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Duisenberg school of finance is a collaboration of the Dutch financial sector and universities, with the ambition to support innovative research and offer top quality academic education in core areas of finance.
TI 2009-032/3
Tinbergen Institute Discussion Paper
Entrepreneurship and the Business
Cycle
P.D. Koellinger
A.R. Thurik
Erasmus School of Economics, Erasmus University Rotterdam, EIM Business and Policy Research,
Zoetermeer, The Netherlands, and Tinbergen Institute.
Tinbergen Institute is the graduate school and research institute in economics of Erasmus
University Rotterdam, the University of Amsterdam and VU University Amsterdam.
More TI discussion papers can be downloaded athttp://www.tinbergen.nl
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Duisenberg school of finance is a collaboration of the Dutch financial sector and universities,
with the ambition to support innovative research and offer top quality academic education in
core areas of finance.
DSF research papers can be downloaded at:http://www.dsf.nl/
Duisenberg school of finance
Gustav Mahlerplein 117
1082 MS Amsterdam
The Netherlands
Tel.: +31(0)20 525 8579
1
ENTREPRENEURSHIP AND THE BUSINESS CYCLE
PHILIPP D. KOELLINGER
a,b,c,d,e
and A. ROY THURIK
a,b,c,e
Forthcoming in the Review of Economics and Statistics
Version (2.0)
Date of completion of this updated manuscript: Mar 23, 2011
(a) Erasmus University Rotterdam, Rotterdam, NL, Erasmus School of Economics, P.O.
Box 1738, 3000 DR Rotterdam, NL. Tel: +31-10-4082776; Fax: +31-10-4089141; Email:
[email protected]
(b) Tinbergen Institute, Rotterdam, NL
(c) Erasmus Research Institute in Management, Rotterdam, NL
(d) German Institute for Economic Research (DIW Berlin), Berlin, D
(e) EIM Business and Policy Research, Zoetermeer, the Netherlands
2
ENTREPRENEURSHIP AND THE BUSINESS CYCLE
Abstract: We find new empirical regularities in the business cycle in a cross-country panel of
22 OECD countries for the period 1972-2007; entrepreneurship Granger-causes the cycles of
the world economy. Furthermore, the entrepreneurial cycle is positively affected by the
national unemployment cycle. We discuss possible causes and implications of these findings.
JEL Codes: L26, E32
Keywords: Entrepreneurship, business cycle
First version: April 2009
This version: March 2011 (forthcoming in the Review of Economics and Statistics)
Acknowledgements: We are grateful for comments from Boyan Jovanovic, Adriano
Rampini, Dennis Fok, Wim Naude, Erik Canton, Marcus Dejardin, André van Stel, Dick van
Dijk, Michael Fritsch, Maurice Bun, Sander Wennekers, Joao Faria, Christian Roessler,
Avichai Snir, Schizas Emmanouil, Martin Carree, Niels Bosma, Philip-Hans Franses, two
anonymous referees and participants at various conferences, workshops and seminars. This
paper has been written in cooperation with the research program SCALES, carried out by
EIM and financed by the Dutch Ministry of Economic Affairs.
3
I. Introduction
Despite the structural changes in modern economies that have led to the increasing
importance of entrepreneurs (Audretsch & Thurik, 2001; Baumol, 2002; Audretsch, 2007),
macroeconomic models of business cycles usually abstract from entrepreneurship, with only a
few exceptions (Bernanke & Gertler, 1989; Carlstrom & Fuerst, 1997; Rampini, 2004). In
addition, there is very little empirical evidence on this topic.
1
Therefore, in establishing the
relationship between entrepreneurship and the business cycle, we find it worthwhile to ‘let the
data speak freely’ (Hoover et al., 2008; Juselius, 2009) instead of deducing and calibrating a
model from more or less arbitrary assumptions regarding entrepreneurial behavior.
We explore the relationship between entrepreneurship
2
and the business cycle using
panel data from 22 OECD countries for the period 1972-2007. To the best of our knowledge,
this is the first study of its kind. We differentiate between the aggregate and the national level.
The aggregate level refers to the weighted average of business cycle fluctuations across
countries. We loosely refer to the aggregate-level business cycle as the global or world,
economy
3
. The national level analyzes the data for each of the 22 countries separately and in a
panel framework.
Differentiating between these two research levels of the relationship between the
entrepreneurship and the business cycle, we obtain four results. First, global fluctuations in
entrepreneurship are an early indicator of the world business cycle: they Granger-cause
increases in GDP. Second, on this aggregate level, GDP and unemployment cycles do not
predict the entrepreneurial cycle. This suggests that other factors besides the world business
climate influence global trends in entrepreneurial activity. Third, at the national level, the
1
The only other empirical contributions on the topic that we are aware of are the work of Congregado et al. (2009) and Golpe
(2009). In contrast to the present article, the work of these authors uses only self-employment data as a measure of
entrepreneurial activity from a smaller number of countries covering a shorter time frame. Also, the focus of their
analysis is different from ours, e.g., they focus on hysteresis effects and cross-country heterogeneity. Faria et al. (2009;
2010) focus on technical aspects of the dynamics and cyclicality of the relationship between unemployment and
entrepreneurship.
2
Entrepreneurship is defined in terms of owner-managers of firms.
3
The 22 OECD countries account for more than 55% of the world GDP in all years included in our analysis (OECD 2010).
4
impact of entrepreneurship on the cycle seems to be weaker than at the aggregate level.
Fourth, again at the national level, an upswing in the unemployment cycle leads to a
subsequent upswing in the entrepreneurship cycle. Numerous tests using various methods and
different data confirm the robustness of these main results. Taken together, our results suggest
that entrepreneurship is intertwined with business cycle dynamics in ways that do not follow
from existing theories.
In the following section, related literature is discussed. Section Three presents our
empirical evidence, including a robustness check using another data set. Section Four
discusses the empirical finding and concludes. The Appendix in Koellinger and Thurik (2009)
and updated in March 2011 reports on various robustness checks using our main data set.
II. Related literature
Bernanke and Gertler (1989) study the influence of entrepreneurs’ net worth on
borrowing conditions and the resulting investment fluctuations in a neoclassical model of the
business cycle. The key to their analysis is the principal-agent problem between entrepreneurs
and lenders: only entrepreneurs can costlessly observe the returns on their individual projects,
whereas outside lenders must jointly incur fixed costs to observe these returns. The greater the
“collateralizable” net worth of the entrepreneur’s balance sheet, the lower the expected
agency costs will be, as implied by the optimal financial contract. Because entrepreneurs’ net
worth is likely to be pro-cyclical (i.e., entrepreneurs are more solvent during good times),
there will be a decline in agency costs and an increase in real investments during booms. The
opposite happens during recessions. Hence, an accelerator effect emerges due to the principal-
agent problem between entrepreneurs and lenders. The focus of Bernanke and Gertler (1989)
is on the real effects caused by random fluctuations in balance sheets (e.g., due to an
unanticipated fall in real estate prices) and not on entrepreneurship per se. They assume that
the potential share of entrepreneurs in the economy is independent of business cycle
fluctuations, whereas the fraction of entrepreneurs who get funding and produce is pro-
cyclical.
