Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Is Ride-sharing Good for Environment?
Yoshifumi Konishi, Akari Ono
6 June, 2024
DP2024-014
https://ies.keio.ac.jp/en/publications/23908/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
6 June, 2024

Is Ride-sharing Good for Environment?
Yoshifumi Konishi, Akari Ono
Keio-IES DP2024-014
6 June, 2024
JEL Classification: L91, Q53, R4, R11
Keywords: Air pollution, congestion, commuting choice, staggered difference-indifferences, instrumental variable, ride-hailing, ride-sharing, transportation andenvironment
Abstract
We estimate the causal effect of ride-hailing entry on transport-related air pollution in
U.S. cities, using granular satellite-based NO₂ concentration data in the staggered
difference-in-differences research design. Our empirical strategy accounts fortreatment effect heterogeneity both within and across cities, coupled with two
additional strategies to strengthen identification: using geography-based instruments
and exploiting a sharp, unanticipated change in ride-hailing activity in Austin due to its
rule change. We find robust evidence that ride-hailing tends to improve air quality inhighly dense cities, but has no significant impact in cities with low and medium density.
We also find evidence that the NO₂ reduction in highly dense cities is associated with adecrease in private car use and an increase in public transit use. Taken together, ourfindings suggest that the environmental effect of ride-hailing depends on thecomplementarity between ride-hailing and public transit: While ride-hailing may
increase congestion by inducing deadheading or displacing of mass transit for parts ofdaily trips, it may still decrease overall air pollution if a combined use of ride-hailingwith other transit displaces private car use more than such adverse behavior.
Yoshifumi Konishi
Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345
ykonishi@econ.keio.ac.jp
Akari Ono
Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345
ono.akari@keio.jp
Acknowledgement: The study was in part supported by the financial supports fromJapan Society for the Promotion of Science (JSPS), Grant-in-aid for Scientific Research(Grant numbers: 18K01562, 21H00712, and 23K20599). Sho Kuroda, Takuya
Shimamura, and Hazuki Yanagida provided valuable research assistance. We thankMichiko Namazu, a data scientist at Uber Inc., for sharing Uber's official launch dates in
the U.S. We also thank Yuta Toyama, Toshi Arimura, and other seminar participants at
Waseda University's Empirical Micreconomics Workshop for their comments.

Is Ride-sharing Good for Environment?

Yoshifumi Konishi
Faculty of Economics
Keio University
andAkari Ono
Faculty of Economics
Keio University

Draft: June 2024

Corresponding author. Mailing address: 2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japan. E-mail:ykonishi@econ.keio.ac.jp. Phone: +81-3-5427-1725.
Acknowledgement: The study was in part supported by the …nancial supports from Japan Society for
the Promotion of Science (JSPS), Grant-in-aid for Scienti…c Research (Grant numbers: 18K01562, 21H00712,
and 23K20599). Sho Kuroda, Takuya Shimamura, and Hazuki Yanagida provided valuable research assistance. We thank Michiko Namazu, a data scientist at Uber Inc., for sharing Uber’s o¢ cial launch dates in
the U.S. We also thank Yuta Toyama, Toshi Arimura, and other seminar participants at Waseda University’s
Empirical Micreconomics Workshop for their comments. This is a substantially improved version of the April
2021 draft.

Abstract: We estimate the causal e¤ect of ride-hailing entry on transport-related air pollution in U.S. cities, using granular satellite-based NO2 concentration data in the staggereddi¤erence-in-di¤erences research design. Our empirical strategy accounts for treatment effect heterogeneity both within and across cities, coupled with two additional strategies tostrengthen identi…cation: using geography-based instruments and exploiting a sharp, unanticipated change in ride-hailing activity in Austin due to its rule change. We …nd robust
evidence that ride-hailing tends to improve air quality in highly dense cities, but has no
signi…cant impact in cities with low and medium density. We also …nd evidence that theNO2 reduction in highly dense cities is associated with a decrease in private car use and anincrease in public transit use. Taken together, our …ndings suggest that the environmentale¤ect of ride-hailing depends on the complementarity between ride-hailing and public transit:
While ride-hailing may increase congestion by inducing deadheading or displacing of masstransit for parts of daily trips, it may still decrease overall air pollution if a combined use of
ride-hailing with other transit displaces private car use more than such adverse behavior.

JEL Codes: L91, Q53, R4, R11
Key Words: Air pollution, congestion, commuting choice, staggered di¤erence-in-di¤erences,
instrumental variable, ride-hailing, ride-sharing, transportation and environment11. Introduction"Ride-hailing is an attractive option for many travelers, and can increase mobility
for households who lack a private vehicle. Yet in communities across the country,ride-hailing is increasing vehicle travel, climate pollution, and congestion." — theUnion of Concerned Scientists (2020)
Over the last decade, Uber, Lyft, and other ride-hailing services have expanded rapidly,
bringing innovations into the transport sector in numerous cities around the world. Economists have attempted to quantify the economic gains from this transport innovation for anumber of important economic margins: Consumer’s gain from Uber’s congestion pricing(Cohen et al., 2018; Castillo, 2023) and from reducing information asymmetry (Liu et al.,2018) and drivers’ gain from Uber’s compensation scheme (Angrist et al., 2021) and from‡exible workstyle (Chen et al., 2019). Economists have also refuted some of the criticisms
against these ride-hailing companies, and instead, …nd: Uber’s entry increased public transit
riderships (Hall et al., 2018), reduced driving under the in‡uence, fatal accidents, arrests for
assault and disorderly [Dills and Mulholland (2018), Anderson and Davis (2023)], and had
no signi…cant e¤ects on taxi driver’s labor supply although reducing their relative earnings
by about 10 percent (Berger et al., 2018). Against these economic bene…ts, however, a rising number of studies have also started to document the negative environmental impacts ofride-hailing services [Diao et al. (2021), Erhardt et al. (2019), Kong et al. (2020), Rayle etal. (2016), Tarduno (2021), Krishnamurthy and Ngo (2024)].
Ride-hailing is thought to increase air pollution and congestion primarily for two reasons— ‘deadheading’ (miles without a passenger between hired rides) and ‘displacing’ (miles
that might have displaced mass transit or other low-emission travel modes). For example,
the Union of Concerned Scientists (UCS) estimates that the former contributes 47% more
emissions per trip while the latter adds 69% more per trip, based on the data from U.S. cities(UCS, 2020). Such claims often receive support from observational studies that document asurge in road tra¢ c upon ride-hailing entry (e.g., T&E, 2019). While we agree with the general sentiment of these studies, they miss an important economic channel: Complementarity
between ride-hailing and other transit modes. Ride-hailing customers may use public transit
in combination with hired rides, and such a combined use may displace the use of private
driving, not only for primary commuting but also for daily errands. Put di¤erently, the UCS
study compares hired rides with other transport modes that could have replaced the rides,
but that isn’t a valid counterfactual if the aforementioned channel exists. In this case, avalid counterfactual comparison would be, instead, to compare a sequence of daily transport2

choices in the presence of ride-hailing against those in its absence. The goal of our study
is to establish credible evidence on the causal e¤ect of ride-hailing on ambient air pollution,
based on the empirical design better suited to make such a counterfactual comparison.
To do so, we start by the canonical two-way …xed e¤ect (TWFE) regression with thestaggered di¤erence-in-di¤erences (DD) design, exploiting the variation in ride-hailing entry
over time and across cities. Our basic empirical strategy is to estimate this TWFE-DD
regression on monthly observations of ambient air pollution on a panel of 348 MSAs during
the 9-year period, 2010-2018. Thus, our causal inference relies on how we carefully design
treatment-control structures on the study sample. This basic strategy itself is analogous toprevious studies [Berger et al. (2018), Hall et al. (2018), Li et al. (2021), Ward et al. (2019),
Kim and Sarmiento (2021)].
We, however, take three new approaches to strengthen the identi…cation of the causale¤ect. First, we construct a measure of de facto ride-hailing entry into a MSA boundary,using both Google’s keyword search trends for both Uber and Lyft. This measure of entry
is more complete in coverage, is more accurate in both entry timing and location, and also
helps us avoid the possibility of falsely refuting the critics’argument in favor of ours. We
demonstrate these points more forcefully in Section 4.
Second, we use satellite-based nitrogen dioxides (NO2 ) concentration data. We specifically avoid use of U.S. Environmental Protection Agency (EPA)’s monitoring-based dataon other pollutants for the identi…ability of ride-hailing’s impacts on transport-related air
pollution. Monitoring data may not be reliable for credible inference for various reasons:Monitoring sites are spatially unevenly located and are not necessarily located in high pollution areas (Fowlie et al.,2019); site locations change over time, making it di¢ cult to compare
data consistently over time; and there is an "unwatched pollution problem" in that local
governments may have incentives to strategically locate monitoring sites or avoid recording
high pollution episodes (Zou, 2021; Grainger and Schreiber, 2019). In contrast, we use thesatellite-based NO2 concentration data to calculate the ambient air pollution on spatiallydelineated subareas within each MSA. This allows us to compare, for example, urban-area
NO2 concentrations of ride-hailing entry city against that of no-entry city consistently overtime.
Third, we take three carefully designed identi…cation strategies so as not to rely on asingle set of identifying assumptions for credible inference. The major identi…cation threat
to the staggered DD design is that ride-hailing entry may occur in cities exactly when
and where residents’ transport-related behavior is expected to change. This leads to theviolation of the parallel trend in unobservables. Although we include a MSA-speci…c linear
trend along with other …xed e¤ects and time-varying controls, this may not completely
3eliminate all unobservables that are correlated with both the treatment and the outcome.
Our …rst strategy is to mimic the idea of matched DD (Heckman et al., 1997). We start
by noting that entry dates are highly correlated with population density, which are also
correlated with other MSA-level socioeconomic variables such as income, manufacturing
employment, and the share of public transit commuters. Hence, we use MSA-level population
density as a su¢ cient statistic for unobservable trends in transport-related behavioral change,and estimate the causal e¤ect on each subsample of MSAs strati…ed by population density
quintile. We then employ the de Chaisemartin-D’Haultfoeulle (dCDH, 2020; 2024) estimator
to estimate heterogeneous dynamic treatment e¤ects.1 The second strategy exploits the
sharp, unanticipated change in the supply of ride-hailing service in Austin, Texas, due to
its rule change on …ngerprint checks on ride-hailing drivers. We apply the synthetic control
method (SCM) on the residualized outcomes to visualize the impacts of both the entry andthe rule change on Austin’s air quality, using this incidence as a quasi-experiment. Our last
strategy applies an instrumental variable (IV) method to the TWFE-DD regression. Our
instruments are reported entry dates, taken from Hall et al. (2018), interacted with thegeography-based instruments, which are widely used in the empirical economic geography
literature [Baum-Snow (2007), Duranton and Turner (2011; 2012), Faber (2014), Redding
and Turner (2015)]. The basic idea here is that the geography-based instruments create‘hypothetical highway routes’, which predict the current routes (hence, the current economic
size of the cities) well, yet are not correlated with contemporaneous economic shocks after
controlling for observables today.
Using these approaches, we …nd robust evidence that ride-hailing entry tends to decrease
ambient NO2 concentrations (in terms of both monthly mean and maximum) in MSAs withhigh population density, particularly in their urban areas. We also …nd no evidence ofride-hailing entry leading to an increase in ambient NO2 concentrations in low- and mediumdensity MSAs. We also con…rm these …ndings in the event study of dynamic treatment e¤ects
using a version of the dCDH estimator. The signs of the estimated impacts are largely
consistent across di¤erent identi…cation/estimation strategies, although their magnitudes
vary. In particular, our DD-IV estimation leads to unreasonably large estimates in lower
density MSAs. We suspect that given the form of the DD-IV estimand, the lack of reported
entry dates for these MSAs overly in‡ate the estimates. Nonetheless, the estimates for theurban areas of MSAs in the highest density quartiles are always negative, have relatively small
standard errors, and range from -0.034 log points (TWFE-DD) to -0.104 log points (DD1
Alternatively, we may use the Callaway-Sant’Anna (CS) estimator, which is theoretically identical withthe dCDH estimator in the absence of covariates. As we explain in Section 5, we absorb the in‡uence of all
time-varying covariates using the imputation method (Catenao et al., 2023). Hence, we should obtain thesimilar estimates using either estimator.

