Gender disparities between young and adult NEETs: do we need a more refined policy approach?

ABSTRACT

Italy has the highest rate of individuals Not in Employment, Education or Training (NEET) in Europe, and its NEET has the highest gender bias in Western Europe. A marked heterogeneity, however, accompanies the age sub-groups, and women’s conditions become systematically worse with advancing age. Using data from 2019, we investigate the association between the probability of being NEET and a set of individual and regional (socioeconomic and institutional) characteristics, examining whether and to what extent the role of these determinants varies depending on gender within two distinct age groups: young (15–24) and adult (25–34) NEETs. In general, we find clear evidence that women are at a relative disadvantage compared to men and, as age increases, both positive and negative determinants show relations that tend to weaken for men and to worsen for women. The results of our analyses also suggest that social/family obligations affect men and women differently, to the detriment of women, and that this disparity widens with age. The contrast between the position of men and women within marriage is empirically confirmed and perfectly captured by marginal effects with opposite signs. The policy implications of our analysis are discussed in the concluding section of this paper.

KEYWORDS:

• NEET
• unemployment
• Italian regions
• gender disparities

JEL CLASSIFICATION:

• E24
• J16
• R10
• J24

Disclosure statement

The authors declare that they have no competing interests.

Author contributions

All authors contributed equally to this work. All authors read and approved the final manuscript.

Data availability statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request. All data used in this manuscript are freely accessible online. Istat: http://dati.istat.it/ Development Policy Statistics - Istat: https://www.istat.it/it/statistiche-politiche-sviluppo Labour Force Survey - Istat: https://www.istat.it/it/archivio/127792 Regional Economic Accounts - Eurostat: https://ec.europa.eu/eurostat/web/main/data/database Institutional Quality - Nifo and Vecchione: https://sites.google.com/site/institutionalqualityindex/dataset.

Notes

¹ See Section 2.1 for details about the definitional aspects of NEET.

² Interestingly, Maguire (2015) questions whether parenthood is a cause for British women to be NEET, considering that around 30% have no children. Such evidence signals the possibility of there being highly heterogenous contexts within which the phenomenon of NEET can be studied..

³ For example, male NEETs 20–24 benefit more from the presence of more vacant jobs.

⁴ The Covid pandemic has, if possible, worsened the effects of such a mentality (Del Boca et al. 2022)..

⁵ We focused on 2019, since the latest (2020) release is obviously influenced by the pandemic crisis. Given that the Labour Force Survey is released by ISTAT on a quarterly basis – in our econometric analysis the four waves referring to 2019 have been pooled together, and dummies to control for the survey wave have been included..

⁶ The change of the independent variable of interest is from zero to one if it is a discrete regressor; an infinitesimal change if it is a continuous variable.

⁷ Another well-known feature of probit models is that the marginal effect of each independent variable is not just its coefficient but depends also on the value of all the other independent variables included in the regression.

⁸ Recalling also the previous footnote, Berry, DeMeritt, and Esarey (2010, 250) well clarify the point: ‘In a binary logit or probit model, two distinct unobserved dependent variables are of potential interest: (1) an unbounded latent variable assumed to be measured by the observed binary outcome, Y, and (2) the probability that the event will occur, Pr(Y), a variable constrained to the range between zero and one’. Then, on p. 252: “[…] for any two variables (e.g. X₁ and X₂) in a logit or probit model, the marginal effect of X₁ on Pr(Y) will vary with X₂ — i.e. there will be some interaction between X₁ and X₂ in influencing Pr(Y) [actually the interaction is among all the independent variables; authors’ note, emphasis added] — even when the model includes no product term, due to what we have called compression. Indeed, in a model without a product term, there is no interaction between X₁ and X₂ in influencing the unbounded variable, Y*, and all interaction between X₁ and X₂ in influencing Pr(Y) is due exclusively to compression. Adding an X₁X₂ term to the model allows for interaction between X₁ and X₂ in influencing the unbounded variable, Y*, and creates an additional source of variation in the marginal effect of X₁ on Pr(Y) owing to the fact that the marginal effect of X₁ on Y* (∂Y*/∂X₁) is no longer constant”...

⁹ According to EUROSTAT, the 2021 share of young people leaving their parental household in Italy is below 30%, after more than a decade above that threshold.