Hitotsubashi University
Institute of Innovation Research

The Determinants of Venture Capital:
A Panel Data Analysis of 16 OECD Countries

Astrid Romain
Bruno van Pottelsberghe de la Potterie

IIR Working Paper WP#03-25

December 2003

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

The Determinants of Venture Capital:
A Panel Data Analysis of 16 OECD Countries

Astrid Romain
Bruno van Pottelsberghe de la Potterie
December 2003
WP#03-25

THE DETERMINANTS OF VENTURE CAPITAL:
A Panel Data Analysis of 16 OECD Countries
Astrid ROMAIN∆ and Bruno VAN POTTELSBERGHE DE LA POTTERIEα
December 2003

Abstract: The objective of this paper is to identify the main determinants of venture capital (VC). We
develop a theoretical model where three main types of factors affect the demand and supply of VC. These
factors are related to macroeconomic conditions, research efforts and technological opportunity, and theentrepreneurial environment. The model is evaluated econometrically with a panel dataset of 16 major
OECD countries over the period 1990-1998. The estimates confirm that VC intensity is highly pro-cyclical it reacts positively and significantly to GDP growth. Short-term (one-year) interest rates have a positive
impact on VC intensity, which means that they affect more the demand side of VC (entrepreneurs) than the
supply side. Indicators of technological opportunity, such as the growth rate of R&D investment, the stock ofknowledge and the number of triadic patents affect positively and significantly the relative level of VC. The
factors associated with the entrepreneurial environment also explain a substantial part of cross-country
variations in VC intensity. An increase in corporate income tax rate has a negative effect on VC intensity.
Labour market rigidities reduce the impact of the GDP growth rate and of the stock of knowledge, whereas aminimum level of entrepreneurship is required in order to have a positive effect of the available stock ofknowledge on VC intensity. One important policy implication emerges from these results. It is not by
providing money for VC that public decision makers will stimulate VC, but by providing knowledge andimproving the entrepreneurial environment.
Keywords: Venture Capital, Technological Opportunity, Entrepreneurship, Labour Market Rigidities
JEL: G24, O33, M13, C33
Acknowledgment:
Research financed by the Région de Bruxelles-Capitale. We would like to thank Lydia Greunz (ULB,
DULBEA), Ant Bozkaya (ULB, DULBEA), Marie-Paule Laurent (ULB, CEB), Pierre Monhen (Maastricht,
MERIT), Philip Marey (Maastricht, ROA), Sadao Nagaoka (IIR), Reinhilde Veugelers (KUL) and theparticipants to three academic seminars for their useful comments: KUL in November 2002, MERIT in
January 2003, and IIR in July 2003. The final version of this paper was performed when Bruno vanPottelsberghe was Visiting Professor at the Institute of Innovation Research (IIR), Hitotsubashi University,
Tokyo, July-December 2003.

∆

Astrid Romain, Université Libre de Bruxelles, Solvay Business School, Centre Emile Bernheim and DULBEA, CP
140, 50 av. F.D. Roosevelt, B-1050, Brussels (e-mail: astrid.romain@ulb.ac.be)αBruno van Pottelsberghe, Université Libre de Bruxelles, Solvay Business School, Solvay Chair of Innovation, Centre
Emile Bernheim, DULBEA and CEPR, CP 145/01, 21 av. F.D. Roosevelt, B-1050, Brussels (e-mail:bruno.vanpottelsberghe@ulb.ac.be).

11. Introduction
Venture capital (VC) is a financial intermediary that aims at fitting innovative start-up’s needs, mainly
because these firms are generally associated with large growth potentials and high levels of uncertainty. A
growing number of scholars have documented the positive impact that venture funds have on the probabilityof success of start-ups, as well as on the growth of their sales and employees1. Most government bodies inindustrialized countries now recognize the importance of VC as a factor of firm creation and sustainablegrowth.
Despite this wide recognition of venture funds as key players underlying a country’s entrepreneurial
performances, there are huge differences across industrialized countries in the relative amounts invested in
VC. The level of VC intensity is relatively high in the USA and Canada for instance, whereas it is very low
in Japan. The diversity of national financial systems is undoubtedly one important factor underlying theseinternational differences. Other factors also play an important role, as shown by Jeng and Wells (2000) and
Gompers and Lerner (1998). With a panel dataset of 21 countries Jeng and Wells show that labour market
rigidities, the level of Initial Public Offerings (IPO), government programs for entrepreneurship, andbankruptcy procedures explain a significant share of cross country variations in VC intensity.
The objective of this paper is to contribute to this recent stream of research in three ways. We first develop atheoretical model which takes into account the factors that affect the demand and supply of VC. These
factors include the growth of GDP, short-term and long-term interest rates, several indicators oftechnological opportunity, and of entrepreneurial environment. Second, we exploit a panel dataset composedof 16 countries over a nine years period. Third, we investigate to what extent the level of entrepreneurshipand of labour market rigidities affect the impact of the GDP growth rate and the stock of available
knowledge on VC intensity.
The results show that interest rates and the corporate income tax rate significantly influence VC intensity.
The countries with lower labour market rigidities benefit from a higher impact of the GDP growth rate andthe available stock of knowledge on the relative level of VC. Higher levels of entrepreneurship – i.e., thepercentage of people being involved in a start-up – induce a positive and significant relation between the
R&D capital stock and VC intensity.
The paper is structured as follows: The next section summarizes the main findings of the few existingevaluation of the determinants of venture capital. A theoretical model of demand and supply of venture
capital and the econometric model are developed in section 3. The empirical results are presented andinterpreted in section 4. Section 5 concludes.