4IV). Assuming a linear relationship between satellite-based and EPA monitoring records,
these impacts translate into the reductions of monthly mean NO2 concentrations by roughly
0.53-1.62 ppb.
As a supplement to our main analysis, we also use annual household-level data on commuting modes to work from the American Community Survey (ACS) to further explore
the economic mechanism underlying our main results. For this, we use commuting modeindicators as the outcome variables, and apply all three identi…cation strategies discussed
above. The results are quite supportive to our argument. We …nd that the air-pollution impacts of ride-hailing is signi…cantly associated with changes in commuting patterns. In thehighest density MSAs, where ride-hailing entry is estimated to decrease NO2 concentrations,ride-hailing entry is also signi…cantly associated with a decrease in private car commuters,
and increases in public transit commuters and in the other commuting modes. Interestingly,
we also …nd that the sharp decrease in the supply of ride-hailing in Austin due to its rulechange is estimated to reduce, rather than increase, the ambient NO2 pollution and the shareof private car commuters while increasing the share of public transit and other commuting
modes. Taken together, these results support our argument that the environmental e¤ect ofride-hailing depends critically on the degree of complementarity (or substitutability) between
ride-hailing and other transit modes. In highly dense cities, such a complementarity is highso that a combined use of ride-hailing with mass transit can reduce private car use whereas
in lower dense cities, the complementarity is weak so that ride-hailing tends to steer people
away from mass transit. These results are in sharp contrast to critics’views cited above, but
are indeed consistent with Hall et al. (2018), who …nd complementarity between ride-hailingand other transport modes.
Our work complements several vibrant areas of research: (a) empirical studies that estimate the causal e¤ects of ride hailing on various economic outcomes [Anderson and Davis(2023), Angrist et al. (2021), Berger et al. (2018), Chen et al. (2019), Cohen et al. (2018),
Dills and Mulholland (2018), Hall et al. (2018), Liu et al. (2018), Tarduno (2021), Krishnamurthy and Ngo (2024)], (b) a large body of literature that examines pollution- orcongestion-relief e¤ect of public transportation infrastructures [Chen-Whalley (2012), Li etal. (2019), Gendron-Carrier et al. (2022), Gu et al. (2021), other papers cited in Anasand Lindsey (2011)], and (c) the economic studies that structurally investigate the generalequilibrium impacts of ride-hailing into the taxi industry [Buchholz (2023), Fréchette et al.
(2019), Hall et al. (2020), Rosaia (2023)].
Of these, our work is most closely related to Krishnamurthy and Ngo (2024) and Kim andSarmiento (2021). Krishnamurthy and Ngo (2024) use hourly freeway tra¢ c and daily air
pollution data from California and apply a DD design exploiting the staggered rollouts of ride5

hailing entry at the country-level. They …nd that ride-hailing entry reduces weekday freeway
congestion and PM2:5 concentrations in the average county entered, although congestion andPM2:5 concentrations are increased during the evening rush hour and in the most populated
counties. Our study nicely complements theirs in that our …ndings are quite consistent,
yet our scope is quite di¤erent from theirs — we use more aggregate data for virtually all
MSAs in the U.S. while they use more disaggregate data only for California. Kim andSarmiento (2021) use the Callaway-Sant’Anna estimator in an empirical design similar toours and …nd that Uber’s entry is estimated to improve air quality (as measured in the Air
Quality Index and ground-level ozone), particularly during the summer when bad air quality
episodes are expected. Our study complements and further strengthens their …ndings infour regards. First, while they rely mostly on state-level reported entry dates of Uber, weconstruct MSA-level de facto entry dates from the Google Trends Index for both Uber andLyft. We show in Section 4 that our entry dates are likely to be more complete in its
coverage, more accurate in both location and timing, and correctly capture the ride-hailing
activity. Second, while they rely on EPA’s monitoring data and use county-level observations,
we rely on satellite-based data and urban areas of MSAs as study units. Third, while they
rely on one identi…cation strategy (staggered DD with various speci…cations, controls, androbustness checks), we take three alternative identi…cation strategies and …nd consistent
results. Fourth, while they focus only on ambient air quality as outcomes, we also explore
the economic mechanism underlying the main results. We …nd the estimated impacts ofride-hailing entry on commuting modes are indeed consistent with the estimated impacts onambient air quality.
Lastly, our analysis gives a clear answer to an ongoing debate among policy practitioners
and scientists (i.e., "Does ride-hailing decrease or increase air pollution and congestion?)in ways that can embrace and reconcile seemingly con‡icting empirical …ndings in the literature. On one hand, studies based on disaggregate data tend to …nd that ride-hailingincreases congestion in particular segments of transport demand (e.g., rush hours, urban
freeways, populated counties) while decreasing congestion in other segments (e.g., non-rush
ours, non-urban freeways, less populated counties) [Erhardt et al. (2019), Tarduno (2021),
and Krishnamurthy and Ngo (2024)]. On the other hand, studies that rely on more aggregate data such as MSA-level or monthly-level data tend to …nd that ride-hailing increases
use of mass transit (Hall et al., 2018), decrease congestion (Li et al., 2021), decrease ambient air pollution (Kim and Sarmiento, 2021), and vehicle emissions (Ward et al., 2019).
Our results suggest that the key to reconciling these mixed …ndings is the heterogenouse¤ects of ride-hailing on various segments of transport demand. That is, ride-hailing may
decrease air pollution by encouraging a combined use of ride-hailing and mass transit in6

cities that have dense public transit networks, yet may increase congestion in some partof the cities, due to deadheading by ride providers or use of hired rides in place of masstransit for part of the daily trips (but not the entire sequence of daily trips). Our results
is also consistent with Agrawal and Zhao (2023), who argue, based on a simulation-based
study using the monocentric city model, that subsidizing ride-hailing services as a “last-mile”
provider can be welfare-increasing and whether ride-hailing and public transit are substitutes
or complements is a policy choice. Thus, our manuscript provides an important insight into
an important policy debate that seeks to strike a balance between increasing mobility and…ghting air pollution in cities around the world.
2. Background and Motivation

2.A. Ride-hailing Service and Its Environmental Concerns
Uber Technologies Inc. ("Uber") started as a developer of a smartphone application thatwould make ride-hailing as simple as "tapping a button". Uber launched its …rst ride-hailingservice in San Francisco in July, 2010 and in New York in May, 2011. During the initial phase,
Uber mainly operated the ride-hailing platform for expensive limousines ("black car"), butlater introduced a more a¤ordable service, UberX. UberX was …rst launched in San Francisco
in January, 2013, quickly became Uber’s standard ride-hailing service throughout the U.S.,
and is often seen as a direct competitor against the traditional yellow cab service. Lyft Inc.
("Lyft"), on the other hand, started as a long-distance ride-sharing service between college
campuses in 2007, with a brand name Zimride. Its …rst short-distance ride-sharing service
appeared in San Francisco in August, 2012 as a complementary service of Zimride. In 2013,
the company changed its name from Zimride to Lyft and sold it to Enterprise Holdings.
Uber and Lyft have entered roughly 80% of U.S. cities and stayed active there. Today, Uberaccounts for roughly 70% of the ride-hailing service sales in the U.S., with Lyft holding theremaining 30%, and users are royal to their service providers — most customers use only
one service and rarely switch services (Second Measure, 2021).
Ride-hailing services are known to provide a number of economic bene…ts: ease of accessto transit, ‡exible workstyle, increased employment opportunities for the poor, reducing
information asymmetry and mismatch in the taxi market, and promoting smartphone-based
innovations in other areas of the economy. Against these bene…ts, however, they are often
criticized for the downsides of their business model. One of the important controversies iswhether ride-hailing services decrease or increase congestion and road tra¢ c, particularly in7