Carlstrom and Fuerst (1997) extend the work of Bernanke and Gertler (1989) by
developing a computable general equilibrium model that can quantitatively capture the
propagation of productivity shocks through agency costs. Similar to that of Bernanke and
Gertler, the model by Carlstrom and Fuerst also does not focus on entrepreneurship per se and
assumes that the potential share of entrepreneurs in a population is a constant that does not
5
fluctuate with the cycle. However, due to simplifying assumptions, they end up with the
somewhat counter-intuitive result that bankruptcy rates and risk premia are highest during
boom periods as a result of positive technology shocks and higher capital prices. Hence, the
number of solvent entrepreneurs would then be counter-cyclical. Furthermore, the bankruptcy
probability is the same across entrepreneurs, independent from their net worth. However, the
authors point this out as one of the shortcomings of their model.
The only theoretical business cycle model we are aware of that explicitly focuses on the
share of entrepreneurs in the labor force is that of Rampini (2004). In this real business cycle
model, the risk associated with entrepreneurial activity implies that the amount of such
activity should be pro-cyclical, which also results in the amplification and inter-temporal
propagation of productivity shocks. Agents are assumed to be risk-averse and can choose
between a risk-free production technology (i.e., wage employment) and a risky production
technology (i.e., entrepreneurship). Productivity shocks shift the output of both technologies
by a constant. As a result, all agents are wealthier during economic booms. The risk-free
production technology is always available, which implies no structural unemployment.
Furthermore, it is assumed that the expected value of risky entrepreneurship exceeds the
opportunity costs of risk-free employment. Hence, all agents prefer entrepreneurship to
employment. However, the share of entrepreneurs is restricted by a financial intermediary that
determines the optimal rate of entrepreneurship, given the productivity shock of the period
and the wealth and preferences of the agents. The intermediary designs an optimal incentive
contract that allows entrepreneurs to insure a part of their risk via leverage. Because all agents
are wealthier as a result of positive productivity shocks and because risk aversion is assumed
to decrease with wealth, it is optimal to have a higher share of entrepreneurs during economic
booms.
4
Furthermore, it is also argued in the spirit of Bernanke and Gertler (1989) that agency
costs are counter-cyclical because more utility is lost due to the moral hazard problem when
productivity is low. Hence, Rampini (2004) concludes that entrepreneurship is pro-cyclical,
even if agents have access to financial intermediaries.
Aside from the abovementioned direct analyses of the relationship between
entrepreneurship and the business cycle, there are several labor market-related effects
identified in the entrepreneurship literature. A literature survey by Parker (2009, pp. 142-143)
4
Alternatively, one might argue that risk preferences remain constant over time, but the higher level of wealth of agents
during booms reduces liquidity constraints and hence increases entrepreneurial activity (Evans and Jovanovic, 1989).
6
discusses evidence from the US that new firm formation is pro-cyclical. He also points to the
effect of falling wages in recessions, which may lower the opportunity costs for starting a
business and encouraging marginal types of entrepreneurship. Yet, low-quality businesses
may be removed in recessions, exerting a countervailing force on the total number of business
owners. Congregado et al. (2009) discuss the recession-push and prosperity-pull concepts as
well as numerous studies supporting these concepts. The recession-push argument would lead
to a counter-cyclical and the prosperity-pull argument to a pro-cyclical effect.
5
The vast majority of the business cycle literature, however, does not explicitly model
entrepreneurial activity. This implies the hypothesis that entrepreneurship is either
independent from the cycle or irrelevant for the real economy. The results are mixed and often
indirect in the entrepreneurship literature (Thurik et al., 2008 and Congregado et al., 2009).
This ambiguity does not lead to dominant hypotheses. Hence, we will focus on the data and
link our results to the existing literature afterwards.
III. Analysis
In general, there are two ways of analyzing our data. Either observations can be
averaged across countries to focus on global trends or coefficients can be averaged, putting
more emphasis on national conditions. Of course, the two approaches address somewhat
different questions: the first investigates if global trends in entrepreneurial activity exist and
how they relate to the cycles of the world economy; the second approach investigates the
average relationship between entrepreneurship and the cycle at the national level. These two
perspectives are likely to yield diverging results if different factors influence the data at the
aggregate and national levels. For example, low-skilled individuals who consider starting a
business are more likely to be influenced by national labor market policies than by global
technological trends, whereas the opposite can be expected for highly skilled opportunity
entrepreneurs. Because the former constitute the majority of entrepreneurs (Kirchhoff, 1994),
one can expect to find different relationships between unemployment and entrepreneurial
activity at the national and global levels if labor market conditions are imperfectly correlated
across countries.
5
See also Thurik et al. (2008) and Parker (2009) discussing the interplay between unemployment and entrepreneurship.
7
Furthermore, economic variables at the country level are more likely to be influenced
by national policies and the conditions in specific, closely related nations. The world economy
is hardly influenced by the idiosyncratic policies of particular countries. Instead, global-scale
business cycle fluctuations reflect more directly developments of global importance, such as
major geopolitical changes, world market prices of commodities or technological
breakthroughs. Of course, these global developments also impact on national business cycles,
but the additional influence of national policies and conditions leads to country-specific
patterns and dilutes the correlation of cycles across countries.
The present section consists of three parts. First, we present the global results of the co-
movement of GDP, unemployment and business ownership, using data from 22 OECD
countries for the period 1972-2007. The second part deals with the co-movement of these
variables at the country level. The third part is a robustness check we carried out using a
different data source, with the aim of replicating the results of our initial analysis using an
alternative measure of entrepreneurship.
A. Aggregate analysis of entrepreneurship, unemployment and the cycle
We construct a balanced cross-country panel of 22 OECD countries
6
with annual data
for the period 1972-2007 using various sources. OECD data are used to determine annual real
GDP in constant 2000 prices in national currencies and standardized unemployment rates.
Entrepreneurial activity per country and per year is measured as the share of business
owners in the total labor force
7
, using data from Compendia 2007.1 that corrects for
measurement differences across countries and over time.
8
This is a broad measure of
entrepreneurial activity that includes incorporated, self-employed individuals (owner-
managers of incorporated businesses) and (unincorporated) self-employed persons with and
6
The included countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Greece, Iceland, Ireland, Japan,
Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom and
the USA. These are the 23 old OECD countries, with Germany excluded because we are unable to correct for the
influence of its unification on the time series.
7
The total labor force is the sum of the employed and the unemployed.
