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By P.Cella*, T.Laureti°, S.Rossetti* and C.Viviano*

NEW SUCCESSFUL ENTREPRENEURS IN ITALY: A STATISTICAL PORTRAIT. By P.Cella*, T.Laureti°, S.Rossetti* and C.Viviano* * ISTAT - Italian National Statistical Institute ° University of Tuscia OECD ENTREPRENEURSHIP INDICATORS STEERING GROUP Istanbul, 26-27 June 2007. Objectives. EISG ».

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By P.Cella*, T.Laureti°, S.Rossetti* and C.Viviano*

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  1. NEW SUCCESSFUL ENTREPRENEURS IN ITALY: A STATISTICAL PORTRAIT By P.Cella*, T.Laureti°, S.Rossetti* and C.Viviano* * ISTAT - Italian National Statistical Institute ° University of Tuscia OECD ENTREPRENEURSHIP INDICATORS STEERING GROUP Istanbul, 26-27 June 2007

  2. Objectives EISG » The purpose of this study is to contribute to the debate about new entrepreneurs and entrepreneurial process by: describing economic behaviour of new entrepreneurs in Italy verifying whether their performance behaviours fit the conceptual framework we adopted depicting different profiles of new entrepreneurs EISG Istanbul, 25-26 June 2007

  3. Conceptual Framework EISG » » To describe profiles of differently performing entrepreneurs, an interesting framework is proposed by Bruyat & Julien (2001) and recently reckoned by Seymour (2006). According to their notion entrepreneurship is the “dialogic” between the individual and new value creation, in which the environment can play an active role. The value created by the action of the entrepreneur can be captured by him(her)self and/or by others (employees, government, customers, …). Combining the different “dimensions” of value creation, different types of entrepreneurs may emerge. EISG Istanbul, 25-26 June 2007

  4. Conceptual Framework EISG » . EISG Istanbul, 25-26 June 2007

  5. Background EISG » • Previous work • (Istat-Eurostat-Oecd, workshop on EI, 6-7 december Rome) • “Profiles of new entrepreneurs and associated outcomes” • 1) Cluster techniquebased on demo-social variables • then • 2) ex-post characterization of clusters with economic information • Present work • Multivariate techniquesbased on economic performance to • measure “dimensions” of value creation (stick to the conceptual • framework) • then • 2) Descriptionof profiles using socio-economic characteristics EISG Istanbul, 25-26 June 2007

  6. Data base EISG » Main source of data: “Factor of Business Success” survey (Fobs) The main drawbackwith this source is its “thin” coverage of economic variables In order to have measures of the economic performance of new entrepreneurship theFobs dataset has been integrated with other statistical and administrative sources: a) Business Register b) Fiscal survey purpose to support Tax Administration to control small and medium firms c) Balance sheets (d) Statistics on Italian Foreign trade. EISG Istanbul, 25-26 June 2007

  7. Data base EISG » Questionnaire The questionnaire consisted of 29 tick box questions, divided up into three sections: SECTION 1 Profile of the entrepreneur and conditions of the enterprise at start-up. This section is relevant only if the current manager or director of the enterprise is the entrepreneur who started up the enterprise. Questionnaire asks questions on the entrepreneur’s profile, such as education, gender, age, motivation, experience running an enterprise, previous branch experience, training received, sources of financing,etc. EISG Istanbul, 25-26 June 2007

  8. Data base EISG » SECTION 2 The enterprise’s present situation at survey date This section focuses on employment, turnover, market position, co-operations, innovation and possible obstacles to the enterprise’s development. SECTION 3 Future plans This section finishes the questionnaire with the questions on the expectations for the future, about business activity development with respect to turnover, employment, profitability. EISG Istanbul, 25-26 June 2007

  9. Data base EISG » The integration process has given satisfactory results. Thanks to the fiscal and balance sheets data, it is possible to gather information of economic accounts and some assets variables for about the 75% of Fobs entrepreneurs The integration process brings to the reconstruction of the following information : 1. Enterprise characteristics (size, economic activity sector, localization) 2. Input variables (Material and service costs, Labour costs, Interest payments, Fixed assets) 3. Output variables (Turnover, Value added, Operating surplus, export value) EISG Istanbul, 25-26 June 2007

