What Drives Pricing Behavior in Peer-to-Peer Markets...

1

What Drives Pricing Behavior in Peer-to-Peer Markets?

Evidence from the Car-Sharing Platform BlaBlaCar

Mehdi Farajallah (Marsouin)

Bob Hammond (North Carolina State University)

Thierry Pénard (CREM, University of Rennes 1)

March 2016

Abstract: What determines prices and market outcomes in peer-to-peer platforms? We study

the leading European car-sharing platform, BlaBlaCar, to understand price determination and

factors such as reputation that influence demand. Our econometric model controls for the

joint determination of price and quantity demanded. Controlling for price endogeneity, we

find that reputation matters in interesting ways. Drivers with more positive feedback ratings

set lower prices and (controlling for price) sell more seats. It is surprising that more reputable

drivers set lower prices, given that evidence from other peer-to-peer markets suggest that

brand loyalty effects that are seen offline are also common in online markets. We interpret

this, along with other of our results, as evidence that prices and market outcomes on “sharing

platforms” such as BlaBlaCar are determined differently than on other types of peer-to-peer

markets such as eBay.

2

Introduction

The rise of the “sharing economy” and the success of sharing platforms like AirBnB have

attracted the attention of economists and other academics as well as the popular press. Einav

et al. (2016) define these platforms as peer-to-peer markets and emphasize their role in

matching buyers to sellers to facilitate transactions with reduced scope for opportunistic

behavior. Similarly, Rochet and Tirole (2006) or Evans (2011) define these platforms as two-

sided markets that bring together two groups of economic agents. The two sides of the

market can be sellers and buyers, hosts and guests, or drivers and riders and are characterized

by cross-network externalities. The traditional analysis of these types of markets focuses on

the first group in markets such as eBay or AmazonMarketPlace ; recently however, these

analyses have begun to focus on markets for carsharing, lending, accommodation, home

services, deliveries, or task assignments(Sriram et alii, 2014).

Our analysis focuses on the leading European carsharing platform, BlaBlaCar, which is valued

at $1.5 billion as of September 2015.1 BlaBlaCar connects drivers with empty seats to riders to

share an intercity trip. Similar to many other peer-to-peer markets, BlaBlaCar receives fees on

each ride that is reserved through the platform. A carsharing platform like BlaBlaCar has some

features that make it particularly interesting to study. Most empirical studies of peer-to-peer

market focus on settings where interaction are limited to online communication (e.g., eBay).

In contrast, on BlaBlaCar, the interaction between the two parties begins online, but ends

offline in the driver’s car where they share a small space for up to a few hours together.

Obviously, BlaBlaCar users may be motivated by monetary gains (revenue as a driver or

1 http://www.wsj.com/articles/blablacar-joins-ranks-of-billion-dollar-venture-backed-startups-1442433577

3

savings as a passenger relative to alternative transportation). However, nonmonetary factors,

such as social and ecologicalmotivations (i.e. to create social ties or reduce road congestion

and gas emissions) may be as important in determining supply and demand in these markets

(Schor and Fitzmaurice, 2015). For more detail on social motivations, see the behavioral

economics literature on prosocial behavior, such as Benabou and Tirole (2006) or Gneezy,

Meier, and Rey-Biel (2011). .

In this context, we study price determination and the role of reputation in the functioning of

BlaBlaCar. Our main contribution is an empirical analysis of the joint determination of price

and the quantity of seats demanded. BlaBlaCar presents an interesting empirical setting for

such a study because its price-setting environment is different from other carsharing peer-to-

peer markets (e.g., Uber and Lyft). On platforms like Uber and Lyft, the price is set by the

market maker and thus any driver offering a given trip at a given moment has the same price.

In contrast, on BlaBlaCar, drivers set their own price for each trip. This allows a more

interesting empirical analysis of price setting by increasing price variation and allowing for

factors such as reputation and social preferences to have more scope to influence prices.

Our econometric model addresses the endogeneity of a driver’s price and, controlling for

price, models the number of seats sold by the driver. Not surprisingly, we find that higher

prices are associated with fewer seats sold. The effect size suggests that the fraction of seats

sold decreases by 10 percentage points for each one euro higher price. Reputation matters in

surprising ways because more highly reviewed drivers set lower prices. In contrast, many

offline markets exhibit brand loyalty effects, where more reputable firms fetch higher prices.

Evidence from other peer-to-peer markets such as eBay has confirmed these effects are

present online as well. We interpret our finding that drivers lower their prices as they gain

4

more feedback ratings as suggestive of the importance of nonmonetary factors in price setting

on BlaBlaCar. Specifically, it may be the case that new drivers have a mix of monetary and

nonmonetary motives for offering a trip but the more experienced drivers are those for whom

nonmonetary motives are most important. Another explanation is that new drivers are

systematically overestimating the optimal price and drivers learn to decrease their price with

experience. In either case, we conclude that prices and market outcomes on “sharing



Peer-to-peer markets serve an important role and have been argued to provide efficiency

gains (Edelman and Geradin, 2015). Specifically, these markets lower search and transaction

costs (i.e., reduce information asymmetries) and allow fuller use of resources (i.e., increase

car occupancy and reduce road congestion). Despite these benefits, these platforms have

raised a lot of regulatory issues, especially in the transportation and accommodation sectors.

Many municipalities and countries have taken steps to limit the use of residential property for

short-term rental through AirBnB or banned nonprofessional drivers on Uber. Our

contribution is to analyze supply and demand factors in a specific peer-to-peer market, which

is essential to understanding the tradeoffs that are highlighted by these policy concerns.

The effect of peer-to-peer markets on prices and consumer welfare remain open questions

because the literature is limited (Einav et al., 2016). Edelman and Luca (2014) study the prices

posted by hosts on AirBnB. They show that rental prices depend on the characteristics of the

apartment (size, location), but also on the demographics of the landlord: non-black hosts

charge approximately 12% more than black hosts for an equivalent rental. Zervas et al. (2014)

study the effect of AirBnB on the hotel industry; they find that a 1% increase in AirBnB listings

5

in an area results in a 0.05% decrease in hotel revenues, with a larger effect on lower-end

hotels. There is also a literature on the effect of online ratings and reputation in peer-to-peer

markets (Cabral and Hortacsu, 2010; Cabral, 2010; Houser and Wooders, 2006; Resnick and

Zeckhauser, 2002; Resnick et al. 2006; Haley and Van Scyoc, 2010). However rating a product

is quite different from rating a personal experience with a driver or a host. Thus, we know

much less about rating behavior and their effects on sharing platforms such as BlaBlaCar. One

exception is Zervas et al. (2015), who find that ratings on AirBnB platform are overwhelmingly

positive (the average AirBnB property rating is 4.7 stars) but there exists significant variability.