8
Data are constructed by EIM (Zoetermeer, NL) on the basis of OECD material. Seehttp://www.ondernemerschap.nl for the
data and van Stel (2005) for an explanation of the method. Quarterly data regarding business ownership rates are not
available for most countries.
8
without employees; conversely, the measure excludes unpaid family workers
9
. The business
ownership rate also excludes so-called “side-owners," who generate less than 50% of their
income by running their own businesses.
A disadvantage of using business ownership as a measure of entrepreneurial activity is
that it does not fully capture early-stage ventures that do not yet generate a substantial
contribution to the owner’s income. In addition, business ownership rates reflect to some
extent the existing industry structures in place rather than the introduction of new economic
activity in the Schumpeterian (1934) and Kirznerian (1973) sense.
10
To address these
conceptual shortcomings of business ownership rates as a measure of entrepreneurial activity,
we also use data from the Global Entrepreneurship Monitor (GEM) (Reynolds et al., 2005) as
a second measure for robustness checks.
Following the convention of defining the business cycle as a series of deviations from
long-term trends in GDP data, we decompose time series into trends and cycles using the
Hodrick-Prescott filter (Hodrick & Prescott, 1997), referred to below as the HP filter. The HP
filter is a standard method of removing trend movements that has been applied both to actual
data and to artificial data in numerous studies.
11
The smoothing parameter ? of the filter,
which penalizes acceleration in the trend relative to the business cycle component, needs to be
specified. Most of the business cycle literature uses quarterly data and a ? value of 1600, as
has been suggested by Hodrick and Prescott (1997). Unfortunately, business ownership rates
are only available on an annual basis in most countries. Because the time period over which
aggregation takes place affects the variance in the process at discrete time intervals, the ?
value must be adjusted. Ravn and Uhlig (2002) show that the appropriate ? value for annual
data is 6.25; this is the value we use for our analysis.
9
Unpaid family owners can be regarded as irrelevant in measuring the extent of entrepreneurship, as they do not own the
businesses they work for and do not bear responsibility or risk in the way that ‘real’ entrepreneurs do.
10
Despite these disadvantages, the business ownership rate is widely used: in Thurik et al. (2008), investigating the
interrelationships between entrepreneurship and unemployment; in Erken et al. (2009), measuring the influence of
entrepreneurship on total factor productivity; and in Carree et al. (2002), studying the influence of economic
development. See also Parker (2009, Chapter 1).
11
See Ravn and Uhlig (2002) and Jaimovich and Siu (2009), for example.
9
To test if our results are robust to different methods of de-trending the data, we repeat
all analyses using a ? value of 100 and first differences of growth rates.
12
The main results we
present below have been computed using the HP filer with a ? value of 6.25. They are not
sensitive to the method of de-trending. The additional results are reported in the Appendix in
Koellinger and Thurik (2009).
>> Table I about here > Figure I about here 99% confidence). A countercyclical relationship between
GDP and entrepreneurship can be clearly rejected since the contemporaneous correlation
between the two series is positive (0.3, significant at >90% confidence). A feedback between
unemployment and entrepreneurship seems likely because labor market opportunities
determine to a large extent the opportunity costs of entrepreneurship (Thurik et al., 2008).
Indeed, the contemporaneous correlation between unemployment and entrepreneurship is -
0.43 (significant at >98% confidence).
This interrelation between GDP, unemployment and entrepreneurship suggests a joint
analysis of these three variables in an autoregressive context. Given the stationarity of
13
Results are available from the authors on request.
11
detrended data
14
, we estimate a vector auto-regression model with two lags, VAR(2),
including deviations from trends in terms of business ownership, real GDP and
unemployment (Lütkepohl, 2007; Greene, 2003). The optimal lag length of two is
unanimously suggested by the Akaike (1974) information criterion, the Hannan-Quinn (1979)
criterion and the Schwarz (1978) criterion for 1 < p
max
< 7.
Our reduced-form VAR(2) expresses each variable as a linear function of its own two
past values and the two past values of the other two variables. The vector of errors is assumed
to be serially uncorrelated with contemporaneous covariance across equations. Specifically,
we estimate
(1)
t t t t
u y A y A v y + + + =
? ? 2 2 1 1
,
where
( )
?
=
t t t t
y y y y
3 2 1
, , is a 3 x 1 random vector with
=
1
y real GDP cycle,
=
2
y unemployment cycle, and
=
3
y business ownership cycle,
1
A and
2
A are fixed 3 x 3 matrices of parameters,
v is a 3 x 1 vector of fixed parameters, and
t
u is assumed to be white noise; that is
( )
s t u u E
u u E
u E
s t
t t
t
? ? =
?
?
?
?
?
?
?
? =
?
?
?
?
?
?
?
=
0
0
.
The model is estimated with least squares. Confidence intervals are based on common t-
values, which have been shown to yield reasonably accurate estimates even for small samples
(Lütkepohl, 2007; p. 94).
>> Table II about here > Table III about here > Figure II about here 90% confidence. Again, the effect of the entrepreneurship shock levels
off in later years, as the cycle progresses. Although this pattern is partly a result of the general
upswing in economic activity that tends to follow an expansion of entrepreneurial activity, it
is equally possible that part of the effect stems from the additional economic activity and the
jobs created by new firms.
16
In summary, these observations suggest that an impulse from global entrepreneurial
activity is typically followed by a recovery of the world economy and a decrease in
unemployment.
>> Figure III about here > Table IV about here > Table V about here > Table VI about here 90% confidence
** denotes significance at >95% confidence
*** denotes significance at >99% confidence
Table A2b – Vector autoregressive model on world economy, first differences of growth
rates
Y
1
= GDP Y
2
= Ent Y
3
= Unempl
Coef. Std. Dev. Coef. Std. Dev. Coef. Std. Dev.
GDP (t-1) 0.39* (0.20) 0.05 (0.13) -7.97*** (-1.39)
Unempl (t-1) 0.09*** (0.03) -0.01 (0.02) -0.99*** (0.20)
Ent (t-1) 0.75** (0.30) -0.03 (0.19) -3.87* (-2.06)
Model diagnostics
Portmanteau (16) test of residual
autocorrelation, modified (Ahn 1988)
0.28
LJB test for nonnormality of
residuals (Doornik and Hansen 2008)
0.77
LMF (5) statistic of residual
autocorrelation (Edgerton and Shukur
1999)
0.64 MARCHLM (2) (Lütkepohl and
Krätzig 2004)
0.23
Notes:
* denotes significance at >90% confidence
** denotes significance at >95% confidence
*** denotes significance at >99% confidence
33
We computed the Akaike (1974) information criterion, the Hannan-Quinn (1979) criterion and the Schwarz (1978)
criterion for 1 < p
max
< 7.
