1
Government Support for Entrepreneurial Finance in the UK:
From “Market Failures” to “Thin Markets”
Paul Nightingale (Sussex), Charles Baden-Fuller (City), Gordon Murray (Exeter), Marc
Cowling (Exeter), Colin Mason (Strathclyde), Josh Siepel (Sussex), Mike Hopkins (Sussex),
Joe Tidd (Sussex), Charles Dannreuther (Leeds)
Abstract: Many national governments have attempted to replicate US success in early stage
venturing by supporting private VC funds, often basing such interventions on supply-side
market-failure arguments about funding gaps. Using the full dataset of all UK firms supported
through hybrid public-private VC schemes the paper explores a range of managerial and
financial factors that influence firm performance by analysing how the behaviour of funded
firms compares to a matched unfunded sample. The results strongly suggest the supply side
problem in the UK is over-rated. However, the analysis does find strong positive evidence that
public interventions produce desired VC-like effects. Shifting the framing of public policy
from ‚market failures‛ to ‚thin markets‛ – where small numbers of high quality firms, and
small numbers of high quality investors have difficultly finding each other - would allow
analysis of both supply and demand in the context of the variety of institutions involved in the
provision of effective entrepreneurial financing. Policy recommendations are provided.
1.1 Introduction
Venture Capital (VC) is a major source of finance for innovative, high growth American firms
and plays an important role in the commercialisation of advanced ICT, medical, clean and
new material technologies (Gompers and Lerner, 2001; Reynolds et al, 2000; Shane, 2008;
Audretsch et al 2008).1 However, US success has been difficult to replicate and while the UK,
Israel, Finland and Sweden have produced competitive, high-tech start-ups, they have not
been as successful at growing them into global firms or developing a viable VC sector
(European Commission 2006). Outside the US market institutional investors, such as pension
funds, have tended to shy away from early stage VC funds making it increasingly difficult to
create such funds. As a result, outside the US overall levels of VC investment are lower,
comparable firms receive smaller amounts of funding (Bottazzi and Da Rin, 2002), and the ‘full
service’ venture capital funds that provide both funding and active managerial support are
rare.
This lack of institutional investment, despite substantial growth in private equity since the
1990s, is a simple reflection the largely low and erratic returns of early stage VC funds outside
the USA (Dantas et al., 2006). Particularly in recent years fund returns have been low and in
many instances negative. Many funds and their investors have moved to later stage private
equity investment, where returns have been higher and more persistent. In the UK, for
example, this has left a funding gap for investments over £500,000 (€700,000) that are too large
1 Intel, Apple, eBay, Google and Genentech for example were all VC backed.
2
for business angels, with implications for early stage funding and the commercialisation of
innovation. Similar funding constraints across the industrialised world have drawn
governments to directly support VC investing (Lerner, 1996) through the provision of equity
support (Toschi and Murray, 2009). Public support for early stage venture capital is now a
major phenomena, but given the significant variety in performance, it is currently unclear how
such funds should be best structured.
This government intervention is often justified because the moral hazards, information
asymmetries, scale economies, institutional failures, inherent uncertainties, and spill-overs
associated with VC investment create market failures. As a result, economic actors cannot
capture the full economic value of their investments and invest less than the socially optimal
amount. Government intervention to address this under-investment is particularly warranted
if funding problems constrain the small number of high-potential firms that drive job creation
(Storey, 1998).2
The UK Government has developed a range of innovative early stage, equity finance support
programmes involving public co-investment or ‘hybrid’ funding (Almeida Capital, 2005;
OECD, 2004). Such investment now make up approximately 68% of the early stage investment
(by number) identified in official data (M, 2010). In this paper we evaluate the impact of six
UK government-backed VC schemes on 782 funded firms by exploring their transformational
impact on recipient firms compared to a matched control sample in order to better understand
which sector, managerial and financial factors influence early stage firm performance.3
The econometric analysis suggests the schemes have improved firm performance, but the
impact remains small, so far. This suggests it is over-simplistic and misleading to frame the
UK SME funding problem in terms of an exclusive supply-side financing constraint that can
be solved by the provision of funding. The policy model suggested that funding constraints
was the key problem and funding provision the key solution. Our results clearly show it is
not. This suggests the quality of the firms, their support, their management and their
environment should also be considered. However, the analysis does find repeated
encouraging evidence disruptive changes that build firms’ capabilities. This can be seen across
a number of performance metrics when funding has an initial negative impact on firm
performance that rebounds strongly after 4-5 years.
Together these findings suggest that the UK has a demand side problem due to a lack of high
potential firms worth investing in, a supply side problem in the provision of funding and a coordination problem between them. In the discussion we suggest that the current ineffective
provision of risk capital should be understood as a ‘thin market’ where limited numbers of
While these arguments make sense in theory, there are good reasons to be sceptical about
them in practice.
2
3 The six schemes are the Enterprise Capital Funds (ECFs); Early Growth Funds (EGFs); Regional
Venture Capital Funds (RVCFs); Scottish Enterprise backed Funds; University Challenge Funds
(UCFs); and Welsh Hybrid Funds.
3
investors and entrepreneurial firms have difficulty finding and contracting with each other at
reasonable costs (Steinmeuller, personal communication).
The rest of the paper discusses these issues in detail. Section 2 provides a brief review of the
academic and policy debates. Section 3 discusses the econometric and qualitative
methodologies used. Section 4 presents the econometric results and robustness tests. Section 5
provides a discussion and section 6 provides conclusions and recommendations for public
policy makers in other countries.
1.2 Section 2: Venture Capital and the VC System
Venture capital is the process of external equity finance provision by professional investors,
using funds drawn from external investors, in new or young (i.e. early stage) companies to
create new assets for the primary purpose of reaping substantial economic gain through an
attractively priced market flotation (initial public offering) or trade sale.4 Venture capital can
be thought of as a form of corporate governance for putting together complementary assets to
create value that is captured through sale/IPO. Venture capitalists primarily invest equity in
young companies in return for a significant part ownership of a business they think has
exceptional growth prospects. They are typically highly informed professional investors who
actively engage and provide advice and governance to the entrepreneurial management team
as they grow the company and move towards a successful exit. Despite the significant
economic impact of venture capital, it remains poorly understood. Characteristic features of
VC include it being:
Small as a percentage of the total number of investments in firms in the economy as only a
small cadre of exceptional high-growth companies as suitable for VC funding. Even in the
United States only approximately 3000 firms get VC funding each year and only between 500
and 750 of these are start ups (Shane, 2006:90). VC investments make up about 1.9% of US
early stage investment (ibid). The boundary between VC and Private Equity (PE) is not
consistently defined, which has the potential to distort statistics, overestimate the size of the
VC industry and make comparisons difficult, as PE investments are typically much larger.5
Specialised as almost all investments take place in a very few sectors that generate the high
returns that VC requires. These sectors are mainly biotechnology and healthcare, information
and communications technology, and increasingly green-tech (Deloitte, 2009). The very high
costs of VC equity necessitates high returns, which means that VC investment is largely
4 Specialised terms such as VC have confusingly different meanings in America and Europe
(European Commission, 2005). Accordingly, we use the term Venture Capital as it is commonly
understood in the USA (Bygrave and Timmons, 1992; European Commission, 2006).
5 PE involves refinancing and restructuring existing assets (rather than the creation of new assets)
through management buy-outs, buy-ins and other later stage development finance. The paper does
not address PE because governments rarely intervene to promote it. The US PE industry is
organisationally distinct from VC, but elsewhere their activities often overlap, promoting confusing
terminology and misleading statistics. In Europe private equity funds take full advantage of this lack
of clarity when lobbying for tax breaks on their capital gains.
4
focused on new technologies that have the potential to disrupt existing business practice and
incumbent firms.
