Publications of the University of Eastern FinlandDissertations in Social Sciences and Business Studies

Publications of the University of Eastern Finland

Dissertations in Social Sciences and Business Studies

isbn 978-952-61-1454-5

issn 1798-5757

Marika Miettinen

The Lending and Performance Determinants of Very Small Start-upsInsight into the Lenders’ Evaluation

Using the research setting of new

micro firms which have been granted

a loan from state-owned financial

institutions, the objective of this

study is to identify factors affecting

the financial performance of new

micro firms (measured by creditwor-

thiness), lender’s evaluation of firms’

prospects, and the factors affecting

the capital structure of new firms.

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Marika MiettinenThe Lending and

Performance Determinants of Very Small Start-ups

Insight into the Lenders’ Evaluation

The Lending and Performance Determinants

of Very Small Start-ups

Dissertations in Social Sciences and Business Studies No 83

MARIKA MIETTINEN

The Lending and Performance Determinants

of Very Small Start-upsInsight into the Lenders’ Evaluation

Publications of the University of Eastern FinlandDissertations in Social Sciences and Business Studies

No 83

Itä-Suomen yliopistoYhteiskuntatieteiden ja kauppatieteiden tiedekunta

Kuopio2014

Kopijyvä OyJoensuu, 2014

Senior Editor: Prof. Kimmo KatajalaEditor: Eija Fabritius

Sales: University of Eastern Finland Library

ISBN (print): 978-952-61-1453-8ISSN (print): 1798-5749

ISSN-L: 1798-5749ISBN (PDF): 978-952-61-1454-5

ISSN (PDF): 1798-5757

Miettinen, MarikaThe Lending and Performance Determinants of Very Small Start-upsInsight into the Lenders’ Evaluation 74 p.University of Eastern FinlandFaculty of Social Sciences and Business Studies, 2014Publications of the University of Eastern Finland, Dissertations in Social Sciences and Business Studies, no 83ISBN (print): 978-952-61-1453-8ISSN (print): 1798-5749ISSN-L: 1798-5749ISBN (PDF): 978-952-61-1454-5ISSN (PDF): 1798-5757Dissertation

ABSTRACT

The objective of this study is to identify factors affecting the financial perfor-mance of start-ups (measured by creditworthiness) and the capital structure, and lender’s evaluation of firms’ prospects. Little research has focused on these fac-tors of new micro firms which have been granted a loan from state-owned finan-cial institutions. Although prior research has mainly concentrated on accounting variables when modelling the financial performance and lender’s evaluation of business prospects, this study examines the impact of non-accounting variables. Furthermore, this study is one of the rare ones to examine the capital structure of new micro firms in a bank-centred economy.

The empirical data include 440 start-up firms and are collected from the database of a specialized financing company owned by the Finnish state, Finnvera Plc. This study suggests that a firm’s financial performance is influenced by a special mix of entrepreneurial, firm, and external characteristics. The findings show that founder-related variables are more important in examining multiple-point financial per-formance and good creditworthiness. Firm-funding variables and market-related variables are important in all models. When examining the capital structure of the firms, it is found that the firms have lower leverage when founders are wealthy and have no prior work experience in the same sector, and when firms are located in big cities, and have a good demand for products. In addition it is found that the determi-nants of creditworthiness and business prospects differ to a certain extent, thereby suggesting that the lenders ascribe importance to societal issues in granting loans.

The results obtained can be useful for prospective entrepreneurs, lenders, academics, and policymakers. The findings may enable a new firm to create the preconditions for successful firm development. If the lenders’ ability to predict the financial performance in micro business improves, it may encourage them to finance more relatively opaque and risky micro firms at the start-up stage.

Keywords: small business, finance, credit, government financial institutions, evaluation

Miettinen, MarikaAloittavien pienyritysten luotonantoon ja tulokseen vaikuttavat tekijät: Katsaus luotonantajan arviointiin. 74 s.Itä-Suomen yliopistoYhteiskuntatieteiden ja kauppatieteiden tiedekunta, 2014Publications of the University of Eastern Finland, Dissertations in Social Sciences and Business Studies, no 83ISBN (print): 978-952-61-1453-8ISSN (print): 1798-5749ISSN-L: 1798-5749ISBN (PDF): 978-952-61-1454-5ISSN (PDF): 1798-5757Väitöskirja

ABSTRAKTI

Tavoitteena on identifioida tekijät, jotka vaikuttavat aloittavan yrityksen taloudel-liseen tulokseen (mitattuna luottokelpoisuudella) ja pääomarakenteeseen sekä luo-tonantajan arvioon yrityksen kehitysnäkymistä. Aiempi tutkimus on pääasiassa keskittynyt tilinpäätöstietoihin mallintaessaan luottokelpoisuutta ja luotonantajan päätöksentekoa. Erityisen vähän on tutkittu aloittavia mikroyrityksiä, jotka ovat saaneet valtio-omisteisen rahoitusinstituution lainaa. Tämä tutkimus perustuu ei-tilinpäätöstietojen hyödyntämiseen ja on myös yksi harvoista, joka tutkii uusien yritysten rahoitusrakennetta pankkikeskeisessä taloudessa.

Empiirinen aineisto koostuu 440 toimintansa aloittavasta yrityksestä. Aineisto on kerätty Finnvera Oyj:n tietojärjestelmästä. Kun tutkitaan yritysten luottokelpoi-suutta, väitöskirjan tulokset viittaavat siihen, että vaaditaan erilaisia yhdistelmiä muuttujista, jotka liittyvät yrittäjään, yritykseen ja ympäristöön. Tulokset osoitta-vat, että yrittäjään liittyvät muuttujat ovat tärkeämpiä tutkittaessa moniportais-ta tulosta tai hyvää luottokelpoisuutta. Yrityksen rahoitukseen ja markkinoihin liittyvät muuttujat ovat tärkeitä kaikissa malleissa. Kun tutkitaan yritysten pää-omarakennetta, näyttää että alhaisempi velkaisuus on yrityksillä suuremmissa kaupungeissa ja joiden perustajalla on henkilökohtaista omaisuutta eikä saman alan työkokemusta. Kun verrataan luottokelpoisuutta ja luotonantajan arviota yri-tyksen näkymistä, tutkimuksessa havaitaan, että vaikuttavat tekijät ovat jossain määrin erilaisia. Tämä viittaa siihen, että luotonantajat painottavat yhteiskunnal-lisia tekijöitä myöntäessään lainaa.

Väitöskirjan tulokset ovat hyödyllisiä niin yrittäjiksi aikoville, luotonantajilla, tutkijoille kuin poliitikoille. Tutkimuksen tulokset voivat opastaa uutta yritystä luomaan edellytyksiä onnistuneelle yrityksen kehittämiselle. Jos luotonantajien kyky ennustaa mikroyritysten luottokelpoisuutta paranee, voi se rohkaista heitä rahoittamaan enemmän suhteellisen vaikeaselkoisia ja riskialttiita mikroyrityksiä toiminnan aloitusvaiheessa.

Asiasanat: mikroyritykset, rahoitus, luotonanto, luottokelpoisuus, Finnvera, arviointi

Acknowledgements

Having reached this point of completing this dissertation, I would like to express my gratitude to the people and institutions who have helped me in completing this project. I would like to express my warm gratitude to my supervisors: Professor Jyrki Niskanen, who in the first place gave me the opportunity to begin the re-search, to him and Professor Mervi Niskanen for supervising and advising me during the research process. I thank my co-authors, Professor Hannu Littunen and Professor Markku Virtanen for supporting and advising with the articles. I am also grateful to the official dissertation pre-examiners, Professor Tensie Steijvers and Professor Ari Hyytinen, for their valuable comments to improve my work.

I consider myself lucky to have access to special data I needed for this research from the financial institution Finnvera. I am grateful to the former Vice President of Risk Management, Jorma Lappalainen, for providing the permission to collect the data in the Master’s thesis phase. He was the first one to give me the inspira-tion to continue this scientific work. I also appreciate the further contribution of Vice President of Risk Management, Ilpo Jokinen. For help in data collection, I would like to thank software designer Jouko Lappi, with whom I was able to cooperate well, thereby enabling me to work in a productive manner.

Further, discussions with experts in the field provided me useful insights. I ap-preciate the comments received from Professor Herbert Glöckle from Reutlingen University, the comments and suggestions of reviewers and participants at the European Accounting Association (EAA) 26th in Istanbul Turkey 2010, at the Nordic Conference on Small Business Research 17th in Helsinki Finland 2012, RENT XXVI—Research In Entrepreneurship And Small Business 2012 in Lyon, and at the INBAM Conference, International Network of Business and Management Journals 2013 in Lisbon.

For financial support, I thank the Jenny and Antti Wihuri Foundation and the Foundation for Economic Education, University of Eastern Finland, Business School, the Foundation for Economic Education, the OP-Pohjola Group Research Foundation, the Finnish Cultural Foundation, and the Savings Banks’ Research Foundation.

I would like to express my warmest gratitude for the understanding and sup-port given to me by my parents and family. In particular, I would like to give heartfelt thanks to my mother for supporting me in whatever I have undertaken in during my life. I am also grateful to my daughter Rosanna for providing me with the much needed distraction from this project, which helped me to renew my energies. I also thank all my friends, especially Katri, for supporting me and expressing interest in my work.

Gloria in excelsis Deo.

Kuopio, April 2014

Marika Miettinen

Contents

1 INTRODUCTION ....................................................................................... 111.1 Background and Research Environment .........................................................111.2 Financial Systems and Economic Development ............................................. 121.3 Government Intervention ...................................................................................14

1.3.1 Rationales for Government Intervention .................................................141.3.2 Acts on Governmental Funding ...............................................................151.3.3 Finnvera ........................................................................................................161.3.4 Impact of Government Intervention ........................................................18

1.4 Purpose of the Dissertation ................................................................................191.5 Research Design and Methods ..........................................................................211.6 Contribution ......................................................................................................... 251.7 Conclusions .......................................................................................................... 301.8 Definitions .............................................................................................................321.9 Structure of the Study ......................................................................................... 34

2 LITERATURE REVIEW ............................................................................. 352.1 Arguments Regarding the Risks Faced by New Ventures ............................ 35

2.1.1 Liability of Newness and Adolescence ................................................... 352.1.2 Liability of Smallness ................................................................................ 352.1.3 Framework of Asymmetric Information ................................................ 362.1.4 Evidence of Information Asymmetry Problems .................................... 38

2.2 Firms’ Financial Performance ........................................................................... 392.3 Financial Structure .............................................................................................. 422.4 Lender’s Evaluation of the Business Prospects ............................................... 49

3 SUMMARY OF THE ARTICLES .............................................................. 533.1 Article 1: Factors Contributing to the Success of Start-Up Firms Using

Two-Point or Multiple-Point Scale Models ..................................................... 533.2 Article 2: Capital Structure of Start-Ups: Evidence on Non-Accounting

Characteristics..................................................................................................... 543.3 Article 3: Lender Evaluations of Start-Up Business Prospects ..................... 543.4 Summary of Overall Results .............................................................................. 55

SOURCES ......................................................................................................... 59

ARTICLES ........................................................................................................ 75

TABLESTable 1: Definitions of credit risk categories ................................................... 22Table 2: The age of failed firms ......................................................................... 22

FIGURESFigure 1: Research design. ....................................................................................24Figure 2: Framework for analyzing new micro firms’ performance

given by Watson, Hogarth-Scott, and Wilson (1998). ...................... 40Figure 3: Summary of significant relationship in the articles ....................... 56

1 Introduction

1.1 BACKGROUND AND RESEARCH ENVIRONMENT

The contribution made by the small business sector to generating employment, innovativeness, wealth distribution, and increasing the vitality of the economy is frequently brought to the fore in current political and economic debate (Bonet, Armengot and Martín 2011; Giovannetti, Ricchiuti, and Velucchi 2011). Self-employment through one’s own business is a reasonable option for the (long-term) unemployed. New firms have been a popular research topic since Birch (1979). Because of their incubator role in the economy, success and survival continue to be important issues among small firms. Moreover, there is a large share of small firms in most economies. In Finland, there were 263,000 enterprises in 2011. Most of them (99.8%) were small and medium-sized enterprises (SMEs). Micro enterprises accounted for 93.6% of all SMEs. Generally, a micro firm employs one person, the owner; however, some of them provide work for 2–9 persons. Further, the number of entrepreneurs in Finland was 255,000 (excluding agriculture and forestry); 63% of all entrepreneurs were self-employed. At the end of 2009, there were approximately 84,000 female entrepreneurs, constituting one-third of all en-trepreneurs (Entrepreneurship Review 2012). However, the survival or success of new ventures is more essential than having a big number of new firms (Schjutjens and Wever 2000). It is known that chance plays a minimal role in the survival and growth of new ventures (O’Gorman 2012). With time, as an entrepreneur develops the firm’s resource base, including entrepreneurial and managerial resources, new market opportunities are discovered or created (O’Gorman (2012). In recent de-cades, policy-makers have sought interventions that increase the ‘quality’ of start-ups (Greene, Mole and Storey 2008). This dissertation explores the performance of new micro enterprises, in particular, creditworthiness and factors which impact loan availability and the role of lenders in evaluating business prospects.

For the welfare of any economy, apart from its institutions, it is important to select and fund the best new ventures. Previous literature assumes that, because of market imperfections, loan availability may not be optimal for small, young firms that are informationally opaque. A natural interpretation of this is that, despite the development of markets and institutions, the fundamental problem of investment selection continues to exist. Both acceptance errors and rejection errors are possible.

The widely held view that small ventures contribute to economic growth and national innovation capacity has encouraged government interventions in mar-kets for small venture finance (Beck and Demirgüc-Kunt 2006). The current study concentrates on the financing granted by Finnvera Plc, which is a leading state-

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owned financial institution that operates in the market for small business finance in Finland. Although government intervention is common in the small venture finance market in Finland as well as in other nations, there exists surprisingly little evidence regarding the factors that lenders pay attention to. In addition, while gov-ernment funding can enhance the activity of small ventures, it is important to pay attention to efficient allocation of subsidized financing. This dissertation aims to partially close the gap in the literature. This study also provides a new insight into the debate on applicant characteristics, entrepreneurship, and lending by focus-ing on the factors affecting financial performance measured by creditworthiness.

1.2 FINANCIAL SYSTEMS AND ECONOMIC DEVELOPMENT

In the literature, we encounter various theories and views on the comparative importance of different factors in financial development and aspects of utmost importance in economic growth. Beck and Levine (2002) and Levine (2002) classify these factors into four categories. The bank-centred view emphasizes the positive role of banks. Banks screen and monitor their borrowers. Powerful banks can insist on more information and can negotiate contracts due to long-term financ-ing relationships with firms. The market-centred view posits that markets have a relative advantage over banks in capital allocation. Powerful banks may cause more imperfections in the market. For example, if banks protect their customers from competition posed by new entrants and therefore hinder the effective devel-opment of firms, they could be inhibiting effective cost allocation. Furthermore, banks can take an excessively large share of the returns of successful firms. According to the financial services view, it is not relevant whether a bank- or market-centred financial system prevails; the one that reduces information asym-metry and transaction costs is better. What is more relevant is the country’s overall ability to handle financial issues through its financial costs. Banks and markets may even complement each other in reducing information asymmetry and costs. The law and finance view states that the better the legal protection of external financiers, the more developed the financial system, which enhances the amount of capital invested in firms (Beck and Levine 2002; Levine 2002). According to Beck and Levine (2004), it is the overall level of financial development and not the role of banks and stock markets in providing finance that promotes economic growth and creation of new ventures. Moreover, the legal system of a country influences financial development (Beck, Demirgüc-Kunt and Levine 2001). Beck et al. (2001) find that the legal heritage in Britain, France, Germany, and Scandinavia influenc-es the current state of the nations’ monetary institutions. Countries with stronger financial institutions seem to have in common a law similar to the German legal tradition. The reason the legal system influences financial development is that legal traditions define adaptability to developing economic conditions and may differ among countries. An example is the ability to support new financial transac-tions. One factor in financial development is the different priorities that countries ascribe to the right of individual investors (Beck et al. 2001).

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It has been suggested that bank-centred financial systems may have more ben-efits than market-centred systems, because banks monitor their customers while markets do not. However, the severe banking crises in the 1990s in the United States, Europe, and Japan reveal that this proposed superiority is not necessarily justified (Kanniainen 2003: 294). The effects of banking sector shocks can have adverse consequences for firms and the economy (Ashcraft 2005; Dell’Ariccia, Detragiache, and Rajan 2008; Chava and Purnanandam 2011). The evidence sug-gests that bank-dependent borrowers face disruptions in the availability of fi-nance. In particular, small firms face obstacles in their borrowing and have in-creased financial problems because of this difficulty (Carbo, Rodriquez, and Udell 2008; Chava and Purnanandam 2011). On the contrary, there is also proof that bank-centred financial systems may function well with smaller firms when com-pared to stock market-oriented financial systems (Bugamelli et al. 2001). Beck and Levine (2002) are inconclusive regarding whether a bank- or a market-centred system enhances the formation of new firms or makes the cost allocation more efficient. However, they find that legal system efficiency and overall financial de-velopment help in industrial growth and formation of new firms. This finding is supported by La Porta et al. (2000; 2002). These studies state that improvements in corporate finance could expand financial markets and increase firms’ liquidity, thereby easing the access of external finance to new establishments.

Although financial systems are becoming more integrated throughout the world, the domestic financial institutions continue to play a relevant role. Evidence suggests that local financial development is an important determinant of the eco-nomic success of an area, particularly among the smallest firms (Guiso, Sapienza, and Zingales 2009; Hyytinen et al. 2003c: 386). Using Italian data, Guiso et al. (2009) find that development of the local financial market increases the probability of an individual establishing her/his own business and enhances growth of firms, particularly the growth of small businesses. There is also evidence that the lack of capital related to new business establishments and the growth of small business is positively associated with the deficiencies in a country’s financial and legal institutions (Beck, Demirgüc-Kunt, and Maksimovic 2008).

The Finnish financial system, which is similar to that of the European coun-tries, has traditionally been bank-centred (see, e.g., Hyytinen, Kuosa, and Takalo 2003b; Niskanen and Niskanen 2004). During the period 1980–2000, protection of shareholders was strengthened, while the protection of creditors was weakened. One of the reasons is that the Act on Reorganisation of Companies of 1993 has weakened creditors’ control of bankruptcy situations. In addition, reforms re-lated to accounting, auditing, and disclosure rules have strengthened the rights of shareholders. In Finland, the rights of shareholders are comparable to those in the United States (Hyytinen, Kuosa, and Takalo 2003a: 66). This implies a shift from relationship-based lending towards increasing the influence of the stock market. However, for new micro firms, public markets are not relevant. It might be even more difficult for them to obtain loans because of the weakening of credi-tors’ control.

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1.3 GOVERNMENT INTERVENTION

1.3.1 Rationales for Government InterventionThere are two main ways in which governmental interventions operate, to sub-sidize or offer funding to small businesses (Lerner 1999). Small firms are consid-ered to be the source of economic growth; moreover, because of market imperfec-tions, it is appropriate that the government supports these firms (e.g., Beck and Demirgüc-Kunt 2006).

The first rationale for government intervention relates to capital market im-perfections. Corporate finance literature indicates that small, young businesses are more likely to face a lack of capital (Beck and Demirgüc-Kunt 2006; Beck et al. 2006; Berger and Udell 1998). This financial constraint stems from the asymmetric information problem between firms and financiers and impedes the birth of new ventures (e.g., Cressy 2002). In addition, these informationally opaque firms may suffer from a lack of assets to be used as collateral (Berger and Udell 1998). The second rationale rests on beneficial externalities. For example, small firms may be sources of new ideas that generate positive externalities to society and other firms (Arrow 1962; Lerner 1999; 2002). If governmental institutions had better resources to identify good loan applicants than private sector financiers, governmental in-tervention might encourage private sector financiers to fund some firms (Lerner 1999). The general view is that governmental intervention is reasonable in the event of capital market failure. By rectifying the market, government funding could promote firm creation, innovation, and growth.

However, the situation described above is disputed. The common argument against intervention is that the government faces the same difficulty in predicting the probability of business success as private sector finance (Holzt-Eakin 2000). He (2000) emphasizes that certain unexamined issues remain, such as whether SMEs provide relatively more innovations in the economy or whether the level of innovativeness is sufficient to generate beneficial externalities. He proceeds to state that with regard to the common assumption that small, young firms face funding gaps, there is no evidence that too few businesses are established every year or that rather poor quality firms obtain finance. He calls for future research on the ‘optimal’ business failure rate. Similarly, de Meza (2002) states that govern-mental intervention necessitated by capital market imperfections may attract more low-quality loan applicants and consequently decrease efficiency. He concludes that to increase efficiency, this special treatment needs to be reduced rather than expanded. Correspondingly, Holtz-Eakin highlights that government aid should, if it is to be provided at all, be directed towards SMEs selectively, identifying firms that indeed have good quality. The idea that the government should not eliminate firm failures is related to the idea of creative destruction by Joseph Schumpeter, particularly in his book Capitalism, Socialism and Democracy first published in 1942.

In Schumpeter’s vision of capitalism, this term describes innovative entries by entrepreneurs which results in the generation of economic growth. This happens because of more efficient allocation of resources and competition in the market. It

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is vital for start-ups to distinguish themselves from what is offered by monopo-lies. Such start-ups threaten monopolists and force them to invest in innovation. Schumpeter doubted whether this process would be sustainable; he believed that it could eventually undermine the framework of capitalism.

The implication of government funding rectifying market imbalance is that it should finance firms that have been identified by private financiers as unprofit-able. If the government funds start-ups identified as profitable, then it would be competing with the private sector and not solving the screening problem and tak-ing sufficient risks (Hyytinen and Väänänen 2003: 330). Overall, there are different opinions regarding the necessity of public funding. However, there is consensus that governmental funding should not distort competition.

1.3.2 Acts on Governmental FundingThe Finnish legislation includes two acts on governmental funding and business subsidies. The legislation is available in Finnish in the database at www.finlex.fi. Translations of the quotes are from the webpage, by Hyytinen and Väänänen (2003: 331-332) or by the author. The Act on Discretionary Government Transfers 688/2001 applies to the use of government funds; the grounds and procedures when granting government aid; and deals with financing including loans and subsidies, interest subsidies, and guarantees. The general conditions for the grant-ing of government support are

1) the purpose for which transfer is applied is socially acceptable; 2) granting a transfer is justified by the objective of its use; 3) granting a transfer is necessary considering other public aid received by the applicant and the type and extent of the project or activity in question; or 4) granting a transfer is judged to cause only minor distortion to competition and the market in a state belonging to the European Economic Area (688/2001, Section 7).

The Act on the General Terms and Conditions of Business Subsidy (786/1997) serves in the granting of business support directly or indirectly from government sources (excluding agriculture, forest and fishing industry). Business subsidies include gov-ernment aid, interest subsidies, loans, guarantees, or other financing involving a subsidy to the recipient. The objectives of a business support program are to

promote the growth potential of the economy as well as increase the efficiency of business activity. A business support program must be targeted primarily to such purposes, which remove deficiencies in the market. be composed in such a way that the distortion on competition is minimized (786/1997, Section 3, translation by Hyytinen and Väänänen 2003: 331).be targeted primarily to research, product development, education, interna-tionalization or else immaterial development activity, or to improve long-term competitiveness of small and medium-sized enterprises. Support of normal investments and financing of working capital of large companies can be done only on special occasions (786/1997, Section 3, translation by the author).

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The conditions regarding business subsidies are described in Section 5:

Business subsidies can only be granted for such business activity, which is estimated to have the requisites for continuous profitable activity. The giver of the subsidy, when making the business subsidy decision, must establish the amount of public support as well as the total financing, profitability, and effects on competition of the project in question (translation by Hyytinen and Väänänen 2003: 332).

In Finland, the Ministry of Employment and the Economy (MEE) is responsible for providing an operating environment that promotes the creation of new ventures and their growth, wherein corporate financing development is important. The MEE has the objectives of maintenance, improvement, and development of the financial conditions affecting firms. It monitors and intervenes in time, guaran-teeing—regardless of economic volatility and considering regional aspects—that all types of enterprises can access adequate funding. ‘The MEE is the central ad-ministrative authority for budgeting subsidies, developing relevant legislation, providing guidelines for and corporate analyses of the recipients, and developing and maintaining related tools for granting and payment of subsidies such as forms and IT systems’ (MEE).

In addition, the ministry is responsible for the management and control of Finnvera Plc, which is a state-owned specialist financial institution and is the focus of this dissertation. To be more precise, this study utilizes the financial decision data of Finnvera Plc and its credit risk ranking data. Finnvera provides subsidized loans and guarantees for new ventures. It is the main governmental provider of funding for new, young firms (0–4 years). The regional Centres for Economic Development, Transport, and the Environment (ELY Centres) can grant aid for new ventures in the initial stage of business. The ELY Centres are also controlled and managed by the MEE. There are also other state-owned finan-cial institutions that provide aid and funding for small firms and their establish-ment. The Finnish Funding Agency for Technology and Innovation (Tekes) and the Foundation for Finnish Interventions support innovative business ventures. The Finnish Innovation Fund, Sitra, offers government venture capital to new technology companies.

1.3.3 FinnveraFinnvera is a specialized financial institution owned by the Finnish state. Its activi-ties are regulated by many acts. The Act on the State-Owned Specialized Financing Company 443/1998 sets the official objective of Finnvera. According to Section 1 of the Act, Finnvera’s purpose is ‘to provide financing services to promote and develop business, particularly that of small and medium-sized enterprises, and to promote and develop the exports and internationalization of enterprises. In its operations, the State-owned specialised financing company shall promote the State’s regional policy goals. Operations shall focus on shortcomings in the supply of financing services’ (443/1998, Section 1, www.finlex.fi, accessed on 9 August 2013).

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Finnvera’s duty is to make up for the deficiencies in the supply of financial services. New ventures do not have sufficient collateral, if any, and not even a history or reputation. Private sector financiers generally do not finance start-ups without collateral. Nascent entrepreneurs only have a business idea, an interest, or ability to work with the idea. The quality of human capital cannot be con-firmed either. Therefore, there is scope for financing offered by a state-owned financial institution. SMEs who applied for loans to private creditors and were refused have a higher probability to apply and receive Finnvera’s funding than their counterparts whose credit application has not been rejected in the private market (Hyytinen and Väänänen 2003: 357). Finnvera may provide direct loan or guarantees for bank loans.

With each financial service applicant, Finnvera evaluates its business pros-pects. The analysis contains widespread detailed investigation and understanding of the venture’s business. The steps involved are meeting with the entrepreneur, examining all available material, and negotiating regional partners. Finnvera utilizes its acquired knowledge in assessing all small start-ups and analyzing the venture’s management, business, and finance. The credit risk classification is based on these three factors, which includes subdivisions. The risk classifica-tion system contains eight categories including one category for insolvent firms (Finnvera’s Annual Report 2012; Finnvera Financial Review 2010: 24).

Section 4 of the Act on the State-Owned Specialized Financing Company 443/1998 describes the economic principles directing Finnvera’s operations: ‘In the long term, the company should aim to cover its operating expenses the credit, guarantee, and export credit guarantee losses for which it bears responsibility out of operating income. Regardless of the self-sufficient goal, the state is also committed to compensating for some guarantee and credit losses’. The Act on the State Guarantee Fund (444/1998) and The Act on Credits and Guarantees Provided by the State-Owned Specialised Financing Company (445/1998) contain provisions on State liability for certain contingent liabilities binding on the com-pany (443/1998, Section 4). The compensation is approximately 50% of the credit losses that arise in domestic operations. Nonetheless, the state has the ultimate responsibility for Finnvera’s operations. Any losses resulting from Finnvera’s op-erations are covered from the fund for domestic operations on the balance sheet, fund for export credit guarantees, special guarantees or from the State Guarantee Fund (Finnvera’s Annual Report 2012). Recently, the Finnish Parliament decided to amend the Act on the State’s Export Credit Guarantees and the Government (445/1998). Consequently, the state compensates up to 75% when credit losses arise from loans granted to start-up and growth firms (Finnvera’s Annual Report 2012).

As a consequence of the state’s compensation for credit losses, Finnvera can take greater risks in export credit guarantees and in the financing of both enter-prises than private funding providers. Even during insecure economic conditions, Finnvera can promote ventures in their establishment or growth actions. If this were not the case, the results for economy would be remarkably slower. In the case of multiple financiers, the total risks involved in financing are shared among the financiers (Finnvera’s Annual Report 2012).

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The MEE supervises Finnvera’s operating systems and effectiveness and sets goals for its operations. ‘When determining these goals, attention is paid to the Finnish Government Programme, the Ministry’s corporate strategy, the policy objectives concerning the Ministry’s branch of administration, and the goals of EU programmes’ (Finnvera Annual Report 2012). According to audit (Heinonen et al. 2012), Finnvera has met the goals and objectives of legislation and MEE-steering relatively well. Finnvera’s strength lies on its highly professional staff (Heinonen et al. 2012). One of Finnvera’s aims in 2012 was creating approximately 10,000 new jobs in Finland with the help of Finnvera’s financing; 8,660 new jobs were created through co-financing by Finnvera (Finnvera Annual report 2012). Another target is to provide finance for approximately 3,500 start-up firms. The amount is approxi-mately 10% of firms founded every year in Finland. In 2012, Finnvera participated in the financing of 3,123 start-ups (Finnvera Annual Report 2012). The total num-ber of Finnvera’s clients is approximately 30,050 clients, which includes micro-firms (69%), other SMEs (11%), large enterprises (less than 1%), and ‘entrepreneurs who had been granted an Entrepreneur Loan for investment in share capital or for their contribution to a partnership’ (20%) (Finnvera Annual Report 2012).

1.3.4 Impact of Government InterventionThere are three different viewpoints on the background of the effects of govern-mental funding. The first is the development view, which states that states cre-ate their own financial institutions to alleviate market imperfections in welfare maximization (Gerschenkron 1962). This view emphasizes the social returns of projects (La Porta et al. 2002; Sapienza 2004). The second is the agency view which challenges the motivation of credit analysts working in state-owned financial in-stitutions. Do these people have incentives to allocate the resources effectively (Sapienza 2004)? The third view is the political one according to which politicians steer these financial institutions to allocate financial aid to their voters. Therefore, politicians are keen on maximizing their own political goals rather than rectifying market failures (La Porta et al. 2002). Politically connected ventures may obtain loans from a state-owned financial institution more easily, despite their high prob-ability of default (Khwaja and Mian 2005) or during elections (Dinc 2005).

The prior findings on subsidized lending and the effectiveness of state-owned financial institutions in credit allocation has been inconsistent. Many prior stud-ies argue that state-owned financial institutions are less profitable than financi-ers in the private market (e.g., Berger et al. 2005; Micco, Panizza, and Yanez 2007; Iannotta, Nocera, and Sironi 2007). Berger et al. (2005) suggest that the long-term performance of state-owned financial institutions is generally weak. Finnish gov-ernment funding has promoted firms to expand their operations (Ylhäinen 2013) and effectively complemented the private market funding for SMEs (Hyytinen and Väänänen 2003: 364).

The allocation of state-owned financial institutions is related to slower eco-nomic and productivity growth (La Porta et al. 2002; Oh et al. 2009; Ylhäinen 2013) or no increase in growth at all (Galindo and Micco 2004). Labour productivity is negatively or insignificantly related to subsidized finance (Ylhäinen 2013). In

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Japan, the firms that participated in a support program have shown deteriorated performances compared to their non-participating counterparts (Uesugi, Sakai, and Yamashiro 2010).

Although Criscuolo et al. (2012) and Ylhäinen (2013) find no evidence that subsidized financing could enhance long-term productivity and growth of an economy, both studies suggest that governmental finance can enhance the activity of small firms; they are able to invest more, grow faster, and employ more persons. In Oh et al. (2009), credit guarantees strongly enhance firms’ ability to maintain their size and probability of survival. A recent study by Adrianova et al. (2012) indicates that governmental funding is related to higher long-term growth rates. In the sectors that are more dependent on external finance, government fund-ing strongly promotes the growth of firms (Hyytinen and Toivanen 2005). Honjo and Harada (2006) show the influence of governmental support on the growth of young SMEs. Similarly, firms that are financially constrained and obtain subsidies increase the total productivity (Girma, Görg, and Strobl 2007).

Adrianova et al. (2012) argue that, for example, the institutional quality may explain the negative relationship between state-owned financial institutions and economic growth. Berger et al. (2005) admit that their results could reflect the different goals of these institutions; one example is development goals instead of profit maximization. Beck, Levine, and Loayza (2000) emphasize that more effi-cient work of financial intermediaries enhances growth in productivity as well as in the economy. In lending, improvements in quality are more important than the increase in volume when considering higher growth (Jayaratne and Strahan 1996).

Another reason for inefficient outcomes in government finance may be because government funding may attract lower-quality firms (de Meza 2002; Cole 2009), or because of encouraging entrepreneurs to overinvest (Innes 1991). Therefore, state-owned financial institutions may have ‘lower profitability, weaker loan quality and higher insolvency risk than other banks’ (Iannotta et al. 2007). Bertrand, Schoar, and Thesmar (2007) argue that a better functioning banking sector follows the idea of ‘creative destruction’ defined by Schumpeter. The lowering of government interven-tion in the private capital market in France increased the entry and exit of firms and enhanced the efficiency of resource allocation (Bertrand et al. 2007).

Taken together, while government funding can enhance the activity of small ventures, it is important to pay attention to efficient allocation of subsidized fi-nancing. The capital allocation of scarce resources is improved by providing funds for more projects with high expected returns and less for projects with weak prospects (Wurgler 2000).

1.4 PURPOSE OF THE DISSERTATION

The purposes of this dissertation are to present and discuss factors affecting the financial performance of start-up micro firms measured by their creditworthi-ness and lender’s evaluation of firms’ prospects, as well as to identify the factors affecting the capital structure of start-ups. Previous research into business suc-

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cess and failure does not provide a comprehensive explanation for the financial performance of start-ups. Very little research has focused on factors affecting the financial performance of new micro firms. Furthermore, to the best of my knowl-edge, this study is the first to examine the capital structure of new micro firms in a bank-centred economy, particularly the factors that impact external lending. This study utilizes data from the main institution that currently provides government funding to Finnish micro firms, the state-owned specialized financing company Finnvera Plc. Finnvera’s intervention in financial markets is based on three crite-ria: a) addressing market failure, b) catalysing private financial institutions, and c) stimulating competition between banks and ensuring fair pricing (Heinonen et al. 2012). The company could be considered an active player in information production and risk sharing.

The objective is to identify and support profitable new micro firms and to shed some light on how to improve lender’s efficiency and, hence, profitability for small business lending. This study provides new insight into the debate on applicant characteristics, entrepreneurship, and lending: a focus on the factors affecting financial performance, consequences of non-accounting characteristics of the criteria used in evaluating business prospects, and lending decisions of the state-owned financial institution Finnvera Plc. There is surprisingly little previ-ous research on small business financing by state-owned financial institutions, despite the prevalence of government funding in the markets of many countries. This dissertation aims to fill this gap.

The data enables an investigation of the factors at the time of establishment that affect financial performance in the short run (maximum five years). In the establishment phase, there is a very limited amount of financial information. Therefore, the study employs variables that investigate the type of funding used, background of the entrepreneur and other non-accounting characteristics of the firm, credit analysts’ evaluation of the firms’ business prospects, and the market and competitive position.

Using the research setting of Finnish start-up firms, this dissertation examines the importance of non-accounting characteristics in the lending process and firm’s financial performance using three separate articles. The following research ques-tions are addressed in the articles:

(1) Do the non-accounting variables play an important role in the success (measured by creditworthiness) of a start-up micro firm when using two-point or multiple-point scale models? Does the contribution of non-accounting variables vary between success measured by two-point or multiple-point models in small start-up firms?

(2) Do the non-accounting variables influence the external funding of genu-inely new micro firms? Are non-accounting characteristics important in financial lending (from the financial institution’s view)?

(3) How are the funding, entrepreneur-, and firm-related non-accounting variables reflected in the credit analysts’ evaluations of the firms’ business prospects? Are the credit analysts’ subjective evaluations of the business prospects of the firm similar to the firm’s actual creditworthiness?

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1.5 RESEARCH DESIGN AND METHODS

The analysis is conducted on a sample of 440 Finnish micro firms in different in-dustries, including 101 defaults, spanning the time period from 1998 to 2003 and from 2000 to 2005. The data is collected from the list of start-up firms registered in the database of a specialized financing company owned by the Finnish state (Finnvera Plc). All the firms in the sample are genuinely new: mergers and buyouts have been excluded from the database. All the firms in the study were established at around the same time and in the same geographic area. This insulates the da-tabase from differences in overall economic conditions.

The data was collected from loan applications from 1998 or 2000. In every credit risk category, the selection began from the first loan application of the year until the samples were sufficient. Even though the applications were examined systematically, the data can be considered random, because the borrowers were of a similar nature throughout the year. During these two years, 3397 new ventures applied for a loan. However, the database includes some firms that are not con-sidered micro firms or new establishments. The firms which were bought from a previous owner and those where one owner did not represent a private person or micro enterprise were excluded. The entrepreneurs which began their business in traditional agriculture or as part of a franchise chain were also excluded. The final sample was 13% of the initial sample of 3397 firms. It is important to bear in mind that the firms in the data do not represent a random sample of all new ventures in Finland. Many of these loan applicants have been refused loans from private creditors (Hyytinen and Väänänen 2003: 357).

The sample represents six credit risk categories based on the situation five years after beginning the business. Category six represents the lowest credit risk and category two the highest. Each category contains 61–75 firms. Category one includes enterprises that have gone bankrupt (74 firms) or put an end to their business because of financial distress (27 firms) within the first five consecutive years. Lenders assessed borrowers’ creditworthiness and placed them in the ap-propriate rating category. The rating has three important dimensions. The first includes financial ratios. The second includes the industry and markets where the business operates. The third describes the quality of firm management. Each of the dimensions has at least three subsections. For example, the first dimension con-tains profitability, liquidity, and capital structure of the firm. The finance manager who rates firms follows detailed instructions regarding each section and assigns specific limit values and weights to rate firms. New ventures are given individual weights (Finnvera Annual Report 2012, Finnvera Financial Review 2010: 24).

Finnvera actively monitors its clients and the credit rating is then updated reg-ularly for clients. The institution describes it in the following words: ‘Monitoring of clients is based on annual financial statements, regular contacts, and moni-toring of the constant payment behaviour and operations. When monitoring the payment behaviour, Finnvera utilises data from various sources: from Finnvera’s own control systems, from beneficiaries of guarantees, and from public payment

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default registers. Clients with risen risks are selected for special monitoring’ (Finnvera Financial Review 2010: 24). Table 1 presents the distribution of the 440 firms, including 101 default observations over 6 different credit risk categories.

Table 1: Definitions of credit risk categories

Category DefinitionNumber of firms

6 Operation is profitable since years. 75

5 Operation is profitable. 68

4 Difficulties in tolerating disturbances. Changes in profitability. 61

3 Operational and financial difficulties. Need for reorganization. 72

2 Clear threat of bankruptcy. 63

1 Unable to pay back their loan. 101

Total 440

New or young firms (in the first four years after establishment) have a greater probability of failure than more mature small businesses (DeYoung, Glennon, and Nigro 2008; Headd 2003). Geroski (1995) has documented that, during the first three years, 30% of new projects fail and during the first five years, the failure rate increases up to 50%. Therefore, the time period used in this dissertation is five years. After five years, the survivors are considered to have come through the famous ‘death valley’. In the data, the youngest failed firm was 1.2 years old. Table 2 presents more detailed information on failed firms’ ages.

Table 2: The age of failed firms

Age in years Number of firms Percentage

1.2-1.9 14 13.9

2.0-2.9 19 18.8

3.0-3.9 11 10.9

4.0-4.9 10 9.9

5.0-5.6 47 46.5

Total 101 100

There is no consensus regarding the best method for modeling prediction. Many methods have been applied, such as discriminant analysis, logistic regression, recursive partitioning, neural networks, survival analysis, and data envelopment analysis (Laitinen and Kankaanpää 1999; Psillaki, Tsolas, and Margaritis 2010).

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Some argue that the time dimension must not be neglected (e.g. Laitinen 2005; Balcaen and Ooghe 2006). In contrast, another line of studies finds no evidence for the importance of considering default time. Laitinen and Kankaanpää (1999) examined the performance of different methods: linear discriminant analysis, logit analysis, recursive partitioning, survival analysis, neural networks, and the human information processing approach. They did not find that any method was superior for analysis two and three years before failure, but logit analysis per-formed better than survival analysis one year before failure. Furthermore, Hu and Ansell (2007) compared five credit scoring methods: Naïve Bayes, logistic re-gression, recursive partitioning, artificial neural network, and sequential minimal optimization. Similarly, they did not find one best method for classifying ordinal performance.

In this dissertation, the methods applied to model the data analysis are logistic regression and ordinal regression analysis with a logit link function and linear regression analysis. Discriminant analysis (DA) and logit analysis (LA) are the most popular methods in the classification context (Laitinen and Kankaanpää 1999). However, the assumptions of multivariate DA, multivariate normal dis-tributed variables, or equal dispersion matrices are often violated, whereas the LA method has less demanding assumptions. The latter method assumes only a dichotomous dependent variable and that there is no multicollinearity among independent variables (Balcaen and Ooghe 2006; Ohlson 1980). However, LA is sensitive to missing values (Joos et al. 1998). In this study, logistic regression is used to investigate the relationship between survival/success and failure, and be-tween actual creditworthiness and the lender’s evaluation of business prospects. Ordinal regression analysis enables the modelling of the dependence of a polyto-mous ordinal response on a set of predictors, which can be factors or covariates. Like credit risk, the dependent variable is assumed to be ordinal. The ordering is determined by sorting the values of the dependent variable in descending order. Furthermore, the factor variables are assumed to be categorical. There are dif-ferent kinds of ordinal regression analysis. The model used in this dissertation is based on the methodology of McCullagh (1980). The program uses the logistic cumulative probability function in predicting financial health status. The main difference between logistic regression and ordinal regression analysis is that the dependent variable in ordinal regression can have more than two values. The ordinal regression is used to examine the association between the multiple-point scale success (i.e. credit risk categories) and non-accounting variables. Linear re-gression analysis is used to investigate leverage. This method is appropriate in an analysis where the variables are continuous. All the models are implemented using SPSS 19.0. This dissertation includes three separate publications. Figure 1 connects objectives and specific research questions, and summarises the datasets and research methods of this study. Independent variables used in the studies are more or less the same and relate to funding, founder, firm, and market.

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1.6 CONTRIBUTION

This dissertation is related to a number of studies in entrepreneurship and fi-nance literature. It offers a new perspective on vast and important literature in firm financial performance, lending, and capital structure. This study deals with the determinants of firm financial performance (measured by creditworthiness) and capital structure within very small and new ventures. Previous literature has reached unambiguous conclusions regarding the determinants of firm finan-cial performance and capital structure (e.g. Lussier and Halabi 2010; Graham and Leary 2011). Moreover, there are many approaches to modelling credit risk, begin-ning with Beaver (1967) and Altman (1968). Nonetheless, most models are applica-ble only to large (listed) companies and financial ratios. In response to the above, there is a need to examine credit risk models that pertain specifically to SMEs and non-accounting variables (Blumberg and Letterie 2008; Gama and Geraldes 2012).

