Unpacking the polarization of workplace skills

Skill dependencies uncover nested human capital

Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities-occupations require one skill conditional on another. This directional nature suggests that advanced, specific skills and knowledge are often built upon broader, fundamental ones. We examine 70 million job transitions to show that human capital development and career progression follow this structured pathway in which skills more aligned with the nested structure command higher wage premiums, require longer education and are less likely to be automated. These disparities are evident across genders and racial/ethnic groups, explaining long-term wage penalties. Finally, we find that this nested structure has become even more pronounced over the past two decades, indicating increased barriers to upward job mobility.

The disparities and development trajectories of nations in achieving the sustainable development goals

The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an 'SDG space of nations'. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators - areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs.

Course-Skill Atlas: A national longitudinal dataset of skills taught in U.S. higher education curricula

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity. Here, we fill this gap by presenting Course-Skill Atlas - a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market. Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Leveraging the discrete skill and knowledge worker requirements of each occupation provided by O*NET, our empirical approach employs network-based tools from the Economic Complexity framework to characterize the US occupational network. This approach provides insights into the interplay between wages and the complexity or relatedness of the skill sets within each occupation, complementing conventional human capital frameworks. Our empirical strategy is threefold. First, we construct the Job and Skill Progression Networks, where nodes represent jobs (skills) and a link between two jobs (skills) indicates statistically significant co-occurrence of skills required to carry out those two jobs, that can be useful tools to identify job-switching paths and skill complementarities Second, by harnessing the Fitness and Complexity algorithm, we define a data-driven skill-based complexity measure of jobs that positively maps, but with interesting deviations, into wages and in the bottom-up and broad abstract/manual and routine/non-routine job characterisations, however providing a continuous and endogenous metric to assess the degree of complexity of each occupational skill-set. Third, building on relatedness and corporate coherence metrics, we introduce a measure of each job's skill coherence, that negatively maps into wages. Our findings may inform policymakers and employers on designing more effective labour market policies and training schemes, that, rather than fostering hyper-specialization, should favor the acquisition of complex and "uncoherent" skill sets, enabling workers to more easily move throughout the job and skill progression networks and make informed career choices decisions while unlocking higher wage opportunities.

Occupations and careers within organizations: Do organizations facilitate unequal wage growth?

Recent research suggests that occupations and organizations intersect during the formation of wage inequality. Using administrative data from the Netherlands, I investigate whether workers who are employed in different occupations experience unequal wage growth when staying in an organization. Results reveal that workers in professional and managerial positions realize larger wage growth than workers who work initially in lower-status occupations. After six years of staying at the same organization, predicted wage growth rates vary between 5.44% for production workers and 10.18% for technical professionals. The findings indicate that occupations compound present and future wage advantages at the organizational level. I test whether occupational sorting across organizations with differing pay quality mediates part of the occupation-based heterogeneity in wage growth. The results show that occupational sorting is marked but that sorting explains only up to around 8% of inequality in firm-internal wage growth between different occupational classes in the Dutch labor market.

Unveiling complementarities between national sustainable development strategies through network analysis

The 2030 agenda of the United Nations provides a framework of 17 Sustainable Development Goals (SDGs) and 232 indicators for its members to fulfill. The overall achievement critically depends on how nations understand the interactions between these SDGs and set priorities for development pathways. This study provides a comprehensive network analysis of global SDG complementarities, measured by the co-occurrences of SDG pairs' comparative advantages in the same region by adopting the 'product space' concept from economics. We construct the 'SDG space' at goal and indicator levels with the most recently available data and then validate its robustness by comparing it to the commonly used correlation network and confirm its predictive power using historical data. Network analysis reveals a strongly connected socioeconomic-related core and an environmental-related periphery, with 'bridge' indicators connecting different clusters. The goal-level space identifies the 'bridge' goals as SDG 17 (Partnerships for the Goals), SDG 8 (Decent Work and Economic Growth), and SDG 15 (Life on Hand) in the environmental-related cluster, while identifying SDG 7 (Affordable and Clean Energy), SDG 6 (Clean water and Sanitation), and SDG 16 (Justice and Strong Institutions) in the socioeconomic cluster. The indicator-level space provides details to explain how they act as 'bridges' in the network. In particular, 16-9: Free Press Index is the 'bridge' indicator with the highest betweenness centrality value and acts as the bottleneck indicator in China for its overall sustainable development. Improving it can enhance connected indicators' performance, leading to positive cascading effects on different aspects of sustainability.

