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- Publisher Website: 10.1016/j.cities.2022.104112
- Scopus: eid_2-s2.0-85142375600
- WOS: WOS:000946289300007
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Article: Who are the gig workers? Evidence from mapping the residential locations of ride-hailing drivers by a big data approach
Title | Who are the gig workers? Evidence from mapping the residential locations of ride-hailing drivers by a big data approach |
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Authors | |
Keywords | Big data Digital platform economy Gig workers Mobile phone data Residential location Residential segregation |
Issue Date | 1-Jan-2023 |
Publisher | Elsevier |
Citation | Landscape and Urban Planning, 2023, v. 132 How to Cite? |
Abstract | One of the challenges faced in understanding platform gig work is who are the gig workers involved in this novelty labour relationship. We attempt to answer it by using mobile phone trajectories to map the residential locations of gig drivers in the ride-hailing platform economy in Chengdu, China. Then, we reconstruct the relationships between the spatial structure of the ride-hailing drivers and their neighbourhood-level sociodemographic characteristics and residential built environments. We found that ride-hailing drivers are more likely to come from neighbourhoods with lower-income and less regular job opportunities. Their residences formed living clusters with urban villages, resettlement houses, and shanty towns. Our study suggests that ride-hailing platforms do not liberate gig workers from the structural rural-urban disparities but form a continuation of the structural barriers and discrimination in the division of labour, even if the technological innovation discourse in the platform economy argues that the gig driving can attract the well-educated and other minorities. |
Persistent Identifier | http://hdl.handle.net/10722/340195 |
ISSN | 2021 Impact Factor: 8.119 2020 SCImago Journal Rankings: 1.938 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qiao, S | - |
dc.contributor.author | Huang, G | - |
dc.contributor.author | Yeh, A | - |
dc.date.accessioned | 2024-03-11T10:42:23Z | - |
dc.date.available | 2024-03-11T10:42:23Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.citation | Landscape and Urban Planning, 2023, v. 132 | - |
dc.identifier.issn | 0169-2046 | - |
dc.identifier.uri | http://hdl.handle.net/10722/340195 | - |
dc.description.abstract | <p>One of the challenges faced in understanding platform gig work is who are the gig workers involved in this novelty labour relationship. We attempt to answer it by using mobile phone trajectories to map the residential locations of gig drivers in the ride-hailing platform economy in Chengdu, China. Then, we reconstruct the relationships between the spatial structure of the ride-hailing drivers and their neighbourhood-level sociodemographic characteristics and residential built environments. We found that ride-hailing drivers are more likely to come from neighbourhoods with lower-income and less regular job opportunities. Their residences formed living clusters with urban villages, resettlement houses, and shanty towns. Our study suggests that ride-hailing platforms do not liberate gig workers from the structural rural-urban disparities but form a continuation of the structural barriers and discrimination in the division of labour, even if the technological innovation discourse in the platform economy argues that the gig driving can attract the well-educated and other minorities.<br></p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Landscape and Urban Planning | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Big data | - |
dc.subject | Digital platform economy | - |
dc.subject | Gig workers | - |
dc.subject | Mobile phone data | - |
dc.subject | Residential location | - |
dc.subject | Residential segregation | - |
dc.title | Who are the gig workers? Evidence from mapping the residential locations of ride-hailing drivers by a big data approach | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.cities.2022.104112 | - |
dc.identifier.scopus | eid_2-s2.0-85142375600 | - |
dc.identifier.volume | 132 | - |
dc.identifier.eissn | 1872-6062 | - |
dc.identifier.isi | WOS:000946289300007 | - |
dc.identifier.issnl | 0169-2046 | - |