Discriminatory attitudes between ridesharing passengers

  • Scott Middleton
  • Jinhua ZhaoEmail author


Prior studies have provided evidence of discrimination between drivers and passengers in the context of ridehailing. This paper extends prior research by investigating passenger-to-passenger discriminatory attitudes in the context of ridesharing. We conducted a survey of 1110 Uber and Lyft users in the US using Mechanical Turk, 76.5% of whom have used uberPOOL or Lyft Shared rides, and estimated two structural equation models. The first model examines the influence of one’s demographic, social and economic characteristics on discriminatory attitudes toward fellow passengers in ridesharing, and how such influence varies by the targets of discrimination (i.e., race and class). The second model examines the influence of one’s generic social dominance orientation on discriminatory attitudes in the ridesharing context. We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children. A respondent’s race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences). The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election. Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences. We also find that one’s generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes. Further research is required to identify policy interventions that mitigate such attitudes in the context of ridesharing.


Ridesharing Discrimination Social dominance Transportation Network Companies (TNCs) Class Race 



Funding was provided by MIT IDSS Seed Fund.


  1. Adams, D.: Boston-Based Attorney Argues Uber’s Star Ratings are Racially Biased. Boston Globe, Dorchester (2016)Google Scholar
  2. Ambinder, L.P.: Dispelling the myth of rationality: racial discrimination in taxicab service and the efficacy of litigation under 42 USC 1981. Geo. Wash. L. Rev. 64, 342 (1995)Google Scholar
  3. Ayres, I., Banaji, M.R., Jolls, C.: Race effects on eBay. Rand J. Econ. 46(4), 891–917 (2011)Google Scholar
  4. Ayres, I., Vars, F.E., Zakariya, N.: To insure prejudice: racial disparities in taxicab tipping. Yale LJ 114, 1613 (2004)Google Scholar
  5. Bartlett, K.T., Gulati, G.M.: Discrimination by customers. Duke Law School Public Law & Legal Theory Series, No. 2015-4 (2016)Google Scholar
  6. Bentler, P.M.: Comparative fit indices in structural equation models. Psychol. Bull. 107(2), 238–246 (1990)Google Scholar
  7. Brown, A.E.: Ridehail revolution: ridehail travel and equity in Los Angeles. PhD thesis, UCLA (2018)Google Scholar
  8. Chaube, V., Kavanaugh, A.L., Perez-Quinones, M.A.: Leveraging social networks to embed trust in rideshare programs. In: 2010 43rd Hawaii International Conference on System Sciences, pp. 1–8 (2010)Google Scholar
  9. Delbosc, A.: Delay or forgo? A closer look at youth driver licensing trends in the United States and Australia. Transportation 44(5), 919–926 (2017)Google Scholar
  10. DeLoach, S.B., Tiemann, T.K.: Not driving alone? American commuting in the twenty-first century. Transportation 39(3), 521–537 (2012)Google Scholar
  11. Doleac, J.L., Stein, L.C.: The visible hand: race and online market outcomes. Econ. J. 123(572), F469–F492 (2013)Google Scholar
  12. Dueker, K., Bair, B., Levin, I.: Ride sharing: psychological factors. Transp. Eng. J. 103, 685–692 (1977)Google Scholar
  13. Edelman, B.G., Luca, M.: Digital discrimination: the case of Working Paper, Harvard Business School (2016)Google Scholar
  14. Ferguson, E.: The rise and fall of the American carpool: 1970–1990. Transportation 24(4), 349–376 (1997)Google Scholar
  15. FHWA: 2017 National Household Travel Survey. (2018). Accessed 15 Oct 2018
  16. Ge, Y., Knittel, C.R., MacKenzie, D., Zoepf, S.: Racial and gender discrimination in transportation network companies. Working Paper 22776, National Bureau of Economic Research (2016).
