Ample evidence indicates that a person’s human capital is important for success on the labor market in terms of both wages and employment prospects. However, unlike the efforts to identify the impact of school attainment on labor-market outcomes, the literature on returns to cognitive skills has not yet provided convincing evidence that the estimated returns can be causally interpreted. Using the PIAAC Survey of Adult Skills, this paper explores several approaches that aim to address potential threats to causal identification of returns to skills, in terms of both higher wages and better employment chances. We address measurement error by exploiting the fact that PIAAC measures skills in several domains. Furthermore, we estimate instrumental-variable models that use skill variation stemming from school attainment and parental education to circumvent reverse causation. Results show a strikingly similar pattern across the diverse set of countries in our sample. In fact, the instrumental-variable estimates are consistently larger than those found in standard least-squares estimations. The same is true in two “natural experiments,” one of which exploits variation in skills from changes in compulsory-schooling laws across U.S. states. The other one identifies technologically induced variation in broadband Internet availability that gives rise to variation in ICT skills across German municipalities. Together, the results suggest that least-squares estimates may provide a lower bound of the true returns to skills in the labor market.

This field experiment investigates the causal impact of mothers’ perceptions of gender norms on their employment attitudes and labor-supply expectations. We provide mothers of young children in Germany with information about the prevailing gender norm regarding maternal employment in their city. At baseline, over 70% of mothers incorrectly perceive this gender norm as too conservative. Our randomized treatment improves the accuracy of these perceptions, significantly reducing the share of mothers who misperceive gender norms as overly conservative. The treatment also shifts mothers’ own labor-market attitudes towards being more liberal – and we show that specifically the shifted attitude is a strong predictor of mothers’ future labor-market participation. Consistently, treated mothers are significantly more likely to plan an increase in their working hours one year ahead.

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.

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.

Our results reveal that higher lending spreads between foreign and home markets redirect real estate backed lending towards foreign markets offering a higher interest rate, which provides evidence for "search for yield" behavior. This re-allocation is found especially for banks with more expertise on the foreign market due to a higher local activity and holds for commercial and residential real estate backed loans. Furthermore, "search for yield" behavior and a resulting increase in foreign real estate backed lending is found when macroprudential regulation is missing or misaligned between a bank’s country of residence and the destination country. When turning to the question of whether the detected search for yield behavior results in more risk, we find that especially better capitalized banks report higher forbearance ratios as they might face less stigma effects compared to low capitalized banks.

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% points higher annual employment growth, a 1.1% points higher annual exit probability, and 10% points 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.

The Transition Report 2024-25 focuses on industrial policies in the EBRD regions and beyond. Such policies have seen a resurgence, seeking to address market failures such as environmental degradation. However, their track record is mixed. Their growing popularity is shaped primarily by domestic political economy considerations and rising geopolitical tensions. While industrial policies are typically employed by higher-income economies, they are also now used more frequently in economies with less administrative and fiscal capacity to implement them.

Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.