Banking Loans and the Survival of New Firms

Transcription

1 The Survival of ew Firms: Do Bank Loans at Birth Matter? Françoise Bastié a, Sylvie Cieply b, Pascal Cussy a,1 a CREM, U.F.R. Sciences Economiques, Université de Caen, esplanade de la paix, Caen, France b CREM, I.U.P. Banque Assurance, Université de Caen, esplanade de la paix, Caen, France Abstract: While many studies deal with policy implications to reduce the problem of financial constraints on new firms, the role of access to banking loans and its consequences on entrepreneurship remain unclear. In this article, we investigate the extent to which the survival of new firms, once started, is affected by a limited use of banking loans at the time of their start. As bankers assess the probability of new firms survival when they decide to grant a loan, the endogeneity of financial variables is taken into account in an empirical model that introduces all usual determinants of new firms survival as well. This model is tested on a sample of French new firms by using the SINE database of the INSEE. Our results stress the significant and positive impact of banking loans on the survival time of new firms even after the correction of the omitted-variables bias. Bank loans persistently impact the survival of start-ups at the turning of the last century in France. Our study shows as well that the screening of banks on the population of new firms allows them to distinguish (however not perfectly) among good firms and bad ones. Banks cannot perfectly identify the future successful new firms but their screening must be taken into account by both academic studies and policy makers. Key-words: Survival, New firms, Bank loans, Screening, Duration. JEL Classification: M13, D82, G21 1 addresses : francois.bastie@unicaen.fr, Sylvie.cieply@unicaen.fr, pascal.cussy@unicaen.fr 1

2 The Survival of ew Firms: Do Bank Loans at Birth Matter? Abstract: While many studies deal with policy implications to reduce the problem of financial constraints on new firms, the role of access to banking loans and its consequences on entrepreneurship remain unclear. In this article, we investigate the extent to which the survival of new firms, once started, is affected by a limited use of banking loans at the time of their start. As bankers assess the probability of new firms survival when they decide to grant a loan, the endogeneity of financial variables is taken into account in an empirical model that introduces all usual determinants of new firms survival as well. This model is tested on a sample of French new firms by using the SINE database of the INSEE. Our results stress the significant and positive impact of banking loans on the survival time of new firms even after the correction of the omitted-variables bias. Bank loans persistently impact the survival of start-ups at the turning of the last century in France. Our study shows as well that the screening of banks on the population of new firms allows them to distinguish (however not perfectly) among good firms and bad ones. Banks cannot perfectly identify the future successful new firms but their screening must be taken into account by both academic studies and policy makers. Key-words: Survival, New firms, Bank loans, Screening, Duration. JEL Classification: M13, D82, G21 1. Introduction Entrepreneurship has become a popular research topic since small firms have been recognized to create more new jobs than large firms (Birch, 1979, Acs and Audrestch, 1985). Because of their high dissolution rate, this fragile job generation effect leads the academic and popular business press to be flushed with recurring articles on the factors associated with entrepreneurial success (for example, Cooper, Woo and Dunkelberg, 1988, Cooper, Gimeno- Gascon and Woo, 1994, Storey, 1994, Van Praag, 2003, Reid, 1991, Astebro and Bernhardt, 2003). Among them, access to finance is often stressed as a determinant of the performance of new firms in general, and their likelihood to survive in particular (for example, Cooper and Gimeno-Gascon, 1992 and Cooper, Gimeno-Gascon and Woo, 1994). A large strand of academic literature focuses on the founder s personal wealth that influences positively the likelihood of a new firm to survive (Evans and Jovanovic, 1989, Evans and Leighton, 1989, Holtz-Eakin, Joulfaian and Rosen, 1994a, 1994b, Lindh and Ohlsson, 1996, Blanchflower and Oswald, 1998, Van Praag, 2003, Hurst and Lusardi, 2004, Schäfer and Talavera, 2009). Other studies focus on debt and in particular on banking debt. For many reasons, in most developed countries, banking debts are, with trade credit, the sole available financial means 2 for new firms that look for external finance (Berger and Udell, 1998, Reid, 2003, Francks and 2 In most countries, first-time entrepreneurs, like more generally small firms, must rely on the limited equity funding both from family and friends and from the equity market. Raising equity on financial markets is without any track record usually forbidden and too costly for small amounts of financing. Moreover, the venture capital industry does not generally concern traditional sectors and its focus is rather firms in the early growth stage rather than the start-up phase, especially in continental Europe (Ooghe, Manigart and Fassin, 1991, Brouwer and Hendrix, 1998). 2

3 Sussman, 2005). Despite the liquidation policy of suppliers are more lenient than those of banks, trade credit is generally considered to be an expensive financing source that small firms only use when banking debt is constrained (Petersen and Rajan, 1997). Banks provide other advantages to new firms. First, by granting overdrafts, they can supply new firms with an option to be financed in the future. This option makes new firms stronger at the time of tightness as they can maintain a buffer stock of cash. Then, bank loans are a rather flexible mean of financing. Banks, like possibly venture capitalists, provide staged financing in the form of loans that are renewed and expanded as the entrepreneur makes his case for financing more compelling (Stulz, 2001). Finally, banking finance could be interpreted as a positive signal by stakeholders (supplier, customers and potential workers). Banks exist indeed because they screen and monitor borrowers more efficiently than other investors could do (Bhattacharya and Thakor, 1993, Allen and Santomero, 1998). For all these reasons, bank loans should influence positively the likelihood of a new firm to survive. However, empirical studies do not confirm overwhelmingly this expectation. Asterbro and Bernhardt (2003) found a negative correlation between having a bank loan and business survival and a positive correlation between having a non bank loan and survival. However the study of the marginal value of having a bank loans shows that it was a ceteris paribus positive predictor of the survival of start-up companies. On French data, Crepon and Duguet (2004) evaluated the impact of bank loans and start-up subsidies on the survival of new firms between 1994 and These authors showed that, in France, during the midnineties, for formerly unemployed people, the bank loan alone has no significant effect on the survival of start-up companies while the best performance is achieved by projects financed both by loans and subsidies. They insisted on the effect of subsidies that is stronger than the effect of the bank loans surely because the former funding is attributed to a larger number of recipients. Greffe and Simmonnet (2008) worked on a sample of French new firms between 1998 and 2003 but they focus on cultural firms. They modeled jointly the probability of receiving a bank loan, the probability of having private means and the probability of being financed by another firm by using a multivariate Probit. These financial variables are instrumented to take into account the endogeneity of the source of financing in the model of survival. The authors showed how this correction modifies the results concerning the effect of financing on the survival of new firms. After this instrumentation, banking loans influence no more the survival time of new firms. In this article, we use the SINE Database provided by the INSEE and we investigate the extent to which the survival of ex nihilo new firms created in 1998 is affected by a limited use of banking loans at the time of their start. We question the duration of this effect as well and we distinguished the effects linked to the screening of banks from the pure effect of bank loans on the survival of French new firms. Our results suggest that the use of banking loans by ex nihilo new firms at their founding influences significantly their likelihood to survive. The effect of banking loans on the survival duration even after the correction of the endogenous bias and it is persisting over time. The paper is organized as follows. Section 2 describes the data and the empirical method. Section 3 introduces the endogenous variables Section 4 discusses the results and compares them to those of previous studies and section 5 concludes with a number of observations about what these results might mean for both theory of financing and public aids to new firms. 2. Methodology 3

