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SPATIAL DETERMINANTS OF THE UNEMPLOYEMENT OF GRDADU ATES:

THE TUNISIAN EXPERIENCE

HANÈNE BEN OUADA JAMOUSSI 1, FOUED BEN SAID2 & MAHER GASSAB 3

1Department of Quantitative Methods, Business School, Manouba University, Larma, Fseg Tunis, El Manar University,

Manouba, Tunis, Tunisia 2Department of Quantitative Methods, Business School, Manouba University, L Larequad,

Fseg Tunis El Manar University, Manouba, Tunis, Tunisia 3Department of Economics, Business School, Manouba University, Larma, Fseg Tunis El Manar University,

Manouba, Tunis, Tunisia

ABSTRACT

Both in individual and community standards; education is a factor for increasing social status as well as the

chance of finding a job. Most economists acknowledged the idea that the laboring skills of a country represent one of its

most important competitive assets.What is happening in emerging countries and revolutions called "Arab Spring" is

changing the rules.A discuss about these changes is done through the Tunisian events.A high unemployment rate of

graduates was the origin of the revolution. But the revolution which was supposed to solve this problem has worsened it.

A mismatch between the constituted human capital and the capacities of its absorption by the market drags the loss of

positive effects that may create added value and generate economic growth. This is even worse than the initial

situation.Once more, unemployment and feelings of frustration and discontent felt before the first revolution may lead to a

second revolution. A vicious circle develops. In this paper we try to analyze the hypothesis that the unemployment of

graduate people is more likely clustered in specific regions by using the scan statistic cluster detection test. High graduate

unemployment incidence rates are detected in Southern West and Northern West governorates. A binary logit model shows

that in these regions, graduates are less likely to get a job than less educated people.

KEYWORDS: Unemployment, Labor Mobility, Regional Development, Mismatching, Logit Model

Jel Codes: R11, J61, C25

1. INTRODUCTION

Since the sixties Tunisia opted for generalization of education. The 1991 reform makes school compulsory till the

age of sixteen. This polity allowed a very important retreat of illiteracy during the sixties and the seventies and led

nowadays to the creation of a large human capital potential constituted by university-graduated.

Nowadays, instead of being an invaluable tool for growth and development for the country, this capital constitutes

a heavy burden that hinders its proper social and economic evolution. It constitutes one of the major raisons of the Tunisian

revolution.

International Journal of Human Resource Management and Research (IJHRMR) ISSN(P): 2249-6874; ISSN(E): 2249-7986 Vol. 4, Issue 5, Oct 2014, 1-18 © TJPRC Pvt. Ltd.

2 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

Adam Smith identifies in his book1 the improvement of employees' skills as a fundamental source of economic

progress. In the work of Schultz T.W. (1961)2, «Investment in individuals and in knowledge are critical factors to ensure

the standard of living and to resolve paradoxes inherent with economic growth», and investments in population quality and

in knowledge determine to a large extent the future prospects of humanity. «Moreover, the human capital theory has indeed

helped explain economic growth and the formation of individual remuneration». The main idea of the theory of human

capital developed by Gary Becker 3 is that learning and knowledge for an individual as for the community are sources of

wealth and thus are rewarding. From the perspective of the community, investment in human capital is also a profitable

investment, knowing that education provides higher social gains than private gains4. Samuelson and Nordau5add that it

constitutes the "stock of technical knowledge and skills characterizing the labouring standards of a nation.

This vision continues to be recommended today. For the OECD, «Investment in human capital works for economic

prosperity, employment and social cohesion by promoting the well-being of populations »6.

Indeed, one of the major challenges of the Tunisian economy is to solve the problem of unemployment.

The unemployment rate in Tunisia remains one of the highest in the MENA region. Being excessive before the Tunisian

revolution (almost 14% in 2010), the unemployment rate accuses a sustainable increase (around 18% in 2013).

A feature of unemployment in Tunisia is the unemployment of graduates. The unemployment rate for this

category of young people is growing dramatically in recent years from 16.9% in 2006 to 21.9% in 2009. This rate has

increased since the Tunisian revolution. It is expected to grow next years if arrangements to insert young graduates into the

labour market are not taken.

To understand what determines this type of unemployment and its social and political underground, this paper is

based on the diagnosis of the situation that led to the revolution of January 14th, 2011. The unemployment of graduates was

considered as being the main reason behind the Tunisian revolution.

The notable rising of Higher Education and insufficient creation of adequate jobs are the main causes of the

exponential rise in the unemployment rate of graduates. This situation has forced many students to further their studies,

minimizing paradoxically their chances of being recruited, because of their over-qualification.

With the exception of a few specialties such as medicine, computing, telecommunications and architecture, where

opportunities are available especially abroad, other types of graduates endure more or less difficulty in the labor market.

This causes migration through a “brain drain” to developed countries, leaving a shortage of qualified workers that could

increase labor productivity and enhance economic growth.

