technology based ventures

8/18/2019 Technology Based Ventures

1/23

This article was downloaded by: [islem khefacha]On: 03 September 2015, At: 04:39Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: 5 Howick Place, London, SW1P 1WG

Click for updates

The Journal of InternationalTrade & Economic Development:

An International and

Comparative ReviewPublication details, including instructions for authors

and subscription information:

http://www.tandfonline.com/loi/rjte20

Technology-based ventures

and sustainable development:

Cointegrating and causal

relationships with a panel data

approach

Islem Khefachaa

& Lotfi Belkacema

a Laboratory Research for Economy, Management and

Quantitative Finance, IHEC - University of Sousse 4054,

Tunisia

Published online: 15 Jun 2015.

To cite this article: Islem Khefacha & Lotfi Belkacem (2015): Technology-based venturesand sustainable development: Cointegrating and causal relationships with a panel data

approach, The Journal of International Trade & Economic Development: An Internationaland Comparative Review, DOI: 10.1080/09638199.2015.1048707

To link to this article: http://dx.doi.org/10.1080/09638199.2015.1048707

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the

information (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, orsuitability for any purpose of the Content. Any opinions and views expressedin this publication are the opinions and views of the authors, and are not theviews of or endorsed by Taylor & Francis. The accuracy of the Content shouldnot be relied upon and should be independently verified with primary sources

http://dx.doi.org/10.1080/09638199.2015.1048707http://dx.doi.org/10.1080/09638199.2015.1048707http://www.tandfonline.com/action/showCitFormats?doi=10.1080/09638199.2015.1048707http://www.tandfonline.com/loi/rjte20http://crossmark.crossref.org/dialog/?doi=10.1080/09638199.2015.1048707&domain=pdf&date_stamp=2015-06-15


2/23

of information. Taylor and Francis shall not be liable for any losses, actions,claims, proceedings, demands, costs, expenses, damages, and other liabilitieswhatsoever or howsoever caused arising directly or indirectly in connectionwith, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly

forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

D o w

n l o a d e d b y [ i s l e m k

h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions


3/23

The Journal of International Trade & Economic Development , 2015

http://dx.doi.org/10.1080/09638199.2015.1048707

Technology-based ventures and sustainable development:

Cointegrating and causal relationships with a panel data

approach

Islem Khefacha ∗ and Lotfi Belkacem

Laboratory Research for Economy, Management and Quantitative Finance, IHEC - University of Sousse 4054, Tunisia

( Received 27 January 2014; accepted 3 May 2015)

The aim of this article is to provide new empirical evidence on the causality between proxy variables of technology entrepreneurship and proxy variable of sustainable economic performance in a vector error correction model. It coversa sample of 13 countries participated to Global Entrepreneurship Monitor studies under the period 2002–2013. Building on a theoretical background that considers the adoption of new technologies through a dynamic processof creative destruction based on innovation as the most important factor for achieving long-term economic growth, the empirical investigation uses robusteconometric techniques that are capable of estimating long-run cointegrating

relationships in panel data.Our results support the idea that total entrepreneurship activity related tothe technology sector leads to improve the sustainability of a nation in thelong run. More importantly, our paper helps understand the nature of liaison between the creation of innovative and high-technology business and the presence of favorable social and environmental conditions for the well-beingof a population.

Keywords: technology entrepreneurship; sustainable economic growth;GEM; cointegration tests; vector error correction model

JEL Classification: C32, M13, Q01

1. Introduction

It is well known that economic growth is the key to higher living standards. For

this, economic theory suggests several key institutions and policy factors that

are important to make way for sustainable economic growth (see Onipede 2003).

Recent studies emphasize entrepreneurship as a driver of economic development

and some authors include entrepreneurship as a fourth production factor in the

macroeconomic production function (see Audretsch and Keilbach 2004 cited in

∗Corresponding author. Email: [email protected]

C 2015 Taylor & Francis

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

http://dx.doi.org/10.1080/09638199.2015.1048707mailto:[email protected]:[email protected]://dx.doi.org/10.1080/09638199.2015.1048707


4/23

2 I. Khefacha and L. Belkacem

Szirmai, Naud ́e, and Goedhuys [2011]). Therefore, several economists focused

their research on the dynamic role of entrepreneurial activity in promoting inno-

vation, economic growth and employment.

In this body of literature, emphasis seemed to be placed on high-potentialtechnology opportunities and technical systems. Within this context, technology

entrepreneurship – a relatively new field of study – has received increasing atten-

tion from the scholars of various streams of business and technology disciplines.

For Abdullah and Ahcene (2011), in the current context of globalization and lib-

eralization economy, technology entrepreneurship is indeed becoming vital as it

provides greater opportunities and enables effective optimization of resources to

attain high profit margins.

This research leverages on the Global Entrepreneurship Monitor (GEM) data

to explore the relationship between investing in technologies and sustainable eco-

nomic growth. So far, however, the analysis of the link between entrepreneurial

activity rates as measured by GEM and economic growth has been limited to

bivariate correlations with short-term GDP rates, with no attempt to control for

other factors (see Reynolds et al. 2000, 2001, 2002 cited in Wong et al. [2005])

related notably with the well-being of a nation.

The aim of this article is to provide new empirical evidence on the impact

of investment in technologies and economic welfare in 13 developing countries

belonging to the Group of Twelve (G12). It covers the period 2002–2013, and car-

ries out tests for non-stationary panels using the Granger causality and the vector

error correction model (VECM) to investigate both the short-run and the long-rundynamic relationship between variables. To the best of our knowledge, there is no

study that tries to evaluate the evolution of technological entrepreneurship as a

factor for sustainable economic development.

The layout of the article is as follows. Section 2 provides an overview of the

literature of the two main concepts of this study: technology entrepreneurship

and sustainable economic growth. Section 3 provides the model specification,

definitions of the variables and econometric tools. Section 4 estimates the model

by panel cointegration techniques and, finally, Section 5 concludes with a summary

of the main findings and policy implications.

2. Theoretical background

2.1. The concept of technology entrepreneurship

Although over the last years, technology entrepreneurship has developed as a

distinct stream of research at the nexus of entrepreneurship and the management

of technology and innovation, this field is in its infancy compared to others such

as economics, entrepreneurship and management (see Bailetti 2012). Yet, despite

several special issues and an increased level of interest, no conventional definitionhas been developed so far.

Several expressions are used in research papers to point out technology en-

trepreneurship such as technological entrepreneurship, technical entrepreneurship,

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


5/23

The Journal of International Trade & Economic Development 3

techno-entrepreneurship, high-tech firm, knowledge-based firm, new technology-

based firm, technology-based ventures and several definitions are applicable. It

follows that technology entrepreneurship is basically the merge of two words from

two disciplines: technology from the innovation discipline and entrepreneurshipfrom the business discipline.

