ENTREPRENEURIAT RESPONSABLE : PRATIQUES ET ENJEUX...

9e Congrès de l’Académie de l’Entrepreneuriat et de l’Innovation

ENTREPRENEURIAT RESPONSABLE : PRATIQUES ET ENJEUX THEORIQUES

Nantes, France, 20-22 mai 2015

Exploring the relationship between micro-enterprises and regional development:

Evidence from Tunisia

Faten GAZZAH

PhD in Economics and Management

Economics and Management Research Center

CNRS UMR 6211 – France

University of Caen Basse Normandie

Research Laboratory for Economy, Management and Quantitative Finance

IHEC - University of Sousse-Tunisia

E-mail address: [email protected]

Jean Bonnet

Professor of economics

Economics and Management Research Center

CNRS UMR 6211 – France

University of Caen Basse Normandie


Telephone: +216 23221270

Sana El HARBI

Professor of economics and business

Research Laboratory for Economy, Management and Quantitative Finance

University of Sousse-Tunisia


Telephone: +216 23221270

Abstract

The impact of entrepreneurship in regional development has been largely studied in

developed economies but less so in countries in the process of development.

This paper investigates empirically the impact of micro-enterprise creation on the

regional development at the delegation level. We analyzed the data of 263 Tunisian delegation

observed in the year 2010 by using spatial measures and spatial econometric techniques. Our

results show that micro-enterprise creation influence positively and significantly the regional

development.

Key words: Micro-enterprises, spatial autocorrelation, spatial regression, Tunisia

mailto:[email protected]



Exploring the relationship between micro-enterprises and regional development:

Evidence from Tunisia

Abstract

The impact of entrepreneurship in regional development has been largely studied in

developed economies but less so in countries in the process of development.

This paper investigates empirically the impact of micro-enterprise creation on the

regional development at the delegation level. We analyzed the data of 263 Tunisian delegation

observed in the year 2010 by using spatial measures and spatial econometric techniques. Our

results show that micro-enterprise creation influence positively and significantly the regional

development.

Key words: Micro-enterprises, spatial autocorrelation, spatial regression, Tunisia

1

Introduction

It is generally acknowledged in entrepreneurship literature that regional conditions tend to

influence new rates of business creation and that "the social and economic environment are

the most important in the promotion of new business creation."(Garofoli,1994). The

immediate environment, family relationships, networks also have an important role in

entrepreneurship (Julien, 2007). An inverse relationship presented in the recent special issue

of the economy of small businesses (number 4 Volume 36) entitled "dynamic entrepreneurial

and regional growth" deals this time with the impact of entrepreneurship on regional

development, particularly the growth of regional productivity and job creation (Andersson

and Noseleit, 2011; Koster, 2011). The general agreement on the latter relationship is that the

spirit of enterprise has a positive impact on regional development (Cornett, 2009, Desjardin

and (long-term / short-term), the choice of region, the direct and indirect effect of

entrepreneurship on the region (Van Stel and Subtil, 2008).

These two approaches (Entrepreneurial Region / Region Entrepreneurial) exhibit two

parallel discourses:

One discussion draws attention to the regional conditions that can be linked to areas that have

a large amount of entrepreneurial activity. Context, regional structure or spatial features are

shown in this research to have an influential impact on entrepreneurial activity.

The second discussion, on the other hand, is centered on how the roles of the entrepreneurs

and entrepreneurship is a pioneer in regional development ( Berglund and Johansson 2007),

and studies are often concentrated on discussing the contribution of entrepreneurship to

regional development and growth.

However the link between these two trends is addressed lightly apart from a limited number

of works such as Kodithuwakku and Rosa (2002), West, Bamford, and Marsden (2008),

Anyadike-Danes, Hart, and Lenihan (2011).

This paper discusses the impact of entrepreneurship on regional development while

incorporating the spatial aspect, characteristic of the first discourse. In other words, a

crossover between these two approaches will occur. Obviously, the spatial context has

received relatively little attention in the entrepreneurship field compared to other areas

(Welter, 2011).

The perspectives of the entrepreneurial contribution to the regional development are largely

studied in Western countries, like Sweden (Davidsson and al 1994, Berggren and Dahlstrand

2009), Germany (Audretsch and Fritsch 1994, Mueller 2006, Fritsch and Mueller, 2007,

2

Fritsch and Schroeter, 2011), Italy (Friedman and Desivilya2010), Great Britain (Mueller and

al 2008); and the United States (Flora and al 1997, Renski 2009). A contrario, limited

attention has been given to economies still in development , especially in highly ranked

journals. Work on Argentina (Jorge Moreno, Castillo , et de Zuani Masère, 2007) , Indonesia

(Ndoen, Gorter, Nijkamp, et Rietveld, 2002), China (Yang et Xu 2008; Dai et Liu 2009)

,Ethiopia (Kimhi, 2010) and South Africa (Naudé, Gries, bois, et Meintjes, 2008) has been the

exception.

Our study joins this line of research for emerging African countries, particularly for

Tunisian regions where research is still missing.

According to the United Nations Program for Development calculated in 2010,

Tunisia has achieved honorable national socio-economic progress over the last decade (1997-

2010). Such progress has placed it 81st among 169 developing countries. Despite this, the

objectives were far from being achieved at a regional level (Dhaher, 2010).

Moreover, if growth has contributed significantly to reducing the incidence of poverty at the

national level, it would still have exacerbated the disparities between regions. A priority index

identify areas where poverty is to be treated as a priority shows that the center of West

concentrated 41.3% of those with lower levels of consumption in the low threshold, it is the

region with a priority index high and equal to 3.11. Northwest, Southeast and Southwest

regions are also potentially identified as priorities. Central West and Northwest are considered

two large pockets of extreme poverty, while the Northeast, Central East and Greater Tunis

(the coast) are not considered a priority in 2010 (Ratio measurement Poverty INS2010).

