HIAS-E-115

Do Economic Incentives Promote
Physical Activity?
Evidence from the London Congestion
ChargeRyota Nakamura (a), Andrea Albanese (b), Emma Coombes (c),Marc Suhrcke (b) (d)(a) Hitotsubashi Institute for Advanced Study, Hitotsubashi University(b)
Luxembourg Institute of Socio-Economic Research(c)University of East Anglia(d)University of York

December, 2021

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

All rights reserved.

Do Economic Incentives Promote Physical Activity? Evidence
from the London Congestion Charge *Ryota Nakamura a, Andrea Albanese b, Emma Coombes c, Marc Suhrckeb,dThis version: December 2021
Abstract
This study investigates the impact of economic incentives on travelrelated physical activity, leveraging the London Congestion Charge’s
disincentivising of sedentary travel modes via increasing the cost of
private car use within Central London. The scheme imposes charges
on most types of cars entering, exiting and operating within the Central
London area, while individuals living inside the charging zone are
eligible for a 90% reduction in congestion charges. Geographicallocation information provides the full-digit postcode data necessary to
precisely identify the eligibility for the discount of participants in the
London Travel Demand Survey for the period 2005–2011. Using aboundary regression-discontinuity design reveals a statisticallysignificant but small impact on active commuting (i.e. cycling andwalking) around the border of the charging zone. The effect is larger
for lower-income households and car owners. The findings are robust
against multiple specifications and validation tests.

Keywords: economic incentive; health behaviour; London Congestion Charge;
geographical information system; regression-discontinuity.
JEL Classification Codes: D04, I12, R48

This study was funded by the UKCRC Centre for Diet and Physical Activity Research (CEDAR). The funders
had no role in the study design, data collection, analysis or interpretation.
aCorresponding author: Hitotsubashi Institute for Advanced Study, Hitotsubashi University. Email:ryota.nakamura@r.hit-u.ac.jpb
Luxembourg Institute of Socio-Economic Research.
c
Norwich Medical School, University of East Anglia.
dCentre for Health Economics, University of York.
*1

1. Introduction
Economic incentives, especially when established through large-scale fiscal policies, havelong been considered an effective economic tool for reducing unhealthy behaviours such as
smoking, alcohol consumption and unhealthy diet (Gruber and Koszegi, 2001; Yaniv et al.,2009; Volpp et al., 2009; Giné et al., 2010; Cawley and Ruhm, 2011; Cawley et al., 2019).
However, the evidence is limited and mixed as to whether economic incentives can sustainably
improve physical activity levels. The literature has examined the impact of personal economic
incentives within micro-settings such as gym attendance (Charness and Gneezy, 2009; Royer etal., 2015). A recent literature review focused on randomised controlled trials (RCTs) concludes
that there is no compelling evidence that economic incentives in these settings are effective
upon the incentive’s discontinuation, especially over the longer term (Finkelstein et al., 2019). 1
Existing RCTs focus on micro settings and follow up participants for short periods (i.e. one or
two years typically).
This study presents quasi-experimental evidence on the long-term impact of economic
incentive on physical activity at a population level. To date, such evidence is extremely scarce(Martin et al., 2012). 2 We focus specifically on active commuting in the form of walking or
cycling. Because commuting is a habitual daily activity for many people, and because it isassociated with significant broader health benefits (Dinu et al., 2019), it is an important target
area for public policy interventions (Andersen, 2017).

See Mantzari et al. (2015) for a similar conclusion; meanwhile, see Mitchell et al. (2020) for amore positive evaluation.

1

Rare examples of the indirect effects of such “macro” policies or factors on physical activity havebeen identified by studies on the relationship between higher gasoline prices (induced by tax
increases) and physical activity (see, for example, Courtemanche, 2011; Sen, 2012).

2

2

The London Congestion Charge (LCC) represents one of the world’s most prominenttransportation policies. 3 The LCC was implemented in 2003, imposing charges (initially £5 andthen £10 per day) on most types of cars entering, exiting and operating within the Central
London area. Although the policy’s primary aim was to reduce congestion rather than directly
promote physical activity, the associated increase in the cost of private car use may substantially
incentivise active commuting in the area. For example, Pucher et al. (2012) rank the LCC as thepolicy with the most potential to encourage cycling in the city. Elsewhere, Maibach et al. (2009)
describes the congestion charge as a policy option promoting increased physical activity on thebasis of data indicating that cycling increased substantially in Central London following the
LCC’s implementation. Reviewing studies evaluating the different potential outcomes of thecongestion charge (i.e. congestion, the environment, revenue and changes in travel behaviour),Givoni (2012) concludes that most findings suggest beneficial outcomes. 4 However, Givoni
also expresses concerns about the extent to which these positive effects can be causally
attributed to the LCC. A recent evaluation of the literature also recognises the need to produce
more rigorous evidence of congestion pricing’s impact on active commuting (Brown et al.,2015).
Despite prior investigations into the LCC’s various direct and indirect outcomes, its potential
effects on physical activity remain to be assessed via a formal impact evaluation. The mostnotable existing study, the findings of which are often cited in the related literature, is the seriesof reports by Transport for London, the local governmental body responsible for the city’stransport services. 5 These reports provide detailed information on the changes observed forSome environmental and epidemiologic studies have addressed the potential health impacts ofreduced air pollution in the Central London area (see, for example, Beevers et al., 2005; Tonne et al.,2008).
4
See Tang (2021) for a more recent evaluation of the effect of the LCC on housing prices.
3

The reports are available online at: http://www.tfl.gov.uk/roadusers/congestioncharging/6722.aspx(accessed September 2021).
53different modes of travel, indicating that the amount of travel-related physical activity increased
following the implementation of the LCC. For instance, cycling distances increased by about
30% after the charge’s introduction (Transport for London, 2006). However, a major concern
regarding such assessments is the difficulty of attributing the observed change exclusively to
specific economic incentives deriving the scheme, given its introduction was accompanied byother significant changes in transportation policy, including improvements to bus networks andbicycle paths (Givoni, 2012).
To identify the effects of this economic incentive on active commuting, we analyse cases where
the charge is discounted for individuals living inside the Central London area (i.e. inside thecharging zone). These residents are eligible to have their charges reduced by 90%; meanwhile,
those living outside the zone must pay the full amount. This significantly changes the incentive
for those living just inside the charging zone compared to those living just outside it. Ourestimation exploits this legislation by employing a boundary discontinuity design (Black, 1999;
Dell, 2010).
Eligibility for the 90% discount is determined by postcode. We use a geographical information
system (GIS) to geocode the charging zone’s border and the residential locations of participantsin the London Travel Demand Survey (LTDS) for the period 2005–2011. The data include fulldigit postcode information, enabling precise calculation of the distance between residential
locations and the charging zone’s border, which we define as the treatment assignment variable.
Using GIS, we also construct geographical segments of the study area, which we add as spatial
fixed effects to our regression model, capturing time-invariant neighbourhood characteristics
across the study area (Goldstein and Udry, 2008).
Our fixed-effect boundary discontinuity estimates for 2005-2011 show that the LCC’s
economic incentive did increase physical activity levels, as measured by the duration of time4