Skewed in its returns with the majority of profits going to the top quartile of firms from a small
percentage of exceptional investments. 6 Most investments in a portfolio either fail or return (at
best) a negligible net present value when the time cost of money and an appropriate risk
premium are considered (Murray and Marriott, 1998).7
Skilled in its implementation. Because VC funding, unlike debt funding, transfers part of the
ownership risk from the entrepreneur to the investor, it encourages Venture Capitalists to
provide managerial support to entrepreneurs (Sapienza et al., 1996). The persistent superior
performance of the top quartile funds reflects both network effects and the value of this
human capital for investee firms (Gompers, 1996).8 However, when key partners leave and
move to new funds, their new funds also tend to perform strongly.
Scale intensive, as VC funds’ ‚2+20‛ structure means they take (at a minimum) 2% of the fund a
year for their operating costs and then 20% of the carried return.9 Small funds may have
difficultly paying for the required size investment team and infrastructure needed to find,
evaluate, and support investee firms without a higher yearly percentage, which has a dramatic
impact on returns. They will also have fewer investments to spread costs, generate returns and
diversify their portfolio risks and may not be able to make follow-on investments to maintain
their ownership share. As a result, their returns will be diluted by deeper pocketed coinvestors over repeated funding rounds as new shares are issued.10 In the UK the minimum
size of a 10 year commercial fund would be approximately £30-50m (40-60m Euros) (Murray
2009), while early stage VC funds below £25-30 million generate poorer returns for investors
(Murray and Marriott, 1998; Jääskeläinen et al, 2007).11 This economic scale imperative often
forces successful non US funds that raise more money to move out of early stage investing as
6 As an asset class VC returns are more skewed than hedge funds so anecdotal statements about
‘premiership’ funds rarely apply to the average fund.
7 As a rough rule of thumb, half the investment portfolio will fail to make a return, about a third will
make a small return, and the remainder will hopefully make a large enough return to create value.
8 Key investment managers are ‘locked in’ by skewing rewards to the final distribution of the ‘carry’
(i.e. capital gain shared by the general partners) of the fund. To acquire a senior manager a new
general partnership will have to buy the new hire out of his or her existing entitlement which may
cost tens of millions of dollars in a top quartile fund.
9 This makes it very difficult for investors to make a good return. If Warren Buffet had adopted a
‚2+20‛ he would have retained $57bn of the $62bn his investment portfolios have generated (Kay,
2009).
10 In order to stand a good chance of capturing one of these investments a syndicated portfolio needs
to have at least 8-10 investments, while a portfolio closer to 20-30 investments would more adequately
spread their risks. Funds require a portfolio that is not so large that investment managers cannot
actively intervene and coach firms.
11 The minimum size will be greater in funds specialising in sectors, such as biotech, where larger
investments over longer periods are needed and for funds specialising in earlier stage investments in
immature enterprises. Such firms often do not need large amounts of external finance, but can be more
costly to manage as they need to be nurtured over a long period.
5
the size of their average investment increases. By contrast, US early stage funds are often
extremely large, and may have $1bn under management, an issue we return to in the
conclusion (cite).
Significant in its impact on the US economy. While only a small number of investments are
made their impact is disproportionately large. As Shane has noted:
‚Since 1970, venture capitalists have funded an average of 820 new companies
per year. These 820 supported start ups – out of the more than 2 million efforts to start
businesses in this country every year – have enormous economic impact. By 2003,
companies that had been backed by venture capital employed 10 million people, or 9.4
per cent of the private sector labour force, and generated $1.8 trillion in sales... In 2000,
the 2,180 publicly traded companies that had received venture capital backing between
1972 and 2000 comprised 20 per cent of all public companies, 11 per cent of sales, 13
per cent of profits, 6 per cent of employees, and one third of total market value, a
figure in excess of $2.7 trillion dollars... In short, almost all of the value generated by
start ups has come from this handful of firms‛ (Shane, 2008:162).
Supported by national governments. The historical record suggests that it is impossible to start
up and maintain a viable VC industry without some kind of government support given the
fragility of the market (Learner, 1999). Even in the US there is substantial government
intervention through direct financial support for investors and subsidies to R&D. The US
government provides some 20-25% of all the funds invested in start up firms which is about
equal to business angels’ total investments and about two to eight times the amount invested by
private venture capital firms (Branscomb and Auerswald, 2003).
Systemic, as VC funds do not operate in isolation. They need investors that accept the higher
risks and skewed returns (often because VC is largely uncorrelated with other parts of their
portfolios). Funds also need a supply of high quality, high potential, investment-ready firms
worthy of investment and a supportive institutional environment of (expensive) experts,
managers and advisors - lawyers, accountants, consultants and industry contacts. Finally,
there need to be viable exit routes to generate large enough returns for investors to justify the
higher risks involved
When all the parts of this system are working effectively it can generate a self-sustainable
cycle where VC funds generate high returns for investors who sustain new funds that support
the next wave of entrepreneurial firms and generate learning environments for managers,
entrepreneurs, advisors and investors (Gompers and Learner, 1999). As this system develops
subsequent investments become easier and cheaper. However, the system is fragile and suffers
from ‘simultaneity problems’ (Gilson, 2003) because all its elements must each be present and
working together over decades to sustain itself. Shocks, such as the dot.com crash or the
current global credit crunch, can disrupt investment and exit markets which increase the time
investments are held and therefore investment costs which decreases fund performance and above all - the willingness of institutional investors to support VC as an asset class.
6
1.3 Government Policy to support Venture Capital investment
The combination of enormous potential impact and system fragility has meant that virtually
every major economy has implemented initiatives to promote venture capital (Bottazzi and Da
Rin, 2002; Lerner, 2002; Murray, 2008). Government support has been important because the
simultaneity problem creates a ‘chicken and egg’ situation, where an effective funding system
depends on skills and networks, that can only be built up within an effective funding system.
However, the rhetorical justifications for policies are almost always made in terms of market
failures. Such justifications draw on well developed theory that suggests market failures are
likely because of the incomplete appropriability conditions associated with investment in
knowledge assets that emerge because knowledge is a non-rival and non-excludable good.
This is a particular problem for small firms that typically lack the complementary assets to
appropriate the returns from their innovations (Teece, 1984) creating spill-overs to other parts
of the economy (Griliches, 1992) and a gap between private and public investment returns.
Other theoretical problems include asymmetric information and moral hazard problems
leading to credit and liquidity constraints (Carpenter and Petersen, 2002), the fact that
knowledge intensive firms have intangible-assets that cannot be used to guarantee loans,
network externalities and diseconomies associated with underdeveloped infrastructure and
signalling (Lerner, 2002), scale effects because of the higher costs of screening early stage firms
with immature technologies, and because of imperfections in the market for LP-GP funding.
These problems lead to the financing hierarchy proposed by Stiglitz and Weiss (1981) and help
explain why VC is an appropriate form of finance under such conditions (Gompers and
Lerner, 2001; Kaplan and Stromberg, 2001) because VC firms can screen funding proposals
(Chan, 1983), monitor firms and assist in their management (Kaplan and Stromberg, 2003).
However, finding the correct practical policies to address these theoretical market failures is
not easy. Many governments initially established their own VC funds, but these have been
largely abandoned as political influences distort investment decisions and government
officials lacked the ability to assess and manage investment opportunities. Today government
policy typically takes the form of capital participation in which the State invests as a special
Limited Partner in a fund managed by a commercial VC who takes responsibility for
commercial investment decisions once the general focus, objective and distribution of
incentives of the fund are negotiated and agreed (OECD, 2004).
Examples include the Australian Industry Investment Fund, the German High-tech
Gründerfonds, the Israeli Yozma programme and the New Zealand Venture Investment
Fund.12 Such schemes can also involve downside protection against losses or upside leverage
of private Limited Partners’ investments (Jääskeläinen Maula and Murray 2007). They rarely,
however, address ‘demand side’ problems associated with poor quality investee firms or local
VC skills (Toschi and Murray, 2009).
