Information barriers and social stratification in higher education: evidence from a field experiment.

Our contribution assesses the role of information barriers for patterns of participation in Higher Education (HE) and the related social inequalities. For this purpose, we developed a large-scale clustered randomised experiment involving over 9,000 high school seniors from 62 Italian schools. We designed a counseling intervention to correct student misperceptions of the profitability of HE, that is, the costs, economic returns and chances of success of investments in different tertiary programs. We employed a longitudinal survey to test whether treated students' educational trajectories evolved differently relative to a control group. We find that, overall, treated students enrolled less often in less remunerative fields of study in favour of postsecondary vocational programmes. Most importantly, this effect varied substantially by parental social class and level of education. The shift towards vocational programmes was mainly due to the offspring of low-educated parents; in contrast, children of tertiary graduates increased their participation in more rewarding university fields. Similarly, the redistribution from weak fields to vocational programmes mainly involved the children of the petty bourgeoisie and the working class, while upper class students invested in more rewarding university fields. We argue that the status-maintenance model proposed by Breen and Goldthorpe can explain these socially differentiated treatment effects. Overall, our results challenge the claim that student misperceptions contribute to horizontal inequalities in access to HE.

Merging Datasets of CyberSecurity Incidents for Fun and Insight.

Providing an adequate assessment of their cyber-security posture requires companies and organisations to collect information about threats from a wide range of sources. One of such sources is history, intended as the knowledge about past cyber-security incidents, their size, type of attacks, industry sector and so on. Ideally, having a large enough dataset of past security incidents, it would be possible to analyze it with automated tools and draw conclusions that may help in preventing future incidents. Unfortunately, it seems that there are only a few publicly available datasets of this kind that are of good quality. The paper reports our initial efforts in collecting all publicly available security incidents datasets, and building a single, large dataset that can be used to draw statistically significant observations. In order to argue about its statistical quality, we analyze the resulting combined dataset against the original ones. Additionally, we perform an analysis of the combined dataset and compare our results with the existing literature. Finally, we present our findings, discuss the limitations of the proposed approach, and point out interesting research directions.