2. Literature review
Few articles have so far focused on the determinants of VC. To the best of our knowledge, only two articles
attempted to evaluate quantitatively the macroeconomic determinants of VC. Jeng and Wells (2000) develop
a model aiming at identify the determinants of VC and test it on a cross-section of 21 countries over a periodof 10 years. Gompers and Lerner (1998) focus on the US economy over the period 1969-1994. The main
results are summarised in Table 1.

1

See Engel (2002), Hellmann and Puri (2002), Kortum and Lerner (2000), Romain and van Pottelsberghe (2003) for
empirical evidence on the economic impact of VC.

2

Table 1: Potential determinants of VC
Jeng and Wells (2000)

Gompers and Lerner (1998)

21 countries, 1986-1995

US, 1972-1994+ Except for early stage funds

No effect at aggregate levelNot significant+

Stock Market Opportunities

Not significant+

Finance reporting standards-/

Labour market rigidities

Not significant for total VC investment but for early stage funds

n.a.

(Level and growth of pension funds)(Dummy for changes in ERISA’s prudent
man rule)

Potential DeterminantsInitial Public Offering
Gross Domestic Product1

Private pension funds+ Over time but not across countries+

Corporate Gains Tax Rate

Not significant-

Level of interest raten.a.

+ At aggregate level and – at state level

Industrial and academic R&D
expenditures

n.a.

+ At the state level VC activity

1.
This variable is proxied by an indicator of market capitalization growth by Jeng and Wells (not significant, but probably correlatedwith GDP and IPO) and by an indicator of equity market return by Gompers and Lerner (positive and significant).

Initial Public Offering is considered as being a very important determinant of VC. It is the strongest driver of
VC according to Jeng and Wells (2000) because it reflects the potential return to VC funds. Gompers andLerner (1998) take it as a proxy for fund performance but cannot find any significant effect in theirmultivariate regressions. It seems that the IPO variable is strongly correlated with the expected return on
alternative investments and the Gross Domestic Product (GDP), which also proxy exit opportunities. GDP
and Market Capitalization Growth (MCG) are part of the impact of IPOs and therefore turn out to be notsignificant for Jeng and Wells (2000). However the reverse is true for Gompers and Lerner who find apositive and significant impact of Equity Market Return and GDP on VC but no impact of IPO. Higher GDP
growth implies higher attractive opportunities for entrepreneurs, which lead to a higher need for venture
funds.
For Jeng and Wells (2000), getting the basic legal and tax structures into place appears to be an importantfactor influencing VC. Gompers and Lerner (1998) also recognize the importance of government decisions
on the private equity funds. The labour market legislation is typically put in place to protect employees fromarbitrary, unfair or discriminatory actions by employers. Some authors argue that venture financing cansuffer from the rigidity of the labour market in Europe. Jeng and Wells (2000) show that it does not
significantly influence total VC but affects negatively the early stage of VC investment.
It has been widely accepted in the literature that VC investments in the US have been positively influenced
by the clarification of the Employee Retirement Income Security Act (ERISA) “prudent man” rule of 1979.
As a result pension funds started to invest substantial amounts of money into VC funds. In 1978 pension
funds accounted for 15% of VC funds in the US and in the middle of the 80’s, the share had risen to morethan 50 %. Jeng and Wells (2000) find that the level of investment by private pension funds in VC is asignificant determinant of VC over time but not across countries. Gompers and Lerner (1998) use a proxy for3the amendment of the “prudent man” rule to show the impact of pension regulation and came to the sameconclusion. After 1979, the additional capital provided by pension funds led to a dramatic shift incommitments to VC.
Concerning the impact of the Corporate Gains Tax Rate (CGTR) on VC activity, Gompers and Lerner (1998)
reach the conclusion that a decrease in CGTR has a positive and important impact on commitment to new
VC funds. In fact, they confirm the result of Poterba (1989) who built a model of decision to become anentrepreneur. He found that decreases in CGTR might increase the raising of VC funds not throughstimulation on the supply side (i.e., the potential fund providers) but rather on the demand side. Indeed,decreases in CGTR often encourage entrepreneurship and thus the desire of people to create their own firm
and to engage in R&D activities. Anand (1996, cited by Gomper and Lerner) also highlights the fact that thelevel and composition of investments appear to be negatively affected by increases in the CGTR but
investments in one industry may be affected by myriad of other factors like technology shifts, tastes, etc.
Both industrial and academic R&D expenditures are significantly related to venture capital activity at theState level in the model of Gompers and Lerner (1998). For them, the growth VC fundraising in the mid1990s may be due to increases in technological opportunities.
Interest rates might also be an important factor influencing VC. Although Jeng and Wells (2000) do not takethis factor into account into their cross country investigation, Gompers and Lerner (1998) show that it affects
positively the demand for VC funds in the US. Economic theory would suggest a reverse relationship: if
interest rates rise, the level of investment should fall. The positive impact estimated by Gompers and Lerneris probably due to the fact that they use a short-term interest rate. If short-term interest rates increase, theattractiveness of venture financing versus credit through usual financial institutions increases from theentrepreneur’s viewpoint.
Some scholars have also focused on the micro determinants of VC. Gompers and Lerner (1998) show thatthe individual firm performance and reputation computed by firm age and size, positively impact thecapacity to raise larger funds. Hellmann and Puri (2000) use a probit model to show that the strategy of acompany is one of the determinants of VC investment when controlling for the age of the company and its
industrial sector. If the strategy is an innovative one (the company is the first to introduce a new product orservice on the market), it has a higher probability to benefit from VC compared to companies that follow animitation strategy (the company uses existing technologies to develop and improve products and processes).
They also find that innovating companies are able to raise VC earlier in their life cycle than companies witha strategy of imitation. In other words, their analysis suggests that VC is stimulated by technologicalopportunities. However there is less evidence of such a relationship at the aggregate macroeconomic level.
In a nutshell, there are several potential determinants of VC. Some of them can be measured qualitatively or
quantitatively at the macro level whereas others like the fund reputation and the strategy of the venture
funded firms are microeconomic factors. In the next section we develop a theoretical model that takes into
account the macroeconomic factors that might affect the demand and supply of VC.