12 Murray and Liu in their 2009 unpublished review of 16 developed economies found 31 hybrid
schemes in operation.
7
In the UK hybrid VC funds emerged from a long series of policy interventions to improve
small firm financing that expanded significantly after the election of the Thatcher government
in 1979 with 108 new policies introduced between 1979 and 1983 (Mason and Harrison,
1986).13 The main early schemes included a the Small Firm Loan Guarantee Scheme, the Business
Start Up Scheme - a tax relief based equity investment support scheme, which was poorly
designed and led to substantial tax avoidance, before it was replaced with the Business
Expansion Scheme. These were followed in the 1990s by the Enterprise Investment Scheme which
provided tax relief based incentives for investment in small private firms, the retail Venture
Capital Trust scheme for passive equity investment in small firms, and the founding of the
Alternative Investment Market to provide low cost capital market access for growing firms.
The New Labour government (1997) introduced a range of additional policies in the 1998
Competitiveness White Paper drawing on New Economy ideas that competition was increasingly
driven by knowledge and intangible assets that cannot be put up as collateral for loans,
creating an emphasis on equity investments (Bank of England, 1996; Storey and Tether, 1998).
As a result, the White Paper announced the formation of a £270m Enterprise Fund to address
‘market failure in the provision of finance in amounts below £500,000 for SMEs with growth
aspirations’ (DTI, 1999, paragraph 1.11) through the SFLGS, Regional Enterprise Funds, a UK
High Tech Fund of Funds, and an Early Growth Fund.
The funds had three objectives: to increase equity finance for SMEs; to ensure all nine English
regions had access to local small scale equity investors; and, to ‘demonstrate to investors that
robust returns can be made by funds investing in the equity gap’ (Mason and Harrison,
2003:856). The market weakness these funds targeted could be addressed by either a)
subsidising management costs, b) providing guarantees, or c) co-investing to attract other
investors. Because subsidies weaken fund managers’ exposure to poor investment choices
and guarantees nullify the aim of demonstrating that money can be made in the equity gap
(Mason and Harrison, 2003, 857) an equity enhancement structure was used (OECD, 2004).
Returns to the public investors were either capped and/or ‘subordinated’ to bare the first loss
(DTI, 1999, para 2.5). The performance of these hybrid funds is evaluated in the next section.
Year
1981
1981
Scheme
Small Firm Loan
Guarantee Scheme
(SFLGS)
Business Start up
Scheme (BSS)
Summary
A loan guarantee scheme which has provided over 100,000
guarantees of almost £4 billion to over 90,000 eligible
businesses.
An external equity funding support scheme that was over
complicated and liable to tax avoidance abuses.
13 This coincided with David Birch’s suggestion that 80% of net new jobs creation in the United States
was by small firms. Later research found that most of the small establishments that generated the
majority of new jobs were owned by large firms (Arington and Odel, 1982), and that most of the jobs
created by small firms quickly disappear (Brown, Hamilton and Medoff, 1990). Today academics are
more sceptical about public policy that supports small firms’ market entry and suggest that policy
should focus on the small minority of high impact firms (Storey, 1998; see Santarelli and Vivarelli,
2007).
8
1983
1990s
1993
1995
2002
2002
2003
Business Expansion
Scheme (BES)
Follow up scheme that provided tax relief on investments in
unquoted growth oriented companies. Many investments
went into low risk, asset-backed enterprises, particularly
after 1988 when investments in private rented housing were
permitted.
Enterprise Investment EIS provides small, higher risk unquoted companies with
Scheme (EIS),
access to external growth capital through tax relief-based
incentives for private investors (including business angels).14
Venture Capital Trust Investment scheme for retail investors to invest indirectly in
(VCT)
a portfolio of companies through a professionally managed
fund. VCTs qualify for tax breaks and help channel
investment and liquidity into the AIM market.15
Alternative Investment A stock market focused on supporting smaller firms. With
Market (AIM)
EIS and VCT, AIM provides an implicit funding escalator
going from a few hundred thousand pounds (EIS), to over £1
million (VCT), to tens of millions of pounds (AIM).
Regional Venture
Funds for each of the nine English regions run by private VC
Capital Fund (RVCF)
general partnerships making commercial investments below
£500k (later £660k) with initial investments of up to £250k. By
2006 £74.4m of public finance was committed to RVCFs
which had ~£250m funds under management (NAO,
2006:24).16 Each fund typically raised 50% of its money from
the private sector, 30% from the UK State and 20% from the
European Investment Fund.
University Challenge
19 funds, co-funded by the government, the Wellcome Trust,
Funds (UCFs)
the Gatsby Foundation and universities to provide proof-oftechnology and proof-of-market funding for the
commercialisation of academic research, covering more than
50 institutions.17
High Technology Fund Fund of fund to for existing technology focused VC funds.
of Funds (HTFoF)
Aimed to demonstrate that commercial returns were possible
from investing in technology. Government investment of £20
14 This involves 20% tax relief up to £500k per year, a capital gains deferral at 40% on investment held
for at least three years, and income tax relief on losses. Over time the minimum holding time declined
from five to three years (April 2000) and the limits on investment have declined from £150k in 2000, to
£200k in 2004, to £400k in 2006 and £500k in 2008.
15 VCTs can invest up to £1m/year in a qualifying company and tax relief is given if the shares are
held for 3 years (previously 5 years). To overcome the £1m investment constraint VCT managers
often run a series of VCTs. The 2006 Finance Act tightened the range of allowable investments, and
since then investments have declined.
16 In 2004-05, £20 million of Government expenditure supported investment in 61 firms and by the
end of 2005, 218 firms had been supported (ibid).
17 15 seed funds were set up in 1999 (£45m) and four more in 2001 (£15m). The funds were small and
varied in their investing approach and eventually, the replaced by the University Higher Education
Innovation Fund (HEIF).
9
2003
2003
2003
million was used to raise £106 million from institutional
investors.
Early Growth Funds
Provides small amounts of equity finance based on angel co(EGFs)
investment, employing a quasi-equity approach, linked to
business support to enhance the recipient firms’ chances of
success. Originally established to ensure every English region
has access to early growth funding of up to £100,000 per
firm.18 Problems with the regional focus of the RVCFs, meant
that they evolved into a mixture of regional and national
funds.
Scottish Co-Investment Co-investment fund that invests alongside approved
Fund
investment partners, (mainly angel groups) set up after the
2000 technology crash and supported by the European
Regional Development Fund. Any business that the
investment partner has invested in that meets the scheme’s
eligibility rules can raise matching funds up to £1m. To date
the fund has made approximately 300 investments, investing
£45 million alongside £95million from the private sector.
Enterprise Capital
SBIC-type scheme to increase the flow of capital to growthFunds
orientated businesses seeking up to £2 million. The funds
modify private investors’ (regulated fund managers’ and
business angel syndicates’) risk-reward profiles and reduce
the amount of capital needed to establish a fund by covering
up to two thirds of the capital. Potential moral hazard issues
are addressed by providing no downside protection with
government money acting as a loan that is repaid first, with
4.5% interest.
Econometric Analysis
Given the importance of VC funds on economic performance in the US, it is important to
understand how hybrid funds have performed and what lessons can be learned about their
future design. This section presents an econometric analysis of the effects of the Enterprise
Capital Funds (ECF); Early Grow Funds (EGF); Regional Venture Capital Funds (RVCF);
Scottish Enterprise Funds (SEF); University Challenge Funds (UCF); Welsh Hybrid Equity
Funds (WF) that is designed to (i) compare the performance of funded firms against the
performance of similarly matched unfunded firms, (ii) to compare the performance of funded
firms dynamically, i.e., before and after, they received their initial investment, to (iii) quantify
the effects of key variables including and controls on performance, and (iv) help discriminate
between rival theoretical approaches to hybrid fund design.