From the standpoint of economics, there are a number of authors who associate

entrepreneurship with innovation (see Abdullah and Ahcene 2011). Schumpeter

(1950) viewed entrepreneurship as a dynamic process of creative destruction, in

which he put forward the idea of innovation as the creation, development and

introduction of new products, processes, systems and organizational forms that

change the basic technological and demand parameters of the economy.

Freeman (1998) recognized that innovation is developed from technology and

an outcome of new scientific results. Schumpeter (1912) showed that technology

is driven by entrepreneurs, and it is the entrepreneur who plays a major role in

creating inventions through the appropriate implementation of technology. In this

setting, technology is regarded as one of the crucial components in an innovation

activity by creating new things and matching it with market needs.

Technological innovation has long been viewed as an integral part of en-

trepreneurship (see Drucker 1985). In fact, technology provides the solution of

some problems that are generated by innovation. A technological change comes

from the new and innovative ideas and the firms implement those ideas into reality

on international level. It is from this point of view that Dopfer (1992) defined

technology ‘as an engine of growth, and its application is seen in the branch of Neo-Schumpeterian research like Technological Paradigm (see Dosi 1988), “fo-

cusing devices” (Rosenberg 1976), “Technological Trajectory” (see Nelson and

Winter 1977), and others’ (cited in Abdullah and Ahcene 2011).

To develop innovation and to achieve competitiveness, the accent must be

put on acquiring technological capability that offers the required human skills

such as entrepreneurial, managerial and technical to set up and operate industries

efficiently (see Lall 1990) and to deploy and utilize various resources and know-

how (see Anderson and Tushman 1990; Song et al. 2005; Khefacha, Belkacem,

and Mansouri 2014).The question here is what distinguishes technology entrepreneurship from

entrepreneurship as defined by the traditional managerial literature.

In fact, the concept of technology entrepreneurship was addressed from various

perspectives relative to economics, business and management sector.

Bailetti (2012) makes the widest research on definitions proposed by different

authors, and in the end, he offers his own definition which identifies and incor-

porates the various distinctive aspects of technology entrepreneurship discussed

above and its links to the existing domains of economics, entrepreneurship and

management: ‘Technology entrepreneurship is an investment in a project that as-sembles and deploys specialized individuals and heterogeneous assets that are

intricately related to advances in scientific and technological knowledge for the

purpose of creating and capturing value for a firm.’ He adds that technological

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


6/23


entrepreneurship can be distinguished from other entrepreneurship types (e.g. so-

cial entrepreneurship, small business management and self-employment) by the

collaborative experimentation and production of new products, assets and their

attributes, which are intricately related to advances in scientific and technologicalknowledge and the firm’s asset ownership rights.

Specifically, the technological entrepreneurship process is based on four main

sets of activities related to the creation of new technologies or the identification of

the existing one but not yet previously exploited; the recognition and matching op-

portunities, thanks to the application of these new technologies to emerging market

needs; technology/applications development; and business creation. It, therefore,

follows that technological entrepreneurship occurs at the intersection of technol-

ogy development (science and engineering) and business creation (management

and business) involving individuals, businesses and governments that transform

new ideas into economic and societal values.

2.2. Technology entrepreneurship as a factor for sustainable

economic growth

Several economists focused their research on the dynamic role of entrepreneurial

activity in promoting innovation, economic growth and employment (see

Audretsch, Keilbach, and Lehmann 2006; Van Stel 2006; Fritsch and Mueller

2004, 2008). The contribution of technological innovation to national economic

growth has been well established in the economic literature, both theoretically(see Solow 1956; Romer 1986) as well as empirically (see Mansfield 1972; Nadiri

1993). These studies have established that the level of technological innovation

contributes significantly to economic performance, particularly at the firm and

industry levels (see Wong et al. 2005).

Acs and Audretsch (2003), for example, showed that entrepreneurship stimu-

lates and generates economic growth in several ways. Notably, entrepreneurs may

introduce important innovations by entering markets with new products or pro-

duction processes. Namely, Evangelista (2000) and Miles (2005) considered the

adoption of new technologies through a dynamic process of creative destruction based on innovation as the most important factor for achieving long-term eco-

nomic growth. Autio, Ho, and Wong (2005) using an augmented Cobb–Douglas

production to explore firm formation and technological innovation as separate

determinants of growth showed that only high growth potential entrepreneurship

is found to have a significant impact on economic growth. More recently, Rado-

sevic and Yoruk (2013) state that the introduction of databases such as the GEM

has enabled research on the impact of entrepreneurship on economic growth to

be tested at the levels of country, industry or firm (see Yli-Renko, Autio, and

Sapienza 2001; Acs and Varga 2005; Minniti, Bygrave, and Autio 2005; Wonget al. 2005).

However, a closely related concept, technology entrepreneurship, has for a

long time not found a proper place in mainstream empirical economic research on

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


7/23


the sources of sustainable economic growth. Although copious amounts have been

written theoretically and descriptively on how entrepreneurship affects the eco-

nomic sustainability (see Fritsch and Mueller 2004, 2008; Radosevic and Yoruk

2013; Wong et al. 2005), there is a dearth of evidence based on empirical data.This is partially due to the difficulty in defining the concept of technology en-

trepreneurship and formalizing its measurement for empirical modeling.

3. The empirical study

3.1. Data and variables

GEM, initiated in 1999, is widely acknowledged to be the best source of compara-

tive entrepreneurship data in the world (see Shorrock 2008) and has been utilized

in studies published in leading journals (see Bowen and De Clercq 2008).A key outcome of the GEM project is the consistent and internationally com-

parable measures of entrepreneurship, the Total Entrepreneurship Activity (TEA)

rates.

TEA captures percentage of adults (aged 18–64) who are either involved in

the process of starting up a business in two populations: nascent entrepreneurs and

young business owners. Nascent entrepreneurs are individuals who have, during

the last 12 months, taken tangible action to start a new business, would personally

own all or part of the new firm, would actively participate in the day-to-day

management of the new firm and has not yet paid salaries for anyone for morethan 3 months. Young business owners are defined as individuals who are currently

actively managing a new firm, personally own all or part of the new firm and the

firms in question are not more than 42 months old.

TEA indices have high validity and reliability (see Reynolds et al. 2005).

Within TEA, the present study is concerned with harmonized GEM measures

for technology entrepreneurship and utilizes the following three measures:

• TEATEC: Percentage of all TEA business entities reporting business activity

in a technology sector (high or medium), according to OECD classification.

1

• TEANT1: Percentage of all TEA business entities reporting that they use

the VERY LATEST technology, not available one year ago.