4 2.1. Database and sample selection In this article, we use the SINE database produced by the French National Institute of Statistical and Economic Studies (INSEE) in the framework of the New firms Information System (SINE). In this survey, new firms are identified on the basis of their registration in the Système d'informations et de Répertoire des Entreprises et des Etablissements (SIRENE). SIRENE gives information on their identity, localization, economic activity and size. The SINE database completes this information on the population of new firms. Data are generated by a compulsory survey which has been driven on a representative population of new firms every five years since These data give us qualitative information on the entrepreneur s profile, the project he develops, the conditions of their setting up, in particular the characteristics of goods and services, the market and the financing, and the difficulties he must cope with at the beginning of the venture. In this article, we study the cohort of firms which set up in 1998 and which had survived at least for one month. This compulsory survey is carried out among the population of new firms. This sample is representative 3 of the total population of new firms ( firms). A second survey is carried out in 2001 and a third one in They give information about the status of the same firms (closed down or still running). Because we analyze the impact of the entrepreneur s characteristics, firms taken over or set up by existing companies (subsidiaries) have been removed from the sample. In this study, we focus on ex nihilo creations which correspond to new means of production. We exclude takeovers, which correspond to the purchase of the whole or part of another firm's activity and means of production, are excluded to avoid an underestimation of bank loan s effect. Finally, we observed the survival time of enterprises Econometric method In order to investigate factors affecting new firm s survival, the life duration is analyzed. In the sample, the data are right censored. This situation is due to firms which were still alive at the time when the data was last updated. To overcome this problem we use a hazard rate approach that considers not only the potential mortality of firm but the length of survival time. Finally, we model the conditional probability that a firm stops its activity over a specified period. In this article, different parametric specifications of the hazard function were implemented to take into account different duration dependence behaviors. Dealing with the role of bank loans survival function, researchers must cope with an omitted variable bias. As bankers screen applicants for credit, the use of banking loans by new firms and their survival may have common determinants. We think in particular to the human capital of entrepreneurs and the economic characteristics of projects. Without any doubt, some of them are unobservable factors and consequently the impact of bank loan upon survival can be overestimated. To solve this problem, this variable must be instrumented. The statistical procedure is directly inspired by Heckman and Robb (1985) and used by Brunet and Lesueur (2004). In the first step, a probit model is implemented to estimate access to bank loan. We use the instrument introduced by Greffe and Simonet (2008) that concerns the firm s location. This place can be far or not of the entrepreneur s main residence. This variable is correlated with bank loan and not with survival. This first step leads to the estimation of a predicted value of bank loans which is introduced as a covariate in the duration model in a 3 A weight variable is used for the sample to fit the entire population. Firms belong to 9 sectors of activity; financial and agricultural activities and units established abroad are not taken into account in this database. 4

5 second step. In this study, like in Audrestsch and Mahmood (1995), we use the Cox specification. This semi-parametric approach specifies the baseline hazard very flexibly. Nevertheless, the Cox model is based on the proportional hazard assumption. This one requires that the initial mode of financing remains the same regardless of the age of the firm. In order to analyze the impact over time of financial variables, these variables are interacted with the age of firms 4. To complete the analysis of the persistent effect of bank loans, simultaneous equations of the use of bank loans and the survival at n years (conditional or not to survival at n-1 years) are estimated. In these bivariate probit models, the omitted bias variable is corrected by the introduction of the same instrumental variable (the firm s location) in the equation of bank loans. The selection bias inherent to the estimation of conditional survival is neglected as we cannot find an instrument that impacts the survival at n-1 years but not the survival at n years (for n=2 to 5). Nevertheless, as the objective is to analyze the evolution of this impact and to compare the result of different models, this problem is less tricky. 3. Explanatory variables: the determinants of new firms survival A description of variables is given in Appendix 1. In this section, only independent variables are introduced. All variables are dichotomous variables, except the entrepreneur s age. They all take the value one (and zero otherwise) for the modality that is presented in the text and in Table Financial variables: bank loans and other modes of financing Among firm-specific factors, the way new firms are financed is often mentioned as a significant factor of new firm s survival. In SINE 1998, firms are questioned about their use of both banking debts and other debts. This last category gathers heterogeneous creditors: public agencies, local private institutions, and financial companies granting consumer credits to the entrepreneur. Due to this heterogeneity, we consider them separately. We distinguish five sources of funding, which are not exclusive: bank loans, other loans, financing by private equity firms, by industrial or commercial firms, subsidies (or other public helps) and personal funds. Bank loans are used by 21.84% of new firms. When they are financed by banks, new firms use more than others the other kinds of loans (10.57% of firms with bank loans against 8.28% for the others). Few new firms (1.71%) are financed by third parties (private equity firms and industrial or commercial firms). This proportion is higher for firms with banking debt (2.42% against 1.51% for start-ups without banking debt). We observe that subsidies (and other public aids) are more granted to firms with banking loans (43.75% of the subsample) than to firms without banking loans (21.08% of the subsample). Finally, 64.43% of business starters use personal funds or funds coming from family and/or relatives. This proportion is lower when firms are financed by banks (57.65% against 66.33% for firms without banking loan). In a first analysis, survivor functions are plotted in the same graph for two subsamples: firms financed by bank loans and firms not financed by bank loans. The Figure 1 illustrates the Kaplan-Meir product limit estimator survival function for these two subsamples. Firms that are not financed by bank loans appear to have shorter survival times. The log rank test for equality of survivor functions confirms that this difference of survival is significant. 4 Disney, Haskel and Heden (2003) also used this methodology. 5