18 The first fund started in October 2002, by 2005 £5.3m had been invested in 65 firms and by the end
of 2005, 107 small firms had been supported (NAO, 2006:25). By 2006 seven operational funds had
invested nearly £16m of public money matched by ~£43.5m of private money into 136 firms.
10
As the previous sections have shown there are a range of different ways of framing the VC
problem. The capabilities approach stresses scale, human capital and institutional setups (i.e.
effective exit markets), while agency based approaches explore how VC funds are structured
to address moral hazards and uncertainties. These theoretical approaches can be positioned
within the current theory of the firm (Nightingale, 2008), and are contingently applicable. No
agency theorist considers exit markets unimportant, but they are interested in different
questions in situations when such markets are in place. Similarly, capability theorists
recognise the serious moral hazard problems in equity investment, but are interested in
different questions and the pre-conditions for such problems to arise. When institutional
structures are functioning well, as is arguably the case in the US, their analysis can be put to
one side. However, in the UK, it is an open question if they are functioning well.
The different assumptions of these theoretical approaches generate different practical ‘policy
models’. A pure market failure approach, for example, that assumed that effectively
functioning institutions and labour markets were in place would focus on informational
failures in micro-capital markets and suggest these could be addressed by increasing the
amount of money being invested. This, in turn, would lead to the suggestion, common in the
policy justifications, that simply providing additional finance should be enough to solve the
problem. This might be formulated as a hypothesis:
H1: The UK suffers from a pure supply-side market failure and providing funding will
lead to US style VC portfolio performance. In capital intensive industries funding should
generate a strong immediate effect.
An alternative approach might be that there is no failure in the capital markets, because they
perform far more like markets in theory than markets for human capital, managerial and VC
talent, technology or innovation. As a result, the policy problem is unlikely to relate to a lack
of funding, as in a well functioning financial market investors do not leave profitable
investment opportunities lying around for long. Under this interpretation, the problem in the
UK is largely one of a lack of decent firms worth investing in.
H2: The UK suffers from a pure demand side problem in a lack of decent firms worth
investing in, and therefore providing additional supply side funding will have no impact on
the performance of funded firms compared to a matched control.
A third approach would be to suggest that treating supply or demand in isolation is
misleading and we should focus on the equilibrium conditions where they meet, and keep
‘market failure’ for situations where conditions are not in place for the market to clear, which
might be caused by a supply or demand shortfall, or, a short-fall in both, and/or problems in
linking small them together.
H3: The UK suffers from a ‘thin market’ in early stage equity finance, and as a
consequence simply providing funding will have a limited (if any) impact on firm
performance. However, this does not imply that there is no funding problem; only that there
are other problems that also need to be addressed. These will reveal themselves in initially
small effects, AND those effects should increase through time as funding allows firms to
11
overcome growth constraints (for example, by attracting new managerial talent), AND those
effects should differ between capital and labour intensive industries.
Differentiating between these alternatives is non-trivial. As with any evaluation there are
major problems in assessing the relationship between policies and outcomes as not all
observed change can be attributed to policy interventions. There is therefore a need to
estimate the counterfactual outputs that would have occurred without the intervention to
understand how much of the observed output has been caused by the intervention (Smith,
2000). With the available data we observe (a) the sample of firms that were treated (i.e.
funded), after treatment; and (b) a sample of firms that were untreated. However, to
understand the effect of the policy, one needs to compare (a) the sample of treated firms, after
treatment against (c) the same sample of treated firms under counterfactual conditions in
which they had they not been treated (which is unobserved).
In an experimental situation with random sampling substituting (b) the untreated firms for (c)
the counterfactual treated firms had they not been treated, is not a problem as they are
randomly selected. However, in real life (b) and (c) cannot be treated as equal as firms that get
VC funding of any kind are atypical (i.e. they grow much faster, etc). Using (c) for (b) would
therefore over-estimate the effect of the policy.
Evaluation therefore has to address methodological problems in linking inputs (i.e. the policy
intervention (public support for VC) measured in financial and opportunity costs) to outputs
(i.e. the change associated with the input) to outcomes (i.e. the output minus the
counterfactual change that would have happened anyway with the inputs) to impacts (the
long term effect across the wider economy of the short term outcome).
The jump from ‘outputs to outcomes’ is non-trivial because of selection effects and difficulties
of defining the appropriate counterfactual scenarios. Similarly, the jump from outcomes to
impacts is non-trivial because of crowding in and crowding out problems which can make it
incorrect to assume that a given input of public funds will lead to a proportional increase in
overall funding. Investment projects are parts of portfolios and additional money might turn
unprofitable projects into profitable ones, speed up projects, reduce fixed costs, and generate
spill-overs, all of which will stimulate future innovation and create crowding-in effects that
lead evaluators to under-estimate the impact of policy (Crespi, 2006).
These need to be balanced by substitution effects as when public policy biases investments
towards fundable projects that would have been funded anyway, leading to a reduction in
total funding and evaluations that over-estimate the impact of policy. This can be seen with
R&D subsidies (Gonzalez and Pazo, 2004 for Spain, and Lach 2002, for reductions in overall
R&D from subsidies due to constraints on human resources in Israel). Since there is little
evidence that such subsidies lead firms to undertake new projects (David, 2000), this may be
the case with hybrid-VC subsidies. Mason and Harrison (2001), for example, suggested the
exclusive focus in UK policy on the supply side failed to address the possibility that the lack of
investment arose from a limited number of investment opportunities. As a consequence,
subsidised funding ran the risk of driving existing private sector VC funds out of the market.
12
1.4 Inputs and Outputs
The policy inputs and outputs are taken from the full sample of all 782 firms backed by hybrid
venture capital schemes in the UK. The initial pilot sample was taken from the Library House
data-base (LH), a commercial dataset of UK VC investments. While this is the most commonly
used dataset in UK VC research, when the data was hand cleaned and checked we found that
it was poor quality (the firm supplying the data subsequently went out of business).19 The data
was improved by cross checking it against the Venture Expert dataset, the accounts of the
hybrid funds, accounts of funded firms and public announcements by the funds. A second
round of analysis has now been undertaken based on the full dataset of all funded firms
provided by the UK Government Department of Business Innovation and Skills. Crosschecked against this initial sample suggests coverage is good.
Output (and additional input) data were taken from financial information taken from the
FAME database that provides standard accountancy variables taken from firms’ accounts. The
inputs analysed include age, size, sector, SIC code, region and funding type, while outputs are
measured by standard accountancy variables including: trading performance (profitability
and sales); capital structure (fixed assets, capital formation); factor utilisation (labour
productivity); and survival. These were analysed as real, per capita and dynamic effects, as
appropriate.
To address omitted variable problems, multi-collinearity and other econometric problems we
used a panel dataset approach. We also gathered a panel of data on a control sample that was
matched to the treated firms by sector (measured by 1 and 4 digit Standard Industrial
Classification codes) and by the date of incorporation (which matched age and employment
levels). Approximately 10 firms were taken as the control sample for each firm in the treated
sample. The numbers are not exact as small modifications were made to ensure representative
regional coverage. The 10:1 control-to-treatment sample ratio was chosen to ensure a
sufficiently large number of firms could be analysed to make the results statistically
meaningful (i.e. approximately 400 observations or higher). In the second round of analysis
using the full BIS dataset, the controls were taken from the Office of National Statistics data,
which is higher quality and comprehensive, but only provides data on employee numbers and
sales. 88,000 firms were included.
In the initial analysis, reported here, the number of observations is often lower than the total
sample size because of missing data problems that plague analysis of small firm finance.
