Want more high-growth entrepreneurs? Then control corruption with less ineffective bureaucracy

Abstract

For decades, scholars have been concerned with the role of public policy in stimulating entrepreneurial activity. Aside from pro-entrepreneurship policy, governments can also erect barriers to startup activity. Researchers have concluded that the degree of corruption in a country can become a significant deterrent to entrepreneurship, while research on the relationship between bureaucracy and startup rates has been inconclusive. In this study, we apply the theory of planned behaviour – in particular, the perceived behavioural control construct – to clarify the role of corruption and ineffective bureaucracy both independently and jointly in their relationships with entrepreneurship participation rates. Data on individuals from 53 nations for the 2006–2015 period were utilized to test the hypotheses. This research confirms that both are negatively associated with rates of startup activity and that in the context of highly corrupt countries, the two constructs interact to further reduce startup activity.

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Want more high-growth entrepreneurs?
Then control corruption with less ineffective
bureaucracy
Antonio Lecuna, Boyd Cohen & Vesna Mandakovic
To cite this article: Antonio Lecuna, Boyd Cohen & Vesna Mandakovic (2020) Want more high-
growth entrepreneurs? Then control corruption with less ineffective bureaucracy, Interdisciplinary
Science Reviews, 45:4, 525-546, DOI: 10.1080/03080188.2020.1792128
To link to this article: https://doi.org/10.1080/03080188.2020.1792128
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Want more high-growth entrepreneurs? Then control
corruption with less ineffective bureaucracy
Antonio Lecuna
a
, Boyd Cohen
b
and Vesna Mandakovic
c
a
School of Business and Economics, Universidad del Desarrollo, Santiago, Chile;
b
EADA Business
School, Barcelona, Spain;
c
Entrepreneurship Institute, Universidad del Desarrollo, Santiago, Chile
ABSTRACT
For decades, scholars have been concerned with the role of
public policy in stimulating entrepreneurial activity. Aside
from pro-entrepreneurship policy, governments can also
erect barriers to startup activity. Researchers have
concluded that the degree of corruption in a country can
become a significant deterrent to entrepreneurship, while
research on the relationship between bureaucracy and
startup rates has been inconclusive. In this study, we apply
the theory of planned behaviour –in particular, the
perceived behavioural control construct –to clarify the role
of corruption and ineffective bureaucracy both
independently and jointly in their relationships with
entrepreneurship participation rates. Data on individuals
from 53 nations for the 2006–2015 period were utilized to
test the hypotheses. This research confirms that both are
negatively associated with rates of startup activity and that
in the context of highly corrupt countries, the two
constructs interact to further reduce startup activity.
ARTICLE HISTORY
Received 26 October 2018
Revised 7 June 2020
Accepted 2 July 2020
KEYWORDS
Entrepreneurship; corruption;
procedural bureaucracy;
theory of planned behaviour;
perceived behaviour control;
high-growth entrepreneurs;
multilevel approach; global
entrepreneurship monitor
1. Introduction
Entrepreneurship policy seeks to influence the level of entrepreneurial activity in
a particular region (Lundstrom and Stevenson 2005) since increased levels of
entrepreneurship have been found to support job growth (Birch 1979) and
country competitiveness (Audretsch and Peña-Legazkue 2012). Researchers
continue to pursue the question of what factors, and which entrepreneurship
policies, if any, are actually successful in stimulating rates of entrepreneurship
and country competitiveness (Lecuna and Chávez 2018; Acs and Amorós 2008).
However, results have been mixed at best regarding what role governments
play in supporting more productive entrepreneurship in their territories (Capel-
leras et al. 2008; Ribeiro-Soriano and Galindo-Martín 2012). Baumol (1990)
suggested that policy may, perhaps, not be able to produce more entrepreneurs
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives
License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduct ion
in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Antonio Lecuna alecuna@udd.cl School of Business and Economics, Universidad del Desarrollo,
Ave. La Plaza 700, Las Condes, Santiago, Chile
INTERDISCIPLINARY SCIENCE REVIEWS
2020, VOL. 45, NO. 4, 525–546
https://doi.org/10.1080/03080188.2020.1792128

but could possibly direct, or ‘allocate,’entrepreneurs to more desirable pursuits.
Shane (2009) went further by suggesting that most entrepreneurship policy is
unlikely to have any measurable impact on local economies, because most entre-
preneurial activity, with or without government support, fails to generate
employment anyway.
The lack of consensus regarding the potential for government policy to posi-
tively impact the rate, or quality, of startups in a region has led some scholars
to turn their attention to the other side of the equation regarding government bar-
riers. For example, Acs et al. (2009) provided empirical support to the notion that
a range of barriers to entrepreneurial activity, including legal restrictions and
taxes, are negatively correlated with startup activity, while Murdock (2012)
showed that business regulation has a negative impact on entrepreneurial activity.
Leveraging institutional theory while investigating eighteen Latin American
economies during the 2002–2014 period, Lecuna and Chávez (2018) found
weak evidence for an association between strengthening of the institutional
framework and the number of newly registered firms as a percentage of an econ-
omy’s working-age population. To further expand our knowledge concerning the
barriers erected against entrepreneurial activity, this study uses a global sample
instead of a region-specific sample, multilevel data instead of country-level data
alone, and most importantly, given that multilevel analysis focuses on the individ-
ual, the theory of planned behaviour (TPB) –and the perceived behaviour control
construct (PBC) –instead of institutional theory as the relevant conceptual
framing for understanding the study’s entrepreneurial-related data.
The TPB, first developed by Ajzen (1991), suggests that three types of beliefs,
behavioural, normative, and control, influence an individual’s intentions to act.
Ajzen’s work has been leveraged in the following decades by entrepreneurship
scholars to identify factors that influence entrepreneurial intentions (Boyd
and Vozikis 1994) and to predict rates of nascent entrepreneurship (Serida
and Morales 2011). Recently, TPB was utilized to understand what factors
influence the growth intentions of entrepreneurs in developing countries
(Lecuna, Cohen, and Chávez 2017). Within Ajzen’s(1991) TPB is the construct
of control beliefs. An individual’s self-efficacy –that is, their belief in their own
capability to be successful in a specific pursuit –has been shown to be highly
related to entrepreneurial intentions and actions (Zhao, Seibert, and Hills 2005).
However, even in the context where an entrepreneur may have internal self-
efficacy, their beliefs regarding control over the success of their venture could be
influenced by exogenously erected barriers, which are out of the entrepreneur’s
control. Specifically for this research, we are interested in two of the most com-
monly researched governmental barriers to entrepreneurship: corruption and
ineffective bureaucracy (captured mainly by the degree of government corrup-
tion and the amount of procedural bureaucracy).
The study of corruption has been a source of debate. From one perspective,
according to Leff(1964), corrupt public employees should be more efficient if
526 A. LECUNA ET AL.

