Transcript The Global Entrepreneurship and Development Index Dr. Habil László Szerb Associate professor University of Pécs, Faculty of Business and Economics Acknowledgements: OTKA Research Foundation.

The Global Entrepreneurship and Development Index
Dr. Habil László Szerb
Associate professor
University of Pécs, Faculty of Business and Economics
Acknowledgements: OTKA Research Foundation (K 81527)
provided financial support for the project, thanks for it.
Economic development and entrepreneurship
• Theoretical setup
– Joseph Schumpeter (1911) - Innovation
– Paul Douglas (1934) – (K,L)
– Robert Solow (1957) - TFP
– W. W. Rostow (1960) – Stages of Growth
– Paul Romer (1990) - Knowledge
– Porter and Sachs (2002) Stages of Development
– Samuelson (2009) Acs, Audretsch Strom 2009
• TFP is what remains unexplained after (capital and labor) accounted
for.
– Knowledge (Romer)
– Institutions (North)
– Entrepreneurship (Schumpeter)
• How to combine inputs is key to development.
• Need entrepreneurs.
The connection between entrepreneurship and
economic development
Entrepreneurship and Prosperity
• Factor driven stage most people are
involved in underproductive, unproductive
or destructive entrepreneurship.
• Efficiency driven stage people shift out of
destructive entrepreneurship
• Innovation most people shift out of
unproductive and most destructive
entrepreneurship.
Explaining development – the role of indices
•
•
•
•
•
Global Competitiveness Index – World Economic Forum
The Index of Economic Freedom – Heritage Foundation
Ease of Doing Business - World Bank
Global Creativity Index – Richard Florida
(Prosperity Index – Legatum)
– None of these focus on entrepreneurship!
Entrepreneurship definitions, concepts, measure
• Entrepreneurship definitions – different in terms of aims, can be societal,
academic or teaching phenomenon (Shane and Venkatamaran 2000)
– One-dimensional definitions
• Factor of production
• Assemble resources
• New business creation
• Innovation
• Opportunity recognition and exploitation
– Multidimensional definitions – includes more than one from the
followings:
• Innovation
• Opportunity exploitation
• New venture creation
• Risk taking
• (judgemental) decision making
• Specific behavior – entrepreneurial attitudes
• (positive) result orientation – wealth, growth, value creation
Entrepreneurship definitions, concepts, measure
• Entrepreneurship concepts:
– Process perspective
• examining business gestation, life-cycle issues
– Context perspective
• The environmental (outside) factors of entrepreneurship
• What is not entrepreneurship?
– Small business,
– Self employment
– Ordinary, routine type managerial tasks,
– Activities with no or very low risk
– Change of ownership
– Mergers, buyouts
Entrepreneurship definitions, concepts, measure
• Measures of entrepreneurship
– One dimensional measures dominantly
• Self employment, ownership rate – attitude towards SE
• Total Early-phase Entrepreneurial Activity (TEA) Index
• Business density
• Entry/exit rate
• Innovation rate (e.g. share of innovative businesses)
• High growth businesses
– Multidimensional measures – can be interpreted as quasi
entrepreneurship measures
• Doing business
• Index of Economic Freedom
• Global Competitiveness Index
• Creativity Index
Entrepreneurship definitions, concepts, measure
• Basic problems
– multidimensional definition and concept –
one-dimensional measure
– Environmental, institutional factors are
missing
– Measures mainly quantity and not quality
– Correlation between economic development
and the measure of entrepreneurship is
negative – contradicts to mainstream
dominant theories
Spearman Correlations between TEA and the Business
Indexes
GCI
IEF
-.533**
-.323*
-.430**
TEA
GCI = Global Competitiveness Index
IEF = Index of Economic Freedom
EDB = Ease of Doing Business Index
TEA = Early-stage entrepreneurial activity
n=57
EDB
Spearman Correlations between COMPENDIA and
the Business Indexes
GCI
IEF
-.760**
-.073
EDB
-.209
COMP
GCI = Global Competitiveness Index
IEF = Index of Economic Freedom
EDB = Ease of Doing Business Index
COMP = COMPENDIA, Business Ownership rate (the number of business owners divided by total labor force)
n=22
Index building – the building blocks
• The GEDI is a complex measure of entrepreneurship
• The Building Blocks
– Entrepreneurial attitudes is defined as a population’s general
attitudes about entrepreneurship including opportunity recognition,
networking, start-up skills taking risk, acceptance of entrepreneurs
with high status
– Entrepreneurial activity is defined as the quality of startup including
the motivation of start-up, the level of education of the
entrepreneur, the sector (high or medium), and the potential not to
be overly competitive.
