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The Attractiveness of Central Eastern European Countries for
Venture Capital and Private Equity Investors
Alexander Peter Groh*, Heinrich von Liechtenstein**, and Karsten Lieser***
Abstract
We address the attractiveness of Central Eastern European countries for Venture Capital and
Private Equity investors by the construction of a composite index. For the index’s
composition we refer to the results of numerous prior research papers that investigate
relevant parameters determining entrepreneurial activity and/or the engagements of
institutional investors. We aggregate the index via five different methods and receive
country rankings that vary only slightly, signaling a robust index calculation. We
benchmark the Central Eastern European countries with the EU 15 countries, Norway and
Switzerland and identify six tier groups of attractiveness for all of our sample countries. We
compare our index with the actual Venture Capital and Private Equity activities in the
individual countries and reveal a reasonable correlation of both figures. The results highlight
the strengths and weaknesses of the particular economies and provide guidelines for policy
improvements to attract Venture Capital and Private Equity and hence, to spur innovation,
entrepreneurship, employment, and growth.
JEL codes: G23, G24, M13, O16, P34, P52
Keywords: Central Eastern Europe, Economic Transition, Venture Capital, Private Equity
*corresponding author, GSCM Montpellier Business School, 2300 Avenue des Moulins,
34185 Montpellier Cedex 4, France, a.groh@supco-montpellier.fr and IESE Business
School – University of Navarra, Finance Department, Av. Pearson, 21, 08034 Barcelona,
Spain
**IESE Business School – University of Navarra, Finance Department, Av. Pearson, 21,
08034 Barcelona, Spain, hl@iese.edu
*** IESE Business School – University of Navarra, Finance Department, Av. Pearson, 21,
08034 Barcelona, Spain, klieser@iese.edu
mailto:a.groh@supco-montpellier.frmailto:hl@iese.edumailto:klieser@iese.edu
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The Attractiveness of Central Eastern European Countries for
Venture Capital and Private Equity Investors
Abstract
We address the attractiveness of Central Eastern European countries for Venture Capital and
Private Equity investors by the construction of a composite index. For the index’s
composition we refer to the results of numerous prior research papers that investigate
relevant parameters determining entrepreneurial activity and/or the engagements of
institutional investors. We aggregate the index via five different methods and receive
country rankings that vary only slightly, signaling a robust index calculation. We
benchmark the Central Eastern European countries with the EU 15 countries, Norway and
Switzerland and identify six tier groups of attractiveness for all of our sample countries. We
compare our index with the actual Venture Capital and Private Equity activities in the
individual countries and reveal a reasonable correlation of both figures. The results highlight
the strengths and weaknesses of the particular economies and provide guidelines for policy
improvements to attract Venture Capital and Private Equity and hence, to spur innovation,
entrepreneurship, employment, and growth.
JEL codes: G23, G24, M13, O16, P34, P52
Keywords: Central Eastern Europe, Economic Transition, Venture Capital, Private Equity
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1. Introduction
The Central Eastern European (CEE)1 countries are still in a transitional stage. EBRD
(2005) emphasizes that improvements in governance, enterprise restructuring, and the
financial sector have been the main features of the transition process in the last years. The
CEE countries lessened the burden of business regulation, such as licensing and tax
administration, and they progressed in reducing corruption and organized crime. EBRD
(2006) highlights that the speed of the transition process varies in each country; some of
them show strong attempts to reform while others have decreased the pace of reform, partly
influenced by lately elected new governments.
Kolodko (2000) and Wagner and Hlouskova (2005) argue that the CEE countries are in a
period of catch-up that might last for several decades. This view is typically based on the
observation that per-capita GDP are still below the level of the current EU member states,
while education in CEE countries is at a high level, and institutional structures have been
converging for some time, as Süppel (2003) highlights. Growth estimates above the
European average, and the policy will to promote innovative enterprises should lead to a
strong demand for risk capital in the CEE countries and hence, to a high attractiveness for
Venture Capital and Private Equity (VC/PE) investors.
Hellmann and Puri (2000), and Kortum and Lerner (2000) show that VC/PE-backed
companies are more efficient innovators, and Belke et al. (2003), and Fehn and Fuchs
(2003) prove that they create more employment and growth than their peers. Levine (1997)
documents well the role of VC/PE funds in fostering innovative firms, and indeed, there
now exists a broad consensus that a strong VC/PE culture is a cornerstone for
commercialization and innovation in modern economies. Hence, policymakers should focus
on the creation of an adequate setting for a prospering VC/PE market to support
entrepreneurial activities and growth, especially in transition countries. However, the risk
capital supply is rather small compared to other European economies and relative to the
1 We define CEE countries as those Central Eastern European countries that lately (i.e. 2004, and 2007 respectively) accessed the European Union, namely Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, Slovenia, and the Baltic States, including Estonia, Latvia, and Lithuania.
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expected growth opportunities in the CEE countries, even if institutional investors are
increasingly looking internationally for new investment opportunities. The first funds were
raised shortly after the fall of communism. According to EVCA (2004, 2005 and 2006),
since then only a little more than € 9bn have been raised by VC/PE funds dedicated to CEE
countries. This raises questions about the reasons that constrain the development of the
VC/PE market in that region.
In this paper, we address the attractiveness of the CEE countries for VC/PE investors by
means of the construction of a composite index. For the index’ composition we refer to the
results of numerous prior research papers that investigate relevant parameters determining
entrepreneurial activity and/or the engagements of institutional investors. We aggregate the
information to the index via five different methods. As benchmarks, we also calculate the
index scores for the 15 countries that belonged to the European Union before May 1st 2004
(the EU-15 countries), and for the non-EU countries Switzerland and Norway. As a result,
we obtain a ranking of all the individual economies based on their attractiveness for VC/PE
allocation by institutional investors. The rankings vary only slightly with the five different
index calculation methods, signaling a robust index definition. We clearly identify six tier
groups of attractiveness for all of our sample countries and three tier groups for the CEE
countries, that all rank below the EU-15 average. We compare our index with the
fundraising activities in the individual countries and reveal a reasonable correlation of both
figures.
Policy makers will benefit from our results by realizing the weaknesses of their countries to
attract international VC and PE. Improvements of the revealed weaknesses shall lead to
more supply of risk capital and will hence spur innovation, entrepreneurship, employment,
and growth.
The paper is structured as follows: After a brief introduction to our assumptions about
supply and demand in the VC/PE market, we review the most important related literature,
and discuss the relevant parameters for our model. Next, we explain the data and the
technical background for our index calculations. We verify the appropriateness of the sub-
indicators included, and discuss different normalization techniques, weighting, and
aggregation methods. Then, we present the index’s results, the strengths, and weaknesses of
the individual countries, perform robustness checks and determine tier groups regarding the
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attractiveness of the various economies for VC/PE investors. Finally, we summarize this
paper.
2. Supply and Demand in the VC/PE Market
Models and conditions for achieving equilibrium in supply and demand in a VC/PE market
are comprehensively discussed in Gompers and Lerner (1998), Balboa and Martí (2003),
and Jeng and Wells (2000). We assume that the institutional investors supplying VC/PE
analyze several economies and choose among them for their international asset allocation.
The usual fundraising process implies, and statistics on supply and demand for VC/PE, such
as EVCA (2006) confirm this, that usually there is no lack of supply of funds. On the
contrary, the amount of funds raised in a particular year is generally higher than the amount
invested in the same period, and the funds raised are invested progressively in subsequent
years. This leads us to conclude that the suppliers of capital estimate the demand for VC/PE
with a one-to two-year horizon and make their allocations accordingly. Consequently, they
judge the individual countries’ attractiveness, which is determined primarily by expectations
about the ability of local VC/PE funds to perform a sufficient number of transactions with
satisfactory risk and return ratios. Hence, the predominant issue regarding the attractiveness
of a particular region for an institutional investor is probably the availability of adequate
investment opportunities. These opportunities are probably depending on local
entrepreneurial activities and are associated, among other factors, with innovations,
restructurings, the size of the economy, growth expectations, and the entrepreneurial spirit
of people. However, it is not clear to what extent these and related factors influence the
attractiveness of individual economies for investors in VC/PE funds. Therefore, in the
following section, we provide an overview of the literature dealing with success factors for
entrepreneurial activities, and the volume of VC/PE investments.