This corroborates the findings of Fradkin et al. (2014), who document under-reporting of

negative experiences and other reviewing biases on AirBnB.

The paper is organized as follows. In the next section, we briefly introduce the carsharing

platform BlaBlaCar. Section 2 describes our data and section 3 explains the empirical

methodology to analyze market outcomes and address endogeneity bias. Section 4 displays

the main econometric results and section 5 provides several extensions. Section 6 concludes.

1. The car-sharing platform BlaBlaCar

BlaBlaCar is a French start-up founded in 2006 and has now become the leading European

carsharing platform.2 BlaBlaCar offers intercity ridesharing services, connecting drivers with

empty seats to people who are traveling on the same trip (see Figure 1 in appendix). Drivers

earn money and passengers save on travel expenses (given that the typical trip on BlaBlaCar

2 The name BlaBlaCar comes from the French word “blabla” that is the English equivalent of blah.

Driver profiles can display their “talking” preference: Bla if they do not like to talk with passengers,

BlaBla if they like to talk a little, and BlaBlaBla if they like to talk a lot.

6

is cheaper than the corresponding train ticket). BlaBlaCar now operates in 19 countries (mainly

in Europe, but also in Mexico and India). In April 2015, BlaBlaCar acquired the second largest

European carsharing company carpooling.com, which expanded BlaBlaCar to more than 20

million members. In September 2015, BlaBlaCar has raised $200 million, bringing its total

funding as of 2015 to over $300 million. The company has a $1.5 billion valuation. BlaBlaCar

has not seen the types of regulatory battles faced by carsharing companies like Uber, because

BlaBlaCar is considered as a not-for-profit ride service (the stated purpose of the money

received by drivers is only to share the fuel cost of the trip).

Over 2 million people use BlaBlaCar every month, around 29% of whom are drivers. In 2015,

the average BlaBlaCar user is 34 years old. The platform is popular among young people, with

14% of drivers and 36% of passengers being students. Registration on BlaBlaCar is free but

passengers pay fees that are about 15% of the price of the ride paid to the drivers.3 Similar to

most other peer-to-peer markets, passengers and drivers are asked to provide rating of each

other and write reviews.

For each trip, BlaBlaCar suggests a “recommended price” based on the trip distance and the

estimated price of fuel and tolls.4 The recommended price does not depend on the number of

seats offered. The driver is allowed to adjust the price up or down, with the minimum

(maximum) price set as 50% (150%) of the recommended price.5 In February 2012, BlaBlaCar

introduced a price color classification, where the driver’s price displays to potential riders as

3 Booking fees and VAT are added to the price that the passenger pays to the driver. The fees earned

by BlaBlaCar are composed of a fixed component (€ 0.89) and a variable component (9.90%, of the

price paid to the driver). A VAT of 20% is added to these fees. 4 For instance, in 2015, the suggested price of BlaBlaCar was automatically calculated as follows: .065

€ per kilometer and per seat if the driver takes a toll road and .048 € per kilometer and per seat

otherwise. 5 https://www.blablacar.in/faq/question/how-do-i-set-my-price

7

green if the driver chooses a price that does not exceed the recommended price. Otherwise,

the price is orange (up to 125% of the recommended price) or red (up to 150%).

2. Data collection

Data were collected from February 2013 to March 2014, but our analysis focused on the

period from August 2013 to March 2014, when data were collected daily. We selected 43

French intercity trips, chosen to ensure a representative sample of trips that are offered on

BlaBlaCar. Of these 43 trips, 40 trips were offered at least several times and our final sample

contains these 40 trips. The shortest trip is Nimes-Montpellier (56 km) and the longest trip is

Paris- Marseille (774 km). Trips from all around the country were selected: trip between

provincial cities as well as trip between Paris and a provincial city. The list of trips is available

in the appendix, including descriptive statistics (distance, number of observations, and unique

drivers for each trip). There are 41 unique cities (33 of more than 100,000 inhabitants and 8

of less than 100,000 inhabitants).

The data collection procedure was automated. For each trip, we collected all offers, resulting

in 948,789 observations from 297,582 individual drivers. The data collection script scraped the

BlaBlaCar website, resulting in multiple snapshots of each observation (e.g., three days before

departure, two days, etc.). Here, we focus on the last observation for each offer. The data

contain the departure and arrival cities; departure date and hour; driver name; and their

profile (gender, age, etc.); whether the driver’s photo is shown; and declared preferences for

smoking, pets, music, and talking (dislikes talking, likes a little talking, or likes a lot of talking).

For each trip, we have the number of seats available, the price, and price color (green =

[50%,100%] of recommend price, orange = (100%,125%], or red = (125%,150%]).

8

When these data were collected, the rating mechanism of BlaBlaCar allowed only a positive

or negative rating. In our data set, members’ reputation is, therefore, measured by the

number and percentage of positive ratings that were received.6 A related measure of drivers’

reputation is the five-level status assigned by BlaBlaCar on four criteria (profile completion,

membership seniority, number of ratings, and percent positive rating). This status is publicly

observable on the drivers’ profile. The following table shows how a driver is classified as

newcomer, intermediate, experienced, expert, or ambassador. Finally, we also have the

comfort of the driver’s car (that takes values between zero and four where higher values

indicate a more luxurious vehicle).

Newcomer Intermediate Experienced Expert Ambassador

Profile completion > 60% > 70% > 80% > 90%

Number of positive

ratings

>1 rating >3 ratings >6 ratings >12 ratings

Seniority >1 month >3 months >6 months >12 months

Table 1 presents summary statistics of the data. Panel A summarizes the outcomes of interest

for the entire sample, while Panel B presents our explanatory variables, including each driver

only once. Drivers are 36 years old, on average, and around 40% of listings are by females.