they were to charge directly for their remunerations, because by independently
charging their supposed salary, the incentives to work should increase. Hunting-
ton (1968) obtained similar results by arguing that corruption should reduce the
governmental interference that adversely effects those economic decisions that
would be favourable for growth. Lui (1985) extended this idea by proposing
that corruption should accelerate slow and rigid bureaucratic processes.
However, the more classical view regarding corruption will argue that corrupt
activities should not be considered a solution to government inflexibility,
because government inflexibility was deliberately instituted in the first place to
generate opportunities to commit acts of corruption, such as extortions and
bribes. Moreover, corruption should never be considered an element of
efficiency, because the acceleration of bureaucratic process by corrupting
public management decisions will eventually decelerate average times, since
corrupt public employees and elected politicians will benefit from this
deceleration.
Based on the World Bank’s Worldwide Governance Indicators (WGIs), cor-
ruption is defined as the perception of the extent to which public power is exer-
cised for private gain, including both petty and grand forms of corruption, as
well as capture of the state by elites and private interests. This definition is
similar to that of Bayley (1996), who suggested that corruption is the abuse of
a public management position for personal or third parties’benefits against
those interests of society and its institutions. Other authors, such as Harch
(1993), developed a more practical definition based on specific corrupt
actions, such as extortion, payoffs, bribery, collection of charge fees, illegal
gifts, illicit contributions, tax evasion or fraud, open public robbery, nepotism,
unlawful appropriation of public funds or state property, and the abuse of
public authority. The definition of Harch (1993) also includes trafficofinflu-
ences, acceptance of compensations and gifts, use of privileged information,
and any other activity that influences the political system with the objective of
obtaining benefits, either personal or for groups of interests.
This study defines corruption using the theoretical lenses of political science
and economics. In contrast, procedural bureaucracy is measured from the per-
spective of public administration (i.e. the number of procedures required to start
a business), while entrepreneurial activity is defined based on the management
and business literatures. According to Shane and Venkataraman (2000, 218),
entrepreneurship is the process and the set of enterprising individuals who dis-
cover (or create), evaluate, exploit, and respond to situational cues and existing
sources of opportunity. Essentially, entrepreneurship is the nexus of two
phenomena: the work of entrepreneurs and the presence of lucrative opportu-
nities (Shane and Venkataraman 2000, 218). Using the Global Entrepreneurial
Monitor dataset, this study measures entrepreneurial activity from two dimen-
sions: total early-stage entrepreneurial activity (TEA) and high-growth expec-
tation (i.e. high-aspiration) entrepreneurial activity (HAE). HAE is a
INTERDISCIPLINARY SCIENCE REVIEWS 527

percentage of TEA ventures that have better opportunities to grow as measured
by the number of employees. Consistent with how Shane (2009) implores policy-
makers to shift their attention and resources towards high-potential and high-
growth ventures, the focus of the study is on high-growth entrepreneurs,
instead of on the opportunity-driven versus necessity-driven entrepreneurship
dichotomy.
There are many reasons an individual may choose to become an entrepre-
neur. In the past several decades, entrepreneurship researchers have chosen to
differentiate necessity-driven entrepreneurs from opportunity-driven entrepre-
neurs (Williams 2009). Necessity-driven entrepreneurship emerges when indi-
viduals have no job prospects; consequently, they start a business as the only
alternative to unemployment. In contrast, opportunity-driven entrepreneurship
occurs when individuals identify a new and profitable business opportunity
(Lecuna, Cohen, and Chávez 2017, 143–144). According to Shane (2009),
however, the significant attention invested in differentiating between opportu-
nity- and necessity-driven entrepreneurship is misguided, which is principally
justified by the assertion that the distinction between opportunity- and neces-
sity-driven entrepreneurship does not exist, since entrepreneurs can build
high-growth, job-creating, wealth-generating ventures even if their motivation
for starting a business is out of sheer necessity (Shane 2009). Moreover, most
opportunity-driven entrepreneurs have founded businesses that have more in
common with self-employment than with the creation of high-growth compa-
nies (Lecuna and Chávez 2018,33–34) and ‘are not interested in growing
their businesses, and fewer still manage to do so’(Shane 2009, 142), whereas
necessity-driven entrepreneurs have strong growth potential based on the neces-
sity to survive as a motivation for successful entrepreneurship (Lecuna 2019, 13).
In the next section, we provide an overview of the TPB from an applied
psychological lens and its application to entrepreneurship research. We then
review the evolving literatures on both corruption (political science) and pro-
cedural bureaucracy (public administration) in independently affecting entre-
preneurship (management science) in regions around the globe. This is
followed by the formal development of three hypotheses. We then detail our
data sources and present our methodology for testing the hypotheses. We con-
clude with a discussion of the results and the implications of our findings on TPB
and potential avenues for future research.
2. Literature review
2.1. TPB in entrepreneurship research
The TPB was developed as an extension to Ajzen and Fishbein’s(1980) prior
theory development known as the Theory of Reasoned Action. TPB has been
applied to a range of social science disciplines and has generally been found
528 A. LECUNA ET AL.