Regional development and disadvantaged areas are a key element of the current

political agenda and academic debate in Tunisia. In this sense, the Ministry of Regional

Development and Planning of the Tunisian Institute for Competitiveness and Quantitative

Studies have suggested a synthetic regional development indicator. The values of the index

corresponding to an area of reference will allow a much clearer analysis of the level of

Tunisian territory development.

Extreme inequality of development index indicates the governorates of Kasserine

(Central West ) against the governorate of Tunis: the least developed delegation of Hassi Ferid

in the governorate of Kassarine with a development index of zero against the most developed,

in the order of 1, that of the delegation of Bab Bhar belonging to the governorate of Tunis.

This problem of regional disparities encourage the migration of a poor disadvantaged

areas which accumulate delays and disabilities to the relatively more developed coastal

regions, creating around the poor neighborhoods of cities and sub-groups integrated into their

3

urban systems (Chabbi 1986) .We also note that if inland areas are lagging behind in

development compared to coastal areas as we have noted, the delegations of the coastal

governorates know theirtheir towers significant heterogeneity in term of development.

Our study addresses the following question: To what extent does micro-

entrepreneurship contribute to the development of Tunisian delegation? The originality of our

paper lies in the fact of conducting spatial analyses of the density of micro-enterprises in

order to study their impact on regional development indicator. Our plan is organized as

follows: In a first section we will present a review of literature according to the subject of the

study. In the second section we draw attention to regional disparities in Tunisia. The third

part is dedicated to the presentation of the empirical strategy. The interpretation of the results

of the exploratory spatial analysis and econometric estimation is presented in the fourth part.

1. Review of the literature

1.1 The issues of entrepreneurship on the region

Definitions of regional development are diverse as they require complex consideration of

“what local and regional development is for and what it is designed to achieve” (Pike et al.,

2007). Overall, regional development is understood as a dynamic process (Fischer and

Nijkamp, 2009); it refers to the provision and assurance of equal opportunities as well as

sustainable economic and social well-being of individuals in areas that are typically less

developed. Regional development studies are traditionally dominated by economic concerns

such as growth, income and job creation (Pike and al., 2007; Armstrong and Taylor, 2000).

However, “growth must be distinguished from development: growth means to get bigger,

development means to get better” (Pike et al., 2007). Thus, besides growth, regional

development is also about social change and transformation (Berglund and Johansson, 2007).

Spatial and regional science has devoted much focus on the factors determining structural

disparity in relation to regional development. This has primarily led to an integration of

various scientific perspectives and theories, such as growth theory, agglomeration theory,

regional innovation systems theory, location theory, new economic geography, and

entrepreneurship (Fischer and Nijkamp, 2009). Previous studies suggest that regional

development is influenced by a number of driving forces, namely availability and access to

human capital, the level and speed of innovation, the presence physical or communicative

infrastructures, existing welfare and institutional structures. Finally, the existence of

entrepreneurial activity in regions is also found to be an important driving force for regional

development as it creates jobs, income and growth (Cornett, 2009; Audretsch and Keilbach,

4

2004; Naudé et al, 2008).

Generally the relationship between entrepreneurship and regional development is two-sided:

direct/indirect effects (van Stel and Suddle 2008). The direct effects are linked to the creation

of jobs while the indirect effects (or long term effects) are mainly a result of negative eviction

from enterprises and rivals, closing enterprises and loss of jobs that generally appear after a

period of implantation of new enterprises (van Stel and Suddle 2008 , Mueller and al 2008).

To clarify the idea of entrepreneurial perspective in the development of a territory, Cravo

and Resende (2013), accentuate the importance of both the regional and temporal framework.

Studies show that for the first three years, the creation of start-ups in Germany, in the United

Kingdom, in Holland and in Portugal causes relatively weak effects on jobs but that this

evolves in a significant positive way after the sixth year (Caliendo et Kritikos 2010,Fritsch

and Mueller 2004, Mueller and al 2008), and the eighth year in Portugal (Baptista and al

2008).

The complex mechanisms of the effects of new-firm startups on the creation of jobs on a

local market have been deepenly described by Fritsch (2008). If the setting up of a new firm

has generally a positive net effect in the long term on the creation of jobs, this is not

immediate (Dejardin and Fristch, 2011). In the medium term, new firms may induce

displacements that lead to a higher productivity but also to a decline in employment.

According to Fristch, the most important positive effects upon the growth and the creation of

jobs are subject to a delay of 5 or 6 years and a time lag that may reach 10 years. “The

employment effects of new business formation will probably be rather positive in high

productivity regions with high-quality entries, abundant resources and a well-functioning

innovation system. They will be much smaller or may even be negative in low productivity

regions with low-quality entries, scarcity of relevant resources and an inefficient innovation

system” (Fritsch, 2008, p.5).

Another strand of literature is related to the motives of setting-up a firm (push/pull

effects). Some evidence of unemployment-push into self-employment is proved in the

majority of the regions of France (Abdesselam et al, 2014). Yet The Île-de-France region is an

exception since the “Schumpeter” effect prevails in the long term (Aubry et al, 2014).

The question of the contribution of entrepreneurship to regional development has sparked a

major economic and political interest to this day.

Previous research has extensively explored the question of whether small/large firms or new

firms contributed to growth and to regional development. In a regional context, numerous

works in research have focused on measures more closely related to the dynamic nature of

5

entrepreneurship (Audretsch and Feldman, 1996, Berglund and Johansson 2007, Haltiwanger

and al 2010, Nanda 2009, Rosenthal and Strange 2010, Glaeser and Kerr 2011). Certain

researchers advocate that the regions dominated by small enterprises have high rates of

creation of new companies while those dominated by big enterprises have weaker rates

(Davidsson and al 1994). The majority of studies have found that small enterprises are the

driving force of regional growth and job creation (Audretsch and Fritsch, 2002).

