nach oben

The Annals of Regional Science

Erschienen in:

14.03.2022 | Original Paper

The impact of multi-homing in a ride-sharing market

verfasst von: Oksana Loginova, X. Henry Wang, Qihong Liu

Erschienen in: The Annals of Regional Science | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Platforms such as Uber, Lyft and Airbnb serve two-sided markets with drivers (property owners) on one side and riders (renters) on the other side. Some agents join multiple platforms (multi-home). In the case of ride-sharing, a driver may drive for both Uber and Lyft, and a rider may use both apps and request a ride from the company that has a driver close by. In this paper, we are interested in welfare implications of multi-homing in such a market. Our model abstracts away from entry/exit by drivers and riders as well as pricing by platforms. Both drivers’ and riders’ surpluses are determined by the average time between a request and the actual pickup. The benchmark setting is a monopoly platform and the direct comparison is a single-homing duopoly. The former is more efficient since it has a thicker market. Next, we consider multi-homing on the rider side, multi-homing on the driver side, and multi-homing on both sides. Relative to single-homing duopoly, we find that multi-homing on either side improves the overall welfare. However, multi-homing drivers potentially benefit themselves at the cost of single-homing drivers. In contrast, multi-homing riders benefit themselves as well as single-homing riders, representing a more equitable distribution of gains from multi-homing.

Vorheriger Artikel Locational error in the estimation of regional discrete choice models using distance as a regressor

Nächster Artikel Do workers negative self-select when they commute? Evidence for the Chilean case of long-distance commuting

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Bryan and Gans (2019) assume that platforms choose “idleness” parameter \(\delta _i\) which benefits the platform by reducing the distance a rider has to travel to join platform i (à la Hotelling). Investment in idleness \(\delta _i\) comes in the form of a fixed cost to the platform. Drivers are reduced to the exogenous parameter of marginal cost w (wage), which is also independent of \(\delta _i\). This stylized treatment of wait time makes the model tractable enough to allow price competition and multi-homing.

As a result, allowing endogenous market division of agents between the platforms (e.g., by allowing price competition) would lead to the following technical problem. The expected wait/pickup time affects drivers and riders’ platform participation decisions, but these platform participation decisions also affect the wait time. That is, the wait time and platform participation decisions are jointly determined. We are unable to solve such a model analytically with multi-homing, and thus have to rely on exogenous market division.

Uber’s surge pricing has been featured in the popular media such as NPR, “Uber Plans to Kill Surge Pricing, Though Drivers Say It Makes Job Worth It,” NPR All Tech Considered, May 3, 2016.

For example, Choi (2010) examines tying in two-sided markets with multi-homing. Jeitschko and Tremblay (2020) endogenize single- vs. multi-homing decisions on both sides. Belleflamme and Peitz (2019) show that the possibility of multi-homing may benefit or hurt the side which can multi-home. In similar spirit, in our model, the impacts of multi-homing depends on which side multi-home and also differ across single- and multi-homing agents on the same side. On the empirical side, Landsman and Stremersch (2011) analyze the seller-level multi-homing decisions in the video game console market. Gal-Or (2021) focuses on single-homing and shows that intensified competition among peer-to-peer lodging platforms may lead to higher prices, lower consumer surplus and higher platform profit.

This may include the time from the dropoff point to a nearby local center, where the driver will wait for the next request.

After a drop off, a driver can drive around or find a free parking spot, while waiting for a ride. See “What Can Uber Drivers Do While Waiting for a Ride? (Other than Scroll Your Facebook Feed),” http://therideshareguy.com/what-can-uber-drivers-do-while-waiting-for-a-ride-other-than-scroll-your-facebook-feed/.

All of our results hold if we replace (2) by \(y=\omega \phi (N_Dx)\), where \(\phi (\cdot )\) is a strictly decreasing and strictly convex function. Note that (2) corresponds to \(\phi (z)=z^{-1/2}\).

Assuming that \(N_D\) is sufficiently large, the equilibrium in which all riders get a ride exists. That is, the two curves do intersect.

Convexity of curve D in Fig. 1 (i.e., convexity of the right-hand side of (3) as a function of x) is an intrinsic property of the model. If curve D is concave, then the stable equilibrium is the lower intersection point \(\underline{x}\) (i.e., \(x^*=\underline{x}\)). Qualitatively, this equilibrium has the same comparative statics properties as stated in the text (i.e., it also decreases in \(N_R\) and increases in \(N_D\)). Accordingly, all results in the next section continue to hold.

According to Statista.com, in July 2021, Uber’s share of the US ride-sharing market was 69% and Lyft’s was 31%.

