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.