spent cycling and walking for travel purposes, but only modestly so, i.e. by about three minutes
around the border of the charging zone. Exploiting an expansion of the charging zone to West
London in 2007 reveals small and immediate effects of the economic incentive. The main effects
are larger for lower-income households and car owners and are robust against multiple
specifications and validation tests. Our overall findings suggest that the economic incentives
exhibit immediate and persistent yet limited effects from a public health perspective on travel
related physical activity. These do not fully support the long-standing aspirations for the LCC’s
economic incentive to increase physical activity levels, expressed in the epidemiological andpublic health literature.
The rest of the paper is organised as follows. Section 2 provides an overview of the LCC
scheme. Section 3 describes our boundary discontinuity design. Section 4 explains the detailsof the data utilised. Section 5 reports the empirical findings, followed by extensions androbustness checks in Section 6. Finally, Section 7 concludes the paper.
2. The London Congestion Charge scheme
The idea of a congestion charge in London emerged in 1964 as a large-scale transportation
policy with the main purpose of reducing congestion in the Central London area and using therevenue generated to improve the city’s transport system. The scheme’s economic rationale wasbuilt around the need to internalise the cost of congestion. As the cost of car travel within thezone increased, it was expected that congestion would retract to an optimal level of congestion,
producing efficiency gains.
The LCC was implemented in February 2003, using the London Inner Ring Road as the
charging zone’s border. This approximately 20-kilometre-long route was included in the 1943
County of London Plan and established after World War II to comprise several major roads thatsurround the Central London area, including the City of London and the West End. Before the5charge was implemented, about 25% of vehicles driving on the Inner Ring Road passed by the
charging zone; hence, it was chosen as the border to enable such drivers to navigate the route
without entering the zone (Leape, 2006).
The LCC scheme charges drivers of chargeable vehicles (i.e. private vehicles other thanmotorcycles, vehicles with nine or more seats and ultra-low-emission cars) when they enter,leave, travel through or park within the charging zone during the charging window, which is
between 7:00 and 18:00 (changed from 18:30 in January 2007) Monday to Friday, excluding
public holidays and the week of Christmas and New Year. Approximately 200 cameras operate
across the charging zone, mostly around the border. Alongside “patrol” cars equipped with
CCTV, these cameras photograph car number plates and send the images to a central system,
which matches the images with driver information provided by the Driver and Vehicle Licensing
Agency. Drivers are expected to take responsibility for paying the charge without being
prompted to do so; that is, they are not notified that they have been captured within the charging
zone unless they fail to pay, at which point a penalty notice is sent several days later. The initialrate was £5 per day if paid before 22:00 on the day of travel. 6 This was increased to £8 in July
2005 and then £10 in January 2010.
In February 2007, the charging zone was expanded to include western London, a largely
residential area including the City of Westminster and parts of Kensington and Chelsea. Knownas the “western expansion” (see Figure 1), this expansion was repealed in January 2011.
The scheme provides a resident discount, meaning that registered residents of areas within the
charging zone (and some “buffer areas” just outside the zone) receive a 90% discount. In 2014,
this discounted rate was £20 per month (if paid monthly); the full rate was £200 a month.
In 2014, the standard rate was £10 per day if payment was made by 22:00 on the day of travel. The rate increasedto £12 if paid the next day. If the driver failed to pay the charge, a penalty charge notice for £130 would be sent tothe car owner. If the penalty charge was paid within 14 days, the charge was reduced to £65.
6

6

Eligibility for the discount is determined by the residential location’s full postcode. 7 This createsa significant economic difference for those living outside the charging zone compared to thoseliving inside the charging zone. Residents living inside the zone are not charged if their carsremain parked in a registered location during the charging window. However, upon driving theircar, they risk being charged if they are captured by the cameras.
All revenues generated from the congestion fees are invested in improving transportation in
the area. About 80% of the revenues are used to expand the local bus network and improve theservices, including the number of services and a new payment system (Givoni, 2012). Also,
there have been significant investments to promote cycling in the area, including the expansionof the cycling network, construction of cycling superhighways, establishment of new bicycle
parking spaces and development of a bicycle-sharing scheme (Pucher et al., 2012; Martin et al.,2021).
According to earlier descriptive evidence, following the LCC’s implementation, active travelincreased, the number of cars entering Central London during the charging window decreased,
and the number of bicycles entering the area increased (Transport for London, 2007). During
the same period, the average distance cycled by residents increased by 30%, and the number ofbus passengers (who tend to partake in more physical activity than door-to-door car drivers)
increased substantially (Transport for London, 2006). Nonetheless, given that those related
policies, implemented during the same period, might also have impacted active travel, the extent
to which the economic incentive of the LCC prompted the observed changes in physical activity
levels remains unclear.

Transport for London operates a “zone checker” on their website, which informs residents whether
they are eligible for the discount.

77

3. Methods
This study’s primary goal is to estimate the causal effect of increasing the cost of chargeable
sedentary travel modes (i.e. private car use) on travel-related physical activity levels. However,this estimation engenders at least two concerns. The first is a standard endogeneity problem
related to residential location eligibility for the discount scheme; residential location is likely
correlated with neighbourhood characteristics that independently determine travel modes,
including the built environment (e.g. shops, roads, bus stops) and socioeconomic characteristics.
The second concern relates to isolating the effect of the congestion charge from effects
produced by relevant complementary policies. As Section 2 discussed, the LCC’s
implementation was buttressed by other policies aimed at improving urban transportation,
including improving the bus network and expanding cycling routes. As these complementarypolicies can also increase commuters’ physical activity levels, an uncontrolled before-and-after
comparison would fail to identify the LCC’s causal effect.
To address these two concerns, we exploit the geographic discontinuity of eligibility for the
discount. Focusing on individuals who live close to the charging zone’s border, and thus sharesimilar neighbourhood characteristics, mitigates concerns due to unobserved neighbourhood
characteristics. Additionally, the benefits of improved urban transportation infrastructure are
expected to be enjoyed almost equally by residents on both sides of the border. For example, if
a person living on a border street has access to a better bus service, someone living on the
opposite side of the street could easily utilise that same service. This suggests that theaccompanying transportation policies are not likely to introduce bias to our analysis.

8As mentioned, eligibility for the 90% discount is determined by postcode. 8 Our data containfull six-digit postcode information. Using a GIS, we locate each survey participant of LTDS
within London (see Figure 1).
[Figure 1 about here]
In our analysis, the treatment group comprises individuals who live just outside the charging
zone and pay the full congestion charge, and the control group comprises those who live justinside the zone and are eligible for the discount. Implementing a regression discontinuity design(RDD) estimation, we employ distance to the congestion charge area as the treatment
assignment variable. To ensure we are comparing individuals who live close to each other, wefollow the approach of Dell (2010) and implement a spatial RDD estimator that controls forborder segment fixed-effect, by dividing the border into segments and assigning a segment
identifier for each residential location. The same border segments are also used to cluster
standard errors. 9
To implement the boundary RDD, we follow the standard approach of the literature and run a
local linear regression using the optimal mean squared error criterion with triangular kernel
weights (Calonico et al., 2014) 10 as estimated by the following linear model:

𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝜏𝜏𝜏𝜏𝜏𝜏𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝑡𝑡𝑖𝑖𝑖𝑖 ∗ 𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝑡𝑡𝑖𝑖𝑖𝑖 (1 − 𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 ) + 𝒛𝒛′ 𝑖𝑖 𝜸𝜸 + 𝜑𝜑𝑠𝑠 + 𝜖𝜖𝑖𝑖𝑖𝑖