dense urban metro areas. Increased congestion and road tra¢ c, if true, are a cause of serious
concerns from an environmental perspective. Internal combustion of fossil fuels by vehicles isthe leading source of harmful air pollutants such as CO and NO2 . Such pollutants are known
to increase the risk of stroke, heart disease, lung cancer, and chronic and acute respiratory
diseases. There is a large body of literature that establishes a positive relationship between
increased congestion/tra¢ c and air pollution/carbon emissions from on-road vehicles [SeeAnas and Lindsey (2011) for a nice review on this issue]. Hence, the congestion problem
could also be directly linked to climate and pubic health concerns. Recent reports from two
high-pro…le organizations tout for such concerns [T&E (2019); UCS (2020)].
There are two competing views on the environmental e¤ect of ride-hailing. On one hand,
proponents of Uber and Lyft (including the companies themselves) argue that ride-hailingapps provide easy access to shared mobility on demand, allowing commuters to rely lesson private car ownership and more on public transit and other transport modes such as
bicycling and walking. On the other hand, critics argue that ride-hailing can add roadtra¢ c either because of ‘deadheading’(driving without a passenger between hired rides) or
because ride-hailing increases reliance on hired rides rather than on public transit and othertransit modes. Previous studies indeed …nd mixed results on this issue. Studies that rely oninterviews, surveys, and micro-level trip data in speci…c cities [Erhardt et al. (2019), Konget al. (2020), Rayle et al. (2016); UCS (2020)] tend to …nd results in support of the critics’arguments while studies that rely on quasi-experimental research design [Hall et al. (2018);
Li et al. (2021); Ward et al. (2019)] tend to refute the critics’arguments.2
One point we wish to clarify in this paper is that these mixed …ndings arise mostly
because they make empirically quite di¤erent counterfactual comparisons. Studies that …nd
in support of the critics’ arguments compare hired rides with other transport modes that
could have potentially replaced the rides. For example, in the UCS study, a hired ride with anaverage level of deadheading is compared against other modes of travel an average commuter
might opt for in the absence of ride-hailing services. However, in our view, that is not a fairor valid counterfactual. Consumers make a sequence of daily transport choices, and hence,
use ride-hailing in combination with other transport modes (We discuss this point more fullyin the next section). A valid counterfactual in this case, instead, is to compare a sequence oftransport choices over a course of the day in the presence of ride-hailing against those in its
absence. In other words, we need a counterfactual comparison that accounts for behavioral
2

An exception is Diao et al. (2021), who uses a di¤erence-in-di¤erences research design analogous to Hallet al. or Li et al., but …nds, in contrast, that ride-hailing entry increases congestion and decreases public
transit ridership. In our view, however, Diao et al. fails to deliver credible results because they fail to control
for MSA-speci…c time trends and instead use mostly endogenous controls and highly suspicious IVs alongwith a prohibited second-stage regression using the predicted probability from the …rst-stage logit regression.

8

changes in equilibrium commuting patterns in a city. We discuss this point more forcibly inthe next section.

2.B. Why May Ride-hailing Decrease or Increase Transport Emissions?

U.S. cities have experienced substantial suburbanization over the last half century. Central city population declined by 17% whereas total MSA-level population increased by 72%
between 1950 and 1990 for large MSAs, due primarily to the rapid development of limitedaccess highways over this period (Baum-Snow, 2007). As a result of this suburbanization, amajority of MSA residents make either suburb-to-central city or suburb-to-suburb commutefor their work. About 63% of MSA commuters make such trips and about 87% use private
cars for daily commuting in 2000 (U.S. Census Bureau, Journey to Work). Thus, the impactof ride-hailing entry on transport-related emissions depend on how it a¤ects commuters’transport choices in such a suburbanized city.
In this regard, a recent empirical …nding by Hall et al. (2018) — that ride-hailing services
are a complement to public transportation, particularly in large cities — gives us an important insight. In large cities where commuters have access to su¢ ciently dense public transit
networks, ride-hailing can complement public transit in a variety of ways. For example, public transit stations may be far from commuters’homes but may be close to their workplace.
Ride-hailing can connect such commuters to their nearest transit, potentially allowing themto switch from private driving to public transit. For another example, commuters may run
a variety of chores while at work. Commuters may opt to drive their own car to work, notjust for convenience of commuting but for such anticipated chores. Low-cost, easy-to-hailride services may alleviate this latter need for driving their own cars. This line of reasoning
suggests that ride-hailing can potentially reduce private driving more than simply replaces
it — the ride-hailing service is used in place of private driving, but the use of public transit(or ride-hailing) for other parts of travel that comes with it can replace private driving as
well.
We demonstrate this point using Chicago as an illustrative example. Figure 1 showsmajor train and bus routes in the urban and suburban areas of Chicago, along with a stylized
diagram intended to conceptualize a certain segment of the city. As with many cities in the
U.S., Chicago has a radial network of public transit routes that are highly concentrated
around the central business district (CBD) and extend radially outward from the CBD to
the suburban areas. As a result, many suburban areas have sparse transit networks thatcannot directly connect their residents to either the CBD or other employment centers.

9

Imagine a commuter who lives in a suburb and commute to a city’s employment center forwork. Often, the nearest station is not located within a walking distance from/to commuter’shome or workplace. Hence, the commuter may need to take another mass transit to thenearest station. Given this inconvenience, the commuter may opt to drive her car to workin the absence of low-cost, easy-to-hail ride services. This driving distance is denoted by sc
in the diagram. In addition, the commuter may use her car to run a few ‘daily chores’whileat work: attending meetings at other places, buying things at stores, going to restaurants,
etc. For simplicity, the driving distance is denoted by a radius se from her workplace. In
the presence of convenient ride-hailing services, however, the commuter could hire rides tothe nearest station, either from her home or from her workplace, and take the mass transit
instead. The driving distances by hired rides in this case are denoted rh and rw , respectively.
Furthermore, such a commuter may also use hired rides to replace the driving distance fordaily chores se . Assuming that the mass transit operation stays the same, the total amountof air pollution depends only on the total driving distance, which would change from sc + se
to rh + rw + se if a complementarity between ride-hailing and public transit exists. In thiscase, ride-hailing would decrease the total driving distance if sc > rh + rw , yet increase thecongestion in the urban area because se < rw + se . Of course, if such complementarity does
not exist or is not strong enough, hired rides might simply displace mass transit commuting
m. Figure A1 in the Appendix provides some anecdotal evidence in support of suchcomplementary use of ride-hailing services. The …gure displays the observed geographic
distribution of ride-hailing trips during the weekday rush hours in Chicago, demonstratingthat roughly 75% of the rush-hour trips from the suburban community areas do not travel
the full distance to the CBD.
It is, therefore, largely an empirical question to what extent such complementarity exists,
and whether it is strong enough to outweigh the increase in driving activity by ride-service
providers. In addition, these discussions suggest that the impact of ride-hailing can behighly heterogeneous across cities because the degree of complementarity (or substitutability)
between ride-hailing and mass transit is likely to depend on the density of public transit.
3. Estimation and Identi…cation Strategy

3.A. Overview
Our empirical strategy employs a staggered di¤erence-in-di¤erences (DD) research design,building on earlier empirical studies [Berger et al. (2018); Hall et al. (2018); Li et al. (2021)].
10

For all analyses, we use metropolitan statistical areas (MSAs) as our study units. Our study
covers the period 2010-2018 and uses the 2009 core-based statistical area (CBSA) boundaries
for all years’observations. This coverage ensures that each treated MSA has su¢ cient leads
and lags before and after its ride-hailing entry, and that the period does not overlap with
Uber Eats activity.
As a basis of our analysis, we start with the standard two-way …xed e¤ect (TWFE)
speci…cation of the staggered DD regression, following Hall et al. (2018) and Berger et al.
(2018). For an outcome variable yctm in year t and month m of city (MSA) c, we specify our
TWFE regression as:
j
yctm
=

c+

tm +

X

0
s Dctm + Xctm+

c (t) + ctm ;(1)swhere Dctm is our treatment variable and equals 1 if ride-hailing service enters/exists in
MSA c and 0 otherwise in period tm, s is the heterogenous treatment e¤ect parametercorresponding to s-th quartiles of MSA-level population density with s 2 S f1; 2; 3; 4g,
Xctm is a vector of (exogenous) time-varying covariates, c and tm are MSA and year/month…xed e¤ects, and c (t) is a MSA-speci…c linear time trend. Our main outcome variables ofinterest are ambient levels of nitrogen oxides (NO2 ) concentrations in logged terms. Section
4 justi…es the use of NO2 concentrations as the most appropriate measure of transport-related
air pollution for our purpose. We use two measures of our outcome: the MSA-level means(over girded cells) of (1) monthly average NO2 concentrations and (2) monthly maximum NO2
concentrations. Time-varying controls include temperature, wind speed, their polynomials,
gasoline prices, and non-attainment status for 1997 CAAA standards for PM2.5 and O3 .
Temperature, wind speed, and gasoline prices are aggregated at the state level to avoid
substantial data attrition as well as confounding with our treatment.3 We carefully choose
our time-varying controls since inclusion of endogenous time-varying controls is known tocause severe bias in the estimates. The population density quintile is based on the 2010 data
to ensure that it is the pre-treatment status.
There are several important empirical challenges in estimating eq. (1), and we addresseach of them as follows.
First, earlier studies use reported Uber/Lyft entry dates, making use of information
drawn from local newspapers, o¢ cial blogs/websites, and social networking services. These3

We use temperature and wind data from the monitoring records available at the EPA’s AirData. As weshall discuss in Section 4, monitoring data are available only sparsely. Hence, a non-negligible share of oursample would need to be dropped if we were to aggregate these data at the MSA level. Furthermore, wewould like only the supply-side variation in gasoline prices, excluding the in‡uence of MSA-level demand-side
factors for gasoline consumption.