The literature shows that the aspects of regional development such as regional learning,

identity creation or the regional well-being that go beyond job creation and growth, have not

been given as much attention as the studies concerning new enterprises reflecting macro

economical impacts. Hence the attention that was paid to the study of the economic impact of

entrepreneurial activity.

1.2 The region's issues on entrepreneurship

Why do enterprises locate themselves at one such place rather than another? One

answer immediately comes to mind: certain areas are better suited for business than

others. The presence of arable lands, mines, oil wells, and an access to the sea or strong

sunlight is liable to influence enterprise locations. But these benefits of "first nature" do

not suffice to explain the distribution of economic activities in the area. Previous

empirical researches on regional determinants of entrepreneurship are many explanatory

factors.

The regions or employment zones densely populated are more likely to have higher rates of

enterprise creation because the services and resources infrastructure is more developed in the

most populated areas (Combes and al 2009, Martin and al 2010, Joffre-Monseny and al 2011).

For their part rural areas tend to show a higher level of activity in the agricultural sector itself

characterized by a prevalent self-employment rate (Keber, 2013, Meccheri and Pelloni, 2006).

Areas with a strong proportion of highly skilled and specified labor benefit from high rates of

enterprise creation compared to areas with unskilled workers (Davidsson and al 1994,

Audretsch and al 2010). In a study on Canadian provinces, Coulombe and Tremblay (2006)

have shown that the dynamic of a human capital is at the heart of regional growth. If the

human capital has a vocation to be concentrated in certain regions over others, its geographic

behavior could be both a potential determinant of corporate entries and a source of their

growth (Monfort 2002). The high level of income and the gross value added per person

(Audretsch and Fritsch 1994) have in turn attractive regional factors.

6

Wealthy region is more likely to support newly created firms (Bergmann, 2005) in reducing

loan rates authorized for the funded firms and most of all in offering a risk-free investment

climate (Stam, 2010).

Michelacci and Silva (2007) show that the share of local entrepreneurs that setup their firm

in their native region is more important than the corresponding share of salaried people. And

moreover this share is more pronounced in the more developed regions and increases

according to the degree of development of the local finance such as it has been measured by

Guiso et al. (2004). This suggests that local entrepreneurs are more able to exploit the

available financial opportunities in the regions where they are born and that there is a

correlation between entrepreneurship and wealth.

Aoyama (2009) points out that cultural and historical heritage bring forward important

underlying mechanisms in terms of localization. Feldman (2001) and Audretsch and al (2010)

confirmed this logic in turn. Localities that are endowed with an entrepreneurial culture grow

faster than localities that are not endowed of this characteristic (Holmqvist 2001,Beugelsdijk

and Noorderhaven 2004). This cultural characteristic influences regional development

(Mueller 2006, Minniti 2005).

Johannisson and Nilsson (1989), Jack and Anderson (2002), Lawton Smith and al (2005),

define the social network as a local determinant supporting local business entry. The personal

network of the entrepreneur and the exchange of information play an important role for

entrepreneurs (Kodithuwakku and Rosa 2002). Benneworth (2004) finds that local

entrepreneurs extend the reach of enterprise networks while allowing others to benefit from

these networks.

Also, areas with strong local politics in favor of entrepreneurship recorded higher

entrepreneurial activity (O'Gorman and Kautonen 2004) and a higher starting-up rate

(Garofoli, 1994, Bosma and Schutjens, 2011).Finally, the choice of area location depends on

the proximity of university zones and research institutions (Berggren and Dahlstrand 2009).

7

Table 1: Preview of the literature of regional structures on entrepreneurship

Structure of the region Effect Authors

Economic Structures Population density

The presence of human capital

(Proportion of highly skilled

workers, education)

Access to financial capital

Unemployment rate

Specialized industry

High household income

The presence of SME

High proportion of women in the population

+

+

+

+/-

+/-

+

+

-

Combes and al 2009, Martin and al 2010,

Joffre-Monseny and al 2011.

Davidsson and al.1994 , Coulombe and Tremblay 2006

Florida and Kenney 1988a, Malecki 1997, Bergmann,

2005, Naudé et al.2008 , Avdeitchikova 2009, Stam,

2010

(+) Georgellis and Wall 2000, Fritsch and Falck 2007

(-) Davidsson et al. 1994, Santarelli and al 2009

Garofoli 1994, Davidsson et al. 1994

Krugman 1991, Audretsch and Fritsch 1994, Garofoli

1994, Feldman 2001

Davidsson et al. 1994, Aoyama 2009

Georgellis and Wall (2000)

Geographical Structures

Regional Resources

Attractive living conditions

Regional Spirit, the local ethics

Regional Entrepreneurial

Capacity

Infrastructure

Proximity to urban areas

Proximity to research

institutions or universities

+

+

+

+

+

+

+

Benneworth (2004)

Keeble, 1992; Meccheri, 2006

Mueller 2006, Aoyama 2009, Johansson 2009, and

Audretsch and al 2010

Naudé et al. 2008 , Gaddefors and Cronsell 2009

Benneworth 2004, Florida 2007, Keber 2013

Fritsch 1997, Mueller et al. 2008, Keber 2013

Acs et al. (1994), Audretsch and Feldman (2004);

Berggren and Dahlstrand (2009)

Social Structures

Networks and social and

capital

+

Flora and Flora 1993, Jack and Anderson 2002,

Johannisson and Dahlstrand 2009

8

2. Regional development in Tunisia

2.1 The regional plans of Tunisia

In the history of Tunisia, the regional development policy of the country has

experienced three main periods. The first, dating between (1962-1969), put forward a

redistributive and voluntarism political strategy aimed at sector development, regardless of

the region of implantation. The strongly committed to the establishment of large industrial

centers created in the interior regions and in the south of the country, generated a strong flow

of rural exodus notably towards coastal regions and the great Tunis (Chabbi ,1998). The rural

exodus is due to system modernization of traditional agriculture causing the destruction of the

rural economy and the impoverishment of farmers. Very early on, many doubts arose

concerning the effectiveness of the policy of growth poles.

