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Would You Ride with Me? Discrimination in Shared Mobility Platforms. The Hungarian Case

Would You Ride with Me? Discrimination in Shared Mobility Platforms. The Hungarian Case

  Borbala Simonovits, PhD, Eotvos Lorand University Division: Sociology &humanities  


Sharing economy platforms are rapidly growing worldwide, especially peer-to-peer (P2P) online marketplaces operating in the travel and of tourism industry. We focus our attention on a Hungarian ridesharing platform in order to understand the working mechanisms of discriminative selection by service providers against service users of various minorities, as the literature in this area is limited. In our ongoing innovative research, we are collaborating with the most widely-used ridesharing platform in Hungary: As a result of our joint work we are going to apply an intervention-based research in 2019/2020 to test whether the experimental stimuli have any effect on drivers’ behaviour towards minorities. In my proposed presentation, I am go to show the collaboration of the platform and our research team as well as the research design, the spots for the intervention and our first empirical results of the first wave. My ongoing research (2018-2021) is carried out within the framework of Trust and Discrimination in the Sharing Economy—With a Special Focus on Collaborative Consumption Platforms, financed by the Young Researcher’s Grant of the National Research, Development and Innovation Fund (FK 127978). Beyond the experimental research module, qualitative research is also applied to explore how online trust and discrimination works on the platforms. See more on the project at:

Background and relevance of the project

In Hungary, discrimination based on ethnicity is likely to occur everyday. Since 2006 several projects using the technique of controlled field experiment have been carried out in Hungary, mostly to explore the mechanisms of discrimination in the labour market against various vulnerable social groups, i.e. the Roma, overweight people, and people with disabilities (see: Pálosi, Sik, & Simonovits, 2007; Sik & Simonovits, 2008). Most recently, we conducted a small scale experimental research in 2017 testing racial and ethnic discrimination on one of the most popular ridesharing platforms (BlaBlaCar) also operating in Hungary. The findings of the field experiment were in line with the previous research results: racial and ethnic background both serve as a basis for discrimination on online platforms, especially in the case of male passengers (Simonovits et al, 2018). Ridesharing platforms can be understood as social markets involving one-off, face-to-face interactions in informal settings. Private service providers (drivers) in these informal markets are providers of risky, “high-stakes” offline experiences, which makes the trust between users a crucial resource. In many cases service providers have to share their narrow personal spaces (practically their cars or flats) with foreigners. Analysing these kinds of informal social markets enables us to explore more diverse everyday interactions, where various minorities may face unequal treatment (Tjaden, et al. 2017). As opposed to classic business models (i.e. public transport companies in the field of transportation), in the case of shared mobility platforms (e.g. Blablacar, Oszkár or Uber) the individual service providers (the drivers) themselves are in charge of deciding whether to accept a request from a potential passenger. There is more and more research evidence that passengers of various racial and ethic minorities are significantly more frequently rejected by the service providers (Edelman and Luca, 2014; Edelman et al. 2016; Ge et al. 2016; Simonovits et al. 2018). On the other hand, drivers belonging to various ethnic and national minorities are also discriminated against, paying a discriminatory price premium of about 32% of the average market price in Germany (Tjaden, et al. 2017). in a nutshell, (available at was established in 2007 by two university students whose primary goal was to create a sustainable and environment friendly transportation solution for medium and long distance travellers in Hungary. As of 2019 they have five full time employees and 700 thousand—mostly Hungarian—users. In the past 10 years they administered all together 3.2 million rides, out of which 200 thousand (6.25% of the total rides) potential passengers were rejected by the drivers, who gave various reasons for their decisions such as “the passenger doesn’t seem to be likable”, or “the passenger is not available”, or “the driver is no longer travelling.”