6 FIGURE 1:Kaplan-Meier survival estimates analysis time BANK_LOANS = 0 BANK_LOANS = 1 This impact appears to be persistent across time. The Table 1 presents the descriptive statistics on the link between survival at n years and bank loans. The percentage of surviving firms at n years financed by bank loans at their start is significantly higher than the percentage of died firms at n years financed by bank loans. This significant difference remains when the conditional survival is considered (survival at n years given survival at n-1 years). In the same way, the percentage of firms financed by bank loans which survive at n years is significantly higher than the percentage of firms not financed by bank loans. The same result is observed for the conditional survival. Nevertheless, the difference between the two variables decreases for the conditional survival after four and five years (but remains significant). Note that the percentage of conditional survival for firms financed by bank loans is decreasing during the first three years and is increasing after. In the first years of firm s life, the rather high rate of mortality can be due to the presence of bad firms (which are seldom financed by banks). This selection impact of bad firms is high at start and is decreasing after. We can wonder why this percentage is again increasing for the survivors at three years (conditionally to the survival at two years). When very bad firms disappeared, the residual effect of bank loans may be linked to the necessary minimal efficient size. Table 1. Univariate analysis Percentage of the firms financed by bank loans survival at n years Percentage of the survivors financed by bank loans Percentage of the dead firms financed by bank loans Percentage of survivors Percentage of the firms financed by bank loans which survive gi ve n su Percentage of the firms not financed by bank loans which survive One year survival 21,84 23,66 10,68 85,97 93,16 83,96 Two years survival 21,84 25,04 12,85 73,73 84,54 70,71 Three years survival 21,84 27,18 13,48 61,00 75,91 56,83 Four years survival 21,84 28,61 14,13 53,19 69,70 48,58 Five years survival 21,84 29,48 14,79 48,01 64,78 43,33 rvtwo years survival/one years 23,66 25,04 15,33 85,77 90,78 84,22 6

7 Three years survival/two years Four years survival/three years Five years survival/four years 25,04 27,18 14,80 82,73 89,79 80,37 27,18 28,61 17,38 87,20 91,81 85,48 28,61 29,46 20,76 90,27 92,94 89,19 Finally, this univariate analysis stresses that bank loans affect the survival of new firms and that the effect remains over time on financial endogenous variables Other variables influence both the survival of new firms and their use of banking loans. A large strand of literature shows that entry and exit of firms are conditioned by industry attractiveness, its growth, and the existence of entry barriers (for example, Audretsch and Mahmood, 1994, 1995, Audretsch and Mata, 1995, Mata and Portugal, 1994). However, market conditions are not the only determinants of new firms performance. The theory of entrepreneurship shows that other factors, associated with the entrepreneur, the firm, and macroeconomic conditions, can matter (Geroski, Mata and Portugal, 2007, Carree and Thurik, 1998). These factors influence access to banking loans as well. Banks own private information on borrowers creditworthiness and use it to screen and monitor borrowers (Diamond, 1984, Rajan, 1992). Despite informational asymmetries, the main objective of screening is for bankers to assess borrowers risk of default and to appreciate if banks can support it. When they screen new firms, bankers analyze not only the ability of new firm to reimburse loans but finally their ability to survive during the time of credit as well. Bankers appreciate successively and, according to practitioners in this order, the characteristics of entrepreneurs, in particular their human capital, the characteristics of projects and the value of collaterals The characteristics of the entrepreneur in the banking screening The characteristics of the entrepreneur relate to the concept of human capital, which can be defined as an investment in skills and knowledge that can boost earning power (Becker, 1962). Following Becker, Arribas and Vila (2007) distinguished between the general human capital and the specific human capital. The general human capital takes into account the personal characteristics of entrepreneurs in particular their educational background, demographic factors, like gender, age or race, and motivations. Few works studied the influence of these variables in the screening process used by banks and their results are rather divergent 5. Concerning education, Cressy (1996), on British data, and, more recently, Parker and Van Praag (2006), on a sample of Dutch entrepreneurs, showed that capital constraints 6 are decreasing with respectively the level of diploma or the number of schooling years. In Blumberg and Letterie (2007), on Dutch data, high education is not significant in the banking denial estimations, but reduces the chance that a business founder applies for credit. They conclude, like Asterbro and Bernhardt (2003), that education is more a demand factor than a supply determinant. Focusing on Internet-related services, Grilli (2005) confirmed that highly qualified human resources of Internet founders do not represent an important driver for gaining access to bank credit. To analyse the impact of education, four variables are 5 «However, very little empirical knowledge exists about the characteristics of individuals who would like to start a business, but are restricted in doing so because of credit rationing by banks» (Blumberg and Letterie, 2007, p. 2). 6 These studies include all debts and not only banking ones. 7