The solutions to overcome this crisis of unemployment are very difficult, requiring enormous resources over

several years. These solutions involve several areas; higher education, vocational training, investment and regional

integration. They also require participatory planning, management and sustainable governance promotion.

1Smith A. (1776), « An inquiry into the nature and causes of the wealth of nations”, (1 ed). London: W.Strahan. Retrieved 2013, volume2, via Google Books: http://books.google.tn/books?id=AwgHAAAAQAAJ&printsec=frontcover&hl=fr&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false 2 Schultz T. W. (1961), « Investment in Human Capital », the American Economic Review 51(1), 1-17. 3 Becker G. (1975), « Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education», 2nded, NBER. 4This positive externality justifies, moreover, for some, interventions made by the State in support of the educational system. 5Samuelson P.A. and Nordhaus W.D. (2005), « Economie », 18e édition, Economica. 6 www.oecd.org

Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience 3

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Tounder stand the determinants of this type of unemployment, the paper is basedonthediagnosis of the situation

before the Tunisian revolution.

An examination of the unemployment vicious circle process is done through a synthesis of key findings, of survey

conducted in 2009 by the national institute of statistics. This diagnosis was supported by an econometric model linking the

unemployment indicator and key indicators of educational attainment and regional localization.

2. UNEMPLOYMENT SITUATION BEFORE THE TUNISIAN REVOL UTION

An examination of the employment situation before the Tunisian revolution (January 2011) helps to the

developing and the understanding of the socio-economic and environmental conditions in the education policy as well as

the employment market dynamics, to identify the bases of the crisis.

With over half a million unemployed in a population of 10.5 million strong youths§- the age group 15-29 years

accounts for 29% of the total population - Tunisia is experiencing a serious unemployment problem. Almost one third of

the unemployed have received higher education qualifications and two thirds of the additional job demands emanate from

this category of job seekers. Tunisia has experienced for some years a real breakdown of the “social elevator”, a sign of the

failure of economic and social policy in recent years.

This alarming situation has been reached despite the fact that Tunisia has given special and constant attention to

the problem of unemployment since the early sixties.7 During the last decade, fighting against unemployment and

employment promoting, especially for young people, were among the major projects of the development policy of the

Tunisian government, with emphasis on developing skills.

The economic literature, including human capital literature, since its first version with Schultz (1960)8 and

Becker (1964)9 to the most recent developments, reinforces the idea that employment rates increase with the level of

training. This is mainly due to the fact that more educated individuals who have invested more in human capital seek to

enhance their investment.

According to Fraisse-D'Olimpio (2009)10, human capital determines various areas of daily life of individuals

which indicates that this notion is now at the heart of public policies in developed countries and increasing in developing

countries. The public choice in particular is moving towards improving the education and training of populations

throughout the life cycle but also the degree of social integration of societies, with emphasis on the role of qualifications in

improving growth.

However, these assumptions known since the "Chicago school" literature and then with the followers of the theory

of endogenous growth have been increasingly subject to criticism particularly in situations characterized by high rates of

unemployment, youth issues and especially graduates.

Spence (1973)11, with signal theory, attempted to explain why people are studying. In fact, youths not attending

school to acquire skills but to obtain a degree will serve as a signal to future employers. Moreover, according to the filter

7Ben Sedrine H. (2009), “Etudes de cas : La Tunisie”, In B. Labraki (Ed) Enseignement supérieur et marché du travail dans le monde arabe, Collection électronique de l’IFOP. 8Schultz T. W. (1960), "Capital Formation by Education," Journal of Political Economy, University of Chicago Press, vol. 68, pages 571 9Becker G. (1964), Human Capital: A Theoretical and Empirical Analysis; 1st ed. New York: Columbia University Press for the National Bureau of Economic Research. 10Fraisse-D’Olimpo S. (2009), Les fondements théoriques du concept du capital humainhttp://ses.ens-lyon.fr/1242027840910/0/fiche___article/&RH=05. 11Spence, A. Michael (1973), "Job Market Signaling" The Quarterly Journal of Economics, MIT Press, vol. 87(3), pages 355-74, August.

4 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

theory, Arrow (1973)12, individuals that are hired are those who show characteristics with the lowesttraining costs, not the

most productive. The Diploma also acts as the indicator of the ability to be formed.

Balsan (2000)13 points out that the human capital theory was developed in a period of full employment, where

individuals make their choices without uncertainty about the possibility to hold employment after training. Today, in

economies experiencing high rates of unemployment among graduates of higher education, the assumption of the absence

of the influence of unemployment on the choice of investment in education is difficult to sustain.

The strategy requires investment in training, taking into account the risk of unemployment, Guillon (2010)14.

Thus, as Giret (2000)15 stated, investment in human capital depends not only on the expected wage, but also the risk of

unemployment.

The extension studies can then be explained by lower opportunity costs. In this sense, Kodde (1989)16 shows that

the prospect of unemployment risk leads the individual to invest more in education, to improve employability, to reduce

the risk of unemployment.

Lepage (1999)17 points out that the relationship between human capital and unemployment remains complex.

