January, 2004
Are Entrepreneurs Eternal Optimists or do they ‘Get Real’?
Stuart Fraser* and Francis Greene
Centre for Small and Medium Sized Enterprises,
Warwick Business School,
The University of Warwick
JEL Classifications: Self-Employment J23; Government Policy, I28; and Discrete
Regression and Qualitative Choice Models, C25.
Abstract
The first contribution of this paper is a model of entrepreneurial learning in which
both optimistic bias and uncertainty diminish with experience. The shape of the
business failure distribution, by experience, is explained by the relative effects of
learning on optimism and uncertainty respectively. The second contribution is an
empirical analysis using British data on optimism spanning the period 1984-1999.
This analysis supports the propositions that entrepreneurs are optimists but that
optimism and uncertainty diminish with experience. Changes in public policy
account for related findings: i) entrepreneurs’ optimism is higher in the 1980s relative
to the 1990s; and ii) uncertainty rose in the 1980s but remained stable in the 1990s.
Acknowledgements: We would like to thank Andrew Burke, Peter Elias, Andrew
Oswald, Simon Parker, David Storey and Mirjam van Praag for their edifying
comments on earlier drafts of this paper which led to many improvements. Thanks
also go to lunchtime seminar participants at the Institute for Employment Research, in
Warwick Business School, and to Marie Screene for her assistance. We are, of
course, solely responsible for all remaining errors.
*Corresponding Author: Stuart Fraser, Centre for Small and Medium Sized
Enterprises, Warwick Business School, The University of Warwick, Coventry CV4
7AL, United Kingdom. Tel: (0) 24 765 22899, Fax: (0) 24 765 23747, email:
Stuart.Fraser@wbs.ac.uk.
1
Are Entrepreneurs Eternal Optimists or do they ‘Get Real’?
Section 1: Introduction
It is often argued that entrepreneurs are a self-selected group of optimists. This is
perhaps unsurprising given the inherent uncertainties in running a business. In this
paper we explore, theoretically and empirically, the notion that both optimism and
uncertainty vary with experience. In particular: i) experience reduces optimistic bias
in expected outcomes; and ii) experience reduces the uncertainty in these outcomes.
To put it succinctly, entrepreneurs ‘get real’ over time. The idea that experience alters
entrepreneurs’ subjective beliefs is not new (see Jovanovic, 1982). It is an issue,
though, that has been largely ignored in previous empirical analyses of selfemployment but which is addressed in this paper.
The implications of optimism are not to be underestimated. It is well known, for
example, that optimists may fall victim to the ‘winner’s curse’ (Thaler, 1988). That
is, they end up with a negative return on their investment since they overestimate the
value of resources. An allocation of resources favouring realists would, accordingly,
increase economic welfare. The welfare loss arising from optimism formed the basis
for Adam Smith’s otherwise uncharacteristic defence of interest ceilings (see Niehans,
1997). Indeed, Smith warned of the prevalence of optimism: ‘The chance of gain is by
every man more or less over-valued, and the chance of loss is by most men
undervalued, and by scarce any man, who is in tolerable health and spirits, valued
more than it is worth’ [Smith, 1805, p. 123]. On the other hand, a key implication of
2
entrepreneurial learning is that the welfare loss from optimism will vanish, over time,
among a given generation of entrepreneurs.1
In more recent times, research in the psychology literature supports Smith’s view that
optimism prevails (Taylor, 1989) whilst realism is more or less confined to depressed
individuals (Alloy and Abrahamson, 1979). Super-optimism among entrepreneurs
follows naturally from other psychology based evidence that optimistic excess is
greatest for events that are seemingly under the person’s control (Zackay, 1984;
McKenna, 1993). In fact, entrepreneurship is such a tempting prospect for many
precisely because they believe that it will afford them a more independent lifestyle
(Blanchflower and Oswald, 1998).
In the economics literature de Meza and Southey (1996) argue that super-optimism
can explain several stylized facts about small business including high start-up failure
rates. Here, we argue that entrepreneurial learning is able to account for the shape of
the business failure distribution by experience. Indeed, this shape is explained by the
relative effects of entrepreneurial learning on optimism and uncertainty respectively.
The speed of decline in optimism determines the early phases of the distribution
whereas it is the rate of decline in uncertainty (equivalently, the rate of increase in
confidence) which determines the tail-shape. Specifically, high start-up failure rates
are explained by a relatively rapid initial decline in optimism. The positive skew in
the distribution, on the other hand, is determined by the rate at which confidence
accumulates – the slower the accumulation the greater the skew. It seems plausible
that the tail-shape has more to do with changes in confidence rather than optimism.
1
However, we may expect optimism to be ‘reborn’ with each new generation of entrepreneurs. Further
developments of this idea include the possibility that the old generation of entrepreneurs mentor
younger generations thereby mitigating the optimistic excesses of the latter group (see also section 3).
3
Optimists, after all, will tend to be forced out of business as soon as their lack of
talent is realised. Confidence, on the other hand, takes time to accumulate; not only
self-confidence but also the confidence of entrepreneurial backers, such as banks,
requires some track-record of success.
The limited empirical evidence seems to support a state of super-optimism among
entrepreneurs. In particular, Arabsheibani et al (2000) have shown that, in the UK
context, the self-employed were more likely to forecast an improvement in their
financial situation, compared to employees, but were also more likely to experience
worse outcomes. However, regarding the prevalence of optimism, the same study
showed that even employees were susceptible to baseless optimism.
Our empirical analysis seeks to develop this limited literature by examining changes
in optimism, experience and self-employment choices in Britain, over the 1980s and
1990s, using data from the British Social Attitudes Survey (BSAS). These data,
uniquely to our knowledge, allow analysis of changes in optimism over two decades.
The principal findings from this analysis are that: i) entrepreneurs are more optimistic
than wage-workers; and ii) experience reduces optimism and its influence on selfemployment choices.
The changing policy backdrop for the analysis explains some important further
findings. In particular, three successive Thatcher administrations of the 1980s (19791983, 1983-1987, 1987-1991) offered public support for the creation of new ventures.
Arguably, this was an era of renewed entrepreneurial optimism (Bannock and
Peacock, 1989). Young people, females and the previously unemployed, with no
previous experience, received support and encouragement to start businesses (Meager
4
et al. 1996). This is echoed in our results where we find that optimism was relatively
high, and uncertainty rose markedly, in the 1980s. In the 1990s however the focus of
policy in the UK switched to enhancing the quality of existing businesses. Arguably,
this policy switch may have helped to inject a dose of realism into entrepreneurial
forecasts. Indeed, we find the level of optimism is lower in the 1990s and uncertainty
is stable which support the predictions of the learning model.
The remainder of this paper is divided along the following lines. In Section 2, we
outline the ways in which enterprise support policy has changed over the last 20
years. In Section 3, we set out a theoretical framework for the analysis of selfemployment, optimism and uncertainty. In Section 4 five propositions are set out for
empirical testing. The principal data sources – the BSAS for 1984-1999 – and selfemployment trends are then presented in section 5. In section 6 we present an
empirical analysis of the relationship between optimism and experience. In section 7
the analysis turns to examining the role of optimism and uncertainty in selfemployment choices using structural heteroscedastic probits. The final section of the
paper discusses the findings and highlights some policy implications.
Section 2: Quantity vs. Quality - Policy Background to the 1980s and 1990s;
Until the 1980s, self-employment in the UK was largely seen as a marginal activity
(Stanworth and Curran, 1973) and the UK small firm sector - as a whole - was seen as
lacking vitality (Bolton, 1971) or vibrancy (Boswell, 1973). Indeed prior to the
1980s, policy initiatives towards the small firm sector were limited. For example,
Beesley and Wilson (1984) showed that there were only two programmes from 19461960. In the 1960s, this increased to thirteen (1961-1970) and then, in the 1970s, on
5
to thirty-three (1971-81). Very many of these schemes sought not to improve access
to self-employment or improve its attractiveness but, instead, to provide employment
subsidies for the unemployed or the young2.
In the 1980s, however, the intensity of policy initiative increased markedly: by 1989
there were 103 public policy initiatives directed at the small firm sector (Curran and
Blackburn, 2000). Given this number is would be unsurprising if there was not some
heterogeneity in terms of objectives. That said, there would appear to be two
discernible public policy thrusts throughout the 1980s. First, there was an attempt to
increase the number of individuals entering self-employment. To support this,
‘enterprise zones’ were created (1981), the Small Firms Loan Guarantee Scheme
(SFLGS) (1981 onwards) and schemes such as the Enterprise Allowance Scheme
(EAS) (1982-1991) were set up. The EAS provided the unemployed (unemployed for
more than 13 weeks – later reduced to 8 weeks) with a payment of £40 a week for a
year. Individuals also had to have access to capital of £1,000, work full-time on the
business, were between 18-59, had to be based in Britain and have an idea that was
suitable for public support (the last requirement was introduced after a massage
parlour was set up in South Wales).
The second public policy thrust in the 1980s was to shift the non-pecuniary career
preferences of individuals (Lawson, 1984). To achieve this, a number of schemes
were set up: the Technical and Vocational Educational Initiative, ‘Compacts’ between
schools and business, the Mini Enterprise in Schools Project, and Youth Training
initiatives (e.g. ‘Training for Enterprise’).
2
For example: Job Creation Programme (October 1975 - December 1977); the Recruitment Subsidy
for School Leavers (October 1975 – September 1976); the Work Experience Programme (September
1976 – April 1978); Youth Employment Subsidy (October 1976 – March 1978); Small Firms
6
In the 1980s, however, enterprise support was not confined solely to public policy.