36
Tables A2a and A2b show that entrepreneurship is an early indicator of the business
cycle. The first year lag of entrepreneurship in the model with HP(? = 100)-filtered data is not
significant, but the second year lag is. The model using first differences of growth rates
strongly confirms that entrepreneurship is an early indicator of the cycle. Both models show
no signs of a specification problem in the test statistics. In particular, there is no indication of
remaining autocorrelation in the error terms. Both models also confirm that entrepreneurship
at the aggregate level is not influenced by GDP or unemployment.
The Granger causality tests in Tables A3a and A3b strongly confirm these conclusions.
Table A3a – Granger-Causality Wald Tests on world economy, HP-filtered data (? =
100)
Dependent Variable in Regression
Regressor GDP Ent Unempl
GDP 0.50 0.00
Ent 0.01 0.03
Unempl 0.07 0.31
Notes: Results were computed from a vector autoregression with two lags and a constant term over the annual
cross-country averages for the 1972-2007 period. Entries show the p-values for Chi2-tests that lags of the
variable in the row labeled Regressor do not enter the reduced form equation for the column variable labeled
Dependent Variable.
Table A3b – Granger-Causality Wald Tests on world economy, first differences of
growth rates
Dependent Variable in Regression
Regressor GDP Ent Unempl
GDP 0.72 0.00
Ent 0.01 0.05
Unempl 0.00 0.75
Notes: Results were computed from a vector autoregression with two lags and a constant term over the annual
cross-country averages for the 1972-2007 period. Entries show the p-values for Chi2-tests that lags of the
variable in the row labeled Regressor do not enter the reduced form equation for the column variable labeled
Dependent Variable.
37
Tables A4a and A4b confirm that entrepreneurship is a less reliable or unreliable early
indicator of the business cycle at the country level. In HP(? = 100)-filtered data,
entrepreneurship Granger-causes GDP only in the US and Denmark, whereas six countries
show this relationship in the data that has been de-trended using first differences of growth
rates. Both Tables reaffirm that the aggregate level results are not due to a few countries with
particularly strong influence of entrepreneurship. This conclusion is supported if the US is
excluded from the aggregate level analysis (results available from the authors on request).
Table A4a - Granger causality of business ownership on real GDP cycles across
countries, HP-filtered data (? = 100)
Country Granger causality
Wald test
Australia 0.51
Austria 0.23
Belgium 0.26
Canada 0.13
Denmark 0.09
Finland 0.91
France 0.94
Greece 0.68
Iceland 0.82
Ireland 0.12
Italy 0.37
Japan 0.43
Luxembourg 0.13
Netherlands 0.36
New Zealand 0.73
Norway 0.31
Portugal 0.26
Spain 0.33
Sweden 0.15
Switzerland 0.93
United Kingdom 0.68
USA 0.01
Notes: Results were computed from country specific
VARs for the period 1972-2007.
38
Table A4b - Granger causality of business ownership on real GDP cycles across
countries, first differences of growth rates
Country Granger causality
Wald test
Australia 0.00
Austria 0.06
Belgium 0.98
Canada 0.64
Denmark 0.41
Finland 0.96
France 0.76
Greece 0.99
Iceland 0.06
Ireland 0.08
Italy 0.37
Japan 0.59
Luxembourg 0.49
Netherlands 0.34
New Zealand 0.18
Norway 0.48
Portugal 0.31
Spain 0.08
Sweden 0.96
Switzerland 0.92
United Kingdom 0.52
USA 0.04
Notes: Results were computed from country specific
VARs for the period 1972-2007.
39
The panel regressions in Tables A5a and A5b, which report average coefficients across
countries under the conservative fixed-effects assumption, confirm the conclusion that
entrepreneurship at the country level does not predict GDP or unemployment, but is instead
positively driven by past increases in unemployment.
Table A5a - Fixed effects regressions on cross-country panel, HP-filtered data (? = 100)
Y = GDP Y = Unempl Y = Ent
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
GDP (t-1) 0.81** (0.04) -3.28** (0.33) 0.02 (0.05)
GDP(t-2) -0.33** (0.04) 1.69** (0.31) -0.05 (0.05)
Unempl (t-1) -0.00 (0.00) 0.45** (0.03) 0.01** (0.04)
Unempl (t-2) -0.00 (0.00) -0.05** (0.02) 0.00 (0.00)
Ent (t-1) 0.00 (0.03) -0.22 (0.23) 0.58** (0.04)
Ent (t-2) 0.01 (0.03) -0.25 (0.23) -0.20** (0.04)
Notes: All models include time dummies and a constant and including time dummies. OLS
and system GMM estimators (with 136 instruments, collapsed) deliver almost identical results.
N=22, T=32, observations=726
* denotes significance at 90% confidence
** denotes significance at >95% confidence
Table A5b - Fixed effects regressions on cross-country panel, first differences of growth
rates
Y = GDP Y = Unempl Y = Ent
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
GDP (t-1) -0.40** (0.04) -3.23** (0.52) 0.04 (0.06)
GDP(t-2) -0.26** (0.04) -1.33** (0.51) 0.05 (0.05)
Unempl (t-1) -0.00 (0.00) -0.58** (0.04) 0.00 (0.00)
Unempl (t-2) -0.00 (0.00) -0.36** (0.03) 0.01** (0.00)
Ent (t-1) 0.03 (0.03) -0.45 (0.35) -0.58** (0.04)
Ent (t-2) 0.04 (0.03) -0.11 (0.34) -0.39** (0.04)
Notes: All models include time dummies and a constant and including time dummies. OLS
and system GMM estimators (with 136 instruments, collapsed) deliver almost identical results.
N=22, T=32, observations=726
* denotes significance at 90% confidence
** denotes significance at >95% confidence
40
A comparison of Figures 1, A1a and A1b shows that the detrending method influences the
cyclical pattern that is extracted from the raw data. HP(? = 100)-filtered data (Figure A1a)
emphasizes the longer-term cyclical peaks and troughs, while the first differences of growth
rates (Figure A1b) emphasizes short-term fluctuations compared to the reference of HP(? =
6.25)-filtered data (Figure 1 in the text). The spectra of the extracted series also look markedly
different for the three detrending methods. In particular, HP(? = 100)-filtered data almost
entirely filters out low frequencies and forces a common spectral shape on self-employment,
GDP and unemployment, whereas first differences of growth rates emphasizes low
frequencies and maintain a high degree of heterogeneity among the spectra of the three
variables.
Although the detrending procedure has a large influence on the resulting time-series, the main
results of our analyses are replicated.
Figure A1a - Average deviations of real GDP and business ownership rates from trend
in percent across 22 OECD countries, HP-filtered data (? = 100)
-4
-3
-2
-1
0
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3
1
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GDP deviation from trend Business ownership rate deviation from trend
41
Figure A1b - Average deviations of real GDP and business ownership rates from trend
in percent across 22 OECD countries, first differences of growth rates
-0,080
-0,060
-0,040
-0,020
0,000
0,020
0,040
0,060
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GDP deviation from trend Business ownership rate deviation from trend
doc_220410240.pdf
Duisenberg school of finance is a collaboration of the Dutch financial sector and universities, with the ambition to support innovative research and offer top quality academic education in core areas of finance.
