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The labor market increasingly rewards social skills. Between 1980 and 2012, jobs requiring high levels of social interaction grew by nearly 12 percentage points as a share of the U.S. labor force. Math-intensive but less social jobs—including many STEM occupations—shrank by 3.3 percentage points over the same period. Employment and wage growth were particularly strong for jobs requiring high levels of both math skill and social skills. To understand these patterns, I develop a model of team production where workers “trade tasks” to exploit their comparative advantage. In the model, social skills reduce coordination costs, allowing workers to specialize and work together more efficiently. The model generates predictions about sorting and the relative returns to skill across occupations, which I investigate using data from the NLSY79 and the NLSY97. Using a comparable set of skill measures and covariates across survey waves, I find that the labor market return to social skills was much greater in the 2000s than in the mid-1980s and 1990s.

Using a broad panel of advanced economies, we document that increases in GDP per capita are associated with a systematic shift in the composition of value added to sectors that are intensive in high-skill labour, a process we label as skill-biased structural change. It follows that further development in these economies leads to an increase in the relative demand for skilled labour. We develop a quantitative two-sector model of this process as a laboratory to assess the sources of the rise of the skill premium in the U.S. and a set of ten other advanced economies, over the period 1977 to 2005. For the U.S., we find that the sector-specific skill neutral component of technical change accounts for 18–24% of the overall increase of the skill premium due to technical change, and that the mechanism through which this component of technical change affects the skill premium is via skill-biased structural change.

We show that skill requirements in job vacancy postings differentially increased in MSAs that were hit hard by the Great Recession, relative to less hard-hit areas. These increases persist through at least the end of 2015 and are correlated with increases in capital investments, both at the MSA and firm levels. We also find that effects are most pronounced in routine-cognitive occupations, which exhibit relative wage growth as well. We argue that this evidence is consistent with the restructuring of production toward routine- biased technologies and the more-skilled workers that complement them, and that the Great Recession accelerated this process.

This paper documents the pervasiveness of job polarization in 16 Western European countries over the period 1993-2010. It then develops and estimates a framework to explain job polarization using routine-biased technological change and offshoring. This model can explain much of both total job polarization and the split into within-industry and between-industry components.

Does adoption of broadband internet in firms enhance labor productivity and increase wages? Is this technological change skill biased or factor neutral? We combine several Norwegian data sets to answer these questions. A public program with limited funding rolled out broadband access points and provides plausibly exogenous variation in the availability and adoption of broadband internet in firms. Our results suggest that broadband internet improves (worsens) the labor market outcomes and productivity of skilled (unskilled) workers. We explore several possible explanations for the skill complementarity of broadband internet. We find suggestive evidence that broadband adoption in firms complements skilled workers in executing nonroutine abstract tasks, and substitutes for unskilled workers in performing routine tasks. Taken together, our findings have important implications for the ongoing policy debate over government investment in broadband infrastructure to encourage productivity and wage growth.

We construct a structural model of on-the-job search in which workers differ in skills along several dimensions and sort themselves into jobs with heterogeneous skill requirements along those same dimensions. Skills are accumulated when used, and depreciate when not used. We estimate the model combining data from O*NET with the NLSY79. We use the model to shed light on the origins and costs of mismatch along heterogeneous skill dimensions. We highlight the deficiencies of relying on a unidimensional model of skill when decomposing the sources of variation in the value of lifetime output between initial conditions and career shocks.

We study variation in skill demands for professionals across firms and labor markets. We categorize a wide range of keywords found in job ads into 10 general skills. There is substantial variation in these skill requirements, even within narrowly defined occupations. Focusing particularly on cognitive and social skills, we find positive correlations between each skill and external measures of pay and firm performance. We also find evidence of a cognitive social skill complementarity for both outcomes. As a whole, job skills have explanatory power in pay and firm performance regressions beyond what is available in widely used labor market data.

This paper argues that several of the poor labor market outcomes observed in the Great Recession can be traced back to a change in the demand pattern for skilled workers that started with the tech bust of 2000. In particular, we show that around the year 2000, the demand for cognitive tasks underwent a reversal. In response, highskilled workers moved down the occupational ladder and increasingly displaced lower-educated workers in less skill-intensive jobs. While these effects were present before the financial crisis of 2008, they became more obvious after jobs associated with the housing bubble disappeared.

The Covid-19 pandemic has led to the rise of digitally enabled remote work with consequences for the global division of labour. Remote work could connect labour markets, but it might also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, in how far could remote work connect employers and workers in different countries? Does it bring jobs to rural areas because of lower living costs, or does it concentrate in large cities? And how do skill requirements affect competition for employment and wages? We use data from a fully remote labour market—an online labour platform—to show that remote platform work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote platform workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to large cities; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that agglomerative forces linked to the unequal spatial distribution of skills, human capital, and opportunities shape the global geography of remote work. These forces pull remote work to places with institutions that foster specialisation and complex economic activities, i. e. metropolitan areas focused on information and communication technologies. Locations without access to these enabling institutions—in many cases, rural areas—fall behind. To make remote work an effective tool for economic and rural development, it would need to be complemented by local skill-building, infrastructure investment, and labour market programmes.

This paper analyzes the role of specialized high-skilled labor in the disproportionate growth of the service sector. Empirically, the importance of skill-intensive services has risen during a period of increasing relative wages and quantities of high-skilled labor. We develop a theory in which demand shifts toward more skill-intensive output as productivity rises, increasing the importance of market services relative to home production. Consistent with the data, the theory predicts a rising level of skill, skill premium, and relative price of services that is linked to this skill premium.