Indeed, what is particularly noteworthy about the period was that it saw the rise of
corporate responsibility in the UK. This led to the creation of schemes such as Shell
LiveWIRE, the Prince’s Youth Business Trust (now the Prince’s Trust) and Business
in the Community: all of which sought to convert individuals into self-employment
(IMS, 1989, 1987; Pilkington, 1984). It is also hard to ignore cultural influences (e.g.
films such as Wall Street and comic characters such as ‘Loads-a-Money’ and ‘DelBoy’).
By the early 1990s, however, it would appear that there was a discernable shift in
policy towards self-employment. Partly, this may be because Thatcher left office in
1991. Another reason may have been that the UK’s recession of the early 1990s spelt
the end of the euphoric optimism of the late 1980s. Government policy itself towards
self-employment also changed in three important ways in the 1990s. First, in the
early part of the decade the government moved away from delivering training through
a centralised mechanism (the Manpower Services Commission) to the provision of
localised support (Business Links and Training and Enterprise Councils) (Deakin and
Edwards, 1993). Second, the government also sought to focus upon improving the
quality of existing businesses rather than increasing the quantity of businesses through
the creation of Business Links (House of Commons Trade and Industry Select
Committee, 1996). Third, the government also changed the way it supported
ventures. Financial support provided by schemes such as the SFLGS are now in the
minority. Attention, instead, has instead shifted to providing support to improve the
Employment Subsidy (July 1977 – March 1980); the Special Temporary Employment Programme
(April 1978 – March 1981); and the Youth Opportunities Scheme (April 1978 – September 1983).
7
capabilities and skills of individuals (Straw and Blair, 1991) through changes to
education and training (see DTI, 1994, 1996, 1998).
In summary, there were two observable policy imperatives in the 1980s. First, there
was an attempt to increase the quantity of existing businesses in the UK economy.
The second imperative was to improve the supply of potential entrants into selfemployment. In terms of the 1990s, it is argued that there was a change in focus
towards enterprise support. Hence, whilst support was still concentrated upon
improving the appeal of self-employment, many of the actual policies in the 1990s
were, instead, concentrated upon improving the quality of existing businesses.
Section 3: Theoretical framework for optimism and self-employment choices
The principal aim in this section is to develop the dynamics of entrepreneurial
optimism and uncertainty laying the foundations for the empirical analysis later in this
paper. To emphasize, the focus here is on the dynamics of optimism and not, for
example, the relationship between optimism and finance (see de Meza and Southey,
1996; Manove and Padilla, 1999).
Our first assumption is that individuals have a straight choice between
entrepreneurship (e) and wage-work (w) (in contrast, see Parker, 1996). We assume a
framework in which talent is observed with noise and individuals differ according to
their subjective beliefs about their entrepreneurial talent (Jovanovic, 1982; Frank,
1988). In such a setting, individuals with higher expectations are more likely to
become entrepreneurs. As in Jovanovic (1982), the entrepreneur, through experience,
8
learns to forecast talent with increasing confidence. Unlike Jovanovic, however, we
allow for the possibility of optimistic bias in expectations.
Entrepreneurial income ( y e ) is stochastic embodying the entrepreneur’s uncertainty
about their talent. In particular, we assume this income deviates from a representative
level ( y e ) according to the level of noisy talent:
y e = (θ + ε ) y e
(1)
where θ is actual talent and ε is luck. Assuming θ is non-negative, but that the
support of the distribution for ε is the real line, implies negative self-employed
incomes are possible. This is in accord with empirical evidence on self-employed
incomes (Parker, 1997). 3
The entrepreneur makes an expectation of talent on the basis of accumulated business
experience in order to form an opinion about prospects. Assuming a sequence of luck
with distributions, ε i ~ NIID (0, σ 2 ), i = 1, Κ k , then talent expectations are unbiased.
In that case prospects are given by E [y e Ω(k )] = θy e where {θ + ε i }i =1 ≡ Ω(k ) is the
k
information on talent available to an entrepreneur with k years of experience.
Assuming also that individuals maximize their subjective expected utilities, then a
risk neutral individual will choose entrepreneurship over wage-work, if and only if,
E [u e − u w Ω(k )] = y e − y w + Xβ > 0,
(2)
where u i , i = e, w are utilities in entrepreneurship and wage work; y w is certain wage
income; and X is a vector of personal characteristics (life-style preferences) and other
taste shifters.
3
This representative level of income will vary over different levels of capital and labour inputs. We
choose not to elaborate here on the entrepreneur’s technology since it adds nothing substantive to the
current analysis.
9
This, however, is only the static half of the story. The entrepreneur’s beliefs are
updated as new information on noisy talent accrues over the course of running the
business – the entrepreneur learns. Specifically, a repeated application of Bayes’s
theorem gives the following recursive formula for the posterior talent distribution:
[
f θ Ω(k ), σ
2
]= [
][
f θ Ω(k − 1), σ 2 f ω (k )θ , σ 2
f [ω (k )]
]
(3)
where ω (k ) = θ + ε k is incremental sample information and the recursion is initialized
(
)
with a prior (pre-start up) distribution f θ µ 0 , σ 02 . The derivation of this recursion
[
] [
][
]
can also be seen noting that f θ , Ω(k )σ 2 = f θ , Ω(k − 1)σ 2 f ω (k )θ , σ 2 since
ω (k ) is independent from Ω(k − 1) (recall, the ε i are independent). The posterior
distribution is normal if we assume a normal prior and given the normality
assumption on the ε i . From this formulation of the posterior distribution we show, in
Appendix 1, that the posterior variance has the following recursive representations:
[
[
]
var θ Ω(k ), σ 2 = W (k ) var θ Ω(k − 1), σ 2
=σ
2
0
]
(4)
k
∏W (i ),
i =0
where,
−1
⎡
1
1
k ⎤⎥
+
W (k ) = ⎢
, k > 0,
k −1
2
2
2
⎢ σ 0 ∏ W (i ) σ ⎥ σ 0 ∏k −1W (i )
i =0
i =0
⎣
⎦
W (0) = 1.
(5)
Since we see that 0 ≤ W (k ) < W (k − 1) < Κ < W (0) = 1 , it follows that the posterior
variance diminishes as experience is gained. The interpretation of this result is that
confidence in talent forecasts grows with experience. In terms of the occupational
choice model (2) this adds a supplementary condition for the variance
10
var[u e − u w Ω(k )] = y e2σ 02 ∏ W (i ) ,
k
(6)
i =0
indicating that uncertainty in utility forecasts also diminishes with experience.
We now relax the assumption that expectations of talent are unbiased. The rationale
for this is the empirical observation that individuals, and especially entrepreneurs,
tend to forecast events with an optimistic bias. In terms of self-employment choices,
quite straightforwardly, this entails the inclusion of a bias function Β into (2)
E [u e − u w Ω(k )] = y e − y w + Xβ + Β > 0.
(7)
where Β = (µ − θ ) y e and µ are biased talent expectations. According to (7),
optimists (Β > 0) are more likely than realists (Β = 0) or pessimists (Β < 0) to be
entrepreneurs holding actual earnings and tastes constant. Note that, in the optimistic
scenario, the marginal entrepreneur’s (i.e., the individual with a zero ex-ante relative
utility) ex-post relative utility is negative which implies there is over-investment in
entrepreneurship. The variance of net utility remains as in (6) assuming that
optimistic bias affects only the location of the posterior talent distribution.4
The final development is to synthesize optimism with entrepreneurial learning. By
this we mean optimistic entrepreneurs learn to form unbiased forecasts of relative
utility and their confidence in these forecasts grows over time. This replaces the static
prediction that entrepreneurs are optimists with the dynamic prediction that
experience increases realism.
To begin this analysis we observe that the posterior mean has the following recursive
representation (see Appendix 1)
11
µ (k ) = W (k )µ (k − 1) + [1 − W (k )]θˆ(k )
(8)
where θˆ(k ) = k −1 ∑i =1 (θ + ε i ) are sample based estimates of talent and µ (0) = µ 0 are
k
[
]
prior expectations. Since µ (k ) − θˆ(k ) = W (k ) µ (k − 1) − θˆ(k ) < µ (k − 1) − θˆ(k ) , it
follows that optimistic bias diminishes with experience - µ (k ) − θ < µ (k − 1) − θ < Κ .
Also, since lim W (k ) = 0 , infinitely experienced entrepreneurs are realists (i.e., talent
k →∞
forecasts are consistent).
This leads to the culmination of the model with the following criteria for selfemployment choices:
E [u e − u w Ω(k )] = y e − y w + Xβ + Β(k ) > 0,
(9)
where Β(k ) = [µ (k ) − θ ]y e Β k ≤ 0 , lim Β(k ) = 0 and the variance of net utility is given
k →∞
by (6). Accordingly, from (9), we see that the corollary to the diminution of optimism
with experience is that its impact on self-employment choices will also attenuate.
One interpretation of this result is that untalented (low actual earnings) optimists drop
out of entrepreneurship, over time, leaving behind a pool of talented realists.
However, it is quite possible that these talented realists were themselves once
optimists but, having sufficient actual talent, managed to survive. Regardless, the
fundamental prediction is that, among a given generation of entrepreneurs, optimistic
bias eventually goes to zero.
The learning process, as set out in this section, is totally individualistic – the only way
to learn is through personal experiences. However, it would be straightforward to
allow for learning across different generations of entrepreneurs. This development
4
A more general formulation in which optimism produces over-confidence could also be envisaged.