On the one hand, the underemployment encourages the unemployed to invest more in human capital. On the other hand,

rising unemployment destroyed part of human capital, following its degradation. Human capital may depreciate if the skills

are not maintained in good condition through regular use. From this point of view, the long-term unemployment and youth

unemployment can lead to deterioration of knowledge and skills (Fraisse-D'Olimpio 2009)18.

In Tunisia, despite efforts in skills development (training, retraining, comprehensive studies) in the period before

January 2011, graduate unemployment has reached a very high rate (13.3% in 2009) and was the main demand and even

the root of the social revolution of January 2011. It is therefore most important, nowadays, to revise the policy guidelines

for investment in human capital in Tunisia, taking into account the problem of unemployment among graduates of

highereducation. This requires a preliminary investigation of this issue very closely, allowing, after a diagnosis of the

situation, the identification of the characteristics and determinants of this type of unemployment. This also allows the

assessment of the impact of training and qualification on employment opportunities process. That is the purpose of our

work. Thus, Section 2 describes the evolution of unemployment since the 1960s to highlight the emerging problem of

unemployment among graduates of higher education from the early 2000s. Section 3 focuses on two main determinants of

graduate unemployment in Tunisia: i) the rapid increase in student numbers, and ii) the insufficient job creation by the

national economy in order to better understand problems of graduate unemployment; Section 4 summarizes the main

results of surveys conducted in 2005 and 2007 on graduates of 2004. This diagnosis is supported in Section 5 by an

econometric model linking the unemployment indicator and key indicators of qualification. Finally, section 6 concludes.

12Arrow, K. J. (1973), “Higher Education as Filter” Journal of Public Economics. Vol 2, pp 193-216.

13BalsanD. (2000), « Évaluation des rendements éducatifs dans un contexte de chômage », Économie. Publique, vol. 1, n° 5. 14 Guillon S. (2010),Le chômage des diplômés de l'enseignement supérieur à la Réunion - Méthodes plurielles, trajectoires hétérogènes; le Harmattan, Paris. 15Giret J. F., (2000), pour une économie de l’insertion professionnelle des jeunes, CNRS Editions, Paris. 16Kodde D. A. (1989), “Unemployment Expectations and Human Capital Formation”, European Economic Review, 36, 1645-1660. 17 Lepage J.-M., (1999), Croissance et multiplicateurs sectoriels, Economica, Paris. 18 Op. cit.

Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience 5

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3. THE EVOLUTION OF UNEMPLOYMENT IN TUNISIA

As shown in Table 1, unemployment in Tunisia is a structural problem. Since the 1960s, it has never dropped

below 12%, regardless of calculation methods used and criticisms that we can make on them.

Table 1: Evolution of Unemployment Rates between 1966 and 2013

Year 1966 1975 1984 1989 1994 1999 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2nd

Quarter 2011

4th Quarter 2011

Male 15,3 13,4 13,7 13,9 15 15,6 15;5 14,9 14,5 13,8 12,9 11,5 11.3 11.2 11.3 10.9 15.0 15,4 Female 13,4 10,6 11 20,9 17,2 16,3 16,1 15,3 16 15,8 16,7 15,1 15.3 15.9 18.8 18.9 27.4 28,2

Total 15,2 12,9 13,1 15,3 15,6 15,8 15,6 15 14,9 14,3 13,9 12,5 12.4 12.4 13.3 13.0 18.3 18.9

Source: National Institution of Statistics

This rate stays at almost the same level after the revolution:

Table 2

Year 1st

Quarter 2012

2nd Quarter

2012

3rd Quarter

2012

4th Quarter

2012

1st Quarter

2013

2nd Quarter

2013

3rd

Quarter 2013

Male 14.9 14.6 14,1 13,9 13,9 13,3 13,1 Female 26.6 25.6 24,9 24,2 23,3 23,0 22,5

Total 18.1 17.6 17,0 16,7 16,5 15,9 15,7

Table 3: Variation in the Overall Rate of Activity

Period May 2005 May 2006 May 2007 May 2008 May 2009 May 2010 May 2011 Male 67,9 67,3 67,7 68,0 68,7 69,5 70,1 Female 23,6 24,4 24,5 24,7 24,8 24,8 24,9 Overall 45,5 45,6 45,8 46,2 46,5 46,9 47,2

Source: National Institution of Statistics

It follows from the data above that the rate of female unemployment since the late 1980s exceeds the male one.

Indeed, if the variation average rate of male labor force has been 2.3% per year between 1966 and 2007, it has been 6.4%

for the female labor force. This reflects the growing entry of women in the labor market, even if the female labor force is

now less than 30% of the total workforce. In addition, it should be noted that there are regional disparities in

unemployment. For example if the unemployment rate nationally is 14.1% in 2007, the South-West and North West have

higher rates of unemployment (around 20%). They are followed by the Midwest and Southeast who have rates above the

overall average. Regions least affected are located on the axis coast (North-East, Central and Eastern District of Tunis) as

shown in Table 4.