Accelerated demand for interpersonal skills in the Australian post-pandemic labour market

The COVID-19 pandemic has led to a widespread shift to remote work, reducing the level of face-to-face interaction between workers and changing their modes and patterns of communication. This study tests whether this transformation in production processes has been associated with disruptions in the longstanding labour market trend of increasing demand for interpersonal skills. To address this question, we integrate a skills taxonomy with the text of over 12 million Australian job postings to measure skills demand trends at the aggregate and occupational levels. We find that since the start of the pandemic, there has been an acceleration in the aggregate demand for interpersonal skills. We also find a strong positive association between an occupation's propensity for remote work and the acceleration in interpersonal skills demand for the occupation. Our findings suggest that interpersonal skills continue to grow in importance for employment in the post-pandemic, remote work friendly labour market.

Location is a major barrier for transferring US fossil fuel employment to green jobs

The green energy revolution may displace 1.7 million fossil fuel workers in the US but a Just Transition to emerging green industry jobs offers possibilities for re-employing these workers. Here, using 14 years of power plant data from the US Energy Information Administration, job transition data from the Census Bureau, as well as employment and skills data from the Bureau of Labor Statistics, we assess whether people employed in fossil fuel resource extraction today are co-located and have the transferable skills to switch to expected green jobs. We find that these workers could leverage their mobility to other industries and have similar skills to green occupations. However, today's fossil fuel extraction workers are not co-located with current sources of green energy production. Further, after accounting for federal employment projections, fossil fuel extraction workers are mostly not located in the regions where green employment will grow despite attaining the appropriate skillsets. These results suggest a large barrier to a Just Transition since fossil fuel extraction workers have not historically exhibited geospatial mobility. While stakeholders focus on re-skilling fossil fuel extraction workers, this analysis shows that co-location with emerging green employment will be the larger barrier to a Just Transition.

Assessing the impact of technological change on similar occupations: Implications for employment alternatives

BACKGROUND

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.

METHOD

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.

RESULTS

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.

CONCLUSION

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.

Influence of skill relatedness on the location choice of heterogeneous labor force in Chinese prefecture-level cities

High-quality regional development should be promoted by facilitating inter-regional mobility of heterogeneous labor force to optimize its spatial allocation. This study incorporates skill relatedness into spatial categorization and selection effects, and explores how skill-relatedness affects the location choice of heterogeneous labor force. To do so, we use labor force migration data and employee data by occupation subcategory from the 2000 National Population Census and 2015 National Population Sample Survey. The empirical evidence provides three major findings. First, there are significant regional differences in labor migration rates by the occupational group between cities in China, and the trend is increasing. Regional concentration of location choice is increasing and six significant agglomerations are formed. Second, skill relatedness positively affects the location choice of the heterogeneous labor force in Chinese cities. When cities' skill-relatedness is more robust, influence on labor location choice is more remarkable. In cities with high-size classes, the effect of high-skill relatedness on labor location choice is higher. Third, labor force with solid skill relatedness with regional employment moves to the location owing to the spatial sorting effect. Labor force without skill relatedness or weak relatedness moves out or does not move to the location owing to the spatial selection effect.

The dynamic resilience of urban labour networks

Both cities and markets are well understood as complex systems which are amenable to analysis using physically inspired methods. Cities have shown fascinating universality with size, while labour markets modelled as networks have considerable explanatory power. Labour markets are a particularly attractive domain of study in this context due to societal importance, the influx of high-resolution data as well as exogenous influence of automation. While much previous work has studied the economic characteristics of cities as a function of size and examined the exposure of urban economies to automation, this has often been from a static perspective. In this work, we examine the diffusive properties of labour markets and examine their variance across cities. More specifically, we identify the occupations which are most important in promoting the diffusion of beneficial or deleterious properties. To this end, we propose a new measure of node centrality empSI. We find that these properties of influence vary considerably with city size.