We leave this development for future work.
12
would allow inexperienced entrepreneurs to learn from the accumulated wisdom of
more experienced entrepreneurs.5 The prediction, in that case, is that mentoring will
reduce both optimistic excess and uncertainty among start-ups. We are, unfortunately,
unable to test this particular prediction with our current data.
The normality of the posterior distribution means that the likelihood of
entrepreneurship is given by
⎡ y − y + Xβ + Β(k ) ⎤
w
⎥
Pr ob[s = 1 Ω(k )] = Φ ⎢ e
⎢ y eσ 0 ∏k W (i )1 2 ⎥
i =0
⎣
⎦
(10)
where Φ is the cumulative standard normal distribution function. Estimation of the
model using a structural probit is therefore appropriate (Lee, 1978). However, since
uncertainty varies with experience, a heteroscedastic probit should be estimated.
Failure to take heteroscedasticity into account will result in biased and inconsistent
parameter estimates (Yatchew and Griliches, 1984).
3.1 Implications of learning for the firm failure distribution
A well documented stylized fact from the small business literature is that the firm
failure distribution, by experience, has an inverted U-shape with a positive skew (see
e.g., Ganguly, 1985). It is important that the learning model, presented above, is able
to offer a plausible explanation for this shape (see Cressy, 1999, for an alternative,
entrepreneurial learning based, explanation for the shape of the firm failure
distribution).
5
This feature could be incorporated in the current model via the parameters of the prior (pre-start up)
distribution. In particular, mentoring by an experienced entrepreneur may yield a prior distribution
which more accurately reflects the start up entrepreneur’s true talent. New entrepreneurs may also
benefit from mentoring by other experienced sources such as banks.
13
Firstly, we note that the failure distribution is given by 1 − Pr ob[s = 1 Ω(k )] (i.e., the
probability of failure given k periods of experience). Accordingly, concavity of this
distribution in experience, which is implied by the inverted U-shape, is equivalent to
the convexity of equation (10) in k . Taking the partial derivative of (10) with respect
to k we obtain
⎡ M (k ) ⎤
⎡ M (k )⎤ Bk (k )σ (k ) − σ k (k )M (k )
Φk ⎢
= φ⎢
⎥
⎥
σ (k )
⎣ σ (k ) ⎦
⎣ σ (k ) ⎦
(11)
using M (k ) and σ (k ) as shorthand notation for the numerator and denominator
respectively of the index function in (10) and φ (⋅) is the standard normal density
function. It can be seen from (11) that the shape of the failure distribution depends on
the relative effects of learning on optimism and uncertainty - the parameters Bk
and σ k - respectively. The convexity of (11) (i.e., the concavity of the failure
distribution) requires the sign of Bk − σ k [M (k ) σ (k )] to be initially negative before
turning positive. In this regard, we note that Bk and σ k are strictly non-positive – the
effects of experience on optimism and uncertainty are strictly non-increasing – and
that M (k ) σ (k ) is strictly positive for an entrepreneur [this follows from the selfemployment criterion (9)]. Therefore, convexity requires optimism to fall fast,
relative to the decline in uncertainty, in the early stages of business with a reverse in
this situation later on. For example, the initial bite of reality from starting a business
may hit optimism hard in the early stages, resulting in a surge in failures. Later on,
with optimism less prevalent, the speed at which confidence is accumulated (i.e., the
rate of decline in uncertainty) becomes the critical factor in survival.
Regarding the positive skew in the distribution, we note firstly that the likelihood of
failure approaches zero as k → ∞ . This follows since uncertainty is asymptotically
14
[
]
zero lim σ (k ) = 0 implying lim Pr ob[s = 1 Ω(k )] = 1 . Accordingly, the degree of skew
k →∞
k →∞
in the distribution is determined by the rate of increase in confidence (rate of decline
in uncertainty); the slower is this increase (decline) the more positively skewed will
be the failure distribution. The rate at which confidence accrues will depend not only
on the rate at which self-confidence accrues but also on the rate at which the
confidence of external backers, such as banks, can be built up. For example, if
entrepreneurs can build up a track record with banks quickly then the chances of
failure will tail-off faster (again, this is an implication that we are unable to explore
with our current data).
Section 4: Propositions
Aided by the predictions from the theoretical analysis, and with the benefit of the
policy background, we are now in a position to state some testable propositions
regarding optimism and uncertainty. We start with general propositions and, from
them, develop specific propositions relating to the policy periods. The actual testing
of the propositions involves a combination of survey questionnaire analysis and
econometric estimation. It will therefore make sense, at the testing stage, to
occasionally depart from the numerical ordering of the propositions used in this
section. We begin with the following basic proposition:
PROPOSITION 1: Entrepreneurs are more optimistic than wage-workers.
This proposition will be tested in the structural probit for self-employment choices.
The central prediction, arising from the entrepreneurial learning model, is framed in
the following proposition:
15
PROPOSITION 2: Optimism diminishes with experience.
This proposition will be tested directly using survey analysis of optimism rates among
entrepreneurs with different levels of experience. In addition, the corollary to this
proposition, i.e., that the impact of optimism on self-employment choices diminishes
with experience [see (9)], will be tested using estimates from the structural probit.
Proposition 2 is further developed in view of the distinct quantity and quality policy
periods. In particular, we expect higher rates of optimism in the 1980s, relative to the
1990s, reflecting greater inexperience among the self-employed during the 1980s. In
contrast, the emphasis on improving the quality of established entrepreneurs in the
1990s is expected to reduce optimism rates. This view is stated in the following:
PROPOSITION 3: Entrepreneurs were more optimistic in the 1980s than in the
1990s.
Testing this proposition can be achieved by comparing optimism rates in the 1980s
and 1990s respectively. The key prediction of the learning model, regarding
entrepreneurial uncertainty, is stated in the following proposition:
PROPOSITION 4: Uncertainty diminishes with experience.
This proposition is tested using estimates of the variance function from the structural
probit. Specifically, the testing entails comparison of the variance estimates for
experienced and inexperienced entrepreneurs respectively. The shift from quantity to
16
quality policies is also expected to manifest itself in terms of changes in uncertainty.
In particular, we anticipate that the level of uncertainty increases in the 1980s due to
rising inflows of inexperienced entrepreneurs. In contrast, we expect a stable or
falling variance in the 1990s:
PROPOSITION 5: Uncertainty rose in the 1980s and remained stable in the 1990s.
This proposition is tested in the structural probit by comparing trends in estimates of
the variance functions in the 1980s and 1990s respectively.
Section 5: Data and trends in self-employment
For the empirical analysis we use repeated cross sectional samples of BSAS data.
The BSAS is an annual survey designed to yield a representative sample of adults,
aged 18 or over, living in private households in Britain (south of the Caledonian
Canal). The survey began in 1983 and, with the exception of 1988 and 1992, it has
used the same set of core questions in each year. In total, we use 13 surveys covering
the years 1984-1999. The omitted years from this analysis are: 1988 and 1992 (no
survey); and 1983 and 1997 (no question relating to optimism – see section 6).
In these surveys, the employment status of individuals is ascertained from their
response to the question regarding their main economic activity in the previous seven
days. To be self-employed, individuals must be working more than ten hours per
week, on their own account, and not paying tax through PAYE.
17
Figure 1 shows that the self employment rates for BSAS respondents increased
throughout the 1980s in a similar fashion to the Labour Force Survey (LFS) rates.6 In
the 1990s, however, when compared to the LFS rates, it is clear that BSAS
respondents were more likely to be in self-employment although it is also clear, as
with the LFS rates, that the rate of self-employment has generally plateaued since the
late 1980s (see section 2 for possible explanations of these trends).
INSERT FIGURE 1 HERE
Section 6: Analysis of optimism
One of the advantages of using BSAS data is that we are able to directly observe
attitudinal shifts including trends in business optimism. In particular, self-employed
individuals were asked, in each survey year except for 1983 and 1997, whether they
felt their business prospects for the following year were better, the same, or worse
than the present. We define optimists as respondents who believe their prospects for
the coming year are better than present.7 The binary optimism variable used in this
section is coded to equal unity if the respondent is optimistic, on the above definition,
and zero otherwise. Analysis of the survey responses, among groups with different
business experience, will allow us to test directly PROPOSITIONs 2 and 3 pertaining
to the relationship between optimism and experience. A test of the more fundamental
PROPOSITION 1, viz., entrepreneurs are more optimistic than wage workers, is
deferred to the analysis of self-employment choices in section 7.
6
Self-employment status is ascertained in the LFS from the answer to the question ‘were you working
as an employee or were you self-employed [reference week]’.
7
Although, unlike Arabsheibani et al (1998), we are unable to compare expected outcomes with actual
outcomes and so we are unable to infer the extent of self-deception in the data.
18
In Figure 2 we start by looking at a time-series plot of optimism rates and GDP
growth.
INSERT FIGURE 2 HERE
The first observation from Figure 2 is that optimism rates vary markedly through
time. Also we see that economic growth and optimism appear highly positively
correlated. Notably, though, optimism lags the economic downturn in 1989. In
contrast optimism appears to lead economic growth in the early mid-1990s. This is
compatible with a state of relative euphoria in the late 1980s which was slow to adjust
to economic reality. By the 1990s, however, levels of optimism are lower and more
attuned to the actuality.