The goal of our experimental research

With our research we are aiming to find out how the above mentioned 6.2% refusal rate changes by groups of passengers belonging to different minority groups before and after our intervention, which serves as the experimental stimulus. As in Hungary, the Roma is the largest ethnic minority (making up approx. 6-8 % of the total population), and discrimination against them is widespread. Our primary goal is to test drivers’ reactions to requests coming from passengers of Roma origin. People with disabilities will also be included as the experimental stimulus, in order to compare levels of refusals across various combinations of minority identities. Based on previous research evidence (see Gneezy et al 2012), we assume that different types of stereotypes and selection mechanisms work with regard to ethnicity on the one hand, and physical disability on the other. Gneezy et al suggests that physical disabilities which are perceived to be outside of the control of the individual, —such as blindness or having to use a wheelchair—are more likely to elicit pity and help from others. Our question is how these types of experimental stimuli interact with each other. Gender will also be included in our design as previous empirical studies on ridesharing have proven that gender has a significant effect on the users’ selection choices both as drivers (Simonovits et al 2018) and as riders (Tjaden et al 2017). The following research design is a joint project of our research group and of the platform. The field-experiment is going to be implemented in two waves, between which we will introduce the intervention. The experimental variables are the following: • ethnic origin (Roma vs. non Roma) • physical disability (disabled vs. non-disabled) and • gender (male vs. female). While ethnic origin will be visualized by photos and confirmed by typical Roma and non-Roma names, testers with disabilities will mention that they are wheelchair users, and will ask for extra space in the car, but not for extra help from the driver. The non-disabled group will ask for extra room for extra luggage in order to keep the requested favour under control.

The intervention

This will be based on 20-second-long animated video spots covering the topic of inclusivity closely connected to the general values of, focusing on 3 different types of content (the Roma, the disabled, and some generic content on inclusiveness). The sample of the targeted drivers will be randomly divided into 3 subsamples, all of whom will receive one of the messages.


The reaction of the drivers will be categorized as positive, negative, or no-response. We expect significant differences of outcome by profiles in terms of ethnicity, disability and gender. In order to test interaction on the one hand, and the impact of the intervention on the other, the following experimental design will be introduced (Table 1): Table 1: Controlled experimental design with intervention (planned number of observations N=1920, in total, 2 waves) After the experiment is completed, we will also conduct a short online survey (N=1000) in order to ask drivers about their decision making process, as well as about their views on topics related to online trust, reputation, and the role of reviews on the platform. Beyond its scientific impact, our innovative study has policy relevance as well, as unequal access to collaborative platforms seems to be a growing concern among both platform owners and policy makers (see the case of Airbnb whose Open Door policy has been combating discrimination since 2016). References Airbnb (2016): Airbnb Launches ‘Open Doors’ Policy to Combat Discrimination Edelman, B., and Luca, M. (2014). Digital Discrimination: The Case of Harvard Business School. Edelman, B., Luca, M., and Svirsky, D. (2016). Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment. Harvard Business School. Ge, Y., Knittel, C., MacKenzie, D., and Zoepf, S. (2016). Racial and Gender Discrimination in Transportation Network Companies. National Bureau of Economic Research. Gneezy, U., List, J., & Price, M. K. (2012). Toward an Understanding of Why People Discriminate: Evidence from a Series of Natural Field Experiments (Working Paper No. 17855). National Bureau of Economic Research. Retrieved from Pálosi, É., Sik, E., & Simonovits, B. (2007). Diszkrimináció a plázában. Szociológiai Szemle, 17(3–4), 135–148. Sik, E., & Simonovits, B. (2008). Egyenlő bánásmód és diszkrimináció. In T. Kolosi & I. G. Tóth (Eds.), Társadalmi Riport 2008 (pp. 363–386). Budapest: TÁRKI. Simonovits, B.; Shvets, I.; & Taylor, H. C. (2018): Discrimination in the sharing economy: evidence from a Hungarian field experiment. Corvinus Journal of Sociology and Social Policy. Vol.9 (2018)1, 55-79. DOI: 10.14267/CJSSP.2018.1.03 Tjaden, J. D., Schwemmer, C., & Khadjavi, M. (2017). Ride with Me – Ethnic Discrimination,