Urban scaling laws arise from within-city inequalities

Theories of urban scaling have demonstrated remarkable predictive accuracy at aggregate levels. However, they have overlooked the stark inequalities that exist within cities. Human networking and productivity exhibit heavy-tailed distributions, with some individuals contributing disproportionately to city totals. Here we use micro-level data from Europe and the United States on interconnectivity, productivity and innovation in cities. We find that the tails of within-city distributions and their growth by city size account for 36-80% of previously reported scaling effects, and 56-87% of the variance in scaling between indicators of varying economic complexity. Providing explanatory depth to these findings, we identify a mechanism-city size-dependent cumulative advantage-that constitutes an important channel through which differences in the size of tails emerge. Our findings demonstrate that urban scaling is in large part a story about inequality in cities, implying that the causal processes underlying the heavier tails in larger cities must be considered in explanations of urban scaling. This result also shows that agglomeration effects benefit urban elites the most, with the majority of city dwellers partially excluded from the socio-economic benefits of growing cities.

Digitalisation of occupations-Developing an indicator based on digital skill requirements

Digitalisation is assumed to have far reaching consequences for workers. So far, these have been analysed using indicators derived from survey data on occupational tasks. Survey-based indicators measure what people do at work but provide little insight into the skills required to perform a task. Since multiple skills may be necessary to perform a task, approximating digital skills through tasks may underestimate the extent of digitalisation of a given occupation. Besides, they provide limited coverage in terms of periodicity, scope and variety of tasks. We therefore suggest to change the perspective from tasks to skills and propose to analyse the digital skill requirements of occupations. To this end, we use detailed information on the classification of European Occupations, Skills and Qualifications, natural language processing tools and network analysis methods to determine digital skills in the database. We construct four different versions of the digital competencies indicator identifying occupations that depend highly on digital skills. Our indicator can be mapped to the ISCO-08 classification and easily be used alongside other data sources. We show that compared to an indicator based on ICT-tasks derived from the OECD 'Programme for the Assessment of Adult Skills', our indicator captures more complex and specialised digitalised occupations. Our results stress the importance of using granular data in order to properly identify digital skill requirements of jobs.

C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation

Artificial Intelligence in Education (AIED) has witnessed significant growth over the last twenty-five years, providing a wide range of technologies to support academic, institutional, and administrative services. More recently, AIED applications have been developed to prepare students for the workforce, providing career guidance services for higher education. However, this remains challenging, especially concerning the rapidly changing labour market in the IT sector. In this paper, we introduce an AI-based solution named C3-IoC (https://c3-ioc.co.uk), which intends to help students explore career paths in IT according to their level of education, skills and prior experience. The C3-IoC presents a novel similarity metric method for relating existing job roles to a range of technical and non-technical skills. This also allows the visualisation of a job role network, placing the student within communities of job roles. Using a unique knowledge base, user skill profiling, job role matching, and visualisation modules, the C3-IoC supports students in self-evaluating their skills and understanding how they relate to emerging IT jobs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40593-022-00317-y.

Novel idea generation in social networks is optimized by exposure to a "Goldilocks" level of idea-variability

Recent works suggest that striking a balance between maximizing idea stimulation and minimizing idea redundancy can elevate novel idea generation performances in self-organizing social networks. We explore whether dispersing the visibility of high-performing idea generators can help achieve such a trade-off. We employ popularity signals (follower counts) of participants as an external source of variation in network structures, which we control across four conditions in a randomized setting. We observe that popularity signals influence inspiration-seeking ties, partly by biasing people's perception of their peers' novel idea-generation performances. Networks that partially disperse the top ideators' visibility using this external signal show reduced idea redundancy and elevated idea-generation performances. However, extreme dispersal leads to inferior performances by narrowing the range of idea stimulation. Our work holds future-of-work implications for elevating idea generation performances of people.

Technological relatedness and industrial transformation:

This article introduces eleven research articles that connect concepts of technological relatedness and diffusion with the transformation of industrial and innovation systems. These studies focus on the role of knowledge spillovers, regional variations in innovation and performance, and the evolution of new technologies, such as green and digital technologies. Regional capabilities and ability to diversify are key in accelerating the transformation process of existing industries

Taken all together, these studies suggest that industrial transformation hinge on firms capability to absorb domestic or foreign knowledge, regions capabilities, development trajectories, and their ability to network. In particular, regions capacity to diversify and leverage existing related knowledge are key in accelerating the green and digital transformation process of existing industries.