To our knowledge, there is no paper which derives the expected wait/pickup time from interactions between drivers and riders, allows multi-homing and considers price competition. If we add price competition to our model, platform participation decisions and the wait time will feed into each other, making it impossible to solve the model analytically.

Both Uber and Lyft have adopted programs to reward active and high-performing drivers. Launched in June 2016, Uber’s Power Driver Plus awards drivers cash bonuses after a set number of trips. Thus, drivers who complete an extra 80–100 trips a week receive 10–20% extra earnings. Surge pricing is another example of complicated pricing schemes. See Cachon et al. (2017) for a theoretical study of pricing schemes on service platforms, including Uber’s surge pricing.

Armstrong M (2006) Competition in two-sided markets. Rand J Econ 37(3):668–691CrossRef

Arnott R (1996) Taxi travel should be subsidized. J Urban Econ 40(3):316–333CrossRef

Bai J, So KC, Tang CS, Chen X, Wang H (2019) Coordinating supply and demand on a on-demand service platform with impatient customers. Manuf Serv Oper Manag 21(3):556–570CrossRef

Belleflamme P, Peitz M (2019) Platform competition: who benefits from multihoming? Int J Ind Organ 64:1–26CrossRef

Berger T, Chen C, Frey CB (2018) Drivers of disruption? Estimating the Uber effect. Eur Econ Rev 110:197–210CrossRef

Bernstein F, DeCroix GA, Keskin NB (2021) Competition between two-sided platforms under demand and supply congestion effects. Manuf Serv Oper Manag 23(5):1005–1331CrossRef

Bryan KA, Gans JS (2019) A theory of multihoming in rideshare competition. J Econ Manag Strategy 28(1):89–96CrossRef

Cachon GP, Daniels KM, Lobel R (2017) The role of surge pricing on a service platform with self-scheduling capacity. Manuf Serv Oper Manag 19(3):368–384CrossRef

Cailalud B, Jullien B (2003) Chicken & egg: competition among intermediation service providers. Rand J Econ 34(2):309–328CrossRef

Choi JP (2010) Tying in two-sided markets with multi-homing. J Ind Econ 58(3):607–626CrossRef

Cohen P, Hahn R, Hall J, Levitt S, Metcalfe (2016) Using the big data to estimate consumer surplus: the case of Uber. NBER Working Paper No. 22627

Cramer J, Krueger AB (2016) Disruptive change in the taxi business: the case of Uber. Am Econ Rev Papers Proc 106(5):177–182CrossRef

Edelman B, Luca M, Svirsky D (2017) Racial discrimination in the sharing economy: evidence from a field experiment. Am Econ J Appl Econ 9(2):1–22CrossRef

Gal-Or E (2021) How does competition among lodging sharing platforms affect welfare and profits? J Econ Manag Strategy 30(1):45–62CrossRef

Jeitschko TD, Tremblay MJ (2020) Platform competition with endogenous homing. Int Econ Rev 61(3):1281–1305CrossRef

Hall J, Kendrick C, Nosko C (2016) The effects of Uber surge pricing: a case study. Technical report

Landsman V, Stremersch S (2011) Multihoming in two-sided markets: an empirical inquiry in the video game console industry. J Mark 75(6):39–54CrossRef

Liu Q, Serfes K (2013) Price discrimination in two-sided markets. J Econ Manag Strategy 22(4):768–786CrossRef

Nikzad A (2017) Thickness and competition in on-demand service platforms. Working Paper

Rochet J-C, Tirole J (2003) Platform competition in two-sided markets. J Eur Econ Assoc 1(4):990–1029CrossRef

Rysman M (2009) The economics of two-sided markets. J Econ Perspect 23(3):125–143CrossRef

Zervas G, Proserpio D, Byers JW (2015) A first look at online reputation on Airbnb, where every stay is above average. Working Paper

Zervas G, Proserpio D, Byers JW (2017) The rise of the sharing economy: estimating the impact of Airbnb on the hotel industry. J Mark Res 54(5):687–705CrossRef

Titel: The impact of multi-homing in a ride-sharing market
verfasst von: Oksana Loginova
X. Henry Wang
Qihong Liu
Publikationsdatum: 14.03.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: The Annals of Regional Science / Ausgabe 1/2022
Print ISSN: 0570-1864
Elektronische ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-022-01120-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2022

How does big-box retail entry affect labor productivity in durable goods retailing? A synthetic control approach

Household asset dynamics and shocks: an empirical assessment of asset-based poverty traps in Peru

Air pollution and income inequality: a spatial econometric approach

Locational error in the estimation of regional discrete choice models using distance as a regressor

(Green) Knowledge spillovers and regional environmental support: do they matter for the entry of new green tech-based firms?

Club convergence of regional housing prices in China: evidence from 70 major Cities