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Archives: IAB-Veranstaltungen

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.

We investigate how negatively reciprocal traits of unemployed individuals interact with “sticks" policies imposing constraints on individual job search effort in the context of the German welfare system. For this we merge survey data of long-term unemployed individuals, containing indicators of reciprocity as a personality trait, to a unique set of register data on all unemployed coached by the same team of caseworkers and their treatments. We find that the combination of a higher negative reciprocity and a stricter regime have a negative interaction effect on search effort exerted by the unemployed. The results are stronger for males than for females. Stricter regimes may therefore drive long-term unemployed males with certain types of social preferences further away from the labor market.

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.

In the light of global megatrends such as ageing, globalisation, technological transformation and climate change, the 2019 ESDE is dedicated to sustainability.

One of the major sustainability challenges is sluggish productivity growth despite accelerating technological change and the increasing qualification levels of the EU labour force. We explore the preconditions for sustained economic growth, based on region-level and firm-level data analysis, focusing on complementarities between efficiency, innovation, human capital, job quality, fairness and working conditions. We identify policies that could boost productivity without increasing inequality.

We examine the impact of climate action on the economy and on employment, income and skills. In the light of EU welfare losses from climate inaction, we examine the sectors in which employment and value generation are taking place in the EU economy, estimate the overall impact of climate action in EU Member States, following a full implementation of the Paris agreement, on GDP and employment, as well as its potential impact on job polarisation.

Our main conclusion is that tackling climate change and preserving growth go hand in hand. We highlight a number of policy options to preserve the EU's competitiveness, sustain growth and spread its benefits to the entire EU population, while pursuing an ambitious transition to a climate-neutral economy.

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut’s Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.

Starting with a comparison between the life-course approach and Bourdieu, the study focuses the relation between social origin and habitus on typical patterns of education- and employment trajectories. Therefore, it tries to provide a test of the social reproduction theory of Pierre Bourdieu using a subsample of longitudinal data from the adult cohort of the German National Educational Panel Study (NEPS). Theoretically, we assume that the social class of one’s origin-family defines the process of socialization and hence the habitus of its members and is cumulative predictive for the generalizable patterns of educational- and employment sequences starting with school entry up to age 30. The individual or class-specific habitus as a “whole set of practices (or those of a whole set of agents produced by similar conditions)” (Bourdieu 1984:170) should hence correspond to differences in successful sequence-patterns, measured personality-traits and attitudes suggesting a stable class-specific realization of the habitus.

The presentation is about the nature and how to clean errors in occupational coding in order to measure patterns of occupational mobility (US, UK and Canada). Furthermore it is shed light on how occupational mobility matters for cyclical earnings inequality (based on Carrillo-Tudela, Visschers and Wiczer, 2019), unemployment and its duration distribution (based on Carrillo-Tudela and Visschers, 2019) and cleansing and sullying effects of the business cycle (based on Carrillo-Tudela, Sumerfield and Visschers, 2019).

Often asked questions concerning business surveys are:

  • What will be the increase in response rates if we apply such-and-such measure(s)?
  • What would be perfect timing for these measures? And,
  • What will be the costs?

Basically these questions ask for an efficient strategy to get response, aiming for a cost-efficient survey design both for the survey organisation (like a National Statistical Institute) and businesses alike, not burdening and chasing businesses too much. The effects of measures to get response for business surveys have not been studied systematically as much as for social surveys. Obvious reasons for this may be the fact that business surveys are mandatory by law, and the costs involved in getting response are not as
high as for social surveys using CAPI or CATI. Nowadays however, with ever decreasing budgets, and the pressure to reduce response burden even more efficient business surveys designs are required. An overview of various measures has been presented by Snijkers et al. (2013), but quantitative information to answer the above mentioned questions was to a large extend still lacking. In a study conducted at Statistics Netherlands (Snijkers et al., 2018) the effects of various measures to get response have been analysed for a number of business surveys, without doing an experiment. These measures include the obvious measures, like sending advance letters to
businesses introducing the survey and soliciting survey response, sending pre-due data reminders, and after the due date sending one or more reminder letters. For one survey (the Survey on International Trade in Goods) we modelled the effects of these measures using survival analysis, to find out what would have happened without any of these measures. At the
lecture the results will be presented.

Almost eight million forced migrants arrived in West Germany after WWII. We study empirically how regional conditions affected their economic, social and political integration. We first document large cross-regional differences in integration outcomes. We then show that high inflows of migrants and a large agrarian base hampered integration. Religious differences between migrants and natives had no effect on economic integration. Yet, they decreased intermarriage rates and strengthened anti-migrant parties. Based on our estimates, we simulate the regional distribution of migrants that maximizes their labor force participation. Inner-German migration in the 1950s brought the actual distribution closer to its optimum.

While R&D tax credits appear to increase R&D expenditures, how they change search strategies and impact private and public value creation remains less clear. We develop a simple model that predicts a stronger focus on exploitation, due to increased opportunity costs and the need to generate profit in order to take the credit. We empirically validate greater exploitation for firms in states that offered credits, and illustrate further implications including increased defensive patenting, decreased new market entry, an increase in valuation, and increased markups and profit margins. Technologically close industry peers exhibit a decrease in valuation. We provide evidence that the subsequent introduction of R&D tax credits in other states had qualitatively similar, although quantitatively smaller, effects. Our results indicate that although R&D tax credits create value, they also have unintended consequences.