# Crowding in public transport: Who cares and why?

Luke Haywood, Martin Koning, Guillaume Monchambert

► To cite this version:

Luke Haywood, Martin Koning, Guillaume Monchambert. Crowding in public transport: Who cares and why?. Transportation Research Part A: Policy and Practice, 2017, 100, pp.215-227.
10.1016/j.tra.2017.04.022. hal-01517820

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<figure><img src="image_1.png" /></figure>
Crowding in public transport: who cares and why?

Luke Haywood<fnref n="1" />, Martin Koning<fnref n="2" />, and Guillaume Monchambert*,<fnref n="3" />

<footnote n="1">¹DIW Berlin, Public Economics Department, Mohrenstr. 58, 10117 Berlin, Germany</footnote>

<footnote n="2">²French Institute of Science and Technology for Transport, Development and Networks,
East Paris University, Marne-La-Vallée Cedex 2, France</footnote>

<footnote n="3">³Ecole Normale Supérieure de Cachan, CES - Université Paris-Saclay, 61, avenue du Président Wilson, 94235 Cachan, France</footnote>

<footnote>*Corresponding author. Present affiliation: University of Lyon, LAET-ISH, 14 avenue Berthelot, 69363 Lyon, France. Email: g.monchambert@univ-lyon2.fr</footnote>
Abstract

Crowding on public transport (PT) is a major issue for commuters around
the world. Nevertheless, economists have rarely investigated the causes of
crowding discomfort. Furthermore, most evidence on the costs of PT crowding
is based on trade-offs between crowding, travel time and money. First, this
paper assesses discomfort with PT crowding at various density levels across
heterogeneous individuals using a different methodology. Based on a survey of
1,000 Paris PT users, the negative relationship of in-vehicle density on reported
satisfaction is similar to previous studies investigating PT crowding costs and
stable across most individual characteristics. We also find a sensitive increase
in crowding costs over users' income. Second, we investigate the causes of
this discomfort effect. We identify three key drivers: (a) dissatisfaction with
standing and not being seated; (b) less opportunities to make use of the time
during the journey; (c) the physical closeness of other travellers *per se*.

**Keywords:** public transport; crowding; stated satisfaction; travel cost; survey
data
# 1 Introduction

Given the low elasticity supply of rail-based public transport (PT), policies implemented to reduce individual motorized traffic in cities have often coincided with less comfortable travel conditions. This indirect consequence of modal shift policies is problematic because availability of space in vehicles is often singled out as one of the most desirable dimensions of PT attractiveness. Using factor analysis or structural equation modelling on survey data, various studies have thus highlighted that crowding is a major component of PT users' satisfaction and service quality, next to travel duration, connectivity of the network, service frequency and fares (see Eboli and Mazzulla 2007, Dell'Olio et al. 2011, de Ona and de Ona 2015). For instance, Cantwell et al. (2009) decompose satisfaction for PT into three elements - crowding, travel time reliability and monetary cost - and test their relative importance using an on-line survey on commuting in Dublin. Satisfaction levels among PT commuters are strongly decreasing for those who travel in crowded situations.

Crowding negatively affects the passenger experience for several reasons (see Tirachini et al., 2013, for a review). It is associated with increased anxiety (Cheng, 2010) and stress (Mahudin et al., 2011), especially if it implies proximity to other passengers (Evans and Wener, 2007) or propensity to arrive late for work (Mahudin et al., 2011). Crowding might also accentuate the perception of risk to personal safety and security (Cox et al., 2006). Moreover, crowding is important for workers' choices about working times (Tirachini et al., 2013) and for firms' scheduling of working hours (Henderson, 1981).
Recognizing these discomfort effects, theoretical models of public transport increase the cost of time in crowded PT - either discretely once passengers can no longer be seated (Kraus, 1991), continuously as a function of in-vehicle density (IVD) (Jara-Díaz and Gschwender, 2003), or both (de Palma et al., 2015). Density can thus be integrated in the analysis of optimal supply and pricing (De Borger and Wouters, 1998; Parry and Small, 2009; Wardman, 2014). Significant welfare costs of crowded PT are also found empirically using both survey data on stated preferences (SP) (Wardman and Whelan, 2011; Haywood and Koning, 2015) and revealed preferences (RP) (Batarce et al., 2015; Tirachini et al., 2016). The latter studies estimate the parameters of the utility function based on trade-offs between travel time, money and IVD. These are then used to calculate monetary value associated with comfort improvements, “willingness to pay” (WTP), or “time multipliers” which evaluate the cost of crowded travel conditions in terms of additional time (see Wardman and Whelan, 2011 or Li and Hensher, 2011). With these valuations, in-vehicle crowding can be integrated as part of the generalized cost of PT, thus allowing an normative assessment of alternative policy choices (Batarce et al., 2016)<fnref n="1" />.

Whilst higher IVD implies spatial limitation for individuals, it only generates eco-

<footnote n="1">¹Note that different studies make different assumptions about how the cost of crowding relates to travel time. Most empirical studies assume that the utility cost of crowding is proportional to the time spent in the crowded condition, interacting travel time and crowding in PT users' utility function (Whelan and Crockett, 2009; Tirachini et al., 2013; Haywood and Koning, 2015; Batarce et al., 2016, among others). Thus, the marginal utility of travel time varies with respect to in-vehicle crowding. Alternatives are a fixed crowding cost per trip (Kroes et al., 2013; de Lapparent and Koning, 2015) or both additively and in interaction with travel time (Li et al., 2016).</footnote>
nomic consequences as an “experimental state” (see Stokols, 1972). Personal and trip
characteristics may modify this experience, hence we take these into account in our
analysis. Furthermore, this experience is intrinsically subjective<fnref n="2" />. Therefore it seems
most relevant to use a subjective indicator to measure it. We use a self-reported sat-
isfaction measure in this paper. Individuals are able to rate their well-being during
long or short periods of time (Van Praag and Ferrer-i Carbonell, 2008). Metcalfe
and Dolan (2012) conclude that reported satisfaction measure is a good measure of
the underlying utility of a transport journey.

This paper focuses on comfort satisfaction (CS), allowing travel time to moderate CS alongside other trip and individual characteristics. The crowding effect is understood as the utility cost due to lack of in-vehicle space and may thus vary across PT users: apart from IVD, it may also depend on travel and individual characteristics. In this framework, we address two research questions:

1. How does IVD relate to subjective CS stated by users and what determines the subjective CS? We use data on individual self-reported measures of satisfaction (derived from a field survey conducted late 2010 on platforms of Paris subways). This data allows a direct assessment of the perception of crowding and its impact on the satisfaction of PT users is in line with the empirical literature on subjective well-being (Kahneman and Krueger, 2006) or job satisfaction (Clark and Senik, 2010). This study contrasts with previous studies

<footnote n="2">²Mohd Mahudin et al. (2012) distinguish three components of the experience of passenger crowd-
ing (evaluation of psychosocial aspects of the crowded situation, emotional reactions to the crowded
situation and evaluation of the ambient environment of the crowded situation) to evaluate the re-
lationship between crowding and stress and feelings of exhaustion.</footnote>
in two aspects. Compared to RP or SP studies, we approximate the utility
cost of higher crowding based on changes in stated CS, rather than through
trade-offs with money or travel time. Our alternative approach and data allow
us to identify a crowding effect dependent on user income but independent of
travel time. We differ from studies (Eboli and Mazzulla, 2007; Cantwell et al.,
2009; Dell'Olio et al., 2011) looking at overall PT satisfaction in that we focus
specifically on the effect of crowding on PT satisfaction.