Table 4: Unemployment Rates by Region 2007 (%)

Region Unemployment Rates South West 20 North West 19,6 South East 17,6 Central West 14,3 Tunis District 13,9 Central East 11,7 North East 10,3

Source: National Institution of Statistiques (2008), from Boubakri (2010)

6 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

According to Boubakri (2010)19, more than half of the governorates have rates above the average. Some inland

areas even have unemployment rates almost twice the national average:

• 24 to 26%: Tozeur, Jendouba and Siliana.

• 20 to 22.5%: Kasserine, Gabes, Gafsa

• 14 to 19%: Tataouine, Mahdia, Manouba, Le Kef, Ben Arous, KebiliMedenine.

• 4 Governorates have a Very Moderate Rate (Less than 10%): Zaghouan, Monastir, Nabeul and SidiBouzid.

By observing the data on the structure of unemployment by educational level, it is obvious that such a structure

has changed dramatically over the past thirty years. Table 4 shows that unemployment among university graduates was

virtually non-existent in the mid-1970s and high school graduates level was quite low. This problem has been amplified

over the last decade.

Table 5: Changes in the Structure of Unemployment by Educational Level (in %)

Year 1975 1984 1994 1999 2001 2002 2003 2005 2006 2007 2008 2009 2010 None 41,5 34,4 24,4 13,1 9,5 10 9,2 6,2 6,6 4,2 3,6 4,8 4,5 Primary 47,8 45,7 47,8 48 45,2 43,8 43,3 42,0 38,3 33,8 30,3 27,0 24,1 Secondary 10,5 19,2 26,2 34 38,2 37,7 37,4 37,4 36,1 39,8 40,1 39,7 39,4 Higher 0,2 0,7 1,6 4,7 7,1 8,5 10,1 14,4 17,0 18,7 20,6 23,4 23,3

Source: National Institution of Statistics

The structure of the additional demand has changed radically in a few years (from 23.1% of global additional

demand jobs in 2001 to 55.2% in 2007and close to 60% now come from higher education). Applications rose from 74,100

in 2001 to 91200 in 2006 and 87100 in 2007, 88300 in 2008 and 85000 in 2009 (BCT. 2009), that leads to a cumulative

volume of 738,700 jobs in 9 years.

Table 6: Evolution of the Structure of the Additional Demand of Employment by Education Level (%)

Year 2001 2002 2003 2004 2005 2006 2007 Highest 23,1 32,6 43,1 51,5 54,1 47,3 55,2 Others 76,9 67,4 56,9 48,5 45,9 52,7 44,8 Total 100 100 100 100 100 100 100

Source: National Institution of Statistics

It is obvious that one of the specificities of unemployment in Tunisia is the unemployment of university graduates.

In fact, in spite of the efforts that were made to insert young graduates into the labor market, the unemployment rate for

this category of young people increased dramatically (from 4.7% in 1999 to 32% in 2010). With only 89.3% of the

additional demand satisfied over the period, this rate is continuously growing up.

Table 7: Annual Evolution of Job Creations

Year 2006 2007 2008 2009 2010 2nd Quarter

2011 4rth Quarter

2011 Male 41.0 60.7 57.5 54.6 66.4 -63.7 10.9 Female 35.4 19.5 12.8 -11.1 12.1 -73.9 20.0 Total 76.4 80.2 70.3 43.5 78.5 -137.6 30.9

19Boubakri H. (2010), « Tunisie : Migration, marché du travail et développement », OIT Institut International d’Etudes Sociales, Genève.

Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience 7

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1st Quarter

2012

2nd Quarter

2012

3rd Quarter

2012

4th Quarter

2012

1st Quarter

2013

2nd Quarter 2013

3rd Quarter 2013

Male 18.9 11.0 9.4 6.7 15.0 16.4 12.9 Female 17.5 13.5 1.9 6.2 10.4 17.7 13.0

Total 36.4 24.5 11.3 12.9 25.4 34.1 25.9

The quest for diplomas was a way to avoid unemployment and this affected illiterate or primary level populations.

The trend was reversed with the proportion of unemployed high school and higher education more than 60% of the

population seeking employment.

With almost one in three unemployed who is graduated from higher education, the educational “social elevator” is

“broken” leading to the rolling back of the economic and social policies adopted and a rethinking of the effectiveness of

the underground literature.

These conditions resulted with the rise of a feeling of injustice, of exclusion and frustration; especially that,

according to the INS statistics, a strong disequilibrium exists in the regional distribution of young graduated unemployed

between cities and poor Tunisian regions (more than 1/5 at Kibili, in the south of Tunisia and only 1/10 in the capital).

4. THE SOCIOECONOMIC DETERMINANTS OF UNEMPLOYMENT A MONG UNIVERSITY

GRADUATES

The socioeconomic determinants of unemployment among graduates of higher education are located at both the

supply and demand in the labor market.