3. Literature Review
Intuitively, the state of a particular country’s economy affects VC/PE activities. Gompers
and Lerner (1998) point out that there are more attractive opportunities for entrepreneurs if
the economy is growing quickly. Wilken (1979) argues that economic development
facilitates entrepreneurship as it provides a greater accumulation of capital for investments.
The ease of start-ups is expected to be related to societal wealth, not only due to the
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availability of start-up financing, but also to higher income among potential customers in
the domestic market. Romain and van Pottelsberghe de la Potterie (2004) find that VC/PE
activity is cyclical and significantly related to GDP growth.
Likewise, Jeng and Wells (2000) stress that the main force behind the cyclical swings is the
IPO activity because it reflects the potential return to the VC/PE funds. Kaplan and Schoar
(2005) confirm this. Black and Gilson (1998), and Gompers and Lerner (2000) point out
that risk capital flourishes in countries with deep and liquid stock markets. Schertler (2003)
uses either the capitalization of stock markets or the number of listed firms as a measure for
the liquidity of stock markets. He finds that the liquidity of stock markets has a significant
positive impact on VC investments at early stages.
The availability of debt financing is another entry key for start-ups, and, emphasized by
Greene (1998), in many countries it is the most important entrepreneurial obstacle.
Entrepreneurs need to find backers who are willing to bear this risk, like banks or VC/PE
funds. Hellmann et al. (2004) argue that banks represent the dominant financial institutions
in most of the countries. They examine the role of banks for the VC/PE industry and stress
that banks invest in VC/PE mainly for strategic reasons. They try to build early relationships
for future lending activities. Cetorelli and Gambera (2001) provide evidence that bank
concentration promotes the growth of those industrial sectors that have a higher need of
external finance by facilitating credit access to younger firms.
Additionally, the VC/PE activity in a particular country relates to the status of the VC/PE
market’s maturity level. Sapienza et al. (1996) mention that acceptance in a country’s
society and the historical evolvement of its VC/PE market determine investor confidence.
Balboa and Martí (2003) find that annual fundraising volume is dependent on the previous
year’s market liquidity. Chemla (2005) argues that the management of VC/PE funds is
costly. Particular regions become attractive to investors when the transaction volumes and
expected payoffs exceed a certain amount to cover the management fees.
Legal structures and the protection of property rights also appear to influence the
attractiveness of a VC/PE market. La Porta et al. (1997 and 1998) confirm that the legal
environment strongly determines the size and extent of a country’s capital market and local
firms’ ability to receive outside financing. They emphasize the difference between law on
books and the quality of law enforcement in some countries. Glaeser et al. (2001), Djankov
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et al. (2003 and 2005) suggest that parties in common-law countries have greater ease in
enforcing their rights from commercial contracts. However, Cumming et al. (2006a) find
that the quality of a country’s legal system is stronger connected to facilitating VC/PE
backed exits than the size of a country’s stock market. Cumming et al (2006b) extend this
finding and show that cross-country differences in legality, including legal origin and
accounting standards, have a significant impact on the governance of investments in the
VC/PE industry. Desai et al. (2006) discuss that fairness and property rights protection
largely determine the growth and emergence of new enterprises. Cumming and Johan
(2007) highlight that the perceived importance of regulatory harmonization increases
institutional investors’ allocations to the asset class. La Porta et al. (2002) find lower cost of
capital for companies in countries with better investor protection. Lerner and Schoar (2005)
confirm these findings. Johnson et al. (1999) show that weak property rights limit the
reinvestment of profits in start-up firms. Even so, Knack and Keefer (1995), Mauro (1995),
and Svensson (1998) demonstrate that property rights significantly affect investments and
economic growth.
Gompers and Lerner (1998) stress that the capital gains tax rate influences VC/PE activity.
In fact, they confirm Poterba’s finding (1989), who builds a decision-model to become
entrepreneur. Bruce (2000 and 2002), and Cullen and Gordon (2002) prove that taxes matter
for businesses entry and exit. Bruce and Gurley (2005) explain that increases in the tax rates
on wages raise the probability of becoming an entrepreneur. Hence, the difference between
personal income tax rates and corporate tax rates tends to be an incentive to create self-
employment.
Rigid labor market policies negatively affect the evolvement of a VC/PE market. Lazear
(1990), and Blanchard (1997) discuss how protection of workers can reduce employment
and growth. Black and Gilson (1998) show that variations in labor market restrictions
correlate with VC/PE activity.
Djankov et al. (2002) investigate the role of administrative and bureaucratic burdens for
start-ups in different countries. They conclude that the highest barriers and costs are
associated with corruption, a larger unofficial economy, and bureaucratic delay. Baughn and
Neupert (2003) argue that bureaucracy in form of excessive rules and procedural
requirements, multiple institutions from which approvals are needed, and numerous
documentation requirements may severely constrain entrepreneurial activity. Lee and
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Peterson (2000) stress that the time and money required to meet such administrative burdens
may discourage new venture creations.
Access to viable investments is probably the most important factor for the attractiveness of a
regional VC/PE market. In order to foster a growing risk capital industry, Megginson (2004)
argues that R&D culture, especially in universities or national laboratories, plays an
important role. Gompers and Lerner (1998) show that both industrial and academic R&D
expenditure is significantly correlated with VC/PE activity. Kortum and Lerner (2000)
highlight that the growth in VC/PE fundraising in the mid-90s may be due to a surge of
patents in the late 1980s and 1990s. Schertler (2003) emphasizes that the number of
employees in the field of R&D, and the number of patents, as an approximation of the
human capital endowment, has a positive and highly significant influence on VC/PE
activity. Furthermore, Romain and von Pottelsberghe de la Potterie (2004) find that the level
of entrepreneurship interacts with the R&D capital stock, with technological opportunities,
and the number of patents. Lee and Peterson (2000), and Baughn and Neupert (2003) argue
that national cultures shape both individual orientation and environmental conditions, which
lead to different levels of entrepreneurial activity in particular countries.
Summarizing this literature overview, we identify six key drivers determining the
attractiveness of an individual country for VC/PE investors: economic activity, size and
liquidity of capital markets, taxation, investor protection and corporate governance, human
and social environment, and entrepreneurial opportunities. We also refer to these key drivers
as level 1 indexes. To proxy the desired characteristics of these latent variables we find 42
different sub-indicators, which we can group into lower index levels (level 2 and 3). These
indicators are available for every individual sample country and ultimately determine its
attractiveness for VC/PE investors. We will describe the data and the framework for the
index aggregation in the following chapter.
4. Data and Aggregation Methodology
4.1 Data Sample
In general, composite indicators are used to summarize a number of underlying individual
indicators or variables. An indicator is a quantitative or qualitative measure derived from a
series of observed facts that can reveal or proxy characteristics. To ensure that our cross-
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country aggregations are comparable, we deflate variables by the sizes of the
economies/countries and use either GDP or population as deflators.
We use several databases with yearly data ranging from 2000 to 2005 and usually refer to
the last data record. Some of the data-points are averages over a certain time-period to
smooth fluctuations. GDP figures, PE activity or M&A transaction volume among others
are such averages considering the period from 2000 to 2005. For large fluctuations and large
differences between the countries we also use logs of the averages (please refer to the
legend of table 1 for detailed information). In less than one percent of all cases, data was not
available for a certain year. If data-points are missing, we apply the three methods suggested
by Nardo et al. (2005a) in the following order: a) We try to find missing data in other
databases or via the Internet, b) we interpolate between the adjacent data records, and c) we
use the latest available data before 2005.