The early 70's marked the end of the "collectivist" experiment and a policy shift based

on the appeal to foreign capital and the creation of export processing zones appears: this is the

second strategic plan.

Obviously, the results of interventions in terms of regional development over the

course of this phase of development were disappointing and the policy hadn't deviated from

its original logic, adopted in the 60's (Rallet, 1995). In terms of employment, there wasn't any

balance between the different regions (Belhadi, 1990). In the same vein, the allocation

strategy of industrial centers adopted during this period could not achieve the desired

objectives (Rallet, 1995). The proliferation of regional development measures over the course

of this period became both a requirement and a priority to counterbalance the negative effects

brought on by the new liberal policy adopted by Tunisia in the 70's. The crisis of the mid-80's,

the adoption of the Structural Adjustment Program (SAP) and the customs union agreement

with the European Union in 1996 will totally change the orientation by being fully inserted in

globalization with a scenario of regional development founded on each regions resources. A

new conception of the regional development policy was installed at the start of the 90's. The

third period in the history of Tunisia drew a regional development policy based on resource

mobilization, local initiatives, and regional planning rather than sectoral planning (Rallet

1995 and PNUD 1999). The region became responsible to trace it's path of development.

Unfortunately, the objectives of the regional development policy during the period 1990-2010

were far from being achieved on the regional plan (Dhaher, 2010).

Moreover, the situation in the Center West and in the North West has strongly

deteriorated between 2000 and 2010. This being said, the most affluent regions of Tunisia, in

9

other words the Grand Tunis and the Center East, have seen their position concerning extreme

poverty improve. The North Western, South Eastern and South Western regions are also

potentially identified as priorities. The Central West and the North West are considered as two

large pockets of extreme poverty, whereas the North East, Central East and Greater Tunis (the

coast) are not considered as a priority in 2010. (Measuring poverty report INS2010).

2.2 Micro-enterprises in the Tunisian territory

The interior regions of the Tunisian territory weigh little in the national economy

compared to the coastal region which, in 2010, contributed to a strong socio-economic

potential and 80% of the national GDP. More than 83% of businesses (economic activities)

are concentrated in the coastal areas of the Tunisian territory, and nearly 40% are located in

the central business districts of both Tunis and Sfax (Ayadi and Matoussi 2012).

The differences are partly related to the situation of the local labor market but also to

the region’s economic performances. The enterprises and the workers benefit from their

location in areas already benefiting from strong economic activity. In the case of development

of the enterprise statistics system, the National Institute of Statistics conducted two National

Surveys of Economic Activities (NEST) respectively in 2002 and in 2007. The scope of these

two investigations on micro-enterprises covers the non agricultural activities.

The results of both surveys allowed us to evaluate the activities of these small units

and to define the share the informal sector plays in the national Gross Domestic Product.

In the Tunisian context, the informal sector is defined following three criteria: the legal

status of the unity (sole proprietorship), the size of the company in terms of employee

numbers (less than 6 employees) and the absence of a bookkeeping (the company does not

hold accounting).

In total, the contribution of micro-enterprises throughout the national economy

reached 68% in 2002 and 63% in 2007. Concerning production, the micro-enterprise sector is

characterized by an added value rate of 68% in 2007. As a result, these small entities are a

fundamental component in the fabric of the Tunisian economy.

A little more than a sixth of jobs covered (15,3%) belongs to enterprises of the

industrial and craftsmanship sectors, whereas 41,1% of these jobs are done in activities of

commerce and repair. The service sectors occupy around 41,2% of the total number of

employees. It should also be noted that three-quarters of jobs (74%) are concentrated in firms

with 1 or 2 employees.

10

However, the value added per capita among the self-employed reached 85% of the average

productivity of micro-enterprises as a whole in 2007.

It should be noted that the survey only covered companies fiscally registered and

localized in institutions (82,4% of the companies are located in an establishment, whereas the

17,6% corresponding to units that operate while traveling, at home, at a local market or on a

building site are not taken into account by the survey).

The sample in 2007 focused on 8172 micro-enterprises against 8251 in 2002, with

7144 enterprises in 2007 without accounting data.

The share of micro-enterprises is around 32,5% of all informal activity according to

the survey of 2007. As a result, informal employment represents 42,2% of total employment

in production, absorbing a high level of labor (also in trade and service). The question that

arises is whether a high density of micro-enterprises in their general form (formal/informal),

improves the development index of Tunisian disadvantaged areas?

The Tunisian Institution of Competitiveness and Quantitative Studies in cooperation

with the Ministry of Regional Development and Planning collected a wide database of 129

variables, covering a wide socio-economic component regrouping the average of 17 available

variables in four components ; knowledge, wealth and employment, health and population and

finally justice and equity for each of the delegations.

The data collected from the National Institute of Statistics and the relevant

departments to build an indicator of regional development (IRD) allows us to evaluate the

state of the level of welfare and of regional development of the Tunisian delegations.

The construction of a composite indicator normalized by the values of the composite

index corresponding to a delegation of reference will allow a much clearer analysis of the

spatial configuration of the level of Tunisian territory development. A ranking of the

development indicator records scores above average in the coastal region encompassing the

capital, Cap Bon, the Sahel, the North East, Central and the South East ; its value decreases

from north to south. This being said, the remainder of the Tunisian territory is marked by low

scores under average.

3 Empirical Strategies

3.1 Data

Our study is based on the mobilization of a range of technical and spatial measures to

answer the following question: To what extent does the density of micro-enterprises reinforce

11

the development of Tunisian delegations?

Our database contains statistical information on the 263 delegations of Tunisia

(delegations represent the second administrative division of the Tunisian territory after the

“gouvernorat”).

The variables used were published by the Ministry of Regional Development and

Planning in 2010 and focus on two sources of information: the General Commissioner for

Regional Development and the Tunisian Competitiveness and Quantitative Studies Institute.