8 introduced: no diploma at all, technical undergraduate degrees, A-level and postgraduate diplomas. In our sample, % of founders have no degree (15.55% of founders whose firms are financed by banking loans and 18.74% of the others) whereas 30.48% of them have a postgraduate degree (24.03% of founders whose firms are financed by banking loans and 32.28% of the others). The log rank test for equality of survivor functions leads to reject this equality and the independence between access to bank loans and level diploma is rejected too (see the Appendix 1). Concerning demographic factors, despite the usual results of a negative influence of being a woman on the success of new firms 7 (Blanchflower and Oswald, 1998, Cooper, Gimeno-Gascon and Woo, 1994, Montgomery, Johnson and Faisal, 2005), many works showed that there is no difference in access to credit according to gender or ethnicities, but for black entrepreneurs in the U.S. (Cavalluzzo and Cavalluzzo, 1998, Levenson and Willard, 2000, Blanchflower, Levine and Zimmerman, 2003, Blumberg and Letterie, 2007). We introduce variables concerning gender and nationality (native or not). In the sample, men represented 72.79% of the sample (74.27 % of founders whose firms are financed by banking loans and 72.37% for the others), and French entrepreneurs 89.51% (93.46% of whose firms are financed by banking loans and 88.40% of the others). The access of bank loans depends on gender and nationality, the survival functions too. Despite age plays no significant effect in most previous studies, we introduce this variable among demographic factors as well. In average the founder is years old. Finally, the motivation of the founder can be expected to influence the banker s decision. Since 2001, the Global Entrepreneurship Monitor has distinguished the opportunity entrepreneurs, who start a business in order to pursue an opportunity, and the necessity entrepreneurs, who create their own employment because they cannot find any paid job on the formal labour market (Reynolds et al., 2005). Despite the importance of this variable among the determinants of new firm s survival (Santarelli and Vivarelli, 2007, Block and Sandner, 2009), the role of this factor in the screening of banks has not been analysed yet. The SINE database gives information about motives of founders. Some founders catch a business opportunity (26.43% of founders in our sample, % of founders whose firms are financed by banking loans and 25.78% of the others). Another motivation is the taste for entrepreneurship (54.37% of founders in our sample, 63.11% of founders whose firms are financed by banking loans and 51.93% of the others). The survival functions and access to bank loans depend on these two motivations that are not exclusive. The specific human capital refers to prior experiences of the entrepreneur. These experiences can be very diverse. Most often, like in Preisendorfer and Voss (1990), researchers consider both experience in the same activity and previous experience as a business owner. The former ensures that entrepreneurs own cognitive abilities. Bruderl, Preisendorfer and Ziegler (1992) argue that this greater experience finally enhances the productivity of the founder, which results in a lower probability of early failure. Blumberg and Letterie (2007) showed, on a sample of Dutch entrepreneurs, that, job similarities exert a negative effect on bankers refusal and that, when entrepreneurs exhibit a low previous income, credits are more often fully denied by bankers. To take into account the role of a professional experience in the main activity, four dummy variables are considered: experience in a same activity, close experience 7 Women may have fewer opportunities to develop relevant experiences, fewer networks to get assistance and greater difficulty in assembling resources (Robinson and Sexton, 1994). Montgomery, Johnson and Faisal (2005) explain these results by a differentiated access to resources and by the low self-confidence of women. 8

9 in the activity, no experience or a multiplicity of experiences % of the founders have acquired an experience in the same activity of the new firm. The percentage is higher when the new firm is financed by banking loans (57.65%). The Pearson test for independence between sector experience and bank loans leads to reject this independence and the log rank test for equality of survivor functions leads to reject this equality in regard to industry experience. To complete this analysis in regard to previous professional experience, three other kinds of previous activity experience are introduced: production activities (23.43% of the sample), management and human resources activities (21.84% of the sample) or commercial activities (33.34% of the sample). The access to bank loans is not independent from this kind of specific experiences and survival functions depend on each of them. On French data, Crépon and Duguet (2004) underlined the differentiated role of bank loans and subsidies according to the previous statute of entrepreneurs on the labour market (employed or unemployed). Prior paid experiences impact positively the access to bank loans and the survival time of new firms. The situation of founders just before the creation is used to proxy this experience. Entrepreneurs can be employed, short term unemployed, long term unemployed or non-worker. Approximately half of the founders in the sample have a job just before the creation. The prior paid experience has an impact upon the survival and is linked with bank loans (see tests in the Appendix 1). The founder can improve his (her) specific human capital when he exerts an effort to prepare the project like writing a business plan and consulting an accountant. Such commitments can signal that new entrepreneurs are well-prepared (Schutjens and Wever, 2000). Blumberg and Letterie (2007) showed that these commitments reduce significantly the incidence of partial loan denial but do not influence the likelihood of fully denied credits. To take into account the preparation of projects, four variables are introduced: the participation to voluntary training activities, carrying out market researches, carrying out prospective financial accounts and receiving advices from specialists. The proportion of projects with training programs is higher when new firms are financed by bank loans (16.21% against 12.23% for the others). New firms financed by bank produce more often than the others market researches (44.45% against 32.83%) and prospective financial accounts (73.43% against 47.91%). They receive more often advices from specialists too (25.72% against 19.23%). Except for market researches, the survival function depends on these factors as well. An experience in setting up a firm should increase the entrepreneur s human capital value and so access to bank loans and the survival of new firms. We introduce a variable equal to one when entrepreneurs answer that they already set up a firm (22.43% of the sample) and 0 otherwise. The proportion of new firms whose founders have already set up a firm is higher when firms are financed by banking loans (23.02%) than among the others (20.31%). The bank loans access and the survival functions depend on the experience in past creation. However, we will have to be careful when interpreting the results, as some individuals can be serial entrepreneurs. This kind of entrepreneurs can prefer selling firms and restarting new businesses (in this case we consider that these firms died) rather than continuing their activity in a unique firm. Arribas and Vila (2007) introduced the concept of accumulated human capital to take into account the case of firms created by several persons. Human capital can indeed be accumulated though a community of individuals as well. Woo et al. (1989) showed that these firms are more successful than those founded by a single entrepreneur. Arribas and Vila (2007) found that they survive longer. Blumberg and Letterie (2007) stressed that applicants who intend to remain the single owner are more likely to face credit rationing. With several owners, the risk of not being fully reimbursed is lower in case of bankruptcy. We introduce variables to proxy this accumulated human capital. We distinguish between founders who 9