Before analysing in detail the determinants of optimism we take a summary look at
the relationship between optimism and experience. The level of business experience
may be ascertained in BSAS data by responses to questioning on the state of the
respondent’s business one year ago; a response that the business did not exist one year
ago is taken to denote less than a year of experience.8 We note that 7.8% (n=1280) of
self-employed had less than one year of experience between 1984 and 1991 whereas
the corresponding figure for the period 1993-1999 is 5.9% (n=1442). The hypothesis
that levels of experience are the same over the two periods is rejected against the
hypothesis that experience is lower in the 1980s (p=0.023). This supports the
contention, underlying PROPOSITION 3, that average levels of experience are lower
in the quantity, relative to the quality, policy periods.
8
This obviously ignores the possibility that the entrepreneur may have had experience running a
different business at an earlier time.
19
In Table 1 we present analysis of optimism rates by sub-groups of different age and
levels of business experience:
INSERT TABLE 1 HERE
Optimism rates are significantly higher among the under 30s and among those with
less than one year of business experience. This supports the central PROPOSITION 2
that optimism diminishes with experience. Comparing rates of optimism between the
quantity and quality policy periods, we see that they are higher in the quantity era
with a statistical significance approaching the 5% level. This offers some support for
the view that optimism was higher in the 1980s than in the 1990s (PROPOSITION 3).
The next stage in this analysis is to estimate a probit model for optimism with sample
selection (recall, views on business prospects are elicited from the sub-sample of selfemployed only). The key determinant of optimism is business experience as indicated
by the theoretical analysis.
Additional controls for: personal characteristics;
education; housing wealth; economic growth; region and industry dummies are also
included in the specifications. A summary analysis of the main exogenous variables,
used in the analysis of both optimism and self-employment choices, is given in
Appendix 2, Table A2.1 (see also section 7.1 for background information on these
variables). In the following Table 2, in which marginal effects from the optimism
probits with selection are reported, the (reduced-form) self-employment selection
equations are not listed to conserve space.9
INSERT TABLE 2 HERE
These estimates again support the contention (PROPOSITION 2) that optimism
diminishes with experience. In particular, entrepreneurs in the 1980s, with less than
9
These equations include an enterprise support variable in addition to the previously mentioned
controls in the optimism equation.
20
one-year’s experience, are around 17 percentage points more likely to be optimistic
regarding their business prospects relative to more experienced entrepreneurs. The
corresponding effect for the 1990s is more than 30 percentage points. Regarding age,
there is no consistent relationship with optimism over the 1980s and 1990s. In the
1993-1991 period there is a significant convex relationship between age and
optimism; optimism rates diminish with age, up to around 64 years, and pick-up
thereafter.
It is also notable that, in Table 2, the apparent association between
optimism and economic growth (Figure 2) vanishes with the inclusion of the
experience variable and other controls. Finally, we note that the correlations between
unobservables in the self-employment selection and optimism equations
(ρ ) are
insignificant.
Aside from showing the determinants of optimism, the probits in Table 2 have a
further important use. Specifically, the predicted marginal probabilities from these
probits are used to construct an optimism variable for wage workers. This is a binary
variable which equals unity if the predicted marginal probability of optimism exceeds
one-half and equals zero otherwise. This variable is joined, with the existing binary
optimism variable for the self-employed, to form an optimism variable spanning the
whole sample.
The conjoined optimism variable will play a central role in the
following analysis of self-employment choices.
Section 7: Analysis of Self-Employment Choices
In this section we will show estimates of a structural probit for self-employment
choices. Firstly, though, we begin with a discussion of the principal determinants of
these choices. These determinants affect choices through expected relative utilities
21
and through uncertainty (see section 3). Within expected relative utilities there is a
further sub-division between pecuniary and non-pecuniary (taste) effects on
preferences
7.1 Expected relative utilities
Pecuniary Effects
Optimism – Optimists tend to over-estimate pecuniary gains in self-employment
increasing their likelihood of self-employment (PROPOSITION 1). The construction
of this variable is discussed at the end of section 6.
Self-employment minus wage income differential –The first issue here is that the
income data are truncated on occupational choices – a working individual reports
either a self-employed or a wage income but not both. Which income is actually
reported is the result of an occupational choice made, possibly, on the basis of where
the individual’s comparative advantage lies.10 This raises the problem of (self)
selection bias in estimating earnings. The second issue is that income variables in
BSAS are recorded as interval data. To address these issues, an income differential
variable is constructed as follows. Ordered probits for earnings, with occupational
selection, are used to predict self-employed and wage income marginal probabilities.11
These probabilities are then used to predict individuals’ self-employed and wage
income categories. Finally, an income dummy variable is constructed which equals
unity if predicted self-employment income exceeds wage income and equals zero
otherwise. (see Appendix 3 for estimates of the income ordered probits).
10
Formally, comparative advantage exists if an individual, from the self-employed (wage-employed)
sub-sample, earns more in self-employment (wage-employment) than would an observably identical
individual, from the wage-employed (self-employed) sub-sample, were they to be placed in selfemployment (wage-employment).
11
Estimates from ordered probits, without selection, would yield probabilities which are conditional on
occupational choices.
22
For identification of the income differential effect, industry dummies are included in
the earnings (and the reduced form self-employment selection) equations but not the
structural self-employment equations. This is the scheme also used by Rees and Shah
(1986) and Taylor (1996) to identify their structural self-employment probits. This
identification is based on the assumption that individuals do not switch industry on
changing between occupations.
Non-pecuniary effects
Personal characteristics and education – Unlike Taylor (1996), Blanchflower and
Oswald (1998) and Burke et al (2000), the BSAS does not have ‘desirable job
characteristic’ variables to directly measure desire for independence.12 In this study,
we use variables for individuals’: sex (Male); race (Black and Asian); marital status
(Married); age (Age and Age squared); trade union membership (Union); manual
occupational status (Manual); and education as controls for non-pecuniary lifestyle
preferences.
Financial Independence – Black et al (1996) use housing equity and the regional
value of housing to examine the role of collateral in business formations. In this
study, we construct a housing wealth variable that is given by the individual’s
predicted probability of being a home owner multiplied by the real average regional
house price to give an estimate of the individual’s expected housing wealth13 (Data
Source: Nationwide Building Society)14. Predicted probabilities of home ownership
12
These authors were able to use measures of the importance of being one’s own boss and job security
as characteristics of the respondent’s job.
13
These predicted probabilties were obtained from reduced form probits for home ownership in which
all the exogenous variables (including the industry dummies) and real interest rates appear as
explanatory variables. Real interest rates are excluded from the mean of the structural self-employment
probit to identify the impact of housing wealth.
14
www.nationwide.co.uk. These data measure mix adjusted average prices. This adjustment ensures
the price series are not biased by changes in, for example, the types of property being sold or property
locations. We also calculated the housing wealth variable using data from the leading UK mortgage
lender – the Halifax Building Society (www.hbos.plc.com/view/housepriceindex/housepriceindex.asp).
Their methodology for constructing price series is similar to that used by the Nationwide. It is
23
are used as instruments since home ownership itself may be endogenous in selfemployment decisions. Note that, given we control for earnings differentials (and
earnings themselves are a function of wealth – see Appendix 3), the housing wealth
variable has a non-pecuniary interpretation in the structural probit. In other words,
the ability to self-finance may raise self-employment utility by increasing
independence from external sources of equity and/or provide opportunities to pursue
non-profit objectives (see Burke et al, 2000).
Enterprise Support – Non-pecuniary effects of enterprise support on self-employment
are examined using (log) real value of lending under the SFLGS (Data Source:
Department of Employment). This measure is deflated by the population aged 15 to
64 as a proxy for the total working population (Data Source: ONS). It might be
expected that greater government involvement in enterprise support would raise
general awareness and tastes for self-employed occupations.
In addition variables for economic growth, regional unemployment rates and region
dummies are included in expected relative utilities. These capture variations in tastes
for self-employment over the business cycle and over different geographical locations
7.2 Uncertainty
The model suggests a parsimonious specification of uncertainty which centres on
experience. The business experience variable, used earlier, cannot be used here since
it is defined on the self-employment sub-sample only. Instead, respondent’s age (and
age-squared) are used as experience proxies. An additional key variable, real interest
rates, is included in the specification of uncertainty. We expect that rising borrowing
costs will increase the prospect of insolvency thereby raising uncertainty.
unsurprising therefore that the empirical results are unaffected by the version of the housing wealth
24
The functional form for the uncertainty function is the typically used exponential
form, σ 2 (w) = [exp{γw}] (see Harvey, 1976), where w , the vector of uncertainty
2
covariates, does not include a constant term. This functional form ensures that a
positive variance is estimated.
7.3 Estimates
Next, we turn to estimates of the structural probits for the quantity (1984-1991) and
quality (1993-1999) policy periods. In Table 3, we show the marginal effects on selfemployment probabilities along with estimates of the uncertainty function. Note that
the estimation samples are diminished due to missing earnings data (cf. Table 2).
INSERT TABLE 3 HERE
Looking firstly at the marginal effects for the 1984-1991 period, the principal result is
the strong effect of optimism. Specifically, optimists are 14 percentage points more
likely to be self-employed than non-optimists. This offers support for the proposition
that entrepreneurs are more optimistic than wage workers (PROPOSITION 1). Also,
individuals with higher expected relative earnings in self-employment are about nine
percentage points more likely to be self-employed. This finding is compatible with
Rees and Shah (1986) and Taylor (1996) who found a positive effect of income
differentials on self-employment probabilities. 15
Turning to taste effects in the 1980s, personal characteristics are individually
significant (except Asian) pointing to the importance of lifestyle preferences in
occupational choices. For example union members have lower preferences, ceteris
variable used in the analysis.
