0,847*** 0,000 0,868*** 0,000

AQ_SD*Firm Type - -0,539 0,135

Creditor Protection + 0,197*** 0,001

AQ_SD*Creditor

Protection

- -0,372** 0,028

Liquidity - -0,162*** 0,001 -0,162*** 0,001 -0,159*** 0,002

Tangibility - -0,130*** 0,004 -0,127*** 0,003 -0,128*** 0,004

Sales Growth + 0,016 0,325 0,017 0,330 0,015 0,315

Performance - -0,102** 0,028 -0,102** 0,028 -0,103** 0,028

Leverage + 0,206*** 0,003 0,206*** 0,003 0,208*** 0,003

Observ. 14.145 Observ. 14.145 Observ. 14.145

Avg. R2 0,3304 Avg. R2 0,3305 Avg. R2 0,3308

F statistic 1361,76 F statistic 14,82 F statistic 166,59

Prob > F 0,000 Prob > F 0,006 Prob > F 0,006

* Statistically significant at the 10 % level ** Statistically significant at the 5 % level *** Statistically significant at the 1 % level

XIX

Appendix C: Robustness check with non-dominant countries

Table C.1 Robustness check – country subsample

Dependent Variable: Credit Access1

Panel A Panel B

Exp.

sign

Coeff.

p value

Coeff.

p value

Constant 0,086*** 0,000 0,116*** 0,000

AQ + 0,687** 0,036 1,753** 0,038

Creditor Protection - -0,006*** 0,000

AQ*Creditor Protection + 0,093* 0,058

Property Rights - -0,000*** 0,003

AQ*Property Rights + 0,020** 0,041

Firm Type - 0,007** 0,015 0,010*** 0,004

Size + 0,007*** 0,000 0,008*** 0,000

Age + 0,001*** 0,000 0,001*** 0,000

Liquidity - -0,344*** 0,000 -0,332*** 0,000

Tangibility + 0,165*** 0,000 0,169*** 0,000

Sales Growth - 0,009 0,190 0,008 0,167

Performance +/- 0,049*** 0,001 0,047*** 0,001

Observ. 14.996 Observ. 14.996

Avg. R2 0,1565 Avg. R2 0,1645

F statistic 54,36 F statistic 40,22

Prob > F 0,000 Prob > F 0,000

Credit Access1 = total debt/total assets, AQ: see section 3; Research design, Creditor Protection =

additive inverse of strengths of legal rights index, AQ*Creditor Protection = interaction term between AQ

and Creditor Protection, Property Rights = additive inverse of property rights index, AQ*Property Rights

= interaction term between AQ and Property Rights, Firm Type = dummy variable taking 1 if firm type is

private – 0 otherwise, Size = ln(total assets), Age = number of years since inception, Liquidity = cash &

CE/total assets, Tangibility = total fixed assets/total assets, Sales Growth = year-over-year percentage

growth in sales, Performance = dummy variable taking 1 if net income is positive – 0 otherwise, Industry

dummies are included in both models – the largest industry (NACE code F42 – civil engineering) serves

as base case. The test variables of interest are highlighted in bold letters.

* Statistically significant at the 10 % level

** Statistically significant at the 5 % level

*** Statistically significant at the 1 % level