• TEANT2: Percentage of all TEA business entities reporting that they use

the NEW technology, not available since 1–5 years ago.

Variables measuring entrepreneurships activities are from GEM (2002–2013)

and data have been collected for 13 developed countries:2 Australia (2002–2006

and 2011), Belgium (2002–2013), Canada (2002–2006 and 2013), France (2002–

2013), Germany (2002–2010 and 2012–2013), Italy (2002–2010 and 2012–2013),Japan (2002–2013), the Netherland (2002–2013), Spain (2002–2013), Sweden

(2002–2010 and 2011–2013), Switzerland (2002–2003; 2005; 2007 and 2009–

2013), United Kingdom (2002–2013) and USA (2002–2013).

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


8/23


Those relating to the sustainable economic growth come from World Bank

Indicators (2013) that is the adjusted net saving indicator (ANS) known informally

as genuine savings (GS). The origin of the approach goes back to Pearce and

Atkinson (1993) when they used the term net savings criterion for sustainabledevelopment. These authors showed that the future well-being will be at risk

if the gross saving in a given economy shortfalls than the combined value of

depreciation on physical capital and depletion on natural capital (see Hamilton

2010). Technically,3 GS is based on traditional gross national income (GNI) and

includes the change rates of the three forms of capital: physical ( K P), human ( K H)

and natural ( K N) (Boos and Holm-Müller, 2013):

Genuine savings = (GNI− CP − CG+ NCT) GNS

− δKP

NNS

+ δKH − δK N

where GNS is the gross national savings, theoretically seen as gross investment in

physical capital; C P and C G are the private and public (governmental) consump-

tions; NCT is the net current transfers that comprise all exchanges with foreign

countries of goods and services as well as income and financial items without a

quid pro quo; NNS is the net national savings, which – at least theoretically – is

equivalent to net investment in physical capital (δ K P).Investment in human capital is calculated as net educational expenditure (δ K H).

This includes both capital expenditure as well as current expenditure such as in-

vestment in school buildings, the purchase of school books or payment of teachers’

salaries, that are counted as consumption rather than investment in the traditional

national accounts. Therefore, GS adds current operating expenditures on educa-

tion as a rather crude approximation for investment in human capital. Finally, net

depreciation of natural capital (δ K N) can be divided at a basic level into resource

extraction, on the one hand, and environmental pollution. The World Bank esti-

mates resource extraction for a range of fossil fuels (oil, natural gas, hard coaland brown coal), minerals (bauxite, copper, iron, lead, nickel, zinc, phosphate, tin,

gold and silver), and one renewable resource (forests). These rents demonstrate

the change in the natural resource asset value associated with their extraction over

the accounting period and, therefore, the change in the natural capital stock (see

Atkinson and Hamilton 2007).

3.2. Hypothesis

The paper is based on the following hypotheses for testing the causality and

cointegration between the World Bank sustainability indicator and technology

entrepreneurship:

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


9/23


(1) there is bidirectional causality between sustainable economic growth and

technology entrepreneurship,

(2) there is unidirectional causality between the two concepts,

(3) there is no causality between GS and technology TEA,(4) there exists a long-run relationship between well-being of a nation and

TEA related to the technology sector.

3.3. Econometric specification

To investigate the relationship between sustainable economic growth and

technology-based ventures, we use the following model:

GSit = β0i + β1iTEATECit + β2iTEANT1it + β3iTEANT2it + εit (1)

where GSit is genuine savings in country i and year t and ε it is an error term.

The test for causal relationship between technology entrepreneurship and sus-

tainable economic growth in a panel context is usually conducted in four steps.

3.3.1. Step 1: unit root tests for panel data

Prior to testing for cointegration and Granger causality, it is necessary to study the

univariate time-series properties of each variable by performing unit root tests.

A series is said to be stationary if the mean and the variance remain constant

over time for all t , and the covariance and hence the correlation between any twovalues taken from different time periods depend on the difference apart in time

between the two values for all t = s. By contrast, a non-stationary time series will

have a time-varying mean or a time-varying variance or both. In the first case,

the variable is described as trend stationary (TS), while for time-varying variance,

the model is called difference stationary (DS). Hence, for the two models, the

moments of the stochastic process depend on time and, therefore, the standard

assumptions for asymptotic analysis in the Granger test will not be valid. We

should hence perform tests for unit root in potentially non-stationary time series.

In fact, a number of unit root tests for panel data have been developed inthe recent literature, including most notably those by Levin, Lin, and Chu (2002)

(herein referred to as LLC), Im, Pesaran, and Shin (2003) (herein referred to as

IPS), Maddala and Wu (1999) (herein referred to as MW), Choi (2001) and Hadri

(2000).

The basic autoregressive model can be expressed following the basic Aug-

mented Dickey Fuller (ADF) specification for panel data:

yit = αit + β1iyit −1 + β2i t +

m

j =1 β

ij yit −j + εit (2)

where yit = yit − yit −1.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


10/23


The lag order for the difference terms ‘m’ is allowed by specification to vary

across the cross section.

Using different null and alternative hypothesis, we can distinguish the follow-

ing three situations:

Hypothesis (A) : Ho : βli = 0 for all i versus H1 : βli


11/23


relationship exists, the most popular developed methodology proposed by Pedroni

(1999, 2004) is employed. Basically, the test involves regressing the variables

along with cross-section-specific intercepts, and examining whether the residuals

are integrated order one (i.e. not cointegrated). Pedroni proposes two sets of teststatistics as follows:

• Four tests are based on pooling the residuals of the regression along

the within-dimension of the panel ( panel tests): v-statistic, rho-statistic,

PP-statistic and ADF-statistic.

• Three tests are based on pooling the residuals of the regression along the

between-dimension of the panel ( group test ): rho-statistic, PP-statistic and

ADF-statistic.

The seven test statistics are distributed as standard normal variates, and diverge

to negative infinity under the alternative hypothesis of panel cointegration. How-

ever, these tests are not always unanimous, but a consensus among the statistics

often is interpreted as evidence in favor of cointegration. As reported in Pedroni

(2004), the group and panel ADF -statistics have the best power properties of the

seven test statistics when T < 100, whereas the panel ν and group rho statistics

perform comparatively worse. The ADF test statistics also perform better if the

errors follow an autoregressive process (see Harris and Sollis 2003).

Unfortunately, testing unit root and cointegration hypotheses by using paneldata instead of individual time series involves several additional complications. As

a major shortcoming, Taylor and Sarno (1998) have criticized panel unit root tests

on the grounds that they have a high probability of rejecting the null hypothesis of

joint non-stationarity. They explain that given the null hypothesis underlying panel

unit root tests, the only possible alternative hypothesis is that at least one unit is a

stationary process. Consequently, we may end up rejecting the null, even if only

one series is stationary. More recently, Breitung and Pesaran (2008) showed that

the panel test outcomes are often difficult to interpret if the null of the unit root

or cointegration is rejected. The best that can be concluded is that ‘a significant

fraction of the cross-section units is stationary or cointegrated.’ The panel tests do

not provide explicit guidance as to the size of this fraction or the identity of the

cross-section units that are stationary or cointegrated.