Drivers include a picture with their profile in 39% of cases. 56% of drivers indicate that they

play music during the trip, 9% allow pets, and 7% allow smoking. Finally, 26% of drivers tend

to offer roundtrip travel, while 12% of drivers allow a seat to be sold only after manually

confirming the sale (as opposed to an immediate sale). Regarding drivers’ experience, the

6 As of 2015, BlaBlaCar’s rating system is richer with a five point scale: 5 points if the passenger rates

the experience as outstanding, 4 points if excellent, 3 if good, 2 if poor, and 1 if very disappointing.

9

number of ratings received is 8.4 on average, while driver status is 2.7 and car class 2.3 on

average. Using these data, we estimate the econometric model explained next.

[Insert Table 1]

3. Econometric Model

To understand the functioning of the BlaBlaCar car-sharing sharing platform, we perform a

regression analysis to explain the prices charged and quantities sold by drivers in the data

described above. We use data listed on BlaBlaCar starting from August 2013 until March 2014.

We cleaned the data to exclude listings with a departure date after December 2014. The

resulting data set contains 948,789 listings from 297,582 drivers.

Our econometric model uses a fixed-effects panel-data regression, with trip fixed effects. A

trip is defined as a departure city-arrival city pair. There are 40 trips in the data, with the

following five trips as the most commonly offered trips in descending order: (Nimes,

Montpellier), (Nantes, Rennes), (Lille, Paris), (Lyon, Paris), and (Toulouse, Bordeaux). Including

trip fixed effects allows us to control for the general characteristics of the trip and then look

separately at listing specific factors that affect drivers' prices and riders' purchase behavior.

�� = �� + �� + � +�+�� + ��

with i=driver, j=trip and t=departure date.

10

Measuring the quantity sold

To define quantity sold (qijt), we use the panel nature of our data with repeated listings of a

given driver. While the data collection is likely to miss seats sold for a driver on a listing, it is

unlikely to systematically miss seats sold on all listings that a driver ever offers. As such, we

construct a variable that is equal to the maximum number of seats available ever observed by

the driver across all listings. While this measure could be noisy if a driver uses different cars

for different trips, we have characteristics that help to identify the car used in a listing

(specifically the rating of the car). We use the variables to construct an alternative measure

of the maximum number of seats available, finding that the two measures largely overlap.

We use the maximum number of seats available for each driver to construct two quantity sold

variables. First, fraction sold measures the proportion of maximum seats available that sold

for the listing, varying between zero and one. Second, all seats sold is a dummy variable that

equals one when fraction sold equals one, that is, when the number of seats available equals

zero at the close of the listing. Note that the all seats sold dummy is robust to our approach

for measuring the maximum number of seats available. In particular, while we imperfectly

observe the number of seats initially offered, we perfectly observe the number of seats

available for each listing, irrespective of how soon or how often the data-extraction software

gathered data on a listing. If a listing has zero seats available when it closes, then all of the

listing’s seats sold, by definition. The two quantity sold measures provide similar results in

what follows, providing support for our approach for defining quantity sold.

Controlling for price endogeneity

The outcomes of primary interest are price and quantity sold. Our approach is to present an

instrumental variables regression analysis, where price is considered an endogenous variable

11

that affects quantity sold. As such, we use an instrument that we argue affects the price that

a driver sets but has no effect on riders, except through its influence on price. The instrument

is constructed from the trip-level panel nature of our data. Specifically, we link drivers who

offer a given trip to other trips offered by the same driver to construct the universe of listings

offered by the driver during our sample period.

Importantly, this approach requires that we precisely identify drivers. Unfortunately, unlike

other online marketplaces, BlaBlaCar does not use unique user IDs as part of its listing

interface, an approach that is useful with eBay data, for example. As a result, we need to

identify drivers as carefully as possible to be able to say which listings were offered by the

same driver. To do so, we use three variables in our data: first name, age, and gender. We

have explored including other variables to identify drivers uniquely, including whether a photo

is shown and whether smoking is allowed in the car. Reassuringly, each approach classifies

the vast majority of drivers in the same way and all the results that follow are robust to

alternative driver classifications.

Having identified drivers, we construct driver characteristics in three ways: over all trips, over

the trip in question, and over all trips other than to the trip in question. To be clear, this

implies that if a driver is only ever observed offering trips from Lyon to Grenoble and from

Lyon to Paris, then we use characteristics of the Lyon to Paris trips when referring to the Lyon

to Grenoble trip and using the term “trips other than to the trip in question.”

Using this approach, the instrument is the average price charged by the same driver on all

trips other than the trip in question. For the drivers who only ever offer one trip, then there

are no such trips. We refer to these drivers as single-trip drivers and, for these drivers, the

average-other-price instrument equals zero. This instrument is essentially a combination of

12

two distinct characteristics of drivers. First, does the driver offer trips between a pair of cities

that is different from the pair of cities in question? This factor determines whether the

instrument is positive. Second, if yes, did the driver set prices that were high or low, on

average, on those other trips? This factor determines the continuous variation in the

instrument if it is non-zero. In essence, our instrument is a combination of a dummy variable

for whether the driver offers trips other than the trip in question and, if so, a continuous

variable measuring average price on those trips.

We believe (and will provide evidence to support) that the instrument is very strong. The

intuition behind the “average-other-price instrument” is that a combination of observed and

unobserved characteristics of the driver affects the price she sets. However, the

econometrician has access to all observed characteristics and thus the variation in price that

is affected by the unobserved characteristics should be highly correlated across the driver’s

listings on the trip in question and her listings on trips other than the trip in question.

Further, we believe that the average-other-price instrument is plausibly exogenous because it

reflects underlying factors about the driver that should not affect demand except through the

price set on the listing in question. It is very useful that we have a large number of trips

because constructing the average price the driver set for other listings on the same trip is likely

to itself be endogenous; such an average-same-price instrument is problematic because

potential riders might observe a given driver offering a given trip across multiple listings of the

trip. By using the average price on trips other than the trip in question, we greatly reduce the

possibility that riders have any sense of where the driver falls in the price distribution for other

trips.

13

Using the average-other-price instrument, we conduct an instrumental variables analysis,

where the first stage asks what factors affect the driver’s price and the second stage asks what

factors affect the quantity sold, controlling for the endogeneity of the price in its

determination of quantity sold. Our price measure is straightforward, the price set in integer

Euros. However, quantity sold is based on the number of seats available that is shown to

potential riders and our variable construction requires further explanation. The data-

extraction software used to gather data regularly visited hundreds of thousands of BlaBlaCar

listing pages but instantaneous data collection is infeasible. As a result, the data occasionally

contain a number of seats available variable that already reflects a lower quantity supplied

than the true quantity supplied.