We retain as spatial dependent variables from the General Commission for Regional

Development:

- the density of micro-enterprises, 'densME' calculated by the number of micro-

enterprises (less than 6 employees) in a delegation 'i' divided by the population of the

delegation 'i').

-the density of population, ‘lnPopDens’ , calculated by the number of population in a

delegation 'i' divided by the superficies of the delegation 'i'.

- the number of employments created by the Tunisian Solidarity Bank 'lnEBTS' in

each delegation.

- the number of qualified employment seekers,' lnQualiUnp 'measured by the number

of job applicants in the context category in each delegation.

- finally the road network variable in kilometers 'lninfrastruc'.

The regional dependent variable represented by the regional development indicator

'RDI' is calculated by the Tunisian Institute of Competitiveness and Quantitative Studies (see

report in July 2012- A comparative study in terms of regional development in Tunisia).

Each case is spatially localized through a pair of coordinates (x, y) having the

measuring axis as latitude and longitude of the centroid of the delegation. These observations

are geo-referenced due to the map projection system Carthage / UTM Zone 32N - EPSG:

22391.

Table 2: Variable description and descriptive statistics

Acronym

of variable

Definition Source Descriptive statistics

Observation Mean Std.Dev Min Max

RDI It is a synthetic

indicator that

refers to four

areas: life

convenience,

social

Ministry of Regional

Development and

Planning. Rapport by

Tunisian

Competitiveness and

Quantitative Studies

263

0.318

0.157

0

1

12

envrionment,

economic activity

and labor market

calculated on the

basis of statistics

produced by the

National Statistics

Institute. This

variable

represents the

development

index in each

delegation.

Institute: Comparative

study in terms of

regional development

in Tunisia (July 2012).

DensME The number of

micro-enterprises

having less than 6

employees in a

delegation 'i'

divided by the

population of the

delegation 'i'.

National Statistics

Institute: enterprise

directory in 2010.

263 0.507 0.055 0.001 0.676

EBTS Number of

employment

created by the

Tunisian

Solidarity Bank


Development and

General Commissioner

for Regional

Development :

Governorate in number

in 2010

263

4.936

2.210

0

8.964

Qualiunp Number of

qualified

employment

seekers


Development and


for Regional

Development :


in 2010

263

5.211

2.0014

-0.964

10.313

Infrastruc The road network

in kilometers


Development and


263

5.393

1.191

1.386

7.936

13

for Regional

Development :


in 2010

PopDens The density of

population


Development and


for Regional

Development :


in 2010

263

4.246

1.205

0

6.7310

3.2 Methodology

As noted in the previous section (2-2), the National Survey of Economic Activities

statistics conducted by the INS in 2007 shows that micro-enterprises are an essential

component of the Tunisian economic fabric and that their activities contribute significantly to

the national production. However, at present, work measuring the impact of these small

entities on regional development, especially at the level of Tunisian delegations, is still

missing.

The starting point of our analysis is to test the presence of spatial correlation for the

ensemble of our spatially referenced observations.

This test involves calculating the Moran statistic presented as follows: IM=

i

i j

xxi

xxjxxiwij

K

N2

Where:

xi is the observation for the i delegation, N is the number of delegations in the country, x is

the average of the observations of the retained variable on the N delegations , wij measures the

degree of spatial interdependence between the two regions i and j.

In this study, we determine a spatial weight matrix (matrix Wij) calculated under the standard

of a Euclidian distance of a threshold of 50 kilometers. To facilitate the interpretation of

results, this matrix is standardized in a way that makes the sum of each line equal to 1.

k= wijrepresents the sum of all the elements of the spatial weight matrix. We note E(I) the

14

expectancy of I under the hypothesis of the absence of spatial auto-correlation. A value of I

greater (smaller) than the value of mathematical expectancy: E(I)=-1/(n-1) presents a positive

(negative) spatial auto-correlation composed of a geographic clustering of similar (different)

values whereas the auto-correlation is null if the index is close to zero. In spite of the strength

of this index (see Upton and Fingleton 1985), it has a limit related to the fact that it doesn't

allow appreciation of the significance of spatial clustering. An appeal to a local indicator of

spatial association will there for be presented by the Moran diagram and the LISA statistics

(Anselin 1995) to map the four spatial clustering’s.

A global linear spatial regression is then estimated with two models, a spatial error model

(SEM) and a spatial autoregressive model (SAR). The first involves considering spatial

dependence as a form of nuisance by grasping a spatial process in the terms of errors. In the

second specification, the interactions between observations are characterized by the

introduction of a spatially dependent variable delayed in the aim of measuring the intensity of

interactions between the observations.

The choice of appropriate model is based on the sturdy values of the Lagrange multiplicator

for each model (Anselin and al 1992). The choice of the right global spatial specification is

based on the two Lagrange Multiplicator tests (LM-Lag and LM-Error).

4. Econometric estimation

4.1 Specification of the classic linear model

The classic models of regression involve modelising a phenomenon by defining an

equation of regression that determines the value of a dependent variable on the basis of k

values independent variables (x1…xk).

Le classic linear model of regression is as follows: RDIi= β0 + Xiβ+εi (1)

With RDI the development indicator calculated for each delegation. Xi is a vector

presenting the independent variables. This vector reflects the variables inducing the

development of a delegation.

β is the vector of coefficients estimated using the model of ordinary least squares and εi is the

error term.

This model of regression ill-suited to spatial data holds a total independence between

the observations (Fothe ringham and al 1996 , Anselin and Griffith 1998).The current work

predicts that a model can be very effective in some geographical areas and not in others

(Apparicio and al 2007). In works on entrepreneurship, the behavior of an entrepreneur could

15

be predicted by that of the neighboring entrepreneurs and the small firms are very dependent

of local and/or neighboring resources (Anderson, 2005). As Plummer, 2010, says « the

performance, growth, and survivability of a new firm are not independent from the

performance, growth, and survivability of nearby firms ». It will there for be important to put

forward the hypothesis of the existence of a spatial correlation between our observations

(delegations) by using two spatial specifications: SEM and SAR.

