Entrepreneurial activity in Portugal: Evidence from individual, firm, and regional level Joana Mendonça IN+ Center for Innovation, Technology and Policy

Entrepreneurial activity in Portugal: Evidence from individual, firm, and

regional level

Joana Mendonça

IN+ Center for Innovation, Technology and Policy Research

November 2008

Introduction

18-04-23 Entrepreneurial activity in Portugal 2

•The longitudinal and often all-inclusive nature of large surveys matching firms, owners, and employees makes these data sources particularly useful to answer research questions where interrelated heterogeneous factors concerning firms and individuals require large, unbiased samples with the possibility to investigate a variety of factors simultaneously •In particular, longitudinal data allow us to study the pre-history and post-history of firms and entrepreneurial experiences•There is considerable potential to use detailed longitudinal data to study new issues in entrepreneuship and industry dynamics. Linked data will allow to examine start-up survival and success; industry clusters, employee networks, labor market conditions, and interactions between all of these factors. •There are also limitations!

The Quadros de Pessoal dataset• The Quadros de Pessoal Database is a longitudinal matched employer-employee microdata set covering all business units with at least one wage-earner in the Portuguese economy, including extensive information on firms, establishments, business owners and employees from 1986 to 2002

• Firms, establishments and individuals are fully cross-referenced through the use of a unique identification number, thus allowing for the recognition of entry and exit of firms and plants, as well as tracing the mobility of workers and business owners across firms and establishments over time

• Data on firms include size (employment), age, location, sector and number of plants; data on business owners and employees include gender, age, hierarchical level, tenure, and schooling

18-04-23 3Entrepreneurial activity in Portugal

The Quadros de Pessoal datasetIdentifiers Pecuniary factors and working hours

Year Ernings – Regular,Irregular, ExtraIdentifier of the individual Nr. of hours worked (per month) regular, extraIdentifier of the firm Regular period of work (per week)

Individuals’ demographics Demography of firmsGender Number of persons in the firmAge Number of establishmentsDate of birth Establishing yearNationality NACE code (CAE) 1,2,3 and 6 digit

Individuals’ occupational status Firm Location Professional activity (1,2,3 and 6 digit) Parish, Municipality; District; Professional category Regions: NUTS I, II and IIIProfessional SituationDate of admittance in the firm Financial indicators and type of ownershipDate of the last promotion Sales volumes (Euros)Type of contract Inicial capital (Euros)Partial /full time work Legal type of the business

Share of National capital - Private (%)Individuals’ human capital Share of National capital - Public (%)

Level of qualification Share of Foreign Capital (%)Education

Overview of the main variables and dimensions of analysis

Amaral, 2007


FirmsNumber of firms in the dataset

Year No Firms No Firms KBE

1991 148594 94691992 159191 104261993 165876 111741994 184306 127171995 192270 154781996 197558 163141997 213589 178701998 228819 195461999 244241 213842000 268701 242842001 284006 264242002 299790 285292003 306567 30032


Size Distribution of Firms

KBE= OECD classification of knowledge based enterprise sectors: high and medium-high technology manufacturing; post and communications; finance, insurance and business services (OECD, 2002)

Size Class Nº employess All firms KBE

Frequency Percent Frequency Percent

Micro 1-10 employees 2,355,205 82.75 192,586 80.57

Small 10-50 employees 397,914 13.98 34,616 14.48

Smedium 50-250 72,607 2.55 9,057 3.79

Lmedium 250-500 6,745 0.24 1,433 0.60

Large > 500 employees 13,550 0.48 1,325 0.55


Entry and Exit of FirmsYear Entry Firms Entry Rate Entry

KBE Firms KBE entry Rate

1991 16851 13311992 16661 10.47 1320 12.661993 16929 10.21 1346 12.051994 26207 14.22 2121 16.681995 20589 10.71 1925 12.441996 20168 10.21 1818 11.141997 24013 11.24 2323 13.001998 25704 11.23 2471 12.641999 24472 10.02 2413 11.282000 35859 13.35 3752 15.452001 48847 17.20 4667 17.662002 41323 13.78 4221 14.802003 31395 10.24 3562 11.86


Entry and Exit of Firms


Year Exit firms Exit rate Exit KBE firms KBE Exit Rate1992 14200 8.92 1018 9.761993 19446 11.72 1524 13.641994 15985 8.67 2159 16.981995 16389 8.52 1508 9.741996 16408 8.31 1477 9.051997 18288 8.56 1573 8.801998 19973 8.73 1695 8.671999 24720 10.12 2131 9.972000 35269 13.13 2858 11.772001 37686 13.27 3849 14.572002 45107 15.05 5740 20.12

