This approach relies on the platform having a physical or legal presence, such as a subsidiary company, in a country that can be contacted for survey. However, the online nature of platforms’ business models means that they are often active in countries without having any formal presence there. Furthermore, large international platforms can have complicated structures, with transactions being routed and processed in multiple ways. This can make it challenging for statistical agencies in any one country to get a holistic view of a platform’s activities. Furthermore, it is likely to lead to platform companies receiving data requests from many countries. International co-ordination on collecting data from online platforms has the potential to yield better quality data and to minimise the reporting burden on online platform companies.
Experiences of gathering information directly from platform companies have varied greatly. If working relationships and collection channels can be developed, it is clear that, because online platforms are based entirely around digital systems, they are likely to hold a considerable amount of information that would be useful for statistical purposes. This includes transaction numbers and values, as well as information on the products customers buy and the prices paid (potentially useful for inflation statistics), on supplier and customer locations (relevant for international trade statistics), and other policy-relevant information such as the number of nights for which a property is rented out. However, such information is also likely to be commercially sensitive. This, and concerns about privacy, disclosure, and so on, would need to be managed in any attempt to gather statistical data from platform companies.
Other surveys might also be used to gain information on online platforms and the customers and suppliers that make transactions through them, such as ICT usage surveys, Labour Force Surveys, household expenditure surveys, and time-use surveys. Third-party data sources can also provide useful insights. For example, the JP Morgan Chase Institute used data on millions of transactions by Chase Bank clients in the United States to identify a sample who were active in the platform economy. This allowed analysing the income of individuals who are active on different types of online platforms. Key insights included an apparent high turnover of participants offering services via online platforms, indicated by 58% of the sample having platform earnings for only three or fewer months of the year, and an apparent slowing of uptake as the “traditional” labour market strengthened (Farrell, Grieg and Hamoudi, 2018). Data from tax administration systems and web-scraped data may also be of use.