11

reported dates may have incomplete coverage, potentially multiple dates of entry, and some
reporting errors. More importantly, in some cities, reported entry occurs well before theride-hailing service achieves su¢ cient market penetration. This endangers the risk of false
negative by construction — i.e., Uber/Lyft would not increase transport-related air pollution
until a su¢ ciently large number of commuters respond to it. To overcome this problem, weconstruct a measure of de facto entry, constructed from the Google Trends Index. In thenext section, we de…ne our measure of de facto entry and discuss its empirical properties
more fully.
Second, recent advances in the environmental economics literature indicate that the useof EPA’s monitoring data may lead to substantially biased statistical inferences. EPA’s monitoring stations are spatially unevenly located, and many of them are discontinued or change
locations over time (Fowlie et al., 2019). Furthermore, there is an "unwatched pollution problem”in that local governments may strategically choose monitoring sites or avoid recording
high air pollution episodes (Zou, 2021; Granger-Schreiber, 2019). These characteristics makeit very hard to credibly compare pollution data over time and across space. Instead, we makeuse of the National Aeronautics and Space Administration (NASA)’s satellite-based NO2
concentration data, which are available at relatively high resolutions (0:25
0:25 girded
cells) consistently throughout our study period. We discuss our NO2 data more thoroughly
in Section 4.
Third, although NO2 has a known distance-decay relationship (Cape et al., 2004; EPA,
2008), it may still travel far distance and a¤ect ambient concentration levels several miles
away from its emissions sources (Su et al., 2009).4 This implies that the violation of STUVA(or no-spillover) condition may occur in several ways, particularly between neighboring
MSAs. To avoid such spillover e¤ects, we delineate urban, suburban, and non-urban boundaries within each MSA, calculate the monthly NO2 concentration statistics for each of thesesubareas j 2 furban, suburban, non-urbang, and run a separate regression for each j in eq.
(1). This allows us to compare, for example, urban-area NO2 concentration of entry city
against urban-area NO2 of no-entry city. Because urban areas are quite far apart from eachother, this minimizes the risk of STUVA violation. Furthermore, our discussion in Section
2 suggests that ride-hailing entry may have di¤erent air-pollution impacts on di¤erent subareas of cities. Since we expect some spatial spillovers across subareas of cities, how the4

It is known that the e¤ect of emissions from on-road vehicles on ambient NO2 concentrations declines
quickly with distance — 90% of the decline occurs within just a 10-meter distance and the return to thebaseline concentration levels occurs between 200 and 500 meters [see EPA (2008) and papers cited therein].
However, recent studies have shown that the in‡uence of NO2 emissions may extend beyond this conventional
distance range through complex reactive/mixing processes with background pollutants in the atmosphere.
Applying the land-use regression models in Los Angels, Sue et al. (2009), for example, found that the spatial
extent of in‡uence can reach as far as 5-20 kilometers from the emissions source.

12treatment e¤ects di¤er over subareas of cities is also an important empirical question.
Fourth, the major threat to identi…cation in eq. (1) comes from the violation of parallel
trends in unobservables. As discussed in Hall et al. (2018), ride-hailing services mostly
enter cities in the rank order of population size (or population density). However, even
if ride-hailing entry is mostly exogenous to residents’ transport choices, the parallel-trend
assumption may still be violated because Uber/Lyft happen to enter cities exactly where andwhen transport-related air pollution is expected to decline (say, due to preferences/publice¤orts toward more eco-friendly transportation behavior) or is expected to rise (say, due
to growth of economic activities). Indeed, we show in the Appendix that entry dates arehighly correlated with population density, the share of manufacturing employment, and theshare of workers who use public transit for daily commuting. To overcome this challenge,
we employ three identi…cation strategies. In this section, we discuss our …rst strategy below,
leaving the other two in Sections 6 and 7, respectively.
Our …rst strategy mimics the idea of matched di¤erence-in-di¤erences approach (Heckman et al., 1997). Ideally, we would compare the outcomes of cities with similar air pollution
trends in the absence of Uber/Lyft entry. If we have a large sample of cities with large variation in timing of Uber/Lyft entry, we would match cities based on all available observables.
We, however, have only 348 MSAs with little variation in entry timing. Thus, matching oneven a few observables can quickly exhaust observations that can be used as control units
because Uber/Lyft enter similar cities at similar timings. This makes it infeasible to directly
apply the matched DD method. Instead, we rely on the (arguably heuristic) argument that
population density is likely a su¢ cient statistic for unobserved trends. Speci…cally, our …rst
approach makes the following identifying assumption:
A1. Conditional on exogenous (time-varying) covariates cf c ; tm ; Xctm ; c (t)g,parallel trends in unobservables hold for all MSAs in the same population density cohort:
E[ c;

c;

1 jDc

= 1;

c ] = E[ c;

c;

1 jDc

= 0;

c ];

for each city c within each population density quartile s for all periods .
Note that in the staggered DD setup, parallel trends (in unobservables) need to holdfor each timing group: i.e., unobservables for cities treated in g-th period would move inthe same way as for all cities not treated in that period [Goodman-Bacon (2021), Sun
and Abraham (2021), Callaway-Sant’Anna (2021)]. The assumption A1 helps relax thisassumption by requiring the parallel trends for each timing group to hold only within thesame density cohort. We take two alternative estimation strategies under this assumption.
13

The …rst is to estimate the treatment e¤ect parameter separately on each density-quintile
subsample. This approach is taken in our event-study estimation in Subsection 5.B as wellas our instrumental variable approach in Subsection 7.B. The second is to fully interact the
population density quintile dummies with the treatment variable. The latter is the approach
we take with our TWFE speci…cation in eq. (1).
The speci…cation in (1) also helps our identi…cation in two other regards. As discussed inHall et al. (2018), ride-hailing services mostly entered cities in the rank order of population
size (or, more precisely, population density as shown in the Appendix B). Because we expect the treatment timing to vary by population density, our speci…cation is a parsimonious
way to interact the timing group dummies ( the population density quintile dummies)
with the treatment variable. This is known to remove bias arising from the possible correlation between treatment e¤ect heterogeneity and the timing of entry [Sun and Abraham(2021), Wooldridge (2021)]. Furthermore, our speci…cation also helps us explicitly accountfor heterogeneous treatment e¤ects of ride-hailing entry to vary by (pre-treatment) population density. This is important because we expect the complementarity between ride-hailingservice and public transit to be greater in cities with density (both public transit density
as well as proximity to each other are important). S÷
oczyński (2020) has shown that whensuch heterogeneity exists, the best way to identify the true treatment e¤ect is to interact thetreatment with the sources of heterogeneity rather than to include them as controls.
3.B. Event Study
It is now increasingly common to estimate dynamic treatment e¤ects, using event-study
speci…cations. Recently, a series of studies have documented that severe biases may arise inestimating TWFE event-study regressions in the staggered DD setup, particularly when thee¤ects are both dynamic and heterogeneous [de Chaisemartin-D’Haultfoeulle (2020, 2024)
Goodman-Bacon (2021), Sun and Abraham (2021), Callaway-Sant’Anna (2021)]. In theworst case, the bias may be so severe that the TWFE estimates may have the signs thatare opposite to the true parameters. Several alternative estimators are proposed that canproperly address the problem. Among others, de Chaisemartin-D’Haultfoeulle (dCDH) andCallaway-Sant’Anna (CS) estimators are probably the most robust to misspeci…cations (andprobably the most widely used). The problem with the CS estimator, however, is that it does
not allow for time-varying covariates. In our context, air pollution concentration is highly
seasonal and is a¤ected by time-varying climatic conditions such as wind and temperature.
Hence, failing to control for the e¤ect of such time-varying confounds is likely to give us biased
estimates. Hence, to address this issue, we exploit the imputation approach suggested by by14

Gardner (2021) and Catenao et al. (2023): we can use pre-treatment observations to purge
out the e¤ect of (exogenous) time-varying confounders. Speci…cally, we take the following
steps:

1. Estimate the following using only pre-treatment data:
j
yctm
=

c+

0
tm + Xctm+

c (t) + ctm(2)

2. Estimate “residuals”using all data (incl. post-treatment):j^jctm = yctm0(^ c + ^ tm + Xctm
^ + ^c (t))

3. Apply the following dCDH/CS estimator to the estimated residuals, separately for each
subarea j and each density-cohort subsample s (not pooling all MSAs): For MSAs thatreceive treatment in g-th period, estimate the average treatment e¤ect on the treated(ATT) for -th period as5
AT T j;s (g; ) = E[^jc;^jc;g 1 jGg = 1]

E[^jc;^jc;g 1 jC = 1](3)

where Gg is an indicator of g-th timing group (i.e., equals 1 if MSAs receive treatment ing-th period for the …rst time and 0 otherwise), and C is an indicator of not-yet-treated
MSAs as of -th period (or never treated MSAs if such MSAs exist).

There are several reasons why this approach using steps 1-3 may yield more consistentestimates of ATTs than the TWFE regression (1). First and the foremost, the dCDH/CS
estimator removes the bias that arises from heterogeneous dynamic treatment e¤ects. The
primary reason for such a bias is that the TWFE regression uses ‘already treated’observations as e¤ective control units when estimating the impact of entry on ‘later treated’units.
Although our speci…cation mitigates this problem by including interactions with density cohort dummies, the dCDH/CS estimator removes the bias all together by speci…cally avoiding
use of such units. Second, by step 1, we remove the potential bias that may arise due totime-varying covariates. Time-varying covariates are known to produce biased estimates if
5This is a version of the CS estimator without covariate adjustment, which is essentially the same as thedCDH estimator in the absence of switchers. The original CS estimator adjusts weights for di¤erent units
using generalized propensity scores estimated on pre-treatment time-invariant covariates. As discussed in theprevious subsection, we instead use pre-treatment population density as a su¢ cient statistic and estimate
the dCDH/CS estimator separately on each density-quintile subsample.