Now return to Table 1, which shows summary statistics for the key variables in our data. The

average price set by drivers is around 13 euros, with substantial variation (standard deviation

of 9.4 euros). As discussed in the previous section, we have two measures of quantity sold:

fraction sold measures the proportion of listed seats that sold (average of 62%), while all seats

sold is a dummy variable (average of 53%). The instrumental variable used to handle the

endogeneity of price is the average-other-price variable, which has an average of around 11

euros. Drivers offer 3.8 listings of their modal trip and 2.6 listings of trips other than their

modal trip. Just over half of the drivers in our sample only ever offer one trip, implying that

the average-other-price instrument equals zero just over half of the time.

Figures 2 and 3 present the evolution of mean prices and the average percentage of seats sold

for all trips and for the single trip Paris-Lyon, where Paris-Lyon is chosen as a representative

trip among the most commonly observed trips. Prices exhibit some volatility around a trend

over time. Further, seats sold appear to positively covary with prices, reflecting underlying

14

seasonality in both supply and demand. Finally, there is a noticeable decrease in prices and

seats sold around the end of 2013 that recovers early in 2014. In the appendix, we list the

coefficient of variation by trip over the period, which measures price dispersion for a given

trip. This dispersion measure ranges from 10.5% to 47.5% in these data.

[Insert Figure 2 and 3]

Econometric Specification

The econometric specification throughout uses fixed-effects panel-data regression, with trip

fixed effects. For price, the model is a linear regression. For the fraction of seats sold and the

all seats sold dummy variable, we again use linear regression. In both cases, the appropriate

econometric specification is non-linear: fractional logit in the case of fraction sold (which

continuously varies between zero and one) and logit/probit in the case of all seats sold (which

is a dummy variable). However, econometric models that handle endogeneity and allow for

fixed effects are not available for either fractional logit or logit/probit. We could use a

random-effect panel-data model but the orthogonality assumption on the unobserved effects

imposed by the random-effects model does not hold in these data. Instead of ignoring

endogeneity or ignoring unobserved trip-level effects, we use linear models throughout. Our

approach is consistent with the approach advocated by Angrist and Pischke (2008). In all

specifications, continuous explanatory variables are included in quadratic form, with the

results shown as the marginal effect at the mean.

We now present the results from the first stage (price) and second stage (quantity sold).

15

4. Empirical Results

Table 2 presents the determinants of price, which serves as the first-stage of our instrumental

variables regression analysis. Recall that our econometric specification is a panel-data

regression with trip fixed effects. First, the average-other-price instrument (average price set

on trips other than the trip in question) is highly statistically significant, with an effect size of

one cent for each euro higher the price is set on other trips.

[Insert Table 2]

Regarding driver experience, the number of feedback ratings is statistically insignificant but

driver status is not, probably due to the high correlation between the two measures. For

status, drivers with more experience set lower prices to a quantitatively meaningful degree.

If we omit driver status, we find the same result with the number of feedback received: drivers

with more ratings set lower prices. Intuition from offline markets suggest that brand loyalty

effects should allow more established firms to charge higher prices. Evidence from eBay show

that seller reputation does have a positive effect on prices: buyers are willing to pay more for

items sold by sellers with a good reputation (Cabral, 2016) In contrast, we interpret our

finding as suggestive that new drivers on BlaBlaCar are using a different decision-making

process when setting prices than that of experienced drivers. A first explanation is the new

drivers are unsure of the appropriate price, and set systematically high prices, while

experienced drivers learn about the platform and adjust their prices down to increase their

chance to sell their empty seats. A second explanation is that new drivers are more attracted

to BlaBlaCar by a profit motive, while experienced drivers have gained an appreciation for the

non-pecuniary attributes of riders obtained through the platform (pro-social behavior). In

either case, we interpret this as evidence that prices and market outcomes on “sharing

16



Next, car class measures the luxurious nature of the vehicle, where zero represents no

indication of the class, relative to values between one (basic comfort) to four (luxurious).

Interestingly, when there is no indication given, prices are set as if the vehicle is average (a

class between two and three). Otherwise, more luxurious vehicles have higher prices. The

fact that cars of an undisclosed comfort level are being priced as if they are of average quality

runs counter to the intuition that suggests unraveling would force undisclosed vehicles to be

treated as if they were of the lowest level of comfort. To gain a full understanding, the

question is whether these vehicles of undisclosed comfort level sell at a similar rate to average

quality vehicles.

Regarding other characteristics of the driver, older drivers set higher prices, as do female

drivers. We discuss the result on age in Section 5, which contains extensions of the main

results. Specifically, recall that age (along with the other continuous variables) is included in

quadratic form, where the results shown in Table 2 are the marginal effect at the mean. In

Section 5, we ask whether age has a nonlinear effect, which would not be apparent by

considering only the effect at the mean.

Drivers with a photo set prices that are no different from drivers without a photo. Drivers

who declare to listen to music in their car set lower prices, perhaps suggesting that these

drivers seek riders for enjoyment more so than for profit. This result may also suggest that

riders who do not want to be disturbed by music are willing to pay more for a quiet ride.

Further, drivers who allow pets set lower prices, while drivers who allow smoking set higher

17

prices. Finally, roundtrip listings are set with higher prices, while drivers who require manual

confirmation also set higher prices.

Also shown in Table 2 is the first-stage F statistic, which measures instrumental validity of the

average-other-price instrument. The statistic equals 1683.5, which is very large and

considerably above the rule-of-thumb that it exceed 10 in order to mitigate concerns about

weak instruments (Angrist and Pischke 2008). Further, we run two robustness checks of the

instrument to measure the strength of both components of the variation in our instrument:

first, the zero/non-zero variation in our instrument of whether the driver offers trips other

than the trip in question and, second, the continuous variation in our instrument of the

average price on those trips.

To measure the strength in the zero/non-zero variation, we rerun the first-stage weak

instrument test with an instrument that equals one if the driver offers multiple trips. The first-

stage F statistic in this case equals 173.1. To measure the strength in the continuous variation,

we rerun the first-stage weak instrument test for only multiple-trip drivers to ask whether the

continuous variation in our instrument is strongly associated with price. The statistic in this

case equals 1672.2. These results suggest that most of the strength of the instrument comes

from the continuous variation but that both sources of variation are sufficiently strong to

support the use of the average-other-price instrument as a strong predictor of prices.

