More individualistic countries experience higher economic growth. We provide evidence for a human-capital-based explanation of the growth effects of individualism. Using data from the largest international adult skill assessment, we establish that individualism shapes human capital formation. 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. Individualism is more important in explaining adult skill formation than any other cultural trait that previous literature has emphasized. 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.

In this paper, we use granular trading data from Taiwan between 2012 and 2016 to examine how local social capital influences retail investor behavior during corporate social irresponsibility (CSIR) events. Therefore, we are responding to longstanding calls in the international finance literature to explore investor behavior in non-US markets with distinct institutional and cultural characteristics. We find that investors residing in cities with higher social capital are less likely to purchase underpriced stocks following the announcements of negative events despite the potential for positive abnormal returns. This norm-driven restraint reflects a form of socially responsible investing motivated by community-based values rather than economic rationality. By documenting this behavior in an East Asian market, we extend the external validity of social norm theories developed in Western settings and contribute to a more nuanced understanding of how localized social preferences can influence asset pricing and capital allocation in a global context.

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 study employs bilateral data on external assets to examine the impact of climate policies on the reallocation of international capital. We find that the stringency of climate policy in the destination country is significantly and positively associated with an increase in the allocation of portfolio equity and banking investment to that country. However, it does not show significant effects on the allocation of foreign direct investment and portfolio debt. Our findings are not driven by valuation effects, and we present evidence that suggests diversification, suasion, and uncertainty mitigation as possible underlying mechanisms.

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.