4

3. Modelling the amount of venture capital
As Poterba (1989) and Gompers and Lerner (1998), we argue that changes in the level of VC funds comefrom changes either in the supply or the demand of VC. The demand comes from the entrepreneurs
interested in setting up an innovative start-up. The supply of VC corresponds to the share of risk capitalprovided by private investors, pension funds and banks. The actual amount of VC invested represents theequilibrium between the demand and the supply of VC.
The demand and supply of VC can be modelled through equations (1) and (2) that characterize the demand
price of VC, Pd, and the supply price of VC, Ps, respectively. The supply price of VC is assumed to be apositive function of the available VC funds, the interest rate (r) and the corporate tax rate (TAX). The more
VC is available on the market, the higher will be the supply price of VC, due to increasing marginal costs(avc>0). If interest rates increase we can expect the fund providers to increase their return requirement (ar>0;
otherwise they would opt for alternative investments opportunities). Similarly, an increase in the corporate
gains tax rate would increase the return requirements (atax>0).

Ps

VC

Pd

VC

= aC + avc VC + atax TAX + ar r

(1)

= bC + bvc VC + bYˆ Yˆ + bto TO + ben EN + btax TAX + br r

(2)

The equation of the demand price of VC reflects the entrepreneurs’ viewpoint. Decreasing marginal returns
to VC is assumed (the projects with the largest expected returns are selected first). The more VC is available
the lower is the demand price of VC (bvc<0). The other factors that are assumed to influence the demand of
VC are the GDP growth (Y), technological opportunities (TO), entrepreneurial culture (EN), level ofcorporate gains tax rate (TAX) and interest rates (r). The countries with a high GDP growth, large
technological opportunities and a strong entrepreneurial culture are more likely to be associated with a strong
demand for VC (and hence positive effects on the demand price of VC: bY>0; bTO>0; bEN>0). The generallevel of taxation will probably reduce the rate of entrepreneurship (the demand for VC and therefore btax<0).
Concerning interest rates, we consider that innovative start-up’s need important amounts of money in the
short-term. Therefore if the cost of capital increases entrepreneurs are more likely to switch from the banking
sector to the venture fund providers (br>0).
Equations (3) and (4) show the equilibrium level of VC that equalizes the supply and demand of VC.

(avc − bvc) VC = (bC − aC ) + bYˆ Yˆ + bto TO + ben EN + (btax − atax) TAX it +(br − ar ) r(3)

 a vc > 0 → increasing marginal cost of VC Investment
where  bvc < 0 → decreasing marginal return
( a − b ) → always positivevc
 vc
 (b − a )   bYˆ  ˆ  bto  ben (btax − atax)
 (br − ar ) 
VC =  C C  + 
 Y +
 TO +  EN +  TAX + r
(avc − bvc) (avc − bvc)
(avc − bvc)
(avc − bvc)
 (avc − bvc) (avc − bvc)
(4)
Since the denominator is always positive, the numerator provides the expected sign of the parameters
between brackets. All the right-hand side variables, except the level of taxation and the interest rate, are5

expected to have a positive impact on VC. For interest rate (r), the impact is either negative or positive
depending on the difference between the demand price effect and the supply price effect. If the demand priceeffect of a high interest rate is larger than its supply price effect, then the overall impact of interest rates on
VC should be positive. The effect of the level of corporate gains tax rate on the equilibrium level of VC will
always be negative since (btax – atax) is always negative.
The empirical implementation of equation (4) is presented in equations (5) and (6). The growth rate of GDP
allows testing the cyclicality of VC. Regarding interest rate we suspect that short-term and long-term interest
rates could affect differently the venture fund providers and the ‘high-tech’ entrepreneurs. We therefore plan
to use a short-term (one year, rST) and a long-term (ten years, rLT) interest rate in the empirical model.
Technological opportunity is proxied by three variables, the growth rate of business R&D outlays, thebusiness R&D capital stock and the number of triadic patents. The growth rate of business R&D
expenditures represents the research dynamics of a country. The business R&D capital stock is an indicatorof the available stock of knowledge (or of the cumulated innovative efforts). The number of triadic patents is
an indicator of innovative output. It measures the number of highly valuable inventions invented in eachcountry (it is counted by country of inventor and by priority year). The entrepreneurial environment can bemeasured with three variables: level of taxation, entrepreneurial activity and labour market rigidity. The levelof taxation is measured with the corporate income tax rate (CITR).
The measures of entrepreneurial activity (TEA) and labour market rigidity (RIG) are indices that are only
available for one year. We therefore introduce them in interaction with other variables. For instance, it ispossible to test whether RIG would affect the impact of GDP growth rate on the intensity of VC. This isequivalent to test whether the impact of GDP growth rate on VC intensity is composed of a fixed component(βc ∆ gdp) and a component that varies across countries according to the level of labour market rigidities (i.e.,β ∆ gdp= βc ∆ gdp + βrig ∆ GDP). Similarly, the level of entrepreneurship (TEA) could affect the impact of theavailable stock of knowledge, SBRD. These interactions are illustrated in equation (6).
Model with no interaction

VC it = β∆gdp ∆GDPit + β∆brd ∆BRDit−1 + βsbrd SBRDit−1 + βpat PATit + βcitr CITRit + βr r it
+ σG G + φ i + ϕ t + µ it

(5)

Model with interactions with TEA and RIGc
VC it = β∆cgdp ∆GDPit + β∆brd ∆BRDit−1 + βsbrd
SBRDit−1 + β pat PATit + βtea (SBRDit−1 *TEAi ) + βcitr CITRit+ βrig (∆GDPit * RIG i ) + βr rit + σG G + φi + ϕt + µit(6)where ∆ represents the first logarithmic difference. In this equation, the parameters that are to be estimatedare assumed to be constant across countries and over time; they are defined as follows (the expected signs
are presented between parentheses):β ∆ gdp
β ∆ brd
βsbrd
βpat
βtea
βcitr
βrig
βr

The impact of GDP growth (+).
The impact of business R&D expenditures growth rate (+).
The impact of the level of business R&D capital stock (+).
The impact of the number of triadic patents (+).
The impact of the level of entrepreneurship on βsbrd (+).
The impact of the CITR (-).
The impact of labour market rigidities on β ∆ gdp (-).
The impact of interest rate (?).