15

they are correlated with treatment assignment. We avoid the bias by estimating the parameters using pre-treatment observations. Third, this approach explicitly makes use of theassumption A1, and estimate ATTs using only observations in the same density cohort.
Hence, our approach compares only ‘similar units’in terms of the pre-treatment population
density.6
One potential drawback of this estimator, however, is that we use only ‘relevant’ observations that provide valid comparisons. Thus, this estimator tends to produce larger
standard errors than the TWFE regression when there are a small number of comparable
units. Hence, we should keep in mind that there is a trade-o¤ between consistency ande¢ ciency in interpreting the estimates from the two estimators.
4. Data
A. Ride-hailing Entry: Given our goal of making the counterfactual comparison discussed in Section 2, the chronology of the ride-hailing industry makes it di¢ cult to de…ne‘entry’ of ride-hailing service in a city for a number of reasons. First, earlier studies use
reported Uber/Lyft entry dates, making use of information drawn from local newspapers,o¢ cial blogs/websites, and social networking services. These reported dates are often incomplete and sometimes inaccurate. Second, Uber and Lyft enter each city in di¤erent timings,
with varying levels of market presence. Yet, presence of either of the two companies may besu¢ cient to induce changes in commuting patterns. Third, the o¢ cial dates of entry may be
an imprecise measure of a ride service’s penetration into the city’s market. We need a measure of de facto entry that embodies the su¢ cient market penetration. This is particularly
important in our study context. Changes in transport choice would not have occurred on alarge scale, say, in San Francisco immediately after Uber’s entry in 2010 or UberX’s entry in
2013. De…ning this date as ride-hailing entry would falsely refute critics’argument because
Uber’s entry probably had no immediate e¤ect on ambient air pollution almost by de…nition.
Put di¤erently, we should see the negative e¤ect of ride-hailing (if critics are right) only after
ride-hailing su¢ ciently penetrated the city’s taxi market.
To overcome these challenges, we use Google Trends data to construct a measure of ridehailing entry, building upon Hall et al. (2018) argument that normalized Google Trends
closely coincide with monthly trends in active Uber drivers in their sample of U.S. cities.
Figure 2 reinforces Hall et al.’s as well as our arguments. In Figure 2-(a), we plot normalized Google Trends indices for Uber and Lyft in San Francisco. Search activities were the6

We, however, do not use a generalized propensity score to condition on other pre-treatment covariates
X unlike the original CS estimator. As discussed above, matching on pre-treatment covariates was infeasible
due to the small sample size with little variation in entry timing.

16

highest in 2017 in San Francisco for both Uber and Lyft. Relative to the level we observein 2017, search activities were close to zero during the …rst few years of ride-hailing entry(2010-2012). In particular, search activity for Uber only begins to rise after UberX is o¢ cially announced in 2013. We plot the same indices for Austin, Texas in Figure 2-(b), which
provides a strong case for our argument. In May 2016, both companies o¢ cially announced
their exit from Austin after the city’s voters supported …ngerprint checks for their drivers.
But, they returned a year later after the state passed the bill that requires the minimum
background checks but no …ngerprint checks for their drivers (L.A. Times, May 29, 2017).
The …gure con…rms that both Uber and Lyft search activities rose sharply at the same timeafter their entry, but collapsed immediately after both companies announced their exit upon
the …ngerprint ruling in May 2016, and returned to the pre-2016 level after the passing of
the state bill that overturned the …ngerprint rule. Furthermore, although we do not reporton other cities, we observe all sorts of patterns in these indices: Search activities exist for
both Uber and Lyft in some cities, only one in some, and none in others; Search activity
for Uber precedes Lyft in some cities and vice versa in other cities. For these reasons, we
believe Google Trends indices for Uber and Lyft in each city are good proxies for their market
presence in that city.
Given the above, we construct a uni…ed measure of ride-hailing entry based on these
normalized Google Trends indices as follows. For each MSA in our sample, we obtain search
trend indices from January 2010 to December 2018, using "Uber" and "Lyft" as keyword
entries.7 We then de…ne entry if the maximum of the two indices exceeds a certain threshold.
That is, for each MSA i and for each month t,nt
Entryit = I max(T rendsUit ber ; T rendsLyf
)
it

o

c :

For all subsequent analyses, we use the cuto¤ value of c = 0:2. We tested several values
for the cuto¤, but eventually chose the cuto¤ value of 0:2 to match the ideal of our quasiexperimental design. That is, the cuto¤ value must be such that below the cuto¤, there isvirtually no Uber/Lyft activity and above the cuto¤, Uber/Lyft activity jumps and continues
to grow thereafter. Put di¤erently, we avoid the cuto¤ values that would generate similar
Uber/Lyft activity levels in both sides of the cuto¤.
Another problem is that even in cities where no ride-hailing exists, residents may stillsearch for Uber or Lyft for other purposes. Because the indices are normalized against thehighest month of search trends for each MSA, even low search frequencies can result in alarge search index. This may result in the false signal for ride-hailing entry. We correct for
7

Google Trends are calculated for digital marketing areas (DMAs). Hence, we use 2009 MSA boundaries
to convert DMA boundaries to MSA boundaries.

17

such anomaly in the following way. First, for each MSA, we calculate the maximum searchindex observed before December 2012 — i.e., before any entry would have occurred for mostcities. We then subtract this pre-treatment maximum from the original index values. This
normalization ensures that the adjusted index values exceed zeros only if their original values
exceed the pre-treatment maximum. Second, we hire a graduate student to manually searchfor …ctitious rides for Uber and Lyft on randomly chosen locations in each city using thesmartphone’s google map application. If we don’t …nd any UberX or Lyft service for the…ctitious ride requests, we classify that city as having ‘no service’ as of 2020. We thenvisually check each city individually and con…rm that for no-entry MSAs, the normalized
trend indices never cross the threshold whereas for entry MSAs, the indices continue to growover time and stays above the threshold once they cross it.
Panel (a) of Figure 3 compares the distribution of our de facto entry dates constructedfrom this measure against that of (i) reported entry dates of UberX, taken from Hall et al.
(2018) and (ii) o¢ cial UberX launch dates obtained from Uber Inc.8 We see that bothreported entry and o¢ cial entry precede de facto entry for some cities while a much larger
number of MSAs are recorded with ride-hailing entry in our de facto entry measure. The
di¤erences also come from the fact that we account for Lyft’s activity. Thus, readers should
be cautious in comparing our study with earlier studies because we use a di¤erent measureof ride-hailing entry (and a di¤erent identi…cation strategy as a consequence).
Panel (b) of Figure 3 compares the event-study estimates of the e¤ects of entry on
the normalized Google Trend Index, using the three entry measures.9 There are several
important take-away messages from the …gure. First, the estimated impacts of reported
entry and o¢ cial entry are quite small over the two-year window after entry, and evensmaller than the cuto¤ value (c = 0:2) used to determine the de facto entry. Thus, if we
use reported entry as the treatment variable, we would only get at the average treatmente¤ect from this small activity level of Uber/Lyft. Second, there is a discontinuous jump in
the estimated impact at the de facto entry timing, suggesting that the Google Trend Index
increases rather abruptly around the cuto¤ value. We take this as a sign that our choice of thecuto¤ value is well calibrated to mimic the quasi-experimental design. Third, the estimatedimpacts of de fact entry gradually increase over the two-year window. This implies that wewould expect the treatment e¤ect of Uber/Lyft entry to be dynamic, gradually increasingover time. Fourth, there is no sign of violation of the no-anticipation assumption even if
we use the de facto entry as our treatment variable — the estimated impacts during the8

Our special thanks go to Michiko Numazu, a data scientist at Uber Inc. who kindly provided the datafor us.
9
We use the dCDH/CS estimator without covariates.

18

pre-entry periods are nearly zero and are precise.
B. Ambient Air Quality: Our second data source is the satellite-based nitrogen oxides(NO2 ) data from the National Aeronautics and Space Administration (NASA)’s Goddard
Earth Sciences (GES) Data and Information Services Center (DISC). GES-DISC provides
level-3 daily total column NO2 in molecules per cm2 on the 0.25 0.25 degree global grids.
We focus on ambient NO2 pollution not only because it is an important transport-related
air pollutant but also because we wish to minimize the risk of falsely capturing the e¤ects
of other confounders on air pollution. NO2 is a leading cause of respiratory diseases such asasthma, is a known precursor to ozone (O3 ), and is often used as the indicator for the larger
group of nitrogen oxides (NOx ). Because NO2 is released into the air from combustion offossil fuels, the primary sources of NO2 emissions are cars, trucks and buses, power plants,
and o¤-road industrial equipment. According to the U.S. Environmental Protection Agency(EPA) National Emissions Inventory (NEI), road transportation accounts for roughly 36%
of total NO2 emissions, but accounts for only 2-4% of PM2:5 , PM10 , and VOC emissions in
the United States. The share of road transportation gets even higher in urbanized areas. For
example, road transportation accounts for 48% in Chicago. Therefore, if there is any e¤ectof ride-hailing on transport-related air pollution, we should expect to see it most vividly inNO2 concentration levels.10
We rely on the satellite-based air quality data for its coverage, granularity as well as
temporal consistency. AirData from U.S. EPA provide daily air pollution data from monitoring stations at the ground level for all …ve criteria pollutants as well as climate datasuch as temperatures and wind speeds for all monitoring stations in the U.S. However, themonitoring stations are only sparsely located across U.S. cities. The monitoring data arealso sparsely monitored over time, with missing records for some months and with monitoring stations frequently added and discontinued. As a result, ambient air pollution data
are consistently available over time only for 69 MSAs (18.8% of the full MSA sample). Our
main analysis would thus have to be restricted to this subset of MSAs if we rely on monitoring data. Furthermore, recent studies …nd that air quality data from monitoring sites
may be systematically biased due to strategic compliance behavior, either by local authority(Grainger et al., 2021) or by polluters (Zou, 2021).
For illustration, Figure 4 plots our satellite-based NO2 grid data along with monitoring site locations for three example cities in the U.S. The NO2 data are grid-level monthly
averages for January, 2014. The map also shows the MSA boundary as well as the Census10

Previous studies have also used carbon monoxide (CO) and aerosol optical depth (AOD) for similarreasons. But we chose not to do so because we only have access to the satellite-based CO and AOD data onmuch less granular level (0.5 0.5 grided cells or larger).

