Methodological issues related to the use of online labour market data

Abstract

This report provides a mapping of existing research that employs online labour market data, covering both online job vacancies (demand side) and online applicant data (CVs) (supply side). We discuss and assess a variety of tools and empirical methods that have been used to address specific disadvantages of this data, such as non-representativeness or fluctuations in data quantity and structure; these may be due to external shocks, such as the COVID-19 pandemic. We find that while this research field has expanded rapidly, including with respect to geographical coverage, many empirical studies do not engage with the methodological aspects and weaknesses of online labour market data and take them at face value. We highlight that there are legitimate research approaches, which are inductive in nature, focused on discovering patterns and trends in underlying data. These are by definition less concerned with generalizability of findings, as they have different objectives. For this body of research, online labour market data open new avenues for understanding developments in labour markets. We also argue that biases in online labour market data emerge due to multiple factors. With respect to the order of discrepancies between online labour market data and representative data sources, these are typically not paramount. Different techniques have been adopted to deal with the non-representativeness problem, such as statistical techniques; adapting the research questions and research focus to the quality of data; and use of mixed methods, including qualitative methods, to increase the robustness of results.

Leveraging Online Job Portal Data in Asia and the Pacific

“…OJV analysis can also contribute to mapping skills to occupations (Giabelli et al 2020) as well as identify sector-or industry-specific skills at various levels of granularity (Messum et al 2011, Ternikov 2022. Further, OJV data can be used to improve automated job recommendations, enhance skills development curricula, and assist in talent search (Khaouja, Kassou, and Ghogho 2021;Fabo and Mýtna Kureková 2022).…”

“…Changes in the labor market over time, geographical distribution of jobs, demographic features, and the effect of shocks to employment can be analyzed using job vacancy data (Jony et al 2022). Given its longitudinal nature and near real-time access, Fabo and Mýtna Kureková (2022) note that OJV data is a useful source for studying labor market changes and fluctuations in terms of number of vacancies, nature of working arrangements, wages, and impact of various shocks on the labor market (OECD 2021, Acemoglu et al 2020, Azar et al 2020.…”

“…Non-vacancy data falls broadly into three categories: (i) jobseeker data (biography, educational qualifications, skills, and past work experience), usually found in jobseeker profiles and résumé; (ii) employer data and company profile found in job advertisements on the portal; and (iii) transaction data generated by jobseekers and employers, such as numbers of clicks, numbers of applications per vacancy, timing of application, and time spent on the portal. While research studies using non-vacancy data are limited compared to vacancy data, it has the potential to unlock unique and diverse insights of labor market dynamics (Fabo and Mýtna Kureková 2022).…”

“…This affects the extent to which findings based on the data can be generalized to the labor market. Studies have attempted to overcome the representativeness challenge by using national labor force surveys to adjust for the biases, but this method has been criticized (Mýtna Kureková et al 2015, Fabo andMýtna Kureková 2022). One argument is that the labor force survey data, which provides a snapshot of the job counts in the labor market, is not an accurate reflection of new job openings.…”

“…OJP data are not collected with research objectives in mind. As a result, the data are not consistent or centralized, and some are even self-reported and outdated (Cedefop 2019, Fabo andMýtna Kureková 2022). The lack of standardization in format (text, image, video, audio, etc.…”