Skills-approximate occupations: using networks to guide jobs retraining

An issue often confronting economic development agencies is how to minimize unemployment due to disruptions like technological change, trade wars, recessions, or other economic shocks. Decision makers are left to craft policies that can absorb surplus labor with as little pain to workers as possible. The questions they face include how to re-employ displaced workers and how to fill labor shortages. To address such questions, we quantify the proximity of any two occupations based on the skills inherent in each. Taking labor skills as nodes, we model US labor as a weighted network of interdependent skills, deriving link values from geographical patterns of skill co-occurrence. We use this network to locate occupations, measure their proximity to each other, and identify which missing skills may inhibit workers from easily transitioning from one occupation to another. Thus, given that an occupation is a bundle of skills, we use our skills network to help policy makers identify which other occupations are most proximate a worker's current occupation. Finally, we apply our method to assess various worker retraining pathways for metropolitan Washington, DC, USA, whose economy was simultaneously disrupted by both the COVID-19 pandemic and the arrival of a second headquarters for Amazon.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00487-7.

The latent structure of global scientific development

Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic and economic factors, it remains unclear whether there are universal structures and trajectories of national scientific development that can inform forecasting and policy-making. Here, by examining the scientific 'exports'-publications that are indexed in international databases-of countries, we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective on the evolution of global science.

Intelligent career planning via stochastic subsampling reinforcement learning

Career planning consists of a series of decisions that will significantly impact one’s life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one’s career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one’s career life.

The new paradigm of economic complexity

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.

Industry Interconnectedness and Regional Economic Growth in Germany

Urban systems, and regions more generally, are the epicenters of many of today’s social issues. Yet they are also the global drivers of technological innovation, and thus it is critical that we understand their vulnerabilities and what makes them resilient to different types of shocks. We take regions to be systems composed of internal networks of interdependent components. As the connectedness of those networks increases, it allows information and resources to move more rapidly within a region. Yet, it also increases the speed and efficiency at which the effects of shocks cascade through the system. Here we analyzed regional networks of interdependent industries and how their structures relate to a region’s vulnerability to shocks. Methodologically, we utilized a metric of economic connectedness called tightness which quantifies a region’s internal connectedness relative to other regions. We calculated tightness for German regions during the Great Recession, comparing it to each region’s economic performance during the shock (2007–2009) and during recovery (2009–2011). We find that tightness is negatively correlated with changes in economic performance during the shock but positively during recovery. This suggests that regional economic planners face a tradeoff between being more productive or being more vulnerable to the next economic shock.

Skill-driven recommendations for job transition pathways

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.

Housing Prices and the Skills Composition of Neighborhoods

In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

Universal resilience patterns in labor markets

Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations’ skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city’s job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience.

Recent technological, social, and educational changes are profoundly impacting our work, but what makes labour markets resilient to those labour shocks? Here, the authors show that labour markets resemble ecological systems whose resilience depends critically on the network of skill similarities between different jobs.

Labour market polarisation revisited: evidence from Austrian vacancy data

Recent research suggests that new technologies are important drivers of empirically observed labour market polarisation. Many analyses in the field of economics are conducted to evaluate the changing share of employment in low-skill, medium-skill and high-skill occupations over time. This occupation-based approach, however, may neglect the relevance of specific skills and skill bundles, which potentially can be used to explain the observable patterns of labour market polarisation. This paper adds to the literature in two ways: First, we present the results of an analysis of data on job vacancies rather than the currently employed and, second, we derive occupation-defining skills using network analysis tools. The analysis and tool usage allowed us to investigate polarisation patterns in Austrian vacancy data from 2007 to 2017 and identify changes in the skills demanded in job vacancies in Austria. In contrast to most previous research, we find no evidence for polarisation, but rather a trend towards upskilling.

Occupational mobility and automation: a data-driven network model

The potential impact of automation on the labour market is a topic that has generated significant interest and concern amongst scholars, policymakers and the broader public. A number of studies have estimated occupation-specific risk profiles by examining how suitable associated skills and tasks are for automation. However, little work has sought to take a more holistic view on the process of labour reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this article, we develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro level, our model reproduces the Beveridge curve, a key stylized fact in the labour market. At a micro level, our model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to specific automation shocks. We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. In an automation scenario where low wage occupations are more likely to be automated than high wage occupations, the network effects are also more likely to increase the long-term unemployment of low-wage occupations.

