Including migrant skills in a knowledge economy: The politics of recognition of foreign qualifications,work experience and industry courses

The lack of skilled labour pushes mature knowledge economies towards importing skills. Previous studies have shown that migrants' economic and social integration in their host societies depend on the recognition of their skills. Yet, the degree to which the certificates of migrants and their practical experience are recognised and additional training is offered, is contested. Interests conflict not only between unions and employers, but also among employers and unions themselves. We argue that social partners' preferences on foreign qualification recognition (FQR), recognition of experience and industry-wide training (in the following: FQR and related measures) are mainly determined by the existence of occupational labour markets. An occupational labour market segments the labour market and subsequently restricts access to these segments. FQR and related measures constitute a regulated avenue into these occupational labour markets, protecting these labour markets from an uncontrolled influx of workers. Empirically, this contribution compares whether and how hospitality and construction industries—two sectors that depend heavily on migrants and difficult to organise due to seasonal activity—recognise and remunerate foreign skills. The construction sector recognises and remunerates foreign skills to a much larger extent than the hospitality industry. Adopting the method of difference and based on expert interviews, the paper shows that the more social partners protect their domestic occupational labour market, the more they also support FQR and related measures. The article discusses the implications for migrants' wages and social inclusion, contributing to the analysis of socio-economic tensions in mature knowledge economies.     

Hidden champions: Exploring supportive family relationships of youth experiencing homelessness

The relationships that youth experiencing homelessness (YEH) have with supportive family members (i.e. those who provide them with social support) may be protective against risk behaviours and buffer adverse health outcomes. However, little is known about the nature of YEH's supportive family relationships and the type of social support that these family relationships offer them. Thus, we conducted in-depth interviews with 30 YEH to examine the type of social support received from family relationships and to explore the social dynamics of these relationships. We found that many YEH not only continued to maintain relationships with family members after becoming homeless but also received emotional and/or instrumental support from these family members. Beyond providing this type of social support, family members served as a source of motivation for these youth. Additionally, YEH contributed to their family relationships and gained a sense of connectedness in return. Lastly, we found that YEH's family support was influenced by their family circumstances and their need for autonomy. Collectively, our findings suggest there may be a need for interventions designed to strengthen relationships with supportive family members among YEH, who may derive multiple positive benefits from relating with the ‘hidden champions’ in their lives.     

Robustly assigning unstable items

Journal of Combinatorial Optimization - We study the robust assignment problem where the goal is to assign items of various types to containers without exceeding container capacity. We seek an...     

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

National Statistical Agencies and other data custodian agencies hold a wealth of data regarding individuals and organizations, collected from censuses, surveys and administrative sources. In many cases, these data are made available to external researchers, for the investigation of questions of social and economic importance. To enhance access to this information, several national statistical agencies are developing remote analysis systems (RAS) designed to accept queries from a researcher, run them on data held in a secure environment, and then return the results. RAS prevent a researcher from accessing the underlying data, and most rely on manual checking to ensure the responses have acceptably low disclosure risk. However, the need for scalability and consistency will increasingly require automated methods. We propose a RAS output confidentialization procedure based on statistical bootstrapping that automates disclosure control while achieving a provably good balance between disclosure risk and usefulness of the responses. The bootstrap masking mechanism is easy to implement for most statistical queries, yet the characteristics of the bootstrap distribution assure us that it is also effective in providing both useful responses and low disclosure risk. Interestingly, our proposed bootstrap masking mechanism represents an ideal application of Efron's bootstrap—one that takes advantage of all the theoretical properties of the bootstrap, without ever having to construct the bootstrap distribution.