Digital Technologies and Future of Work

Digital Technologies and Future of Work aims to design a leveraging tool that employers and employee can use to analyze job data for extensive and intensive changes.

New technologies are emerging on a continual basis with drastic trajectories and wider penetration into the job market. During the previous three industrial revolutions, it often took decades to transform the educational systems and to develop new skill sets that were needed. Nowadays, as we are entering the fourth industrial revolution given the pace and the scale of disruption brought about, the adjustment time becomes shorter. Being proactive - by predicting what skills will be in demand is essential to be prepared for these changes.

Extensive

Extensive margin is defined as cross-occupation employment change, therefore, analyzing job demand is required to better understand these changes. This website provides empirical analysis of the occupation employment changes with real data from the job markets.

Extensive Analysis »

Intensive

Intensive margin represents the ways in which technology is changing the composition of jobs themselves. By analyzing the variations in the composition of jobs themselves we can identify the skills that are in lower demand then we can further investigate are they replaced by machines.

Intensive Analysis »

About Us

Meet the team behind Digital Technologies and Future of Work project. What makes our team great is the fusion of professors experience and students enthusiasm. Individually, we all have our own weaknesses and strengths. But together, we can reach great lengths. Check out our dedicated team.

Our Team »

Data Science Tools. Collect, clean and analyze data.

Deployed techniques are Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) for extensive (changes in job demand) analysis; and for the intensive (skill changes) analysis, we used factorization techniques Factor Analysis (FA) and Non-Negative Matrix Factorization (NMF) that can solve increasingly complex data problems. In addition, professional social networks such as LinkedIn were exploited as well as significant numbers of websites for job advertisements. To standardize data, we used USA O*NET database, which contains a comprehensive skill set for almost all occupations.

Data Visualization Techniques. Heatmaps, Topics, Factors.

Using different visualization techniques, results are presented in an intuitive way where job demands from the three main industries, namely, Oil&Gas, Banking&Finance, and Healthcare can be seen. Heatmaps represent the jobs demand, topics and factors represent the extracted topics and factors from the O*NET database.

Policy Recommendations. For decision makers.

Results by the Digital Technologies and Future of Work team can help the decision-making process related to workforce and potentially demonstrate which jobs can be automated in the near future. Moreover, it can be used as a guideline for the countries to transition into the knowledge-based economy, where employees will have the right skill-sets to work in the key industrial sectors.