INTEGRATED REPORTING AND

ACCESS TO FINANCE: EVIDENCE

FROM EUROPEAN PUBLICLY TRADED

FIRMS

Aantal woorden/ Word count: 22932

Marko Josimovski Stamnummer/ student number : 01600850

Promotor/ Supervisor: Prof. dr. Heidi Vander Bauwhede

Masterproef voorgedragen tot het bekomen van de graad van:

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics

Academiejaar/ Academic year: 2016 - 2017

INTEGRATED REPORTING AND

ACCESS TO FINANCE: EVIDENCE

FROM EUROPEAN PUBLICLY TRADED

FIRMS

Aantal woorden/ Word count: 22932

Marko Josimovski Stamnummer/ student number : 01600850

Promotor/ Supervisor: Prof. dr. Heidi Vander Bauwhede

Masterproef voorgedragen tot het bekomen van de graad van:

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics

Academiejaar/ Academic year: 2016 - 2017

Vertrouwelijkheidsclausule/ CONFIDENTIALITY AGREEMENT

Een A-4 met één van volgende clausules dient opgenomen te worden in de masterproef.

Add one of the following clauses in the master’s dissertation.

PERMISSION

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of

gereproduceerd worden, mits bronvermelding.

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

provided that the source is referenced.

Naam student/name student : Marko Josimovski

Handtekening/signature

I

FOREWORD

This paper represents an attempt to provide an answer to some of the most attractive questions

arising when it comes to the concept of integrated reporting. This thesis is the result of a dedicated

work, high academic effort and most importantly, a passionate approach towards the research

topic. I would like to state my immense gratitude towards my supervisor, prof. dr. Heidi Vander

Bauwhede, for her especially helpful advice and remarks during the process of writing the thesis

and for the honest collaboration and communication which we have throughout the whole

academic year. Moreover, I would like to thank the assistants, Thi Na Le and Sebastiaan Laloo,

who helped me refine the overall quality of the thesis. I would also like to thank all of the other

professors and assistants for their contribution to the knowledge and remarkable experience which

I gained here, at Ghent University, during this academic year. Furthermore, I would like to thank

the fellow graduate students at Ghent University with whom I spent so many great moments and

with whom I had so many interesting and challenging discussions with regards to a variety of

academic topics. I would especially like to thank the Flemish Government for their decision to

award me the Master Mind Scholarship for this academic year, during my stay at Ghent University.

I would also like to thank Ghent University for the decision to accept me as a master’s degree

student and for providing me with the resources required to complete this academic journey.

Finally, I would like to thank my family, girlfriend and friends for their huge support during my

stay at Ghent University.

Marko Josimovski Ghent, June 2017

II

TABLE OF CONTENTS

TABLE OF CONTENTS ............................................................................................................... II

LIST OF ABBREVIATIONS ....................................................................................................... IV

LIST OF TABLES ......................................................................................................................... V

1. INTRODUCTION ...................................................................................................................... 1

1.1. The research topic: broad view and relevance ..................................................................... 1

1.2. Objectives of the paper ......................................................................................................... 3

2. LITERATURE REVIEW ........................................................................................................... 5

2.1. The impact of CSR performance and CSR reporting on firms ............................................ 5

2.2. The concept of integrated reporting ..................................................................................... 8

2.3. The impact of integrated reporting on firms ...................................................................... 10

3. HYPOTHESES DEVELOPMENT .......................................................................................... 12

4. MODELS AND SAMPLE ........................................................................................................ 15

4.1. Econometric approach ........................................................................................................ 15

4.2. Variables............................................................................................................................. 16

4.2.1. Dependent variables .................................................................................................... 16

4.2.1.1. KZ index ................................................................................................................ 17

4.2.1.2. WW index .............................................................................................................. 19

4.2.1.3. SA index ................................................................................................................ 20

4.2.1.4. Altman Z-score...................................................................................................... 20

4.2.2. Independent variable .................................................................................................... 21

4.2.3. Control variables.......................................................................................................... 23

4.2.3.1. Size ........................................................................................................................ 24

4.2.3.2. Age ........................................................................................................................ 25

4.2.3.3. Long-term repayment capacity ............................................................................. 27

4.2.3.4. Lagged dependent variable ................................................................................... 27

4.2.3.5. Country-specific and industry-specific factors ..................................................... 28

4.3. Sample and data ................................................................................................................. 28

5. RESULTS ................................................................................................................................. 33

5.1. Descriptive statistics ........................................................................................................... 33

5.2. Correlations ........................................................................................................................ 35

5.3. Regressions results ............................................................................................................. 38

5.4. Practical interpretation of the results .................................................................................. 44

III

6. CONCLUSION ......................................................................................................................... 48

LIST OF REFERENCES .............................................................................................................. VI

ATTACHMENT 1 ......................................................................................................................... X

ATTACHMENT 2 ........................................................................................................................ XI

ATTACHMENT 3 ...................................................................................................................... XII

IV

LIST OF ABBREVIATIONS

CSR Corporate Social Responsibility

GMM Generalised Method of Moments

IIRC International Integrated Reporting Committee (in the source from 2011)

IIRC International Integrated Reporting Council (in the source from 2012)

IIRC International Integrated Reporting Council (in the source from 2013)

OLS Ordinary Least Squares

SASB Sustainability Accounting Standards Board

V

LIST OF TABLES

Table 1: Definitions of variables................................................................................................... 17

Table 2: Sample selection ............................................................................................................. 29

Table 3: Distribution of firms across countries ............................................................................. 31

Table 4: Distribution of firms across business sectors .................................................................. 32

Table 5: Descriptive statistics ....................................................................................................... 33

Table 6: Correlations..................................................................................................................... 36

Table 7: Regression results: Full OLS regression models ............................................................ 39

Table 8: Regression results: OLS regression models without country and industry controls ....... X

Table 9: Regression results: OLS regression models without lagged dependent variable control

....................................................................................................................................................... XI

Table 10: Regression results: OLS regression models without country, industry and lagged

dependent variable controls ........................................................................................................ XII

1

1. INTRODUCTION

1.1. The research topic: broad view and relevance

The need for comprehensive and high-quality information regarding a firm’s financial, social

and environmental performance has been rapidly growing in recent years. In the past and to a large

extent today, firms have tried to anticipate this need by separately issuing both financial and

sustainability reports. However, the practice of separately preparing and issuing financial and

sustainability reports leads to the problem that in most of the cases they are not interconnected and

related to one another (Eccles & Serafeim, 2014). Although the financial reports are prepared in

accordance with the appropriately defined accounting rules, standards and principles and are thus

considered to be more transparent, the informational content depicted in the sustainability reports

is usually characterised with an absence of credibility and reliability (Eccles & Serafeim, 2014).

Moreover, another disadvantage regarding sustainability reports which further deteriorates their

usefulness is derived from the fact that an organisation’s business model and long-term strategy

are almost never properly expressed in these reports (Eccles & Serafeim, 2014). Therefore, the

comprehensive picture of a firm’s overall performance and the way in which the firm produces

value for its stakeholders are not presented completely and appropriately (Eccles & Serafeim,

2014). Following Eccles and Serafeim (2014), it becomes clear that the existing practice of issuing

separate financial and sustainability reports has little value in today’s increasingly dynamic

business world, where the relationship between the firm as an entity and the environment has

reached extremely high levels of interdependence and complexity. In addition to this, firms are

expected to have much more accountability for their operations and the impact they have on the

environment and the society (Ioannou & Hawn, 2016). Hence, it is not only a firm’s financial

performance that seems attractive to the investors and other stakeholders, but the firm’s overall

creation of value as depicted from the aspects of the firm’s stakeholders such as investors and

other capital providers, employees, suppliers, customers, environmental organisations,

governmental institutions and others (IIRC, 2013)1. Integrated reporting is a new concept in the

area of corporate reporting which provides the basis for a firm to prepare and issue comprehensive

reports where the way in which the firm creates value for its stakeholders is presented, through a

clear depiction of the firm’s overall financial, environmental, social and governance performance

(IIRC, 2011)2. Integrated reporting should result into the preparation of one all-inclusive, but at

1 In the source from 2013 IIRC stands for International Integrated Reporting Council. 2 In the source from 2011 IIRC stands for International Integrated Reporting Committee.

2

the same time concise report in which the mutual relationship between different organisational

aspects of financial and non-financial nature is presented alongside the business model and the

long-term strategy of the organisation (IIRC, 2011). Because of its novelty, integrated reporting

poses many challenges to the organisations that decide to implement it. Some of the possible

challenges include the potential high costs related to the preparation of such a report, the need for

collective thinking inside the organisation which requires much more transparent communication

and collaboration between separate organisational divisions and departments, the uncertainty of

the potential benefits arising from the adoption of integrated reporting and whether the benefits

would outweigh the related costs.

Inspired by Cheng, Ioannou, and Serafeim (2014) who investigate the link between a firm’s

corporate social responsibility (CSR) performance and the firm’s access to finance, in this paper I

investigate whether firms which have adopted integrated reporting are characterised with

increased abilities to obtain financial resources relative to firms which have not adopted integrated

reporting. Since integrated reporting so far has been mainly adopted by large, usually publicly

traded corporations, in this paper I focus my research on the effects from the adoption of integrated

reporting on access to finance for European publicly traded firms. Due to the huge importance of

the access to finance for firms regardless of size, age, business sector and country of origin, I

decided to investigate whether integrated reporting contributes to better access to financial

resources for firms. Since most of the European countries are known for their emphasis on CSR

and sustainability performance, in this paper I analyse the concept of integrated reporting on a

sample of European publicly traded firms. The research question in this paper is: “Do firms which

practise integrated reporting have better access to finance relative to firms which do not practise

integrated reporting?”. I investigate this research question through two distinct mechanisms which

directly affect a firm’s ability to obtain financial resources. The first mechanism is the level of

financial constraints which the firm experiences (Cheng et al., 2014), whereas the second

mechanism is the firm’s financial health (Aernoudt, 2017). More specifically, following Cheng et

al. (2014) and Aernoudt (2017) respectively, I assume that firms which are characterised with

lower levels of financial constraints and increased financial health, are more capable of obtaining

financial resources relative to firms which are financially more constrained and have worse

financial health. In contrast with what I initially expected, the results in this research indicate that

in the case of European publicly traded firms, firms which practise integrated reporting are not

financially less constrained and do not have increased financial health relative to firms which do

not practise integrated reporting. With regards to financial health specifically, firms which have

3

adopted integrated reporting even appear to have worse financial health relative to firms which

have not adopted integrated reporting. The evidence in this research indicate that firms which

practise integrated reporting do not have increased abilities to access financial resources relative

to firms which do not practise integrated reporting.

1.2. Objectives of the paper

In contrast with the extensive literature which exists regarding the implications of CSR

performance and CSR reporting for a firm’s financial performance, corporate image,

attractiveness and reputation, not many researchers have made an attempt to investigate how the

adoption of integrated reporting affects firms. One of the reasons why literature about integrated

reporting is scarce is the fact that integrated reporting is still a new, emerging concept and it is

currently far from being widely adopted on a global scale. I believe that this paper contributes to

a large extent to filling the gap in the literature which exists concerning integrated reporting.

Besides filling the literature gap, the main objective of this paper is to get a more precise

picture of the concept of integrated reporting by investigating potential implications for firms

which have adopted this new concept. In this paper, I focus on analysing the effects of integrated

reporting on firms’ abilities to access finance, since financing is one of the most important topics

which concerns firms regardless of their size, age, industry and region of origin. By providing an

answer to the research question, I am convinced that the currently blurry picture of integrated

reporting would become more visible, thereby helping us to understand whether integrated

reporting really provides firms with a crucial benefit, namely better access to finance.

In addition to this, I believe that this paper will serve as an inspiration for many researchers

around the world to devote time and effort to further explore integrated reporting and the

advantages and disadvantages which it poses for firms. In this way, we can help to raise the

awareness of many firms and firms’ stakeholders around the world about what integrated reporting

really is. Summarised in a few words, the main goal of this paper is to better understand the concept

of integrated reporting, by exploring whether integrated reporting influences firms to have better

access to financial resources.

The paper is organised as follows. First, I present the literature in terms of CSR performance,

CSR reporting, integrated reporting and their influence on a firm’s business performance,

corporate image, attractiveness and reputation. Then, based on the research question and the

presented literature, I develop the hypotheses. In the section dedicated to the models and sample,

4

I explain the econometric approach, the dependent, independent and control variables incorporated

in the models and the sample and data. Furthermore, in the section dedicated to the results from

the research, I discuss the descriptive statistics, the correlations and the regression results and I

interpret the results in terms of their practical meaning. Finally, I conclude by mentioning the

limitations and summarising the main findings and contribution of this paper.

5

2. LITERATURE REVIEW

2.1. The impact of CSR performance and CSR reporting on firms

So far, there has been a great amount of research investigating the effects of firms’ CSR

performance and CSR reporting on firms’ business performance and attraction of resources.

However, not much research has been conducted in the field of integrated reporting and its effects

on the financial performance of firms, probably because integrated reporting, as mentioned in the

introductory part, is a relatively new concept which is still not widely spread around the world.

Since integrated reporting combines both financial and sustainability reporting in one concise

report in which the value creation of the firm is highlighted (IIRC, 2011), in the literature review

section I refer not only to previous papers regarding integrated reporting specifically, but also to

papers dedicated on analysing the effects of sustainability, CSR performance and CSR reporting

on firms’ business performance. As indicated by Eccles and Serafeim (2011), the benefits arising

from the adoption of integrated reporting would be even greater than the benefits originating from

the obligatory sustainability reporting. Moreover, I consider integrated reporting to be an act of

CSR itself. More specifically, if a firm has decided to adopt integrated reporting, it means that it

has decided to share more useful, valuable and integrated information with its stakeholders,

indicating that the firm has decided to increase the level of transparency and openness towards its

stakeholders. This further implies the firm’s willingness to build more trustworthy and honest

relationships with its stakeholders. For a firm, the state of being more transparent and keen on

sharing even strategy-related information regarding the business itself in order to satisfy

stakeholders’ demand for high-quality information, represents an act of honesty and a responsible

attempt to build better relations with the stakeholders. Therefore, a firm’s adoption of integrated

reporting can be considered as an act of CSR itself.

Waddock and Graves (1997) emphasise that there exists a two-way relation between a firm’s

engagement in socially responsible activities and the firm’s achievement in a financial sense. By

analysing the S&P 500 firms, it has been found that firms which perform financially well have

higher scores with regards to CSR-related performance (Waddock & Graves, 1997). However, it

has also been suggested that firms with better results alongside the corporate-social dimensions

are subsequently characterised with more superior financial results, so the relationship exists in

both ways (Waddock & Graves, 1997). Moreover, it has been discovered that firms should not

neglect the participation in CSR-linked activities, because this may cause the appearance of severe

risks originating from potential legal actions taken against the firms (McGuire, Sundgren, &

6

Schneeweis, 1988). Lima Crisóstomo, de Souza Freire, and Cortes de Vasconcellos (2011) argue

that there exists neither a positive nor a negative impact of a firm’s CSR on the firm’s financial

success for Brazilian firms, but the influence of a firm’s CSR activism is in fact neutral on the

firm’s financial condition. Similar findings have been earlier confirmed by Elsayed and Paton

(2005) as well. Additionally, CSR has even been proven to negatively influence firm value in the

case of Brazilian firms (Lima Crisóstomo et al., 2011).