15
It is possible, however, that this effect is understated due to unobserved tax advantages in selfemployment (Pissarides and Weber, 1989; Baker, 1993).
25
paribus, for self-employment reflecting better (wage) working environments relative
to non-union workers. Also, individuals with 13 years of education have lower tastes
for self-employed occupations relative to individuals with only 10 years of education.
Notably, however, there is no role for wealth (financial independence) in the quantity
periods. Nor, surprisingly, does government support (SFLGS) appear to influence
attitudes to self-employed occupations during this time. On the other hand, each
percentage point increase in economic growth, in the 1980s, increases selfemployment likelihoods by about 1.5 percentage points. Finally, for the quantity
periods, unemployment has no influence on tastes and there are no apparent regional
variations in attitudes toward self-employed occupations.
Looking at the marginal effects in the quality policy period, we again notice a strong
impact of optimism on self-employment (12 percentage points). Income differentials,
on the other hand, have no effect on self-employment choices in the 1990s (see
Parker, 2003, and Hamilton, 2000, who also found no earnings effect, in the UK and
US respectively, using data from the 1990s).
While pecuniary incentives appear to have a lesser role in the 1990s there are,
however, some interesting non-pecuniary effects. For example, Asians are more
likely than individuals from white ethnic backgrounds to be self-employed in the
1990s.
This effect may reflect variations in cultural resources, such as family
networks, between Asians and non-Asians (see Metcalf et al, 1996 and Clarke and
Drinkwater, 1998).16 There is also an interesting sign shift in the effect of marriage
16
However, interestingly, this effect is not significant in the 1980s. This is surprising since evidence
from alternative data sources suggests the self-employment boom in the 1980s was particularly
important for non-whites (General Household Survey – Clarke and Drinkwater, 1998; Labour Force
Survey – Daly, 1991).
26
between the 1980s and 1990s. Whereas marriage promotes self-employment in the
1980s it appears to act as a barrier in the 1990s.
Significantly, we notice that self-employment choices are increasing in wealth in the
1990s. Specifically, each 1% increase in housing wealth is associated with a 0.076
percentage point rise in self-employment likelihoods. Interestingly, this suggests that,
unlike the 1980s, a desire for financial independence is a motive for choosing selfemployment during the 1990s. In light of this result, it is also notable that enterprise
support (SFLGS) has a significantly negative effect in the quality periods.
Presumably, the 1990s entrepreneurs find the strings attached to external finance
distasteful. Finally, we note that economic growth is negatively associated with selfemployment which contrasts with the effect in the 1980s. On this evidence, falling
self-employment rates in the 1990s may be partly explained by faster economic
growth leading to enhanced wage employment opportunities.
The corollary to PROPOSITION 2 is that the influence of optimism on selfemployment choices diminishes with experience. To test this corollary we examine,
in Figure 3, how the marginal effect of optimism varies with age holding the other
explanatory variable at their sample means.
INSERT FIGURE 3 HERE
This figure shows a concave relationship, in both sub-periods, between the marginal
effect of optimism and age.
The effect rises up to ages in the late 40s (more
accurately, age 47 in the 1984-1991 sub-period and age 48 in the 1993-1999 subperiod) and attenuates thereafter. This attenuation supports the diminishing role of
optimism in self-employment choices beyond a threshold level of experience. To be
more precise regarding this threshold, we note that the average ages of entrepreneurs
27
with more than one year of business experience are 43 and 44 in the 1980s and 1990s
samples respectively. The turning points in Figure 3 are, accordingly, compatible
with an experience threshold which lies above one year.
Next, we turn to estimates of the uncertainty in self-employment choices which are
reported at the end of Table 3. The effects of age (and age-squared) are significant in
both sub-samples which points to the role of experience in uncertainty. Interestingly,
however, the relationship is convex in the 1980s and concave in the 1990s. The
turning points occur at around 46 and 29 years of age respectively. Also, real interest
rates are positively signed, as expected, but are individually insignificant in both subperiods.
To conclude the analysis, the relationship between experience and uncertainty is
explored in more detail. In the following Table 4 we report: i) summary statistics for
uncertainty, by level of experience and policy period; and ii) estimates of regressions
of uncertainty on a trend and trend-squared, in both policy periods.
INSERT TABLE 4 HERE
Firstly, we notice that uncertainty is higher among less experienced entrepreneurs in
both the 1980s and 1990s. This difference is, however, only significant in the quality
policy periods. This finding provides an empirical bolster to PROPOSITION 4 – viz.,
uncertainty diminishes with experience.
Secondly, the regression estimates are
indicative of a concave trend in uncertainty in the 1980s with no trend in the 1990s.
This finding supports PROPOSITION 5 which predicted rising uncertainty in the
quantity policy periods and stability in the quality periods. Note that incomes are
higher in the 1990s (see Table A3.1) so that the greater absolute level of uncertainty
28
in the 1990s, compared to the 1980s, is unsurprising [see also equation (6) which
indicates that the variance is increasing in the level of representative income].
On the evidence presented in this section, the motivation for choosing self-employed
occupations has changed markedly over time. The strong pecuniary incentive, which
characterises entrepreneurship in the Thatcher era, is replaced in the 1990s with a
desire to satisfy non-pecuniary lifestyle preferences. Also, rising uncertainty in the
1980s is contrasted with a more stable environment in the 1990s. The one constant
feature in this analysis is the high impact of optimism on self-employment choices in
both the 1980s and 1990s.
Section 8: Conclusions and policy implications
In this paper we have presented a model in which entrepreneurs’ optimistic excesses
are mitigated by their capacity to learn from their initial errors in judgement. The
model
predicts
that
the
relative
effects
of
learning
on
optimism
and
uncertainty/confidence respectively will determine the shape of the business failure
distribution by experience. High initial failure rates are explained by the forced exit
of optimists soon after start-up. The degree of positive skew in the distribution, on
the other hand, is determined by the speed at which confidence accumulates; the faster
is this accumulation the quicker is the tail-off in failure probabilities.
The empirical analysis supports the central propositions that entrepreneurs are
optimists but that this optimism diminishes with experience. In addition, we find that
rates of optimism are high and uncertainty in self-employment choices is rising in the
1980s. This, we argue, is a result of policies which encouraged young people, and
29
other inexperienced types, into self-employment. In contrast, in the 1990s, optimism
rates are lower and uncertainty is more stable which, we believe, reflects the shift in
policy emphasis toward increasing the quality of incumbent businesses.
The
empirical analysis also lends support to the popular view that entrepreneurship in the
Thatcher era was driven predominantly by the prospect of financial gain. The profile
of the 1990s entrepreneur, on the other hand, is somewhat ‘softer’ with the principal
motivation being to satisfy lifestyle preferences.
The policy implications from this analysis are particularly apposite given recent
developments. For example, the UK government has, through the Davies review
(2002) of enterprise education, promised to provide £60m by 2005/06 so that each
secondary school student will experience five days of ‘enterprise activity’. Similarly,
the government has also recently spent £43.9 million in promoting undergraduate
entrepreneurship education through the Science Enterprise Challenge (DTI, 2000).
One of the stated intentions of this expenditure is to promote entrepreneurship.
However, the opposite intention may be seen as more desirable in the presence of
optimism. Specifically, in helping nascent entrepreneurs form better forecasts of their
future performances, entrepreneurship education may help persuade some individuals
of the wisdom that they choose not to become entrepreneurs.
Another policy implication relates specifically to direct attempts to increase the rate of
new venture creation which the UK government sees as one of its seven ‘pillars’ to
support smaller enterprises (SBS, 2004). The policy lesson from the 1980s is that
quantity policies, implemented largely on their own, may exacerbate optimism and
uncertainty. In contrast, the quality policies in the 1990s appear to alleviate optimism
and uncertainty. A synthesis of the lessons from the quantity and quality policy
30
periods suggests that a combination of hard support and sound advice may result in
more effective start-up policies. Not only would start-ups increase under such a
regime but these new businesses would also be more likely to flourish. This policy
implication is very relevant at a time when the UK government is seeking, once again,
to increase self-employment amongst disadvantaged individuals with little prior selfemployment experience (HM Treasury, 2002).
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Figure 1: LFS and BSAS Self-Employment Rates
in the UK, 1983-1999
25
15
LFS data
BSAS data
10
5
0
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
Percentage
20
Year
34
Figure 2: Annual Rates of Optimism and GDP Growth (%)
optimism
growth
40.97
5.2
growth
optimism
28.93
-1.4
83
84
85
86
87
88
89
90
91 92
year
93
94
95
96
97
98
99
35
Table 1:
Survey Questionnaire Analysis of Optimism Rates (%) by Age,
Business Experience and Policy Sub-Periods – Sub-Sample of Self-Employed
Only (n is the number of observations in the sub-sample) (p – values for tests on
the equality of proportions between sub-samples).