However, we do not always use raw data but sometimes refer to ready-made indexes like
the “doing business indexes” from the World Bank.2 For instance, our indicator for investor
protection and corporate governance is a ready-made index. The following table 1 presents
all the raw data and ready-made indexes and their sources (resp. alternative databases if
data-points are missing), we use to determine the “Venture Capital and Private Equity
Attractiveness Index” (VC/PEAI). For descriptions of the individual index items, we refer to
the sources, where comprehensive definitions and descriptions of the data series are
available.
Table 1: VC/PEAI – List of raw data and ready-made indexes and their sources
1 Economic activity 1.1 Gross Domestic Product 1.1.1 Total GDP [€/capita]* Global Market Inform. Database 1.1.2 Total GDP y-o-y growth [%]** Global Market Inform. Database 1.2 General Price Level [Index=1995]*** Global Market Inform. Database 1.3 Working force (unemployment rate) [%]* Global Market Inform. Database 1.4 Foreign direct investment, net inflows [% of GDP]*** Global Market Inform. Database 2 Capital market 2.1 IPO [IPO volume in % of GDP]**** Thomson Financial Data 2.2 Stock market 2.2.1 Stock market capitalization [% of GDP]* Worldbank Data
2 See http://www.doingbusiness.org.
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2.2.2 Stock Market Total Value Traded / GDP [% of GDP]* Worldbank Data2.3 M&A market [sales % of GDP]* Global Market Inform. Database 2.4 Debt & Credit market 2.4.1 Central bank discount rate [%]* IMF2.4.2 Private Credit by Deposit Money Banks and Other
Financial Institutions [% of GDP]* Worldbank Data
2.4.3 Number of Banks [per Capita] EBRD, EUROSTAT Database2.5 Private equity activity [funds invested in % of GDP]**** Thomson Financial Data3 Taxation 3.1 Highest marginal tax rate, corporate rate (%) Worldbank Data 3.2 Difference between income and corporate tax rate [%] The Heritage Foundation 4 Investor protection and corporate governance 4.1 Extent of disclosure index Worldbank Data 4.2 Extent of director liability index Worldbank Data 4.3 Ease of shareholder suits index Worldbank Data5 Human & social environment 5.1 Education 5.1.1 Government expenditure on education, total [% of GDP]* Global Market Inform. Database 5.1.2 Amount employes as researcher in the university sector
[per capita]EUROSTAT
5.1.3 Amount university students [per capita]* Global Market Inform. Database 5.1.4 Amount university establishements [per capita] Global Market Inform. Database 5.2 Labor regulations 5.2.1 Rigidity of employment 5.2.1.1 Difficulty of hiring index Worldbank Data5.2.1.2 Rigidity of hours index Worldbank Data5.2.1.3 Difficulty of firing index Worldbank Data5.2.2 Hiring cost [% of salary] Worldbank Data5.2.3 Firing costs [weeks of wages] Worldbank Data5.3 Bribing & corruption index Transparency5.4 Crime 5.4.1 Juvenile offenders [per capita]* Global Market Inform. Database 5.4.2 Offences [per 100,000 habitants]* Global Market Inform. Database 6 Entrepreneurial opportunities 6.1 General Innovativeness Index TrendChart.Cordis6.2 R&D expenditure 6.2.1 Public R&D expenditures [% of GDP] EUROSTAT, OECD6.2.2 Business R&D expenditures [% of GDP] EUROSTAT, OECD6.3 Enterprise restructuring 6.3.1 Small-scale privatisation index EBRD 6.3.2 Large-scale privatisation index EBRD 6.3.3 Governance and enterprice restructuring index EBRD 6.4 Enterprise stock activity 6.4.1 Number of enterprises [per capita] World Bank, EUROSTAT, OECD 6.4.2 Enterprise foundation rate [%]* World Bank, EUROSTAT, OECD 6.5 Burden: Starting a Business 6.5.1 Procedures [numbers] Worldbank Data 6.5.2 Time [days] Worldbank Data 6.5.3 Cost of business start-up procedures [% GNI per capita] Worldbank Data 6.5.4 Min. capital [% of income per capita] Worldbank Data
* = arithmetic average of annual data from 2000 to 2005, ** = geometric average of annual data from 2000 to 2005, *** = log of arithmetic average of annual data from 2000 to 2005, **** = arithmetic average of annual data since coverage in the database for CEE countries, arithmetic
average of annual data from 2003 to 2005 for the other countries,
otherwise: 2005 data record.
http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.doingbusiness.org/ExploreTopics/HiringFiringWorkers/http://www.transparency.org/policy_research/surveys_indices/http://www.transparency.org/policy_research/surveys_indices/http://www.transparency.org/policy_research/surveys_indices/http://trendchart.cordis.lu/http://trendchart.cordis.lu/http://trendchart.cordis.lu/http://trendchart.cordis.lu/http://trendchart.cordis.lu/http://trendchart.cordis.lu/
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We choose the 42 sub-indicators according to the referenced literature and according to our
comprehensive search of adequate data-series to proxy the latent variables. However, the
selection of the index-items is arguable. Additional data-series might be included some
might be discarded, or exchanged against others. We propose our selection and emphasize
that changes of particular data-series do not meaningfully affect the overall results. The
results are stable due to the magnitude of index-items and due to the index consistency, as
proofed by the reported and unreported robustness checks.
Due to the large number of index items (42), and data-points (105) per country (including
the data records over a certain period to calculate the averages), we follow the method
proposed by Nicoletti et al. (2000) and determine a pyramidal structure of three sub-index
levels for the index aggregation (see Figure A1 in the Appendix). We group the items that
we expect to correlate with each other. The main advantage of this pyramidal structure is
that we can trace back indicator values to increasing levels of detail. This will help in
interpreting the strengths and weaknesses of the individual countries and in drawing up the
conclusions.
Using this composition technique, we have to prove that the raw data and the ready-made
indexes are consistent for their aggregation. Thus, we perform a reliability analysis of all the
individual items, using Cronbach’s Alpha to ascertain the consistency of the chosen data.
This procedure is described in the subsequent section.
4.2 Analysis of Index Consistency
Cronbach’s Alpha3 is a measure of internal consistency of items in a model or survey.4 It
assesses how well a set of items measures a single one-dimensional object
(unidimensionality). Here, we use it to approve the consistency of our index and all the sub-
indexes we aggregate. Cronbach’s Alpha is defined as:
( )RnRn
11 −+=α (1).
3 Cf. Cronbach (1951). 4 Cf. Raykov (1998), Cortina (1993), Feldt et al. (1987), Green et al. (1977), Hattie (1985), and Miller (1995).
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Thereby, n is the number of the components of a (sub-) index and R is the mean correlation
of the items (e.g. the mean of the non-diagonal terms of the correlation matrix). The
coefficient increases with the number of sub-indicators and with the correlation of each
tuple. Cronbach’s Alpha is equal to zero if no correlation exists and the sub-indicators are
independent. The coefficient is equal to one if sub-indicators are perfectly correlated.
Hence, a high alpha indicates that the underlying items proxy well the desired variable.
Nunnally (1978) suggests a value of 0.7 as acceptable threshold.
The following Table 2 presents the consistency of the six main key drivers measured by the
Cronbach’s Alphas of the underlying level 2 sub-indexes. We do not consider Taxation and
Investor protection in this calculation, because they consist of too few underlying items.
