by p.cella, t.laureti°, s.rossetti and c.viviano* * istat - italian national statistical...

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

NEW SUCCESSFUL ENTREPRENEURS IN ITALY: A STATISTICAL PORTRAIT

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

ObjectivesEISG »

EISG Istanbul, 25-26 June 2007

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.

Conceptual Framework


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Conceptual Framework


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mogul

Self-employed (subsistence

entrepreneurs)

entrepreneur

Social entrepreneurs

environment

Val

ue c

reat

ed c

aptu

red

for

entr

epre

neur

litt

le

a lo

t

little a lot Value created for others (whether employees, government, customers)

hostile

benign

.

BackgroundEISG »


Previous work (Istat-Eurostat-Oecd, workshop on EI, 6-7 december Rome)

“Profiles of new entrepreneurs and associated outcomes”

1) Cluster technique based on demo-social variables

then

2) ex-post characterization of clusters with economic information

Present work1) Multivariate techniques based on economic performance to measure “dimensions” of value creation (stick to the conceptual framework) then 2) Description of profiles using socio-economic characteristics

Main source of data:

“Factor of Business Success” survey (Fobs)

The main drawback with this source is its “thin” coverage of economic variables

In order to have measures of the economic performance of new entrepreneurship the Fobs dataset has been integrated with other statistical and administrative sources:

a) Business Registerb) Fiscal survey purpose to support Tax

Administration to control small and medium firms

c) Balance sheets (d) Statistics on Italian Foreign trade.

Data baseEISG »


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.

Data baseEISG »


Data baseEISG »


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.

Data base


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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)

Data base


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Coverage of Fobs data (1) and of Integrated Fobs and economic data (2) by legal form

Figure 1

sole propretorships48%

freelance10%

self-employed5%

partenership10%

limited company27%

Figure 2

0 10 20 30 40 50 60 70 80 90 100

sole propretorships

freelance

self-employed

partenership

limited company

total

Economic indicators


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Code Description Year Formula

lmps Service and inputs intensity (log)

2004 Ln(purchases of goods and services/employed)

lkad Capital intensity (log) 2004 Ln(fixed assets/employed)

ladd Size (log) 2004 Employed

ldfatt Growth of turnover(log) 2004 Turnover 2004/ turnover 2002

vfat Value added on sales 2004 Value added/sales

indeb Debt intensity 2004 Financial burden/sales

f_exp Exports intensity 2004 Export/sales

lfadd Turnover per capita (log) 2004 Ln(turnover/employed)

lvaladd Labour productivity (log) 2004 Ln(value added/employed)

ldfadd Growth of labour productivity(log)

2002-2004

Ln(turnover per capita04/ turnover per capita02)

ldadd Growth of occupation (log) 2002-2004

Ln(employed04/ employed 02)

Ros Profitability 2004 Return on sales


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The Italian FOBS survey (a sample of 5,868 records) allows to sketch the following identity of the “average” entrepreneur

Descriptive statistics

Male 37 years old

secondary education with previous working experience-

-in the same sector of activity

0 10 20 30 40 50 60 70 80

Administration

Contacts w ith customers

Financing

Alone as entrepreneur

Outstanding invoices

Suitable personnel

Pricing goods

Finding premises

None

Backing from family

Finding suppliers

Information technology

high degree

to some degree

Difficulties at start-up

The profile of new entrepreneurs: methodology

In order to identify potential entrepreneurial profiles according to the theoretical framework, the adopted methodology is based on the application of two techniques:

1) 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.


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The profile of new entrepreneurs: methodology

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 of k-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.


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Results:PCA

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

LADD

VFAT

ROS

INDEBF_EXP

LDADD

LDFADDLDFATT

LFADD04

LMPS

LKADLVALADD

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

Com

pone

nt 2

Component 1


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Results:PCA

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 » Results:Cluster Analysis

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.


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Results:Cluster Analysis

Characterization of the cluster-profile with the introduction of the 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 characteristics a 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


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Results: The Moguls (39% of the sample)

Variable Category I

Profitability 2.1

Increase entrepreneur’s salary yes 1.3

Sector of activity construction 1.4

Education low 1.1

Previous occupational status employee 1.2

Start up difficulties low 1.1

Self-financing yes 1.0

Innovation activity no 1.1

Development obstacles low 1.1

Positive expectation low 1.1

Expect more investment no 1.2


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Results: Classic entrepreneurs (3% of the sample)

Variable Category I

Productivity Growth 5.3

Turnover Growth 4.3

Sector of activity non traditional manufacturing 1.6

Sector of activity business services 1.5

Education high 1.9

Previous experience yes 1.1

Previous occupational status entrepreneur 1.3

Start up difficulties high 1.3

Debt financing yes 1.6

Innovation activity yes 1.3

Exports yes 1.4

Positive expectation high 1.3

Expect more workers. yes 1.1

Expect more investment yes 1.1


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Results: Subsistence entrepreneurs (9% of the sample)

Variable Category I

Gender female 1.5

Sector activity trade 1.7

Education medium 1.1

Location south 1.2

Previous experience no 1.1

Previous occupational status unemployed 1.7

Previous experience in the same branch of activity no 1.6

Debt financing no 1.1

Collaboration with other firms no 1.1

Profitability satisfaction scarce/sufficient 1.3

Will continue activities change 1.7

Profitability 0.2

Productivity Growth 0.1


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Results: Social entrepreneurs (49% of the sample)

Variable Category I

Occupation Growth 1.9

Previous occupational status entrepreneur 1.3

Previous experience in the same branch of act. yes 1.1

Debt financing yes 1.3

Declare export yes 1.4

Development obstacles high 1.1

Number of customers greater than 50 1.2

Expect more workers. yes 1.1

Expect more investment yes 1.1

Increase entrepreneur’s salary no 1.1

Profitability 0.3

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

by p.cella, t.laureti°, s.rossetti and c.viviano* * istat - italian national statistical...

Documents

data baseeisg eisg istanbul

new successful entrepreneurs

backgroundeisg eisg

new value creation

entrepreneurs profile

economic informationpresent

description of profiles

survey date

by p.cella*, t.laureti°, s.rossetti* and c.viviano* * istat - italian national statistical...

by p.cella, t.laureti°, s.rossetti and c.viviano* * istat - italian national statistical...