2. What explains the discomfort associated with crowding in PT? We investigate the reasons for low CS, defined as “causes of crowding discomfort” (CCD), i.e. *those features of a journey that are deteriorated by high passenger IVD*. In this study, we consider eight causes of discomfort, described in detail in Section 2.2. To our knowledge, we are the first to empirically test different candidate CCDs to understand the origins of the deterioration in CS. Having a better idea of the nuisances that really affect users can inform public policies. This study could thus highlight whether individuals will be better-off if they are offered additional seats, efficient cooling systems or more security in carriages.

The paper proceeds as follows. Section 2 presents the data and survey design. The Paris PT network constitutes a perfect case study to address in-vehicle crowding due to the recent growth in its patronage and no evidence of bottleneck effets. An active anti-car policy has been there implemented and succeeded to enhance a huge modal shift toward rail-based PT. Since PT supply could not adapt as fast as PT demand, however, IVD grew by 10% over 2000-2009 whilst service regularity remained
unchanged, see Haywood and Koning (2015). Section 3 estimates the relationship
between crowding (IVD), satisfaction with PT (CS) and other moderating factors.
Section 4 uses original data to assess the most important reasons for this crowding
effect.

2 Data

Our data was collected in the Parisian mass transit network in late 2010. Around
1,000 users were interviewed directly on platforms of subway lines 1 and 4, during
morning (7:30-10am) and evening (5-7:30pm) peaks, whilst waiting for their train
to arrive.<fnref n="3" /> Subway line 1 crosses Paris East-West. It is the busiest service of the
subway network with 750,000 daily users in 2010, serving Europe's largest central
business district *La Défense* and large tourist attractions. Subway line 4 crosses
Paris North-South and is the second most used service of the network, with 670,000
daily travelers in 2010. It connects three long-distance train stations: *Gare du Nord*,
*Gare de Lyon* and *Gare Montparnasse*. Users of lines 1 and 4 are very heterogeneous
since the lines cover both wealthy and poor neighborhoods. This heterogeneity is
useful to assess different individuals' preferences concerning PT crowding.

To elicit assessments of CS, PT users were shown a show-card (see Figure 1) and
asked "Which density of users do you expect to face during your immediate jour-

<footnote n="3">³The stations where the survey has been conducted are, from East to West, *Gare de Lyon*, *Hôtel de Ville*, *Champs Elysées*, *Georges V*, *Argentine* and *Esplanade* for line 1, and, from South to North, *Denfert-Rochereau*, *Montparnasse-Bienvenüe*, *Saint Sulpice*, *Odéon* and *Les Halles* for line 4.</footnote>
ney?". The density levels on the show-card correspond to 0, 1, 2, 2.5, 3, 4 and 6 passengers per square meter respectively. We use this as a measure of IVD. To test the robustness of our results, we independently collected measures of both the density of passengers in carriages and the duration between stations on the same lines. We could have carried out our analyses using this measure. We prefer to use the survey measure which is arguably more precise. All results are robust to using the alternative non-survey measure in the analysis <fnref n="4" />.

CS is assessed by answers to the question: "Given this density, mark your satisfaction associated with the comfort for your immediate journey on a scale from 0 to 10.", where 0 corresponds to highly dissatisfied and 10 to highly satisfied.

To determine the factors causing discomfort in high-density PT, interviewers showed interviewees the most crowded situation on the show-card (6 passengers per square meter) and asked them: "On a scale from 0 to 10, mark the inconvenience associated with the following aspects when traveling in conditions similar to the ones represented on show-card: Over-closeness, Standing, Noise, Smell, Time loss, Waste of time, Fall and Robbery." The scale ranges from 0 (not concerned by this type of discomfort) to 10 (highly relevant cause of nuisance). Section 4 considers which of these causes of crowding discomfort (CCD) are the key features that are deteriorated by high IVD and drives the reported CS.

<footnote n="4">⁴Other measures of density are also available - for a useful overview, see Pel et al. 2014.</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: Density show-card used during the field survey</figcaption></figure>

## 2.1 Descriptive statistics

Equal numbers of respondents were interviewed on lines 1 and 4, and during morning and evening peaks, with almost equal proportions of men (48%) and women (52%). We observe large socioeconomic heterogeneity, e.g. in income and age. A majority of the population is from central Paris (53%). 37% of respondents own a car, representative of the Parisian population. Door-to-door travel time is 49 minutes, with on average 10 minutes on lines 1 or 4. A large majority of the sample commute (71%) and use lines 1 or 4 every day (64%).

Section 4 is based on a sub-sample of 278 individuals. All respondents provide information on IVD and CS necessary for the analysis in Section 3. Interviewees were
then given the option of answering additional questions (used in Section 4). This
usually required taking a later train, given high service frequency at peak times. The
main difference between the two samples concerns the time of interview: 36% of sub-
sample users travel during morning peaks (as opposed to 50% of the whole sample).
This is consistent with the existence of scheduling costs that are more important
in the morning (Small and Verhoef, 2007) and that may occur if individuals would
answer to the longer survey (because deviating from their preferred arrival time).
They are also more likely to be commuters and use line 1. The characteristics of
the trip differ between the sub-sample and the rest of the sample, but the individual
characteristics do not vary significantly. The samples are systematically compared
to each other and differences tested for significance in Table (A.7) in Appendix A.

Table (1) gives the distribution of surveyed IVD. Few individuals will have a seat
in their trip: only 1 interviewee chose the “empty subway” situation and 2.8% chose
the card with 1 passenger per square meter. By contrast, more than 10% indicate
more than 6 passengers per square meter during their journey. More than 50% of
the PT users think they will travel with 2.5-3 passengers per square meter around
them with an average estimated density of 3.2 passengers per square meter. This
distribution is very similar in the sub-sample. Table (2) shows that CS is negatively
related to the level of passenger density IVD. By contrast, we do not observe any
clear relationship between CS and trip duration.
<table><caption>Table 1: Distribution of the in-vehicle density, IVD</caption><thead><tr><td>In vehicle density (pass/m²)</td><td>Frequencies (%)</td></tr></thead><tbody><tr><td>0</td><td>0.1</td></tr><tr><td>1</td><td>2.8</td></tr><tr><td>2</td><td>16.2</td></tr><tr><td>2.5</td><td>26.4</td></tr><tr><td>3</td><td>24.2</td></tr><tr><td>4</td><td>20.0</td></tr><tr><td>6</td><td>10.2</td></tr></tbody></table>

<table><caption>Table 2: Distribution of the comfort satisfaction, CS</caption><thead><tr><td>CS</td><td>Frequency (%)</td><td>Ave. IVD</td><td>Ave. IVTT</td></tr></thead><tbody><tr><td>0</td><td>6.3</td><td>5.1</td><td>9.8</td></tr><tr><td>1</td><td>4.3</td><td>4.3</td><td>9.7</td></tr><tr><td>2</td><td>9.5</td><td>4.0</td><td>10.2</td></tr><tr><td>3</td><td>11.4</td><td>3.4</td><td>10.1</td></tr><tr><td>4</td><td>12.6</td><td>3.0</td><td>10.1</td></tr><tr><td>5</td><td>21.9</td><td>2.8</td><td>9.8</td></tr><tr><td>6</td><td>17.5</td><td>2.7</td><td>9.3</td></tr><tr><td>7</td><td>10.4</td><td>2.6</td><td>8.8</td></tr><tr><td>8</td><td>4.3</td><td>2.5</td><td>9.3</td></tr><tr><td>9</td><td>1.2</td><td>2.0</td><td>11.1</td></tr><tr><td>10</td><td>0.6</td><td>2.6</td><td>7.9</td></tr></tbody></table>