10 manage alone, those who manage with managing partners and those who manage with family members. In the sample, 66.55% of founders manage alone, 22.71% manage with members of family and the rest with shareholders. The survival functions depend on these variables and access to bank loans is not independent from them. Finally, we consider the influence of social capital that is measured by the presence of other entrepreneurs in the individual's family. In our sample, 69.37% % of independent entrepreneurs answer that they belong to an entrepreneurial network (73.37 % of founders whose firms are financed by banks and % of the others). The log rank test for equality of survivor functions leads to reject this equality in regard to social network and the Pearson test of independence between bank loans and entrepreneurial network leads to reject it The characteristics of the project Post-entry performance of a new firm does not depend only on the entrepreneur but on the quality of his (her) project as well. The ability of a new firm to reimburse a banking loan depends on environmental conditions and strategic choices not only at current time but at entry time too (Geroski, Mata and Portugal, 2007). Environmental factors refer to the chosen sector that may involve specific conditions on concentration and entry rates. For example, Kaniovski and Peneder (2008) found, on a sample of Austrian firms, differences in hazard rates among different types of manufacturing industries distinguished by the nature of their sunk costs, their reliance on human resources and inputs from external services. As sectors influence significantly new firm s survival, it is not surprising that bankers introduce information on sector affiliation in their screening device. Very early, Altman (1962) showed indeed that corporate bankruptcy was highly sector-dependent. Finally, in the banking sectors, all expectations concerning firms ability to reimburse credit are generally built in reference to standards linked to firms belonging sector. So, we introduce dummy variables to take into account the activity sector of the firm. The Appendix 1 shows that the use of bank loans and the survival are sector-dependent. Firms strategies relate back to the choice of size at entry and the introduction of innovative activities. The choice of entry size is a strategic variable as it allows either to reach the minimum efficiency scale or to stay small enough to escape predation from potential rivals. Empirical studies showed both the positive influence of size on the life duration of new firms (Dunne, Roberts, and Samuelson, 1989, Bates, 1990, Audretsch and Mahmood, 1994, Mata and Portugal, 2004) and the significant influence of size on financial constraints (Galéotti, Schianterelli and Jarimillo, 1994, Himmelberg and Petersen, 1994, Gilchrist and Himmelberg, 1995, Brito and Mello, 1995). More precisely, the correlation between investment decisions and cash flow is decreasing with the size of the firm. Consequently, size is introduced in quite all empirical models that study the financing of new firms (Cassar, 2004). To proxy the size of firm, we consider both the level of the required initial investment and the number of created jobs. Five levels of financial needs are inrroduced (see the Appendix 1). For more than a half of ex nihilo creations, this level is lower than The number of jobs is a continuous variable. On average, an ex nihilo creation creates 1.75 jobs. The level of required initial investment influences both bank loans and survival. The choice to develop innovative activities is a strategic variable too that influence positively firm growth (Hall, 1987, Mansfield, 1962, Yasuda, 2005) and life expectancy (Hall, 1987, Cefis and Marsili, 2004, Esteve-Pérez and Manez-Castillejo, 2008). Lots of theoretical reasons can be given to support the idea that innovative new firms may suffer from credit rationing (see, for example, Freel, 2007). As new firms, they exhibit high level of risk, greater 10

11 scope for informational asymmetries and fixed costs of screening and monitoring. Innovation exacerbates all these drawbacks; the future of new firms is not only risky but uncertain. In this context, investors are unable to adequately assess the technological viability of project and the related potential market. Moreover, bank perceptions of risks are likely to be further aggravated by the bank s inability to use innovative assets as collateral. On an empirical standpoint, results are more ambivalent. However a great majority of studies documented that innovative small firms are rather financially constrained (for example, Westhead and Storey, 1997, or Freel, 2007), particularly at their birth (Egeln, Licht and Steil, 1997). To take into account the nature of innovations, we distinguish three situations: the introduction of new products, the introduction of new methods of production (process) and the opening of a new market (marketing). A founder can declare to innovate in different and non exclusive ways % of founders declare to have implemented a product innovation, 4.03% a process innovation and 5.96% a marketing innovation. Except for process innovations, the survival function does not depend on innovation and access to bank loans and innovation are bound, except for product innovation Collaterals in the banking screening As soon as the banker s mind is put at rest about the quality of both the entrepreneur and his (her) project, the credit contract can be set up. As borrowers hold private information regarding their project quality, premium s variations alone cannot equilibrate the credit market that suffers from both adverse selection and moral hazard and that produces equilibrium with rationing (Stiglitz and Weiss, 1981). In this informational context, collateral requirement may allow lenders to sort observationally equivalent loan applicants and mitigate these inefficiencies. An important theoretical literature motivates indeed collateral as a mechanism that mitigates inefficiencies that arise when borrowers hold ex ante private information (Bester 1985, 1987, Besanko and Thakor 1987a, 1987b, Chan and Thakor, 1987, Boot, Thakor and Udell, 1991). Lenders may offer a menu of contract terms that links premium and collateral. The choice of a specific menu, with given levels of premium and collateral, reveals the nature of the project and limits the consequences of private information 8. If the business fails, collaterals underlying loans guarantee the repayment of debts. Concerning new firms, these collaterals concern the starter s private wealth and the firm s re-deployable assets (Parker, 2004). In particular, owning property assets may be a positive determinant of access to banking loans. For example, Blumberg and Letterie (2007) found indeed that credit denial is less likely to occur when the applicant owns a house. We select in the SINE database the variable that concerns the ownership of the new firm s buildings that can be held by the entrepreneur himself. The proportion of new firms whose founders are owners of the firm s building is higher when firms are financed by banks (7.09%) than for the others (1.78%). The bank loans access and the survival functions depend on this variable. 8 Applicants with higher-quality projects choose secured debt at lower premiums, while those with lower-quality projects select unsecured debt at higher premiums. 11

12 3.3.Instrumental variable and control variables We select the instrument used by Greffe and Simonet (2008), the distance between the location of new firms and the residence of entrepreneurs, and we introduce some control variables in both the estimation of bank loan access and the function of survival. These variables concern the characteristics of demand. The first variable refers to the entrepreneur s activity. It appreciates if the creation is his sole activity or not. We observe a higher proportion of founder s multi activities when the firm does not use bank loans (18.35% against 16.45%). Another variable is the expectation of founder about future cash flows difficulties. Founder whose firm is financed by banks more often anticipates problems of cash flows. Access to bank loans and survival are not independent on this expectation. Other characteristics of the demand are introduced as well. We consider the geographical dispersion of customers. New firm s market can indeed be local, regional, national or international. We observe that the clientele is mainly local (46.53% of the sample) and that this proportion is higher when new firms are financed by banking loans (56.46% against 43.66% otherwise). Information upon the type of clientele (enterprises or not) and the number of customers (one or two, more than two and less than ten, more than ten, more than ten plus few big clients) are introduced as well. Other variables relate back to the nature of relations between new firms and their industrial and commercial partners. We introduce variables on subcontracting relationships (no subcontracting relationships at all, subcontracting relationships representing a large part of turnover and minor role of subcontracting relationships) and cooperation ties for specific tasks (existence of cooperation links versus inexistence of these links). Finally we introduce two last variables in the estimation of new firm s survival: the entrepreneurs expectations of their expected growth and the use of computers. 4. Results In this paper, we present first the estimation of the probit model concerning the banking screening and second the result of the survival analysis that integrates banking loans after the correction of the endogenous bias The screening of new firms by banks The Table 2 synthesizes the results of the probit model. We analyse successively the role of non banking finance, the characteristics of the entrepreneur, the characteristics of the project and the use of collaterals. Table 2: Probit model results of the likelihood of bank loans Independent Variables MODE OF FINANCING - Other loans - Financing by third parties - Subsidies (and other public helps) - Personal funds CHARACTERISTICS OF THE ENTREPRENEUR General human capital and demographic characteristics Education - No diploma (reference) - Technical undergraduates diploma - Secondary school level (baccalaureate) - Post graduate diploma Demographics characteristics - Gender (man) - Nationality (native) - Entrepreneur s age Estimated (0.000) (0113) (0.000) (0.000) (0.000) (0.000) (0.000) (0.478) (0.000) (0.069) 12

