Nadine Levratto
email: nadine.levratto@u-paris10.fr

Denis Carre
email: denis.carre@u-paris10.fr

Regional dynamics and start-ups: Evidence from French departments in 2011

Keywords: entrepreneurship, entry rate, spatial dependence, French departments JEL Codes: L26, R11, C21

This paper seeks to determine the exact role played by regional dynamics in the creation of companies. The notion that territorial dynamics influence entrepreneurial activity seems to be backed up, first of all, by the fact that it is at the regional level that the direct influence of the ecosystem of wealth and of material, human and organizational resources is strengthened through agglomeration effects. We empirically address this question considering the case of French departments in 2011. In order to take into account the role played by the neighbourhood and the resulting spatial dependence, we estimate the sensitivity of both the overall entry rate and the entry rate in the manufacturing industry using spatial econometric estimation techniques, an approach which enables us to control the effect of spatial autocorrelation. Our results show that the creation of companies highly depends on local factors and that the source of local dependence differs according to the entry rate used as an explained variable. Whereas a spatial lag applies at the overall level, the creation of companies in the manufacturing industry is more oriented by exogenous shocks so that a spatial error model is more appropriate.

Résumé : Ce papier cherche à déterminer le rôle joué par les dynamiques régionales dans la création d'entreprises. L'idée que les dynamiques locales influencent l'activité entrepreneuriale s'appuie sur le fait que les effets d'agglomération qui se produisent qui se produisent au niveau d'un écosystème local renforcent les effets directs des ressources locales que sont les revenus ainsi que les ressources matérielles, humaines et organisationnelles. Nous abordons cette question de manière empirique en considérant le cas des départements français en 2011. Afin de prendre en considération le rôle joué par le voisinage et la dépendance spatiale qui en résulte, nous estimons la sensibilité du taux de création d'entreprises global et du taux de création dans l'industrie en utilisant des techniques d'économétrie spatiale qui permettent de contrôler les phénomènes d'auto-corrélation spatiale. Nos résultats montrent que la cré »ation d'entreprises dépend fortement des facteurs locaux et que la source de dépendance locale diffère selon que l'on mesure le taux de création global ou le taux de création d'entreprises industrielles. Alors qu'un retard spatial intervient au niveau global, la création d'entreprises dans le secteur industriel est davantage influencée par les chocs intervenant au voisinage, si bien que le recours à un modèle avec erreur spatiale s'en trouve légitimé.

INTRODUCTION

Since the very beginning of the 1990s, a large number of policy initiatives have sought to promote business start-ups to foster both economic and job growth. The recent crisis has strengthened this tendency to facilitate firm creation with the implementation of a simplified regime for the self-employed people aiming at launching their own business. All these policies rest upon the idea that new firms contribute to the dynamism of the economy and that they are able to promote structural change, innovation and new job creation as well.

However, despite the large number of papers, books and reports on this subject a lot remains to be done to understand better why and how potential entrepreneurs decide to take action [START_REF] Markman | Inventors and new venture formation: The effects of general self-efficacy and regretful thinking[END_REF].

From [START_REF] Bird | Implementing Entrepreneurial Ideas: The Case for Intention[END_REF], scholars 2 agree that entrepreneurial intentions are the result of individual features (personality, motivation, skills and experience) combined with environmental characteristics (market situation, socioeconomic context). The limited empirical literature clearly shows that entrepreneurs exhibit geographic inertia [START_REF] Keeble | New Firms, Small Firms and Dead Firms: Spatial Patterns and Determinants in the United Kingdom Regional Studies[END_REF]Sorenson and Audi 2000) and supports the assumption that 'regions matter' for entrepreneurship research. A key synthesis of this question has been proposed by [START_REF] Audretsch | The geography of firm birth in Germany[END_REF] in their study about the local determinants of business creation in Germany. This paper inaugurated a long series of empirical studies aiming at enlightening the reasons why European regions or countries differ from an entrepreneurship point of view. [START_REF] Fritsch | New Firms and Regional Employment Change[END_REF] shows that the number of start-ups clearly depends on the industrial structure in the considered region. [START_REF] Armington | The determinants of regional variation in new firm formation[END_REF] also note that traditionally most studies on determinants of regional entry use variables such as unemployment rate and population density as explanatory variables. More recently, theories of new economic geography and endogenous growth theories (e.g. [START_REF] Krugman | Geography and trade[END_REF][START_REF] Aghion | Endogenous growth theory[END_REF] provide significant contributions to explain the choice of location for an entrepreneur who is considering starting a new firm. These theories imply that spatial agglomerations and location generate advantages in terms of spillovers and co-operation between firms. More recently, [START_REF] Kibler | Formation of entrepreneurial intentions in a regional context[END_REF] also shows that individual and local characteristics are mutually reinforcing.

These papers agree to consider that agglomeration and urbanization external effects are capable of boosting entrepreneurial spirit. These relationships may have different aspects: a specialized local labour market creating a large potential skilled labour force, the functioning of local entrepreneurs' networks making it possible to reduce risk and lack of information that 2 Dejardin (2011) proposes a comprehensive review of literature on this topic.

may deter business creation [START_REF] Acs | The knowledge spillover theory of entrepreneurship[END_REF]. As these external and network effects are stronger in high-density areas, these agglomeration effects justify a higher entry rate in urban areas with a high economic and demographic density.

All these papers have in common the fact that they consider any area, all other things being equal, and without taking into account its neighbourhood. However, Plummer (2010) recently pinpointed the importance of spatial dependence in entrepreneurship research. This is all the more important since the data used are spatial in the sense that the location of the observations is observed. So, instead of focusing on the spatial heterogeneity of the entrepreneurship context as previous papers do, we seek at considering the spatial dependence that is equally underlined by theories of entrepreneurship [START_REF] Shane | The promise of entrepreneurship as a field of research[END_REF] This paper contributes to the empirical literature by applying spatial econometric techniques to take into account the spatial auto-correlation in the business creation process. It

shows to what extent the entrepreneurial profile of an area not only depends on its own local characteristics but on the profile of neighbouring areas as well. Such a phenomenon, often intuited but barely demonstrated, is enlightened considering data computed at the "département" "level3 to capture the local capacity to create new business and the local characteristics that may drive this entrepreneurial activity.