Previous sensitivity analysis suggested that missing data in the FAME dataset does not
introduce systemic bias (Cowling, et al 2008). The treated firms and controls together
generated a rich dataset of 7,741 companies tracked over a maximum of thirteen years (1995 to
For example, when we compared LH to the SPRU UK biotech dataset, which we have
strong confidence in, we found that it only captured just over half the data that should be
there. This highlights the importance of checking commercial databases and leads us to
question the extent to which there is a major selection bias in previous research and public
policy that has drawn on LH.
19
13
2008). The data starts before the first scheme was introduced in order to pick up the behaviour
of firms before they received investment in the econometric modelling. Due to the higher
prevalence of firms being born at, or around, the point of their first supported external equity
investment, the majority of the data is concentrated between the years 2003-2008. The average
age of firms is 4.1 years, the mean total investment is £3.5 million, which reflects a strong skew
as the median total investment is £0.7m.
There are some caveats that should be borne in mind when considering sources of potential
bias in the data. Because of the datasets used, many potentially important firm characteristics
are not available for inclusion, such as the quality and experience of the entrepreneurs and
management teams, and how they might dynamically change through time. However, the
data does have some benefits. It is unique and uses recorded performance data which is often
superior to survey based, subjectively reported measures that suffers from Common Method
biases. It permits the examination of the absolute and relative effects of scheme funding across
a reasonable number of observations through time. Moreover, because the random effects
methods analyses performance changes through time at the firm level, modelled in a
multivariate way across the controls, as firms move from being unfunded at t0 to being
funded at t1. This allows a degree of control over firm specific heterogeneity.
Outputs to Outcomes
When moving from outputs to outcomes the counterfactual scenario problem, (i.e. the
difference between the observed untreated sample of firms in our data, and the unobserved
sample of treated firms had they not been treated) was addressed using a number of methods.
The issue is how to avoid cross sectional heterogeneity biasing estimates, through both direct
effects (firms differ) and selection effects (funded firms differ from unfunded firms).
The most common approach is to use a Fixed Effects estimator in which a dummy is used for
each case, and the observations for each case are subtracted from the average for each case.
This approach has major drawbacks. Firstly, since there are some 782 firms, with an average
age of 4.1 years, loosing a year to averaging dramatically reduces the amount of data we can
work with. Second, subtracting time-invariant variables from their average across time makes
them all zero, which means that it is impossible to estimate their coefficients. Thus, such an
approach prevents us exploring very important elements of behaviour. Thirdly, taking the
average as a model, is an extremely simplistic approach, particularly with such short series of
data per firm, and particularly when addressing a dynamic process such as firm development
after VC funding.
A much richer approach involves using a multivariate approach, rather than an average, by
drawing on the large amount of data in the control sample. This is what Dynamic and
Random Effects models do. Dynamic models can not be used here because of the limited
length of data for each firm (average age 4.1 years). Instead we use a Random Effects model.
This allows us to draw on information from a large panel dataset of 7,741 firms rather than
just the 782 treated firms (10.5% of total) and control for various spurious correlations such as
performance changes because firms are in particular sectors, or are particular sizes, or at
particular investment stages.
14
Random Effects models can be biased when there is correlation between the error term and an
explanatory variable – for example if firm performance was being regressed on receiving
funding, and a missing variable – firm quality – effects performance and likelihood of getting
funding, then funding and performance are likely to be correlated, which will bias
estimations. To address this we use the Random Effects model to pick up the dynamic impact
of receiving funding on firm performance through time. To double check the value of this
approach we cross check the coefficients against a Fixed Effect model and find relatively
strong agreement see table 1. Full details of the models and their sensitivity analysis can be
found in Cowling et al, (2008). As the results section will show, while the difference between
funded and unfunded firms might have been a major concern with high performance private
funds or funds in the US, the small differences between funded and unfunded firms suggests
this is not a major concern.
Outcomes to Impacts
In assessing the shift from outcomes to impacts the research is constrained by the lack of
previous qualitative research on the mechanisms through which such changes might take
place in a UK setting (which is potentially very different from the US, but without such
research we cannot be sure). The previous academic literature suggests that impacts are likely
to be influenced by human capacity building, within funds, government, advisors and in
particular serial entrepreneurs, improved network building, scale economies, signalling and
improved liquidity in exit markets (Nightingale, et al 2009). The consensus, at least in the US,
is that these are likely to lead to increasing returns and crowding in (Lerner, 2010). Interviews
conducted as part of this research project found reasonably widespread belief that all these
positive effects had been found in the UK. However, without further research we are unable to
say what the exact mechanisms are, and therefore have great difficulty quantifying or
estimating them.
On the other hand, crowding out of private VC investment could be a potential problem with
poorly designed schemes. However, in this instance the conditional linking of public money to
private money and the specific early stage focus of the schemes in activities poorly covered by
the private sector, suggests that the schemes are not likely to have ‘crowded out’ private
money, particularly because of an almost total lack of private VC investment in this range.
A potentially more important impact assessment problem is that estimates of both outcomes
and impacts need to consider that the differences in performance between funded and
unfunded firms can take several years to become apparent even using large datasets and
sophisticated econometric methods. As a result, the analysis is more informative about
policies the further back in time we look. Ideally investment policies require a decade or more
of data before they can be authoritatively evaluated, which is unrealistic given the pressures of
accountability on government. This means, unfortunately, that there is a trade off in any
evaluation between what can be said about the long term impact of an extant policy and the
current relevance of information about older and sometimes terminated schemes. Thus, the
analysis of more recent schemes is likely to present a more negative picture than might be
expected when they have completed the investment cycle. In VC funding, the ‘J Curve’ effect
means that negative performance results occur before the returns from realised investments
improve fund performance (Burgel, 2000).
15
Results: General Capacity Building
One aim of the schemes is to improve the resources that firms have at their disposal, such
as capital for investment (proxied by net total assets per capita) or more, higher quality
employees. While we lack data on employee quality, the data shows that funded firms
were characterised by a one-off upward shift in both employment and capitalisation. This
effect can be quantified and we find that the 782 recipient firms generated an additional
1,407 jobs (or 1.8 extra jobs per firm) which is better than the EIS and VCT schemes.20
Similarly, funded firms have, on average, £98,455.50 greater capitalisation than untreated
firms. 21
It is possible to explore this behaviour in more fine grained analysis, using a growth
specification model to investigate the time dynamics of this employment effect. This
generates the curvi-linear relationship displayed in figure 1. Employment growth is more
rapid in the years immediately after investment with the majority of the employment
effect achieved by the seventh year. This may reflect the weaker performance of earlier
schemes. The size of the employment effect is relatively small, suggesting that these
schemes are an expensive means of job creation.22
Fig 1: The proportional impact of funding on changes in employment over time since
investment
20 The ‘treatment’ variable has a highly statistically significant and positive impact of employment
(ln) with a coefficient of 0.587 (z-stat = 5.81, P>z=0.0001). The equivalent coefficients are 0.35 for EIS
and 0.65 for VCT using similar techniques.
21 Our preferred, random effects model finds that the ‘treatment’ variable is highly statistically
significant and positive with a coefficient of 0.888 (z-stat = 4.95, P>z=0.0001).
22 The analysis also found that a number of controls had a statistically significant impact on capacitybuilding. Company size was negatively related to capital accumulation (but not employment),
manufacturing and construction firms had higher employment levels, and university spin-outs had
both lower capitalisation (coefficient - 2.42, z-stat=-4.57) and employment even when we control for
age, sector, size and high-tech status. We also find that high tech firms have higher net total assets per
capita (coefficient .38).
.15
.1
.05
0
EmploymentChange
.2
.25
16
0
2
4
6
yearsincefunding
8
10
Profitability
Another aim of the schemes was to illustrate that investors could achieve commercial
returns. While valuation data on privately held firms is difficult to obtain and information
on the returns that funds have generated cannot be obtained until the funds have closed,
we can roughly proxy fund performance by portfolio firm profitability. If the schemes
were illustrating very strong returns we would expect to see disproportionately higher
profits and profit margins among funded firms compared to the untreated control sample.