*Notes.* This table reports descriptive statistics for sub-samples clustered by CS. Column (2) reports the part of each sub-sample into the whole sample. Columns (3) and (4) respectively report the average IVD, in users per square meter, and the average in-vehicle travel time, in minutes, in each sub-sample.
## 2.2 Dimensions of crowding discomfort

Whilst some studies equate the discomfort of crowding with a lack of seating (e.g. Kraus, 1991), we distinguish eight dimensions of CS which may be affected by high IVD and about which individuals were questioned:

* *Over-closeness*: Crowding generates an intrusion in users' individual space. Passengers suffer from stress and lack of control (Epstein, 1981, and for PT in particular, Epstein et al., 1981).

* *Standing*: When passenger density on a train is high, users find no seat. This may lead to pain and discomfort (Boussenna et al., 1982).

* *Noise*: may cause discomfort and mental health problems (Bhattacharya et al., 1995).

* *Bad Smell* increase with many passengers, not least as average temperatures increase with *IVD*.

* *Time Loss*: Crowding may increase dwell times at stations due to slower boarding and alighting. Furthermore, incidents on other points in the network may cause more delays, reducing reliability.

* *Waste of Time*: When passenger density is high, users are not able to perform tasks they would like during their PT journeys, such as read a newspaper or work (Langrehr, 1991).<fnref n="5" />

<footnote n="5">⁵Note that this could help justify the interaction effect implicit in the time multiplier studies, in which crowding costs are necessarily increasing in trip duration: productivity losses due to *Wasted Time* increase in trip duration.</footnote>
* *Fall*: In crowded situations, the risk of falling may increase.

* *Robbery*: Uzzell and Brown (2007) find higher rates of pick-pocketing in more crowded contexts.

Table (3) presents respondents ratings for each of these CCDs and categorizes them as psychological, physical, sensory, temporal and risky. Potential abstract dimensions such as the “lack of control” are hardly quantifiable for users, despite their importance in the psychological literature (see Cox et al., 2006). The interested reader is referred to Mohd Mahudin et al. (2012). Since a rating of 1 for one particular CCD may not have the same meaning if all other dimensions are also rated 1 or if the others are rated 10, we also include information on individuals’ ranking of CCDs (we later include the sum of CCD ratings in our regressions for the same reason). The mean rankings and CCD scores are fairly similar: *Over-closeness* appears as the most relevant CCD. More than half of respondents rank this feature as the most unpleasant. Second and third are *Smell* and *Standing*. *Robbery*, *Wasted Time*, *Noise* and *Time Loss* are moderately rated causes of crowding discomfort. Lastly, risks of *Fall* due to high density are viewed as negligible by subway users.

# 3 The crowding effect

Many studies have documented a crowding effect, i.e. a link between IVD and generalized cost or satisfaction of PT. To do this, they generally use data from hypothetical questions in an SP framework to estimate time multipliers or WTP. To what extent can we confirm these models’ findings using a completely different set-up based on
<table><caption>Table 3: Rank and score statistics for the 8 causes of crowding discomfort, *CCD*</caption><thead><tr><th>Category</th><th>Cause of dis.</th><th>Mean rank</th><th>Mean CCD</th><th>sd CCD</th></tr></thead><tbody><tr><td>Psychological</td><td>Over-closeness</td><td>2.0</td><td>7.7</td><td>2.525</td></tr><tr><td>Physical</td><td>Standing</td><td>3.3</td><td>6.3</td><td>3.208</td></tr><tr><td rowspan="2">Sensory</td><td>Noise</td><td>4.2</td><td>5.2</td><td>2.924</td></tr><tr><td>Smell</td><td>3.1</td><td>6.6</td><td>2.827</td></tr><tr><td rowspan="2">Temporal</td><td>Time Loss</td><td>4.3</td><td>5.1</td><td>2.874</td></tr><tr><td>Waste of Time</td><td>3.9</td><td>5.5</td><td>3.116</td></tr><tr><td rowspan="2">Risky</td><td>Fall</td><td>5.2</td><td>3.9</td><td>6.286</td></tr><tr><td>Robbery</td><td>3.8</td><td>5.5</td><td>3.198</td></tr></tbody></table>

*Notes.* This table reports descriptive statistics for each of the self-reported dissatisfactions with the cause of crowding discomfort, CCD. Column (1) (category) reports the category of the cause of crowding discomfort. Columns (3) reports the mean value of the rank. The rank was obtained by ordering all the dissatisfaction measures for one user. If the two highest dissatisfaction measures are equal, their rank is 1 and the rank of the third highest dissatisfaction mark is 3. Columns (4) and (5) respectively report the mean CCD and the standard deviation of CCD.

linking data from CS with IVD?

In our data, CS is measured on an 11-point discrete scale, and we assume there exists a latent continuous variable $CS^*$, such that:

$$ CS^* = IVD(\alpha + \beta_i x_i) + \sum_{k \in K} \gamma_k x_k + \varepsilon, \quad \forall i \in K. \quad (1) $$

where $x$ is a vector of $K$ individual and trip characteristics, and $\varepsilon$ captures the unobservables.

When $\beta_i$ is constrained to 0, $\alpha$ measures the pure crowding effect on the CS. In order to take into account heterogeneity of the relationship between IVD and CS, we allow
for $\beta_i \neq 0$, thus including interaction terms between IVD and individual and trip characteristics. We also allow individual and journey characteristics to influence CS independently of IVD.

## 3.1 Estimation

Given the discrete nature of our data on CS, we estimate the model using an ordered response model. We tested both logit and probit frameworks and it made no difference in results. We report probit results. Ordered choice models allow us to impose only a weak requirement on the interpretation of the scale: All we require is that a user with a CS of 6 is strictly more satisfied than one with a CS of 5, the difference between a CS of 10 and a CS of 8 may be different from the difference between a CS of 6 and a CS of 4. Note however that these differences must be homogeneous across different individuals. Table (B.8) in Appendix B also estimates a linear regression model of Equation (1) and finds very similar results.