Another contribution is to public sector finance literature. The impact of pub-lic sector finance on the economy has recently been of interest to international researchers (e.g. Gadd, Hansson and Månsson 2008; Zecchini and Ventura 2009; Lopez-Acevedo and Tan 2010; Uesugi et al. 2010). Generally, the previous stud-ies have shown that firms financed by government funding have increased their firm size, but they do not have higher profitability. The same results also hold for Finland; the governmental funding has positive impact on investments, turnover, and the employment situation (MEE 2012). However, it is important that firms are first profitable and can grow their business thereafter (Lappalainen and Niskanen 2012). In the literature, there is a gap between how lenders in state-owned financial institution choose their customers, how they expect their customers to perform, and what are the real factors that impact firm’s financial performance (measured by creditworthiness). Moreover, studies regarding small start-ups as a customer of state-owned financial institutions are scarce. It is worth noting that these firms present the major proportion of the customers of state-owned financial institu-tions’ (Finnvera), and this group of firms have difficulties in obtaining finance from elsewhere.

This study contributes to existing literature by enabling a better understand-ing of financial performance of new micro firms which have obtained a loan from a state-owned financial institution. Furthermore, it provides an insight into the factors which are emphasized by lenders when granting a loan for a start-up firm. Another contribution is that this study uses non-accounting variables instead of financial ratios (which may be backward looking) when modelling the financial performance (creditworthiness), probability of surviving, and lender’s evaluation of business prospects. This dissertation investigates the financial performance and determinants of loan amounts of start-up micro firms with Finnish data. Contrary to many accounts of start-up activity, the firms in this data rely heavily on external debt sources and loans from a state-owned financial institution (Finnvera) and some of them (37.6%) also have bank loans. For 62.4% of the firms, Finnvera is the only possible source of external funding; this is also one of Finnvera’s tasks: to

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operate within market gaps and support start-ups. The data has been collected at establishment time, whereas the credit rating is based on the situation five years after establishment. The dissertation employs variables that investigate the type of funding used, background of the entrepreneur, and other non-accounting variables of the firm and market. Most of the firms have been established around the same time period and within the same geographic area. This insulates the database from differences in overall economic conditions.

The dissertation contributes to existing academic literature by examining three related articles. The first article extends financial performance literature on firms by examining multiple-point scale levelled firm financial performance. There is evidence that the predictors of performance vary, depending on whether we use two-point scale or multiple-point scale models (Cooper et al. 1994; Dahlqvist et al. 2000). However, there are only a few studies on multiple-point scale models of financial performance research. One objective of this dissertation is to contribute to small business research by providing a more comprehensive understanding of the financial performance (creditworthiness) of multiple-point scale performance models for genuinely new ventures. The research on the creditworthiness of start-ups using non-accounting factors has been very scarce.

The second article contributes to the academic literature of capital structure. Prior studies have mainly examined the subsequent stages of financing for large corporations or SMEs. This paper aims to contribute to existing understanding of the capital structure of very small start-ups by analyzing relevant owner, firm, and environment-related attributes at the time of establishment of new ventures when there are no accounting variables available. Only a few studies (Scherr et al. 1993; Chandler and Hanks 1998; Cassar 2004) have analyzed the start-up stage of financ-ing and owner’s characteristics, with inconclusive results. These studies are US-based, where the financing of firms is strongly market-centred, whereas this study examines firms in Finland’s bank-centred economy. The determinants of capital structure are influenced by the financial system of the economy (Antoniou, Guney, and Paudyal 2008). This study seems to have more independent owner-related variables than previous studies, such as attending an entrepreneurial training course, the financial wealth of the founder, and whether the firm is founded by a single founder or a team. In addition, this study includes credit analysts’ evalu-ation of the firms’ business prospects, and its market and competitive position. Furthermore, most previous studies on SMEs do not distinguish between micro, small, and medium enterprises. However, it has been noted that determinants of capital structure are different for micro firms than for small, medium, and large firms (Ramalho and Da Silva 2009; Atherton 2012).

The third article contributes to previous studies examining the credit analysts’ subjective evaluations of the start-up’s business prospects. In the academic litera-ture, there are studies on the ability of an investor to diminish firms’ opaqueness and screening good from bad investees; this has been investigated in the context of banks (Berger and Udell 1998; Berger, Klapper, and Udell 2001), venture capital finance (Gompers and Lerner 1999), angel finance (Wong, Bhatia and Freeman

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2009; Hoberg et al. 2012), lease finance (Myers, Dill and Bautista 1976; Porter 1995), and supplier finance (Tamari 1970). This study concentrates on the loan appli-cant’s assessment by a state-owned financial institution. By comparing the actual creditworthiness model with the model evaluated by lenders, the study examines whether the credit analysts’ subjective evaluations of the business prospects of the firm similarly predict the future of start-ups. The previous studies on individual judgment are based mainly on accounting data (e.g. Gadenne and Iselin 2000), and there are few, if any, studies that have used only non-accounting variables in this setting. It is important to determine whether the decision rules actually work. Are they related to future business creditworthiness? This dissertation provides a better assessment of non-accounting variables and facilitates a deeper understand-ing of the lending process in a state-owned financial institution for a sample of Finnish micro start-ups.

Given the importance of external debt within small start-ups, this study also aims to make a contribution to existing literature on lending by enhancing the un-derstanding of this situation. Today, many of the mechanisms available for found-ers and lenders do not hold for new ventures. For example, lenders cannot rely on a firm’s track record. Lenders can only adjust interest rates, demand for collateral and covenants, and verify the founder’s reputation. The multivariate analyses provide a statistical overview for lenders and enable them to understand how non-accounting variables may impact survival and good financial performance, as measured by creditworthiness. The information available for this purpose is the same as lenders have access to when deciding to reject or approve a loan ap-plication. This study provides an understanding of how this information differs among various non-accounting and dependent variables.

Collectively, this dissertation contributes to previous literature on genuinely new micro firms. There is a lack of understanding of the lending and financial performance context of new micro firms. This dissertation aims to shed additional light on the factors affecting good financial performance, capital structure, and evaluation of these firms by the credit analysts of a state-owned financial institu-tion. The empirical results of the dissertation enhance our understanding of well-performing firms, not mere survival, which might only be marginal in many cases (Cooper, Gascon, and Woo 1991), and lending in the context of very small start-ups.

According to the results of this dissertation, share of equity has a positive impact in different financial performance (creditworthiness) models, irrespective of whether the models examine survival, three-point-scale or six-point-scale finan-cial performance (creditworthiness), or good creditworthiness. Further, found-er-related variables were found to be statistically significant in other financial performance models, except in the survival model; older founders seem to have lower credit risk. In the prospect model, age is also positively viewed by lenders. In the six-point scale model, firms which are founded by males have lower credit risk than firms which have both male and female owners. However, if we exam-ine firms with good versus medium and weak creditworthiness, females seem to have better creditworthiness than males. The difference in results is caused

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by the different reference group used in the models. In the good creditworthi-ness model, the founders who do not own personal property (i.e. they have not paid at least half of their mortgage) have a higher probability of having better creditworthiness. It may be that these founders do not own any house and have rented a house or their spouse owns the house where they live. Therefore, these founders are not struggling with mortgage at all and thus have better creditwor-thiness. Further, the founders who have a degree from college or university have a higher probability of having better creditworthiness. The results also reveal that being employed prior to establishing the business has a negative impact on credit risk as well as on business prospects evaluated by the lender. One explana-tion is that they are risk averse. In addition, this may be a sign that the founders who have been unemployed before establishing a firm work harder to not return to the situation of unemployment, or they have more energy to work efficiently because they have had the chance to recover from their previous working period. In contrast, prior experience in running a firm has a positive influence on cred-itworthiness. Lenders ascribe importance to prior work experience in the same sector, even though this aspect is not captured in other models. If the founders have work experience, they have a higher probability of belonging to the medium or weak prospects group. Perhaps the lenders believe that these founders are used to working for others and lack some qualifications that would be good for an entrepreneur, such as independence and initiative. If the firms are located in the same city as the state-owned financial institution—that is, in a big city—they seem to have a higher probability of survival and better creditworthiness. Further, a high level of competitiveness negatively influences survival and three-point-scale financial performance. Similarly, firms with good demand seem to have a higher credit risk. Competitiveness and demand may become negative if there have been changes in the business environment. Another explanation for the negative im-pact on competitiveness and demand may be that entrepreneurs place excessively trust the favourable situation at the start-up phase; in doing so, for example, they do not control basic financial matters and invest less in marketing. An alternative explanation for the negative impact is that lenders fail in their evaluation of these factors, maybe due to a lack of sufficient available information. Lenders evaluate firms with good demand to have medium or weak business prospects. However, in addition to good demand, there may be other stronger factors that cause the as-sessment of medium/weak business prospects and are not captured in the model. It is possible that the evaluation of business prospects may be based on the intu-ition of the lenders. Moreover, firms with good business prospects seem to have lower credit risk; therefore, lenders’ evaluation of business prospects is a good predictor of business. In addition, the loan amount has a positive impact on credit risk. Further, the results regarding leverage show that higher leverage is associ-ated with founders who have no personal property; no prior work experience in the same sector; and with firms which are sole proprietorships or partnerships, not located in the same city as state-owned financial institutions, and have medium or weak demand at start-up phase.

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Providers of external funding have an important disciplinary role in risk as-sessment process. Their decision forces firm founders to exit markets and steers the resources to more efficient firms (e.g., Bertrand et al. 2007). Evaluation of loan applicants is a challenging task for financial institutions due to increasing com-petition. This is particularly true for state-owned financial institutions, whose intervention is based on the existence of market failures. Better information is particularly important to a state-owned financial institution, which may have less transparent customers than banks (Daniels and Ramirez 2008). Further, for credit analysts, this dissertation provides useful information on identifying promising entrepreneurs, which may be taken into account during the loan approval process; it could help credit analysts better evaluate risk, guide micro enterprises towards greater profitability, and secure enough resources for firms with the greatest po-tential. This is an important implication, because the firms that obtain government funding are more likely to obtain it again in the future (Ylhäinen 2013, 21). In ad-dition, as a result of the Euro-crisis in the 2010s, European governments are fac-ing budgetary problems and there is a stronger need to rationalize limited public resources to the most value-added activities. This dissertation answers the need to select the most potential traditional firms, whose proportion is lower in Finnvera’s financing portfolio as they are replaced by more firms with growth, innovation, and internationalization potential (Heinonen et al. 2012: 102).

The analysis and the results obtained can also be useful to prospective entre-preneurs, investors, academics, and policymakers. The findings may steer a new venture to create the preconditions for successful firm development. All those involved in business start-ups should provide encouragement, but also prevent losses. This study seeks to contribute to the lending process of start-up micro firms and the ongoing debate in the literature. Hopefully, it will lead to a better understanding of the pre-entry factors of human capital, firm, and environment that are related to the financial performance (creditworthiness) of start-up micro firms and help in identifying the most important factors that predict good finan-cial performance. Since there are numerous micro firms, they are exposed to a relatively high share of bank loan portfolios. If the lenders’ ability to predict micro business financial performance improves, it may encourage them to finance more relatively opaque and risky micro firms at start-up stages. On the other hand, public funding may encourage banks to invest in some of the firms that would otherwise receive negative responses to their loan applications. For the signal to be positive, government organizations must be successful in identifying good applicants. In addition, it can help to recognize low-quality firms and thus the number of high-quality entrepreneurs in the pool could be higher. The availability of start-up finance is an important requirement for many new ventures (Parker 2002). In general, it is in public interest to develop appropriate conditions in the establishing period to be able to avoid inefficient resource allocation (Parker 2002). If risk can be measured, there is an opportunity to manage it; thus, in this regard, better information is the key factor.

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1.7 CONCLUSIONS

This research is motivated by the aim of fulfilling the gap in the literature and en-gagement of real-world problems in financing new micro ventures. The main aim in this dissertation is to investigate the financial performance (measured by cred-itworthiness) of new micro firms funded by a state-owned financial institution. Although the firms’ financial performance is a much studied concept in general, there is a consensus in the literature that micro firms have their special features, and this aspect needs further examination. Another significant aspect here is that these micro firms obtained finance from a state-owned financial institution, which makes the sample extraordinary and fulfils some of the gaps in the literature on small-firm finance. The customers of state-owned financial institutions are rarely included in existing studies. Prior research on state-owned financial institutions is mainly based on macroeconomic issues, whereas this study concentrates on microeconomic issues. The second aim is to explore the factors impacting the level of leverage. This study attempts to partially fill the gap in the literature by investigating new firms in a bank-centred economy. Similarly, there is evidence that we need to study the capital structure of micro firms separately from SMEs. In the latter aim, the focus is to investigate how the lender’s perception of business prospects differs from their actual creditworthiness five years after establishment. Earlier research has concentrated on accounting variables, whereas this study ex-amines the impact of non-accounting variables. This section provides a short and general summary of the results. More detailed answers to the research questions are provided in the third chapter.

The dissertation concludes that a firm’s financial performance, here defined as survival or creditworthiness evaluated by Finnvera, requires a special mix of entrepreneurial, firm, and external characteristics. Therefore, research on a firm’s performance that focuses exclusively on entrepreneurial aspects, firm aspects, or environmental factors will tend to miss some of the important determinants. Consistent with the prior findings of equity share, the current findings confirm that firms with higher equity share seem to succeed better, that is, have lower credit risk. The increase in the loan amount also enhances the probability of good finan-cial performance. It can be concluded that a firm needs a certain level of capital to be able to succeed. However, having a bank loan does not have an impact on good creditworthiness. This can be interpreted in the following manner: Credit analysts of Finnvera manage to find customers with good potential among the founders of new micro firms who cannot obtain a loan in the private sector. This is an important finding for public sector finance literature, which has been rather scarce in terms of examining new micro firms. In addition, founders who have no personal property seem to perform financially better. It may be that these people have less duties at home (e.g. taking care of the property) and they have more time to take care of their business, and therefore they are also able to run their business better.

Four founder-related variables play an important role in firm’s financial per-formance: the age of the founder, gender, employment status before starting the

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business, and prior experience in running a firm. The founder-related variables are of less significance when examining the business prospects evaluated by the lender or the leverage. To some extent, the findings contradict those of Unger et al. (2011), who suggest that human capital is particularly essential for young firms. Education, which is often used as a measure of human capital, is only statistically significant in explaining good creditworthiness (third article). The results also contradict those of Bosma et al. (2004), who emphasise that a single founder alone is a proxy for the performance of the firm. The findings suggest that, in addi-tion to funding and founder-related variables, firm- and market-related variables also explain the firm’s financial performance, the business prospects evaluated by the lender, and the leverage level. The results are consistent with those of Storey and Wynarczyk (1996), who showed that the talent of the entrepreneur is not the unique determinant of performance. However, they investigated the survival of young firms. This dissertation is one of the few studies that uses a multiple-point scale performance model and also a two-scale creditworthiness model. Furthermore, the investigation of the business prospects and capital structure of new micro firms contributes to lending and performance literature. The study of business prospects adds knowledge to the literature by using non-accounting variables. Earlier studies have mainly used financial ratios in predicting business prospects. The results of the second article confirm the findings of Scherr et al. (1993) that owner-related characteristics are important factors in explaining capital structure. Moreover, this is one of the rare studies that examines micro firms in a bank-centred economy.

Overall, this dissertation sheds additional light on small business lending. New micro firms that obtain loans from a state-owned financial institution are rarely included in prior studies. This research adds to existing understanding of these firms’ actual creditworthiness and what lenders expect from their busi-ness prospects. In addition, the factors that impact loan availability are examined. There is literature on how public financial institutions succeed in their duties to finance firms that have difficulties obtaining a loan elsewhere. This research em-phasises the factors that exist at the loan application phase, when firms are just beginning their businesses. It can be said that this research concentrates on micro-economic issues, whereas most existing studies concerning state-owned financial institutions are based on a macroeconomic framework. For an overall perspective of how the entire economy works, we need to gain an understanding of economics at both the micro and macro levels.

As with all studies, this research also has limitations. Moreover, as with most research on small ventures, this study also uses data that is collected at a sin-gle point in time. Therefore, a longitudinal study could show changes in some variables over time or even include different variables, such as short-term debt. The owner’s personal situation and its direct impact can be critical. For example, illness and divorce might cause deterioration of the business. Furthermore, the competence and motivation of the entrepreneur may change over time. On the other hand, the change in the owner’s situation might not necessarily be the only

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influential factor; market circumstances can change extremely fast. Consequently, the financial performance of small firms suffers and different features are re-quired to succeed. Moreover, it was not possible to control the decision-making process. The experiment was not implemented in controlled circumstances for a predetermined fixed set of decision-makers since a reading of loan applications in the database was used. This dissertation examines start-ups in years (five years after establishment in 2003 and 2005) when economic conditions were prosper-ous. However, it is common knowledge that financial development is a fluctuating process. At this point, it is difficult to ascertain whether funding decisions would be different in recessions. For example, the customers may include more firms that during normal economical situation would have received financing from the private market. Perhaps during economic recessions, adverse selection become even more relevant as overall risk tends to be higher. Moreover, it is worth noting that the results from regression analysis are not necessarily representative of the entire Finnish micro firm population. These firms represent the group of high-leverage firms and all of them are not able to gain financing from banks, that is, the private sector.

Credit risk is the single most important risk for a large number of financial institutions. However, research on credit risk management for micro firms is rela-tively scarce. Future investigations should focus on collateral in micro firm lend-ing, particularly on how the amount of collateral affects the availability of loans in this context. Moreover, it would be worthwhile to investigate the factors that lead to rejection in the loan application phase. It is widely recognized that avail-ability of external finance is related to information asymmetries faced by lenders. In addition, including the aspects of growth, innovation, and internationalization would be very useful; for example, how these aspects help in obtaining finance and impact firms’ financial performance is very important. Furthermore, the ques-tion of whether there are differences between recession and non-recession groups among new micro firms in the state-owned institutional environment remains unanswered. This study identified the important factors that influence financial performance, identified here as credit risk. It is evident that the financial perfor-mance of start-up firms presents challenges for future research; it is necessary to improve information availability and develop adequate risk models for these firms to reduce moral hazard and adverse selection problems.

1.8 DEFINITIONS

In this dissertation, financial performance is measured by creditworthiness. In evaluating the creditworthiness of a firm, the lender assesses the likelihood of future borrower defaults; in other words, the repayment prospects of the borrower (Krahnen and Weber 2001). In his seminal paper, Stinchcombe (1965: 171-173) em-phasizes the importance of creditworthiness of a new firm in shaping the firm’s level of legitimacy and external reputation. The examination of creditworthiness

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has a long history in economics, finance, and accounting literatures (Hillegeist et al. 2004; Wiklund et al. 2010). Studies on creditworthiness rely strongly on finan-cial ratios, representing liquidity, leverage, and profitability which appear to influ-ence the chances of failure. However, there is new evidence that lenders should consider soft qualitative factors ‘when setting internal systems and procedures to manage credit risk’ to minimize their expected and unexpected losses (e.g. Gama and Geraldes 2012). Expected loss (‘the anticipated, annual level of credit losses across the credit cycle’) and unexpected loss (‘the annual volatility of credit losses across the business credit cycle’) are both measures for credit risk (Westgaard and Wijst 2001). Ratings of creditworthiness of firms are offered by a group of rating companies, such as Dun and Bradstreet and Moody’s and Suomen Asiakastieto in Finland. In this dissertation, evaluations by the state-owned financial institution Finnvera, are applied.

Creditworthiness studies are based on bankruptcy studies, which mainly apply financial ratios. However, this study applies non-accounting variables. Terming this a creditworthiness study is difficult because there is no literature on the relationship between non-accounting attributes of start-ups and their credit-worthiness. The assessment of creditworthiness involves an evaluation of finan-cial ratios, industry, markets, and firm management of incumbent firms. In this study, creditworthiness is based on the situation five years after the establishment of the firm, and the defaults have been within this time period. Owing to the lack of literature on creditworthiness and new ventures, this study defines creditwor-thiness as part of success because creditworthiness includes profitability, which is a common success measure. Undoubtedly, success is a much broader construct than creditworthiness. However, success and financial performance studies have applied a diversified range of non-accounting variables.

The definition of success requires some background information. Research suggests that success is a complex and multidimensional phenomenon (e.g. Combs, Crook and Shook 2005). Firm performance can be conceptualized and measured in different ways. Venkatraman and Ramanujam (1986) differentiate financial and operational performance of enterprises. A firm’s economic achieve-ments are measured by financial performance, whereas factors of operational per-formance (such as innovativeness) may influence financial performance. Further, firm success can have different measures, for example, survival, profit, return on investment, sales growth, number of employed, happiness, reputation (Dess and Robinson 1984; Robinson et al. 1984; Smith et al. 1988; Storey 1994a; Stucki 2014). A prerequisite for success is survival; firms have to make sufficient profit to en-sure continuity of operations. In the long term, one of the most important targets is profitability. It is important that firms are first profitable; they can grow their business thereafter (Davidsson, Steffens, and Fitzsimmons 2009; Lappalainen and Niskanen 2012).

The difference in performance criteria is one possible reason for the relative impact of different explanatory factors (Unger et al. 2011). In addition, the results obtained in empirical studies may even contradict each other. Consequently, gen-

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eralization of findings is very challenging or impossible. However, there could also be differences in focus, because researchers examine different stages of firms (e.g. start-ups vs. older firms) or researchers investigate different specific sectors or countries (Unger et al. 2011). Moreover, a comparison among studies is far from easy when the time frames differ (Delmar et al. 2003).

There are also different definitions of business failure, the other side of sur-vival. It can be described using several terms, for example, bankruptcy, insolvency, liquidation, death, deregistering, discontinuance, ceasing to trade, closure, and exit (Balcaen and Ooghe 2006). It must be noted that some of the definitions are based on the concept of voluntary exit, which should be separated from failures. In most studies, the event of bankruptcy is used to define failure, probably because it is objective and clear (Balcaen and Ooghe 2006). Some studies have used ‘finan-cial distress’, but this criterion is ambiguous. In this study, the start-up period includes the first five years. Survival means the continuation of the firm for at least the first five years. In contrast, failure means the disappearance of the firm because of bankruptcy or financial distress in the first five years. Failure is based on bankruptcy (74 firms) or financial distress (27 firms). Both failure types lead to a closure of businesses and are included in the worst credit risk category.

In the first article, six success levels are distinguished in terms of credit risk. Rating grade 6 corresponds to the lowest and grade 1 to the highest degree of cred-it risk. The firms categorized in grades 6 to 2 are those that are still functioning, whereas grade 1 represents failed firms. In the third article, firms are categorized into those with good versus medium/weak creditworthiness.

1.9 STRUCTURE OF THE STUDY

The remainder of the dissertation is organized in the following manner: The next section presents the underlying literature for the articles included in the disser-tation. Section three briefly reviews the individual articles and provides a sum-mary of overall results. Finally, the three articles are presented at the end of the dissertation.

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2 Literature Review

2.1 ARGUMENTS REGARDING THE RISKS FACED BY NEW VENTURES

2.1.1 Liability of Newness and AdolescenceEvery entrepreneur begins with high hopes of success, but market entry is risky and no venture can be run without any risk of failure. This is a major problem par-ticularly for new and small firms (Aldric and Auster 1986). Founders face several barriers in the start-up phase. Their own resources may not be sufficient to cover start-up investment costs and they lack commercial experience. There are two spe-cial theoretical arguments regarding the risks faced by new ventures. The first one is with regard to the firm’s age. The liability of newness provided by Stinchcombe (1965: 148) argues that the hazard rate is the highest in the initial phase of new busi-nesses and, as time passes, the hazard rate keeps declining until it reaches a constant level. The liability of newness can be explained by the lack of specific resources and capacities. Start-ups also need to create and manage new roles, tasks, and social in-teractions. New firms have low levels of legitimacy in the eyes of resource providers (e.g. lenders, employees, customers, and suppliers), and they have to enter markets and take their place among incumbent firms (Stinchcombe 1965: 148–149; Freeman, Carroll, and Hannan 1983; Brüderl and Schüssler 1990). Stinchcombe (1965: 148) ar-gues that relatively more new firms fail than old ones. Similarly, Geroski (1995) has documented that during the first three years, 30% of new projects fail and during the first five years, the failure rate increases up to 50%.

Brüderl and Schüssler (1990) developed the theoretical age-dependent argu-ment further and introduced the concept of a ‘liability of adolescence’. They argue that, in the establishment phase, firms have a higher likelihood of survival if they have sufficient initial resources and if lenders monitor their performance. Later, when the firms become independent, the death risk reaches high levels and con-tinuously declines until 10 years after founding, by which time it remains broadly constant (Headd 2003; Strotmann 2007). According to Cressy (2006), approximate-ly half of all new firms survive for at least four years. Brüderl and Schüssler (1990) state that the adolescence of a firm depends on the legal form of the firm. Some forms of business have requirements on the minimal initial capital to start with, while for others there are no such requirements. Sole proprietorships have a peak in their hazard rate during the first year, whereas for limited liability companies (LLC) it varies between 5–6 years (Brüderl and Schüssler 1990).

2.1.2 Liability of SmallnessIn addition to the age-dependent decline in the death ratio of firms, the liability of smallness has also been observed. According to Brüderl and Schüssler (1990),

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smaller firms have higher and earlier risk peaks than larger organizations. In ad-dition, smaller borrowers suffer from a higher probability of default ( Jacobson, Lindé, and Roszbach 2005). Liability of smallness coexists with liability of new-ness (Freeman et al. 1983; Brüderl and Schüssler 1990). The probability of failure decreases with the size of the new venture at the time of entry (Acs and Audretsch 1989; Audretsch, Santarelli, and Vivarelli 1999; Nurmi 2006). The performance of survivors is very volatile in the short, medium, and long run; moreover, this volatility is difficult to predict (Storey 2011). Therefore, understanding the deter-minants of success and failure is a central concern for those weighing the risk of starting a business (Baron and Hannan 2002; Farrell 2003), and it is also a critical factor in maintaining the stability and health of the economy (Carter, Williams, and Reynolds 1997; Pompe and Bilderbeek 2005).

Corporate finance literature states that new ventures and SMEs are informa-tionally more opaque, which makes them statistically more likely to default; con-sequently, it is more difficult to judge their relative creditworthiness (Jacobson et al. 2005). However, the results of Hyytinen and Pajarinen (2008) show that disa-greements regarding the creditworthiness of SMEs as assessed by credit informa-tion companies decreases with the age of the firms but not so strongly with their size. Both size and age of the firm are important in the availability of finance (Beck et al. 2006).

2.1.3 Framework of Asymmetric InformationThe main reason for imperfections in the capital market is the information asym-metry between borrowers and lenders. It is assumed that insiders in a firm have more information than external agencies like banks on their business and the like-lihood of the firm making a breakthrough or going bankrupt. There are two types of information asymmetry in capital markets: adverse selection and moral hazard.

‘Adverse selection’ refers to the problem that lenders encounter before granting a loan (ex-ante). With the limited amount of information available, lenders face dif-ficulties in distinguishing good applicants from bad ones. They have to evaluate the applicant’s capacity to repay credit and also whether the applicant is willing to pay back. The credit department uses all the relevant information at its disposal to place the applicant in a specific credit risk category. However, some information can be very specific, such as technological knowledge or proprietary information, and this causes difficulties for lenders in assessing the quality of the investment. Furthermore, it is possible that applicants can masquerade as good ones. This is because start-ups do not have a track record for the lenders to refer to.

On the other hand, ‘moral hazard’ refers to the situation after entrepreneurs have raised funds from lenders. It arises from the lack of information on the ex-post behaviour of the entrepreneur. The firm may allocate the financing granted to it for other purposes than those intended by the lender or make other commit-ments that imperil the repayment and benefit the firm disproportionately (Denis 2004). Alternative causes of action for the entrepreneur are slow work, making riskier choices than would be optimal from the lender’s point of view, and getting seriously ill.

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The classical asymmetrical information problem obviously creates demand in the less-informed party to acquire information. The acquiring of information depends on the nature of the information asymmetry. Freixas and Rochet (2008: 29) classified the monitoring into three types: (i) ex-ante (situation of adverse selec-tion), (ii) interim (preventing moral hazard), and (iii) ex-post (punishing or audit-ing a borrower who fails to meet repayment obligations). An active monitor can be beneficial for both parties concerned. It can help to resolve information asym-metry problems. If the borrower is financial sound, s/he could borrow more capital from less-informed investors if they are assured of the borrower’s creditworthi-ness (Holmström and Tirole 1997; Tirole 2006: 356-359). Monitor activity may also attract other financiers even if the firm is not worthy of it, thereby creating a ‘free ride’ effect (Tirole 2006: 480). Monitoring is also important for the lenders in the sense that they want to be more efficient than their competitors. Good ex-ante, interim, and ex-post monitoring attracts better quality clientele and can generate a competitive advantage, whereas weak predictions can lead to adverse selec-tion (Hyytinen and Toivanen 2004). Jappelli and Pagano (2000) suggest that the availability of founder-specific information in creditworthiness evaluation has im-proved with technological innovations and the development of credit bureaus and credit rating agencies. Financial institutions collect quantitative information (e.g., financial ratios) which may be analyzed statistically. Then, they often complement it with qualitative information, which needs data processing by humans. Such information may be more circumstantial; for example, it may involve the knowl-edge of the local economy and the competitive position of local small businesses.

Credit rationing can be a consequence of adverse selection and moral haz-ard problems. In credit rationing, banks invest less compared to the social opti-mum, that is, there is underinvestment in markets and demand for credit exceeds the supply; even good investments are not financed because riskier investments replace the less risky ones. The assumption is that banks have asymmetric in-formation on loan applicants. There are two kinds of demand rationing. Type I rationing signifies a situation in which the full amount that is required is not sanc-tioned, whereas in Type II rationing the application itself is rejected (Blumberg and Letterie 2008). If banks would raise loan prices and some applicants accept the higher interest rate, it could attract riskier borrowers (i.e. adverse selection effect). This ‘boomerang effect’ makes it inadvisable for banks to raise interest rates; riskier lending would not yield adequate higher returns (i.e. if banks raise the interest rate, borrowers would undertake more risky projects to earn higher expected return with a lower chance of repayment; moral hazard effect) (Stiglitz and Weiss 1981). The basic idea is that it is not necessarily profitable for a bank in the long run to try to maximize its profit by increasing the interest rate to cover credit risk. There might be an interest rate level at which the expected return of loans granted are at the maximum; at a higher interest rate the expected return from loans begins to decrease due to higher defaults. If, despite the higher interest rate, there is a lot of demand, not all applicants would obtain a loan despite simi-larity between the two groups (of those granted a loan and those who are not). In

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this Stiglitz-Weiss adverse selection model, a new venture has difficulties in rais-ing external finance, because it has a serious opaqueness problem and therefore a severe adverse selection problem. This opacity leads to a credit constraint.

On the contrary, the model by de Meza and Webb (1987) shows that asymmetric information will cause overinvestment in the market and may lead to many bad investments being funded. This is possible when good investments compensate for bad ones. The differences in outcomes stem from the different assumptions. Stiglitz and Weiss (1981) assume that banks lack information about risk types. Therefore, banks have to offer the same interest rates on all investments that have the same expected value of return. On the other hand, de Meza and Webb (1987) assumed that banks are familiar with the expected return on investment, but they are unaware of the success probability of entrepreneurs. In this case, banks have to offer the same interest rate on all possible investments which yield the same return despite the success level.

Diamond (1989) shows that young although good firms have to pay higher interest rates in the beginning, if they are not able to convince the lender of their quality (adverse selection problem). In such a situation, firms prefer more risky projects (moral hazard problem) and benefit more if their business is successful; if not, then the bank suffers from investment credit risk. This combined influence of adverse selection and moral hazard is most severe for start-ups.

However, it remains ambiguous whether there is under- or over-investment in the market. In addition, an important question is whether governments can improve the functioning of the market (e.g., de Meza 2002; Parker 2002). Mankiw (1986) modelled financial markets with assumptions similar to those of Stiglitz and Weiss (1981). The model indicates that government intervention may lead to more efficient credit allocation. When the group of applicants is very heterogeneous, the benefit of government intervention is greater. However, Mankiw emphasizes that to be able to increase the social welfare, the intervention must simultaneously increase the probability of repayment. In his model, government intervention in-creases the inclusion of both riskier and risk-free applicants. According to de Meza (2002), lending subsidies may attract less qualified firms and result in worse ef-ficiency. Banks are cautioned against lending to bad borrowers as the latter could replace good substitutes (Caballero, Hoshi, and Kashyap 2008).

The background reasons why the opacity of young and small firms leads to their facing a credit constraint or higher costs of capital are relatively well under-stood. It is the information asymmetry which makes external finance for such firms expensive and difficult to obtain (e.g., Hubbard 1998; Stein 2003).

2.1.4 Evidence of Information Asymmetry ProblemsAccording to the life cycle pattern of corporate financing, asymmetric information problems and incentive problems are most severe for small businesses and at the start-up phase of firms; these problems decrease over time for firms that mature and acquire a good track record and reputation (Boot and Takor 1994; Diamond 1989; Beck and Demirgüc-Kunt 2006). The assumption underlying the theories

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is that small businesses are a heterogeneous group and information on them is scarce, particularly facts that can be used to distinguish between creditworthy and non-creditworthy firms. In other words, start-up firms and small firms are informationally opaque.

An empirical study by Hyytinen and Pajarinen (2008) showed that younger firms are more opaque than incumbent firms. However, their results do not sup-port the idea that the size of firms is related to their transparency. Which one of the two phenomena related to informational asymmetry on the availability of capital, that is, the problems of adverse selection and moral hazard, is more prevalent empirically? From Finnish SMEs data, the evidence shows that adverse selection is more prevalent. Moreover, asymmetric information is more explanatory in re-gression modelling and has a greater effect than moral hazard (Hyytinen and Väänänen 2006). As proposed by Diamond (1989), the proxies also have an inverse relationship to the age of firms. The correlation is negatively related with the age of firms (Hyytinen and Väänänen 2006). This confirms the finding of previous literature that credit constraints are particularly acute for young firms.

2.2 FIRMS’ FINANCIAL PERFORMANCE

There is no theory explaining prediction variables of financial performance be-cause there is a wide discrepancy in the results of researches (Lussier and Halabi 2010). It has been noted that prediction variables vary in different countries (Benzing, Chu, and Kara 2009; Lussier and Halabi 2010). However, it has been documented that non-financial prediction models are more suitable for small busi-ness research, since such firms have highly concentrated ownership, and personal and business activities may be intertwined (Cooper, Gimeno-Gascon, and Woo 1994; Dennis and Fernald 2001; Van Auken et al. 2009; Lussier and Halabi 2010). Many theoretical frameworks for analyzing the influence of non-accounting fac-tors on new firm financial performance have been provided. Watson, Hogarth-Scott, and Wilson (1998) have developed an extensive framework for new ventures, formatting it particularly for very small or micro businesses (Figure 2). Naturally, there are many determinants of business performance, both internal and external factors, even in very small businesses. They indicate the importance of the firm’s characteristics, business infrastructure, and firm’s customer markets, as well as the characteristics and experiences of the founder and the influence of the envi-ronment affecting business performance (Watson et al. 1998). All start-ups carry skills and routines embodied in their founders that are likely to influence the new firm’s future development and success. For independent start-ups that do not con-tinue the business of parents, or that result from mergers by existing firms, pre-entry capabilities are associated primarily with the founders, who usually play an important role in the management of the start-up (Watson et al. 1998).

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Figure 2: Framework for analyzing new micro firms’ performance given by Watson, Hogarth-Scott, and Wilson (1998).

Some studies have found that soft (non-accounting) factors play an important role in determining the credit ratings for loans to small and medium-sized firms (e.g. Blumberg and Letterie 2008; Altman, Sabato, and Wilson 2010; Unger et al. 2011). Altman et al. (2010) developed a model that combined both hard and soft factors for very small firms; it is one of the few studies that have examined the credit risk of small firms. Their study confirmed that different procedures are required for small enterprises. In addition, there is evidence that the combined use of hard and soft factors clearly increases classification power (Grunert, Norden, and Weber 2005; Altman et al. 2010; Mures-Quintana and García-Gallego 2012). However, these previous studies did not investigate start-up firms. Until recently, the role of non-accounting factors has remained unclear, mainly because they are inherently private and market data are not available for unlisted firms (Grunert et al. 2005; Altman et al. 2010).

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The major problem facing new and small ventures is survival, while in more mature stages, it is more the problem of strategic transformation (Aldrich and Auster 1986: 193). Predicting entrepreneurial financial performance is an impor-tant research area (Westhead, Wright, and Ucbasaran 2001; Pompe and Bilderbeek 2005), because bankruptcy is expensive and harmful to owners and their families, investors, customers, suppliers, and communities (Van Auken, Kaufmann, and Herrmann 2009). Examining factors that may lead to firm success, survival, or failure is an unfulfilled purpose of entrepreneurial research (Rogoff, Lee, and Sub 2004). The dynamic nature of the economy makes financial performance studies continue to be an important research topic (Carter and Van Augen 2005).

The survival of a firm concerns the continuity of the business. For some en-trepreneurs, reaching an area out of the red may already be an accomplishment. To survive in the long run, the business must have good solvency and be able to maintain its competitive advantage (Gorgievski, Ascalon, and Stephan 2011). Firms must survive to succeed; however, factors affecting survival may be differ-ent from factors affecting success. Survival and success are distinct concepts of business performance (Haynes, Danes, and Stafford 2011). Moreover, the meaning of success differs considerably between entrepreneurs and firms. For example, a typical Schumpeterian start-up strives for growth and maximum profit, whereas for a self-employed person sufficient income may be enough. These self-employed people are also not interested in hiring more personnel (Schutjens and Wever 2000).

There are many studies focusing on the importance of non-accounting vari-ables in predicting the future financial performance of start-up firms. Some of them examine owner characteristics (e.g. Bosma et al. 2004). For example, Pfeiffer and Reize (2000) investigate the connection between business start-ups and un-employed owners and find that start-ups by previously unemployed owners are less likely to succeed. Studies that have used a theoretical framework of human capital have found that entrepreneurial human capital enhances the productiv-ity of the founder-manager (Brüderl, Preisendörfer, and Ziegler 1992; Cooper et al. 1994; Bosma et al. 2004). According to Cooper (1982) the strengths and weak-nesses of the founders are also the strengths and weaknesses of the ventures. Similarly, some researchers suggest that the new firm is an extension of the start-up (Chandler and Jansen 1992; Dyke, Fischer, and Reuber 1992). Human capital has also been used to explain new venture financial performance, venture scale, and growth, and it is an important contributor to the start-up’s quality (Stuart and Abetti 1987; Cooper et al. 1994; Bosma et al. 2004; Cassar 2004; Coleman and Cohn 2000; Unger et al. 2011). Originally, human capital theory was developed to enlighten the relationship between financial returns of employees and their char-acteristics and background (Becker 1993). According to the theory, people strive to receive compensation and economic benefits for their human capital investments over their lifetime. Thus far, authors have distinguished different types of human capital. Rauch, Frese, and Utsch (2005) distinguished among three types of human capital: an individual’s education, experiences, and skills that help in the task of

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getting one’s work done. Some authors have distinguished between general (e.g. schooling) and specific human capital (e.g. work experience in the same sector), demonstrating the importance of the task context (Becker 1993; Madsen, Neergard, and Ulhoi 2003; Bosma et al., 2004). Currently, human capital is emerging as the most crucial asset (Zingales 2000).

When it comes to findings of financial and firm-related variables, Cooper et al. (1994) and Gimeno, Cooper, and Woo (1997) suggest that human capital provides a better competitive advantage than physical capital. Cooper et al. (1994) find that increase in initial financial capital increases the likelihood of survival. According to Cressy (1996), human capital is a better determinant of start-ups’ survival, and financial capital cannot predict survival. Storey and Wynarczyk (1996) argued that financial variables are likely to reflect human capital, which implies that bankers will grant a loan if the level of human capital is high. They indicate the importance of examining both personal characteristics and business factors, and of studying different industries. Bates (1990) and Saridakis, Mole, and Storey (2008) find that both financial and personal factors have an impact on survival.

Generally, studies on financial performance have used two-point scale per-formance models (success vs. failure) (e.g., Lussier and Halabi 2010). However, it has been shown that the predictors of financial performance vary, depending on whether we use a two-point scale or multiple-point scale performance model (Cooper et al. 1994; Dahlqvist, Davidsson, and Wiklund 2000). The literature of multileveled financial performance research remains very scarce. In particular, the relationship between different factors and financial performance measured by creditworthiness is unknown. Furthermore, there is variation in the kinds of measures used in previous research studies on firm performance. Comparison between studies is difficult as the time frame, performance indicators, and the performance formula often differ (Delmar, Davidsson, and Gartner 2003).

2.3 FINANCIAL STRUCTURE

Capital structure, or the distortions related to different ways of funding, is one of the main areas of corporate finance. Corporate finance examines the manner in which firms are financed. Obtaining finance is a major issue for many start-up businesses; it is also universally accepted as the key barrier to business start-up (Black and Strahan 2002; Hill, Leitch, and Harrison 2006). The availability of fi-nance for SMEs is scarce both in bank-centred financial systems, such as those in Germany and Finland, and in market-centred ones, such as the Anglo-Saxon systems (Hubbard 1998; Carpenter and Petersen 2002). Firms that rely on banks are hit hard by fiscal policy restrictions and damages sustained by the banking sector (e.g. Kroszner, Leaven, and Klingebiel 2007; Dell’Ariccia et al. 2008; Khwaja and Mian 2008). There is international evidence that large firms with financing needs have better access to funding because they can obtain better and different types of external finance (Berger and Udell 1998; Beck et al. 2008). Niskanen and

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Niskanen (2006) have a similar finding among big and small SMEs in Finland. The financial problems of Finnish micro firms were triple those of large firms in 2012. The financial problems are also more serious for micro firms (Business Financing Survey 2012).

The determinants of new venture financing structures are affected by different factors. If financial institutions are not able to solve information asymmetries, the new venture may lack external debt or equity or external capital may be expensive (Parker 2002; Hall 2002; Huyghebaert and Van de Gucht 2007). This lack of suf-ficient information can be termed supply-side reason (Atherton 2009). Moreover, certain characteristics of the new firm affect the capital structure of small busi-nesses; for example, the liability of newness and the higher ex-ante failure risk compared to existing ventures (Chaganti, Decarolis, and Deeds 1995; Cassar 2004; Huyghebaert and Van de Gucht 2007). One reason for reduced finance is that smaller firms have less, if any, available collateral to hedge the risk of debt (Parker 2002; Hyytinen et al. 2003c: 454; Niskanen and Niskanen 2006). In addition, the market period during debt issuance has an impact on capital structure. In an eco-nomic boom, firms with high adverse selection costs issue significantly more debt than in a recession (Doukas, Guo, and Zhou 2011). There are also differences be-tween the institutional frameworks in different countries, which may impact new venture financing structure (Hall, Hutchinson, and Michaelas 2004). Nevertheless, finance decisions may also reflect the choice of the entrepreneur; in other words, it may be driven by demand-side factors. Entrepreneurs may be unwilling to opt for external financing, thereby keeping all the control of the business themselves (Gibson 1992; Kuratko, Hornsby, and Naffziger 1997; La Rocca et al. 2011). It also re-flects the entrepreneurs’ attitude towards risk (Atherton 2009). On the other hand, the decision to raise debt may be due to necessity rather than choice, because small firms cannot rely on public-held debt or equity or commercial paper.

