Deutschland befindet sich schon im zweiten Jahr in einer Rezession, und Wachstum ist laut den Prognosen der Wirtschaftsforschungsinstitute auch 2025 kaum zu erwarten. Viel schlimmer ist allerdings, dass neue Prognosen des langfristigen Wachstums im Gleichgewicht (das so genannte Produktionspotenzial) dramatisch niedrigere Wachstumsraten für die nächsten 20 Jahre vorhersagen, wenn sich die Wirtschaftspolitik nicht deutlich ändert. Eine wesentliche Ursache für die Wachstumsschwäche ist die Investitionsschwäche. Um diese anzugehen, reicht das beschlossene Sondervermögen für Infrastruktur nicht aus. Die neue Bundesregierung muss die Bürokratie abbauen und Planungsverfahren verkürzen, eine rationale Klima- und Energiepolitik verfolgen, das Rentensystem mutig in Richtung Kapitaldeckungsverfahren umbauen und eine Einkommensteuerreform auf den Weg bringen, die mittlere Einkommen entlastet.

Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers’ automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.

This paper uses a new empirical design to explore how labor market polarization affects individuals’ incentive to pursue education funded on the margin by student debt. We argue that the labor market polarization–where automation replaces mid-skill and mid-education-level job–changes the marginal benefits of education and training and sharpens incentives to incur student debt. We advance a new measure of labor market polarizations that allows to capture the heterogeneity of this phenomena across geographies and time. Using this measure, we find that U.S. CBSAs that experience deeper labor market polarization see an increase in student debt balances and in the number of people pursuing student debt. On average, the decline in middle-skill jobs and wages has little effect on individuals’ ability to pay down existing student debt. The effects are most pronounced in ZIP codes with lower average credit scores, lower incomes, and higher share of the minority population.

Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.