One result of this study that should be underlined and brought to the debate is that, the presumption of digital discrimination for drivers with a predominantly Arabic name is quite robust. « Comparing French and Arabic-sounding names, Arabic drivers earn 8.6 euros less in revenue. This is an extremely large effect relative to a mean revenue of 14.8 (58.4% reduction in revenue), which gives an estimate of the cost of being discriminated against on a carsharing platform.»
On this first empirical analysis of the world’s leading intercity carsharing platform, BlaBlaCar, Robert G.Hammond (North Carolina State University), Thierry Pénard (CREM, from University of Rennes 1), and Mehdi Farajallah, from Rennes School of Business, highlights other several interesting points :
- how prices are determined on peer-to-peer markets
- whether experience and reputation in sharing economy platforms have the same impact as in traditional markets
- more-experienced drivers on BlaBlaCar set lower prices and sell more seats than less-experienced drivers.
« We examine how price and demand are determined on peer-to-peer platforms and whether experience and reputation have the same impact as in traditional markets. We use data from the world’s leading intercity carsharing platform, BlaBlaCar, which connects drivers with empty seats to riders. We find that pricing decisions evolve as drivers gain experience with the platform. More-experienced drivers set lower prices and, controlling for price, sell more seats. Our interpretation is that more-experienced drivers on BlaBlaCar learn to lower their prices as they gain experience; accordingly, more-experienced drivers earn more revenue per trip. In total, our results suggest that peer-to-peer markets such as BlaBlaCar share some characteristics with other types of peer-to-peer markets such as eBay but remain a unique and rich setting in which there are many new insights to be gained. »
How to deal with Digital discrimination on online markets
As explain the three researchers, the prevalence of digital discrimination on online platforms is increasingly addressed by researchers on this topic. Including studies of AirBnB (Cui et al., 2016; Edelman and Luca, 2014; Edelman et al., 2017 and Kakar et al., 2018), Craigslist (Doléac and Stein, 2013), Uber (Ge et al., 2016), and Prosper.com (Pope and Sydnor, 2011).
« These studies show evidence of discrimination on both sides of the market (toward suppliers and demanders) and seek to understand the underlying mechanisms (statistical versus taste-based discrimination). »
In order to improve things and to avoid « bad buzz » for Blablacar or other similar platforms the authors have made the following suggestions:
« First, drivers could be identified by user IDs (as done on eBay for example) without pictures, which would remove the identifiers that might reveal characteristics that might be subject to differential treatment. Second, BlaBlaCar could encourage or require automatic confirmation (which is similar to Instant Book on AirBnB); in conjunction with the removal of names and pictures, this instantaneous booking of trips removes the potential for screening on the part of drivers who might have preferences over certain characteristics of riders. »
- Read the entire study on www.sciencedirect.com