19

tract-level population density. The monitoring sites are located mostly around the city
centers, but only sparsely. As a result, the monitoring sites do not necessarily coincide withhigh-pollution areas. Furthermore, the map shows that the high-pollution incidence can go
beyond the central urban areas to suburban or non-urban areas. This occurs partly due towind direction but also due to suburb-to-urban or suburb-to-suburb commuting. Our argument outlined in Section 2 suggests that ride-hailing entry may have di¤erent air-quality
impacts for di¤erent subareas of cities, say, urban versus non-urban areas. Another bene…tof using the satellite-based data is that it allows us to calculate (area-weighted) monthly
means and maximums for urban, suburban, and non-urban areas within each MSA. We use
the 2010 Census de…nition of urban-area boundaries for all study periods. To validate thesatellite-based data, we regress these satellite-based means and maximums on EPA’s monitoring data. Consistent with earlier studies, the satellite-based data are highly signi…cantly
correlated with the monitoring data, with the urban-area statistics having the most predictive power. See also Figure A3 in the Appendix, which plots the satellite-based NO2 data
against the EPA monitoring data using MSA-level monthly averages.
C. Commuting Patterns and Other Variables: We also use yearly household-levelcommuting mode data in order to explore the economic mechanism behind our main resultson NO2 concentration levels. To do so, we compile all micro…les from the U.S. American
Community Survey (ACS) to construct repeated cross-section data. We only have panel
structures at the MSA level since the micro…les do not allow us to trace out household
identi…ers over years to construct panels at the household level. We also use the ACS datato construct a variety of pre-treatment covariates such as the median age, the median income,the share of manufacturing employment, and the share of college graduates.
We also supplement our data from a few other sources. We obtain monthly temperature
and wind data from the EPA’s Air Data, monthly regional gasoline price data from North
American Electric Reliability Corporation (NERC), MSA-level population density from U.S.
Census Bureau, and historical time series of county-level nonattainment status for …ve criteria
pollutants from EPA’s Greenbook website. We use these to construct time-varying control
variables.
As discussed in Section 3, the parallel-trend assumption may be violated if there exist
confounding factors that would systematically a¤ect the trends in NO2 concentrations thatare also correlated with the location and timing of Uber/Lyft entry. One plausible confounderis the local compliance e¤orts for the National Ambient Air Quality Standards (NAAQS).
The 1990 Clean Air Act Amendments requires the EPA to set the NAAQS for …ve criteria
pollutants. Under the CAAA, if a county is designated as "nonattainment" of the standards,
then states were mandated to regulate plant-level sources of these pollutants. Although the20

NAAQS stayed the same for NO2 , major changes to the NAAQS occurred in 1997, 2006,
and 2012 for PM2.5 and in 1997, 2008, and 2015 for O3 . Because NO2 is a known precursor
to both pollutants, the nonattainment status for either pollutant may a¤ect the time trends
for NO2 concentrations at the MSA level. Thus, we control for these in our regression.11
D. Descriptive Statistics and Treatment-Control Structures: Table 1 provides
a statistical overview of main outcome and pre-treatment variables by treatment status. The
earliest de facto entry occurs in 2013 whereas the majority of entry occurs in 2014. Roughly
18% of the MSAs in our sample do not show su¢ cient Uber/Lyft activity to be classi…ed asde facto entry. The table con…rms that entry is correlated with MSA-level socioeconomic
covariates: Uber/Lyft enter cities with higher population density, higher median income,higher share of college graduates, and higher share of public transit commuters earlier thanothers. Furthermore, there is an indication that cities with early Uber/Lyft entry may be
more polluted, as measured in EPA monitoring data. However, the use of satellite-based NO2
data reveals that this may be due to systematic bias in monitoring data. EPA monitoringdata are available for about 22-27% of observations for cities where Uber/Lyft enter before
2015 whereas only 5-8% are available for cities where Uber/Lyft entry never occurs or occurs
after 2015. If we use the satellite-based data, di¤erences in NO2 concentrations across cities
by entry status become less obvious.

5. Estimation Results
5.A. TWFE Estimates
We start by presenting the estimation results from the TWFE regression of eq. (1).12 We
use Sergio Correia’s Reghdfe package in Stata to e¢ ciently absorb multi-way …xed e¤ects.
Table 2-A presents the results using (the area-weighted means of) monthly means (in logged
values) as the outcome whereas Table 2-B uses (the area-weighted means of) monthly
11

There is a subtle discussion as to whether the nonattainment statuses for these pollutants are ‘bad
controls’in our context. On one hand, the local authorities with nonattainment status for PM2.5 or O3 may
take a variety of compliance measures, some of which may a¤ect the local emissions sources of NO2 directly
or indirectly. Hence, the failure to control for them is likely to overstate the estimated impact of ride-hailingentry if the nonattainment status coincides with the entry timing/location. On the other hand, ride-hailing’s
impact on ambient NO2 may also a¤ect ambient levels of PM2.5 or O3 , which in turn may trigger changes
in the nonattainment statuses for these pollutants. If this were the case, the inclusion of these controls maylead to the overstatement of the entry’s impact. As we discussed above, transport-related emissions accountfor only 2-4% of PM and VOC pollutants. Hence, the latter e¤ect must be quite small even if it exists. Ourestimation results seem to con…rm this point — the estimated impacts of entry on ambient NO2 are smallerwhen we include these controls.
12
We use Sergio Correia’s reghdfe package in Stata to e¢ ciently absorb two-way …xed e¤ects.

21

maximums (in logged values). Each table shows the results from three regressions, withvarying sets of controls, for each of the subareas (urban, suburban, non-urban). The …rstcolumn controls for MSA/year/month …xed e¤ects, the second controls for climate conditions,and the third includes other time-varying covariates such as MSA-speci…c linear time trend,
regional gasoline price, and nonattainment status for O3 and PM2:5 . Inclusion of endogenous
time-varying controls can potentially bias our estimates since they can be correlated withunobservables in the estimating equation. Hence, we avoid use of such controls. Standard
errors are clustered at the MSA level.
In both tables, there is a tendency that the estimated impacts tend towards positive
values with more controls, suggesting that our estimates may be biased towards negativevalues. Hence, we focus on the results with full controls. In Table 2-A, we see that de facto
ride-hailing entry is estimated to decrease ambient concentration levels of NO2 for cities
in the highest population density quartile. The estimates are statistically signi…cant, andrange from -0.032 log points (in non-urban areas) to -0.034 log points (in urban areas). If
we use a linear transformation evaluated at the mean concentration levels, these estimates
imply that ride-hailing entry reduces NO2 concentrations by 0.50-0.53 ppb for these cities.
Interestingly, the estimates get smaller in magnitude for less densely populated cities, andeventually turn positive for cities in the lowest density quartile. The results are consistent
with our prediction in Section 2 — In cities with su¢ cient public transit networks, acombined use of ride-hailing with public transit can displace use of private cars, reducing
overall vehicle emissions, but in cities without such public transit networks, ride-hailing may
simply induce more driving, increasing overall vehicle emissions.
Next, we turn to the results on monthly maximums in Table 2-B. The results are generally consistent with those of Table 2-A. That is, the estimates are negative and statisticallysigni…cant for cities in the highest density quartiles; the estimates get smaller for cities in
the lower density cohorts, and turn positive for non-urban areas in the lowest density cities.
There is one important di¤erence, however. The estimated impacts are larger, more statistically signi…cant, and range from -0.049 log points (in suburban areas) to -0.050 log points(in urban areas). From these, we infer that ride-hailing entry mostly a¤ects the peaks oftransport-related air pollution. That is, it tends to reduce transport-related air pollution
on days when pollution levels are high (i.e., when heavy vehicle tra¢ c are expected). This
also explains the relatively large estimates on the mean concentration levels because themonthly means are highly sensitive to the maximum concentration levels. These results arealso consistent with Kim and Sarmiento (2021), who …nds that much of the air qualitydCDH
improvement comes from a decline in the number of bad air quality days in summer.

22

5.B. Event Study Estimates

Next, we present the event study estimates from the dCDH/CS estimator.13 As explainedin Section 3, we implement the dCDH estimator separately for each density quartile (rather
than pooling all observations) after purging out the in‡uence of exogenous time-varying
covariates. Hence, the identi…cation is much clearer here. By construction, this estimatoruses only ‘not-yet-treated’ MSAs for each treatment timing within the same population
density cohort as comparison units. As a result, the estimates are not contaminated fromuse of either already treated MSAs or other non-similar MSAs in di¤erent density cohorts.
Figure 5 plots the estimates of the ATTs in relative time to entry ( = 0). The …rst
two graphs on the top panel plot the estimates pooling all MSAs. The other graphs plot
the estimates using a subsample of MSAs on each population density quartile. To avoid
busy graphs, we only present the results using monthly maximum NO2 except for the …rst
graph on the pooled sample. For the highest density cohort, eventually all units get treated,and hence, we are able to estimate the ATTs only within the two-year window. Thus, forconsistency, we use only not-yet-treated units as control units and estimate the ATTs over
the two-year window for all density cohorts.
We see that in line with the results from the TWFE regression, on the pooled sample aswell as on the highest density cohort, NO2 concentrations start to decline after ride-hailingentry (relative to not-yet-treated units), and the magnitudes of the decline get larger overtime. This is also consistent with Figure 2, which shows that Uber/Lyft activity grows
over time. On the other hand, we do not see any sign of either a rise or decline in NO2
concentrations (relative to not-yet-treated units) for the medium- and low-density cohorts.
The graphs also indicate that there is no sign of violation of parallel-trend assumption during
the pre-treatment period. There results boost our con…dence in the estimated impacts ofride-hailing entry.