Turning to the determinants of quantity sold, Table 3 displays the results for the fraction sold

measure in Column (1) and the all seats sold dummy in Column (2). The two sets of results

are very similar, leading us to only discuss Column (1). Having a higher price is associated with

fewer seats sold, where fraction sold decreases by around 10 percentage points for each one

euro higher price.

18

[Insert Table 3]

Drivers with more experience sell more seats, where the results are consistent between the

feedback rating variable and the driver status indicators. For status in particular, the results

suggest that more experience has a small return when moving from status one to status four

but a meaningful return of moving to status five, which BlaBlaCar refers to as Ambassador

status. Ambassadors sell around five percentage points more seats. Given the interesting

results, we see for experience with regard to price and quantity demanded, we further

consider these results in the next section of extensions.

For car class, more luxurious vehicles are associated with more sold seats, which is intuitive.

Vehicles of the lowest comfort levels, one and two, sell more often than the omitted category,

which refers to vehicles with undisclosed comfort level. Recall that drivers who do not disclose

the comfort level set prices as if their vehicles are of average quality. When considering

quantity demanded, these vehicles of undisclosed quality are treated as if they are of the

lowest quality level, consistent with the unraveling result economists often predict under

voluntary quality disclosure.

Concerning other driver characteristics, the following are associated with more sales: older

drivers, females, photo shown, playing music, and roundtrips. In contrast, allowing pets and

requiring manual confirmation are associated with fewer sales, while allowing smoking is

associated with neither more nor less sales. The statistically insignificant result for smoking is

the only case in which the two measures of quantity demanded give different results: Column

(2) (all seats sold dummy) shows a statistically significant negative effect of allowing smoking,

while Column (1) (fraction of seats sold) shows a statistically insignificant negative effect. In

19

both case though, allowing smoking appears to have a small effect on quantity demanded,

which could suggest that the price premium smokers are willing to pay to smoke partially

offsets the price premium non-smokers are willing to pay to avoid smoking.

Finally, Table 4 presents the final set of covariates, displaying the effects of departure

time/day characteristics on price and quantity. That is, Column (1) of Table 4 presents results

from the same regression as Column (1) of Table 2, while Columns (2) and (3) of Table 4

present results from the same regressions as Columns (1) and (2) of Table 3, respectively. For

departure day of week, days with higher prices are also those days with more quantities sold,

showing a strong preference for departures on Saturday and Sunday (the omitted day). From

these results, a Tuesday departure is associated with the lowest prices yet also the lowest

quantity sold (controlling for the endogeneity of price). For departure time in six hour

intervals, we find that prices are lower for trips that depart later in the day, especially after

6PM, while more seats are sold between 6AM and 6PM, relative to nighttime hour departures.

Finally, a time trend is included to control for patterns over our eight month sample period.

We find that prices trend downward, as do quantities; however, the aggregate data in Figure

2 do not suggest a downward trend other than a dip around the end of the calendar year.

These trends are interesting but, as our sample is less than one year, we cannot control for an

overall trend separately from monthly seasonality. As a result, we do not draw much in the

way of interpretation of any potential time trend.

[Insert Table 4]

20

5. Extensions

a) Nonlinearities in the Effect of Age on Price and the Probability of Sale

Figures 4 and 5 present an analysis of the effects of the age of the driver on price and quantity

demanded, respectively. This analysis uses the fraction of seats sold as the measure of

quantity demanded, but results are very similar when using the all seats sold dummy variable.

These results are generated using Stata’s margins command and show the marginal effect of

age on each outcome, along with 95% confidence intervals. Age is included in the earlier

specifications in quadratic form but the earlier regression tables displayed only the marginal

effect at the mean.

[Insert Figures 4 and 5]

Now, we ask whether the effect of age is linear, or whether incremental increases in the age

of the driver are associated with different effects at different ages. For the age of the driver,

an additional year of age is associated with higher prices at all ages; the relationship shows

minimal curvature, such that the slope is slightly diminishing in age. The magnitude of the

effect is small but highly statistically significant, as can be seen by the 95% confidence

intervals. The effect size suggests that comparing a 25 year old driver to a 41 year old driver

(the 25th and 75th age percentiles, respectively) results in a price increase from 13.33 euros to

13.44 euros.

In contrast to the linear effect on price, there is a nonlinear effect of age on quantity

demanded such that the effect is increasing at first and then decreasing. That is, among

younger drivers, an additional year of age is associated with more sales, while, among older

21

drivers, an additional year of age is associated with fewer sales. The effect of age changes

from positive to negative around 38 years of age.

Putting the results on price and quantity demanded together, we see that drivers in their 30s

fare better than drivers in their 20s, in terms of quantity demanded, despite the fact that

drivers in their 30s set higher prices. Once drivers reach around age 40, the effect of further

increases in age are associated with lower demand, yet these older drivers continue to set

higher prices. This pattern is consistent with homophily, that is, younger drivers wanting to

travel with young riders, perhaps because they share common interests. As younger riders

are likely to be more price sensitive, young drivers may be setting lower prices to attract their

peers. Similarly, older drivers are probably more selective in with whom they ride and high

prices serve as a screening device to select riders.

b) Heterogeneity in the Price Elasticity of Demand: Driver Reputation

Next, the analysis considers whether our main results on the price elasticity of demand mask

important heterogeneity in price sensitivity across characteristics of the driver. We focus on

the driver’s reputation, as measured by her number of feedback evaluations. We split drivers

into four categories using quartiles of the reputation variable, splitting drivers from least to

most experience as follows: zero ratings, one to three ratings, four to 13 ratings, and more

than 13 ratings. The results are very similar if we instead categorize drivers using the driver

status variable; using the number of feedback ratings instead allows comparison with related

work in online markets such as eBay.

22

The results in Table 5 suggest a monotonic relationship such that riders are more sensitive to

the prices of more experienced drivers, relative to less experienced drivers. The effect of each

category is statistically significantly different from the effect of the neighboring category.

However, the main takeaway from Table 5 is that the effects in Columns (2)-(4) are relatively

similar, while the effect in Column (1) is markedly smaller. That is, price matters more for

experienced drivers than for new drivers. Our interpretation is that a new driver cannot simply

sell her seats by setting a very low price; instead, riders appear to be cautious when buying

from new drivers. It may be the case that riders are relying on other signals of a new driver’s

quality and that price alone does not allow new drivers to generate an initial level of demand.