Berger and Udell (2006) propose a conceptual framework to analyze SME fi-nance availability issues. According to the framework, two features impact the availability of credit and the nature of the credit facility. The first is the nation’s financial institution structure and lending infrastructure, which are affected by government policy. The policies influence market shares and competitive condi-tions for different financial institutions (e.g. state-owned versus private, or large versus small financial institutions). This context forms the basis of the financial institution structure. The lending infrastructure includes information, legal, ju-dicial, bankruptcy, social, tax, and regulatory environments in which financial institutions operate. These financial structures then affect different lending tech-nologies, which are second in the causal chain. Technology use is affected by the structures of financial institutions, as various institutional types have varied com-parative advantages. Whether these technologies are legal and profitable depends on the lending infrastructure. Lending technology includes different transaction technologies (e.g. financial statement lending, small business credit scoring), and relationship lending. Transaction lending relies on ‘hard’ quantitative data such as financial statement information, while relationship lending depends on ‘soft’

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qualitative information. Each technology has its own kind of combination of pri-mary information source, screening and underwriting policies and procedures, loan contract structure, and monitoring mechanisms that are applied in the lend-ing processes.

The most important finance source for start-ups is the founders themselves (Berger and Udell 1998; Cassar 2004; Atherton 2009). Approximately half the nas-cent entrepreneurs reported the need for external finance in the UK in 2011 (GEM UK 2011). For start-ups, among other sources, external entrepreneurial capital may be provided by banks, venture capitalists, private individuals, and leasing and factoring. For start-ups in the UK, funding from banks or other financial insti-tutions was approximately 20% for the 2006–2011 period (GEM UK 2011). Some studies find external debt an extremely important source for new firms, even if they are at the very initial stage of their business (Scherr, Sugrue, and Ward 1993; Robb and Robinson 2009; La Rocca, La Rocca, and Cariola 2011). New ventures use more long-term debt than short-term debt (Atherton 2009). Subsequently, when the firms mature and generate internal funds, they use less debt (López-Gracia and Sogorb-Mira 2008). There is also a positive relationship between firm size and the amount of loan (Sogorb-Mira 2005; López-Gracia and Sogorb-Mira 2008; Degryse, de Goeij, and Kappert 2010).

A firm’s decision on its capital structure affects the debt-to-equity ratio for the firm. In the start-up phase, entrepreneurs actively narrow the so-called funding gap independently. For example, they minimize the required running cost and look for informal sources of finance (Lam 2010). The interrelationship between demand and supply of entrepreneurial finance is a dynamic and complex issue, where the business owner plays an important role in managing the firm’s finan-cial needs (Lam 2010). According to some studies, financial bootstrapping is the most important means of funding among small ventures (Harrison, Mason, and Girling 2004; Lam 2010; Winborg and Landström 2001). Financial bootstrapping can be defined as ‘a collection of methods used to minimize the amount of outside debt and equity financing needed from banks and investors’ (Ebben and Johnson 2006). Winborg and Landström (2001) classified these methods into four types: (1) customer-related (e.g. minimization of the accounts receivable), (2) owner-related financing and resources (e.g. use of private credit card or loans from the founder’s family or friend(s), having a spouse in employment to support the financial needs of the new venture), (3) joint utilization of resources with other firms (e.g. sharing employees, assets, or facilities with other firms, purchasing with other firms to take advantage of economies of scale.), (4) delaying payments (e.g. delaying pay-ments to suppliers or leasing of equipment).

Demirgüc-Kunt and Maksimovic (2001) argued that the development level of a country’s legal and financial infrastructure is related to the use of trade credit. In countries with highly developed legal systems, firms use more bank loans than trade credit. In other countries, firms use and offer more trade credit. Similarly, Carbo et al. (2008) find that firms with restricted access to bank loans may use more trade credit. Non-financial firms may have a comparative advantage in find-

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ing information on the borrowers’ repayment ability, or may offer trade credit to fi-nancially restricted customers because of their easier access to a less costly source of funding, that is, bank loans (Carbo et al. 2008). New firms primarily utilize owner-related financing and resources and joint-utilization techniques, whereas the use of customer-related and delaying-payment techniques is initially low and increases over time (Ebben and Johnson 2006). In Robb and Robinson (2009), bor-rowing from family and friends and other financing sources like credit cards is found to be less relevant in funding newly founded firms. In the United Kingdom, loans from friends and family are as important as bank overdraft (GEM UK 2011).

The capital structure of Finnish SMEs is rather similar to that of US SMEs. Hyytinen and Pajarinen (2003: 203) found Finnish SMEs comparable to US ones, as given in the results of Berger and Udell (1998). In general, SME source of finance depends strongly on both equity and debt. Hyytinen and Pajarinen estimated distributions of equity and debt by firm size and age. According to their defini-tion, small SMEs have less than 20 employees and less than 1 million euros in annual sales. In Finnish SMEs, the debt ratio, that is, the ratio of the sum of debt and capital loans to the sum of debt, capital loans, and equity financing is 54%. The results reveal that small SMEs are less indebted than large ones. The debt percentage of small SMEs is 43%, while that of large SMEs is 59% (Hyytinen and Pajarinen 2003: 211). The most important source of finance for small SMEs is 35% (29% in large SMEs) of the total debt and equity, provided by the principal owner’s equity. In Finnish SMEs, venture capital firms are not important holders of equity; their share is approximately 1%, which is not surprising because such capital firms choose their candidates carefully. The second most important source of finance for small SMEs is non-financial institutions; this accounts for 16% of the total debt and equity, including trade credit. The credit provided by financial institutions constitutes the third most important source (15%) (Hyytinen and Pajarinen 2003: 212). This source accounts for 17% for large Finnish SMEs, while in the United States, it is approximately 27% for all SMEs (Berger and Udell 1998).

The debt ratio changes over the lifecycle of firms. For ‘infant’ SMEs (0–4 years old), it is 56%. Then the ratio decreases somewhat, and is highest at 61% for SMEs that are 9–24 years old. Hyytinen and Pajarinen’s (2003) finding of evolution of the capital structure is similar to that of Berger and Udell (1998) pertaining to the United States. The proportion of total funds provided by the principal owner is relatively low for infant SMEs (22%). ‘Principal owner is defined either as a share-holder who is one of the five largest owners with significant control over the firm’s capital structure and governance or, for some firms, as the largest shareholder if such a shareholder unambiguously exists.’ (Hyytinen and Pajarinen 2003: 211). Moreover, 58.2% is provided by ‘managers and employees who are actively in-volved in the daily business of firms (but do not have control over the firm required by the definition of the principal owner)’ (Hyytinen and Pajarinen 2003: 214).

Hyytinen and Pajarinen (2003: 218) divide the sources of SME debt into nine institutional creditor categories (excluding capital loans). Financial institution debt and non-financial (business and governmental) debt have four types each;

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the ninth is a collection of public debt instruments. A tenth category includes other debt, including debt from individuals. The most important source of debt for start-ups is trade credit, which is 34% of the total debt. The second most important source of debt is provided by domestic banks (26% of the total debt). The third most important source is that of ‘other debt’, which mainly includes debt from in-dividuals, whereas governmental bodies are the fourth largest sources. The share of Finnvera is 9% of the total debt. Commercial papers and other instruments of public debt account for only 0.2%. Notably, Hyytinen and Pajarinen (2003) exclude SMEs that are proprietorships or partnerships. Furthermore, the study oversam-pled firms in technology (60%).

The age categorization shows that the ratio of bank loans to total debt fluctu-ates over the lifecycle of a firm. It is at its highest, 26%, when SMEs are infants and reaches its peak at 35%, when firms are 5–8 years old. Then, the ratio decreases strongly in the 9–24 year-old firm category (22%), increasing thereafter as firms mature (25 years and older). The contribution of debt from finance firms, other non-financial firms, and governmental bodies is strongest during the early stages of the SME lifecycle. The debt proportion by finance companies is particularly high among the 0–4 year-old SMEs. The sources of debt are more heterogeneous for small and, in particular, for young SMEs (Hyytinen and Pajarinen 2003: 219).

As in many other countries, the Finnish government is interested and offers funding to Finnish firms, particularly to SMEs. However, one difference between the capital structure of SMEs in Finland and the United States is that infant SMEs use less debt provided by financial institutions in Finland (22%), whereas in the United States it is 30%. Therefore, it can be stated that the debt market in Finland is less favourable for entrepreneurship and new firm creation than in the United States. However, this could be due to differences in demand, which naturally invalidates the conclusion (Hyytinen and Pajarinen 2003: 214). Further, Hyytinen and Väänänen (2003: 325) emphasizes that government funding to SMEs should be made available selectively. Moreover, they indicate that government funding faces problems in the screening of applications from SMEs that depend entirely on this source.

Funding is of central importance to new ventures, and understanding how it affects the financial performance of these firms is a focal question in entrepre-neurial finance. There are many possible explanations for the failure of new firms. One often cited factor is resource poverty (Welsh and White 1981). One aspect of this poverty is insufficient financial capital (e.g. Chaganti et al. 1995; Cassar 2004), which is stated to be a major cause for small business failure (Wucinich 1979; Cooper et al. 1994; Altman et al. 2010). It is important to understand the factors that determine the initial combination of equity and debt in determining business prospects (e.g. Storey, 1994a; Chaganti et al. 1995; Atherton 2012). Too much leverage at the beginning leads to liquidity problems (e.g. Wiklund, Baker, and Shepherd 2010). Higher levels of loan also indicate prior claims on future cash flows (Panno 2003). These arguments indicate the importance of understand-ing the initial capital structure decisions made by the entrepreneur and lender.

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Establishing a new firm typically requires a considerable investment of resources to succeed. A new venture with little internal capital needs external funding.

There is no universal theory of capital structure. The development of a theoret-ical body in explaining firm’s capital structure configuration was first structured by Modigliani and Miller (1958). Assuming perfect financial markets (no taxes and no agency and transaction costs) and rational economic behaviour, firms have suf-ficient resources and the value of the firm is independent of its financial structure; this determines the firm’s ability to generate cash flow. Modigliani and Miller (1963) stated that firms tend to favour debt to equity due to the tax deductibility of interest payments, thereby maximizing the firm’s value. In a context in which taxes, costs of possible financial distress including reorganization costs, and agent costs are considered during the same time period, the so-called trade-off theory emerges, which helps to minimize their weighted average cost of capital. Firms seek a balance between the tax advantages of debt financing over equity and bankruptcy and agent costs to maximize the firm’s value (Verheul and Thurik 2001; Sogorb-Mira 2005). Small firms generally have relative higher bankruptcy costs (Ang 1992). They borrow at less favourable interest rates (Ferri and Jones 1979) and have a lower likelihood of being able to fully use tax shields from inter-est payments, thereby lowering the expected tax benefits of debt (Smith and Stulz 1985). The conflicts between external creditors and insiders are more severe for small firms (Grinblatt and Titman 1998). Theoretically and empirically, it is stated that firm size is positively related to leverage (Gaud et al., 2005; Sogorb-Mira 2005).

In imperfect markets, firms have to choose how to finance their investments. Contrary to the trade-off theory, Michaelas, Chittenden, and Poutziouris (1999) and López-Gracia and Sogorb-Mira (2008) find that managers of SMEs do not weigh the tax deductibility of interests when deciding or planning the capital structure of their firm.

The Jensen and Meckling (1976) model explains that ownership structure func-tions as an incentive to take actions that reduce the total value of the firm in an environment where information is asymmetric. This could be the case in a static world in a market where the behaviour of actors (managers, bond holders, and eq-uity holders) is supremely opportunistic. However, the Jensen and Meckling mod-el can be criticized because its orientation to the corporate finance market does not take into account the aspects of entrepreneurship. Entrepreneurship is a dynamic and progressive process, where the objective of an owner-manager could be the maximization of wealth through value creation in his or her firm. According to Virtanen (1996), at least in the early stages of new ventures, bootstrapping through wage and perquisite flexibility is a common phenomenon, rather than the short-term opportunistic pursuit of self-benefit.

One of the assumptions in Modigliani and Miller (1958) is that lenders and investors have access to the same firm-specific information. However, the infor-mation of the firm is often incomplete, inaccurate, or simply unavailable. The in-formational asymmetries between lenders and managers lead to pecking-order theorizations. Myers (1984) and Myers and Majluf (1984) suggest that firms prefer

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internal cash flows to external capital sources. In external finance, firms first seek to obtain debt and, as a last source, external equity. These preferences are based on the related costs of different sources of finance due to asymmetric informa-tion and transaction costs. The pecking-order hypothesis states that there is no optimal level of leverage; rather, it depends on the firm’s situation over time. The pecking-order theory predicts that young firms use debt because they do not have generated cash flows due to short time since existence (Sogorb-Mira 2005).

In a study of the capital structure of small business, Berger and Udell (1998) assert that the financial needs and options of a firm are based on the phase of a firm’s life cycle. The life cycle pattern takes into consideration the availability of financial sources and the cost of capital. The cost of capital depends on the degree of information asymmetry, which changes during the financial life cycle (Kaplan and Strömberg 2003). Firms without track records or assets that could be used as collateral must rely mainly on internal finance. In doing so, firms attempt to minimize issue costs; then, debt after internal finance is exhausted. However, they may not obtain a significant amount of debt from financial intermediaries. Subsequently, when firms are more transparent and bigger (i.e. more collateral is available), they gain better access to debt finance. One reason for this is that a firm has a longer relationship with an external financier, such as financial institutions (Petersen and Rajan 1994; Berger and Udell 1998). The financial needs of the firm differ according to its life cycle, how it is able to generate cash flow, whether it has growth opportunities, and what is the related risk in realizing them (La Rocca et al. 2011). Therefore, the debt-equity ratio is life-cycle-specific. In the presence of market imperfections and agency risks, firms face financial constraints (Berger and Udell 1998). Investors may reject capital applications and firms may raise only certain types of finance (Cosh, Cumming, and Hughes 2009). Because of the asymmetric information and lack of collateral, the availability of bank finance and new equity finance may be particularly problematic for start-ups (Berger and Udell 1990; Cassar 2004). If they obtain external finance, it may be prohibitively expensive (Berger and Udell 1998; Cassar 2004). Firms have to pay relatively high rates of interest. Lenders’ demand for collaterals or personal guarantees naturally adds to the risk of entrepreneurs. In addition, it restricts their flexibility (Coleman and Cohn 2000). According to Berger and Udell (1998), debt is more important and easier to obtain on a later stage of the lifecycle. Myers (2001) summarizes the knowledge of capital structure choices that the debt ratio of each firm reflects its cumulative need for external finance (Myers 2001).

An alternative framework to explain capital structure in small start-ups busi-nesses is the human capital theory. Cressy (1996) says that there is no relation-ship between financial capital and survival, and that firms’ performance is ex-plained by human capital. Thus, it is worthwhile to investigate human capital and the availability of external debt. Studies of investors’ selection criteria suggest that founders with higher levels of human capital have more access to finance (MacMillan et al. 1985; Blumberg and Letterie 2008: Levie and Gimmon 2008). It is termed the ‘wealth effect’ of founders’ human capital (Gimmon and Levie 2009).

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For lenders, human capital provided by founders represents important informa-tion to investigate the risk of a start-up (Colombo, Delmastro, and Grilli 2004). Accordingly, investors consider that founders with greater human capital will be less uncertain regarding their efficiency and learn about market conditions faster than those with less human capital; hence, their firms would be more likely to sur-vive the initial years of the market selection process (Cooper et al. 1994; Coleman and Cohn 2000). The qualifications may also serve as signals of the commitment level of the founder. On the other hand, the human capital theory (Becker 1993) argues that founders with high human capital levels are more likely to generate higher incomes and, hence, are able to contribute more to the start-up’s initial financial capital (Astebro and Bernhardt 2005).

2.4 LENDER’S EVALUATION OF THE BUSINESS PROSPECTS

The forces that simultaneously affect the founding of a firm and assessment of a lending proposition from a small business can be categorized into three catego-ries: (1) starting resources of the founder(s), (2) characteristics of the venture, and (3) environmental factors (Alkhavein, Frame, and White 2005). The three parts need to be combined appropriately for survival and success to be achieved. This implies that it is very difficult at the start-up stage to predict whether a firm will be a success (Storey 1994a). It is said that a firm is an extension of the entrepreneur (Chandler and Hanks 1994). Many founder characteristics, such as education and prior experience, can be considered as fixed in the short and medium terms, some even in the long term. They may be associated positively with the firm’s average financial performance (Storey 2011). Firm-specific, industry-specific, and macro-economic variables have been stated to have different impacts on survival of new SMEs and micro enterprises (Holmes, Hunt, and Stone 2010). The environment provides market opportunities that the firm attempts to match with its resources and capabilities. However, because of the small size, restricted resources, and lack of market power, new firms are more vulnerable to environmental influences than larger firms (Chandler and Hanks 1994; Man, Lau, and Chan 2002).

Traditionally, many financiers have used the so-called 5 Cs (character, capital, capacity, collateral, conditions) method to evaluate the creditworthiness of a loan applicant. The first factor, character, refers to the borrower’s reputation. It esti-mates his/her willingness to repay the debt. Critical information includes business history and managerial experience. Capital refers to the borrower’s ability to repay a debt, that is, does the borrower have sufficient resources to respond to the obliga-tion? Capacity is a subjective measure of borrower’s solvency. The lenders evaluate business experience and ability to manage the business without being effective. When considering collateral, lenders estimate if collateral is sufficient and if the borrower takes care of its value. Collateral comprises the assets used to secure the loan. Finally, the conditions of the loan, such as the interest rate and environ-mental conditions, will influence the borrower’s desire and ability to repay the

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loan despite the changes in business and economy (Levy and Sarnat 1999: 193194).There are different types of funders: bankers, venture capitalists, business an-

gels, and state-owned financial institutions. According to Mason and Stark (2004), bankers, venture capitalists, and business angels assess loan applications differ-ently and pay attention to different types of information. Bankers emphasize the borrowers’ ability to repay loans. They emphasize fewer environmental or entre-preneur-related attributes (Mason and Stark 2004). The main focus is the degree of credit risk that the loan represents for the lender (Wynant and Hatch 1991: 132). Lenders consider the sufficiency of collateral or personal guarantees in the case of liquidation of the business and the firm’s ability to generate adequate cash flow to be able to repay the loan (Wynant and Hatch, 1991: 132133). Similar findings are provided by Storey (1994b), which concentrates on the founders of new firms, and Deakins and Hussain (1994); bank lending decisions are dominated by financial considerations, whereas personal characteristics are only weakly related to grant-ing loans. The primary concerns of venture capitalists and business angels are market- and finance-related attributes. Business angels are also interested in the entrepreneur (Mason and Stark 2004). Some studies find that venture capitalists base their judgment on the ‘quality’ of the management (e.g., MacMillan, Siegel, and SubbaNarisimha 1985). According to Zacharakis and Shepherd (2005), in ad-dition to human capital, venture capitalists also consider social capital. Intuition and herd behaviour are also included in decision-making in venture capital (Zider 1998). Moreover, it is notable that venture capitalists seek ventures that are likely achieve high growth; often these firms are very young and innovative and lack sufficient collateral to be able to borrow from banks. Only rarely do new firms have venture capital (Kanniainen and Keuschnigg 2004); in the United States, approximately 8% of new ventures have venture capital (Reynolds 2007). It is ex-pected that state-owned financiers adopt a different approach, because they stress the societal aspects of the proposal. The focus on start-ups financed particularly by a state-owned financial institution provides an insight into an area that has been subjected to too few studies and is of considerable interest to policymak-ers, as such ventures have a positive impact on employment (Bonet et al. 2011; Giovannetti et al. 2011). One difference between governmental funding and others is that the former financiers claim some protection to secure the loan.

Availability of external capital is a critical institutional factor for new ven-tures (Zacharakis and Meyer 2000; Gimmon and Levie 2009). Efficient funding for businesses at the establishment and early growth phases is also important for maintaining a competitive SME sector (Cressy 2002). The accuracy of lenders in selecting successful entrepreneurs is a societal issue, since poor judgment will lead to a loss of the invested capital. The capital sources of fund managers should be selected in a way that they are encouraged to invest their money successfully. Most banking theories state that a bank faces a given demand for credit, so that the pre-lending screening of loan applicants’ creditworthiness is one of the primary roles of the lenders (Jaffee and Stiglitz 1990; Freixas and Rochet 2008). This is a fundamental aspect in the discussion of the resources of a state-owned financial

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institution. In academic literature, there are studies on the ability of an investor to diminish firms’ opaqueness and screening good from bad investees, and this has been investigated in the context of banks (Berger and Udell 1998; Berger, Klapper, and Udell 2001), venture capital finance (Gompers and Lerner 1999), angel finance (Wong, Bhatia and Freeman 2009; Hoberg et al. 2012), lease finance (Myers, Dill and Bautista 1976; Porter 1995), and supplier finance (Tamari 1970). This study con-centrates on the loan applicant’s assessment by state-owned financial institution.

Literature on lender’s evaluation in the lending application phase is scarce. Most theoretical studies on the loan market adopt the view that entrepreneurs know their business, future possibilities, and their ability to repay better (Garmaise 2007). However, loan and investment risk evaluation are very subjective in nature, and an alternative information structure may also be empirically relevant (Jaffee and Stiglitz 1990). Garmaise (2007) stated that banks and venture capitalists have much accumulated experience, which helps them to evaluate the ventures better than entrepreneurs themselves. Firms financed by banks have lower failure rates than firms financed by others. The literature in behavioural corporate finance sug-gests that entrepreneurs are overly optimistic regarding their future financial per-formance (Baker and Wurgler 2012; Landier and Thesmar 2009). One-third of the entrepreneurs believe that their success is certain (Cooper, Woo, and Dunkelberg 1988).

According to Kahneman and Tversky (1982), there are three reasons why it is important to examine decision-makers’ systematic errors and inferential biases. First, investigating inferential limitations may suggest ways to enhance decision quality. Second, mistakes and biases can reveal the psychological processes that impact judgment. Third, errors help the charting of human intuitions by signify-ing which factors are statistical and not intuitive. Under unsure and complex envi-ronmental conditions, biases and heuristics can improve decision-making quality (Pitz and Sachs 1984; Tversky and Kahneman 1974). On the other hand, biases can lead to mistakes (Kahneman, Slovic, and Tversky 1982).

Lender evaluation has been investigated using financial factors (Casey 1980; Houghton and Sengupta 1984; Laitinen and Laitinen 1997; Gadenne and Iselin 2000). However, the objective of these studies is to examine the ability of users to predict a firm’s financial performance by means of accounting information. Generally, the focus has been on prediction accuracy and the importance of dif-ferent financial ratios. To the best of my knowledge, the only study that includes non-accounting variables is that by Laitinen (1999), in which the non-accounting variables are based on the investigation of a credit analyst. The variables included the amount of personnel, recent ownership, the owner’s previous activities and property, and the legal form of the firm. The results suggest the importance of us-ing non-accounting as well as accounting variables in determining a credit rating. However, to the best of my knowledge, there are no previous studies that model the role of non-accounting factors available at the time of start-up in the evaluation of the loan applicant in the start-up phase. It is important to examine the factors prevalent in a firm at the time of loan application, because the founders as well as

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lenders have restricted resources which need to be invested carefully. In addition, sufficient start-up funding is vital to a firm’s subsequent financial performance. The aim is to provide preliminary findings on the loan applicant’s assessment by state-owned financial institutions, which differs from other lenders in that it is able to take more risk and pays attention to societal environment. This study also aims to provide knowledge on how non-accounting variables impact the assess-ment of financial ratios, which are available only for incumbent firms.

One of the most difficult issues for many financiers is evaluating the human capital of the founder (Levie and Gimmon 2008). Among start-ups, the human capital of the founder can be considered as a key factor of venture performance (Stoner 1987; Chandler and Jansen 1992), because the concentration of decision-making power is typically very high (Shepherd, Douglas, and Shanley 2000). Human capital refers to knowledge that is not easily appropriable and that gener-ates competitive advantages (Barney 1991). Moreover, human capital of the found-ers serves as a signal of quality to external financiers. This is particularly valuable in an environment with high levels of information asymmetry, as it increases the probability that financiers will provide loans to uncertain entrepreneurial ventures (Hallen 2008). However, in the literature, there are unclear theoretical predictions regarding which indicators of human capital commonly employed in the entrepreneurship literature and by investors in new ventures might be associ-ated with performance (Gimmon and Levie 2009; Unger et al. 2011).

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3 Summary of the articles

This chapter introduces the three articles; their objectives and main findings are introduced in a condensed manner. The overall objective of this dissertation is to explore lending to start-up firms. Towards this end, special focus is placed on the firms that have received finance from a state-owned financial institution (Finnvera Plc). Another objective of this dissertation is to explore the financial performance (creditworthiness) of these firms. Here, the articles are presented in chronological order. The first article begins by examining which factors affect the financial per-formance of these new micro firms. The second article concentrates on the factors that impact the amount of the loan granted by Finnvera. The third article expands the examination of firms’ financial performance by investigating the factors that lenders pay attention to when they evaluate business prospects during the loan ap-plication phase. Their evaluation is also compared with actual creditworthiness.

3.1 ARTICLE 1: FACTORS CONTRIBUTING TO THE SUC-CESS OF START-UP FIRMS USING TWO-POINT OR MULTI-PLE-POINT SCALE MODELS

The first article investigates the determinants of start-up success (measured by creditworthiness) in a sample of Finnish firms. The data has been collected at the time of the firm’s establishment. The study employs variables that investigate the type of funding used, the background of the entrepreneur, other non-accounting variables of the firm and market-related variables. This study seeks to compare the factors affecting success five years after start-up as derived from two-point and multiple-point scale models. To generate a two-point scale model, the logis-tic regression model is applied; for the multiple-point scale model, the ordinal (logit) regression model is applied. For ordinal regression, both three-point and six-point scale success is applied. In the latter case, the method identifies the fac-tors distinguishing the five survival (in terms of credit risk) categories and the failure category.

The results indicate that the contribution of the variables differs if the financial performance is measured on more than a two-point scale in the sample. The mod-els show that there are more differences than similarities between the multivariate models. An increase in equity investments by the owners and an increase in the loan amount increase the likelihood of success and decrease the credit risk of the firms. In addition, the likelihood of success decreases if the firm has a good competitive position. There are three additional attributes in the multiple-point scale model com-pared to the two-point scale model. The age of the founder, gender and demand for

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the products or services are only statistically significant in the multiple-point scale model. Furthermore, the six-point scale model requires more attributes than the three-point scale model (employment status, business experience and location). The results indicate that a multiple-point scale model yields more statistically significant attributes compared to a two-point scale model. The results support previous find-ings that suggest that the predictors of financial performance vary depending on whether a two-point or a multiple-point scale performance model is used.

3.2 ARTICLE 2: CAPITAL STRUCTURE OF START-UPS: EVI-DENCE ON NON-ACCOUNTING CHARACTERISTICS

The second article examines the factors affecting availability of external financing for firms, utilising a sample of Finnish start-up micro firms. The overall purpose is not to produce a comprehensive model for capital structure, but to illustrate the importance of non-accounting characteristics in explaining the attractiveness of the new venture from the lender’s viewpoint. This study is one of the few to investigate genuinely new micro firms. The sample includes 90 firms that failed within the first five years of their existence. The dataset tracks firms from their loan application phase onwards. This enables one to study financing decisions at an early establishment stage of a firm. Since this study focuses on the very early stage, the use of cash flows cannot be examined. For these firms, funding from relatives and friends and the equity of venture capitalists is not considered. This study distinguishes three sources of financing: the equity provided by the found-er, a loan from a state-owned financial institution, and a loan from a commercial bank. In this article, the focus is on long-term leverage. The statistical method applied in the study is linear regression analysis.

The results show that a founder who has prior work experience from the same sector and has little or no personal financial property seems to have higher leverage. After dividing the database into survived firms and failed firms, lenders viewed work experience positively only in the sample of failed firms. In addition, limited liability companies, firms located in bigger cities, and firms with good demand—as evaluated by the lender—have a lower level of leverage. The previous findings indi-cate that there are contradictory results on how human capital influences the capital structure of start-ups. In this study, indicators of a founder’s human capital are, to a certain extent, important in explaining the capital structure of start-ups.

3.3 ARTICLE 3: LENDER EVALUATIONS OF START-UP BUSINESS PROSPECTS

The third article investigates lender evaluations of start-up business prospects in a sample of Finnish firms. Our database permits us to examine how qualitative information—based on personal history, firm-specific characteristics, subjective

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evaluations of business prospects and market position by credit analysts—impacts firms’ financial performance. The data for this study was collected in 2003 and 2005 from the database of Finnvera, a state-owned financial institution. The data are analysed using logistic regression to employ one model for business prospects and another model for actual creditworthiness.

The estimated models show that the founder’s age and employment status and the demand for products are important in both models. As far as the other variables are concerned, the lenders seem to ignore certain factors that are statis-tically significant in actual creditworthiness models. These lenders seem to em-phasize the less relevant cue, in this case, founders without prior work experience in the same sector. This can be related to the social environmental purpose of the lender. Then, lenders may underestimate the more relevant clues, including equity percentage, female-operated firms, the personal property of the founder and the founder’s education and prior experience in running a firm. It may be that lenders trust their intuition more, which is not captured using statistical models. This type of inner feeling combines many types of information that may require more sophisticated measures. On the other hand, the differences can be partially explained by the fact that, as a state-owned lender, Finnvera can choose riskier borrowers like founders with less equity.

3.4 SUMMARY OF OVERALL RESULTS

The purpose of this section is to provide the reader with a clear idea of the most relevant relationships found to be significant within the framework of this dis-sertation. Figure 3 sums up all the relevant findings of the three articles. The key concepts of the dissertation are presented within ovals in the figure. The financial performance oval includes all the different models used to investigate financial performance (measured by creditworthiness). The boxes are the factors that are used in the analysis as dependent variables.

When studying firms’ financial performance and lending in the context of new start-ups, the main finding is that prior work experience in the same sector is not statistically significant in actual financial performance models. However, it is appreciated by the lender; founders with prior work experience seem to obtain more loans; on the other hand, their business future is evaluated as me-dium/weak by the lender. This finding contradicts previous findings where ex-perience has a general positive impact on a firm’s survival performance. The conflict may be due to the features of the sample. This dissertation focuses on new micro firms whose main source of financing is provided by a state-owned financial institution. It might be that the relationship between those who start their business with plenty of equity and less debt, or perhaps without debt, and prior working experience is meaningful and positive. This finding highlights the importance of studying different samples. The results are only generalized among heterogeneous groups.

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Figure 3. Summary of significant relationship in the articles

When we consider other founder-related variables, there are four common vari-ables for most of the models. The age of the founder is important in three-point and six-point scale models, a good creditworthiness model, and a prospect mod-el evaluated by the lender. The interpretation of the results indicates that older founders seem to have firms with lower credit risk and the older the founder, the better his/her business prospects. The age of the founder is statistically insignifi-cant in the two-point scale model, which divides the data into failed firms and survived firms. It seems that the founder’s age has less strong influence on the firm’s survival than it does on a firm with better financial performance, which is the case, for example, in the good creditworthiness model. The results of gender only have an effect on the financial performance models, except in the case of the survival model. The results reveal that in the three-point scale model and the

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six-point scale model, a male has better probability of having the lowest credit risk for his business than a firm with both genders. Conversely, females seem to have a better probability of good creditworthiness than males. Indeed, the re-sults depend upon the focus of the examination. This dissertation emphasizes the good financial performance (measured by creditworthiness) of firms because plain marginal survival is not in the interest of anyone involved. The worst pos-sibility is that the invested resources of the counterparties and their connections are wasted. Then, the results of employment status suggest that the founders who were employed before they established a firm have a lower probability of success (six-point scale model and good creditworthiness model). Prior working experi-ence from the same sector is negatively viewed by lenders. While prior experience in running a firm has a positive relationship in the six-point scale model and good creditworthiness, it seems that these individuals have the courage to start a new venture and have learnt to run their own business. This is an aspect that lenders should pay attention to, particularly when they are identifying a firm with good financial performance. Education is important only in the good creditworthiness model. The founders who graduated from a collage/polytechnic school or a uni-versity have the highest probability of good creditworthiness.

The three firm-related variables are important to some extent; the firm’s geo-graphic location is significant in explaining six-point scale success, thereby sug-gesting that if the firms are located in the same city as the lender, the probability of credit risk is lower. Moreover, firms located in bigger cities seem to have a lower level of leverage. This is also the case if the legal form of the firm is limited liability. However, team variables remain statistically insignificant.

When it comes to the results of the variables evaluated by the lender, it must be noted that they are statistically significant in explaining firms’ financial perfor-mance, capital structure, and business prospects. Competitive position is captured in the two-point scale model (failed vs. survived) and the three-point scale model, thereby revealing that firms with a good competitive position have a higher credit risk. Firms in high demand in the market have a lower probability of having a low-er credit risk (three-point and six-point scale) and good creditworthiness. Good demand is also codetermined with medium/weak business prospects. Overall, it seems that good demand and competitive position of firms at the loan application stage are not very stable aspects. An explanation for this is that the entrepreneurs excessively trust the aspects of good demand and competitiveness at the start-up phase and invest relatively lesser amounts in marketing, for example. The results regarding funding variables are natural. The share of equity is significant in all financial performance (measured by creditworthiness) models, which is in line with the findings of previous studies. However, the equity share does not impact the business prospects evaluated by the lender. This is rather logical because it is the duty of a state-owned financial institution to financially support start-ups that have difficulties obtaining finance elsewhere. Even though they represent risk financiers, they are careful in selecting their customers. The founders who have more personal property do not seem to have good creditworthiness. Furthermore,

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they require less external financing. The loan amount variable is only included in the first article, where an increase in the loan amount has a positive effect on the firm’s financial performance. The third article considers the aspect of whether the founder has a bank loan. However, that factor remains insignificant. This is interesting because prior evidence suggests that firms financed by banks have lower failure rates than firms financed by others. It seems that the existence of a bank loan does not impact high financial performance.

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Articles

I FACTORS CONTRIBUTING TO THE SUCCESS OF START-UP FIRMS USING TWO-POINT OR MULTIPLE-POINT SCALE MODELSMiettinen, Marika & Littunen, Hannu (2013): Factors contributing to the success of start-up firms using two-point or multiple point scale models. Entrepreneurship Research Journal, Vol. 3, No. 4, 449-481, ISSN (Online) 2157-5665, ISSN (Print) 2194-6175, DOI: 10.1515/erj-2012-0012, June 2013.

II CAPITAL STRUCTURE OF START-UPS: EVIDENCE ON NON-ACCOUNTING CHARACTERISTICSMiettinen, Marika & Virtanen, Markku (2013): Capital structure of start-ups: Evidence on non-accounting characteristics. Journal of Modern Accounting and Auditing, Vol. 9, No. 7, 889-907. ISSN 1548-6583.

III LENDER EVALUATIONS OF START-UP BUSINESS PROSPECTSMiettinen, Marika & Niskanen, Mervi (2014): Lender evaluations of start-up business pros-pects. Working paper, University of Eastern Finland.

Article I

Miettinen, Marika & Littunen, Hannu (2013): Factors contributing to the success of start-up firms using two-point or multiple point scale models. Entrepreneurship Research Journal, Vol. 3, No. 4, 449-481, ISSN (Online) 2157-5665, ISSN (Print) 2194-6175, DOI: 10.1515/erj-2012-0012, June 2013.

Reprinted with kind permission by Walter de Gruyter, Inc.

Competitive Research Article

Marika Rosanna Miettinen* and Hannu Littunen

Factors Contributing to the Success ofStart-Up Firms Using Two-Point orMultiple-Point Scale Models

Abstract: This study investigates the determinants of start-up success in asample of Finnish firms. Our database allows us to investigate how qualitativeinformation based on the personal history and characteristics of the owner,along with firm and market attributes, affect success 5 years after start-up. Itseeks to compare the factors affecting success as derived from two-point andmultiple-point scale models. The results indicate that a multiple-point scalemodel yields more statistically significant attributes compared to a two-pointscale model. Furthermore, the founder attributes are not as important aspredicted.

Keywords: small firms, start-ups, success, creditworthiness

*Corresponding author: Marika Rosanna Miettinen, Department of Business, University ofEastern Finland, Microkatu 1 G, PL 1627, Kuopio 70211, Finland, E-mail: marika.miettinen@uef.fiHannu Littunen, Department of Health and Social Management, University of Eastern Finland,Kuopio 70211, Finland, E-mail: hannu.littunen@uef.fi

1 Introduction

Over the last few decades, an extensive body of literature has examined andtried to explain the success of small and medium-sized firms. Generally, studiesof success have used a two-point scale (dichotomous) performance models(success vs. failure) (e.g. Lussier and Halabi 2010). However, it has beenshown that the predictors of success vary, depending on whether there aretwo-point or multiple-point scale of success used in the analyses (Cooper,Gimeno-Gascon, and Woo 1994; Dahlqvist, Davidsson, and Wiklund 2000).Literature describing research that models multiple-point scale of success hasremained scarce. Furthermore, there has been some variation in the kinds of

doi 10.1515/erj-2012-0012 ERJ 2013; 3(4): 449–481

Brought to you by | De Gruyter / TCSAuthenticated | 173.9.48.25

Download Date | 11/6/13 7:00 PM

Competitive Research Article

Marika Rosanna Miettinen* and Hannu Littunen

Factors Contributing to the Success ofStart-Up Firms Using Two-Point orMultiple-Point Scale Models

Abstract: This study investigates the determinants of start-up success in asample of Finnish firms. Our database allows us to investigate how qualitativeinformation based on the personal history and characteristics of the owner,along with firm and market attributes, affect success 5 years after start-up. Itseeks to compare the factors affecting success as derived from two-point andmultiple-point scale models. The results indicate that a multiple-point scalemodel yields more statistically significant attributes compared to a two-pointscale model. Furthermore, the founder attributes are not as important aspredicted.

Keywords: small firms, start-ups, success, creditworthiness

*Corresponding author: Marika Rosanna Miettinen, Department of Business, University ofEastern Finland, Microkatu 1 G, PL 1627, Kuopio 70211, Finland, E-mail: marika.miettinen@uef.fiHannu Littunen, Department of Health and Social Management, University of Eastern Finland,Kuopio 70211, Finland, E-mail: hannu.littunen@uef.fi

1 Introduction

Over the last few decades, an extensive body of literature has examined andtried to explain the success of small and medium-sized firms. Generally, studiesof success have used a two-point scale (dichotomous) performance models(success vs. failure) (e.g. Lussier and Halabi 2010). However, it has beenshown that the predictors of success vary, depending on whether there aretwo-point or multiple-point scale of success used in the analyses (Cooper,Gimeno-Gascon, and Woo 1994; Dahlqvist, Davidsson, and Wiklund 2000).Literature describing research that models multiple-point scale of success hasremained scarce. Furthermore, there has been some variation in the kinds of

doi 10.1515/erj-2012-0012 ERJ 2013; 3(4): 449–481

Brought to you by | De Gruyter / TCSAuthenticated | 173.9.48.25

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measures of success used in previous research studies. Many previous studieshave used size, growth or profitability as a measure (Schutjens and Wever 2000;Shepherd and Wiklund 2009; Unger et al. 2011). Comparison between studies isdifficult as the time frame, the success indicator and the success formula oftendiffer (Delmar, Davidsson, and Gartner 2003).

There are many theoretical frameworks for analyzing the influence ofnon-accounting factors on the performance of new firms. Watson, Hogarth-Scott, and Wilson (1998) developed an extensive framework for new ventures,designing it especially for very small or micro-businesses. Of course, there aremany factors that determine business performance, both internal and external,even in very small businesses. They pointed out the importance of a firm’scharacteristics, the business infrastructure and the firm’s customer markets aswell as the characteristics and experiences of the founder and the influence ofthe environment on the firm’s business performance (Watson, Hogarth-Scott,and Wilson 1998).

Our objective is to contribute to small business research by providing amore comprehensive understanding of the success of genuinely new ventures.The first objective of the study is to clarify whether funding, founder, firm andmarket attributes are important in predicting the success of start-ups using amultiple-point scale model. Another objective is to investigate how the out-comes differ between successes measured using two-point and multiple-pointscales. This paper investigates the potential success of start-up firms usingFinnish data. The data have been collected when the firms were established.The data allow us to investigate which factors, operating at the time of thestart-up, affect success in the short term (a maximum of 5 years). Most ofthe firms have been established at around the same time and within the samegeographic area. This isolates our database from differences in overalleconomic conditions.

We find that the contribution of the variables differs if success is derivedusing more than two-point scale in our sample. The models show that thereare more differences than similarities between multiple-point scale and two-point scale models. As a conclusion, we can say that modeling multiple-pointscale of success requires more indicators overall to differentiate between thedifferent scales. In the loan application phase, it is useful to know whichfactors promote the likelihood of survival, especially which factors promotehigh success.

The paper is structured as follows: The relevant literature and reasons forusing the chosen variables applied in the analysis are discussed in Section 2,where the data is also outlined. In Section 3, there is a description of theempirical results; the final section concludes the study.

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2 Theory and hypotheses

2.1 Prior research

Many theoretical frameworks for analyzing the influence of non-accountingfactors on the performance of a new firm have been developed. Watson,Hogarth-Scott, and Wilson (1998) developed an extensive framework for newventures, designing it especially for very small or micro-businesses (Figure 1). Ofcourse, there are many factors that determine business performance, both inter-nal and external, even in very small businesses. They pointed out the

Figure 1: Framework for analyzing new micro-firms’ performance from Watson, Hogarth-Scott,and Wilson (1998).

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measures of success used in previous research studies. Many previous studieshave used size, growth or profitability as a measure (Schutjens and Wever 2000;Shepherd and Wiklund 2009; Unger et al. 2011). Comparison between studies isdifficult as the time frame, the success indicator and the success formula oftendiffer (Delmar, Davidsson, and Gartner 2003).

There are many theoretical frameworks for analyzing the influence ofnon-accounting factors on the performance of new firms. Watson, Hogarth-Scott, and Wilson (1998) developed an extensive framework for new ventures,designing it especially for very small or micro-businesses. Of course, there aremany factors that determine business performance, both internal and external,even in very small businesses. They pointed out the importance of a firm’scharacteristics, the business infrastructure and the firm’s customer markets aswell as the characteristics and experiences of the founder and the influence ofthe environment on the firm’s business performance (Watson, Hogarth-Scott,and Wilson 1998).

Our objective is to contribute to small business research by providing amore comprehensive understanding of the success of genuinely new ventures.The first objective of the study is to clarify whether funding, founder, firm andmarket attributes are important in predicting the success of start-ups using amultiple-point scale model. Another objective is to investigate how the out-comes differ between successes measured using two-point and multiple-pointscales. This paper investigates the potential success of start-up firms usingFinnish data. The data have been collected when the firms were established.The data allow us to investigate which factors, operating at the time of thestart-up, affect success in the short term (a maximum of 5 years). Most ofthe firms have been established at around the same time and within the samegeographic area. This isolates our database from differences in overalleconomic conditions.

We find that the contribution of the variables differs if success is derivedusing more than two-point scale in our sample. The models show that thereare more differences than similarities between multiple-point scale and two-point scale models. As a conclusion, we can say that modeling multiple-pointscale of success requires more indicators overall to differentiate between thedifferent scales. In the loan application phase, it is useful to know whichfactors promote the likelihood of survival, especially which factors promotehigh success.

The paper is structured as follows: The relevant literature and reasons forusing the chosen variables applied in the analysis are discussed in Section 2,where the data is also outlined. In Section 3, there is a description of theempirical results; the final section concludes the study.