A range of control variables is included in all the regressions.
G

is a dummy equal to 1 for Germany in 1991, and 0 otherwise; in order to take into account theexogenous shock of the German unification.
6

φi
ϕt

are country dummies which take into account country-specific framework conditions that might
affect VC intensity.
are time dummies which take into account exogenous shocks that are common to several countries,
such as changes in exchange rates.

The variables (for country i and time t) are defined as follows:
VC
GDP
BRD
SBRD

PAT
CITR
RIG

TEA

r

is the venture capital intensity, i.e. the VC funds2 divided by GDP (Sources: EVCA and OECD).
is the gross domestic product (Source: OECD, Main Science and Technology Indicators).
is the business R&D expenditures (Source: OECD, Main Science and Technology Indicators).
is the business R&D capital stock. It has been computed using the perpetual inventory method fromtotal intramural business R&D expenditures, in constant 1990 GDP prices and US PPPs (seeappendix 1). The depreciation rate is 15%. Sensitivity analysis show that the results of theregressions do not change significantly with the chosen depreciation rate (Guellec and vanPottelsberghe, 2002) (Source: OECD Main Science and Technology Indicators).
is the number of Triadic patents (Source: OECD). These patents have been applied at the USPTO,
the Japanese Patent Office and the European Patent Office. We can therefore assume that they reflect
patents with a very high value.
is the corporate income tax rate (Source: OECD).
is the employment protection index drawn up by the OECD (1994a) and based on the strength of thelegal framework governing hiring and firing of employees. It is a measure of labour market rigidities.
The countries are ranked from 1 to 20 with 20 being the most strictly regulated. Since the indicator isfixed over time, it is introduced in interaction with GDP.
is the Total Entrepreneurship Activity (TEA)-index computed by adding the proportion of adults
involved in the creation of nascent firms and the proportion involved in new firms (Source: The
Global Entrepreneurship Monitor 2001). The variable is a ranking from 1 to 20. This measure ofentrepreneurial activity can be meaningfully used for international comparisons.
is the one-year national deposit rate (Source: IMF) or the long-term national interest rate (10 years,
Source: OECD).

The estimates are performed with a panel data set of 16 OECD countries over the period 1990-1998. These
16 countries are Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, theNetherlands, Norway, Spain, Sweden, United Kingdom and United States. The period can vary acrosscountries based on availability of information. Descriptive statistics of all the variables are presented inTable 2. The average value of the dependent variable (VC intensity) varies from 0.01 percent in Denmark to
0.16 percent in Canada, as shown in the last column.

2

The European definition of VC is not exactly the same as the US one. For example, in Europe, the European Venture
Capital Association included management buy-outs (MBOs) and management buy-ins (MBIs) in the definition of the
VC. The management and the composition of the funds also differ in each country.

7

Table 2: Descriptive statistics (%)
Country

PeriodGDP

Business
Business Number One-year
Long-Term
R&D
R&D capitalofInterest
Interest rate
investment
stock
Patentrate

Corporate
IncomeTax Rate

Growth rates (%)

