Methodological issues related to the use of online labour market data
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Organización Internacional del Trabajo (OIT)
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| Format: | Entrada de enciclopedia |
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International Labour Organization
2022
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| _version_ | 1874525252724195333 |
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| author | Brian Fabo (Author) Lucia Mytna Kurekova (Author) |
| author_facet | Brian Fabo (Author) Lucia Mytna Kurekova (Author) Brian Fabo (Author) Lucia Mytna Kurekova (Author) |
| contents | Methodological issues related to the use of online labour market data Brian Fabo (Author) Lucia Mytna Kurekova (Author) 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. ILO working paper, 68, ILO 10.54394/ZZBC8484 DOI https://doi.org/10.54394/ZZBC8484; ISBN 9789220372838,9789220372814 publication.encyclopediaEntry |
| format | Entrada de enciclopedia |
| id | ilo_995219478802676 |
| institution | Organización Internacional del Trabajo (OIT) |
| publishDate | 2022 |
| publisher | International Labour Organization |
| spellingShingle | Methodological issues related to the use of online labour market data Brian Fabo (Author) Lucia Mytna Kurekova (Author) Methodological issues related to the use of online labour market data Brian Fabo (Author) Lucia Mytna Kurekova (Author) 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. ILO working paper, 68, ILO 10.54394/ZZBC8484 DOI https://doi.org/10.54394/ZZBC8484; ISBN 9789220372838,9789220372814 publication.encyclopediaEntry |
| title | Methodological issues related to the use of online labour market data |
| url | https://researchrepository.ilo.org/esploro/outputs/encyclopediaEntry/Methodological-issues-related-to-the-use/995219478802676 |