From the supply side, we see a continued increase in the flow of graduates. From the demand side, we note an

insufficient adapted job creation by different economic structures.

4.1 The Massive Access to Higher Education

The dynamics of unemployment among graduates is partly explained by the arrival of a huge number of new

graduates in the labor market. The workforce has changed from 13,000 students in 1975-1976 to 40,000 students in

1986-1987 to 137,000 in 1997-1998 to 357,472 during 2009-2010. Student numbers have been multiplied by 27 between

1976 and 2010 with an average annual growth rate of 10.2%. The average global graduation rate for all fields of Bachelor

degree is 89.1% (excluding Master and Doctorate cycles).

With regard to the huge number of graduates, the average annual growth rate is 11.1% over the period 2001-2009,

from 24,500 in 2001 to 59,300 in 2009. According to Ben Sedrine (2009)20, the massive flow direction of academic

achievement of basic education to secondary school and then to higher education has produced the massive increase of this

last cycle. This has been encouraged through free public education since the Tunisian independence (in 1956) and

automatic access to the university for any pupil who obtains his final secondary school graduation. In the context of low

growth in employing the highly skilled, strong growth in the number of graduates is an issue of employability.

This phenomenon could have worse without the birth control policy implemented since the 1960s, generating population

growth only among the lowest classes.

20Op. cit.

8 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

In addition, Ben Sedrine (2009) adds that this high skilled unemployment expansion can be explained by the slow

implementation of the reform of vocational training. Indeed, the vocational training system, which targets the qualification

of the workforce and middle management staff, has undergone, since the mid-ninety, a comprehensive reform to bring it

into harmony with requirements of the new economic context of upgrading the economy, due to the introduction of a free

trade zone with the European Union. This reform, based on flying training vocational qualifications needs of the economy

and work and training with the company, began to produce positive effects on quality. Furthermore, the search for

appropriate modalities of articulation between vocational and academic education, on a matching basis, as was predicted in

the reforms of 1991 and 2002, has been slow to materialize because of the absence of a system of information and advice

in schools which would have facilitated the voluntary guidance of young people to vocational tracks at the same time, the

same way and under the same conditions as the school types.

4.2 Insufficient Job Creations

During the period (2001-2009), an average of 73,300 jobs had been created every year to reach a total of 659,700

jobs for the whole period. Therefore, only 89.3% of the additional demand over the period had been satisfied, so as to

increase the overall unemployment rate.

However, the job creations tend to reflect the needs of jobseekers from secondary school better than those from

higher education. This reflects one of the fundamental weaknesses of the Tunisian labour market, namely a persistent

mismatch, even increasing, between supply and demand on the labour market, Boubakri (2010)21. Indeed, over the period

2001-2007, created jobs for high school graduates cover 71.7% of the additional demand in this category. However, the

creation of jobs for secondary graduates accounted for 112% of additional demand in this category. While over the same

period, the additional demand for university graduates averaged 41% of global demands, jobs created on average

accounted for 34.2%.

The evolution of the unemployment rate by educational level (table 5) shows a significant and negative correlation

between uneducated people and the university's level of education.

The Tunisian economy is facing a very specific problem; this economy does not create enough jobs, especially for

university graduates. A loop movement appears installing the country in a "vicious circle of unemployment". In what

follows we will attempt to highlight the underlying mechanisms.

The unemployment “vicious circle”

In a very schematic way, basing ourselves on academic performance and the sphere of studies, we can divide

graduates into three categories: the elite, the good and the worse.

Overqualified for the jobs offered by the country and sought abroad. The elite (consisting mainly of engineers,

computer scientists, doctors and architects) leave the country to go mainly to Europe, Canada and, in a smaller percentage,

to the United States.

In the last years an acceleration of the migration of highly qualified people has been noted. Indeed, according to

OECD estimates, 15% to 18% of Tunisian migrants living in OECD countries have a university level: teachers and

researchers, engineers, computer scientists, doctors and pharmacists.

21Op. Cit.

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The second category is recruited on the Tunisian market in small part. Those who do not have the chance to find a

job or cannot find a job that meets their aspirations pursuing a Master or PhD. Being more skilled after a doctoral cycle;

they are hardly absorbed by the Tunisian market and remain unemployed.

The third category (not having good reports and not having obtained the required qualifications) persists in a

situation of long-term unemployment. Their knowledge deteriorates which makes their chance of employment even

weaker.

Recent economic literature on human capital and unemployment risk better depicts these situations: «On the one

hand, underemployment encourages the unemployed to invest more in their human capital. On the other hand, the

extension of the duration of unemployment destroys a portion of the capital gains and leads to its deterioration »,

Lepage (1999)22.

«The long-term unemployment and youth unemployment lead to the deterioration of knowledge and

qualifications », Fraisse-D'Olimpio (2009)23…etc.

