Entrepreneurship, institutional economics, and economic growth: an ecosystem perspective
Entrepreneurship, institutional economics, and economic growth: an ecosystem perspective
Zoltan J. Acs 0 1 3
Saul Estrin 0 1 3
Tomasz Mickiewicz 0 1 3
László Szerb 0 1 3
0 T. Mickiewicz Aston University , Birmingham , UK
1 Z. J. Acs George Mason University , Fairfax, VA , USA
2 London School of Economics , London , UK
3 L. Szerb University of Pecs , Pecs , Hungary
We analyze conceptually and in an empirical counterpart the relationship between economic growth, factor inputs, institutions, and entrepreneurship. In particular, we investigate whether entrepreneurship and institutions, in combination in an ecosystem, can be viewed as a Bmissing link^ in an aggregate production function analysis of cross-country differences in economic growth. To do this, we build on the concept of National Systems of Entrepreneurship (NSE) as resource allocation systems that combine institutions and human agency into an interdependent system of complementarities. We explore the empirical relevance of these ideas using data from a representative global survey and institutional sources for 46 countries over the period 2002-2011. We find support for the role of the entrepreneurial ecosystem in economic growth.
Economic growth; Entrepreneurship; Ecosystem; Efficiency; Technology; Solow residual; GEM; GEI
Using an aggregate production function,
found that only around 13% of US growth in GDP was
due to increases in measured inputs, labor, and capital.
The remainder was unexplained, and he proposed that
the large residual, 87% of the change in growth,
represented technological change. But, explaining the
determinants of, and measuring, this technological change
has proved to be elusive. Thus, the original notion of
inputs generating outputs through an aggregate
production function has been extended by more sophisticated
measures of inputs, including human capital
, as well as more complex conceptualizations of
the functional relationship and the factors underlying it
(Barro and Sala-i-Martin 1995)
. Models of endogenous
growth have also extended the framework to consider
research and development, patents, and policy
1986; Aghion and Howitt 1992; Aghion 2017)
However, less attention has been paid to the joint role of
entrepreneurship and institutions in the growth process.
In a little cited article by Martin L. Weitzman, we
have a clue to how these might affect economic growth.
replicated the Solow model for the
Soviet Union. He estimated that the Solow residual was
only in the range of 20%. In other words, in the Soviet
Union, increases in factor inputs explained most of
economic growth. On this basis, Weitzman correctly
foresaw a decline in Soviet growth rates because per
worker capital accumulation cannot sustain positive
aggregate growth in a Solow framework. What was
different between the Soviet Union and the USA was not so
much the availability of new technology (as the quality
of technical research in the former country was high) but
rather in the institutional structure and the incentives for
The idea that entrepreneurship and institutions are
pivotal in explaining the variation in economic growth
not accounted for by changes in factor inputs was a
central implication of the ideas of Baumol
1993; see also Bjørnskov and Foss 2013, 2016)
argued that, even if all countries had similar supplies of
entrepreneurs, the institutional structure would
determine the allocation to productive, unproductive, and
destructive forms of activity. Countries with weak
institutions would not incentivize productive
entrepreneurship but rather either unproductive or even destructive
(see also Murphy et al. 1993; Parker
Baumol and Strom (2007)
to argue that as a result of these differing incentives for
entrepreneurs, economic growth and performance
would vary along with heterogeneity in institutions.
Aidis et al. (2008)
argue that, because the
Soviet Union had poor Bmarket supporting institutions^
(Acemoglu and Robinson 2012)
as well as weak
incentives for wealth-creating entrepreneurship, much of its
entrepreneurship was indeed of the unproductive or
even destructive type.
Aidis et al. (2008)
even post-transition, productive entrepreneurial activity
has remained extremely low in many former socialist
economies, especially the former Soviet Union.1
There has been a longstanding literature linking
entrepreneurship and growth
, and over the past 25 years, a large
literature has also emerged on institutions and economic
(North 1990; Acemoglu and Johnson 2005;
Acemoglu and Robinson 2012)
. However, most of the
literature has focused on either entrepreneurship
Koellinger and Thurik 2012)
and Mihov 2013)
, with less emphasis on the joint effects
1 The problem was systemic; in the Soviet legal code, entrepreneurship
of the productive type was seen as criminal activity. See
of entrepreneurship and institutions on economic
growth. This leads us to consider whether
entrepreneurship and institutions, in combination as an ecosystem,
might represent the Bmissing link^ in explaining
crosscountry differences in economic growth
et al. 2010; Acs et al. 2017a, b; Sussan and Acs 2017)
The idea is that the stronger the entrepreneurial
ecosystem, the more productive will be the technology, and
hence the stronger the impact of technology on
economic growth. Entrepreneurs thereby act as the agents who,
by commercializing innovations, provide the
transmission mechanism transferring advances in knowledge
into economic growth. However, even where
entrepreneurial initiative is present, this process of transmission
may be either hampered or facilitated by the institutional
(Baumol and Strom 2007)
. To formalize
these ideas empirically, we measure entrepreneurship
and institutional arrangements independently and
combine them in a National System of Entrepreneurship
(NSE). The NSE brings together human agency and
the institutional context and therefore allows us to
compare the combined roles of entrepreneurship and
institutions in economic growth.2
To develop these ideas, we need also to consider
what we mean by entrepreneurship at the national level.
Is it self-employment
(Reynolds et al. 2005)
, or is it
(Lumpkin and Dees 1996;
Henrekson and Sanandaji 2014)
, or individual-level
(Shane and Venkataraman 2000;
.3 According to Acs et al. (2014: 476), BThe
measurement challenge becomes even more complex
when discussing entrepreneurship in countries. If we
have difficulty defining entrepreneurship as an
individual or firm-level phenomenon, what hope do we have of
deciding what ‘entrepreneurship’ means as a
countylevel phenomenon?^ Researchers at the country-level
use measures of self-employment, new firm startups, or
the Global Entrepreneurship Monitor defined as Total
Entrepreneurship Activity (TEA) rate
Thurik 2003; Erken et al. 2016)
. In contrast, we propose
that country-level entrepreneurship should be treated as
a systemic phenomenon similar to the way the literature
2 See two special issues of the Journal of Technology Transfer on
National Systems of Innovation
(Acs et al. 2017a, b)
Business Economics on National Systems of Entrepreneurship
et al. 2016)
3 For a clearer discussion on the issue, see
. He focuses on
the definition of entrepreneurship as a process rather than an event
embodiment as a type of person.
on National Systems of Innovation (NSI) treats
countrylevel innovation, institutions, and policies. A key
message of NSI was that the structure rather than individual
processes ultimately determines the innovation
productivity of nations
We make three contributions to the literature about
the relationship between entrepreneurship and economic
growth. First, we review and develop the literature about
the relationship between entrepreneurial activity,
institutions, and economic growth. One stream has
highlighted the crucial role of institutions
Acemoglu et al. 2005; Acemoglu and Robinson 2012)
but has not focused on the impact of entrepreneurship.