The Transport for London website offers a “charging zone checker” allowing residents to check
whethertheyareeligibleforthediscountbyentering
theirpostcode:
https://tfl.gov.uk/modes/driving/congestion-charge (accessed September 2021).
9
We use the quantile-based method to divide the border into 50 segments of similar sample sizes. In
two sensitivity analyses, we split the border into either 30 or 70 segments. This delivers similar results(see Appendix Table A1).
10
Because the values of the forcing variable in our data are highly asymmetric (i.e. the minimum valueof the forcing variable is 2 km inside the congestion charge zone and 29.5 km outside the zone) weallow for two different MSE-optimal bandwidth selectors for each side of the cut-off.
89

where 𝑦𝑦𝑖𝑖𝑖𝑖 is the outcome variable for an individual i closer to the border segment s, α is theconstant term and 𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 is the treatment indicator, which equals 1 if the individual lives outside

the congestion charge zone and 0 if they live inside the charge zone. On 9 February 2007, thecharging zone was extended to the western side of Central London, making the 𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 0 if they
were surveyed before 9 February 2007 and 1 if they were surveyed after that date. 11 We analyse

the effects of the western expansion separately. The assignment variable 𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑖𝑖 is the distanceto the border s from the the individual’s residential location. The linear spline on the left side of

the cut-off (i.e. inside the congestion charge zone) is represented by the interaction 𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑖𝑖 ∗𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 , and 𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑖𝑖 (1 − 𝐿𝐿𝐿𝐿𝐶𝐶𝑖𝑖𝑖𝑖 ) is the linear spline on the right side of the cut-off (i.e. outside thecongestion charge zone). A sensitivity analysis also specifies a local quadratic spline. The spatial
segment fixed effect proposed by Dell (2010) is represented by 𝜑𝜑𝑠𝑠 .

Finally, we control for a set of covariates in the vector 𝒛𝒛𝑖𝑖𝑖𝑖 , including calendar time (fixed

effects for year, month, and day of the week), age (quadratic specification), gender, ethnicity(White, Asian, Other), employment status (employee, self-employed, student, non-employed,
unknown status), possessing a driver’s license, and household gross yearly income.
The literature indicates that the estimated treatment effect should be considered a weighted
average treatment effect, where the weight is the probability of living close to the charging
zone’s border (see, for example, Lee and Lemieux, 2010). This raises concerns regarding theexternal validity of the estimated effects, which we acknowledge as a limitation of our approach.

We run a series of placebo tests on false cut-off points by implementing an RDD estimation forevery 2 km outside the congestion charge zone. We start from the second kilometre to have at least 2
km on the left of the false cut-off (as in the main analysis) and isolate the true discontinuity byremoving units inside the congestion charge zone. Appendix Table A2 shows that no estimate for these
false cut-off points is statistically significant at 5%.
1110

4. Data
We use data from the LTDS for the period 2005–2011, beginning with the 2005/6 financial
year. Unfortunately, data for the period between the congestion charge’s implementation in 2003
and the middle of 2005 are not available, meaning our analysis addresses the congestion
charge’s long-term rather than immediate effects. The LTDS is an annual cross-sectional
household survey for residents of London’s boroughs and some parts of the area outside Greater
London (restricted to those within the M25 motorway).
The sample households are representative in terms of both household characteristics andresidential locations within the area. For each survey year, the total sample size is around 5,000
to 8,000, with a response rate of approximately 50–55%. Conducted via household interviews,
the survey comprises three sets of questionnaires. The first is a household questionnaire
addressing household characteristics (socioeconomic and demographic information), 12 vehicle
ownership (i.e. how many and what kind of vehicles) and housing tenure (i.e. when thehousehold began residing at the current residence). The second is an individual questionnairecompleted by all household members aged five and above and covering individual-level
demographic characteristics, including age and gender, employment or educational status,
workplace/school details and travel-related characteristics, such as whether the individual
possesses a driving license. A third questionnaire completed by all household members aged
five or above provides information regarding trips made during the day before the householdinterview (i.e. a single-day travel diary) and records main travel mode, trip origin, trip

If the household income band is missing, it is imputed using the following household characteristics:i) the number of full and part-time workers in the household; ii) household size and structure; iii)
housing tenure; iv) whether the household has a home computer; v) number of vehicles available tothe household; and vi) whether the household has another home.
1211

destination, time of departure, time of arrival and trip purpose. Each trip is further divided into
travel stages to also generate this information at the travel-stage level. 13
Thus data include detailed geographic information, including full-digit postcode information
for home, workplace and school addresses alongside detailed insights into resident travel
behaviour. This enables us to use GIS to precisely geocode household locations andgeographical characteristics of participant trips (e.g. distance). Although eligibility for the 90%
reduction on the congestion charge derives from full postcode information, the data do notindicate whether eligible individuals actually registered to receive the discount. Accordingly,
our effect estimates should be interpreted as intention-to-treat effects. The key outcome
variables are the time spent on active travel (i.e. walking and cycling) and the distance travelledby car during the congestion charge window. The former derives from computing the time an
individual spends on all travel stages involving either walking or cycling on the same day. Trips
outside of the congestion charge window are recorded as zero.
Our initial sample includes 124,333 individuals, which we narrow down as follows. First, weremove individuals with missing information in their travel diaries (1,187 individuals). Second,
we remove individuals for whom the treatment is unlikely to affect car use, such as individuals
under 18 years old (28,426), those with long-term health problems or disabilities that limit their
daily activity (10,090), and those who did not leave their place of residence during the day of
the interview (16,066). Third, because the congestion charge only applies on weekdays andduring working hours, we exclude travel data for weekends and public holidays (17,869
observations). Finally, we exclude 102 individuals who report values for trips during thecongestion charge window positioning them above the 99.9 percentile for total time spent
For an illustrative example, consider a trip to their workplace during which a person walks fromtheir home to the nearest bus stop (stage 1) and then takes a bus to the city centre (stage 2) before
walking to their workplace (stage 3). The total time spent walking during this trip is the sum of thedurations of stages 1 and 3.
13

12

actively commuting (>326 minutes) or distance travelled by car (>285 km). Upon cleaning up
the data, 50,593 observations remain.
5. Main results
Summary statistics
The analysis focuses on individuals living near the congestion charge zone’s border. Table 1
presents summary statistics for two different subsamples: 1) inner London residents (individuals
living within 2 km range of the charge zone border); and 2) the rest of the sample.
[Table 1 about here]
On average, the total time spent on active travel (walking or cycling) is half an hour per day
per individual. Inner London residents spend more time on active travel than those in other areas(30 vs 22 minutes). Conversely, while inner London residents travel about 2.4 km in their cars
per day, the rest of the sample travels about 8.1 km. This contrast could arise from inner London
residents living closer to their workplace or school. No substantial differences are observed for
distances covered by other means of transport (7 km) or for gender (52% are women). However,inner residents tend to be younger (40.6 vs 43.8 years old) and ethnically white (61% vs 55%).
They also tend to report a lower gross household income and are more likely to be students(11.0% vs 6.0%). Finally, a smaller proportion of inner London residents possess a driver’s
license (64% vs 77%). No large differences are observed for calendar dates. Ultimately,
differences between inner London residents and the rest of the sample implies that the travel
environment in the inner London area is somewhat distinctive; as such, empirical findings fromthe inner London sample may not be generalisable to the broader London population.

