SPATIAL DETERMINANTS OF THE

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.

SPATIAL DETERMINANTS OF THE UNEMPLOYEMENT OF GRDADUATES: THE TUNISIAN EXPERIENCE HANÈNE BEN OUADA JAMOUSSI1, FOUED BEN SAID2 & MAHER GASSAB3 1

Department of Quantitative Methods, Business School, Manouba University, Larma, Fseg Tunis, El Manar University, Manouba, Tunis, Tunisia 2

Department of Quantitative Methods, Business School, Manouba University, L Larequad, Fseg Tunis El Manar University, Manouba, Tunis, Tunisia

3

Department 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.

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Hanène Ben Ouada Jamoussi, Foued Ben Said & Maher Gassab

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.

1 Smith 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. 4 This positive externality justifies, moreover, for some, interventions made by the State in support of the educational system. 5 Samuelson P.A. and Nordhaus W.D. (2005), « Economie », 18e édition, Economica. 6 www.oecd.org

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Spatial Determinants of the Unemployement of Grdaduates: The Tunisian Experience

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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 REVOLUTION 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 7

Ben 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. 8 Schultz T. W. (1960), "Capital Formation by Education," Journal of Political Economy, University of Chicago Press, vol. 68, pages 571 9 Becker G. (1964), Human Capital: A Theoretical and Empirical Analysis; 1st ed. New York: Columbia University Press for the National Bureau of Economic Research. 10 Fraisse-D’Olimpo S. (2009), Les fondements théoriques du concept du capital humainhttp://ses.ens-lyon.fr/1242027840910/0/fiche___article/&RH=05. 11 Spence, A. Michael (1973), "Job Market Signaling" The Quarterly Journal of Economics, MIT Press, vol. 87(3), pages 355-74, August.

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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.

12

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

13

BalsanD. (2000), « Évaluation des rendements éducatifs dans un contexte de chômage », Économie. Publique, vol. 1, n° 5. Guillon S. (2010),Le chômage des diplômés de l'enseignement supérieur à la Réunion - Méthodes plurielles, trajectoires hétérogènes; le Harmattan, Paris. 15 Giret J. F., (2000), pour une économie de l’insertion professionnelle des jeunes, CNRS Editions, Paris. 16 Kodde 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. 14



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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 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 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 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 Source: National Institution of Statistics

2nd Quarte r 2011 15.0 27.4 18.3

4th

Quarte r 2011 15,4 28,2 18.9

This rate stays at almost the same level after the revolution: Table 2 Year Male Female Total

1st Quarter 2012 14.9 26.6 18.1

nd

2 Quarter 2012 14.6 25.6 17.6

3rd

4th

Quarter 2012 14,1 24,9 17,0

Quarter 2012 13,9 24,2 16,7

1st Quarter 2013 13,9 23,3 16,5

2nd Quarter 2013 13,3 23,0 15,9

3rd

Quarter 2013 13,1 22,5 15,7

Table 3: Variation in the Overall Rate of Activity Period May 2005 May 2006 May 2007 Male 67,9 67,3 67,7 Female 23,6 24,4 24,5 Overall 45,5 45,6 45,8 Source: National Institution of Statistics

May 2008 68,0 24,7 46,2

May 2009 68,7 24,8 46,5

May 2010 69,5 24,8 46,9

May 2011 70,1 24,9 47,2

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)

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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 None 41,5 34,4 24,4 13,1 Primary 47,8 45,7 47,8 48 Secondary 10,5 19,2 26,2 34 Higher 0,2 0,7 1,6 4,7 Source: National Institution of Statistics

2001 9,5 45,2 38,2 7,1

2002 10 43,8 37,7 8,5

2003 9,2 43,3 37,4 10,1

2005 6,2 42,0 37,4 14,4

2006 6,6 38,3 36,1 17,0

2007 4,2 33,8 39,8 18,7

2008 3,6 30,3 40,1 20,6

2009 4,8 27,0 39,7 23,4

2010 4,5 24,1 39,4 23,3

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 Highest 23,1 32,6 43,1 51,5 Others 76,9 67,4 56,9 48,5 Total 100 100 100 100 Source: National Institution of Statistics

2005 54,1 45,9 100

2006 47,3 52,7 100

2007 55,2 44,8 100

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

19

Year

2006

2007

2008

2009

2010

Male Female Total

41.0 35.4 76.4

60.7 19.5 80.2

57.5 12.8 70.3

54.6 -11.1 43.5

66.4 12.1 78.5

2nd Quarter 2011 -63.7 -73.9 -137.6

4rth Quarter 2011 10.9 20.0 30.9

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



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1st Quarter 2012 Male Female Total

18.9 17.5 36.4

2nd Quarter 2012 11.0 13.5 24.5

3rd Quarter 2012 9.4 1.9 11.3

4th Quarter 2012 6.7 6.2 12.9

1st Quarter 2013 15.0 10.4 25.4

2nd Quarter 2013

3rd Quarter 2013

16.4 17.7 34.1

12.9 13.0 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 AMONG 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.

20

Op. cit.

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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. 21

Op. 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).

