Social distancing has become worldwide the key public policy to be implemented during the COVID-19 epidemic and reducing the degree of proximity among workers turned out to be an important dimension. An emerging literature looks at the role of automation in supporting the work of humans but the potential of Artificial Intelligence (AI) to influence the need for physical proximity on the workplace has been left largely unexplored. By using a unique and innovative dataset that combines data on advancements of AI at the occupational level with information on the required proximity in the job-place and administrative employer-employee data on job flows, our results show that AI and proximity stand in an inverse U-shape relationship at the sectoral level, with high advancements in AI that are negatively associated with proximity. We detect this pattern among sectors that were closed due to the lockdown measures as well as among sectors that remained open. We argue that, apart from the expected gains in productivity and competitiveness, preserving jobs and economic activities in a situation of high contagion may be the additional benefits of a policy favouring digitization.
Veranstaltungsreihe: IAB-Colloquium (en)
The discussion series “Labour Market and Occupational Research (IAB-Colloquium zur Arbeitsmarkt- und Berufsforschung)” is a forum where primarily external researchers present the results of their work and discuss these with experts from IAB. Practitioners from the political, administrative and business fields are naturally also welcome.
OECD-Bericht Getting Skills Right: Continuing Education and Training in Germany
Germany has a strong skill development system. The country’s 15 year old students performed above the OECD average in the last (2018) edition of the Programme for International Student Assessment (PISA), continuing a trend of significant improvement since PISA’s first edition in 2000. Its adult population also has above average literacy and numeracy skills, according to the OECD Survey of Adult Skills (PIAAC). A strong and well-respected vocational education and training system is seen as one of the success factors behind these achievements. However, participation in learning beyond initial education lags behind other high-performing OECD countries and varies considerably across different groups of the population. This is problematic in a rapidly changing labour market, where participation in continuing education and training is a precondition for individuals, enterprises and economies to harness the benefits of these changes. This report assesses the current state of the German continuing education and training (CET) system. It examines how effectively the system prepares people and enterprises for the changes occurring in the world of work, and identifies what changes are necessary to make the CET system more future ready. The report makes recommendations for the further development of the CET system based on international good practice.
Ms. Meierkord plans to give the lecture in German.
Can Peer Mentoring Improve Online Teaching Effectiveness? An RCT During the COVID-19 Pandemic
Online delivery of higher education has taken center stage but is fraught with issues of student self-organization. We conducted an RCT to study the effects of remote peer mentoring at a German university that switched to online teaching due to the COVID-19 pandemic. Mentors and mentees met one-on-one online and discussed topics like self-organization and study techniques. We find positive impacts on motivation, studying behavior, and exam registrations. The intervention did not shift earned credits on average, but we demonstrate strong positive effects on the most able students. In contrast to prior research, effects were more pronounced for male students.
The Effects of UI Benefits on Labor Supply During and Before the COVID-19 Pandemic
During the COVID-19 crisis the U.S. increased UI benefits substantially, leading to earnings replacement rates above 100% for many workers. In this paper, we use the universe of micro records on UI claims from the state of California going back over 15 years to study the impact of UI benefits on labor supply and job outcomes during the COVID-19 crisis, and contrast it with the variation of effects in booms and recessions before the crisis. Our main estimation strategy exploits the fact that UI benefits rise linearly with earnings up to a maximum, leading to a sharp kink that allows us to implement a Regression Kink Design (RKD) to estimate the effect of UI benefit changes on a range of outcomes. We also analyze the effect of sharp changes in UI benefits during the COVID-19 crisis. Preliminary estimates suggest that increase in UI benefits during the COVID-19 crisis raised unemployment durations for affected workers. These estimates do not imply increases in unemployment or reduction in hiring rates because they may be offset by workers not covered by UI.
Victims of Crime
We analyse who falls victim of crime, the immediate and long term consequences of victimization for victims and their families and the heterogeneity in these effects using rare administrative data on the population of reported victims in Denmark. Victims are more likely to grow up in disadvantaged environments and to be of lower SES. Moreover, victimization has long-lasting negative effects on the labor market outcomes of victims, and these effects are larger and more persistent for female than for male victims.
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
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.
CANCELLED – Higher Education Dropout and Labor Market Integration: Experimental Evidence
Thousands of students leave higher education without graduating, and worry about the negative consequences of dropping out on labour market success. However, research on how employers evaluate higher education dropouts is lacking. And while studies on school-to-work transitions are plentiful, most of them focus on the consequences of successfully attained educational qualifications – and ignore the consequences of unsuccessfully attempted qualifications.
Drawing on human capital, signalling, and credentialism theories, we conducted a series of factorial survey experiments with random samples of employers (N = 1350) to answer the following research questions: First, what is the causal effects of a dropout on the hiring prospects for different types of positions? Second, which factors facilitate labor market entry for dropouts?
Our findings indicate that employment chances depend heavily on the type of job dropouts compete for, and on the mode and duration of the study episode.
Sustainable growth in the EU: enhancing productivity growth while respecting the planetary boundaries
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.
What is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
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.