The remainder of the paper is structured as follows. Section 2 recalls the relationship between entry rate and geographic characteristics leading us to define an empirical model of business creation at the local level. Section 3 describes the French situation and the difference observed among the 93 mainland "départements" and presents the variables introduced in the model to depict them. Section 4 presents the econometric strategy and the results obtained.

Section 5 concludes and proposes a knowledge building research agenda for this field of research.

THE LOCAL CONDITIONS OF BUSINESS CREATION

The literature tends to present entrepreneurship as a multifaceted phenomenon driven by three groups of explanatory variables: those relating to the newly founded firm, to the entrepreneur, and to the external factors embracing the geographical and industrial environment in which entrepreneurial phenomena occur [START_REF] Schutjens | Determinants of new firm success[END_REF]. In this paper we mainly focus on the third one as we aimed at explaining the local determinants of business creation.

The analysis of local differences in the entrepreneurial process began at in the very beginning of the 1990s with international comparisons aiming at explaining the difference in entry rates among countries. They concluded that economic conditions, large companies' restructuring, households' incomes, consumption, economic policy and technological change explain why some countries are more entrepreneurial than others [START_REF] Keeble | New firm formation and small business growth in the United Kingdom: spatial and temporal variations and determinants[END_REF]. They were rather soon replicated at a regional level. The main results were very similar. For instance, Reynolds and Storey (1993) and [START_REF] Reynolds | Cross-national comparisons of the variation in new firm formation rates[END_REF] conclude that local demand approximated by demographic growth, the share of SMEs in the local productive system and the degree of urbanization drive business creation.

In this field of research, the most documented analysis has been proposed by [START_REF] Keeble | New Firms, Small Firms and Dead Firms: Spatial Patterns and Determinants in the United Kingdom Regional Studies[END_REF] who insist upon the diversity of local determinants of firm creation. They identify 31 factors possibly influencing entrepreneurship in a given area and show that the entry rate is inasmuch important as the banking system is developed, the local labour market is specialized, and cities are big. If entrepreneurial spirit also depends on the average size of already operating companies, the role of this factor differs according the industry: in the consumer goods industry, the entry rate positively depends on the share of small companies (seedbed effect) whereas in the services industries the higher the share of large companies, the greater the creation of new firms. From these extensive empirical analyses of the spatial differences in business creation it is possible to identify three major categories of factors able to explain why some areas are more or less entrepreneurial than others. Following [START_REF] Keeble | New firm formation and small business growth in the United Kingdom: spatial and temporal variations and determinants[END_REF] and [START_REF] Johnson | Spatial variations in the determinants and effects of firm births and deaths[END_REF], one may differentiate between local demand factors, the supply of founders and the policy environment.

Local demand factors

One can expect start-up location decisions to be influenced by the market size and potential demand of the areas under consideration. The solvent demand in a given region depends on the number of companies and inhabitants in the area and their incomes. In this section we focus on households whereas inter-enterprise relations are discussed in the agglomeration section below.

 Income. Higher levels of income increase demand, but they also provide access to capital that a potential entrant needs in order to start a firm [START_REF] Reynolds | Autonomous firm dynamics and economic growth in the United States, 1986-1990[END_REF]. But high levels of income also mean a high labour cost as in large cities. It is thus possible to consider that a high level of income might therefore also deter entry in industries that are sensitive to high labour costs. In any case, the literature broadly agrees that the demand effects dominate so that higher incomes in an area tend to facilitate firm creation.

 Population. If firms prefer to locate in regions with a large population, this may have a self-reinforcing effect. Areas experiencing a high entry rate tend to become more and more attractive for new entrepreneurs [START_REF] Krugman | Space: The final frontier[END_REF]). On the opposite, the same self-reinforcing mechanism exists in areas where an already small population is also decreasing, deterring potential entrepreneurs from launching their business.

Incentives to create a business

Firm creation depends not only on personal characteristics, such as the fact that some people have a higher entrepreneurial spirit than others (Casson 1983), but also on their economic environment.

 Among the different reasons why an individual decides to act as an entrepreneur, difficulties in accessing a job due to a high level of unemployment in the area is a major one. This idea is developed by [START_REF] Storey | The birth of new firms-Does unemployment matter? A review of the evidence[END_REF], according to whom high unemployment rates can cause higher entry rates since they force unemployed workers to start their own companies as an alternative to unemployment. This assumption has often been tested as recalled in the survey of literature proposed by Carree (2002).

However, the results are quite unclear as they are highly sensitive to the nature of the data. Empirical studies using time series often confirm a positive relation between unemployment and firm creation, whereas cross-section studies find a negative relationship. [START_REF] Santarelli | Unemployment and Firm Entry and Exit: An Update on a Controversial Relationship, Regional Studies[END_REF] investigate the relationship between regional unemployment and firm entry and exit in Italian regions. Taking into account the effect of unemployment on firm exit, in addition to that on firm entry, they do not find evidence for the 'unemployment push' hypothesis. Considering to what extent unemployment pushes people to create their own business we can, nevertheless, reasonably expect a positive relationship.

 The size structure of the productive system in a given area is expected to play a role in the propensity to create new companies. A high rate of large companies in a given area may then encourage or deter the creation of new business depending on the nature of the industry. In some activities characterized by economies of scale, a higher rate of large companies may deter new entrepreneurs from entering the market. On the opposite, in industries where entry barriers are low, starting a new firm is easier as incumbents do not have the possibility to erect barriers to entry thanks to a growth strategy [START_REF] Shapiro | The determinants of exit and entry reconsidered[END_REF].