Level ofentrepreneurship

Labourmarketrigidities

Average

VC
Intensity(GDP)% SharesAU

1995-98 3.47

-4.64

5.79

6.87

-13.95-15.67

0.47

15.2

4

0.09

BE

1990-98 1.68

5.28

3.72

6.77

-8.51

-9.09

0.45

4.5

17

0.06

CA

1995-98 3.14

4.00

4.95

10.47

-11.06-13.14

0.40

12.2

3

0.16

DK

1990-98 2.53

7.06

7.18

7.11

-11.15

-8.91

0.47

7.6

5

0.01

FI

1990-98 1.55

8.58

7.33

12.36

-14.21-11.89

0.49

12.5

10

0.04

FR

1990-98 1.42

0.70

2.80

0.89

-4.13

-9.08

0.46

5.0

14

0.04

GE

1990-98 2.94

-0.05

1.43

4.23

-10.62

-7.71

0.63

6.9

15

0.04

IR

1990-97 7.03

17.37

15.49

4.60

-31.18

-6.84

0.10

9.1

12

0.05

IT

1990-98 1.28

-0.65

2.34

1.20

-9.13

-11.97

0.43

8.1

20

0.03

JP

1994-98 0.94

4.86

3.55

5.83

-36.87-22.91

0.55

5.7

8

0.02

NL

1990-98 2.55

2.17

1.78

3.63

-0.82

-7.87

0.45

6.4

9

0.10

NO

1990-97 3.64

3.81

3.23

11.14

-13.07

-8.15

0.51

10.9

11

0.06

SP

1990-98 2.19

1.06

4.07

4.83

-14.93-12.90

0.34

6.6

19

0.03

SW

1990-97 0.95

8.19

5.75

12.00

-17.88

-9.36

0.52

6.6

13

0.03

UK

1990-98 2.15

-0.85

0.79

2.99

-12.07

-9.06

0.44

6.9

7

0.08

US

1990-98 3.07

3.25

2.72

3.05

-4.86

-5.88

0.43

16.7

1

0.08

Sources: OECD, MSTI, EVCA and own calculations

4. Empirical results
Each variable of equation (5) has first been included separately in the empirical model. The estimatedparameters are presented in Table 3. All variables have the expected impact as far as their sign andsignificance is concerned. Results concerning the growth rate of GDP (Table 3, column 1) are in line withthose of Gompers and Lerner (1998) for the US but do not confirm the non-significant impact obtained byJeng and Wells (2000). Several tests have been carried out in order to determine whether a time lag isnecessary. However, only the contemporaneous GDP growth rate has a significant impact on VC intensity.
Both one-year and ten-year interest rates have a positive and significant impact, suggesting that the demand
effect of interest rates is stronger than the supply effect. Since entrepreneurs must have a shorter vision offinancial constraints (or return) than fund providers, we would have expected a negative impact of the longterm interest rate. However, the adjusted R-squared is the smallest with the ten-year interest rate. Column 4of Table 3 reports a variable representing the difference between the long-term and short-term interest rates.
The negative and significant impact suggests a stronger supply-side effect. What matters is not only the levelof the long-term interest rate but also the difference between the long-term and short-term rates. The larger
this difference, the less venture fund providers would be attracted towards risky investment.
The three variables representing technological opportunity and research efforts play a significant role indetermining VC intensity. The strong and positive impact of the growth rate of business R&D expenditures,the Business R&D capital stock and the number of triadic patents show that the demand of VC is sensitive to8the dynamics of research activities, to the available stock of knowledge and to the level of innovation output,
as proxied by the number of high value patents.
It is worth noticing that the three variables that yield the highest adjusted R-squared are the short-term
interest rate, the growth of business R&D investment and the difference between the long-term and shortterm interest rates. Then come the GDP growth rate, the stock of knowledge and the number of triadic
patents. The short-term cost of money and technological opportunity seem to be the strongest drivers of VC.
High taxation of income reduces the relative level of VC probably due to an induced lower entrepreneurial
will.
Table 4 presents the results of the estimates with several variables introduced simultaneously in the model.
The sign and significance of the impact of all these variables remain unchanged when they are introduced
simultaneously in the model. Columns 1 to 3 present the basic model described in equation (5), with different
indicators for the interest rates.
The short-term interest rate (column 1) is still highly significant, whereas the long-term interest rate is no
more significant (column 2), probably due to the simultaneous introduction of GDP growth in the model.
However, the difference between short-term and long-term interest rates has a significant and negative
impact (column 3). It seems therefore that the short-term cost of capital and its difference with the long-term
cost play a more important role in explaining the intensity of VC. These results witness a stronger influenceof the short-term cost of capital on the demand side (entrepreneurs) than on the supply side (investors).
However, the larger the difference between long-term and short-term interest rates, the lower the VC
intensity.
As predicted, corporate income tax rate has a negative impact on the VC intensity and the parameters
associated with the three variables of technological opportunity and research efforts (the growth rate ofbusiness R&D expenditures, the business R&D capital stock and the number of triadic patents) are positiveand significant. This result about triadic patents is consistent with the results of Kortum and Lerner (1998)
who show that a surge of patents may increase the VC fundraising. In other words, the property of highly
valued intellectual assets (triadic patents are associated with a much higher value than the patents applied
only in one country or region) seems to stimulate the demand for VC.
The remaining columns test other specifications described in equation (6), with two interaction variables
representing a country’s entrepreneurial environment. The index of labour market rigidities is first interactedwith the GDP growth rate variable (see column 4). Results show that the impact of GDP growth rate on the
VC intensity is composed of a fixed and significant component (0.0067) and a country specific component
that depends on labour market rigidities (-0.00047). The positive impact of GDP on the VC intensity istherefore reduced in countries with high labour market rigidities. Jeng and Wells (2000) obtain a similar
result but only for early stage funding. Over the threshold of 14.2 in the index of labour market rigidities, theimpact of GDP growth becomes negative.
The level of entrepreneurship is interacted in a similar way with the stock of available knowledge (the R&D
capital stock, in column 5). Estimates indicate that the impact of the R&D capital stock on the VC intensity
is composed of a fixed negative component and a country specific component that depends on the relative
level of entrepreneurship (TEA): the higher the level of entrepreneurship, the stronger the impact of thebusiness R&D capital stock on VC intensity. In order to have a positive impact of the available stock ofknowledge on VC performances, a minimum level of entrepreneurship is required. The estimated parameterssuggest that the impact of the business R&D capital stock on the VC intensity becomes positive andsignificant above a threshold of 12.8 in the TEA index (level of entrepreneurship).

9

Table 3: Estimation results of the VC intensity, single explanatory variables
Dependent variable: VC intensity (VC/GDP)

Regressions

GLS

GLS

GLS

GLS

GLS

GLS

GLS

GLS

1

2

3

4

5

6

7

8

Economic variables∆GDPitGDP growth rate
One-year Interest rateritLong-Term Interest rate (10 years)

rit

Log [r10/r1]

rit

0.003***(3.32)
0.00004***(3.26)
0.00004**(2.18)-0.0001***(-3.05)

Technological opportunity
Business R&D investment growth rate (t-1)∆BRDit −1

Business R&D capital stock (t-1)

SBRDit −1

Log Number of triadic Patents

PATit

0.001***(4.19)
7*10-15***(5.15)
0.0003***(2.67)

Entrepreneurial environment
Corporate Income Tax Rate

-0.002***(-3.20)

CITRitControl variables
German reunification dummy (t)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Country-specific intercept

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Time dummies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R-squared

0.726

0.755

0.628

0.737

0.742

0.713

0.682

0.669

Note: Panel data, 16 OECD countries, 1990-98, 130 observations. * Indicates the parameters that are significant at a 10% probability threshold, ** 5% probability threshold and *** 1%
probability threshold. All equations include country dummies (within estimates), time dummies, and a dummy variable for the German reunification. The econometric method is GLS.