13

Graphical analysis
We now graphically analyse our estimation sample after restricting it to the optimal bandwidthderived with reference to Calonico et al. (2014). Figure 2 plots the total duration of active traveland distance travelled by car during the day of travel against the distance between residential
location and the charging zone border.
[Figure 2 about here]
In Figure 2, distance (horizontal axis) is 0 at the border; it is positive if an individual lives
outside the border and negative if an individual lives inside the border. The plots demonstrate asmall increase in the duration of active commuting and a small decrease in kilometres travelledby car for those living just outside the cut-off. The next subsection formally estimates theseeffects.
Regression analysis
Table 2 presents our benchmark regression analysis.
[Table 2 about here]
Panel A presents the effects on the total duration of active travel per individual per day. Column
1 indicates that the treatment effect is only about 3 extra minutes of active travel. The effect isstatistically significant at 5% and is robust to different estimation methods, including
incorporating individual covariates (Column 2) and relying on a quadratic spline (Column 3).
Retaining only individuals with a car on the day of travel (Column 4) delivers larger effects(+7.8 minutes). 14 This implies that the economic incentive increases overall activity levels fromNo significant effect is found for the subset of individuals with no car available during the day.
These estimates are available upon request.
14

14

a statistical significance perspective, but only moderately so from a population healthperspective.
Column 1 of Panel B demonstrates a reduction in the total distance travelled by car during thecongestion charge window of only about 0.9 km. However, the effect is statistically significant
at 10% for only some specifications, possibly owing to the inability to detect small reductions
in car use. Finally, Panel C represents the statistically insignificant effect on the distancetravelled using other means of transportation.
6. Extension and robustness
Income heterogeneity
According to our main results, the LCC has only a small overall impact on active commuting.
A potential explanation for this finding is that the charge insufficiently disincentivises those
who can already afford to live in the costly Central London area from driving. Accordingly, we
estimate the heterogeneous effects for individuals above or below the median observed
household income (£25,000). If the estimated effect is due to the economic incentive of thecongestion charge area, and not due to something else, we should observe a larger effect forlower-income households. In Table 3, our estimates confirm this prediction (see Column 1 ofPanel A): the effect on active commuting is larger and only statistically significant forindividuals from lower-income households (+4.5 minutes). Results are robust to the inclusionof covariates (Column 2) or relying on a quadratic spline (Column 3). Similar differences
between income groups are observed for the distance travelled by car during the day (Panel B).
[Table 3 about here]15

Expansion of the charging zone and its repeal
Given the congestion charge was first implemented in 2003, but our data begins in the 2005/6
financial year, our main analysis should thus be viewed as assessing the long-term effects of theeconomic incentive. However, the aforementioned “western expansion”, which occurred in
February 2007 before being repealed in December 2010, can be exploited using the LTDS’stime dimension to estimate the “border effect” for West London. We first implement a placebo
test for when there was no congestion charge (before February 2007 and after December 2010)
and then estimate the treatment effect during the period of the congestion charge. This analysis
employs distance from the western border of the congestion charge area as the treatment
assignment variable. 15
In Table 4, we do not observe a statistically significant difference in travel behaviour for theperiod during which residents of the West London area were not eligible for a reduced
congestion charge (Panel A). Instead, during the period of the expansion (Panel B), we estimate
a higher level of active commuting among individuals living outside the Western congestion
charge zone (about 3.9 minutes; Column 3) and a reduction in the distance travelled by car (1.9km; Column 4 ), indicating an immediate but modest impact of the economic incentive on active
travel.
[Table 4 about here]
Sorting around the charging zone
Depending on the availability of housing properties, individuals can choose where they live,
which can violate a basic assumption of regression discontinuity design, namely, the lack ofprecise sorting around the cut-off (Black, 1999; Dell, 2010; Magruder, 2012). If individuals with
We remove 737 individuals living in the central area due to there being no treatment differencealong that border during the western expansion.
15

16

a preference for a sedentary lifestyle self-select to live inside the congestion charge zone to
avoid such a charge, the effects of the economic incentive on active commuting would beoverestimated, meaning the true effects would be even smaller than our main analysis’ modest
estimates.
To formally address this concern, we implement the density test proposed by Cattaneo et al.
(2020, 2021) to explore potential manipulation around the cut-off. The test result does not reject
the null hypothesis of a discontinuity in density at the cut-off, with the p-value being 0.340 (fora graphical representation, see Appendix Figure A1).
We test for compositional changes around the cut-off by running the same RDD estimatorusing each covariate as an alternative dependent variable. As Appendix Table A3 demonstrates,of the 45 estimates, only one (travel year = 2007) is statistically significant at 5%, which is
below the 5% rejection rate of a false positive.
We further estimate the effect by restricting the sample to those who resided in the area before
the congestion charge was implemented. In Table 5, Panel A reveals that the charge increases
active commuting by 6.9 minutes and reduces the distance travelled by private car by 1.4 km;
however, only the former is statistically significant. The magnitude of these impacts is slightly
greater than the estimates using the full sample.
[Table 5 about here]
Travelling outside the congestion charge window
The congestion charge only applies from 7:00 to 18:00 on weekdays. Individuals might thuschoose to commute before and after the charge window to avoid being charged. Accordingly,
we estimate the effect of the congestion charge on the probability of travelling outside thecharging hours, with Panel B of Table 5 revealing that the effect is not statistically significant

17

for either the whole sample or for the subsample of individuals with a car available during theday, thereby rejecting the possibility of the economic incentive affecting when individuals
choose to travel.
7. Concluding remarks
As reported by Transport for London, there have been increases in travel-related physicalactivity levels in the Central London area since the congestion charge’s implementation.
However, it has long been unclear whether this trend is indeed causally attributable to thecongestion charge. While this study does observe statistically significant positive effects on
active commuting overall, especially among car-owners and residents of lower-income
households, the estimated magnitude suggests, at best, a very moderate impact on physicalactivity and health. Our findings do not support the high expectations expressed in theepidemiological and public health literature (Andersen, 2017), in terms of the effectiveness of
economic incentives in increasing physical activity at the population level. This suggests that
other related policies might have contributed substantially to the trend; these include theexpansion of cycling routes and bicycle-rental schemes, which are partly funded by revenues
generated by the congestion charge. This underscores the need for a more holistic, multicomponent intervention approach, of which economic incentives represent one component,
albeit a component that raises the revenue necessary to fund accompanying measures, thereby
indirectly impacting active commuting.
The daily charge rate has been increased several times since the LLC’s introduction (initially£5 per day; £8 from July 2005 to January 2010; £10 henceforth). While the estimates for travel
before 2010 are consistent with the main results, our data do not provide sufficient power to18

detect significant effects after 2010 due to the small sample size, 16 precluding assessment of the
impact of changes to the congestion charge.
Overall, our results have implications for policy-makers seeking to plan community-level
economic incentives to stimulate a preferred behaviour. In a health context, as previous studies
on gym attendance have demonstrated, personal economic incentive schemes are, at best,
effective for one or two years (Charness and Gneezy, 2009; Royer et al., 2015). However,achieving sustained behavioural change in terms of physical activity – in this case, in the formof active commuting – may require either larger economic incentives or a set of adjacentinterventions to accompany the economic incentive.

The number of individuals living inside the congestion charge zone in the 2010-2011 data is only
193.

16

19

References
1. Andersen, L.B., 2017. Active commuting is beneficial for health. The BMJ. 357:j1740.
2. Beevers, S.D., Carslaw, D.C., 2005. The impact of congestion charging on vehicle
emissions in London. Atmospheric Environment. 39(1):1-5.
3. Black, S.E., 1999. Do better schools matter? Parental valuation of elementary education.
Quarterly Journal of Economics. 114(2):577-599.
4. Brown, V., Moodie, M., Carter, R., 2015. Congestion pricing and active transport–evidence
from five opportunities for natural experiment. Journal of Transport & Health. 2(4):56879.
5. Calonico, S., Cattaneo, M.D., Titiunik, R., 2014. Robust nonparametric conﬁdence
intervals for regression-discontinuity designs. Econometrica. 82(6):2295-2326.
6. Cattaneo, M.D., Jansson, M., Ma, X., 2020. Simple local polynomial density estimators.
Journal of the American Statistical Association. 115(531):1449-145.
7. Cattaneo, M.D., Jansson, M., Ma, X., 2021. Local regression distribution estimators.
Forthcoming in Journal of Econometrics.
8. Cawley, J., Ruhm, C.J., 2011. The economics of risky health behaviors. In: Handbook of
Health Economics. 2:95-199.
9. Cawley, J., Thow, A.M., Wen, K., Frisvold, D., 2019. The economics of taxes on sugarsweetened beverages: a review of the effects on prices, sales, cross-border shopping, andconsumption. Annual Review of Nutrition. 21(39):317-38.
10. Charness, G., Gneezy, U., 2009. Incentives to exercise. Econometrica. 77(3): 909-931.
11. Courtemanche, C., 2011. A silver lining? The connection between gasoline prices andobesity. Economic Inquiry. 49(3):935-957.

