Diapositiva 1

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Data Integration for Entrepreneurship Indicators
in Italy
Performance, Impact and Context Analysis
By M.G. Calza, P. Cella, F. Oropallo, S.
Rossetti, C. Viviano ISTAT-Eurostat-OECD
Workshop on Entrepreneurship Indicators Rome,
December 6-7, 2006
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Outline

• Objectives
• Background
• Data Integration Issues
• Framework of analysis
• Entrepreneurship Performance Indicators
• Entrepreneurship Impact Indicators
• Entrepreneurship Determinants
• Barriers to competition
• Profiles of new entrepreneurs and associated
  outcomes
• An Entrepreneurship Scoreboard
• Conclusions

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Objectives

• The objective of this work is to contribute to
  research on entrepreneurship indicators by
• Building indicators on new/young firms
• Developing indicators which focus on factors that
  hinder new entries and, more generally,
  competition
• Using FOBS data to characterize the profiles of
  new entrepreneurs and associated outcomes

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Background (1)
Data on new/young firms are needed for various
entrepreneurship indicators. Currently, there
are two main sources of data 1) The Business
Demography database It permits to identify real
births and real deaths, and to calculate
performance indicators, e.g. rate of new firms,
business churn, survival rates 2) The Factor of
Business Success Survey It helps us to uncover
the socio-demographic characteristics of new
entrepreneurs and to understand their motivations
and difficulties The main drawbacks with both
sources are (i) their thin coverage of
economic variables (ii) the hitches allied to
their linkage with SME (PMI) survey data, since
new firms are inadequately represented in the
latter For this reasons it is useful and indeed
rewarding to integrate the information on new
entrepreneurship with data from other sources
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Background (2)
The development of new entrepreneurship is deeply
influenced by market entry barriers and the
regulatory context Market barriers and
competition are believed to be important factors
in the study of the Italian economy Since these
phenomena are multifaceted, their study requires
different indicators and data sources To this
effect Data integration is of paramount
importance
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Data integration issues (1)
Advantages - exploit existing statistical or
administrative information - enhance the
statistical information system on new firms -
provide more and better information for policy
making - open up new vistas for economic
analysis Drawbacks- confidentiality problems
related to the access to micro data-
administrative data are customarily collected for
different purposes- they may refer to legal
units not statistical units
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Data integration issues (2)
Matching different data sources
(statistical/administrative) means tackling a
host of issue, e.g. Identifying business
unitsi.e. find an identifying variable which is
a unique key that is a natural join between
different sources. In almost all firm databases
we choose the fiscal code Dealing with
Matching Problemsi.e. whenever a key variable is
unavailable or is not sufficient to identify the
statistical unit. In case of mis-matches or when
sources do not contain the same unit Identifying
changes in business units Changes involving a
single unit (changes in kind of business
classification, in legal form or
localisation) Changes in the number of units
(death, birth, breaks up and splits off, mergers
and acquisitions) Addressing sampling problems
When merging survey data with exhaustive data
from a subset of the population Reconciling
definitions and values among sourcesWhenever a
variable has not the same definition or value
across different sources Handling data editing
and data reconstruction issuesMeasurement
Errors, Missing data, Outliers etc.
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Framework of analysis (1)

• Following the OECD (Davis 2006) three types of
  entrepreneurship indicators can be distinguished
• - Performance which measure how a country
  performs in terms of entrepreneurship. Most of
  these indicators can be calculated using Business
  Register and Business Demography
• Impact which measure the outcome(s) of
  entrepreneurship.To focus our attention on these
  indicators we need to expand the information on
  new/young businesses and integrate it with
  information from other sources
• Determinants/Context which measure various
  aspects of the conditions and qualities that
  foster or hinder entrepreneurship.We focus our
  attention on two subgroups barriers to
  competition and entrepreneurs profiles

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Framework of analysis (2)