10

Table 4: Estimation results of the VC intensity, complete model and interactions
Dependent variable: VC intensity (VC/GDP), GLS estimates
Regressions

1

2

3

4

5

6

7

8

0.007***

Economic variables
GDP growth rate

∆GDPit0.001*

0.001

0.001*

0.007***

0.001*

0.007***

0.007***(1.69)(1.37)(1.76)(2.98)(1.86)(3.77)(3.55)

rit

0.00003***0.00003**0.00003***0.00003***0.00002(2.45)(2.96)(2.72)(1.55)

One-year Interest rateLong-Term Interest rate (10 years)

rit

Log [r10/r1]

rit(2.70)(3.17)∇

0.00002(0.96)-0.0001***-0.0001**-0.00004(-3.35)(-2.23)(-0.67)0.001***

Technological opportunities
Business R&D investment growthrate (t-1)
Business R&D capital stock (t-1)
Log Number of triadic patents∆BRDit −10.001**

0.001**

0.001***

0.001**

0.001***

0.001**

0.001***(2.25)(2.46)(2.52)(2.19)(2.62)(2.46)(2.67)(2.56)

SBRDit −1

6*10-15***

6*10-15***

6*10-15***

4*10-15***-2*10-14***-1.5*10-14***-1.1*10-14***-1.4*10-14***(4.44)(4.90)(4.66)(2.91)(-5.14)(-5.25)(-2.76)(-3.17)PATit

0.0004***0.0003**0.0004***0.0003**0.0003**0.0003*0.0002*0.0003*(2.72)(2.24)(2.67)(2.28)(2.19)(1.91)(1.72)(1.91)-0.001**-0.001*-0.001**-0.001*-0.001**-0.001*-0.001*-0.001*(-2.23)(-1.67)(-2.17)(-1.76)(-2.29)(-1.82)(-1.78)(-1.77)-0.0005***-0.0006***-0.0005***

Entrepreneurial environment

CITRitCorporate Income Tax Rate
Labour Market Rigidities
Level of entrepreneurship-0.0005***∆GDPit * RIGi(-2.55)

SBRDit −1 * TEAi(-3.07)(-3.26)(-2.94)1.4*10-15***

1.1*10-15***

1*10-15***

1*10-15***(6.49)(5.92)(3.55)(3.90)

Control variables
German reunification dummy (t)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Country-specific intercept

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Time dummies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R-squared

0.833

0.805

0.821

0.820

0.833

0.860

0.827

0.830

Note: Panel data, 16 OECD countries, 1990-98, 130 observations. ∇ Indicates the parameters that are significant at a 15% probability threshold, * 10% probability threshold, ** 5% probability threshold and ***1% probability threshold. All equations include country dummies (within estimates), time dummies, and a dummy variable for the German reunification. The econometric method is GLS.

11

The estimated parameters associated with the interaction between the two country-specific variables
representing the entrepreneurial environment are stable in the subsequent models. Columns 6 to 8 show thatthe simultaneous introduction of the two indicators (RIG and TEA), and the use of short-term and long-term
interest rates do not affect the results. The last column introduces simultaneously the short-term interest rate
and the difference between the long-term and short-term interest rates. The non significant impact of thedifference indicator suggests that what matters more is the short-term interest rate influence on the demandside of VC.

Table 5a: Estimation results of the VC intensity, interactions with RIG
Dependent variable: VC intensity (VC/GDP)
Regressions

GLS

GLS

GLS

GLS

1

2

3

4

0.007***(2.98)
0.00003**(2.45)

0.001∇(1.45)
0.00003**(2.33)

0.001(1.06)
0.00003***(3.00)

0.001(1.03)
0.00002**(2.02)

0.001**(2.19)
4*10-15***(2.91)
0.0003**(2.27)

0.002***(2.75)
5*10-15***(4.30)
0.0004***(3.02)

0.001**(2.06)
6*10-15***(4.66)
0.0003*(1.76)

0.001***(2.61)
5*10-15***(3.58)
0.001***(2.78)-0.001*(-1.76)-0.0005***(-2.55)-0.001**(-2.38)-0.001**(-1.99)-0.001**(-2.29)Economic variables∆GDPitGDP growth raterit

One-year Interest rate
Technological opportunities
Business R&D investment growth rate (t-1)∆BRDit −1

Business R&D capital stock (t-1)

SBRDit −1

Log Number of triadic patents

PATit

Entrepreneurial environment
Corporate Income Tax Rate

CITRitLabour Market Rigidities 1∆GDPit * RIGi

Labour Market Rigidities 2∆BRDit −1 * RIGi

Labour Market Rigidities 3

SBRDit −1 * RIGi

Labour Market Rigidities 4

PATit * RIGi

-0.0001*(-1.93)-1*10-15***(-3.92)0.00003∇(-1.57)

Control variables
German reunification dummy (t)

Yes

Yes

Yes

Yes

Country-specific intercept

Yes

Yes

Yes

Yes

Time dummies

Yes

Yes

Yes

Yes

Adjusted R-squared

0.820

0.840

0.814

0.844

Note: Panel data, 16 OECD countries, 1990-98, 130 observations. ∇ Indicates the parameters that are significant at a 15%
probability threshold, * 10% probability threshold, ** 5% probability threshold and *** 1% probability threshold. All equations
include country dummies (within estimates), time dummies, and a dummy variable for the German reunification. The econometricmethod is GLS.