20

12. Dell, M., 2010. The persistent effects of Peru’s mining mita. Econometrica 78(6):18631903.
13. Dinu, M., Pagliai, G., Macchi, C., Sofi, F., 2019. Active commuting and multiple healthoutcomes: a systematic review and meta-analysis. Sports Medicine. 49(3):437-52.
14. Finkelstein, E.A., Bilger, M., Baid, D., 2019. Effectiveness and cost-effectiveness of
incentives as a tool for prevention of non-communicable diseases: a systematic review.
Social Science & Medicine. 232:340-50.
15. Giné, X., Karlan, D., Zinman, J., 2010. Put your money where your butt is: A commitment
contract for smoking cessation. American Economic Journal: Applied Economics.
2(4):213-235.
16. Givoni, M., 2012. Re-assessing the results of the London congestion charging scheme.
Urban Studies. 49(5):1089-1105.
17. Goldstein, M., Udry, C., 2008. The profits of power: land rights and agricultural investment
in Ghana. Journal of Political Economy. 116(6):981-1022.
18. Gruber, J., Köszegi, B., 2001. Is addiction “rational”? Theory and evidence. Quarterly
Journal of Economics. 116(4):1261-1303.
19. Leape, J., 2006. The London Congestion Charge. Journal of Economic Perspectives.
20(4):157-176.
20. Lee, D.S., Lemieux, T., 2010. Regression discontinuity designs in economics. Journal of
Economic Literature. 48(2):281-355.
21. Magruder, J.R., 2012. High unemployment yet few small firms: the role of centralized
bargaining in South Africa. American Economic Journal: Applied Economics. 4(3):138166.

21

22. Maibach, E., Steg, L., Anable, J., 2009. Promoting physical activity and reducing climatechange: opportunities to replace short car trips with active transportation. Preventive
Medicine. 49(4):326-7.
23. Mantzari, E., Vogt, F., Shemilt, I., Wei, Y., Higgins, J.P., Marteau, T.M., 2015. Personal
financial incentives for changing habitual health-related behaviors: a systematic review
and meta-analysis. Preventive Medicine. 75:75-85.
24. Martin, A., Morciano, M., Suhrcke, M., 2021. Determinants of bicycle commuting and theeffect of bicycle infrastructure investment in London: evidence from UK census microdata.
Economics & Human Biology. 41:100945.
25. Martin, A., Suhrcke, M., Ogilvie, D., 2012. Financial incentives to promote active travel:an evidence review and economic framework. American Journal of Preventive Medicine.
43(6):e45-e57.
26. Mitchell, M.S., Orstad, S.L., Biswas, A., Oh, P.I., Jay, M., Pakosh, M.T., Faulkner, G., 2020.
Financial incentives for physical activity in adults: Systematic review and meta-analysis.
British Journal of Sports Medicine. 54(21):1259-68.
27. Pucher, J., de Lanversin, E., Suzuki, T., Whitelegg, J., 2012. Cycling in megacities: London,
Paris, New York, and Tokyo. In: Pucher J & Buehler R (eds.) City Cycling. MIT Press,
Cambridge MA.
28. Royer, H., Stehr, M.F., Sydnor, J.R., 2015. Incentives, commitments and habit formationin exercise: Evidence from a field experiment with workers at a Fortune-500 company.
American Economic Journal: Applied Economics. 7(3):51-84.
29. Sen, B., 2012. Is there an association between gasoline prices and physical activity?
Evidence from American time use data. Journal of Policy Analysis and Management.
31(2):338-66.

22

30. Tang, C.K., 2021. The Cost of traffic: Evidence from the London Congestion Charge.
Journal of Urban Economics. 121:103302
31. Tonne, C., Beevers, S., Armstrong, B., Kelly, F., Wilkinson, P., 2008. Air pollution andmortality benefits of the London Congestion Charge: spatial and socioeconomic
inequalities. Occupational and Environmental Medicine. 65(9):620-627.
32. Transport for London, 2006. Central London congestion charging: Impacts monitoring.
Fourth annual report. Mayor of London, London.
33. Transport for London, 2007. Central London congestion charging: Impacts monitoring.
Fifth annual report. Mayor of London, London.
34. Volpp, K.G., Troxel, A.B., Pauly, M.V., Glick, H.A., Puig, A., Asch, D.A., Galvin, R., Zhu,
J., Wan, F., DeGuzman, J., Corbett, E., Weiner, J., Audrain-McGovern, J., 2009. A
randomized, controlled trial of financial incentives for smoking cessation. New England
Journal of Medicine. 360:699-709.
35. Yaniv, G., Rosin, O., Tobol, Y., 2009. Junk-food, home cooking, physical activity andobesity: The effect of the fat tax and the thin subsidy. Journal of Public Economics. 93(56):823-830.

23

Figure 1: Distribution of study households (Central London area), LTDS, 2005–2011

Note: The blue area represents the congestion charge area for the period between February 2007 and January
2011, when the charging zone incorporated the western expansion.

24

Figure 2: Graphical analysis of the boundary discontinuity at the border of the LLC
zone (duration of active travel and kilometres travelled by car during the day)A) Total duration of active travel in minutes

B) Total distance by car in kilometres

Note: These graphs show RDD plots for the following dependent variables: a) total duration of active travel(walking or cycling) in minutes and b) total distance by car during the day in km. The treatment assignment
variable is the distance to the congestion charge zone from the residential location. The splines are obtainedusing a local linear polynomial regression with triangular weights and bandwidth following the two optimal
MSE selectors for the two sides of the discontinuity in accordance with Calonico et al. (2014). We impose
the variance quantile-spaced method using a spacings estimator to select the number of bins.

25

Table 1: Summary statistics (LTDS, 2005–2011)
Inner London

Outside of inner London

Total active travel time during the congestion charge period (minutes)

30.49

22.43

(Standard deviation)(32.87)(31.12)

Total distance travelled by car during the congestion charge period (km)

2.41

8.08

(Standard deviation)(10.99)(19.33)

Total distance travelled by other transport means during the congestion charge period (km)

7.04

7.23

(Standard deviation)(24.70)(19.69)Age (Mean)

40.65

43.85

Female0.52

0.52

Ethnicity: Asian

0.07

0.04

Ethnicity: White

0.12

0.09

Ethnicity: Other

0.11

0.09

Income: below £5,000

0.11

0.09

Income: £5,000–9,999

0.10

0.09

Income: £10,000–14,999

0.13

0.14

Income: £15,000–19,999

0.11

0.16

Income: £20,000–24,999

0.12

0.15

Income: £25,000–34,999

0.06

0.08

Income: £35,000–49,999

0.09

0.07

Income: £50,000–74,999

0.09

0.14

Income: £75,000–99,999

0.61

0.55

Income: £100,000 and above

0.30

0.31

Employee

0.58

0.62

Self-employed

0.04

0.04

Unemployed or inactive

0.25

0.28

Unknown working status

0.03

0.00

Student

0.11

0.06

Driver’s license holder

0.64

0.77

N

5,356

45,237

Note: Inner London includes individuals living within 2 kilometres of the charging zone border. Calendar time dummies are not reported.