to have strong predictive capabilities. In a meta-study of the accumulated results
of the application of the TPB across 185 studies published through 1997, Armi-
tage and Conner (2001) found that TPB accounted for 39% of the variation in
intentions and 27% of the variation in behaviour.
In entrepreneurship research, TPB has often been leveraged to predict entre-
preneurial intentions (Politis et al. 2016) as opposed to behaviour (Kautonen, Gel-
deren, and Fink 2013). Such use has occurred despite TPBs having been developed
to predict intentions and behaviour, and TPB has been used for both purposes in
numerous social science disciplines (Ajzen 1991; Armitage and Conner 2001).
Entrepreneurship scholars have also found consistent results in predicting entre-
preneurial intentions from TPB’s belief constructs, with approximately 35% of the
variation in intentions explained in TPB models (Aloulou 2016). Kautonen, Gel-
deren, and Fink (2013) published one of the first complete tests of TPB in entre-
preneurship research by leveraging a longitudinal approach to explore the
relationships between beliefs, intentions, and actions. With a sample of nearly
1000 individuals in Austria and Finland between 2011 and 2012, Kautonen, Gel-
deren, and Fink (2013) found that 59% of the variation in intention and 31% of the
variation in action to form a venture were predicted by the TPB model. Interest-
ingly, PBC, measured through survey questions associated with the capability to
form a venture and perceived control of the outcome, was a significant factor in
predicting both intention and behaviour.
Early entrepreneurship traits research sought to confirm that individuals with an
internal locus of control were more apt to launch new ventures. Entrepreneurship
scholars have long abandoned trying to identify universal personality traits that
predict entrepreneurial action and success. Nevertheless, the PBC construct from
TPB has continued to show predictive capability in many disciplines, including
entrepreneurship. However, entrepreneurship scholars have yet to fully determine
the full range of factors that influence PBC in its relationship with entrepreneurial
action. For this study,we are particularly interested in the relationship between two
governmental barriers, corruption and ineffective bureaucracy, which are measured
for testing purposes as the perception of the degree of corruption and procedural
bureaucracy. Below, we will provide a brief literature review of the extant research
pertaining to corruption, procedural bureaucracy, and entrepreneurship.
2.2. Corruption and entrepreneurship
Exogenous variables can influence an individual’s attitudes and moderate the
relationship between entrepreneurial intentions and behaviour (Krueger,
Reilly, and Carsrud 2000). Government corruption and procedural bureauc-
racy are two ways in which governments can inhibit entrepreneurial action.
A growing body of research has argued that decreasing the level of corruption
encourages entrepreneurial activity (Anokhin and Schulze 2008; Aidis, Estrin,
and Mickiewicz 2012; Lecuna and Chávez 2018). In the absence of strong rule
INTERDISCIPLINARY SCIENCE REVIEWS 529

enforcement –which is a common trait of highly corrupt governments –it
becomes risky to rely on legal contracts and/or the goodwill of service providers
(Alchian and Woodward 1988). Alternatives to trust as foundations of entrepre-
neurship, such as affect, kinship, and/or ethnic identity, are economically
inferior because they necessarily limit the size of the provider pool and expose
promising entrepreneurs to a greater risk of adverse selection. Corruption also
creates disincentives for investment in innovation and other economic activities,
with payoffs that are difficult or costly to monitor because they are uncertain
and/or temporally distant (Teece 1981).
In particular, we support the specific argument that corruption may encou-
rage unproductive and destructive forms of entrepreneurship and breed negative
societal attitudes towards entrepreneurs (Baumol 1990). This is mainly because
corruption increases agency costs (Alchian and Woodward 1988), transaction
costs (Luhmann 1988), and institutional risks for prospective entrepreneurs,
forcing them to rely on one-sided trust (Anokhin and Schulze 2008). Thus,
there are examples, such as the so-called ‘China Conundrum,’whereby entrepre-
neurs among a country’s elite can actually benefit from a corrupt system
(Bhoothalingam 2012); we would consider this unproductive entrepreneurship
and not always representative of productive or market-based entrepreneurial
activity. In contrast, better control over corruption should increase cash flow
reliability and allow entrepreneurs across political and economic spectra to
capture a greater share of revenue (Anokhin and Schulze 2008).
2.3. Ineffective bureaucracy and entrepreneurship
Government bureaucracy that can inhibit startup activity is associated with
extensive government procedures for new firm formation and burdens associ-
ated with growing a new venture, such as labour policy, credit restrictions, tax
policy and firm closure (van Stel, Storey, and Thurik 2007). For this research,
we are particularly interested in the extant literature pertaining to the relation-
ship between the number of government procedures imposed on startups and
the rate of startups in a country.
The number of procedures and lengths of time required to start a firm in
countries around the globe varies widely.
To meet government requirements for starting to operate a business in Mozambique, an
entrepreneur must complete 19 procedures taking at least 149 business days and pay US
$256 in fees. To do the same, an entrepreneur in Italy needs to follow 16 different pro-
cedures, pay US$3946 in fees, and wait at least 62 business days to acquire the necessary
permits. In contrast, an entrepreneur in Canada can finish the process in two days by
paying US$280 in fees and completing only two procedures. (Djankov et al. 2002,1)
In contrast to theoretical expectations, van Stel, Storey, and Thurik (2007) did
not find a relationship between the number of procedures for startups and the
rate of nascent entrepreneurship. van Stel, Storey, and Thurik (2007) did
530 A. LECUNA ET AL.

however find that higher capital requirements for startups was negatively corre-
lated with the rate of nascent entrepreneurship. Although more procedures for
startups should intuitively have a negative effect on startup activity in a country,
the extant research on the topic has been inconclusive. A primary objective of
this study is to clarify the impact that higher numbers of procedures have on
startup activity rates across countries.
3. Hypotheses
We have developed three hypotheses in order to determine if two different forms
of government barriers serve independently or collectively to hinder new firm
formation. Below we develop the hypotheses, present our data and methodology,
interpret the results, and discuss implications for TPB and entrepreneurship
policy research. While Krueger, Reilly, and Carsrud (2000) posited that exogen-
ous variables would be weak predictors of entrepreneurial activity, our hypoth-
eses predict that the perceived loss of behaviour control associated with
increasing corruption and ineffective bureaucracy will be significantly associated
with decreased rates of entrepreneurship.
3.1. Hypothesis 1: corruption and rates of nascent entrepreneurship
As discussed previously, corruption rates in a country have been found to be
negatively associated with entrepreneurship behaviour. Prior results are con-
sistent with what would be expected utilizing TPB and, in particular, the
PBC construct. TPB scholars have found that individuals with high degrees
of self-efficacy may be deterred from acting on their intentions towards a
new behaviour if they perceive that exogenous factors limit their volitional
control (Ajzen 2002).
A corrupt environment distorts entrepreneurial opportunities and returns: it facili-
tates the development of entrepreneurs willing and able to engage in corrupt prac-
tices while acting as a barrier that hinders the entry or growth of businesses by
entrepreneurs who are unwilling to engage in corrupt practices. (Aidis, Estrin,
and Mickiewicz 2012, 122)
Corruption has been observed to be negatively associated with entrepreneurial
entry for three related reasons (Aidis, Estrin, and Mickiewicz 2012): (1) it dis-
courages entrepreneurs who are unwilling to engage in corruption to advance
their enterprise; (2) it encourages destructive forms of entrepreneurship; and
(3) it can prevent businesses from growing in order to avoid governments
extracting increased revenues and resources from the company. Referring
back to the PBC construct from TPB, we therefore hypothesize that higher
rates of corruption will lead to lower levels of new firm formation due to the per-
ception of prospective entrepreneurs that the exogenous lack of corruption
control will negatively influence their entry and growth prospects.
INTERDISCIPLINARY SCIENCE REVIEWS 531