TI 2009-032/3
Tinbergen Institute Discussion Paper
Entrepreneurship and the Business
Cycle
P.D. Koellinger
A.R. Thurik
Erasmus School of Economics, Erasmus University Rotterdam, EIM Business and Policy Research,
Zoetermeer, The Netherlands, and Tinbergen Institute.
Tinbergen Institute is the graduate school and research institute in economics of Erasmus
University Rotterdam, the University of Amsterdam and VU University Amsterdam.
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1
ENTREPRENEURSHIP AND THE BUSINESS CYCLE
PHILIPP D. KOELLINGER
a,b,c,d,e
and A. ROY THURIK
a,b,c,e
Forthcoming in the Review of Economics and Statistics
Version (2.0)
Date of completion of this updated manuscript: Mar 23, 2011
(a) Erasmus University Rotterdam, Rotterdam, NL, Erasmus School of Economics, P.O.
Box 1738, 3000 DR Rotterdam, NL. Tel: +31-10-4082776; Fax: +31-10-4089141; Email:
[email protected]
(b) Tinbergen Institute, Rotterdam, NL
(c) Erasmus Research Institute in Management, Rotterdam, NL
(d) German Institute for Economic Research (DIW Berlin), Berlin, D
(e) EIM Business and Policy Research, Zoetermeer, the Netherlands
2
ENTREPRENEURSHIP AND THE BUSINESS CYCLE
Abstract: We find new empirical regularities in the business cycle in a cross-country panel of
22 OECD countries for the period 1972-2007; entrepreneurship Granger-causes the cycles of
the world economy. Furthermore, the entrepreneurial cycle is positively affected by the
national unemployment cycle. We discuss possible causes and implications of these findings.
JEL Codes: L26, E32
Keywords: Entrepreneurship, business cycle
First version: April 2009
This version: March 2011 (forthcoming in the Review of Economics and Statistics)
Acknowledgements: We are grateful for comments from Boyan Jovanovic, Adriano
Rampini, Dennis Fok, Wim Naude, Erik Canton, Marcus Dejardin, André van Stel, Dick van
Dijk, Michael Fritsch, Maurice Bun, Sander Wennekers, Joao Faria, Christian Roessler,
Avichai Snir, Schizas Emmanouil, Martin Carree, Niels Bosma, Philip-Hans Franses, two
anonymous referees and participants at various conferences, workshops and seminars. This
paper has been written in cooperation with the research program SCALES, carried out by
EIM and financed by the Dutch Ministry of Economic Affairs.
3
I. Introduction
Despite the structural changes in modern economies that have led to the increasing
importance of entrepreneurs (Audretsch & Thurik, 2001; Baumol, 2002; Audretsch, 2007),
macroeconomic models of business cycles usually abstract from entrepreneurship, with only a
few exceptions (Bernanke & Gertler, 1989; Carlstrom & Fuerst, 1997; Rampini, 2004). In
addition, there is very little empirical evidence on this topic.
1
Therefore, in establishing the
relationship between entrepreneurship and the business cycle, we find it worthwhile to ‘let the
data speak freely’ (Hoover et al., 2008; Juselius, 2009) instead of deducing and calibrating a
model from more or less arbitrary assumptions regarding entrepreneurial behavior.
We explore the relationship between entrepreneurship
2
and the business cycle using
panel data from 22 OECD countries for the period 1972-2007. To the best of our knowledge,
this is the first study of its kind. We differentiate between the aggregate and the national level.
The aggregate level refers to the weighted average of business cycle fluctuations across
countries. We loosely refer to the aggregate-level business cycle as the global or world,
economy
3
. The national level analyzes the data for each of the 22 countries separately and in a
panel framework.
Differentiating between these two research levels of the relationship between the
entrepreneurship and the business cycle, we obtain four results. First, global fluctuations in
entrepreneurship are an early indicator of the world business cycle: they Granger-cause
increases in GDP. Second, on this aggregate level, GDP and unemployment cycles do not
predict the entrepreneurial cycle. This suggests that other factors besides the world business
climate influence global trends in entrepreneurial activity. Third, at the national level, the
1
The only other empirical contributions on the topic that we are aware of are the work of Congregado et al. (2009) and Golpe
(2009). In contrast to the present article, the work of these authors uses only self-employment data as a measure of
entrepreneurial activity from a smaller number of countries covering a shorter time frame. Also, the focus of their
analysis is different from ours, e.g., they focus on hysteresis effects and cross-country heterogeneity. Faria et al. (2009;
2010) focus on technical aspects of the dynamics and cyclicality of the relationship between unemployment and
entrepreneurship.
2
Entrepreneurship is defined in terms of owner-managers of firms.
3
The 22 OECD countries account for more than 55% of the world GDP in all years included in our analysis (OECD 2010).
4
impact of entrepreneurship on the cycle seems to be weaker than at the aggregate level.
Fourth, again at the national level, an upswing in the unemployment cycle leads to a
subsequent upswing in the entrepreneurship cycle. Numerous tests using various methods and
different data confirm the robustness of these main results. Taken together, our results suggest
that entrepreneurship is intertwined with business cycle dynamics in ways that do not follow
from existing theories.
In the following section, related literature is discussed. Section Three presents our
empirical evidence, including a robustness check using another data set. Section Four
discusses the empirical finding and concludes. The Appendix in Koellinger and Thurik (2009)
and updated in March 2011 reports on various robustness checks using our main data set.
II. Related literature
Bernanke and Gertler (1989) study the influence of entrepreneurs’ net worth on
borrowing conditions and the resulting investment fluctuations in a neoclassical model of the
business cycle. The key to their analysis is the principal-agent problem between entrepreneurs
and lenders: only entrepreneurs can costlessly observe the returns on their individual projects,
whereas outside lenders must jointly incur fixed costs to observe these returns. The greater the
“collateralizable” net worth of the entrepreneur’s balance sheet, the lower the expected
agency costs will be, as implied by the optimal financial contract. Because entrepreneurs’ net
worth is likely to be pro-cyclical (i.e., entrepreneurs are more solvent during good times),
there will be a decline in agency costs and an increase in real investments during booms. The
opposite happens during recessions. Hence, an accelerator effect emerges due to the principal-
agent problem between entrepreneurs and lenders. The focus of Bernanke and Gertler (1989)
is on the real effects caused by random fluctuations in balance sheets (e.g., due to an
unanticipated fall in real estate prices) and not on entrepreneurship per se. They assume that
the potential share of entrepreneurs in the economy is independent of business cycle
fluctuations, whereas the fraction of entrepreneurs who get funding and produce is pro-
cyclical.