4.2 Specification of the global spatial model

The Spatial Autoregressive model (SAR) is estimated according to the following

equation:

RDIi=β0+ρWRDIi+β1 lnEBTSi+ β2 DensiteMEi + β3 ln PopDensi+ β4 lnQualiUnpi + β5lnInfrastruci + εi

(Model 1)

ρ is an autoregressive spatial parameter indicating the degree of inter-delegation interaction.

W is the matrix of spatial interaction representing the structure of spatial dependency between

the observations (delegations).

The second spatial specification involves applying spatial auto-correlation in the terms

of error. In this case, the Spatial Error Model (SEM) takes the following form:

RDIi= β0 + β1 lnEBTSi+ β2 DensiteMEi + β3 ln PopDensi+ β4 lnQualiUnpi + β5lnInfrastruc + εi

εi =λwijεj + vi (Model 2)

v is an error term, it follows a normal law of a zero mean and a constant variance. λ presents a

spatial parameter reflecting the intensity of the persistent spatial correlation between the

residue.

5. Result and analysis

5.1 results of the spatial exploratory analysis

The central hypothesis of the analysis of spatial auto-correlation has been defined by

Tobler's law (1970). This law indicates that observations close to other observations in the

area have a tendency to be alike (correlated positively) more so than others more distant.

«Everything is related to everything else, but closer things more so (Tobler 1979) ».

On the delegation scale, we confirm the presence of a positive global spatial auto-

correlation for all of our variables, especially the value of the density of micro-enterprises. A

positive and significant value of the Moran index translates the multidirectional dimension of

the phenomenon. The density of enterprises of delegation "i" positively affects the density of

micro-enterprises localized in neighboring delegations. Thereafter, a construction of the

16

Moran diagram defines in the x-axis by the value of the variable 'densME' standardized and in

the y-axis it's standardized spatial shift, shows four types of spatial associations.

The statistics of LISA show a total of 235 delegations having significant values of

local spatial auto-correlations against 27 delegations not belonging to any spatial regrouping

(figure 1).

Figures 1: LISA significance map and LISA cluster map

Density of micro-enterprises in Tunisia

The map showing the LISA index of the densities of micro-enterprises shows that the

cluster of type HH regroups 126 delegations. This regrouping localized all along the coastline

is also shown in the four South Tunisian delegations, the South Tataouine delegation, South

Mednine, Djerba Houmet Souk and Mareth. The regrouping of type HH is associated with

positive spatial auto-correlations as it presents delegations of micro-enterprises with strong

densities surrounded by neighboring delegations having a strong density of micro-enterprises.

The second type of regrouping situated in the LL shows positive auto-correlations translating

a similar spatial regrouping. The type of clustering LL formed by the delegations of the North

West and the Center West and a part of the South West that regroups 57 delegations with a

weak density of micro-enterprises surrounded by neighbors with weak density. HL and LH

type associations represent a negative auto-correlation explained by a spatial regrouping of

dissimilar values. However, it is relevant to note that the hypothesis of the existence of a

positive spatial dependence between the density of micro-enterprises and the level of the

index of delegation development holds.

17

5.2 Result of econometric estimations

We are beginning our study by the specification of the spatial model. The tests LM-

Lag and LM-Error reject the null hypothesis of the absence of spatial auto-correlation

concerning our endogenous variable as well as the term of error (LM-lag= 113.35, p-value

=0.000 and LM –Error=169.17, p-value = 0.000).

The estimated values of the positive and significant parameters (λ and ρ) at a threshold

of 5% indicate the presence of spatial dependence between the indicator of regional

development of a delegation and that of its neighbors as well as a dependence of terms. In this

case, we compare the values of Robust LM of the parameters λ and ρ. The results shown in

Table 1 confirm the presence of a positive spatial auto-correlation concerning the error term.

This first result is due to the omission of one or many explicative variables strongly correlated

in the area. In this case, the spatial auto-correlation of errors is considered as a principal

component of the variation of regional development. We therefore limit ourselves to the

results of the SEM model for the ‘RDI’ variable. The results of the estimation of the three

models are shown in table 3.

Table 3: Estimation results of the three models

Variables

Model 1 a spatial

model

Model 2

SAR

Model 3

SEM

DensiteSM

0.79***

(0.105)

0.77***

(0.092)

0.79***

(0.093)

Ln EBTS

0.014***

(0.0028)

0.008***

(0.0025)

0.0077***

(0.0026)

LnPopDens

0.043***

(0.0038)

0.033***

(0.0036)

0.036***

(0.0037)

LnQualiUnp

0.0088*

(0.004)

0.018***

(0.004)

0.016***

(0.004)

LnInfrastruc

0.019***

(0.005)

0.6***

(0.005)

0.013***

(0.004)

Constante

-0.077***

(0.0234)

-0.208***

(0.0262)

-0.10**

(0.048)

RHO (ρ) --- 0.524 ---

--- 0.064 ---

Lambda (λ) --- ---- 0.882

---- ---- 0.071

18

R-Squared 0.73 0.78 0.78

Lagrange Multiplier --- 63.195 111.323

Robust Lagrange

Multiplie --- 20.744 68.872

Number of observation 262 262 262

Number of variable 7 7 7

The values in parentheses are standard deviations. The stars are defined as: *** significant to 1%, ** significant

to 5%, *significant to 10%.

It comes from the SEM model that our hypothesis 'micro-enterprises participate in

delegation development' is confirmed. The variable 'DenseteME' correlates positively and

significantly to the dependant variable. Indeed, any rise of 10% in the variable of the density

of micro-enterprises in an 'i' delegation leads to a rise of 7,9% in the development level of the

'i' region as well as it's neighboring regions in a perimeter of 50 kilometers.