On the other hand, some analysts have sought to
associate entrepreneurial activity with economic growth
, but the underlying mechanisms have
rarely been spelt out and there is as yet limited convincing
empirical evidence of the relationship
Thurik 2003; van Praag and Versloot 2007; Acs and
. We consider whether entrepreneurship
and institutions in combination in an ecosystem can
improve the explanation provided by an aggregate
production function analysis of cross-country differences in
Further, we suggest a mechanism whereby greater
rates of entrepreneurship in the context of inclusive
institutions might raise economic growth. We return to
the notion of the entrepreneur as the coordinator of the
production process, bringing together labor, capital, and
technology to produce output. As
understood, there is an important distinction between
replicating existing economic activities in which case growth
relies solely on the supply of inputs, and changing the
production function which links inputs to output, which
generates technical change, raising levels of aggregate
(Lafuente et al. 2016)
. The entrepreneur
achieves this by introducing new forms of technology
to the production process, but if the rewards to such
innovations depend on the institutional arrangements,
increased entrepreneurial activity will only raise growth
if the institutional environment is benign. We propose a
construct which seeks to encapsulate the joint ecosystem
of entrepreneurial activity and institutions and which
influences the process of economic growth
independently from the traditional factor inputs.
Our final contribution is empirical. We use the Global
Entrepreneurship Index (GEI) as a measure of the NSE
(Acs et al. 2014)
and use this construct to test our ideas
about the individual and combined impacts of
entrepreneurship and institutions on economic growth.
We use a panel fixed effects model (Islam 1995) to test
the hypothesis that a NSE as measured by the GEI is
positively associated with economic growth. We find
support for the role of the entrepreneurial ecosystem in
economic growth but only a marginal role for the
entrepreneur or institutions acting independently.
2 The theoretical background
proposed to separate variation in national
output per head due to technical change from that due to
changes in the availability of capital per head. Thus, if Q
represents output and K and L represent capital and labor
inputs in physical units, then, the Solow aggregate
production function can be written as
Q ¼ FðK; L; AðtÞÞ:
The variable A(t) allows for productivity to rise over
time without additional factor inputs, technical change.
Solow explored empirical specifications of the function
˙q A˙ ˙k
q ¼ A þ wk k ;
using output per man hour, capital per man hour, and the
share of capital to decompose growth into the elements
caused by capital inputs and technical change,
respectively. Using American data for the period 1909–1949,
Solow concludes the following: technical change (A(t))
during that period was neutral on average; the upward
shift in the production function was, apart from
fluctuations, at a rate of about 1% per year for the first half of
the period and 2% per head for the last half; Gross
output per man hour doubled over the interval with
87.5% of the increase attributed to technical change
and the remaining to increased use of capital.
Technological change is the product of endeavor,
especially in the fields of science and engineering. The
literature has sought to explain the mechanism
enabling the transition from inventions to economic
applications which raise total factor productivity
. The process is not automatic; in practice, many
inventions have never been commercialized, and many
economies have been for long periods stagnant
(Acemoglu and Robinson 2012)
. We argue that this
prolonged absence of convincing and unambiguous
results on this mechanism of transition arises because
the modeling fails to take sufficient account of
potential complementarities and bottlenecks in the
r e l a t i o n s h i p b e t w e e n i n s t i t u t i o n s a n d
entrepreneurship. In an early attempt to address this
pointed out that the
standard theory of competition gives the impression that
there is no need for entrepreneurs. If all inputs and
outputs are marketed and their prices are known, and
if there is a production function that relates inputs to
outputs, then we can always predict the returns for any
activity that transforms inputs into outputs. But, one to
one correspondence between sets of inputs and outputs
is a very strong assumption
(see also March and Simon
. There are many reasons why that
correspondence is broken. Contracts for labor are incomplete, the
production function is not completely specified or
known, and not all factors of production are marketed
. Returning to the question of the Solow
residual, we are left with the issue of what constitutes
growth-generating technical change. According to
Weitzman (1970: 686), writing about the Soviet
economy, BIt is at the point that our ignorance of what
constitutes the residual becomes really annoying. What
is it that should be pushed—increasing returns, labor
skills, new innovations, optimal use of resources,
better organization, or what?^
Jones and Romer (2009)
identify two types of attempts to explain the Solow
residual. The first is to include the stock of human
capital in the production function for a cross section
of countries. The switch from a time series for one
(as in Solow 1957)
to a cross section has
certain advantages. It allows us to look at different
levels of development.
in a series of
studies for almost 100 countries for the period 1960–
1985 found that the growth rate of real per capita GDP
was positively related to initial human capital, proxied
by school enrolment rates, and negatively related to the
initial (1960) level of real per capita GDP, suggesting
convergence in growth rates.
The more recent advance—endogenous growth
theory—has been based on the emergence of research and
development focused models of growth in the seminal
Aghion and Howitt (1992)
This class of models explicitly aims to explain the role
of technological progress in the growth process.
R&Dbased models view technology as the primary
determinant of growth yet treats it as an endogenous variable.
These are two-sector-models, in which the stock of ideas
is an input in the knowledge production function and the
variety of ideas creates value
.4 In the
Romer model, long-run per capita growth is driven by
technological progress, but the latter is conditioned by
growth in knowledge.
Jones and Romer (2009)
bring these points together
arguing that progress in growth theory resulted from a
tractable description of production possibilities based on
a production function and a small list of inputs. Modern
growth theory has added ideas, institutions, population,
and human capital. Physical capital has been pushed to
the periphery. Summarizing the stylized facts, they list
& Increased flows of goods, ideas, finance, and
people—via globalization and urbanization—have
increased the extent of the market for all workers and
& The variations in rate of growth of per capita GDP
increase with the distance from the technological
& Large income and TFP differences persist.