Age<30
Age≥30
59.1
(n=303)
35.2
(n=1988)
< 1year experience
≥ 1year experience
65.7
(n=134)
36.6
(n=2157)
p=0.000
p=0.000
1984-1991 (excl. 1988)
1993-1999 (excl. 1997)
40.4
(n=896)
37.0
(n=1395)
p=0.051
36
Table 2: Optimism: Probit model with selection (Self-employment selection
equations not reported) – Marginal Effects ( ∂Φ ∂x ) (p-values in parenthesis)
1984-1991 (excl. 1988)
1993-1999 (excl. 1997)
Business experience
0.174 (0.023)
0.301 (0.000)
< 1 year experience
Personal characteristics
Male
Black
Asian
Married
Age
Age squared/100
Manual
Years of Education
(relative to 10 years)
11
12
13
14 or more
0.111 (0.009)
0.443 (0.021)
−0.025 (0.806)
−0.012 (0.852)
0.002 (0.864)
−0.011 (0.305)
−0.164 (0.061)
0.083 (0.024)
−0.090 (0.448)
−0.153 (0.039)
0.025 (0.652)
−0.025 (0.020)
0.020 (0.044)
−0.104 (0.064)
0.011 (0.839)
0.041 (0.570)
0.132 (0.166)
0.111 (0.076)
0.011 (0.798)
0.011 (0.862)
0.005 (0.931)
0.048 (0.363)
(log) Real Housing wealth
Real GDP Growth
−0.263 (0.218)
−0.033 (0.499)
−0.141 (0.398)
0.062 (0.068)
Regional Unemployment
rate
Region dummies (p-value)
−0.030 (0.167)
−0.061 (0.053)
0.497
0.853
Trend
Year dummies (p-value)
Industry dummies
(p-value)
0.051 (0.359)
0.391
0.067
−0.035 (0.233)
0.329
0.000
ρ
χ (p-value)
0.159 (0.544)
0.000
0.069 (0.703)
0.000
Log-likelihood
n
Censored obs.
Uncensored obs.
−2835.180
9183
8295
888
−4250.701
9349
8016
1333
2
Notes: Survey weights are used in estimation.
Discrete changes in probabilities calculated for dummy variables.
37
Table 3: Structural Self-Employment Probits (Marginal Effects ∂Φ ∂x ) with
Estimates of the Uncertainty Equation (p-values in parenthesis).
1984-1991 (excl. 1988)
1993-1999 (excl. 1997)
Pecuniary
0.140 (0.000)
0.121 (0.000)
Optimism
0.091 (0.000)
−0.019 (0.556)
Income Differential
Non-pecuniary
Personal characteristics
0.041 (0.000)
0.074 (0.000)
Male
−0.043 (0.000)
−0.025 (0.235)
Black
0.022 (0.423)
0.078 (0.029)
Asian
0.023 (0.004)
−0.020 (0.051)
Married
0.007 (0.000)
0.018 (0.000)
Age
−0.007 (0.001)
−0.018 (0.000)
Age squared/100
−0.062 (0.000)
−0.106 (0.000)
Union
0.028 (0.001)
0.044 (0.000)
Manual
Years of Education
(relative to 10 years)
−0.002 (0.747)
0.009 (0.313)
11
−0.003 (0.714)
−0.031 (0.018)
12
−0.027 (0.000)
−0.011 (0.341)
13
−0.004 (0.653)
0.009 (0.455)
14 or more
0.054 (0.598)
0.015 (0.864)
Still at college
Financial independence
0.027 (0.166)
0.076 (0.000)
(log) Real Housing wealth
Enterprise Support
0.019 (0.118)
−0.215 (0.002)
(log) SFLGS
0.013 (0.005)
0.004 (0.344)
−0.147 (0.000)
0.002 (0.617)
0.812
0.337
0.010 (0.076)
0.117
−0.040 (0.000)
0.022
0.038
0.034
Age
Age squared/100
Real Interest Rates
χ 2 (p-value)
−0.063 (0.001)
0.068 (0.009)
0.037 (0.165)
0.000
0.030 (0.016)
−0.053 (0.001)
0.012 (0.774)
0.000
Log-likelihood
n
Wage workers
Self-Employed
−2047.583
8491
7763
728
−3126.194
8679
7536
1143
Real GDP Growth
Regional unemployment
rate
Regional Dummies (pvalue)
Trend
Year dummies (p-value)
χ 2 (p-value)
UNCERTAINTY
Notes: Survey weights are used in estimation.
Discrete changes in probabilities calculated for dummy variables.
38
Table 4: Uncertainty: i) summary analysis by experience and policy sub-period;
and ii) regression on time trend and trend-squared.
1984-1991 (excl. 1988)
1993-1999 (excl. 1997)
0.351
1.474
< 1 year experience (σ 1 )
0.338
1.379
≥ 1 year experience (σ 2 )
0.155
0.002
p-value
(H 0 : σ 1 = σ 2 vs. H 1 : σ 1 > σ 2 )
Regression
0.021 (0.010)
0.026 (0.541)
Trend
−0.002 (0.002)
−0.001 (0.525)
Trend-sq
0.000
0.001
Year dummies (p-value)
Robust standard errors used in regression analysis
39
Figure 3: Optimism: Marginal Effects on Self-Employment Probabilities by Age.
optimism_mfx8491
optimism_mfx9399
.6
.5
.4
.3
.2
.1
0
20
25
30
35
40
45
50
55
60
age
65
70
75
80
85
90
95 100
40
Appendix 1
Recursive formula for the posterior variance
Given the normality of the prior distribution, and assuming σ 2 is known, we may
write the posterior variance for k = 1 as
var θ Ω(1), σ 2 = W (1)σ 02
[
]
where
[
−1
]
⎡
⎤
( prior var iance ) = ⎢ 12 + 12 ⎥ 12
W (1) = ( prior var iance ) + (sample var iance )
⎣σ 0 σ ⎦ σ 0
(see e.g., Judge et al., 1982, p. 123). The process of Bayesian learning implies that
the posterior density, from the current period, forms the prior density in the following
period [see equation (3)]. Accordingly,
var θ Ω(2 ), σ 2 = W (2 )W (1)σ 02
−1 −1
−1
[
]
−1
[
= W (2 ) var θ Ω(1), σ 2
]
where
−1
⎡ 1
2 ⎤
1
+ 2⎥
W (2 ) = ⎢
.
2
2
⎣W (1)σ 0 σ ⎦ W (1)σ 0
The recursive formula for var θ Ω(k ), σ 2 , given in equation (4), is arrived at by a
[
]
repeated application of the preceding logic.
Recursive formula for the posterior Mean
The posterior mean for k = 1 is given by
E θ Ω(1), σ 2 = W (1)µ 0 + [1 − W (1)]θˆ(1)
[
]
(see e.g., Judge et al., 1982, p. 123) which is simply a weighted average of the prior
mean and the initial sample estimate of talent. In the next period: this posterior mean
will form the prior mean; new sample information will give rise to a new sample
estimate; and the weighting matrices are updated (as in the posterior variance).
Repeated application of this logic gives rise to the recursive formula for
E θ Ω(k ), σ 2 given in equation (8).
[
]
41
Appendix 2
Table A2.1: Summary statistics for principal exogenous variables (standard
errors in parenthesis)†
Male
Black
Asian
Married
Age
Union
Manual
Years of
education
11
12
13
14 or more
Still at college
(log) Real
Housing
Wealth
(log) SFLGS
Real GDP
Growth (%)
Real interest
rates (%)
n
†
1984-1991
excl 1988
(self-employed)
1984-1991
excl. 1988
(wage-workers)
1993-1999
excl. 1997
(self-employed)
1993-1999
excl. 1997
(wage-workers)
0.761 (0.012)
0.010 (0.003)
0.028 (0.005)
0.804 (0.011)
42.581
(12.375)
0.410 (0.014)
0.467 (0.014)
0.532 (0.005)
0.014 (0.001)
0.017 (0.001)
0.714 (0.005)
38.966
(12.734)
0.608 (0.005)
0.429 (0.005)
0.722 (0.012)
0.011 (0.003)
0.036 (0.005)
0.721 (0.012)
43.665
(12.311)
0.203 (0.011)
0.427 (0.013)
0.482 (0.005)
0.021 (0.002)
0.020 (0.002)
0.659 (0.005)
39.260
(11.873)
0.421 (0.005)
0.373 (0.005)
0.254 (0.012)
0.097 (0.008)
0.059 (0.007)
0.168 (0.010)
0.002 (0.001)
0.294 (0.005)
0.096 (0.003)
0.081 (0.003)
0.147 (0.004)
0.001 (0.000)
0.318 (0.012)
0.074 (0.007)
0.089 (0.007)
0.210 (0.011)
0.001 (0.001)
0.325 (0.005)
0.101 (0.003)
0.114 (0.003)
0.202 (0.004)
0.002 (0.000)
10.527 (0.443)
10.424 (0.474)
10.716 (0.307)
10.670 (0.335)
−17.689 (0.611)
2.270 (1.950)
−16.964 (0.506)
3.015 (0.788)
6.269 (1.669)
3.641 (0.779)
1280
8527
Standard errors for binary variables are calculated as
1442
8338
⎛ πˆ (1 − πˆ ) ⎞
⎜
⎟ where πˆ is the proportion of
⎝ n ⎠
observations on the binary variable equal to unity and n is the number of observations in the (sub-)
sample.
Note that the estimation sub-samples are smaller than reported in this table due to missing observations
on business prospects and income.
42
Appendix 3: Construction of the income differential variable and estimates of
heteroscedastic ordered income probits with selection
We begin this appendix with a brief look at the income distributions for selfemployed and wage workers.
Table A3.1: Income distributions
1984-1991
Income
1984-1991
(excl. 1988)
(thousands £)
(excl. 1988)
self-employed wage workers
(%)
(%)
51.73
65.15
<10
22.02
20.58
10-15
14.04
8.78
15-20
10.00
4.94
20-30
0.77
0.33
30-35
1.44
0.23
>35
1993-1999
(excl. 1997)
self-employed
(%)
32.87
20.67
16.68
14.56
2.36
12.86
1993-1999
(excl. 1997)
wage workers
(%)
35.91
24.62
17.09
15.62
2.06
4.69
The consistent finding in these distributions is the higher proportion of self-employed
‘superstars’ relative to wage-workers (i.e., incomes in the upper sextile). At the other
end of the income distributions, it is notable that the proportion of wage workers in
the bottom sextile is 44% lower in the 1990s, relative to the 1980s, as compared to a
fall of only 36% among the self-employed. This suggests that, among the very lowest
earners, the self-employed are the most disadvantaged in terms of income growth.
