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The trend over time of labour market opportunities for young people in Italy

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Abstract

We analyse the re-employment probabilities of young people (ages 15–24) from 1985 to 2004. We find that this 20 year period decades were characterized by an increase in youth employment, especially since the mid-1990s. Nonetheless, the employment opportunities offered to disadvantaged workers were primarily atypical and therefore did not imply a stable and permanent increase in the bulk of youth employment. In addition, although the increase in re-employment probabilities by atypical contract would be largely explainable by flexibility policies, the evolution of re-employment probabilities by permanent and fixed-term contracts would be a consequence of competing causes, including a selection of higher productive workers.

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Notes

  1. In Italy, the youth unemployment rate is slightly higher with respect to the mentioned 25 %. The overall youth unemployment rate was approximately 27.7 % in 1999. This figure is the average of approximately 26.6 % for males and 37.4 % for females. The unemployment gender gap in Italy is also quite relevant for young people. These figures are available on the Internet at http://stats.oecd.org/.

  2. The study by Azmat et al. (2006) emphasizes that in countries where the unemployment gender gap is high (Mediterranean countries), i.e., where the female unemployment rate is significantly greater than that of males, the unemployment problem is largely a problem of female unemployment. Typically, these countries also have very high youth unemployment rates and high youth unemployment gender gaps.

  3. Until the second half of the nineties, the standard work arrangement in Italy was full-time, open-ended, and characterized by one of the strictest employment protection laws, mostly against dismissals, in the OECD area (Lazear 1990; Kugler and Pica 2008; Leonardi and Pica 2013).

  4. The reform process also involved countries with relatively little employment protection regulation, such as the UK and the US (Booth et al. 2002).

  5. Other studies on the ‘dead end’ or ‘springboard’ effects of temporary contracts include D’Addio and Rosholm (2005) and Güell and Petrongolo (2007).

  6. For a description of the features and limits of the WHIP data, see Mussida and Sciulli (2015).

  7. Other legislative changes of the Italian labour market concerned the reform of the “Cassa integrazione guadagni” (CIG) (see Dell’Aringa and Lucifora 2000, for more details), the decentralization of public employment services and the liberalization of employment services, allowing the entry of private companies.

  8. Since the 1990s, single laws or more-complex reforms have been introduced in the Italian labour market: the so-called “Treu Package” introduced by Law No. 196/1997, Legislative Decree No. 368/2001, Law No. 30/2003 (“Biagi’s Law”), and Legislative Decree No. 276/2003.

  9. The characteristics of our data, i.e., interval-censored data, allow estimating discrete-time hazard models (Prentice and Gloecker 1978).

  10. Specifically, a binary dependent variable was created. If individual i’s survival time is censored, then the dependent binary variable always takes value zero. If instead individual i’s survival time is not censored, the dependent binary variable is zero in the first j-1 observation and one in the last observation.

  11. We divided the total spell of non-employment into nine sub-spells for these groups of months: 1–3, 4–6, 7–9, 10–12, 13–18 (base category), 19–24, 25–36, 37–48, and over 48 months.

  12. Because of the independence assumption, the total log-likelihood function logL(β,γ) for the two type of employment is the sum of the partial log-likelihood functions derived for the contracts of destination PC and AC.

  13. We use STATA (ver 12.1) statistical software, which provides a command, xtcloglog, to estimate random-effect complementary log–log models.

  14. For details, see Sect. 2.

  15. WHIP data do not present attrition problems because if the worker or the firm is enrolled with INPS, they must provide INPS with all of the information. In addition, as stated in the relevant literature/empirical evidence based on the WHIP data (e.g., Contini and Grand 2010; Grand and Quaranta 2011; Contini and Poggi 2012) and in the specific documentation of those data (LABORatorio Revelli 2009), the residual attrition that we observe is the product of perfectly explainable patterns of workforce utilization that do not relate to data collection.

  16. When constructing our sub-sample, if an individual was simultaneously in more than one work relationship, we eliminated the shorter job relationship; if the relationships were of the same duration, we removed the part-time job or the work relationship characterized by fewer days of actual work. Finally, when the second job started before the end of the first job but ended after the end of the first job, we censored the second work spell to the left and hypothesized that the second job started only when the first ended. Thus, the passage from a double job to a single one is viewed as a transition from one job to another. This strategy is adopted to reconstruct the non-employment duration spells with accuracy.

  17. We also control for time by using yearly dummies for the overall period examined, which are not reported here for brevity. Nonetheless, these statistics are available upon request.

  18. The related results for both cohort dummies and their interactions are showed in Table 4 in Appendix. In addition, main estimations (Table 1) result from a specification accounting for the role cohort dummy variables and related interactions.

