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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.

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.

Using the text from job ads, we introduce a new dataset to describe the evolution of work from 1950 to 2000. We show that the transformation of the US labor market away from routine cognitive and manual tasks and toward nonroutine interactive and analytic tasks has been larger than prior research has found, with a substantial fraction of total changes occurring within narrowly defined job titles. We provide narrative and systematic evidence on changes in task content within job titles and on the emergence and disappearance of individual job titles.

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.

We examine the role of technological change in explaining the large and persistent decline in earnings following job loss. Using detailed skill requirements from the near universe of online vacancies, we estimate technological change by occupation and find that technological change accounts for 45 percent of the decline in earnings after job loss. Technological change lowers earnings after job loss by requiring workers to have new skills to perform newly created jobs in their prior occupation. When workers lack the required skills, they move to occupations where their skills are still employable but are paid a lower wage.

The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.

What are the directions of change in the complexity of work and the required skill levels of the labour force in Europe? Three prominent strands of literature suggest conflicting expectations – upskilling, deskilling and polarisation. This question is answered by employing a novel work complexity indicator that measures how tasks are performed at work according to three dimensions: routinisation of tasks, autonomy at work and continuous skill-building. The measurements rely on the European Working Conditions Surveys carried out in 2005, 2010 and 2015. The results show that the European labour markets witness upskilling with some polarisation, although there are significant cross-national differences. They also show that, individually, neither shifts in work complexity within occupations (deskilling hypothesis), nor changes in employment structure (the focus of the upskilling and polarisation hypotheses) can provide an adequate view of trends in the European labour markets. Instead, both vectors of change should be analysed collectively.

Development is associated with systematic changes in the firm size distribution. I document that the mean and dispersion of firm size are larger in rich countries, and increased over time for US firms. To analyze the firm size-development link, I construct a frictionless general equilibrium model of occupational choice with skill-biased change in entrepreneurial technology (i.e., technical progress favors better entrepreneurs). The model accounts for key aspects of the US experience with only changes in aggregate technology. It attributes half the variation in mean and dispersion of firm size across countries to technical change. Distortions also affect the size distribution.

The digitalisation of processes is a current topic in accounting. New technologies can change activities which in turn may require different skills from accounting graduates. This paper aims to shed light on the changes that digitalisation brings about in various areas of accounting by assessing the types of activities (non-routine and routine) and corresponding competences in the context of progressing stages of digitalisation. In addition, it is analysed how different technologies are used in these activities and where their execution is placed within the supply chain. The systematic literature review shows a lack of expertise in the field of digitalisation that enables graduates and employees to successfully manage respective processes in the workplace. While routine activities are continuously being automated or digitalised, non-routine activities and the corresponding skills have a similarly increasing importance for employees in accounting as the acquisition of general digital competences.

We develop an equilibrium model of the US labor market, fit to Panel Study of Income Dynamics data from 1968–96. Our main innovation is a finer differentiation of types of labor than in prior work (i.e., by occupation, education, gender, and age). This lets us fit wage and employment patterns better than simpler models. We obtain a good fit to wages and occupational choices over the 29-year period while also explaining college attendance rates. We use the model to assess factors driving changes in the wage structure. Occupational demand shifts and shifts in demand for college labor and female labor within occupations are key factors.