10 older workers.? The simp! -st Cox specification, which enters schooling as a continous linear variable, reveals no effect of education on the durao;on of unemployment. However, the specification with school dummies shows chat relative to those with university-level schooling, those with complete secondary education have a higher hazard and therefore shorter unemployment spells. Similarly, the results suggest a weak link between primary education and shorter unemployment duration, and between higher secondary education and shorter duration.

The estimates also show that household heads have significantly higher hazards, and thus shorter unemployment spells. This finding is explained by the fact that individuals with household responsibilities have a much higher cost associated with job search. Similarly, having children is associated with a higher hazard and shorter unemployment duration.

For women, the Cox estimates are much weaker, perhaps reflecting the _rit:_ l -naller sample size. In the simplest specification, none of the explanatory variables appear to I significant effect on the hazard. When age is entered as a quadratic, however, it becomes <. ,e a.d almostsignificant, indicating that the hazard decreases with age (these are the results pr'- in the table). The continous school variable does not reveal any significant effect of education .. Lemployment duration. However, the dummies for no formal education and for complete secone -y -eakly suggest that those groups have higher hazards of exiting unemployment. Neither the househo:U iia- dummy nor the dummy for children are significant in any of the specifications.

# **V.** PERSISTENCE AND TURNOVER

Previous analysis showed that the incidence of unemployment (probability of being unemployed at a given time) and to some extent also the duration of unemployment, vary according to sex, age, education and family characteristics. The active population is thus heterogeneous: certain population subgroups appear more vulnerable than others to unemployment. Policy and programs can therefore be especially designed and targeted towards them.

An interesting question, which the previous analysis did not address is whether each population subgroup is homogeneous (with each member facing equal risk) or on the contrary, heterogeneous (some members facing a higher risk than others). The extreme case of heterogeneity would correspond to a situation in which the same people would be repeatedly hit by unemployment, alternating periods of unemployment with periods of employment, while others would face zero risk of being unemployed. The extreme case of homoaeneity would, on the contrary, correspond to a situation in which only a fraction of the labor force would be hit by unemployment at one point of time, but, as time goes by, an increasing portion of the labor force would end up experiencing unemployment. When the period of observation becomes sufficiently long, this proportion approximates one. This distinction, and in particular, the possibility of identifying high risk individuals or subgroups, has obvious implications for an appropriate policy design. Heterogeneity calls for more precise targeting and raises issues on the distributive impact of support programs.

Methodologv and Data.

**9,** We tried a quadratic specificaticn for **age.** but the data stronoly favor the simple linear specification.