13 Personal goals and motivations - Personal goal : entrepreneur catches an opportunity - Taste for entrepreneurship Specific human capital Situation before creation - Employed (reference) - Short term unemployed - Long term unemployed - Non-worker Industry experience in the activity of creation - Without industry experience (reference) - Experience in the main activity - close experience - Multiplicity of experience Previous activity experience - production - management and human resources management - commercial activities (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.003) (0.262) (0.000) (0.057) (0.000) Experience in setting up a firm (0.000) The preparation on entry - Training program (0.912) - Market researches (0.000) - Prospective financial accounts (0.000) - Entrepreneurs receive professional advises (0.001) Interactions variables - Market research*prospective financial (0.075) - Training program* Prospective financial (0.473) - Training program* Market researches (0.218) - Advising* Prospective financial (0.181) - Advising*Market researches 0.203(0.000) Accumulated human capital Management of firm - Manage alone (reference) - Manage with associates - Manage with members of family (0.000) (0.000) Entrepreneurial network (0.000) CHARACTERISTICS OF THE PROJECT Sector - Trade (reference) - Agribusiness - Industry - Building - Transportation - Real estate - Private person services - Enterprise services - Education and social activities Innovation - Product - Process - Marketing Subcontracting relationship - No relation (reference) - Relation but no essential - Strong subcontracting relationship (0.000) (0.000) (0.007) (0.000) (0178) (0.000) (0.771) (0.000) (0.295) (0.000) (0.000) (0.000) (0.611) No cooperation link with other firms (0.000) Size(proxy1) : level of financial need : - Less than 1500 (reference) - Less than 3800 and more than (0.000) - Less than 8000 and more than (0.000) - Less than and more than (0.000) - Less than and more than (0.000) - Less than and more than (0.000) - more than (0.000) Size (proxy 2): number of jobs (continue) -0,028(0.000) 13

14 COLLATERAL : Owner's status of buildings of enterprise (0.000) INSTRUMENTAL VARIABLE AND CONTROL VARIABLES Place of setting-up of the enterprise far of the place of residence (0.000) Multi activity of the founder (0.001) Anticipation : problem of cash flows (0.000) Customers geographical dispersion - Local (reference) - Regional - National - International (0.000) (0.000) (0.611) Clientele of enterprises (0.000) Number of customers - [1,2] (reference) - [3,10] - More than ten - More than ten plus few big LR chi2(66)= , Prob>chi2=0.0000, Pseudo R2= Classification table : Classified + if predicted Pr(E) >=.5 (True E defined as EMPRUNTS = 1) Sensitivity (Pr( + D)) = 41.49%; Specificity (Pr( - ~D)) = 94.97% Positive predictive value (Pr( D +)) = 69.74%; Negative predictive value (Pr(~D -)) = 85.32% Correctly classified = 83.29% The influence of non banking financial means (0.000) (0.000) (0.000) In Table 2, we observe that personal funds are negatively linked with banking debts. This empirical result is rather consistent with the Pecking Order Theory that demonstrates the preference of firms for self-finance before using debt (Myers and Majluf 1984, Myers 1984). Personal funds and banks loans appear to be rather substitutable means. This substitution effect is observed with other loans as well. Other loans are negatively linked with banking loans. New firms seem to trade off between different kinds of debts, in particular trade credits and bank loans. This result confirms the analysis of Petersen and Rajan (1997) or Berger and Udell (1998) on the choice of small firms between trade credit and banking loans. These two financing means are rather substitute and using trade credits does not produce any leverage effect on the access to bank loans (or the reverse). On the contrary, a complementary effect is observed between bank loans and subsidies. The subsidies affect significantly and positively the probability to use banking debt. We can explain this result by either the leverage effect produced by public aids or by the signal effect sent by public aids if these last are granted by public agencies that screen very deeply projects. The financing by third parties, other firms or private equity funds, is not significantly linked with banking loans The characteristics of the entrepreneur As many previous studies, we do not find a positive link between high level of education and access to bank loans. On the contrary, we observe that only entrepreneurs with technical undergraduate diploma use more than the others bank loans. To explain these results, we can rely on the argument given by Cressy (1996) or Asterbo and Bernhardt (2003). Education would be more a demand factor than a supply determinant. High educated people should ask less credit to banks than the others as they are able to find other sources of financing, in particular venture capital. So they should use less banking debts than the others. On the contrary, entrepreneurs who possess an undergraduate diploma should ask more bank loans than the others and consequently use it significantly more than the others. Our results show that the oldest entrepreneurs have a lower probability to be financed by banking loans and there is no difference in access to credit according to the gender. These results are consistent with the predictions of Blumberg and Letterie (2007). On the contrary, the nationality of founder is a significant determinant. French entrepreneurs have a higher 14

