Evaluating skills dimensions: Case study on occupational changes in the UAE

Reem Al Junaibi, Mohammed Omar, Zeyar Aung, Armin Alibasic, George Westerman, Wei Lee Woon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Rapid technological advances have led to profound changes to skills composition in the workplace. Low skilled jobs are gradually being replaced by automated systems, while there is a gaining demand for jobs which require interpersonal and technological skills. In this study, a combination of data science techniques are used to study this phenomenon. Firstly, matrix factorization and clustering methods are used to extract skills dimensions from O∗NET, a database of occupations-skills matchings compiled by the US Department of Labor. A method of evaluating the performance of each of these methods is proposed and used to determine the ideal extraction procedure. Next, the relative importance of different occupations was estimated using a corpus of job advertisements collected from local job sites, which we adopt as a proxy for demand. Finally, the results of this analysis are used to measure shifts in the demand for the skills and abilities associated with these occupations. This procedure is applicable to any job market. However as a test of its effectiveness, it was used to study two important economic sectors in the United Arab Emirates (UAE): Oil Gas and Banking Finance. The findings of this study will help us to determine the jobs and skills which will be most impacted by structural and technological change, and provide recommendations to ensure that the UAE workforce is well equipped to adapt to these changes.

Original languageBritish English
Title of host publication16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728150529
DOIs
StatePublished - Nov 2019
Event16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019 - Abu Dhabi, United Arab Emirates
Duration: 3 Nov 20197 Nov 2019

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2019-November
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period3/11/197/11/19

Keywords

  • Data Science
  • Occupations
  • O∗Net
  • Skill
  • Text Mining

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