• Units of analysisNew firms real births from the
  Business Demography Young firms four-year
  surviving from Business Demography
• Incumbent firms taken as benchmark firms, i.e.
  standing on markets for at least 5 years
• Analyzed indicators
• - Rate of new firm start-ups
• - Business churn
• - Survival rate for new businesses
• - Turnover of new firms
• - Number of SMEs in total economy
• - Importance of SMEs in total economy
• - Share of employment in SMEs
• - Growth rate of employment of young firms-
  Growth rate of turnover of young firms
• Contribution of new firms to productivity growth
  - Share of new/young firms that export
• Exports per employee of new/young firms
• Return on sales (Ros) for new/young firms-
  Share of labour cost for new/young firms-
  Financial burden on sales
• Barriers to competition
• Profiles of new entrepreneurs and associated
  outcomes

Sources Business Register, Business Demography,
SBS data, Foreign Trade Statistics, Fiscal
Data, Balance Sheets
Performance
Impact
Determinants
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Entrepreneurship Performance Indicators
NF real births from Business
Demography NF(t,tn) young firms (t-year
surviving from Business Demography) DF real
deaths from Business Demography TF All firms
from Business RegisterSME Small-medium firms T_
Turnover E_ Employment Breakdown by sector
Nace, legal form, geographical area, size class

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Entrepreneurship Impact indicators

• NF real births from Business Demography
• NF(t,tn) young firms (t-year surviving from
  Business Demography)
• T_ Turnover
• E_ Employment
• Exp_ Exports
• OS_ Operating Surplus
• LC_ Labour Costs
• TC_ Total Costs
• IP_ Interest Payments
• P Labour Productivity measured by Turnover on
  Employment
• Share of employment
• Breakdown by sector Nace, legal form,
  geographical area, size class

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Entrepreneurship Impact indicators
When Business Register, Business Demography,
Foreign Trade, Balance Sheets and SBS are
integrated a great deal of new opportunities open
up


PopulationB.R. B.D. Linked with
Foreign Trade Exporting/Imp. firms
Balance Sheets corporate firms
SBS (sample) domain level analysis
- Growth rate of employment of young firms-
Growth rate of turnover of young firms -
Contribution of new firms to productivity growth
- Share of new/young firms that export -
Exports per employee of new/young firms
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Entrepreneurship Impact indicators
Growth rate of turnover and employment of young
firms
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Entrepreneurship Impact indicators
Contribution of entries and exits to productivity
growth of micro firms
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Entrepreneurship Impact indicators
Share of new, young and incumbent firms that
export ()
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Entrepreneurship Impact indicators
Exports per employee in firms at birth, after
four years (young firms) and in incumbent firms
(thousand )
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Entrepreneurship Impact indicators
Opportunities that open up when Fiscal data are
integrated



Fiscal survey purpose To support Tax Admin.
control action on small and medium firms
(Turnover less than 5 million ) (Coverage all
business sectors except Energy (E), Financial
(J), Social (O)
Available information sectors from D to I and M
48 balance-sheets-comparable variables
sectors K and N excluded from the analysis
since available variables are not comparable
Population Linked
New firms 69
Young firms 73
Incumbents 81
RosReturn on sales LCIShare of labour cost
IPSFinancial burden on sales
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Entrepreneurship Impact indicators
ROS () - Profitability
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Entrepreneurship Impact indicators
Share of labour cost () on total costs
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Entrepreneurship Impact indicators
IPSFinancial burden on sales ()
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Entrepreneurship Determinants
For the analysis of entrepreneurship determinants
more complex methodologies have to be applied

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Entrepreneurship Determinants Barriers to
competition
Barriers to competition Barriers to entry are an
essential condition to the existence of
non-competitive behaviour Several indices
relating to the existence of entry barriers have
been computed (at 3-digit level of
aggregation) - Herfindahl index (H) (Sum of
squared market sale shares) - Concentration
Ratio (CR5) (Cumulative market sale share of the
largest 5 firms)- Share of advertising expenses
(AE) (Advertising costs on sales)- Share of
Fixed assets (FA) (Fixed assets on total
Assets)- Dominant firm size (Size class that
represents at least 50 of workers) Sources
Business Register, Structural Business
Statistics, Balance Sheets Coverage D-K (Nace
rev. 1.1)