15 probability to be financed by banks than other entrepreneurs. Several explanations can be advanced. Some are based on the analysis of the demand side of the credit market. Foreigners can indeed be specialized in activities which need low level of capital to start and their external financing can rely on alternative networks inside, for example, an ethnical community. Others explanations can be based on the analysis of the supply side of the credit market. Bankers are perhaps better informed on French entrepreneurs than on foreigners. They avoid risks and it is possible that French natives would have a better knowledge on French markets than foreigners and so would bear less risk of failure than foreigners. To finish, we cannot omit the hypothesis of a discrimination of banks against foreigners on the credit market. This hypothesis of a potential discrimination by banks according to ethnical factors is confirmed by several studies on developing countries (Raturi and Swamy, 1999, Storey, 2004) and on the United-States (Cavaluzzo and Cavaluzzo, 1998, Levenson and Willard, 2000). Our results show that motivations can influence the use of bank loans. Being an opportunity entrepreneur and being driven by a taste for entrepreneurship increase the probability of bank loans. Concerning specific human capital, we observe first that the entrepreneurs who were unemployed or without any working activity before self employment are less often financed by banks in comparison with base category (workers). Second, the previous activity exerts an influence on the use of bank loans. Surprisingly, only the past experience in production influences positively the use of banking loans. The others kinds of experience, in management or trade and marketing activities, influence negatively the use of banking debts. Two reasons can be given. Entrepreneurs with a past experience in production can develop more costly projects than the others and so ask for more credit. Banks can interpret a past experience in management or trade and marketing activities as not well fitted to make a start-up succeed. Third, experience in the main or close activity increases the probability to obtain bank loans. Fourth, experience in setting-up a firm affects negatively the probability to use banking debts. Either the serial founders ask less credit than the others as he is wealthier than the others because of past entrepreneurial successes or past experiences in business can be a failure that influences negatively the supply of banks. In theory, the preparation of the entry should convey information to bankers on the credibility of the project. We find that training programs do not affect the probability of bank loans. On the contrary, market researches and financial forecasting statements play a significant role. Financial forecasting statements increase the probability to use banking debt. This probability does not increase when founders make certified the quality of this information by an advisor or when they follow a training program. One explanation can be that bankers do not need supplementary signal and expertise when they analyze financial forecasting statements. The positive impact of financial forecasts is higher when entrepreneurs drive jointly a research market (significant at 7.5%). On the contrary, when entrepreneurs only declare to realize market researches or have been advised by an expert the probability to use banking debt decreases. Concerning the accumulated human capital, we observe that managing with family members (in regard to manage alone) or belonging to an entrepreneurial network increase the probability to use banking debts. These factors can be interpreted by bankers as the signal that new firms will benefit from strong support in case of difficulties. Managing with associates does not affect the probability to use bank loans. This result can be explained by a demandside argument. Several owners can be correlated with an access to more financial means so that new firms ask less for banking loans. 15

16 The characteristics of project Except for real estate sector and enterprise services sectors, all sectors appear to affect significantly the use of banking debt comparatively to the base sector (the trade sector). This result confirms that bankers use information upon activity sector to screen potential borrowers. When innovation is focused on process, innovative firms use less debt than the others. The result is strictly opposed when innovations concern the marketing function. The result is not significant for product innovation. We can explain this result because the former implies more expansive investments than the latter. Concerning links with other firms, the subcontracting relationships decreases the probability to be financed by banks when they do not represent a major part of the turnover. The absence of cooperation with other firms increases the use of bank loans. Once again we can explain these results by a demand effect. When firms develop strong links with other firms, we can expect they benefit from inter-firm financing. Finally, our empirical investigation shows that high levels of capital are associated with rather high probabilities to use banking debts. The impact of a change in the amount of capital on the use of banking debts (in comparison with the reference level - less than ) increases with the level of capital 9. For the other proxy of size (number of employees), we find a strictly opposite results The use of collaterals and control variables As expected, we observe a positive significant effect of the ownership of buildings by new firms on their use of bank loans. The control variables concerning characteristics of demand are by and large significant. Except for international market, the dispersion of customers (in regard to local customers) decreases the probability to use banking loans. On the contrary, the likelihood of banking loans increases with the number of customers, except when customers are other firms. Finally, when entrepreneurs expect a problem of cash flow, they use more bank loans The impact of bank loans on the survival of new firms Only the results concerning the impact of financial variables on the survival are presented 10. Four models are summed up in the Table 3: without and with correction of omitted variables bias (respectively the models 1 and 2 and the models 3 and 4) and without and with continuous time-varying financial covariates (respectively the models 2 and 4). If the sign of coefficients is significantly negative, the impact of the corresponding variables corresponds to a lower hazard and therefore a longer survival time. These models allow us to identify the impact of bank loans on the survival of new firms. We comment results on the other variables as well. 9 The coefficient of CAPITALi, for i={2,...,7} are indeed superior to one and they increase with the level of capital. 10 the result concerning the others variables are given in Appendix 2 16

17 Table 3: The impact of mode of financing on the survival Exogenous bank loans Endogenous bank loans MODE OF FINANCING Independent Variables (1) (2) (3) (4) - Bank loans - Estimated bank loans - Other loans - Financing by third parties - Subsidies (and other public aids) - Personal funds - Bank loans*age - Estimated bank loans *age - Other loans *age - Financing by third parties*age - Subsidies (and other public aids)*age - Personal funds*age *** *** ** *** *** Legend: coefficient significant at: 10%( + ),5%(*),1%(**),1 per thousand (***) *** *** ** *** *** *** *** *** 4.48e *** *** *** ** *** *** *** *** ** *** *** *** * ** *** Our empirical study stresses a significant and positive impact of banking loans at birth on the survival time of new firms even after the correction of the omitted-variables bias. Our results are in line with those given by Parker and Van Praag (2006) on a Dutch database. More surprisingly, we do not confirm those of Greffe and Simonnet (2008) who yet worked on the same database. This difference can be justified because we do not work on the same population. Greffe and Simonnet (2008) focused on the cultural sector and worked on all new firms whatever may be their mode of entry. By contrast, in this article, we work on all sectors but we focus on ex nihilo new firms. According to us, this last difference of focus has a lot of consequences. For takeovers, bank loans after correction of omitted variables bias should be less important. Bankers screen indeed more efficiently takeovers than start-ups for at least three reasons. First, takeovers are less risky than ex nihilo new firms. They survive a longer time than start-ups. Moreover, a bank can share risks with other banks and with private equity funds, which are in France more incline to finance takeovers than start-ups. Second, they are less opaque than ex nihilo new firms and some track records can be exhibited. Third, the amount of required banking debt is higher for takeovers than for start-ups. Average fixed costs are so smaller for takeovers than for ex nihilo new firms. Thus, bankers should be more efficient in screening firms when potential customers are takeovers than other new firms and so access to banking loans for takeovers should not give an advantage in the length of survival as bankers appreciate with a relative good screening survival chance of takeovers. The situation is different for ex nihilo new firms for which bankers have to cope not only with informational asymmetries but surely uncertainty as well. For these firms the screening of banks is less efficient and banks can finance bad firms that should disappear and that will survive a longer time thanks to bank loans. Preliminary estimations on the subsample of takeovers confirm this view. Ex nihilo creations and takeovers are not the same and their simultaneous treatment can introduce a bias. Our empirical study stresses another important result. The effect of banking loans is persisting over time. As soon as the screening of banks is taken into account, the (still positive) impact of banking debt on the survival of new firms is decreasing. The basic proportional hazard regression assumes the following relationship: h =h t exp where are covariates and the coefficient. After the introduction of times-varying covariates ( ), the proportional hazard is equal to h =h t exp +. The coefficient of bank loans is higher after correction of 17