However, Gond, El-Akremi, Igalens, and Swaen (2010) show that CSR has a positive impact

regarding the recruitment of future employees, in the sense that potential employees could be

attracted by a socially responsible firm and therefore CSR can be considered as a very important

element of the human resource management strategy. A firm’s involvement in CSR leads to

specific benefits for the existing employees as well and among them are employees’ identification

with the firm and satisfaction and respect with regards to the workplace (Gond et al., 2010).

Furthermore, the findings of Sen and Bhattacharya (2001) indicate that companies known for their

good CSR performance can increase their attractiveness, especially if their customers are mainly

consisted of people with similar beliefs regarding CSR and hence CSR is well-suited for

supporting various marketing campaigns. In fact, Sen and Bhattacharya (2001) find that while

poor CSR performance of a given company is negatively perceived by all of the consumers,

specifically those consumers possessing similar CSR-related attitudes perceive the company

positively for its satisfactory CSR performance. However, Öberseder, Schlegelmilch, and Gruber

(2011) argue that although the engagement of a certain firm in socially-related activities might

positively influence consumers’ opinions regarding the firm and its products, this would not in

fact trigger the consumers to buy products from the firm.

Hillman and Keim (2001) indicate that engaging in socially responsible activities by means of

investments and support for these activities can have a positive influence on the wealth of the

shareholders of the firm, although this relationship holds only when the social activities in which

it is invested are directly related to the most important, primary stakeholders of the firm. If there

does not exist a direct link between the social activities in which a firm engages and the firm’s

primary stakeholders, then even a negative influence on the shareholders’ wealth of the firm can

be experienced (Hillman & Keim, 2001). Moreover, Waddock and Graves (1997) point out the

importance of managing the relationships with the primary stakeholders as well, stating that

unsatisfactory performance of firms in areas which are tightly connected to the main stakeholders

might adversely affect the financial results of the firm. Additionally, the importance of a strong

7

relationship with the firm’s primary stakeholders, that is built in a socially responsible manner,

has been confirmed in the case of Indian firms as well (Mishra & Suar, 2010).

Eccles, Ioannou, and Serafeim (2014) argue that firms which implement sustainability-related

policies are characterised with a greater financial achievement in comparison with firms which do

not implement policies of this kind. Furthermore, relying on a sample of firms dispersed across 49

countries, Cheng et al. (2014) analyse the impact of the engagement of firms in CSR-related

activities on their access to finance and suggest that firms which exhibit excellent results in terms

of CSR performance are characterised with lower capital constraints and therefore increased

abilities to obtain financial resources. Moreover, it has been indicated that the CSR performance

of a firm has an impact on the firm’s cost of equity capital (Wang, Feng, & Huang, 2013).

Especially in North America and Europe, firms which perform well regarding CSR experience

lower cost of equity capital (Wang et al., 2013). The cultural and institutional diversity of countries

across different geographic regions around the world leads to the fact that the framework of CSR

is not perceived the same everywhere, for example in terms of Asia, firms’ activism concerning

CSR does not lead to a reduction of the cost of equity capital for the majority of the firms (Wang

et al., 2013). Dhaliwal, Li, Tsang, and Yang (2014) analyse the relationship between a firm’s

practice of disclosing CSR-based information and the cost of equity capital. Namely, firms which

publicly announce more non-financial information connected to a variety of CSR activities,

experience lower cost of equity capital (Dhaliwal et al., 2014). However, by investigating large

French companies, it has been discovered that companies’ practice of issuing non-financial, CSR-

related information, does not have a specific influence on the cost of debt (Najah & Jarboui, 2013).

On the other hand, after taking into account companies’ size and age by including them as control

variables, a negative relationship has been confirmed between CSR-related reporting and the cost

of debt (Najah & Jarboui, 2013). Furthermore, security analysts’ recognition of firms’

performance regarding CSR activities has experienced a dramatic change, since in the past security

analysts mostly saw CSR-related activities as value deteriorating for firms, while nowadays

analysts display a much more positive view towards the sustainability and CSR performance of

firms, meaning that well-performing firms in the CSR areas are expected to be positively evaluated

by security analysts (Ioannou & Serafeim, 2015). Similar to this, Luo, Wang, Raithel, and Zheng

(2015) confirm that analysts become increasingly interested in firms’ activism and engagement in

socially responsible activities, looking at their social performance as an important and valuable

asset and using it when evaluating firms for potential investors.

8

Relying on the Sustainability Accounting Standards Board (SASB), Khan, Serafeim, and Yoon

(2016) have made a successful attempt to divide sustainability topics into two distinct types,

material and immaterial topics, where a specific topic or area is material to a certain industry only

if it is considered essential and important to firms operating in that industry. It is discovered that

firms which exhibit excellent results with regards to material sustainability, perform much better

in the future relative to firms which score low in terms of material sustainability areas, implying

that allocating resources to material sustainability areas could be a value-increasing factor for

firms (Khan et al., 2016). However, it has also been noted that providing resources for immaterial

sustainability areas has neither significant value-adding, nor value-deteriorating consequences for

firms (Khan et al., 2016).

Ioannou and Serafeim (2017) analyse the effects of obligatory sustainability reporting for firms

located in China, South Africa, Denmark and Malaysia. More specifically, it is concluded that

Chinese and South African firms which are required to issue sustainability reports on a compulsory

basis not only increase the level of sustainability information which they present in their periodic

reports, but they are even expected to increase the credibility and reliability of this type of

information as well, relative to comparable firms which were not affected by the obligatory policy

for sustainability reporting (Ioannou & Serafeim, 2017). These findings could be definitely useful

when considering whether to introduce or not, obligatory integrated reporting in a given country,

a topic which Eccles and Serafeim (2011) argue about.

2.2. The concept of integrated reporting

Integrated reporting can be described as a new, original and fresh communication concept in

the field of corporate reporting, which has recently become especially attractive (Serafeim, 2015).

In a similar manner, Eccles and Serafeim (2011) indicate that there has been an increasing demand

for the adoption of integrated reporting by firms. The focus of integrated reporting is to combine

the practices of separately issuing financial and sustainability reports and to provide the basis for

the establishment of an integrated reporting framework which would enable the preparation and

issuance of integrated, clear and concise information about a firm’s financial and sustainability

performance and how the firm creates and delivers value to all of its stakeholders (IIRC, 2011).

Harrison and Wicks (2013) note that a firm’s prosperity depends on the extent to which the firm

exerts efforts to design mechanisms which would facilitate the understanding of the firm’s value

from the aspects of its different stakeholders and hence cause these stakeholders to take a

participation in the value production process of the firm. Valuable, credible and reliable

9

information prepared and presented by firms is thought to be essential for a successful transfer of

the required resources from the capital providers to the demanding firms and integrated reporting

is one of the key concepts targeting this (Zhou, Simnett, & Green, 2017). One of the most

important motives behind the concept of integrated reporting is a more successful transfer of

capital resources to the demanding firms, through refinement of the overall quality of the

information which firms disclose in the periodic reports (IIRC, 2013). As implied by the IIRC

(2011), the incorporation of information concerning a firm’s overall performance depicted from

its business and social aspects in relation to its long-term strategic orientation and business model,

is the cornerstone of integrated reporting. In fact, what makes an integrated report distinct,

specific, innovative and more valuable as a reporting and a communication mechanism, is that an

integrated report actually describes how a firm produces value, not only in the short and medium

term, but especially in the long term, thus indicating the sense for sustainability of the firm (IIRC,

2011). The concept of integrated reporting tries to improve upon the idea of separately performing

financial and sustainability reporting by firms, since it has been confirmed that separate financial

and sustainability reports add less and less value in the interwoven, complex, and dynamic

business environment of the twenty-first century (Eccles & Serafeim, 2014).

Owen (2013) highlights the importance of defining a new accounting curricula which can

respond better to the new needs and challenges in the field of corporate reporting in this century.

It is argued that integrated reporting would necessitate accounting curricula to take a more

strategic perspective, to be more long-term oriented and to supplement the financial type of

information with more qualitative information regarding a firm’s overall performance (Owen,

2013).

However, except for South Africa where integrated reporting is obligatory for publicly traded

firms for instance, in the majority of the countries around the world integrated reporting is the

result of organisations’ self-initiated acts (Eccles & Serafeim, 2011). The bio-industrial products

company Novozymes originating from Denmark, is known as the world’s first company to adopt

integrated reporting and it published its first report of this kind in 2002, whereas in 2003, the

cosmetics and fragrances company Natura, based in Brazil, became the second company to publish

an integrated report and it was followed by the diabetes care company Novo Nordisk from

Denmark, which in 2004 published its own integrated report for the first time (Eccles & Serafeim,

2011).

10

2.3. The impact of integrated reporting on firms

Eccles and Serafeim (2014) indicate that the real problem with firms’ current practice of

corporate reporting is that it does not allow for a superior completion of the information and

transformation functions, because the financial and sustainability reports are mostly being

separately prepared and issued from one another, sustainability reports are considered to be less

credible, comparable and trustworthy and do not appropriately depict a firm’s business model and

strategic course. The great advantage of integrated reporting is that by combining financial and

sustainability reporting into one comprehensive report, it aims to perform the two functions in a

more successful manner (Eccles & Serafeim, 2014).

Searfeim (2015) concludes that more future-oriented investors are characteristic for firms

which practise integrated reporting to a greater extent, explained by the fact that in these firms the

number of dedicated, long-term oriented investors is larger than the number of investors who are

temporary and short-term oriented and this result is especially significant for firms which have

been performing integrated reporting on a more consistent basis throughout the time.

Zhou et al. (2017) investigate integrated reporting with regards to the direct benefits for firms

arising from firms’ more proficient practice of integrated reporting. Namely, by investigating the

effects related to the level of quality of integrated reporting performed by firms listed on the

Johannesburg Stock Exchange, it has been concluded that firms which tend to produce reports that

to a greater extent follow the integrated reporting framework are characterised with a reduction of

analysts’ forecast error and experience lowering of the cost of equity capital (Zhou et al., 2017).

The cost of equity capital reduction has been confirmed by Wang et al. (2013) as one of the

benefits originating from a firm’s excellent performance with regards to CSR-linked areas. Similar

findings in terms of socially-related reporting have been confirmed by Dhaliwal et al. (2014) as

well. Especially important is the finding that integrated reports present additional valuable

information which is not incorporated in the usually separately issued financial and sustainability

reports, thereby adding marginal reporting value to the capital providers and stakeholders who

rely on integrated reports (Zhou et al., 2017).

Eccles and Serafeim (2011) emphasise the necessity for a global adoption of integrated

reporting, because of its huge importance arising from its superior ability to appropriately depict

a firm’s overall performance including the sustainability aspect and the overall value creation of

the firm.

11

Frias-Aceituno, Rodriguez-Ariza, and Garcia-Sanchez (2013) find that bigger and more

diversified boards of directors are expected to successfully practise integration of corporate

information, because people with different educational and professional backgrounds are more

capable of preparing and evaluating different types of information and therefore the periodic

reports can be produced such that they cover a broad set of corporate information in a more

integrated manner.

Finally, Adams and Simnett (2011) refer to the importance of integrated reporting for all the

organisations originating from Australia which issue reports, even for organisations coming from

the not-for-profit sector.

In the following section, based on the research question mentioned in the introductory part and

the summarised literature, I present the development of the hypotheses.

12

3. HYPOTHESES DEVELOPMENT

As summarised in the literature review section, it has been shown that firms which perform

well along the CSR dimensions, face lower capital constraints and have better access to finance in

comparison with poor-performing CSR firms (Cheng et al., 2014). As already explained, a firms’

adoption of integrated reporting can be considered as a socially responsible act itself due to the

firm’s decision to present to the stakeholders even strategy-related information which is

comprehensive and it is related to the firm’s financial and sustainability performance. In this

manner, by the issuance of integrated reports, firms satisfy their stakeholders’ increasing demand

for integrated and comprehensive corporate information, which indicates that firms become more

transparent. Wang et al. (2013) also suggest that firms which perform well in the CSR areas exhibit

lower cost of equity capital, while Dhaliwal et al. (2014) confirm that firms which disclose more

non-financial, socially-related information, face lower cost of equity capital as well. In addition,

Zhou et al. (2017) highlight the decreasing cost of equity capital as a typical financial benefit for

firms resulting from a more proficient practice of integrated reporting. Motivated by Eccles and

Serafeim (2011) who suggest that the potential benefits of integrated reporting are much greater

than those originating from the sustainability reporting, I believe that firms which issue integrated

reports have increased abilities to access finance. The logic behind this assumption is that if a firm

discloses the holistic picture of its financial and sustainability performance into a cohesive,

comprehensive and concise report, while at the same time emphasising the value it delivers from

the aspects of all the stakeholders (IIRC, 2011), then the firm should be perceived as being more

transparent and oriented towards openly sharing useful and valuable information with the public,

including investors and other capital providers. This method of disclosing information could

reduce one of the most prevalent market frictions, information asymmetry, which apparently exists

between a firm’s managers on one side and investors and capital providers on the other side. The

reduction of information asymmetry as a result of the issuance of integrated reports would

contribute to better, more transparent and trustworthy relationships between the firm and the

investors, leading to an increased ability of the firm to negotiate and obtain financial resources

under better terms, so the firm would become better able to finance its operations and fund its

planned investments. Since in this study I am particularly focused on investigating the effects of

integrated reporting on access to finance for European publicly traded firms, I assume that

integrated reporting contributes to firms not only in terms of an increased likelihood for obtaining

loans from financial institutions, but also in terms of attracting new, fresh capital, from the

13

issuance of new shares as well. It is natural to assume that investors would be much more

interested in buying shares from a more transparent firm which is known for establishing

trustworthiness with its stakeholders through the disclosure of relevant financial and non-financial

information in a comprehensive manner in the form of an integrated report. Additionally, since an

integrated report clearly depicts the perspectives of the business model and long-term strategy of

a firm, thereby indicating a firm’s future prospects, targets and intended market position in

accordance with sustainability plans (IIRC, 2011), it becomes likely for a firm which has adopted

integrated reporting to attract new investors and fresh capital in an easier way relative to firms

which do not disclose this type of integrated information. Before presenting the hypotheses, I again

refer to the research question of this paper which I already mentioned in the introductory part. The

research question is: “Do firms which practise integrated reporting have better access to finance

relative to firms which do not practise integrated reporting?”. I evaluate the ability of a firm to

access finance through analysing two distinct mechanisms. The first mechanism is the level of

financial constraints which the firm experiences (Cheng et al., 2014), while the second mechanism

is the firm’s financial health (Aernoudt, 2017). Cheng et al. (2014) use the level of financial

constraints faced by a firm as a measurement for the firm’s ability to access financial resources.

