Existing studies on the downward trend in the labor share of income mostly focus on changeswithin individual countries. I document, however, that half of the global decline in the laborshare of income can be traced to the relocation of activities between countries. I develop atwo-country model...

This paper draws on existing empirical literature and an original theoretical model to argue that globalization and skill supply affect the extent to which technology adoption in developing countries favors skilled workers. Developing countries are experiencing technical change that is...

The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a...

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and...

The U.S. labor share of income has been on a secular downward trajectory since thebeginning of the new millennium. Using data that are disaggregated across both state andindustry, we show the decline in the labor share is broad-based but the extent of the fallvaries greatly. Exploiting a new...

Cross-border migration can act as an important adjustment mechanism to country-specific shocks. Yet, depending on who moves, it can have unintended consequences for business cycle stability. This paper argues that the skill composition of migration plays a critical role. When migration flows...

We exploit a survey data set that contains information on how 11,000 workers across advanced and emerging market economies perceive the main forces shaping the future of work. In general, workers feel more positive than negative about automation, especially in emerging markets. We find that...