3.3.3. Step 3: robustness tests: panel causality test and the error correction

model (ECM)

The finding of cointegration between variables indicates the existence of causality

and an ECM must be estimated. However, cointegration analysis can evaluatewhether the panel variables follow each other’s suit but it does not provide any

information about the cause and effect, or the direction of causality between the

variables.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


12/23


For this we will use the Granger causality test. This technique tests short-term

causality and validates a long-term relationship. Two stages are suggested by Engle

and Granger (1987) in order to investigate the short-run and long-run relationships

between these variables. The first stage is to recover the estimated residuals inEquation (1) and the second stage estimates the parameters related to the short-

run adjustment while incorporating the estimated residuals from Equation (1) in

the following ECM equations:

GSit = α1i +

mj =1

β11ij GSit −j +

mj =1

β12ij TEATECit −j

+

m

j =1

β13ij TEANT1it −j +

m

j =1

β14ij TEANT2it −j

+λ1iECTit −1 + ε1it (3)

TEATECit = α2i +

m j=1

β21ij GSit −j +

mj =1


+

mj =1


mj =1



TEANT1it = α3i +

mj =1

β31ij GSit −j +

mj =1


+

mj =1


mj =1



TEANT2it = α4i +

mj =1

β41ij GSit −j +

mj =1


+

mj =1


mj =1



With α1i and α2i are individual fixed effects,

denotes the first difference of the variable, GSit , TEATECit ; TEANT1it and TEANT2it are the two cointegrated

variables; ECT is the error correction term; m denotes the lag length determined

automatically by the Schwarz Information Criterion; and ε it are the error terms.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


13/23


The parameters of the previous equation include the following important short-

term and long-term implications: λi parameters can be thought of as speed of

adjustment parameters. If λi = 0, then there would be no evidence for a long-run

relationship. This parameter is expected to be significantly negative under the prior assumption that the variables show a return to a long-run equilibrium.

3.3.4. Step 4: panel long-run estimates

After having established the existence of a cointegration relationship and the di-

rection of causality between the dependent variable and the explanatory variables,

we proceed to estimate the long-term structural coefficients using various methods

of panel estimation which are more efficient than the ordinary least square (OLS)

method. Among them, Pedroni (2001) proposed a fully modified OLS (FMOLS)that provides consistent estimates of the β coefficients, together with ‘t -ratios’

that are asymptotically distributed as standard normal variates. A mathematical

derivation of the estimator is beyond the scope of this paper, but a practical discus-

sion will inform the interpretation of the results. Roughly explained, the FMOLS

estimator is constructed by making corrections for endogenity and serial correla-

tion to the OLS estimator by using the long-run covariance matrices to remove the

nuisance parameters (see Eberhardt 2009).

Improved by Kao and Chiang (2000) and Mark and Sul (2003) of the case

of panel data, the dynamic OLS (DOLS) is another approach of panel esti-mation which consists of adding to the cointegration equation lags of the ex-

planatory variables in order to clean the error term from any autocorrelation and

heteroscedasticity. Monte Carlo testing has suggested it may perform slightly bet-

ter than the FMOLS estimator (see Eberhardt 2009). One practical issue with the

estimator is choosing the appropriate lag and lead length, but this may be a less

serious issue than the non-parametric correction used by FMOLS. It should be

noted that under both the FMOLS and DOLS regressions, time dummies have

been added, in order to correct for homogeneous cross-sectional dependence (see

Pedroni 2001).

4. Empirical results and discussion

As mentioned in the previous section, panel cointegration testing requires variables

integrated in the same order. Since none of the panel unit root test is free from

some statistical shortcomings in terms of size and power properties (see Hurlin

and Mignon 2008), it is better for us to perform several unit root tests to infer an

overwhelming evidence to determine the order of integration of the panel variables.

Table 1 shows the individual test statistics and p-values with a lag lengthselection of one. We know macroeconomic variables tend to exhibit a trend over

time, thus it is more appropriate to consider the regression equation with constant

and trend terms at level form.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


14/23


T a b l e 1 .

P a n e l u n i t r o o t t e s t s .

M W

U n i t r o o t t e s t

L L C

I P S

A D F - F i s h e r

P P - F i s h

e r

V a r i a b l e s

L e v e l

F i r s t d i f f e r e n c e

L e v e l


L e v e l


L e v e l

F i r s

t d i f f e r e n c e

G S

0 . 4 3 7

− 7 . 6 7 4

0 . 3 9 5 3

− 3 . 2 7 1

2 5 . 8 8 4

5 8 . 7 5 9

2 0 . 8 8 5

6 1 . 3 1 7

( 0 . 6 6 9 )

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 6 5 3 )

( 0 . 0 0 5 ) ∗ ∗ ∗

( 0 . 4 6 9 5 )

( 0 . 0 0 2 ) ∗ ∗ ∗

( 0 . 7 4 7 8 )

( 0 . 0 0 1 ) ∗ ∗

T E A T E C

− 0 . 9 0 3 9

1 1 . 0 8 7

− 1 . 1 0 8

− 8 . 3 1 0

3 1 . 3 3 4

8 8 . 4 3 6

4 4 . 1 1 1

1 1 4 . 2 0 0

( 0 . 1 8 3 )

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 1 3 3 )

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 2 1 6 )

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 0 1 4 ) ∗ ∗

( 0 . 0 0 0 ) ∗ ∗ ∗

T E A N T 1

− 4 . 3 3 6

− 7 . 4 0 3

− 2 . 4 3 1

− 4 . 2 3 6

3 9 . 8 0 4

5 3 . 7 6 8

4 5 . 9 3 3

8 1 . 1 3 1

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 0 5 9 ) ∗

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 0 3 0 ) ∗ ∗

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 0 3 1 ) ∗ ∗

( 0 . 0 0 0 ) ∗ ∗ ∗

T E A N T 2

3 . 2 4 9

− 7 . 8 9 0

1 . 4 3 3

− 2 . 5 8 8

5 . 4 1 4

5 6 . 1 4 7

2 2 . 7 6 5

1 2 0 . 5 9 2

( 0 . 9 9 9 )

( 0 . 0 0 0 ) ∗ ∗ ∗

( 0 . 9 2 4 )

( 0 . 0 0 4 ) ∗ ∗

( 0 . 9 9 9 )

( 0 . 0 0 0 ) ∗ ∗

( 0 . 3 0 0 )

( 0 . 0 0 0 ) ∗ ∗ ∗

N o t e s : N u l l h y p o t h e s i s : u n i t r o o t ( n o n - s t a t i o n a y ) .