Following Cheng et al. (2014), the idea is that if a specific firm exhibits lower level of financial

constraints, then that firm has an increased ability to obtain financial resources. Derived from the

explanation stated above, the first hypothesis in this paper states as follows:

Hypothesis A: Firms which practise integrated reporting have lower financial constraints relative

to firms which do not practise integrated reporting.

It is not only the financial constraints experienced by firms which are likely to be affected by

the implementation of integrated reporting. The second mechanism assumes that firms which issue

integrated reports have better financial health. Through the act of preparing integrated reports,

firms are inclined to present the broader picture of their overall financial and non-financial

performance. In order to prepare and issue an integrated report, there should exist an organisational

cohesion inside the firm, the firm should also apply collective thinking at all management levels

and engage many people from the firm in the preparation of such reports. In this manner, during

the process of preparing integrated reports, firms become much more capable of analysing and

evaluating the broad picture of their own financial and non-financial performance in a more

methodical, structural, comprehensive and detailed manner. This enables a firm to precisely detect

specific problems, including financial problems which might exist in the firm and identify the real

14

causes behind the problems. The thorough analysis and identification of problems inside the firm

itself resulting from the implementation of integrated reporting can enable the firm to cope and

probably solve the problems appropriately and on time, before the problems become too big and

impossible to be dealt with. In fact, I assume that integrated reporting can serve as a tool for a

better internal evaluation of the firm itself, through which organisational problems can be

identified accurately and on time and hence solved successfully. Following this logic, I believe

that firms which have adopted integrated reporting detect financial problems before the problems

get more serious and hence cope with them more successfully. Therefore, I assume that firms

which have adopted integrated reporting have better financial health relative to firms which have

not adopted integrated reporting. The increased financial health translates into a higher probability

for these firms to obtain a loan from financial institutions such as banks for instance (Aernoudt,

2017). This indicates that firms which exhibit higher levels of financial health have better access

to finance (Aernoudt, 2017). I expect that firms which practise integrated reporting have an

increased level of financial health and therefore they have increased abilities to access financial

resources relative to firms which do not practise integrated reporting. Accordingly, the second

hypothesis in this paper states:

Hypothesis B: Firms which practise integrated reporting have increased financial health relative

to firms which do not practise integrated reporting.

In the following section I elaborate in detail the models and the sample of firms employed in

this research. More specifically, I first describe the econometric models. Then, I explain the

dependent, independent and control variables incorporated in the models. After the explanation of

the variables, I describe the sample and the data set which I use in order to run the regressions.

15

4. MODELS AND SAMPLE

4.1. Econometric approach

Regarding Hypothesis A, in order to investigate whether firms which practise integrated

reporting exhibit lower financial constraints, based on a cross-sectional data set, I specify three,

separate, ordinary least squares (OLS) regression models in which the dependent variable

represents the level of financial constraints of firms. There have been established several indices

in the literature which measure the level of financial constraints that firms experience and arguably

the most important and popular indices are the KZ index, originating from Kaplan and Zingales

(1997), the WW index constructed by Whited and Wu (2006) and the SA index, developed by

Hadlock and Pierce (2010). Following Cheng et al. (2014), I use the three aforementioned indices

of financial constraints as a dependent variable in each of the three separate OLS regression

models in order to test Hypothesis A and assess the robustness of my findings. The three, full OLS

regression models for testing Hypothesis A can be written as follows:

𝐾𝑍𝐼𝑗,𝑡 = 𝛽0 + 𝛽1𝐼𝑅𝑗 + 𝛽2 (ln(𝑇𝐴𝑗,𝑡)) + 𝛽3 (ln(𝐴𝑔𝑒𝑗,𝑡)) + 𝛽4𝐾𝑍𝐼𝑗,𝑡−3 + 𝑐 + 𝑖 + 𝜀𝑗,𝑡 (1)

𝑊𝑊𝐼𝑗,𝑡 = 𝛽0 + 𝛽1𝐼𝑅𝑗 + 𝛽2 (ln(𝐴𝑔𝑒𝑗,𝑡)) + 𝛽3𝑊𝑊𝐼𝑗,𝑡−3 + 𝑐 + 𝑖 + 𝜀𝑗,𝑡 (2)

𝑆𝐴𝐼𝑗,𝑡 = 𝛽0 + 𝛽1𝐼𝑅𝑗 + 𝛽2𝑆𝐴𝐼𝑗,𝑡−3 + 𝑐 + 𝑖 + 𝜀𝑗,𝑡 (3)

The variables 𝐾𝑍𝐼𝑗,𝑡, 𝑊𝑊𝐼𝑗,𝑡 and 𝑆𝐴𝐼𝑗,𝑡 are the dependent variables, the indices which express

the level of financial constraints which a firm experiences and they are calculated for the year

2015; 𝐼𝑅𝑗 is the independent variable and it is actually a dummy variable that receives the value

of 1 for a specific firm j if firm j has issued integrated reports for 2013 and 2014 and otherwise

this variable receives the value of 0; ln(𝑇𝐴𝑗,𝑡) is a control variable and it is actually the natural

logarithm of a firm’s total assets in 2015 (Cheng et al., 2014); ln(𝐴𝑔𝑒𝑗,𝑡) is a control variable for

firm age, calculated such that I take the natural logarithm of firm’s absolute age in 2015; motivated

by Cheng et al. (2014), I control for 𝐾𝑍𝐼𝑗,𝑡−3, 𝑊𝑊𝐼𝑗,𝑡−3 and 𝑆𝐴𝐼𝑗,𝑡−3 which are the financial

constraints of the firms three years before the year of calculation of the dependent variables which

is 2015, so these control variables are calculated for 2012; 𝑐 and 𝑖 denote the country-specific and

industry-specific factors for which I control in the form of country and industry dummy variables;

𝜀𝑗,𝑡 stands for the error term; t indicates the year 2015; and j stands for firm.

Concerning Hypothesis B where I test whether firms which practise integrated reporting have

increased financial health relative to firms which do not practise integrated reporting, I use the

16

Altman Z-score of 2015 as a dependent variable, which is based on the model constructed by

Altman (1968). As Altman (1968) argues, the Altman Z-score is obtained from a model based on

a discriminant function analysis and can be used in order to assess the probability that a specific

firm would go bankrupt in the two upcoming years. This model has also been suggested by

Aernoudt (2017) for measuring the financial health of a specific firm. The full OLS regression

model for testing Hypothesis B states:

𝑍𝑗,𝑡 = 𝛽0 + 𝛽1𝐼𝑅𝑗 + 𝛽2 (ln(𝑇𝐴𝑗,𝑡)) + 𝛽3 (ln(𝐴𝑔𝑒𝑗,𝑡)) + 𝛽4𝐿𝑇𝑅𝐶𝑗,𝑡 + 𝛽5𝑍𝑗,𝑡−3 + 𝑐 + 𝑖 + 𝜀𝑗,𝑡

(4)

In the regression model stated above, 𝑍𝑗,𝑡 is firm’s Altman Z-score calculated for 2015; 𝐼𝑅𝑗 is

the independent variable and it is actually a dummy variable which receives the value of 1 for a

specific firm j if firm j has issued integrated reports for 2013 and 2014 and otherwise this variable

receives the value of 0; ln(𝑇𝐴𝑗,𝑡) represents a firm’s size and it is the natural logarithm of a firm’s

total assets in 2015 (Cheng et al., 2014); ln(𝐴𝑔𝑒𝑗,𝑡) is the natural logarithm of a firm’s absolute

age in 2015; 𝐿𝑇𝑅𝐶𝑗,𝑡 stands for a firm’s long-term repayment capacity computed for 2015 and it

is actually calculated such that the firm’s cash flow from 2015 is divided by the long-term

liabilities of the firm from 2015 (Aernoudt, 2017); similar to Cheng et al. (2014) who rely on the

lagged dependent variable as a control variable, in the model I include the control variable 𝑍𝑗,𝑡−3

which is the Altman Z-score of firms calculated for 2012; 𝑐 and 𝑖 respectively express controlling

for country-specific and industry-specific factors in the form of country and industry dummy

variables; 𝜀𝑗,𝑡 represents the error term; t indicates the year 2015; and j stands for firm. A more

detailed explanation regarding all of the variables and their method of calculation can be found in

the part dedicated to the explanation of the variables.

4.2. Variables

In Table 1 I present the labelling and a short description for each of the dependent, independent

and control variables which I incorporate in this research.

4.2.1. Dependent variables

As already presented, for the purpose of testing Hypothesis A, based on a cross-sectional data

set, I construct three OLS regression models in which the dependent variable is an index of

financial constraints calculated for 2015. In terms of the OLS regression model constructed in

order to investigate Hypothesis B, the dependent variable is the Altman Z-score calculated for

17

2015. First, I elaborate on the construction of the dependent variables used for testing Hypothesis

A and then I discuss the dependent variable that I use to test Hypothesis B.

Table 1: Definitions of variables

Variable Definition

KZI 2015

(dependent)

A firm’s KZ index, a measure of the firm’s financial constraints calculated for the

year 2015

WWI 2015

(dependent)

A firm’s WW index, a measure of the firm’s financial constraints calculated for the

year 2015

SAI 2015

(dependent)

A firm’s SA index, a measure of the firm’s financial constraints calculated for the

year 2015

Z-score 2015

(dependent)

A firm’s Altman Z-score calculated for the year 2015, used for measuring the

financial health of a firm in 2015

IR

(independent)

A dummy variable which for a specific firm takes the value of 1 if the firm has issued

integrated reports for 2013 and 2014 and otherwise this variable takes the value of 0

Ln(TA 2015)

(control) Natural logarithm of a firm’s total assets in 2015

TA 2015

A firm’s actual total assets in 2015 (the variable TA is only presented in the

descriptive statistics due to a more understandable interpretation, because the

interpretation of its natural logarithm is more abstract)

Ln(Age 2015)

(control) Natural logarithm of a firm’s absolute age in 2015

Age 2015

A firm’s absolute age in 2015 calculated as the difference between 2015 and the year

in which the firm was founded (the variable Age is only presented in the descriptive

statistics due to a more understandable interpretation, because the interpretation of its

natural logarithm is more abstract)

LTRC 2015

(control) A firm’s long-term repayment capacity in 2015

KZI 2012

(control)

A firm’s KZ index, a measure of the firm’s financial constraints calculated for the

year 2012

WWI 2012

(control)

A firm’s WW index, a measure of the firm’s financial constraints calculated for the

year 2012

SAI 2012

(control)

A firm’s SA index, a measure of the firm’s financial constraints calculated for the

year 2012

Z-score 2012

(control)

A firm’s Altman Z-score calculated for the year 2012, used for measuring the

financial health of a firm in 2012

4.2.1.1. KZ index

For the calculation of the KZ index I rely on the method employed by Cheng et al. (2014) who

refer to Baker, Stein, and Wurgler (2003), who actually follow Lamont, Polk, and Saá-Requejo

(2001), while the last refer to Kaplan and Zingales (1997) and accordingly the KZ index states:

18

𝐾𝑍𝐼𝑗,𝑡 = −1.002𝐶𝐹𝑗,𝑡

𝑇𝐴𝑗,𝑡−1− 39.368

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠𝑗,𝑡

𝑇𝐴𝑗,𝑡−1− 1.315

𝐶𝑎𝑠ℎ𝑗,𝑡

𝑇𝐴𝑗,𝑡−1+ 3.139𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗,𝑡 +

0.283𝑄𝑗,𝑡, (5)

where the variable 𝐶𝐹𝑗,𝑡/𝑇𝐴𝑗,𝑡−1 stands for the cash flow of firm j in year t divided by the firm’s

total assets from the previous year t-1; the variable 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠𝑗,𝑡/𝑇𝐴𝑗,𝑡−1 represents the dividends

payed by firm j to its shareholders in year t also divided by the firm’s total assets from the previous

year t-1; the variable 𝐶𝑎𝑠ℎ𝑗,𝑡/𝑇𝐴𝑗,𝑡−1 refers to a firm’s cash and cash equivalents in year t divided

by its total assets from the previous year t-1; 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗,𝑡 stands for the leverage of a firm; the

last variable 𝑄𝑗,𝑡 refers to Tobin’s Q; t stands for year; and j indicates firm.

Moreover, similar to the debt-to-capital ratio as defined by Palepu, Healy, and Peek (2016), I

calculate the leverage of a firm in year t in the following way:

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗,𝑡 =𝑆𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡+ 𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡

𝑇𝐴𝑗,𝑡, (6)

where the variables 𝑆𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡 and 𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡 stand for a firm’s short-term and long-

term liabilities respectively, while 𝑇𝐴𝑗,𝑡 depicts a firm’s total assets in year t for firm j.

Tobin’s Q, serves as a measure of the investment opportunities which the firm has (Kaplan &

Zingales, 1997). According to Cheng et al. (2014), who follow Baker et al. (2003), who actually

refer to Lamont et al. (2001), while the last are inspired by Kaplan and Zingales (1997), Tobin’s

Q is calculated in the following way:

𝑄𝑗,𝑡 =𝑀𝑉 𝑒𝑞𝑢𝑖𝑡𝑦𝑗,𝑡+(𝑇𝐴𝑗,𝑡− 𝐵𝑉 𝑒𝑞𝑢𝑖𝑡𝑦𝑗,𝑡)

𝑇𝐴𝑗,𝑡, (7)

where 𝑀𝑉 𝑒𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 refers to the market value of a firm’s equity; 𝐵𝑉 𝑒𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 stands for the book

value of a firm’s equity; 𝑇𝐴𝑗,𝑡 refers to a firm’s total assets; and all of these variables correspond

to firm j in year t. For the market value of equity, I use the annual market capitalisation of the

firms. Since the sample of firms which I investigate is consisted of only publicly traded firms, I

had no specific difficulties in obtaining the market value of equity and subsequently the Tobin’s

Q and the KZ index.

The original coefficients appearing in the KZ model are estimated in an ordered logit model

established by Kaplan and Zingales (1997), in which they test which variables might influence the

likelihood of a firm to belong to a specific category of financial constraints. Perhaps, it would be

much better if I run similar ordered logit regressions, just like Kaplan and Zingales (1997), on the

19

sample of firms that I collect for the purpose of this research and then, based on the estimated

coefficients to construct a financial constraints index of my own. However, since calculating the

most accurate coefficients for the indices is not the main focus in this paper, I decided to rely on

the already estimated coefficients. The same also holds for the other two indices, the WW index

and the SA index. The lower the value of each of the three indices of financial constraints employed

in this study, the less financially constrained the firm is (Cheng et al., 2014). The KZ index has

been used by Lamont et al. (2001) who investigate financial constraints and returns of shares, by

Baker et al. (2003) who explore investment sensitivity to share price changes and by Cheng et al.