Carlstrom and Fuerst (1997) extend the work of Bernanke and Gertler (1989) by
developing a computable general equilibrium model that can quantitatively capture the
propagation of productivity shocks through agency costs. Similar to that of Bernanke and
Gertler, the model by Carlstrom and Fuerst also does not focus on entrepreneurship per se and
assumes that the potential share of entrepreneurs in a population is a constant that does not
5
fluctuate with the cycle. However, due to simplifying assumptions, they end up with the
somewhat counter-intuitive result that bankruptcy rates and risk premia are highest during
boom periods as a result of positive technology shocks and higher capital prices. Hence, the
number of solvent entrepreneurs would then be counter-cyclical. Furthermore, the bankruptcy
probability is the same across entrepreneurs, independent from their net worth. However, the
authors point this out as one of the shortcomings of their model.
The only theoretical business cycle model we are aware of that explicitly focuses on the
share of entrepreneurs in the labor force is that of Rampini (2004). In this real business cycle
model, the risk associated with entrepreneurial activity implies that the amount of such
activity should be pro-cyclical, which also results in the amplification and inter-temporal
propagation of productivity shocks. Agents are assumed to be risk-averse and can choose
between a risk-free production technology (i.e., wage employment) and a risky production
technology (i.e., entrepreneurship). Productivity shocks shift the output of both technologies
by a constant. As a result, all agents are wealthier during economic booms. The risk-free
production technology is always available, which implies no structural unemployment.
Furthermore, it is assumed that the expected value of risky entrepreneurship exceeds the
opportunity costs of risk-free employment. Hence, all agents prefer entrepreneurship to
employment. However, the share of entrepreneurs is restricted by a financial intermediary that
determines the optimal rate of entrepreneurship, given the productivity shock of the period
and the wealth and preferences of the agents. The intermediary designs an optimal incentive
contract that allows entrepreneurs to insure a part of their risk via leverage. Because all agents
are wealthier as a result of positive productivity shocks and because risk aversion is assumed
to decrease with wealth, it is optimal to have a higher share of entrepreneurs during economic
booms.
4
Furthermore, it is also argued in the spirit of Bernanke and Gertler (1989) that agency
costs are counter-cyclical because more utility is lost due to the moral hazard problem when
productivity is low. Hence, Rampini (2004) concludes that entrepreneurship is pro-cyclical,
even if agents have access to financial intermediaries.
Aside from the abovementioned direct analyses of the relationship between
entrepreneurship and the business cycle, there are several labor market-related effects
identified in the entrepreneurship literature. A literature survey by Parker (2009, pp. 142-143)
4
Alternatively, one might argue that risk preferences remain constant over time, but the higher level of wealth of agents
during booms reduces liquidity constraints and hence increases entrepreneurial activity (Evans and Jovanovic, 1989).
6
discusses evidence from the US that new firm formation is pro-cyclical. He also points to the
effect of falling wages in recessions, which may lower the opportunity costs for starting a
business and encouraging marginal types of entrepreneurship. Yet, low-quality businesses
may be removed in recessions, exerting a countervailing force on the total number of business
owners. Congregado et al. (2009) discuss the recession-push and prosperity-pull concepts as
well as numerous studies supporting these concepts. The recession-push argument would lead
to a counter-cyclical and the prosperity-pull argument to a pro-cyclical effect.
5
The vast majority of the business cycle literature, however, does not explicitly model
entrepreneurial activity. This implies the hypothesis that entrepreneurship is either
independent from the cycle or irrelevant for the real economy. The results are mixed and often
indirect in the entrepreneurship literature (Thurik et al., 2008 and Congregado et al., 2009).
This ambiguity does not lead to dominant hypotheses. Hence, we will focus on the data and
link our results to the existing literature afterwards.
III. Analysis
In general, there are two ways of analyzing our data. Either observations can be
averaged across countries to focus on global trends or coefficients can be averaged, putting
more emphasis on national conditions. Of course, the two approaches address somewhat
different questions: the first investigates if global trends in entrepreneurial activity exist and
how they relate to the cycles of the world economy; the second approach investigates the
average relationship between entrepreneurship and the cycle at the national level. These two
perspectives are likely to yield diverging results if different factors influence the data at the
aggregate and national levels. For example, low-skilled individuals who consider starting a
business are more likely to be influenced by national labor market policies than by global
technological trends, whereas the opposite can be expected for highly skilled opportunity
entrepreneurs. Because the former constitute the majority of entrepreneurs (Kirchhoff, 1994),
one can expect to find different relationships between unemployment and entrepreneurial
activity at the national and global levels if labor market conditions are imperfectly correlated
across countries.
5
See also Thurik et al. (2008) and Parker (2009) discussing the interplay between unemployment and entrepreneurship.
7
Furthermore, economic variables at the country level are more likely to be influenced
by national policies and the conditions in specific, closely related nations. The world economy
is hardly influenced by the idiosyncratic policies of particular countries. Instead, global-scale
business cycle fluctuations reflect more directly developments of global importance, such as
major geopolitical changes, world market prices of commodities or technological
breakthroughs. Of course, these global developments also impact on national business cycles,
but the additional influence of national policies and conditions leads to country-specific
patterns and dilutes the correlation of cycles across countries.
The present section consists of three parts. First, we present the global results of the co-
movement of GDP, unemployment and business ownership, using data from 22 OECD
countries for the period 1972-2007. The second part deals with the co-movement of these
variables at the country level. The third part is a robustness check we carried out using a
different data source, with the aim of replicating the results of our initial analysis using an
alternative measure of entrepreneurship.
A. Aggregate analysis of entrepreneurship, unemployment and the cycle
We construct a balanced cross-country panel of 22 OECD countries
6
with annual data
for the period 1972-2007 using various sources. OECD data are used to determine annual real
GDP in constant 2000 prices in national currencies and standardized unemployment rates.
Entrepreneurial activity per country and per year is measured as the share of business
owners in the total labor force
7
, using data from Compendia 2007.1 that corrects for
measurement differences across countries and over time.
8
This is a broad measure of
entrepreneurial activity that includes incorporated, self-employed individuals (owner-
managers of incorporated businesses) and (unincorporated) self-employed persons with and
6
The included countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Greece, Iceland, Ireland, Japan,
Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom and
the USA. These are the 23 old OECD countries, with Germany excluded because we are unable to correct for the
influence of its unification on the time series.
7
The total labor force is the sum of the employed and the unemployed.
8
Data are constructed by EIM (Zoetermeer, NL) on the basis of OECD material. Seehttp://www.ondernemerschap.nl for the
data and van Stel (2005) for an explanation of the method. Quarterly data regarding business ownership rates are not
available for most countries.