T h e p r o b a b

i l i t i e s f o r t h e F i s h e r - t y p e t e s t s a r e c o m p u t e d u s i n g a n a s y m p t o t i c c h i - s q u a r e ( χ 2 ) a s y m p t o t i c d i s t r i b u t i o n .

A u t o m a t i c l a g s e l e c t i o n b a s e d o n S c h w a r z I n f o r m a t i o n C r i t e r i a ( S I C ) .

‘ ∗ ’ , ‘ ∗ ∗ ’ a n d ‘ ∗ ∗ ’ i n d i c a t e s t a t i s t i c a l s i g n i fi c a n c e a t 1 0 % , 5 % a n d 1 % l e v e l s , r e s p e c t i v e l y .

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


15/23


Table 2. Pedroni residual cointegration test results (GS as dependent variable).

Statistic Prob.

Alternative hypothesis: common AR coefficients (within-dimension)Panel υ -stat −3.924128 1.0000Panel r-stat 2.414450 0.9921Panel PP-stat −9.484897 0.0000∗∗∗

Panel ADF-stat −8.494431 0.0000∗∗∗

Alternative hypothesis: individual AR coefficients (between-dimension)Group r-stat 3.496559 0.9998Group PP-stat −5.288141 0.0000∗∗∗

Group ADF-stat −5.649528 0.0000∗∗∗

Notes: Null hypothesis: no cointegration.‘∗∗’ and ‘∗∗∗’ indicate statistical significance at 5% and 1% levels, respectively.

The test statistics suggest that the sustainable economic growth as measured by

genuine savings and two of the three variables measuring technology entrepreneur-

ship have a unit root which implies non-stationarity. However, when we apply the

first difference of all variables we can reject the null hypothesis of unit root, and

GS, TEATEC and TEANT2 are stationary. It means that these three variables are

integrated of order one I (1) and are appropriate for cointegration analysis (except

for TEANT1 which is I (0)).Hence, the variables with the same order facilitate the examination of possible

long-run relations through cointegration panel tests of Pedroni. Table 2 indicates

two within-dimension tests and two between-dimension tests providing the pres-

ence of cointegration. In fact, the null hypothesis of no cointegration is rejected

for the two statistical tests panel-ADF and group-ADF at 5% and 1% levels, re-

spectively. In addition, panel and group PP statistics are also significant. Due to

these promising results, it is possible to estimate the long-run relationship between

the variables.

The finding of cointegration between variables indicates the existence of

causality and an ECM must be estimated based on the following regressions:

GSit TEATECit TEANT2it

=

α1iα2iα3i

+

mj =1

β11ij β12ij β13ij β21ij β22ij β23ij β31ij β32ij β33ij

GSit −j TEATECit −j TEANT2it −j

+

λ1iλ2iλ31i

ECTit −1 +

ε1it ε2it ε3it

Table 3 reports the short-run and long-run causality results for sustainable eco-

nomic growth model and indicates that there is evidence of bidirectional causality

between TEATEC, TEANT2 and GS at 1% level of significance in the short-run.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


16/23


Table 3. Panel Granger causality test results (short-run and long-run causalities).

Short-run Long-run

GS

TEATEC

TEANT2 ECT

GS – 0.109049 0.230188 −0.018125[0.18012] [0.53713] [−2.18653]∗∗∗

TEATEC 0.061875 – 0.236518 −0.094011[2.12772]∗∗∗ [3.20118] [−3.15969]

TEANT2 0.067754 0.176450 – −0.107691[1.67713] [1.21687] [−5.11334]∗∗∗

Note: ‘∗∗∗’ indicates statistical significance at the 1% level. The t -statistic is listed in brackets.

Table 4. Panel DOLS–FMOLS long-run estimates.

Variables TEATEC TEANT2

DOLS 0.692409 (0.0034)∗∗∗ −0.053601 (0.8583)FMOLS 0.036999 (0.0013)∗∗∗ 0.110594 (0.1771)

Notes: Cointegrating equation deterministics: constant and trend.‘∗∗∗’ indicates statistical significance at 1% levels.

P -value listed in parentheses.

The error correction term is statistically significant only for percentage of allTEA business entities reporting business activity in a technology sector (high or

medium), according to OECD classification and sustainable economic growth at

1% level. This finding indicates that there is evidence of long-run equilibrium

between technology entrepreneurship and sustainable development.

Finally, after having established the existence of a cointegration relationship

and the direction of causality between sustainable growth and TEA, the last step

consists in the long-run estimation of Equation (1) where the dependent vari-

able is genuine savings, and the independent variables the two TEA measures of

technology entrepreneurship.The long-run structural coefficients are estimated using the DOLS and FMOLS

panel approaches (Pedroni, 2001, 2004).

The results are presented in Table 4 which shows positive relationship between

technology entrepreneurship measured by the percentage of all TEA business enti-

ties reporting business activity in a technology sector (high or medium), according

to OECD classification and sustainable growth.

These results indicate that technology entrepreneurship is an important de-

terminant of economic development for the period under study and for the 13

developed countries. The pace of progress in information technologies, microelec-tronics, robotics, new materials, biomedical sciences, space and other advanced

fields continues to quicken, and in turn, to significantly change the way we live

and work.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


17/23


Particularly, creating business based on new technologies stimulates the wel-

fare of a nation’s economy by enhancing the social and environmental conditions

of the living beings.

Socially, the creation of technology-based ventures by investing in researchand development as well as innovation in the discovery of new technologies or

the perfection of existing ones improves the quality of life of people and the

satisfaction of newly originated needs. It will also underpin economic, social and

territorial cohesion. For instance, a greater capacity for technological research and

development in the industrial sector promote innovation and knowledge transfer,

make full use of information and communication technologies, and ensure that

innovative ideas can be turned into new products and services that create growth,

quality jobs and global societal challenges. Combined with the increased resource

efficiency, technological entrepreneurship will also improve competitiveness and

foster job creation by pushing the businesses to do what they know best, that is,

create productive enterprises with high-potential employability.

Environmentally, the implementation of new and better technologies in differ-

ent productions could minimize the effects of economic activity on the environ-

ment. Investing in cleaner and exploiting fully the potential of new technologies

such as carbon capture and sequestration possibilities would significantly help

limit emissions and contribute to fighting climate change.

Many firms do recognize that caring for the environment is good business –

energy efficiency, waste reduction and pollution prevention save money which

can increase profits and boost economic growth by creating new business and employment opportunities.

