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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.
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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
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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
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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
platforms” such as BlaBlaCar are determined differently than on other types of peer-to-peer
markets such as eBay.
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
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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.
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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
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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%]).
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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.
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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.
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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
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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
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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.
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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
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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).
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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
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platforms” such as BlaBlaCar are determined differently than on other types of peer-to-peer
markets such as eBay.
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
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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.
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[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
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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
29
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)***
Driver Status=3 -0.278
(0.010)***
Driver Status=4 -0.399
(0.010)***
Driver Status=5 -0.445
(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)***
Driver Status=3 0.011
(0.002)***
0.008
(0.002)***
Driver Status=4 0.009
(0.002)***
0.005
(0.002)***
Driver Status=5 0.053
(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