– Entrepreneurial aspiration is defined as the early-stage
entrepreneur’s effort to introduce new products and/or services,
develop new production processes, penetrate foreign markets,
substantially increase their company’s number of employees, and
finance the business with formal and/or informal venture capital.
Index building – the combination of the variables
• The GEDI combines together individual and institutional variables
– following the logic of the interaction variables applied in
regression technique. Each of our 14 pillars is a result of the
multiplication of an individual variable and an associated
institutional variable.
• A critical part of index-building is identifying the proper weights - a
novel approach to determining/interpreting weight
– In this case, institutional variables can be viewed as particular
(country-level) weights of the individual variables.
• Individual variables – source Global Entrepreneurship Monitor survey
• Institutional variables – sources, other than GEM (UNESCO, WEF,
OECD etc.)
Index building – Attitudes variables
Example
• SKILL is defined as the percentage of the 18-64 aged
population claiming to posses the required
knowledge/skills to start business
• Source: GEM adult population survey
• EDUCPOSTSEC is defined as the gross enrolment ratio in
tertiary education
• Source: UNESCO
• SKILL x EDUCPOSTSEC = STARTUP SKILLS , combines
together the start-up skills with high level education
– Benchmarking individuals: high growth ventures are
lead by educated skillful entrepreneurs
– Potential support for business start-up in terms of skills
– Higher probability to provide informal finance –
business angel
Example – an exception
• MARKETAGGLOM = MARKETDOM*URBANIZATION - The size of the
market: A combined measure of the domestic market size and the
urbanization that later measures the potential agglomeration effect
– MARKETDOM - Domestic market size that is the sum of gross
domestic product plus value of imports of goods and services,
minus value of exports of goods and services. (World Economic
Forum)
– Urbanization that is the percentage of the population living in
urban areas (Population Division of the United Nations)
• OPPORTUNITY - The percentage of the 18-64 aged population
recognizing good conditions to start business next 6 months in area
he/she lives.
• OPPORTUNITY PERCEPTION = MARKETAGGLOM* OPPORTUNITY - This
pillar captures the potential “opportunity perception” of a population
by considering the size of its country’s domestic market and level of
urbanization.
Index building – Activity variables
Index building – Aspirations variables
Index building - pillar combination
• Another novelty of our index-building is the way the pillars are combined
into sub-indexes.
• Combination of the pillars – connection to weighting
– use the (weighted) average of the pillars;
– dimension reduction methodology – principal component-, factor
analysis
– Regression technique
– (Grey) relational analysis – not really applied
• Problems:
– Does not really take into account the interdependencies of the
pillars.
– Relies on the statistical properties – no real theoretical foundation.
– Sensitive to sample size - smaller sample may not provide acceptable
solution
Index building – the Penalty for Bottleneck
• Assumptions, theoretical setup
– Configurational theory: not the individual elements but the
integration of the elements (pillars) to the whole system is
important (Miller 1986).
– The Theory of Weakest Link: The elements of the system
can only be partially substitutable with each other
• A bad performance in one pillar cannot be compensated
fully with a good performance in another pillar
– The Theory of Constraints: The performance of the system
is determined by the worst performing element (pillar)
• The Penalty for Bottleneck provides a solution
– Optimal configuration: the pillars should be about the same
level
The general methodology of the PFB
Pi (xi1, xi2,.,, xij,.. xkm) → Ri, where i = 1,2,...k, and j = 1,2...m,
(1)
P: is a matrix of the data set containing k x m elements
m: is the number of units (country, region firm, etc.)
k: is the number of features/variables
xij: is the observed value of unit j with respect of feature i
Ri : is a vektor containing the unique index numbers for each unit 1,2,...m
Now, let’s select unit 1 and rearrange the features from the lowest to the highest:
0  x1  x 2 
 xk  1
we define the variability with the range, called bottleneck, which is the difference
between the value of actual feature and the value of the worst feature. The
bottleneck vector (Ri) can be defined as
Ri = xi - x1, where i= 1, 2, 3,….k
The general methodology of the PFB
Now, we apply a penalty function in general form such as:
x i  x1  f ( x i  x1 )
 The index value representing the overall performance of the unit over the k
features is calculated as the arithmetic mean (hereafter mean) after
applying the PFB methodology:
k
1 k
1
x i   x1   f

k i 1
k i 1
xi
 x1 
 The value of the index is mainly determined by the variable with the worst
value that can be considered as the weakest link amongst all the variables
(features).