Differences in measured inputs explain less than half of
the enormous cross-country differences in per capita
& Poor countries are poor not only because they have
less physical and human capital but also because
they use their inputs much less efficiently.
They conclude their paper with the observation that
Bthere is very broad agreement that differences in
institutions must be the fundamental source of the wide
differences in growth rates observed for countries at
low levels of income and for low income and TFP levels
themselves^ (p. 20).
What exactly are institutions? North (1990: 3) offers
the following definition: BInstitutions are the rules of the
game in a society or, more formally, are the humanly
devised constraints that shape human interaction....In
consequence they structure incentives in human
exchange, whether political, social or economic.^ In their
survey of institutions as a fundamental cause of growth,
Acemoglu et al. (2005: 385) write:
4 Thus, Romer assumes a knowledge production function in which
new knowledge is linear in the existing stock of knowledge, holding
the amount of research labor constant. The idea is expressed in the
simple model where the growth rate is proportional to Å/A = F(H, A),
where A is the stock of knowledge and H is the number of knowledge
…though this theoretical tradition is still vibrant in
economics and has provided many insights about
the mechanics of economic growth, it has for a
long time seemed unable to provide a fundamental
explanation of economic growth. As
, p.2) put it: Bthe factors we have
listed (innovation, economies of scale, education,
capital accumulation etc.) are not causes of
growth; they are growth^ (italics in original).
Factor accumulation and innovation are only
proximate causes of growth. In North and Thomas’s
view, the fundamental explanation of comparative
growth is differences in institutions.
Of particular importance to growth are the economic
institutions in society such as the structure of property
rights and the presence of effective market frameworks,
Binclusive or market supporting institutions^
(Acemoglu and Robinson 2012)
. Without property
rights, individuals will not have the incentive to invest
in physical or human capital or adopt more efficient
technologies. More generally, economic institutions are
important because they help to allocate resources to their
most efficient uses; they determine who gets profits,
revenues, and residual rights of control. As we noted
for the Soviet Union, when markets were highly
restricted, there was little substitution between labor and capital
and technological change was limited.
How can we think about the combined role of
entrepreneurship and institutions in growth?
argued that entrepreneurial talent can be allocated
among a range of choices with varying effects from
productive to destructive effects on economic welfare.
If the same actor can be engaged in such different
activities, then the mechanism through which talent is
allocated has important implications for economic
(Desai et al. 2013)
, and the quality of this
mechanism becomes the key criterion in evaluating a given
set of institution with respect to growth. Murphy et al.
(1993: 506) proposed that countries’ institutions create
incentives and that the entrepreneurial talent is allocated
to activities Bwith the highest private return, which need
not have the highest social returns^.5 The comparison of
the USA and the Soviet Union is important in this
context because it enables us to isolate the impact of
technological innovation from the institutional change.
5 This implies that it may be hard to make inferences about
externalities or overall social welfare effects based on generic measures of
What was different between the Soviet Union and the
USA was not so much in their generation of technology
(they both had nuclear weapons and successful space
programs) but in technological progress in economic
applications. We follow many others, for example
, in proposing that the
explanation for this rests upon the institutional system
and the incentives that it created for agents to generate
decentralized knowledge; we differ in simultaneously
stressing the role of entrepreneurs. In the USA,
institutions of private property and contract enforcement gave
entrepreneurs the incentive to invest in physical and
human capital, to combine inputs in ways to create
new production functions, and to complete markets. In
the Soviet Union, there was also entrepreneurship, but it
tended to take unproductive and destructive forms
(Aidis et al. 2008)
We therefore propose that entrepreneurs, operating in
productive institutional environments, provide the
transmission mechanism from innovation to economic
growth. This leaves open the question of how to
operationalize the features which make the economic
system efficient in this process. If we accept that the
entrepreneurs are important for the efficient working of
the system, to create or carry on an enterprise where not
all the markets are well established or clearly defined
and in which the relevant parts of the production
function are not completely known, an obvious way to
approach the problem is to try to incorporate this into
an aggregate production function. However, this is not a
simple task. We suggest that one way to explore the
efficiency of the process is to incorporate
entrepreneurship into a system that combines institutions and agency
(Acs et al. 2014)
. The basic Solow model has already
been extended to take account of the quality of factor
inputs, such as human capital
(e.g. Barro 1991; Barro
and Lee 1993)
. Indeed, according to Bergeaud et al.
(2017), the quality of labor and capital and the diffusion
of innovation explain slightly more than half the share of
TFP growth 1913–2010. However, the unexplained
residual remains large and this leads us to ask the question:
Does entrepreneurship within a context of specific
institutions supplement the explanation of the growth
process offered by factor inputs?
In particular, we consider the role of entrepreneurship
and institutions jointly within an ecosystem. On the
institutional side, we build on the ideas of National
Systems of Innovation (NSI)
(Acs et al. 2017a, b)
though entrepreneurship remains mostly absent from
this literature with its institutional-centric focus. The
other side of the coin has been the tendency of the
entrepreneurship literature to concentrate on individual
agency but with insufficient reference to the wider,
system-level constraints and outcomes of
entrepreneurial action.6 Central to the entrepreneurship process is not
whether opportunities exist but rather, what is done
about them and by whom (McMullen et al. 2007). Thus,
action by individuals and regulations thereof
(bottlenecks) becomes key to the entrepreneurial
process. This leads us to think about the role of the
entrepreneur’s context not only as a regulator of opportunities
and personal feasibility but also as the regulator of
outcomes. From a systems perspective, we emphasize
the interactions between individuals and their
institutional contexts in producing entrepreneurial action.
Entrepreneurship can be viewed as individual-led behavior
that mobilizes resources for opportunity exploitation
through the creation of a new production function. This
is subject to complex population-level interactions
between attitudes, abilities, and aspirations embedded
within a multifaceted economics social and institutional
context that drives productivity through the allocation of
resources to efficient ends. This leads us to propose the
following definition of National Systems of
(Acs et al. 2014)
A NSE is the dynamic institutionally embedded
interaction between entrepreneurial attitudes,
abilities and aspirations, by individuals, which
drives the allocation of resources through the
creation and operation of new ventures.
The NSE can be conceived of as a dynamic
interaction between entrepreneurial attitudes, abilities, and
aspiration. It must also consider entrepreneurial processes
within their institutional contexts and recognize the
multifaceted multi-level nature of the phenomenon. In
our empirical counterpart, we present an empirical
measure of the NSE across countries and explore whether it
represents a significant additional phenomenon
explaining differences in cross-country rates of growth
using an aggregate production function.
