Extracting value from job vacancy information

This paper presents a comprehensive methodology to collect and standardise vacancy information systematically from job portals. Describes available information in Colombian job portals. Describes the methodology (web scraping) and challenges to automatically and rapidly collect a massive number of online job vacancies. Also explains the methods that can be used to homogenise variables, and explains challenges involved in standardising two of the most relevant variables for the economic analysis of the labour market: skills and occupations. This paper develops a method to automatically identify skills patterns in job vacancy descriptions based on international skill descriptors and text mining. In addition, it conducts a novel mixed-method approach (software classifiers and machine learning algorithms) to properly classify job titles into occupations. Furthermore, it deals with duplication and missing value issues, by using predictors such as occupation, city, and experience requirements.