Table 2: Consistency analysis of the underlying items on the level of the six key drivers
Sub-indicator
Cronbach's Alpha
Economic activity 0.553Capital market 0.729Taxation -Investor protection -Human and Social environment 0.750Entrepreneurial opportunities 0.785
Cronbach’s Alpha for economic activity lies below Nunnally’s (1978) cut-off value of 0.7.
This could lead us to exchange or to drop some items that proxy economic activity.
However, the aggregation of the six key drivers to the overall VC/PE Attractiveness Index
yields a Cronbach’s Alpha of 0.784. Thus, we propose that our selection of items represents
well a country’s attractiveness for VC/PE investors and we do not discard sub-indexes from
our sample.
4.3 Normalization and Standardization
All data-points need to be normalized for their index aggregation. There exist various
techniques, each one with particular advantages and disadvantages as discussed by
Freudenberg (2003), Jacobs et al. (2004), and Nardo et al. (2005a). We use two different
methods - standardization and rescaling - in our calculations. Lastly, we analyze the
differences resulting from using both methods in a robustness check.
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Standardization (or z-scores) converts the underlying data to a common scale of the normal
distribution with a mean of zero and a standard deviation of one. Hence, variables with
extreme values have a greater effect on the indicator. The z-score is defined as:
sxxz −= (2).
The rescaling method is used to normalize indicators to an identical range by linear
transformation. This method is vulnerable for extreme values or outliers that can distort the
transformation. However, rescaling can widen the range of indicators lying within small
intervals more than using the z-scores transformation. The rescaling method is defined as:
)min()max()min(
xxxxy
−−
= (3).
Ebert and Welsch (2004) discuss that the selection of a suitable normalization method is not
trivial and requires special attention. The method shall consider the properties of the
underlying data, as well as the objective of the summarized indicator. The z-scores and the
rescaling approach are the most commonly used because they have desirable characteristics
when it comes to aggregation.
Considering our data, where the values of the variables are rather close to each other, the
rescaling method seems most appropriate because it widens the countries’ spread, and
hence, allows better interpretations. Accordingly, we use the rescaling method and convert
all variables of the particular sub-indexes to a common scale from 0 to 100 points. Thereby,
100 represents the best score, while 0 is the worst. For every individual variable, we define
if high values positively or negatively influence the attractiveness for VC/PE investors,
regarding the above-cited literature findings. In our robustness checks, we investigate the
difference resulting from using z-scores for standardization. The next step deals with the
weighting of the individual factors and sub-indexes, and the aggregation of all items to the
VC/PE Attractiveness Index.
4.4 Weighting of the Index Items
If there are no statistical or empirical grounds for choosing a different scheme, we could use
equal weights for the index items to calculate the index. This implies an equal contribution
of all sub-indicators to the VC/PE attractiveness, which is arguable. Equal weighting, as
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discussed by Nardo et al. (2005a), can be the result of insufficient knowledge about causal
relationships, ignorance about the correct model to apply or even stem from the lack of
consensus on alternative solutions. There are a number of weighting techniques derived
from statistical models. Manly (1994) discusses principal component analysis. Nardo et al.
(2005a) propose factor analysis, and data development analysis. Kaufmann et al. (1999 and
2003) use an unobserved component model. Other weighting techniques are derived from
analytic hierarchy processes, as described in Forman (1983), or Saaty (1987), or from
conjoint analysis, as in Green and Srinivasan (1978), Hair et al. (1998), and McDaniel and
Gates (1998).
We use both, one approach with equal weights among all the sub-index items and one
approach based on factor analysis where we differentiate between the index levels. Level 3
sub-indexes are still equally weighted, but for the level 2 and level 1 sub-indexes we follow
Berlage and Terweduwe (1988). In this weighting method, each component of the level 1
and level 2 sub-indexes is weighted according to its contribution to the total variance in the
data. This is an attractive feature, because it ensures that the resulting summary indicators
account for a large part of the cross-country variance of the underlying items. That makes
this method independent of prior views on their relative economic importance, which is an
arguable issue, but, as highlighted in Nicoletti et al. (2000), these properties are particularly
desirable for cross-country comparisons. However, using the two weighting approaches, we
can investigate the results of both in our robustness check.
A detailed discussion of factor analyses is carried out e.g. in Hair et al. (1998). The general
linear factor model for p observed variables and q factors or latent variables takes the form:
iqiqiii eFFFx ++++= ααα ...2211 (i = 1,…,p) (4).
Where xi represent standardized variables, and αi1,…,αiq are factor loadings related to the
factors Fi,…,Fq, while ei are residuals. We assume that the factors are uncorrelated with each
other, and with the residuals. Further, they have zero means, and unit variance. Additionally,
the residuals are uncorrelated with each other, have zero means, but not necessarily equal
variances.
Now, the most common method used to extract the first m components is principal
component analysis. The decision of when to stop extracting factors depends on the point
when only little “random” variability remains. Various stopping rules have been developed
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as described in Dunteman (1989): Kaiser’s Criterion, Scree Plot, variance explained criteria,
Joliffe Criterion, Comprehensibility, Bootstrapped Eigenvalues and Eigenvectors. However,
Kaiser’s Criterion is one of the most widely used stopping rules and recommends to drop all
factors with an Eigenvalue below one. Due to Kaiser (1958), most of the total variance is
determined by components beyond the Eigenvalue of one. However, regarding the
Eigenvalues in our sample, there is another large decrease of explained variance below
Kaiser’s mark. As demonstrated in Table 3, we obtain three components that represent
83.8% of the total variance given by the underlying data.
Table 3: Total Variance explained by Components
Total Variance Explained
2,747 45,776 45,776 2,747 45,776 45,776 2,578 42,961 42,9611,306 21,766 67,541 1,306 21,766 67,541 1,272 21,195 64,156,976 16,268 83,810 ,976 16,268 83,810 1,179 19,654 83,810,446 7,431 91,241,300 5,005 96,246,225 3,754 100,000
Component123456
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
The next step deals with the rotation of factors (see Table 4). According to Hair et al. (1998)
the usual rotation method is the Varimax Rotation. Rotation is used to minimize the number
of sub-indicators that have a high loading on the same factor. Ideally, each indicator is
loaded exclusively on one of the factors. Kline (1998) points out, that the rotation changes
the factor loadings, and hence, the factors’ interpretation, but leaves the analytical solutions
ex-ante and ex-post rotation unchanged.
Table 4: Rotated Component Matrix
Rotated Component Matrixa
,849 ,077 ,249,700 -,457 ,391,018 ,924 ,154,089 ,133 ,966
,796 ,376 -,088
,851 -,211 ,004
Economic activityCapital marketTaxationInvestor protectionHuman & SocialenvironmentEntrepreneurialopportunity
1 2 3Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 5 iterations.a.
Table 4 presents the component matrix after Varimax Rotation and allows an interesting
interpretation of the resulting factors: Economic activity, capital market, human & social
13
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environment and entrepreneurial opportunities have high loadings on the first factor. Hence,
it represents the general socio-economic conditions. Taxation and investor protection have
each high loadings on the remaining two factors. Consequently, we can name the two other
factors correspondingly. As a result, the socio-economic environment, the tax regime and
property rights protection determine the index.
The last step (see Table 5) of the weighting procedure deals with the construction of the
weights from the matrix of factor loadings after rotation. The square of a factor loading
represents the proportion of the variance of the indicator explained by the factors. Now, the
three intermediate components are aggregated by weighting each composite using the
proportion of the explained variance in the dataset: 0.513 for the first
(0.513 = 2.578/(2.578+1.272+1.179)), 0.254 for the second component and 0.235 for the
third one. We rescale the final weights to sum up to one to preserve the comparability.
Finally, we can calculate the overall weights as a linear combination of the different
components.