6 BAlthough Schumpeter elaborated on the role of entrepreneurship as
a novelty introducing function in economic landscapes, this aspect has
not been properly picked up by entrepreneurship researchers, who have
tended to focus on the individual and on the new venture while largely
ignoring the considerations of system-level constraints and outcomes^
(Acs et al. 2014: 478)
3 National Systems of Entrepreneurship
Composite indices can capture the multifaceted
characteristics like those of NSE
. Our measure
of NSE, the Global Entrepreneurship Index (GEI),
further incorporates (1) systemic combination of the
elements, (2) system dynamics (interaction), and (3) the
optimal resource allocation to improve the system
performance. We assume that the system of
entrepreneurship does not work perfectly, with system failure
operationalized by recognizing bottlenecks
Casadio Tarabusi and Guarini 2013)
.7 Hence, we
propose that the building blocks (pillars) of entrepreneurial
activity constitute a system where the final outcome is
moderated by the weakest performing pillar. Index
building is at four levels: (1) variables, (2) pillars, (3)
sub-indices, and finally (4) the super-index. All three
sub-indices contain several pillars, which can be
interpreted as quasi-independent building blocks. The
sub-indices of attitudes, abilities, and aspiration
constitute the entrepreneurship super-index (GEI). The
detailed structure of the GEI is presented in
Acs et al.
To summarize, the GEI scores are calculated as
1. Selection of variables: These variables can be at the
individual-level (personal or business) derived from
the Global Entrepreneurship Monitor (GEM), Adult
P o p u l a t i o n S u r v e y, o r t h e i n s t i t u t i o n a l /
environmental level. We employ 16 individual and
15 institutional variables.
2. The construction of the pillars: We calculate pillars
by multiplying the individual variable with the
appropriate institutional variable. All pillars were
normalized and capped.
7 The NSE includes the stock of institutions, and entrepreneurship,
bound together by a theory of interdependence and complementarities.
There are parallels with
) O-ring Theory of Economic
Development, in which quantity cannot be substituted for quality and
strategic complementarities in production lead to endogenous sorting
by worker skill. BThis O-ring production function differs from the
standard efficiency units’ formulation of labor skill, in that it does not
allow quantity to be substituted for quality within a single production
chain. For example, it assumes that it is impossible to substitute two
mediocre advertising copywriters, chefs, or quarterbacks for one good
one (1993: 553).^ In the GEI, entrepreneurial skills will sort
endogenously as the entrepreneurial ecosystem creates the incentives and
entrepreneurs drive resource allocation to the most efficient uses.
Furthermore, the penalty for bottleneck methodology in the GEI is
consistent with the lack of substitution in the O-ring theory.
zi; j ¼ indi; j x insi; j
for all j = 1 ... k, the number of pillars, individual, and
where zi, j is the original pillar value for country i and
pillar j, indi, j is the original score for country i and
individual variable j, insi, j is the original score for
country i and institutional variable j.
3. Average pillar adjustment: The different averages of
the normalized values of the indicators imply that
reaching a given value requires different effort and
resources. The additional resources for the same
marginal improvement of the indicator values
should be the same for all indicators. Therefore,
we need a transformation to equate the average
values of the components.
Pillars are adjusted so the potential minimum value is
0 and the maximum value is 1, calculated for the 2002–
2011 time period.
4. Penalizing: The penalty for bottleneck (PFB)
methodology was used to create indicator-adjusted
values; a loss in one pillar is compensated by the
same increase in another pillar at an increasing rate.
Casadio Tarabusi and Palazzi (2004
define the penalty function as
hðiÞ; j ¼ min yðiÞ; j þ a 1−e−bðyðiÞ j−min yðiÞ; jÞ
where hi, j is the modified, post-penalty value of pillar j
in country i, yi, j is the normalized value of index
component j in country i, ymin is the lowest value of yi,
j for country i. i = 1, 2,……n is the number of countries.
j = 1, 2,.……m is the number of pillars. 0 ≤ a and b ≤ 1
are the penalty parameters; the basic setup is a = b = 1.
5. Pillars and sub-indices: The pillars are the basic
building blocks of t. The value of the three
subindices—entrepreneurial attitudes, entrepreneurial
abilities, and entrepreneurial aspirations—is the
arithmetic average of its PFB-adjusted pillars for that
sub-index multiplied by a 100. The maximum value
of the sub-indices is 100 and the potential minimum
GEI: This is simply the average of the three
The description of individual variables used in GEI is
presented in Table 1.
4 Data and estimation issues
4.1 Specif ication
To explore empirically whether the entrepreneurial
ecosystem helps in an explanation of cross-country growth,
we start from Eq. (1) augmented with the National
System of Entrepreneurship. This gives us
Q ¼ F ðK; L; NSE; AðtÞÞ:
For estimation, more specifically, we adopt a
standard Cobb-Douglas function based on a product of
independent variables. Transforming the latter into
logarithms leads to additive functional form. We explore
the relevance of the entrepreneurial ecosystem in
aggregate growth via the sign and significance of the
coefficient on logarithm of NSE in Eq. (5). To address
potential omitted variable bias, we also consider in some
specifications L to be proxied by both employment
and labor quality (human capital).
We noted in our theoretical framework that much of
the literature has proposed the relevance of either
institutions or entrepreneurship, or both, in the growth
process, without reference to the need for an entrepreneurial
ecosystem. We therefore also propose to test a version of
this idea, namely that growth is influenced by
entrepreneurship and institutions separately rather than via an
ecosystem. Hence, we suggest, as an alternative
specification to that indicated in Eq. (5), that, in addition to
the standard factor inputs, output is determined by
national-level institutions and/or individual-level
entrepreneurial activity, separately. The alternative
Q ¼ F ðK; L; I ; E; AðtÞÞ
where I is country-level institutions and E represents an
indicator of entrepreneurial activity at the country-level.
Once again, Eq. (6) is estimated in logarithms. If neither
entrepreneurship nor institutions affect the growth
process, net of factor inputs, then neither E nor I will be
significant in the estimation of Eq. (6). We may also
The percentage of the 18–64 aged population who provided funds for new business in past 3 years excluding stocks
and funds, average
The amount of informal investment calculated as the informal investment mean times business angel
wish to compare the impact of the NSE against the
separate institutional and entrepreneurial factors.