Anything that prevents new firms from actually
entering a market
As a proxy for monopolistic competition due to
product differentiation
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Entrepreneurship DeterminantsBarriers to
competition
To measure the intensity of entry barriers for
each market, a composite index (summing up
indicators re-scaled by their own mean values) is
computed To avoid redundancy, indicators have
been selected on the basis of the correlation
matrix below

Market Barriers Intensity CR5/m(CR5)AE/m(AE)FA
/m(FA) Thanks to the above index, markets can be
ranked and distinguished for example according to
their dominant size (micro, small, medium and
large)
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Context Barriers to competition
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Indicators
Market Barriers Intensity Index in 1999 (violet)
and 2003 (beige) in all micro (dominant size)
business sectors
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Entrepreneurship Determinants Profiles of new
entrepreneurs and associated outcomes
The Italian FOBS survey (a sample of 6 th.
records) allows to sketch the following identity
of the average entrepreneur

Male 39 years old secondary education with
previous working experience- -in the same sector
of activity
Given the vast array of information on
entrepreneurs and their characteristic gathered
with the survey, different profiles, not just
one, are likely to coexist Multidimensional
techniques (e.g. cluster analysis) can be used to
group entrepreneurs into homogeneous clusters
subject to the following conditions - the
within clusters distance should be as small as
possible - the between clusters distance should
be as large as possible Each cluster will define
an entrepreneurs profile
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Entrepreneurship Determinants Profiles of new
entrepreneurs and associated outcomes
Steps of the clustering procedure 1. Choice of
variables Only a set of demo-social variables has
been considered (such as geog. area, Nace,
occupation prior to starting-up enterprise, age,
gender, educational background , branch
experience, a synthetic measures of the
motivations at start-up and a synthetic measure
of the difficulties at enterprise start-up) 2.
Distance measure between units Gowers
dissimilarity formula 3. Cluster algorithm
Wards minimum variance method (it minimizes the
within group variance and maximizes the between
groups variance) 4. Choice of the number of
clusters 6 clusters have been chosen according
to the results of pseudo t-test and pseudo
f-test 5. Ex-post characterization of the
cluster with economic information from B.R. and
Fiscal data per capita turnover, employment
variation and ROS, LCI and IPS

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Entrepreneurship Determinants Profiles of new
entrepreneurs and associated outcomes
The empirical analysis leads to portrait the
following profiles
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Entrepreneurship Determinants Profiles of new
entrepreneurs and associated outcomes
ISTAT-Eurostat-OECD Workshop on Entrepreneurship
Indicators
How does each profile perform?
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Entrepreneurship Determinants Profiles of new
entrepreneurs and associated outcomes
ISTAT-Eurostat-OECD Workshop on Entrepreneurship
Indicators
How does each profile perform?
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1999
Towards an entrepreneurship scoreboard
2003
Performance

Impact
1999
Determinants
2003
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Data Integration for Entrepreneurship Indicators
in Italy Performance, Impact and Context Analysis

• CONCLUDING REMARKS AND PUZZLES
• We have tried to contribute to the
  entrepreneurship debate focusing on selected
  key concepts and measuring them for Italy
• We have shown the potential of data integration.
  A wide range of indicators can be developed, but
  further work is needed in other areas (e.g.
  credit access)
• Looking ahead, further analysis is needed on the
  assessment and selection of indicators on grounds
  of their relevance and other quality criteria
  (e. g., robustness, coherence, accurateness ) to
  better support policies with evidence
• From our analysis new/young firms appear to
  perform well. How does this impact on the
  overall economy? What policy implications can be
  drawn?
• AND ABOVE ALL
• How an effective tool the Entrepreneurship
  Scoreboard can be expected to be?

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