Table 5: Calculation of the weights Private Equity AttractivenessRotated Component Matrix(a) Overall weights
1 2 3 1 2 3Economic activity 0.849 0.077 0.249 0.280 0.005 0.053 0.157Capital market 0.700 -0.457 0.391 0.190 0.164 0.130 0.169Taxation 0.018 0.924 0.154 0.000 0.671 0.020 0.174Investor protection 0.089 0.133 0.966 0.003 0.014 0.791 0.191Human & Social environment 0.796 0.376 -0.088 0.246 0.111 0.007 0.156Entrepreneurial opportunity 0.851 -0.211 0.004 0.281 0.035 0.000 0.153Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 5 iterations.Expl. Var 2.578 1.272 1.179 1 1 1 1Expl. /Tot 0.513 0.253 0.235 Sum Sum Sum Sum
Component loadings Component weights
Table 5 presents the weights of the six key drivers (or level 1 sub-indexes). It becomes
obvious that the difference between the weights of the key drivers is not very large,
probably allowing for equal weightings of the sub-indexes as well. However, we will
address this issue in the robustness check.
Anyway, tables 3, 4, and 5 so far present the final procedure for the weights of the already
aggregated level 1 sub-indexes (refer to Figure A1 in the appendix). To determine those
sub-indexes we had to perform the same procedure for all the six key drivers one-step
before. The calculations of the factor analyses for each key driver are described in the
appendix (Tables A1 – A12).
14
-
Nardo at al. (2005b) discuss the advantages and disadvantages of factor analysis. Factor
analysis can summarize a set of sub-indicators while preserving the maximum possible
proportion of the total variation in the original set. This is a very desirable feature for cross-
country comparisons. Contrarily, the determined factor loadings might not represent the real
influence of sub-indicators. Furthermore, factor analysis is highly sensitive towards
modification of the sample due to data revisions or updates of new countries. Factor analysis
is also very sensitive to the presence of outliers, which may introduce a spurious variability
in the data, and to the sample size.
4.5 Aggregation
There also exist various procedures for the index aggregation. Nardo et al. (2005a and
2005b) distinguish additive methods, geometric aggregation and non-compensatory multi-
criteria analysis. We focus on linear and geometric aggregation because they are in common
use.
Linear aggregation is an additive method and defined as:
∑ ∑ =≤≤=i i
iii wandwwherexwx 1,10, (9).
Geometric aggregation is defined as:
∑∏ =≤≤=i
ii
ii wandwwherexwx 1,10, (10).
Ebert and Welsch (2004) recommend that the linear aggregation method is useful when all
sub-indicators have the same measurement unit, and geometric aggregation is better suited,
if non-comparable and strictly positive sub-indicators are expressed in different ratio scales.
Nardo et al. (2005a) highlight that linear aggregation assigns base indicators proportionally
to the weights, while geometric aggregation rewards those countries or those sub-indicators
with higher scores. Overall, a shortcoming in the value of one variable or sub-index can be
compensated by a surplus in another. Compensability is constant in linear aggregation,
while it is smaller in geometric aggregation for the sub-indicators with low values.
Therefore, countries with low scores in some sub-indexes benefit from linear aggregation.
Due to the properties of the rescaling method from 1 to 100 index points, we prefer linear
aggregation. However, in the robustness analyses we will prove if the geometric aggregation
method yields different index results.
15
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16
5. Results
5.1 Base Findings
We calculate the base case of our VC/PE Attractiveness Index according to the procedures
described using rescaling, factor analysis, and linear aggregation. Figure 1 presents the
index rankings for the EU-15 countries, for the CEE countries, for Switzerland and Norway,
and for the GDP-weighted averages of the EU-15 and the CEE. The EU-15 states are chosen
as the benchmark. Their score is indexed to 100 points to simplify country comparisons.
That means that the CEE region, with 85 points, is 15% less attractive compared to EU-15
benchmark.
The top performers are Ireland, Luxembourg, United Kingdom, Sweden and Denmark. The
Central Eastern European Countries lag behind the EU-15 states. The best CEE performer
with 96 index points is Hungary, followed by Slovenia with 95 points, which even rank
before France. The CEE average still ranks before Italy and Spain. However, Bulgaria,
Romania and Slovakia constitute less attractive economies for VC/PE investors while
Greece qualifies as least attractive. The decisive factors for the individual countries shall not
be discussed here, but are presented in the appendix in Tables A12 to A15.
Focusing on the CEE region, and disaggregating the index result to the six key drivers, we
can present the region’s strengths and weaknesses in Figure 2. The chart shows the six key
drivers (and their index weights according to our factor analysis) of attractiveness for the
CEE region, and the EU-15 states as the benchmark. Taxation is the strongest component of
the CEE countries’ attractiveness for VC/PE investors. However, this criterion is highly
dependent on the local legislations, and relatively quickly and arbitrarily adaptable by
politicians. United Nations (2004) reports that CEE governments try to attract investors with
low corporate tax rates and tax incentives within the accession process.
Investor protection & Corporate Governance is another criterion where local legislatives
copied Western European standards to catch up quickly in the accession process. Generally
speaking, investors are as well protected by law on books and by enforcements in the CEE
countries as they are in the average EU-15. Both, the character of the legal rules, and the
quality of law enforcement is covered in the selected sub-indexes. The human & social
environment is also on a par with the EU-15 level. However, the other key drivers cannot
-
reach the EU-15 average. Economic activity, entrepreneurial opportunities, and particularly
capital markets lag (far) behind the EU-15 countries.
Figure 1: Country Ranking According to the VC/PE Attractiveness Index – Method: Rescaling, Factor Analysis, and Linear Aggregation
69
76
77
79
81
82
85
85
89
100
102
102
104
110
111
121
122
124
127
130
99
93
95
95
96
96
0 100
Greece
Slovakia
Romania
Bulgaria
Italy
Spain
CEE
Czech Republic
Poland
Baltic States
France
Slovenia
Hungary
Portugal
Germany
Austria
Belgium
Netherlands
Finland
Switzerland
Norway
Denmark
Sweden
UK
Luxembourg
Ireland
VC/PE-Attractiveness Index
EU15=100 Index Points
17
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Figure 2: Central Eastern European Strengths & Weaknesses (1)
0
50
100
150
200Economic activity ; (0.156)
Capital market; (0.169)
Taxation; (0.174)
Investor protection; (0.190)
Human & Social environment;(0.155)
Entrepreneurial opportunity;(0.153)
CEEEU15
Figure 3 breaks down the index aggregation and presents the level 2 sub-indexes (and their
weights calculated by our factor analysis) for the average of the CEE countries with the EU-
15 states as the benchmark. It reveals that relatively small economies, high unemployment
rates, and small and illiquid capital markets characterize the CEE countries. The capital
markets in particular constitute a strong deficit in every sub-criterion compared to the EU-
15 benchmark.
The Human & social environment of the CEE countries is judged to be equal to the EU-15
average. High educational standards, good labor regulations, and low crime rates constitute
the strengths of the CEE culture. However, bribery and corruption remain higher in the CEE
countries than in the West European benchmarks.
While privatization and large enterprise restructuring processes are nearly completed,
entrepreneurial opportunities are rather weak in CEE. In particular, the burden for starting a
business is much higher than the EU-15 average. Additionally, the innovativeness of the
CEE countries is ranked very poorly. The small number of patents and low public and
private R&D expenditure contribute to that deficit.
18
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Figures A54 and A55 in the appendix present the scores of the six key drivers for all of our
sample countries, and for the CEE and the EU-15 averages. Further, spider charts and bar
charts, as in the appendix Figures A2 to A53, allow comprehensive comparisons of all our
sample countries.