Individuals’ professional occupation - 1986-2003

Business owner

Paid-employee

Member ofcooperative

Non-paid family

member

Total observations

Frequency 1,861,385 27,318,507 67,042 13,927 29,260,861

Percentage(average for 1986-

2003)6.36% 93.36% 0.23% 0.05% 100.00%


Amaral, 2007

BO Age and Gender

Business owners’ gender

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

1986 1987 1988 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2002 2003

Year

Nr.Obs

Males Females

Individuals' mean age when entering business ownership

39

40

41

42

43

44

45

46

1986 1987 1988 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2002 2003

Year

Age

Females Males


Amaral, 2007

BO Education Levels

Number of years of formal education (average per year)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1986 1987 1988 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2002 2003

Year

Nr.Obs

Basic, 1st cycle (4 years) Basic, 2nd cycle (6 years)

Basic, 3rd cycle (9 years) Secondary (12 years)Polytechnical Institute (15 years) University (17 years)


Amaral, 2007

Education levels in KBE


Geographic Distribution of KBEs

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2002

KBE firms per thousand

inhabitants

0 - 0.25

0.25 - 0.5

0.5 - 1

1 - 2

2 - 4

> 4

No data

KBE firms per thousand

inhabitants

0 - 0.25

0.25 - 0.5

0.5 - 1

1 - 2

2 - 4

> 4

No data

1992

Entrepreneurial activity in Portugal

Limitations of the QP dataset

• It does not include public administration and the military sectors. • Exit of firms is identified by exiting the dataset- does not allow controlling

for mergers and acquisitions; • There is no information on R&D and use of technology in the firms.• The data excludes independent workers providing services to firms, or

workers without contract. • There is no information on family background, financial constrains or

psychological traits of the employees and business owners


Research at IN+

Firm Level

R. Baptista, F. Lima and M.T. Preto (2008). “The Role Played by Business Ownership Experience in the Internal Economics of the Firm”.R. Baptista, M. Karaoz and J.Mendonça (2007).“Pre-Entry Capabilities, Entrepreneurial Human Capital, and Start-up Success”R.Baptista , F. Lima and J. Mendonca (2008). “Differentiating the Role Played byFounders’ Human Capital on Firms’ Performance”


Research at IN+


Individual Level

Amaral, A.M. & R. Baptista (2007). Transitions from Paid-Employment into Entrepreneurship: An Empirical Study for Portugal.

Amaral, A.M, R. Baptista & M. Flores-Romero (2008). “Does Entrepreneurial Experience Really Matter for Business Performance?”

R. Baptista, F. Lima and M.T. Preto (2007). “Switching from paid employment to entrepreneurship: the pecuniary impact of becoming a business-owner”.

Amaral, A.M. & R. Baptista (2007). Serial Entrepreneurship: the impact of human capital on time to re-entry.

Amaral, A.M., R. Baptista & Lima, F. (2008). Becoming an ex-entrepreneur: firm performance and the sell-or-liquidate decision.

R. Baptista, F. Lima and M.T. Preto (2008). “Is there a labor market ‘penalty’ to Entrepreneurship?”.

Research at IN+


Regional Level

R. Baptista and M. T. Preto (2008). “Entrepreneurship and Industrial Re-Structuring: What Kinds of Start-Ups Matter Most for Job Creation?”.R. Baptista and M. T. Preto (2007). “New Firm Formation and Employment Growth: Differences between Regions”. R. Baptista and M. T. Preto (2006). “The Dynamics of Causality between Entrepreneurship and Unemployment: the Case of Portugal”.J. Mendonça and R. Baptista, “Proximity to Knowledge Sources and the Location of Knowledge Based Start-ups”J. Mendonça, R. Baptista and F. Lima, ““Creation of Higher Education Institutions and Entry of Knowledge Based firms”

The Role of Universities

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Universities act as an important localized source of knowledge through:• the development of research activities and the development of

company-scientist links;• the regular generation of new, qualified human capital able to use

knowledge in productive activities, and absorb new developments, converting them into product, process and organizational innovations

A significant amount of these wealth-creating changes occur through the creation of new firms

Localized pools of specialized and highly trained human capital are also likely to absorb knowledge developments and convert them into innovations


Scope and Objectives

We study the impact of geographical proximity to knowledge sources and local absorptive capacity on the location of knowledge-based start-ups in regions.

Explore differences in new firm formation in knowledge based

sectors across Portuguese regions, examining the relationship

between these differences and:

• Local presence of knowledge sources, i.e. universities,

students and graduates

• Local availability of human capital capable of exploiting new

knowledge to generate commercial innovations


Methodology I

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Dependent Variable: Count of start-ups in high and medium-high technology manufacturing, and knowledge intensive services in each municipality

Explanatory Variables: 1) No. of universities; No. of students; No. graduates/ year /municipality; 2) No. of Students and No. graduates in Basic sciences and Engineering and in Social Sciences

Control Variables: Stock of KBEs; Per capita sales volume; Av. years of education of the regional labour force (log); Pop. density (log); Distances (km) to major metropolitan centers (Lisbon and Oporto); Distance (km) to the district’s administrative centre