In consequence, we can see that in all cases there is a loss of human capital, an accentuation of graduate

unemployment and hence a « no return on investment » likely to generate economic growth. This reflects, on the one hand,

the failure of educational policy and the academic orientation (nearly 20% of graduates are oriented towards qualifications

without opportunities in the labor market). And secondly the failure of the training policy and employability for graduates

who give advantage essentially to the qualified in the fields the least affected by unemployment (engineers in IT,

telecommunications or electronics).

Such a situation reflects the lack of a clear vision and long-term educational and economic policies which had

prevailed before the revolution.

To face this inadequacy of the labour market for higher education graduates, there are three solutions which could

reduce the evolution of the unemployment rate for this category of job seekers:

• Adjustment of the economy to the supply of existing skills, often inadequate or overqualified.

• Reform of the University and offering additional training to better meet the needs of the labor market.

• Migration of young graduates to Europe and North America in search of jobs suitable to their qualifications.

Certainly, during the last decade, these three movements existed but without much success. The unemployment

rate for graduates has continued its dramatic rise. During the period 1994-2004, two types of

evolution were recorded (Boubakri 2010):

• Strengthening the dynamism of both service sector (including commerce, communication and transport) and

building, construction and manufacturing industries. In terms of job creation, the service sector grew by 3.6% on

average per year while construction and manufacturing industries have evolved more timid rates

(2 to 2.2% per year).

22H. Lepage (1999), Op. Cit. 23Fraisse-D'Olimpio (2009), Op. Cit.

10 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

• A lighter weight of other sectors such as agriculture, fisheries, mining and energy, where the creations of jobs

actually fell between 1994 and 2004.

It is obvious that the economic policies already adopted have been insufficient to absorb the unemployed

university graduates. These policies are mainly focused on attracting foreign direct investment often low value-added and

thus requiring fewer skills. Accordingly, the program of upgrading and modernizing of the industry at this level has not

achieved the desired results.

Similarly, the continuation of the Bologna process in higher education since 2006 has not improved the situation.

As a result, many students want to continue their studies either in Tunisia or abroad to escape unemployment, which can

only aggravate the mismatch between supply and demand in the Tunisian labour market.

Reforms should involve the economic and the academia sphere. However, before any recommendation, each

sphere must undergo a profound investigation. Thus, close monitoring of graduates in their search for work seems like a

much-needed exercise that will identify further characteristics and determinants of graduate unemployment in Tunisia.

The National Observatory for Employment and Qualifications (ONEQ)24, organ of the Ministry of Employment,

initiated such an investigation. The Centre conducted a survey of 2004 graduates. This study (ONEQ 2009) intervenes in

effect in a particular context of the job market. Young graduates have a relatively higher risk of unemployment,

particularly long-term unemployment. This reflects increased higher levels staffing graduates, who outnumber the jobs

generated by the economy for this category. Mismatch between the needs of the economy and the skilled outputs of higher

education institutions. These imbalances are reflected in the lengthening of waiting time before a first job and an

increasingly delayed stabilization in employment. Imbalances that should be better analyzed with appropriate statistical

surveys. Unemployment high rate of graduates can be explained by a spatial mismatch, resulting from the poor connection

between high rate unemployment regions and big employment centers. The analysis of graduates unemployment map can

reveal this spatial mismatch phenomenon.

5. SPATIAL UNEMPLOYMENT CLUSTERS DETECTION

Tunisia the study area of this analysis is located between and it comprises 24 governorats and 264 delegations. It’s

area is about 163000 km2. The National survey of population and employment conducted in 2009 by the National intitute

of Statistics (INS) collects data on unemployed and total active by govarnorats. We use this data to explore the spatial

distrubution of unemployment graduates indicators by using the spatial scan statistic test.

5.1 Scan Statistic Detection

The Scan test is a test developped by (Kulldorf and Nagarwalla, 1995) Usually applied on raw data to detect local

clusters with high or low rates of graduate umemployement the advantage of this test is that it does not depend on the

specification of weights matrix. The null hypothesis of this test is the absence of cluster in region Z, and the alternative

hypothesis is the existence of cluster in region Z.

The most likely cluster and the second most likely cluster are detect by the comparison of the number of cases

obtained according to the null hypothesis and the observed number of cases inside scanning windows. For each scanning

24 ONEQ (2009), Dynamique de l’emploi et adéquation de la formation parmi les diplômés universitaires. Document conjoint du Ministère de l’Emploi et

de l’Insertion Professionnelle des Jeunes et de la Banque mondiale. Juillet.

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window the likelihood ratio is:

Where cin are the observed cases inside a scan window, C is the total amount of cases, nin is the total amount of

cases and controls inside a scan window and N are all cases and controls of the data set. I is an indicator function equal to 1

if > and equal to 0 otherwise. The null hypothesis testing is performed by monte carlo testing process with

999 replication.

5. 2 RESULTS

The results of the cluster detection test are presented in figure 1 and figure 2:

Figure 1: Map of Most Likely Clusters with High Unemployment Rates

The analysis of the spatial repartition of detected clusters using the scan statistic test presented in figure 1 show

that the hypothesis of random spatial distribution of unemployment graduates indicator is rejected. The governorates of

Gafsa, Tozeur and Kebili located in the southern west part of the country concentrate the tree most likely clusters.