(2014) in order to analyse CSR and access to finance.

4.2.1.2. WW index

The second index of financial constraints which I use for the purpose of testing Hypothesis A

is the index suggested by Whited and Wu (2006). By improving on the KZ index, Whited and Wu

(2006) develop the WW index through an application of the generalised method of moments

(GMM) approach for estimating an investment Euler equation. This index of financial constraints

has also been employed by Cheng et al. (2014). For the calculation of the WW index, I follow

Cheng et al. (2014) who actually refer to Whited and Wu (2006) and the index states:

𝑊𝑊𝐼𝑗,𝑡 = −0.091𝐶𝐹𝑗,𝑡

𝑇𝐴𝑗,𝑡− 0.062𝐷𝐼𝑉𝑃𝑂𝑆𝑗,𝑡 + 0.021

𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡

𝑇𝐴𝑗,𝑡− 0.044(ln (𝑇𝐴𝑗,𝑡)) +

0.102𝐼𝑆𝐺𝑗,𝑡 − 0.035𝐹𝑆𝐺𝑗,𝑡, (8)

where the variable 𝐶𝐹𝑗,𝑡/𝑇𝐴𝑗,𝑡 presents a firm’s cash flow divided by its total assets for year t; the

second variable 𝐷𝐼𝑉𝑃𝑂𝑆𝑗,𝑡 is a dummy variable which gets the value of 1 if firm j paid dividends

in year t and otherwise this variable gets the value of 0; the third variable, 𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡/𝑇𝐴𝑗,𝑡,

stands for a firm’s long-term liabilities divided by its total assets in year t; the variable ln (𝑇𝐴𝑗,𝑡)

depicts the natural logarithm of a firm’s total assets and it is a measure of the firm’s size in year t;

the fifth variable, 𝐼𝑆𝐺𝑗,𝑡, represents the sales growth of the industry where firm j belongs to; the

final variable 𝐹𝑆𝐺𝑗,𝑡 stands for a firm’s sales growth; t stands for year; and j denotes firm. For

industry sales growth, due to the unavailability of the required data I did not calculate the different

business sectors’ sales growth rates by myself, but I took them from the website of CSIMarket

which provides computed sales growth rates across a variety of industries3. However, concerning

a firm’s sales growth in a particular year t, I calculate it in a way such that I first compute the

3 The link to the official website of CSIMarket is http://csimarket.com/help/About_us.php

20

difference between a firm’s sales in year t and year t-1 and then I divide this difference with the

firm’s sales from year t-1:

𝐹𝑆𝐺𝑗,𝑡 =𝑆𝑎𝑙𝑒𝑠𝑗,𝑡−𝑆𝑎𝑙𝑒𝑠𝑗,𝑡−1

𝑆𝑎𝑙𝑒𝑠𝑗,𝑡−1 (9)

4.2.1.3. SA index

The third and final index of financial constraints which I incorporate in this research is the SA

index, developed by Hadlock and Pierce (2010) who in addition, severely criticise the KZ index.

For the calculation of the SA index, I again rely on Cheng et al. (2014), who actually follow the

method of Hadlock and Pierce (2010), meaning that the SA index states:

𝑆𝐴𝐼𝑗,𝑡 = −0.737𝑆𝑖𝑧𝑒𝑗,𝑡 + 0.043(𝑆𝑖𝑧𝑒𝑗,𝑡)2 − 0.040𝐴𝑔𝑒𝑗,𝑡, (10)

where for the variable 𝑆𝑖𝑧𝑒𝑗,𝑡 the natural logarithm of a firm’s total assets is taken; for the variable

𝐴𝑔𝑒𝑗,𝑡 I take the difference between year t and the year when the firm was founded; t stands for

year; and j denotes firm.

𝑆𝑖𝑧𝑒𝑗,𝑡 = 𝑙𝑛(𝑇𝐴𝑗,𝑡) (11)

𝐴𝑔𝑒𝑗,𝑡 = 𝑌𝑒𝑎𝑟 𝑡 − 𝑌𝑒𝑎𝑟 𝑜𝑓 𝑓𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑜𝑓 𝑓𝑖𝑟𝑚 𝑗 (12)

Similar to the KZ index, it also holds for the other two indices of financial constraints, the WW

index and the SA index, that the lower the value of the index for a given firm, the less financially

constrained the firm is (Cheng et al., 2014).

4.2.1.4. Altman Z-score

Regarding Hypothesis B where I investigate whether firms which practise integrated reporting

have increased financial health relative to firms which do not practise integrated reporting, based

on a cross-sectional data set I construct an OLS regression model where the dependent variable is

the Altman Z-score of firms calculated for 2015. If the value of the Z-score is between 1.81 and

2.99, then that firm belongs to a so-called ignorance zone, or grey area (Altman, 1968). In addition

to this, the greater the Z-score above the upper threshold of 2.99, the greater the probability that

the firm is not going to go bankrupt in the following two years, suggesting that it is in a good

financial condition (Altman, 1968). This model has also been suggested by Aernoudt (2017) as a

measure of the financial health of a specific firm. For the calculation of the Altman-Z score, which

is based on the model developed by Altman (1968), I follow Aernoudt (2017):

𝑍𝑗,𝑡 = 1.2𝑊𝐶𝑗,𝑡

𝑇𝐴𝑗,𝑡+ 1.4

𝑅𝐸𝑗,𝑡

𝑇𝐴𝑗,𝑡+ 3.3

𝐸𝐵𝐼𝑇𝑗,𝑡

𝑇𝐴𝑗,𝑡+ 0.6

𝑀𝑉 𝑒𝑞𝑢𝑡𝑦𝑗,𝑡

𝑇𝐴𝑗,𝑡+

𝑆𝑎𝑙𝑒𝑠𝑗,𝑡

𝑇𝐴𝑗,𝑡, (13)

21

where the variable 𝑊𝐶𝑗,𝑡 stands for a firm’s level of working capital; the variable 𝑅𝐸𝑗,𝑡 refers to

the firm’s retained earnings; 𝐸𝐵𝐼𝑇𝑗,𝑡 denotes a firm’s earnings before interest and taxes, or also

known as the operating profit of a firm; the variable 𝑀𝑉 𝑒𝑞𝑢𝑡𝑦𝑗,𝑡, which stands for the market

value of equity, can be calculated such that the firm’s share price is multiplied by the number of

shares outstanding of that firm, but in this study I use the already calculated firm’s annual market

capitalisation; the variable 𝑆𝑎𝑙𝑒𝑠𝑗,𝑡 stands for the firm’s sales or revenues; the aforementioned

variables are divided by 𝑇𝐴𝑗,𝑡, which refers to the firm’s total assets; t stands for year; and j stands

for firm.

Furthermore, I calculate a firm’s working capital as the difference between the firm’s current

assets and the firm’s current liabilities (Aernoudt, 2017):

𝑊𝐶𝑗,𝑡 = 𝑆𝑇 𝑎𝑠𝑠𝑒𝑡𝑠𝑗,𝑡 − 𝑆𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡 (14)

In equation (14), the variable 𝑆𝑇 𝑎𝑠𝑠𝑒𝑡𝑠𝑗,𝑡 stands for a firm’s short-term or current assets,

while the variable 𝑆𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡 refers to a firm’s short-term or current liabilities (Aernoudt,

2017).

4.2.2. Independent variable

In the regression models which I construct for the purpose of this research concerning both

Hypothesis A and Hypothesis B, I include only one independent variable, IR. The independent

variable is in fact a dummy variable, also known as an indicator or a binary variable, so-called

since it can only take two values, namely either 1 or 0. More specifically, the independent variable

takes the value of 1 for a specific firm if the firm has issued integrated reports for at least both

2013 and 2014 financial years and otherwise the independent variable receives the value of 0. This

means that the first condition to be satisfied in order a firm to be placed in the group of practitioners

of integrated reporting is that the firm has issued an integrated report for the financial year 2013.

Moreover, the second condition to be satisfied is that the firm has issued an integrated report for

the financial year 2014 as well. If a specific firm satisfies both conditions, meaning that it has

issued integrated reports for both 2013 and 2014, then the firm is placed in the group of

practitioners of integrated reporting. There is a specific way which I use in order to define whether

a given firm belongs to the group of firms issuing integrated reports or not. Before elaborating in

more detail the procedure for classifying the firms in one of the two categories regarding integrated

reporting, I would like to emphasise that there does not exist an accurately defined method to

clearly distinguish between firms which issue integrated reports and firms which do not issue such

22

reports. While some firms prepare reports according to the International Integrated Reporting

Framework, defined by the IIRC (2013), and thereby name them integrated reports or annual

integrated reports, other firms do not intentionally and specifically follow the Framework

developed by the IIRC (2013), but prepare reports which contain integrated information regarding

their financial and sustainability performance. Since integrated reporting is still an emerging

concept, a firm might even issue an annual report which corresponds to the definition of integrated

reports without actually being aware of the fact that it has issued an integrated report. The

aforementioned reasons indicate why, sometimes, it is very difficult to categorise a specific firm

as a practitioner of integrated reporting. However, concerning this particular research, I decided

to rely on the official website of the IIRC on which there exists a list of firms whose reports are

identified and recognised to be integrated reports (IIRC, 2012)4. In order a specific firm’s annual

report to be defined as an integrated report, it does not need to follow exactly every element and

point indicated in the International Integrated Reporting Framework, but it is enough to

incorporate a certain element which corresponds to the IIRC, or the Framework and the definition

which describes integrated reporting (IIRC, 2012). It is easy to deduce that a much better method

for measuring the practice of integrated reporting by firms would be if the extent to which a firm’s

report corresponds to the definition of an integrated report is expressed on a given scale. As I

already indicated, since this is a new concept in the field of corporate reporting, from what I was

investigating, I found that for European firms there does not exist a detailed database with a list

of firms and a measure of the extent to which they practise integrated reporting, or the extent to

which their integrated reports correspond to the International Integrated Reporting Framework.

This is the reason why I construct a dummy variable as the independent variable in the regression

models employed in this research. First of all, in order to categorise a specific firm in the group of

practitioners of integrated reporting, the firm should be listed on the official website of the IIRC.

This means that at least some parts of the firm’s annual reports correspond to the concept of

integrated reporting as proposed by the IIRC and the International Integrated Reporting

Framework (IIRC, 2012). It is not enough for an individual firm just to be listed on the official

website in order to be considered for the group of integrated reporters. Since the dependent

variables in the regression models which I develop are computed for the year 2015, the

independent variable must correspond to at least one year earlier. In this way, I assure that the

independent variable precedes the dependent one. In other words, by including an independent

4 The link to the official website of IIRC is: http://integratedreporting.org/, while the link to the web page with the

list of firms identified as integrated reporters is http://examples.integratedreporting.org

23

variable which happened at least one year before the dependent variable, I make sure that the

independent variable is the potential cause and can actually influence the dependent variable and

not the other way around. To consider a firm as a practitioner of integrated reporting, I set a

condition that the firm has issued its first integrated report for the financial year 2013, or of course

earlier. Firms whose first integrated reports were issued for the financial year 2014, which

probably means that the reports became published and publicly available somewhere in the first

quarter of 2015, did not have much time to experience specific tangible benefits from the adoption

of integrated reporting in terms of better access to finance at the end of year 2015. When

introducing a new, innovative and challenging concept to perform a specific function in a more

superior manner than before, it is likely that the benefits arising from that concept cannot be

immediately experienced. A period of less than a year is probably not long enough for specific

benefits to appear and to be recognised by firms as a result of the adoption of integrated reporting.

I believe that more time is needed in order the benefits from adopting integrated reporting to really

be recognised and felt by firms. That is why, in the group of integrated reporters I only place those

firms which have prepared and published integrated reports for both 2013 and 2014 as financial

years. In the process of evaluating whether a firm has issued an integrated report for a given year

or not, I downloaded the reports of all the firms in the sample which are also listed on the IIRC

website under the section of integrated reporters. As already mentioned, it often happens that a

specific firm’s report might not be named an integrated report, but the firm which has issued it is

still recognised as practitioner of integrated reporting, since some parts of that annual report show

similarities with the definitions proposed in the International Integrated Reporting Framework.

After manually analysing and inspecting the downloaded reports of the firms in order to check

whether integrated reports were issued in both financial years, 2013 and 2014, I constructed the

independent variable. In the final sample of 3011 firms in total, I identify 46 firms as practitioners

of integrated reporting.

4.2.3. Control variables

In this part, I explain all of the control variables which I incorporate in the regression models

that I construct for the purpose of testing the hypotheses in this study. In the regression models

which are developed in this research, I control for firm size, firm age, long-term repayment

capacity of a firm, the lagged dependent variable and country-specific and industry-specific

factors. Besides explaining which control variables are included in each of the regression models,

I also elaborate on the reasons why I find it important to control for these variables.

24

4.2.3.1. Size

Regarding the regression models which I develop in order to test Hypothesis A, for the

regression model in which the KZ index is a dependent variable, I follow Cheng et al. (2014) who

refer to Hadlock and Pierce (2010), and control for firm size. In the regression models in which I

use the WW index and the SA index as a dependent variable, I do not control for firm size because

this variable is included in the computations of the dependent variable (Cheng et al., 2014). For

firm size, I take the natural logarithm of a firm’s total assets (Cheng et al., 2014). Hadlock and

Pierce (2010) indicate that the only variables which have predictive power to categorise a given

firm in one of the categories of financial constraints are in fact firm size and firm age. In addition

to this, Zia (2008) has highlighted a crucial difference between large, publicly traded firms and

smaller, private firms, in the sense that large, publicly traded firms are not financially constrained

relative to small, private firms. Since in this study I include only publicly traded firms, the level

of financial constraints which they experience might be influenced by firm size and therefore I

find it very important to control for size. In other words, I expect firm size to have an explanatory

power to answer why a certain publicly traded firm exhibits a specific level of financial

constraints. Moreover, it is logical that larger firms are financially less constrained than smaller

firms if we take into account that larger firms apparently have more tangible assets which can be

referred to as collateral when applying for loans from financial institutions for instance (Aernoudt,

2017). A large firm, usually implies past growth of the firm. Firms do not become large

immediately after founding. Instead, it usually takes a lot of time, effort and successful running of

the business in order a firm to grow, expand and eventually become large. This implies that larger

firms have either gone, or are still going, through the process of exerting effort in order to grow

the business. Therefore, it is deduced that financial institutions and investors are more likely to

have more confidence in larger firms relative to smaller ones. Larger firms are more likely to have

a proven record of successful operational working and more consistent profitability throughout

the time. Furthermore, the larger the firm, the lower the problem of information asymmetry,

indicating a greater probability that the firm has established successful relationships with various

investors and financial institutions, which might be essential when requesting financial resources

for funding the desired investments (Berger & Udell, 1995). In other words, due to the effects of

information asymmetry, larger firms face less difficulties when obtaining financial resources in

the form of credits, relative to smaller firms (Berger & Udell, 1995). The aforementioned reasons

are relevant enough to believe that indeed, larger firms are perceived as more secure and less risky

25

than smaller ones, which leads to the assumption that larger firms are usually financially less

constrained and thus have better access to finance relative to smaller ones.