On average, however, the schemes have little impact on profit margins which are not
statistically significantly different. In a previous evaluation EIS backed firms were found
to have decreased profit margins (coefficient -5.18), while no effect was found for VCT
funded firms. The analysis does however identify an upward shift in (real) operating
profits in the region of £43,086 for the average funded firm.23 This compares positively
with the £1400 found for EIS funded firms and the zero effect found for VCT backed firms
in Cowling et al, (2008) using the same methods. However, this is substantially below the
performance levels expected by commercial VC and are not compatible with the view that
the simple provision of equity funding will generate US style VC performance. This leads
us to reject Hypothesis 1. It is not compatible with the suggestion that there is (or was) a
large untapped source of high potential firms in the UK that are only constrained by a lack
of funds at the levels funded by the schemes.
Average effects can be misleading, however. When we estimate profit margins using a
using a more nuanced treatment variable, which allows for differential effects as time
elapses, we reveal an interesting time dynamic in the ‘U shaped’ relationship (see figure
23 Our preferred random effects model finds that the ‘treatment’ variable is positive and within the
bounds of conventional statistical significance with a coefficient of 0.89 (z-stat = 4.96, P>z=0.0001).
2).24 As figure 2 shows, there is a substantial, and immediate, collapse in gross profit
margins by the sampled firms compared to the unfunded firms in the three years after
receiving the initial investment. But, by the fourth year, gross profit margins have levelled
out and by year six there is an equally dramatic increase in profit margins.
This is consistent with firms making a trade off between short-run growth and longer
term profit as they reconfigure and invest in new products, technologies and processes.
Once these are in place, firms can then rebuild their profit margins. Such reconfiguring is
likely to be especially important for new technology based firms where technology life
cycles are short and the need to innovate existing products/services in order to maintain
sales and to ward off competitors is high (Burgel at al, 2004).
These differences between funded and unfunded firms are consistent with funding for
firms seeking to undertake ‘equity style’ behaviour being constrained. As with the
previous models we also found that some controls were statistically significant. In
particular, we find poorer profitability performance for university spin-outs (again) and
larger firms, while service sectors were the most profitable. The size of the coefficient for
university spin outs impact on profit margins was very large and negative (-16.79, p.
0.075).
0
10
20
30
Fig 2: Gross Profit Margins over time since investment.
-10
GrossProfitMargins
17
0
2
4
6
yearsincefunding
8
10
24 This involves using a non-linear approach that incorporates both the natural log of time elapsed
and its square to produce the ‘J curve’ in figure 2. To rule out the possibility that the improved
performance is a ‘blip’ in an otherwise downward trend, we also estimated a cubic term and find that
it is not statistically significant.
18
Labour Productivity
A key economic role of US VC is the allocation of financial and managerial resources to
help grow firms that will have a dramatic impact on productivity in the economy. The
analysis finds that funded firms have higher average labour productivity (mean sales per
employee measured in £’000s) than matched unfunded firms, seen in a one-off, upward
shift in per capita labour productivity. After controlling for other influences, in the
‘typical’ supported firm this would equate to £57,800 (sales per worker) in increased
labour productivity.
This result (coefficient 0.37) is similar to the recent results of the evaluation of the EIS
(coefficient 0.33) and VCT schemes (coefficient 0.74) (Cowling et al, 2008).25 Again, using
more subtle analysis we find the full effect is more nuanced with a decline in labour
productivity immediately post-investment and a rebound around four years later with
strong predicted growth thereafter. Again, such a pattern would support a trade-off
between capacity building and short term sales and profitability by innovative young
firms.
Table 1: Results summary (Random and Fixed Effects Coefficients).
(log) Real Sales (log) Real
per Capita
Operating
Profits per
Capita
(log) Real
Capitalisation
per Capita
(log)
Employment
Profit Margins
RE
FE
RE
FE
RE
FE
RE
RE
Treatment
Variable
0.37*
0.48**
0.56
0.55
0.89**
0.94*** 0.59*** 0.59*** -0.55
(log) Years
since
Investment
-0.87***
-1.07***
-0.33
-0.07
-23.43***
Years since
investment
squared
52.75*
73.91**
85.31*
44.35
1976.37***
(log) Size
0.03
-0.13*** -0.22*** -0.45*** -0.71*** -0.81***
FE
-4.26***
25 Our preferred random effects model finds the ‘treatment’ variable is statistically significant (at the
10 per cent level) and positive with a coefficient of 0.368 (z-stat = 1.90, P>z=0.057).
FE
-1.86
-5.46***
19
1.5 Discussion: Designing Policy to Support Venture Capital
Two findings stand out in our analysis (table 1). First, while being funded changes behaviour
and performance, the size of the effect to date is modest. Subject to the caveats noted earlier,
companies that are recipients of funding from hybrid funding schemes do not yet exhibit significantly
better performance. This strongly suggests that the UK does not posses an untapped resource of
high potential firms that are only held back by a lack of funding.
Support for equity investment has been justified in terms of a supply-side market failure in the
provision of finance leading to a policy assumption that if funding is provided then high
potential firms will achieve their full potential. We have tested this assumption and found it to
be largely false. This does not mean that funding is unproblematic, only that the solutions on
offer have not (to date) produced the disproportionately higher performance firms seen in US
VCs’ portfolios. Whatever problems UK firms have, they are more complex than a lack of
funding alone. We therefore reject hypothesis 1.
The evidence in fact leads us to suggest that the UK suffers from a very substantial demand
side problem, due to the limited number of high potential firms on offer to investors. There is
other evidence to support this interpretation. At the time the funds were established such
business Angel investors were complaining of too much money and not enough good firms to
invest in at around the £200,000 mark (Mason and Harrison, 2003:663). Furthermore the
RVCFs only invested in 48 of the first 2,680 applications they received, indicating major
problems with the quality of deal-flow attracted by the programmes (Murray, 2008; Mason
and Harrison 2003). Some funds even had to return funds as they were unable to find suitable
investment opportunities (cite). Even today many of the major VC funds operating from the
UK invest on a pan-European basis, and presumably would invest in the UK if the
opportunities were there.
Lastly, the absolutely dire performance of university spin-outs in our sample suggests that
there are major problems there. This may reflect the operational focus of UCFs to create a
cultural change in university faculty, and the selection effect should caution against
generalising to the entire population of UK university spin outs without further evidence, but
the initial results are worryingly poor, and consistent with the substantially lower levels of
public investment in the university sector in the UK compared to the US (roughly half). While
the UK science system is extremely productive, productivity data is misleading here as this
partly reflects the lower levels of inputs. Furthermore, this is entirely consistent with the
theoretical suggestions of Dosi et al (2005), that the European approach of attempting to
develop technology on the cheap in universities by academics with poor understanding of
either markets or management, is a poor approach compared to the US model of funding high
quality academic research in universities and funding commercial development of technology
in firms. This problem is increasingly recognised (HMT, SBS, 2003:6, 33).
The second key finding in our analysis is the repeated encouraging evidence of the kinds of
growth-oriented behaviour that the schemes were intended to stimulate. The funded firms
engage in disruptive (asset development) behaviour that lowers short term performance while
generating longer term improvements. This suggests that the supply of risk equity is
constrained in some way.
20
Two further pieces of evidence support the suggestions that there is a supply side problem.
First, there is a threshold effect. Below a total investment threshold of approximately £1m little
effect is observed. Firms may be using these smaller sums of money as operating capital.