Column (1) of Table (4) gives results of the restricted specification $\beta_i = 0$: As expected, CS decreases with IVD. One additional user per square meter decreases predicted latent $CS^*$ by 0.55 (around 3/4 of a standard deviation). Interestingly, the effect appears near-linear: Given our estimates find that a very small variance in the estimated cut-offs for the latent variable $CS^*$ (the cut-off distance is around 0.55 points and the s.d. only 0.011), increases in density affect comfort similarly at different points in the density distribution. This regularity in the relationship between density and satisfaction is remarkably consistent with results according to
<table><caption>Table 4: Effects of the density and the income on the comfort satisfaction</caption><thead><tr><th></th><th colspan="2">(1)</th><th colspan="2">(2)</th><th colspan="2">(3)</th></tr><tr><th></th><th colspan="2">CS*</th><th colspan="2">CS*</th><th colspan="2">CS*</th></tr><tr><th></th><th>Coef.</th><th>Std. err.</th><th>Coef.</th><th>Std. err.</th><th>Coef.</th><th>Std. err.</th></tr></thead><tbody><tr><td colspan="7"><strong>Crowding effects:</strong></td></tr><tr><td>IVD (users/m²)</td><td>-0.550***</td><td>0.044</td><td>0.699**</td><td>0.334</td><td>0.651*</td><td>0.349</td></tr><tr><td>IVD × ln(In. net monthly inc.)</td><td></td><td></td><td>-0.166***</td><td>0.043</td><td>-0.127**</td><td>0.049</td></tr><tr><td>IVD × Line (1=line 1/0=line 4)</td><td></td><td></td><td></td><td></td><td>-0.072</td><td>0.086</td></tr><tr><td>IVD × Door to door travel time</td><td></td><td></td><td></td><td></td><td>-0.009</td><td>0.061</td></tr><tr><td>IVD × In-vehicle travel time</td><td></td><td></td><td></td><td></td><td>0.160</td><td>0.283</td></tr><tr><td>IVD × Peak hour</td><td></td><td></td><td></td><td></td><td>-0.065</td><td>0.066</td></tr><tr><td>IVD × Daily usage</td><td></td><td></td><td></td><td></td><td>-0.069</td><td>0.070</td></tr><tr><td>IVD × Car available</td><td></td><td></td><td></td><td></td><td>0.052</td><td>0.065</td></tr><tr><td>IVD × Age</td><td></td><td></td><td></td><td></td><td>-0.438</td><td>0.291</td></tr><tr><td>IVD × Gender</td><td></td><td></td><td></td><td></td><td>-0.004</td><td>0.064</td></tr><tr><td colspan="7"><strong>Journey controls:</strong></td></tr><tr><td>Line (1=line 1/0=line 4)</td><td>0.074</td><td>0.077</td><td>0.085</td><td>0.078</td><td>0.297</td><td>0.247</td></tr><tr><td>Door to door travel time (hours)</td><td>0.070</td><td>0.052</td><td>0.065</td><td>0.051</td><td>0.094</td><td>0.196</td></tr><tr><td>In-vehicle travel time (hours)</td><td>-0.086</td><td>0.310</td><td>-0.085</td><td>0.315</td><td>-0.607</td><td>0.952</td></tr><tr><td>Morning Peak dummy</td><td>0.179***</td><td>0.067</td><td>0.184***</td><td>0.067</td><td>0.378*</td><td>0.063</td></tr><tr><td>Daily usage of the line (1=Y/0=N)</td><td>-0.125*</td><td>0.070</td><td>-0.127*</td><td>0.070</td><td>0.092</td><td>0.214</td></tr><tr><td colspan="7"><strong>Individual controls:</strong></td></tr><tr><td>Male</td><td>0.126*</td><td>0.066</td><td>0.117*</td><td>0.066</td><td>0.139</td><td>0.196</td></tr><tr><td>Car available</td><td>-0.060</td><td>0.071</td><td>-0.085</td><td>0.071</td><td>-0.261</td><td>0.209</td></tr><tr><td>ln(Individual net monthly income)</td><td>-0.094*</td><td>0.049</td><td>0.406***</td><td>0.132</td><td>0.289*</td><td>0.149</td></tr><tr><td>Age (centuries)</td><td>0.321</td><td>0.316</td><td>0.238</td><td>0.320</td><td>1.603*</td><td>0.908</td></tr><tr><td>cut1</td><td>-4.139***</td><td>0.335</td><td>-0.459</td><td>0.979</td><td>-0.602</td><td>1.016</td></tr><tr><td>cut2</td><td>-3.754***</td><td>0.329</td><td>-0.065</td><td>0.978</td><td>-0.206</td><td>1.016</td></tr><tr><td>cut3</td><td>-3.189***</td><td>0.320</td><td>0.511</td><td>0.976</td><td>0.370</td><td>1.015</td></tr><tr><td>cut4</td><td>-2.721***</td><td>0.314</td><td>0.985</td><td>0.974</td><td>0.845</td><td>1.012</td></tr><tr><td>cut5</td><td>-2.306***</td><td>0.312</td><td>1.405</td><td>0.974</td><td>1.265</td><td>1.012</td></tr><tr><td>cut6</td><td>-1.655***</td><td>0.310</td><td>2.060**</td><td>0.973</td><td>1.922*</td><td>1.012</td></tr><tr><td>cut7</td><td>-1.036***</td><td>0.307</td><td>2.680***</td><td>0.973</td><td>2.543**</td><td>1.012</td></tr><tr><td>cut8</td><td>-0.414</td><td>0.304</td><td>3.303***</td><td>0.971</td><td>3.169***</td><td>1.012</td></tr><tr><td>cut9</td><td>0.181</td><td>0.309</td><td>3.902***</td><td>0.973</td><td>3.776***</td><td>1.018</td></tr><tr><td>cut10</td><td>0.598*</td><td>0.324</td><td>4.323***</td><td>0.969</td><td>4.207***</td><td>1.023</td></tr><tr><td>Number of observations</td><td>999</td><td></td><td>999</td><td></td><td>999</td><td></td></tr><tr><td>Likelihood function</td><td>-1953.041</td><td></td><td>-1943.405</td><td></td><td>-1939.915</td><td></td></tr><tr><td>Pseudo R²</td><td>0.086</td><td></td><td>0.090</td><td></td><td>0.092</td><td></td></tr><tr><td>Prob > chi2</td><td>0.000</td><td></td><td>0.000</td><td></td><td>0.000</td><td></td></tr><tr><td>Akaike IC</td><td>3.950</td><td></td><td>3.933</td><td></td><td>3.942</td><td></td></tr><tr><td>Number of iterations</td><td>4</td><td></td><td>4</td><td></td><td>4</td><td></td></tr></tbody></table>

Notes. This table reports results from ordered probit estimations of Equation (1) when $\beta_i = 0$ (column (1)), when $x_i$ in Eq. (1) is ln (Individual net monthly income (euros)) (column (2)) and with all interaction effects (column (3)). *significant at 10%; **significant at 5%; ***significant at 1%.
which PT crowding costs grow linearly with IVD (Jara-Díaz and Gschwender, 2003; Haywood and Koning, 2015)).

It is worth noting that the vast majority of individuals in our sample face levels of occupancy that preclude seating. Our study is thus less informative about additional comfort costs involved in moving from a level of IVD that allows passengers to be seated and higher levels of IVD (see for example Wardman and Whelan, 2011).<fnref n="6" />,<fnref n="7" /> This focus is in line with peak travel conditions in many PT systems - fining a seat is a very rare privilege on subways in Paris during rush hours. In other PT systems with lower levels of occupancy ratio, the benefit of seating will be more important.

