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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.

When jobs offered by different employers are not perfect substitutes, employers gain wage-setting power; the extent of this power can be captured by the elasticity of labor supply to the firm. The authors collect 1,320 estimates of this parameter from 53 studies. Findings show a prominent discrepancy between estimates of direct elasticity of labor supply to changes in wage (smaller) and the estimates converted from inverse elasticities (larger), suggesting that labor market institutions may rein in a substantial amount of firm wage-setting power. This gap remains after they control for 22 additional variables and use Bayesian Model Averaging and LASSO to address model uncertainty; however, it is less pronounced for studies employing an identification strategy. Furthermore, the authors find strong evidence that implies the literature on direct estimates is prone to selective reporting: Negative estimates of the elasticity of labor supply to the firm tend to be discarded, leading to upward bias in the mean reported estimate. Additionally, they point out several socioeconomic factors that seem to affect the degree of monopsony power.

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 study how various types of machines, namely, information and communication technologies, software and especially industrial robots, affect the demand for workers of different education, age and gender. We do so by exploiting differences in the composition of workers across countries, industries and time. Our data set comprises 10 high-income countries and 30 industries, which span roughly their entire economies, with annual observations over the period 1982–2005. The results suggest that software and robots reduced the demand for low- and medium-skill workers, the young and women – especially in manufacturing industries; but raised the demand for high-skill workers, older workers and men – especially in service industries. These findings are consistent with the hypothesis that automation technologies, contrary to other types of capital, replace humans performing routine tasks. We also find evidence for some types of workers, especially women, having shifted away from such tasks.

Following Grossman and Rossi-Hansberg (2008) we present a model in which tasks of varying complexity are matched to workers of varying skill in order to develop and test predictions regarding the effects of immigration and offshoring on US native-born workers. We find that immigrant and native-born workers do not compete much due to the fact that they tend to perform tasks at opposite ends of the task complexity spectrum, with offshore workers performing the tasks in the middle. An effect of offshoring and a positive effect of immigration on native-born employment suggest that immigration and offshoring improve industry efficiency. (JEL J24, J41, J61, L24)

Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse . Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

Recent research has focused on students’ knowledge sharing behavior and its driving factors, which promote the knowledge transmission and exchange in different educational settings. However, little research has focused on factors that inhibit knowledge sharing. Based on social learning theory and social cognitive theory, the current study investigates the relationships between knowledge hiding behaviors and students’ belief about interactional justice and professional commitment. Based on 312 reliable questionnaires collected from Chinese and international postgraduate students in China, this study explored postgraduate students. Three knowledge hiding behaviors include playing dumb, evasive hiding, and rationalized hiding, all of which is negatively related to interactional justice. Furthermore, the moderating effect of professional commitment was found to influence evasive hiding more significantly than the other two aspects of knowledge hiding. Based on the findings of the study, implications are elaborated for learners, supervisors, and higher education.

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.