Econometric Estimation: Panel data (275 regions; 10 years); Zero inflated negative binomial regression; Estimations for pooled data Entrepreneurial activity in Portugal

Results

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No. students in each year increase the probability of a new firm entry (1% per additional student)

No. graduates increase the probability of 1 more firm entering the market (0.5% per additional graduate)

No. of universities has a very significant impact (31%)

High and Medium-high Tech Manufacturing

No. students in engineering and basic science have a positive effect on new firm entryNo. students in social sciences display a negative coefficient No. graduates in engineering and basic science gave a negative coefficient


Results

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No. students in each year increase the probability of a new firm entry (3% per additional student)

No. graduates increase the probability of 1 more firm entering the market (1.7% per additional graduate)

Nº of universities in the region has a positive effect on entry in services

Knowledge Intensive Services:

different types of students and graduates gave similar results


Scope and ObjectivesWhat is the real impact of new universities on entrepreneurial activity in regions?

Assess the effect of the establishment of a new higher education institution in a region on the subsequent levels of new firm entry in that region

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Universities have a role in fostering entrepreneurial activity, then the creation of universities in regions will have an impact in the no of start-ups in the same region

The creation of a new higher education institution in a region has a positive effect on subsequent levels of new firm entry in knowledge based sectors in that region


Methodology II

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Econometric Estimation: Estimation with first difference estimator; propensity score matching estimator

Control groups: A)regions where no. institutions is 0 and remains 0 during the entire timeNo. municipalities= 204B) regions where the no. institutions is ≠ 0 and remains constant. No. municipalities= 17

Treatment: creation of a new institution in 1993 and 1994 Treatment variable has the value 1 if there was an increasein the no. higher education institutions in the region; 0 otherwiseNo. treated municipalities= 17

Matching: made with pre-treatment characteristics (t-2)


ATT - Average Treatment effect on the TreatedGroup A = Regions with no. institutions equal to zeroGroup B = Regions with no. institutions constant and different from zero

* significant at 10%; ** significant at 5%; *** significant at 1%

No. Treated No. Control ATT Std. Err.Difference in the share of new firms between t = 3 and t = –2

Control group A+B 15 441 -2.176 1.448Control group A 13 406 -2.806 2.125Control group B 13 37 0.087 2.185

Difference in the share of new firms between t = 5 and t = –2Control group A+B 15 441 0.115 1.799

Control group A 13 406 -1.247 1.995Control group B 13 37 6.511* 2.036

Difference in the share of new firms between t = 7 and t = –2Control group A+B 15 441 -1.489 2.146

Control group A 13 406 -2.712 2.593Control group B 13 37 2.436 2.319

Effect of a new higher education institution on firm entry in regions – ATT Estimation with the stratification matching method

Results


Results

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Note: ATT - Average Treatment effect on the Treated* significant at 10%; ** significant at 5%; *** significant at 1%

No. Treated No. Control ATT Std. Err.Difference in the share of new firms between t = 3 and t = –2

Control group A+B 15 441 23.862* 13.069Control group A 13 406 30.338* 17.132Control group B 15 35 166.945 120.570

Difference in the share of new firms between t = 5 and t = –2

Control group A+B 15 44126.739**

13.286Control group A 13 406 33.068** 15.715Control group B 15 35 172.001 118.146

Difference in the share of new firms between t = 7 and t = –2Control group A+B 15 441 27.014* 16.047

Control group A 13 406 --- ---Control group B 13 37 321.946 225.462

Effect of a new higher education institution on the entry of KBE in regions – ATT Estimation with the stratification matching method


Concluding Remarks

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• There are significant differences in new firm formation in knowledge based sectors among Portuguese regions, persistent over time

• Geographical proximity to knowledge sources and pools of human capital plays a significant role in generating differences in entry by knowledge based firms across regions

• Increase of the No. Universities/higher education institutionshas a positive effect on new firm entry in a region in KBE• Effect is stronger in regions where there was no institutions and when compared with regions where there is no institution

Policy Implication

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• Regional development: focus potential action aimed at fighting

depopulation in certain areas. • Universities enhance regional development - less favored regions would

benefit from establishment of a new institution. They can benefit not only

from knowledge spillovers from the institutions, but also from the setting

of more educated people.• Local educational facilities have to develop effort in technology transfer

and entrepreneurship programs, in order to take advantage of the

concentration of human capital existent in the region. • Understanding the location patterns of knowledge based firms

should be a good base for the design of policy actions regarding

incentives for new firm creation.

Entrepreneurial activity in Portugal: Evidence from individual, firm, and

regional level

Joana Mendonça

IN+ Center for Innovation, Technology and Policy Research

November 2008

Documents

Entrepreneurial activity in Portugal: Evidence from individual, firm, and regional level Joana Mendonça IN+ Center for Innovation, Technology and Policy