Column (3) tests how the crowding effect varies across passengers and trips by including interaction terms. Of all the individual and travel characteristics we test, only income significantly influences the dissatisfaction associated with the IVD. We also included each of the other variables listed in column (3) sequentially as the only interaction. Only the age variable was significant - but only if we do not include income, indicating that the only significant moderating factor is income. We also tested different functional forms for including income. The Akaike Information Criterion (AIC) confirms that using log income produces the best goodness of fit. Thus we focus on the estimates in column (2) where we include only the interaction with

<footnote n="6">⁶Wardman and Whelan (2011) use the density of standees per square meter as a measure of the vehicle occupancy, and not the IVD.</footnote>

<footnote n="7">⁷As a robustness check, we estimated equation (1) testing for a specific effect for the two lowest levels of IVD, corresponding to situations in which passengers are seated. The results did not show any significant difference.</footnote>
income. When IVD increases, the satisfaction of wealthier passengers decreases more quickly. To illustrate this result, Figure 2 draws CS as a function of IVD and various levels of income (400, 2,000 and 5,000 euros per month). Other things being equal, wealthier users have a lower CS when vehicles are very crowded (6 users per square meter). Nevertheless, their CS* increases more quickly when IVD decreases.

Importantly, CS does not seem to be driven by the amount of time spent in the vehicles. The in-vehicle travel time coefficient is not significant - neither individually nor as interactions. Note that these results do not immediately infirm the validity of the time multiplier formulation of utility costs used in Jara-Díaz and Gschwender (2003); Haywood and Koning (2015); Wardman and Murphy (2015). Our data is focused on the level of comfort satisfaction. In order to identify separately a marginal utility of time we would require information on each sub-component of the total travel satisfaction.<fnref n="8" />

Table (4) also reveals that traveling in the morning brings more CS than traveling in the evening. One potential explanation is that these trips often have home as a destination and that users are more impatient to arrive at home than at other destinations. Maybe tired evening commuters also suffer more from the stress of crowding. We find no “habituation”-effect: To the contrary, frequent passengers tend to be less satisfied by their journey comfort than occasional users. This is in

<footnote n="8">⁸Kroes et al. (2013) and de Lapparent and Koning (2015) also find that an additive crowding penalty better fits the data, thus suggesting that PT crowding costs should be specified independently of time costs within the generalized cost function.</footnote>
<figure><img src="image_3.png" /></figure>

Expected CS* of the representative user with a net monthly income of 400 euros.

Expected CS* of the representative user with a net monthly income of 2,000 euros.

Expected CS* of the representative user with a net monthly income of 5,000 euros.

<caption>Figure 2: Latent comfort satisfaction (CS*) and comfort satisfaction (CS) as a function of IVD and income</caption>
line with Baum and Greenberg (1975) who find that expectations do not reduce people's perception of general level of discomfort. Finally, men are more satisfied with comfort than women. The pseudo-R² remind us that only a modest share of stated satisfaction measures depends on objective variables (see Kahneman et al., 1999).

# 4 Causes of crowding discomfort

We now address the causes of the crowding effect we found in section 3. Can we identify reasons for the relationship between density and comfort in PT journeys?

As potential channels through which IVD may decrease satisfaction, we test our eight causes of crowding discomfort (CCD) discussed in sub-section 2.2. If cause *d* is an important channel, we expect the associated interaction effect $\alpha_d$ to be significant in Equation (2). Note that the causes of discomfort, $CCD_d$, are assumed to be cardinal measures of the dissatisfaction. We also need to assume that differences in $CCD_d$ across users are stable for all levels of IVD.

$$ CS^* = IVD \left( \sum_d \alpha_d CCD_d \right) + \delta \overline{CCD} + \sum_k \gamma_k x_k + \varepsilon, \quad (2) $$

where $CCD_d$ is the stated dissatisfaction with the cause of crowding discomfort *d* described in Table 3, $\overline{CCD}_i = \sum_d CCD_{i,d}$ is the sum of CCD rating by an
individual *i*, *x* is a set of *K* control variables: line, duration of journey, journey time on line, morning peak, daily usage of the line, gender, car ownership, ln (Individual net monthly income (euros)), age and residence in Paris. A negative value of *α*<sub>d</sub> means that a user who is more dissatisfied by the cause of crowding discomfort *d* is less tolerant to crowding. *CCD* controls for an individual fixed effect, e.g. some individuals may have a tendency of reporting higher values in all categories due to a different understanding of the scale.

Given that we have information on CCD only for a sub-sample of individuals, we want to control for non-random selection of these individuals. We thus estimate a Heckman selection model. The estimate of ρ in Table (5) indicates that there is indeed a selection issue. We require instruments which are correlated with the probability of answering the whole survey, but not correlated with the mark given to CS. We rely on two instruments here: The reason for the trip (Motive 1=work/0=others) and the gender mismatch (a dummy indicating that interviewer and interviewee are not of the same gender). The *Motive* instrument is chosen because trips related to work are, in average, more time-constrained than others. The surveyed traveler may have chosen not to answer the additional questions because he did not know the duration of these questions. This ensures an informative instrument. Whilst individuals commuting are more time constrained, they appear to be similar along important dimensions - thus we find no significant differences in gender, CS rating, trip duration, car ownership or location of residence. This gives us hope that the instrument is indeed exogenous. The interviewer gender effect on survey participation has been documented in the literature (see Kane and Macaulay, 1993; Catania et al.,
1996; Huddy et al., 1997). Whilst the effect may also influence survey responses in some specific cases such as sexual behavior (Catania et al., 1996), gender inequality (Kane and Macaulay, 1993) or feminism and political activism (Huddy et al., 1997), we think that this is unlikely in our survey, which has no obvious gender dimension. There is thus no reason for answers to be influenced by the interviewer gender, suggesting that the instrument is indeed exogenous.

Given the small size of the sub-sample we use a linear regression. Since there are no thresholds to estimate, we save ten degrees of freedom.<fnref n="9" /> The results in Section 3 reassure us that the grid of CS is fine enough so that assuming that CS is a continuous variable does not strongly influence results.

## 4.1 Results

Table (5) reports selected results. Since the main individual and journey effects have been discussed in the previous section, our discussion now focuses on the estimated coefficients of the interaction between *IVD* and *CCD*. These are negative and significant for *Standing*, *Over-closeness* and *Wasted Time*. Users who are relatively more dissatisfied by one of these three CCDs perceive a higher disutility of crowding. We therefore consider these as principal channels of the crowding effect. *Standing* and *Wasted Time* seem to have the highest impact on *CS*, followed by *Over-closeness*. Note that the most important CCDs here follow quite closely the ranking of CCDs