450 Marika Rosanna Miettinen and Hannu Littunen

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2 Theory and hypotheses

2.1 Prior research

Many theoretical frameworks for analyzing the influence of non-accountingfactors on the performance of a new firm have been developed. Watson,Hogarth-Scott, and Wilson (1998) developed an extensive framework for newventures, designing it especially for very small or micro-businesses (Figure 1). Ofcourse, there are many factors that determine business performance, both inter-nal and external, even in very small businesses. They pointed out the

Figure 1: Framework for analyzing new micro-firms’ performance from Watson, Hogarth-Scott,and Wilson (1998).

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importance of a firm’s characteristics, the business infrastructure and the firm’scustomer markets as well as the characteristics and experiences of the founderand the influence of the environment on the firm’s business performance(Watson, Hogarth-Scott, and Wilson 1998). All start-ups begin with the skillsand routines embodied in their founders, things that are likely to influence thenew firm’s future development and success. For independent start-ups that donot continue a family business, or are the result of mergers of existing firms, pre-entry capabilities are associated primarily with the founders, who usually playan important role in the management of the start-up (Watson, Hogarth-Scott,and Wilson 1998).

Most of the studies of success have used two-point scale performancemodels (e.g. Lussier and Halabi 2010). However, it has been shown that thepredictors of performance are dependent on whether two-point (dichotomous) ormultiple-point scale models are used (Cooper, Gimeno-Gascon, and Woo 1994;Dahlqvist, Davidsson, and Wiklund 2000).

One of the first studies of multiple-point scale performance using non-accounting data (Cooper, Gimeno-Gascon, and Woo 1994) divided performanceinto three outcomes: failure, marginal survival and high growth. They exploredsome of the human capital and financial capital variables that can be observedat the start-up of an enterprise, with the industry serving as a control variable.However, not all the firms in the study are genuinely new: some have beenhanded over from parents for example. The results suggest that the contributionof some factors can influence whether the outcome is marginal survival or highgrowth. For example, firms with women founders are less likely to grow but justas likely to survive.

The other general human capital variables (e.g. education) influence bothsurvival and growth. The management know-how variable and having parentswith business experience both contribute to marginal survival, but not togrowth. In contrast, the number of partners contributes to growth but not tosurvival. In both models, prior industry experience and the amount of initialfinancial capital are significant. Dahlqvist, Davidsson, and Wiklund (2000)replicated and extended the study of Cooper, Gimeno-Gascon, and Woo (1994)with almost the same outcome. In both studies, the predictors of high perfor-mance partly differ from the predictors of marginal survival. However,Dahlqvist, Davidsson, and Wiklund (2000) reported some different findings.For example, prior industry experience reveals not to be important; parentalbusiness experience is negatively associated with marginal survival and seemsto be insignificant for high performance; management know-how does not affectmarginal survival or high performance; and initial financial capital does notaffect marginal survival. Dahlqvist, Davidsson, and Wiklund (2000) used, in

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addition to employment growth, sales growth and profitability, making thestudy more reliable. Hence, they used the concept of high performance insteadof high growth.

Bosma et al. (2004) investigated how human and social capital measuresaffect three aspects of start-up performance: survival, profit and employmentgenerated. They found that specific investments in human and social capitalgenerate more promising start-ups. They did not utilize any firm-specific factors,because they emphasized the single founder as a proxy for the performance ofthe firm. It can be said that tacit knowledge is the basis for firm-specificattributes. Furthermore, the results imply that the factors affecting the threeoutcomes are partly different. Similar effects are obtained only with “experiencein industry”, gathering information from “commercial relations”, “gender” and“hours worked at the start” (Bosma et al. 2004).

Previous studies have examined a variety of attributes – many of themsignifying human capital: the founders’ education, training, experience, parent’sbackground and occupational background and others such as financial attri-butes, firm and environment-related attributes. However, previous studies arenot unanimously agreed about which variables contribute to the success ofsmall businesses. Comparisons across success studies are also difficult becausethe methodologies, samples, choice and definition of variables differ from oneresearch study to another (Murphy, Trailer, and Hill 1996). Furthermore, thesuccess factors vary in different countries (e.g. Benzing, Chu, and Kara 2009).

2.2 Model development

We include two attributes that measure the funding sources used by the firms.The percentage share of equity refers to the share of equity capital provided bythe owner(s) related to the total assets. It seems that a high percentage of equityinvestment by the founder(s) produces a positive effect on success (Duchesneauand Gartner 1990; Brüderl and Preisendörfer 1998; Bosma et al. 2004).Successful entrepreneurs seek to reduce risk in their firms. It can also beinterpreted as a reflection of the commitment of the founder(s), which is some-thing that lenders value. On the other hand, van Praag (2003) found no differ-ences in performance between people who started with their own capital andthose who started with borrowed capital.

The second funding attribute is the total amount of loan. Having more fundsis generally considered better for new venture survival and growth compared tohaving fewer funds available (Cooper, Gimeno-Gascon, and Woo 1994; Becchettiand Trovato 2002; Lussier and Halabi 2010). However, Schutjens and Wever

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importance of a firm’s characteristics, the business infrastructure and the firm’scustomer markets as well as the characteristics and experiences of the founderand the influence of the environment on the firm’s business performance(Watson, Hogarth-Scott, and Wilson 1998). All start-ups begin with the skillsand routines embodied in their founders, things that are likely to influence thenew firm’s future development and success. For independent start-ups that donot continue a family business, or are the result of mergers of existing firms, pre-entry capabilities are associated primarily with the founders, who usually playan important role in the management of the start-up (Watson, Hogarth-Scott,and Wilson 1998).

Most of the studies of success have used two-point scale performancemodels (e.g. Lussier and Halabi 2010). However, it has been shown that thepredictors of performance are dependent on whether two-point (dichotomous) ormultiple-point scale models are used (Cooper, Gimeno-Gascon, and Woo 1994;Dahlqvist, Davidsson, and Wiklund 2000).

One of the first studies of multiple-point scale performance using non-accounting data (Cooper, Gimeno-Gascon, and Woo 1994) divided performanceinto three outcomes: failure, marginal survival and high growth. They exploredsome of the human capital and financial capital variables that can be observedat the start-up of an enterprise, with the industry serving as a control variable.However, not all the firms in the study are genuinely new: some have beenhanded over from parents for example. The results suggest that the contributionof some factors can influence whether the outcome is marginal survival or highgrowth. For example, firms with women founders are less likely to grow but justas likely to survive.

The other general human capital variables (e.g. education) influence bothsurvival and growth. The management know-how variable and having parentswith business experience both contribute to marginal survival, but not togrowth. In contrast, the number of partners contributes to growth but not tosurvival. In both models, prior industry experience and the amount of initialfinancial capital are significant. Dahlqvist, Davidsson, and Wiklund (2000)replicated and extended the study of Cooper, Gimeno-Gascon, and Woo (1994)with almost the same outcome. In both studies, the predictors of high perfor-mance partly differ from the predictors of marginal survival. However,Dahlqvist, Davidsson, and Wiklund (2000) reported some different findings.For example, prior industry experience reveals not to be important; parentalbusiness experience is negatively associated with marginal survival and seemsto be insignificant for high performance; management know-how does not affectmarginal survival or high performance; and initial financial capital does notaffect marginal survival. Dahlqvist, Davidsson, and Wiklund (2000) used, in

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addition to employment growth, sales growth and profitability, making thestudy more reliable. Hence, they used the concept of high performance insteadof high growth.

Bosma et al. (2004) investigated how human and social capital measuresaffect three aspects of start-up performance: survival, profit and employmentgenerated. They found that specific investments in human and social capitalgenerate more promising start-ups. They did not utilize any firm-specific factors,because they emphasized the single founder as a proxy for the performance ofthe firm. It can be said that tacit knowledge is the basis for firm-specificattributes. Furthermore, the results imply that the factors affecting the threeoutcomes are partly different. Similar effects are obtained only with “experiencein industry”, gathering information from “commercial relations”, “gender” and“hours worked at the start” (Bosma et al. 2004).

Previous studies have examined a variety of attributes – many of themsignifying human capital: the founders’ education, training, experience, parent’sbackground and occupational background and others such as financial attri-butes, firm and environment-related attributes. However, previous studies arenot unanimously agreed about which variables contribute to the success ofsmall businesses. Comparisons across success studies are also difficult becausethe methodologies, samples, choice and definition of variables differ from oneresearch study to another (Murphy, Trailer, and Hill 1996). Furthermore, thesuccess factors vary in different countries (e.g. Benzing, Chu, and Kara 2009).

2.2 Model development

We include two attributes that measure the funding sources used by the firms.The percentage share of equity refers to the share of equity capital provided bythe owner(s) related to the total assets. It seems that a high percentage of equityinvestment by the founder(s) produces a positive effect on success (Duchesneauand Gartner 1990; Brüderl and Preisendörfer 1998; Bosma et al. 2004).Successful entrepreneurs seek to reduce risk in their firms. It can also beinterpreted as a reflection of the commitment of the founder(s), which is some-thing that lenders value. On the other hand, van Praag (2003) found no differ-ences in performance between people who started with their own capital andthose who started with borrowed capital.

The second funding attribute is the total amount of loan. Having more fundsis generally considered better for new venture survival and growth compared tohaving fewer funds available (Cooper, Gimeno-Gascon, and Woo 1994; Becchettiand Trovato 2002; Lussier and Halabi 2010). However, Schutjens and Wever

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(2000) reported that the size of the start-up capital is not important for a firm’ssuccess. Moreover, initial capital does not affect marginal survival according toDahlqvist, Davidsson, and Wiklund (2000). Our first research hypothesis is:

H1: Funding attributes influence start-up success.

The age of the owner can have both positive and negative influences on perfor-mance. It can be said that very young people lack both experience and capital.Consequently, the younger the entrepreneur, the more likely he is to fail. Middle-aged entrepreneurs likely have most experience, credibility and energy.Increasing the age of the owner is stated to have a negative relationship withgrowth (Kangasharju 2000). An entrepreneur’s age seemed to have a negativeassociation with performance in the study by Harada (2003). However, theowner’s age does not have an impact on survival, earnings or employmentgenerated in the study by Bosma et al. (2004).

In regard to gender of the entrepreneur, the previous studies are contra-dictory. Cooper, Gimeno-Gascon, and Woo (1994) found that firms with womenfounders are less likely to grow but just as likely to survive. Harada (2003) foundthat female gender seems to have a negative association with success. It alsoidentified that male business founders/owners have a better likelihood to sur-vive, to make better profits and to create employment (Kangasharju 2000;Bosma et al. 2004). On the other hand, Díaz-Garcí and Brush (2012) found intheir quantitative study that there are no differences between female and maleowners’ financial performance. They suggested that possible gender differenceshave to be investigated using a qualitative approach.

The financial status of the entrepreneur refers to the borrower’s resources andhis ability to manage them. Personal property facilitates establishing a new firm;it can also enhance the survival prospects of the firm (Bates 1990; Parker 2004,182). In their review article, Georgellis, Sessions, and Tsitsianis (2005) agreed: awealthier individual can establish a business with more efficient levels of capitaland thereby have a better probability of success in the business than a poorerone. However, van Praag (2003) found no significant effect of personal assets orhome ownership on the success.

A good deal of the literature has focused on what makes an entrepreneursuccessful. The concept of human capital investments or outcomes can be basedon an assessment of education, experience, entrepreneurs’ knowledge, skillsand competencies (Unger et al. 2011). Most authors have argued that there is apositive relationship between human capital and success (e.g. Cooper, Gimeno-Gascon, and Woo 1994; Barringer and Jones 2004; Bosma et al. 2004; Haber andReichel 2005; Van der Sluis, Van Praag, and Vijverberg 2005; Bonet, Armengot,

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and Martín 2011). It has been suggested that human capital in particular isessential for young firms (Davidsson and Honig 2003; Unger et al. 2011).Founders with greater human capital learn the market conditions faster thanthose with less human capital and want to have compensation for their humancapital investments; hence their firms should be more likely to survive the earlyyears of the market selection process. In addition, some studies have found thatthese soft factors have played an important role in determining the ratings ofloans to small and medium-size firms (Treacy and Carey 2000; Blumberg andLetterie 2008). However, the importance of the relationship between humancapital and firm success is different between the studies. Some studies havefound a significant relationship, where others have found little or none (Ungeret al. 2011). According to Unger et al. (2011), this could be due to differentindependent variables included in the models. The most common indicators ofhuman capital construct are education, work experience in the same sector,management experience and business experience in the family (Reuber andFisher 1994; Unger et al. 2011). The human capital attributes we use are educa-tion, entrepreneurial training, prior employment status, work experience inyears in the same sector and experience of running a firm.

Education can symbolize the entrepreneurs’ capacity to adapt and developknowledge of the environment (Haber and Reichel 2005). According to manystudies, entrepreneurs who have higher levels of education have a greater chanceof prospering in their business compared to individuals who have invested less intheir human capital (e.g. Lussier and Pfeifer 2001; Bosma et al. 2004; Cassar2006; Lussier and Halabi 2010). It has been suggested that the level of formaleducation is correlated to an owner’s drive, energy, motivation and dedication tothe business and thus to better business performance (Kim, Aldrich, and Keister2006; West and Noel 2009). Educational attainment can also reflect rationalability which is required in running a new or small business (Storey 2011).Furthermore, formal education can be an indicator of good communication,teamwork and problem-solving skills (Soriano and Castrogiovanni 2012). Honjo(2004), as well as Pereira and St. Aubyn (2009), found that an increase in primaryand secondary levels of education enhances growth. Both Almus (2002) and Vander Sluis, Van Praag, and Vijverberg (2005) showed a positive associationbetween education and business growth; Almus (2002) reported that ownerswith a university degree seem to have a better chance to quickly grow theirbusiness. On the other hand, some studies have found no relationship betweenhigher education and the probability of performance (e.g. Schutjens and Wever2000; Harada 2003; Littunen and Niittykangas 2010).

The success of the business is said to be enhanced by the use of outsideprofessionals and advisors for consulting during the establishment phase

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(2000) reported that the size of the start-up capital is not important for a firm’ssuccess. Moreover, initial capital does not affect marginal survival according toDahlqvist, Davidsson, and Wiklund (2000). Our first research hypothesis is:

H1: Funding attributes influence start-up success.

The age of the owner can have both positive and negative influences on perfor-mance. It can be said that very young people lack both experience and capital.Consequently, the younger the entrepreneur, the more likely he is to fail. Middle-aged entrepreneurs likely have most experience, credibility and energy.Increasing the age of the owner is stated to have a negative relationship withgrowth (Kangasharju 2000). An entrepreneur’s age seemed to have a negativeassociation with performance in the study by Harada (2003). However, theowner’s age does not have an impact on survival, earnings or employmentgenerated in the study by Bosma et al. (2004).

In regard to gender of the entrepreneur, the previous studies are contra-dictory. Cooper, Gimeno-Gascon, and Woo (1994) found that firms with womenfounders are less likely to grow but just as likely to survive. Harada (2003) foundthat female gender seems to have a negative association with success. It alsoidentified that male business founders/owners have a better likelihood to sur-vive, to make better profits and to create employment (Kangasharju 2000;Bosma et al. 2004). On the other hand, Díaz-Garcí and Brush (2012) found intheir quantitative study that there are no differences between female and maleowners’ financial performance. They suggested that possible gender differenceshave to be investigated using a qualitative approach.

The financial status of the entrepreneur refers to the borrower’s resources andhis ability to manage them. Personal property facilitates establishing a new firm;it can also enhance the survival prospects of the firm (Bates 1990; Parker 2004,182). In their review article, Georgellis, Sessions, and Tsitsianis (2005) agreed: awealthier individual can establish a business with more efficient levels of capitaland thereby have a better probability of success in the business than a poorerone. However, van Praag (2003) found no significant effect of personal assets orhome ownership on the success.

A good deal of the literature has focused on what makes an entrepreneursuccessful. The concept of human capital investments or outcomes can be basedon an assessment of education, experience, entrepreneurs’ knowledge, skillsand competencies (Unger et al. 2011). Most authors have argued that there is apositive relationship between human capital and success (e.g. Cooper, Gimeno-Gascon, and Woo 1994; Barringer and Jones 2004; Bosma et al. 2004; Haber andReichel 2005; Van der Sluis, Van Praag, and Vijverberg 2005; Bonet, Armengot,

454 Marika Rosanna Miettinen and Hannu Littunen

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and Martín 2011). It has been suggested that human capital in particular isessential for young firms (Davidsson and Honig 2003; Unger et al. 2011).Founders with greater human capital learn the market conditions faster thanthose with less human capital and want to have compensation for their humancapital investments; hence their firms should be more likely to survive the earlyyears of the market selection process. In addition, some studies have found thatthese soft factors have played an important role in determining the ratings ofloans to small and medium-size firms (Treacy and Carey 2000; Blumberg andLetterie 2008). However, the importance of the relationship between humancapital and firm success is different between the studies. Some studies havefound a significant relationship, where others have found little or none (Ungeret al. 2011). According to Unger et al. (2011), this could be due to differentindependent variables included in the models. The most common indicators ofhuman capital construct are education, work experience in the same sector,management experience and business experience in the family (Reuber andFisher 1994; Unger et al. 2011). The human capital attributes we use are educa-tion, entrepreneurial training, prior employment status, work experience inyears in the same sector and experience of running a firm.

Education can symbolize the entrepreneurs’ capacity to adapt and developknowledge of the environment (Haber and Reichel 2005). According to manystudies, entrepreneurs who have higher levels of education have a greater chanceof prospering in their business compared to individuals who have invested less intheir human capital (e.g. Lussier and Pfeifer 2001; Bosma et al. 2004; Cassar2006; Lussier and Halabi 2010). It has been suggested that the level of formaleducation is correlated to an owner’s drive, energy, motivation and dedication tothe business and thus to better business performance (Kim, Aldrich, and Keister2006; West and Noel 2009). Educational attainment can also reflect rationalability which is required in running a new or small business (Storey 2011).Furthermore, formal education can be an indicator of good communication,teamwork and problem-solving skills (Soriano and Castrogiovanni 2012). Honjo(2004), as well as Pereira and St. Aubyn (2009), found that an increase in primaryand secondary levels of education enhances growth. Both Almus (2002) and Vander Sluis, Van Praag, and Vijverberg (2005) showed a positive associationbetween education and business growth; Almus (2002) reported that ownerswith a university degree seem to have a better chance to quickly grow theirbusiness. On the other hand, some studies have found no relationship betweenhigher education and the probability of performance (e.g. Schutjens and Wever2000; Harada 2003; Littunen and Niittykangas 2010).

The success of the business is said to be enhanced by the use of outsideprofessionals and advisors for consulting during the establishment phase

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(Duchesneau and Gartner 1990). In Spain, Peña (2002) found entrepreneurialtraining an important determinant of new firm growth. However, expert help atstart-up has no effect on a firm’s high growth during the first 4 years; after5–8 years’, however, this type of help has a positive effect (Littunen andNiittykangas 2010).

Employment status before establishing the business can be expected toinfluence the performance of a start-up. A firm started as an escape fromunemployment (push reason, business opportunities) seems not to show anydifferences in its success measured by growth in number of employees com-pared to entrepreneurs who wanted to be their own boss (pull reasons)(Schutjens and Wever 2000). Ritsilä and Tervo (2002) emphasized the negativerelationship between push motivations (necessity) and firm growth. Similarly,Littunen and Tohmo (2003) found that positive situational and pull factorsstrongly motivate and precipitate the creation of a high-growth business. Incontrast, motives at start-up are not statistically significant for rapid growthduring the first 4 years according to Littunen and Niittykangas (2010).

Prior work experience in the same sector represents a form of specific humancapital. Industrial knowledge and experience is associated with the particularskills, insights and abilities transferable to a sector or industry, e.g. understand-ing of markets and customers and, of course, understanding of the specifictechnologies (Reuber and Fischer 1999). Industrial experience prior to startingof the firm has a positive effect on the new firm’s performance (Cooper, Gimeno-Gascon, and Woo 1994; Harada 2003; van Praag 2003; Bosma et al. 2004;Blumberg and Letterie 2008; Lussier and Halabi 2010; Soriano andCastrogiovanni 2012). These individuals have developed their expertise andexperience which they then bring to their new firms. Nevertheless, empiricalevidence on this issue remains unclear. West and Noel (2009) and Shrader andSiegel (2007) have not verified the impact of previous industrial experience onnew venture performance. Grilli (2011) reported a negative effect between priorwork experience and high-tech start-up survival. It could be that the environ-ment is so dynamic that the value of previous experience is very quickly eroded(Newbert 2005).

The final human capital attribute is personal business experience.Individuals who have been running their own firm have gained valuableexperience, which has been found to be a critical success factor for smallfirms (e.g. Yusuf 1995; Harada 2003; Coleman 2007). Individuals with priorbusiness experience tend to have larger social networks and are thereforemore able to develop networked relationships than novice entrepreneurs, whohave fewer skills to help diversify their network team (Mosey and Wright 2007;Zolin, Kuckertz, and Kautonen 2011). On the other hand, people with experience

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in running a firm could be called “opportunists” (Storey 1994). They may lackmanagerial skills and they have not learned from their mistakes. According toBlumberg and Letterie (2008), banks do not value this experience during theloan application phase. Furthermore, some authors believe that entrepreneurialexperience does not have a significant influence on small firm performance (e.g.van Praag 2003; Bosma et al. 2004).

All start-ups have skills and routines embodied in their founders that arelikely to influence the new firm’s future development and success. For indepen-dent start-ups that are not continuations of family businesses, or are the result ofmergers of existing firms, pre-entry capabilities are associated primarily with thefounders, who usually play an important role in the management of the start-up(Watson, Hogarth-Scott, and Wilson 1998). Since very small firms are usuallyheaded by one owner, it can be expected that the role of the owner in such firmsis central. Thus, the next research hypothesis argues as follows:

H2: Founder attributes influence success.

We include three firm-specific attributes. These attributes are legal form, teamand location. Generally, the existing literature indicates that limited liabilitycompanies have a higher potential for growth (Almus 2002; Davidsson et al.2002; Coad and Tamvada 2011). However, legal form has not been shown to belinked to the probability of survival (Åstebro and Bernhardt 2003; Saridakis,Mole, and Storey 2008).

Another relevant issue deals with the effect of the entrepreneurial team onthe success of a firm. There are several studies that show a positive associationbetween firms founded by teams and their success and growth (e.g. Cooper,Gimeno-Gascon, and Woo 1994; Littunen 2000; Schutjens and Wever 2000; Friarand Meyer 2003; Littunen and Niittykangas 2010; Lussier and Halabi 2010;Lafuente and Rabetino 2011). This is because they have a wide array of skillsand resources needed for the firm’s success. However, some studies have takenthe possible disadvantages of teams into account. Lechler (2001) suggested thatamong firms with multiple founders, personal conflicts and inefficient commu-nication can occur. In his study, the team variable had only weak predictivevalue for performance.

Empirical evidence on geographical location is inconclusive. Strotmann(2007) and Storey and Wynarczyk (1996) found that firms located in ruralareas have higher chances of survival than those located in urban areas. Theareas with high rates of new firm formation and where the environment offeredgood opportunities are also those which have seen the highest closure rates(Littunen, Storhammar, and Nenonen 1998). Fotopoulos and Louri (2000) found

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(Duchesneau and Gartner 1990). In Spain, Peña (2002) found entrepreneurialtraining an important determinant of new firm growth. However, expert help atstart-up has no effect on a firm’s high growth during the first 4 years; after5–8 years’, however, this type of help has a positive effect (Littunen andNiittykangas 2010).

Employment status before establishing the business can be expected toinfluence the performance of a start-up. A firm started as an escape fromunemployment (push reason, business opportunities) seems not to show anydifferences in its success measured by growth in number of employees com-pared to entrepreneurs who wanted to be their own boss (pull reasons)(Schutjens and Wever 2000). Ritsilä and Tervo (2002) emphasized the negativerelationship between push motivations (necessity) and firm growth. Similarly,Littunen and Tohmo (2003) found that positive situational and pull factorsstrongly motivate and precipitate the creation of a high-growth business. Incontrast, motives at start-up are not statistically significant for rapid growthduring the first 4 years according to Littunen and Niittykangas (2010).

Prior work experience in the same sector represents a form of specific humancapital. Industrial knowledge and experience is associated with the particularskills, insights and abilities transferable to a sector or industry, e.g. understand-ing of markets and customers and, of course, understanding of the specifictechnologies (Reuber and Fischer 1999). Industrial experience prior to startingof the firm has a positive effect on the new firm’s performance (Cooper, Gimeno-Gascon, and Woo 1994; Harada 2003; van Praag 2003; Bosma et al. 2004;Blumberg and Letterie 2008; Lussier and Halabi 2010; Soriano andCastrogiovanni 2012). These individuals have developed their expertise andexperience which they then bring to their new firms. Nevertheless, empiricalevidence on this issue remains unclear. West and Noel (2009) and Shrader andSiegel (2007) have not verified the impact of previous industrial experience onnew venture performance. Grilli (2011) reported a negative effect between priorwork experience and high-tech start-up survival. It could be that the environ-ment is so dynamic that the value of previous experience is very quickly eroded(Newbert 2005).

The final human capital attribute is personal business experience.Individuals who have been running their own firm have gained valuableexperience, which has been found to be a critical success factor for smallfirms (e.g. Yusuf 1995; Harada 2003; Coleman 2007). Individuals with priorbusiness experience tend to have larger social networks and are thereforemore able to develop networked relationships than novice entrepreneurs, whohave fewer skills to help diversify their network team (Mosey and Wright 2007;Zolin, Kuckertz, and Kautonen 2011). On the other hand, people with experience

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in running a firm could be called “opportunists” (Storey 1994). They may lackmanagerial skills and they have not learned from their mistakes. According toBlumberg and Letterie (2008), banks do not value this experience during theloan application phase. Furthermore, some authors believe that entrepreneurialexperience does not have a significant influence on small firm performance (e.g.van Praag 2003; Bosma et al. 2004).

All start-ups have skills and routines embodied in their founders that arelikely to influence the new firm’s future development and success. For indepen-dent start-ups that are not continuations of family businesses, or are the result ofmergers of existing firms, pre-entry capabilities are associated primarily with thefounders, who usually play an important role in the management of the start-up(Watson, Hogarth-Scott, and Wilson 1998). Since very small firms are usuallyheaded by one owner, it can be expected that the role of the owner in such firmsis central. Thus, the next research hypothesis argues as follows:

H2: Founder attributes influence success.

We include three firm-specific attributes. These attributes are legal form, teamand location. Generally, the existing literature indicates that limited liabilitycompanies have a higher potential for growth (Almus 2002; Davidsson et al.2002; Coad and Tamvada 2011). However, legal form has not been shown to belinked to the probability of survival (Åstebro and Bernhardt 2003; Saridakis,Mole, and Storey 2008).

Another relevant issue deals with the effect of the entrepreneurial team onthe success of a firm. There are several studies that show a positive associationbetween firms founded by teams and their success and growth (e.g. Cooper,Gimeno-Gascon, and Woo 1994; Littunen 2000; Schutjens and Wever 2000; Friarand Meyer 2003; Littunen and Niittykangas 2010; Lussier and Halabi 2010;Lafuente and Rabetino 2011). This is because they have a wide array of skillsand resources needed for the firm’s success. However, some studies have takenthe possible disadvantages of teams into account. Lechler (2001) suggested thatamong firms with multiple founders, personal conflicts and inefficient commu-nication can occur. In his study, the team variable had only weak predictivevalue for performance.

Empirical evidence on geographical location is inconclusive. Strotmann(2007) and Storey and Wynarczyk (1996) found that firms located in ruralareas have higher chances of survival than those located in urban areas. Theareas with high rates of new firm formation and where the environment offeredgood opportunities are also those which have seen the highest closure rates(Littunen, Storhammar, and Nenonen 1998). Fotopoulos and Louri (2000) found

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that firms located in larger urban areas are more likely to survive. Schutjens andWever (2000) used proxies for urban and rural areas and found that the locationvariable did not contribute anything (Schutjens and Wever 2000).

H3: Firm attributes influence start-up success.

In the data there are two attributes related to the business environment: demandfor the product(s) and service and competitive position. In evaluating these areas,the loan officer takes into account the development of the industry sector. S/healso observes the start-up firm’s ability to cope with the challenges of thebusiness environment, which naturally is weaker than the ability of establishedorganizations. Another important factor in determining the firm performance inthese critical early years is pre-entry knowledge of demand and competitivenessof a firm. The lender evaluates the density of markets, how a start-up may entermarkets and how it may take its place among incumbent firms. The firm shouldbe capable of adapting its competencies to market conditions as quickly aspossible. The market environment is clearly a factor that influences the devel-opment opportunities of new firms (Littunen and Niittykangas 2010; Nunes andSerrasqueiro 2012). The environmental dynamism can enhance firm performance(Chandler and Hanks 1994). The unemployment rate can be a proxy for demandalthough Holmes, Hunt, and Stone (2010) did not find it to be a statisticallysignificant attribute for the survival of new micro-firms. However, they con-cluded that macroeconomic variables do impact on micro-firms and the effectsare more complex for them than for SMEs. They used interest rates to examinecompetitive position, and found that low and stable interest rates improve thesurvival chances of new micro-firms. As a firm grows and develops, strategycomponents continue to be critically important (Kazanjian 1988) and changes instrategy processes, e.g. market conditions are key factors in the success of a firm(Porter 1985; Smallbone, Leigh, and North 1995; Littunen and Virtanen 2009).

H4: Market attributes influence start-up success.

3 Methods

3.1 Sample and data

The sample consists of 440 start-up micro-firms chosen in 2003 and in 2005 fromthe list of start-up firms registered on the database of Finnvera, which is aspecialized financing company owned by the Finnish state. Loans from this

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corporation had been granted to all the firms in the sample and all the firms alsohad a documented business plan. The data were collected from loan applica-tions made during 1998 or 2000. In every credit risk category, the selection wasstarted from the first loan application of the year through until there weresufficient samples. Even though we went through the applications systemati-cally, the data can be considered as random. This is based on the fact that theborrowers are of a similar nature throughout the year. We excluded firms whichwere bought from a previous owner and where one owner did not represent aprivate person or micro-enterprise. The entrepreneurs whose businesses were intraditional agriculture or as part of a franchise chain were also excluded.

In our literature review, we have included studies that did not investigatecreditworthiness but instead examined survival, profitability or growth potential.Because there has been no previous research into creditworthiness, we have usedthe outcomes of these studies to compare our results with, even where they maynot be completely comparable. However, it is worth keeping in mind that firmsthat want to achieve a high level of growth and profitability, have to achieve first aprofitable low growth stage (Davidsson, Steffens, and Fitzsimmons 2009).Although creditworthiness is made up of liquidity and leverage, which are neededfor the survival of a firm, it also has an aspect of profitability (Laitinen 1992).

The relationship between different factors and success measured by cred-itworthiness is not understood. In his seminal paper, Stinchcombe (1965, 171–3)emphasized the importance of the creditworthiness of a new firm in shapingtheir firm-level legitimacy and external reputation. Creditworthiness has beenlargely ignored in the literature (Wiklund, Baker, and Shepherd 2010). It reliesstrongly on financial ratios, but also includes the lender’s evaluation of thebusiness environment. It encompasses liquidity, leverage and profitability andenhances a firm’s legitimacy (Wiklund, Baker, and Shepherd 2010).

3.2 Variables and measures

A list of attributes used in this study, including their definitions, is shown inTable 1. We used three dependent variables for success. They are based oncreditworthiness, which is evaluated by a lender from the state-owned financialinstitution, Finnvera Plc. The rating has three important dimensions. The firstincludes financial ratios. The second includes the industry and markets wherethe business operates. The third describes the quality of firm management. Eachof the dimensions has at least three sections. For example, the first dimensioncontains profitability, liquidity and capital structure of the firm. The financemanager who rates firms follows detailed instructions regarding each section

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that firms located in larger urban areas are more likely to survive. Schutjens andWever (2000) used proxies for urban and rural areas and found that the locationvariable did not contribute anything (Schutjens and Wever 2000).

H3: Firm attributes influence start-up success.

In the data there are two attributes related to the business environment: demandfor the product(s) and service and competitive position. In evaluating these areas,the loan officer takes into account the development of the industry sector. S/healso observes the start-up firm’s ability to cope with the challenges of thebusiness environment, which naturally is weaker than the ability of establishedorganizations. Another important factor in determining the firm performance inthese critical early years is pre-entry knowledge of demand and competitivenessof a firm. The lender evaluates the density of markets, how a start-up may entermarkets and how it may take its place among incumbent firms. The firm shouldbe capable of adapting its competencies to market conditions as quickly aspossible. The market environment is clearly a factor that influences the devel-opment opportunities of new firms (Littunen and Niittykangas 2010; Nunes andSerrasqueiro 2012). The environmental dynamism can enhance firm performance(Chandler and Hanks 1994). The unemployment rate can be a proxy for demandalthough Holmes, Hunt, and Stone (2010) did not find it to be a statisticallysignificant attribute for the survival of new micro-firms. However, they con-cluded that macroeconomic variables do impact on micro-firms and the effectsare more complex for them than for SMEs. They used interest rates to examinecompetitive position, and found that low and stable interest rates improve thesurvival chances of new micro-firms. As a firm grows and develops, strategycomponents continue to be critically important (Kazanjian 1988) and changes instrategy processes, e.g. market conditions are key factors in the success of a firm(Porter 1985; Smallbone, Leigh, and North 1995; Littunen and Virtanen 2009).

H4: Market attributes influence start-up success.

3 Methods

3.1 Sample and data

The sample consists of 440 start-up micro-firms chosen in 2003 and in 2005 fromthe list of start-up firms registered on the database of Finnvera, which is aspecialized financing company owned by the Finnish state. Loans from this

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corporation had been granted to all the firms in the sample and all the firms alsohad a documented business plan. The data were collected from loan applica-tions made during 1998 or 2000. In every credit risk category, the selection wasstarted from the first loan application of the year through until there weresufficient samples. Even though we went through the applications systemati-cally, the data can be considered as random. This is based on the fact that theborrowers are of a similar nature throughout the year. We excluded firms whichwere bought from a previous owner and where one owner did not represent aprivate person or micro-enterprise. The entrepreneurs whose businesses were intraditional agriculture or as part of a franchise chain were also excluded.

In our literature review, we have included studies that did not investigatecreditworthiness but instead examined survival, profitability or growth potential.Because there has been no previous research into creditworthiness, we have usedthe outcomes of these studies to compare our results with, even where they maynot be completely comparable. However, it is worth keeping in mind that firmsthat want to achieve a high level of growth and profitability, have to achieve first aprofitable low growth stage (Davidsson, Steffens, and Fitzsimmons 2009).Although creditworthiness is made up of liquidity and leverage, which are neededfor the survival of a firm, it also has an aspect of profitability (Laitinen 1992).

The relationship between different factors and success measured by cred-itworthiness is not understood. In his seminal paper, Stinchcombe (1965, 171–3)emphasized the importance of the creditworthiness of a new firm in shapingtheir firm-level legitimacy and external reputation. Creditworthiness has beenlargely ignored in the literature (Wiklund, Baker, and Shepherd 2010). It reliesstrongly on financial ratios, but also includes the lender’s evaluation of thebusiness environment. It encompasses liquidity, leverage and profitability andenhances a firm’s legitimacy (Wiklund, Baker, and Shepherd 2010).

3.2 Variables and measures

A list of attributes used in this study, including their definitions, is shown inTable 1. We used three dependent variables for success. They are based oncreditworthiness, which is evaluated by a lender from the state-owned financialinstitution, Finnvera Plc. The rating has three important dimensions. The firstincludes financial ratios. The second includes the industry and markets wherethe business operates. The third describes the quality of firm management. Eachof the dimensions has at least three sections. For example, the first dimensioncontains profitability, liquidity and capital structure of the firm. The financemanager who rates firms follows detailed instructions regarding each section

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Table 1: Definition of attributes.

Success 6 6 = operation is sustainable over some years5 = operation is sustainable4 = difficulty in coping with fluctuations, changes in profitability3 = operational and financial difficulties, need for reorganization2 = clear threat of bankruptcy/1 = unable to pay back their loan

Success 3 3 = operation is profitable/2 = difficulty in coping with fluctuations,operational and financial difficulties, clear threat of bankruptcy/1 =unable to pay back their loan

Success 2 0 = the firm has survived/1 = the firm has failed

Funding attributesEquity % percentage share of equityLn (Loan amount) Ln (Total loan amount €)

Founder attributesLn (1+Age) Ln (1+Age of the founder(s) when business started),

mean age if there is more than one founderGender 1 = male(s) / 2 = female(s) / 3 = the firm founders represent both

genderFinancial status dummy, 1 = the founder(s) has propertyEmploymentstatus

0 = the founder(s) was unemployed prior to starting in business1 = employed

Ln (WorkExpYrs) Ln (Work experience in years in the same sector)Businessexperience

dummy, 1 = the founder(s) has prior experience of running a firm

Education 1 = no professional education / 2 = the founder(s) has degree ofvocational school / 3 = college / 4 = university

Training 1 = the founder was in receipt of entrepreneurial training course priorstarting in business

Firm attributesLegal form 0 = sole proprietorship or partnership / 1 = limited liability companyTeam 0 = single founded / 1 = teamLocation dummy, 1 = the firm locates in the same city like the state-owned

financial Institution, i.e. in bigger cities

Market attributesDemand 0 = demand for products or service is medium/weak/1 = goodCompetitiveness 0 = competitive position is medium/weak/1 = good

ControlIndustry 1 = farming, fishing, forestry or manufacturing or construction

2 = whole and retail trade industry/3 = hotels and restaurants4 = transport, communications5 = financial intermediation, real estate, renting and business activitiesor Education, health and social work, or other community, social andpersonal service activities

Marital status 1 = single; 2 = marriage/cohabitation without marriage3 = divorced/widowed

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and specific limit values and weights to rate firms. New ventures have theirown weights. The dependent variables describe the situation 5 years after start-ing the business, or at the time the firm failed. The three-point scale is acombined version of the categories in the six-point scale. The purpose of thisstudy is to investigate whether there is a tendency to move more multiple-scalemodels. The three-point scale models are statistically better than six-point scalemodels.

Table 1 also shows the independent variables which describe the firm atthe time it was started. The second funding attribute, the total amount ofloan, includes the loan from the bank and the state-owned financial institu-tion. The financial status of the entrepreneur has the value of one if thefounder owns at least half of his house. The market attributes, demand forthe product(s) and service and competitive position, are both evaluated bythe loan officer.

The final two attributes in our study serve as control variables: industryand marital status. The industry is a general attribute in explaining the firm’sperformance. We distinguish between farming, fishing, forestry, manufactur-ing, construction, wholesale/retail trade, hotels and restaurants, transporta-tion, communications, financial intermediation, real estate, renting andbusiness activities, education, health and social work, other community,social and personal service activities. The data do not include any traditionalagriculture firms. Marital status can have one of three values: single, mar-ried/cohabitation without marriage and divorced/widowed. Being marriedhas been suggested as having a positive effect on survival and growth(Fajnzylber et al. 2006). Being married is also positively related to new firmsurvival by Bates (1995). However, it is statistically insignificant. Maritalstatus is rarely included in the studies; here we include it as a controlvariable.

3.3 Data analysis

The methods we apply to model the data for univariate analysis are based ont-tests and mean ranks for ordinal data. For multivariate analysis, we use logisticregression and ordinal regression analysis with a logit link function. We inves-tigate the success of start-up firms using three dependent variables. The depen-dent variable SUCCESS2 is a dichotomous variable which equals one for firmsthat have failed and zero for firms that are still operating 5 years after start-up.Logistic regression is used to investigate the relationship between success andfailure (SUCCESS2). For the multiple-point scale model, the dependent variables

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Table 1: Definition of attributes.

Success 6 6 = operation is sustainable over some years5 = operation is sustainable4 = difficulty in coping with fluctuations, changes in profitability3 = operational and financial difficulties, need for reorganization2 = clear threat of bankruptcy/1 = unable to pay back their loan

Success 3 3 = operation is profitable/2 = difficulty in coping with fluctuations,operational and financial difficulties, clear threat of bankruptcy/1 =unable to pay back their loan

Success 2 0 = the firm has survived/1 = the firm has failed

Funding attributesEquity % percentage share of equityLn (Loan amount) Ln (Total loan amount €)

Founder attributesLn (1+Age) Ln (1+Age of the founder(s) when business started),

mean age if there is more than one founderGender 1 = male(s) / 2 = female(s) / 3 = the firm founders represent both

genderFinancial status dummy, 1 = the founder(s) has propertyEmploymentstatus

0 = the founder(s) was unemployed prior to starting in business1 = employed

Ln (WorkExpYrs) Ln (Work experience in years in the same sector)Businessexperience

dummy, 1 = the founder(s) has prior experience of running a firm

Education 1 = no professional education / 2 = the founder(s) has degree ofvocational school / 3 = college / 4 = university

Training 1 = the founder was in receipt of entrepreneurial training course priorstarting in business

Firm attributesLegal form 0 = sole proprietorship or partnership / 1 = limited liability companyTeam 0 = single founded / 1 = teamLocation dummy, 1 = the firm locates in the same city like the state-owned

financial Institution, i.e. in bigger cities

Market attributesDemand 0 = demand for products or service is medium/weak/1 = goodCompetitiveness 0 = competitive position is medium/weak/1 = good

ControlIndustry 1 = farming, fishing, forestry or manufacturing or construction

2 = whole and retail trade industry/3 = hotels and restaurants4 = transport, communications5 = financial intermediation, real estate, renting and business activitiesor Education, health and social work, or other community, social andpersonal service activities

Marital status 1 = single; 2 = marriage/cohabitation without marriage3 = divorced/widowed

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and specific limit values and weights to rate firms. New ventures have theirown weights. The dependent variables describe the situation 5 years after start-ing the business, or at the time the firm failed. The three-point scale is acombined version of the categories in the six-point scale. The purpose of thisstudy is to investigate whether there is a tendency to move more multiple-scalemodels. The three-point scale models are statistically better than six-point scalemodels.

Table 1 also shows the independent variables which describe the firm atthe time it was started. The second funding attribute, the total amount ofloan, includes the loan from the bank and the state-owned financial institu-tion. The financial status of the entrepreneur has the value of one if thefounder owns at least half of his house. The market attributes, demand forthe product(s) and service and competitive position, are both evaluated bythe loan officer.

The final two attributes in our study serve as control variables: industryand marital status. The industry is a general attribute in explaining the firm’sperformance. We distinguish between farming, fishing, forestry, manufactur-ing, construction, wholesale/retail trade, hotels and restaurants, transporta-tion, communications, financial intermediation, real estate, renting andbusiness activities, education, health and social work, other community,social and personal service activities. The data do not include any traditionalagriculture firms. Marital status can have one of three values: single, mar-ried/cohabitation without marriage and divorced/widowed. Being marriedhas been suggested as having a positive effect on survival and growth(Fajnzylber et al. 2006). Being married is also positively related to new firmsurvival by Bates (1995). However, it is statistically insignificant. Maritalstatus is rarely included in the studies; here we include it as a controlvariable.

