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How to quantify what is not seen? Two proposals for measuring platform work

2019Science for policyInnovation and growth
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Digital labour platforms are defined as digital networks that coordinate labour services in an algorithmic way. The rise of digital labour platforms can reshape work organisation and tasks distribution across the workforce, posing new policy challenges. A crucial problem for the design of an adequate policy response is the lack of clear estimates of the prevalence of platform workers. This paper proposes two approaches for measuring platform work. The first approach attempts to measure platform work as individual participation in the labour force through surveys, similarly to what is done by the Labour Force Survey (LFS) for traditional employment. Given the structural differences between traditional employment and platform work, the identification of the latter through surveys should include measures that assess also the regularity, intensity and significance of platform work, with a specific focus on the task performed. The second approach aims at deriving estimates of platform work as labour input. In other words, instead of asking workers if they provide services via platform, the data can be collected from the platform itself. The vast amount of information platforms collect could be used to estimate the number of hours worked via platforms and gather more detailed evidence on wages. However, the mixed use of platforms and the ambiguous identification criteria of individuals on platforms could raise issue of double counting when measuring employment using this second approach.
Pesole, A., Fernandez Macias, E., Urzi Brancati, M.C. and Gomez Herrera, M.E., How to quantify what is not seen Two proposals for measuring platform work, European Commission, 2019, JRC117168.
2019-06-26
European Commission
JRC117168
Language Citation
ENGPesole, A., Fernandez Macias, E., Urzi Brancati, M.C. and Gomez Herrera, M.E., How to quantify what is not seen Two proposals for measuring platform work, European Commission, 2019, JRC117168.
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