<footnote n="9">⁹The ordered logit IV-regression did not converge.</footnote>
<table><caption>Table 5: Effect of different causes of crowding discomfort (CCD) on comfort satisfaction (CS)</caption><thead><tr><th></th><th colspan="2">CS*</th></tr><tr><th>Main model</th><th>Coef.</th><th>Std. err.</th></tr></thead><tbody><tr><td><strong>Crowding effect:</strong></td><td></td><td></td></tr><tr><td>IVD × Standing CCD</td><td>-0.033 ***</td><td>0.013</td></tr><tr><td>IVD × Over-closeness CCD</td><td>-0.028*</td><td>0.016</td></tr><tr><td>IVD × Noise CCD</td><td>-0.020</td><td>0.012</td></tr><tr><td>IVD × Robbery CCD</td><td>-0.002</td><td>0.015</td></tr><tr><td>IVD × Fall CCD</td><td>0.012</td><td>0.014</td></tr><tr><td>IVD × Smell CCD</td><td>-0.012</td><td>0.013</td></tr><tr><td>IVD × Time Loss CCD</td><td>-0.022</td><td>0.014</td></tr><tr><td>IVD × Wasted Time CCD</td><td>-0.030 **</td><td>0.014</td></tr><tr><td><strong>Journey characteristics:</strong></td><td colspan="2">Y</td></tr><tr><td><strong>Individual characteristics:</strong></td><td colspan="2">Y</td></tr><tr><td>Σ CCD</td><td>0.026 **</td><td>0.011</td></tr><tr><td>Constant</td><td>5.085 ***</td><td>1.246</td></tr><tr><td>Likelihood function</td><td colspan="2">-1121.009</td></tr><tr><td>Wald chi2(18)</td><td colspan="2">225.58</td></tr><tr><td>Prob > chi2</td><td colspan="2">0.000</td></tr><tr><th>Selection model</th><th colspan="2">sub-sample participation (dummy)</th></tr><tr><td><strong>Excluded Instruments:</strong></td><td></td><td></td></tr><tr><td>Motive (1=work/0=other)</td><td>-0.140</td><td>0.091</td></tr><tr><td>Gender mismatch (dummy)</td><td>0.152 **</td><td>0.075</td></tr><tr><td><strong>Controls:</strong></td><td></td><td></td></tr><tr><td>Morning peak (dummy)</td><td>-0.469 ***</td><td>0.086</td></tr><tr><td>Door to door travel time (hours)</td><td>0.039</td><td>0.074</td></tr><tr><td>Daily usage of the line</td><td>0.170*</td><td>0.095</td></tr><tr><td>Age (years)</td><td>0.015</td><td>0.352</td></tr><tr><td>Constant</td><td>-0.492 ***</td><td>0.170</td></tr><tr><td>ρ</td><td>0.774 ***</td><td>0.096</td></tr><tr><td>Wald test of indep. eqns. (rho = 0):</td><td></td><td></td></tr><tr><td>chi2(1)</td><td colspan="2">18.51</td></tr><tr><td>Prob > chi2</td><td colspan="2">0.000</td></tr><tr><td>Number of observations</td><td colspan="2">999</td></tr><tr><td>Censored observations</td><td colspan="2">721</td></tr><tr><td>Uncensored observations</td><td colspan="2">278</td></tr></tbody></table>

*Notes.* This table reports result estimating Equation (2) taking into account selection. ρ is the estimated correlation between residual of Equation (2), ε, and residuals of the selection equation. Significance levels: * 10%; ** 5%; *** 1%.
in Table (3) - with one exception: Whilst *Smell* is apparently judged an important nuisance in the sample overall, it is ranked highly especially by individuals who are not very sensitive to crowding. In summary, when passenger density is high, users incur a disutility because they have to stand, because they are not able to spend their time usefully and because they suffer from the physical proximity of others.

From a public policy perspective, we can go beyond highlighting the costs of PT congestion and thus the benefits of higher service frequency or better rolling stock. Our results suggest that the CS of Paris subway users may be increased by focusing in particular on one of the three channels identified here - *Standing, Over-closeness* and *Wasted Time*:

* Reducing the discomfort caused by *Standing* should not simply consist in adding more seating, since additional seating may generate higher levels of *Over-closeness* if seats take away room used for standing in crowded conditions. However, it is possible to install fold-up seating that uses very little space in crowded times when individuals must stand.

* Regarding *Over-closeness*, policy options are limited without changing IVD. The Parisian transport operator is already running ad campaigns exorting passengers to stand up from foldable seats and remove rucksacks from their backs.<fnref n="10" />

* The comfort cost of *Wasted Time* could be reduced if access to wireless com-

<footnote n="10">¹⁰See http://www.citylab.com/commute/2012/08/paris-metro-system-forced-admit-parisians-act-jerks/2857/.</footnote>
<table><caption>Table 6: Main effects of socio-economic variables on different causes of comfort dissatisfaction</caption><thead><tr><th></th><th>Standing</th><th>Wasted time</th><th>Over-clos.</th></tr></thead><tbody><tr><td>Male</td><td>(-)</td><td>(-)</td><td>(-)</td></tr><tr><td>Income</td><td></td><td>(+)</td><td>(+)</td></tr><tr><td>Age</td><td></td><td>(-)</td><td></td></tr><tr><td>Car available (1=Y)</td><td></td><td>(-)</td><td></td></tr></tbody></table>

*Notes.* The signs displayed in this table are the signs of significant coefficients obtained through regressing CCD ratings on these individual characteristics (controlling for selection). Table (C.9) in Appendix C provides all results.

Reading: (+) means that a policy addressing this cause of crowding discomfort would increase more the CS of users with these characteristics. (-) means that a policy addressing this cause would increase less the CS of users with these characteristics.

munication (wi-fi or phone networks) was facilitated.

Finally, PT users do not perceive different causes of discomfort in the same way. Addressing one specific CCD may favor certain users over others. In order to investigate this issue, Appendix C looks at the role of individual characteristics for key CCDs *Standing*, *Wasted Time* and *Over-closeness*. Table (6) summarizes the findings: Women are generally more sensitive to our key CCD causes. Passengers with higher incomes are more likely to suffer from *Wasted Time* and *Over-closeness*. Car-owners perceive *Wasted Time* as a less important feature of crowding, maybe because they know that they can occupy their travel time in a better way than if they had to focus on the road traffic, whatever the level of density. Alternately, individuals who enjoy working and reading during transport (and hence are very sensitive to *Wasted Time*) do not own a car. Finally, old people tend to be less affected by this nuisance.
# 5 Conclusion

A growing body of research focuses on the cost of PT crowding in terms of passenger welfare. This paper has used a survey on stated satisfaction collected on Paris subway platforms to investigate this crowding effect. We add evidence from an interesting new type of data to a literature that has mostly focused on trade-offs between crowding and time or money to estimate the utility parameters and infer time multipliers or WTP. In addition, our analysis includes an original discussion on the causes of the crowding effect.

Our main conclusions can be summarized as follows: First, we confirm previous findings that crowding costs grow linearly with IVD. Second, wealthier users' satisfaction decreases more quickly with IVD. Third, we identify three causes of dissatisfaction with crowding: a higher probability to stand for all or part of the journey, a poorer use of the time during the journey, and a shorter average distance from other users during the journey. Finally, women and wealthy individuals are more likely to benefit from any policy addressing one or more of these three channels.

# Acknowledgements

This research did not benefit any financial support. We thank the editor and two referees for very useful comments and suggestions. We also would like to thank André de Palma, Marc Ivaldi, Stef Proost, and participants at the Annual Conference of
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# Appendices

## A Representativeness of the sample

The sample consists of 999 passengers, the sub-sample contains 278 interviewees. Table (A.7) contrasts our sample to a representative sample of the overall peak hour subway user population taken from the “Enquête Globale Transport” (EGT). Peak hours are here defined as the 7:30-10am and 5-7:30pm periods. The EGT survey is conducted every ten years by the PT regulator in the Île-de-France region. 18,000 households are surveyed and weighted to ensure sample representativeness at the regional scale. When compared with the EGT sample, our sample is on average more manly, younger, less likely to live in central Paris, poorer and more likely to own a car. Despite this, we find that our sample is fairly representative.

## B Crowding effect estimation using OLS

Table (B.8) shows results using a linear specification of the crowding effect, i.e. estimating Equation (1) using OLS.

