Who Gets the Lion's Share in the Sharing Economy: A Case Study of Social Inequality in AirBnB

Sharing economy platforms have rapidly disrupted and transformed many traditional markets. Companies such as AirBnB, in the housing market, and Uber, in the ride-sharing space, have thrived by creating opportunities for so-called “micro-entrepreneurs”, allowing them to leverage existing personal assets, such as a spare room or car, to generate additional income. While often heralded as an opportunity to reduce income inequality, opening opportunities through technology to a much larger segment of the population, there is however a latent concern that these platforms are in practice not as inclusive as advertised. In this paper we study the AirBnB listings in Chicago and examine a number of different dimensions regarding the hosts, their property and the environment within which they operate. Specifically we examine who the hosts are by detecting hosts’ ethnicity, gender and age using images posted publicly on the site. Leveraging this information and socio-economic metrics from the Census, we examine the properties different hosts offer and what is received in return. Finally we study how these hosts present their properties by measuring the aesthetic score of the main listing photographs using a deep learning algorithm. Our results suggest an ethnical discrepancy that affects minorities from lower socio-economic backgrounds, even when taking into account location and other attributes such as price of AirBnB listings. The findings also suggest that a wider range of factors, such as poorer pictures of listings, maybe affecting the inclusion and that could be corrected with internal policies and assistance of the platform owners.

[1]  Giovanni Quattrone,et al.  There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets , 2015, CSCW.

[2]  Brent J. Hecht,et al.  A Tale of Cities: Urban Biases in Volunteered Geographic Information , 2014, ICWSM.

[3]  Michael Luca,et al.  Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment , 2016 .

[4]  Dennis J. Zhang,et al.  Reducing Discrimination with Reviews in the Sharing Economy: Evidence from Field Experiments on Airbnb , 2016, Manag. Sci..

[5]  Dennis J. Zhang,et al.  Discrimination with Incomplete Information in the Sharing Economy: Field Evidence from Airbnb , 2016 .

[6]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[7]  Naila Murray,et al.  AVA: A large-scale database for aesthetic visual analysis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  K. Frenken,et al.  Putting the sharing economy into perspective , 2017, A Research Agenda for Sustainable Consumption Governance.

[9]  Georgios Zervas,et al.  A first look at online reputation on Airbnb, where every stay is above average , 2015, Marketing Letters.

[10]  Giovanni Quattrone,et al.  Who Benefits from the "Sharing" Economy of Airbnb? , 2016, WWW.

[11]  Giovanni Quattrone,et al.  Putting ubiquitous crowd-sourcing into context , 2013, CSCW '13.

[12]  Loren G. Terveen,et al.  Towards a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit , 2022 .

[13]  Kristina Lerman,et al.  Analyzing Uber's Ride-sharing Economy , 2017, WWW.

[14]  D Hémon,et al.  Assessing the significance of the correlation between two spatial processes. , 1989, Biometrics.

[15]  J. Schor Does the sharing economy increase inequality within the eighty percent?: findings from a qualitative study of platform providers , 2017 .

[16]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[17]  Giovanni Quattrone,et al.  Mind the map: the impact of culture and economic affluence on crowd-mapping behaviours , 2014, CSCW.

[18]  Matthew Pearson,et al.  Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb , 2015, EC.

[19]  Eric Gilbert,et al.  Faces engage us: photos with faces attract more likes and comments on Instagram , 2014, CHI.

[20]  R. Sprague Worker (Mis)Classification in the Sharing Economy: Trying to Fit Square Pegs in Round Holes , 2015 .

[21]  A. Sundararajan,et al.  Peer-to-Peer Rental Markets in the Sharing Economy , 2015 .

[22]  Christopher R. Knittel,et al.  Racial and Gender Discrimination in Transportation Network Companies , 2016 .

[23]  Xin Jin,et al.  Deep image aesthetics classification using inception modules and fine-tuning connected layer , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).