22 23

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

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•

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:

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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 AGE GRAND_TUNIS NORTH_WEST EAST_CENTER WEST_CENTER SOUTH_EAST SOUTH_OUEST GRADUATE_DUMMY ACTIF_DUMMY Gender Valid N (listwise)

N 221176 221176 221176 221176 221176 221176 221176 221176 221176 221176 221176

Minimum 15,00 ,00 ,00 ,00 ,00 ,00 ,00 ,00 ,00 ,00

Maximum 59,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00

Mean 37,2289 ,3076 ,1668 ,1761 ,1275 ,1130 ,1089 ,1552 ,8616 ,7346

Std. Deviation 12,60787 ,46152 ,37276 ,38094 ,33353 ,31664 ,31154 ,36211 ,34531 ,44154

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 = LR chi2(8) = Prob> chi2 = Pseudo R2 = Log likelihood Dependant : ACTIF_DUMMY SEX AGE NORTH_WEST EAST_CENTER WEST_CENTER SOUTH_EAST SOUTH_OUEST GRADUATE_DUMMY CONSTANT

221176 28997.40 0.0000 0.1631 = -74419.705 Coefficients. .3753318 .1000143 -.5760506 .0254765 -.1918371 -.3115417 -.5296764 -.8305654 -1.280402

Std. Err. .0141006 .0007899 .0201044 .0206074 .022928 .0225852 .0221315 .0159019 .0273037

z 26.62 126.62 -28.65 1.24 -8.37 -13.79 -23.93 -52.23 -46.89

P>z 0.000 0.000 0.000 0.216 0.000 0.000 0.000 0.000 0.000

[95% Confidence Interval] .3476951. 4029686 .0984662. 1015625 -.6154544 -.5366467 -.0149132. 0658662 -.2367753-. 146899 -.3558079-. 2672755 -.5730533-. 4862995 -.8617326-. 7993983 -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.

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Table 10: Classification Results True Classified D ~D + 189690 29309 878 1299 Total 190568 30608 Correctly Classified

Total 218999 2177 221176 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.

26

These probabilities pi are deducted from the odds ratio values (OR) by: details.


See Hosmer D.W. and S. Lemeshow (1989) for more


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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. 27

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

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8. REFERENCES 1. Becker G. (1964). Human Capital: A Theoretical and Empirical Analysis; 1st ed. New York: Columbia University Press for the National Bureau of Economic Research. 2. Fraisse-D’Olimpo S. (2009).Les fondements théoriques du concept du capital humain 3. http://ses.ens-lyon.fr/1242027840910/0/fiche_article/&RH=05. 4. Giret J. F., (2000).pour une économie de l’insertion professionnelle des jeunes, CNRS Editions, Paris. 5. Guillon S. (2010). Le chômage des diplômés de l'enseignement supérieur à la Réunion - Méthodes plurielles, trajectoires hétérogènes; le Harmattan, Paris. 6. Hosmer D.W. & S. Lemeshow (1989). Applied Logistic Regression, Wiley, 2d edition, 2000 7. Lepage J.-M. (1999). Croissance et multiplicateurs sectoriels, Economica, Paris. 8. Boubakri H. (2010). « Tunisie : Migration, marché du travail et développement », OIT Institute International d’Etudes Sociales, Genève. 9. Ben 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. 10. ONEQ (2009). Dynamique de l’emploi et adéquation de la formation parmi les diplômés universitaires. Document conjoint du Ministèrere de l’Emploi et de l’Insertion Professionnelle des Jeunes et de la Banque mondiale. Juillet. 11. Tunisian Central Bank, BCT (2009). Annualreport. 12. Institut National de la Statistiques (INS).Tunisia. 13. Arrow. K. J. (1973., Higher Education as Filter,Journal of Public Economics, Vol 2, pp 193-216. 14. Balsan D. (2000). Évaluation des rendements éducatifs dans un contexte de chômage, Économie Publique. Vol., n°5. 15. Kodde D. A. (1989).Unemployment Expectations and Human Capital Formation, European Economic Review, 36, 1645-1660. 16. KulldorffM., & Nagrawalla N (1995). Spatial disease clusters: detection and inference, Statistics in Medicine 14: 799-810. 17. Schultz T. W. (1960).Capital Formation by Education,Journal of Political Economy, University of Chicago Press, vol. 68, pages 571. 18. Spence A. Michael (1973). Job Market Signaling" The Quarterly Journal of Economics, MIT Press, vol. 87(3), pages 355-74, August.



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APPENDICES Table 11 Dependant : ACTIF_DUMMY

Odds Ratio

Std. Err

z

P>z

[95% Conf. Interval]

SEX AGE NORTH_WEST EAST_CENTER WEST_CENTER SOUTH_EAST SOUTH_OUEST GRADUATES_DUMMY

1.455474 1.105187 .562114 1.025804 .8254413 .7323171 .5887955 .4358028

.0205231 .000873 .0113009 .0211391 .0189258 .0165395 .0130309 .0069301

26.62 126.62 -28.65 1.24 -8.37 -13.79 -23.93 -52.23

0.000 0.000 0.000 0.216 0.000 0.000 0.000 0.000

1.415801 1.49626 1.103477 1.106899 .5403953. 5847056 .9851974 1.068084 .7891686. 8633812 .7006072. 7654622 .5638014. 6148976 .4224296. 4495994

CONSTANT

.2779256

.0075884

-46.89

0.000

.2634436. 2932038

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