Im Koalitionsvertrag von CDU, CSU und SPD vom 7. Februar 2018 formuliert die neue Bundesregierung ihre rentenpolitischen Ziele. Diese sind vor dem Hintergrund der Bevölkerungsdynamik in Deutschland zu sehen. Ab dem Jahr 2020 wird sich die Altersstruktur der deutschen Bevölkerung deutlich verändern. In diesem Beitrag werden Simulationsrechnungen zu den Konsequenzen der rentenpolitischen Maßnahmen aus dem Koalitionsvertrag für die Finanzierung der gesetzlichen Rentenversicherung mit Hilfe eines Simulationsmodells dargestellt. Die im Koalitionsvertrag vorgesehenen Leistungsausweitungen verursachen langfristig Kosten in Höhe von etwa 2½ Prozentpunkten beim Beitragssatz zur gesetzlichen Rentenversicherung. Es werden ferner Maßnahmen – auch im Vergleich zu den Rentensystemen anderer Länder – diskutiert, mit denen der Anstieg des Beitragssatzes begrenzt werden könnte.

Job 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 job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.

We examine effects of government-imposed employment targets on firm behavior. Theoretically, such policies create “polarization,“ causing low-productivity firms to exit the market while others temporarily distort their employment upward. Dynamically, firms are incentivized to improve productivity to meet targets. Using novel data from East German firms post-privatization, we find that firms with binding employment targets experienced 25% higher annual employment growth, a 1.1% higher annual exit probability, and 10% higher annual productivity growth over the target period. Structural estimates reveal substantial misallocation of labor across firms and that subsidizing productivity growth would yield twice the long term increases in employment.