They contain about 25% of the unemployed graduates. The forth most likely clusters are located in governorates of Beja

and Jandouba located in the northern west part of Tunisia, they concentrate more than 13% of the unemployed graduates.

Coastal governorates that include Tunis the capital, Sfax and Sousse the three biggest governorates of the country have the

lowest incidence rates of unemployment. The following map shows the local unemployment risk by governorates:

12 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

Figure 2: Map of Local Incidence Rate Unemployment among Graduates

The analysis of the local risk map indicates that the unemployment risk inside Gafsa the most likely cluster is

twice higher than unemployment risk outside. The unemployment risk inside southern governorates of Tozeur and kebeli is

one and half higher than risk outside. These results indicate that highest rates of unemployment rates are concentrated in

southern west and northern west governorates of the country with probability to be jobless twice higher than other

governorates.

6. ECONOMETRIC ANALYSIS

The econometric analyze is done to confirm the previous results. We define first the econometric specification and

then the results of the estimation.

To follow the problematic of this study we specify an econometric model that explains the probability of being in

a situation of unemployment by different determinants.

6.1 The Econometric Model

The impact of regional and educational attainment variables on employment chance is analyzed by using a logit

binary model:

(1)

Where and

(2)

yi is considered as a realization of a random variable Yi that can take the values one and zero with probabilities

piand (1-pi) respectively.

(3)

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The impact of educational attainment is measured by a binary variable that takes 1 for high graduate level and 0

otherwise.

6. 2 Estimation and Results

We consider for the econometric investigation the database of national survey on population and employment

conducted by the INS in 200925. The survey, collect data on the occupation status, following the ILO definition

(employed or unemployed), the socio-economic characteristics and other variables of about 221176 person.

Binary variables are created to detect regional and gender effects. Descriptive statistics on used variables are presented in

the following table:

Table 8: Descriptive Statistics

Variable N Minimum Maximum Mean Std. Deviation AGE 221176 15,00 59,00 37,2289 12,60787 GRAND_TUNIS 221176 ,00 1,00 ,3076 ,46152 NORTH_WEST 221176 ,00 1,00 ,1668 ,37276 EAST_CENTER 221176 ,00 1,00 ,1761 ,38094 WEST_CENTER 221176 ,00 1,00 ,1275 ,33353 SOUTH_EAST 221176 ,00 1,00 ,1130 ,31664 SOUTH_OUEST 221176 ,00 1,00 ,1089 ,31154 GRADUATE_DUMMY 221176 ,00 1,00 ,1552 ,36211 ACTIF_DUMMY 221176 ,00 1,00 ,8616 ,34531 Gender 221176 ,00 1,00 ,7346 ,44154 Valid N (listwise) 221176

This table shows that the mean age is 37 year, 30% of active persons are located in the region of Grand Tunis. 16,

68% are located in North Western regions of the country. Graduate active persons represent 15, 52% of the total active

persons. The global unemployment rate is about 13, 4%. Results obtained by logistic regression estimation, after iterations,

are resumed in the following table:

Table 9: Logit Model Estimation Results

Number of obs = 221176 LR chi2(8) = 28997.40 Prob> chi2 = 0.0000

Pseudo R2 = 0.1631

Log likelihood = -74419.705 Dependant : ACTIF_DUMMY Coefficients. Std. Err. z P>z [95% Confidence Interval] SEX .3753318 .0141006 26.62 0.000 .3476951. 4029686 AGE .1000143 .0007899 126.62 0.000 .0984662. 1015625 NORTH_WEST -.5760506 .0201044 -28.65 0.000 -.6154544 -.5366467 EAST_CENTER .0254765 .0206074 1.24 0.216 -.0149132. 0658662 WEST_CENTER -.1918371 .022928 -8.37 0.000 -.2367753-. 146899 SOUTH_EAST -.3115417 .0225852 -13.79 0.000 -.3558079-. 2672755 SOUTH_OUEST -.5296764 .0221315 -23.93 0.000 -.5730533-. 4862995 GRADUATE_DUMMY -.8305654 .0159019 -52.23 0.000 -.8617326-. 7993983 CONSTANT -1.280402 .0273037 -46.89 0.000 -1.333916 -1.226887

25

This survey is done one year before the Tunisian revolution of 2010-2011. The main trigger of this revolution is the high rates of graduate unemployment in interior regions.

14 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

Table 10: Classification Results

True Classified D ~D Total

+ 189690 29309 218999 - 878 1299 2177

Total 190568 30608 221176 Correctly Classified 86.35%

The reference person in this model is an unemployed female under graduated from the region of Grand Tunis.

The model is validated on both global and individual valuation (given the results of the LR test and the student

test of individual signification).

Global valuation

The null hypothesis of no overall fit of the model is rejected, KHI2 statistic used is significant with p=0,000).

