Veranstaltungsformat: Präsenz

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.

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.

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.