Conversely, this may reflect that hybrid funds are competing with Business Angels at the
levels where there is limited evidence of an existing lack of funding. However, once funding
moves beyond the levels funded by BAs, then effects are found. 26 Secondly, large numbers of
funded firms were judged by professional investors to be good enough to received further
funding from private sector VC funds. Even though their average age is only 4.1 years, roughly
a quarter of the treated firms received a third round of funding (many hybrid schemes can
only invest in the first two rounds).27 This is substantially higher than would be expected if
these were typical firms or there were no funding problem. It appears that these funds were
finding firms with potential and addressing a gap in the market. This leads us to reject the
pure demand-side hypothesis 2.
Percentage of portfolio firms receiving different numbers of funding rounds28
60
50
40
30
20
10
0
One
Two
Three
Four
Five
Six
Together these findings suggest that rather than only a supply or demand side problem, the
UK suffers from a ‘thin market’ in the provision of specialised venture capital funding and
managerial expertise. ‘Thin markets’ occur when small numbers of high potential firms and
small numbers of investors with the skills to help them grow find it difficult to find one
another without incurring unacceptable transaction and/or search costs. As a result, firms
26 When the funds were formed Mason and Harrison (2003) raised concerns that the funding gap had
been misidentified because of misleading statistics that ignored investments by Business Angels (BA).
Most BAs invest less than £100k per firm, focused on the seed and start up phases and regularly form
organised groups of between 10 and 100 investors to invest over £250k per deal. More than one in ten
of the deals funded through BA Networks in 2000-2001 exceeded £250k (Mason, 2002: Mason and
Harrison, 2003:863). As a consequence, the gap in funding was less likely to be under £250k, where
informal but poorly co-ordinated investments by BAs operate, than between £250k and £1 million.
27 Care must be taken in interpreting this data as it may reflect that hybrid funds were unable to
negotiate a good price for their sale, and that VCs were picking up investment opportunities on the
cheap. Even if this were the case, the issue is that they were prepared to spend any money at all.
28 It is important to note that the data used in this histogram is skewed to the portfolio firms of more
recent funds. It would be expected that more firms receive multiple rounds of finance over the life of
the fund.
21
complain about difficulties in getting funding while investors bemoan the difficulties in
finding attractive portfolio firms. Both are telling the truth because thin markets make it
difficult for the supply and demand for finance to match thereby reducing overall levels of
investment and investment performance.
Partly this reflects constraints on the development of human capital in the UK industry. This
could be seen when the RVCFs were formed when only 17 prospective managers, (two of
whom withdrew), applied for the nine regional funds causing the Regional Development
Agencies to complain about a lack of competition, particularly when two VCs - WM
Enterprise and Yorkshire Fund Managers - were selected to manage five of the nine funds
(Mason and Harrison, 2003:862). These human capital constrains can also be seen in the
highest performing UK funds which are often oversubscribed by their investors. The constraint
on their expansion is not funding, as their investors would like to invest more, but limited human
capital in the industry and the managerial problems of increasing the size of the partnerships.
A thin market also reflects the challenges of co-ordination between entrepreneurs and
investors, constraints that prevent firms growing fast enough to generate the returns investors
require and limited opportunities for funds to exit.
Within such a system, simply adding more money is unlikely to be sufficient as an informed
policy response. Without a steady stream of high potential ‘investment ready’ firms, skilled
equity investors able to grow firms into high value assets, etc., additional investment is likely
to be allocated to lower quality firms, and without the infrastructure high impact firms need
even high quality firms will have difficulty growing. Similarly increasing the ‘demand side’
alone, for example, by producing higher potential start ups, is unlikely to have an economic
impact unless funds, advisors and effective exit markets are available.
1.5.1 Thin markets and business models
Evidence for a thin market can be seen in the way it constrains the business models that VCs
can adopt. When economic advantage comes from the scale and profitability of investment
(funds under management) funds tend to adopt business models that either increase the size
of investments for a given level of profitability or increase profitability for a given size of
investment.29 Thin markets severely constrain the ability of funds to follow a profitability
focused business models. As a result, VCs default to the increasing their total funds under
management with a corresponding increase in their average investment size over time. This
leads to ‘style drift’ away from early stage investments towards larger, later stage,
management buyout related investments (Sohl, 2003; Bygraves and Timmons, 1992). VC firms
follow this strategy move out of early stage investing because only a small number of
exceptional investors can make a profit there.
29 Institutional investors prefer funds with well established and clear business models and corporate
governance structures. These are often distorted by ‘multi-play’ investment strategies.
22
However, in the United States where the markets are ‘thicker’ the majority of successful seed
activity is undertaken by integrated VC funds, each managing in excess of a billion dollars
suggesting they can get bigger without moving out of early stage investing. Instead they focus
increasing the profitability of investments and use the scale of their funds to make repeated
follow-on investments across their portfolios in an attempt to make a number of extremely
high return investments (often producing returns 20 or 30 times their original investment)
(Dimov and Murray, 2006). This requires technically and commercially well-informed fund
managers to identify major technological opportunities, create new assets, and actively build
them into outstanding portfolio firms that can be sold for substantial profit. This business
model is higher risk, more fragile, and requires the entire financing system to work effectively.
When, as is arguable the case in the UK, these components are not working effectively, even
fund managers that adopt this model will eventually be forced to back to a lower risk, sizefocused business model.
With hindsight it seems clear that the hybrid funds were too small and too constrained to
follow such a profitability focused business model: the average RVCF was £27m and could
only invest up to £660k, the average EGF just over £5m and could invest up to £200k, the UCFs
just over £3m each and could invest up to £200k, and the ECFs range have an average of £26m.
Such funds can’t make decent sized follow on investments and therefore get diluted in each
subsequent investment round, are forced to make more investments than can be effectively
supported, which hampers their portfolio firms’ development, and can’t accumulate
managerial knowledge to grow higher impact firms.
Moreover, because the funds focused on a single ‘funding gap’ they miss the importance of
multiple gaps. As firms grow, different sized tranches of finance are needed that are not always
willingly funded by the market (Murray, 1994). Early stage funding may resolve initial
funding problems but without providing an effective funding escalator to enable firms to
grow, the end result may simply be to set firms up to fail at a later date, which obviously
constrains the ability of the funds to pursue a profitability focused business model.
1.6 Conclusion
The previous sections highlight that a viable VC industry requires its constituent parts to be
working effectively together for extended periods of time in order to build human capital,
social networks and investor confidence. These inter-related parts include informed
institutional investors; a strong deal flow of high potential portfolio firms; large professional,
and specialised VC funds; a supportive network of professional advisors; and effective and
liquid exit markets. Such an ecosystem is fragile, particularly when subject to major shocks
such as investment bubbles and financial crises. As a result, public intervention has been
historically necessary while the institutions supporting the VC ecosystem grow and learn. If
this occurs effectively markets thicken to the point that high levels of repeated interaction
between VC and high-growth firms build up human capital in the sector, provide a large
enough market for an ecosystem of high quality specialised advisors to develop, and
encouraged more entrepreneurial investment.
23
However, finding ways to make this happen is not easy. The United States, for example, has
taken over 50 years of repeated cycles of experimentation to generate its current forms of
support, which only seem to generate world class VC expertise in a few locations on the East
and West coasts. Many early interventions were of limited value with tax incentives used for
tax avoidance and other schemes subject to partisan political control that diverted resources to
unproductive investments (Lerner, 1999; Hsu and Kenny, 2005). The Small Business
Investment Company scheme, the Advanced Technology Programme and the Small Business
Innovation Research program, which are often seen as exemplars of effective policy in Europe
have all suffered major operational problems which have resulted in either major programme
changes or cancellation.
The difficulties that Americans have had in getting their own support institutions to work
suggest that it is unrealistic to expect US style institutions to be easily transferable elsewhere.