Creativity in temporal social networks: how divergent thinking is impacted by one's choice of peers

Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials (N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers' ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network.

Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system “tightness” of a city’s labor force, capturing the degree of integration or internal connectedness of a city’s economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city’s resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies.

An empirical study of displaceable job skills in the age of robots

Purpose

The purpose of this paper is to contribute to the literature on issues regarding the influence of skill-polarized workplace on jobs, human capital and organization from human resource development’s (HRD’s) perspective, this research identified 30 displaceable skills from endangered jobs and examined 423 adult employees’ awareness and level of technological redundancy based on the displaceable kills.

Design/methodology/approach

By using survey methodology, the findings discovered four displaceable skill sets – repeated physical motion and performance, information process and analysis, repeated physical control of equipment, and individual affective performance – existing in 23 occupations with varying degrees.

Findings

Evidently, about half of the respondents were not aware of their level of technological redundancy and the current changes caused by automation and advancing technology in the job market. Proper HRD interventions are needed to assist employees to adjust the job changes and coexist with machines and robots in the technologically dynamic workplace. Specific approaches and strategies to help employees to become robot-proof were provided and discussed.

Originality/value

This research offers important insights for HRD professionals to understand the phenomena of the current skill-polarized workplace and to potentially address the related issues of talent shortage, endangered jobs, and technological unemployment.

The value of complementary co-workers

As individuals specialize in specific knowledge areas, a society's know-how becomes distributed across different workers. To use this distributed know-how, workers must be coordinated into teams that, collectively, can cover a wide range of expertise. This paper studies the interdependencies among co-workers that result from this process in a population-wide dataset covering educational specializations of millions of workers and their co-workers in Sweden over a 10-year period. The analysis shows that the value of what a person knows depends on whom that person works with. Whereas having co-workers with qualifications similar to one's own is costly, having co-workers with complementary qualifications is beneficial. This co-worker complementarity increases over a worker's career and offers a unifying framework to explain seemingly disparate observations, answering questions such as "Why do returns to education differ so widely?" "Why do workers earn higher wages in large establishments?" "Why are wages so high in large cities?"

Network-driven differences in mobility and optimal transitions among automatable jobs

The potential for widespread job automation has become an important topic of discussion in recent years, and it is thought that many American workers may need to learn new skills or transition to new jobs to maintain stable positions in the workforce. Because workers' existing skills may make such transitions more or less difficult, the likelihood of a given job being automated only tells part of the story. As such, this study uses network science and statistics to investigate the links between jobs that arise from their necessary skills, knowledge and abilities. The resulting network structure is found to enhance the burden of automation within some sectors while lessening the burden in others. Additionally, a model is proposed for quantifying the expected benefit of specific job transitions. Its optimization reveals that the consideration of shared skills yields better transition recommendations than automatability and job growth alone. Finally, the potential benefit of increasing individual skills is quantified, with respect to facilitating both job transitions and within-occupation skill redefinition. Broadly, this study presents a framework for measuring the links between jobs and demonstrates the importance of these links for understanding the complex effects of automation.

Toward understanding the impact of artificial intelligence on labor

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

Urban scaling and the regional divide

Superlinear growth in cities has been explained as an emergent consequence of increased social interactions in dense urban environments. Using geocoded microdata from Swedish population registers, we remove population composition effects from the scaling relation of wage income to test how much of the previously reported superlinear scaling is truly attributable to increased social interconnectivity in cities. The Swedish data confirm the previously reported scaling relations on the aggregate level, but they provide better information on the micromechanisms responsible for them. We find that the standard interpretation of urban scaling is incomplete as social interactions only explain about half of the scaling parameter of wage income and that scaling relations substantively reflect differences in cities' sociodemographic composition. Those differences are generated by selective migration of highly productive individuals into larger cities. Big cities grow through their attraction of talent from their hinterlands and the already-privileged benefit disproportionally from urban agglomeration.

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for "soft" social skills, like teamwork and communication, increase with greater demand for "hard" technical skills and tools.