Efficiency frontier and matching process on the labor market: Evidence from Tunisia Imed DRINE United Nations University World Institute for Development Economics Research Helsinki, Finland 16th International Conference on Panel Data 2010, Amsterdam

Introduction The economic performance in Tunisia (average growth rate over the last thirty years around 5%) contrasts with an unemployment rate that is persistently high with important regional disparities. A major concern in Tunisia is the fact that the unemployment rate for the age groups of 15 and 24 years is reaching 30%. MENA Including Tunisia Eastern Europe Latin America and the Caribbean OECDAsiaSouth and East Asia Unemployment rate 14%9.7%7.7%6.7%4.7%3.8%

World Bank 2008 Unemployment affects all kinds of graduates Graduates with two year professional degrees are actually the most vulnerable to unemployment. University studies have a higher rate of unemployment  Better align graduates’ skills with the needs of the economy

Objective Our main objective in this paper is to estimate and explain the efficiency changes in labor market process that may have taken place both over time and across regions.

Methodology This study takes advantage of the recent development in the stochastic frontier techniques and estimates, for the first time, the matching function for Tunisia using disaggregated data. In addition, we include additional variables as determinants of matching efficiency and regional disparities.

Outline 1.Characteristics of the Tunisian Labour market 2. Matching in the Tunisian labour market 3. Stochastic frontier estimates for the matching process 4. Conclusion

Characteristics of labour market in Tunisia (1/3)

Characteristics of labour market in Tunisia (2/3)

Characteristics of labour market in Tunisia (3/3) Gross unemployment rate 15,7%16,4%16,2%14,2% Male 16,4%17%15,2%12,9% Female 13,4%13,7%17,4%16,7% Less than 25 years 26,3%25%26,5%30,3% Between years 7,4%8,1%12,5% More than 50 years 7,1% 5,6% Illiterate 9,9%10,3%17,6%13,8% Primary 19,2% 18,3%15,1% Secondary 10,6%12,2%13,1%14,1% Tertiary 1,5%2,2%3,8%10,2%

What can we learn? The labour force is heavily male-dominated because of the low participation rate of females The reduction in the unemployment rate recorded during the decade has benefited the men The unemployment rate for tertiary graduates increased from 1.5% in 1984 to 3.8% in 1994 and 10.2% in 2004, while gross unemployment has tended to stabilize  The education and training system is not properly linked to the economic environment

In addition Recorded unemployment was at its lowest in the central east region (10%) (with a diversified and dynamic economy) and at its worst in the agricultural region of the north-west (18%) and the mining region of the south-west (18%). Raisons : Low mobility and lack of inter-regional migration are among the reasons for this regional disparity

Matching Process The matching process is generally represented by the following function: where H, U, and V denote new hiring, unemployment, and vacancies, respectively. The constant A shows the 'efficiency parameter'.

The stochastic logarithmic production frontier model is defined by: The model is called true fixed-effect model since it separates the true fixed effect from inefficiency (Greene, 2005) The technical efficiency component :

The objective of our study is to identify factors explaining inefficiencies in the matching process The data are yearly and concern 23 regional labour offices over the period The data source is the Tunisian Ministry of labour and different local labour offices. HU V Unemployed less than 35 years Female Unemployed Long-run unemployment share Urban unemploym ent Graduated unemployed Min/max 205/ / / ,45/0,910,05/0, / 0.70,6/0,790,10/0,41 Average ,750, ,770,23 Std.Dev ,070, ,10,06 Number of observations 460

Main characteristics A substantial numbers of job seekers have been unemployed for more than two years (almost 15% on average). 75% of jobseekers are aged less than 35 years and almost 80% are male. Only 23% of the job seekers, on average, have a higher level of education. Rq: Overall, the male activity rate in Tunisia is close to 75% while that of women is still only 25%.

Regional disparity in terms of labour market dynamics is associated with social, human and physical capital endowment and the lack of policies that increase workers’ interregional mobility Hiring by region Average-period Number of job seekers by region Average-period Vacancies by region Average-period

Regional Mismatch( Jackman and Roper (1987) indicator) : The upper limit, m=1, appears when all unemployment is concentrated in one unique region and all vacancies are concentrated in another

Stochastic frontier estimates for the matching process Variables Stochastic Frontier (Battese et Coelli) Stochastic Frontier (Greene 2005) Ln(U t-1 ) 0,08 (0,00) 0,12 (0,02) Ln(V t-1 ) 0,89 (0,00) 0,80 (0,00) Constant 0,35 (0,00) 0,77 (0,01) Long-run unemployment share -0,15 (0,04) Unemployed less than 35 years 0,06 (0,03) Urban unemployment -0,02 (0,08) Female Unemployed -0,24 (0,01) Graduated unemployed (US) 4,3 (0,00) Dummy96 -0,12 (0,00) FDI 0,35 (0,01) Return to scale 0,97 RDC0,92 RDD Number of observations 460

Main Findings Hiring flows seem to be driven more by the stock of vacancies, with the stock of job seekers having a limited effect A 1% increase in the share of long-term unemployment decreases the matching efficiency by 0.15%. The share of jobseekers aged below 35 years significantly and positively affects the matching process The effect of the share of women job seekers on the matching efficiency is significantly negative

A 1% increase in the group of qualified job seekers increases the efficiency by 4% Negative effect of density on the efficiency of matching process. We find a positive and significant effect of foreign direct investment (FDI) on the efficiency of matching The flexibility of the Tunisian labour market has a negative and significant effect on the matching efficiency

A very substantial variation in labour market efficiencies scores among the regions Regional distribution of efficiency scores 2004

Conclusion 1. Rising and persistence unemployment may be interpreted as the inefficiency of the matching process and differences in structural factors across regions. 2. Changes in the composition of the stock of job seekers such as age and educational structure, structure by gender, and level of urbanization, contribute significantly to explaining regional efficiency disparities. 3. To improve efficiency of matching we need: -Better transmission of information -Better functioning of employment agency -Better regional mobility