Meeting these targets should mobilize our collective attention. All sectors of the

economy, not just emission-intensive, are concerned: governments, environmental

experts and industry itself, as the principal source and user of technological knowl-

edge. Especially, entrepreneurs would do well to heed the lessons of ecology as new

technologies will be needed to clean up past mistakes and achieve new industrial

growth safely. Businesses must make the ethic for living sustainably an integral

part of their corporate goal, taking care that their practices, processes and products

conserve energy and resources and have a minimum impact on ecosystems.The implication of these findings is that countries that promoted technologi-

cal entrepreneurship make their economy and businesses more sustainable while

protecting environmental aspects and improving social conditions. Therefore, the

government and other stakeholders should put in place measures that develop new

processes and technologies, including green technologies, accelerating the roll out

of smart grids using information and communication technologies and creating

a good business environment that encourages the creation and growth of new

technology- based firms. This can mean the following:

• Adopting practices that take into account not only the well-being of humans

but also the ecosystem as a whole; practices that avoid damage and require

consultation with local communities and the public at large;

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


18/23


• Introducing or improving technological innovative processes that minimize

the use of raw materials and energy, reduce waste and prevent pollution;

• Making products that are ‘environmentally friendly’ with minimum impact

on people and the Earth.

5. Conclusion

In this paper, we investigate the causal relationship between sustainable economic

growth and technology entrepreneurship for an unbalanced panel of 13 countries

participating in the GEM data-set for the period 2002–2013. This empirical analy-

sis is interesting because there is no previous study that worked on the causal link

between genuine savings as proxy variable of sustainable development and TEA

rates as proxy variable of entrepreneurship.

Using robust econometric techniques that are capable of estimating long-

run cointegrating relationships in panel data, our findings provide a clear and

compelling account by establishing that the level of technological innovation

contributes significantly to economic performance.

For robustness tests, we have used the error correction approach. Our results

support the idea that there is evidence of bidirectional causality running from the

percentage of all TEA business entities reporting business activity in a technology

sector (high or medium), according to OECD classification to sustainable eco-

nomic growth in the short-run. In the long-run, we find that the error correction

term corresponding to the genuine savings equation is negative and statisticallysignificant. It means that there is evidence of long-run relationship running from

TEA related to the technology sector to sustainable development.

This is an important area of concern in entrepreneurship which improves our

knowledge about the nature of relationship between technological entrepreneur-

ship and economic welfare.

The knowledge gained from this research can be helpful in many ways. In fact,

much of this research will provide concrete and scientific evidence for analyzing

the relationship between investing in new technologies and sustainability by using

a cointegration analysis. Thanks to this analysis, we are better able to understand what is happening within the creation of innovative and high-technology business

and the sustainability of a nation economy.

We can use this study to push government to promote entrepreneurial activities,

especially for the ones that constantly innovate in technology sector and that

ensuring favorable social and environmental conditions for the well-being of a

population.

This research can be extended by introducing variables related to the estab-

lished businesses – the number of adults (18–64 years old) per 100 involved in

established firm as owner and manager for which salaries or wages have been paid for more than 42 months – and reporting that they use the very latest technology,

not available one year ago or that they use the new technology, not available since

1–5 years. We can also use panel cointegration techniques to examine the causal

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


19/23


relationship between technology entrepreneurship and sustainability for develop-

ing countries and make a comparison with the results obtained for developed

countries.

Acknowledgements

This research has received funding from the Global Entrepreneurship Research Association(GERA), London Business School, Regents Park, London NW1 4SA, UK.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

Global Entrepreneurship Research Association (GERA).

Notes

1. High technology: aerospace, computers, office machinery, electronics communica-tions, pharmaceuticals, scientific instruments. Medium--high technology: motor vehi-cles, electrical machinery, chemicals, other transport equipment, non-electrical ma-chinery. Medium--low technology: rubber and plastic products, shipbuilding, other manufacturing, non-ferrous metals, non-metallic mineral products, fabricated metal products, petroleum refining, ferrous metals.

2. Countries belong to the Group of Twelve or G12 which is a group of industriallyadvanced countries whose central banks co-operate to regulate international finance.

3. Detailed calculation descriptions and definitions can be found in Bolt, Matete, and Clemens (2002), Hamilton (2006) or World Bank (2011).

ORCID

Islem Khefacha http://orcid.org/0000-0002-1635-5274

References

Abdullah, S., and L. Ahcene. 2011. “The Understanding of Technology EntrepreneurshipAccording to Shariah Principles.” Proceeding of International Conference on Businessand Economics Research. Singapore. 144–150.

Abdullah, S., and M. Amran. 2008. “The Development of Entrepreneurship in Malaysia:State-Led Initiatives.” Asian Journal of Technology Innovation 16 (1): 101–116.

Acs, Z. 2006. “How Is Entrepreneurship Good for Economic Growth?” Innovations 1 (1):

97–107.Acs, Z.-J., and D.-B. Audretsch. 2003. “Innovation and Technology Change.” In Hand-

book of Entrepreneurship Researcher , edited by Z.J. Acs and D.B. Audretsch, 55–79.Dordrecht: Kluwer.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

http://orcid.org/0000-0002-1635-5274http://orcid.org/0000-0002-1635-5274


20/23


Acs, Z.-J., and A. Varga. 2005. “Entrepreneurship, Agglomeration and TechnologicalChange.” Small Business Economics 24 (3): 323–334.

Anderson, P., and M. Tushman. 1990. “Technological Discontinuities and Dominant De-signs: A Cyclical Model of Technological Change.” Administrative Science Quarterly

35: 604–633.Antoncic, B. 2006. “Impacts of Diversification and Corporate Entrepreneurship Strategy

Making on Growth and Profitability: A Normative Model.” Journal of Enterprising Culture 14 (1): 49–63.

Atkinson, G., and K. Hamilton. 2007. “Progress Along the Path: Evolving Issues in theMeasurement of Genuine Saving.” Environmental and Resource Economics 37 (1):43–61.

Audretsch, D.-B., and M. Keilbach. 2004. “Entrepreneurship Capital and Economic Perfor-mance.” Regional Studies 38: 949–959.

Audretsch, D.-B., M. Keilbach, and E. Lehmann. 2006. Entrepreneurship and EconomicGrowth. Oxford: Oxford University Press.

Autio, E., Y.-P. Ho, and P.-K. Wong. 2005. “Entrepreneurship, Innovation and EconomicGrowth: Evidence from GEM Data.” Small Business Economics 24: 335–350.

Bailetti, T. 2012. “Technology Entrepreneurship: Overview, Definition, and DistinctiveAspects.” Technology Innovation Management Review 2 (2): 5–12.

Bolt, K., M. Matete, and M. Clemens. 2002. Manual for Calculating Adjusted Net Savings.Washington, D.C.: World Bank Environment Department.

