There is an ongoing debate about the economic effects of individualism. We establish that individualism leads to better educational and labor market outcomes. Using data from the largest international adult skill assessment, we identify the effects of individualism by exploiting variation between migrants at the origin country, origin language, and person level. Migrants from more individualistic cultures have higher cognitive skills and larger skill gains over time. They also invest more in their skills over the life-cycle, as they acquire more years of schooling and are more likely to participate in adult education activities. In fact, individualism is more important in explaining adult skill formation than any other cultural trait that has been emphasized in previous literature. In the labor market, more individualistic migrants earn higher wages and are less often unemployed. We show that our results cannot be explained by selective migration or omitted origin-country variables.

I estimate management opposition to unions in terms of hiring discrimination in the German labor market. By sending 13,000 fictitious job applications, revealing union membership in the CV and pro-union sentiment via social media accounts, I provide evidence for hiring discrimination against union supporters. Callback rates are on average 15% lower for union members. Discrimination is strongest in the presence of a high sectoral share of union members and large firm size. I further explore variation in regional and sectoral strike intensity over time and find suggestive evidence that discrimination increases if a sector is exposed to an intense strike. Discrimination is positively associated with the sectoral share of firms that voluntarily orientate wages to collective agreements. These results indicate that hiring discrimination can be explained by union threat effects.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.