 The size of the penalty depends on the difference between the value of the
worst variables and the value of the particular variable: The higher the
difference the higher the penalty is.
Logarithmic penalty function
Now, we are defining a concrete penalty function following as
f z   ln 1  z 
Therefore after penalty:
x i   x 1  ln(1  x i  x 1 ) i = 1,…k.
 For example, assume the normalized score of a particular feature is 0.90,
and the lowest value indicator is 0.50. The difference is 0.40. The natural
logarithm of 1.4 is equal to 0.336. Therefore the final adjusted value of the
feature is 0.50 + 0.336 = 0.836 instead of 0.90.
 The largest potential difference between two indicators can be 1, when a
particular country has the highest value in one indicator and the lowest
values in another. In this case the natural logarithm of 2 = 0.693, so the
maximum penalty is 1-0.693 = 0.307.
The logarithmic penalty function:
original and after penalty values
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
Original
Adjusted values: X(min)=0
Adjusted values: X(min)=0,5
Application of PFB to GEDI - steps
• Multiplication of the variables to calculate pillar values
• Normalization: We normalized the scores of all the 14 pillars, to maximum 1 and
the lowest value to zero in each pillar.
– Outliers are handled with truncation - The maximum value is set to reduce
the difference between the first and the second countries and the second and
third countries in a particular entrepreneurial feature indicator to 5 percent.
• This method preserves the ranking of the countries in a particular
entrepreneurial feature, but reduces the relative differences between
the leading country and the other nations.
• The calculation of the PFB adjusted values in each country: The penalty is
calculated in each of the three sub-index level of the particular country. The
normalized value of each pillar in a country is penalized by linking it to the score
of the indicator with the weakest performance of the sub-index in that country.
• The calculation of the PFB adjusted sub-indexes: The PFB adjusted sub-indexes
can be received by averaging the PFB adjusted pillars for each sub-index.
• The calculation of the PFB adjusted GEDI: The GEDI is derived by calculating the
simple non-weighted averages of the three sub-indexes.
PFB properties
• Assumption: all pillars should be positively correlated with each other
– If not, the improvement of one pillar decreases the value of
another pillar,
• Sensitivity to outliers: goes back to the problem of selecting the
benchmark properly
• Problem of the potentially different distributions of the pillars and
sub-indexes
– Aspiration has the lowest and attitudes has the highest average
– Question: is normalization (0 mean and 1 variance) a better
solution? – over-normalization
Variables
•
•
•
•
•
•
•
•
71 countries
31 variables
16 from GEM
15 from other data sources
14 pillars
The 3As
1 super index
Used the 2002-2008 pooled data – it is rather a stock than a flow
concept
• (Moved to examine changes over time: two years moving average
– in some cases that is not enough data – e.g. high growth
businesses)
• Institutional data: two years average 2006-2007 – various
sources
The GEDI rank of the countries
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Country
Denmark
Canada
United States
Sweden
New Zealand
Ireland
Switzerland
Norway
Iceland
Netherlands
Australia
Belgium
Finland
United Kingdom