Boos, A., and K. Holm-Müller, 2013. “The Relationship Between the Resource Curse and Genuine Savings: Empirical Evidence.” Journal of Sustainable Development 6 (6):59–72.

Bowen, H.P., and D. DeClercq. 2008. “Institutional Context and the Allocation of En-trepreneurial Effort.” Journal of International Business Studies 39 (4): 747–767.

Breitung, J., and M.H. Pesaran. 2008. “Unit Roots and Cointegration in Panels.” In The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice. 3rd ed. edited by L. Matyas and P. Sevestre, 279–322. Springer Publishers,Ch.9.

Burgelman, R.A., and R.S. Rosenbloom. 1997. “Technology Strategy: An EvolutionaryProcess Perspective,” In Managing Strategic Innovation and Change, edited by M.L.Tushman and P. Anderson, 273–286. New York: Oxford University Press.

Burgelman, R.A., M.A. Maidique, and S.C. Wheelwright. 1996. Strategic Management of Technology and Innovation. http://www.duc.auburn.edu/∼ boultwr/5intstgy.pdf .

Choi, I. 2001. “Unit Root Tests for Panel Data.” Journal of International Money and Finance20 (1): 249–272.

Costantini, V., and C. Martini, 2010. “The Causality Between Energy Consumption and Economic Growth: A Multi-sectoral Analysis Using Non-stationary Cointegrated PanelData.” Energy Economics 32 (3): 591–603.

Dejardin, M. 2000. Entrepreneurship and Economic Growth: An Obvious Conjunction? Namur: University of Namur.

Dietz, S., E., Neumayer, and I. Soysa. 2007. “Corruption, the Resource Curse and GenuineSaving.” Environment and Development Economics 12 (1): 33–53.

Dopfer, K. 1992. “The Phenomenon of Economic Change: Neoclassical vs. Schumpete-rian Approaches.” Evolutionary and Neo-Schumpeterian Approaches to Economics, Netherlands: Springer, 125–171.

Dosi, G. 1988. “Sources, Procedures, and Microeconomic Effects of Innovation.” Journal

of Economic Literature 26: 1120–1171.Drucker, P. 1985. Innovation and Entrepreneurship. New York: Harper and Row.Eberhardt, M. 2009. Non-stationary Panel Econometrics and Common Factor Models: An

Introductory Reader . Oxford: University of Oxford.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

http://www.duc.auburn.edu/boultwr/5intstgy.pdfhttp://www.duc.auburn.edu/boultwr/5intstgy.pdfhttp://www.duc.auburn.edu/boultwr/5intstgy.pdfhttp://www.duc.auburn.edu/boultwr/5intstgy.pdfhttp://www.duc.auburn.edu/boultwr/5intstgy.pdf


21/23


Engle, R.-F., and C.-W.-J. Granger. 1987. “Co-integration and Error Correction: Represen-tation, Estimation, and Testing.” Econometrica 55 (2): 251–276.

Evangelista, R. 2000. “Sectoral Patterns of Technological Change in Services, Economicsof Innovation.” Economics of Innovation and New Technology 9: 183–221.

Freeman, C. 1998. “The Economics of Technical Change.” In Trade, Growth and Technical Change, edited by D. Archibugi and J. Michie, 16–54. Cambridge, MA: CambridgeUniversity Press.

Fritsch, M., and P. Mueller. 2004. “The Effects of New Business Formation on RegionalDevelopment over Time.” Regional Studies 38: 961–975.

Fritsch, M., and P. Mueller. 2008. “The Effect of New Business Formation on RegionalDevelopment over Time: The Case of Germany.” Small Business Economics 30: 15–29.

Gaglio, C.M., and J.A. Katz. 2001. “The Psychological Basis of Opportunity Identification:Entrepreneurial Alertness.” Small Business Economics 16: 95–111.

Granger, C.W.J. 1981. “Some Properties of Time Series Data and Their Use in EconometricModel Specification.” Journal of Econometrics 16: 121–130.

Hadri, K. 2000. “Testing for Stationary in Heterogeneous Panel Data.” Econometrics Journal 3 (2): 148–161.

Hamilton, E. 2010. “Wealth, Saving and Sustainability.” In Is Economic Growth Sustain-able?, International Economic Association (IEA) Conference Volume 148, edited byGeoffrey Heal. UK: Palgrave Macmillan.

Hamilton, K. 2006. “Where is the Wealth of Nations? Measuring Capital for the 21st century.” Washington, D.C.: The World Bank.

Harris, R.I.D., and R. Sollis. 2003. “ Applied Time Series Modelling and Forecasting .”Chichester: J. Wiley.

Hurlin, C., and V. Mignon. 2008. “Une Synthèse des Tests de Cointégration Sur Donnéesde Panel.” Economie & Pr ́ evision 4: 241–265.

Im, K.S., M.H. Pesaran, and Y. Shin. 2003. “Testing for Unit Roots in HeterogeneousPanels.” Journal of Econometrics 115: 53–74.Jin, Z. 2002. “Soft Technology – the Essential of Innovation.” Futures Research Quarterly

18 (2): 1–24.Johansen, S. 1988. “Statistical Analysis of Cointegration Vectors.” Journal of Economic

Dynamics and Control 12: 231–254.Johansen, S. 1991. “Estimation and Hypothesis Testing of Cointegration Vectors in Gaus-

sian Vector Autoregressive Models.” Econometrica 55: 1551–1580.Kao, C., and M.H. Chiang. 2000. “On the Estimation and Inference of a Cointegrated

Regression in Panel Data.” In Nonstationary Panels, Panel Cointegration, and Dynamic Panels, edited by B.H. Baltagi. Amsterdam: Elsevier.

Khefacha, I., L. Belkacem, and F. Mansouri. 2014. “An Estimated Model of New VentureCreation: Theories and Determinants in Tunisia.” Journal of Enterprising Culture 22(1): 1–23.

Lall, S. (1990). Building Industrial Competitiveness in Developing Countries. Paris: OECD.Levin, A., C. Lin, and C.J. Chu. 2002. “Unit Root Test with Panel Data: Asymptotic and

Finite-Sample.” Journal of Econometrics 108 (1): 1–24.Licht, A.N. 2007. “The Entrepreneurial Spirit and What the Law Can Do About it.” Com-

parative Labor Law & Policy Journal 28 (4): 817–862.Maddala, G., and S. Wu. 1999. “A Comparative Study of Unit Root Tests with Panel Data

and a New Simple Test.” Oxford Bulletin of Economics and Statistics 61: 631–652.Mansfield, E. 1972. “Contribution of Research and Development to Economic Growth of the

United States.” Papers and Proceedings of a Colloquim on Research and Development and Economic Growth Productivity. Washington, DC: National Science Foundation.Mark, N.C., and D. Sul. 2003. “Cointegration Vector Estimation by Panel Dynamic OLS and

Long-Run Money Demand.” Oxford Bulletin of Economics and Statistics 65: 655–680.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5


22/23


Nadiri, I. 1993. “Innovationsand Technological Spillovers.” WorkingPaper 423. Cambridge.MA: National Bureau of Economic Research.