Figure 3: Central Eastern European Strengths & Weaknesses (2)
Central Eastern Europe
69.71
58.80
53.48
102.49
24.76
10.80
70.77
31.53
19.03
320.31
100.37
72.34
105.33
126.54
88.83
110.13
52.87
120.79
26.84
39.39
83.65
165.78
67.29
0 50 100 150 200 250 300 350
1.1 Gross Domest ic Product; (0.27)
1.2 General Price Level; (0.25)
1.3 Working force (unemployment rate); (0.25)
1.4 Foreign direct investment, net inf lows; (0.21)
2.1 IPO Volume; (0.23)
2.2 Stock market ; (0.17)
2.3 M &A market; (0.14)
2.4 Credit and Debt market ; (0.20)
2.5 Private equity act ivity; (0.24)
3.1 Highest marginal tax rate, corporate rate; (0.5)
3.2 Dif ference between income and corporate tax rate; (0.5)
4.1 Extent of disclosure index; (0.33)
4.2 Extent of director liability index; (0.33)
4.3 Ease of shareholder suits index; (0.32)
5.1 Education; (0.27)
5.2 Labor regulat ions; (0.22)
5.3 Bribing & corruption; (0.25)
5.4 Criminality; (0.24)
6.1 General Innovativeness; (0.22)
6.2 R&D expenditure; (0.20)
6.3 Enterprise restructuring; (0.19)
6.4 Enterprise stock; (0.20)
6.5 Burden: Start ing a Business; (0.17)
EU 15 =100
5.2 Attractiveness Regarding Actual VC/PE Activity
Klonowski (2005) argues that the activity of VC/PE fundraising indicates the attractiveness
of a particular country to foreign and domestic investors. Hence, there should be a strong
correlation between our VC/PE attractiveness ranking and the fundraising activities in the
various countries. In Figure 4, we present this relationship, adding the size of the individual
19
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20
economies measured by GDP per capita into the chart. The expected VC/PE activity is
measured as averages of the ratios between raised funds and GDP for several years to
smooth fluctuations. The data is taken from EVCA (2003, 2004, 2005, and 2006 and an
additional EVCA database on the CEE market). Especially for the CEE countries, the raised
funds fluctuate strongly among different periods. Therefore, we average the raised
funds/GDP ratios for the CEE countries for all the periods since coverage in the EVCA
database.5 For the other countries, we use the average of the ratios from 2003 to 2005. The
chart shows a strong correlation between the actual VC/PE activity in the individual
countries and our index results. This indicates that our index is a good proxy for the
countries’ attractiveness for VC/PE investors. The Pearson Correlation coefficient is 0.571
at a 0.002 significance level (two tailed). If we drop outliers beyond the percentile marked
by two times the standard deviation of one of the variables (namely Ireland because of its
high ranking, and the United Kingdom because of its high activity), the correlation becomes
0.676 at a 0.000 significance level.
Regarding Figure 4, it should be emphasized that there is a large volume of cross border
transactions in some of the countries with large raised fund figures. Especially in the United
Kingdom, a certain amount of the funds raised is allocated to other European economies.
For the CEE market, Poland plays a similar role as a hub for fundraising activities.
Furthermore, a high level of fund raising activities in the CEE region is, in some cases,
attributable linked to a small number of (larger and regional) funds.
5 The coverage in the EVCA database differs for the particular countries. Some countries such as the Czech Republic, Hungary, or Poland are covered since 1998. The Baltic States are least covered, since 2003.
-
Figure 4: VC/PE-Attractiveness vs. VC/PE-Activity
Slo
vaki
a
Rom
ania B
ulga
riaCze
ch R
epub
lic
Pol
and
Balti
c St
ates
-0.2
5
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
6575
8595
105
115
125
135
VC/P
E-At
trac
tiven
ess
Inde
x 200
6 [EU
15 =
100
]
Expected VC/PE-Activity [raised funds in % of GDP]
Gre
ece
Slo
vaki
aR
oman
iaB
ulga
riaIta
lyS
pain
Cze
ch R
epub
licP
olan
dBa
ltic
Stat
esFr
ance
Slov
enia
Hun
gary
Por
tuga
lG
erm
any
Aus
tria
Bel
gium
Net
herla
nds
Finl
and
Sw
itzer
land
Nor
way
Den
mar
kSw
eden
UK
Luxe
mbo
urg
Irela
nd
Bub
ble
size
= T
otal
GD
P pe
r cap
ita
21
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5.3 Robustness Check
As discussed before, the index is affected by the normalization, the weighting, and the
aggregation technique. For the results commented above we used rescaling, factor analysis,
and linear aggregation for the index calculation. Now, we investigate how other
normalization, weighting, and aggregation techniques impact the results. Table 6 presents
the ranks of the individual countries with respect to the approach chosen. The first two
columns show the results for rescaling and linear aggregation, with altered weighting
schemes. In column three and four, we use rescaling and geometrical aggregation, and again
alter the weighting schemes. In column five, we use z-scores as standardization with equal
weights and linear aggregation. The last column presents the volatility of the rankings
measured by the standard deviation of the ranks for each country. The volatility ranges from
0.4 to 2.5 with an average of 1.09. This means that ranks change very little across the
different approaches.
Table 6: Robustness of the VCPEAI
Score Ranking Score Ranking Score Ranking Score Ranking Score RankingAustria 100.10 11 99.94 11 82.80 11 83.85 11 99.42 12 0.4Baltic States 89.63 17 92.61 17 68.65 19 73.16 18 88 17 0.9Belgium 100.01 12 102.01 10 79.61 12 82.17 12 100 10 1.1Bulgaria 74.49 22 79.00 22 40.06 23 44.12 22 75 23 0.5Czech Republic 82.39 19 85.01 19 70.86 17 73.51 17 80 19 1.1Denmark 121.02 5 120.60 5 112.46 3 110.50 3 120 4 1.0Finland 109.16 8 104.22 8 96.35 9 91.21 8 108 8 0.4France 94.94 13 94.96 16 78.61 13 78.37 15 95 13 1.4Germany 100.62 10 99.49 12 89.86 10 87.90 9 99 11 1.1Greece 67.20 25 69.38 25 43.63 22 42.10 23 63 25 1.4Hungary 93.29 15 95.87 14 77.07 14 79.83 14 92 15 0.5Ireland 127.85 1 130.03 1 114.56 2 111.77 2 127 1 0.5Italy 80.99 21 81.22 21 69.44 18 68.69 19 77 21 1.4Luxembourg 124.30 3 127.21 2 96.38 8 98.59 6 121 3 2.5Netherlands 105.00 9 102.34 9 97.24 7 94.05 7 106 9 1.1Norway 110.36 7 110.63 6 105.70 5 105.97 5 110 7 1.0Poland 83.00 18 89.23 18 55.43 21 64.23 20 85 18 1.4Portugal 92.69 16 95.89 13 76.79 15 80.34 13 90 16 1.5Romania 73.34 24 76.69 23 31.15 25 36.85 25 74 24 0.8Slovakia 73.81 23 76.27 24 38.80 24 41.41 24 77 22 0.9Slovenia 94.14 14 95.34 15 75.87 16 77.72 16 93 14 1.0Spain 81.97 20 82.41 20 59.10 20 56.97 21 79 20 0.4Sweden 124.96 2 122.26 4 111.77 4 109.31 4 122 2 1.1Switzerland 117.90 6 109.83 7 97.72 6 87.57 10 114 6 1.7UK 121.37 4 124.26 3 115.54 1 117.61 1 116 5 1.8
Z-scoreEqual weights - LIN
Volatility
Re-scalingEqual weights - GME
Re-scalingFactor analysis - GME
Re-scalingEqual weights - LIN
Re-scalingFactor analysis - LIN
Summarizing, the index is robust against different normalization, weighting, and
aggregation approaches. Figure 5 shows the rankings for the different approaches. The lines
present the span of the ranks determined by the different approaches, while the dots indicate
22
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23
the average rank among the five scenarios from Table 6.6 We can clearly identify six tier
groups of attractiveness as marked by the dashed lines. Within those tier groups, the ranks
might change slightly due to the different approaches. However, there is hardly any
transition across the tier groups.