26

Table 2: The effects of the LLC on (A) time spent actively travelling, (B) distancetravelled by car and (C) distance travelled by other means of transport
Local linear

Local linear(A) Active travel time (minutes)
Local quadratic

(1)

(2)

(3)

(4)

3.067**

3.816**

4.441

7.769**[0.176 ; 8.404][0.862 ; 9.359][-2.754 ; 8.154][1.853 ; 18.124]

0.041

0.018

0.332

0.016

Order Loc. Poly. [p]

1

1

2

1

Order Bias [q]

2

2

3

2

Covariates

No

Yes

Yes

Yes

Total N

50,59350,59350,59336,494

Eff. N estimate [h]14,95012,36823,336

6,532

Eff. N bias [b]35,44825,71635,126

15,812

Local linear

Local linear(B) Distance travelled by car (km)Local quadratic

Local linear, car available

(1)

(2)

(3)

(4)

-0.934-1.523*-1.620-3.317*[-3.486 ; 1.127][-3.900 ; 0.333][-5.04 ; 1.718][-8.836 ; 0.329]

0.316

0.099

0.335

0.069

Order Loc. Poly. [p]

1

1

2

1

Order Bias [q]

2

2

3

2

Covariates

No

Yes

Yes

Yes

Total N

50,59350,59350,59336,494

Eff. N estimate [h]

9,180

9,353

16,966

5,092

Eff. N bias [b]18,184

18,884

27,861

10,431

Local linear

Local linear

Local quadratic

(1)

(2)

(3)

(4)

-0.9800.421-0.578-0.499[-4.572 ; 1.110][-6.875 ; 4.998][-9.102 ; 6.133][-3.523 ; 2.64]

0.232

0.757

0.702

0.779

Order Loc. Poly. [p]

1

1

2

1

Order Bias [q]

2

2

3

2

Covariates

No

Yes

Yes

Yes

Total N

50,59350,59350,59336,494

Eff. N estimate [h]13,853

13,11015,94911,335

Eff. N bias [b]30,10924,78524,76928,244

Beta
Robust CI
Robust p-value

Beta
Robust CI
Robust p-value

Local linear, car available(C) Distance travelled by other means of transport

Beta
Robust CI
Robust p-value

Local linear, car available

Notes: The dependent variables are (A) time (in minutes) of active travel undertaken during the day, (B) the distance travelled by car during
the day (km) and (C) the distance travelled by other means of transport during the day (km). RDD estimates use distance from residential
location to the congestion charge zone as the treatment assignment variable. The table presents results from a local regression with triangularweights. Two different MSE-optimal bandwidth selectors for the two sides of the discontinuity are used, as in Calonico et al. (2014). The
models control for and cluster into 50 border segments (Dell, 2010). Different models: (1) local linear regression without covariates, (2)local linear regression with covariates, (3) local quadratic regression with covariates, (4) local linear regression with covariates on thesubsample of individuals with a car available during the day. The table shows the total number of observations (N) and the effective numberof observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence intervals are reported. * significant at the 10%
level, ** significant at the 5% level, *** significant at the 1% level.

27

Table 3: Heterogeneous effects by household income of the LLC on (A) active travel
time and (B) distance travelled by car(A) Active travel time (minutes)
Lower-income households

Beta
Robust CI

Higher-income households

Local linear

Local linear

Local quadratic

Local linear

Local linear

(1)

(2)

(3)

(4)

(5)

Local quadratic(6)4.564***

5.708***

8.306***

1.187

1.557

-1.769[3.009 ; 12.956][3.612 ; 14.093][5.292 ; 17.012][-8.076 ; 7.191][-7.337 ; 6.659][-14.778 ; 2.829]

Robust p-value
Order Loc. Poly.
[p]

0.002

0.001

0.000

0.909

0.924

0.183

1

1

2

1

1

2

Order Bias [q]

2

2

3

2

2

3

Covariates

No

Yes

Yes

No

Yes

Yes

Total N

21,02021,02021,02029,57329,57329,573

Eff. N estimate [h]

6,368

6,322

11,135

6,668

7,022

15,150Eff. N bias [b]13,59814,133

15,511

16,561

16,832

23,453(B) Distance travelled by car (km)Lower-income households

Beta

Higher-income households

Local linear

Local linear

Local quadratic

Local linear

Local linear

Local quadratic

(1)

(2)

(3)

(4)

(5)

(6)

-1.548-2.123*-0.993-1.618-0.851[-5.506 ; 0.850][-5.940 ; 0.115]-4.261**[-10.626 ; 0.207][-4.356 ; 2.030][-4.883 ; 1.343][-2.839 ; 3.665]

Robust p-value
Order Loc. Poly.
[p]

0.151

0.059

0.042

0.475

0.265

0.803

1

1

2

1

1

2

Order Bias [q]

2

2

3

2

2

3

Covariates

No

Yes

Yes

No

Yes

Yes

Total N

21,02021,02021,02029,57329,57329,573

Eff. N estimate [h]

5,089

4,951

11,008

4,779

4,807

8,117

Eff. N bias [b]10,142

10,174

15,443

9,144

9,255

13,903

Robust CI

Notes: The dependent variables are (A) time (in minutes) of active travel undertaken during the day and (B) the distance travelled by car
during the day (km). Subsample: lower-income households with a gross yearly income below £25,000 and higher income households witha gross yearly income above £25,000. RDD estimates use distance from residential location to the congestion charge zone as the treatment
assignment variable. The table presents results from a local regression with triangular weights. Two different MSE-optimal bandwidth
selectors for the two sides of the discontinuity are used, as in Calonico et al. (2014). The models control for and cluster into 50 bordersegments (Dell, 2010). Different models: (1, 4) local linear regression not controlling for individual covariates, (2, 5) local linear regression
controlling for individual covariates, (3, 6) local quadratic regression controlling for individual covariates. The table shows the total numberof observations (N) and the effective number of observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence
intervals are reported. * significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level.

28

Table 4: The effects of the congestion charge on participants in the western borderzone (A) in placebo periods and (B) during the western expansion
(A)

(B)

Placebo: before or after the western expansion
Active travel time
Distance travelled by car(minutes)
(km)
(1)
(2)
Beta
Robust CI
Robust p-value

During the western expansion
Active travel time
Distance travelled by car(minutes)
(km)
(3)
(4)

-3.746-0.515

3.949**-1.921*[-20.412 ; 6.253][-3.329 ; 1.542][0.034 ; 11.02][-5.073 ; .397]

0.298

0.472

0.049

0.094

Order Loc. Poly. [p]

1

1

1

1

Order Bias [q]

2

2

2

2

Covariates

Yes

Yes

Yes

Yes

Total N

21,674

21,67428,182

28,182

Eff. N estimate [h]

3,454

4,398

4,398

2,996

Eff. N bias [b]8,920

11,702

11,509

7,226

Notes: The analyses concern (A) trips before or after the western expansion period and (B) trips during the western expansion period. The
dependent variables are: (1, 3) time (in minutes) of active travel undertaken during the day; and (2, 4) the distance travelled by car during
the day (km). RDD estimates use distance from residential location to the congestion charge zone as the treatment assignment variable. The
table presents results from a local linear polynomial regression with triangular weights.Two different MSE-optimal bandwidth selectors forthe two sides of the discontinuity are used, as in Calonico et al. (2014). The modelscontrol for and cluster into 50 border segments (Dell,2010). We control for individual covariates as described in Section 3. The table shows the total number of observations (N) and the effective
number of observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence intervals are reported. * significant at
the 10% level, ** significant at the 5% level, *** significant at the 1% level.