 The level of education and skills have been identified as an important determinant of the probability for people to start a new firm. However, the sign of the relation highly depends on the period during which such a relation is estimated. [START_REF] Evans | Small business formation by unemployed and employed workers[END_REF] suggest that the probability to start a new firm is higher for well-educated people who are supposed to be more able to identify and exploit entrepreneurial opportunities. However, and this is narrowly connected with the previous point, unskilled people have a higher probability to be unemployed and, thus, may find in business creation a solution to their exclusion from the labour market. Indeed, as brought up by Leger-Jarniou ( 2001) from a study on young French engineering and business schools students, the decision to become an entrepreneur is a second choice option since they prefer an ordinary job, which generates higher personal income and greater professional stability than being an entrepreneur. [START_REF] Lee | Creativity and Entrepreneurship: A Regional Analysis of New Firm Formation[END_REF] reconsider this question taking into account skills, the social context and the diversity of the population. They conclude that business creation is strongly associated with cultural creativity when controlled for the variables suggested in the literature.

Agglomeration effects

Based on [START_REF] Marshall | Principles of Economics; An Introductory[END_REF] suggestion about co-location, agglomeration effects refer to the advantages available to a firm when it is located close to other firms [START_REF] Puga | The magnitude and causes of agglomeration economies[END_REF]. Three main reasons explain why companies in the same industry tend to locate near each other [START_REF] Rosenthal | Evidence on the nature and sources of agglomeration economies[END_REF].

 Sharing: the enlargement of the market of input providers enables agglomerated companies to exploit economies of scale in their production process (average production costs decrease as the output increases). As they know they are able to sell a high quantity of goods, the providers of input may propose a supply adapted to the specific needs of their various customers. In this way, proximity increases the global profit in the economy.

 Matching: Lower transport costs and proximity to suppliers make it easier for different types of customers to find an appropriate seller. They lower consumers search costs increasing the likelihood of visitation, and thereby heightening the demand experienced by agglomerated firms. Agglomeration also helps improve matching on the labour market so that firms and workers are less likely to settle for unproductive matches. Moreover, a high density of employers and employees in the same area reduces time spent looking for suitable jobs and other transaction costs so that the labour market becomes more efficient and the productive process as well.

 Learning: agglomeration provides more opportunities for people and firms to learn from one another and from the environment around them. Thanks to geographical proximity, opportunities for face-to-face contacts, which facilitate knowledge exchange, increase. The learning process enhanced by agglomeration also increases the possibility of knowledge spill-overs and innovation.

These localisation economies give economic advantages to dense areas. The prevailing rule is that colocation of enterprises generates over-performance [START_REF] Krugman | Geography and trade[END_REF][START_REF] Combes | The productivity advantages of large cities: distinguishing agglomeration from firm selection, WP, Crest[END_REF][START_REF] Martin | Spatial concentration and plant-level productivity in France[END_REF]) because of the positive external effects. The different factors generating these effects may also play in favour of an increased entry rate (Joffre-Monseny et al. 2011). Dense areas should thus be characterized by a higher rate of entry because potential entrepreneurs foresee that they have better chances of surviving in such places.

The challenging problem of spatial auto-correlation

Spatial location of individuals is seldom considered as a determinant of entrepreneurs choices. When scholars consider this point, they examine the influence of region on entrepreneurship either by introducing dummy variables [START_REF] Blanchflower | What Makes an Entrepreneur?[END_REF][START_REF] Fairlie | Ethnic and Racial Self-Employment Differences and Possible Explanations[END_REF] or by adding regional characteristics as explained variables in the model to be estimated [START_REF] Blanchflower | What Makes an Entrepreneur?[END_REF][START_REF] Glaeser | Entrepreneurship and the city[END_REF][START_REF] Glaeser | Entrepreneurship and the city[END_REF][START_REF] Glaeser | Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain[END_REF]. The main drawback of this technique lies in the high number of dummy variables introduced in the model. In addition, none of these studies allow for the determination of neighbourhood effects whereas they are more and more considered as key factors in explaining the entrepreneurial process. Plummer (2009) and [START_REF] Plummer | The Geography of Entrepreneurship[END_REF] emphasise the spatial dimension of entrepreneurship. Drawing upon [START_REF] Cooper | Entrepreneurship and high technology clusters[END_REF], who demonstrated that start-up performance is sensitive to local resources, Plummer (2009) Two challenges result from the spatial feature of entrepreneurship. The first one consists in detecting the spatial dependence affecting the "georeferenced" data used to estimate the propensity to create new business. The second one lies in determining the method to adopt in order to mitigate this spatial dependence in the context of linear regression. They are successively addressed in the next Section.

DATA, EMPIRICAL MODEL AND ECONOMETRIC STRATEGY

In These data are barely available yearly; we succeeded in getting the maximum number of them for 2011. This date is appropriate at it is a very recent one. In addition the majority of the data collected are no available for the subsequent years.

This Section presents the dataset used to determine to what extent local characteristics influence entry rate. We specify the empirical model and the econometric strategy adopted to estimate it below.

The empirical model

We assume that the entrepreneurial intensiveness of an area is given by the rate of new companies created a given year. It is defined as the number of new companies divided by the total number of companies operating in the same area over the same year. This definition is known to pay more attention to the growth rate or the renewal rate of the global productive sector. This makes a difference with the ratio (new companies/resident population) which, according to [START_REF] Garofoli | New Firm Formation and Regional Development: The Italian Case[END_REF], allows for a more accurate appreciation of the local entrepreneurial spirit. We did not keep it in this paper as our main concern has more to do with the productive system than with the propensity to create new business.

