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The rise of digital labor platforms, which link service providers directly with customers, is a potentially promising solution for job creation, empowerment, and poverty reduction (World Bank 2023). In Indonesia, the gig economy took off in 2015 with the launch of online motorbike taxis, allowing anyone with a smartphone and a bike to work in ride-hailing digital platforms. Since then, this gig economy has rapidly grown, expanding into new areas like food delivery and logistics, transforming how people work and earn in the country. In this study, we partnered with an Indonesia-based gig work platform to understand the characteristics and experience of digital gig workers—specifically, motorcycle taxi and delivery drivers. To keep our partner’s identity confidential, we’ll simply refer to them as [platform] throughout our report. Our research seeks to shed light on a few key areas:
We divided our study findings into two main themes. The first theme focuses on economic lives of workers: the socio-economic characteristics and economic lives of gig workers, covering aspects such as demographics, motivations for joining platform work, labor supply, income, financial inclusion and security, and comparisons with offline drivers. The second companion report explores gig workers’ experiences using the platform, including participation in social programs, work-hour preferences, interactions with others while working, safety, unionization, the experiences of female drivers, as well as exit and inactivity. This main report covers the first theme, along with an overview of the study methodology. The second theme is discussed in our companion paper, Experiences Working on Gig Platforms: The Case of Delivery Drivers in Indonesia.
We focus on three groups of motorcycle drivers:
In this study, the samples for active and inactive drivers were drawn from [platform]’s nationwide administrative data, allowing us to assess the representativeness of our sample and apply post-stratification weights for improved accuracy of the estimates. Although women make up only a small percentage of active platform drivers (2%), we intentionally over-sampled female drivers to provide meaningful insights into gendered experiences. When presenting our results, we highlight how drivers stack up against the broader urban or national population and examine how our findings either support or challenge the narratives from previous studies and reports (Brailovskaya 2023). For offline drivers, we used random sampling methods to draw a sample of drivers in places where they usually wait for passengers.
Case of motorbike on taxi and delivery drivers in Indonesia
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