18 endogeneity (without and with introduction dependence on time) than before. So his impact on hazard rate increases and therefore its effect on survival diminishes. This result means that the screening of banks on the population of new firms allows them to distinguish (however not perfectly) among good firms and bad ones. To study the persistence of the effect of bank loans on firm s survival, we estimate the joint probability of obtaining bank loans and being still alive at n years (conditional or not to the survival at n-1 years). The results of bivariate probit models with correction of omittedvariable bias for bank loans are presented in the table 4. This analysis confirms the persisting impact of debt. Concerning the conditional survival at n years given the survival at n-1 years, the effect of bank loans is decreasing for two years of survival and is increasing for three years. This result confirms our view concerning the role of bank loans. At the beginning of firm s life, the screening of banks explains the survival of firms, but after a second effect appears: the positive link between the use of bank loans and minimal efficient size of firm. Table 4: Bivariate analysis: survival at n years and access to bank loans Estimated model Bank loans CI for bank loans* One year survival 0,573 [0,411; 0,734] simultaneous estimation of survival at n years and bank loans Two years survival 0,658 [0,506; 0,810] Three years survival 0,931 [0,797;1,066] Four years survival 0,920 [0,787; 1,053] simultaneous estimation of survival at n years given survival at n-1 years and bank loans Five years survival 0,976 [0,854; 1,097] Two years survival/one years 0,488 [0,276; 0,700] Three years survival./two years 0,933 [0,725; 1,142] Four years survival/three years 0,403 [0,157; 0,649] Five years survival/four years 0,460 [0,208; 0,713] *95% confidence interval As a linear dependency of times is assumed, this last effect cannot appear in survival analysis. In the models 2 and 4, the impact of debt is strong at the beginning but decreases over time. Nevertheless, the coefficient of bank loans crossed with age is small. The total impact of debt loans on hazard rate can be approached by the sum of bank loans coefficient plus the coefficient of bank loans crossed with time age 11. This total impact on hazard rates is equal to age It is negative if age is inferior to days. In our sample, the maximum time of life observed is 2098 days; consequently the positive effect of bank loans on survival persists during the period of observation. This measure does not allow concluding on the significant effect over time. Nevertheless, it confirms the result of bivariate probit models. 5. Conclusion Financial constraints are among the most cited impeding factors for entrepreneurial dynamics to flourish. Finding the proof of credit rationing is a very difficult empirical task. In this article, we decide to go beyond this issue and to question directly the role of the use of banking loans in the survival of French new firms. We test empirical models that incorporate 11 This expression is the derivate of hazard rate relative to bank loans. 18

19 not only financial factors but all the variables linked with the entrepreneurs and the firms given by the academic literature on entrepreneurship. We take into account the screening process used by bankers too. With this correction of the omitted variables bias, we deal with the endogeneity between bank loans and the other variables and we prevent from the overestimation of the impact of bank loans on the survival of new firms. In this article we use the SINE database that gives information on the start-up at birth, three years after the beginning and five years after. We observe that only 21.84% of new firms use bank loans at their birth. Despite the fact that the use of bank loans remains relatively low among French new firms in 1998, our results stress that that the influence of this mode of financing on the survival of new firms is significant and positive. This result is confirmed even after the correction of the omitted-variables bias. Consequently, we can conclude that banking debts matter for new firms as using this mode of financing at birth makes their likelihood to survive increase. Another important is the persistence of this positive effect of banking debt over time. Our study provides an empirical analysis of the determinants of the use of banking loans too. As expected we show the role of collaterals and the influence of sector specialization. The influence of innovation depends on their nature with a low use of debt when the innovation concerns the process. As many studies, we show that education does not impact positively the use of debt. Consequently, we confirm that education would be more a demand factor than a supply factor on the credit market. We stress the negative role played by the nationality as being a foreigner reduce the use of debt. Like for education, we explain this result by both demand and supply side factors. We stress the role of personal goals and motivations and the influence of specific human capital as well such as past activities. We show in particular the positive influence on using banking debt to have a past paid activity, an experience in a close activity or in production function, and to be able to produce forecasting financial statements. Our study shows as well that the screening of banks on the population of new firms allows them to distinguish (however not perfectly) among good firms and bad ones. The coefficient of banking debts in the survival function of new firms is indeed lower after the correction of the omitted-variables bias. Banks cannot perfectly identify the future successful new firms and those that will default but their screening must be taken into account both by academic studies and by policy makers. Our results show that bankers evaluate rather well the chances of success of new firm despites the lack of track record. Our study supports the idea that banks can be used as a rather interesting channel to distribute public aids to new firms. Granting banking loans to new firms is indeed a factor of longevity for them and the screening process of banks is rather efficient. According to our results, state-financed measures, like loan guarantee schemes and/or start-up loans (PCE), that are distributed by commercial banks should be a rather good solution to help new firms. Finally this study shows the need to distinguish among the modes of entry (ex nihilo creation versus takeovers) and the demand side and supply side determinants when the use of banking debt is analyzed. These subjects will be the focus of future researches. 6. References Acs, Z. and D. Audretsch, (Eds.), (1985), Innovation and Technological Change: An International Comparison, New York: Harvester Wheatsheaf. Allen, F. and Santomero, A., (1998), The Theory of Financial Intermediation, Journal of Banking and Finance, 21, pp

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