  17. Greenwald, A.G., Nosek, B.A., Banaji, M.R.: Understanding and using the implicit association test: an improved scoring algorithm. J. Personal. Soc. Psychol. 85(2), 197–216 (2003)Google Scholar
  18. Hannak, A., Wagner, C., Garcia, D., Mislove, A., Strohmaier, M., Wilson, C.: Bias in online freelance marketplaces: evidence from TaskRabbit and Fiverr. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, pp. 1914–1933. ACM Press (2017)Google Scholar
  19. Hanson, A., Hawley, Z., Martin, H., Liu, B.: Discrimination in mortgage lending: evidence from a correspondence experiment. J. Urban Econ. 92(Supplement C), 48–65 (2016)Google Scholar
  20. Hauser, D.J., Schwarz, N.: Attentive Turkers: Mturk participants perform better on online attention checks than do subject pool participants. Behav. Res. Methods 48(2), 400–407 (2015)Google Scholar
  21. Hawkins, A.J.: Lyft’s carpooling service is expanding to six us cities next week. The Verge (2016a). Accessed 30 Oct 2017
  22. Hawkins, A.J.: Lyft’s president says majority of rides will be in self-driving cars by 2021. The Verge (2016b). Accessed 30 Oct 2017
  23. Ho, A.K., Sidanius, J., Kteily, N., Sheehy-Skeffington, J., Pratto, F., Henkel, K.E., Foels, R., Stewart, A.L.: The nature of social dominance orientation: theorizing and measuring preferences for intergroup inequality using the new SDO scale. J. Personal. Soc. Psychol. 109(6), 1003–1028 (2015)Google Scholar
  24. Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6(1), 1–55 (1999)Google Scholar
  25. Hughes, R., MacKenzie, D.: Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators. J. Transp. Geogr. 56, 36–44 (2016)Google Scholar
  26. Ipeirotis, P.G.: Demographics of Mechanical Turk. NYU Working Paper, Social Science Research Network: CEDER-10-01 (2010)Google Scholar
  27. Kalanick, T.: Uber response to Senator Franken. (2016). Accessed 30 Nov 2017
  28. Kearney, A., De Young, R.: A knowledge-based intervention for promoting carpooling. Environ. Behav. 27, 650–678 (1995)Google Scholar
  29. Klein, N.J., Smart, M.J.: Millennials and car ownership: less money, fewer cars. Transp. Policy 53(Supplement C), 20–29 (2017)Google Scholar
  30. Li, C., Zhao, J.: Humanizing travel: how e-hail apps transform stakeholder relationships in the US taxi industry. In: Transportation Research Board 94th Annual Meeting (2015)Google Scholar
  31. Lyft: About Lyft Line ridesharing. (2017a). Accessed on 04 Dec 2017
  32. Lyft: Lyft anti-discrimination policies. (2017b). Accessed 20 Oct 2017
  33. MacCallum, R.C., Browne, M., Sugawara, H.: Power analysis and determination of sample size for covariance structure modeling. Psychol. Methods 1(2), 130–149 (1996)Google Scholar
  34. MacDonald, A.: Uber economic study: Uber serves underserved neighborhoods in Chicago as well as the Loop. Does taxi? Uber Blog (2014). Accessed on 19 Oct 2017
  35. Martyn, A.: Uber and Lyft are Being Accused of Redlining Again, But is That Actually Happening?. Dallas Observer, Dallas (2014)Google Scholar
  36. McGovern, T.: United States general election presidential results by county 2016. (2018). Accessed 30 Nov 2017
  37. Moody, J., Middleton, S., Zhao, J.: Rider-to-rider discriminatory attitudes and ridesharing behavior. Transp. Res. Part F Traffic Psychol. Behav. 62, 258–273 (2019)Google Scholar
  38. Moody, J., Zhao, J.: Using the implicit association test to understand travel behavior: a case study on social status bias in car vs. bus mode choice. In: Proceedings of the Transportation Research Board 95th Annual Meeting (2018)Google Scholar
  39. Newbold, K.B., Scott, D.M.: Driving over the life course: the automobility of Canada’s Millennial, Generation X, Baby Boomer and Greatest Generations. Travel Behav. Soc. 6(Supplement C), 57–63 (2017)Google Scholar