Next, heteroscedastic ordered probits, with selection, are estimated using the above
income categories (coding the first category as zero). These earnings equations
include as explanatory variables: human capital, wealth, enterprise support (selfemployed earnings only); and industry dummies. Using the estimated marginal
probabilities we may estimate wage earnings as:
E ( y w ) = prob( y w = 1) + 2 × prob( y w = 2) + ... + 5 × prob( y w = 5) .
Similarly, estimated self-employment income is given by:
E ( y e ) = prob( y e = 1) + 2 × prob( y e = 2 ) + ... + 5 × prob( y e = 5) .
The income differential variable, used in the structural self-employment equation, is
then calculated as a dummy variable equal to unity if E ( y e ) > E ( y w ) and zero
otherwise.
The presence of comparative advantage in career choices will depend on the
correlations between occupational choice and earnings. Denoting latent earnings by
y i* , i = e, w and prob(s = 1) by Φ (Zγ ) , where Zγ is the index function from the
reduced form self-employment equation, then we may write
φ (Z γ )
E y w* s = 1 = E y w* − ρ yws
Φ(Zγ )
φ (Zγ )
.
E y w* s = 0 = E y w* + ρ yw s
1 − Φ (Z γ )
If ρ yws , the correlation between unobservables in wage earnings and occupation
(
(
)
)
( )
( )
choices, is positive then self-employed individuals earn below average earnings in
wage employment whereas wage earners earn above average in wage employment E ( y w* s = 1) < E ( y w* ) < E ( y w* s = 0 ) (comparative advantage). Similarly,
43
φ (Z γ )
1 − Φ (Z γ )
φ (Zγ )
.
E ( y e* s = 1) = E ( y e* ) − ρ ye s
Φ (Z γ )
E ( y e* s = 0 ) = E ( y e* ) + ρ ye s
If ρ ye s is negative then wage earners earn below average incomes in self-employment
whereas self-employed individuals earn above average incomes in their chosen
careers - E ( y e* s = 0 ) < E ( y e* ) < E ( y e* s = 1) (comparative advantage).
In the following tables marginal effects on income cell probabilities are reported. The
reduced form self-employment selection equations are not reported to conserve space:
Table A3.2 Heteroscedastic Ordered Probits with Selection (Marginal Effects)
and Estimates of the Uncertainty Equation (p-values in parenthesis): SelfEmployed Income; 1984-1991 (excl. 1988)
Income category(thousands £)
<10
10-15
15-20
20-30
30-35
>35
Personal
characteristics
-0.561762
0.436046
0.0937575
0.031142
0.0004953
0.0003213
Male
(0.001)
(0.004)
(0)
(0.001)
(0.024)
(0.046)
Black
Asian
Age
Age squared/100
Manual
Union
Married
0.1131402
(0.286)
0.1453861
(0.287)
-0.0312871
(0.007)
0.000379
(0.005)
0.3280526
(0.005)
0.0735005
(0.067)
-0.1362563
(0.044)
-0.0878207
(0.291)
-0.1128503
(0.294)
0.0242854
(0.012)
-0.0002942
(0.01)
-0.2546381
(0.01)
-0.0570519
(0.085)
0.1057637
(0.055)
-0.018883
(0.286)
-0.0242648
(0.284)
0.0052218
(0.005)
-0.0000633
(0.004)
-0.0547516
(0.002)
-0.0122672
(0.041)
0.022741
(0.039)
-0.0062721
(0.3)
-0.0080597
(0.296)
0.0017344
(0.016)
-0.000021
(0.014)
-0.018186
(0.008)
-0.0040746
(0.046)
0.0075535
(0.058)
-0.0000997
(0.331)
-0.0001282
(0.325)
0.0000276
(0.063)
-0.0000647
(0.346)
-0.0000831
(0.339)
0.0000179
(0.092)
-0.000000334
(0.061)
-0.000000217
(0.09)
-0.0002892
(0.046)
-0.0000648
(0.081)
0.0001201
(0.112)
-0.0001876
(0.072)
-0.000042
(0.103)
0.0000779
(0.139)
-0.0169861
(0.724)
-0.0655302
(0.286)
-0.054321
(0.458)
-0.1176408
(0.089)
2.49314
(0.002)
0.0131848
(0.725)
0.0508653
(0.29)
0.0421646
(0.458)
0.0913141
(0.103)
-1.935204
(0.007)
0.002835
(0.725)
0.0109369
(0.289)
0.0090661
(0.465)
0.0196341
(0.077)
-0.4161025
(0)
0.0009416
(0.726)
0.0036327
(0.304)
0.0030114
(0.475)
0.0065216
(0.093)
-0.1382103
(0.003)
0.000015
(0.729)
0.0000578
(0.337)
0.0000479
(0.494)
0.0001037
(0.14)
-0.002198
(0.03)
0.00000971
(0.73)
0.0000375
(0.352)
0.0000311
(0.503)
0.0000673
(0.164)
-0.0014258
(0.054)
0.1438718
(0.391)
0.1265875
(0.027)
-0.1116749
(0.394)
-0.0982587
(0.039)
-0.024012
(0.392)
-0.0211273
(0.017)
-0.0079757
(0.401)
-0.0070175
(0.029)
-0.0001268
(0.422)
-0.0001116
(0.073)
-0.0000823
(0.432)
-0.0000724
(0.1)
0.0639766
(0.231)
-0.0496594
(0.239)
-0.0106776
(0.226)
-0.0035466
(0.24)
-0.0000564
(0.275)
-0.0000366
(0.292)
0.0210705
(0.329)
-0.0163551
(0.337)
-0.0035166
(0.318)
-0.0011681
(0.323)
-0.0000186
(0.343)
-0.0000121
(0.354)
Years of Education
(relative to 10
years)
11
12
13
14 or more
Still at college
(log) Real
Housing wealth
(log) SFLGS
Real GDP Growth
Regional
Unemployment
rate
χ 2 (p-value)
0.9864
44
UNCERTAINTY
Age
Age squared/100
Real Interest
Rates
ρy s
e
Log-likelihood
n
0.0207446
(0.177)
-0.0001873
(0.289)
-0.040480
(0.009)
-0.2457243
(0.000)
-3500.1883
8775
Table A3.3 Heteroscedastic Ordered Probits with Selection (Marginal Effects)
and Estimates of the Uncertainty Equation (p-values in parenthesis): Wage
Income; 1984-1991 (excl. 1988)
Income category(thousands £)
<10
10-15
15-20
20-30
30-35
>35
Personal
characteristics
0.3914347
0.1087805
0.0254897
0.0003134
0.0000699
Male -0.5260882
(0)
(0)
(0)
(0)
(0)
(0)
Black
Asian
Age
Age squared
Manual
Union
Married
0.122638
(0.029)
0.2012271
(0)
-0.0647874
(0)
0.0007216
(0)
0.2507401
(0)
0.0038361
(0.754)
-0.0408409
(0.048)
-0.0912485
(0.03)
-0.1497225
(0)
0.0482049
(0)
-0.0005369
(0)
-0.1865625
(0)
-0.0028542
(0.754)
0.0303875
(0.049)
-0.0253581
(0.026)
-0.0416082
(0)
0.0133962
(0)
-0.0001492
(0)
-0.0518461
(0)
-0.0007932
(0.754)
0.0084448
(0.045)
-0.005942
(0.025)
-0.0097497
(0)
0.003139
(0)
-0.000035
(0)
-0.0121487
(0)
-0.0001859
(0.754)
0.0019788
(0.043)
-0.0000731
(0.023)
-0.0001199
(0)
0.0000386
(0)
-0.00000043
(0)
-0.0001494
(0)
-0.00000229
(0.754)
0.0000243
(0.042)
-0.0000163
(0.023)
-0.0000267
(0)
0.00000861
(0)
-0.0000000959
(0)
-0.1105018
(0)
-0.1572529
(0)
-0.2175503
(0)
-0.3511501
(0)
-0.1790357
(0.54)
0.0822186
(0)
0.1170036
(0)
0.1618678
(0)
0.2612724
(0)
0.1332111
(0.54)
0.0228487
(0)
0.0325156
(0)
0.0449834
(0)
0.0726081
(0)
0.0370196
(0.54)
0.005354
(0)
0.0076191
(0)
0.0105406
(0)
0.0170137
(0)
0.0086745
(0.54)
0.0000658
(0)
0.0000937
(0)
0.0001296
(0)
0.0002092
(0)
0.0001067
(0.541)
0.0000147
(0)
0.0000209
(0)
0.0000289
(0)
0.0000467
(0)
0.0000238
(0.541)
-0.0951087
(0.082)
0.0322298
(0.081)
0.0707654
(0.083)
-0.0239805
(0.081)
0.0196659
(0.079)
-0.0066642
(0.081)
0.0046082
(0.077)
-0.0015616
(0.081)
0.0000567
(0.076)
-0.0000192
(0.082)
0.0000126
(0.076)
-0.00000428
(0.083)
-0.0083833
(0.245)
0.0062376
(0.246)
0.0017334
(0.242)
0.0004062
(0.239)
0.00000499
(0.236)
0.00000111
(0.235)
-0.0000333
(0)
-0.00000051
(0.754)
0.00000543
(0.041)
Years of Education
(relative to 10
years)
11
12
13
14 or more
Still at college
(log) Real
Housing wealth
Real GDP Growth