When it comes to the regression model for testing Hypothesis B where the dependent variable

is the Altman Z-score, I also include firm size as a control variable. This variable is calculated as

the natural logarithm of firm’s total assets (Cheng et al., 2014). Following Hadlock and Pierce

(2010) who show that firm size has an influence on the financial constraints experienced by firms,

a similar reasoning can be applied when considering the financial health of a given firm. It is

logical to assume that larger firms are less likely to get into financial difficulties relative to smaller

firms. In a similar way, larger firms should be expected to absorb financial difficulties more easily

than smaller firms. For example, a larger firm probably has a larger amount of internal cash and

other assets which can be used to immediately cope with financial difficulties. These are the

reasons behind the assumption that firm size can influence the financial health of a given firm.

4.2.3.2. Age

Firm age as a control variable is computed as the natural logarithm of a firm’s absolute age:

ln(𝐴𝑔𝑒𝑗,𝑡) = ln( 𝑌𝑒𝑎𝑟 𝑡 − 𝑌𝑒𝑎𝑟 𝑜𝑓 𝑓𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑜𝑓 𝑓𝑖𝑟𝑚 𝑗) (15)

In equation (15) t refers to year 2015. The difference between 2015 and the year in which a

firm was founded can never be zero since in the sample I include firms which have been founded

up to 2010 and firms founded in 2010 at latest. Additionally, since I calculate the natural logarithm

of firm age in 2015, the youngest possible firm was five years old in 2015, so the difference

between the two years always remains positive. I control for firm age in those regression models

in which firm age is not one of the variables included in the computation of the dependent variable

(Cheng et al., 2014). This means that I include the natural logarithm of firm age as a control

variable in the regression models where the dependent variable is the KZ index, the WW index and

the Altman Z-score. As equation (10) shows, firm age is used for the calculation of the SA index

and therefore I do not control for firm age when the SA index is the dependent variable (Cheng et

al., 2014). There exist a lot of firms operating in highly competitive technology sectors. These

firms base their business operations on highly skilled human capital, knowledge that is likely

developed inside the firm, specific technology, patents and other industrial property rights.

Furthermore, regardless of the sector where a firm belongs to, many firms are dedicated on

developing a unique brand through huge investments in marketing campaigns. In addition to this,

Palepu et al. (2016) argue that investments in research and development, brands, advertising

campaigns and promotions, are in fact not included in the balance sheets of firms. Even though

26

these investments are considered to be intangible assets, from an accounting point of view they

are considered to be expenses and are only included in the income statement (Palepu et al., 2016).

In this way, a firm’s size calculated as the natural logarithm of total assets might not indicate the

real size of the firm. For instance, this is the case with software, bio-technology and

pharmaceutical firms (Palepu et al., 2016). Therefore, I also include firm age as another control

variable. The older the firm, the more extensive its networks of customers, suppliers and other

stakeholders. An older firm means that the firm succeeded not only to survive, but to successfully

compete against rival firms throughout the years, implying a history of successful running of the

business. Additionally, it is logical to assume that older firms have well-established relations with

financial institutions such as banks, investors and other capital providers, relative to younger firms.

For instance, Berger and Udell (1995) highlight that firms which are characterised with longer

lasting relationships with financial institutions such as banks, are being granted loans with lower

interest rates. Moreover, for client firms which banks have known for a long time and with whom

strong relationships have been formed, even guarantees in the form of collateral might not be

requested by banks (Berger & Udell, 1995). In addition to this, as the relationship between a bank

and a firm gets older and stronger, banks absorb more essential, private information regarding the

firm, that can be used later in the process of credit assessment and loan granting, which means that

older, more experienced firms and firms with stronger relationships with financial institutions can

be considered to be more transparent, less risky and more capable of paying back the granted loans

(Berger & Udell, 1995). There must be good business reasons why older firms existed for long

periods of time. Also, older firms are more likely to have developed valuable knowledge inside

the organisation, a stronger brand throughout the years, they are more recognisable among people

and hence are considered to be less risky relative to younger firms. Accordingly, older firms can

be expected to obtain financial resources under better conditions and they can thus be considered

to be financially less constrained relative to younger firms. Therefore, I control for firm age when

the dependent variable is an index of financial constraints, on the condition that firm age is not

incorporated in the calculation of the dependent variable (Cheng et al., 2014). In a similar manner,

I believe that firm age has an influence on the firm’s financial health as measured by the Altman

Z-score. The older the firm, the greater the probability that it is experienced with dealing with

different financial situations and it is likely that its experience is helpful in terms of coping with

financial difficulties in a more superior way relative to younger firms.

27

4.2.3.3. Long-term repayment capacity

With regards to Hypothesis B specifically, as implied by Aernoudt (2017), one of the

indicators of the solvency of a given firm which could influence a bank’s decision to grant a credit

to the firm or not, is the repayment capacity of the firm. Aernoudt (2017) indicates that the long-

term repayment capacity of a given firm provides a good picture of whether the firm’s net cash

flow is able to cover all of its long-term liabilities which include long-term loans granted from

banks and other financial institutions. The fact that banks use the long-term repayment capacity

in their screening process of the applying firm, implies that the long-term repayment capacity

might indicate the financial health of the firm (Aernoudt, 2017). This means that the repayment

capacity can be at least used to partially explain the state of a given firm with regards to its

financial condition. Accordingly, I control for a firm’s long-term repayment capacity, but only in

the regression model where the dependent variable is the Altman Z-score of firms. As Aernoudt

(2017) notes, the formula for calculating the long-term repayment capacity states:

𝐿𝑇𝑅𝐶𝑗,𝑡 =𝐶𝐹𝑗,𝑡

𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡, (16)

where the numerator, 𝐶𝐹𝑗,𝑡, refers to a firm’s cash flow; the denominator, 𝐿𝑇 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑗,𝑡, stands

for the firm’s long-term liabilities; t represents year; and j stands for firm. Due to the fact that in

the regression model concerning Hypothesis B the dependent variable Altman Z-score is

calculated for 2015, the long-term repayment capacity of firms is also calculated for 2015.

4.2.3.4. Lagged dependent variable

The level of financial constraints which a given firm faces today might partially be influenced

by the historical financial constraints which the firm experienced several years ago. Following

Cheng et al. (2014), who additionally explain that including the dependent variable from several

years ago as a control variable in the regression models captures the historical factors which might

have affected the level of financial constraints that firms previously experienced, in the regression

models concerning Hypothesis A in this study, I also include a firm’s level of financial constraints

of three years before the year for which the dependent variable is calculated. This means that in

the separate regressions with the KZ, WW and SA indices computed for 2015 being the dependent

variable, I respectively include the KZ, WW and SA indices computed for the year 2012 as control

variables. In a similar manner with regards to Hypothesis B, in the regression model where the

dependent variable is the Altman Z-score calculated for 2015, I include the Altman Z-score

28

computed for 2012 as a control variable. In this way, I capture the influence of a firm’s historical

financial health on the financial health of the firm in 2015.

4.2.3.5. Country-specific and industry-specific factors

I also control for country-specific and industry-specific factors. I include controls for country-

specific and industry-specific factors in all of the full regression models, regardless of what the

dependent variable is, an index expressing the financial constraints which firms exhibit, or the

Altman Z-score. For example, it is well-known that different European countries have different

economic and business conditions, a different political situation and differently organised

legislative bodies. European countries differ from one another when it comes to the level of

financial development, financial institutions, the development of capital markets and the overall

financial infrastructure. The aforementioned factors are country-specific factors which are specific

and consistent for a certain country and they differ among different countries. Similar to this, it is

worthwhile noting that differences among industries exist in terms of business regulations,

competitiveness, market dynamics, market attractiveness, technology dynamics, legislative

procedures and other related aspects. These factors are industry-specific factors which are specific

and consistent for a particular industry and they differ among different industries. Controlling for

country-specific and industry-specific factors which remain relatively stable over time in a given

country and industry respectively, refers to a technique known as fixed effects, which is usually

used when dealing with longitudinal or also called panel data and in previous research the fixed

effects approach has been applied by Schneper and Guillen (2004) and Cheng et al. (2014) for

instance. Although I rely on a cross-sectional data set for the purpose of this research, I still control

for the country-level and industry-level factors which are specific for each country and industry

respectively. In this way, by the inclusion of country and industry dummy variables, I manage to

capture the factors on the country and industry levels which are specific for each separate country

and industry and differ among different countries and industries respectively.

4.3. Sample and data

For the purpose of this research, I downloaded almost all of the required data from the widely

used Orbis database, which is a product of Bureau van Dijk5. To be more specific, I downloaded

the required firms’ financial data from the Orbis database, except for the industry sales growth

which I took from the website of CSIMarket. A detailed overview of the search strategy which I

5 The link to the official website of Bureau van Dijk is: https://www.bvdinfo.com/en-gb/home

29

implemented in order to come to the final sample of 3011 European publicly traded firms, is

presented in Table 2.

Table 2: Sample selection

Search criteria

Search result (number of

firms)

1.

Region of origin of firms: Europe (Eastern and Western

Europe) 89,270,724

2. Status of firms: Active firms 63,329,758

3. Listed/Unlisted firms: Publicly listed firms 16,261

4. Type of firms’ accounts: Firms with consolidated accounts only 7,629

5. Year of incorporation: every year up to and including 2010 7,146

6.

Industry/business sectors of firms: All of the industries/business

sectors listed in Table 4; industries/business sectors which are

not included in the sample are banking, insurance and public

administration 6,873

7. Cash flow of firms in 2012 and 2015 4,590

8. Ordinary dividends paid by firms in 2012 and 2015 3,903

9. Cash and cash equivalent of firms in 2012 and 2015 3,875

10. Short-term liabilities of firms in 2012 and 2015 3,875

11. Long-term liabilities of firms in 2012 and 2015 3,875

12. Annual market capitalisation of firms in 2012 and 2015 3,411

13. Book value of firms’ equity in 2012 and 2015 3,411

14. Total assets of firms in 2011, 2012, 2013, 2014 and 2015 3,243

15. Operating revenue of firms in 2011, 2012, 2014 and 2015 3,182

16. Sales of firms in 2011, 2012, 2014 and 2015 3,148

17. Current assets of firms in 2012 and 2015 3,148

18. Retained earnings of firms in 2012 and 2015 3,013

19. Operating profit/loss (EBIT) of firms in 2012 and 2015 3,013

20. Sample after removal of firms for which the year of founding

was unavailable 3,011

21. Final sample (total number of firms) 3,011

The firms’ financial data used in this research is taken from the consolidated financial accounts

regarding all European, active, publicly traded firms which have been founded up to 2010,

including 2010 as well, for which the required financial data is available in the Orbis database.

Initially, after downloading the required data from the Orbis database, the sample included 3013

firms in total. However, the year of incorporation was not available for two firms and that caused

problems when calculating these firms’ age. Therefore, I decided to remove these two firms from

the sample. Luckily, none of the removed firms is a practitioner of integrated reporting. The final

sample includes exactly 3011 firms from 35 different European countries and dispersed across 16

different business sectors. In the sample of 3011 firms, only 46 firms or 1.528 % fulfilled the

30

conditions to have issued integrated reports for 2013 and 2014 and were thus categorised as

practitioners of integrated reporting.

When dealing with a lot of observations, especially with detailed, granular data at the firm

level, it should always be taken into consideration the fact that there might exist some data errors.

Brav (2009) has relied on using truncated variables in order to avoid the data errors and scaling

problems. Namely, Brav (2009) has removed the most extreme 0.5% of the observations at the

ends of each of the variables. Since I do not want to lose any of the 3011 observations, I rely on a

technique known as winsorising, previously applied by Abarbanell and Lehavy (2003) and Cheng

et al. (2014), for instance, of which the former use winsorising, in their case on quarterly forecast

errors, at the 1st and 99th percentile. It has been suggested that winsorising is a widely adopted

methodology for dealing with data errors in the past literature (Abarbanell & Lehavy, 2003).

Before the construction of the dependent and control variables, I applied the technique of

winsorising on the financial variables at the 1st and 99th percentile. The procedure was only applied

to the financial variables of both 2012 and 2015, such as cash flow, dividends, cash and cash

equivalents, short-term and long-term liabilities, annual market capitalisation, book value of

equity, total assets, sales, current assets, retained earnings and operating profit, since these

variables are included in the calculation of the dependent and control variables, or are themselves

included as control variables. It is worthwhile mentioning that the variables age and industry sales

growth were not the part of the winsorising process. Moreover, I did not apply the winsorising

technique on the dummy variables since they can only receive two values, either 1 or 0. The rest

of the variables are winsorised at the 1st and 99th percentile. This means that the observations with

values below the 1st percentile receive the value of the 1st percentile and the observations whose

values are above the 99th percentile receive the value of the 99th percentile. To sum up, I construct

the dependent and the control variables based on the winsorised variables at the 1st and 99th

percentile.

I present the distribution of firms across countries in Table 3. In total, 3011 firms originating

from 35 different European countries are included in the sample. The United Kingdom dominates

the entire sample with 596 firms. This means that 19.794% of the total number of firms in the

sample have their headquarters in the UK. Moreover, there are 413 firms or 13.716% of the total

number of firms, originating from Germany. Additionally, 355 firms or 11.790% of the total

number of firms are based in France, while 218 or 7.240% of all the firms in the sample are located

in Sweden. Of all the 35 countries represented in the sample, the Czech Republic and Slovakia are

31

the countries which are represented with the lowest number of firms. Namely, only 3 firms or

0.100% of the firms originate from each of these two countries.

Table 3: Distribution of firms across countries

Country

Number of

firms

Percentage of

firms

1. Austria 42 1.395%

2. Belgium 60 1.993%

3. Bulgaria 15 0.498%

4. Croatia 46 1.528%

5. Cyprus 14 0.465%

6. Czech Republic 3 0.100%

7. Denmark 81 2.690%

8. Estonia 14 0.465%

9. Finland 65 2.159%

10. France 355 11.790%

11. Germany 413 13.716%

12. Greece 158 5.247%

13. Hungary 9 0.299%

14. Iceland 4 0.133%

15. Ireland 32 1.063%

16. Italy 130 4.318%

17. Latvia 9 0.299%

18. Lithuania 16 0.531%

19. Luxembourg 26 0.864%

20. Macedonia 10 0.332%

21. Malta 13 0.432%

22. Netherlands 70 2.325%

23. Norway 51 1.694%

24. Poland 204 6.775%

25. Portugal 38 1.262%

26. Romania 4 0.133%

27. Russia 28 0.930%

28. Serbia 4 0.133%

29. Slovakia 3 0.100%

30. Slovenia 9 0.299%

31. Spain 70 2.325%

32. Sweden 218 7.240%

33. Switzerland 141 4.683%

34. Turkey 60 1.993%

35. United Kingdom 596 19.794%

Total number of firms 3011 100%

32

In Table 4 I present the distribution of firms across the different industries/business sectors.