The result of the participation of micro-enterprises in the improvement of the

delegation development index is explained as follows: The presence of micro-enterprises in

an area guaranties a secure and attractive climate for investors. These regions characterized by

a high level of income and wealth managing to support micro-enterprises while offering

positive agglomeration externalities. The result of our estimation confirms those found in the

study by Bosma, van Stel, and Suddle 2008, for low countries and those, Bosma and al in

2009, for European regions. Pe'er and Vertinsky 2008 certify our analysis in the context of

Canadian provinces.

We should mention that the debate on the role of small firms in job creation and

development was first launched in the Birch's work (1979, 1981) work. It stated that small

enterprises constituted the most powerful force of job creation in the American economy.

While in Sub-Saharan Africa, Itaddy(2014) concluded that micro entrepreneurship is limited

to satisfying household needs in the Congo Brazzaville area.

The 'lnEBTS' variable has a positive and significant impact on the regional

development indicator. The result of our estimation confirms the one offered by the joint

report by the World Bank and the Ministry of Employment and employability of young people

conducted in 2007 on the micro-enterprises of Tunisia. This report indicates that more than

half of resources allocated to the active employment policy in Tunisia are dedicated to micro-

enterprises. However the regional distribution of created projects show that the weakest

sectors (agriculture, trade and crafts) are overrepresented in the areas that record the lowest

19

survival rates (West Central, Greater Tunis to North East). Thereby we can say that the

participation of the Tunisian bank of solidarity in an area contributes to the progress of its

development.

Likewise the results of our estimation shows a positive and significant effect of the

variables ‘lnQualiUnp’,‘lninfrastrucrure’ and ‘lnPopdens’ on ‘RDI’. The rural exodus towards

coastal areas marks well it's negative role on disadvantaged areas. This is supported by the

study by Chabbi (1986, 2005) and Belhaj (1994). Naudé and al (2008) suggest that the level

of regional participation in higher education contributes to a high entrepreneurial intensity in

rural areas and thereafter to their development. These author's results are confirmed by the

positive and significant impact of the variable ‘lnQualiUnp’ that then explain that any increase

of 10% concerning superior education generates 0,016% of the level of delegation

development.

Conclusion

Recently numerous works emphasize the role of entrepreneurship and entrepreneurial firms

(new and innovative ones) in the development of industrialized countries (Bonnet et al., 2010)

and developing countries (Acs et al, 2008, Naudé, 2010).

In this paper we investigate the effect of micro-enterprises and other regional control

variable on the regional development indicator. A spatial configuration indicated the spatial

associations that exist on the Tunisian territory and the interaction between the delegation and

it's neighbors. These associations announce a first idea on the role played by micro-enterprises

at the regional level .The estimation of the spatial error model rejects the null hypothesis of

the randomness of our observations. The results indicate that micro-enterprises creation

impact positively regional development.

18

Reference

Acs, Z.J., S. Desai, and J. Hessels (2008). Entrepreneurship, economic development and

institutions.Small Business Economics 31, 219–234.

Anselin.L (1995).Local Indicator of Spatial Association LISA,Geographical Analysis 27,93-115.

Anselin. L, Artur.G 1992.Spatial Statistical Analysis and Geographic information Systems .The

Annals of Regional Science 26,19-33.

Audretsch, D., D. Dohse, and A. Niebuhr (2010). "Cultural diversity and entrepreneurship: a

regional analysis for Germany," Annals of Regional Science, 45 (1), 55-85.

Audretsch, David B., and Michael Fritsch (1994). "The Geography of Firm Births in Germany,"

Regional Studies, 28 (4), 359-359.

Audretsch, D. B., and Maryann P. Feldman (2004). "Knowledge spillovers and the geography of

innovation," Handbook of regional and urban economics, 4 ,2713-2739.

Audretsch, D. B. (2001). "The role of small firms in US biotechnology clusters," Small Business

Economics, 17 (1-2), 315.

Aoyama, Y. (2009). "Entrepreneurship and Regional Culture: The Case of Hamamatsu and

Kyoto, Japan,"Regional Studies, 43 (3), 495-512.

Benneworth, P. (2004). "In what sense 'regional development?’ Entrepreneurship,

underdevelopment and strong tradition in the periphery," Entrepreneurship and Regional

Development, 16 (6), 439-458.

Berggren, E., and A. L. Dahlstrand (2009). "Creating an Entrepreneurial Region: Two Waves of

Academic Spin-offs from Halmstad University," European Planning Studies, 17 (8), 1171-

1189.

Beugelsdijk, S. 2007. "Entrepreneurial culture, regional innovativeness and economic

growth".Journal of Evolutionary Economics 17, 187–210.

Beugelsdijk, S., and N. Noorderhaven (2004). "Entrepreneurial attitude and economic growth: A

cross-section of 54 regions," Annals of Regional Science, 38 (2), 199-218.

Bonnet J., Garcia Perez-De-Lema D., Van Auken, H., (2010), "The Entrepreneurial society: how

to fill the gap between knowledge and innovation”, Edited by Jean Bonnet, Domingo García-

Pérez-de-Lema, Howard Van Auken, 260 pages, Edward Elgar Publishing.

Bosma, N., and V. Schutjens (2011). " Understanding regional variation in entrepreneurial

activity and entrepreneurial attitude in Europe". Annals of Regional Science ,47,711–742.

Bosma, N., V. Schutjens, and E. Stam (2009). "Entrepreneurship in European regions:

Implications for Public Policy. In Public Policies for Fostering Entrepreneurship": A

European Perspective,eds. J. Leitao, and R. Baptista, 59–89. New York: Springer.

Bosma, N., A. van Stel, and K. Suddle(2008). "The geography of new firm formation: Evidence

from independent start-ups and new subsidiaries in the Netherlands". International

Entrepreneurship and Management Journal ,4,129–146.