3.3 Data analysis

The methods we apply to model the data for univariate analysis are based ont-tests and mean ranks for ordinal data. For multivariate analysis, we use logisticregression and ordinal regression analysis with a logit link function. We inves-tigate the success of start-up firms using three dependent variables. The depen-dent variable SUCCESS2 is a dichotomous variable which equals one for firmsthat have failed and zero for firms that are still operating 5 years after start-up.Logistic regression is used to investigate the relationship between success andfailure (SUCCESS2). For the multiple-point scale model, the dependent variables

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SUCCESS3 and SUCCESS6 are assumed to be ordinal. The ordering is determinedby sorting the values of the dependent variable in ascending order, the highestvalue defining the first category. SUCCESS3 has two and SUCCESS6 has fivecategories for non-defaulted borrowers. Rating grade 6 or 3 corresponds tothe lowest and grade 1 to the highest degree of credit risk (for more details,see Table 2). The reference group is the most successful firms and all other firmsare compared with these ones. Ordinal regression analysis allows the modelingof the dependence of a polytomous ordinal response on a set of predictors. Likecredit risk, the dependent variable is assumed to be ordinal. The ordering isdetermined by sorting the values of the dependent variable in ascending order.The model of ordinal regression analysis which we use is based on the metho-dology of McCullagh (1980) and performed using SPSS 19.0. The program usesthe logistic cumulative probability function in predicting financial health status(SUCCESS3 and SUCCESS6).

Logistic regression uses maximum likelihood estimation after transforming theresponse variable into a logit function. A logit is the natural log of the odds ofthe response equaling a certain value or not (usually 1 in logistic models and thehighest or lowest value in ordinal ranked models). Logistic regression estimatesthe log-odds of a certain value happening. This means that logistic regressionreveals changes in the log-odds of the response (Hosmer and Lemeshow 2000).Ordinal regression can be thought of as an extension of the logistic regressionmodel that allows the simultaneous comparison of three or more orderedresponse categories. It is used to predict the probabilities of the differentpossible outcomes. One approach to analyzing ordinal data is to perform

Table 2: Definitions of credit risk categories.

SUCCESS6category

SUCCESS3Category

Definition Number offirms

6 3 Operation is sustainable over some years. 755 3 Operation is sustainable. 684 2 Difficulty in coping with fluctuations.

Changes in profitability.61

3 2 Operational and financial difficulties.Need for reorganization.

72

2 2 Clear threat of bankruptcy. 631 1 Unable to pay back their loan. 101

Total 440

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many binary logistic regressions. The downside of this approach is that usefulinformation in the ordering is ignored and the model may not be very powerful(Armstrong and Sloan 1989).

In ordinal regression, an underlying model assumption is that of parallelregression, i.e. the proportional odds assumption. It means that the relation-ship between each pair of outcome groups should be the same. Thus, thereis only one set of regression coefficient estimates (Ananth and Kleinbaum1997).

The correlation coefficients indicate that the problem of multicollinearitybetween explanatory variables may not be particularly relevant in this study.The correlation coefficients of the independent variables do not exceed 0.8,which is often considered to be a sign of harmful multicollinearity. In thedata, the highest correlation coefficient is 0.519, which gives the correlationbetween team and firm form variables (Table 3).

4 Findings

4.1 Influences on success

While the average percentage of equity to total assets for all the firms in thesample is 14.23%, the successful firms have a higher equity share than the failedfirms (Table 4). The average total loan amount is 31 138 euros, whilst amongsuccessful firms the average loan amount is even higher, at 35 768 euros. Thecorrelation coefficients (not reported) between success as defined by the six-point scale and funding attributes, as well as work experience years, are lowbeing less than 0.217.

Turning to founder attributes, we can see that the average age of entrepre-neurs is 36.70 years. Over half of the firms (52.0%, n=229) are owned by males.Women own 174 enterprises (39.5%) and 37 enterprises (8.5%) are owned bymales and females together. Founders who have personal property seem tosucceed more often and they have a higher likelihood of having a lower creditrisk. As Parker (2004) wrote, personal property facilitates establishing a newfirm; it can also enhance the survival prospects of the firm. Furthermore, eventhough only 38% of the entrepreneurs in the sample were employed beforestarting the business, being employed increases the likelihood that the firmwill succeed its start-up phase and that the firm has a low credit risk. This isconsistent with the results of Avery, Calem, and Canner (2004), who investigated

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SUCCESS3 and SUCCESS6 are assumed to be ordinal. The ordering is determinedby sorting the values of the dependent variable in ascending order, the highestvalue defining the first category. SUCCESS3 has two and SUCCESS6 has fivecategories for non-defaulted borrowers. Rating grade 6 or 3 corresponds tothe lowest and grade 1 to the highest degree of credit risk (for more details,see Table 2). The reference group is the most successful firms and all other firmsare compared with these ones. Ordinal regression analysis allows the modelingof the dependence of a polytomous ordinal response on a set of predictors. Likecredit risk, the dependent variable is assumed to be ordinal. The ordering isdetermined by sorting the values of the dependent variable in ascending order.The model of ordinal regression analysis which we use is based on the metho-dology of McCullagh (1980) and performed using SPSS 19.0. The program usesthe logistic cumulative probability function in predicting financial health status(SUCCESS3 and SUCCESS6).

Logistic regression uses maximum likelihood estimation after transforming theresponse variable into a logit function. A logit is the natural log of the odds ofthe response equaling a certain value or not (usually 1 in logistic models and thehighest or lowest value in ordinal ranked models). Logistic regression estimatesthe log-odds of a certain value happening. This means that logistic regressionreveals changes in the log-odds of the response (Hosmer and Lemeshow 2000).Ordinal regression can be thought of as an extension of the logistic regressionmodel that allows the simultaneous comparison of three or more orderedresponse categories. It is used to predict the probabilities of the differentpossible outcomes. One approach to analyzing ordinal data is to perform

Table 2: Definitions of credit risk categories.

SUCCESS6category

SUCCESS3Category

Definition Number offirms

6 3 Operation is sustainable over some years. 755 3 Operation is sustainable. 684 2 Difficulty in coping with fluctuations.

Changes in profitability.61

3 2 Operational and financial difficulties.Need for reorganization.

72

2 2 Clear threat of bankruptcy. 631 1 Unable to pay back their loan. 101

Total 440

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many binary logistic regressions. The downside of this approach is that usefulinformation in the ordering is ignored and the model may not be very powerful(Armstrong and Sloan 1989).

In ordinal regression, an underlying model assumption is that of parallelregression, i.e. the proportional odds assumption. It means that the relation-ship between each pair of outcome groups should be the same. Thus, thereis only one set of regression coefficient estimates (Ananth and Kleinbaum1997).

The correlation coefficients indicate that the problem of multicollinearitybetween explanatory variables may not be particularly relevant in this study.The correlation coefficients of the independent variables do not exceed 0.8,which is often considered to be a sign of harmful multicollinearity. In thedata, the highest correlation coefficient is 0.519, which gives the correlationbetween team and firm form variables (Table 3).

4 Findings

4.1 Influences on success

While the average percentage of equity to total assets for all the firms in thesample is 14.23%, the successful firms have a higher equity share than the failedfirms (Table 4). The average total loan amount is 31 138 euros, whilst amongsuccessful firms the average loan amount is even higher, at 35 768 euros. Thecorrelation coefficients (not reported) between success as defined by the six-point scale and funding attributes, as well as work experience years, are lowbeing less than 0.217.

Turning to founder attributes, we can see that the average age of entrepre-neurs is 36.70 years. Over half of the firms (52.0%, n=229) are owned by males.Women own 174 enterprises (39.5%) and 37 enterprises (8.5%) are owned bymales and females together. Founders who have personal property seem tosucceed more often and they have a higher likelihood of having a lower creditrisk. As Parker (2004) wrote, personal property facilitates establishing a newfirm; it can also enhance the survival prospects of the firm. Furthermore, eventhough only 38% of the entrepreneurs in the sample were employed beforestarting the business, being employed increases the likelihood that the firmwill succeed its start-up phase and that the firm has a low credit risk. This isconsistent with the results of Avery, Calem, and Canner (2004), who investigated

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Table3:

Correlations

.

Equity

%

Ln (Loa

n

amou

nt)

Ln (1+Ag

e)

Gen

derFina

ncial

status

Employ

men

t

status

Ln (WorkE

xpYrs)

Bus

iness

expe

rien

ce

Education

Training

Firm

form

Team

Location

Dem

and

Compe

titivene

ssIndu

stry

Marital

status

Equity

%1

Ln(Loa

n

amou

nt)

–0.021

1

Ln(1+A

ge)

0.129

0.114

1

Gen

der

–0.022

–0.021

0.146

1

Fina

ncial

status

0.23

50.163

0.315

0.08

81

Employ

men

t

status

0.04

60.318

0.017

0.00

50.159

1

Ln (WorkE

xpYrs)

0.173

0.26

80.477

–0.047

0.23

60.219

1

Bus

inessexpe

r.–0

.027

0.186

0.194

–0.042

0.05

70.06

50.09

91

Education

0.08

60.07

90.07

70.22

20.04

10.152

0.03

50.00

71

Training

–0.040

–0.070

0.06

10.04

2–0

.048

–0.124

–0.016

–0.150

–0.134

1

Firm

form

0.23

40.40

40.126

0.03

70.148

0.271

0.23

10.26

20.191

–0.188

1

Team

0.136

0.410

–0.003

0.171

0.104

0.210

0.24

90.219

0.162

–0.117

0.519

Locatio

n0.07

5–0

.044

0.016

–0.015

–0.148

0.06

90.07

5–0

.063

0.121

0.03

4–0

.008

0.03

41

Dem

and

0.155

0.116

–0.051

0.07

7–0

.023

0.160

0.134

–0.063

0.08

7–0

.030

0.164

0.217

0.100

1

Compe

titiven

ess

0.09

30.153

0.02

00.04

30.05

10.139

0.136

0.06

20.00

8–0

.025

0.07

60.126

–0.071

0.38

01

Indu

stry

–0.062

–0.226

–0.127

0.166

–0.151

0.03

2–0

.097

–0.093

0.177

–0.117

–0.061

–0.050

0.132

0.155

0.05

71

Marita

lstatus

–0.062

–0.091

0.276

0.119

0.05

8–0

.011

0.04

60.114

0.04

40.02

90.04

1–0

.015

0.07

7–0

.052

0.07

50.00

31

Notes:Tablepresen

tsPe

arso

ncorrelations

.Co

rrelations

sign

ificant

atthe1%

confiden

celevelarerepo

rted

inita

lics.

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consumer credit scoring. The univariate findings are contradictory for successmeasured using two-point scale or six-point scale analysis. Participation in anentrepreneurial training course in connection with the establishment of thebusiness (21.8%) increases the likelihood of success when measured using thetwo-point scale model, which is consistent with the findings of Storey andWynarczyk (1996). However, when using an output with multiple-point scales,participation in a course decreases the likelihood of success. This course may

Table 4: Model of firm success derived using univariate analysis.

Variable Total sample(n = 440)

Success2(failed)

Success2(survived)

Equalityof means

(n = 101) (n = 339)

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. p-value

Funding attributesEquity% 14.238 16.716 9.233 13.047 15.695 17.392 0.000Ln (loan amount 1,000€) 2.830 0.902 2.540 0.705 2.910 0.938 0.000Loan amount 1,000€ 31.186 98.793 16.307 14.688 35.768 112.342 0.104

Founder attributesFounder age 36.704 8.434 35.869 8.819 36.952 8.314 0.262Male 0.520 0.5 0.495 0.502 0.528 0.5 0.561Female 0.395 0.489 0.436 0.498 0.383 0.487 0.348Both gender 0.084 0.277 0.069 0.255 0.088 0.284 0.543Financial status 0.617 0.486 0.465 0.501 0.662 0.474 0.001Employment status 0.383 0.486 0.241 0.43 0.422 0.495 0.001Vocational school 0.387 0.488 0.406 0.382 0.494 0.406 0.669College/Polytechnic 0.376 0.485 0.487 0.367 0.494 0.083 0.487University 0.109 0.312 0.483 0.116 0.277 0.297 0.360Entrepr. training 0.218 0.413 0.321 0.195 0.459 1.386 0.045Work experience yrs 10.480 9.706 9.100 10.011 10.800 9.626 0.232Ln (WorkExpYrs) 2.002 0.953 1.745 1.002 2.064 0.933 0.019Business experience 0.209 0.407 0.218 0.415 0.206 0.405 0.806

Firm attributesLegal form 0.340 0.474 0.230 0.426 0.370 0.484 0.009Team 0.299 0.458 0.823 0.323 0.415 40.238 0.031Location 0.375 0.485 0.554 0.500 0.322 0.468 0.000

Market attributesDemand 0.642 0.480 0.495 0.502 0.685 0.465 0.001Competitiveness 0.423 0.495 0.250 0.435 0.475 0.500 0.000

Notes: Reported statistics are means and standard deviations for total sample. The reported p-valuesare from a comparison of means.

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Table3:

Correlations

.

Equity

%

Ln (Loa

n

amou

nt)

Ln (1+Ag

e)

Gen

derFina

ncial

status

Employ

men

t

status

Ln (WorkE

xpYrs)

Bus

iness

expe

rien

ce

Education

Training

Firm

form

Team

Location

Dem

and

Compe

titivene

ssIndu

stry

Marital

status

Equity

%1

Ln(Loa

n

amou

nt)

–0.021

1

Ln(1+A

ge)

0.129

0.114

1

Gen

der

–0.022

–0.021

0.146

1

Fina

ncial

status

0.23

50.163

0.315

0.08

81

Employ

men

t

status

0.04

60.318

0.017

0.00

50.159

1

Ln (WorkE

xpYrs)

0.173

0.26

80.477

–0.047

0.23

60.219

1

Bus

inessexpe

r.–0

.027

0.186

0.194

–0.042

0.05

70.06

50.09

91

Education

0.08

60.07

90.07

70.22

20.04

10.152

0.03

50.00

71

Training

–0.040

–0.070

0.06

10.04

2–0

.048

–0.124

–0.016

–0.150

–0.134

1

Firm

form

0.23

40.40

40.126

0.03

70.148

0.271

0.23

10.26

20.191

–0.188

1

Team

0.136

0.410

–0.003

0.171

0.104

0.210

0.24

90.219

0.162

–0.117

0.519

Locatio

n0.07

5–0

.044

0.016

–0.015

–0.148

0.06

90.07

5–0

.063

0.121

0.03

4–0

.008

0.03

41

Dem

and

0.155

0.116

–0.051

0.07

7–0

.023

0.160

0.134

–0.063

0.08

7–0

.030

0.164

0.217

0.100

1

Compe

titiven

ess

0.09

30.153

0.02

00.04

30.05

10.139

0.136

0.06

20.00

8–0

.025

0.07

60.126

–0.071

0.38

01

Indu

stry

–0.062

–0.226

–0.127

0.166

–0.151

0.03

2–0

.097

–0.093

0.177

–0.117

–0.061

–0.050

0.132

0.155

0.05

71

Marita

lstatus

–0.062

–0.091

0.276

0.119

0.05

8–0

.011

0.04

60.114

0.04

40.02

90.04

1–0

.015

0.07

7–0

.052

0.07

50.00

31

Notes:Tablepresen

tsPe

arso

ncorrelations

.Co

rrelations

sign

ificant

atthe1%

confiden

celevelarerepo

rted

inita

lics.

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consumer credit scoring. The univariate findings are contradictory for successmeasured using two-point scale or six-point scale analysis. Participation in anentrepreneurial training course in connection with the establishment of thebusiness (21.8%) increases the likelihood of success when measured using thetwo-point scale model, which is consistent with the findings of Storey andWynarczyk (1996). However, when using an output with multiple-point scales,participation in a course decreases the likelihood of success. This course may

Table 4: Model of firm success derived using univariate analysis.

Variable Total sample(n = 440)

Success2(failed)

Success2(survived)

Equalityof means

(n = 101) (n = 339)

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. p-value

Funding attributesEquity% 14.238 16.716 9.233 13.047 15.695 17.392 0.000Ln (loan amount 1,000€) 2.830 0.902 2.540 0.705 2.910 0.938 0.000Loan amount 1,000€ 31.186 98.793 16.307 14.688 35.768 112.342 0.104

Founder attributesFounder age 36.704 8.434 35.869 8.819 36.952 8.314 0.262Male 0.520 0.5 0.495 0.502 0.528 0.5 0.561Female 0.395 0.489 0.436 0.498 0.383 0.487 0.348Both gender 0.084 0.277 0.069 0.255 0.088 0.284 0.543Financial status 0.617 0.486 0.465 0.501 0.662 0.474 0.001Employment status 0.383 0.486 0.241 0.43 0.422 0.495 0.001Vocational school 0.387 0.488 0.406 0.382 0.494 0.406 0.669College/Polytechnic 0.376 0.485 0.487 0.367 0.494 0.083 0.487University 0.109 0.312 0.483 0.116 0.277 0.297 0.360Entrepr. training 0.218 0.413 0.321 0.195 0.459 1.386 0.045Work experience yrs 10.480 9.706 9.100 10.011 10.800 9.626 0.232Ln (WorkExpYrs) 2.002 0.953 1.745 1.002 2.064 0.933 0.019Business experience 0.209 0.407 0.218 0.415 0.206 0.405 0.806

Firm attributesLegal form 0.340 0.474 0.230 0.426 0.370 0.484 0.009Team 0.299 0.458 0.823 0.323 0.415 40.238 0.031Location 0.375 0.485 0.554 0.500 0.322 0.468 0.000

Market attributesDemand 0.642 0.480 0.495 0.502 0.685 0.465 0.001Competitiveness 0.423 0.495 0.250 0.435 0.475 0.500 0.000

Notes: Reported statistics are means and standard deviations for total sample. The reported p-valuesare from a comparison of means.

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provide the necessary skills for survival, whereas success encompasses moreaspects which may be impossible to include in an entrepreneurial trainingcourse, such as a change in market conditions.

Considering the firm attributes in the two-point scale (Table 4), successfulfirms in our sample are more likely to be limited liability companies. Ourfindings on this variable support those of Brüderl et al. (1992). In addition,most of the successful firms are run by a single person. This result contradictsthat of a number of previous studies. For example, Cressy (1996) found that teamfirms have higher probabilities of survival than firms owned by a single person.Firm location is given the value of one if the firm is located in the same city asthe lender. The results from this variable indicate that firms that are locatedoutside the city are more likely to succeed. This somewhat puzzling result hastwo possible explanations. One is that location is a proxy for urban versus ruraland the result indicates that rural firms are more likely to survive. Another isthat it is a proxy for personal connections between the lender and the entrepre-neur. In this case, the result might indicate that the existence of a personalconnection increases the likelihood that the firm will obtain funding even withpoorer credentials.

All firm and market attributes have the same effect whether we analyzesuccess with the two-point or six-point scale. The sole proprietor or partnershipfirms, firms run by a single entrepreneur and firms which are located in thesame city as the state-owned lender are more likely to have higher credit risk.Moreover, the firms with a medium or weak demand and competitivenessposition seem to have a higher credit risk (Table 5).

The first research hypothesis (H1) proposed that the funding attributesinfluence start-up success. It is supported by the two-point scale model(Table 4), which indicates that both funding attributes are important. Thesecond hypothesis that founder attributes influence start-up success is onlyweakly supported by the univariate analysis, whether we use two-point or six-point scales. In both models, the same three from seven founder attributes areimportant, whereas all firm and market attributes show a statistically significantdifference, which indicates that H3 and H4 are clearly supported by the analysis.These results contradict those of Bosma et al. (2004), emphasizing that a singlefounder alone is not a proxy for the performance of the firm. However, theresults are consistent with those of Storey and Wynarczyk (1996), who showedthat the talent of the entrepreneur is not the unique determinant of performance.In their study, firm attributes were dominant in explaining the survival of youngfirms.

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Table 5: Model of six-point scale success derived using univariate analysis.

Variable n Mean rank p-value Test

Founder Gender 440 0.354 Kattributes Male 229 212.88

Female 174 230.98Both genders 37 218.35

Financial status 433 0.001 MNo 166 242.26Yes 267 201.30

Employment status 381 0.000 MUnemployed 235 209.40Employed 146 161.38

Professional education 431 0.874 KNo 55 227.92Vocational School 167 212.11College/Polytechnic 162 216.45University 47 214.31

Entrepreneurial training 440 0.006 MNo 344 211.92Yes 96 251.25

Business experience 440 0.179 MNo 348 216.38Yes 92 236.09

Firm attributes Legal form / Ltd 390 0.008 MNo 258 206.21Yes 132 174.58

Team 438 0.038 MNo 307 227.59Yes 131 200.54

Location in a bigger city 440 0.004 MNo 275 207.07Yes 165 242.88

Market attributes Demand 438 0.000 MMedium/Weak 157 263.65Good 281 194.83

Competitiveness 437 0.000 MMedium/Weak 252 243.90Good 185 185.09

Notes: Reported statistics are mean ranks and p-values for non-parametric tests: Mann-Whitney Utest (M) or Kruskall-Wallis test (K).

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provide the necessary skills for survival, whereas success encompasses moreaspects which may be impossible to include in an entrepreneurial trainingcourse, such as a change in market conditions.

Considering the firm attributes in the two-point scale (Table 4), successfulfirms in our sample are more likely to be limited liability companies. Ourfindings on this variable support those of Brüderl et al. (1992). In addition,most of the successful firms are run by a single person. This result contradictsthat of a number of previous studies. For example, Cressy (1996) found that teamfirms have higher probabilities of survival than firms owned by a single person.Firm location is given the value of one if the firm is located in the same city asthe lender. The results from this variable indicate that firms that are locatedoutside the city are more likely to succeed. This somewhat puzzling result hastwo possible explanations. One is that location is a proxy for urban versus ruraland the result indicates that rural firms are more likely to survive. Another isthat it is a proxy for personal connections between the lender and the entrepre-neur. In this case, the result might indicate that the existence of a personalconnection increases the likelihood that the firm will obtain funding even withpoorer credentials.

All firm and market attributes have the same effect whether we analyzesuccess with the two-point or six-point scale. The sole proprietor or partnershipfirms, firms run by a single entrepreneur and firms which are located in thesame city as the state-owned lender are more likely to have higher credit risk.Moreover, the firms with a medium or weak demand and competitivenessposition seem to have a higher credit risk (Table 5).

The first research hypothesis (H1) proposed that the funding attributesinfluence start-up success. It is supported by the two-point scale model(Table 4), which indicates that both funding attributes are important. Thesecond hypothesis that founder attributes influence start-up success is onlyweakly supported by the univariate analysis, whether we use two-point or six-point scales. In both models, the same three from seven founder attributes areimportant, whereas all firm and market attributes show a statistically significantdifference, which indicates that H3 and H4 are clearly supported by the analysis.These results contradict those of Bosma et al. (2004), emphasizing that a singlefounder alone is not a proxy for the performance of the firm. However, theresults are consistent with those of Storey and Wynarczyk (1996), who showedthat the talent of the entrepreneur is not the unique determinant of performance.In their study, firm attributes were dominant in explaining the survival of youngfirms.

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Table 5: Model of six-point scale success derived using univariate analysis.

Variable n Mean rank p-value Test

Founder Gender 440 0.354 Kattributes Male 229 212.88

Female 174 230.98Both genders 37 218.35

Financial status 433 0.001 MNo 166 242.26Yes 267 201.30

Employment status 381 0.000 MUnemployed 235 209.40Employed 146 161.38

Professional education 431 0.874 KNo 55 227.92Vocational School 167 212.11College/Polytechnic 162 216.45University 47 214.31

Entrepreneurial training 440 0.006 MNo 344 211.92Yes 96 251.25

Business experience 440 0.179 MNo 348 216.38Yes 92 236.09

Firm attributes Legal form / Ltd 390 0.008 MNo 258 206.21Yes 132 174.58

Team 438 0.038 MNo 307 227.59Yes 131 200.54

Location in a bigger city 440 0.004 MNo 275 207.07Yes 165 242.88

Market attributes Demand 438 0.000 MMedium/Weak 157 263.65Good 281 194.83

Competitiveness 437 0.000 MMedium/Weak 252 243.90Good 185 185.09

Notes: Reported statistics are mean ranks and p-values for non-parametric tests: Mann-Whitney Utest (M) or Kruskall-Wallis test (K).

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4.2 Comparing measures of success

The results of the logistic regression for firm success are presented in Table 6,which shows five hierarchical regression models. The control variables are inputinto Model 1. Then the funding attributes are input into Model 2. Likewise,founder attributes, firm attributes and market attributes are added in Models3, 4, and 5. The results of the analysis of funding attributes show that theincrease in equity share as well as the amount of loan, increases the probabilityof success. The results of own investments agree with those of Bosma et al.(2004). Furthermore, having more funds during the start-up phase and itsassociation with success is in line with the results of Cooper, Gimeno-Gascon,and Woo (1994), Becchetti and Trovato (2002) and Lussier and Halabi (2010).When we examine the impact of other attributes, we find that any one of thefounder attributes is not statistically important. The geographical location of thefirm suggests that firms located in the same city as the state-owned financier (i.e.in the bigger cities) have a higher probability of success. However, when marketattributes are added, the location does not become more important. It might bethat the market attributes have a stronger impact on success. The competitiveposition of the firm, evaluated by the lender, has statistically significant coeffi-cients. If the firms have good competitiveness, the probability of successincreases. In addition, firms operating in the Industry 3 sector (Hotels andrestaurants) are more likely to succeed. In general, the first four models haveweaker classification rates. In Table 6, only the total classification rate isreported. When we consider the classification rate separately, we find thatclassification among failed firms is only 0–37.5%, whereas in Model 6, it is46.9% (not reported).

Estimates for success measured using three-point scale and six-point scalemodels are presented in Tables 7 and 8. Both tables show five hierarchicalmodels, as in Table 6. In all models, where funding attributes are included,the Equity% shows statistically significant values, meaning that an increase inequity investments by the owners decreases the credit risk. This agrees withDuchesneau and Gartner (1990), who suggested that high percentage equityhas a positive effect on a firm’s success. It can also reflect the founder’scommitment. The total loan amount (in euros) is also important for all models:the higher the loan amount, the lower the credit risk. This is in line with,amongst others, Cooper, Gimeno-Gascon, and Woo (1994), Becchetti andTrovato (2002), Bosma et al. (2004) and Lussier and Halabi (2010), who foundthat having more funds is better for survival and growth. The observed correla-tion between Equity% and Loan amount is −0.021 and is not statistically sig-nificant (Table 3).

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Table 6: Estimates of success in two-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.029 0.004 0.052 0.005 0.063 0.003 0.058 0.009

Ln (Loan amount) 0.635 0.002 1.628 0.000 1.644 0.001 1.707 0.001

Founder attributes

Ln (1+Age) 0.789 0.517 0.872 0.524 1.388 0.349

Male(s) 0.605 0.644 0.737

Female(s) –0.151 0.878 0.730 0.599 0.990 0.503

Both genders 0.432 0.676 1.268 0.410 1.236 0.435

Financial status 0.556 0.334 0.811 0.195 0.796 0.223

Employment status –0.396 0.504 –0.924 0.180 –0.626 0.387

No prof. education 0.059 0.148 0.188

Vocational school 1.031 0.370 0.726 0.572 0.627 0.624

College/Polytechnic 0.458 0.588 0.431 0.644 0.433 0.651

University –0.972 0.258 –1.008 0.303 –0.992 0.319

Training 0.406 0.455 0.139 0.821 0.151 0.816

Ln (WorkExpYrs) 0.230 0.409 0.501 0.140 0.294 0.428

Business experience –0.457 0.558 –0.196 0.820 0.027 0.975

Firm attributes

Legal form –0.174 0.825 –0.206 0.801

Team –0.759 0.452 –0.588 0.570

Location 0.982 0.079 0.886 0.126

Market attributes

Demand –0.442 0.460

Competitiveness 1.153 0.085

Control

Industry 1 0.769 0.256 0.276 0.378 0.402

Industry 2 –0.004 0.989 –0.358 0.341 –0.996 0.105 –1.082 0.145 –1.194 0.124

Industry 3 –0.399 0.235 –0.883 0.025 –1.463 0.043 –1.694 0.060 –1.645 0.081

Industry 4 –0.142 0.815 –0.728 0.270 –0.932 0.486 –1.006 0.465 –1.079 0.441

Industry 5 0.158 0.818 –0.667 0.375 0.263 0.854 0.123 0.936 0.409 0.809

Marital status/

single

0.337 0.766 0.639 0.753 0.967

Married/cohabit 0.626 0.235 0.418 0.468 –0.014 0.987 –0.203 0.830 0.156 0.879

Divorced/widowed 0.606 0.147 0.243 0.587 0.548 0.474 0.364 0.669 0.227 0.796

Constant –0.754 0.076 –0.730 0.233 –6.050 0.239 –7.207 0.210 –8.408 0.173

Total class’ n rate (%) 77.1 75.3 83.3 81.6 85.4

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.016 0.118 0.338 0.431 0.475

Notes: The results are obtained through a logistic regression where the dependent variable SUCCESS2 has

the value of zero if the firm has failed and one otherwise.

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4.2 Comparing measures of success

The results of the logistic regression for firm success are presented in Table 6,which shows five hierarchical regression models. The control variables are inputinto Model 1. Then the funding attributes are input into Model 2. Likewise,founder attributes, firm attributes and market attributes are added in Models3, 4, and 5. The results of the analysis of funding attributes show that theincrease in equity share as well as the amount of loan, increases the probabilityof success. The results of own investments agree with those of Bosma et al.(2004). Furthermore, having more funds during the start-up phase and itsassociation with success is in line with the results of Cooper, Gimeno-Gascon,and Woo (1994), Becchetti and Trovato (2002) and Lussier and Halabi (2010).When we examine the impact of other attributes, we find that any one of thefounder attributes is not statistically important. The geographical location of thefirm suggests that firms located in the same city as the state-owned financier (i.e.in the bigger cities) have a higher probability of success. However, when marketattributes are added, the location does not become more important. It might bethat the market attributes have a stronger impact on success. The competitiveposition of the firm, evaluated by the lender, has statistically significant coeffi-cients. If the firms have good competitiveness, the probability of successincreases. In addition, firms operating in the Industry 3 sector (Hotels andrestaurants) are more likely to succeed. In general, the first four models haveweaker classification rates. In Table 6, only the total classification rate isreported. When we consider the classification rate separately, we find thatclassification among failed firms is only 0–37.5%, whereas in Model 6, it is46.9% (not reported).

Estimates for success measured using three-point scale and six-point scalemodels are presented in Tables 7 and 8. Both tables show five hierarchicalmodels, as in Table 6. In all models, where funding attributes are included,the Equity% shows statistically significant values, meaning that an increase inequity investments by the owners decreases the credit risk. This agrees withDuchesneau and Gartner (1990), who suggested that high percentage equityhas a positive effect on a firm’s success. It can also reflect the founder’scommitment. The total loan amount (in euros) is also important for all models:the higher the loan amount, the lower the credit risk. This is in line with,amongst others, Cooper, Gimeno-Gascon, and Woo (1994), Becchetti andTrovato (2002), Bosma et al. (2004) and Lussier and Halabi (2010), who foundthat having more funds is better for survival and growth. The observed correla-tion between Equity% and Loan amount is −0.021 and is not statistically sig-nificant (Table 3).

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Table 6: Estimates of success in two-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.029 0.004 0.052 0.005 0.063 0.003 0.058 0.009

Ln (Loan amount) 0.635 0.002 1.628 0.000 1.644 0.001 1.707 0.001

Founder attributes

Ln (1+Age) 0.789 0.517 0.872 0.524 1.388 0.349

Male(s) 0.605 0.644 0.737

Female(s) –0.151 0.878 0.730 0.599 0.990 0.503

Both genders 0.432 0.676 1.268 0.410 1.236 0.435

Financial status 0.556 0.334 0.811 0.195 0.796 0.223

Employment status –0.396 0.504 –0.924 0.180 –0.626 0.387

No prof. education 0.059 0.148 0.188

Vocational school 1.031 0.370 0.726 0.572 0.627 0.624

College/Polytechnic 0.458 0.588 0.431 0.644 0.433 0.651

University –0.972 0.258 –1.008 0.303 –0.992 0.319

Training 0.406 0.455 0.139 0.821 0.151 0.816

Ln (WorkExpYrs) 0.230 0.409 0.501 0.140 0.294 0.428

Business experience –0.457 0.558 –0.196 0.820 0.027 0.975

Firm attributes

Legal form –0.174 0.825 –0.206 0.801

Team –0.759 0.452 –0.588 0.570

Location 0.982 0.079 0.886 0.126

Market attributes

Demand –0.442 0.460

Competitiveness 1.153 0.085

Control

Industry 1 0.769 0.256 0.276 0.378 0.402

Industry 2 –0.004 0.989 –0.358 0.341 –0.996 0.105 –1.082 0.145 –1.194 0.124

Industry 3 –0.399 0.235 –0.883 0.025 –1.463 0.043 –1.694 0.060 –1.645 0.081

Industry 4 –0.142 0.815 –0.728 0.270 –0.932 0.486 –1.006 0.465 –1.079 0.441

Industry 5 0.158 0.818 –0.667 0.375 0.263 0.854 0.123 0.936 0.409 0.809

Marital status/

single

0.337 0.766 0.639 0.753 0.967

Married/cohabit 0.626 0.235 0.418 0.468 –0.014 0.987 –0.203 0.830 0.156 0.879

Divorced/widowed 0.606 0.147 0.243 0.587 0.548 0.474 0.364 0.669 0.227 0.796

Constant –0.754 0.076 –0.730 0.233 –6.050 0.239 –7.207 0.210 –8.408 0.173

Total class’ n rate (%) 77.1 75.3 83.3 81.6 85.4

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.016 0.118 0.338 0.431 0.475

Notes: The results are obtained through a logistic regression where the dependent variable SUCCESS2 has

the value of zero if the firm has failed and one otherwise.

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Table 7: Estimates for the measurement of success in three-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.029 0.000 0.035 0.000 0.037 0.001 0.033 0.004

Ln (loan amount) 0.498 0.000 0.763 0.000 0.813 0.001 0.765 0.003

Founder attributes

Ln (1+Age) 0.916 0.269 1.595 0.088 1.873 0.051

Male 0.612 0.268 1.227 0.075 1.566 0.029

Female(s) 0.347 0.545 0.733 0.316 0.878 0.240

Both genders

Financial status 0.102 0.772 0.095 0.804 0.000 0.999

Employment status –0.426 0.226 –0.558 0.157 –0.397 0.324

No Prof. Edu 0.852 0.207 0.766 0.314 0.636 0.411

Vocational school 0.638 0.209 1.016 0.074 0.904 0.120

College/Polytechnic 0.214 0.671 0.649 0.253 0.631 0.276

University

Training 0.253 0.492 0.166 0.683 0.148 0.723

Ln (WorkExpYrs) 0.034 0.864 0.139 0.546 –0.031 0.897

Business experience 0.484 0.284 0.830 0.108 0.827 0.116

Firm attributes

Legal form –0.476 0.329 –0.640 0.202

Team –0.278 0.602 –0.210 0.705

Location 0.375 0.296 0.261 0.478

Market attributes

Demand –0.720 0.081

Competitiveness –0.703 0.085

Control

Industry1 0.322 0.186 –0.556 0.053 –1.035 0.014 –1.375 0.005 –1.277 0.011

Industry2 –0.704 0.010 –1.031 0.001 –1.364 0.003 –1.709 0.001 –1.677 0.001

Industry3 –0.517 0.282 –0.645 0.224 –0.201 0.816 –0.119 0.894 –0.096 0.916

Industry4 0.366 0.481 –0.259 0.654 –0.789 0.334 –1.586 0.094 –1.689 0.081

Industry5

Marital status/single 0.258 0.555 –0.049 0.916 –0.779 0.214 –0.812 0.239 –0.515 0.469

Married/cohabit 0.544 0.134 0.098 0.798 –0.305 0.567 –0.549 0.357 –0.530 0.380

Divorced/widowed

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.034 0.134 0.233 0.345 0.395

Model fitting info 0.101 0.000 0.003 0.000 0.000

Goodness-of-fit 0.670 0.701 0.246 0.308 0.465

Test of parallel lines 0.429 0.720 0.173 0.085 0.094

Notes: The results are obtained using an ordinal regression where the link function is logit. The dependent

variable is SUCCESS3. The lowest value defines the first success category where the success is lowest, i.e.

all failed firms. The highest category serves as a reference group including most successful firms.

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Table 8: Estimates for the measurement of success in six-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.024 0.000 0.031 0.000 0.035 0.001 0.034 0.001

Ln (loan amount) 0.375 0.002 0.512 0.008 0.523 0.017 0.431 0.052

Founder attributes

Ln (1+Age) 0.868 0.246 1.738 0.041 1.915 0.027

Male 0.796 0.109 1.680 0.006 2.077 0.001

Female(s) 0.380 0.459 1.073 0.100 1.222 0.063

Both genders

Financial status 0.051 0.871 0.151 0.660 0.051 0.882

Employment status –0.636 0.046 –0.908 0.011 –0.830 0.022

No Prof. Education 0.851 0.163 0.863 0.208 0.818 0.238

Vocational school 0.756 0.100 0.953 0.062 0.795 0.123

College/Polytechnic 0.268 0.555 0.480 0.344 0.461 0.368

University

Training 0.158 0.633 0.138 0.707 0.026 0.943

Ln (WorkExpYrs) 0.082 0.651 0.134 0.518 –0.055 0.800

Business experience 0.366 0.370 0.769 0.097 0.730 0.116

Firm attributes

Legal form –0.182 0.673 –0.306 0.485

Team –0.654 0.170 –0.717 0.146

Location 0.693 0.032 0.567 0.085

Market attributes

Demand –0.765 0.037

Competitiveness –0.525 0.144

Control

Industry1 –0.329 0.151 –0.464 0.080 –0.881 0.019 –1.244 0.004 –1.092 0.013

Industry2 –0.707 0.006 –0.958 0.000 –1.225 0.002 –1.540 0.001 –1.448 0.002

Industry3 –0.712 0.118 –0.724 0.142 –0.254 0.745 –0.100 0.900 –0.006 0.993

Industry4 0.397 0.413 –0.056 0.915 –0.487 0.509 –1.217 0.150 –1.249 0.144

Industry5

Marital status/single 0.439 0.289 0.161 0.711 –0.456 0.419 –0.434 0.482 –0.222 0.726

Married/cohabit 0.640 0.064 0.290 0.422 0.009 0.984 –0.169 0.749 –0.128 0.810

Divorced/widowed

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.039 0.106 0.212 0.322 0.369

Model fitting info 0.040 0.000 0.002 0.000 0.000

Goodness-of-fit 0.881 0.599 0.146 0.077 0.103

Test of parallel lines 0.903 0.012 0.001 0.000 0.066

Notes: The results are obtained using an ordinal regression where the link function is logit. The dependent

variable is SUCCESS6. The lowest value defines the first success category where the success is lowest, i.e.

failed firms. The highest category serves as a reference group including most successful firms.

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Table 7: Estimates for the measurement of success in three-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.029 0.000 0.035 0.000 0.037 0.001 0.033 0.004

Ln (loan amount) 0.498 0.000 0.763 0.000 0.813 0.001 0.765 0.003

Founder attributes

Ln (1+Age) 0.916 0.269 1.595 0.088 1.873 0.051

Male 0.612 0.268 1.227 0.075 1.566 0.029

Female(s) 0.347 0.545 0.733 0.316 0.878 0.240

Both genders

Financial status 0.102 0.772 0.095 0.804 0.000 0.999

Employment status –0.426 0.226 –0.558 0.157 –0.397 0.324

No Prof. Edu 0.852 0.207 0.766 0.314 0.636 0.411

Vocational school 0.638 0.209 1.016 0.074 0.904 0.120

College/Polytechnic 0.214 0.671 0.649 0.253 0.631 0.276

University

Training 0.253 0.492 0.166 0.683 0.148 0.723

Ln (WorkExpYrs) 0.034 0.864 0.139 0.546 –0.031 0.897

Business experience 0.484 0.284 0.830 0.108 0.827 0.116

Firm attributes

Legal form –0.476 0.329 –0.640 0.202

Team –0.278 0.602 –0.210 0.705

Location 0.375 0.296 0.261 0.478

Market attributes

Demand –0.720 0.081

Competitiveness –0.703 0.085

Control

Industry1 0.322 0.186 –0.556 0.053 –1.035 0.014 –1.375 0.005 –1.277 0.011

Industry2 –0.704 0.010 –1.031 0.001 –1.364 0.003 –1.709 0.001 –1.677 0.001

Industry3 –0.517 0.282 –0.645 0.224 –0.201 0.816 –0.119 0.894 –0.096 0.916

Industry4 0.366 0.481 –0.259 0.654 –0.789 0.334 –1.586 0.094 –1.689 0.081

Industry5

Marital status/single 0.258 0.555 –0.049 0.916 –0.779 0.214 –0.812 0.239 –0.515 0.469

Married/cohabit 0.544 0.134 0.098 0.798 –0.305 0.567 –0.549 0.357 –0.530 0.380

Divorced/widowed

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.034 0.134 0.233 0.345 0.395

Model fitting info 0.101 0.000 0.003 0.000 0.000

Goodness-of-fit 0.670 0.701 0.246 0.308 0.465

Test of parallel lines 0.429 0.720 0.173 0.085 0.094

Notes: The results are obtained using an ordinal regression where the link function is logit. The dependent

variable is SUCCESS3. The lowest value defines the first success category where the success is lowest, i.e.

all failed firms. The highest category serves as a reference group including most successful firms.

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Table 8: Estimates for the measurement of success in six-point scale models.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Funding attributes

Equity % 0.024 0.000 0.031 0.000 0.035 0.001 0.034 0.001

Ln (loan amount) 0.375 0.002 0.512 0.008 0.523 0.017 0.431 0.052

Founder attributes

Ln (1+Age) 0.868 0.246 1.738 0.041 1.915 0.027

Male 0.796 0.109 1.680 0.006 2.077 0.001

Female(s) 0.380 0.459 1.073 0.100 1.222 0.063

Both genders

Financial status 0.051 0.871 0.151 0.660 0.051 0.882

Employment status –0.636 0.046 –0.908 0.011 –0.830 0.022

No Prof. Education 0.851 0.163 0.863 0.208 0.818 0.238

Vocational school 0.756 0.100 0.953 0.062 0.795 0.123

College/Polytechnic 0.268 0.555 0.480 0.344 0.461 0.368

University

Training 0.158 0.633 0.138 0.707 0.026 0.943

Ln (WorkExpYrs) 0.082 0.651 0.134 0.518 –0.055 0.800

Business experience 0.366 0.370 0.769 0.097 0.730 0.116

Firm attributes

Legal form –0.182 0.673 –0.306 0.485

Team –0.654 0.170 –0.717 0.146

Location 0.693 0.032 0.567 0.085

Market attributes

Demand –0.765 0.037

Competitiveness –0.525 0.144

Control

Industry1 –0.329 0.151 –0.464 0.080 –0.881 0.019 –1.244 0.004 –1.092 0.013

Industry2 –0.707 0.006 –0.958 0.000 –1.225 0.002 –1.540 0.001 –1.448 0.002

Industry3 –0.712 0.118 –0.724 0.142 –0.254 0.745 –0.100 0.900 –0.006 0.993

Industry4 0.397 0.413 –0.056 0.915 –0.487 0.509 –1.217 0.150 –1.249 0.144

Industry5

Marital status/single 0.439 0.289 0.161 0.711 –0.456 0.419 –0.434 0.482 –0.222 0.726

Married/cohabit 0.640 0.064 0.290 0.422 0.009 0.984 –0.169 0.749 –0.128 0.810

Divorced/widowed

No. of observations 345 292 180 158 157

Pseudo R2 Nagelkerke 0.039 0.106 0.212 0.322 0.369

Model fitting info 0.040 0.000 0.002 0.000 0.000

Goodness-of-fit 0.881 0.599 0.146 0.077 0.103

Test of parallel lines 0.903 0.012 0.001 0.000 0.066

Notes: The results are obtained using an ordinal regression where the link function is logit. The dependent

variable is SUCCESS6. The lowest value defines the first success category where the success is lowest, i.e.

failed firms. The highest category serves as a reference group including most successful firms.