Table 4: Distribution of firms across business sectors

Industry/sector Number of firms Percentage of firms

1. Primary sector 140 4.650%

2. Food, beverages, tobacco 144 4.782%

3. Textiles, wearing apparel, leather 78 2.591%

4. Wood, cork, paper 61 2.026%

5. Publishing, printing 111 3.686%

6.

Chemicals, rubber, plastics, non-metallic

products 280 9.299%

7. Metals and metal products 144 4.782%

8. Machinery, equipment, furniture, recycling 529 17.569%

9. Gas, water, electricity 103 3.421%

10. Construction 94 3.122%

11. Wholesale and retail trade 230 7.639%

12. Hotels and restaurants 63 2.092%

13. Transport 101 3.354%

14. Post and telecommunications 98 3.255%

15. Other services 795 26.403%

16. Education, health 40 1.328%

Total number of firms 3011 100%

In the sample I include firms from all of the Bureau van Dijk major business sectors, except

for the banking, insurance and public administration sectors. To be more concrete, in the sample

I include the following sectors which are 16 in total: Primary sector; Food, beverages, tobacco;

Textiles, wearing apparel, leather; Wood, cork, paper; Publishing, printing; Chemicals, rubber,

plastics, non-metallic products; Metals and metal products; Machinery, equipment, furniture

recycling; Gas, water, electricity; Construction; Wholesale and retail trade; Hotels and restaurants;

Transport; Post and telecommunications; Other services; and Education, health. Of all the firms

included in the sample, 795 in total or as a percentage 26.403% belong to the business sector

labelled as “Other services”. Furthermore, 529 firms which corresponds to 17.569% of the firms

in the sample are placed in the business sector labelled as “Machinery, equipment, furniture,

recycling”. Then, 280 firms or 9.299% of the firms in the sample are categorised in the business

sector named as “Chemicals, rubber, plastics, non-metallic products”, whereas 230 firms which is

7.639% of the total number of firms in the sample represent the business sector of “Wholesale and

retail trade”. On the other hand, the business sector with the lowest number of firms in the sample

is the “Education, health” sector to which only 40 firms or 1.328% of the firms belong.

33

5. RESULTS

5.1. Descriptive statistics

The descriptive statistics concerning the variables which I incorporate in this research, except

for the country and industry dummy variables, is presented in Table 5.

Table 5: Descriptive statistics

Variable N Mean Median Maximum Minimum Std. Dev.

KZI 2015 3,011 1.300 1.426 245.017 -162.775 6.478

WWI 2015 3,011 -1.002 -0.558 0.245 -549.068 12.901

SAI 2015 3,011 -4.189 -3.801 0.350 -22.396 1.828

Z-score 2015 3,011 1.417 1.698 72.307 -211.099 5.958

IR 3,011 0.015 0.000 1.000 0.000 0.123

Ln(TA 2015) 3,011 12.394 12.199 18.103 7.569 2.294

TA 2015 3,011 2,973,883.000 198,619.000 72,748,803.000 1,937.000 9,860,177.000

Ln(Age 2015) 3,011 3.485 3.332 6.207 1.609 0.845

Age 2015 3,011 47.140 28.000 496.000 5.000 45.647

LTRC 2015 2,913 -1.028 0.278 5,635.500 -4,644.333 174.813

KZI 2012 3,011 1.696 1.374 1,585.092 -157.256 29.082

WWI 2012 3,011 -0.780 -0.559 0.052 -187.716 4.979

SAI 2012 3,011 -4.106 -3.705 0.349 -22.274 1.820

Z-score 2012 3,011 2.584 1.656 3,360.821 -74.628 61.305

(definitions of the variables are presented in Table 1)

The mean of the KZ index of 2015 is 1.300, which is a bit lower than the median which has a

value of 1.426. In contrast with the mean, the median of a specific variable is not affected by the

extreme values which the variable takes since the median of a specific data set is actually the

middle value in the data set. In this manner, the typical firm in the sample according to the median

has a KZ index in 2015 of 1.426 and thus it is financially more constrained than the average firm

with a KZ index in 2015 of 1.300. As it can be noted by observing Table 5, despite the

winsorisation which I employed in order to reduce the effect of the extreme observations, the KZ

index computed for 2015 for instance, has a very high maximum value of 245.017 and a very low

minimum value of -162.775. However, observations with either extremely high or extremely low

values are very rare. Furthermore, the WW index of 2015 has a mean and a median which have the

values of -1.002 and -0.558 respectively. The WW index of 2015 varies between 0.245 which is

the maximum and -549.068 which is the minimum. Similar to the KZ index of 2015, despite the

winsorisation at the 1st and 99th percentile of the financial variables, the value of the minimum is

extremely low relative to the mean and the median. The SA index computed for 2015 has a mean

of -4.189, whereas the median is -3.801. In contrast with the KZ and WW indices of 2015, the

34

difference between the extreme values is much lower. Namely, the SA index of 2015 varies

between 0.350 and -22.396. In addition to this, the value of the standard deviation of this variable

is 1.828. Moreover, particularly interesting is that the Altman Z-score in 2015 has a mean and a

median of 1.417 and 1.698 respectively. Following Altman (1968), this means that the typical

firms in the sample according to both the average and the median, have values of the Altman Z-

score in 2015 lower than the bottom threshold of the grey zone that is 1.81 and thus belong to the

group of firms with a certain risk of going bankrupt in the two upcoming years. The Altman Z-

score in 2015 varies between 72.307 and -211.099. For all of the dependent variables, values close

to the extremes are very rare in fact, but still exist despite the winsorisation. Concerning the

independent variable IR, which is a dummy variable which gets the value of 1 if a specific firm

has issued integrated reports for 2013 and 2014 and otherwise it gets the value of 0, since only 46

out of 3011 firms are identified as integrated reporters, the mean of this variable is very low,

namely it is only 0.015. The median has a value of 0 meaning that the typical firm in the sample

according to the median is not placed in the group of firms which have practised integrated

reporting for 2013 and 2014. With regards to a firm’s total assets, there is a substantial difference

between the mean of the firms included in the sample which is € 2,973,883 and the median which

has a value of only € 198,619. After the winsorisation at the 1st and 99th percentile, the maximum

value of the variable total assets is € 72,748,803 and the minimum value is € 1,937. The average

age in 2015 for the firms in the sample is slightly above 47 years, while the typical firm according

to the median was 28 years old in 2015. Since in the sample only publicly traded firms are

included, it makes sense that the typical firm, according to the median, is almost 30 years old. The

oldest firm in the sample was 496 years old in 2015, while the youngest firm had 5 years of age at

the time. Concerning the long-term repayment capacity of firms, I lose 98 observations since for

some firms the long-term liabilities of 2015 have a value of 0 and accordingly, equation (16) could

not be calculated because in this equation the firm’s cash flow is divided with the firm’s long-term

labilities. The variables which are used in the calculation of the long-term repayment capacity are

also winsorised. As shown in Table 5, the mean for this variable is even negative -1.028, while

the median has a positive value which is 0.278. The extreme values for this variable are 5,635.500

as the maximum and -4,644.333 as the minimum. The mean of the KZ index computed for the

financial year 2012 has a value of 1.696, whereas its median, which is a bit lower than the mean,

has a value of 1.374. The KZ index of 2012 is characterised with a substantially higher maximum

value relative to the KZ index of 2015. Namely, the KZ index of 2012 reaches a maximum value

of 1,585.092. On the other hand, the value of the minimum for the variable KZ index of 2012 is -

35

157.256. Relative to the KZ index of 2015 whose standard deviation is 6.478, the KZ index of

2012 has a more pronounced standard deviation which has a value of 29.082. The mean and the

median of the WW index computed for 2012 are -0.780 and -0.559 respectively. Moreover, the

maximum value which the WW index of 2012 receives in the sample of firms is 0.052, whereas

the minimum value for this index is -187.716, which is much higher than the minimum of the WW

index in 2015 which is -549.068. The standard deviation of the WW index of 2012 is 4.979. When

it comes to the SA index computed for 2012, the mean and the median receive the values of -4.106

and -3.705 respectively. The maximum value of the SA index of 2012 is 0.349, whereas its

minimum value is -22.274 and these values are very similar to the extremes of the SA index of

2015. The standard deviation of the SA index of 2012 with a value of 1.820 is also very similar to

the standard deviation of the SA index of 2015 which is 1.828. The Altman Z-score in 2012 has a

mean of 2.584, indicating that the average firm in 2012 is financially healthier relative to the

average firm in 2015, because the mean of the Altman Z-score in 2015 is 1.417. However, the

median value of the Altman Z-score in 2012 is 1.656 which is slightly lower than the median of

the Altman Z-score in 2015 which is 1.698. The Altman Z-score in 2012 varies between 3,360.821

and -74.628.

5.2. Correlations

Table 6 presents the correlation coefficients and their statistical significance regarding the

dependent, independent and control variables, except for the country and industry dummy

variables. Regarding the KZ index of 2015, it is not highly correlated with any other variable.

Concerning the correlation coefficients which are statistically significant, the KZ index of 2015 is

negatively correlated with the Altman Z-scores of 2015 and 2012 and with the natural logarithm

of total assets. It is expected for firms with increased financial health and larger firms to a have

lower KZ index and thus be less financially constrained. There also exists a positive and

statistically significant correlation between the KZ indices of 2015 and 2012, although the

correlation coefficient is only 0.167. However, none of the correlation coefficients between the

WW index of 2015 and any other variable is statistically significant. The SA index of 2015 exhibits

very high and statistically highly significant correlation with the natural logarithm of firm’s age

and with the SA index of 2012. The correlation coefficients are -0.781 and 0.996 respectively. The

reason for the high correlation with firm’s age is the fact that firm’s age is directly incorporated

in the derivation of the SA index. Moreover, it is logical that older firms are in fact less financially

constrained.

36

Table 6: Correlations

Variable 1 2 3 4 5 6

1 KZI 2015 1.000

2 WWI 2015 0.003 1.000

3 SAI 2015 0.006 0.027 1.000

4 Z-score 2015 -0.280*** -0.006 -0.040** 1.000

5 IR -0.007 0.003 0.101*** 0.013 1.000

6 Ln(TA) 2015 -0.062*** -0.007 0.208*** 0.166*** 0.218*** 1.000

7 Ln(Age 2015) -0.024 -0.018 -0.781*** 0.115*** 0.030 0.256***

8 LTRC 2015 -0.029 -0.001 0.007 0.120*** 0.001 0.040**

9 KZI 2012 0.167*** -0.001 -0.015 -0.178*** -0.016 -0.060***

10 WWI 2012 -0.010 -0.001 -0.020 0.052*** 0.000 0.008

11 SAI 2012 0.012 0.028 0.996*** -0.050*** 0.099*** 0.187***

12 Z-score 2012 -0.198*** -0.000 -0.045** 0.539*** 0.026 0.173***

***p<0.01, **p<0.05, *p<0.10; definitions of the variables are presented in Table 1.

Variable 7 8 9 10 11 12

7 Ln(Age 2015) 1.000

8 LTRC 2015 0.017 1.000

9 KZI 2012 -0.003 -0.009 1.000

10 WWI 2012 0.041** -0.000 -0.063*** 1.000

11 SAI 2012 -0.786*** 0.005 -0.011 -0.021 1.000

12 Z-score 2012 0.121*** 0.074*** -0.241*** 0.039** -0.053*** 1.000

***p<0.01, **p<0.05, *p<0.10; definitions of the variables are presented in Table 1.

Regarding the high correlation between the SA indices of 2012 and 2015, a reason could be

that only firm’s total assets and firm’s age are used in order to calculate these indices (equation

(10)). In a period of three years, firms not only became older, but they probably did not experience

a drastic change in their total assets base, leading to the logical reasoning that these two indices

have similar values and are highly correlated. As expected, the Altman Z-score of 2015 is

positively correlated with firm’s size, firm’s age, firm’s long-term repayment capacity and the

Altman Z-score of 2012 and the appropriate p values of the correlation coefficients are lower than

0.01 which implies high statistical significance. In fact, larger and older firms with an increased

level of long-term repayment capacity are expected to have an increased Altman Z-score and thus

be considered as financially healthier. The correlation coefficient between the Altman Z-scores of

2015 and 2012 is relatively high with a value of 0.539. The IR variable is not highly correlated

with any other variable. Although it might seem logical to expect that firm’s size and firm’s age

are highly correlated since usually larger firms are in fact older ones, the coefficient of correlation

between firm size and firm age is only 0.256 and it is highly statistically significant. This is a

strong indicator that it is not likely for multicollinearity to exist in the regression models where I

37

include both firm size and firm age as control variables. Moreover, as shown in Table 6, the natural

logarithm of firm’s total assets is not highly correlated with any other of the control variables. For

example, after firm’s age, the second highest correlation coefficient in absolute terms which is

also statistically significant is with the SA index of 2012 and its value is only 0.187. Concerning

the natural logarithm of firm’s age, even though it exhibits high and statistically highly significant

correlation with the SA index of 2012, I never include these two variables as control variables in

a same model, so again multicollinearity is hopefully avoided. With regards to the long-term

repayment capacity for 2015, it exhibits relatively low correlation with all of the other variables.

Namely, for this variable, the highest correlation coefficient which is also statistically significant

is the coefficient presenting the correlation with the Altman Z-score of 2015 which is in fact a

dependent variable. The value of this correlation coefficient is only 0.120. The variable KZ index

in 2012 is also not highly correlated with any other control variable. The highest correlation

coefficient in absolute terms regarding the KZ index in 2012 is the one depicting the correlation

with the Altman Z-score of 2012. This coefficient is -0.241 and its p value is lower than 0.01

making it highly statistically significant. The WW index of 2012 is not highly correlated with any

other variable as well. Despite the high and statistically significant correlation between the SA

index in 2012 and the SA index in 2015 and the high correlation between the SA index in 2012 and

firm’s age in 2015, the control variable SA index in 2012 is not highly correlated with any other

variable. The Altman Z-score in 2012 is not statistically significantly correlated with the

independent variable. Finally, it is not highly correlated with any of the control variables with

which it is included in the regression model. For instance, in absolute terms, the highest correlation

concerning the Altman Z-score in 2012 and any one of the independent and control variables is

with the KZ index of 2012. This correlation coefficient with a value of only -0.241 is statistically

highly significant.

As it can be seen from the correlations tables, there does not exist high and statistically

significant correlation between the independent variable and any other control variable which are

included in a same regression model at the same time. Moreover, there does not exist high and

statistically significant correlation between any two control variables which are at the same time

included in a certain regression model. The aforementioned facts lead to the conclusion that

multicollinearity is most likely avoided concerning the regression models employed in this

research.