Noted CREA, the entry rate is thus computed as:

CREA d,t = NewEnt d,t / ExistEnt d,t (1)
where NewEnt However, there is a major difference with the total entry rate as the number of new businesses almost always exceeds the number of operating manufacturing companies. This surprising phenomenon has a twofold origin. On one hand, the crisis accelerated the collapse of the manufacturing industry a slow decline long recognised in France [START_REF] Askenazy | Face à la désindustrialisation, quelle stratégie industrielle ?[END_REF][START_REF] Aubert | Délocalisations et réductions d'effectifs dans l'industrie française[END_REF]. It explains the relatively low number of companies operating in each department. On the other hand, the closure of production sites and number of insolvent firms in the manufacturing industry has increased sharply over the last few years, pushing down the survival probability of these companies and, thus, increasing the value of the entry rate.

Looking at the maps, it is quite clear that the entry rate in a given department is not indifferent to the characteristics of its neighbours. Instead, hot and cold spots emerge. One may identify macro-areas characterised by uniformly high (low) entry rates. This The incentives to create a business depend firstly on the capacity to find a job. The different aspects of the relation between unemployment and firm creation are discussed in Audresch and Dhose (2010). Two opposite influences may be identified, the refugee effect on one hand, and the shumpeterian opportunity effect on the other. The refugee effect is strong inasmuch as regional economic business climate is poor and that people a led to consider firm creation as means to improve their skills, to accumulate experience and to be occupied [START_REF] Aubry | Les cycles macroéconomiques et le comportement entrepreneurial sur données régionales françaises, 6ème Atelier Stratégies, Espaces et Territoires[END_REF]. Refugee effect tends to take precedence over opportunity effect during an economic slowdown. Due to the economic crisis France is still facing in 2011, the coefficient associated with the variable UNEMPL is expected to be positive.

As education and skills have been identified as able to influence firm creation, we introduce the share of people having at least a bachelor degree in the labour force as an explanatory variable in the model. If the sign associated is positive, it means that skilled people are more able than other ones to detect business opportunities. If it is negative, it means that low skilled workers may face more difficulties finding a job and have a stronger incentive to create their own.

Average size and competition in a given area may either encourage entrepreneurs to carry out their projects or deter them from doing so. We consider that scale economies are the most powerful driving force in such a process. To capture them, we compute the share of employees working in plants employing 100 or more full time workers in the total number of employees in a department. Its effect is expected to be negative.

When the "net benefits to being in a location together with other firms increase with the number of firms in the location" (Arthur 1990: 237), the entry rate is supposed to be higher in areas where a large number of firms are already located. We adopt this perspective and, as in [START_REF] Ciccone | Productivity and the Density of Economic activity[END_REF], we approximate agglomeration effects using the ratio, total number of employees in a given department divided by the area measured in square kilometres of this department. As an alternative measurement ratio, we also consider the number of plants located in a department divided by its area (square kilometres). Last but not least, the number of employees in industrial manufacturing compared to the total number of employees may also be introduced as a proxy for agglomeration effects. Whenever the sign is positive, one may conclude that new industrial business tends to locate in industrial areas; whenever it is negative it is possible to consider that entry and firm creation is a complement to industrial activities. As required by spatial econometric techniques, all the variables are standardized subtracting the mean and dividing by the standard deviation5 . This reduces the problems with too large variance due to heterogeneity in department economic performance and demography. The characteristics of the variables used to run the estimations are presented in Table 1, Appendix 1 depicts the souces and the simple correlation matrices are shown in Appendix 2.

Econometric strategy

The spatial dependence or spatial (auto)correlation constitutes a real challenge in explaining entry rate at the local level. We thus pay major attention to this problem and, in accordance with the literature [START_REF] Doh | Using spatial methods in strategy research[END_REF], we renounce the OLS estimator which has been proved to be inappropriate when a variable at one location is spatially dependent [START_REF] Anselin | Spatial dependence in linear regression models with an introduction to spatial econometrics[END_REF]. Instead, we choose to estimate our model explicitly taking into consideration spatial dependence.

Following [START_REF] Lesage | An Introduction to Spatial Econometrics[END_REF] we consider two ways to introduce spatial autocorrelation in regression models. The first one is the spatial-lag model. It refers to a situation where a phenomenon in one region is affected by a similar one in nearby regions. Such a model is appropriate when there are spillover effects from neighbouring regions. The spatial error model assumes that one or more explanatory variables have been omitted from the model whereas they influence the dependent variable and are spatially correlated. It occurs when a random shock in a given area spreads to neighbouring regions.

Both approaches require the preliminary specification of a matrix of spatial weights (W). A spatial weight matrix W is a square N x N matrix with elements w ij (where i and j are observation indexes), such that w ij  0 iff the observations i and j are neighbours. By definition, w ii  0.

There are many possibilities for spatial weight specification identified in the literature [START_REF] Anselin | SpaceStat tutorial: A workbook for using SpaceStat in the analysis of spatial data[END_REF]. In this paper we choose a contiguity spatial weight matrix6 . In such a matrix (named W1), the same weight is attributed to all neighbours of an observation (w ij = 1 if i and j are neighbours). We also scale the individual rows (or columns) of a spatial weight matrix by the row totals to avoid singularity problem. In such a row-standardized contiguity matrix the spatial weights of each observation i depend only on the number of its neighbours n i : To test the spatial autocorrelation we calculate Moran's I [START_REF] Moran | Notes on Continuous Stochastic Phenomena[END_REF][START_REF] Cliff | Spatial processes: Models and applications[END_REF]) and Geary's C statistics [START_REF] Geary | The Contiguity Ratio and Statistical Mapping[END_REF] The spatial-lag model can be defined as:

CREA= ρW.CREA+Xβ+ ε (3)
where CREA is an Nx1 vector of observations on entry rates, with N = 94; W.CREA is the spatial lag of business creation and ρ is the spatial autoregressive parameter; X is the N × k matrix of explanatory variables; β is the vector of regression parameters and ε is a vector of errors. It is worth noting that spatial dependency is similar to having a lagged-dependent variable as an explanatory variable.