29

Table 5: The effect of the LCC on (A) individuals already residing in border zones and(B) individuals travelling outside the charging window
(A)

Beta
Robust CI
Robust p-value
Order Loc.
Poly. [p]Order Bias [q]Covariates
Subsample
Total N
Eff. N estimate[h]Eff. N bias [b](B)

Active travel time(minutes)

Distance travelledby car (km)(1)
6.869***[4.927 ; 16.058]
0.000

(2)
-1.444[-4.681 ; 1.272]
0.262

Travelling outside thecharging window (fullsample)(3)-0.015[-0.086 ; 0.074]
0.882

Travelling outside thecharging window (carusers)(4)-0.057[-0.195 ; 0.109]
0.579

1

1

1

1

2
Yes
No movers
28,781

2
Yes
No movers
28,781

2
Yes
All
50,593

2
Yes
Car available
36,494

6,431

4,229

10,476

7,898

14,072

9,085

20,132

18,908Notes: The dependent variables are: (1) time (in minutes) of active travel undertaken during the day, (2) the distance travelled by car during
the day (km) and (3, 4) the probability of travelling outside the congestion charge hours. RDD estimates use distance from residential
location to the congestion charge zone as the treatment assignment variable. The table presents results from a local linear polynomial
regression with triangular weights.Two different MSE-optimal bandwidth selectors for the two sides of the discontinuity are used, as inCalonico et al. (2014). The models control for and cluster into 50 border segments (Dell, 2010). We control for individual covariates as
described in Section 3. Subsamples: (1, 2) participants who lived in their place of residence before the LLC’s implementation (no movers);(3) all participants; and (4) participants with a car available during the day. The table shows the total number of observations (N) and theeffective number of observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence intervals are reported. *significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level.

30Appendix (not for publication)
Appendix Figure A1: Density test over distance from border

Notes: Manipulation test uses the local polynomial density estimators proposed by Cattaneo et al. (2020, 2021). A local quadratic
approximation with kernel triangular weights is used to construct the density estimators, and a cubic approximation is used for the biascorrected density estimator. The density estimation method is unrestricted (two-sample). Robust bias-corrected statistic with jackknife
standard errors and uniform confidence interval at 95% level (2000 simulations). Stata command rddensity is used.

31

Appendix Table A1: Changing the number of border segments

Beta
Robust CI
Robust p-value
Order Loc. Poly.
[p]
Order Bias [q]Covariates
Subsample
Total N
Eff. N estimate [h]Eff. N bias [b](A)
30 segments
Active travel time
Distance travelled by car(minutes)
(km)
(1)
(2)
4.012**-1.360[0.671 ; 10.236][-3.776 ; 0.678]
0.025
0.173

(B)
70 segments
Distance travelled by car
Active travel time (minutes)
(km)
(3)
(4)
4.352**-1.598*[1.098 ; 9.898][-3.945 ; 0.177]
0.014
0.073

1

1

1

1

2
Yes
All
50,593
13,521
28,1292
Yes
Car available
50,593
10,12920,022

2
Yes
All
50,59312,08025,607

2
Yes
Car available
50,593
9,277
18,876

Notes: The dependent variables are (1, 3) time (in minutes) of active travel undertaken during the day and (2, 4) the distance travelled by
car during the day (km). RDD estimates use distance from residential location to the congestion charge zone as the treatment assignment
variable. The table presents results from a local linear polynomial regression with triangular weights. Two different MSE-optimal bandwidth
selectors for the two sides of the discontinuity are used, as in Calonico et al. (2014). The models control for and cluster into 30 (A) and 70(B) border segments (Dell, 2010). Note that the main analysis uses 50 border segments. We control for individual covariates as describedin Section 3. The sample includes all participants with a car available during the day. The table shows the total number of observations (N)and the effective number of observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence intervals are reported.
* significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level.

32

Appendix Table A2: Placebo tests (effect at false cut-offs)
Beta

Robust CI

Robust pvalue

Order Loc.
Poly. [p]Order Bias[q]

Total N

Eff. Nestimate [h]Eff. N bias [b]Active travel time (minutes) (A, 1)

1.157[-2.510 ; 4.841]

0.534

1

2

48,981

16,09034,212

Active travel time (minutes) (A, 2)

1.041[-3.892 ; 5.851]

0.694

2

3

48,981

18,25028,588Distance travelled by car (km) (B, 1)-0.308[-1.644 ; 1.019]

0.645

1

2

48,981

14,95832,063Distance travelled by car (km) (B, 2)

0.041[-1.467 ; 1.971]

0.774

2

3

48,981

18,991

30,664

Active travel time (minutes) (A, 1)-1.059[-3.164 ; 1.139]

0.356

1

2

48,981

18,29331,737

Active travel time (minutes) (A, 2)-1.391[-5.094 ; 1.504]

0.286

2

3

48,981

32,462

38,590Distance travelled by car (km) (B, 1)

0.674[-0.419 ; 2.152]

0.186

1

2

48,981

13,58029,862

Distance travelled by car (km) (B, 2)

0.573[-0.940 ; 2.223]

0.426

2

3

48,981

17,484

28,632

Active travel (minutes) (A, 1)

1.878[-0.404 ; 4.405]

0.103

1

2

48,981

15,19530,088

Active travel (minutes) (A, 2)

2.428[-0.791 ; 6.033]

0.132

2

3

48,981

23,01334,363Distance travelled by car (km) (B, 1)-0.444[-1.939 ; 0.978]

0.518

1

2

48,981

11,42825,914

Distance travelled by car (km) (B, 2)-0.326[-2.256 ; 1.792]

0.822

2

3

48,981

17,95029,268

Active travel time (minutes) (A, 1)

0.632[-1.351 ; 3.122]

0.438

1

2

48,981

18,33330,900

Active travel time (minutes) (A, 2)

1.150[-2.023 ; 4.573]

0.449

2

3

48,981

22,91031,198Distance travelled by car (km) (B, 1)

0.278[-1.041 ; 1.722]

0.629

1

2

48,981

18,30029,072

Distance travelled by car (km) (B, 2)

0.479[-1.216 ; 2.181]

0.578

2

3

48,981

26,571

34,458

Active travel time (minutes) (A, 1)

0.042[-2.610 ; 2.448]

0.950

1

2

48,981

18,11331,318

Active travel time (minutes) (A, 2)-0.091[-3.361 ; 3.085]

0.933

2

3

48,981

25,74031,792Cut-off (2)Cut-off (4)Cut-off (6)Cut-off (8)Cut-off (10)Distance travelled by car (km) (B, 1)-0.640[-2.146 ; 0.598]

0.269

1

2

48,981

14,242

23,643Distance travelled by car (km) (B, 2)-0.788[-2.487 ; 0.737]

0.288

2

3

48,981

22,16629,565

Active travel time (minutes) (A, 1)

1.141[-0.803 ; 3.221]

0.239

1

2

48,981

17,72330,391

Active travel time (minutes) (A, 2)

1.843[-0.445 ; 4.877]

0.103

2

3

48,981

24,96032,664

Distance travelled by car (km) (B, 1)-0.980*[-2.321 ; 0.151]

0.085

1

2

48,981

15,23824,470Distance travelled by car (km) (B, 2)-1.011[-2.562 ; 0.471]

0.177

2

3

48,981

20,17728,156

Active travel time (minutes) (A, 1)

0.627[-1.411 ; 2.450]

0.598

1

2

48,981

9,647

20,795

Active travel time (minutes) (A, 2)-0.011[-2.219 ; 2.009]