Hypothesis 1: Increasing corruption will decrease the probability that individuals
engage in early-stage entrepreneurial activities.
3.2. Hypothesis 2: ineffective bureaucracy and rates of nascent
entrepreneurship
As discussed previously, governments may also ‘get in the way’of entrepre-
neurial action by having high barriers to startup through bureaucratic pro-
cedures for firm formation. In a highly cited study of procedural
bureaucracy in 85 countries, Djankov et al. (2002) found that procedural
bureaucracy led to several negative outcomes for aspiring entrepreneurs and
the economy. van Stel, Storey, and Thurik (2007) suggested that aspiring
nascent-stage entrepreneurs would be more likely to be deterred by govern-
mental barriers to entry more than by barriers. van Stel, Storey, and Thurik
(2007) identified several potential government deterrents of new firm for-
mation, including minimum capital requirements, labour market regulations,
and procedural bureaucracy.
Contrary to van Stel, Storey, and Thurik’s(2007)findings, and consistent with
Djankov et al. (2002), we posit that the number of procedures required for firm
formation, which we have referred to as procedural bureaucracy, will in fact
deter new firm formation. Because procedural bureaucracy is an exogenous
factor outside the control of the entrepreneur, the PBC associated with this
aspect of firm formation is low and can result in impeding the relationship
between an entrepreneur’s intentions and their behaviour, as represented by
the formalization of their new firm.
Hypothesis 2: Ineffective bureaucracy, captured by higher rates of procedural bureauc-
racy, will decrease the probability that individuals engage in early-stage entrepreneur-
ial activities.
3.3. Hypothesis 3: combined effects of increasing corruption and ineffective
bureaucracy on new firm formation
The two constructs tied to our first two hypotheses, corruption and procedural
bureaucracy, have been linked to each other in the extant policy literature.
Djankov et al. (2002) introduced the tollbooth hypothesis, which suggested
that higher procedural bureaucracy leads directly to increased corruption as gov-
ernment officials offer to ‘grease the wheels’in return for financial
compensation.
A direct implication of the tollbooth hypothesis is that corruption levels and the inten-
sity of entry regulation are positively correlated. In fact, since in many countries in our
sample politicians run businesses, the regulation of entry produces the double benefit
of corruption revenues and reduced competition for the incumbent businesses already
affiliated with the politicians. (Djankov et al. 2002, 26)
532 A. LECUNA ET AL.

This interconnection between corruption and procedural bureaucracy has been
confirmed in follow-up studies of the tollbooth hypothesis (Guriev 2004; Ahlin
and Bose 2007). Surprisingly, however, the two constructs of corruption and pro-
cedural bureaucracy have rarely been incorporated into empirical studies with
regard to their combined effects on startup rates (Djankov 2009). As the tollbooth
hypothesis demonstrates, corruption and procedural bureaucracy combine to
form an even more insurmounicic barrier to new firm formation. This barrier,
when perceived by aspiring nascent entrepreneurs, would even further lower
the entrepreneur’sPBCofnewfirm formation. We suggest that the mixed
results pertaining to the impact of the number of procedures on startup rates
reported in earlier studies may be due to the lack of empirical examination of
the connection between procedures and corruption. Leveraging PBC, it is reason-
able to expect that the combination of corruption and procedural bureaucracy
leads to reduced control beliefs of entrepreneurs, resulting in further detrimental
impacts on startup rates. Therefore, we hypothesize the following.
Hypothesis 3: Corruption rates and levels of procedural bureaucracy combine to
further lower rates of entrepreneurial activity
4. Methodology
Because our data feature a hierarchical structure –namely, individual and
country-year levels –we apply a multilevel approach to test our hypotheses.
Our source for individual-level data derives from the global entrepreneurship
monitor (GEM) adult population survey (APS), which covers a representative
sample of the population in each participant country (Autio, Pathak, and Wenn-
berg 2013). We use data from the 10-year period 2006–2015. Our analysis
includes 53 countries
1
and covers responses from 725,153 individuals.
Data for country-year variables were gathered from the WGI and the
World Economic Forum’s Global Competitiveness Index (GCI). While
other studies employ data from the Heritage Foundation/Wall Street
Journal to measure institutional factors (see Aidis, Estrin, and Mickiewicz
2012; McMullen, Bagby, and Palich 2008), including ‘freedom from corrup-
tion,’as key variables of interest, the dataset presented here uses the World
Bank’s measurement for government institutions based on the WGI, as
suggested by Djankov et al. (2002). As in many other studies, including Acs
and Amorós (2008),weusetheGCItomeasurethecompetitivenessfactors,
whereas the macroeconomic control variables were drawn from the IMF
World Economic Outlook (WEO) database.
1
Sample: Argentina, Australia, Austria, Belgium, Canada, Chile, Croatia, Czech Rep., Denmark, Finland, France,
Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Rep. of Korea, Latvia, the Netherlands,
New Zealand, Norway, Poland, Portugal, Singapore, Slovenia, Spain, Sweden, the United Kingdom, Algeria, Bosnia
and Herzegovina, Brazil, Colombia, Jamaica, Kazakhstan, Malaysia, Mexico, Panama, Peru, Romania, the Russian
Federation, Serbia, Turkey, Uruguay, Egypt, Indonesia, Jordan, Morocco, the Philippines, and Thailand.
INTERDISCIPLINARY SCIENCE REVIEWS 533