We apply the spatial-error model as an alternative to the spatial-lag one. It is more relevant than the spatial-lag approach when the distribution of residuals in different departments displays spatial correlation. Residuals may be spatially correlated if there are unobservable risk factors concentrated across the areas. The spatial-error model is defined as:

CREA 4

(5)

where λ is the spatial-autoregressive coefficient and μ is a vector of errors that are assumed to be independently and identically distributed. Note from equation ( 5) that errors depend on the weighted average of errors in neighbouring regions.

nonzero ρ; likewise, when testing for ρ = 0, LM ρ also respond to nonzero λ. The robust tests * and * have been designed to solve this problem [START_REF] Anselin | Simple diagnostic test for spatial dependence[END_REF]. The results of the tests lead us to accept H0 (no spatial dependence) in the test for spatial error and to reject it in the diagnosis of spatial dependence. We will thus only discuss the results obtained from the estimation of a spatial-lag specification in the remainder of this Section. Preliminary OLS regressions show a R² adjusted equals to 0.42 for Model 1 and 0.77 for Models 2 and 3. This suggests that overall "regressors" have a significant effect on the dependant variable. Moreover, the Variance Inflation Ratio (VIF) is lower than 4. Our initial tests however indicate that the OLS model can be rejected in favour of the spatial lag model.

We estimate the spatial models using the maximum likelihood procedure developed by [START_REF] Pisati | Tools for Spatial Analysis[END_REF] for Stata. Specifications of models 2 and 3 presented in Table 4 have a superior explanatory power than the one of the first model. We only comment on them 7 . In addition, the variable of interest introduced in this model is not significant, a fact which leads us to leads to infirm the existence of a relationship between skill and entry rate. Acceptable range for rho in the spatial error models: -1.644 < ρ < 1.000 7 The results obtained estimating the first model strongly differ from the conclusion reached by [START_REF] Guesnier | Regional variation in new firm formation in France[END_REF] according to rate of graduate had a positive effect in the firm creation rate measured at the regional level. One may expect that the general increase in working force training that happened since the end of the 1990s causes this change. Spatial Weights matrix (W) is first-order queen-continuity with 1 if departments i,j (i≠j) share common border and 0 otherwise ; estimated standard errors in parentheses. OLS= ordinary least squares; LM = Lagrange Multiplier; AIC is Akaike's information criterion [START_REF] Akaike | A new look at the statistical model identification[END_REF] and BIC is the Bayesian information criterion or Schwarz criterion (Schwarz, 1978) estimated thanks to the Stata procedure.

8

As expected, the coefficient associated with N_Chom is significantly positive confirming a refugee effect frequently mentioned in the literature. The effect of unemployment remains high in the third model which includes the density in establishments instead of employment. Despite a very different method applied to another period, Lasch et al.

(2005) find a similar result. Our conclusion is, however, radically opposed to the result obtained by [START_REF] Baptista | The Relationship between Entrepreneurship and Unemployment: is Portugal an Outlier?[END_REF] according to whom this variable is weakly significant to explain firm creation in Portugal.

The coefficient associated with the variable N_Indus is negative and significant too.

The propensity to create new firms observed in a department is thus negatively correlated to the share of manufacturing companies in a given department. This also confirms the findings of [START_REF] Lasch | L'influence de l'environnement socio-économique sur la création d'entreprises dans le secteur TIC : le cas de la France[END_REF] and [START_REF] Moati | les determinants territoriaux de la creation d'entreprises[END_REF] who also conclude that a higher manufacturing profile tends to deter new business creation. The effect of this variable remains roughly the same regardless of the model. The variable N_GE which depicts the degree of concentration of the productive sector in a given area has a positive influence on firm creation. We may thus conclude, as [START_REF] Lasch | L'influence de l'environnement socio-économique sur la création d'entreprises dans le secteur TIC : le cas de la France[END_REF] do, that these large plants are considered as a solvent market by some new entrepreneurs and, in addition, that they may give some incentives to some of their employees to develop their own business. The role played by the demand size in the entrepreneurial process is confirmed by the positive and significant coefficient associated with N_Rev_med. It makes it possible to conclude that expectations about the capacity of the market to absorb an additional supply are taken into account by potential entrepreneurs when they decide to create their own activity.

The main counterintuitive results come from the two variables introduced to depict agglomeration effects. Indeed, N_Dens-etab and N_Dens_empl are both associated with negative coefficients. Considering AIC and BIC criteria, it appears that Model 2 should be preferred as it minimises the value of these criteria. However, the main result remains unchanged and agglomeration effects deter firm creation. This contradicts a study proposed by the French national institute of Statistics and Economic Studies over the changes in entry rates at the department level between 1993 and 1999 [START_REF] Tellier | Croissance plus faible des créations d'entreprises en 2008[END_REF]. The results they reach using an OLS model laid emphasis on positive agglomeration externalities and the share of employed people in the labour force living in the area. Our conclusions are radically different as, on the opposite, a higher density deters firm creation. Our results are however quite robust as the sign of the coefficient is almost the same in Model 2 and 3, a finding which lead us to consider that, for the studied year, agglomeration effects do not grant a high propensity to create new firms.

A spatial estimation of the entry rate in the Manufacturing Industry

A second level analysis, which is also a check for the robustness of estimates, rests upon the same models as in the previous Section with entry rate in the Manufacturing Industry as the explained variable. The tests reveal the same positive spatial autocorrelation as for the global entry rate (Table 5). A major difference, however, arises when we run the tests to detect the origin of the spatial autocorrelation. The robust LM tests for spatially autoregressive error fail to identify such a scheme and confirm H0 (no spatial dependence). On the opposite, H0 is rejected for the spatial error test which examines spatial autocorrelation between the residuals of adjacent areas. This suggests that error terms are correlated across observations, i.e., the error of an observation affects the errors of its neighbours (Table 6). The source of correlation may come from unmeasured variables that are related through space. Correlation can also arise from aggregation of spatially correlated variables and systematic measurement error. For this reason we will only comment on the results of the estimations obtained running the spatial error models. The values of AIC and BIC criteria obtained by estimating different models and using different techniques lead us to consider model 2 as the most reliable as these two tests exhibit the lowest value for this model (Table 7).