## C Users preferences for the nuisance factors

Sub-sample respondents rate their level of dissatisfaction about the causes of discomfort assuming that the IVD is the highest, i.e 6 users per square meter. We wish to
<table><caption>Table A.7: Individual and journey characteristics for the whole sample, the sub-sample and the Enquête Globale Transport (EGT)</caption><thead><tr><th></th><th></th><th>Sample</th><th>Sub-sample</th><th>Diff. sign.</th><th>EGT Sample</th></tr></thead><tbody><tr><td>N</td><td></td><td>999</td><td>278</td><td></td><td>2,414</td></tr><tr><td>Female (%)</td><td></td><td>51.5</td><td>52.6</td><td>n.s.</td><td>55.1</td></tr><tr><td>Age (Years)</td><td></td><td>35.8<br>(sd: 12.4)</td><td>35.5<br>(sd: 13.4)</td><td>n.s.</td><td>38.1<br>(sd: 14.4)</td></tr><tr><td>Car available (%)</td><td></td><td>37.4</td><td>36.4</td><td>n.s.</td><td>33.5</td></tr><tr><td>Income (Euros)</td><td></td><td>2,422<br>(sd: 2,293)</td><td>2,282<br>(sd: 2,126)</td><td>n.s.</td><td>2,321<br>(sd: 1,861)</td></tr><tr><td>Live in Paris (%)</td><td></td><td>52.7</td><td>44.5</td><td>n.s.</td><td>61.6</td></tr><tr><td>Interviewed during morning peak (%)</td><td></td><td>50</td><td>36</td><td>***</td><td>-</td></tr><tr><td>Interviewed during evening peak (%)</td><td></td><td>50</td><td>64</td><td>***</td><td>-</td></tr><tr><td>Motive (%)</td><td>Work</td><td>70.2</td><td>66</td><td>*</td><td>56</td></tr><tr><td></td><td>Other</td><td>29.8</td><td>34</td><td>*</td><td>44</td></tr><tr><td>Line (%)</td><td>Line 1</td><td>50.1</td><td>55.1</td><td>**</td><td>-</td></tr><tr><td></td><td>Line 4</td><td>49.9</td><td>44.9</td><td>**</td><td>-</td></tr><tr><td>Total travel time (minutes)</td><td></td><td>48.1<br>(sd: 36.7)</td><td>46.9<br>(sd: 35.4)</td><td>n.s.</td><td>41.5<br>(sd: 35.4)</td></tr><tr><td>Surveyed travel time (minutes)</td><td></td><td>9.7<br>(sd: 6.5)</td><td>9.6<br>(sd: 6.25)</td><td>n.s.</td><td>-</td></tr><tr><td>Daily use of the line (%)</td><td></td><td>63.3</td><td>66.9</td><td>n.s.</td><td>-</td></tr><tr><td>In-vehicle density (users/m²)</td><td></td><td>3.153<br>(sd: 1.203)</td><td>3.232<br>(sd: 1.191)</td><td>n.s.</td><td>-</td></tr><tr><td>Comfort satisfaction (0-10)</td><td></td><td>4.464<br>(sd: 2.186)</td><td>4.230<br>(sd: 2.218)</td><td>**</td><td>-</td></tr></tbody></table>

*Notes.* This table summarizes a specialized survey collected in the Parisian subway and the EGT sample of users using the Paris subway during peak periods. Percentages denote frequencies. Age and income means and standard developments are computed with the center of the categories. Significance levels: *** (1%), **(5%), and *(10%), using a two sided t-test comparing variable means of sample and subsample.
<table><caption>Table B.8: Effects of the density and the income on the comfort satisfaction (OLS)</caption><thead><tr><th></th><th>(1)<br>CS*</th><th>(2)<br>CS*</th><th>(3)<br>CS*</th><th></th></tr><tr><th></th><th>Coef.</th><th>Std. err.</th><th>Coef.</th><th>Coef.</th><th>Std. err.</th></tr></thead><tbody><tr><td colspan="6"><strong>Crowding effects:</strong></td></tr><tr><td>IVD (users/m²)</td><td>-0.981 **</td><td>0.052</td><td>1.032 **</td><td>0.488</td><td>0.985*</td><td>0.510</td></tr><tr><td>IVD × ln(In. net monthly inc.)</td><td></td><td></td><td>-0.265 ***</td><td>0.064</td><td>-0.208 ***</td><td>0.075</td></tr><tr><td>IVD × Line (1=line 1/0=line 4)</td><td></td><td></td><td></td><td></td><td>-0.084</td><td>0.114</td></tr><tr><td>IVD × Door to door travel time</td><td></td><td></td><td></td><td></td><td>-0.007</td><td>0.091</td></tr><tr><td>IVD × In-vehicle travel time</td><td></td><td></td><td></td><td></td><td>0.216</td><td>0.432</td></tr><tr><td>IVD × Peak hour</td><td></td><td></td><td></td><td></td><td>-0.109</td><td>0.099</td></tr><tr><td>IVD × Daily usage</td><td></td><td></td><td></td><td></td><td>-0.104</td><td>0.105</td></tr><tr><td>IVD × Car available</td><td></td><td></td><td></td><td></td><td>0.078</td><td>0.099</td></tr><tr><td>IVD × Age</td><td></td><td></td><td></td><td></td><td>-0.691</td><td>0.441</td></tr><tr><td>IVD × Gender</td><td></td><td></td><td></td><td></td><td>-0.033</td><td>0.097</td></tr><tr><td colspan="6"><strong>Journey controls:</strong></td></tr><tr><td>Line (1=line 1/0=line 4)</td><td>0.115</td><td>0.128</td><td>0.132</td><td>0.127</td><td>0.377</td><td>0.365</td></tr><tr><td>Door to door travel time (hours)</td><td>0.122</td><td>0.097</td><td>0.112</td><td>0.096</td><td>0.135</td><td>0.305</td></tr><tr><td>In-vehicle travel time (hours)</td><td>-0.128</td><td>0.550</td><td>-0.129</td><td>0.546</td><td>-0.837</td><td>1.552</td></tr><tr><td>Peak hour (1=morning, 0=evening)</td><td>0.338 ***</td><td>0.118</td><td>0.344 ***</td><td>0.117</td><td>0.678 **</td><td>0.333</td></tr><tr><td>Daily usage of the line (1=Y/0=N)</td><td>-0.228*</td><td>0.122</td><td>-0.230*</td><td>0.121</td><td>0.105</td><td>0.349</td></tr><tr><td colspan="6"><strong>Individual controls:</strong></td></tr><tr><td>Gender (1=male, 0=female)</td><td>0.221*</td><td>0.116</td><td>0.204*</td><td>0.115</td><td>0.323</td><td>0.327</td></tr><tr><td>Car available (1=Y/0=N)</td><td>-0.113</td><td>0.124</td><td>-0.156</td><td>0.123</td><td>-0.426</td><td>0.343</td></tr><tr><td>ln(Individual net monthly income (euros))</td><td>-0.180 **</td><td>0.087</td><td>0.630 ***</td><td>0.214</td><td>0.454*</td><td>0.247</td></tr><tr><td>Age (years)</td><td>0.632</td><td>0.547</td><td>0.491</td><td>0.544</td><td>2.671*</td><td>1.505</td></tr><tr><td>Constant</td><td>8.452 ***</td><td>0.576</td><td>2.415</td><td>1.565</td><td>2.551</td><td>1.633</td></tr><tr><td>Number of observations</td><td>999</td><td></td><td>999</td><td></td><td>999</td><td></td></tr><tr><td>R²</td><td>0.321</td><td></td><td>0.333</td><td></td><td>0.336</td><td></td></tr><tr><td>Prob > chi2</td><td>0.000</td><td></td><td>0.000</td><td></td><td>0.000</td><td></td></tr></tbody></table>