8
without employees; conversely, the measure excludes unpaid family workers
9
. The business
ownership rate also excludes so-called “side-owners," who generate less than 50% of their
income by running their own businesses.
A disadvantage of using business ownership as a measure of entrepreneurial activity is
that it does not fully capture early-stage ventures that do not yet generate a substantial
contribution to the owner’s income. In addition, business ownership rates reflect to some
extent the existing industry structures in place rather than the introduction of new economic
activity in the Schumpeterian (1934) and Kirznerian (1973) sense.
10
To address these
conceptual shortcomings of business ownership rates as a measure of entrepreneurial activity,
we also use data from the Global Entrepreneurship Monitor (GEM) (Reynolds et al., 2005) as
a second measure for robustness checks.
Following the convention of defining the business cycle as a series of deviations from
long-term trends in GDP data, we decompose time series into trends and cycles using the
Hodrick-Prescott filter (Hodrick & Prescott, 1997), referred to below as the HP filter. The HP
filter is a standard method of removing trend movements that has been applied both to actual
data and to artificial data in numerous studies.
11
The smoothing parameter ? of the filter,
which penalizes acceleration in the trend relative to the business cycle component, needs to be
specified. Most of the business cycle literature uses quarterly data and a ? value of 1600, as
has been suggested by Hodrick and Prescott (1997). Unfortunately, business ownership rates
are only available on an annual basis in most countries. Because the time period over which
aggregation takes place affects the variance in the process at discrete time intervals, the ?
value must be adjusted. Ravn and Uhlig (2002) show that the appropriate ? value for annual
data is 6.25; this is the value we use for our analysis.
9
Unpaid family owners can be regarded as irrelevant in measuring the extent of entrepreneurship, as they do not own the
businesses they work for and do not bear responsibility or risk in the way that ‘real’ entrepreneurs do.
10
Despite these disadvantages, the business ownership rate is widely used: in Thurik et al. (2008), investigating the
interrelationships between entrepreneurship and unemployment; in Erken et al. (2009), measuring the influence of
entrepreneurship on total factor productivity; and in Carree et al. (2002), studying the influence of economic
development. See also Parker (2009, Chapter 1).
11
See Ravn and Uhlig (2002) and Jaimovich and Siu (2009), for example.
9
To test if our results are robust to different methods of de-trending the data, we repeat
all analyses using a ? value of 100 and first differences of growth rates.
12
The main results we
present below have been computed using the HP filer with a ? value of 6.25. They are not
sensitive to the method of de-trending. The additional results are reported in the Appendix in
Koellinger and Thurik (2009).
>> Table I about here > Figure I about here 99% confidence). A countercyclical relationship between
GDP and entrepreneurship can be clearly rejected since the contemporaneous correlation
between the two series is positive (0.3, significant at >90% confidence). A feedback between
unemployment and entrepreneurship seems likely because labor market opportunities
determine to a large extent the opportunity costs of entrepreneurship (Thurik et al., 2008).
Indeed, the contemporaneous correlation between unemployment and entrepreneurship is -
0.43 (significant at >98% confidence).
This interrelation between GDP, unemployment and entrepreneurship suggests a joint
analysis of these three variables in an autoregressive context. Given the stationarity of
13
Results are available from the authors on request.
11
detrended data
14
, we estimate a vector auto-regression model with two lags, VAR(2),
including deviations from trends in terms of business ownership, real GDP and
unemployment (Lütkepohl, 2007; Greene, 2003). The optimal lag length of two is
unanimously suggested by the Akaike (1974) information criterion, the Hannan-Quinn (1979)
criterion and the Schwarz (1978) criterion for 1 < p
max
< 7.
Our reduced-form VAR(2) expresses each variable as a linear function of its own two
past values and the two past values of the other two variables. The vector of errors is assumed
to be serially uncorrelated with contemporaneous covariance across equations. Specifically,
we estimate
(1)
t t t t
u y A y A v y + + + =
? ? 2 2 1 1
,
where
( )
?
=
t t t t
y y y y
3 2 1
, , is a 3 x 1 random vector with
=
1
y real GDP cycle,
=
2
y unemployment cycle, and
=
3
y business ownership cycle,
1
A and
2
A are fixed 3 x 3 matrices of parameters,
v is a 3 x 1 vector of fixed parameters, and
t
u is assumed to be white noise; that is
( )
s t u u E
u u E
u E
s t
t t
t
? ? =
?
?
?
?
?
?
?
? =
?
?
?
?
?
?
?
=
0
0
.
The model is estimated with least squares. Confidence intervals are based on common t-
values, which have been shown to yield reasonably accurate estimates even for small samples
(Lütkepohl, 2007; p. 94).
>> Table II about here > Table III about here > Figure II about here 90% confidence. Again, the effect of the entrepreneurship shock levels
off in later years, as the cycle progresses. Although this pattern is partly a result of the general
upswing in economic activity that tends to follow an expansion of entrepreneurial activity, it
is equally possible that part of the effect stems from the additional economic activity and the
jobs created by new firms.
16
In summary, these observations suggest that an impulse from global entrepreneurial
activity is typically followed by a recovery of the world economy and a decrease in
unemployment.
>> Figure III about here > Table IV about here > Table V about here > Table VI about here 90% confidence
** denotes significance at >95% confidence
*** denotes significance at >99% confidence
Table A2b – Vector autoregressive model on world economy, first differences of growth
rates
Y
1
= GDP Y
2
= Ent Y
3
= Unempl
Coef. Std. Dev. Coef. Std. Dev. Coef. Std. Dev.
GDP (t-1) 0.39* (0.20) 0.05 (0.13) -7.97*** (-1.39)
Unempl (t-1) 0.09*** (0.03) -0.01 (0.02) -0.99*** (0.20)
Ent (t-1) 0.75** (0.30) -0.03 (0.19) -3.87* (-2.06)
Model diagnostics
Portmanteau (16) test of residual
autocorrelation, modified (Ahn 1988)
0.28
LJB test for nonnormality of
residuals (Doornik and Hansen 2008)
0.77
LMF (5) statistic of residual
autocorrelation (Edgerton and Shukur
1999)
0.64 MARCHLM (2) (Lütkepohl and
Krätzig 2004)
0.23
Notes:
* denotes significance at >90% confidence
** denotes significance at >95% confidence
*** denotes significance at >99% confidence
33
We computed the Akaike (1974) information criterion, the Hannan-Quinn (1979) criterion and the Schwarz (1978)
criterion for 1 < p
max
< 7.
36
Tables A2a and A2b show that entrepreneurship is an early indicator of the business
cycle. The first year lag of entrepreneurship in the model with HP(? = 100)-filtered data is not
significant, but the second year lag is. The model using first differences of growth rates
strongly confirms that entrepreneurship is an early indicator of the cycle. Both models show
no signs of a specification problem in the test statistics. In particular, there is no indication of
remaining autocorrelation in the error terms. Both models also confirm that entrepreneurship
at the aggregate level is not influenced by GDP or unemployment.