Graph 3 -Moran's diagram for the normalized value of entry rate (CREAI) One main point differs, however. Indeed, whereas tests lead us to consider that CREAI is affected by a spatial dependence bias captured by the error term, lambda is not significant regardless of the model considered (Table 7). This unexpected result is robust as the different estimations we run converge. Two elements may justify it. Firstly, computing the entry rate in the manufacturing industry, one observes that it reaches its highest values in the départements of Southern France, a finding which contrasts with their orientation towards services. In some places, the number of new businesses even exceeds the number of existing companies. This spurious phenomenon finds its origin in the scarcity of large and stable industrial companies. This undersupply contrasts with the quite high number of creations which have more often to do with handicraft than with traditional plants. Secondly, the fact that different départements exhibit different industrial profiles [START_REF] Carré | Les Performances des territoires. Les Politiques locales, remèdes au déclin industriel, Paris, Le Manuscrit Carree MA[END_REF]) means that they have not been evenly hit by the industrial crisis. As a consequence, the internal heterogeneity blurs the ratios. Acceptable range for rho in the spatial error models: -1.644 < ρ < 1.000 Spatial Weights matrix (W) is first-order queen-continuity with 1 if departments i,j (i≠j) share common border and 0 otherwise ; estimated standard errors in parentheses. OLS= ordinary least squares ; LM = Lagrange Multiplier; AIC is Akaike's information criterion [START_REF] Akaike | A new look at the statistical model identification[END_REF] and BIC is the Bayesian information criterion or Schwarz criterion (Schwarz, 1978) estimated thanks the Stata procedure. 9 On the opposite, the share of employees working in the manufacturing industry, as well as the employment density, negatively affects the birth rate of firm in the manufacturing 9 See footnote 5. industry. These results show that the new industrial companies instead of locating close to the operating ones are created in departments where the manufacturing industry is weakly represented. This suggests that new companies, even if they operate in the manufacturing industry, present specificities such as they do not benefit from any positive externality from the proximity to existing ones. One possible explanation lies in the fact that these new industrial companies do not work for a local market; instead, they are positioned on a broader one.

The negative value associated with the variable representing the industrial atmosphere is worthy of note as this result contradicts the conclusions presented by Keeble and Walter (1994) or [START_REF] Bryson | The Creation and Growth of Small Business Service Firms in Post-Industrial Britain[END_REF]. The period under study and the methods used are, however, so different that determining the source of this difference is not straightforward. Our estimations make it possible to consider that in the French departments, a high share of industry is no longer an advantage in the effort to boost the creation of industrial companies. New businesses are less launched to respond to a demand emanating from other industrial companies than to serve households demand. This is confirmed by the positive sign of the coefficient associated with N_Rev_med. In this way, our results are very similar to those obtained by Lash et al. (2005).

CONCLUSION

This paper aimed at identifying the variables determining the intensity of the entrepreneurial process measured by entry rates in the French continental departments. For that purpose, and this is one of the originalities of this research, we used a spatial econometric technique to take into account neighbouring effects which are believed to shape the profile of a given area.

The first result we obtain concerns the robustness of a direct relationship between the local context and the propensity to launch a new business in a given area. Essentially, this conclusion goes in the same way as most of the studies devoted to identifying the local determinants of firm creation in French regions [START_REF] Binet | Les déterminants de la dynamique entrepreneuriale dans les régions françaises[END_REF][START_REF] Moati | les determinants territoriaux de la creation d'entreprises[END_REF].

Our second result is more original as using a spatial econometric technique to estimate our models enabled us to pinpoint the importance of the spatial dependence in explaining the entry rate observed in a given area. Easily visible on a map representing the rate of firm creation as computed in the different departments, the presence of hot (resp. cold) spots is demonstrated thanks to the implementation of spatial econometric modelling and estimation techniques.

Thirdly, we also showed that the spatial dependence differs according to the definition of the entry rate adopted. The distinction between the global entry rate covering all industry and the entry rate in the manufacturing industry only makes it possible to highlight the underlying spatial dependence leading to the apparent proximity mechanism. Indeed, whereas the global entry rate is significantly influenced by spillover effects coming from neighbours, business creation in the manufacturing industry mostly depends on the diffusion process of spatial shocks or, in other terms, on a complex set of shock spillovers. Behind the similar apparent spatial autocorrelation driving the two entrepreneurial processes hide thus specific features. This led us to adopt a spatial lag model to analyse the global entry rate and a spatial error model to determine the underlying mechanisms of the entry rate in the manufacturing industry.

Our fourth result concerns the negative effect of agglomeration effects. Indeed if concentration plays a role in the firm creation effect, this effect is, however, negative. We cannot exclude the possibility such an unexpected result, considering the usual theoretical framework and the empirical analysis, probably comes from the dataset used. Indeed, this result holds for 2011 only. The enlargement of the period under review and the introduction of spatial dependence in a fixed effect model for panel should be run to test the validity of such a result. This requires yearly available local data that remain to be computed. This challenging perspective offers a rich future research agenda.