Singapore
Germany
Puerto Rico
France
Slovenia
Korea
Israel
Austria
Hong Kong
GDP* GEDI
35890
0.76
34926
0.74
44474
0.72
36358
0.69
26773
0.68
44402
0.63
40183
0.63
49014
0.62
35490
0.62
38083
0.62
34073
0.60
34584
0.58
33869
0.56
34726
0.56
39508
0.56
34512
0.54
20223
0.54
33412
0.50
24913
0.49
25481
0.49
25868
0.47
36836
0.45
39089
0.45
Rank
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Country
Poland
Croatia
Peru
China
Colombia
South Africa
Turkey
Mexico
Dominican Republic
Indonesia
Hungary
Romania
Macedonia
Egypt
Morocco
Jordan
Panama
India
Brazil
Venezuela
Thailand
Russia
Tunisia
GDP* GEDI
14095
0.29
15599
0.28
7558
0.28
5087
0.28
8336
0.28
9565
0.28
12747
0.27
14135
0.27
7709
0.26
3459
0.26
18639
0.25
13217
0.25
9632
0.24
5383
0.24
4248
0.24
5092
0.23
11947
0.23
2656
0.23
9376
0.23
11333
0.22
7974
0.22
14121
0.22
7758
0.22
The GEDI rank of the countries
24
25
26
27
28
29
30
31
32
33
34
35
United Arab Emirates
Czech Republic
Chile
Italy
Spain
Japan
Saudi Arabia
Malaysia
Latvia
Portugal
Greece
Uruguay
39900
22110
13609
30248
31241
33288
23428
12681
15574
22595
28024
10844
0.42
0.42
0.41
0.41
0.40
0.40
0.38
0.36
0.36
0.35
0.32
0.30
60
61
62
63
64
65
66
67
68
69
70
71
Jamaica
Algeria
Serbia
Kazakhstan
Bosnia and Herzegovina
Ecuador
Bolivia
Syria
Guatemala
Iran
Philippines
Uganda
6848
7887
10853
10477
8077
7597
4242
4476
4661
10625
3186
918
0.21
0.19
0.18
0.18
0.18
0.17
0.16
0.16
0.15
0.15
0.13
0.10
Global Entrepreneruship Development Index values
1.000
R2 = 0.79
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
0
10000
20000
30000
40000
GDP per capita in Purchasing Power Parity
50000
60000
Table 7: The correlation coefficients between GEINDEX and other major indexes
1
2
3
4
5
6
Global Entrepreneurship Index
Index of Economic Freedom
Doing Business Rank (normalized)
Global Competitiveness Index
Corruption Perception Index
Per capita GDP
All coefficients are significant at a level better than 0.001
1
1.00
2
,70
1.00
3
,81
,76
1.00
4
,88
,70
,84
1.00
5
,92
,77
,82
,88
1.00
6
,89
,66
,76
,84
,87
1.00
Differences over development
OPPORTUNITY PERCEPTION
STARTUP SKILLS
NONFEAR OF FAILURE
NETWORKING
CULTURAL SUPPORT
OPPORTUNITY STARTUP
TECH SECTOR
QUALITY OF HUMAN RESOURCE
COMPETITION
NEW PRODUCT
NEW TECHOLOGY
HIGH GROWTH
INTERNATlONALIZATION
RISK CAPITAL
Number of countries
Stage 1
T-test
Stage 2
T-test
Stage 3
All
0.45
0.42
0.38
0.25
0.46
0.32
0.25
0.39
0.01
0.55
0.25
0.06
0.37
0.00
0.77
0.15
0.10
0.23
0.00
0.53
0.20
0.02
0.32
0.00
0.66
0.25
0.97
0.24
0.00
0.64
0.18
0.04
0.28
0.00
0.64
0.24
0.18
0.33
0.00
0.55
0.28
0.91
0.28
0.00
0.63
0.03
0.00
0.12
0.00
0.48
0.30
0.24
0.23
0.00
0.52
0.21
0.20
0.29
0.01
0.42
0.22
0.03
0.35
0.00
0.67
0.09
0.50
0.12
0.00
0.48
14
27
30
0.43
0.45
0.52
0.34
0.44
0.41
0.41
0.41
0.43
0.26
0.37
0.33
0.46
0.26
71
Cluster analysis
Pillars/
Cluster names
Entrepreneurial Attitudes
Index score (ATT)
Entrepreneurial Activity
Index score (ACT)
Entrepreneurial
Aspirations Index score
(ASP)
Starters
Efficiency
Transformer
s
Innovation
Developers
Innovation
Followers
Innovation
Leaders
Average
0.24
0.35
0.45
0.50
0.75
0.40
0.20
0.32
0.43
0.73
0.71
0.39
0.14
0.22
0.44
0.50
0.52
0.30
Not included in the cluster analysis
Global Entrepreneurship
Index score (GEI)
Institutional variable
averages
Individual variable
averages
Per capita GDP PPP ($US)
Number of countries
Countries
0.19
0.30
0.44
0.58
0.66
0.37
0.27
0.40
0.61
0.73
0.79
0.48
0.36
7364
23
Algeria
Bolivia
Bosnia and
