The use of anonymous job applications (or blind recruitment) to combat hiring discrimination is gaining attention and interest. Results from field experiments and pilot projects in European countries (France, Germany, the Netherlands, and Sweden are considered here), Canada, and Australia shed light on their potential to reduce some of the discriminatory barriers to hiring for minority and other disadvantaged groups. But although this approach can achieve its primary aims, there are also important cautions to consider.

Common proxies, such as years of education, have been shown to be ineffective at capturing cross-country differences in skills acquisition, as well as the role they play in the labor market. A large body of research shows that direct measures of skills, in particular cognitive and socio-emotional ones, provide more adequate estimations of individuals’ differences in potential productive capacity than the quantity of education they receive. Evidence shows that cognitive skills in particular are quite relevant to explain wages, while socio-emotional skills are more associated with labor force and education participation decisions.

The importance of benefit portability is increasing in line with the growing number of migrants wishing to bring acquired social rights from their host country back to their country of residence. Failing to enable such portability risks impeding international labor mobility or jeopardizing individuals’ ability to manage risk across their life cycle. Various instruments may establish portability. But which instrument works best and under what circumstances is not yet well-explored.

Central Europe has experienced one of the most impressive growth and convergence stories of recent times. In particular, this has been achieved on the back of foreign-owned, capital-intensive manufacturing production in the automotive sector. With large domestic supplier networks and high skill intensity, the presence of complex industry yields many economic benefits. However, this developmental path is now reaching its limits with the exhaustion of the available skilled workforce, limited investments in upgrading and research, and persistent regional inequalities.

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.

Do workplace programs help reduce worker sickness absence? Many programs are based on the principle that the employee’s decision to report an absence can be influenced if it is costly to be absent. Firms can reduce absenteeism by implementing broad programs, including performance pay, general improvements of working conditions, and strengthening workers’ loyalty to the firm. Specific programs, such as grading partial absence, seem to be effective at reducing long-term absences. However, firms will be less inclined to implement such programs if they can shift the financial burden to social security programs.

Job displacement poses a serious earnings threat to long-tenured workers through unemployment spells and lower re-employment wages. The prevailing method of insuring job displacement losses involves an uncoordinated combination of unemployment insurance and severance pay. Less developed countries often rely exclusively on public mandating of employer severance pay due to the administrative complexity of unemployment insurance systems. If both options are operational, systematic integration of the two is important, although perhaps not possible if severance pay is voluntarily provided.

Measuring employment and unemployment is essential for economic policy. Internationally agreed measures (e.g. headcount employment and unemployment rates based on standard definitions) enhance comparability across time and space, but changes in real labor markets and policy agendas challenge these traditional conventions. Boundaries between different labor market states are blurred, complicating identification. Individual experiences in each state may vary considerably, highlighting the importance of how each employed or unemployed person is weighted in statistical indices.