  10. Data base EISG » Coverage of Fobs data (1) and of Integrated Fobs and economic data (2) by legal form EISG Istanbul, 25-26 June 2007

  11. Economic indicators EISG » EISG Istanbul, 25-26 June 2007

  12. Descriptive statistics EISG » The Italian FOBS survey (a sample of 5,868 records) allows to sketch the following identity of the “average” entrepreneur Male 37 years old secondary education with previous working experience- -in the same sector of activity Difficulties at start-up EISG Istanbul, 25-26 June 2007

  13. The profile of new entrepreneurs: methodology EISG » • In order to identify potential entrepreneurial profiles according to the theoretical framework, the adopted methodology is based on the application of two techniques: • As a first step, we perform a Principal Component Analysis (PCA) using the economic indicators • PCA is generally used: • to reduce the number of variables before proceeding with clustering techniques • to derive a small number of independent linear combinations (principal components) of a set of variables • 2) In the following step,a cluster analysis is carried out on the main components identified by the PCA. EISG Istanbul, 25-26 June 2007

  14. The profile of new entrepreneurs: methodology EISG » Cluster analysis is an exploratory data-analysis technique which attempts to determine the natural groupings (or clusters) of observations. This method is used to divide observations into groups such that entrepreneurs belonging to the same group are relatively similar and entrepreneurs belonging to two different clusters are relatively different. Each cluster will define an entrepreneur’s profile In this analysis, we have used a partition cluster method ofk-means since it is particularly indicated when the data set contains a large amount of observations. Criteria used to identify the correct number of clusters (by Calinski and Harabasz) are based on a pseudo F-test. EISG Istanbul, 25-26 June 2007

  15. Results:PCA EISG » The results of PCA can be presented on graphs that represent the plot of the original variables in the space defined by the first two principal components. Figure Position of the variables in the first two components space EISG Istanbul, 25-26 June 2007

  16. Results:PCA EISG » Observing the plot of the original variables in the space defined by the first two components(accounting for 40.4% of the total variance) we can derived the following economic interpretation. The horizontal axis identifies behaviours aimed at creating value for others as pointed by the negative correlation with ROS and the positive one with the purchase intensity of inputs (LMPS), a proxy of strong relations with other firms. The vertical axis can be assumed to represent more egoistic behaviours, since the growth of turnover is pursued neglecting employment. It indicates behaviours aimed at creating value for themselves Thus, the previous empirical partition suggests that the conceptual framework can be applied. EISG Istanbul, 25-26 June 2007

  17. Results:Cluster Analysis EISG » To perform the cluster analysis we retain the first three components that explain the 53.9% of the total variance. The methods of the k-means is applied, choosing to obtain four clusters in order to verify their suitability to the analytical framework adopted. EISG Istanbul, 25-26 June 2007

  18. Results:Cluster Analysis EISG » Characterization of the cluster-profile with the introduction ofthe other socio-demographic variables from Fobs. However, in order to sketch the relevant characteristics of each profile we have selected only the variables which have resulted to be significantly different from the average. For each characteristicsa percentage of category into the group greater than the average will show an indicator (I) >1 For example: Given a category x (i.e. education=low) total units in category x = 30% units that belong at group n in category x =40% then I=40/30 =1.3 EISG Istanbul, 25-26 June 2007

  19. Results: The Moguls (39% of the sample) EISG » EISG Istanbul, 25-26 June 2007

  20. Results: Classic entrepreneurs (3% of the sample) EISG » EISG Istanbul, 25-26 June 2007

  21. Results: Subsistence entrepreneurs (9% of the sample) EISG » EISG Istanbul, 25-26 June 2007

  22. Results: Social entrepreneurs (49% of the sample) EISG » EISG Istanbul, 25-26 June 2007

  23. Concluding remarks and future plans • This empirical work contributes to the entrepreneurship debate proposing an application of the conceptual framework proposed by Bruyat and Julien to Italian new entrepreneurs • The “dimensions” of value created by entrepreneurs - for themselves and/or for others – have been measured through a multivariate statistical approach • Results have been achieved thanks to the integration process involving statistical ad hoc survey (Fobs), SBS data and administrative sources • Future work • To investigate the role played by environmental factors • To analyse the entrepreneurial behaviour in different stages of firms’ life EISG Istanbul, 25-26 June 2007

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