However, Eqs. (5) and (6) are non-nested, so our comparison
in this case is based on a J-test
MacKinnon 1981, 1993)
The data on real GDP growth, fixed capital
investment, a nd labor derive from the Penn Wo rld
Table (PWT version 8).8 For robustness, we also used
data derived from the World Bank to measure GDP
growth. As noted above, the Global Entrepreneurship
Monitor (GEM) forms the individual basis for
measures of E and NSE (Reynolds et al. 2005), and the
institutional dimensions are largely derived from the
World Bank and the World Economic Forum. Our
8 The PWT project originates with the Center for International
Comparisons of Production, Income and Prices at the University of
Pennsylvania and is now run jointly by the team at the University of
California at Davis and University of Groningen
(Feenstra et al. 2015)
sample for the table is drawn on the 2003–2011 period
that is available for all indicators. Note that GEI
measures both the NSE as a whole, while its
components include E—average country-level individual
individual and institutions, respectively. The definitions
of variables used in our regression analysis and their
descriptive statistics are presented in Table A0 in the
online appendix and Table 2, respectively.
4.3 Estimation issues
We face serious data constraints that limit both the range
of feasible estimators and the power of econometric tests
we can apply to investigate the relationship between our
proposed empirical measure of NSE, individual
entrepreneurship, institutions, and economic growth. Despite
possible endogeneity, these data limitations make the
application of estimation techniques which rely on
instrumenting hard to implement. For example,
successfully applying dynamic panel data models based on
generalized methods of moments proved to be
impossible, due to the fact that we do not have a
sufficient number of longer sequences of data for countries in
our sample. For that reason, we use the robust, if less
efficient, fixed effects estimators. At the same time, to
make the tests stronger, we apply one additional
measure in our base regressions: We take all our variables in
first differences—therefore, we have economic growth
regressed on changes in employment, fixed capital, the
measure of the NSE, and its components. Two issues of
estimation are worthy of note. The first is whether to
estimate the GEI in logs or levels. It is not clear whether
it is appropriate to put an index into logs, though it is
often done for consistency. Our aim is to test for
robustness, so we present both logs and levels for the GEI and
The second estimation issue has to do with the use
of both first differences and fixed effects in our
estimation of the underlying production function, a strong
specification applied to handle unexplained
countryspecific heterogeneity in the growth process across
countries. This exacting specification is suitable to test
our hypothesis on this dataset because data limitations
mean that our time period is not very long and the
panel is not balanced. If we had a longer time period
and a balanced panel, we could regress growth
averages on values of the independent variables measured
at the beginning of the period, as for example in
Sala-iMartin et al. (2004
We therefore start by estimating the first differenced,
fixed effects specification. We believe it to be more
convincing to obtain significant results about the effects
of entrepreneurship and institutions on growth in such a
demanding specification. Our findings can be compared
with those obtained using first differencing only and
those based on fixed effects only, as reported in the
Appendix (Online Resource).
5 Empirical results
We first estimate a model which only includes our (first
difference) measures of the log of capital and labor in
column (1) of Table 3. Next, we introduce the full
system version of the logged GEI index, estimated in
column (2). Finally, we investigate the separate effects
of agency and institutions, in which the components
making up the GEI index—the individual system
(entrepreneurs) index and the institutional system
index—enter the equation independently in columns
(3) and (4).9
We observe in Table 3 that the effect of log capital
and labor always comes as positive and highly
significant. The estimated coefficient on GEI is positive and
statistically significant at the 5% level in column (2), the
institutional component is mildly significant at the 10%
level in column (3), and the entrepreneurial component
is also mildly significant at the 10% level.
The comparison of the GEI ecosystem variable as
against the individual components is non-nested, so we
apply a Davidson-MacKinnon (1981, 1993) J-test to
choose between them as better representations of the
data. We find that the inclusion of the predicted values
from GEI equation into the components specification
leaves the institutional components insignificant at the
10% level, but applying the reverse does not eliminate
the significance of the GEI index at the 5% level. On
this basis, we conclude that GEI does stand against the
two sub-components separately for the dataset, as a
whole, but the components do not hold against the
GEI variable. Hence, while both representations are
found to have some significance in explaining the
growth process, for the entire sample, the
DavidsonMacKinnon test indicates that the specification based
on independent components is not preferred to that of
the ecosystem. This is in line with our theoretical
argument stressing a distinctive role of entrepreneurial
We have undertaken numerous additional regressions
to explore our results in more depth. These results on
GEI hold when the dependent variable is specified as
GDP per capita rather than GDP (always in logs) and the
factors are loaded as capital per unit of labor (hence
assuming the production function is linearly
homogeneous). Thus, in regressions reported in Appendix
(Table A.1, Online Resource), we re-estimate columns
(1)–(4) using capital per employee instead of capital and
labor separately. In these models, GEI is significant at
0.01 level, individual entrepreneurship component loses
all significant, and the institutional component gains in
significance. Next, we estimate the models utilizing data
in levels rather than rate of change. The GEI index is
statistically marginally significant in this specification.
9 In unreported regressions, we repeat the results of columns (1) and
(2) but using the World Bank data instead of the Penn tables as a
robustness test. The Penn and World Bank data generate very similar
results in terms of estimated coefficients and patterns of significance.
The institutional variable is significant, while the
individual entrepreneurship variable is not. In turn, when we
apply first difference but without fixed effect, GEI index
is significant at the 1% level, the individual
(entrepreneurship) component is not, and the
institutional component is. One might also be concerned about the
effects of the recession given that our sample covers this
period. When a time dummy for the years of the
recession is included, the results of interest are not
statistically altered, and the recession variable is negative and
statistically significant for the years 2008–2011.10
The first three columns of Table 4 show results for
using first differences of GEI as an alternative and the
first logarithmic differences for other variables as
10 Perhaps equally important results during our sample period would
have been influenced by the great recession (depression) of 2008–2009
(Posner 2009; Solow 2009)
. The production frontier may in fact
deteriorate during a depression. If a downturn is a recession, the issue
is one of a lack of effective demand in the short term and the supply
side should not be fundamentally affected. Once the level of demand
returns, perhaps in less than a year, the former level of efficiency will be
achieved again, and the economy can expand on the previous path.
