EDUCATION, SOCIAL ORIGINS AND CAREER (IM)MOBILITY IN CONTEMPORARY ITALY

ABSTRACT

Italy has always been characterized by low rates of career mobility. However, whilst the essential features of intragenerational (im)mobility in Italy are relatively well known, much less is known about the pattern of the specific paths linking the class of the first job with later class positions. To date, no studies of career mobility have been conducted in holistic terms; that is, based on examination of the entire sequence of the class positions occupied by an individual during his/her work history. This paper develops a typology of sequences of transition from one social class to another which preserves the qualitative differences among individual sequences. We introduce a new method to measure resemblance among sequences in order to identify the main career paths observable in Italy. We then examine how these profiles have changed over time because of transformations in the economic system and of the changing size of different social classes. Furthermore, we will try to determine whether and how social origins and education affect the probabilities of following those paths. The results show that social origins: 1) still influence the career patterns of younger cohorts); and 2) play a very important role in modifying the chances of highly-educated people to enter the upward career patterns leading to the service class or to white-collar positions.

Keywords:

• education
• class career
• social mobility
• sequences
• lexicographic index

Acknowledgements

I wish to thank the anonymous referee who reviewed the first version of this article for their comments and fruitful suggestions. I am also indebted to Colin Mills for his very valuable comments on the various drafts of this article, and to Antonio Schizzerotto who strongly encouraged me to write this article and carefully read through its final version.

Notes

¹We are obviously referring to unemployment episodes or labour market exits whose cumulative durations did not exceed 11 months over the 10 years of observation.

²Classes IIIb, V–VI and VIIa were merged together because the employment relations of their members are quite similar. Basically they all share a labour contract (sensu Goldthorpe 1997; Rose and Harrison 2010). Class V–VI is admittedly a partial exception. Because of their assets specificity, members of this class are usually hired on the basis of a mixed contract. Unfortunately, we cannot keep this class apart because it is too small.

³Social origin is derived from the father's social class; if the father's class is missing, from the mother's social class; if we miss the information for both parents, we refer to the head of household.

⁴The appendix is available as supplementary content to the online version of this article.

⁵As clarified in the methodological Web appendix, each multinomial sequence of states expressing individual career trajectories can be broken down into a set of six binary sequences: one for each of the six class positions that we took into account. Obviously, each binary sequence can be completely identified by two coordinates. This is why a full class career trajectory has to be identified by a set of coordinates.

⁶The statistical appendix explains how to determine similarities between larger sets of class career trajectories starting from pair-wise comparisons.

⁷The cluster analysis program mentioned in the main text is ‘FuzzyMe’. Further information about its technical characteristics, and how we used it, are given in the statistical appendix. As explained in the appendix, the final cluster solution is robust to the choice of different fuzziness levels.

⁸The graphs expressing the composition of the above 15 clusters of career trajectories (i.e., types of class career paths envisaged by our typology) are reported in the statistical appendix of this article.

⁹Further comments on the cluster analysis solution we preferred are supplied in the statistical appendix.

¹⁰This figure is somewhat larger than that estimated by Barone et al. in their article. However, the sample that they studied is quite different from that analysed in this paper.

¹¹As stated in section 2, and as shown by previous empirical studies (Pisati and Schizzerotto 1999; Schizzerotto and Marzadro 2010), downward career episodes are extremely uncommon in Italy. We shall confirm this observation later in this section.

¹²Because very few women experience career mobility episodes and, conversely, most of them experience career immobility, the SLR models were carried out on men and women taken together.

¹³The parameters of the SLR models that we specified, together with measures of goodness-of-fit, are reported in the online appendix of this article.

¹⁴In this case the SLR required a one-component solution.

¹⁵Once again, in order to approximate estimates from SLR model to those from the corresponding MLR, a two-component solution is needed.