[Insert Table 5]

c) Pricing Decisions Relative to the Suggested Price

Table 6 presents summary statistics on the fraction of prices set at each color level (green,

orange, red), overall and separately for drivers in each quartile of the reputation distribution.

Recall that the price is green when the driver sets a price equal or below the suggested price.

If the driver chooses a fare up to 125% of the recommended price, the color becomes orange.

The price color is red if the price is more than 125% of the recommended price. Table 6 shows

that the vast majority of drivers stay in the green color (only 11.9% of prices are orange and

1.5% red).

[Insert Table 6]

23

We previously saw that less experienced drivers set higher prices. Table 6 allows us to see

whether this finding also holds for prices relative to the recommended price and whether this

translates to a meaningful difference in prices set at each color. The results clearly suggest

that it does, with around 83% of the least experienced drivers setting green prices and 90% of

the most experienced drivers setting green prices. Further, Table 6 allows us to see whether

the movement toward green prices is relatively uniform in driver experience, or whether

drivers quickly shift toward green prices as they gain experience. The results here suggest that

the trend toward green prices is steadily increasing in experience and does not sharply jump

at any particular level of experience. For the most experienced drivers, only 1% of prices are

set so far above the reference price that they display to drivers as red; for the least

experienced drivers, the number is 2.5%. Finally, the ratio of the frequency of orange prices,

relative to red prices, stays approximately constant as experience grows.

6. Discussion and Conclusion

Despite the increasing importance of carsharing, these platforms have received only limited

economic analysis. Our paper studies a large carsharing platform to understand price

determination and factors such as reputation that affect demand. These peer-to-peer

platforms allow both online and offline interactions between users. As a result, pricing and

reputation effects are likely to present novel insights relative to the large literature that

studies these questions using data from electronic marketplaces such as eBay.

Our analysis focuses on the leading European carsharing platform, BlaBlaCar, which allows

sharing of an intercity trip by connecting drivers with empty seats to potential riders. The

main reason to study pricing and market outcomes on BlaBlaCar is that prices are set by

24

individual drivers, relative to a “recommended price” that is suggested by BlaBlaCar. In

contrast, on other carsharing peer-to-peer markets such as Uber and Lyft, the price is set by

the market maker and thus any driver offering a given trip at a given moment has the same

price. Our focus is on reputation and the potential for social motives in pricing behavior, thus

driver-level variation in prices is important for a full understanding of strategic behavior

(Benabou and Tirole, 2006; Gneezy, Meier, and Rey-Biel, 2011)..

In an econometric model that explicitly accounts for price endogeneity, we find that drivers

with more positive feedback ratings set lower prices and (controlling for price) sell more seats.

However, experience has a significant return when the drivers reach the highest status,

Ambassador, who sell around five percentage points more seats. Moreover, riders are more

sensitive to the prices of more experienced drivers, relative to less experienced drivers.

Further, we find that driver demographics matter in interesting ways: females set higher

prices, yet (controlling for price) sell more seats. Finally, older drivers set higher prices, while

there is a nonlinear effect of age on quantity demanded; in total, drivers in their late 30s fare

best in terms of revenue.

The rich nature of our BlaBlaCar data allows us to present a detailed analysis of market

outcomes in an important type of peer-to-peer market. However, as usual with data from

online markets, there are some features of the data that limit the questions we can ask. First,

we do not observe information about the riders who are buying seats. Thus, we cannot

measure the degree of homophily or social links between drivers and their passengers.

Second, we have only a binary scale for ratings (positive or negative). Since the time of our

data collection, BlaBlaCar has adopted a five-level reputation measure, which would be useful

to verify our findings on the effects of reputation on price and quantity demanded. Moreover,

25

as the platform has matured, it will be interesting to analyze how the role of reputation has

evolved.

In total, our study represents a first step toward an understanding of pricing behavior and

market outcomes on peer-to-peer markets and the effect of social motivations (sharing) in

these types of markets.

26

References

Angrist, J. D., Pischke, J. S. (2008). Mostly Harmless Econometrics: An Empiricist's

Companion. Princeton University Press.

Aperjis, C., Johari, R, (2010). Designing Reputation Mechanisms for Efficient Trade, mimeo,

Stanford University.

Ba, S., Pavlou, P. (2002). Evidence of the Effect of Trust Building Technology in Electronic

Markets: Price Premiums and Buyer Behaviour, MIS Quarterly 26(3), p. 243-268.

Benabou, R., Tirole, J. (2006). Incentives and Prosocial Behavior. The American Economic

Review, 96(5), 1652-1678.

Bolton, G., Greiner, B., Ockenfels, A., (2013). Engineering Trust: Reciprocity in the Production

of Reputation Information, Management Science, 59, 2, 265-285.

Cabral, L., Hortaçsu, A. (2010). The Dynamics of Seller Reputation: Evidence from eBay, The

Journal of Industrial Economics, LVIII 58: 54–78.

Cabral, L. (2012). Reputation on the Internet, in Martin Peitz and Joel Waldfogel (Eds), The

Oxford Handbook of the Digital Economy, Chapter 13.

Dellarocas C., (2003). The Digitization of Word-of-Mouth: Promise and Challenges of Online

Reputation Mechanisms, Management Science 49, p. 1407-1424.

Edelman, M., Luca, M. (2014). Digital Discrimination: The Case of airbnb.com, Harvard

Business School, Working Paper 14-054.

27

Edelman B. G., Geradin D. (2015). Efficiencies and Regulatory Shortcuts: How should we

regulate companies like AirBnB and Uber? Harvard Business School Working Paper 16-026

Einav L., Farronato C., Levin J. (2016). Peer-to-Peer Markets, Annual Review of Economics, 8,

September, forthcoming.

Evans, D. S. (2011). Platform Economics: Essays on Multi-Sided Businesses. Competition

Policy International. http://ssrn.com/abstract=1974020)

Fradkin, Andrey, Elena Grewal, David Holtz, Matthew Pearson. 2014. Reporting Bias and

Reciprocity in Online Reviews: Evidence From Field Experiments on AirBnB. Working paper.

Available at http://andreyfradkin.com/assets/long_paper.pdf

Ghose, A., Ipeirotis, P., Sundararajan, A, (2007). Opinion Mining Using Econometrics: A Case

Study on Reputation Systems, Proceedings of the Association for Computational Linguistics.