4.1. Measures
4.1.1. Individual-level dependent variables
We use two dependent variables to test our hypothesis: the first is the early-stage
entrepreneur (TEA), and the second is a subset of the early-stage entrepreneurs
who are involved in a high-growth-expectation venture (HAE). TEA is based on
the life cycle of the entrepreneurial process, which covers nascent entrepreneurs
who have taken some action to create a new business in the past year but have
not paid any salaries or wages in the last three months, or the owners/managers
of businesses that have paid wages and salaries for more than three months but
less than 42 months. TEA is composed of both opportunity-driven entrepre-
neurship as well as necessity-based entrepreneurship. While some scholars
have sought to distinguish these metrics in studying rates of entrepreneurship,
others have argued that the distinction is largely irrelevant because ‘people
can build high-growth, job-creating, wealth-generating companies even if
their motivation for starting a business was necessity’(Shane 2009, 142). HAE
considers the high-aspiration ventures that are part of the TEA. HAE is thus
defined by entrepreneurs who expect to employ at least 5 employees 5 years
from now. HAE is negatively correlated with TEA. In our results section, we
further interpret the relationship between our hypotheses and the two
different dependent variables identified in this section.
4.1.2. Country-level predictors
Corruption. The corruption indicator (CORR) is the inverse value of ‘control of
corruption,’which is drawn from the WGI. We transformed this variable so that
the sign would be harmonized; moreover, for ease of interpretation, we centred
the variable on zero. CORR reflects the perception of the extent to which public
power is exercised for private gain, including both petty and grand forms of cor-
ruption, as well as capture of the state by elites and private interests. The
expected direction of the corruption coefficient in the regression models is nega-
tive, which implies that higher levels of corruption negatively affects entrepre-
neurial activity. Following Lecuna (2012, 144), we tested the ‘control of
corruption’variable as a valid country-level predictor against four potential
endogenous factors from the 2008–2009 GCI: property rights, strength of audit-
ing and reporting standards, judicial independence, and reliability of police ser-
vices. The correlation coefficients between the five measures, including the
‘control of corruption’indicator, ranged anywhere from .76 to .96, which was
expected due to all simply being variants of a lack of corruption.
Procedural bureaucracy. The information for the second independent variable
of interest, the number of procedures required to start a business, or procedural
bureaucracy, is measured using the GCI. The World Economic Forum’s Global
Competitiveness report remains the most comprehensive worldwide assessment
of national competitiveness, providing a platform for dialogue between
534 A. LECUNA ET AL.

government, business, and civil society about the actions required to improve
economic prosperity. In line with the ‘H2’hypothesis, procedural bureaucracy
is expected to enter the regression model with a strongly negative sign, indicating
that fewer bureaucratic procedures lead to increased entrepreneurial activity.
4.1.3. Individual-level control variables
Following previous research (see, for example, Arenius and Minniti 2005), we
include four perceptual variables that have been linked to entrepreneurial beha-
viours as proxies of (1) social capital within the entrepreneurial ecosystem, (2)
the individual’s perceived self-efficacy in entrepreneurial efforts, (3) the fear of
failure when undertaking new business venture activities, and (4) opportunity
recognition,reflecting the individual’s alertness to opportunities. We also
include demographic characteristics, such as age,sex and level of education.
These variables were obtained from the GEM APS data. Table 1 presents the
summary statistics of the variables used in the empirical exercise at the individ-
ual and the country levels of analysis.
4.1.4. Country-level control variables
Drawing from prior studies of rates of entrepreneurship, we employ a series of
macroeconomic indicators as control variables for testing our hypotheses. The
first macroeconomic explanatory variable is the Gross Domestic Product per
capita (GDP), as expressed in current U.S. dollars per person. Log GDP per
capita values are used to better interpret the GDP per capita explanatory variable
in the regression models and to avoid excessive weighting of extremely high and
low observations. The rates of unemployment (number of unemployed persons
Table 1. Descriptive statistics.
Mean Std.Dev Min Max
LEVEL 1 variables
TEA 0.09 0.283 0 1
HAE 0.16 0.369 0 1
Social capital 0.36 0.479 0 1
Self-efficacy 0.49 0.500 0 1
Fear of failure 0.40 0.491 0 1
Opportunity recognition 0.32 0.468 0 1
Age 42 15 18 99
Gender 0.48 0.500 0 1
Education 0.27 0.444 0 1
LEVEL 2 Variables
PB 6.95 3.452 1 18
CORR 0.00 1.000 −1.87 1.66
GDP (in logs) 1.43 0.106 1.15 1.61
Inflation 0.04 0.035 −0.09 0.22
Unemployment 0.09 0.054 0.01 0.31
Investment 0.23 0.051 0.14 0.47
Savings 0.22 0.087 −0.04 0.58
Political stability 0.23 0.833 −1.83 1.49
Infraestructure 4.71 1.172 1.97 6.73
Capacity of innovation 3.95 0.970 1.87 6.14
Wage flexibility 4.68 0.989 2.20 6.42
INTERDISCIPLINARY SCIENCE REVIEWS 535