Miles, I. 2005. “Innovation in Services.” In The Oxford Handbook of Innovation, edited by J. Fagerberg, D.C. Mowery, and R.R. Nelson., 433–458. Oxford: Oxford University

Press.Minniti, M., W. Bygrave, and E. Autio. 2005. 2005 Global Entrepreneurship Monitor .

Babson Park, MA: Babson College. Nelson, R., and S. Winter. 1977. “In Search of a Useful Theory of Innovation.” Research

Policy 6 (1): 36–75Onipede, S. 2003. Towards Creating an Environment for Sustainable Eco-

nomic Growth in Nigeria, International Experience. http://www.economist.com/countries/Nigeria/profile.cfm?folder = profileEconomic%20structure.

Pearce, D.W., and G. Atkinson. 1993. “Capital Theory and the Measurement of SustainableDevelopment: An Indicator of Weak Sustainability.” Ecological Economics 8: 103–108.

Pedroni, P. 1999. “Critical Values for Cointegration Tests in Heterogeneous Panels

with Multiple Regressors.” Oxford Bulletin of Economics and Statistics 61: 727– 731.

Pedroni, P. 2001. “Fully Modified OLS for Heterogeneous Cointegrated Panels.” Advancesin econometrics 15: 93–130.

Pedroni, P. 2004. “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis.” Econometric Theory20: 597–625.

Porter, M. 1990. The Competitive Advantage of Nations. New York: Free Press.Radosevic, S., and E. Yoruk. 2013. “Entrepreneurial Propensity of Innovation Systems:

Theory, Methodology and Evidence.” Research Policy.Reynolds, P.D., M. Hay, and M. Camp. 1999. Global Entrepreneurship Monitor 1999

Executive Report . Babson Park, MA and London: Babson College and London BusinessSchool.Reynolds, P.D., M. Hay, W.D. Bygrave, S. Michael Camp, and E. Autio. 2000. “Global

Entrepreneurship Monitor: 2000 Executive Report .” Kansas City: Kaufman Centre.Reynolds, P.D., S. Michael Camp, W.D. Bygrave, E. Autio, and M. Hay. 2001. “Global

Entrepreneurship Monitor Global 2001 Executive Report .” Kansas City: KaufmanCenter.

Reynolds, P.D., S. Michael Camp, W.D. Bygrave, E. Autio, and M. Hay. 2002. “Global Entrepreneurship Monitor Global 2002 Executive Report .” Kansas City: KaufmanCenter.

Reynolds, P., N. Bosma, E. Autio, S. Hunt, N. De Bono, I. Servaias, P. López Garcı́a,

and N. Chin. 2005. “Global Entrepreneurship Monitor: Data Collection Design and Implementation 1998–2003.” Small Business Economics 24: 205–231.Rosenberg, N. 1976. Perspectives on Technology. Cambridge: Cambridge University Press.Romer, P. 1986. “Increasing Returns and Long Run Growth.” Journal of Political Economy

94: 1002–1037Ruttan, V.W. 1997. “Induced Innovation, Evolutionary Theory and Path Dependence:

Sources of Technical Change.” Economic Journal 107: 1520–1529Schumpeter, J. 1912. “The Theory of Economic Development.” Cambridge. Cambridge,

Mass: Harvard University Press.Schumpeter, J. 1950. “Capitalism, Socialism and Democracy.” 3rd ed. New York: Harper

and Row.

Shane, S., and S. Venkataraman. 2003. “Guest Editors’ Introduction to the Special Issue onTechnology Entrepreneurship.” Research Policy 32 (2): 181–184.Shorrock, A. 2008. “Opening Address Presented at the, United Nations University-WIDER

Conference on Entrepreneurship and Economic Development .” Helsinki, Finland.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

http://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structurehttp://www.economist.com/countries/Nigeria/profile.cfm?folder=profileEconomic%20structure


23/23


Solow, R. 1956. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70: 65–94

Song M., C. Droge, S. Hanvanich, and R. Calantone. 2005. “Marketing and Technology Re-source Complementarity: An Analysis of their Interaction Effect in Two Environmental

Contexts.” Strategic Management Journal 26 (3): 259–276.Stam, E., and A. van Stel. 2009. “Types of Entrepreneurship and Economic Growth.”

Research Paper 2009/47 . Helsinki: UNUWider.Szirmai, A., W. Naud ́e, and M. Goedhuys. 2011. Entrepreneurship, Innovation, and Eco-

nomic Development . Oxford: Oxford University Press.Tan, S. 2004. Ecological Perspectives in Entrepreneurship: Science and Sustainability.

Singapore: Nanyang Technological University.Taylor, M.P., and Sarno, L. 1998. “The Behavior of Real Exchanges During the Post-Bretton

Woods Period.” Journal of International Economics 46: 281–312.Twiss, B., and M. Goodridge. 1989. Managing Technology for Competitive Advantage

Integrating Technological and Organizational Development: From Strategy to Action.

Aulander, NC: Pitman Publishing.Uysal, A.H. 2010. CT Development and Economic Growth: An Analysis of Cointegrat-

ing and Causal Relationships with a Panel Data Approach. Stockholm: School of Architecture and the Built Environment Royal Institute of Technology.

Van Stel, A.J. 2006. “Empirical Analysis of Entrepreneurship and Economic Growth.” Vol.13 of International Studies in Entrepreneurship. New York: Springer Science.

Wong, Po, Kam Ping, Ho Yuen, and A. Erkko. 2005. “Entrepreneurship, Innovation and Economic Growth: Evidence from GEM Data.” Small Business Economics 24 (3):335–350.

World Bank. 2011. “The Changing Wealth Of Nations: Measuring Sustainable Development in the New Millennium.” Washington, D.C.: World Bank.

Wright, M., K.M. Hmieleski, D.S. Siegel, and M.D. Ensley. 2007. “The Role of HumanCapital in Technological Entrepreneurship.” Entrepreneurship Theory and Practice 31(6): 791–806.

Yli-Renko H, E. Autio, and H.J Sapienza. 2001. “Social Capital, Knowledge Acquisition,and Knowledge Exploitation in Young, Technology-Based Firms.” Strategic Manage-ment Journal 22 (6–7): 587–613.

D o w


h e f a c h a

] a t 0 4 : 3 9 0 3 S e p t e m b e r 2 0

1 5

technology based ventures

Documents