38

5.3. Regressions results

The results from the full OLS regression models depicted with equations (1), (2), (3) and (4)

are presented in Table 7 under specifications (1), (2), (3) and (4) respectively. For the regression

models in which either heteroscedasticity or serial correlation, or both of them were detected, I

present the robust standard errors. For the regression models in which neither heteroscedasticity,

nor serial correlation was detected, I present the normal standard errors. In order to assess the

sensitivity of my findings, I construct several different specifications for each of the full regression

models, in which I exclude some of the control variables. In the section containing the attachments,

I present Tables 8, 9 and 10 which contain the regression results from different specifications of

the models (1), (2) (3) and (4). The results from the different specifications serve as a test for the

sensitivity of the regression results obtained from the full regression models. In Table 8 I present

the results from basically the same regression models as explained above with the difference being

the fact that I exclude the controls for country-specific and industry-specific factors. Moreover, in

Table 9 I present the results from generally the same regression models including the country and

industry controls, but excluding the lagged dependent variable in each of the four specifications.

Finally, in Table 10 I present the regression results from the same regressions after removing the

country, industry and lagged dependent variable controls out of the models.

Before discussing the regression results in detail, I refer again to the two hypothesis developed

in this research.

Hypothesis A: Firms which practise integrated reporting have lower financial constraints relative

to firm which do not practise integrated reporting.

Hypothesis B: Firms which practise integrated reporting have increased financial health relative

to firms which do not practise integrated reporting.

In addition to this, the regression models presented with equations (1), (2) and (3) are used for

testing Hypothesis A, while the regression model corresponding to equation (4) is used for testing

Hypothesis B.

39

Table 7: Regression results: Full OLS regression models

Dependent variable

Independent and

control variables

KZI 2015

(1)

WWI 2015

(2)

SAI 2015

(3)

Z-score 2015

(4)

Constant 3.469*** 1.091 -0.081*** -1.691*

(1.267) (2.294) (0.022) (0.963)

IR 0.447 0.057 0.059*** -0.462**

(0.998) (1.957) (0.020) (0.190)

Ln(TA 2015) -0.167*** 0.168***

(0.060) (0.054)

Ln(Age 2015) -0.240 -0.339 0.096

(0.161) (0.310) (0.113)

LTRC 2015 0.002**

(0.001)

KZI 2012 0.004

(0.004)

WWI 2012 -0.001

(0.048)

SAI 2012 1.002***

(0.002)

Z-score 2012 0.681***

(0.191)

Country dummy

variables Yes Yes Yes Yes

Industry dummy

variables Yes Yes Yes Yes

Observations 3,011 3,011 3,011 2,913

R squared 0.022 0.009 0.993 0.318

Adjusted R squared 0.005 -0.009 0.993 0.305

F-statistic 1.260* 0.494 8,069.668*** 24.656***

***p<0.01, **p<0.05, *p<0.10; the standard errors presented in parenthesis are robust only for those

regressions in which either heteroscedasticity or serial correlation, or both of them were detected, while for

the regressions in which neither heteroscedasticity, nor serial correlation was detected the normal standard

errors are presented (in this case for specifications (3) and (4) the robust standard errors are presented,

while for specifications (1) and (2) the normal standard errors are presented); definitions of the variables

are presented in Table 1.

40

With regards to specification (1) in Table 7 which corresponds to the regression model

expressed with equation (1), the constant is positive and highly statistically significant.

Surprisingly, the estimated coefficient for the independent variable IR is positive. Its value is

0.447. If this coefficient was statistically significant, it would mean that firms which have issued

integrated reports for 2013 and 2014 have a higher KZ index in 2015 for 0.447 units and are hence

financially more constrained relative to firms which have not issued integrated reports for both

2013 and 2014. However, this coefficient is not statistically significant. Although the estimated

coefficient is positive, since it is not statistically significant even at the 10% level, it is not

appropriate to say that integrated reporting firms are financially more constrained than non-

integrated reporting firms. If this estimated coefficient of the independent variable IR was

statistically significant, then it would be appropriate to say that firms which have adopted

integrated reporting are financially more constrained relative to firms which have not adopted

integrated reporting. However, I also cannot say that integrated reporters are financially less

constrained relative to non-integrated reporters. Following the results from specification 1 and in

contrast with the initial expectations, I reject Hypothesis A. In other words, firms which have

issued integrated reports for 2013 and 2014 are not financially less constrained in 2015 relative to

firms which have not issued integrated reports for both 2013 and 2014. Moreover, in line with the

expectations, the estimated coefficient of firm size is negative and statistically significant at the

level of 1%. This means that larger firms are characterised with lower KZ index and are thus

financially less constrained relative to smaller firms. In terms of firm age, although the estimated

coefficient is negative as it was initially expected, it is not statistically significant. Finally, the

estimated coefficient for the KZ index of 2012, although positive, is also not statistically

significant, which could be interpreted that the historical levels of financial constraints do not

statistically significantly affect the current level of financial constraints which firms face. As it

can be noted from Table 7, the specification includes controls for country-specific and industry-

specific factors. The value of the R squared is 0.022 or as a percentage 2.2%. The adjusted R

squared, which takes into account the number of variables included in the model, is only 0.005 or

0.5%. Although the values of the R squared and subsequently the adjusted R squared are very low,

the F-statistic is statistically significant at the 10% level, indicating that all of the included

independent and control variables in the regression model can jointly influence the KZ index of

2015. The results remain the same across the different specifications presented in Tables 8, 9 and

10 respectively. These tables are given in the attachments section. To be more specific, in all of

41

the different specifications the IR variable remains statistically not significant, confirming the

robustness of the findings and the rejection of Hypothesis A.

The regression results with regards to the regression model where the dependent variable is

the WW index of 2015, referring to equation (2), are presented under specification (2) in Table 7.

It can be observed that the estimated coefficients of the constant, the independent variable and the

control variables are not characterised with statistical significance. Although the estimated

coefficient of the IR variable is positive, it is statistically insignificant, just like in the regressions

where the dependent variable is the KZ index of 2015. If this estimated coefficient was statistically

significant, it could be said that integrated reporting firms have higher WW index in 2015 and are

hence financially more constrained relative to non-integrated reporting firms, which is in contrast

to the initial expectations. Anyway, the fact that the estimated coefficient of the independent

variable IR is not statistically significant leads to the rejection of Hypothesis A. Firm size is

excluded from the model since it is used as one of the variables for calculating the dependent

variable WW index of 2015 (Cheng et al., 2014). The estimated coefficient of firm age is negative,

but this variable is also statistically not significant. Finally, the lagged dependent variable whose

coefficient has a negative value, is also statistically insignificant. Even the R squared value is very

low, namely it is only 0.009. Since none of the independent and control variables is statistically

significant even at the level of 10%, it is logical to expect that the F-statistic is statistically

insignificant as well. As it can be seen from Table 7, the F-statistic is not statistically significant

implying that the included variables together do not impact the dependent variable. With regards

to the different specifications in which the dependent variable is the WW index of 2015 depicted

in Tables 8, 9 and 10, the results remain the same. Namely, the estimated coefficients of the

independent and the control variables remain statistically not significant, confirming the

robustness of the findings and the rejection of Hypothesis A.

Concerning the regression model depicted with equation (3) where the dependent variable is

the SA index of financial constraints in 2015, the results are shown under specification (3) in Table

7. Since both firm size and firm age are used for the computation of the SA index, I omit these

variables and do not control for them in this model (Cheng et al., 2014). The estimated coefficient

of the constant is negative and it is statistically highly significant. However, in contrast to what

was initially expected, the estimated coefficient of the IR variable is positive and it is statistically

highly significant since the appropriate p value is lower than 0.01. Since the value of the estimated

coefficient of the IR variable is 0.059, the interpretation of this result would be that firms which

42

have issued integrated reports for 2013 and 2014, on average have a higher SA index in 2015 for

0.059 units and are hence financially more constrained relative to firms which have not issued

integrated reports for 2013 and 2014. In this manner, I reject Hypothesis A which states that firms

which practise integrated reporting have lower financial constraints relative to firms which do not

practise integrated reporting. Moreover, as previously highlighted, there is a very high correlation

between the SA index of 2015 and the SA index of 2012. The correlation coefficient between these

two variables is 0.996 and it is highly statistically significant (Table 6). Similar to this, in the

regression model the estimated coefficient of the SA index of 2012 is positive and highly

statistically significant since the appropriate p value for this estimated coefficient is lower than

0.01. Additionally, the estimated coefficient of the SA index of 2012 is 1.002 and the appropriate

interpretation of this result is that as the SA index in 2012 increases for one unit, the SA index in

2015 increases for 1.002 units, or basically the increase in units in the dependent variable is almost

identical to the increase in units of the control variable. The p value with regards to the F-statistic

is also lower than 0.01 implying highly statistically significant result. This means that the included

variables together have a significant impact on the dependent variable, the SA index of 2015.

Particularly high values can be observed when it comes to the R squared and the adjusted R

squared. Their rounded values amount to 0.993, indicating that 99.3% of the variation of the

variable SA index of 2015 can be explained with the included independent and control variables

in the model. Interesting to note is that after removing the lagged dependent variable out of the

model, the R squared and the adjusted R squared have the values of 0.142 and 0.128 respectively,

implying the huge impact of the SA index of 2012 on the dependent variable SA index of 2015

(attachments section, Table 9, specification (3)). The results remain generally the same across the

other three specifications depicted in Tables 8, 9 and 10 respectively, which confirms the rejection

of Hypothesis A. To sum up, after presenting the regression results from the three full OLS

regression models where the dependent variable is an index of financial constraints, I reject

Hypothesis A. Firms which have issued integrated reports for 2013 and 2014 are not financially

less constrained in 2015 relative to firms which have not issued integrated reports for 2013 and

2014. This means that firms which practise integrated reporting are not financially less constrained

relative to firms which do not practise integrated reporting. Additionally, the regression results

from the regression model presented with equation (3), show that firms which have issued

integrated reports for 2013 and 2014 are financially more constrained in 2015 in comparison with

firms which have not issued integrated reports for 2013 and 2014. The results remain generally

43

the same across the different specifications given in Tables 8, 9 and 10 respectively, confirming

the rejection of Hypothesis A.

In order to test Hypothesis B, I construct an OLS regression model as depicted with equation

(4). The results from this full regression model are presented under specification (4) in Table 7.

The constant term is negative and statistically significant at the 10% level. Surprisingly and

opposite from the initial expectations, the estimated coefficient of the IR variable is negative and

its corresponding p value is lower than 0.05, which means it is statistically significant at the 5%

level. Since the estimated coefficient of the IR variable is -0.462, it can be interpreted that firms

which have issued integrated reports for 2013 and 2014, on average experience lower Altman Z-

score in 2015 for 0.462 units and thus have worse financial health relative to firms which have not

issued integrated reports for 2013 and 2014. This means that firms which have adopted integrated

reporting have worse financial health relative to firms which have not adopted integrated

reporting. Therefore, Hypothesis B which states that firms which practise integrated reporting

have increased financial health relative to firms which do not practise integrated reporting, is

rejected. Moreover, the estimated coefficient of firm size, which is 0.168, is positive and

statistically highly significant since the corresponding p value is lower than 0.01. This result means

that larger firms have higher values of the Altman Z-score and can be considered to have better

financial health relative to smaller firms. Although firm age has a positive sign, it is not statistically

significant. As expected, the long-term repayment capacity of a firm has a positive sign and it is

statistically significant at the level of 5%. However, this estimated coefficient has a relatively low

value, namely it is only 0.002. This means that if the long-term repayment capacity increases for

one unit, the Altman Z-score of 2015 will increase for only 0.002 units, still meaning that firms

which have higher long-term repayment capacity are financially healthier. Furthermore, the

estimated coefficient of the lagged dependent variable is positive and its appropriate p value is

lower than 0.01, which means that it is statistically highly significant. So, the historical financial

health of a firm in 2012 has a significant influence on the firm’s financial health in 2015. The

values of the R squared and the adjusted R squared are 0.318 and 0.305 respectively. The p value

of the F-statistic is lower than 0.01, implying a high statistical significance. This means that the

included independent variable and control variables, together influence the dependent variable, in

this case the Altman Z-score of 2015. In the other specifications of this regression model the results

remain generally the same. More specifically, the estimated coefficient of the IR variable remains

negative and statistically significant across the other specifications presented in Tables 8, 9 and

10 respectively which confirms the robustness of my findings that firms which have issued

44

integrated reports for 2013 and 2014 exhibit lower financial health in 2015 relative to firms which

have not issued integrated reports for 2013 and 2014 and therefore the rejection of Hypothesis B

is confirmed as well.

5.4. Practical interpretation of the results

Following the results from the regression models, I can say that in the sample of European

publicly traded firms, firms which have issued integrated reports for the financial years 2013 and

2014 are not financially less constrained and are not financially healthier in 2015 in comparison

with firms which have not issued integrated reports for 2013 and 2014. The results even indicate

that firms which have issued integrated reports for 2013 and 2014 have worse financial health in

2015 in comparison with firms which have not issued integrated reports for 2013 and 2014. Based

on the regression results, I reject both Hypothesis A and Hypothesis B. These findings are in

contrast with what I initially expected. For instance, as mentioned in the literature review section,

the practice of integrated reporting has been proven to lower the cost of equity capital and to

provide the analysts and the public with additional information which is not available in the

separately issued financial and sustainability reports (Zhou et al., 2017). Regarding Hypothesis A,

the regression results which I obtain indicate that the adoption of integrated reporting does not

lead to a reduction of the financial constraints faced by firms. In fact, except for the model in

which the dependent variable is the SA index of 2015, the IR variable is not statistically significant,

implying that there does not exist significant difference between integrated reporters and non-

integrated reporters in terms of the financial constraints which they experience. Although Cheng

et al. (2014) argue that increased socially responsible performance of firms lowers the financial

constraints faced by them, in this study I do not get similar results with regards to the concept of

integrated reporting. Furthermore, Dhaliwal et al. (2014) suggest that increased announcement of

CSR-based information causes reduction of the cost of equity capital. However, the results that I

obtain do not confirm specific benefits for the firms which have adopted integrated reporting. On

the other hand, the results which I present are similar to the findings of Lima Crisóstomo et al.