as a percentage of the labour force), inflation (percentage change in average con-
sumer prices), investment (total investment as a percentage of GDP), and savings
(gross national savings as a percentage of GDP) are included to reflect the
soundness of a country’s monetary policy. All the macroeconomic variables
are measured using the WEO database. The WEO database contains selected
macroeconomic data series from the statistical appendix of the WEO report.
To capture the influences of additional institutional factors, we use variables
drawing from the WGI and GCI. The WGI measurements report on broad
characteristics of government institutions, including political stability,infra-
structure,capacity of innovation, and wage flexibility.
4.2. Estimation technique
Because we use two levels of analysis (the individual and country levels), we
analyse the data using hierarchical linear modelling (HLM) methods. Multilevel
modelling is appropriate when data are hierarchically structured –that is, when
they consist of units grouped at different levels of a hierarchy (Aguinis, Gottfred-
son, and Culpepper 2013; Rabe-Hesketh and Skrondal 2006). Autio, Pathak, and
Wennberg (2013) recommend the use of a multilevel approach in studies of
institutions and entrepreneurship, and they encourage GEM data entrepreneur-
ship research to use this technique. Thus, we estimate a model specified by the
following equation (Equation (1)):
Yijt =
b
0+
b
1−2Country pred jt +
b
3−9Indiv controlsijt
+
b
10−11Country controls jt +
m
ijt +1jt,
where Yijt represents the dependent variables (TEA or HAE); Country pred jt
represents the country predictors; Indiv controlsijt represents the individual
controls; and Country controlsjt represents the country control variables.
(Table 2 presents the correlations among the controls, predictors, and dependent
variables). The combination of
m
ijt +1jt comprises the random part of the
equation, where 1jt represents the country-level residuals, and
m
ijt represents
the individual-level residuals.
5. Results
Table 3 reports the results from estimating Equation (1) for two measures of
entrepreneurial activity. The first interesting point to highlight is that different
measurements of entrepreneurship are affected in different ways, which was
expected, because HAE tends to capture registered firms with high-growth
aspirations. The main focus in Table 3 is the effect of the degree of corruption
and ineffective bureaucracy measured by the number of procedures required
to start a business.
536 A. LECUNA ET AL.

Table 2. Correlation matrix.
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) TEA 1
(2) HAE - 1
(3) Social capital 0.1836 0.0534 1
(4) Opportunity recognition 0.1628 0.068 0.197 1
(5) Self-efficacy 0.2364 0.0411 0.2445 0.184 1
(6) Fear of failure −0.0919 −0.0517 −0.0455 −0.0913 −0.1502 1
(7) Education 0.0141 0.0802 0.0438 0.0059 0.0448 −0.0064 1
(8) Gender 0.0628 0.1205 0.0921 0.0587 0.1389 −0.0744 0.0065 1
(9) Age −0.0844 −0.0311 −0.1339 −0.0776 −0.0314 −0.0193 −0.0348 −0.0266 1
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) PB 1
(2) CORR 0.4752 1
(3) Political stability −0.3605 −0.8375 1
(4) Infraestructure 0.1691 0.5078 −0.5317 1
(5) Capacity of innovation 0.3282 0.2531 −0.1996 −0.0148 1
(6) Wage flexibility 0.2022 0.2455 −0.1756 0.2364 −0.0472 1
(7) GDP (in logs) −0.0225 −0.0297 −0.0056 −0.109 −0.278 0.4567 1
(8) Inflation −0.497 −0.7437 0.6627 −0.3004 −0.3525 −0.2261 0.0534 1
(9) Unemployment −0.4061 −0.8091 0.7552 −0.5072 −0.1208 −0.1756 0.1212 0.5876 1
(10) Investment −0.4529 −0.776 0.7396 −0.4337 −0.4582 −0.3216 0.0881 0.6579 0.6885 1
(11) Savings −0.1925 0.122 −0.225 0.0444 −0.2587 −0.0398 −0.1299 −0.1976 −0.1464 −0.0385 1
INTERDISCIPLINARY SCIENCE REVIEWS 537

5.1. Hypothesis 1: relationship between corruption and entrepreneurial
activity
As reported in Column 1 of Table 3, corruption enters the TEA model with a
strongly negative coefficient that is reinforced with significant a highly signifi-
cant p-value (
b
=−0.132; p,0.01). Statistically speaking, corruption
Table 3. Estimation results.
Variables (1) (2) (3) (4)
TEA TEA HAE HAE
Country-level predictors
CORR −0.132*** −0.110* 0.0827 0.124
(0.0440) (0.0579) (0.0928) (0.123)
PB 0.0591*** 0.0608*** −0.0310*** −0.0278**
(0.00486) (0.00566) (0.0116) (0.0131)
CORR*PB .−0.0033*** −0.00687
(0.00919) (0.0133)
Country-level controls
Political stability 0.255*** 0.255*** 0.237*** 0.237***
(0.0334) (0.0334) (0.0787) (0.0787)
Infraestructure 0.0182 0.0170 −0.0733 −0.0771*
(0.0194) (0.0195) (0.0458) (0.0464)
Capacity of innovation −0.461*** −0.462*** −0.0961 −0.0984
(0.0285) (0.0286) (0.0651) (0.0652)
Wage flexibility 0.161*** 0.160*** 0.289*** 0.287***
(0.0227) (0.0227) (0.0505) (0.0505)
GDP (in logs) −33.20*** −33.91*** 23.56** 22.08*
(4.831) (4.947) (10.92) (11.31)
GDP * GDP 11.92*** 12.16*** −7.385* −6.857*
(1.751) (1.791) (3.964) (4.101)
Inflation −0.00655 −0.00997 0.561 0.569
(0.277) (0.277) (0.719) (0.719)
Unemployment −2.591*** −2.591*** 1.251* 1.252*
(0.282) (0.282) (0.760) (0.760)
Investment −0.702*** −0.706*** −0.255 −0.270
(0.248) (0.248) (0.593) (0.593)
Savings 0.321 0.320 1.601*** 1.611***
(0.232) (0.232) (0.551) (0.551)
Individual-level controls
Social capital 0.666*** 0.666*** 0.354*** 0.354***
(0.00900) (0.00900) (0.0239) (0.0239)
Opportunity recognition 0.478*** 0.478*** 0.291*** 0.291***
(0.00909) (0.00909) (0.0235) (0.0235)
Self-efficacy 1.550*** 1.550*** 0.242*** 0.242***
(0.0119) (0.0119) (0.0349) (0.0349)
Fear of failure −0.382*** −0.382*** −0.164*** −0.164***
(0.00973) (0.00973) (0.0268) (0.0268)
Education 0.0250** 0.0250** 0.232*** 0.232***
(0.00992) (0.00992) (0.0239) (0.0239)
Gender 0.223*** 0.223*** 0.568*** 0.568***
(0.00873) (0.00874) (0.0236) (0.0236)
Age 0.0866*** 0.0866*** −0.0184*** −0.0184***
(0.00213) (0.00213) (0.00471) (0.00471)
Age*Age −0.00122*** −0.00122*** 0.000144** 0.000144**
(2.59e-05) (2.59e-05) (5.62e-05) (5.62e-05)
Constant 18.66*** 19.16*** −21.74*** −20.69***
(3.309) (3.397) (7.480) (7.763)
Observations 725,153 725,153 69,424 69,424
Number of groups 53 53 53 53
Standard errors in parentheses.
*** p< 0.01, ** p< 0.05, *<0.1.
538 A. LECUNA ET AL.