0.922

2

3

48,981

20,022

32,078Distance travelled by car (km) (B, 1)

0.431[-0.795 ; 1.815]

0.444

1

2

48,981

15,40827,163Distance travelled by car (km) (B, 2)

0.644[-1.132 ; 2.477]

0.465

2

3

48,981

20,50528,054

Active travel time (minutes) (A, 1)

1.664[-0.977 ; 4.667]

0.200

1

2

48,981

15,521

29,671

Active travel time (minutes) (A, 2)

2.633[-0.924 ; 6.720]

0.137

2

3

48,981

21,32329,283Distance travelled by car (km) (B, 1)-0.698[-2.700 ; 1.262]

0.477

1

2

48,981

14,59230,711Distance travelled by car (km) (B, 2)-0.932[-3.563 ; 1.261]

0.350

2

3

48,981

21,81830,690Cut-off (12)Cut-off (14)Cut-off (16)Notes: The dependent variables are A) time (in minutes) of active travel undertaken during the day and B) the distance travelled by car
during the day (km). RDD estimates use distance from residential location to the congestion charge zone as the treatment assignment
variable. Placebo cut-offs every two kilometers outside the congestion charge zone. To isolate the treatment effect, we retain only units to
the right of the discontinuity. The table presents results from a local linear (1) or quadratic (2) polynomial regression with triangular weights.
Two different MSE-optimal bandwidth selectors for the two sides of the discontinuity are used, as in Calonico et al. (2014). The models
control for and cluster into 50 border segments (Dell, 2010). We control for individual covariates as described in Section 3. The table shows
the total number of observations (N) and the effective number of observations for the estimates (h) and the bias (b). Robust bias-corrected pvalue and confidence intervals are reported. * significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level.

33

Appendix Table A3: Discontinuity of covariates at the cut-off

Beta

Robust CI

Robust pvalue

Order Loc.
Poly. [p]Order Bias[q]

Covariates

Subsample

Total N

Eff. Nestimate [h]Eff. N bias
[b]

Age

0.891

[-1.20 ; 5.44]

0.211

1

2

No

All

50,593

15,427

27,043

Female0.017[-0.07 ; 0.09]

0.808

1

2

No

All

50,593

12,29422,309

Income:<£5,000-0.028[-0.08 ; 0.01]

0.109

1

2

No

All

50,59326,262

38,647-0.020[-0.10 ; 0.04]

0.405

1

2

No

All

50,593

9,983

19,117-0.055[-0.11 ; 0.02]

0.178

1

2

No

All

50,593

13,54623,5030.029[-0.03 ; 0.10]

0.330

1

2

No

All

50,593

13,13522,8470.013[-0.03 ; 0.07]

0.375

1

2

No

All

50,593

15,741

27,0920.035[-0.01 ; 0.11]

0.133

1

2

No

All

50,593

15,83028,625-0.001[-0.07 ; 0.04]

0.634

1

2

No

All

50,593

14,40026,370-0.002[-0.09 ; 0.07]

0.769

1

2

No

All

50,593

13,44925,4730.028[-0.02 ; 0.07]

0.331

1

2

No

All

50,593

15,35628,189

Income:£100,000 +0.016[-0.04 ; 0.08]

0.514

1

2

No

All

50,593

14,942

24,739Ethnicity:
Asian

0.038

[-0.03 ; 0.10]

0.308

1

2

No

All

50,593

6,347

13,549Ethnicity:White-0.032[-0.16 ; 0.06]

0.421

1

2

No

All

50,593

8,362

14,610Ethnicity:
Other

0.005

[-0.08 ; 0.11]

0.723

1

2

No

All

50,593

11,24821,071

Employee

0.081[-0.043 ;0.205]

0.201

1

2

No

All

50,59320,00433,906

Selfemployed-0.016[-0.051 ;
0.03]

0.614

1

2

No

All

50,593

13,07921,798

Nonemployed-0.028[-0.098 ;0.088]

0.920

1

2

No

All

50,593

17,84333,907

Student-0.005[-0.076 ;0.055]

0.758

1

2

No

All

50,593

19,68928,886

Driver’s
licenseholder

-0.037[-0.121 ;0.013]

0.114

1

2

No

All

50,593

10,441

21,021

Monday

0.01

0.653

1

2

No

All

50,593

17,612

33,749

0.997

1

2

No

All

50,593

18,61537,562

0.477

1

2

No

All

50,593

20,59835,073

0.161

1

2

No

All

50,593

13,82125,507

0.351

1

2

No

All

50,593

14,69427,875

0.466

1

2

No

All

50,593

11,451

20,751

0.662

1

2

No

All

50,593

10,042

18,368

0.006

1

2

No

All

50,593

12,221

21,970

0.400

1

2

No

All

50,593

20,76436,066

0.443

1

2

No

All

50,593

12,492

24,594

Income:£5,000–9,999
Income:£10,000–
14,999
Income:£15,000–
19,999
Income:£20,000–
24,999
Income:£25,000–
34,999
Income:£35,000–
49,999
Income:£50,000–
74,999
Income:£75,000–
99,999Tuesday-0.02

Wednesday-0.043Thursday

0.021

Friday-0.0142005-0.013[-0.059 ;0.093][-0.094 ;0.094][-0.126 ;0.059][-0.027 ;0.164][-0.171 ;0.061][-0.037 ;0.017][-0.124 ;0.079][0.032 ;0.185][-0.153 ;0.061][-0.054 ;0.124]

2006-0.0072007

0.056***

2008-0.015

2009

0.011

2010

0.025

[-0.07 ; 0.13]

0.578

1

2

No

All

50,593

11,016

18,3562011-0.034[-0.18 ; 0.06]

0.318

1

2

No

All

50,593

8,286

16,35134

2012-0.004[-0.06 ; 0.04]

0.783

1

2

No

All

50,593

20,03934,152January-0.064[-0.15 ; 0.07]

0.451

1

2

No

All

50,59313,02922,956

February-0.026[-0.11 ; 0.07]

0.683

1

2

No

All

50,593

17,33031,611March-0.001[-0.12 ; 0.07]

0.632

1

2

No

All

50,59318,15630,453April-0.054[-0.09 ; 0.04]

0.488

1

2

No

All

50,5939,135

19,544May

0.03[-0.07 ; 0.10]

0.754

1

2

No

All

50,593

11,854

20,864

June

0.021

[-0.07 ; 0.08]

0.868

1

2

No

All

50,59321,07035,901July

0.03[-0.04 ; 0.12]

0.301

1

2

No

All

50,593

14,93826,865

August-0.013[-0.08 ; 0.06]

0.790

1

2

No

All

50,593

11,23618,995September-0.015[-0.05 ; 0.06]

0.905

1

2

No

All

50,593

7,524

16,864

October

0.070*[-0.00 ; 0.14]

0.061

1

2

No

All

50,593

15,191

32,907

November-0.043[-0.16 ; 0.07]

0.463

1

2

No

All

50,593

18,88529,265

December

0.000[-0.04 ; 0.05]

0.902

1

2

No

All

50,593

12,50421,941

Notes: The dependent variables are the covariates listed in Section 3. RDD estimates use distance from residential location to the congestion
charge zone as the treatment assignment variable. The table presents results from a local linear polynomial regression with triangularweights. Two different MSE-optimal bandwidth selectors for the two sides of the discontinuity are used, as in Calonico et al. (2014). The
models control for and cluster into 50 border segments (Dell, 2010). The table shows the total number of observations (N) and the effective
number of observations for the estimates (h) and the bias (b). Robust bias-corrected p-value and confidence intervals are reported. * significant at
the 10% level, ** significant at the 5% level, *** significant at the 1% level.

35