Notes. This table reports results from OLS estimations of Eq. (1) when $\beta_i = 0$ (column (1)), when $x_i$ in Eq. (1) is ln(Individual net monthly income (euros)) (column (2)) and with all interaction effects (column (3)). *significant at 10%; **significant at 5%; ***significant at 1%.
test whether socioeconomic variables drive these self-reported marks. We therefore estimate the following equation:

$$CCD_d^* = \beta_{1d}X + \beta_{2d}Z + \varepsilon_d \quad (C.3)$$

where $CCD_d^*$ is the latent variable associated with the dissatisfaction mark given to the cause of discomfort $d$. $X$ is a set of individual characteristics: gender (dummy), car availability (dummy), ln(Individual net monthly income (euros)), age (centuries) and live in Paris (dummy). It is conceivable that answers are affected by the current journey of users. To control for these effects, we also include a characteristics of the journey, $Z$: line where the user is surveyed (dummy) and the immediate journey travel time (hours).

We control the selection bias with the Heckman selection model. Reported ratings for different CCD are assumed to be cardinal measures of dissatisfaction. We focus on the causes which we have found to influence comfort dissatisfaction most strongly, i.e. *Standing*, *Wasted Time* and *Over-closeness*.

Tables (C.9) reports results from estimating Equation (C.3) using a Heckman selection model (specified as above). First, there is a clear gender effect: men are a lot less dissatisfied than women by the three nuisance factors. This is in line Meyers-Levy and Maheswaran (1991) and Meyers-Levy and Sternthal (1991) who find that women process information in more detail, resulting in a greater sensitivity to environmental factors. Second, wealthier users are more affected by *Wasted Time* and *Over-closeness*. This effect is not surprising and corresponds to results found in Section 3. It may be consistent with their higher value of time. Third, car
ownership influences the perception of crowding nuisances. Car-owner users seem
to compare the crowding conditions in PT with the individual car travel conditions.
As a consequence, they find the *Wasted Time* less penalizing than other users do,
maybe because they know that they can occupy their travel time in a better way
than if they had to focus on the road traffic, whatever the level of density. Finally,
a negative age effect is perceptible for *Wasted Time*.
<table><caption>Table C.9: Correlates of our prime causes of comfort dissatisfaction</caption><thead><tr><th></th><th>(1)<br>Standing CCD<br>Coef.</th><th>Std. err.</th><th>(2)<br>Wasted Time CCD<br>Coef.</th><th>Std. err.</th><th>(3)<br>Over-closeness CCD<br>Coef.</th><th>Std. err.</th></tr></thead><tbody><tr><td colspan="7"><strong>Individual effects:</strong></td></tr><tr><td>Male</td><td>-1.023 ***</td><td>0.390</td><td>-0.694*</td><td>0.366</td><td>-1.039 ***</td><td>0.354</td></tr><tr><td>Car available</td><td>-0.036</td><td>0.439</td><td>-0.964 **</td><td>0.427</td><td>0.187</td><td>0.395</td></tr><tr><td>ln(Ind. net monthly inc.))</td><td>0.110</td><td>0.315</td><td>0.639 **</td><td>0.274</td><td>0.468*</td><td>0.275</td></tr><tr><td>Age (years)</td><td>-1.038</td><td>1.841</td><td>-3.500 **</td><td>1.778</td><td>-0.401</td><td>1.649</td></tr><tr><td>Live in Paris (1=Y/0=N)</td><td>-0.609</td><td>0.403</td><td>0.022</td><td>0.394</td><td>-0.589</td><td>0.373</td></tr><tr><td colspan="7"><strong>Journey controls:</strong></td></tr><tr><td>Line (1=line 1/0=line 4)</td><td>-0.625</td><td>0.438</td><td>-0.383</td><td>0.420</td><td>-0.166</td><td>0.392</td></tr><tr><td>In-vehicule travel time (hours)</td><td>3.318*</td><td>1.720</td><td>-0.841</td><td>1.802</td><td>0.386</td><td>1.770</td></tr><tr><td>Constant</td><td>5.448 ***</td><td>2.040</td><td>1.606</td><td>1.796</td><td>3.094</td><td>1.994</td></tr><tr><td>Likelihood function</td><td>-1284.816</td><td></td><td>-1271.519</td><td></td><td>-1256.504</td><td></td></tr><tr><td>Wald chi2(7)</td><td>12.61</td><td></td><td>18.45</td><td></td><td>15.06</td><td></td></tr><tr><td>Prob > chi2</td><td>0.082</td><td></td><td>0.010</td><td></td><td>0.035</td><td></td></tr><tr><td colspan="7"><strong>Selection model</strong></td></tr><tr><td colspan="7"><strong>Excluded Instruments:</strong></td></tr><tr><td>Motive (1=work, 0=other)</td><td>-0.184*</td><td>0.098</td><td>-0.203 **</td><td>0.101</td><td>-0.170*</td><td>0.103</td></tr><tr><td>Gender mismatch (dummy)</td><td>0.151*</td><td>0.086</td><td>0.162*</td><td>0.086</td><td>0.134</td><td>0.096</td></tr><tr><td colspan="7"><strong>Controls:</strong></td></tr><tr><td>Peak hour (1=morning, 0=evening)</td><td>-0.455 ***</td><td>0.087</td><td>-0.448 ***</td><td>0.088</td><td>-0.461 ***</td><td>0.087</td></tr><tr><td>Door to door travel time (hours)</td><td>0.039</td><td>0.075</td><td>0.024</td><td>0.074</td><td>0.027</td><td>0.073</td></tr><tr><td>Daily usage of the line</td><td>0.184*</td><td>0.095</td><td>0.171*</td><td>0.096</td><td>0.178*</td><td>0.097</td></tr><tr><td>Age (years)</td><td>0.037</td><td>0.357</td><td>0.042</td><td>0.356</td><td>0.028</td><td>0.358</td></tr><tr><td>Constant</td><td>-0.483 ***</td><td>0.174</td><td>-0.462 ***</td><td>0.172</td><td>-0.466 ***</td><td>0.174</td></tr><tr><td>ρ<sub>d</sub></td><td>0.260</td><td>0.186</td><td>0.363</td><td>0.194</td><td>-0.162</td><td>0.466</td></tr><tr><td>Wald test of indep. eqns. (rho = 0):</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>chi2(1)</td><td>1.77</td><td></td><td>2.88</td><td></td><td>0.12</td><td></td></tr><tr><td>Prob > chi2</td><td>0.183</td><td></td><td>0.090</td><td></td><td>0.733</td><td></td></tr><tr><td>Number of observations</td><td>999</td><td></td><td>999</td><td></td><td>999</td><td></td></tr><tr><td>Censored observations</td><td>721</td><td></td><td>721</td><td></td><td>721</td><td></td></tr><tr><td>Uncensored observations</td><td>278</td><td></td><td>278</td><td></td><td>278</td><td></td></tr></tbody></table>

Notes. This table reports result from Equation (C.3). ρ<sub>d</sub> is the estimated correlation between residual of Eq. (C.3), ε<sub>d</sub>, and residuals of the first step, μ. *significant at 10%; **significant at 5%; ***significant at 1%.