The Granger causality tests in Tables A3a and A3b strongly confirm these conclusions.
Table A3a – Granger-Causality Wald Tests on world economy, HP-filtered data (? =
100)
Dependent Variable in Regression
Regressor GDP Ent Unempl
GDP 0.50 0.00
Ent 0.01 0.03
Unempl 0.07 0.31
Notes: Results were computed from a vector autoregression with two lags and a constant term over the annual
cross-country averages for the 1972-2007 period. Entries show the p-values for Chi2-tests that lags of the
variable in the row labeled Regressor do not enter the reduced form equation for the column variable labeled
Dependent Variable.
Table A3b – Granger-Causality Wald Tests on world economy, first differences of
growth rates
Dependent Variable in Regression
Regressor GDP Ent Unempl
GDP 0.72 0.00
Ent 0.01 0.05
Unempl 0.00 0.75
Notes: Results were computed from a vector autoregression with two lags and a constant term over the annual
cross-country averages for the 1972-2007 period. Entries show the p-values for Chi2-tests that lags of the
variable in the row labeled Regressor do not enter the reduced form equation for the column variable labeled
Dependent Variable.
37
Tables A4a and A4b confirm that entrepreneurship is a less reliable or unreliable early
indicator of the business cycle at the country level. In HP(? = 100)-filtered data,
entrepreneurship Granger-causes GDP only in the US and Denmark, whereas six countries
show this relationship in the data that has been de-trended using first differences of growth
rates. Both Tables reaffirm that the aggregate level results are not due to a few countries with
particularly strong influence of entrepreneurship. This conclusion is supported if the US is
excluded from the aggregate level analysis (results available from the authors on request).
Table A4a - Granger causality of business ownership on real GDP cycles across
countries, HP-filtered data (? = 100)
Country Granger causality
Wald test
Australia 0.51
Austria 0.23
Belgium 0.26
Canada 0.13
Denmark 0.09
Finland 0.91
France 0.94
Greece 0.68
Iceland 0.82
Ireland 0.12
Italy 0.37
Japan 0.43
Luxembourg 0.13
Netherlands 0.36
New Zealand 0.73
Norway 0.31
Portugal 0.26
Spain 0.33
Sweden 0.15
Switzerland 0.93
United Kingdom 0.68
USA 0.01
Notes: Results were computed from country specific
VARs for the period 1972-2007.
38
Table A4b - Granger causality of business ownership on real GDP cycles across
countries, first differences of growth rates
Country Granger causality
Wald test
Australia 0.00
Austria 0.06
Belgium 0.98
Canada 0.64
Denmark 0.41
Finland 0.96
France 0.76
Greece 0.99
Iceland 0.06
Ireland 0.08
Italy 0.37
Japan 0.59
Luxembourg 0.49
Netherlands 0.34
New Zealand 0.18
Norway 0.48
Portugal 0.31
Spain 0.08
Sweden 0.96
Switzerland 0.92
United Kingdom 0.52
USA 0.04
Notes: Results were computed from country specific
VARs for the period 1972-2007.
39
The panel regressions in Tables A5a and A5b, which report average coefficients across
countries under the conservative fixed-effects assumption, confirm the conclusion that
entrepreneurship at the country level does not predict GDP or unemployment, but is instead
positively driven by past increases in unemployment.
Table A5a - Fixed effects regressions on cross-country panel, HP-filtered data (? = 100)
Y = GDP Y = Unempl Y = Ent
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
GDP (t-1) 0.81** (0.04) -3.28** (0.33) 0.02 (0.05)
GDP(t-2) -0.33** (0.04) 1.69** (0.31) -0.05 (0.05)
Unempl (t-1) -0.00 (0.00) 0.45** (0.03) 0.01** (0.04)
Unempl (t-2) -0.00 (0.00) -0.05** (0.02) 0.00 (0.00)
Ent (t-1) 0.00 (0.03) -0.22 (0.23) 0.58** (0.04)
Ent (t-2) 0.01 (0.03) -0.25 (0.23) -0.20** (0.04)
Notes: All models include time dummies and a constant and including time dummies. OLS
and system GMM estimators (with 136 instruments, collapsed) deliver almost identical results.
N=22, T=32, observations=726
* denotes significance at 90% confidence
** denotes significance at >95% confidence
Table A5b - Fixed effects regressions on cross-country panel, first differences of growth
rates
Y = GDP Y = Unempl Y = Ent
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
GDP (t-1) -0.40** (0.04) -3.23** (0.52) 0.04 (0.06)
GDP(t-2) -0.26** (0.04) -1.33** (0.51) 0.05 (0.05)
Unempl (t-1) -0.00 (0.00) -0.58** (0.04) 0.00 (0.00)
Unempl (t-2) -0.00 (0.00) -0.36** (0.03) 0.01** (0.00)
Ent (t-1) 0.03 (0.03) -0.45 (0.35) -0.58** (0.04)
Ent (t-2) 0.04 (0.03) -0.11 (0.34) -0.39** (0.04)
Notes: All models include time dummies and a constant and including time dummies. OLS
and system GMM estimators (with 136 instruments, collapsed) deliver almost identical results.
N=22, T=32, observations=726
* denotes significance at 90% confidence
** denotes significance at >95% confidence
40
A comparison of Figures 1, A1a and A1b shows that the detrending method influences the
cyclical pattern that is extracted from the raw data. HP(? = 100)-filtered data (Figure A1a)
emphasizes the longer-term cyclical peaks and troughs, while the first differences of growth
rates (Figure A1b) emphasizes short-term fluctuations compared to the reference of HP(? =
6.25)-filtered data (Figure 1 in the text). The spectra of the extracted series also look markedly
different for the three detrending methods. In particular, HP(? = 100)-filtered data almost
entirely filters out low frequencies and forces a common spectral shape on self-employment,
GDP and unemployment, whereas first differences of growth rates emphasizes low
frequencies and maintain a high degree of heterogeneity among the spectra of the three
variables.
Although the detrending procedure has a large influence on the resulting time-series, the main
results of our analyses are replicated.
Figure A1a - Average deviations of real GDP and business ownership rates from trend
in percent across 22 OECD countries, HP-filtered data (? = 100)
-4
-3
-2
-1
0
1
2
3
1
9
7
2
1
9
7
4
1
9
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6
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8
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0
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0
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2
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0
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4
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0
0
6
GDP deviation from trend Business ownership rate deviation from trend
41
Figure A1b - Average deviations of real GDP and business ownership rates from trend
in percent across 22 OECD countries, first differences of growth rates
-0,080
-0,060
-0,040
-0,020
0,000
0,020
0,040
0,060
1
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0
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6
GDP deviation from trend Business ownership rate deviation from trend
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