6. Exploring Economic Mechanism

So far, our results are consistent with the economic mechanism outlined in Section
2. That is, while ride-hailing may increase congestion and vehicle emissions by inducing
deadheading or displacing of mass transit for parts of daily trips, it may still decrease overall
13
We use multiplegt in Stata to implement the dCDH estimator. The CS estimator (using csdid package
in Stata) produces essentially the same results, but was far slower in our computing environment.

23

air pollution if a combined use of ride-hailing with mass transit displaces private car use
more than such adverse behavior. Thus, transport-related air pollution may decline in cities
with high public transit density where the complementarity between ride-hailing and public
transit is strong. In this subsection, we explore whether such an economic mechanism indeed
exists behind our results.
To do so, we draw household-level data from the American Community Survey (ACS)
on workers’ commuting mode choice. We construct an indicator Iijt of each household i’scommuting mode j to work in year t, and run the TWFE regressions similar to eq. (1), with
Iijt as outcome variables. We run the regression separately for each commuting mode j. We
do not impose any structural restriction on the parameters across these regressions. Because
we only have access to PUMA data (so our geographic identi…ers are county and city ofresidence), we only have panel structures at the MSA level. Hence, we include householdlevel demographic controls such as education, income, and race.
In the ACS survey, respondents are asked to record only one commuting mode they used
to get to work during the week before the survey, and if they use more than one method,they are asked to record one that is used for most of the distance. The ACS’s choice set
includes 12 methods of transportation. For ease of interpretation, we consolidate these into
three primary commuting modes for the …rst set of regressions: commuting by private car(Ic ), commuting by public transit (Ipt ), and commuting by other modes (Io ). These threemodes account for 99.1% of all commuting mode choices in our sample (85.6%, 4.8%, 8.7%,
respectively for Ic , Ipt , and Io ). The remaining 1% of the sample commutes by taxicab,
motorcycle, or bicycle.14 In the second set of regressions, we disaggregate ‘other modes’
further into three modes: commuting by walking (Iwalk ), working at home (Ihome ), and other(Iother ). Thus, Io = Iwalk + Ihome + Iother by de…nition.
The di¢ culty we have is that none of the transport modes in the ACS survey directly
captures the use of ride-hailing services. Our argument here is that Ipt + Io is a good proxy
for the use of ride-sharing, Ipt + Io
Irs , for several reasons. The starting point for ourargument is that the ACS survey format does not allow respondents to record the combined
use of several transport methods, and hence, some of the actual daily transport choices arelikely to be absorbed into one of the chosen transport modes. On one hand, commuters
using ride-hailing mostly to connect to public transit stations are likely to appear as anincrease in Ipt whereas commuters using ride-hailing mostly for daily errands would show
up as an increase in Iwalk . On the other hand, only those who use ride-hailing as a primary
14

There is a possibility that Uber/Lyft may be synonymized with conventional taxi, and the commuters
using Uber/Lyft to work may record ‘taxi’instead of ‘other’as their primary commuting mode. Hence, we
also estimated the same regressions in Table 3 and 7 using Io = Iwalk + Ihome + Itaxi + Iother instead, but
the results were quite similar.

24

commuting mode (e.g., car-pooling or using ride-hailing directly to the employment center)would appear in Iother . This line of reasoning suggests that much of the increase in the useof ride-hailing might be unobserved by nature. However, there must be an increase in the
supply of ride-hailing to accommodate such an unobserved increase in the use of ride-hailing(i.e., associated with increases in Ipt , Iwalk , or Iother ). We would expect this would show up
as an increase in Ihome . Admittedly, this is an important limitation to the use of the ACS
data for our study purpose, and hence, we refrain from making strong inferences from the
ACS data.
With this limitation in mind, we estimate the impact of ride-hailing entry on commuting
mode indicators Iijt and see if the estimates are consistent with the economic mechanism
we discussed in Section 2. Per our discussion, we expect vehicle emissions to increase if
commuting by public transit declines (Ipt #) and commuting by ride-hailing increases (Io ")while commuting by private car stays roughly constant ( Ic 0). This is the case of ridehailing being a substitute for public transit. In contrast, we expect vehicle emissions todecrease if both commuting by public transit and by ride-hailing increase (Ipt " and Io ")while commuting by private car declines (Ic #). This is the case of ride-hailing and public
transit being complements to each other, and a joint substitute for commuting by private car.
An ambiguous case occurs when commuting by private car declines (Ic #) and commuting by
ride-hailing increases (Io ") while commuting by public transit declines (Ipt #). There are,of course, other plausible explanations, given the limitation of our measurement on Iijt . We
discuss this point below when discussing the results.
Table 3-A reports the results of the TWFE regressions on the …rst set of outcomes (Ic ,Ipt , and Io ). In all regressions, we include the MSA and year …xed e¤ects as well as householdlevel demographic controls. For each outcome, the second column includes weather controls
whereas the third column also includes other time-varying controls such as gasoline priceand nonattainment status. Table 3-B repeats the same, but use the sub-categories of Io as
outcomes (Iwalk , Ihome , and Iother ). As with our main results, there is an indication that theestimates get smaller in magnitude as we include more controls. Hence, to be conservative,
we focus on results with full controls.
The results in Table 3-A are not only consistent with our main results in Table 2
but also with the economic mechanism discussed above. In the highest density MSAs, theride-hailing entry is estimated to decrease the share of commuting by private car by 1.3 ppt,
increase that of public transit by 0.4 ppt, and increase that of other modes by 0.7 ppt. The
estimated impacts roughly sum to zero, and hence, the estimated impacts are consistent
with each other (despite that we make no structural restriction on them). As discussed
above, the estimates for the highest density MSAs correspond to the case of ride-hailing25

and public transit being complements to each other while being a substitute for private car
commuting. Hence, these estimates are also consistent with the pollution-decreasing e¤ectof ride-hailing entry for these MSAs in Table 2. Furthermore, in the third-quartile MSAs,
the estimates are signi…cantly negative for private car commuting, insigni…cant for public
transit commuting, and signi…cantly positive for other modes. This corresponds to the caseof ambiguous pollution impacts. For the second quartile MSAs, we essentially see no impactof ride-hailing on commuting modes. For the lowest density quartile, we see that ride-hailingentry is associated with increases in private car commuting. All of these are indeed consistent
with the signs of the e¤ect of ride-hailing entry reported in Table 2.
Although the results so far are consistent with our expectation, turning to the resultsin Table 3-B reveals something we did not expect a priori. In the highest density MSAs,
ride-hailing entry is signi…cantly associated with increases in commuting by walking (Iwalk )and working at home (Ihome ), but has no e¤ect on the other mode (Iother ). As discussed
above, this may imply that much of the use of ride-hailing is absorbed into the combined
use of ride-hailing with either public transit or walking. We also interpret the increase in theshare of those working at home as the sign of the associated increase in ride-service providers.
We, however, found it somewhat puzzling to see there is no impact on the other commuting
mode. A priori, we expected Iother to be a good ‘direct’indicator of the use of ride-sharing
service, and hence, we expected its share to increase. But, the result suggests that this was
not the case, and instead, the ride-hailing entry did not change the share of those who use
ride-sharing or carpooling as the primary commuting mode. Taken together, these results
seem to suggest that commuters are using ride-hailing service mostly as a complementarymeans of commuting rather than the primary commuting mode.
Interestingly, in the third quartile, virtually all of the increase in the share of other modes
is explained by the increase in the share of those working at home, and the magnitude of the
impact (+0.5 ppt) is roughly the same as that of the reduction in the share of commuting
by private car (-0.4 ppt). On one hand, these two e¤ects tend to cancel out each other interms of their impact on mean NO2 concentrations in these MSAs because the shares of theother commuting modes did not change. On the other hand, these e¤ects tend to decreasemaximum NO2 concentrations in these MSAs because private car commuters decline while
commuters using ride-hailing service as the primary mode do not change — ride-hailingservice providers are mostly o¤ering services to those who use the services as complements
to their primary modes of travel. Hence, these results are consistent with …ndings from Hallet al. (2018) and Kim and Sarmiento (2021).

7. Alternative Identi…cation Strategies
26

The results so far rely on the conditional parallel trend assumption A1 to hold on each
population density cohort. Because we include a MSA-speci…c linear trend, this identifying
assumption would be valid as long as there are no unobservable shocks that happen to occur
at the same timing as the ride-hailing entry that are correlated with the outcomes in thesame cohort of MSAs. Here, one disadvantage of our empirical strategy arises from the factthat we construct our de facto entry measure from the Google Trends Indices. The Google
Trends Indices are the equilibrium outcomes, rather than the measures of exogenous changes
in supply of ride-hailing service (Hall et al., 2018). Suppose, for example, highly dense cities
consist of a larger share of residents who have higher demand for more eco-friendly meansof transportation. The changes in their behavior over time may be correlated with changes
in demand for ride-hailing services. In such a case, the estimated impacts in the previous
section get at the combined e¤ect of their demand for eco-friendly transportation and thatfor ride-hailing. To address this issue, we take two alternative approaches. The …rst is
to exploit quasi-experimental settings in Texas, and the other is to apply the instrumental
variable (IV) approach using geography-based instruments.