Herzegovina
Brazil
Ecuador
Egypt
Guatemala
India
Iran
Jamaica
Jordan
Kazakhstan
Macedonia
Morocco
Panama
Philippines
Russia
Serbia
Syria
Thailand
Tunisia
Uganda
Venezuela
0.43
13888
19
Argentina
China
Colombia
Croatia
Dominican
Republic
Greece
Hungary
Indonesia
Latvia
Malaysia
Mexico
Peru
Poland
Portugal
Romania
South Africa
Spain
Turkey
Uruguay
0.50
29015
12
Austria
Chile
Czech
Republic
France
Hong Kong
Israel
Italy
Japan
Korea
Saudi Arabia
Slovenia
United Arab
Emirates
0.55
35448
7
Belgium
Germany,
Ireland
Puerto Rico
Singapore
Switzerland
United
Kingdom
0.58
36895
10
Australia
Canada
Denmark
Finland
Iceland
Netherlands
New Zealand
Norway
Sweden
United States
0.45
19697
71
Cluster analysis
Traffic Light Method
• Bad
• Better
• Best
Italy’s position
Components of Entrepreneurial Attitudes Sub-index (normalized scores)
OPPORTUNITY
PERCEPTION
STARTUP SKILLS NONFEAR OF FAILURE NETWORKING
Italy
33% percentile
67% percentile
0,44
0,28
0,51
0,52
0,34
0,54
0,67
0,35
0,69
Components of Entrepreneurial Activity Sub-index (normalized scores)
OPPORTUNITY
QUALITY OF HUMAN
STARTUP
TECH SECTOR
RESOURCE
Italy
33% percentile
67% percentile
0,46
0,23
0,56
0,36
0,26
0,49
0,27
0,24
0,48
0,44
0,18
0,38
CULTURAL
SUPPORT
0,43
0,28
0,57
COMPETITION
0,38
0,27
0,52
Components of Entrepreneurial Aspirations Sub-index (normalized scores)
Italy
33% percentile
67% percentile
NEW PRODUCT
NEW
TECHOLOGY
0,28
0,08
0,31
0,35
0,20
0,47
HIGH GROWTH
INTERNATlONA
LIZATION
RISK CAPITAL
0,35
0,24
0,37
0,62
0,31
0,62
0,27
0,09
0,29
Improvement – marginal analysis
• Selection of the worst pillar:
– QUALITY OF HUMAN RESOURCES = 0.27
– RISK CAPITAL= 0.27
– NEW PRODUCT = 0.28
• Increase the worst pillar value by 0,1
• Effect:
– QUALITY OF HUMAN RESOURCES = 0.37
– ACTINDEX increase from 0.363 to 0.393
– GEDI increase from 0.407 to 0.417
• Effect:
– RISK CAPITAL = 0.37
– ASPINDEX increase from 0.362 to 0.381
– GEDI increase from 0.407 to 0.413
– New bottleneck: NEW PRODUCT,
– More than one bad pillar, the marginal improvement is smaller than
in the previous case
1. OPPORT UNIT Y PERCEPT ION
(AT T )
14. RISK CAPIT AL (ASP)
1,00
2. ST ART UP SKILLS (AT T )
0,80
13. INT ERNAT lONALIZAT ION (ASP)
3. NONFEAR OF FAILURE (AT T )
0,60
0,40
12. HIGH GROWT H (ASP)
4. NET WORKING (AT T )
0,20
0,00
11. NEW T ECHOLOGY (ASP)
5. CULT URAL SUPPORT (AT T )
10. NEW PRODUCT (ASP)
6. OPPORT UNIT Y ST ART UP (ACT )
9. COMPET IT ION (ACT )
7. T ECH SECT OR (ACT )
8. QUALIT Y OF HUMAN RESOURCE
(ACT )
France
Germany
Italy
1. OPPORT UNIT Y PERCEPT ION
(AT T )
14. RISK CAPIT AL (ASP)
0,80
2. ST ART UP SKILLS (AT T )
0,60
13. INT ERNAT lONALIZAT ION
(ASP)
3. NONFEAR OF FAILURE (AT T )
0,40
0,20
12. HIGH GROWT H (ASP)
4. NET WORKING (AT T )
0,00
11. NEW T ECHOLOGY (ASP)
5. CULT URAL SUPPORT (AT T )
10. NEW PRODUCT (ASP)
6. OPPORT UNIT Y ST ART UP (ACT )
9. COMPET IT ION (ACT )
7. T ECH SECT OR (ACT )
8. QUALIT Y OF HUMAN RESOURCE
(ACT )
Italy
China
India
Questions
• Is there some flaw in it that needs to be corrected?
• What about the theoretical foundation of the index?
– How to improve it?
• Is the PFB methodology applicable?
– What about the marginal analysis?
– How to deal with the different substitution effects?
• How do you move forward ? What are the issues?
– Adding new years?
– What kind of changes – conceptual, technical – are necessary to apply to
the regional level? What regional level?
• Are the policy suggestions acceptable?
Thank you for your attention!