APPENDIX

  affirms that. "there is good theoretical motivation for expecting that many firms-level variables are more spatially dependent in the case of new firms rather than older firms." (p. 150) This requires thus on one hand the use of spatially oriented variables and on the other the adoption of econometric techniques capturing this spatial effect.Indeed, the strong dependence on local elements linked to the behaviour of nearby firms is often omitted from the analysis of entrepreneurship resting upon simple OLS which is unable to integrate it. As mentioned byde Graaff et al. (2006) "The multidirectional nature of spatial dependence in the spatial-error model implies that generalized least squares estimators are inconsistent." (p. 95) As a result, there is a miscalculation of the effect on explanatory variables taken into consideration and, consequently, a misunderstanding of the entrepreneurial phenomenon.

  phenomenon of consolidation leads us to suspect a spatial auto-correlation problem in our data. Graph 1 -Firm creation at the department level in 2011 Global entry rate Entry rate in the manufacturing industry To illustrate the local demand characteristics we take into account the median value of households' income in department d. It is made available thanks to the annual population survey and depicts the market size and solvability in a given area. Its sign is expected to be positive.

  Moran's diagram (Graph 2) compares normalized values of CREA in the 94 departments with the normalized neighbour's average, generating a two-dimensional plot of CREA versus W1, so we can visualize the spatial dependence of such a variable. The first quadrant, Q1, the high-high quadrant, shows high values for the variable for both the region and its neighbours. Q2, the low-low quadrant, shows low values for regions as well as their neighbours. If the region has low values, but is surrounded by neighbours with high values, it will be plotted in the Q3 quadrant (low-high) and regions with high levels surrounded by low levels regions will be in Q4 (high-low). It confirms the positive spatial dependence in the data. Graph 2 -Moran's diagram for the normalized value of entry rate (CREA) Moran scatterplot (Moran's I = 0

  Moran's diagram (Graph 3) displays another configuration than the one characterizing the total entry rate. Instead, of being predominantly located in the HH area of the diagram, the majority of departments belong to the LL quadrant. The macroeconomic shock hitting industry and other invisible elements affecting the business climate may cause this phenomenon.

  coefficients estimated in the spatial error model, one fails to detect any difference between the determinants of the firm creation rate in the manufacturing industry and the general case. Entry rate in manufacturing industry is positively correlated to the current unemployment rate, the share of establishments employing 100 employees or more and the median income.

  this paper we use a dataset constructed from different sources made available by the French National Institute of Statistics and Economic Studies (INSEE). It merges various series representing local information, all of them being provided at the departmental level 4 .

These different sources enable us to estimate different proxy variables to illustrate the explanatory factors presented above and to determine the entry rate in every département.

  We compute the entry rate at a double level. The global entry rate (CREA below) compares then the total number of new firms to the total number of operating ones in every département whereas the entry rate in the manufacturing industry (CREAI below) only focuses on new and already existing firms in the manufacturing industry (levels B, C, D and E of the NAFRev. 2, 2008). This double level analysis is motivated by the high level of spillover effects generated by industry[START_REF] Bonaccorsi | Knowledge spillover effects at the sub-regional level: Theory and estimation[END_REF].The different kinds of local variables identified by the literature as possibly influencing the rate of new business created in an area are inserted in an empirical model aiming at explaining the entry rate at the département level. The model reads as follows:

	CREA d,t = a DEM u,d + b INCENT v,d + c AGGLO u,d	(2)
	where DEM

d,t is the number of new firms created in département d, year t and ExistEnt d,t the number of already operating companies in département d, year t. 9 ud , a set of the u variables used to depict the demand addressed to companies in department d, INCENT v,d , , the set of v variables inserted into the model to approximate the local incentives to create a business, and AGGLO u,d , the set of the u variables introduced within the model to take into consideration agglomeration effects in the département d.

3.2. The explained and explanatory variables

Graph 1 presents the global and sectoral entry rate computed at the departmental level in 2011. The average global entry rate is about 11%. It is the highest in the departments of the Paris area (except the city of Paris itself) where it exceeds 16% and in Southern France with values around 15%. The lowest levels are observed in the departments départements located in rural areas and the center of France where the entry rate is below 7%. A similar geographical structure appears looking at the entry rate in the manufacturing industry.

Table 1 -

 1 Measure and definition of the variables used in the model

	Variables*	Definition	Mean	Std. Error	Min	Max
	CREA	Entry rate = Number of entering firms in department d divided by number of firms in the same department	0.095737 (0.000)	0.024 (1.000)	0.044 (-2.137)	0.153 (2.273)
		Entry rate in manufacturing industry = Number of
	CREAI	entering firms in manufacturing industry in department d divided by number of firms in	1.237487 (-0.000)	0.448 (1.000)	0.506 (-1.649)	2.685 (3.029)
		manufacturing industry for the same department
	CHOM	Unemployment rate: Share of unemployed aged 16-64 in department d divided by population aged 16-64 in department d	9.16 (-0.000)	1.643 (1.000)	5.1 (-0.964)	13.2 (6.022)
	INDUS	Share of employees in Manufacturing Industry compared to the total number of employees in department d	0.17 (0.000)	0.054 (1.000)	0.041 (-2.392)	0.274 (1.896)
	GE	Share of plants employing 100 workers and more in the total number of employees in department d	0.34 (0.000)	0.078 (1.000)	0.176 (-2.189)	0.620 (3.500)
	Dens-Etab	Agglomeration economies: Number of establishments in department d divided by the area in square kilometres of department d	75.29 (0.000)	473.933 (1.000)	2.142 (-0.154)	4518.11 (9.374)
	Dens-Empl	Urbanisation economies: Number of employees in department d divided by the area in square kilometres of department d	315.29 (-0.000)	1749.17 (1.000)	4.68 (-0.178)	15910.27 (8.916)
	Rev_med	Median total income of households in department d	18924.89 (-0.000)	1450.59 2 (1.000)	16600 (-1.603)	24520 (3.857)
	Qual	Share of college-educated employees compared to the total number of employees in department d	17.47 (-0.000)	7.128 (1.000)	10.6 (-0.964)	60.4 (6.022)