provides reasonably good explanatory power for entrepreneurial activity when
judged by the usual t-test of significance. This finding provides support for
the ‘H1’hypothesis, which specifically tests whether higher levels of corruption
in a country have a negative and significant impact on total entrepreneurial
activity. However, in the case of HAE in Column 3, we do not find a significant
coefficient.
5.2. Hypothesis 2: relationship between procedural bureaucracy and
entrepreneurial activity
In the case of procedural bureaucracy, the impact of more bureaucracy in entre-
preneurial activity is significant in all the specifications, but the direction of the
effect is different when comparing the different definitions of entrepreneurial
activity. HAE is negatively associated with more bureaucracy
(
b
=−0.0311; p,0.01), whereas TEA is positively related
(
b
=0.0591; p,0.01). An additional procedure to start a business reduces
the HAE by approximately 3%. Conversely, as TEA likely also includes large
numbers of informal ventures, the increase in levels of bureaucracy could even-
tually become an additional exogenous barrier leading aspiring entrepreneurs to
avoid formality. Therefore, in the case of TEA, more procedural bureaucracy
increases the percentage of informal entrepreneurs.
5.3. Hypothesis 3: interaction effects between corruption and procedural
bureaucracy
The interaction term between corruption and procedural bureaucracy is
reported in Table 3 (Columns 2 and 4) for all the definitions of entrepreneurial
activity. We find this interaction effect to be significant only for the TEA
definition of entrepreneurial activity (
b
=−0.0033; p,0.01), which implies
that the combination of high corruption and relatively greater procedures
required to start a business provides an additional boost to total entrepreneurial
activity over and above the direct effects.
6. Limitations, discussion, and future research
This research draws on panel data from 53 economies to empirically test three
hypotheses derived from TPB. Specifically, we set out to determine the
relationships among corruption, procedural bureaucracy, and two measures
of entrepreneurial activity (TEA and HAE) in 53 countries around the
globe. We were particularly interested in the application of PBC from TPB,
which suggests that entrepreneurs who may otherwise be inclined (entrepre-
neurial intention) to start a new firm and who believe they are capable of
doing so may choose not to act on those intentions because there are
INTERDISCIPLINARY SCIENCE REVIEWS 539

important factors out of their control that may negatively affect their ability to
actually incorporate a new firm.
All three of our hypotheses were confirmed to some degree: corruption is
associated with lower rates of total entrepreneurial activity (H1); ineffective
bureaucracy, as measured by the number of procedures required of a startup,
is associated with lower rates of high-aspiration entrepreneurial activity (H2);
and procedural bureaucracy moderates the relationship between corruption
and total entrepreneurial activity (H3). These conclusions are consistent with
prior conceptual work by Djankov et al. (2002) and later empirical work explor-
ing the tollbooth hypothesis as it pertains to the relationship between corruption
and procedural bureaucracy (Guriev 2004; Ahlin and Bose 2007).
However, we cannot fully support our three hypotheses, because corruption
was not significant in the HAE specification; moreover, procedural bureaucracy
enters the TEA specification with a highly significant negative sign, indicating
that as procedural bureaucracy increases, it becomes likely that more formal
entrepreneurs will consider informal startups, which is likely captured by the
TEA measurements from the GEM (Valdez and Richardson 2013). Similarly,
though we were able to confirm that corruption and procedural bureaucracy
jointly have a greater detrimental impact on TEA, we were not able to fully
extend prior research by finding a significant interaction effect between high
levels of corruption and procedural bureaucracy as they pertain to HAE.
While the link between corruption and entrepreneurial activity has been
confirmed consistently in the extant literature (Aidis, Estrin, and Mickiewicz
2012), the role of ineffective bureaucracy, independently or jointly with corrup-
tion, had yet to be clearly confirmed in prior research. Given the largely incon-
clusive or inconsistent results found in numerous studies of pro-
entrepreneurship policy and the rates or quality of entrepreneurial activity, we
believe our results contribute to the conversation about the different positive
and negative roles governments may play in affecting the entrepreneurial
phenomenon in their country or region.
These results suggest new lines of research related to governmental barriers to
entrepreneurship in the context of TPB. Longitudinal studies, similar to that of
Kautonen, Gelderen, and Fink (2013), using the same or expanded government
barrier indicators could allow for deeper insights into the relationship between
intentions and behaviour. Due to our reliance on secondary data, we were unable
to test a full TPB model that could capture data from aspiring entrepreneurs at
the concept stage and explore which aspects of TPB and PBC affected the
relationship between initial intention and eventual action or inaction.
Measuring corruption also presents a significant challenge. Because corruption
is a criminal activity, methodologies measuring it must be sustained on the sub-
jective perceptions reflected in questionnaires and surveys, which distorts any
possibility of achieving precise measurements. Moreover, the intrinsic problem
with relying on the perception of corruption is that corruption itself has
540 A. LECUNA ET AL.

