Abstract
The availability of new longitudinal data on the Italian labour market for the time period 2004–2009 allows us to depict new scenarios for both labour mobility and unemployment, the latter having increased in relevance since the beginning of the current economic downturn. To achieve an exhaustive picture of the Italian labour market we propose investigation in two directions: we provide both a descriptive analysis and an econometric investigation by estimating multinomial logit models. We find that as far as our analyses go, possessing some particular characteristics reduces the negative impact of the economic downturn. More precisely, individuals with work experience and high educational qualifications have a higher probability of finding a job. On the other hand, older workers with temporary contracts (and also with permanent contracts) are more likely to become unemployed.
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Notes
- 1.
The changes introduced in 2004 can be summarised as follows: increased frequency of the interviews, new criteria for classifying individual economic status consistent with the International Labour Organisation’s (ILO) definitions, wider-ranging information content, the use of a professional surveying network involving more than 300 surveyors, the use of computer-assisted instead of traditional techniques, the implementation of a sophisticated IT system supporting the carrying out of the survey. For a more detailed discussion of the characteristics of the Italian LFS (see Gazzelloni 2006; ISTAT 2009).
- 2.
For a description of the definitions ‘employed’, ‘unemployed’ and ‘inactive’, see ISTAT (2010a).
- 3.
For in-depth details on sampling design, see Discenza and Lucarelli (2009).
- 4.
It should be borne in mind that this description refers to the dynamics internal to the longitudinal population which, however, is clearly affected by the latest trends observable from stock data on the labour market. Conversely, no conclusions can be drawn for individuals not belonging to the longitudinal population. As previously stated, these groups represent a small proportion of the overall population and also include a statistically insignificant quota of exits due to death. It is essential to make this distinction, as exits from the longitudinal population often conceal relocations to areas offering higher opportunities of finding a job for the unemployed, or to new jobs offering better conditions for the employed. These phenomena are extremely important but are beyond the scope of the panel-type analysis proposed in this study.
- 5.
For a detailed analysis of the attainment of educational qualifications, see ISTAT computations on data from the Ministry of Education, Universities and Research (MIUR) contained in ISTAT (2010b).
- 6.
From the statistical point of view, the International Labour Organisation (ILO) classifies as long-term unemployed (LTU) those individuals who have been unemployed for 12 months or more.
- 7.
For an in-depth investigation see ISTAT reports on MIUR data (ISTAT 2010b).
- 8.
For a detailed investigation into the application of such models, see Cook et al. (2002).
- 9.
Aeschimann et al. (1999) explain and make use of a Markov chain approach to describe the evolution of labour market transition probabilities in the Swiss labour market.
- 10.
The issue of youth unemployment in Italy has been analyzed in the literature by employing data from different sources. Among these we cite Barbieri and Sestito (2008) who use the ISTAT LFS, Picchio (2008), who analyzes data from the SHIW, the Survey of Italian Households’ Income and Wealth conducted by the Bank of Italy, and Berton et al. (2008) who use data from INPS (National Institute of Social Security) administrative archives.
- 11.
By “structural component of unemployment” we mean more permanent (or long-term) unemployment. For a definition of’structural component of unemployment’, see Jackman and Roper (1987). The idea of structural unemployment became popular in the 1980s (see, e.g. Sahin et al. 2010), when economists were trying to understand why unemployment was rising steadily in many European countries (including Italy).
- 12.
Regional differentiation of the unemployment rate makes it possible to determine the impact of the geographical component on labour market transitions. The unemployment rate thus absorbs both geographical and structural components. This indicator in fact precludes the inclusion of other structural variables.
- 13.
A similar estimation exercise was conducted for the decade 1993–2003 (Fabrizi and Mussida 2009) on the longitudinal data of the former so-called quarterly survey adopted until 2003 by the ISTAT.
- 14.
The cyclical and structural impact on labour market of last crisis is beyond the aim of this paper and need of a longer period after the downturn, due to the usual lagged and persistent impact of a crisis on labour market.
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Lucarelli, C., Mussida, C. (2012). Labour-Market Dynamics and Unemployment: New Scenarios in the Italian Economy. In: Parodi, G., Sciulli, D. (eds) Social Exclusion. AIEL Series in Labour Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2772-9_12
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