Because the decline in output is relatively small and the duration short,
the impact on the supply side is limited with no deterioration in the
quality of labor or in the quantity of capital. However, in a depression
the situation is different. The downturn is deeper and lasts for longer.
Hence, because labor is idle for a prolonged period, it can experience
deskilling. Moreover, a depression can destroy capital, which will be
written off and scrapped. Because of this, the technological frontier can
in fact decline and the economy become less productive. With respect
to measurement, the value of capital and the quality of labor may be
overstated, so production function estimates may suffer from
before. The results in column (4) replicate the
specification from Table 3 for comparison with model (5),
which has human capital variable added (Barro and
Lee 1993). The human capital variable taken from the
Penn Tables is not statistically significant, while the
ecosystem variable retaining significance. Similar to
Table 3, all these models are replicated in the Appendix
(Online Resource), applying per capita specifications,
models in levels with fixed effects, and models in first
differences without fixed effects (Tables A.1b, A.2b,
6 Discussion and conclusions
The original theoretical insight that entrepreneurship
should have a positive effect on growth comes from
. He argued that entrepreneurship
represents the introduction of new combinations of
factors in the economy and that the role of the entrepreneur
is to shift the production function upwards. Therefore,
for Schumpeter, innovation is at the heart of growth and
development. The key role of efficiency in growth was
also emphasized by
. However, most
growth theory scholars do not consider the role of
entrepreneurship but concentrate on human capital and in
endogenous growth theory, R&D, and innovation. We
used the example of growth in the USA and the Soviet
Union to suggest that they both had technological
development via R&D, and the crucial difference for their
long-run growth performance was perhaps in the quality
of institutions and the implications for entrepreneurship.
Capital stock = log difference
Employment = log difference
GEI = log difference
Individual = log difference
Institutional = log difference
Number of countries
Robust standard errors in parentheses
***p < 0.001; **p < 0.01; *p < 0.05; +p < 0.10
This leads us to suggest considering entrepreneurship
and institutions in combination in explaining the growth
In this paper, we used the concept of the
entrepreneurial ecosystem measured by the GEI and it is
important to note its limitations as well as its strengths. The
GEI is a composite index that combines both agency
Dependent variable: GDP growth rate (approximated by logarithmic difference) (1)
Dependent variable: GDP growth rate (approximated by logarithmic difference)
Capital stock = log difference
Employment = log difference
GEI = difference
Index of human capital = difference
GEI = log difference
Index of human capital = log difference
Number of country name no.
Robust standard errors in parentheses
***p < 0.001; **p < 0.01; *p < 0.05; +p < 0.10
and institutions, a way of thinking consistent with the
De Soto (2000
). With any composite index, there
are necessary ambiguities regarding certain
components. For instance, in GEI, if we consider the domestic
market indicator, in what ways is it good or bad for
institutional entrepreneurship if the domestic market is
big or small? The Likert scale on which much of the
index is based can be thought of being rather opaque. It
may be that the appropriate measurement of GEI should
be at a more disaggregated level, such as a city, MSA, or
some other region that represents an agglomeration
which takes account of knowledge spillover and density.
We have sought to explain the source of the Solow
residual in terms of institutions and entrepreneurship,
whether singly or in combination. Explanations of the
Solow residual over the past almost 50 years have
focused on stocks of capital, labor, human capital, and
knowledge, but none of them have provided a full
explanation of the variance in growth. We therefore
explored the question of whether the interaction of
private initiative and adequate institutional frameworks
shaped by collective choice captured by the concept of
the entrepreneurial ecosystem may be important in the
growth process. We provided a preliminary empirical
exploration of this idea based on the inclusion of a
measure of an NSE, the GEI, in an aggregate production
function framework. We have shown that the NSE is
positively and significantly associated with economic
growth. Hence, though the number of countries under
consideration is relatively small and the estimation
methods employed are relatively unsophisticated, our
results suggest that analyses of entrepreneurial
ecosystems could be a promising way forward to
understanding variation in cross-country growth rates as well as
providing a systemic basis for policy interventions.
Acknowledgements This paper grew out of a GEDI Institute
research project first at Imperial College and then at the London
School of Economics. We would like to thank David B. Audretsch,
Phil Auerswald, Erik Stam, Mark Sanders, Erkko Autio, David
Soskice and seminar participants at the ZEW conference on
National Systems of Entrepreneurship in Mannheim, Germany, 2014,
The Oxford Residence Week for Entrepreneurship Scholars at
Green Templeton College, Oxford University, 2015, two
anonymous referees, and the editors of this special issue of SBE. We
gratefull ackowledge support from the European Union’s Horizon
2020 agreement programme under grant no. 649378 (FIRES
project). We would also like to thank Gabor Markus and Dustin Voss
for valuable research assistance. Any remaining errors are our own.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
Acemoglu , D. , & Johnson , S. ( 2005 ). Unbundling institutions . Journal of Political Economy , 113 ( 5 ), 949 - 995 . https://doi. org/10.1086/432166.
Acemoglu , D. , Johnson , S. , & Robinson , J. A. ( 2005 ). Institutions as a fundamental cause of long-run growth . In P. Aghion & S. N. Durlauf (Eds.), Handbook of economic growth (pp. 385 - 472 ). Amsterdam: Elsevier.
Acemoglu , D. , & Robinson , J. A. ( 2012 ). Why nations fail . New York: Crown Business.
Acs , Z. , Szerb , L. , & Autio , E. , ( 2015 ) Global Entrepreneurship and Development Index , 2014 . (p. 132 ). Cham: Springer.
Acs , Z. J. , Audretsch , D. B. , Lehmann , E. , & Licht , G. ( 2016 ). National Systems of Entrepreneurship. Special Issue. Small Business Economics , 46 ( 4 ), 527 - 535 . https://doi. org/10.1007/s11187-016-9705-1.
Acs , Z. J. , Audretsch , D. B. , Lehmann , E. , & Licht , G. ( 2017a ). National Systems of Innovation. Special Issue. Journal of Technology Transfer , 42 ( 5 ), 997 - 1008 . https://doi. org/10.1007/s10961-016-9481-8.
Acs , Z. J. , Autio , E. , & Szerb , L. ( 2014 ). National systems of entrepreneurship: Measurement issues and policy implications . Research Policy , 43 ( 3 ), 476 - 494 . https://doi. org/10.1016/j.respol. 2013 . 08 .016.