Effective public policy development is path dependent and reflects local conditions and
historical accidents (David, 1985). The US has many unique features that such as a pro-active
industrial policy, levels of public research funding larger than all other G7 countries
combined, risk-receptive securities market regulations, large innovative IT firms in Silicon
Valley and beyond, the world’s best universities, sophisticated technology development
policies that focus on supporting research in universities and technology development in
firms, (rather than the confused focus on university technology development in Europe), a
large number of entrepreneurial and well-informed early stage investors and well developed
entrepreneurial training that make the US VC industry pre-eminent and highly distinctive.
When designing such policy interventions there is now a considerable amount of empirical
evidence that could be drawn on. Unfortunately, public policy in the EU tends to be driven by
theory, and in particular theories of market failures, that were developed for situations where
very different sets of assumptions hold. As a consequence, policy has tended to focus almost
exclusively on supply side constraints on funding, and has overlooked other features of the
VC market outside the US. This treatment of supply without attention to demand would strike
an economist as a rather curious analytical approach. Moreover, its support from small firm
owners should be assessed with caution as they are notorious for presenting false information
about their financial position (to avoid tax). Their self assessments should always be treated
with caution as they are often delusional about their own abilities (Asterbro, 2008) and the
potential of their firms (Shane, 2006; Asterbro and Thompson, 2010). Indeed, Cressy (1996)
infamously showed that while it is possible to find empirical evidence of funding constraints
for UK small firms, when one controls for managerial skill, their statistical effect disappears.
Just because firms have difficulty finding funding does not mean that a market failure exists or
that government intervention is warranted. In a sophisticated, competitive market economy
many firms will enter the market with little chance of success. Most will simply generate
economic ‘churn’ and will either rapidly exit the marketplace or displace similarly marginal
firms (Santarelli and Vivarelli, 2007). As a result, many firms seeking funding will be refused
and many firms that are refused funding will fail. A perceived funding gap could therefore be
the result of investors rationally assessing firms and deciding that they are not worth investing
in given the levels of incurred risk. Government policies that encourage the market entry of
such substandard firms are likely to be a waste of money (Santarelli and Vivarelli, 2007).
24
Justifications for policy intervention that are made in terms of such market failures have other
major problems. Such market failures are presumably the same in the US, so the explanation
does not explain why there is less of a problem there. Nor does it explain why one of the most
competitive financial services sectors in the world (i.e. the UK) seems unable to find ways to
exploit profitable investment opportunities. Nor does it provide any evidence of the extent of
any failure – and it makes a lot of difference to public policy if it is closer to a dollar than a
billion dollars. Lastly, the existence of market failures does not imply anything about
government success, or the ability of government policies to make the situation any better. It is
not clear that public officials can address such objectives easily as they are unlikely to have
well developed abilities to screen firms and address the information asymmetry problems and
moral hazard problems associated with early stage, high tech ventures. Since uncompetitive
subsidised firms may remain in the market at the expense of more competitive entrants, firms
may be supported that do not require public support creating deadweight effects and there is
a potential problem of substitution effects if officials pick ‘winners’ rather than focusing their
attention on the gap between public and private returns, or if political considerations bias the
selection of firms. Once funds are diverted by rent seeking groups, market failure arguments
can provide a useful way of rationalising rent seeking in hindsight.
This paper analysed the performance of a set of schemes that focus on a ‘small but important
minority of innovative, growth orientated business that continue to have difficulties in
attracting funding, particularly between £250,000 and £2 million’ (HMT/SBS, 2003:61).30 When
we analysed the performance of a range of hybrid UK funds the analysis found that the
programmes’ funding had a clear impact on recipients firms’ performance over time
compared to a matched set of controls. The evidence suggests that above a threshold level,
treated firms forgo immediate financial performance while capabilities and assets are built up
to increase future competitiveness. This capability building indicates that government
intervention may be providing firms with the potential to out-perform their matched
unfunded controls firms in future years. The robustness and longevity of the performance
premium will not be able to be fully ascertained for a number of years because currently the
data does not exist.
While the performance results to date may appear modest, it is worth noting that it is common
for independent econometric evaluations of government support schemes to find no tangible
or material positive outcomes. In some instances programmes that have existed for many
years and have consumed considerable resources generate negative impacts. That these hybrid
schemes have shown a positive outcome over a relatively short period should be seen as
substantive grounds for optimism
All the same, the analysis and interviews do suggest that young UK firms face very real
problems in raising equity finance for early stage development. The analysis suggests that
these difficulties are unlikely to be caused purely by a market failure in the supply of capital.
30 On government assumptions, equity investment in this range would be attractive to between 6,000
and 12,000 unfunded firms each year (ibid) which is more than ten times the number of firms that
receive early stage VC funding in the US, an economy nearly ten times the size of the UK.
25
Instead, the analysis suggests there is a thin market in the simultaneous supply of critical
resources including finance, managerial expertise (in both recipient firms and VC funds) and
high-quality, investment-ready firms.
Conceptualising the small-firm equity-finance problem in terms of ‘thin markets’ would
produce a more systemic framework for developing policy that draws attention to the
simultaneity problems associated with building a funding system of many complex
component parts. In doing so, it would need to address both demand and supply side
problems (including issues of managerial expertise), whilst still allowing public policy to be
justified in terms of market failure.
Under such conditions, a key issue relates to the long time that is needed to build up
managerial capabilities in both funds and firms. This suggests a shift in emphasis towards
encouraging firm growth, (and building up the human capital needed to grow high impact
firms in both VC funds and investee firms), rather than firm formation. The performance data
casts severe doubt on the wisdom of unfocused policy to support greater firm foundation
without paying attention to the quality of the firms and the quality of the support they can
receive.
Our analysis provides a number of key lessons for public policy:
1. Hybrid funds should be large, well funded and knowledgeable. This has been
insufficiently recognised by European policy makers. This problem was dramatically
highlighted in the operation of the EU Seed Capital Fund Scheme in the early 1990s, where the
small fund size meant that a number of funds would exhaust their investment capital in less
than five years even if they did not make any investments (Murray, 1998; Mason and Harrison,
2003:862).
2. High quality deal flow and human capital development is essential for ensuring VCs learn
how to operate a profitability-focused business model.
3. Constraining funds can prevent them generate commercial returns. For example,
compelling them to invest in geographically mandated areas hugely reduces the size of the
pool of firms they can invest in, which limits their profitable investments and ability to
specialise. Similarly keeping funds operations within a ‘funding gap’ prevents them
‘following-on’ their investments in high potential portfolio companies through syndicating
with later stage VC funds. This has a negative effect on both the fund – which gets diluted,
and the investee businesses - as a ‘drip feed’ of finance means that entrepreneurial and
managerial time is spent searching for the next round of funding (that may not be available)
rather than growing the business. Policies that fill ‘vertical’ gaps in finance by providing funds
that address different levels of investment create artificial barriers between successive funding
schemes that force growing firms to undertake disruptive and costly changes in their search
for new or additional investors. Policy should focus on improving the flow of funding to high
potential, growth oriented firms (through a ‘funding escalator) as they move from formation
to a successful market exit.
26
Given that the results of this positive behaviour take many years to become apparent, early
evaluations (i.e. less than a decade of comparative data) may therefore produce misleadingly
negative findings. While a more sophisticated modelling exercise using data that will not be
available for many years would be needed to analyse the full impact of these schemes, the
analysis has produced a number of encouraging findings. It is particularly encouraging to note
that because the majority of the data is drawn from the earlier (more constrained) schemes,
one might expect superior performance from more recent schemes in future evaluations.
While there is good reason to be sceptical about supply side market failures in this instance,
the overall assessment of the schemes is positive.
Acknowledgements The authors are grateful to the Engineering and Physical Sciences
Research Council for supporting this research through Grant EP/E037208/1; for financial and
research support from NESTA and the BVCA; and for Financial Support from the ESRC,
NESTA, BIS, and TSB as part of the IRC Distributed Projects Programme.
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