Gneezy, U., Meier, S., Rey-Biel, P. (2011). When and why incentives (don't) work to modify

behavior. The Journal of Economic Perspectives, 25(4), 191-209.

Jian, L., MacKie-Mason, J., Resnick (2010). I Scratched Yours: The Prevalence of Reciprocation

in Feedback Provision on eBay, The B.E. Journal of Economic Analysis & Policy (Volume 10).

Jin, G., Kato A. (2006). Price, Quality and Reputation: Evidence from an Online Field

Experiment. Rand Journal of Economics 37, p. 983-1004.

Jolivet, G., Jullien, B., Postel-Vinay, F. (2014). Reputation and Prices on the e-market:

Evidence from a Major French Platform.

Klein, T., Lambertz, C., Spagnolo, G., Stahl, K., (2006). Last Minute Feedback, CEPR Discussion

Papers 5693, C.E.P.R

28

Li, L., (2010). What is the Cost of Venting? Evidence from eBay, Economics Letter 108, p. 215-

218.

Melnik, M. I. and Alm, J. (2002), Does a Seller’s eCommerce Reputation Matter? Evidence

from eBay Auctions. The Journal of Industrial Economics, 50: 337–349.

Resnick, P., Zeckhauser, R. (2002). Trust Among Strangers in Internet Transactions: Empirical

Analysis of eBay's Reputation System, M. Baye (Ed), Advances in Applied Microeconomics

(Volume 11), Elsevier Science, Amsterdam, p. 667-719.

Resnick, P., Zeckhauser, R., Swanson, J., Lockwood, K. (2006). The Value of Reputation on

eBay: a controlled experiment, Experimental Economics 9, p. 79-101.

Rochet, J.-C., Tirole, J. (2006). Two-Sided Markets: A Progress Report. Rand Journal of

Economics 37: 645-667.

Schor J., Fitzmaurice, C. (2015) “Collaborating and Connecting: The emergence of the sharing

economy” in: Handbook on Research on Sustainable Consumption, eds., Lucia Reisch and

John Thogersen, (Cheltenham, UK: Edward Elgar), 2015.

Sriram et al. (2014) « Platforms: A Multiplicity of Research Opportunities », Working Paper

Michigan Ross School of Business.

Zervas, G., Proserpio D., Byers J. W. (2014). The rise of the sharing economy: Estimating the

impact of AirBnB on the hotel industry. Working paper. Available at

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2366898.

Zervas, G., Proserpio D., Byers J. W. (2015). A First Look at Online Reputation on AirBnB,

Where Every Stay is Above Average. Available at http://ssrn.com/abstract=2554500

30

Figure 1: Screenshots of BlaBlaCar website

31

Figure 2: Average percentage seats sold and mean prices over time for all trips

0,000

0,200

0,400

0,600

0,800

1,000

1,200

4

6

8

10

12

14

16

8/19/13 9/19/13 10/19/13 11/19/13 12/19/13 1/19/14 2/19/14

% s

ea

ts s

old

Me

an

Pri

ce (

€)

All Trips

Mean Price (€) % seats sold

32

Figure 3: Average percentage seats sold and mean prices over time for Paris-Lyon

0

0,2

0,4

0,6

0,8

1

1,2

20

21

22

23

24

25

26

27

28

29

30

31

8/19/13 9/19/13 10/19/13 11/19/13 12/19/13 1/19/14 2/19/14

% s

ea

ts s

old

Me

an

Pri

ce (

€)

Paris-Lyon

Mean Price (€) % seats sold

33

Figure 4: Nonlinearities in the Effect of Age on Price

Figure 5: Nonlinearities in the Effect of Age on Pr(Sale)

13.3

13.4

13.5

13.6

Pre

dict

ed P

rice

20 30 40 50 60Age

Predictive Margins with 95% CIs.6

05.6

1.6

15.6

2.6

25P

redi

cted

Pr(

Sal

e)

20 30 40 50 60Age

Predictive Margins with 95% CIs

34

Table 1: Summary Statistics

Panel A: Unit of Observation = Trip

Price 13.386

(9.421)

Fraction Sold 0.618

(0.435)

All Seats Sold 0.529

(0.499)

Avg. Price, Other Trips 11.149

(7.867)

N 948789

Panel B: Unit of Observation = Driver

Number of Same Trips 3.787

(6.944)

Number of Other Trips 2.565

(6.760)

Single-Trip Driver 0.547

(0.498)

Feedback Rating 8.344

(18.456)

Driver Status 2.655

(1.447)

Car Class 2.263

(1.053)

Age 36.034

(13.307)

Female 0.401

(0.490)

Photo Shown 0.388

(0.487)

Plays Music 0.555

(0.497)

Allows Pets 0.089

(0.284)

Allows Smoking 0.067

(0.250)

Roundtrip 0.260

(0.439)

Manual Confirmation 0.116

(0.320)

N 297582

35

Table 2: First-Stage Regression Results

(1)

Price

Avg. Price, Other Trips 0.010

(0.000)***

Feedback Rating -0.000

(0.000)

Driver Status=2 -0.150

(0.009)***


(0.010)***


(0.010)***


(0.012)***

Car Class=1 -0.326

(0.018)***

Car Class=2 -0.183

(0.012)***

Car Class=3 0.018

(0.012)

Car Class=4 0.313

(0.016)***

Age 0.006

(0.000)***

Female 0.137

(0.007)***

Photo Shown -0.007

(0.007)

Plays Music -0.113

(0.007)***

Allows Pets -0.201

(0.011)***

Allows Smoking 0.141

(0.013)***

Roundtrip 0.168

(0.007)***

Manual Confirmation 0.509

(0.008)***

N 948789

First-Stage F Stat 1683.508

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

36

Table 3: Main Regression Results

(1) (2)

Fraction Sold All Seats Sold

Price -0.097

(0.003)***

-0.086

(0.003)***

Feedback Rating 0.000

(0.000)***

0.000

(0.000)***

Driver Status=2 0.018

(0.002)***

0.013

(0.002)***


(0.002)***

0.008

(0.002)***


(0.002)***

0.005

(0.002)***


(0.002)***

0.058

(0.002)***

Car Class=1 0.010

(0.003)***

0.013

(0.003)***

Car Class=2 0.035

(0.002)***

0.037

(0.002)***

Car Class=3 0.032

(0.002)***

0.039

(0.002)***

Car Class=4 0.044

(0.003)***

0.046

(0.003)***

Age 0.000

(0.000)***

0.000

(0.000)***

Female 0.035

(0.001)***

0.057

(0.001)***

Photo Shown 0.007

(0.001)***

0.008

(0.001)***

Plays Music 0.019

(0.001)***

0.020

(0.001)***

Allows Pets -0.018

(0.002)***

-0.016

(0.002)***

Allows Smoking -0.002

(0.002)