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It is interesting that when we add founder attributes to the model, none of themprove statistically significant. This changes after including firm attributes(Models 5 in Tables 7 and 8). The results of founder age indicate that the olderthe person is, the lower the credit risk. It may be interpreted that older foundersare more stable and motivated to start their business. They may also have betterlong-term planning ability. For the performance of a firm, increasing age isstated to have a negative effect (Kangasharju 2000; Harada 2003). Moreover,our results contradict those of Bosma et al. (2004) who found no relationshipbetween owner’s age and survival, earnings or generated employment. In regardto gender of the entrepreneur, the results show that a male has the best prob-ability of having the lowest credit risk for his business than a firm where theowner is a female. This agrees with the findings of Bosma et al. (2004), Cooper,Gimeno-Gascon, and Woo (1994) and Kangasharju (2000). In this data, the worstprobability of having the lowest credit risk exists in a business where the team ofowners represent both genders. According to many studies, the better educatedthe founder, the greater the chances of the firm surviving (e.g. Bates 1990;Bosma et al. 2004; Brüderl et al. 1992; Saridakis, Mole, and Storey 2008). Ourresults suggest that founders who graduated from vocational school have thelowest credit risk. However, this is only the case for both Model 5s. The six-pointscale model has two important additional founder attributes. The results showthat firms led by persons who were working before starting a firm (employmentstatus) have a higher likelihood of being part of a better credit risk category(Table 8). This finding is consistent with Ritsilä and Tervo (2002) and Littunenand Tohmo (2003) who studied growth. The last important founder attributeconcerns personal business experience. It is surprising but individuals who haveno prior business experience have a higher likelihood of having a lower creditrisk. This finding is in line with those of Storey (1994), who termed these people“opportunists”.

When considering firm attributes, the results indicate that geographic loca-tion has an impact on credit risk when success is measured with six-point scalemodel. To be more precise, if the firms are in the same city as the lender, theprobability of credit risk is higher. In these cases, both lender and borrowers arein big cities and, according to Strotmann (2007), firms in rural areas have ahigher chance of survival. He reported that, in bigger cities, higher costs mayhamper survival. Another possible explanation might be that the lenders thinkthat they know the local business environment and may therefore be less carefulin evaluating potential borrowers.

With respect to the attributes evaluated by the lender, the results indicatethat the market attributes are important to success. The results suggest thatfirms in high demand in the market have a higher likelihood of having a lower

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credit risk in both Model 5s (Tables 7 and 8). Similarly, firms with a goodcompetitive position seem to have a lower credit risk in the SUCCESS3 model(Table 7). It is also important in the two-point scale model (Table 6). It seemsthat a good competitive position is more relevant when observing two-point orthree-point scale performance, but when we examine more multiple-point scaleperformance, its impact becomes weak. It may be that in the six-point scalemodel, the competitiveness is explained by demand which is statistically sig-nificant. The correlation between these two variables is 0.38.

Regarding control variables, we see that firms operating in Industry 2 andIndustry 4 seem to have the highest credit risk. Industry 2 covers whole andretail trade firms, whilst Industry 4 consists of transport and communicationsfirms. The firms in the hotel and restaurant industry (3) and Industry 5 (financialbrokering, real estate, renting and business activities, or education, health andsocial work, or other community, social and personal service activities) have thehighest probability of being in the lowest credit risk categories.

The research hypotheses proposed that the funding, founder, firm andmarket attributes influence start-up success. In the logistic regression model,where success is measured with the two-point scale, funding attributes andcompetitiveness of the firm are significant for estimating success (Table 6).The hypothesis regarding founder attributes is not supported by the analysis.We can say that the founder does not embody the factors that influence the newfirm’s success. Our findings are similar to those of Storey and Wynarczyk (1996)who showed that the talent of the entrepreneur is not the unique determinant ofsurvival. Nonetheless, our findings contradict those of Unger et al. (2011) whosuggested that human capital is particularly essential for young firms. However,R² increases from model to model, being the highest in the last model whichincludes all independent variables. This supports all hypotheses H1–H4. Whenwe add founder attributes to the model, R² changes from 0.118 to 0.338 whichindicates that they improve the model.

With respect to the second hypothesis, the results of the multivariate ana-lysis indicate that the founder attributes are important to success measured bythe multiple-point scale models (Tables 7 and 8). The support of the hypothesisis based on four founder attributes: founder age, gender, employment status andbusiness experience; they are statistically important. However, two fundingattributes, one firm attribute and two market attributes are also significant inmeasuring success with the multiple-point scale models. Thus, it can be statedthat H1, H3 and H4 are also supported by the multiple-point scale models. If welook at the R² of the models, we can see that the four hypotheses are supportedby the increase of R² (Nagelkerke) when more attributes are added into themodels.

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It is interesting that when we add founder attributes to the model, none of themprove statistically significant. This changes after including firm attributes(Models 5 in Tables 7 and 8). The results of founder age indicate that the olderthe person is, the lower the credit risk. It may be interpreted that older foundersare more stable and motivated to start their business. They may also have betterlong-term planning ability. For the performance of a firm, increasing age isstated to have a negative effect (Kangasharju 2000; Harada 2003). Moreover,our results contradict those of Bosma et al. (2004) who found no relationshipbetween owner’s age and survival, earnings or generated employment. In regardto gender of the entrepreneur, the results show that a male has the best prob-ability of having the lowest credit risk for his business than a firm where theowner is a female. This agrees with the findings of Bosma et al. (2004), Cooper,Gimeno-Gascon, and Woo (1994) and Kangasharju (2000). In this data, the worstprobability of having the lowest credit risk exists in a business where the team ofowners represent both genders. According to many studies, the better educatedthe founder, the greater the chances of the firm surviving (e.g. Bates 1990;Bosma et al. 2004; Brüderl et al. 1992; Saridakis, Mole, and Storey 2008). Ourresults suggest that founders who graduated from vocational school have thelowest credit risk. However, this is only the case for both Model 5s. The six-pointscale model has two important additional founder attributes. The results showthat firms led by persons who were working before starting a firm (employmentstatus) have a higher likelihood of being part of a better credit risk category(Table 8). This finding is consistent with Ritsilä and Tervo (2002) and Littunenand Tohmo (2003) who studied growth. The last important founder attributeconcerns personal business experience. It is surprising but individuals who haveno prior business experience have a higher likelihood of having a lower creditrisk. This finding is in line with those of Storey (1994), who termed these people“opportunists”.

When considering firm attributes, the results indicate that geographic loca-tion has an impact on credit risk when success is measured with six-point scalemodel. To be more precise, if the firms are in the same city as the lender, theprobability of credit risk is higher. In these cases, both lender and borrowers arein big cities and, according to Strotmann (2007), firms in rural areas have ahigher chance of survival. He reported that, in bigger cities, higher costs mayhamper survival. Another possible explanation might be that the lenders thinkthat they know the local business environment and may therefore be less carefulin evaluating potential borrowers.

With respect to the attributes evaluated by the lender, the results indicatethat the market attributes are important to success. The results suggest thatfirms in high demand in the market have a higher likelihood of having a lower

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credit risk in both Model 5s (Tables 7 and 8). Similarly, firms with a goodcompetitive position seem to have a lower credit risk in the SUCCESS3 model(Table 7). It is also important in the two-point scale model (Table 6). It seemsthat a good competitive position is more relevant when observing two-point orthree-point scale performance, but when we examine more multiple-point scaleperformance, its impact becomes weak. It may be that in the six-point scalemodel, the competitiveness is explained by demand which is statistically sig-nificant. The correlation between these two variables is 0.38.

Regarding control variables, we see that firms operating in Industry 2 andIndustry 4 seem to have the highest credit risk. Industry 2 covers whole andretail trade firms, whilst Industry 4 consists of transport and communicationsfirms. The firms in the hotel and restaurant industry (3) and Industry 5 (financialbrokering, real estate, renting and business activities, or education, health andsocial work, or other community, social and personal service activities) have thehighest probability of being in the lowest credit risk categories.

The research hypotheses proposed that the funding, founder, firm andmarket attributes influence start-up success. In the logistic regression model,where success is measured with the two-point scale, funding attributes andcompetitiveness of the firm are significant for estimating success (Table 6).The hypothesis regarding founder attributes is not supported by the analysis.We can say that the founder does not embody the factors that influence the newfirm’s success. Our findings are similar to those of Storey and Wynarczyk (1996)who showed that the talent of the entrepreneur is not the unique determinant ofsurvival. Nonetheless, our findings contradict those of Unger et al. (2011) whosuggested that human capital is particularly essential for young firms. However,R² increases from model to model, being the highest in the last model whichincludes all independent variables. This supports all hypotheses H1–H4. Whenwe add founder attributes to the model, R² changes from 0.118 to 0.338 whichindicates that they improve the model.

With respect to the second hypothesis, the results of the multivariate ana-lysis indicate that the founder attributes are important to success measured bythe multiple-point scale models (Tables 7 and 8). The support of the hypothesisis based on four founder attributes: founder age, gender, employment status andbusiness experience; they are statistically important. However, two fundingattributes, one firm attribute and two market attributes are also significant inmeasuring success with the multiple-point scale models. Thus, it can be statedthat H1, H3 and H4 are also supported by the multiple-point scale models. If welook at the R² of the models, we can see that the four hypotheses are supportedby the increase of R² (Nagelkerke) when more attributes are added into themodels.

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When we consider the results of the test of parallel lines, we can see that thecomparison between the ordinal regression models has one weakness. Theparallel lines/proportional odds assumption does not hold for all of them. Inthe three-point scale success models, all five models do not violate the propor-tional odds assumption (at the 5% confidence level). In the six-point scalemodels, the assumption holds only for the first and last models. On the otherhand, for all models (except the first model for SUCCESS3), the model fit issignificant which indicates a good fit. In other words, there is no significantdifference between the model and the data.

Moreover, the results reveal that there are more differences than similaritiesbetween the multivariate models. The models show three similarities betweenthe multiple-point scale and two-point scale versions: an increase in equityinvestments by the owners and an increase in loan amount increases the like-lihood of success and decreases the credit risk of the firms. In addition to, thelikelihood of success increases if the firm has a good competitive position. Thereare three additional attributes in the multiple-point scale model compared to thetwo-point scale model. Age of the founder, gender and demand of the productsor services are only statistically significant in the multiple-point scale model.Furthermore, the six-point scale model requires more attributes than the three-point scale model (employment status, business experience and location).

In addition, we examined if we can find a trend if we have models where thecategories of dependent variable increases by one from model to model.Therefore, we also analyzed the SUCCESS4 and SUCCESS5 models, whichincluded all the independent variables (not reported). The results are similarto those from the SUCCESS3 and SUCCESS6 models. The only exception con-cerns education. Founders who have graduated from vocational school have thebest probability of having the lowest credit risk in the SUCCESS4 and SUCCESS5models. The number of statistically significant variables increases by one fromthe SUCCESS2 model to the SUCCESS6 model. However, R² is the best for theSUCCESS2 model (0.475) and ranges from 0.355 to 0.395 for the other models.This may imply that we need a larger database to validate the results. Naturally,we would need more data if we were to examine more sophisticated classifica-tions. Moreover, the assumption of parallel lines does not hold for the SUCCESS4and SUCCESS5 models.

As a conclusion, we can say that modeling multiple-point scale performanceneeds more indicators overall to indicate the different scales. This is consistentwith the findings of Cooper, Gimeno-Gascon, and Woo (1994) and Dahlqvist,Davidsson, and Wiklund (2000), who studied human and financial capitalvariables. In the loan application phase, it is good to know which factorspromote the likelihood of survival, especially which factors promote low credit

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risk. A study based mainly on financial ratios and only two qualitative variables,by Psillaki, Tsolas, and Margaritis (2010), found that there is no variationbetween models with differing number of scales. The difference in the resultscould be interpreted as meaning that so-called soft variables are more sensitiveto analysis in differing numbers of scales with financial ratios being unable todivide the firms into multiple categories.

5 Conclusions

This research investigates the success of start-up firms using Finnish data. Thedata have been collected at a very early stage in the life of the firms. Therefore,we employ attributes that investigate the type of funding used, background ofthe entrepreneur and other non-accounting characteristics of the firm. Our maincontribution to the literature on start-up success is that we are able to investi-gate the determinants of multiple-point scale of success in very small firms.

As far as the results are concerned, we find that the founder attributes are,to some extent, important in univariate analyses. The same applies for the modelthat measured multiple-point scale of success that was derived using multi-variate analyses. In the two-point scale model, all founder attributes are insig-nificant. Typical measures of human capital, like work experience, are not veryimportant. Education is important only in models measuring success with four-point or five-point scales. This contradicts studies such as Unger et al. (2011),who suggested that human capital is particularly essential for young firms. Oursecond hypothesis, which argued that founder attributes influence success, isonly partly supported by the analyses. They are most important in the six-pointscale model. A specific feature in our sample is that we could investigate thecontribution of firms founded by both genders. Generally, the studies concen-trate only on a comparison between females and males. Firms founded by malesseem to be less risky. The least successful firm was established by both genders.It may be that males and females have such different approaches to business, forexample their attitude to risk, that they have contradictory ideas. The fundingattributes, along with firm and market attributes are statistically significantwhen predicting success. This supports H1, H3 and H4. Funding attributes andmarket attributes explain success particularly well. It seems that sufficientfinance when starting up a new firm is the most important factor. However, tobe able to reach a very low credit risk also requires specific founder attributes.

When considering the results of comparing two-point and multiple-pointscale success models, we observe that the contribution of attributes varies

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When we consider the results of the test of parallel lines, we can see that thecomparison between the ordinal regression models has one weakness. Theparallel lines/proportional odds assumption does not hold for all of them. Inthe three-point scale success models, all five models do not violate the propor-tional odds assumption (at the 5% confidence level). In the six-point scalemodels, the assumption holds only for the first and last models. On the otherhand, for all models (except the first model for SUCCESS3), the model fit issignificant which indicates a good fit. In other words, there is no significantdifference between the model and the data.

Moreover, the results reveal that there are more differences than similaritiesbetween the multivariate models. The models show three similarities betweenthe multiple-point scale and two-point scale versions: an increase in equityinvestments by the owners and an increase in loan amount increases the like-lihood of success and decreases the credit risk of the firms. In addition to, thelikelihood of success increases if the firm has a good competitive position. Thereare three additional attributes in the multiple-point scale model compared to thetwo-point scale model. Age of the founder, gender and demand of the productsor services are only statistically significant in the multiple-point scale model.Furthermore, the six-point scale model requires more attributes than the three-point scale model (employment status, business experience and location).

In addition, we examined if we can find a trend if we have models where thecategories of dependent variable increases by one from model to model.Therefore, we also analyzed the SUCCESS4 and SUCCESS5 models, whichincluded all the independent variables (not reported). The results are similarto those from the SUCCESS3 and SUCCESS6 models. The only exception con-cerns education. Founders who have graduated from vocational school have thebest probability of having the lowest credit risk in the SUCCESS4 and SUCCESS5models. The number of statistically significant variables increases by one fromthe SUCCESS2 model to the SUCCESS6 model. However, R² is the best for theSUCCESS2 model (0.475) and ranges from 0.355 to 0.395 for the other models.This may imply that we need a larger database to validate the results. Naturally,we would need more data if we were to examine more sophisticated classifica-tions. Moreover, the assumption of parallel lines does not hold for the SUCCESS4and SUCCESS5 models.

As a conclusion, we can say that modeling multiple-point scale performanceneeds more indicators overall to indicate the different scales. This is consistentwith the findings of Cooper, Gimeno-Gascon, and Woo (1994) and Dahlqvist,Davidsson, and Wiklund (2000), who studied human and financial capitalvariables. In the loan application phase, it is good to know which factorspromote the likelihood of survival, especially which factors promote low credit

474 Marika Rosanna Miettinen and Hannu Littunen

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risk. A study based mainly on financial ratios and only two qualitative variables,by Psillaki, Tsolas, and Margaritis (2010), found that there is no variationbetween models with differing number of scales. The difference in the resultscould be interpreted as meaning that so-called soft variables are more sensitiveto analysis in differing numbers of scales with financial ratios being unable todivide the firms into multiple categories.

5 Conclusions

This research investigates the success of start-up firms using Finnish data. Thedata have been collected at a very early stage in the life of the firms. Therefore,we employ attributes that investigate the type of funding used, background ofthe entrepreneur and other non-accounting characteristics of the firm. Our maincontribution to the literature on start-up success is that we are able to investi-gate the determinants of multiple-point scale of success in very small firms.

As far as the results are concerned, we find that the founder attributes are,to some extent, important in univariate analyses. The same applies for the modelthat measured multiple-point scale of success that was derived using multi-variate analyses. In the two-point scale model, all founder attributes are insig-nificant. Typical measures of human capital, like work experience, are not veryimportant. Education is important only in models measuring success with four-point or five-point scales. This contradicts studies such as Unger et al. (2011),who suggested that human capital is particularly essential for young firms. Oursecond hypothesis, which argued that founder attributes influence success, isonly partly supported by the analyses. They are most important in the six-pointscale model. A specific feature in our sample is that we could investigate thecontribution of firms founded by both genders. Generally, the studies concen-trate only on a comparison between females and males. Firms founded by malesseem to be less risky. The least successful firm was established by both genders.It may be that males and females have such different approaches to business, forexample their attitude to risk, that they have contradictory ideas. The fundingattributes, along with firm and market attributes are statistically significantwhen predicting success. This supports H1, H3 and H4. Funding attributes andmarket attributes explain success particularly well. It seems that sufficientfinance when starting up a new firm is the most important factor. However, tobe able to reach a very low credit risk also requires specific founder attributes.

When considering the results of comparing two-point and multiple-pointscale success models, we observe that the contribution of attributes varies

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between models. The founder’s age, gender, employment status, education,business experience, geographic location and demand are statistically signifi-cant only in the multiple-point scale models. There is a clear trend when wecompare the different models. When the dependent variable is divided into morecategories, it seems to require more statistically significant variables.Furthermore, the specific variables impacting on success may change, depend-ing on the number of scales of the dependent variable. For example, the geo-graphic location is only important in the model where success is divided intosix-point scales. Correspondingly, competitiveness of the firm is important for allother models except the SUCCESS6 model. As a conclusion, we can say thatmodeling multiple-point scales of success requires more indicators overall todifferentiate between the different scales. This is consistent with the findings ofCooper, Gimeno-Gascon, and Woo (1994) and Dahlqvist, Davidsson, andWiklund (2000). Validation of the results needs a larger database. Our studyonly confirms that there is difference when examining the firms under investiga-tion with two-point or multiple-point scales of success.

As is the case with all research, there are some issues that have to be takeninto account when considering the reliability, significance and generalizabilityof the results obtained. First, entrepreneurs and the processes they use instarting their firms will vary due to their background and line of business. Ininterpreting the results of this study, we should bear in mind that the influenceof other variables could produce different models. For example, the influence ofa founder’s motivation remains unstudied. Furthermore, the owner’s personalsituation can be critical: for example, illness can immediately affect the busi-ness. Caution must be exercised when generalizing the results. Even though thisstudy included all industries except traditional farming, bigger samples couldyield more conclusive findings. Second, the idiosyncratic features of the regionwithout doubt play a role in the nature of the findings. Therefore, we cannotexpect that the results would have been identical if the study had been con-ducted in a country or in a region with significantly different characteristics. Inparticular, the firms located in peripheral and rural areas are often forced to relyon generally available information sources, due to the lack of relevant localnetwork partners and the inadequacies of public support instruments aimed atstart-ups (see North and Smallbone 2000).

Our analysis and the results obtained may guide financial institutions,entrepreneurs, academics and policy makers in their evaluation of small start-ups. It is important for all those involved in business start-ups to prevent losses.With this study, we seek to contribute to the lending process of start-up firmsand the ongoing debate in the literature. There are many small firms and hencesmall firms’ exposure is a relatively high share of bank loan portfolios. If the

476 Marika Rosanna Miettinen and Hannu Littunen

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lenders’ ability to foresee the attributes that predetermine small business suc-cess improves, it may help them finance more small firms at the start-up stage.

Credit risk is the single most important risk for a large number of financialinstitutions. However, research on credit risk management for small firms isrelatively rare. Future investigations should include the growth aspect in amodel with multiple-point scale of success, for example, how the effects of tooslow or too fast a growth rate are related to credit risk. The link betweenincreasing the number of employees or turnover and a firm’s success (creditrisk) is not as straightforward as it seems. Davidsson, Steffens, and Fitzsimmons(2009) pointed out that high profitability firms are likely to achieve sustainablegrowth and retain their high profitability at the same time. This study hasidentified the important factors which influence financial performance, herealso identified as credit risk. It is clear that the performance of start-up firmspresents challenges for future research that seeks to improve the informationand develop adequate risk models for these firms, in order to reduce moralhazard and adverse selection problems.

References

Almus, M. 2002. “What Characterizes a Fast-Growing Firm?” Applied Economics 34(12):1497–508.Ananth, C. V., and D. G. Kleinbaum. 1997. “Regression Models for Ordinal Responses: A Review

of Methods and Applications.” International Journal of Epidemiology 26(6):1323–33.Armstrong, B. G., and M. Sloan. 1989. “Ordinal Regression Models for Epidemiologic Data.”

American Journal of Epidemiology 129:191–204.Åstebro, T., and I. Bernhardt. 2003. “Start-Up Financing, Owner Characteristics, and Survival.”

Journal of Economics and Business 55(4):303–19.Avery, R. B., P. S. Calem, and G. B. Canner. 2004. “Consumer credit scoring: Do situational

circumstances matter?” Journal of Banking and Finance 28:835–56.Barringer, B. R., and F. F. Jones. 2004. “Achieving Rapid Growth: Revisiting the Managerial

Capacity Problem.” Journal of Developmental Entrepreneurship 9(1):73–86.Bates, T. 1990. “Entrepreneur Human Capital Inputs and Small Business Longevity.” The Review

of Economics and Statistics 72(4):551–9.Bates, T. 1995. “A Comparison of Franchise and Independent Small Business Survival Rates.”

Small Business Economics 7(5):377–88.Becchetti, L., and G. Trovato. 2002. “The Determinants of Growth for Small and Medium

Sized Firms. The Role of the Availability of External Finance.” Small Business Economics19(4):291–306.

Benzing, C., H. M. Chu, and O. Kara. 2009. “Entrepreneurs in Turkey: A Factor Analysis ofMotivations, Success Factors, and Problems.” Journal of Small Business Management47(1):58–91.

Blumberg, B. F., and W. A. Letterie. 2008. “Business Starters and Credit Rationing.” SmallBusiness Economics 30(2):187–200.

Start-Up Firms Using Two-Point or Multiple-Point Scale Models 477

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between models. The founder’s age, gender, employment status, education,business experience, geographic location and demand are statistically signifi-cant only in the multiple-point scale models. There is a clear trend when wecompare the different models. When the dependent variable is divided into morecategories, it seems to require more statistically significant variables.Furthermore, the specific variables impacting on success may change, depend-ing on the number of scales of the dependent variable. For example, the geo-graphic location is only important in the model where success is divided intosix-point scales. Correspondingly, competitiveness of the firm is important for allother models except the SUCCESS6 model. As a conclusion, we can say thatmodeling multiple-point scales of success requires more indicators overall todifferentiate between the different scales. This is consistent with the findings ofCooper, Gimeno-Gascon, and Woo (1994) and Dahlqvist, Davidsson, andWiklund (2000). Validation of the results needs a larger database. Our studyonly confirms that there is difference when examining the firms under investiga-tion with two-point or multiple-point scales of success.

As is the case with all research, there are some issues that have to be takeninto account when considering the reliability, significance and generalizabilityof the results obtained. First, entrepreneurs and the processes they use instarting their firms will vary due to their background and line of business. Ininterpreting the results of this study, we should bear in mind that the influenceof other variables could produce different models. For example, the influence ofa founder’s motivation remains unstudied. Furthermore, the owner’s personalsituation can be critical: for example, illness can immediately affect the busi-ness. Caution must be exercised when generalizing the results. Even though thisstudy included all industries except traditional farming, bigger samples couldyield more conclusive findings. Second, the idiosyncratic features of the regionwithout doubt play a role in the nature of the findings. Therefore, we cannotexpect that the results would have been identical if the study had been con-ducted in a country or in a region with significantly different characteristics. Inparticular, the firms located in peripheral and rural areas are often forced to relyon generally available information sources, due to the lack of relevant localnetwork partners and the inadequacies of public support instruments aimed atstart-ups (see North and Smallbone 2000).

Our analysis and the results obtained may guide financial institutions,entrepreneurs, academics and policy makers in their evaluation of small start-ups. It is important for all those involved in business start-ups to prevent losses.With this study, we seek to contribute to the lending process of start-up firmsand the ongoing debate in the literature. There are many small firms and hencesmall firms’ exposure is a relatively high share of bank loan portfolios. If the

476 Marika Rosanna Miettinen and Hannu Littunen

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Download Date | 11/6/13 7:00 PM

lenders’ ability to foresee the attributes that predetermine small business suc-cess improves, it may help them finance more small firms at the start-up stage.

Credit risk is the single most important risk for a large number of financialinstitutions. However, research on credit risk management for small firms isrelatively rare. Future investigations should include the growth aspect in amodel with multiple-point scale of success, for example, how the effects of tooslow or too fast a growth rate are related to credit risk. The link betweenincreasing the number of employees or turnover and a firm’s success (creditrisk) is not as straightforward as it seems. Davidsson, Steffens, and Fitzsimmons(2009) pointed out that high profitability firms are likely to achieve sustainablegrowth and retain their high profitability at the same time. This study hasidentified the important factors which influence financial performance, herealso identified as credit risk. It is clear that the performance of start-up firmspresents challenges for future research that seeks to improve the informationand develop adequate risk models for these firms, in order to reduce moralhazard and adverse selection problems.

References

Almus, M. 2002. “What Characterizes a Fast-Growing Firm?” Applied Economics 34(12):1497–508.Ananth, C. V., and D. G. Kleinbaum. 1997. “Regression Models for Ordinal Responses: A Review

of Methods and Applications.” International Journal of Epidemiology 26(6):1323–33.Armstrong, B. G., and M. Sloan. 1989. “Ordinal Regression Models for Epidemiologic Data.”

American Journal of Epidemiology 129:191–204.Åstebro, T., and I. Bernhardt. 2003. “Start-Up Financing, Owner Characteristics, and Survival.”

Journal of Economics and Business 55(4):303–19.Avery, R. B., P. S. Calem, and G. B. Canner. 2004. “Consumer credit scoring: Do situational

circumstances matter?” Journal of Banking and Finance 28:835–56.Barringer, B. R., and F. F. Jones. 2004. “Achieving Rapid Growth: Revisiting the Managerial

Capacity Problem.” Journal of Developmental Entrepreneurship 9(1):73–86.Bates, T. 1990. “Entrepreneur Human Capital Inputs and Small Business Longevity.” The Review

of Economics and Statistics 72(4):551–9.Bates, T. 1995. “A Comparison of Franchise and Independent Small Business Survival Rates.”

Small Business Economics 7(5):377–88.Becchetti, L., and G. Trovato. 2002. “The Determinants of Growth for Small and Medium

Sized Firms. The Role of the Availability of External Finance.” Small Business Economics19(4):291–306.

Benzing, C., H. M. Chu, and O. Kara. 2009. “Entrepreneurs in Turkey: A Factor Analysis ofMotivations, Success Factors, and Problems.” Journal of Small Business Management47(1):58–91.

Blumberg, B. F., and W. A. Letterie. 2008. “Business Starters and Credit Rationing.” SmallBusiness Economics 30(2):187–200.

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Lussier, R. N., and S. Pfeifer. 2001. “A Crossnational Prediction Model for Business Success.”Journal of Small Business Management 39(3):228–39.

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Start-Up Firms Using Two-Point or Multiple-Point Scale Models 479

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Lussier, R. N., and C. E. Halabi. 2010. “A Three-Country Comparison of the Business Successversus Failure Prediction Model.” Journal of Small Business Management 48(3):360–77.

Lussier, R. N., and S. Pfeifer. 2001. “A Crossnational Prediction Model for Business Success.”Journal of Small Business Management 39(3):228–39.

McCullagh, P. 1980. “Regression Models for Ordinal Data.” Journal of the Royal StatisticalSociety, Series B, (Methodological) 42(2):109–42.

Mosey, S., and M. Wright. 2007. “From Human Capital to Social Capital: A Longitudinal Study ofTechnology-Based Academic Entrepreneurs.” Entrepreneurship Theory and Practice31(6):909–35.

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Start-Up Firms Using Two-Point or Multiple-Point Scale Models 479

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Newbert, S. L. 2005. “New Firm Formation: A Dynamic Capability Perspective.” Journal of SmallBusiness Management 43(1):55–77.

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van Praag, M. 2003. “Business Survival and Success of Young Small Business Owners.” SmallBusiness Economics 21(1):1–17.

Psillaki, M., I. Tsolas, and D. Margaritis. 2010. “valuation of Credit Risk Based on FirmPerformance.” European Journal of Operational Research 201(3):873–81.

Ritsilä, J., and H. Tervo. 2002. “Effects of Unemployment on New Firm Formation: Micro LevelPanel Data Evidence from Finland.” Small Business Economics 19(1):31–40.

Reuber, R., and E. Fischer. 1994. “Entrepreneur’s Experience, Expertise, and the Performance ofTechnology Based Firms.” IEEE Transactions on Engineering Management 41(4):365–74.

Reuber, A. R., and E. Fischer. 1999. “Understanding the Consequences of Founders’Experience.” Journal of Small Business Management 37(2):30–45.

Saridakis, G., K. Mole, and D. J. Storey. 2008. “New Small Firm Survival in England.” Empirica35(1):25–39.

Schutjens, V. A. J. M., and E. Wever. 2000. “Determinants of New Firm Success?” Papers inRegional Science 79(2):135–59. Doi:10.1111/j.1435-5597.2000.tb00765.x.

Shepherd, D., and J. Wiklund. 2009. “Are We Comparing Apples with Apples or Apples withOranges? Appropriateness of Knowledge Accumulation across Growth Studies.”Entrepreneurship: Theory & Practice 33:105–23.

Shrader, R., and D. Siegel. 2007. “Assessing the Relationship between Human Capital and FirmPerformance: Evidence from Technology-Based New Ventures.” Entrepreneurship Theoryand Practice 31(6):893–908.

Smallbone, D., R. Leigh, and D. North. 1995. “The Characteristics and Strategies of High GrowthSMEs.” International Journal of Entrepreneurial Behaviour & Research 3(1):44–62.

Soriano, D. R., and G. J. Castrogiovanni. 2012. “The Impact of Education, Experience and InnerCircle Advisors on SME Performance: Insights from a Study of Public DevelopmentCenters.” Small Business Economics 38(3):333–49. Doi:10.1007/s1187-010-9278-3.

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Storey, D. 2011. “Optimism and Chance: The Elephants in the Entrepreneurship Room.”International Small Business Journal 29(4):303–21.

Storey, D. J. 1994. Understanding the Small Business Sector. New York: Routledge.Storey, D. J., and P. Wynarczyk. 1996. “The Survival and Non Survival of Micro Firms in the UK.”

Review of Industrial Organization 11(2):211–29.Strotmann, H. 2007. “Entrepreneurial Survival.” Small Business Economics 28(1):87–104.

480 Marika Rosanna Miettinen and Hannu Littunen

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Treacy, W. F., and M. Carey. 2000. “Credit Risk Rating Systems at Large US Banks.” Journal ofBanking and Finance 24(1–2):167–201.

Unger, J. M., A. Rauch, M. Frese, and N. Rosenbusch. 2011. “Human Capital and EntrepreneurialSuccess: A Meta-Analytical Review.” Journal of Business Venturing 26(3):341–58.

Van der Sluis, J., C. M. Van Praag, and W. Vijverberg. 2005. “Entrepreneurship, Selection andPerformance: A Meta-Analysis of the Role of Education.” World Bank Economic Review 19(2):225–61.

Watson, K., S. Hogarth-Scott, and N. Wilson. 1998. “Small Business Start-Ups: Success Factorsand Support Implications.” International Journal of Entrepreneurial Behaviour & Research4(3):217–38.

West, G. P., and T. W. Noel. 2009. “The Impact of Knowledge Resources on New VenturePerformance.” Journal of Small Business Management 47(1):1–22.

Wiklund, J., T. Baker, and D. Shepherd. 2010. “The Age-Effect of Financial Indicators as BuffersAgainst the Liability of Newness.” Journal of Business Venturing 25(4):423–37.

Yusuf, A. 1995. “Critical Success Factors for Small Business: Perceptions of South PacificEntrepreneurs.” Journal of Small Business Management 33(2):68–73.

Zolin, R., A. Kuckertz, and T. Kautonen. 2011. “Human Resource Flexibility and Strong Ties inEntrepreneurial teams.” Journal of Business Research 64(10):1097–103.

Start-Up Firms Using Two-Point or Multiple-Point Scale Models 481

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Newbert, S. L. 2005. “New Firm Formation: A Dynamic Capability Perspective.” Journal of SmallBusiness Management 43(1):55–77.

North, D., and D. Smallbone. 2000. “Innovative Activity in SMEs and Rural EconomicDevelopment: Some Evidence from England.” European Planning Studies 8(1):87–106.

Nunes, P. M., and Z. Serrasqueiro. 2012. “Are Young SMEs’ Survival Determinants Different?Empirical Evidence Using Panel Data.” Applied Economics Letters 19(9):849–55.

Parker, S. 2004. “The Economics of Self-Employment and Entrepreneurship.” Cambridge:Cambridge university press.

Peña, I. 2002. “Intellectual Capital and Business Start-Up Success.” Journal of IntellectualCapital 3(2):180–98.

Pereira, J., and M. St. Aubyn. 2009. “What Level of Education Matters Most for Growth?:Evidence from Portugal.” Economics of Education Review 28(1):67–73.

Porter, M. E. 1985. Competitive Advantage: Creating and Sustaining Superior Performance. NewYork: The Free Press.

van Praag, M. 2003. “Business Survival and Success of Young Small Business Owners.” SmallBusiness Economics 21(1):1–17.

Psillaki, M., I. Tsolas, and D. Margaritis. 2010. “valuation of Credit Risk Based on FirmPerformance.” European Journal of Operational Research 201(3):873–81.

Ritsilä, J., and H. Tervo. 2002. “Effects of Unemployment on New Firm Formation: Micro LevelPanel Data Evidence from Finland.” Small Business Economics 19(1):31–40.

Reuber, R., and E. Fischer. 1994. “Entrepreneur’s Experience, Expertise, and the Performance ofTechnology Based Firms.” IEEE Transactions on Engineering Management 41(4):365–74.

Reuber, A. R., and E. Fischer. 1999. “Understanding the Consequences of Founders’Experience.” Journal of Small Business Management 37(2):30–45.

Saridakis, G., K. Mole, and D. J. Storey. 2008. “New Small Firm Survival in England.” Empirica35(1):25–39.

Schutjens, V. A. J. M., and E. Wever. 2000. “Determinants of New Firm Success?” Papers inRegional Science 79(2):135–59. Doi:10.1111/j.1435-5597.2000.tb00765.x.

Shepherd, D., and J. Wiklund. 2009. “Are We Comparing Apples with Apples or Apples withOranges? Appropriateness of Knowledge Accumulation across Growth Studies.”Entrepreneurship: Theory & Practice 33:105–23.

Shrader, R., and D. Siegel. 2007. “Assessing the Relationship between Human Capital and FirmPerformance: Evidence from Technology-Based New Ventures.” Entrepreneurship Theoryand Practice 31(6):893–908.

Smallbone, D., R. Leigh, and D. North. 1995. “The Characteristics and Strategies of High GrowthSMEs.” International Journal of Entrepreneurial Behaviour & Research 3(1):44–62.

Soriano, D. R., and G. J. Castrogiovanni. 2012. “The Impact of Education, Experience and InnerCircle Advisors on SME Performance: Insights from a Study of Public DevelopmentCenters.” Small Business Economics 38(3):333–49. Doi:10.1007/s1187-010-9278-3.

Stinchcombe, A. L. 1965. “Social Structure and Organizations.” In Handbook of Organizations,edited by J. G. March, 142–193. Chicago: Rand McNally.

Storey, D. 2011. “Optimism and Chance: The Elephants in the Entrepreneurship Room.”International Small Business Journal 29(4):303–21.

Storey, D. J. 1994. Understanding the Small Business Sector. New York: Routledge.Storey, D. J., and P. Wynarczyk. 1996. “The Survival and Non Survival of Micro Firms in the UK.”

Review of Industrial Organization 11(2):211–29.Strotmann, H. 2007. “Entrepreneurial Survival.” Small Business Economics 28(1):87–104.

480 Marika Rosanna Miettinen and Hannu Littunen

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Treacy, W. F., and M. Carey. 2000. “Credit Risk Rating Systems at Large US Banks.” Journal ofBanking and Finance 24(1–2):167–201.

Unger, J. M., A. Rauch, M. Frese, and N. Rosenbusch. 2011. “Human Capital and EntrepreneurialSuccess: A Meta-Analytical Review.” Journal of Business Venturing 26(3):341–58.

Van der Sluis, J., C. M. Van Praag, and W. Vijverberg. 2005. “Entrepreneurship, Selection andPerformance: A Meta-Analysis of the Role of Education.” World Bank Economic Review 19(2):225–61.

Watson, K., S. Hogarth-Scott, and N. Wilson. 1998. “Small Business Start-Ups: Success Factorsand Support Implications.” International Journal of Entrepreneurial Behaviour & Research4(3):217–38.

West, G. P., and T. W. Noel. 2009. “The Impact of Knowledge Resources on New VenturePerformance.” Journal of Small Business Management 47(1):1–22.

Wiklund, J., T. Baker, and D. Shepherd. 2010. “The Age-Effect of Financial Indicators as BuffersAgainst the Liability of Newness.” Journal of Business Venturing 25(4):423–37.

Yusuf, A. 1995. “Critical Success Factors for Small Business: Perceptions of South PacificEntrepreneurs.” Journal of Small Business Management 33(2):68–73.

Zolin, R., A. Kuckertz, and T. Kautonen. 2011. “Human Resource Flexibility and Strong Ties inEntrepreneurial teams.” Journal of Business Research 64(10):1097–103.

Start-Up Firms Using Two-Point or Multiple-Point Scale Models 481

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Article II

Miettinen, Marika & Virtanen, Markku (2013): Capital structure of start-ups: Evidence on non-accounting characteristics. Journal of Modern Accounting and Auditing, Vol. 9, No. 7, 889-907. ISSN 1548-6583.

Reprinted with kind permission by David Publishing Ltd.

Capital Structure of Start-ups: Evidence on Non-Accounting

Characteristics

Research is scarce on non-financial characteristics, including owner characteristics and their influence on capital structure in the

establishment phase. The objective of this study is to examine the factors affecting firms’ availability of external finance, utilizing a

sample of Finnish start-up micro firms. The overall purpose is not to produce a comprehensive model for capital structure, but to

illustrate the importance of non-accounting characteristics in explaining the attractiveness of the new venture from the lender’s point

of view. The results show that a founder who has prior work experience from the same sector and has little or no personal financial

property seems to have higher leverage. After splitting the data into survived and failed firms, lenders viewed work experience

positively only in the sample of failed firms. In addition, limited liability companies, firms located in bigger cities, and firms with good

demand evaluated by the lender have a lower level of leverage.

Keywords: capital structure, financing, start-ups, micro firms

1. Introduction

Small businesses are the backbone of the economy of every nation. They can be considered the seeds

for future development. Thus, public policy encourages business start-ups by offering grants and subsidies.

The role of finance is a critical element in the performance and development of small enterprises. Literature

on capital structure and its connection to the performance of companies is extensive. However, prior studies

have mainly examined the later stages of financing of large corporations and used traditional theories of

corporate finance. This paper aims to contribute to the understanding of the capital structure of small start-ups

by analyzing non-accounting attributes at the time of establishment of new ventures. Few studies (Cassar,

2004; Chandler & Hanks, 1998; Scherr, Sugrue, & Ward, 1993) have analyzed the start-up stage of financing

and non-accounting characteristics, and their results are inconclusive.

This study has three contributions. First, it examines firms in Finland, which has a bank-centered

economy. The majority of previous studies are from an Anglo-American context, where firm financing is

strongly market-centered. There is evidence that the determinants of capital structure are influenced by the

financial system of the economy (Antoniou, Guney, & Paudyal, 2008). Second, in addition to owner- and firm-

related variables, this study includes credit analysts’ evaluation of the firms’ business prospects and their

market and competitive position. Third, most previous studies on small and medium enterprises (SMEs) have

not distinguished between micro, small, and medium enterprises. However, it has been noted that determinants

of capital structure are different for micro firms than for small, medium, and large firms (Atherton, 2012;

Ramalho & da Silva, 2009).

It has been stated that general financial theory does not apply to new ventures (Atherton, 2009; Atherton,

2012) or to all small firms (Berger & Udell, 1998). The phase of the firm’s life cycle determines the available

financial sources (Berger & Udell, 1998; La Rocca, La Rocca, & Cariola, 2011). Firms at the early phase do

not have generated cash flows. Therefore, they have to finance their operations with equity or debt. La Rocca

et al. (2011) proposed that new firms have more debt, and the level of debt is then adjusted to the operating

time. For firms that are unable to enter the external debt-equity market, bootstrapping is a common way to

extend their financial resource base (Winborg & Landström, 2001).

New ventures have some unique features from a supply-side perspective. Lending to an SME is

generally riskier than lending to a large company (Altman & Sabato, 2007; DeYoung, Glennon, & Nigro,

2008; Dietsch & Petey, 2004; Saurina & Trucharte, 2004). The most important reason for this is the

information asymmetry in SMEs; in other words, they are often informationally opaque (e.g., Berger & Frame,

2007; Nofsinger & Wang, 2011; Stiglitz & Weiss, 1981). Because of information asymmetry and moral hazard

problems, it is difficult for entrepreneurs to obtain external finance (Denis, 2004). Nonetheless, at the start-up

phase, there is a strong need for external financing even though it can be costly (Berger & Udell, 1998).

Undercapitalization can be one reason for early failure (Yilmazer & Schrank, 2010). Failure threats are

common, especially in the first four years after establishment (Headd, 2003). Small firms are also more

sensitive to economic change; for example, an economic downturn is more likely to have serious consequences

for small firms (Diamond, 1984). To reduce information asymmetry and moral hazard problems in a low-

information environment, lenders evaluate the characteristics of the entrepreneur and firm by using business

plans and other documents (e.g., feasibility studies of business models) of the company as the basis of their

decisions. Since business capitalization is one factor influencing future performance, it is important to examine

it within different contexts, such as within micro start-ups.

This paper investigates whether the non-accounting attributes influence the external funding of

genuinely new micro firms. For this purpose, we used a sample composed of 399 Finnish micro firms in

different industries. The sample included 90 firms that have failed within the first five years of their existence.