  40. Nunley, J.M., Pugh, A., Romero, N., Seals, R.A.: An examination of racial discrimination in the labor market for recent college graduates: estimates from the field. Auburn University Department of Economics Working Paper Series, AUWP 2014-06 (2014)Google Scholar
  41. Oppenheim, N.: Carpooling: problems and potentials. Traffic Q. 33(2), 253–262 (1979)Google Scholar
  42. Oswald, F., Mitchell, G., Blanton, H., Jaccard, J., Tetlock, P.E.: Predicting ethnic and racial discrimination: a meta-analysis of iat criterion studies. J. Personal. Soc. Psychol. 105(6), 171–192 (2013)Google Scholar
  43. Pager, D., Shepherd, H.: The sociology of discrimination: racial discrimination in employment, housing, credit, and consumer markets. Annu. Rev. Sociol. 34, 181–209 (2008)Google Scholar
  44. Perez, S.: Waze Carpool’s new app lets riders get more choosy about their drivers. Tech Crunch (2018). Accessed on 17 Apr 2018
  45. Pisarski, A.: Commuting in America: national report on commuting patterns and trends. Technical report, Eno Foundation for Transportation, Inc. (1987)Google Scholar
  46. Pope, D.G., Sydnor, J.R.: What’s in a picture? Evidence of discrimination from J. Hum. Resour. 46(1), 53–92 (2011)Google Scholar
  47. Rayle, L., Shaheen, S., Chan, N., Dai, D., Cervero, R.: App-based, on-demand ride services: comparing taxi and ridesourcing trips and user characteristics. Technical report, University of California Transportation Center (2014)Google Scholar
  48. Ridley, S., Bayton, J.A., Outtz, J.H.: Taxi service in the District of Columbia: Is it influenced by patron’s race and destination? Washington, DC Lawyer’s Committee for Civil Rights Under the Law (1989)Google Scholar
  49. Rosenblat, A., Levy, K.E., Barocas, S., Hwang, T.: Discriminating tastes: Uber’s customer ratings as vehicles for workplace discrimination. Policy Internet 9(3), 256–279 (2017)Google Scholar
  50. Sandercock, L.: Planning in the ethno-culturally diverse city: a comment. Plan. Theory Pract. 4(3), 319–323 (2003)Google Scholar
  51. Sarriera, J.M., Alvarez, G.E., Alesbury, A., Scully, T., Zhao, J.: To share or not to share: investigating the social aspects of dynamic ridesharing. Transportation Research Board 2017 (2016)Google Scholar
  52. Schreiber, J.B., Nora, A., Stage, F.K., Barlow, E.A., King, J.: Reporting structural equation modeling and confirmatory factor analysis results: a review. J. Educ. Res. 99(6), 323–337 (2006)Google Scholar
  53. Sennett, R.: The Fall of Public Man. Cambridge University Press, Cambridge (1977)Google Scholar
  54. Sennett, R.: Building and Dwelling: Ethics for the City. Farrar Straus and Giroux, New York City (2018)Google Scholar
  55. Siegelman, P.: Racial discrimination in everyday commercial transactions: What do we know, what do we need to know, and how can we find out. In: Proceedings of the Urban Institute Conference on Testing for Racial and Ethnic Discirmination in American Economic Life (1998)Google Scholar
  56. Smith, A.: Shared, collaborative and on demand: the new digital economy. Technical report, Pew Research Center (2016)Google Scholar
  57. Thebault-Spieker, J., Terveen, L.G., Hecht, B.: Avoiding the south side and the suburbs: the geography of mobile crowdsourcing markets. In: Proceedings of the 2015 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 265–275. ACM Press (2015)Google Scholar
  58. Uber: It’s a beautiful (pool) day in the neighborhood. Uber Blog (2017a). Accessed 04 Dec 2017
  59. Uber: What is uberPOOL? (2017b). Accessed on 04 Dec 2017
  60. Wortham, J.: Ubering while black. Matter (2014)Google Scholar
  61. Zhang, H., Zhao, J.: Mobility sharing as a preference matching problem. IEEE Trans. Intell. Transp. Syst. 1–9 (2018).

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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