6 Note that the order of the individual countries in Figure 5 is according to their average rank across the different calculation approaches from Table 6. Hence, their order is not the same as in Figure 1.
-
Figure 5: Box plot of Rankings According to Different Index Calculation Approaches
0 5 10 15 20 25
Irelan
d
UKSw
eden D
enma
rkLu
xemb
ourg
Norw
ay Switz
erlan
dFin
land
Nethe
rland
s Germ
any
Austr
iaBe
lgium
Fran
ceHu
ngary
Portu
gal Sl
oven
iaBa
ltic S
tates
Czec
h Rep
ublic
Polan
d
Italy
Spain
Bulga
riaSlo
vakia
Gree
ce Rom
ania
Average Ranking Position
24
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25
6. Conclusions and Outlook
In this paper, we present a composite index to measure the attractiveness of Central Eastern
European countries for VC/PE investors. The index relies on several sub-indexes and raw
data series. We review the literature on attractiveness determinants and refer to the most
important findings for choosing the relevant data series and sub-indexes. For the index
construction, we follow a pyramidal structure of three index levels and show that the
selected data lead to a consistent result. We aggregate the data as suggested in literature and
propose different methods for calculating the index, namely for normalization, weightings,
and aggregation. The different methods give only slightly different results regarding the
sample countries’ attractiveness, indicating the calculations’ robustness. We find six tier
groups regarding the sample countries’ attractiveness rankings. The CEE region lags behind
the average of the EU-15 states. Some of the CEE countries are more attractive for VC/PE
investors than certain EU-15 states. A detailed analysis of the strengths and weaknesses of
the individual countries, as presented in numerous figures in the appendix, provides
conclusions for policy attempts to attract risk capital investors, and hence, to spur
innovation, entrepreneurship, employment, and growth.
The calculation of the index is based on publicly available data and ready-made sub-
indexes. All of the data used are only proxies for the characteristics of the latent drivers. We
have chosen the data series carefully, and we believe to present the best available selection
of proxies for the desired parameters. However, some data series might not adequately
indicate the latent variables in particular countries. Additionally, there are differences
regarding definitions of the data series and regarding the methods to measure and aggregate
the data among the individual countries. This is even valid for the EU-15 states. On the
other hand, it has to be emphasized that whenever the ranking of a country might
unwarrantedly benefit from particularities of an individual index item, the situation might be
reversed at another stage. This is similar to the question about adding, discarding or
exchanging particular data series. Adding, discarding, or exchanging individual index items
will not influence the overall results meaningfully as proved in many unreported robustness
checks.
The availability of the necessary data series limits the scope of our index. The full data set
with the required quality, and based on single sources, is not (yet) available to any useful
-
26
extent for other regions of the world (beside North America). Hence, further research should
tackle how the quality of our findings changes with a reduced number of index items.
Additionally, other worldwide available data series could qualify as proxies for creating a
worldwide VC/PE Attractiveness Index, and including other emerging regions.
Our economic approach cannot cover special situations or special opportunities in particular
countries. This is notably the case for tax considerations. It is impossible to cover and
compare individual countries’ tax regimes on a general level, especially considering taxes
on dividends, and capital gains taxes, which might be of particular importance for the asset
class in question. Moreover, our approach relies on least available and historic (averaged)
data, and cannot consider the latest changes of individual items. Anyway, we attempt to
contribute to the transparency of the VC/PE fund allocation process, and to discover
strengths and weaknesses of the CEE economies to spur innovation, entrepreneurship,
employment, and growth.
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27
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Appendix
Table(s) A1: Factor Analysis - Economic activity
Communalities
1,000 ,8231,000 ,7911,000 ,789
1,000 ,640
Gross Domestic ProductGeneral Price LevelWorking forceForeign direct investment,net inflows [% of GDP]
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
1,892 47,289 47,289 1,892 47,289 47,289 1,715 42,887 42,8871,152 28,810 76,099 1,152 28,810 76,099 1,328 33,212 76,099,697 17,414 93,513,259 6,487 100,000
Component1234
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
,881 ,216-,141 ,878,556 ,693
,781 -,175
Gross Domestic ProductGeneral Price LevelWorking forceForeign direct investment,net inflows [% of GDP]
1 2Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.a.
Table A2: Weights - Economic activity Economic activityRotated Component Matrix(a) Overall weights
1 2 1 2 WeightsGross Domestic Product 0.881 0.216 0.453 0.035 0.270General Price Level -0.141 0.878 0.012 0.580 0.260Working force 0.556 0.693 0.180 0.362 0.259Foreign direct investment, net inflows [% of GDP] 0.781 -0.175 0.355 0.023 0.210Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 3 iterations.Expl. Var 1.715 1.328 1 1 1Expl. /Tot 0.564 0.436 Sum
Component loadings Component weights
Sum
34
-
Table(s) A3: Factor Analysis - Capital market
Communalities
1,000 ,8991,000 ,6401,000 ,5421,000 ,7701,000 ,907
IPOStock marketM&A market activityCredit and Debt marketPrivate equity activity
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
2,606 52,117 52,117 2,606 52,117 52,117 2,011 40,226 40,2261,153 23,064 75,181 1,153 23,064 75,181 1,748 34,955 75,181,740 14,805 89,986,357 7,150 97,136,143 2,864 100,000
Component12345
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
,948 -,009,522 ,607,068 ,733,108 ,871,908 ,289
IPOStock marketM&A market activityCredit and Debt marketPrivate equity activity
1 2Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.a.
Table A4: Weights - Capital market Capital marketRotated Component Matrix(a) Overall weights
1 2 1 2 WeightsIPO 0.948 -0.009 0.447 0.000 0.239Stock market 0.522 0.607 0.135 0.211 0.170M&A market activity 0.068 0.733 0.002 0.308 0.144Credit and Debt market 0.108 0.871 0.006 0.434 0.205Private equity activity 0.908 0.289 0.410 0.048 0.241Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a 1 components extracted.Expl. Var 2.011 1.748 1 1 1Expl. /Tot 0.535 0.465 SumSum
Component loadings Component weights
35
-
Table(s) A6: Factor Analysis - Investor protection
Communalities
1,000 ,767
1,000 ,760
1,000 ,729
Extent of disclosure indexExtent of director liabilityindexEase of shareholdersuits index
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
1,147 38,248 38,248 1,147 38,248 38,248 1,133 37,765 37,7651,109 36,983 75,231 1,109 36,983 75,231 1,124 37,466 75,231,743 24,769 100,000
Component123
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
-,167 ,860
,839 -,239
,634 ,572
Extent of disclosure indexExtent of director liabilityindexEase of shareholdersuits index
1 2Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.a.
Table A7: Weights - Investor protection Investor protectionRotated Component Matrix(a) Overall weights
1 2 1 2Extent of disclosure index -0.167 0.860 0.025 0.658 0.340Extent of director liability index 0.839 -0.239 0.621 0.051 0.337Ease of shareholder suits index 0.634 0.572 0.354 0.292 0.323Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 3 iterations.Expl. Var 1.133 1.124 1 1 1Expl. /Tot 0.502 0.498 SumSum
Component loadings Component weights
36
-
Table(s) A8: Factor Analysis - Human & Social environment
Communalities
1,000 ,8721,000 ,7101,000 ,8121,000 ,794
EducationLabor regulationsBribing & corruptionCrime
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
2,045 51,131 51,131 2,045 51,131 51,131 2,045 51,128 51,1281,143 28,586 79,718 1,143 28,586 79,718 1,144 28,590 79,718,525 13,118 92,835,287 7,165 100,000
Component1234
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
,145 ,923,666 -,517,893 ,117
-,885 -,107
EducationLabor regulationsBribing & corruptionCrime
1 2Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.a.
