We investigate the relationship between county-level social capital in the U.S. and asymmetric earnings timeliness (accounting conservatism). We measure social capital by the strength of civic norms and the density of social networks in a community. We find that firms headquartered in regions with higher social capital have earnings that reflect bad news more quickly than good news. Two potential mechanisms driving this connection are evident in our findings. First, the positive link between social capital and asymmetric earnings timeliness is more pronounced in firms with weaker external oversight, suggesting that social capital compensates for weaknesses in these mechanisms by discouraging managers from delaying the recognition of bad news. Second, we illustrate that firms in high social capital regions are more likely to recruit senior executives with higher asymmetric earnings timeliness coefficients. This result implies a preference for managers who adopt more conservative accounting practices. We find similar results using an international sample of firms from 21 countries. Our findings offer new insights into how local social norms influence corporate financial reporting.

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.

We study how idiosyncratic income risk shapes the aggregate and distributional effects of labor and capital income taxation in dynamic general equilibrium models. To this end, we compare a heterogeneous-agent (HA) model with uninsurable idiosyncratic labor productivity risk and a ten-representative-agent (TE) model in which households correspond to fixed wealth deciles without such risk. At the aggregate level, both models generate qualitatively similar responses; however, the HA model exhibits a smaller recessionary impact driven by precautionary savings behavior, which stabilizes investment. At the distributional level, the models differ sharply. In the HA framework, tax shocks trigger endogenous mobility across wealth deciles. These inter-decile transition dynamics tend to benefit lower deciles. In contrast, the TA model features fixed household positions. Our findings highlight that while simpler multi-representative-agent models can approximate aggregate dynamics well, they may miss important distributional adjustment channels. The relevance of these mechanisms ultimately depends on the empirical importance of mobility across the wealth distribution, pointing to a key trade-off between model simplicity and accuracy.

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.

Many countries and regions remain below the level of economic activity of the world’s most advanced economies. Some countries form growth clubs, some are stuck in the middle-income trap, and some stay on a very low level of economic activity. Although this situation is well documented on the country level, there is less evidence at the sub-national level within countries. We estimate county-level capital stocks and price indices and provide a comprehensive county-level data set for Germany. We find no evidence of convergence across all counties even if we condition on important drivers of long-term growth such as physical and human capital accumulation. Instead, we identify five convergence clubs, using endogenous clustering. We analyze differences in growth paths and describe the identified clusters based on variations in contributions of capital, labor, and total factor productivity to economic growth. Additionally, we examine the role of migration for regional development and find that net migration has in particular contributed to growth in richer regions.

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.

This paper examines how initial public offerings (IPOs) affect firms' internal organization. We find that IPO firms become more hierarchical and standardized organizations, characterized by additional layers, more managers, smaller control spans, and larger administrative functions. These changes occur mostly in preparation for the IPO and can be only partially explained by growth. IPO firms with greater human capital risk experience larger hierarchical changes. Hierarchical changes help firms standardize employee roles and formalize internal processes. Our results suggest that firms reorganize to reduce their dependence on key individuals' human capital when transitioning to public markets.

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.

This paper examines how firms adjust the number and types of workers they hire in response to increased biodiversity transition risk. Using the adoption of the Key Biodiversity Areas Standard of 2016 as a source of variation that increases the risk of future land-use restrictions, we find that firms reduce job postings in affected areas and reallocate labor to less exposed regions. This effect is concentrated among firms that make negative impacts on biodiversity. Cuts are stronger among production roles, while hiring in green and adaptive occupations increases. The effect is not driven by changes in capital investment or workers’ labor supply decisions. Our findings contribute to the ongoing debate on the costs and benefits of biodiversity conservation policies and their implications for labor market outcomes.

This paper examines the macroeconomic and distributional effects of the European Union’s transition to Net Zero emissions through a gradually increasing carbon tax. I develop a New Keynesian Environmental DSGE model with two household types and distinct energy and non-energy sectors. Five alternative uses of carbon tax revenues are considered: equal transfers to households, targeted transfers to Hand-to-Mouth households, subsidies to green energy firms, and reductions in labor and capital income taxes. In the absence of technological progress, the carbon tax policy induces a persistent increase in energy prices and a reduction in GDP, investment, and consumption. Headline inflation falls below zero in the medium run, reflecting weaker aggregate demand. Distributional outcomes vary significantly depending on the implemented revenue recycling scheme: targeted transfers are the most progressive but entail larger macroeconomic costs, while subsidies and tax cuts mitigate output and investment losses but are less effective in narrowing the consumption gap. A limited foresight scenario, in which agents learn about policy targets sequentially, generates more volatile adjustment paths and temporary inflationary spikes around announcements, but long-run outcomes remain close to the baseline.