The dataset tracked firms from their loan application phase. This enabled us to study financing decisions at the

early establishment stage of a firm. Since this is an early stage, the cash flows generated through sales cannot

be examined. The firms in the dataset rely heavily on external financial sources. We distinguished three sources

of financing: the equity provided by the founder (which rarely includes informal finance obtained from

relatives), loans from a state-owned financial institution, and loans from a commercial bank. In this paper, the

focus is on long-term leverage. Leverage measures the share of debt used and is related to the ability of the

firm to repay their lenders (Foster, 1978, p. 31).

We find that the owner characteristics are, to some extent, important in explaining the capital structure

of micro start-ups. A firm with a founder who has less personal property and who has work experience from

the same sector seems to have higher leverage. After splitting the data into survived and failed firms, lenders

viewed work experience positively only in the sample of failed firms. In addition, if the firm is located in a

bigger city and if the firm is organized as a limited liability company, the leverage level seems to be lower.

One of the variables evaluated by the lender also had a statistically significant coefficient; in particular, firms

with good demand have a lower level of leverage.

The remainder of this paper is structured as follows: Section 2 discusses previous literature that is

relevant to this study. Section 3 details the data and variables used for the study. Section 4 presents the

empirical findings, and Section 5 concludes the paper.

2. Literature Review

Theories and models of capital structure usually start from the Modigliani and Miller theorem. It states

that the market value of a firm in a certain risk class is independent of its capital structure (debt-equity ratio)

(Modigliani & Miller, 1958). Empirical observations of the importance of capital structure have been attributed

to the imperfections of the capital market. These imperfections include the existence of taxes and bankruptcy

costs. Different forms of income and capital are taxed differently. The Modigliani and Miller theory was

extended by Miller (1963; 1988) to a more general approach including the effects of taxes and bankruptcy

costs. Even if the basic Modigliani and Miller (1958) theorem is labeled as a capital structure irrelevance

theorem, they did not deny the importance of capital structure; rather, they pointed out that under certain

conditions, asymmetric information and the size of the firm should be taken into account in evaluating the

financial decisions (Modigliani & Miller, 1958, footnote 52). They noticed the different status of small and

new enterprises in capital markets and suggested that some kind of equity gap could exist. Modigliani and

Miller (1958) concluded that the entrepreneur’s unwillingness to share the business (i.e., control aversion)

would be a reason for a shortage of capital. However, it should be noted that the structure of the financial

market and the lack of supply of capital could also cause the insufficiency of capital. In addition, industrial

differences influence the demand for financial capital (Mizruchi & Stearns, 1994). On the other hand,

Modigliani and Miller’s argument supports some of the crucial elements of entrepreneurship, namely the need

for independence and the risk-taking propensity of the entrepreneur (Bhaird & Lucey, 2010; Virtanen, 1996).

One of the assumptions in Modigliani and Miller’s approach (1958) is that lenders and investors have

access to the same firm-specific information. However, the information of the firm is often incomplete,

inaccurate, or simply unavailable. The existence of asymmetric information raises problems such as the owners

exaggerating the value of the firm or managers taking some return-decreasing actions (Stiglitz, 1988, pp. 123–

124). Generally, the market is hardly ever perfect and efficient for unlisted companies to which the start-up

firms belong. Because of the information asymmetry, lending to an SME is said to be riskier than lending to a

large company (Altman & Sabato, 2007; Berger & Frame, 2007; DeYoung et al., 2008; Dietsch & Petey, 2004;

Saurina & Trucharte, 2004; Stiglitz & Weiss, 1981).

Modigliani and Miller (1963) stated that firms tend to favor debt to equity due to the tax deductibility

of interest payments, thereby maximizing the firm value. Under the context when taxes, bankruptcy costs, and

agency costs are considered at the same time the so-called trade-off theory emerges, which helps to minimize

their weighted average cost of capital. The firms seek balance between the tax advantages of debt financing

over equity and bankruptcy and agency costs to maximize the firm value (Sogorb-Mira, 2005; Verheul &

Thurik, 2001). Small firms generally have relatively higher bankruptcy costs (Ang, 1992). Therefore,

theoretically and empirically, firm size is positively related to leverage. According to the trade-off theory,

young firms are less leveraged (Sogorb-Mira, 2005).

One of the major advancements in the theory of finance is the agency cost theory, which suggests viewing

external financing as a principal-agent problem (Jensen & Meckling, 1976). According to this theory, agency

costs may accrue from external financing, since conflicting interests exist. Agency costs can be defined as the

costs of structuring, monitoring, and bonding a set of contracts among agents with conflicting interests (Fama

& Jensen, 1983, p. 304).

The Jensen and Meckling (1976) model explains that ownership structure acts as an incentive to take

actions that reduce the total value of the firm. This could be the case in a static world in a market where the

behavior of actors is supremely opportunistic. However, the Jensen and Meckling model can be criticized

because its orientation to the corporate finance market does not take into account the aspects of

entrepreneurship. Entrepreneurship is a dynamic and progressive process, where the objective of an owner-

manager could be the maximization of wealth through value creation in his or her firm. According to Virtanen

(1996), at least in the early stages of new ventures, bootstrapping through wage and perquisite flexibility is a

common phenomenon rather than the short-term opportunistic pursuit of self-benefit.

Fama and Jensen (1983) argued that when the investors hold residual claims in organizations—even

minority shares—the management and control of decisions are separated. However, the purpose of state-owned

financiers is not to abolish classical entrepreneurial decision makers. On the contrary, as Fama and Jensen

(1983, p. 309) stated, the separation and diffusion of decision management and decision control, meaning the

absence of a classical entrepreneurial decision maker, limits the power of the individual decision agent to

expropriate the interests of residual claimants. Moreover, most empirical studies have suggested that the

entrepreneur is the most important decision criterion when making investments (Virtanen, 1996).

Even though the theory generally assumes differing goals between the principal and agent and that agency

costs accrue mainly from conflicting interests of different parties involved in the contract (Eisenhardt, 1989,

pp. 58–59, 61), sometimes the interests may be similar. Landström (1992, p. 218) emphasized that the relationship

between the private investor and the entrepreneur has a more “positive” character, where the interaction is based

on support and mutual trust. This “positive” kind of relationship could actually make the use of agency models

controversial. It should be noted that Landström (1992, p. 200) defined private investors broadly to include

professional investors. This study hypothesizes that the interests are not conflicting. Instead, there should be

goal congruence, with both parties working on the success of the new venture.

The asymmetric information theory may be seen as a component of agency theory. It states that managers,

owners, and investors of capital have different objectives and different information. Asymmetry of information

causes deviations in the level of knowledge inside and outside the firm. This means that managers and owners

have better information about the firm’s prospects than external investors do. Therefore, the agent has the

opportunity to use the information for his or her own objectives (Stiglitz, 1984).

This informational asymmetry causes adverse selection and moral hazards, which typically affect the

initial financing decisions and the terms of debt (Huyghebaert & Van de Gucht, 2007; La Rocca et al., 2011).

In the case of adverse selection, the lenders choose businesses that later fail (type one error), or they do not

choose the businesses that have the potential to be successful (type two error). The second risk, moral hazard,

may occur if the behavior of the entrepreneur changes to the detriment of another after the loan has been

granted. A solution for this problem is to secure the loan with personal guarantees or collateral, or with higher

interest rates to hedge against the higher default probability (Berger & Udell, 1998; Paul, Whittam, & Wyper,

2007).

According to Alchian and Demsetz (1972), the theory of rational expectations stands in the background of

the demand on monitoring. In this concept, the decision makers (e.g., lenders) utilize all available information that

have an impact on their decision to accept or reject a loan application. They use this information rationally,

consequently avoiding systematic mistakes. The principals are able to foresee the differences between their own

interests and that of the agents, and they avoid misleading the agents (Alchian & Demsetz, 1972). As a result, the

principals insist on compensation for the risk of loss they carry (Wallace, 1987). This encourages the agent to

reduce agency costs as well as the demand for monitoring activities (Alchian & Demsetz, 1972).

Signaling theory (Akerlof, 1970) states that managers of good firms seek help from investors to

differentiate good and bad firms. Managers offer the set of information that they believe responds to the

demand from the investors. On the other hand, they can keep the information advantage they have over the

external investors. If the investors are not able to distinguish between good and bad firms, they tend to treat

both types as averages. Thus, investors will “underrate” the good ventures and “overrate” the bad ventures.

This will result in inefficiencies in the markets.

The pecking order hypothesis of Myers (1986a; 1986b) and Myers and Majluf (1984) also emphasizes the

relevance of asymmetric information in capital structure decision-making situations. The pecking order of the use

of different instruments is based on the existence of asymmetric information. Firms use internal funds first.

Thereafter, if external funds are needed, the firm will issue safe securities (i.e., debt) followed by hybrid forms

of finance, and equity as a last resort (Brealey & Myers, 1988, p. 433). The pecking order hypothesis states

that there is no optimal level of leverage; rather, it depends on the firms’ situation over time. Pecking order

theory predicts that young firms use debt because they do not have generated cash flows due to the short time

available (Sogorb-Mira, 2005).

In a study on the capital structure of small business, Berger and Udell (1998) asserted that the financial

needs of a firm are based on the phase of a firm’s life cycle. The life cycle pattern considers the availability of

financial sources and the cost of capital. The cost of capital depends on the degree of information asymmetry,

which changes during the financial life cycle (Kaplan & Strömberg, 2003). The financial needs of the firms

differ according to their life cycle, how they are able to generate cash flow, whether they have growth

opportunities, and the related risk in realizing them (La Rocca et al., 2011). Thus, the debt equity ratio is

specific to the life cycle. In the presence of market imperfections and agency risks, firms face financial

constraints (Berger & Udell, 1998). Investors may reject capital applications and firms may raise only certain

types of finance (Cosh, Cumming, & Hughes, 2009). Because of the asymmetric information and lack of

collateral, the availability of bank finance and new equity finance may be particularly problematic for start-

ups (Berger & Udell, 1990; Cassar, 2004). If they obtain external finance, it may be prohibitively expensive

(Berger & Udell, 1998; Cassar, 2004). The firms have to pay relatively high rates of interest. Lenders also

insist on collaterals or personal guarantees. This naturally adds to the risk for entrepreneurs. In addition, it

restricts their flexibility (Coleman & Cohn, 2000). According to Berger and Udell, (1998), debt is more

important and easier to obtain in the later stages of the life cycle.

Literature on capital structure includes many studies that attempt to apply capital structure theory to

small firms. According to Berger and Udell (1998), the concept of the financing life cycle, which examines

the sequential financing choices, does not hold for all small businesses. In a case study, Atherton (2012)

suggested that rational theories of financing may not suit small start-up firms. La Rocca et al. (2011) tested

these theories and compared the results with young and old SMEs, and found that the reverse financial life

cycle pattern seems to fit young businesses (i.e., a loan is a fundamental financial source in the early stages).

Similarly, Robb (2002), Denis (2004), and Coleman and Robb (2012) found that the younger businesses and

start-ups have generally more outside capital, particularly debt, than older businesses. The leverage decreases

when the firms mature, then the firms are operated with generated positive cash flows and equity (La Rocca et

al., 2011; Petersen & Rajan, 1994). In the mature stage, when the profitability increases, the firms follow a

pecking order in financing; they prefer to use the internal financial resources first and the external sources

when the internal ones are exhausted (e.g. La Rocca et al., 2011). This is in the line with Diamond (1989;

1991), who suggested that, in the start-up stage, firms allow themselves to be monitored by a bank to go

through the period of launching.

Genuinely new ventures are the most informationally opaque firms in the economy. They have a lack

of operating history. One method to reduce the severe information asymmetry problem in funding new ventures

is to evaluate the characteristics of the founder, firm, and environment. The owner characteristics contain

valuable information for choices of financial sources in such a low-information environment. It can be argued

that small businesses cannot be viewed as entirely separate from their owners; there are dependencies between

owners and firms (Cassar, 2004; Olson et al., 2003; Åstebro & Bernhardt, 2003). Information asymmetries

between the new venture and the investors can also be mitigated by rich information on the environment, such

as market-specific information (Healy & Palepu, 2001). According to Callen, Livnat, and Segal (2006),

financial statements contain less valuable information for investors, and they are not available in the start-up

phase.

The characteristics of a founder include human capital aspects. The range of human capital signals

includes formal education, training, employment experience, start-up experience, owner’s experience, and

experience, skills, and knowledge on running parental firms. Human capital attributes embody an asset for an

individual as well as for a firm. Human resources may be the most important resource for a firm (Chandler &

Jansen, 1992; Cooper, Gimeno-Gascon, & Woo, 1994). Most authors have concluded that human capital

increases the likelihood of entrepreneurial success and is an important contributor to the start-up quality

(Bosma, van Praag, Thurik, & de Wit, 2004; Cassar, 2004; Coleman & Cohn, 2000; Cooper et al., 1994; Unger,

Rauch, Frese, & Rosenbusch, 2011; Van der Sluis, van Praag, & Vijverberg, 2005).

Since human capital has an impact on future performance, it is also recognized by investors. The

entrepreneur’s higher human capital is an important determinant in raising funds (Coleman & Robb, 2012;

Colombo, Delmastro, & Grilli, 2004; Colombo & Grilli, 2005; Cooper et al., 1994; Peña, 2002; Watson,

Hogarth-Scott, & Wilson, 1998). For the lenders, human capital provided by the founders represents important

information in investigating the risk of a start-up (Colombo et al., 2004). Investors find that founders with

greater human capital are more certain about their efficiency; such founders learn the market conditions faster

than those with less human capital, hence their firms should be more likely to survive the early years of the

market selection process (Coleman & Cohn, 2000; Cooper et al., 1994). In addition, founders with higher

human capital have better availability of funds and rely more on external funding (Coleman & Robb, 2012;

Cressy, 1996). However, according to Storey (1994a; 1994b), the human capital characteristics appear to be

unrelated to bank financing. On the other hand, to some extent, a high human capital level can compensate for

the lack of financial capital, and relatively higher human capital may be positively related to the proportion of

initial equity provided internally (Chandler & Hanks, 1998).

There is scarce research on the influence of non-accounting characteristics on the capital structure of the

start-up firm. Empirical research on truly new firms is scant, mainly because the data are not readily available.

Research by Scherr et al. (1993) considered broader issues concerning owner characteristics and start-up

capital structure. Their study also included firm characteristics. The results suggested that capital structure is

related to owner characteristics, industry, and the profitability and size of the firm. A study by Chandler and

Hanks (1998) also used owner characteristics in explaining the financial structure for start-ups. They

investigated the relationship of human capital measure with the amount of initial equity provided by the

founders. Their results suggested that invested capital by the founder is not significantly related to human

capital. The following variables were entered in the study: management experience, experience of managing

one’s own business, the number of previous successful start-ups, experience in a technical or functional area,

and the level of education. Their findings also suggested that human and financial capital are, to some extent,

substitutes. Founders with a good career history and a lower level of leverage had a similar level of sales or

earnings as the firms with lower human capital and higher leverage.

Cassar (2004) examined firms less than two years old with less than 200 employees. His study had a

smaller range of variables associated with the owner characteristics, but more firm-related characteristics than

the study of Scherr et al. (1993). Contrary to the findings of Scherr et al. (1993), the owner characteristics had

no influence on the financing of the firm after firm characteristics were considered. The study used the

following owner characteristics: gender, tertiary education, and years of experience within the same industry.

It examined the following independent variables of firm characteristics: firm size, industry, legal firm form,

and asset structure. Results showed that firm size had a significant influence on business start-up financing.

Some studies have included only a single or a few owner characteristics in examining the start-up capital

structure. For example, for high-tech start-ups, the number of owners and the work experience of the founders

decrease the level of financial leverage (Colombo & Grilli, 2007). The previous findings indicate that there are

contradictory results regarding the influence of non-accounting variables on the capital structure of the start-

ups.

In the case study by Atherton (2012), one common feature of micro firms was that they had professional

advice and had taken out loans from public financial institutions, whose loans are mainly offered at subsidized

rates and on special terms. Overall, the results of the case study suggested that funding acquisition at start-up

is affected by many factors, and there are huge differences between the firms’ capital structure. In this study,

we concentrate only on micro firms. It is important for founders and potential lenders to reduce the information

asymmetries and potential agency costs within these start-ups. The circumstances where external debt plays a

great role and where loans are provided by state-owned financial institutions provide an interesting research

topic.

3. Data and Variables

3.1. Data

The sample consists of 399 start-up micro firms chosen in 2003 and in 2005 from the list of starting

firms registered in the database of Finnvera, which is a specialized financing company owned by the Finnish

state. The data were collected from loan applications from 1998 and 2000. In every credit risk category, the

selection began from the first loan application of the year until the samples were sufficient. Even though the

applications were examined systematically, the data can be considered random because the borrowers were of

a similar nature throughout the year. The sample includes 90 firms that have failed within the first five years

of their existence. The dataset tracks firms from their loan application phase. This enables us to study financing

decisions at the early establishment stage of a firm. The method applied to model the data is linear regression.

The definition of micro firms used in this study is that of the European Union (Commission

recommendation 2003/361/EC), according to which the number of employees should be less than ten, and the

annual turnover or the balance sheet total of a firm should be €2 million or less. (For more details, see

Commission recommendation 2003/361/EC.)

3.2. Variables

Dependent variable. Our measure of the capital structure is the leverage at the start-up time. Consistent

with the literature, leverage is calculated as the fraction of total financing sources (e.g., Scherr et al., 1993; La

Rocca et al., 2011). The total debt encompasses long-term debt from state-owned financial institutions, and in

some cases, from banks. Entrepreneurs’ loans to their own firms are considered a source of equity finance

rather than a loan.

Independent variables. The independent variables are classified into three main types: founder

attributes, firm attributes, and environment-related variables. Founder attributes focus on entrepreneurial pre-

entry capital that is based on individuals’ backgrounds and experiences. The three environment-related

variables are evaluated by the lender. The definition of variables is provided in Table 1.

There is a notable difference between the demand and the supply of external finance. One side concerns

the founder and which financial sources he or she prefers to use. For example, the founder’s preferences toward

risk and control affect his or her financial decisions. Another side concerns the availability of preferred

financial sources, such as the banks’ willingness to borrow for these founders. This study concentrates on the

supply side of leverage. However, some attributes are also discussed in light of the demand side or the other

side of the coin.

We included eight founder attributes in the model. The gender variable has three outcomes in the data:

the firms are founded by male(s), female(s), or both genders together. Scherr et al. (1993) and Coleman and

Robb (2009) found that male founders have more debts than female founders. Women are more risk averse

than men (Verheul & Thurik, 2001), or their debt amount is due to credit discrimination (Coleman & Cohn,

2000). In addition, women may need less debt because their business is more modest and they are less willing

to grow their business (Coleman & Robb, 2009). Some studies found no relationship between gender and

leverage (Coleman, 2000; Storey, 1994a; Storey, 1994b; Wilson, Carter, Tagg, & Shaw, 2007). In the study of

Carter, Shaw, Lam, and Wilson (2007), the independent effect of gender was weak. The expectation regarding

gender is consequently unclear.

The personal financial wealth of the entrepreneur refers to the borrower’s resources and his or her ability

to manage them. This variable was assigned the value of one if the founder owns at least half of his house. The

commitment of a small venture owner’s personal wealth helps the firm to obtain external finance (Avery,

Bostic, & Samolyk, 1998; Blumberg & Letterie, 2008). Owners with higher personal wealth are more likely

to have used external finance (Haynes, Onochie, & Lee, 2008). This link may also be a question within micro

start-ups, when the founders have to give personal guarantees of their loans. Thus, personal assets may make

the supply side more willing to borrow, which increases the likelihood of higher leverage.

The age of the owner can have negative connections with capital structure. Scherr et al. (1993) found

that older owners were more risk averse or had accumulated property that enhanced their ability to find internal

equity. However, Blumberg and Letterie (2008) and Storey (1994a) found that the age of founders was

insignificantly related to the use of loans. Our measure of age is the age of the owner, and in the case of multiple

owners, their average age. The expectation regarding this variable is ambiguous.

Many studies have found that the founder’s education level and obtained experience provided signals of

better human capital (e.g. Bosma et al., 2004; Cooper et al., 1994; Unger et al., 2011). Generally, individuals

with higher human capital are able to raise more loans (Colombo et al., 2004). We measured education using

four different levels: no professional education, vocational school, college/polytechnic, and university. There

is evidence that education level is positively related to external loans (Storey, 1994a; Coleman & Cohn, 2000).

On the other hand, Blumberg and Letterie (2008) found that highly educated business founders had a lower

need for loans. According to Scherr et al. (1993), education was not significant in explaining the leverage of

firms borrowing only from financial institutions.

The second human capital attribute, entrepreneurial training, indicates whether the founder has

participated in some entrepreneurial training course in connection with establishing the business. Scherr et al.

(1993) found that a formal business education was not associated with leverage level, whereas Ando (1988)

found that lenders are more willing to lend to entrepreneurs with business education. Our expectation on

whether lenders are attracted to founders who attended a training course is ambiguous.

Prior work experience in the same sector has been found to decrease leverage (Chandler & Hanks, 1998;

Coleman & Robb, 2012; Colombo & Grilli, 2007). Owners who have more experience may have had more

time to collect capital, which they can utilize for their own business. Nonetheless, Blumberg and Letterie

(2008) found that bankers appreciated the comparable work experience. Storey (1994b) and Coleman and

Cohn (2000) found no support for the relationship of leverage level with prior work experience in the same

sector. The expectation for this variable is unclear.

Individuals who have been running their own firm have gained valuable experience, which has been

found to be an important factor determining the capital structure in the start-up phase (Cassar, 2004). Venture

capitalists also appreciate founders with relevant experience (Hall & Hofer, 1993; Hsu, 2007; Kaplan &

Strömberg, 2004). On the contrary, research by Scherr et al. (1993) and Blumberg and Letterie (2008) found

that the lending decision of funders was not influenced by experience variables. The expectations for these

four human capital variables are consequently unclear.

Another factor is employment status before establishing the business. People who gave up their current

work to start a new business were likely to do so due to a promising business opportunity, whereas individuals

who had unemployment experience prior to starting the business had a higher likelihood of default (Georgellis,

Sessions, & Tsitsianis, 2007; Taylor, 1999; Pfeiffer & Reize, 2000). Unemployed founders had difficulties in

obtaining finance resources (Hinz & Jungbauer-Gans, 1999). We expect that prior unemployment may be

associated with lower levels of leverage.

Marital status could be a proxy for personal stability and financial responsibility. Married owners may

have a separate income stream from their spouse, and therefore may have more diversified financing. The

bankers pay attention to this variable, and they favor borrowers with financial diversification (Carter et al.,

2007; Scherr et al., 1993). We expect that founders who are married have a higher proportion of debt. In a sole

proprietorship or partnership, one reason might be that founders have debts in their own name, even though

they use it for business purposes.

We also included three firm-specific attributes. Legal form differentiates between limited liability

companies and sole proprietorship/partnership, and obtains the value of one if the firm is structured as a limited

liability company. Generally, the literature indicates that limited liability companies have an increased amount

of bank finance (Cassar, 2004; Storey, 1994b). However, Coleman and Cohn (2000) found no empirical

evidence on this issue. Therefore, our expectation is ambiguous. According to Laitinen (1999), because the

assets of the owner and the firm are not separated in a partnership as they are in a limited company, we also

have to examine the owner’s personal assets to investigate the financial risk of the firm. A variable related to

this issue, namely personal financial wealth, is included in our study.

The team variable indicates whether the start-up firm is run by a team as opposed to an individual. In a

study by Colombo and Grilli (2007), the team variable showed a negative coefficient for leverage. This may

mean that a team has more personal wealth to begin a new venture. In contrast, Blumberg and Letterie (2008)

found that single owners faced credit rationing, and bankers did not seem to pay attention to the increased

agency problem in a firm run by a team. Our expectation for this variable is consequently unclear.

Table 1

Definition of Variables

LEVERAGE = Total debt / Total assets, capital

Founder attributes

Ln (1+Founder Age) = Ln (1+Age of the founder when business started,

mean age if there is more than one founder)

Male = Indicator variable, 1 = male(s)

Female = Indicator variable, 1 = female(s)

Both genders = Indicator variable, 1 = the firm’s founders represent both genders

Personal property = Indicator variable, 1 = the founder(s) has property

Employment status =

Indicator variable, 1 = the founder(s) was employed prior to starting the

business

Marital status/single = Indicator variable, 1 = marital status of founder(s) is single

Married/cohabiting = Indicator variable, 1 = founder(s) is married/cohabiting

Divorced/widowed = Indicator variable, 1 = founder(s) is divorced/widowed

Professional education = Indicator variable, 1 = the founder(s) has a degree

Vocational school = Indicator variable, 1 = the founder(s) has a degree from a vocational school

College/polytechnic =

Indicator variable, 1 = the founder(s) has a degree from a polytechnic school/

college

University = Indicator variable, 1 = the founder(s) has a degree from a university

Entrepreneurial training = Indicator variable, 1 = the founder attended an entrepreneurial training

course prior to starting the business

Work experience = Indicator variable, 1 = the founder(s) has work experience in the same sector

Business experience = Indicator variable, 1 = the founder(s) has experience in running a firm

Firm attributes

Legal form =

Indicator variable, 1 = limited liability company,

0 = proprietorship/partnership

Team = Indicator variable, 1 = team

Location = Indicator variable, 1 = the firm is located in the same city like the state-owned

financial institution

office

Lender’s evaluation

Demand =

Indicator variable, 1 = demand for products or services is good,

0 = medium/weak

Competitiveness = Indicator variable, 1 = competitive position is good, 0 = medium/weak

Prospects = Indicator variable, 1 = business prospects are good, 0 = medium/weak

Control variables

Industry 1 = Indicator variable, 1 = Farming, fishing, forestry, manufacturing, or

construction

Industry 2 = Indicator variable, 1 = Whole and retail trade industry

Industry 3 = Indicator variable, 1 = Hotels and restaurants

Industry 4 = Indicator variable, 1 = Transport, communications

Industry 5 = Indicator variable, 1 = Financial intermediation, real estate, renting and

business activities; education, health, and social work; or other

community, social, and personal service activities

Location indicates whether the firm is located in the same city as the office of the state-owned lender.

The offices are located in bigger cities; therefore, this variable is a proxy for urban location. This variable is

rare in capital structure studies. In a study by Carter et al. (2007), the location of the business was one of the

key criteria used by bankers to assess the loan application. However, the result on this variable is unclear. On

the other hand, there is empirical evidence on this topic in firm survival. Strottmann (2007) found that firms

located in rural areas have higher chances of survival. On the contrary, Fotopoulos and Louri (2000) found

that firms located in greater urban areas are more likely to survive, and Heimonen (2012) concluded that firms

in urban areas are innovative growing ventures. Our expectation on this variable is unclear.

The data included three factors related to the business environment: prospects of business, demand for

the product(s) and service(s), and competitive position—all of which are evaluated by the loan officer. The

loan officer takes into account the development of the industry sector. He or she also observes the start-up

firms’ ability to cope with the challenges of the business environment, which naturally is weaker than the

ability of established organizations. In addition, the lender evaluates the density of markets and how the start-

ups can enter the markets. The firm should be capable of adapting its competencies to market conditions as

fast as possible. Coleman and Robb (2012) found that high credit quality start-ups utilized higher levels of

outside debt. Similarly, firms with good values in prospects, demand, and competitive position are expected

to have a higher likelihood of obtaining a loan.

We also controlled for the industry. The data did not include any traditional agriculture firms. It is

generally accepted that there is a strong relationship between the industry and the level of leverage among

SMEs (Hall, Hutchinson, & Michaelas, 2000; La Rocca et al., 2011; Scherr et al., 1993). However, in a study

by Cassar (2004), the industry was an insignificant determinant of leverage.

4. Empirical Results

4.1. Descriptive statistics

Table 2 provides descriptive statistics for the whole sample, as well as two subsamples resulting from a

split based on the median for leverage. Results on the founder-related attributes show that the males own over

half of the firms. However, females have an advantage in terms of available outside financing. Furthermore,

founders who have personal property, were employed before starting the business, and are married seem to

have fewer loans. Results on the level of education indicate that founders who graduated from a vocational

school obtain more loans. Similarly, prior work experience in the same sector increases the likelihood of a

higher leverage level. Finally, lenders grant fewer loans to those with a good demand, good competitive

position and good business prospects.

4.2. Determinants of capital structure

Our measure of capital structure is leverage. First, we estimated a model that is based on all the variables

(Appendix A). Then, we developed a model that includes only the statistically significant variables (Table 3).

The first model for leverage is based on the whole sample, the second one is based solely on failed firms, and

the third one includes only survived firms.

As far as the owner-related variables are concerned, the results of personal financial wealth show that

the founders who have personal property have lower leverage (Table 3). This finding is inconsistent with that

of Avery et al. (1998) and Haynes et al. (2008). From a lender’s point of view, a founder who has personal

property should be more attractive because the borrower has more assets to use as collateral. However, in this

case, there might be less demand since the founders have more equity to start with. This is supported by

empirical surveys conducted by the Confederation of Finnish Entrepreneurs, Finnvera Plc, and the Ministry of

Employment and the Economy (2012). Micro firms generally have fewer loans. In Finland, approximately 34

percent of firms with less than 5 employees have loan from financial institutions, and respectively 51 percent

of firms with 5 to 9 employees have loan from financial institutions.

The firms whose owners have prior work experience in the same sector seem to have higher leverage.

Storey (1994a) and Coleman and Cohn (2000) found no relationship between industry-specific experience and

leverage. It seems that lenders count on the founders’ work experience and believe that these founders will

launch a successful business. The majority of lenders demand business plans, and experienced entrepreneurs

may be able to show better capabilities in their plans. In particular, their connectedness across the value chain

(Mullins, 2006) could provide value-added perspectives that lenders appreciate. Therefore, they might be more

willing to borrow for them.

With respect to firm characteristics, limited liability companies seem to have a decreased level of

leverage. However, Cassar (2004) and Storey (1994b) had opposite findings. This indicates the risk that the

lender is ready to carry. In sole proprietorship or partnership firms, the assets of the owner and the firm are not

separated as they are in a limited company. In this sense, these firms present lower credit risk for the lenders.

In addition, if the firm is located in the same city as the office of the state-owned financier, the leverage seems

to be lower. In these cases, borrowers are in bigger cities where there might be more loan applicants, and the

lender may prefer applicants with a higher equity share.

Table 2

Descriptive Statistics by Median of Leverage

Descriptive statistics reported are means and standard deviations for the total sample.

The reported p-values are for the comparison of

means.

Total Sample Leverage Leverage Equality

≤ 91 > 91 of

(n = 399) (n = 206) (n = 193) Means

Variable Mean Std. Dev. Mean

Std.

Dev. Mean

Std.

Dev. p-value

Loan amount from bank 1000€ 12.586 54.733 11.182 29.662 14.548 73.641 0.562

Loan amount from Finnvera

1000€

18.306 43.011

18.360 21.877

18.606 60.627

0.956

Bank debt to total debt 0.176 0.251 0.190 0.247 0.159 0.251 0.242

Founder attributes

Founder age ln(1+age) 3.603 0.230 3.620 0.221 3.582 0.239 0.101

Male 0.520 0.500 0.519 0.500 0.502 0.501 0.737

Female 0.395 0.489 0.349 0.477 0.455 0.499 0.030

Both genders 0.084 0.277 0.131 0.338 0.041 0.199 0.001

Financial status 0.616 0.486 0.710 0.454 0.505 0.501 0.000

Employment status 0.383 0.486 0.422 0.495 0.335 0.473 0.091

Marital status/single 0.136 0.343 0.119 0.325 0.157 0.365 0.331

Married/cohabiting 0.776 0.416 0.823 0.382 0.710 0.454 0.017

Divorced/widowed 0.086 0.282 0.056 0.231 0.132 0.339 0.021

Professional education 0.872 0.334 0.911 0.284 0.847 0.360 0.001

Vocational school 0.387 0.487 0.357 0.480 0.442 0.497 0.088

College/polytechnic 0.375 0.484 0.411 0.493 0.315 0.466 0.047

University 0.109 0.312 0.142 0.350 0.089 0.286 0.101

Entrepreneurial training 0.218 0.413 0.213 0.410 0.233 0.423 0.639

Work experience 0.924 0.264 0.897 0.304 0.948 0.222 0.055

Business experience 0.209 0.407 0.199 0.400 0.212 0.410 0.741

Firm attributes

Legal form 0.338 0.473 0.434 0.497 0.202 0.402 0.000

Team 0.299 0.458 0.390 0.488 0.181 0.386 0.000

Location 0.375 0.485 0.388 0.488 0.352 0.478 0.458

Lender’s evaluation

Demand 0.641 0.480 0.718 0.450 0.549 0.498 0.000

Competitiveness 0.423 0.494 0.470 0.500 0.380 0.486 0.069

Prospects 0.452 0.498 0.5 0.501 0.376 0.485 0.013

Control variables

Industry

Industry 1 0.311 0.463 0.305 0.461 0.305 0.461 0.997

Industry 2 0.184 0.387 0.199 0.400 0.176 0.381 0.560

Industry 3 0.040 0.198 0.043 0.204 0.041 0.199 0.912

Industry 4 0.052 0.222 0.043 0.204 0.051 0.222 0.704

Industry 5 0.411 0.492 0.407 0.492 0.424 0.495 0.729

When we investigated the impact of environmental characteristics evaluated by the lender, we observed

that firms who have good demand have a lower level of leverage. This contradicts the findings of Coleman

and Robb (2012). A good predictor of demand may indicate that the firm will generate fast positive cash flows;

thus, it needs less leverage at the beginning. The launching period is shorter than that of firms with medium or

weak demand. The Pearson correlation coefficient between the environmental variables was 0.380–0.415.

The final two columns of Table 3 split the data according to the firms’ performance five years after their

establishment (failed vs. survived). Survived firms look very much like the total sample in the first column.

The only exception is prior work experience in the same sector, which seems to be important only within failed

firms. Therefore, it is dangerous to value prior work experience too much when granting loans to new ventures

and their founders. This can also imply that these founders’ attitude toward their own business is too light.

They may take business success for granted because they have work experience. In contrast, the founders

without similar work experience have to work harder and more carefully to keep their business running.

When we evaluated the models, we concluded that the best model included five explanatory variables—

two variables each for founder attributes and firm attributes, and one for lender’s evaluation. These variables

explained about one tenth of the variation of the dependent variable. The ANOVA test indicated that the

models were statistically significant. However, one problem in using regression analysis is the fact that

majority of the variables are binary variables, which means their variation is limited.

Table 3

Estimates for Leverage

The results are obtained through linear regression where the dependent variable is leverage.

Subgroups are based on firms’ survival.

LEVERAGE LEVERAGE LEVERAGE

Survival = No Survival = Yes

Coeff. p Coeff. p Coeff. p

Founder attributes

Personal property -7.649 0.000 -1.300 0.627 -9.096 0.000

Work experience 6.667 0.038 14.469 0.003 4.318 0.265

Firm attributes

Legal form -7.242 0.000 -6.348 0.056 -7.224 0.001

Location -3.280 0.074 2.959 0.267 -6.267 0.006

Lender’s evaluation

Demand -3.984 0.033 0.046 0.986 -3.932 0.092

Constant 89.957 0.000 78.410 0.000 92.865 0.000

ANOVA 0.000 0.034 0.000

F 10.469 2.550 8.966

R2 0.134 0.142 0.149

Adjusted R2 0.121 0.086 0.132

Number of observations 381 92 289

5. Conclusion

Small business borrowers tend to be more informationally opaque than large companies; thus, they pose

greater challenges for lenders. Lenders must overcome the asymmetric information problems that are inherent

in such borrowers and investigate the distinctive features of small firms separately. Overall, indicators of

founder, firm, and environment characteristics are important in explaining the capital structure of start-ups.

This result is consistent with that of Scherr et al. (1993). On the other hand, Cassar (2004) and Chandler and

Hanks (1998) found no relationship between owner characteristics and the proportion of debt. These three

studies used data from the United States. Our study examines new micro firms in a bank-centered economy.

In this context, the leverage level depends on the characteristics of the founder, firm, and environment.

One caveat of the study is that it focuses only on leverage without considering collaterals, personal

guarantees, and interest rates. In the bank lending process, the bank may influence the amount of debt.

However, state-owned financial institutions are prepared to take more risks. For them, the side effects of new

ventures’ operations (e.g., product development, employment) are more valuable. It would also be important

to include possible public subsidies obtained by the start-ups and possible bootstrapping issues in the study

(e.g., the use of credit cards).

This study has more implications for risk financiers and founders who have to rely heavily on external

loans than for commercial creditors who are more concerned about adverse selection and risk shifting problems

when lending to new ventures. This study also hopes to encourage people who do not have industry-specific

experience to establish their own venture.

Further theoretical work could examine how growth opportunities affect the lenders’ willingness to grant

loans, whether the founders of micro firms are eager for new challenges and firm growth opportunities, or

whether they are content with being able to employ themselves. In addition, a larger database and data from

other bank-centered economies are required to validate the results. Future studies could also include

investigations of an economic recession, during which firms could have different financing patterns and the

founder population could also be different.

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Appendix A

Estimates for Leverage

The results are obtained through linear regression where the dependent variable is leverage.

Subgroups are based on firms’ survival.

LEVERAGE LEVERAGE LEVERAGE

Survival = No Survival = Yes

Coeff. p Coeff. p Coeff. p

Founder attributes

Ln (1+Founder Age) -3.822 0.409 5.828 0.566 -3.764 0.510

Male

Female 3.252 0.134 -0.193 0.968 3.855 0.131

Both genders 2.402 0.539 -0.484 0.955 1.107 0.813

Personal property -7.610 0.000 0.201 0.958 -9.343 0.000

Employment status 4.298 0.048 0.352 0.942 5.270 0.037

Professional education

Vocational school -0.256 0.938 2.598 0.693 -1.390 0.725

College/polytechnic -3.067 0.370 2.393 0.742 -4.485 0.270

University -1.859 0.658 0.688 0.940 -3.049 0.535

Entrepreneurial training 0.774 0.745 1.547 0.724 0.095 0.973

Work experience 7.059 0.051 19.704 0.006 4.195 0.321

Business experience 1.535 0.549 -1.793 0.790 1.224 0.690

Firm attributes

Legal form -7.737 0.004 -13.319 0.043 -7.008 0.023

Team 2.239 0.446 9.800 0.245 1.996 0.541

Location -1.979 0.341 4.323 0.264 -4.317 0.090

Lender’s evaluation

Demand -4.373 0.060 0.044 0.991 -4.403 0.127

Competitiveness 0.661 0.769 -4.088 0.477 1.480 0.572

Prospects -3.790 0.102 -1.570 0.775 -3.971 0.136

Control variables

Industry 1

Industry 2 0.724 0.805 0.761 0.918 1.296 0.703

Industry 3 -1.726 0.742 1.187 0.876 -1.192 0.873

Industry 4 -4.162 0.366 8.003 0.380 -6.659 0.218

Industry 5 1.492 0.522 0.313 0.947 1.457 0.600

Constant 101.343 1.291 48.745 0.195 105.382 1.519

ANOVA 0.000 0.630 0.001

F 2.691 0.866 2.381

R2 0.174 0.292 0.198

Adjusted R2 0.109 -0.045 0.115

Number of observations 290 66 224

Journal of Modern Accounting and Auditing, ISSN 1548-6583 July 2013, Vol. 9, No. 7, 889-907

Capital Structure of Start-ups: Evidence on Non-accounting

Characteristics

Marika Miettinen University of Eastern Finland, Kuopio, Finland

Markku Virtanen Aalto University School of Business, Mikkeli, Finland

Research is scarce on non-financial characteristics, including owner characteristics and their influences on capital

structure in the establishment phase. The objective of this study is to examine the factors affecting firms’

availability of external finance, utilizing a sample of Finnish start-up micro firms. The overall purpose is not to

produce a comprehensive model for capital structure, but to illustrate the importance of non-accounting

characteristics in explaining the attractiveness of the new venture from the lender’s point of view. The results show

that a founder who has prior work experience from the same sector and has little or no personal financial property

seems to have higher leverage. After splitting the data into survived and failed firms, lenders viewed work

experience positively only in the sample of failed firms. In addition, limited liability companies, firms located in

bigger cities, and firms with good demand evaluated by the lender have a lower level of leverage.

Keywords: capital structure, financing, start-ups, micro firms

Introduction Small businesses are the backbones of the economy of every nation. They can be considered as the seeds

for future development. Thus, public policy encourages business start-ups by offering grants and subsidies. The role of finance is a critical element in the performance and development of small enterprises. Literature on capital structure and its connection to the performance of companies is extensive. However, prior studies have mainly examined the later stages of financing of large corporations and used traditional theories of corporate finance. This paper aims to contribute to the understanding of the capital structure of small start-ups by analyzing non-accounting attributes at the time of establishment of new ventures. Few studies (Cassar, 2004; Chandler & Hanks, 1998; Scherr, Sugrue, & Ward, 1993) have analyzed the start-up stage of financing and non-accounting characteristics, and their results are inconclusive.

This study has three contributions. First, it examines firms in Finland which has a bank-centered economy. The majority of previous studies are from an Anglo-American context, where firm financing is strongly market-centered. There is evidence that the determinants of capital structure are influenced by the financial system of the economy (Antoniou, Guney, & Paudyal, 2008). Second, in addition to owner- and firm-related variables, this study includes credit analysts’ evaluation of the firms’ business prospects and their market and

Marika Miettinen, researcher, Accounting and Finance Department of Business, Faculty of Social Sciences and Business Studies, University of Eastern Finland. Email: marika.miettinen@uef.fi.

Markku Virtanen, research director, D. Sc., Small Business Center, Aalto University School of Business.

DAVID PUBLISHING

D

Article III

Miettinen, Marika & Niskanen, Mervi (2014): Lender evaluations of start-up business prospects. Working paper, University of Eastern Finland.

Lenders Evaluations of Start-up Business Prospects

Marika Miettinen and Mervi Niskanen

March 21, 2014

ABSTRACT

The purpose of this study is to investigate lender evaluations of the creditwor-thiness of start-up firms in a sample of Finnish firms. Our database allows us to examine how qualitative information, based on personal history, firm-specific characteristics, credit analyst evaluations of business prospects, and market posi-tion impact firm creditworthiness. The data was collected in 2003 and 2005 from the database of Finnvera, a state-owned financial institution. The results suggest that the determinants of creditworthiness and business prospects evaluated by the lender are, to some extent, different. The age of the founder and his/her em-ployment status are important determinants in both models. Moreover, lenders seem to pay more attention to founders without prior work experience in the same sector. The creditworthiness model captures five additional variables: the equity percentage, female-operated firms, personal property of the founder, education, and prior experience in running a firm. The differences can be partially explained by the fact that a state-owned lender can choose to lend to riskier borrowers, such as founders with low equity. This study is one of the few that sheds light on lender evaluations using non-accounting variables. In addition, the study data includes only the customers of state-owned financial institutions, which are rarely consid-ered in existing literature. One weakness of the study is related to pre-selection bias—the data excludes information on loan applicants who were rejected, which was not recorded in the lender’s files. The findings of this study provide lenders, policymakers, and entrepreneurs with clearer implications regarding the impor-tant aspects of a firm’s period of establishment.

JEL Classification: M13; G21Keywords: Start-ups, creditworthiness, evaluation, lenders, non-accounting vari-ables

1 Introduction

Lenders encounter asymmetrical information when assessing the loan applica-tions of new ventures, which have a high risk of failure and highly concentrated ownership (Huyghebaert and Van de Gucht 2007). It is important to study start-ups, because the conditions prevailing at the time of their founding affect firm fi-nancial performance throughout its lifetime (e.g. Geroski et al. 2010). Furthermore, it has been stated that new firms are not a group that we can analyze without di-viding the data into sub-samples. The factors affecting firm financial performance differ strongly between micro firms and small and medium establishments (SMEs) (Holmes et al. 2010).