  19. Employment growth is measured with respect to the next quarter employment level using data from the ‘Rilevazione sulle Forze di Lavoro’ gathered by ISTAT.

  20. A fixed-term contract of employment is defined as a contract of employment that has a definite start and end date, terminates automatically when a particular task is completed, or terminates after a specific event (other than retirement or summary dismissal). Legislative Decree No. 368/2001 liberalized the use of fixed-term contracts to allow firms to use them to adapt quickly to changes in economic conditions.

  21. The Figures in the Appendix show the interactions between gender, i.e., (M)ale and (F)emale, and area, i.e., (N)orth and (S)outh, and we obtain a total of four combinations (MN, MS, FN, and FS in the legends of the figures). In a first attempt, we also included the hazard for men and women living in the Centre of Italy. Nonetheless, to keep the graphs more clearly interpretable, we chose to keep only the north and the south. These two partitions, indeed, do show the highest gender gap in employment opportunities. The results including the gender and Centre interactions are available upon request.

  22. For brevity, we did not report the duration dependence parameters for the sub-spells of non-employment duration (see footnote 11). The full set of estimates is available upon request.

  23. Thus, an estimated coefficient with a positive sign indicates that the explanatory variable positively affects the re-employment probability rather than favouring permanence in the non-employment state. Moreover, as the non-employment state is the common base-category, the sign and the magnitude of the same explanatory variable estimated for different transitions (NE-PC or NE-AC in Table 1) define the differential effect (due to a specific covariate) on the probability of transition to alternative employment states.

  24. In this regard, Fig. 8 in Appendix reports a graphical representation of estimated coefficients, for the whole sample, and for sub-analysis of exits by permanent and fixed-term contracts and atypical contracts, respectively.

  25. Graphically, this analysis decomposes by sub-groups the continuous gray lines of the first analysis in Fig. 8 in Appendix.

  26. In addition, by running equality tests between the coefficients of our yearly binary variables, we find absence of time-homogeneous probabilities of exiting non-employment, for both genders and geographical areas. These results are available upon request.

  27. To provide evidence on the increase in employment opportunities for disadvantaged or blue-collar workers, we re-estimated our models by occupational classification, i.e., blue-collar and white-collar (according to the International Standard Classification of Occupations, ISCO-88). Although the hazards to AC (both blue and white collars) are lower compared with the hazards to PC through the overall period (Fig. 6 in Appendix), the re-employment probabilities with AC contracts, especially for blue-collars, were mostly affected by the changes of the period, both institutional and due to economic facts (Fig. 10 in Appendix).

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Acknowledgments

The authors wish to thank two anonymous referees for their useful suggestions.

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Authors and Affiliations

  1. Department of Economic and Social Sciences, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy

    Chiara Mussida

  2. Department of Economic Studies, University “G. d’Annunzio” of Chieti-Pescara, Viale Pindaro 42, 65127, Pescara, Italy

    Dario Sciulli

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  1. Chiara Mussida
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Correspondence to Chiara Mussida.

Appendix

Appendix

See Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10 and Tables 2, 3, 4.

Fig. 2
figure 2

GDP growth and the Italian labour market in 1985–2004. Source: OECD statistics (2013)

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Fig. 3
figure 3

Hazard rates to overall employment by gender and geographical area

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Fig. 4
figure 4

Hazard rates to PC by gender and geographical area. Source: our elaborations on WHIP data

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Fig. 5
figure 5

Hazard rates to AC by gender and geographical area. Source: our elaborations on WHIP data

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Fig. 6
figure 6

Hazard rates by occupation, overall employment

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Fig. 7
figure 7

Hazard rates by occupation and contract type. Source: our elaborations on WHIP data

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Fig. 8
figure 8

Step-by-step analysis, graphical representation of estimated coefficients, over the period 1985–2004 (1985, base). Source: our elaborations on WHIP data

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Fig. 9
figure 9

Residual year-to-year dynamics by gender and geographical area over the period 1985–2004 (1985, base). Source: our elaborations on WHIP data

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Fig. 10
figure 10

Residual year-to-year dynamics by occupation over the period 1985–2004 (1985, base). Source: our elaborations on WHIP data

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Table 2 EPL in some EU and OECD countries
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Table 3 Description of variables and summary statistics by gender and total, 1985–2004
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Table 4 Random effects cloglog coefficient estimates: cohort trends by gender, geographical area and sector of economic activity, 1985–2004
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Mussida, C., Sciulli, D. The trend over time of labour market opportunities for young people in Italy. Econ Polit 33, 291–321 (2016). https://doi.org/10.1007/s40888-016-0028-0

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