Labour Market
Regional
Unemployment
rate
χ 2 (p-value)
0.0000
UNCERTAINTY
45
Age
Age squared
Real Interest
Rates
ρy
ws
-0.0498216
(0)
0.0005964
(0)
-0.0196204
(0.035)
0.3779155
(0.000)
-7768.6719
Log-likelihood
n 8775
Table A3.4 Heteroscedastic Ordered Probits with Selection (Marginal Effects)
and Estimates of the Uncertainty Equation (p-values in parenthesis): SelfEmployed Income; 1993-1999 (excl. 1997)
Income category(thousands £)
<10
10-15
15-20
20-30
30-35
>35
Personal
characteristics
-0.39042
0.026072
0.1249157
0.1332219
0.022061
0.0841494
Male
(0)
(0.742)
(0)
(0.029)
(0.087)
(0.176)
0.1226205
(0.161)
0.0308813
(0.748)
-0.0329611
(0)
0.0003672
(0)
0.2499082
(0.001)
0.0083296
(0.796)
-0.1146483
(0.032)
-0.0081885
(0.755)
-0.0020622
(0.809)
0.0022011
(0.752)
-0.0000245
(0.751)
-0.0166887
(0.742)
-0.0005562
(0.849)
0.0076562
(0.746)
-0.0392327
(0.16)
-0.0098805
(0.747)
0.010546
(0)
-0.0001175
(0)
-0.0799586
(0)
-0.0026651
(0.796)
0.036682
(0.03)
-0.0418414
(0.199)
-0.0105375
(0.751)
0.0112472
(0.011)
-0.0001253
(0.011)
-0.0852755
(0.037)
-0.0028423
(0.796)
0.0391211
(0.089)
-0.0069288
(0.237)
-0.001745
(0.753)
0.0018625
(0.047)
-0.0000208
(0.049)
-0.0141213
(0.098)
-0.0004707
(0.795)
0.0064783
(0.146)
-0.0264291
(0.289)
-0.006656
(0.757)
0.0071043
(0.122)
-0.0000792
(0.125)
-0.0538641
(0.186)
-0.0017953
(0.796)
0.0247108
(0.222)
-0.112166
(0.017)
-0.049262
(0.376)
-0.0843596
(0.155)
-0.2372522
(0.001)
-0.487073
(0.255)
0.0074904
(0.743)
0.0032897
(0.76)
0.0056335
(0.75)
0.0158436
(0.744)
0.0325265
(0.755)
0.0358877
(0.015)
0.0157615
(0.375)
0.026991
(0.152)
0.0759093
(0.001)
0.15584
(0.253)
0.0382741
(0.076)
0.0168095
(0.397)
0.0287858
(0.204)
0.0809569
(0.035)
0.1662025
(0.288)
0.006338
(0.137)
0.0027836
(0.417)
0.0047668
(0.247)
0.0134061
(0.093)
0.0275224
(0.318)
0.0241757
(0.217)
0.0106177
(0.444)
0.0181825
(0.303)
0.0511363
(0.178)
0.1049815
(0.36)
0.1770178
(0.123)
0.0092265
(0.793)
-0.0118212
(0.752)
-0.0006161
(0.841)
-0.0566372
(0.121)
-0.002952
(0.793)
-0.0604033
(0.168)
-0.0031483
(0.793)
-0.0100025
(0.211)
-0.0005213
(0.794)
-0.0381536
(0.27)
-0.0019886
(0.795)
-0.0039821
(0.862)
0.0002659
(0.88)
0.0012741
(0.862)
0.0013588
(0.862)
0.000225
(0.862)
0.0008583
(0.862)
Labour Market
Regional
Unemployment
rate
-0.008926
(0.729)
0.0005961
(0.81)
0.0028559
(0.729)
0.0030458
(0.731)
0.0005044
(0.733)
0.0019239
(0.737)
χ 2 (p-value)
0.9974
Black
Asian
Age
Age squared/100
Manual
Union
Married
Years of Education
(relative to 10
years)
11
12
13
14 or more
Still at college
(log) Real
Housing wealth
(log) SFLGS
Real GDP Growth
UNCERTAINTY
46
Age
Age squared/100
Real Interest
Rates
ρy s
e
Log-likelihood
n
-0.0029757
(0.819)
0.000082
(0.546)
0.1382268
(0.001)
-0.1041501
(0.006)
-4885.9402
8722
Table A3.5 Heteroscedastic Ordered Probits with Selection (Marginal Effects)
and Estimates of the Uncertainty Equation (p-values in parenthesis): Wage
Income; 1993-1999 (excl. 1997)
Income category(thousands £)
<10
10-15
15-20
20-30
30-35
>35
Personal
characteristics
-0.0264633
0.1790892
0.2183856
0.0230115
0.0337957
Male -0.4278187
(0)
(0.293)
(0)
(0)
(0)
(0)
Black
Asian
Age
Age squared/100
Manual
Union
Married
0.1163536
(0.022)
0.1533183
(0)
-0.0591171
(0)
0.0006586
(0)
0.3042053
(0)
-0.0832779
(0)
-0.0558866
(0.002)
0.0071972
(0.345)
0.0094837
(0.308)
-0.0036568
(0.29)
0.0000407
(0.289)
0.0188171
(0.295)
-0.0051513
(0.302)
-0.0034569
(0.317)
-0.0487068
(0.022)
-0.0641806
(0)
0.024747
(0)
-0.0002757
(0)
-0.1273434
(0)
0.034861
(0)
0.0233947
(0.001)
-0.0593942
(0.032)
-0.0782633
(0.001)
0.0301771
(0)
-0.0003362
(0)
-0.1552856
(0)
0.0425103
(0)
0.0285281
(0.005)
-0.0062584
(0.042)
-0.0082467
(0.003)
0.0031798
(0)
-0.0000354
(0)
-0.0163626
(0)
0.0044793
(0)
0.003006
(0.009)
-0.0091914
(0.05)
-0.0121114
(0.005)
0.00467
(0)
-0.000052
(0)
-0.0240308
(0)
0.0065786
(0.001)
0.0044148
(0.013)
-0.0987545
(0)
-0.1560326
(0)
-0.2242277
(0)
-0.3345015
(0)
-0.0965213
(0.524)
-0.0061086
(0.309)
-0.0096516
(0.299)
-0.0138699
(0.299)
-0.0206911
(0.297)
-0.0059705
(0.593)
0.0413396
(0)
0.0653168
(0)
0.093864
(0)
0.1400257
(0)
0.0404048
(0.524)
0.0504105
(0)
0.0796489
(0)
0.11446
(0)
0.1707506
(0)
0.0492706
(0.527)
0.0053118
(0.001)
0.0083927
(0)
0.0120607
(0)
0.0179921
(0)
0.0051917
(0.53)
0.0078011
(0.003)
0.0123258
(0.001)
0.0177129
(0.001)
0.026424
(0.001)
0.0076247
(0.532)
0.0702099
(0.072)
0.0108798
(0.164)
0.0043429
(0.36)
0.000673
(0.411)
-0.0293906
(0.072)
-0.0045544
(0.163)
-0.0358396
(0.083)
-0.0055537
(0.176)
-0.0037764
(0.092)
-0.0005852
(0.186)
-0.0055463
(0.1)
-0.0008595
(0.193)
0.0102432
(0.27)
0.0006336
(0.44)
-0.0042879
(0.269)
-0.0052288
(0.275)
-0.000551
(0.281)
-0.0008092
(0.285)
Years of Education
(relative to 10
years)
11
12
13
14 or more
Still at college
(log) Real
Housing wealth
Real GDP Growth
Labour Market
Regional
Unemployment
rate
χ 2 (p-value)
UNCERTAINTY
Age
Age squared/100
0.1507
-0.0312191
(0)
0.0003901
(0)
47
Real Interest
Rates
ρy
ws
Log-likelihood
n
0.0250339
(0.175)
0.0529026
(0.366)
-13001.605
8722
Notes to Tables A3.2-A3.5: Estimates of region, industry and year dummies as well as a time trend are
not reported to conserve space
A consistent finding in the above tables is that males earn more than females in both
self-employment and wage-employment. Blacks and Asians tend to have lower wage
earnings but their self-employed earnings are the same as Whites, ceteris paribus.
Both self-employed and wage earnings increase with experience (age) at a
diminishing rate. In contrast, formal education has no affect on self-employed
earnings but tends to increase wages. Importantly wealth appears to play no part in
self-employed incomes in either the 1980s or 1990s. This, contrary to Evans and
Jovanovic (1989), suggests the absence of liquidity constraints having controlled for
human capital (Cressy, 1996).
The pattern of correlation coefficients in the 1980s is compatible with occupational
self-selection on the basis of comparative advantage ρ yw s > 0; ρ ye s < 0 . Interestingly
(
)
though, in the 1990s, ρ yw s is statistically insignificant which means that the selfemployed could expect to earn the same as wage workers in wage employment. The
implication here is that the self-employed, in the 1990s, are not a group of employees
who choose self-employment because of low wage earnings ability (Hamilton, 2000,
reaches a similar finding with US data for the 1980s). Indeed, the structural analysis
of self-employment choices, in Table 3, supports the idea that lifestyle preferences
rather than financial necessity is the driver of self-employment in the 1990s.
48