different meanings to different people. As such, corruption varies greatly depend-
ing on the nationality of the corrupt individual. For example, it is common for
foreign entrepreneurs to pay sums of money far in excess of the nominal building
permit fee compared to the fees paid by local entrepreneurs. In addition to the rel-
evant limitation presented by the measurement of corrupt activities, quantifying
the real impact of corruption on society based mainly on the sum of individual
cases is also very misleading. For example, which is more corrupt: to pay a restau-
rant waiter an extra tip for a beachfront window table in Rio de Janeiro, to resell
tickets for a baseball game in Santo Domingo, to collect bribes by a low-paid traffic
officer in La Paz, or to award multi-billion-dollar military contracts for the U.S.
Department of Defense? Based solely on the amount of money involved, there
should be no doubt as to which is more corrupt.
Furthermore, by using the perception of corruption and procedural bureauc-
racy, we omitted other government barriers that may also influence an aspiring
entrepreneur’s PBC. Other government barriers worth exploring include taxa-
tion policy, competition policy, and transparency, among many others. Which
of the aforementioned barriers, independently or in combination, also deter
entrepreneurial activity? Naturally, an extension to this research relates to nor-
mative policy guidance for governments seeking to get out of the way. While the
jury is still out on the efficacy of pro-entrepreneurship policy, the evidence is
mounting that governments can clearly impede entrepreneurial activity
through a range of barriers erected intentionally or unintentionally.
Controlling corruption is an extremely difficult endeavour. Although
strengthening governmental institutions is insufficient, it is a strong first step
in the right direction. In reference to the ‘cures’of corruption, Tanzi (1998,
587) argued the following:
The greatest mistake that can be made is to rely on a strategy that depends excessively
on actions in a single area, such as increasing the salaries of the public sector employ-
ees, or increasing penalties, or creating an anticorruption office, and then to expect
quick results.
However, decreasing the number of procedures required to start a business is
a relatively straightforward public policy measure. Therefore, this research has
important policy implications, because the theory and evidence presented here
indicate that stimulating entrepreneurial activity in an economy is more
effective when policy reforms aimed at better control over corruption are
implemented in combination with decreasing bureaucratic procedures.
Finally, one of the contributions of this research is its use of two different
measures of entrepreneurial activity. As highlighted in the Results section, we
found unique and sometimes conflicting results regarding the relationship
between corruption and procedural bureaucracy on startup activity, depending
on which of the two measures was utilized (TEA versus HAE). As Valdez and
Richardson (2013) suggested, further research is needed to understand the
INTERDISCIPLINARY SCIENCE REVIEWS 541

unique role informal entrepreneurship plays in studies of entrepreneurship
activity. We found that TEA was associated with significantly different behaviour
with respect to procedural bureaucracy. Perhaps one reason for entrepreneurship
scholars’inability to obtain consistent results in studies of entrepreneurship
activity is the lack of consistency in the dependent variable chosen.
7. Conclusion
There is growing consensus that corruption is an impediment to entrepreneurship
(Anokhin and Schulze 2008; Aidis, Estrin, and Mickiewicz 2012; Acs, Desai, and
Flapper 2008). With regard to ineffective bureaucracy and new firm formation,
however, results have been mixed (van Stel, Storey, and Thurik 2007). Further-
more, despite the logical connections between corruption and bureaucracy,
these constructs have rarely been related in empirical studies of government impe-
diments to entrepreneurship (Estrin, Korosteleva, and Mickiewicz 2013).
The focus of this research was to clarify the roles of corruption and ineffective
bureaucracy as they pertain to government barriers to entrepreneurship, thereby
extending our understanding of control beliefs within the TPB. In this research,
we developed three hypotheses. The first two sought to clarify the roles of cor-
ruption and procedural bureaucracy as independent constructs affecting entre-
preneurial activity in an economy. The third hypothesis sought to relate the two
constructs to each other in order to determine if there is an interaction effect
between corruption and procedural bureaucracy as a combined factor
affecting entrepreneurship activity in a region. We obtained data from several
sources in support of this research (GEM, WGI, and GCI).
The evidence is mounting that government barriers can affect the relationship
between an aspiring entrepreneur’s intention to start a firm and their eventual
behaviour. This study contributes to the ongoing conversation about demon-
strating a linkage between corruption, procedural bureaucracy, and entrepre-
neurial activity. In all, our findings suggest direct policy implications:
reducing ineffective bureaucracy influences individuals’engagement in high-
quality entrepreneurship. Policymakers could implement simple regulatory
changes that can facilitate business development, such as cutting bureaucratic
red tape or eliminating unnecessary procedures for firm creation. Alleviating
corruption is a more difficult endeavour, but initiatives that increase the
states’modernization, transparency, and accountability have been implemented
successfully, for example, in some Latin American countries with the support of
the Inter-American Development Bank (IADB).
It is quite possible that Baumol (1990) and Shane (2009) are correct in
arguing that pro-entrepreneurial policy may not increase the number or
quality of entrepreneurs but instead that bad government may impede otherwise
promising startups from ever getting offthe ground. We believe this to be a
fertile area for future research.
542 A. LECUNA ET AL.

Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Antonio is an assistant professor at the School of Business and Economics at Universidad
del Desarrollo in Santiago, Chile. His current research is focused on entrepreneurship in
Latin America. He recently served the Corporación de Fomento de la Producción
(CORFO), the Chilean government institution in charge of promoting economic
growth, as the lead researcher of an initiative to improve the entrepreneurial ecosystem
in the country. Recent journal publications include Business Strategy and the Environ-
ment,International Entrepreneurship and Management Journal,Journal of Private Enter-
prise,International Journal of Production Economics,andJournal of Applied Economics.
Antonio holds a PhD in Management Science from Escuela Superior de Administración
y Dirección de Empresas (ESADE).
Boyd Cohen (born 1970) is an urban and climate strategist working in the area of sustainable
development and smart cities. Currently he is Dean of Research at EADA Business School
and co-founder of IoMob. Cohen received a PhD in Strategy & Entrepreneurship from the
University of Colorado (2001). Along with Hunter Lovins, he co-authored Climate Capital-
ism: Capitalism in the Age of Climate Change in 2011. In recent years, Cohen has become
most recognized for his work in smart cities, beginning with his Smart Cities Wheel frame-
work and associated annual rankings of smart cities. In 2016, he published his second book,
The Emergence of the Urban Entrepreneur, followed by the publication of his 3rd book, Post-
Capitalist Entrepreneurship in 2017.
Vesna Mandakovic is an Associate Professor in the Entrepreneurship Institute of Universi-
dad del Desarrollo. She holds a PhD in Economics from the Pontificia Universidad Católica
of Chile. Her research focuses in entrepreneurial activity, analyzing how public policy and
programs influence the creation of new business ventures in developing economies, and
trying to identify which policies are actually successful in stimulating rates of entrepreneur-
ship from an empirical perspective. She is currently Commissioner of the Chilean National
Productivity Commission (CNP) and advisor for the Entrepreneurial Ecosystems Committee
in the Government Agency for Economic Development (CORFO).
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