Acs , Z. J. , Lafuente , E. , Sanders , M. , & Szerb , L. ( 2017b ). The global technology frontier: productivity growth and the relevance of the national system of entrepreneurship . Mineo: University of Pecs.
Acs , Z. J. , & Sanders , M. ( 2013 ). Knowledge spillover entrepreneurship in an endogenous growth model . Small Business Economics , 41 ( 4 ), 775 -796 https://doi.org/10.1007/s11187- 013-9506-8.
Aghion , P. ( 2017 ). Entrepreneurship and growth: lessons from an intellectual journey . Small Business Economics , 48 ( 1 ), 9 -24 https://doi.org/10.1007/s11187-016-9812-z.
Aghion , P. , & Howitt , P. A. ( 1992 ). Model of growth through creative destruction . Econometrica , 60 ( 2 ), 323 - 351 . https://doi.org/10.3386/w3223.
Aidis , R. , Estrin , S. , & Mickiewicz , T. ( 2008 ). Institutions and entrepreneurship development in Russia: a comparative perspective . Journal of Business Venturing , 23 ( 6 ), 656 - 672 . https://doi.org/10.1016/j.jbusvent. 2008 . 01 .005.
Andersson , M. , & Waldenström , D. ( 2017 ). Hernando de Soto: recipient of the 2017 Global Award for Entrepreneurship Research . Small Business Economics , 49 ( 4 ), 721 - 728 . https://doi.org/10.1007/s1118.
Barro , R. J. ( 1991 ). Economic growth in a cross section of countries . Quarterly Journal of Economics , 106 ( 2 ), 407 - 443 . https://doi.org/10.2307/2937943.
Barro , R. J. , & Lee , W. J. ( 1993 ). International comparisons of educational attainment . Journal of Monetary Economics , 32 ( 3 ), 363 - 394 .
Barro , R. J. , & Sala-i- Martin , X. ( 1995 ). Economic growth . Boston: McGraw Hill.
Baumol , W. J. ( 1990 ). Entrepreneurship: productive, unproductive and destructive . Journal of Political Economy , 98 ( 5 ), 893 - 921 . https://doi.org/10.1016/ 0883 - 9026 ( 94 ) 00014 - X .
Baumol , W. J. ( 1993 ). Formal entrepreneurship theory in economics: existence and bounds . Journal of Business Venturing , 8 ( 3 ), 197 - 210 . https://doi.org/10.1016/ 0883 - 9026 ( 93 ) 90027 - 3 .
Baumol , W. J. , & Strom , R. J. ( 2007 ). Entrepreneurship and economic growth . Strategic Entrepreneurship Journal , 1 ( 3- 4 ), 233 - 237 . https://doi.org/10.1002/sej.26.
Bergeaud , A. , Cette , G. , & Lecat , R. ( 2018 ). The role of production factor quality and technology diffusion in twentiethcentury productivity growth . Cliometrica , 12 ( 1 ), 61 - 97 .
Bjørnskov , C. , & Foss , N. ( 2013 ). How strategic entrepreneurship and the institutional context drive economic growth . Strategic Entrepreneurship Journal , 7 ( 1 ), 50 - 69 . https://doi.org/10.1002/sej.1148.
Bjørnskov , C. , & Foss , N. J. ( 2016 ). Institutions, entrepreneurship, and economic growth: what do we know and what do we still need to know? Academy of Management Perspectives , 30 ( 3 ), 292 - 315 . https://doi.org/10.5465/amp. 2015 . 0135 .
Braunerhjelm , P. , Acs , Z. J. , Audretsch , D. B. , & Carlsson , B. ( 2010 ). The missing link: knowledge diffusion and entrepreneurship in endogenous growth . Small Business Economics , 43 ( 1 ), 105 -125 https://doi.org/10.1007/s11187-009-9235-1.
Carree , M. A. , & Thurik , R. ( 2003 ). The impact of entrepreneurship on economic growth . In D. Audretsch & Z. Acs (Eds.), The handbook of entrepreneurship research (pp. 425 - 486 ). Boston: Kluwer.
Casadio Tarabusi , E. , & Guarini , G. ( 2013 ). An unbalanced adjustment method for development indicators . Social Indicators Research , 112 ( 1 ), 19 -45 https://doi.org/10.1007 /s11205-012-0070-4.
Casadio Tarabusi , E. , & Palazzi , P. ( 2004 ). An index for sustainable development . PSL Quarterly Review , 57 ( 229 ), 185 - 206 .
Davidson , R. , & MacKinnon , J. G. ( 1981 ). Several tests for model specification in the presence of alternative hypotheses . Econometrica , 49 ( 3 ), 781 - 793 . https://doi.org/10.2307 /1911522.
Davidson , R. , & MacKinnon , J. G. ( 1993 ). Estimation and inference in econometrics . Oxford: Oxford University Press.
de Soto , H. ( 2000 ). The mystery of capital, why capitalism triumphs in the west and fails everywhere else . New York: Basic Books.
de Soto , H. ( 2017 ). A tale of two civilizations in the era of Facebook and Blockchain . Small Business Economics , 49 ( 4 ), 729 - 739 .
Desai , S. , Acs , Z. J. , & Weitzel , U. ( 2013 ). A model of destructive entrepreneurship: Insights on conflict, post conflict recovery . Journal of Conflict Resolution , 57 ( 1 ), 20 - 40 . https://doi. org/10.1177/0022002712464853.
Erken , H. , Donselaar , P. , & Thurik , R. ( 2016 ). Total factor productivity and the role of entrepreneurship . Journal of Technology Transfer , 1 - 29 . https://doi.org/10.1007/s10961- 016-9504-5.
Fatas , A. , & Mihov , I. ( 2013 ). Policy volatility, institutions, and economic growth . Review of Economics and Statistics , 95 ( 2 ), 362 - 376 . https://doi.org/10.1162/REST_a_ 00265 .
Feenstra , R. C. , Inklaar , R. , & Timmer , M. P. ( 2015 ). The next generation of the Penn World Table . The American Economic Review , 105 ( 10 ), 3150 - 3182 .
Goldman , M. I. ( 1983 ). USSR in Crisis: The Failure of an economic system . New York: Norton.
Hayek , F. A. ( 1945 ). The use of knowledge in society . The American Economic Review , 35 ( 4 ), 519 -530 http://www. jstor.org/stable/1809376.