12

We investigate more thoroughly the role of the two country-specific variables representing theentrepreneurial environment in tables 5a and 5b. The two indexes have been interacted with the GDP growthrate and each of the three variables of technological opportunity and research efforts. Table 5a presents theestimated parameters related to the interaction between labour market rigidities and the four variables. Thereis a negative and significant effect of RIG on the impact of the GDP growth rate, the growth rate of R&D
outlays and the stock of business R&D. The impact of triadic patents is not really affected by labour market
rigidities.
Figure 1 illustrates the results of the estimates presented in table 5a. It shows how the level of labour market
rigidities affects the impact of three determinants of VC. For instance, the impact of the growth rate of R&D
expenses is always positive but declining as RIG increases. The effect of these rigidities on the stock ofknowledge (SBRD) and on the GDP growth rate is steeper, as they become negative over a threshold of
about 13 to 14 in the index of labour market rigidities.

Figure 1: The indirect effect of labour market rigidities on VC
10

0.007
Estimated impact of GDP growth, left-hand scale

0.006

5

0.004

0

0.003
-5

0.002
0.001
0.000

-10

Estimated impact of R&D growth,
left-hand scale

Impact of SBRD, (10E-15)

Impact of growth of BRD and GDP

0.005

-15

-0.001-0.002-20

Estimated impact of R&D capital stock, right-hand scale-0.003-0.004

-25
1

3

5

7

9

11

13

15

17

19

Labour market rigidities index

Note: Estimated impact of the growth rate of GDP, the growth rate of business R&D expenditure and the stock of knowledge on VC
intensity, according to the level of labour market rigidities. See table 5a, columns 1 to 3.

Table 5b presents the estimated parameters related to the interaction between the level of entrepreneurship(TEA) and the four variables. The interaction term is positive and significant with the R&D growth rate andthe business R&D capital stock (see columns 2 and 3). With the lower level of entrepreneurship, the growthrate of business R&D expenditures would have no impact on the VC intensity. The larger the level of13

entrepreneurship is, the larger the impact of business R&D expenditures. In other words, there must be aminimum level of entrepreneurship in order to spur the demand for VC. The business R&D capital stock has
a significant impact on VC intensity only above a threshold of about 12 in the index of entrepreneurship.

Table 5b: Estimation results of the VC intensity, interactions with TEA
Dependent variable: VC intensity (VC/GDP)
Regressions

GLS

GLS

GLS

GLS

1

2

3

4

-0.001(-0.54)0.00003***(2.57)

0.001(1.41)
0.00003***(2.60)

0.001*(1.86)
0.00003***(2.96)

0.001(0.86)
0.00002**(2.15)

0.001**(2.14)
5*10-15***(3.51)
0.0003**(2.42)-0.0004(-0.56)
5*10-15***(4.16)
0.0004***(2.96)

0.001***(2.62)-2*10-14***(-5.14)0.0003**(2.19)

0.001**(2.47)
5*10-15***(3.60)-0.00002(-0.07)-0.001**(-2.24)0.0003(1.15)-0.001**(-2.18)-0.001**(-2.29)-0.001**(-2.06)

Economic variables∆GDPitGDP growth raterit

One-year Interest rate
Technological opportunities
Business R&D investment growth rate (t-1)∆BRDit −1

Business R&D capital stock (t-1)

SBRDit −1

Log Number of triadic patents

PATit

Entrepreneurial environment
Corporate Income Tax Rate

CITRitLevel of entrepreneurship 1∆GDPit * TEAi

Level of entrepreneurship 2∆BRDit −1 * TEAi

Level of entrepreneurship 3

SBRDit −1 * TEAi

Level of entrepreneurship 4

PATit * TEAi

0.0001*(1.62)
1*10-15***(6.49)
0.00004(1.34)

Control variables
German reunification dummy (t)

Yes

Yes

Yes

Yes

Country-specific intercept

Yes

Yes

Yes

Yes

Time dummies

Yes

Yes

Yes

Yes

Adjusted R-squared

0.837

0.837

0.833

0.834

Note: Panel data, 16 OECD countries, 1990-98, 130 observations. ∇ Indicates the parameters that are significant at a 15%
probability threshold, * 10% probability threshold, ** 5% probability threshold and *** 1% probability threshold. All equations
include country dummies (within estimates), time dummies, and a dummy variable for the German reunification. The econometricmethod is GLS.

Table 6 summarizes the main findings of our empirical investigation and compares them with the results
obtained by Jeng and Wells (2000) and Gompers and Lerner (1998). The cyclicality of VC with respect to
GDP growth confirms both our expectation and the results of Gompers and Lerner (1998). Jeng and Wells(2000) did not find any significant effect partly because of the structure of their dataset (cross section ofcountries) and partly because of the use of the IPO variable.
14

Concerning the cost of capital, we confirm the positive impact of the short-term interest rate obtained byGompers and Lerner (1998) at the aggregate level. We also show that the difference between the long-term
interest rate and the short-term interest rate has a negative and significant impact on the VC intensity.
Labour market rigidities and the corporate income tax rate are two factors that reduce the intensity of VC. On
the other hand, a strong entrepreneurial culture and more intense technological opportunities and research
efforts improve the positive effect of the stock of knowledge and GDP growth on the VC intensity.