-0.006

(0.002)***

Roundtrip 0.011

(0.001)***

0.019

(0.001)***

Manual Confirmation -0.420

(0.002)***

-0.530

(0.002)***

N 948789 948789

37

Table 4: Regression Results of Departure Characteristics

(1) (2) (3)

Price Fraction Sold All Seats Sold

Departure=Monday -0.127

(0.011)***

-0.064

(0.002)***

-0.066

(0.002)***

Departure=Tuesday -0.162

(0.013)***

-0.113

(0.002)***

-0.119

(0.002)***

Departure=Wednesday -0.111

(0.013)***

-0.104

(0.002)***

-0.113

(0.002)***

Departure=Thursday -0.009

(0.012)

-0.065

(0.002)***

-0.071

(0.002)***

Departure=Friday -0.054

(0.009)***

-0.026

(0.001)***

-0.028

(0.001)***

Departure=Saturday 0.066

(0.011)***

0.005

(0.002)***

0.003

(0.002)*

Departure=[6AM,12PM] -0.007

(0.023)

0.052

(0.004)***

0.066

(0.004)***

Departure=[12PM,6PM] -0.077

(0.023)***

0.050

(0.004)***

0.064

(0.004)***

Departure=[6PM,12AM] -0.230

(0.024)***

0.002

(0.004)

0.013

(0.004)***

Departure Time Trend -0.006

(0.000)***

-0.002

(0.000)***

-0.001

(0.000)***

N 948789 948789 948789

38

Table 5: Heterogeneity in the Price Elasticity of Demand

(1) (2) (3) (4)

Rating=0 Rating=(1,3) Rating=(4,13) Rating>13

Price -0.053 -0.213 -0.242 -0.309

(0.003)*** (0.013)*** (0.013)*** (0.019)***

N 217345 226896 279355 225193

39

Table 6: Distribution of price color by ratings received by the driver

Rating=0 Rating=(1,3) Rating=(4,13) Rating>13 Total

Green Price 0.829 0.851 0.877 0.902 0.865

Orange Price 0.146 0.133 0.112 0.088 0.119

Red Price 0.025 0.017 0.011 0.010 0.015

40

Appendix: Summary statistics by trip

Trips Number of

observations

Distinct

drivers

Average

price

Coefficient

of variation

Percentage

Sale Sold all

Distance

(km)

Aix-Avignon 15407 10701 5.188 0.290 0.581 0.500 81

Amiens-Beauvais 4485 2472 4.264 0.282 0.546 0.472 66

Angers-Le Mans 23635 16248 6.184 0.223 0.552 0.460 97

Besancon-Dijon 3274 2241 5.893 0.357 0.444 0.342 93

Bordeaux-Nantes 37304 27029 21.169 0.111 0.619 0.521 353

Brest-Saint Brieuc 8049 5059 8.055 0.211 0.440 0.314 144

Caen-Rennes 23293 14371 10.987 0.158 0.499 0.378 185

Clermont-Lyon 29589 20859 12.351 0.161 0.555 0.446 166

Dijon-Besancon 3430 2294 5.876 0.337 0.434 0.330 93

Grenoble-Lyon 24507 15337 6.850 0.217 0.498 0.383 105

Le Havre-Caen 8984 5032 5.841 0.185 0.512 0.388 96

Le Mans-Tours 14716 10694 5.651 0.252 0.538 0.440 102

Lens-Paris 45467 27644 14.306 0.118 0.585 0.487 199

Lille-Paris 50244 29957 14.559 0.111 0.598 0.504 220

Limoges-Toulouse 20093 14779 18.015 0.169 0.543 0.437 291

Lyon-Grenoble 24544 15589 6.889 0.244 0.489 0.370 105

Lyon-Paris 49602 35312 29.155 0.117 0.661 0.573 466

Marseille-Nice 5283 3477 13.213 0.238 0.379 0.271 198

Metz-Nancy 32984 22394 3.544 0.279 0.923 0.912 60

Montpellier-Mars 15636 10843 10.911 0.196 0.506 0.405 169

Nancy-Strasbourg 4426 2960 9.206 0.250 0.453 0.339 156

Nantes-Bordeaux 37241 26965 21.136 0.108 0.609 0.510 353

Nantes-Rennes 53147 28492 5.868 0.237 0.501 0.386 113

Nice-Toulon 2086 1159 10.935 0.257 0.298 0.161 150

Nimes-Montpellier 91661 59904 3.071 0.266 0.962 0.957 58

Orleans-Paris 25468 19028 8.270 0.296 0.544 0.445 133

41

Paris-Brest 17447 13044 36.114 0.105 0.708 0.636 591

Paris-Caen 18308 11947 15.006 0.133 0.520 0.411 234

Paris-Lyon 48023 34552 29.117 0.126 0.654 0.566 466

Paris-Marseille 5471 4798 47.387 0.206 0.604 0.512 774

Pau-Bordeaux 12665 8361 13.927 0.187 0.460 0.336 218

Perpignan-Narbonne 24071 16778 4.197 0.300 0.744 0.698 66

Reims-Troyes 9472 6516 8.086 0.241 0.460 0.338 127

Rennes-Brest 11546 7170 12.763 0.198 0.458 0.325 243

Rouen-Paris 14182 8205 8.772 0.171 0.502 0.392 136

Saint Etienne-

Clermont 36819 22047 22.191 0.475 0.562 0.451 144

Strasbourg-Colmar 8780 6449 4.337 0.269 0.713 0.664 76

Toulon-Aix 11237 7268 5.488 0.325 0.518 0.414 84

Toulouse-Bordeaux 48769 30144 15.114 0.134 0.535 0.413 245

Tours-Paris 27444 20069 15.441 0.184 0.624 0.536 240

Total 948789 13.386 0.618 0.529

What Drives Pricing Behavior in Peer-to-Peer Markets...

Documents

Transcript of What Drives Pricing Behavior in Peer-to-Peer Markets...