Davidsson, P (1991). "Continued entrepreneurship: Ability, need and opportunity as determinants

of small firm growth". Journal of Business Venturing ,6,405–429.

19

Fischer, M. M., and P. Nijkamp (2009). "Entrepreneurship and Regional Development," Working

Paper: Serie Research Memoranda 0035. Amsterdam: VU University Amsterdam, Faculty of

Economics,Business Administration and Econometrics.

Flora, J. L., J. Sharp, C. Flora, and B. Newlon (1997). "Entrepreneurial social infrastructure and

locally initiated economic development in the nonmetropolitan United States," Sociological

Quarterly, 38 (4), 623-645.

Friedman, V. J., and H. Desivilya (2010). "Integrating social entrepreneurship and conflict

engagement for regional development in divided societies," Entrepreneurship and Regional

Development, 22 (6), 495-514.

Fritsch, M., and P. Mueller (2007). The persistence of regional new business formation-activity

over time – assessing the potential of policy promotion programs. Journal of Evolutionary

Economics,17,299–315.

Fritsch, Michael and Alexandra Schroeter. 2011. "Why does the effect of new business formation

differ across regions?" Small Business Economics, 36,383-400.

Fritsch, Michael. 2008. "How does new business formation affect regional development?

Introduction to the special issue." Small Business Economics ,30 (1),1–14.

Fritsch, M., and P. Mueller (2007). The persistence of regional new business formation-activity

over time – assessing the potential of policy promotion programs. Journal of Evolutionary

Economics,17,299–315.

Fritsch, Michael and Pamela Mueller. 2004. "Effects of new business formation on regional

development over time." Regional Studies ,38 (8), 961–975.

Garofoli, G. (1994). "New Firm Formation and Regional Development - the Italian Case,"

Regional Studies, 28 (4), 381- 393.

Jack, Sarah L., and Alistair R. Anderson (2002). "The effects of embeddedness on the

entrepreneurial process," Journal of Business Venturing, 17 (5), 467-487.

Johansson, A. W. (2009). "Regional development by means of broadened

entrepreneurship,"European Planning Studies, 17 (8), 1205-1222.

Johnson, P., and S. Parker 1996. Spatial variations in the determinants and effects of firm births anddeaths.

Regional Studies ,30,679–688.

Johannisson, B, and A Nilsson (1989). "Community entrepreneurs: networking for local

development," Entrepreneurship and Regional Development, 1 (1), 3-19.

Kibler (2013). Formation of entrepreneurial intentions in a regional context .Entrepreneurship and

Regional Development» ,25(3-4),293-323.

Kodithuwakku, S. S., and P. Rosa (2002). "The entrepreneurial process and economic success in

a constrained environment," Journal of Business Venturing, 17 (5), 431-465.

Krugman, P. (1991). "Increasing returns and economic geography," Journal of Political

Economy, 99 (3), 483-499.

Lawton Smith, H., J. Glasson, and A. Chadwick (2005). "The geography of talent:

Entrepreneurship and local economic development in Oxfordshire," Entrepreneurship and

Regional Development, 17 (6), 449-478.

20

Lesage.J.P 2012.An Introduction To Spatial Econometrics. Revue d’Économie Industrielle,

vol.123, 3ème trimestre 2008 .

Martin P, Mayer T, Maynerie F(2010).Spatial concentration and plant-level Productivity in

France. Journal of Urban Economics ,69 (2), 182-195.

Mueller, P. (2006). "Entrepreneurship in the Region: Breeding Ground for Nascent

Entrepreneurs?," Small Business Economics, 27 (1), 41-58.

Mueller, Pamela, Andre van Stel, and David J. Storey (2008). "The effects of new firm formation

on regional development over time: The case of Great Britain," Small Business Economics, 30

(1), 59-71.

Meccheri, N., and G. Pelloni (2006). Rural entrepreneurs and institutional assistance: An

empirical study from mountainous Italy. Entrepreneurship and Regional Development

,18,371–392.

Naudé, W.A., T. Gries, E. Wood, and A. Meintjes. (2008) Regional determinants of

entrepreneurial start-ups in a developing country. Entrepreneurship and Regional

Development ,20,111–124.

O'Gorman, C., and M. Kautonen (2004). "Policies to promote new knowledge-intensive industrial

agglomerations," Entrepreneurship and Regional Development, 16 (6), 459-479.

Pe’er, A., and I. Vertinsky. 2008. Firm exits as a determinant of new entry: Is there evidence of

local creative destruction? Journal of Business Venturing ,23,280–306.

Plummer.LA, Pe’er.A(2010).The Geography of Entrepreneurship. in Handbook of

Entrepreneurship Research. Springer , 5, 519-556

Renski, H. (2009). "New Firm Entry, Survival, and Growth in the United States: A Comparison

of Urban, Suburban, and Rural Areas," Journal of the American Planning Association, 75

(1), 60-77.

Stam, E. (2010). Entrepreneurship, evolution and geography. In The handbook of evolutionary

economic geography, eds. R. Boschma, and R.L. Martin, 307–348. Cheltenham: Edward

Elgar

Stam, E., R. Thurik, and P. van der Zwan (2010). Entrepreneurial exit in real and imagined

markets. Industrial and Corporate Change ,19,1109–1139.

Tobler, W. R 1970.A computer movie simulating urban growth in the Detroit region. Economic

Geography,46, 234-240.

Van Stel, A., and K. Suddle (2008). "The impact of new firm formation on regional development

in the Netherlands," Small Business Economics, 30 (1), 31-47.

Yang, Kaizhong, and Ying Xu (2006). "Regional differences in the development of Chinese small

and medium-sized enterprises," Journal of Small Business and Enterprise Development, 13

(2), 174.

ENTREPRENEURIAT RESPONSABLE : PRATIQUES ET ENJEUX...

Documents

Transcript of ENTREPRENEURIAT RESPONSABLE : PRATIQUES ET ENJEUX...