Using Finnish data of startup micro firms, the objective of this article is to investigate how non-accounting characteristics are reflected in credit analysts’ evaluations of firms’ business prospects. By comparing the actual creditworthi-ness model with the model evaluated by the lenders at the time of loan applica-tion, we examine whether the credit analysts’ evaluations of the prospects of the firm similarly predict the future of start-ups. The data has been collected at a very early stage of the firm’s life cycle, before the first financial statements have been prepared. Therefore, we employ variables that investigate the type of fund-ing used, background of the entrepreneur, characteristics of the firm, and some market-related variables evaluated by the lender. Our aim is to shed more light on the context of genuinely new micro firms, lenders’ evaluation of their business prospects, and firms’ creditworthiness five years after a firm’s establishment.

The aim of human information processing is to describe, evaluate, and im-prove decisions that have been made (Trotman et al. 2011). The previous studies of individual judgment are based mainly on accounting data and used in failure pre-diction (e.g. Gadenne and Iselin 2000; Laitinen and Laitinen 1998). The objective of these studies is to examine the ability of users to predict firm performance by means of given variables. Generally, the focus has been on the accuracy of predic-tions and the importance of accounting information. To our knowledge, the only study that has also included non-accounting variables in the investigation of credit analysts’ evaluations is by Laitinen (1999). The results indicate the importance of using non-accounting variables in calculating credit ratings.

Our study is one of the few that investigates prior judgment while compar-ing human and quantitative models among micro firms. Moreover, because we recognize that a rational entrepreneur will re-enter the job market if the expected utility from the founder’s new business is smaller than the expected utility from paid employment (Millán et al. 2012), we are able to add a new perspective to the literature: namely, our dependent variable divides actual creditworthiness into “good creditworthiness” and “medium/weak creditworthiness”. The operations

of the firms with good creditworthiness are profitable. Other firms have difficul-ties in tolerating disturbances, changes in profitability, operational and financial difficulties, need for reorganization, clear threat of bankruptcy or they are unable to repay their loan. In this sense, previous literature, which differentiates between survival and failure, does not provide any guidelines for the expected behaviour of a rational entrepreneur. The lenders’ evaluation of business prospects is based on the time of establishment, when the firm initially applied for a loan, whereas the measure of the firm’s credit risk category is based on the situation that is prevalent five years after establishment.

Our results show that there are similarities as well as differences between the determinants of actual creditworthiness and those determined by the lender at start-up. We find that age of the founder and his/her employment status are all important determinants of actual creditworthiness, as well as the pre-determined business prospects. Moreover, lenders seem to pay attention to founders without prior work experience in the same sector. The creditworthiness model also cap-tures five additional variables: the equity percentage, female operated firms, per-sonal property of the founder, education, and prior experience in running a firm.

This paper is structured as follows: section two of the study discusses the rel-evant literature. Section three outlines the data and motivates the use of variables applied in the analysis. Section four presents descriptive statistics of variables. Section five provides the empirical results, and the final section concludes the study.

2 Literature Review

Human information processing (HIP) is an area of research that investigates the behaviour of decision-makers. Many HIP studies using accounting information (e.g. when investigating financial distress) apply the lens model approach first presented by Brunswik (1952). The lens model describes the world of the deci-sion-maker by investigating the relationship between the environment and the behaviour of organisms in the environment. The model also examines how the individual uses cues to make a prediction (Ashton 2010). Since the seminal study by Libby (1975), many HIP studies have examined this (e.g. Casey and Selling 1986; Houghton and Sengupta 1984; Zimmer 1980). These studies mainly focus on the accuracy of statistical prediction models and the importance of accounting information. Trotman et al. (2011) provides review on this literature.

Prior findings on loan officers’ predictive accuracy of corporate failure have been inconsistent. The prediction accuracy ranges from 41 per cent (Casey 1980) to 93 per cent (Houghton and Woodliff 1987). In Libby (1975), the bank loan officers’ prediction accuracy is overall 74 per cent, which is higher than the one of their own paramorphic linear models. Similarly, Zimmer (1980) finds bank loan officers and accounting students able to predict bankruptcy fairly regularly. He reports an overall accuracy of 77.1 per cent. In some studies, humans have been found to have lower accuracy (e.g. Casey 1983; Gadenne and Iselin 2000; Zimmer 1981).

These studies have also presented various explanations for the inconsistencies in HIP studies. Prior information of failure proportion in the sample may have a positive impact on an individual’s prediction accuracy (e.g. Lin and Hwang 2000), while some studies find no effect on this matter (Casey 1983; Casey and Selling 1986). In addition, if the split of failed and survived firms in the sample is consist-ent with the real-world situation, the prediction accuracy increases (Houghton and Sengupta 1984; Houghton and Woodliff 1987). However, some studies with the realistic sample proportion report a much lower accuracy of judgement (e.g. Lin and Hwang 2000). The most important issue related to disturbance in pre-dictions is the usefulness of cues (Laitinen and Laitinen 1998). The difference in prediction accuracy might be due to different combinations of financial ratios used (Casey 1983) or earning management (Argenti 1976, 140-144). When considering the accounting information, the age of the data, failure process, and macroeco-nomic factors may impair classification (Altman 1983; Laitinen and Laitinen 1998; Lin and Hwang 2000). Prediction accuracy is negatively affected by outdated in-formation (e.g. Laitinen and Laitinen 1998; Lin and Hwang 2000).

Another possible factor misleading the evaluation is related to the decision-maker. His or her mode of data processing (the form of the rule and the weighting of the cues) can naturally cause misclassification (Laitinen and Laitinen 1998).

There are also indirect effects, with respect to such factors as gender, experi-ence, and personality, which may affect the way in which data is processed (e.g. Shepherd et al. 1998). Furthermore, the attitude toward risky lending decisions dif-fers between institutions. Laitinen and Laitinen (1998) find that credit analysts in a governmental financial institution are more likely to misclassify a bankruptcy. Loan officers, it is suggested, have a tendency to overestimate the less relevant cues and underestimate the more relevant ones (Mear and Firth 1987; Sharma and Iselin 2003). In Riquelme and Watson (2002), venture capitalists’ (VCs) beliefs of why firms fail are similar to the findings of prior empirical studies of SMEs’ failure. However, overconfidence bias is also common, and it is very important in practice (e.g. Zacharakis and Shepherd 2001).

The level of overconfidence depends on the amount and type of information (Zacharakis and Shepherd 2001). The relationship between the amount of infor-mation and information processing quality of humans may be illustrated as an inverted U curve (Driver and Mock 1975). With respect to overconfidence, indi-viduals believe more in their decision making quality when more information is available (Zacharakis and Shepherd 2001). However, decision accuracy actually decreases when VCs have more information to assess a proposal, which might be due to a function of cognitive overload (Zacharakis and Meyer 2000). On the contrary, Simnett (1996) finds no statistically significant impact on decision qual-ity by different amounts of information. Moreover, if people are unfamiliar with the information, and they are not able to rely on their intuition, overconfidence is higher and accuracy is lower (Zacharakis and Shepherd 2001).

Some studies state that previous findings might be inconsistent due to the improper measurement of information loads and the failure to rule out the effects of confounding variables (Iselin 1988), methodological shortcomings, such as limi-tations in recalling past information (Shepherd and Zacharakis 1999; Zacharakis and Meyer 1998), and because of the biases in the decision processes of VCs (Franke et al. 2006; Shepherd et al. 2003; Zacharakis and Meyer 2000; Zacharakis and Shepherd 2001).

As far as we know, the only study that also includes non-accounting vari-ables in investigating credit analysts’ evaluations is performed by Laitinen (1999). The results suggest that non-accounting variables are more important than fi-nancial ratios in calculating credit rating. The non-accounting variables include the amount of personnel, recent ownership, the owner’s previous activities and property, and legal form of the firm. The most important variables are the pay-ment history and personal property of those responsible for the firm, as well as the shareholders’ equity to total assets. In this study, the assessment is dominated by credit analysts.

3 Data and Variables

3.1 SAMPLE FORMATION

The sample consists of 440 start-up micro firms collected in 2003 and in 2005 from the database of Finnvera, which is a specialized financing company owned by the Finnish state. The final sample in regression analyses includes 288 firms because of missing information pertaining to some variables. A loan from Finnvera is granted to all the firms in the sample, and all the firms also have a documented business plan. We included only genuinely new micro firms. The entrepreneurs who started their businesses in traditional agriculture or as part of a franchise chain were excluded. The background variables describe the situation at start-up in 1998 or 2000. Therefore, it is possible to investigate the impact of the factors that prefigure firm creditworthiness five years after establishment. We use logistic regression to investigate actual creditworthiness and prospects. The definition of micro firms that we have used is that of the European Union (Article 2 of the Annex of Recommendation 2003/361/EC).

3.2 VARIABLES

We have two dependent variables, one measuring creditworthiness and another measuring the business prospects as viewed by the lender at the time of the loan ap-plication. The dependent variable, creditworthiness, is a dichotomous variable: firms that have good creditworthiness equal one, otherwise, they get a zero. The operation of these firms is profitable, and they do not have operational or financial difficulties or any threat of bankruptcy. This measure is based on the firm’s credit risk category five years after starting the business. Lenders had assessed the borrowers’ credit-worthiness and placed them in the appropriate category using accounting data as well as some non-accounting information. The other dependent variable, prospects, is also a dichotomous variable, and it equals one for firms with good prospects and zero otherwise. The lenders’ evaluation of business prospects is based on the time of establishment when the firm initially applied for a loan. First, the lenders become familiar with the founder’s own concept of the business prospects and then they reflect this with their larger view of industry and macro economic situation.

We use 16 independent variables divided into four sub-groups. We include two variables measuring the funding sources. The percentage share of equity refers to the share of equity capital provided by the owner(s) to total assets. It seems that a high percentage of equity investments demonstrates a positive effect on post-entry financial performance (e.g. Åstebro and Bernhardt 2003; Brüderl and Preisendörfer

1998). It can also be interpreted to reflect the commitment of the founder(s), which the lenders seem to value (Laitinen 1999). We expect that an increase in equity share improves the probability of success, and it is also noticed by the lenders. The other factor, bank loan, equals one for firms that have a bank loan. Prior re-search implies that having a bank loan promotes firm survival (e.g. Åstebro and Bernhardt 2003; Saridakis et al. 2008). The bankers choose more transparent bor-rowers (Daniels and Ramirez 2008). Therefore, we expect that the existence of a bank loan increases the probability of good creditworthiness.

We include eight founder-related variables. Our measure of the age of the found-er, which is the natural log of (1+age), because it can be argued that the impact of one extra year diminishes as the founder gets older. If there is more than one founder we use mean age. Firms run by mature entrepreneurs may perform fi-nancially better (Van Praag 2003). In contrast, there may be a negative relationship between age and generated profit (Santarelli and Tran 2013). On the other hand, age did not have an impact on firm financial performance in Bosma et al. (2004). Our expectations regarding the age of the owner are unclear.

There are three possible outcomes for the gender variable in the data; the firms are founded by male(s), female(s) or both genders together. Cooper et al. (1994) find that firms with female founders are less likely to grow but just as likely to survive. Some studies find that the male owners have higher survival rates (Baptista et al. 2012; Boden and Nucci 2000; Bosma et al. 2004; Georgellis et al. 2007). On the other hand, some studies find that there are no differences between genders’ financial performance (e.g. Johnsen and McMahon 2005; Watson 2003). Our expectations regarding gender are, consequently, unclear.

The personal property of the entrepreneur refers to the borrower’s resources and his ability to manage them. This variable gets a value of one if the founder owns at least half of his house. A wealthier individual can establish a business with more efficient levels of capital and therefore be more likely to succeed in the business than a poorer one (Georgellis et al. 2005). Van Praag (2003) finds no sig-nificant effect of personal assets or home ownership on the hazard rate. Personal property of those responsible for a business is an important factor when credit analysts are predicting the firm’s credit risk (Laitinen 1999). Our expectation for creditworthiness is ambiguous, but with respect to prospect we expect to find a positive relationship.

We measure education with four different levels: no professional education, vo-cational school, college/polytechnic, or university. According to many studies, a founder’s education enhances financial performance (e.g. Baptista et al. 2012; Bosma et al. 2004; Lussier and Halabi 2010; Santarelli and Tran 2013). Better educated en-trepreneurs may have a greater ability to solve problems, make decisions, and have better social networks, which helps them in business (Ucbasaran et al. 2008). We expect that high education levels increase the probability of good creditworthiness.

The entrepreneurial training variable indicates whether or not the founder has participated in some entrepreneurial training courses in connection with estab-lishing the business. Storey and Wynarczyk (1996) suggest that the participants

are more likely to survive in the long run. Peña (2002) finds it an important deter-minant of new firm growth. Based on these studies, we expect to obtain a positive sign with respect to this variable.

Employment status before establishing the business can be expected to influ-ence the financial performance of a start-up. Individuals who are unemployed prior to starting the business have a higher likelihood of default (Andersson and Wadensjö 2007; Millán et al. 2012; Pfeiffer and Reize 2000). This might be due to a lack of necessary skills. However, it might also provide a stimulus not to return to the situation of unemployment. The association with good creditworthiness is unknown.

A positive effect on new firm financial performance is verified when the found-er has prior work experience in the same sector (e.g. Bosma et al. 2004; Cooper et al. 1994; Millán et al. 2012; Van Praag 2003; Santarelli and Tran 2013). These individu-als have developed their expertise and experience, which they bring to their new firms. They also have an existing network of professional and social contacts, and perhaps with customers as well (Bosma et al. 2004). VCs also value entrepre-neurs with experience in the same industry (e.g. Hall and Hofer 1993; Hsu 2007; Riquelme and Watson 2002). Our measure of work experience is a dummy varia-ble. Based on previous studies, we expect to obtain a positive sign on this variable.

Individuals who have been running their own firm have gained valuable expe-rience, which has been found to be a critical success factor for small firms (e.g. Harada 2003; Millán et al. 2012). In Santarelli and Tran (2013), however, it has a negative relationship with generated profit. These people could be called “oppor-tunists” (Storey 1994). They may lack managerial skills, and they have not learned from their mistakes. Our expectation for this variable is unclear.

We include three firm-related variables in our model. Legal form differenti-ates between limited liability companies and sole proprietorship/partnership. Generally, the literature indicates that limited liability companies have higher survival rates (e.g. Carter and Van Auken 2006). Some studies find no empirical evidence on this matter (Åstebro and Bernhardt 2003; Saridakis et al. 2008). The effect of legal form on good creditworthiness is unclear.

There are many studies that show a positive association between firms found-ed by teams and their financial performance (e.g. Cooper et al. 1994; Delmar and Shane 2006). This is because they have more complementary skills and resources than individual entrepreneurs. VCs also value a team with good qualifications (MacMillan et al. 1985). However, Lechler (2001) suggests that among firms that are founded by multiple people, personal conflicts and inefficient communication can occur. Our expectation on this variable is uncertain.

Location indicates whether the firm is located in the same city as the office of the state-owned lender. The offices are located in bigger cities, therefore this variable works as a proxy for urban location. Empirical evidence on this topic is inconclusive. Firms located in rural areas (Strotmann 2007) or in greater urban areas (Fotopoulos and Louri 2000) may have higher chances of survival. When examining the generated profit of start-ups there is no difference between urban

or rural firms (Santarelli and Tran 2013). Therefore, the effect of geographic loca-tion is unclear.

The measures evaluated subjectively by the lender include demand, competi-tiveness and business prospects, differentiating between good and medium/weak outcomes. The loan officer takes into account the development of the industry sector. S/he also observes the start-up firm’s ability to cope with the challenges of the business environment, which is, naturally, weaker than the ability of es-tablished organizations. Another important factor in determining the financial performance is pre-entry knowledge of demand and competitiveness of a firm. The lender evaluates the density of markets and how the start-ups may enter into markets. Increased concentration implies a higher risk of failure (Baldwin and Rafiquzzaman 1995). The concentration ratio may also be insignificant for start-ups (e.g. Holmes et al. 2010). VCs emphasize the quality of environmental conditions (e.g. Mason and Stark 2004; Riquelme and Watson 2002). We expect that good business prospects, demand, and competition position have a positive effect on firm creditworthiness.

Moreover, we add five different industry sectors to our models to control for industry-specific differences in creditworthiness. Previous results concerning in-dustry are quite diverse. For instance, industry differences are insignificant for small business survival in Cooper et al. (1994) and Saridakis et al. (2008). Persson (2004) finds that, in their sample, only establishments in real estate, hotels, and restaurants have a low probability to survive. In order to control for the existence of non-observable differences in the collection method, we also include a dummy variable to capture whether the collecting date was 2003 or 2005.

3.3 DESCRIPTIVE STATISTICS

Table I reports descriptive statistics for the whole sample, as well as those repre-senting firms with good vs. poor creditworthiness. While the average percentage of equity to total assets for the total sample is 14.23 per cent, it seems that firms with good creditworthiness at t5 have a higher equity share at t0. The results show that the founders who have personal property seem to have better creditworthi-ness. Even if only 38 per cent of the sample of entrepreneurs were employed before starting the business, being employed increases the likelihood that the firm will have good creditworthiness. The same seems to hold true for educated founders. Founders who have participated in an entrepreneurial training course seem to ob-tain significantly higher value among firms with lower creditworthiness. It may be that these founders possess skills that have lower relevance in running a firm, but training courses are not sufficient to equip founders to deal well with their busi-ness. The firms with good creditworthiness are more likely to be limited liability companies and run by teams. Finally, all variables measuring lender evaluations seem to obtain significantly higher values in the group of good creditworthiness firms.

Table 1: Descriptive statistics by firm creditworthiness

Total Sample

(n = 440)

Creditworthiness= 0

(n = 297)

Credit worthiness= 1

(n = 143)

Equalityof

Means

Variable Mean Std. Dev. Mean Std.

Dev. Mean Std. Dev. p-value

Firm Funding

Equity % 14.237 16.716 11.840 15.379 19.198 18.276 0.000

Bank loan 0.376 0.485 0.386 0.487 0.355 0.480 0.542

Founder variables

Founder Age 36.704 8.434 36.438 8.737 37.266 7.754 0.321

Male 0.520 0.500 0.508 0.500 0.545 0.499 0.468

Female 0.395 0.489 0.407 0.492 0.370 0.484 0.461

Both gender 0.084 0.277 0.084 0.278 0.083 0.278 0.993

Personal property 0.616 0.486 0.571 0.495 0.709 0.455 0.005

Employment status 0.383 0.486 0.312 0.464 0.528 0.501 0.000

Professional education 0.872 0.334 0.852 0.355 0.914 0.280 0.050

Vocational School 0.387 0.488 0.381 0.486 0.400 0.491 0.712

Collage/Polytechnic 0.375 0.485 0.350 0.477 0.428 0.496 0.123

University 0.109 0.312 0.120 0.325 0.085 0.280 .0258

Enterpreneurial training 0.218 0.413 0.252 0.435 0.146 0.355 0.007

Work experience 0.924 0.264 0.915 0.278 0.944 0.230 0.254

Business experience 0.209 0.407 0.225 0.418 0.174 0.381 0.206

Firm variables

Legal form 0.340 0.474 0.283 0.451 0.459 0.500 0.001

Team 0.299 0.458 0.263 0.441 0.373 0.485 0.023

Location 0.375 0.485 0.380 0.486 0.363 0.482 0.733

Lender’s evaluation

Demand 0.641 0.480 0.550 0.498 0.830 0.376 0.000

Competitiveness 0.423 0.494 0.353 0.478 0.566 0.497 0.000

Prospects 0.450 0.498 0.350 0.477 0.670 0.472 0.000

Descriptive statistics reported are means and standard deviations for total sample.

The creditworthiness variable has the value of one if the firm’s operation is profitable and zero otherwise. The reported p-values are for compare means.

Table 2: Descriptive statistics by business prospects

Total Sample

(n = 440)

Prospects= 0

(n = 240)

Prospects= 1

(n = 198)

Equalityof MeansEquality

Variable Mean Std. Dev. Mean Std.

Dev. Mean Std. Dev. p-value

Firm Funding

Equity % 14.237 16.716 12.198 15.166 16.953 18.242 0.005

Bank loan 0.376 0.485 0.368 0.483 0.385 0.488 0.729

Founder variables

Founder Age 36.704 8.434 36.299 8.589 37.190 8.225 0.275

Ln (1+Age) 3.603 0.230 3.591 0.235 3.618 0.223 0.234

Male 0.520 0.500 0.520 0.501 0.520 0.501 0.989

Female 0.395 0.489 0.425 0.495 0.358 0.481 0.156

Both gender 0.084 0.277 0.054 0.227 0.121 0.327 0.015

Personal property 0.616 0.486 0.604 0.490 0.627 0.485 0.621

Employment status 0.383 0.486 0.301 0.460 0.482 0.501 0.000

Professional education 0.872 0.334 0.880 0.325 0.866 0.341 0.671

Vocational School 0.387 0.488 0.444 0.498 0.323 0.469 0.010

Collage/Polytechnic 0.375 0.485 0.358 0.481 0.394 0.490 0.445

University 0.109 0.312 0.076 0.267 0.148 0.356 0.020

Enterpreneurial training 0.218 0.413 0.245 -0.431 0.186 0.390 0.134

Work experience 0.924 0.264 0.900 0.301 0.954 0.209 0.027

Business experience 0.209 0.407 0.200 0.401 0.217 0.413 0.660

Firm variables

Legal form 0.340 0.474 0.257 0.438 0.430 0.496 0.000

Team 0.299 0.458 0.245 0.431 0.362 0.481 0.009

Location 0.375 0.485 0.362 0.481 0.393 0.489 0.500

Lender’s evaluation

Demand 0.641 0.480 0.475 0.500 0.843 0.364 0.000

Competitiveness 0.423 0.494 0.235 0.425 0.646 0.479 0.000

Descriptive statistics reported are means and standard deviations for total sample. The prospect variable has the value of one if the firm has good business prospects and zero otherwise. The reported p-values are for compare means.

Table II presents descriptive statistics for firms with good vs. medium/weak busi-ness prospects. The results indicate that the lenders value high equity invest-ments, firms which have owners from both genders, firms whose founders were employed when starting up the firm, and firms whose founders have professional

education and prior work experience in the same sector. Furthermore, the lend-ers seem to believe that firms operating under limited liability and operated by a team have better business prospects. Finally, if they believe that the firm has good demand for its products and a good competition position, they also believe its prospects are good. Moreover, we would like to pay attention that 198 firms have medium/weak prospects and only 16 of them have weak prospects at time of loan request. They did still obtain a loan, which can be explained that risk financier takes intentional risks. Five years after a firm’s establishment 14 firms with weak prospects (including 6 defaults) represent the worse creditworthiness, whereas two of them have good creditworthiness.

4 Empirical results

In creditworthiness model, the result of equity percentage indicates that an in-crease in equity investments increases the probability of good creditworthiness (Table III). The results are in line with prior studies (e.g. Åsterbro and Bernhardt 2003).

When we investigate the impact of founder-related variables, we observe that older founders seem to have firms with good creditworthiness. This contradicts Santarelli and Tran (2013). Age might be an indicator of the accumulated learn-ing, and this capital promotes the establishment of a successful new venture. Furthermore, firms run by females seem to have the highest probability of having firms with good creditworthiness. This is a significant finding, because some pre-vious studies suggest that females are less likely to survive (e.g. Bosma et al. 2004). Our findings also contradict studies claiming that there are no differences be-tween genders’ financial performance (e.g. Johnsen and McMahon 2005). Founders who have no personal property seem to have firms with good creditworthiness, which contradicts with the findings of Georgellis et al. (2005). Further, the results indicate that a person who was working before starting a firm has a lower prob-ability of firm with good creditworthiness. This is inconsistent with prior studies (e.g. Andersson and Wadensjö 2007; Georgellis et al. 2007). Unemployed individu-als may have a strong stimulus not to return to the situation of unemployment. In regards to education, the results show that founders who graduated from a college/polytechnic school or a university have the highest probability of firms with good creditworthiness. This is consistent with many studies (e.g. Millán et al. 2010; Saridakis et al. 2008). Moreover, the model suggests that the firms in which the founder has personal experience in running a firm are more likely to have firms with good creditworthiness. Our results are in line with Harada (2003) and Millán et al. (2010).

As far as variables based on the loan officers’ evaluations are concerned, the results indicate that good demand is not a good predictor, whereas good business prospects seem to be a good predictor of actual creditworthiness. These results suggest that the lenders’ ability to predict firm prospects is quite good. Demand may become negative if there have been changes in the business environment. Another explanation for the negative impact on demand may be that entrepre-neurs trust too much for good demand the start-up phase and invest less in mar-keting for example. The evaluation of prospects may include e.g. some intuition of the founder’s performance in business, whereas the demand is based solely on environmental factors.

In business prospects model, the founder’s age seems to be the most important factor; the older the founder is, the better probability s/he has for good prospects

(Table IV). According to odds ratio, an additional year of age increases the prob-ability of belonging to the group with good prospects by almost five times. The results also suggest that borrowers who are employed at the time of establishment, and prior work experience in the same sector are all viewed negatively by the lenders. The finding of work experience is inconsistent with previous studies (e.g. Hall and Hofer 1993; MacMillan et al. 1985). Finally, we observe that the lenders’ estimates of business prospects are negatively codetermined by their evaluations of demand. This may indicate that there was a change in the demand during these five years, or that entrepreneurs depended excessively on strong demand, or that the evaluation of demand is not very accurate and the boundary between good and medium/weak demand is unclear.

The three variables determined by the lenders’ evaluation process are not harmfully correlated. The correlation coefficients vary between 0.380 and 0.415. The correlation coefficients of the other independent variables are also not too high, which indicates that the problem of collinearity between explanatory vari-ables may not be particularly relevant in this study (Appendix A).

When we compare the results from Tables III and IV, we observe that there are similarities as well as differences in the determinants of actual creditworthiness and those determined by the lender at start-up. We find that age of the founder, employments status, and demand are important determinants in both cases. The creditworthiness model also captures five additional variables: equity percentage, female operated firms, personal property of the founder, education, and prior experience in running a firm. The lenders also seem to consider negatively the founder’s prior work experience in the same sector, while in the actual creditwor-thiness model it is statistically insignificant. Prior studies suggest that industrial experience has a positive impact on performance (e.g. Cooper et al. 1994; Millán et al. 2012; Santarelli and Tran 2013). Our results suggest that this is not the case among genuinely new micro firms. Even though the experienced founders may have more confidence and special knowledge, they may be more willing to take risks or they are overconfident or used to work for others. The results of odds ra-tios (not reported) indicate that in the prospect model, the most important factor is the age of the founder. In the creditworthiness model, the age has a similar impli-cation: a person who is a year older has five times higher probability of belonging to the good creditworthiness group. However, the most important factor in the creditworthiness model is the education. Founders with have college or university level education have approximately nine times higher probability of having good creditworthiness five years after establishment. Moreover, being female increases the likelihood of good creditworthiness by 5.796 times. The founders who have no prior experience in running a firm have 5.366 times higher probability of having good creditworthiness. In the prospects model, the other variables are far less important.

The results in Table III suggest that the lenders’ ability to predict a firm’s pros-pect is rather good. However, if we look at Table IV, we notice that lenders do not take into account all the relevant variables when evaluating a firm. Lenders should consider these differences between the models. The differences between models

are in line with Mear and Firth (1987) and Sharma and Iselin (2003), who suggest that humans are considered to be more fallible because they have a tendency to overestimate the less relevant cues and underestimate the more relevant ones. In addition, the results are in line with Zacharakis and Shepherd (2001) who state that VCs are overconfident in their assessment of a start-up’s success when they predict a very high level of success. On the one hand, one may consider that cred-itworthiness and business prospects measure different aspects. As is evident, the results are not exactly the same. Nonetheless, both measures contain aspects of

Table 3: Estimates for firm creditworthiness

Dependent variable: CREDITWORTHINESS

Variable Coef. p-value

Firm Funding Equity % 0.017 0.091

Bank loan 0.326 0.373

Founder-related variables Ln (1+Age) 1.431 0.079

Male 0.027

Female(s) 1.562 0.015

Both genders 1.015 0.145

Personal property -0.787 0.050

Employment status -1.049 0.004

No professional educ. 0.001

Vocational school 0.199 0.823

College/Polytechnic 2.015 0.001

University 1.895 0.002

Enterpreneurial training 0.410 0.342

Work experience 0.200 0.754

Business experience 1.428 0.003

Firm-related variables Legal form -0.340 0.452

Team -0.628 0.200

Location -0.201 0.566

Lender’s evaluation Demand -1.101 0.009

Competitiveness -0.101 0.793

Prospects -1.171 0.002

Control Industry Yes

Data collection 0.266 0.447

Constant -8.820 0.009

Total classification rate (%) 80.6

Number of obs 288

Pseudo R² Cox & Snell 0.311

Pseudo R² Nagelkerke 0.440

The results are obtained through a logistic regression where the dependent variable has the value of one if the firm’s operation is successful and zero otherwise.

financial performance, entrepreneur-, firm- and market-related variables. It maybe that in the loan application phase, the lender is not able to state whether the po-tential founder can utilize his/her education or not; this could be the case if the founder is young and has not much history behind. Moreover, the data does not define which kind of business experience the founder has; has he had a successful business or was the business driven to bankruptcy? It is possible that the lender has more detailed information and, therefore, the prior business experience is not statistically significant, as shown in Table IV. Another possibility is that the lend-ers do not utilize the available information effectively.

Table 4: Estimates for prospects

Dependent variable: PROSPECTS

Variable Coef. p-value

Firm Funding Equity % 0.014 0.141

Bank loan 0.059 0.860

Founder-related variables Ln(1+Age) 1.608 0.029

Male(s) 0.818

Female(s) -0.197 0.745

Both gender -0.376 0.569

Personal property 0.219 0.524

Employment status -0.624 0.058

No professional educ. 0.614

Vocational School -0.110 0.864

Collage/Polytechnic -0.587 0.257

University -0.315 0.535

Enterpreneurial training 0.176 0.643

Work experience -1.230 0.058

Business experience 0.300 0.461

Firm-related variables Legal form -0.298 0.490

Team -0.152 0.739

Location 0.300 0.378

Lender’s evaluation Demand -1.066 0.002

Competitiveness -1.713 1.116

Control Industry Yes

Data collection 0.148 0.640

Constant -3.876 0.186

Total classification rate (%) 77.4

Number of obs 288

Pseudo R² Cox & Snell 0.320

Pseudo R² Nagelkerke 0.429

The results are obtained through a logistic regression where the dependent variable has the value of one if the firm has good business prospects and zero otherwise.

5 Conclusions

The purpose of the present study is to investigate the differences between actual creditworthiness five years after a firm’s establishment and lenders’ evaluations of business prospects at time of loan application. These factors are classified as funding, founder, firm-, and market related variables. The estimated models show that the founder’s age, employment status, and demand for products are impor-tant in both models. Surprising is that in both models the unemployed founders and firms with low demand have better probability of having good creditworthi-ness and prospects. The findings with regard to work experience in the same sec-tor have a negative sign in prospect model, while in the actual creditworthiness model it is insignificant. As far as the other variables are concerned, the lenders seem to ignore certain factors that are statistically significant in the actual cred-itworthiness models. These lenders may have a tendency to overestimate the less relevant cue, in this case the prior work experience in the same sector, and un-derestimate the more relevant ones, including equity percentage, female operated firms, personal property of the founder, education and prior experience running a firm. Moreover, it may be that lenders hold more trust in their intuition, which is not captured using statistical models. This type of inner feeling combines many types of information that may require more sophisticated measures. On the other hand the differences can be partly explained by the fact that as a state-owned lender Finnvera can choose riskier borrowers such as founders with less equity.

The findings provide lenders, policy-makers, and entrepreneurs with clearer guidance regarding the important aspects of a firm’s period of establishment. For lenders, this might promote effective decision-making. If lenders’ ability to foresee the variables that predetermine micro firm creditworthiness improves, it may help them finance more firms of a similar disposition. Our findings suggest that a ‘common sense theory’ held by lenders has validity and, therefore, lenders should continue to look for potential entrepreneurs who are unemployed and have gained some life experience measured by their age. On the other hand, lenders should pay special attention to founders’ experience in running a business. When it comes to entrepreneurial promotion policies, this study provides more knowl-edge about micro firms’ creditworthiness. Good financial performance is more important than marginal survival, because people need a certain level of income. Moreover, people need to have some compensation from their own firm, and if the income level is lower than a paid salary, they may not want to continue with their own business. As a result, the findings of this study should encourage potential entrepreneurs to establish their own ventures. Prior work experience in the same sector is not as important as people may believe. It is more important that a person has a good educational background.

We believe our study makes an important contribution to the existing literature in terms of describing lenders’ beliefs and comparing them to good creditworthi-ness (not plain survival). However, we must caution readers about limitations of our comparative study. The experiment was not carried out in controlled circum-stances, in other words the credit analysts were not aware of this study. Therefore, it was not possible to control the experience of the credit analysts. Another poten-tial weakness relates to the possibility that the small firm performance studies we have reviewed are so diverse that a comparison of the variables associated with our measure, creditworthiness, is not appropriate.

In the future, a qualitative analysis could be used to evaluate lenders’ decision-making processes with non-accounting variables. At the same time, the decision-maker-specific variables could be included to investigate how these variables impact the classification of firm creditworthiness. This would help us to better understand and improve the human decision-making process in the context of micro firm creditworthiness prediction.

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Appe

ndix

A: C

orre

latio

ns

Equity%

Bank loan

Ln(1+Age)

Gender

Personal property

Employment status

Professional education

Entrepreneurial training

Work experience

Business experience

Legal form

Team

Location

Demand

Competitiveness

Prospects

Industry

Data collection

Eq

uit

y%

1

Ba

nk

loa

n-0

.074

1

Ln(1

+A

ge)

0.13

00.

014

1

Ge

nd

er

-0.0

220.

066

0.14

61

Pe

rso

na

l p

ro-

pe

rty

0.23

50.

045

0.31

50.

088

1

Em

plo

yme

nt

sta

tus

0.046

0.22

60.

017

0.00

50.

159

1

Pro

fess

ion

al

ed

uc.

0.08

60.

016

0.07

80.

222

0.041

0.15

21

En

tre

pr.

tra

inin

g-0

.040

-0.0

200.

061

0.042

-0.0

48

-0.1

24-0

.134

1

Wo

rk e

xp

eri

en

ce-0

.106

-0.0

07-0

.030

-0.0

47-0

.060

0.07

10.

046

0.00

51

Bu

sin

ess

ex

p.

-0.0

270.

069

0.19

4-0

.042

0.05

70.

065

0.00

7-0

.150

0.040

1

Leg

al

form

0.23

40.

202

0.12

60.

037

0.14

80.

271

0.19

1-0

.188

0.07

00.

262

1

Tea

m0.

136

0.21

0-0

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0.17

10.

104

0.21

00.

162

-0.1

170.

054

0.21

90.

519

1

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tio

n0.

075

-0.0

590.

017

-0.0

15-0

.148

0.06

90.

121

0.03

40.

042

-0.0

63-0

.008

0.03

41

De

ma

nd

0.15

50.

052

-0.0

510.

078

-0.0

230.

160

0.08

7-0

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0.05

8-0

.063

0.16

40.

217

0.10

01

Co

mp

eti

tive

ne

ss0.

093

0.15

80.

020

0.043

0.05

10.

139

0.00

8-0

.025

0.02

70.

062

0.07

70.

127

-0.0

710.

380

1

Pro

spe

cts

0.14

10.

017

0.05

70.

052

0.02

40.

185

0.09

7-0

.071

0.10

20.

021

0.18

20.

127

0.03

20.

382

0.41

51

Ind

ust

ry-0

.061

-0.1

13-0

.127

0.16

6-0

.151

0.03

20.

177

-0.1

170.

035

-0.0

92-0

.061

-0.0

490.

132

0.15

60.

057

0.18

21

Da

ta c

oll

ect

ion

-0.0

46-0

.088

-0.0

71-0

.068

-0.0

470.

039

-0.0

00-0

.056

-0.0

41-0

.036

-0.0

93-0

.056

-0.0

150.

080

0.16

0-0

.015

0.00

71

Tabl

e pr

esen

ts P

ears

on c

orre

lati

ons.

ERRATA in “The lending and performance determinants of very small start-ups: Insight into the

lenders’ evaluation” by Marika Miettinen 2014

Errata for Article 1: Factors contributing to the success of start-up firms using two-point or

multiple-point scale models

1) Article 1, p. 460, Table 1, should read (please, see in italics the correct wording):

“Success 2 0= the firm has failed / 1= the firm has survived”

2) Article 1, p. 461, the last full sentence, should read: “The dependent variable SUCCESS2

is a dichotomous variable which equals zero for firms that have failed and one for firms

that are still operating 5 years after start-up.”

3) Article 1, p. 465, Table 4: The notes to the table should also include the following

sentence: "Due to missing values, the number of observations per variable varies."

4) Article 1, p. 468, at the end of first paragraph, should read: “If the firms have good

competiveness, the probability of success decreases.” and “firms operating in the Industry

3 sector (Hotels and restaurants) are more likely to fail”.

5) Article 1, p. 468, the last sentence end of the first paragraph, should read: “whereas in

Model 5, it is 50.0% (not reported).”

6) Article 1, p. 469, Table 6, in Model 5 the coefficient of competitiveness should be

negative.

7) Article 1, p. 472, the second sentence, should read: “This changes after including firm

attributes (Models 4 in Tables 7 and 8).”

8) Article 1, p. 472, at the end of first paragraph, should read: “The results show that firms

led by persons who were working before starting a firm (employment status) have a

higher likelihood of being part of a higher credit risk category (Table 8). ... Individuals

who have prior business experience have a higher likelihood of having a lower credit

risk. This finding is in line with those of Harada (2003) and Coleman (2007).”

9) Article 1, p. 472, at the second full paragraph, should read: “if the firms are in the same

city as the lender, the probability of credit risk is lower. In these cases, both lender and

borrowers are in big cities and, according to Fotopoulos and Louri (2000) firms in urban

areas have a higher chance of survival.” Furthermore, the following sentence should be

removed “He reported that, in bigger cities, higher costs may hamper survival. Another

possible explanation might be that the lenders think that they know the local business

environment and may therefore be less careful in evaluating potential borrowers.”

10) Article 1, p. 472, the last paragraph, should read: "With respect to the attributes evaluated

by the lender, the results suggest, a bit surprisingly, that firms with low demand in the

market have a higher likelihood of having a lower credit risk in Model 5 of Table 7 and

8".

11) Article 1, p. 473, second paragraph, should read: “Regarding control variables, we see

that firms operating in Industry 2 and Industry 4 seem to have the lowest credit risk.

Industry 2 covers whole and retail trade firms, whilst Industry 4 consists of transport and

communications firms. The firms in the hotel and restaurant industry (3) and Industry 5

(financial brokering, real estate, renting and business activities, or education, health and

social work, or other community, social and personal service activities) have the highest

probability of being in the highest credit risk categories.”

12) Article 1, p. 473, the first sentence on the page should read: "Similarly, the estimates

suggest that firms with a good competitive position seem to have a higher credit risk in

the SUCCESS3 model (Table 7)".

13) Article 1, p. 473, at the final paragraph it should read: “With respect to the second

hypothesis, the results of the multivariate analysis indicate that the founder attributes are

important to some extent to success measured by the multiple-point scale models (Table 7

and 8). The partial support of the hypothesis is based on three founder attributes: founder

age, gender and employment status; they are statistically important. Furthermore,

education is statistically significant in the model 4 in both multiple point scale model and

business experience is statistically significant in the model 4 in six-point scale model.

However, two funding attributes, one firm attribute and two market attributes (in the

three-point scale model and one in the six-point scale model) are also significant in

measuring success with the multiple-point scale models.”

14) Article 1, p. 474, middle of the second paragraph, should read: “the likelihood of success

increases if the firm has a good competitive position. However this is significant only in

the two-point and three-point scale models, not in the six-point scale model.”

Furthermore, the last sentence: “the six-point scale model requires more attributes than

the three-point scale model (employment status, business experience (in the model 4) and

location).

15) Article 1, p. 475, with respect to generalizability of results it was not clearly enough

stated that all firms had loan from Finnvera, state-owned financial institution. Therefore,

the first sentence of Conclusion should read: “This research investigates the success of

start-up firms using Finnish data from a state-owned financial institution.” And

furthermore at the end of the paragraph of limitations (last full paragraph on the page

476), it should read: “It is also important to bear in mind that all the firms in the data

have loan from a state-owned financial institution, which differentiates them from firms

which start business without loan. This may create bias.”

16) Article 1, p. 476, second line, should read: “The founder’s age, gender, employment

status, education (Model 4 in Tables 7 and 8), business experience (Model 4 in Table 8),

geographic location and demand are statistically significant only in the multiple-point

scale models.

17) Article 1, p. 476, at the first paragraph, should read: “competitiveness of the firm is

important only in the SUCCESS2 model.

18) Article 1, p. 477, after the second last sentence, should read: “This study has identified

the important factors which influence financial performance, here identified as credit risk.

Evaluation of credit risk includes financial performance measures, but also aspects from

the firm management, the industry and markets.”

Errata for Article 2: Capital structure of start-ups: Evidence on non-accounting characteristics

1) Article 2, p. 890, second last paragraph, should read: “The firms in the dataset rely

heavily on external financial sources. Some of the firms did not obtain or obtain very little

bank finance. All of them obtain debt from a state-owned financial institution.”

2) Article 2, p. 892, final sentence, should read: “The lenders can hedge against default

probability by securing the loan with personal guarantees or collateral, or with higher

interest rates.”

3) Article 2, p. 896, after last sentence should be added: “The correlation coefficients of the

independent variables do not exceed 0.8, which is often considered to be a sign of

harmful multicollinearity. In the data, the highest correlation coefficient is 0.519 which

gives the correlation between team and firm form variables (not reported).”

4) Article 2, p. 902, the second sentence in the second paragraph, should read: “This is

because lower leverage produces a positive effect on survival (e.g. Bosma et al. 2004).

Survived firms have the same type of profile like the total sample in the first column.

5) Article 2, p. 902, in the second paragraph should be removed: “Therefore, it is dangerous

to value prior work experience too much when granting loans to new ventures and their

founders.”

6) Article 2, p. 902, in the last full paragraph should be removed: “This study also hopes to

encourage people who do not have industry-specific experience to establish their own

ventures.”

Publications of the University of Eastern FinlandDissertations in Social Sciences and Business Studies

Publications of the University of Eastern Finland

Dissertations in Social Sciences and Business Studies

isbn 978-952-61-1454-5

issn 1798-5757

Marika Miettinen

The Lending and Performance Determinants of Very Small Start-upsInsight into the Lenders’ Evaluation

Using the research setting of new

micro firms which have been granted

a loan from state-owned financial

institutions, the objective of this

study is to identify factors affecting

the financial performance of new

micro firms (measured by creditwor-

thiness), lender’s evaluation of firms’

prospects, and the factors affecting

the capital structure of new firms.

disser

tation

s | 83 | Ma

rik

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Perform

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Marika MiettinenThe Lending and

Performance Determinants of Very Small Start-ups

Insight into the Lenders’ Evaluation