Ethnic Discrimination on an Online Marketplace of Vacation Rentals

Transcription

1 Ethnic Discrimination on an Online Marketplace of Vacation Rentals Morgane Laouenan (CNRS/Paris 1) Roland Rathelot (Warwick & CEPR) Banque de France - May 2018

2 Motivation Ethnic discrimination pervades many markets Bridging ethnic gaps requires being able to understand the underlying mechanisms behind discrimination Many theoretical and empirical papers on the sources of discrimination but still not conclusive In this paper, we use new data from a website of vacation rentals to investigate which channels cause the price ethnic gap towards hosts

3 An Online Marketplace of Vacation Rentals On Airbnb, hosts post a listing and a lot of information about the property (location, characteristics, pictures, description). To give you an idea : example Potential guests request to stay (or directly book) at a given period and are price-takers: no negotiation can take place Hosts choose to accept or not potential guests: we don t observe this side of the market so we focus on potential discrimination of guests towards hosts Simple supply-demand model: a lower demand for a property should lead its price down

4 Statistical Discrimination Taste-based Discrimination (Becker 1957) Statistical Discrimination (Phelps 1972, Arrow 1973) Idea by Farber & Gibbons (1996); Altonji & Pierret (2001): time reveals some unobservables and the gap should decrease with information Identification either through: (i) audit studies, (ii) natural experiments, (iii) panel data Autor & Scarborough (2008), Oreopoulos (2011), Wozniak (2014), Zussman (2013) List, (2004), Doleac & Stein (2013), Pope & Sydnor (2011), Bayer et al. (2012), Ewens et al. (2014)

5 Statistical Discrimination and this Market Ethnicity is correlated with unobserved determinants of quality and used as a signal by potential guests Short-term rental: perfect market to test statistical discrimination Use of information about reviews and ratings Panel dimension (within-property variation of information) Prediction: conditional on quality, price of minority listings should increase more than majority ones with the number of reviews

6 This paper Does the price depend on property hosts ethnicity? How is this price gap explained by differentials in observables? How does it depend on the information set available to potential guests? What explanations are compatible or can be ruled out by empirical facts?

7 Contributions First large-scale study (19 cities in Europe and North America) on ethnic discrimination on short-term rental markets Makes use of rich longitudinal variation to identify the impact of information on prices We find that empirical evidence suggest that ethnic price differentials are explained by statistical discrimination

8 Database collected online Data obtained from an online marketplace facilitating short-term private rentals (Airbnb) Data on daily rate, property characteristics, hosts first names, ratings/reviews, and location (within a.3 mile radium circle) Website based on reputation in creating trust between hosts and guests: reliability of the peer-reviewing system

9 Example Back Reviews and ratings

10 Our sample 19 cities in Europe/Canada/US (London, Paris, Madrid, Barcelona, Rome, Milan, Florence, Amsterdam, Berlin, Marseille, Vancouver, Toronto, Montreal, Boston, New York City, Miami, Chicago, San Francisco, Los-Angeles) More than 3.5 million observations 400,000 distinct flats 20 waves: each wave collected every 2/3 weeks (from mid-june 2014 to mid-june 2015) Unbalanced panel: 50% flats are observed in more than 6 waves; 11% are observed in all waves

11 Number of observations by City City N Share Amsterdam 142, Barcelona 244, Berlin 215, Boston 52, Chicago 53, Florence 86, London 374, Los-Angeles 219, Madrid 100, Marseille 87, Miami 96, Milan 123, Montreal 110, New-York 489, Paris 646, Rome 207, San-Francisco 135, Toronto 82, Vancouver 61,

12 Airbnb à Paris 69,189 listings 12.5% des listings sont des logements partagés (shared flat) Prix moyen : 134E (entire flat) & 77E (shared flat) Nombre moyen de Reviews : 3 Reviews 32,881 listings with 0 reviews (entre la 1ère et la dernière vague) 76% des logeurs ont un seul logement sur Airbnb

13 Ethnicity is defined using first names and pictures 2 ethnic groups : Arabic/African and Blacks Arabic/African/Muslim first names: Jouniaux (2001) Use of popular websites that help parents pick kids names based on ethnicity Collect pictures from profiles Ethnic-minority listings represent around 6.3% of all observations

14 Ethnic groups Sample size Share Within-city*wave gap Majority 3,233, % - Blacks (US/Can) 67, % 31.5% Blacks (Eur) 32, % 26.6% Arabic/Muslim (US/Can) 42, % 4.9% Arabic/Muslim (Eur) 73, % 9.6%

15 Ethnic price gap Log daily rate (1) (2) (3) (4) Minority *** *** *** *** (0.012) (0.008) (0.008) (0.006) City*Wave FE Yes Yes Yes Yes Neighborhood FE No No Yes Yes Block FE No No Yes Yes Property charac. No Yes No Yes Adj R N obs. 3,450,065 3,450,065 3,450,065 3,450,065 Notes: Standard errors in parentheses. *** p < 0.01.

16 Ethnic price gap Hosts enjoy an average of $26 in surplus per night booked (50 largest US cities in ) from Farronato & Fradkin (2017) Average Price : $109 Approximatively 3.6pp from 25%

17 Mechanisms? Statistical discrimination: test à la Altonji-Pierret Ethnic differences in price-setting behaviors

18 Theoretical framework Price p depends on quality (+) & on minority host (-) Quality is imperfectly observed (but reviews provide information) When #Reviews = 0 : Price set by the minority host is lower to compensate lower demand (due to a lower belief in quality) This belief of quality will be modified by the new information provided by the reviews : Updating prices Statistical discrimination will affect prospective guests beliefs about the quality of the apartment : the ethnic price gap should decrease with the number of reviews

19 Distribution of the number of reviews

20 Theory: If reviews bring some information Price Quintile quality Number of reviews

21 Theory: If there is statistical discrimination 1.0 Price Group Majority Minority Quintile quality Number of reviews

22 Ethnic price gap: pooled cross-sections Log daily rate (1) (2) (3) (4) (5) Minority *** *** *** * (0.008) (0.007) (0.009) (0.011) (0.018) City*Wave FE Yes Yes Yes Yes Yes BlockID FE Yes Yes Yes Yes Yes HoodID FE Yes Yes Yes Yes Yes Property char. Yes Yes Yes Yes Yes Ratings No Yes Yes Yes Yes Nb reviews Minority % 6.4% 6.1% 6.3% 6.3% 6.1% Adj R N obs. 1,110, , , , ,901 All regressions include city*wave FE, neighborhood & Block FE, property characteristics and ratings (if any)

23 A FE model We estimate: p it = r 1{ r i = r}k it β r + K it m i β m + µ i + X it β x + ε it pit : log-price; K it : number of reviews; X it observables of property i at time t r: rating at the last observation; m: minority dummy; µ: property FE Predictions Reviews and ratings matter: β r > β r if r > r Statistical discrimination: βm > 0

24 Fixed-Effects Estimation Minority K/ *** 0.084*** 0.141*** (0.032) (0.029) (0.044) Minority (K/100) ** (0.048) Nb Reviews K N obs. 2,171,788 2,246,786 2,289,933 Notes: (i) All regressions include city*wave FE, property characteristics and listings FE, the number of reviews interacted with the last rating and the minority dummy interacted with wave dummies.

25 Not presented here Structural Estimation Additional Results (ethnic matching) Robustness Checks (outside option : demand)

26 Conclusion Ethnic price differentials seem to be explained by statistical discrimination and not by ethnic differences in price-setting behaviors

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