* Values of normalised variables between brackets

Table 3 -

 3 Identification of the source of spatial dependency for the variable CREA

	Test	Statistic df p-value
	Spatial error:
	Moran's I λ LM λ LM* λ	4.015 11.090 0.306	1 0.000 1 0.001 1 0.580
	Spatial lag :
	LM ρ LM* ρ	22.028 11.245	1 0.000 1 0.001
	Note: LM = simple Lagrange multiplier, LM*= robust Lagrange multiplier

Table 4 -

 4 Regression results of models to estimate the global entry rate

	Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1
	Note:

Table 5 -

 5 Results of tests for spatial dependency for the variable CREAI

		Value	E(I)	Sd(I)	Z	p-value*
	Moran's I	0.529 -0.011 0.063 8.543	0.000
	Geary's C	0.466 1.000 0.083 -6.425	0.000
	Getis & Ord's G -2.613 0.053 0.312 -8.543	0.000
	*1-tail test

Table 6 -

 6 Identification of the source of spatial dependency for the variable CREAI

	Test	Statistic df p-value
	Spatial error:
	Moran's I 	1.135	1 0.256
	LM 	0.373	1	0.542
	LM* 	2.875	1	0.090
	Spatial lag :
	LM 	16.863 1	0.000
	LM* 	19.366 1	0.000
	Note: LM = simple Lagrange multiplier, LM*= robust Lagrange multiplier

Table 7 -

 7 Regression results of models to estimate the entry rate in the Manufacturing Industry

	Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1
	Note :

1 -DATA AND SOURCES Variables (2011) Source Total

  number of companies created Insee, Répertoire des entreprises et des établissements (Sirene) (champ : activités marchandes hors agriculture).Insee, Clap : Établissements actifs au 31 décembre, hors secteurs de l'agriculture, de la défense et de l'intérim p < 0.05, ** p < 0.01, *** p < 0.001

	employees or more
	-establisment operating in
	Manufacturing Industry
	Area				Total de l'effectif salarié (Insee)/superficie
	Median total income of households in department d		Insee et DGFiP, Revenus disponibles localisés
	Percentage of graduated people		Insee, Recensement de la population exploitation
	between 25 and 34 years old		principale
	APPENDIX 2 -CORRELATION MATRICES
		N_CREA	N_Chom	N_Indus N_Qual	N_GE	N_Dens_empl N_Dens_etab N_Rev_med
	N_CREA N_Chom N_Indus N_Qual N_GE N_Dens_empl N_Dens_etab N_Rev_med	1.000 0.334 *** -0.388 *** 0.508 *** 0.662 *** 0.166 0.117 0.488 ***	1.000 -0.127 -0.160 0.070 -0.062 -0.050 -0.431 ***	1.000 -0.482 *** -0.097 -0.330 ** 0.764 *** 0.349 *** 1.000 0.621 *** 1.000 -0.306 ** 0.717 *** 0.278 ** -0.214 * 0.813 *** 0.564 ***	1.000 0.986 *** 0.512 ***	1.000 0.469 ***	1.000
		N_CREAI N_Chom N_Indus N_Qual	N_GE	N_Dens_empl N_Dens_etab N_Rev_med
	N_CREAI N_Chom N_Indus N_Qual N_GE N_Dens_empl N_Dens_etab N_Rev_med	1.000 0.285 ** -0.677 *** 0.585 *** 0.537 *** 0.251 * 0.193 0.500 ***	1.000 -0.127 -0.160 0.070 -0.062 -0.050 -0.431 ***	1.000 -0.482 *** -0.097 -0.330 ** -0.306 ** -0.214 *	1.000 0.621 *** 0.764 *** 0.717 *** 0.813 ***	1.000 0.349 *** 0.278 ** 0.564 ***	1.000 0.986 *** 0.512 ***	1.000 0.469 ***	1.000
	Entry rate in manufacturing industry		Insee, Répertoire des entreprises et des établissements (Sirene) (champ : activités marchandes hors agriculture). NA, 2008, associée à la NAF rév. 2
	Total number of establishments
	/Total number of establishments in	Insee, Clap
	manufacturing industry
	Rate of unemployment		Insee, taux de chômage localisés, lieu de résidence
	Number of employees working
	in :
	-establishments employing 100

*

* p < 0.05, ** p < 0.01, *** p < 0.001

French « départements » correspond to the second level of the Nomenclature of the European Territorial Units for Statistics (NUTS).

French departments correspond to NUTS 3 level in the European nomenclature.

Proceeding this way grants then that each variable's mean is zero and that its standard deviation equals one.

The first-order queen contiguity spatial weight matrix defines all observations that share common boundaries or vertices as neighbours. The first-order rook contiguity spatial weight matrix defines the observations that share common boundaries as neighbours.

AIC = -2*ln(likelihood) + 2*k and BIC = -2*ln(likelihood) + ln(N)*k, where k = number of parameters estimated and N = number of observations.

ACKNOWLEDGMENTS xxxxx

RESULTS AND DISCUSSION

In this Section we present the results of the estimations. The collinearity problems between skills (N_Qual), the share of employees working in large firms (N_GE) and the agglomeration effects measured by N_Dens_empl or N_Dens_etab, lead us to estimate three different models. The first one mixes qualifications, the share of manufacturing Industry and the rate of unemployment; the second one combines the share of manufacturing Industry, the rate of unemployment, the share of large companies, the number of employees per square kilometre and the median income; the third one combines the share of manufacturing industry, the rate of unemployment, the share of large companies, the number of plants per square kilometre and the median income. Three regression methodologies were adopted: conventional regression, spatial lag regression, and spatial error regression analyses. They are run to estimate CREA and CREAI. The results obtained are presented and discussed successively.

A spatial estimation of global entry rate

In order to decide which kind of estimation technique should be adopted in case of spatial autocorrelation, we perform firstly two tests for spatial dependence: Moran's I and Geary's C (