Individual valuation

• The different explicative variables are significant (at 1% risk of error), except the Center East variable which is

not significant with p=0,216.

• The global classification rate is high enough (86, 35%). This value is especially due to a good prediction of the

employment probabilities.

6. 3 Comments

• In the logit model, coefficients associated to binary dependant and explanatory variables can be interpreted easily

by odds ratios (O.R) (table in annex 1). The odds ratio associated to xi is equal to . This odd ratio is the

probability that Y is equal to 1 when X is equal to 1 compared to the probability that Y is 1 when X is 0. If O.R>1

this mean that the probability of Y to be equal to 1 is more likely in situation with X is equal to 1 than otherwise.

• The employment probability increases with male gender, qualified man have (comparing to woman) 1.45 more

chance to be in an employment situation (with a probability of 0.375%)26.

• For graduated persons, the chances to get employed decreases largely (the O.R of 0.43 indicates that the

employment probability of a graduate person is half as likely a non graduate person). This result shows that there

is a negative relationship between the educational level and the employment chance.

• The econometric results of the logit model confirm the regional disparities presented in the section 4.

The employment probabilities decrease in northern west and southern west regions comparing to Grand Tunis.

• The O.R of the age variable indicates that young persons are less likely to be employed.

• These results show that in Tunisian regions the unemployment probability increase with distance from Grand

Tunis and Eastern center regions, and with high levels of educational attainment.

26These probabilities pi are deducted from the odds ratio values (OR) by: See Hosmer D.W. and S. Lemeshow (1989) for more details.

Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience 15

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7. CONCLUSIONS

This work has tried to show the existence of a mismatch between supply and demand in the Tunisian labour

market, affecting particularly higher education graduates. It is a specificity of unemployment in the Tunisian economy over

the past ten years. “Social elevator” is broken, and assumptions of human capital theory are, in the case of the

unemployment of graduates, undetermined.

The mass increase of higher education and insufficient creation of adequate jobs are the main causes of the

exponential rise in the unemployment rate of graduates. This situation has forced many students to further their studies,

minimizing paradoxically their chances of being recruited, due to their over qualification.With the exception of a few

specialties such as medicine, computing, telecommunications and architecture, where opportunities are available especially

abroad, other types of graduates meet more or less difficulties to find a job.

Since January 14 (date of the Tunisian revolution), the country lives an unstable situation with a growth rate near

zero and an unemployment rate predicted to reach 17% at the end of 2011.

Government efforts should be accentuated and targeted.

Solutions to overcome this unemployment plight are very difficult and require enormous resources over several

years.

On the one hand, we must continue the LMD (license, master doctorate) reform, along with reviewing some

choices. The reform now requires additional financial and human resources that are missing from the

University of Tunisia. Meanwhile, it is necessary to enhance the system of vocational training with additional resources.

This would reduce the number of students and ensure better employability of young early leavers and undergraduates.

On the other hand, the Tunisian economy has to create more skilled jobs, with more domestic and foreign

investment with high added value, particularly in disadvantaged areas. This is conditioned by the consolidation of regional

integration in Maghreb and with Mediterranean countries.

Some practical recommendations fall out of this study:

Creating retention structures on over-qualification by investing in areas that create added value (ICT).

Review of foreign direct investment, until now it the turns towards areas of unskilled labour and furthermore,

non-creators of plus-value.

Revision of the university system at both program and evaluation levels so that it adds specialties that should meet

a goal of employability and performance. Various international surveys on education in Tunisia show deterioration in the

quality of education27.

Revision of university orientation system based on an assessment of carrying capacities and needs of the

economy.

Develop research structures to serve the environment and business.

Promote training and professional integration policies.

27The new Programme for International Student Assessment of the OECD.www.oecd.org/pisa/

16 Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

Impact Factor (JCC): 4.9135 Index Copernicus Value (ICV): 3.0

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trajectoires hétérogènes; le Harmattan, Paris.

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conjoint du Ministèrere de l’Emploi et de l’Insertion Professionnelle des Jeunes et de la Banque mondiale. Juillet.

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Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience 17

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APPENDICES

Table 11

Dependant : ACTIF_DUMMY Odds Ratio Std. Err z P>z

[95% Conf. Interval]

SEX 1.455474 .0205231 26.62 0.000 1.415801 1.49626

AGE 1.105187 .000873 126.62 0.000 1.103477 1.106899 NORTH_WEST .562114 .0113009 -28.65 0.000 .5403953. 5847056 EAST_CENTER 1.025804 .0211391 1.24 0.216 .9851974 1.068084 WEST_CENTER .8254413 .0189258 -8.37 0.000 .7891686. 8633812 SOUTH_EAST .7323171 .0165395 -13.79 0.000 .7006072. 7654622 SOUTH_OUEST .5887955 .0130309 -23.93 0.000 .5638014. 6148976 GRADUATES_DUMMY .4358028 .0069301 -52.23 0.000 .4224296. 4495994

CONSTANT .2779256 .0075884 -46.89 0.000 .2634436. 2932038