Table 6: Comparison of our results with the state of the art
Jeng and Wells (2000),
21 countries, Panel data andcross section

Gompers and Lerner (1998)
US industry aggregate data

16 countries, panel data

0

+

+

+ at aggregate level and – atstate level+

Our analysis

Macroeconomic conditions
Gross domestic ProductInterest rate 1 year
Interest rate 10 years

0

Difference between 10 years and 1
year Interest rate

+ Over time
0 Across countries+ Over time

Corporate income tax rate0-

Labour market rigidities- at the early stage
0 at expansion stage

Private Pension Funds
Entrepreneurial environment

Initial Public Offering
Stock Market Opportunities

0 at early stage acrosscountries+ at expansion stage(Market Capitalization
Growth)0reduces the impact of GDP
and R&D on VC

0(Equity Market Return).
++
Increases the impact of R&D
on VC

Level of entrepreneurshipTechnological opportunity
Number of Triadic Patents+

Business R&D growth++

Stock of knowledge

+

+

15

5. Concluding Remarks
This paper aims at contributing to the literature on the determinants of VC. Our contribution consists in (1)developing a theoretical model that takes into account the supply-side and demand-side variables to explain
VC intensity; and (2) introducing simultaneously traditional determinants of VC and new potential
determinants like the cost of capital, the level of entrepreneurship, and novel proxies aiming at measuring
technological opportunity. Empirical results can be summarized as follows.
Interest rates have a significant impact on VC intensity. Whereas short-term interest rates influence
positively the relative level of VC via a strong demand-side effect, the difference between long-term andshort-term interest rates has the opposite impact, via a stronger supply-side effect. Long term interest rates do
not affect significantly the equilibrium level of VC.
We show that the impact of corporate income tax rates on VC investment is negative. In other words, highcorporate income tax rates reduce the relative level of VC investment across countries and over time.
VC is highly cyclical. It follows a similar evolution than GDP growth rate. In periods of high growth, theflow of venture capital outperforms the GDP growth rate, and vice versa. This cyclicality is reduced by the
degree of labour market rigidities. A high level of labour market rigidity reduces the positive impact of GDP
growth on VC intensity, as well as the positive impact of the knowledge capital stock and of the growth inbusiness R&D.
We also show that indicators of technological opportunity, such as the growth rate of R&D investment, theavailable stock of knowledge and the number of high value patents (triadic patents), influence significantly acountry’s investment in VC. The positive impact of the stock of knowledge is strongly reinforced in the
countries were the rate of entrepreneurship is very high.
One important policy implication that emerges from these results is that in order to stimulate VC in a country,
demand-side factors have to be taken into account. The most important factors affecting the demand of VC
are the stock of knowledge, innovative outputs, business R&D, the corporate income tax rate and short-term
interest rates. In addition, labour market rigidities and the level of entrepreneurship do play an important role.
Changing these factors would require adjustment in structural policies (labour market and education), whose
impact can only appear in the long term.

References
Anand B., 1996, Tax effects on venture capital, Unpublished Working paper, Yale School of Management.
Berger A.N. and G.F. Udell, 1998, The economics of small business finance: The roles of private equity anddebt markets in the financial growth cycle, Journal of Banking & Finance, 22 (6-8), pp. 613-673.
Engel D., 2002, The impact of venture capital in firm growth: an empirical investigation, ZEW Discussion
paper, 02-02, 30p.
Gompers, P. and J. Lerner, 1998, What Drives Venture Fundraising?, Brookings Papers on Economic
Activity, Microeconomics, pp. 149-192.
Gompers P. and J. Lerner, 2001, The Venture Capital Revolution, Journal of Economic Perspectives, 15 (2),
pp 145-168.
Hellmann T. and M. Puri, 2000, The Interaction between Product Market and Financing Strategy: The Roleof Venture Capital, Review of Financial Studies, 13, pp. 959-84.
Hellmann T. and M. Puri, 2002, Venture Capital and the Professionalization of Startup Firms: Empirical
Evidence, Journal of Finance, 57 (1), pp. 169-197.

16

Kortum S. and J. Lerner, 2000, Assessing the Contribution of Venture Capital to Innovation, RAND Journalof Economics, 31 (4), pp. 674-692.
International Monetary Fund, 2002, International Financial Statistics, Yearbook 2002, vol LV.
Jeng L.A. and Ph.C. Wells, 2000, The determinants of venture capital funding: evidence across countries,
Journal of Corporate Finance, 6 (3), pp. 241-289.
O’shea M. and C. Stevens, 1998, Quand le gouvernement apporte du capital-risque, L’Observateur del’OCDE, n°213 , pp. 26-29.
OECD, 1994a, Jobs study, Organisation for Economic Co-operation and Development, Paris.
OECD, 1994b, Employment outlook, Organisation for Economic Co-operation and Development, Paris.
OECD, 1997, 1999, 2001, OECD statistics: Main science and technology indicators, 1, Organisation for
Economic Co-operation and Development, Paris.
Poterba J.M., 1989, Venture Capital and Capital Gains Taxation, NBER working paper series, Working
Paper n°2832, NBER, Cambridge.
Romain A. and B. van Pottelsberghe, 2003, The Impact of Venture Capital on Productivity, IIR Working
Paper, WP#03-20; Hitotsubashi University.

17

APPENDIX 1: Calculation of the variables

Business R&D capital stocks
R&D capital stocks are calculated following the perpetual inventory method. The stock at time t is equal to
the new investment at time t plus the stock at time t-1 minus depreciation:

SRt = rt +(1 − δ )SRt −1(A1.1)

SRt = rt +(1 − δ )rt −1 +(1 − δ ) 2 rt −2 +(1 − δ ) 3 rt −3 +...

(A1.2)

To construct the initial stock we assume a constant annual rate of growth of the past investments,

SRt = rt +(1 − δ )λ rt +(1 − δ ) 2 λ2 rt +(1 − δ ) 3 λ3 rt +...
SRt =
where

rt
1 − λ (1 − δ )
SRt
rt

δ

λ=

(A1.3)(A1.4)=

R&D capital stock at time t.

==

R&D investment at time t.
Depreciation rate (constant over time).

1
1+η

and

η

is the mean annual rate of growth of rt .

This formula has been used to calculate the business R&D capital stock (SBRD).

18