The impacts of technological change on jobs have been a topic of much interest over recent decades. Existing economic statistics are in general prone to mismeasuring the value of digital activities and investments, because these are often not directly related to production, but to development, design, and marketing, whose value is harder to establish. Existing labour market statistics in particular are missing online work because of definitional and measurement issues. A standard ILO definition of employment used by statistical agencies counts as employed anyone gainfully employed for at least one hour either in a week or a day. This measure fails to capture any incremental effects of online work – if someone already has a job and does a second job online, their additional efforts are not captured in employment statistics. In addition, it is not clear to what extend online workers choose to report their earnings to tax agencies, especially if the earnings are small. This might be an especially relevant concern for the large share of online workers living in developing countries, where the informal economy dominates and tax evasion is common. Even when online earnings are correctly reported, the existing statistical categories do not allow such earnings to be distinguished from contingent income earned from the local labour market.
If the digital economy presents new challenges for statistics production, it also presents new opportunities. Many digital platforms provide application programming interfaces (APIs) for software developers to integrate the platform with other applications. Such APIs can frequently be used to access and automatically collect data from the digital services. Our paper aims to fill gaps in traditional labour market statistics.
The paper presents an experimental economic indicator which tracks the volume of new vacancies over time. The Online Labour Index measures the utilisation of online labour platforms over time and across countries and occupations, providing a solid evidence base for future policy and research.
We will publish a series of blog posts in the following days which describe various pieces of information we have learned from the Online Labour Index since the start of its data collection.
For more details, see the full paper:
Kässi, O., and Lehdonvirta, V. (2018). Online Labour Index: Measuring the Online Gig Economy for Policy and Research. Technological Forecasting and Social Change (forthcoming)