Table A9: Weights - Human & Social environment Human & Social environmentRotated Component Matrix(a) Overall weights
1 2 1 2Education 0.145 0.923 0.010 0.745 0.274Labor regulations 0.666 -0.517 0.217 0.233 0.223Bribing & corruption 0.893 0.117 0.390 0.012 0.255Crime -0.885 -0.107 0.383 0.010 0.249Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 3 iterations.Expl. Var 2.045 1.144 1 1 1Expl. /Tot 0.641 0.359 SumSum
Component loadings Component weights
37
-
Table(s) A10: Factor Analysis - Entrepreneurial opportunity
Communalities
1,000 ,9291,000 ,8571,000 ,7821,000 ,823
1,000 ,696
General InnovativenessR&D expenditureEnterprise restructuringEnterprise stock activityBurden: Starting aBusiness
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
2,988 59,754 59,754 2,988 59,754 59,754 2,421 48,412 48,4121,100 22,009 81,763 1,100 22,009 81,763 1,668 33,351 81,763,486 9,727 91,490,372 7,431 98,921,054 1,079 100,000
Component12345
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
,854 ,447,855 ,355,875 -,132,019 -,907
,441 ,709
General InnovativenessR&D expenditureEnterprise restructuringEnterprise stock activityBurden: Starting aBusiness
1 2Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.a.
Table A11: Weights - Entrepreneurial opportunity Entrepreneurial opportunityRotated Component Matrix(a) Overall weights
1 2 1 2General Innovativeness 0.854 0.447 0.301 0.120 0.227R&D expenditure 0.855 0.355 0.302 0.076 0.210Enterprise restructuring 0.875 -0.132 0.316 0.010 0.191Enterprise stock activity 0.019 -0.907 0.000 0.493 0.201Burden: Starting a Business 0.441 0.709 0.080 0.301 0.170Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 3 iterations.Expl. Var 2.421 1.668 1 1 1Expl. /Tot 0.592 0.408 SumSum
Component loadings Component weights
38
-
Table A12: Level I Performance – EU (EU15=100)7
Economic activity
Capital market Taxation
Investor protection
Human & Social environment
Entrepreneurial opportunity
Weights 0.16 0.17 0.17 0.19 0.16 0.15Ireland 125.96 62.39 281.63 135.61 115.17 88.84Luxembourg 155.34 120.63 152.76 130.12 112.33 98.86UK 109.84 214.36 79.08 142.08 100.19 110.04Sweden 111.96 124.99 192.45 82.38 110.29 143.39Denmark 115.25 70.06 187.88 112.29 120.89 125.35Norway 114.87 68.37 135.41 118.21 112.78 107.62Switzerland 119.23 113.03 135.91 70.72 119.44 118.28Finland 97.97 87.81 93.63 100.38 99.85 136.70Netherlands 116.29 115.57 117.26 76.81 99.99 102.58Belgium 107.52 73.11 80.50 130.12 96.47 102.18Austria 110.19 46.52 179.97 65.33 109.59 104.99Germany 92.94 70.57 138.49 94.64 91.83 112.94Portugal 99.00 55.74 134.34 106.60 110.96 67.54Hungary 95.14 22.34 224.69 82.09 91.71 82.35Slovenia 96.46 21.76 179.97 100.46 104.23 73.82France 93.75 85.09 74.04 95.15 96.79 114.18Baltic States 82.23 27.55 178.48 100.50 99.85 74.15Poland 53.06 23.15 200.66 112.04 90.45 68.25Czech Republic 95.27 43.78 86.79 88.07 98.37 84.75CEE 63.36 27.91 169.03 98.72 91.10 66.79Spain 89.48 66.92 46.63 83.22 108.60 82.61Italy 87.40 54.33 59.61 83.14 94.90 91.53Bulgaria 35.40 35.21 171.86 94.56 95.85 55.72Romania 74.94 26.07 124.30 101.21 69.36 59.63Slovakia 66.26 39.69 104.84 70.77 103.00 72.33Greece 79.84 38.65 56.97 59.11 94.40 75.46
7 Single observation and comparison of each item is made possible in this table.
39
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Table A13: Level II Performance – EU (1) (EU15=100)8
Gross Domestic Product
General Price Level
Working force (unemployment rate)
Foreign direct investment, net inflows IPO Volume Stock market M&A market
Credit and Debt market
Private equity activity
Code 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 2.5Weights 0.27 0.26 0.26 0.21 0.24 0.17 0.14 0.20 0.24Ireland 174.00 94.90 135.74 169.20 37.39 49.11 115.08 101.47 11.01Luxembourg 233.45 100.77 141.52 325.67 1.00 78.70 397.05 157.81 80.64UK 115.74 98.92 129.31 91.22 327.94 179.80 187.53 96.77 330.37Sweden 117.68 101.57 129.89 95.86 69.15 144.83 112.54 94.27 218.13Denmark 138.62 99.94 128.10 114.17 10.54 57.93 74.43 113.49 69.29Norway 188.00 95.21 137.77 49.88 45.09 44.07 80.40 99.49 57.75Switzerland 135.74 103.46 142.42 97.94 18.82 266.65 118.93 122.95 54.02Finland 106.38 101.07 90.02 99.50 28.79 178.47 117.74 87.93 52.35Netherlands 118.42 97.67 136.33 130.19 38.62 169.65 166.68 113.35 123.20Belgium 104.79 101.34 102.95 147.48 114.84 54.50 61.43 87.12 31.39Austria 109.07 102.23 135.17 71.78 15.26 10.41 31.56 109.43 17.34Germany 89.35 102.70 85.95 78.29 38.47 66.91 152.81 97.99 21.62Portugal 55.27 95.62 123.19 89.84 12.51 39.76 24.07 113.64 37.13Hungary 83.95 79.60 117.07 105.67 21.66 19.17 45.29 16.48 23.64Slovenia 92.32 88.69 116.48 74.58 1.00 9.42 58.04 27.49 26.19France 100.68 101.84 85.40 78.45 97.34 94.55 42.66 91.50 75.51Baltic States 85.66 91.27 62.11 100.17 2.29 8.76 15.33 56.76 29.15Poland 40.11 87.13 1.00 80.74 41.20 10.77 55.58 8.75 22.62Czech Republic 61.67 93.24 99.61 126.38 13.73 15.23 110.78 70.29 23.60CEE 69.71 58.80 53.48 102.49 24.76 10.80 70.77 31.53 19.03Spain 107.67 97.26 67.46 102.20 3.77 137.55 38.52 97.08 34.63Italy 89.65 98.33 90.81 32.61 36.17 61.56 19.62 88.94 28.71Bulgaria 67.28 1.00 36.00 134.68 1.00 1.30 147.11 62.07 1.00Romania 199.08 14.71 104.67 95.63 25.09 1.54 28.04 47.27 14.04Slovakia 72.90 92.60 5.23 128.92 1.00 2.58 219.40 47.64 8.91Greece 93.51 93.56 83.64 1.00 1.09 66.82 1.00 80.55 3.98
8 Single observation and comparison of each item is made possible in this table.
40
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Table A14: Level II Performance - EU (2) (EU15=100)9
Highest marginal tax rate, corporate rate
Difference between income and corporate tax rate
Extent of disclosure index
Extent of director liability index
Ease of shareholder suits index Education
Labor regulations
Bribing & corruption Criminality
Code 3.1 3.2 4.1 4.2 4.3 5.1 5.2 5.3 5.4Weights 0.50 0.50 0.34 0.34 0.32 0.27 0.22 0.25 0.25Ireland 468.77 196.70 131.19 117.15 155.36 102.86 121.96 97.51 135.
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