Online Archive - Seite 15 von 15 - IAB - Institut für Arbeitsmarkt- und Berufsforschung

Digital Tools to Facilitate Job Search

Unemployment insurance systems in modern labor markets are riddled with a multitude of rules and regulations governing job seekers' economic situation and their incentives to search for employment. These include, for instance, detailed regulations specifying individuals' benefit level and potential benefit duration, job search requirements, conditions for avoiding benefit sanctions, possibilities for earning extra income or additional benefit entitlements by working in part-time or short-term jobs, etc. The complexity of UI systems makes it challenging for job seekers to understand the prevailing rules, their built-in incentives, and the resulting consequences for their personal economic situation. This is potentially problematic, as a lack of understanding may distort individuals' job search incentives and employment prospects.

In this paper, we report the results from a randomized controlled trial among the universe of registered Danish job seekers that studies how reducing complexity affects individuals' understanding of UI benefit rules and labor market behavior. Our intervention exploits an online information tool that provides individuals with continuously updated, personalized information on their remaining UI benefit period, their accumulated working time that can be used to prolong the potential benefit duration, as well as information on essential rules regarding job seekers' benefit duration and benefit sanctions. We match the data from our experiment with data from an online survey and rich information from administrative records to evaluate the causal effects of our intervention on individuals' understanding of the prevailing labor market rules, their job search behavior, and resulting labor market outcomes.

Statistical Profiling and Machine Learning in the area of Labour Market Policy

I will talk a bit on how we use machine learning in general in the area of labour market policy in DK, and how we relate this to our core business of producing results on employment and education.
As a specific example of our work, I will illustrate our statistical profiling of newly unemployed, both the technical/methodological side as well as the practical implementation and general experiences in this area, and some thoughts on further development.
Finally I will talk a bit on other more recent areas of developing datadriven solutions in the field of labour market policy, drawing perspectives to new possibilities deriving from machine-learning and modern Technology.

Marginal Propensities to Consume Before and After the Great Recession

Using a quasi maximum likelihood approach for a semi-structural model, we find highly precise and distinct estimates of consumption responses to idiosyncratic income shocks for different groups of households. Homeowners stratified by liquid wealth exhibit the most dispersion in their marginal propensities to consume. Time-varying estimates support strong patterns of heterogeneity by homeownership status and balance sheet liquidity, with economically and statistically significant increases in the sensitivity of transitory consumption for homeowners, especially those with lower liquid wealth, following the collapse in house prices with the Great Recession. These findings support consumption theories that include housing as an illiquid asset.