Henrekson , M. , & Sanandaji , T. ( 2014 ). Small business activity does not measure entrepreneurship . Proceedings of the National Academy of Sciences of the United States of America (PNAS) , 111 ( 5 ), 1760 - 1765 . https://doi. org/10.1073/pnas.1307204111.
Islam , N. ( 1995 ). Growth empirics: a panel data approach . The Quarterly Journal of Economics , 110 ( 4 ), 1127 - 1170 . https://doi.org/10.2307/2946651.
Jones , C. I. , & Romer , P. M. ( 2009 ). The new Kaldor facts: ideas, institutions, population, and human capital . American Economic Journal: Macroeconomics , 2 ( 1 ), 224 - 245 .
Koellinger , P. D. , & Thurik , A. R. ( 2012 ). Entrepreneurship and the business cycle . Review of Economics and Statistics , 94 ( 4 ), 1143 - 1156 . https://doi.org/10.1162/REST_a_ 00224 .
Kremer , M. ( 1993 ). The O-ring theory of economic development . Quarterly Journal of Economics , 108 ( 3 ), 551 - 575 . https://doi.org/10.2307/2118400.
Lafuente , E. , Szerb , L. , & Acs , Z. J. ( 2016 ). Country level efficiency and national systems of entrepreneurship: a data envelopment analysis approach . Journal of Technology Transfer , 41 ( 6 ), 1260 - 1283 . https://doi.org/10.1007/s10961- 015-9440-9.
Leibenstein , H. ( 1968 ). Entrepreneurship and development . American Economic Review , 58 ( 2 ), 72 -83 http://www.jstor. org/stable/1831799.
Lumpkin , G. T. , & Dees , G. G. ( 1996 ). Clarifying the entrepreneurial orientation construct and linking it to performance . Academy of Management Review , 21 ( 1 ), 135 - 172 . https://doi.org/10.5465/AMR. 1996 . 9602161568 .
March , J. G. , & Simon , H. A. ( 1993 ). Organizations. Cambridge: Blackwell.
McMullen , J. S. , Plummer , L. A. , & Acs , Z. J. ( 2007 ). What is an entrepreneurial opportunity? Small Business Economics , 28 ( 4 ), 273 - 283 . https://doi.org/10.1007/s11187-006-9040-z.
Miller , D. ( 1986 ). Configurations of strategy and structure: towards a synthesis . Strategic Management Journal , 7 ( 3 ), 233 - 249 . https://doi.org/10.1002/smj.4250070305.
Murphy , K. M. , Shleifer , A. , & Vishny , R. W. ( 1993 ). Why is rentseeking so costly to growth? American Economic Review: Papers and Proceedings, 83 ( 2 ), 409 -414 http://www.jstor. org/stable/2117699.
Nelson , R. R. ( 1993 ). National innovation systems: a comparative analysis . Oxford: Oxford University Press.
North , D. C. ( 1990 ). Institutions. Institutional change and economic performance . Cambridge: Cambridge University Press.
North , D. C. , & Thomas , R. P. ( 1973 ). The rise of the western world: A new economic history . Cambridge University Press.
OECD. ( 2008 ). Handbook of constructing composite indicators: Methodology and user guide 2008 . Organization for economic co-operation development . Paris: OECD Publishing.
Ofer , G. ( 1987 ). Soviet economic growth, 1928 - 85 . Journal of Economic Literature, 25 ( 4 ), 1676 -1853 http://www.jstor. org/stable/2726445.
Parker , S. C. ( 2009 ). The economics of entrepreneurship . Cambridge: Cambridge University Press.
Posner , R. ( 2009 ). A failure of capitalism: the crisis of '08 and the descent into depression . Boston: Harvard University Press.
Reynolds , P. , Bosma , N. , Autio , E. , Hunt , S. , De Bono , N. , Servais , I. , & Chin , N. ( 2005 ). Global entrepreneurship monitor: data collection design and implementation 1998-2003 . Small Business Economics, 24 ( 3 ), 205 - 231 . https://doi. org/10.1007/s11187-005-1980-1.
Romer , P. ( 1986 ). Increasing returns and long-run growth . Journal of Political Economy , 94 ( 5 ), 1002 - 1037 . https://doi. org/10.1086/261420.
Romer , P. ( 1990 ). Endogenous technological change . Journal of Political Economy , 98 ( 5 , Part 2), 71 - 102 . https://doi. org/10.1086/261725.
Sala-i- Martin , X. , Lavergne , M. , Doppelhofer , G. , & Miller , R. I. ( 2004 ). Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach . American Economic Review, 94 ( 4 ), 813 - 835 .
Schumpeter , J. ( 1934 ). The theory of economic development . New Brunswick: Transaction Publishers.
Shane , S. ( 2012 ). Reflections on the 2010 AMR decade award: delivering on the promise of entrepreneurship as a field of research . Academy of Management Review , 37 ( 1 ), 10 - 20 . https://doi.org/10.5465/amr. 2011 . 0078 .
Shane , S. , & Venkataraman , S. ( 2000 ). The promise of entrepreneurship as a field of research . Academy of Management Review , 25 ( 1 ), 217 - 226 . https ://doi.org/10.5465 /AMR. 2000 . 2791611 .
Solow , R. M. ( 1957 ). Technical change and the aggregate production function . Review of Economics and Statistics , 39 ( 3 ), 312 - 320 . https://doi.org/10.2307/1926047.
Solow , R. M. ( 2009 ). How to understand the disaster . The New York Review of Books, May 14th, http://www.nybooks. com/articles/2009/05/14/how-to -understand-the-disaster/ . Accessed January 11 2018 .
Stiglitz , J. E. ( 1989 ). Markets, market failures, and development . The American Economic Review , 79 ( 2 ), 197 -203 http://www.jstor.org/stable/1827756.
Sussan , F. , & Acs , Z. J. ( 2017 ). The digital entrepreneurial ecosystem . Small Business Economics , 49 ( 1 ), 55 - 73 . https://doi. org/10.1007/s11187-017-9867-5.
Van Praag , C. M. , & Versloot , P. H. ( 2007 ). What is the value of entrepreneurship? A review of recent research . Small Business Economics , 29 ( 4 ), 351 - 382 . https://doi. org/10.1007/s11187-007-9074-x.
Weitzman , M. L. ( 1970 ). Soviet postwar economic growth and capital-labor substitution . American Economic Review , 60 ( 4 ), 676 -692 http://www.jstor.org/stable/1818411.