(2011) who fail to discover a link between a firm’s CSR and its financial success and even confirm

a negative influence of CSR on firm value for firms originating from Brazil. A potential reason

for the results which I obtain could be that integrated reporting is still a very new, fresh concept,

which is not still precisely defined and fully developed. It has been indicated that integrated

reporting is mainly the result of firms’ voluntary initiatives to adopt this concept (Eccles &

Serafeim, 2011). Moreover, different firms adopt it in different ways without following some strict

45

and well-specified standards, rules, principles and guidelines. Although certain firms follow the

International Integrated Reporting Framework as depicted by the IIRC (2013), still many other

firms which are listed as integrated reporters on the official website of Integrated Reporting, do

not follow the Framework to the same extent. Firms still enjoy freedom to a large extent in terms

of the preparation of integrated reports. For example, firms are very flexible with regards to their

decisions what information to include and the method of presentation of the information in the

integrated reports. That is why the level of quality of integrated reporting differs among different

adopters of this concept. Therefore, for now, integrated reporting still cannot be considered to be

credible, relevant, comparable and reliable enough. So, the lack of credibility coming from the

absence of following certain strict standards and guidelines during the preparation of integrated

reports, could be one reason why integrated reporting still does not perform the expected

communication function in a superior manner and does not lead to the expected benefits for the

firms that adopt it. Moreover, it is likely that due to the fact that integrated reporting is still a new

reporting mechanism, it should take more time for this concept to be more precisely defined and

developed in a way in which it will cause investors, analysts and stakeholders to see it as a more

valuable mechanism. One of the motives behind the preparation of the International Integrated

Reporting Framework is in fact the development and introduction of some well-specified

guidelines which would improve the quality, credibility and usefulness of the concept known as

integrated reporting (IIRC, 2013). Another possible explanation for the results which I obtain

concerning the financial constraints of firms is that it should take more time in order for the

integrated reports to really start bringing financial benefits for firms. Just to remind, I define a firm

as an integrated reporter if the firm has issued integrated reports for 2013 and 2014, whereas I

calculate the financial indices of a firm for 2015. Although some of the firms which are identified

as integrated reporters have started preparing and issuing integrated reports before 2013, even

these firms’ first integrated reports were not issued much earlier than 2013, possibly around 2011

or 2012. A period of only two, or even three years could be too short in order real, tangible benefits

to be realised from the introduction of the concept of integrated reporting. Integrated reporting can

be seen as a sort of an innovation and an investment since its adoption requires a lot of effort,

management support, support from the employees, cohesion inside a firm and of course financial

resources. For the implementation of serious and demanding projects such as integrated reporting,

probably more time should pass in order for some tangible benefits to be observed for the firms

which have adopted it. To sum up, concerning Hypothesis A, probably the main reasons why

integrated reporting does not lead to lowering of the financial constraints are the facts that it is

46

differently adopted by different firms, it is still not properly defined, it does not follow any strict

and specific standards or guidelines, which leads to a lack of credibility, reliability and

comparability. Moreover, this lack of relevance and comparability could negatively impact the

investors and audience in the sense that they do not see integrated reports as that much different

and more useful than the separate financial and sustainability reports. Finally, similar to traditional

investments initiated by firms, after the introduction of a specific innovative concept such as

integrated reporting, more time should pass in order the real benefits to be recognised and

experienced by firms.

When it comes to Hypothesis B, not only that it is rejected, but since the estimated coefficient

of the IR variable is negative and statistically significant in all four specifications of the regression

model in which the dependent variable is the Altman Z-score of 2015, it can be stated that

integrated reporters exhibit worse financial health relative to non-integrated reporters. First of all,

the adoption of integrated reporting is an expensive process. It requires a lot of effort at all

management levels, support from not only the top managers, but also from the board of directors

and even from the employees. It definitely takes a lot of energy, effort at all organisational levels,

good management and time as well, in order for a firm to adopt this concept properly and produce

an integrated report. It is also expected that in order integrated reporting to be implemented

successfully in a specific corporation, all of the corporation’s divisions and departments should

show a cohesion through an open and honest collaboration, which further makes the adoption and

practice of this concept more difficult and more expensive. A possible interpretation of the results

which I obtain regarding the financial health of firms is that the costs incurred to implement

integrated reporting outweigh the realised financial benefits, especially in the short-term, leading

to a worsening of the financial situation of the firms in the first couple of years after the

introduction of integrated reporting. As I already mentioned, I define a firm as an integrated

reporter if the firm has issued integrated reports for 2013 and 2014. Although some firms have

started issuing integrated reports for years earlier than 2013, their first integrated reports have not

been issued much earlier than 2013, possibly one or two years before those issued for 2013. Two,

three, or even four years as a time period is probably overly optimistic for seeing tangible benefits

from the adoption of this challenging concept. As noted previously, another reason could be the

lack of credibility and relevance in terms of preparing integrated reports. It is possible that as more

specific standards and guidelines are defined, more credible, reliable and comparable integrated

reports will be prepared. In this manner, real financial benefits are more likely to start appearing

from the adoption of integrated reporting. However, for now, as indicated from the results, I cannot

47

say that integrated reporting brings tangible financial benefits for firms which adopt it. To sum

up, firms which practise integrated reporting do not have better access to financial resources

relative to firms which do not practise integrated reporting.

48

6. CONCLUSION

I believe that this paper provides some very interesting results with regards to the concept of

integrated reporting and its impact on a firm’s access to finance. Although the findings in this

paper are opposite from what was initially expected, I am convinced that this paper contributes to

a better understanding of the idea of integrated reporting. It is essential that firms, stakeholders

and boards which set accounting standards immediately start to collaborate regarding this topic in

terms of improving the basis for producing more credible, relevant and comparable integrated

reports, so that the expected benefits of this concept can be fully realised in the foreseeable future.

The role of a firm’s stakeholders, including investors, financial institutions, employees, customers,

governmental institutions and environmental organisations is very important in this process, since

integrated reports communicate relevant corporate information towards the firm’s stakeholders

(IIRC, 2011). Stakeholders’ ideas, requests, advice and support could be of tremendous

importance in the process of creating a precise and reliable basis in terms of standards, principles

and guidelines for the preparation of integrated reports. As implied by the IIRC (2013), the

International Integrated Reporting Framework is indeed a very serious approach towards

standardisation and uniformity of integrated reporting, meaning that the Framework could indeed

set a solid ground for the preparation and issuance of more credible, reliable and comparable

integrated reports.

The research question in this paper is “Do firms which practise integrated reporting have better

access to finance relative to firms which do not practise integrated reporting?” In order to

investigate this question, I develop two hypotheses:

Hypothesis A: Firms which practise integrated reporting have lower financial constraints relative

to firms which do not practise integrated reporting.

Hypothesis B: Firms which practise integrated reporting have increased financial health relative

to firms which do not practise integrated reporting.

In order to test Hypothesis A I construct three OLS regression models in which the dependent

variable is an index of financial constraints, whereas for the purpose of testing Hypothesis B I

construct one OLS regression model in which the dependent variable is the Altman Z-score. In the

regression models, the independent variable is a dummy variable which takes the value of 1 if a

specific firm has issued integrated reports for 2013 and 2014 and otherwise this variable takes the

value of 0. The results which I obtain indicate that firms which have issued integrated reports for

2013 and 2014 do not have lower financial constraints in 2015 relative to firms which have not

49

issued integrated reports for both 2013 and 2014. Furthermore, the regression results indicate that

firms which have issued integrated reports for 2013 and 2014 are characterised with worse

financial health in 2015 relative to firms which have not issued integrated reports for 2013 and

2014. The results lead to the rejection of both Hypothesis A and Hypothesis B. This means that

firms which practise integrated reporting do not have better access to financial resources relative

to firms which do not practise integrated reporting. Possible explanation of these results could be

that integrated reporting is still a new, not fully developed, not very precisely defined concept and

it is being adopted differently by different firms without following some strictly defined rules,

standards and guidelines. The reasoning could be that due to the novelty of this concept, it may

still be perceived as less credible and relevant and therefore it does not help firms which adopt it

to lower their financial constraints. On the other hand, since integrated reporting is a serious and

challenging concept to be implemented, maybe more time should pass in order for some tangible

benefits to be experienced by the firms which have adopted it. A period of two years is probably

not enough for specific tangible benefits to outweigh the costs incurred in the process of adoption

and implementation of integrated reporting. These findings can be useful and important for firms

which plan to adopt integrated reporting, for firms which already adopted integrated reporting, but

also for firms’ stakeholders including investors, capital providers, employees, customers,

environmental institutions, governmental institutions and others. Finally, these findings are

especially important for standards setting boards and proponents of integrated reporting in order

to exert effort and in collaboration with firms and stakeholders to improve and fully develop the

appropriate standards, principles and guidelines for a more credible preparation of integrated

reports.

This paper has several limitations as well. First of all, I believe that the “ideal” way of

investigating the effects of firms’ practice of integrated reporting is by using a measure of the

extent to which firms practise integrated reporting. To be more specific, since integrated reporting

is still not precisely defined in terms of strict rules, standards and guidelines and the majority of

the firms adopt it on a voluntary basis, it would be much better if there is a measure of the degree

to which this concept is practised by firm. Due to the fact that I was not able to obtain this kind of

data, I decided to categorise a given firm as either an integrated reporter or a non-integrated

reporter. However, through this method of categorisation I am unable to assess and evaluate not

only the degree to which firms practise integrated reporting, but also the quality of the integrated

reports. Moreover, I believe that investigating the impact of the adoption of a specific concept

such as integrated reporting, can be done in a better way if longitudinal data is used in the research

50

process. In this way, not only the specific relationships between the variables can be analysed, but

the evolution of certain trends throughout time can be presented as well. However, since this is a

new concept, I was unable to obtain longitudinal data for a period of several years. Finally, I was

not able to find a variable which would be a measure of the CSR performance of firms. However,

since the results are completely opposite of what I initially expected, I do not believe that the

inclusion of a control variable which is a measure of firms’ CSR performance would drastically

change the results. Additionally, the different specifications of the full regression models provide

generally the same results.

Despite the limitations, I am convinced that this paper contributes to filling the gap in the

scarce literature which exists regarding integrated reporting. I believe that this paper helps for a

better understanding of the idea of integrated reporting. I am also strongly convinced that this

research would serve as an inspirational ground for many researchers around the world to start

exploring integrated reporting much more in the foreseeable future, so that this concept becomes

not only better understood by academia, but by practitioners themselves. Under the term

practitioners, I recognise large and small, publicly traded and privately owned firms, investors,

financial institutions, customers, environmental organisations, legislative bodies, accounting

boards and other firm’s stakeholders who are interested in this topic. Although in this paper the

main focus is specifically the relation between integrated reporting and a firm’s ability to access

finance, there are plenty of other areas which can be researched in terms of integrated reporting.

For example, it can be researched what causes firms to adopt integrated reporting, what are firms’

ideas and motivations for the adoption of this concept, what kinds of inside governance processes

exhibit firms which adopt integrated reporting, which changes firms go through when adopting

this concept, what are both financial and non-financial benefits which firms experience after the

adoption of this concept and other related topic.

VI

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X

ATTACHMENT 1

Table 8: Regression results: OLS regression models without country and industry controls

Dependent variable

Independent and

control variables

KZI 2015

(1)

WWI 2015

(2)

SAI 2015

(3)

Z 2015

(4)

Constant 3.682*** -0.012 -0.085*** -1.697**

(0.733) (1.002) (0.009) (0.706)

IR 0.335 0.315 0.051** -0.555***

(0.985) (1.918) (0.020) (0.176)

Ln(TA 2015) -0.161*** 0.126***

(0.055) (0.043)

Ln(Age 2015) -0.115 -0.286 0.168**

(0.145) (0.279) (0.076)

LTRC 2015 0.002**

(0.001)

KZI 2012 0.004

(0.004)

WWI 2012 -0.002

(0.047)

SAI 2012 1.000***

(0.002)

Z 2012 0.698***

(0.190)

Country dummy

variables No No No No

Industry dummy

variables No No No No

Observations 3,011 3,011 3,011 2,913

R squared 0.004 0.000 0.992 0.303

Adjusted R squared 0.003 -0.001 0.992 0.302

F-statistic 3.158** 0.359 187,607.4*** 252.710***

***p<0.01, **p<0.05, *p<0.10; the standard errors presented in parenthesis are robust only for those

regressions in which either heteroscedasticity or serial correlation, or both of them were detected, while for

the regressions in which neither heteroscedasticity, nor serial correlation was detected the normal standard

errors are presented (in this case for specifications (3) and (4) the robust standard errors are presented,

while for specifications (1) and (2) the normal standard errors are presented); definitions of the variables

are presented in Table 1.

XI

ATTACHMENT 2

Table 9: Regression results: OLS regression models without lagged dependent variable control

Dependent variable

Independent and

control variables

KZI 2015

(1)

WWI 2015

(2)

SAI 2015

(3)

Z 2015

(4)

Constant 3.501*** 1.093 -3.496*** -3.432***

(1.267) (2.292) (0.199) (1.320)

IR 0.448 0.057 1.253*** -0.544*

(0.998) (1.957) (0.242) (0.283)

Ln(TA 2015) -0.169*** 0.340***

(0.060) (0.079)

Ln(Age 2015) -0.240 -0.339 0.291**

(0.161) (0.310) (0.145)

LTRC 2015 0.003**

(0.001)

Country dummy

variables Yes Yes Yes Yes

Industry dummy

variables Yes Yes Yes Yes

Observations 3,011 3,011 3,011 2,913

R squared 0.022 0.009 0.142 0.080

Adjusted R squared 0.005 -0.008 0.128 0.063

F-statistic 1.265* 0.504 9.833*** 4.713***

***p<0.01, **p<0.05, *p<0.10; the standard errors presented in parenthesis are robust only for those

regressions in which either heteroscedasticity or serial correlation, or both of them were detected, while for

the regressions in which neither heteroscedasticity, nor serial correlation was detected the normal standard

errors are presented (in this case for specifications (3) and (4) the robust standard errors are presented,

while for specifications (1) and (2) the normal standard errors are presented); definitions of the variables

are presented in Table 1.

XII

ATTACHMENT 3

Table 10: Regression results: OLS regression models without country, industry and lagged

dependent variable controls

Dependent variable

Independent and

control variables

KZI 2015

(1)

WWI 2015

(2)

SAI 2015

(3)

Z 2015

(4)

Constant 3.710*** -0.009 -4.212*** -3.173***

(0.732) (0.998) (0.033) (1.041)

IR 0.338 0.315 1.496*** -0.724**

(0.985) (1.918) (0.270) (0.301)

Ln(TA 2015) -0.162*** 0.272***

(0.055) (0.061)

Ln(Age 2015) -0.116 -0.286 0.375***

(0.145) (0.278) (0.099)

LTRC 2015 0.003**

(0.001)

Country dummy

variables No No No No

Industry dummy

variables No No No No

Observations 3,011 3,011 3,011 2,913

R squared 0.004 0.000 0.010 0.046

Adjusted R squared 0.003 -0.000 0.010 0.045

F-statistic 3.918*** 0.537 30.661*** 35.307***

***p<0.01, **p<0.05, *p<0.10; the standard errors presented in parenthesis are robust only for those

regressions in which either heteroscedasticity or serial correlation, or both of them were detected, while for

the regressions in which neither heteroscedasticity, nor serial correlation was detected the normal standard

errors are presented (in this case for specification (4) the robust standard errors are presented, while for

specifications (1), (2) and (3) the normal standard errors are presented); definitions of the variables are

presented in Table 1.