Table 2 below reports the average marginal effects yielded by the regressions run. Overall, there does not appear to be any effect for being a foreign citizen. If the coefficients are of relevant magnitude (M1 and M3), they are not statistically significant. Conversely, it appears that the foreigners who grew up in Italy are less likely (by 4.8–6.7 p.p.) to be over-qualified than natives whilst there is no evidence that migrants who prevalently grew up abroad are more over-qualified than natives (M2 and M4).
On a different note, the effect of informal networks is associated to a decrease in the probability of over-education. While looking for a job, a one-point increase of the intensity of use of informal networks translates into a 0.4 p.p. decrease in the probability of over-education (M1 and M2). Considering the fact of finding a job through informal networks, the probability of over-education decreases by 6.5 p.p. (M3 and M4).
Looking at our estimates on subsamples, there appears to be no effect of the use of networks to look for work for foreign citizens (M5 and M7) while they seem to decrease over-education for natives (M9). Finding work through networks seemingly decreases the probability of over-education for migrants and natives (M6 and M10) but has no effect for second-generation migrants (M8). Interestingly, M5 and M6 indicate that there is no effect of the length of stay or the area of origin (in contrast with previous literature).
In order to control for the potential endogeneity bias introduced by the use of cross-sectional data in the regressions presented in the main text, we provide further results obtained through the application of the same models on PLUS panel data, which covers the years 2016 and 2018. PLUS panel data counts a total of 13,500 observations, fewer than for the cross-section data. Resultantly, the number of foreigners in the panel data is not suitable for all the analyses presented above. In addition, the way the question on citizenship is asked in PLUS 2016 differs than that in PLUS 2018, thus preventing us from splitting the foreign population between migrants and second-generation migrants. Namely, whilst PLUS 2018 allows the split between foreigners who grew up abroad and foreigners who grew up in Italy, PLUS 2016 only identifies those with and those without the Italian citizenship. This is problematic because some of those with the Italian citizenship may have acquired it over the years through naturalization. That being stated, the number of foreigners allows us to re-run M1, M3 and M9, presented in the table below as M1’, M3’ and M9’. We also add a model run on the foreigners in PLUS 2018 cross-sectional data (M11; not included in Table 2) and its equivalent in PLUS panel data (M11’).
The models presented in Table 3 and their comparison with Table 2 suggest consistency in our results and, thus, a limited endogeneity bias. For M1, M1’, M3 and M3’, the coefficients for foreign citizens are not statistically significant. The use of networks in these models is captured by coefficients that are similar in the direction, the magnitude, and the statistical significance of the effect. M9 and M9’ on the native population yield similar conclusions. M11 and M11’ are also widely similar the results yielded for the use of networks (which is not statistically significant and, at any rate, with small magnitude), despite the decrease in number of observations. Note though that some control variables change from M11 to M11’ in their statistical significance. The variables that are significant in both models are alike in terms of direction of the effect; less so in terms of magnitude of said effect.
To further test the results of the probit regressions presented thus far, we use matching techniques through propensity scores to correct the unbalances of our sample.
Firstly, we analyse the dichotomy foreigner-native through PSM. After having matched the observations as described above, the effect of being a foreigner on the probability of mismatch clearly appears. The probit regression on the matched sample yields a positive 12 p.p. difference between natives and foreigner (Table 4, first model). More specifically, whilst natives have, on average, a 12.7% probability of being over-educated; foreigners display a 24.7% probability. Matching the observations relating to foreigners to their close native neighbours, irrespective of the matching method, reveals a statistically significant difference ranging between 6.1 and 11.7 p.p. from one group to another.
In a different fashion, considering the effect of informal networks on matched observations reveals that there is no significant (neither statistically nor substantively) effect of the intensity of informal network use on over-education even though there does appear to be an effect of networks on mismatch when they lead up to employment. Interestingly, the effect is negative, meaning that finding employment through networks consistently decreases the probability of over-education by 7.3 p.p. Figure 4, however, shows the breadth of the effect is no different for natives and foreigners as the slopes are very similar.
Now turning to the difference between types of foreigners, breaking down the foreign category into migrants and foreigners who grew up in Italy points to a notable difference between these two groupsFootnote 13 (Table 4). Namely, migrants are 8.3 p.p. more likely to be over educated than natives and 19.7 p.p. more likely to be so than foreigners who grew up in Italy. Reversely, there does not appear to be any difference between foreigners who grew up in Italy and natives (coefficient of limited magnitude and not statistically significant). This is an important finding as it suggests the existence of an upward mobility for children of foreigners, a phenomenon already established in literature with regard to old immigration countries such as Sweden, the UK, Germany or the Netherlands (Crul et al. 2017) but also with respect to newer immigration destination such as Spain (Portes et al. 2016).
The estimates provided thus far converged towards a greater probability of over-education for the foreigners who migrated than for natives or foreigners who grew up in Italy. It remains to be seen whether the use of informal networks plays a role in this. Table 5 reports our estimates. With models breaking down foreigners between migrants and second generations, it appears that the use of informal networks to look for a job does not decrease over-education. Nor does it decrease it when people found their current job through informal networks (Table 6).
However, said effect does appear to vary from one category to another. When considering the intensity of network use to look for a job, the effect is positive for migrants while it is negative for natives and foreigners who grew up in Italy. The differentiated effect appears clearer where the use of networks is most intense. Figure 5 illustrates that.Footnote 14
If, instead, we consider the instances in which respondents reported having found their current employment through networks, the effect of the latter does not vary from one category to another as the confidence intervals for our two categories of interest overlap significantly. Figure 6 illustrates this.Footnote 15