The COVID-19 pandemic pushed countries to adopt various non-pharmaceutical interventions (NPIs). Due to the features of the pandemic, which spread over time and space, governments could decide whether or not to follow policy choices made by leaders of countries affected by the virus before them. In this study, we aim to empirically model the adoption of NPIs during the first wave of COVID-19 in the 14 European countries with more than 10 million inhabitants, in order to detect whether a policy diffusion mechanism occurred. By means of a multivariate approach based on Principal Component Analysis and Cluster Analysis, we manage to derive three clusters representing different behaviour models to which the different European countries belong in the different periods of the first wave: pre-pandemic, summer relaxation and deep-lockdown scenarios. These results bring a two-fold contribution: on the one hand, they may help us to understand differences and similarities among European countries during the first wave of the COVID-19 outbreak and guide future quantitative or qualitative studies; on the other, our findings suggest that with minor exceptions (such as Sweden and Poland), different countries adopted very similar policy strategies, which are likely to be due more to the unfolding of the pandemic than to specific governmental strategies.

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PurposeThis paper contributes to the empirical analysis of PhD holders' transition into the non-academic labor market (i.e. their intersectoral mobility). The research focuses on doctoral graduates specialized in a field of study supposed to have notable non-academic applications, namely Industrial and Information Engineering. We inspect whether these doctoral graduates experience lower satisfaction with PhD knowledge use on the job when they work outside universities and non-public research centers.Design/methodology/approachWe use cross-sectional survey data collected by the Italian National Institute of Statistics in 2014. Ordinary least squares and ordered logit analyses provide baseline results; furthermore, we apply a multinomial endogenous treatment model to control for potential bias arising from self-selection into employment sectors.FindingsWe find evidence that for PhD holders Industrial and Information Engineering being employed in the industrial and services sector implies lower satisfaction with the use of doctoral knowledge than that reported by their counterparts working in universities or public research centers.Originality/valueThese results complement and extend previous evidence about PhD holders' career outcomes by focusing on the intersectoral mobility issue and on a specific group of doctoral graduates whose intersectoral mobility potential is expected to be high. Our findings call for policies that might trigger a better alignment between doctoral education and non-academic jobs.

This paper contributes to the literature on the gender wage gap by empirically analyzing those workers who hold the highest possible educational qualification, i.e., a Ph.D. The analysis relies on recent Italian cross-sectional data collected through a survey on the employment conditions of Ph.D. holders. The Oaxaca–Blinder decomposition analysis and quantile decomposition analysis are carried out, and the selection of Ph.D. holders into employment and STEM/non-STEM fields of specialization is taken into account. Findings suggest that a gender gap in hourly wages exists among Ph.D. holders, with sizeable differences by sector of employment and field of specialization.

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