7.A. Quasi-experimental Setup in Texas

In this approach, we restrict our study sample only to twenty-four MSAs in Texas. There
are several advantages for doing so. First and foremost, Austin, the state capital of Texas,
voted for the …ngerprint rule (FR) in 2016, which would mandate the …ngerprint check for
all ride-hailing drivers. In response to the rule, Uber and Lyft halted all their services inMay, 2016. A year later, both companies resumed the services at the end of May, 2017 after
the state passed the bill that removes the requirement for …ngerprint checks for their drivers.
As indicated in Figure 2-(b), this incident caused a sharp, unanticipated decline and asubsequent rise in the supply of Uber/Lyft services. Importantly, this incidence occurred
only in Austin, but no other neighboring MSAs. There is one caution here — a numberof small ride-hailing companies, such as GetMe and RideAustin, operate in Austin during
this period. Therefore, Uber and Lyft customers may have switched to their service. Hence,
we take the FR as an event that caused an exogenous change in the supply of ride-hailingservice, but not the complete exit. Second, de facto entry dates mostly correspond toreported entry dates in Texas. Therefore, the treatment assignment is unlikely to be driven
by unobservable demand shocks that are correlated with the outcomes. This minimizes
27the risk of identi…cation failures we discussed above. Third, there is su¢ cient variation intreatment status. Out of twenty-four MSAs, entry occurs in fourteen MSAs in 2014 and in
two MSAs in 2016. We do not observe ride-hailing activity for the remaining eight MSAs.
The urban areas of these MSAs are su¢ ciently far apart from each other (see Figure A4).
Therefore, focusing on Texas allows us to control for state-level confounds while exploiting
variation in timing and location of ride-hailing entry.
There are also disadvantages, however. First, the small sample size precludes the use ofcertain estimation strategies for reliable statistical inference. In particular, we cannot reliably
use the dCDH/CS estimator because there are not enough ‘relevant’MSAs for each treatment
timing for each density cohort. Second, air pollution concentration data are highly volatile,
with large variation remaining even after controlling for many observables. This also prevents
us from using certain (otherwise credible) approaches such as regression discontinuity (RD).
For example, Tarduno (2021) also uses Austin’s FR as a natural experiment and estimates
the di¤erence-in-di¤erences RD regression, comparing hourly tra¢ c speeds before and afterMay 9th, 2016 against those of 2015. Unfortunately, as we shall see below, this approach
fails to deliver credible statistical inference in our case.
Given these pros and cons, we employ two estimation strategies. Our …rst strategy
is to apply a version of the synthetic control method (SCM) à la Abadie et al. (2010)
to visually present the impacts of both the ride-hailing entry and the FR relative to thecounterfactual trend. We implement this strategy as follows. We …rst estimate the auxiliary
regression (2) using only the pre-treatment observations and predict the residuals for allobservations on the Texas sample. We then apply the SCM using lags up to 12 months
as covariates. By construction, this allows us to use all available information to construct
the counterfactual Austin’s residualized NO2 trend. Figure 6 plots the synthetic Austin’s
NO2 residuals (squares in maroon) against the actual Austin’s NO2 residuals (dots in navy).
To visualize the impacts, we also plot the local polynomial smoothing of these residuals,
respectively as dashed and solid lines. Figure 6-A uses monthly means whereas Figure
6-B uses monthly maximums. The …gure indicates that both mean and maximum NO2
concentrations have an upward trend before the ride-hailing entry even after controlling for
observable confounds, but switch to a declining trend after the entry. The declining trend
continues during the FR period, and then returns to the pre-FR level after the FR period.
Importantly, we see these trends relative to the counterfactual NO2 trends, not just relative
to the pre-treatment period. The …gure also illustrates why the RD design is unlikely towork in our case — we see unusually high (non-seasonal) NO2 concentration levels to theright of both the ride-hailing entry and the FR cuto¤s, even after controlling for year/month…xed e¤ects.
28

Our second approach is to estimate the TWFE regression in (1) on Texas MSAs while
adding another FR dummy, which equals 1 for Austin during the FR period. This approach
does not take full advantage of the sharp identi…cation due to the FR, but allows us to
explore its impact on the commuting mode choices. Table 4 presents the results on NO2
concentrations. To make the results comparable to Table 2, we use the same population
density quartiles as in Table 2. The results are somewhat similar to those of the fullsample reported in Table 2, with notable exceptions. The estimated impacts of ride-hailingentry are negative and statistically signi…cant for MSAs with higher population density,
both on monthly means and monthly maximums. As in Table 2, the estimated impacts
are generally larger on monthly maximums than on monthly means. Unlike in Table 2,
however, the estimated impacts on the second quartile MSAs are positive and statisticallysigni…cant when monthly maximums are used. Interestingly, the …ngerprint rule is estimatedto decrease NO2 concentrations, and the estimated impacts are large. Note that the FR
dummy is de…ned as the Austin dummy times the FR period dummy. Hence, the impacts
are estimated relative to no-entry status. Because Austin belongs to the highest population
density quartile, we obtain the pure impacts of the FR by subtracting the estimates for thatquartile: 0.051-0.056 log point decrease in mean NO2 concentrations. This result may seempuzzling at …rst because it implies that both the increase (i.e., ride-hailing entry) and thedecrease (i.e., FR) in the supply of ride-hailing service reduce transport-related air pollution.
However, exploring the economic mechanism behind the results, we …nd that they are indeed
consistent, not only with our argument, but also with each other.
Table 5 reports the results of our supplementary TWFE regressions using yearly householdlevel commuting modes as outcomes. The same explanations as in Section 6 apply to all
regressions in Table 5, except that the regressions are run on the Texas sample along withthe additional FR dummy. The results suggest that the FR is estimated to decrease private
car commuting by 1.1 ppt, increase public transit commuting by 0.5 ppt, and increase othercommuting modes by 0.6 ppt. These changes are also associated with a 1.2-ppt increasein the share of those working at home, a 0.4-ppt decrease in commuting by walking, and a0.2-ppt decrease in the other mode. By subtracting the estimates for the highest population
density quintile, we arrive at the pure impacts of the FR in Austin (relative to the pre-FR
period): -0.5 ppt in private car commute, +0.6 ppt in public transit commute, -0.6 ppt inwalking to work, +0.5 ppt in working at home, and -0.4 ppt in the other mode. Thus, weconclude that the sharp decline in the supply of ride-hailing service due to the FR further decreased commuting by private car while encouraging the use of public transit, and that thesechanges in the commuting patterns decreased transport-related air pollution. Although theexact economic mechanism is still ambiguous, the results are consistent with our argument29

in that the impact of ride-hailing depends on how it changes the overall commuting patterns.
7.B. Instrumental Variable Approach
Our last identi…cation strategy is to apply an instrumental variable (IV) approach to
the DD regression (1), building upon recent advances in empirical research on economic
geography. To arrive at credible instruments, we distinguish two sources of endogeneity.
Our entry variable Dc is an indicator of when and where our normalized Google Trends
Index crosses the threshold. Because the frequency of the keyword searches is an equilibriumoutcome, both the level and the timing of treatment is endogenous and may coincide withunobservables that a¤ect air quality. Therefore, we need instruments that predict both thelocation and the timing of ride-hailing service’s market penetration, yet do not a¤ect ambient
air pollution directly after controlling for pre-treatment covariates.
To arrive at our instruments, we …rst start by constructing three types of geographybased instruments [see Redding and Turner (2015) for a more thorough discussion of these
instruments]. The …rst is based on the highway construction plan as of 1947 developed underthe mandate to serve military services. This IV is known as the "planned route IV" and isused in Baum-Snow (2007) and Duranton and Turner (2011; 2012). The second is based on
the railroad network as of 1870. This IV is known as the "historical route IV" and is used in
Duranton and Turner (2011; 2012). The third is based on the Euclidean spanning network
connecting large cities as of 1860. This is known as the "inconsequential unit IV" and isused in Faber (2014). All three variables are the MSA-level indicators, each of which equals
1 if the route passes through the MSA.
Although these IVs exploit conceptually quite di¤erent quasi-random variations, they areoften used in conjunction with each other and generate quantitatively similar variations inpractice (Redding and Turner, 2015). In essence, these IVs are designed to construct ‘hypothetical’routes for a given geography, which predict the observed routes, but are presumably
uncorrelated with contemporaneous economic shocks after controlling for the current economic conditions. These geography-based instruments are known to work well for estimating
the causal e¤ect of road network on economic outcomes. We tailor this line of argument forour purpose: the ‘hypothetical routes’are a good predictor of the current population size,
which is a good predictor of ride-haling entry/activity, but do not a¤ect the current air quality except through the ride-hailing entry, after controlling for the current (pre-treatment)
economic conditions. Hence, these variables serve as a set of good instruments for the locationof ride-hailing service’s market penetration. Details of how we construct these instruments
are explained in the Appendix F.
30

The geography-based instruments, however, give us only cross-sectional variation, andthus, are a poor predictor of the timing of the market penetration. For this, we use reported
entry dates of UberX and Lyft from Hall et al. (2018), which are collected from newspapers,
blog posts, and web entries, and hence, are plausibly considered o¢ cial entry dates of these
services. Speci…cally, we construct our IVs as follows. For each geography-based instrumentk, Zck for city c and time is:Zck = Rc
Gkc :On one hand, because ride-hailing service’s market penetration into a city occurs only after
the service o¢ cially enters the city, de facto entry dates are monotonic in reported ride-hailingentry dates (DcRc ). On the other hand, as Hall et al. or Berger et al. (2018) argue,
the timing of reported ride-hailing entry is exogenous after controlling for pre-treatment
covariates such as the current population size. Identi…cation is strong because we interact
reported entry with geography-based instruments — we only use the timing of reported
entry to predict the timing of de facto entry whereas using hypothetical routes to predictthe location of de facto entry.
For implementation, we apply the two-stage least squares (2SLS) estimator on the DD
regression in eq. (1), separately on each population density cohort (i.e., not pooling all
MSAs). We do this to ensure we can interpret our estimates as the local average treatmente¤ect (LATE). For the DD-IV estimates to be the valid LATE, we require the following
identifying assumptions (Du‡o, 2001; Hudson et al., 2017):
A2. For MSAs in the same population density cohort s,
1. Relevance and monotonicity: E[Dc jZc = 1]

E[Dc jZc = 0].

2. Exclusion restriction: Zc a¤ects Yc only through Dc .
3. Conditional parallel trends in Yc on Zc : Conditional on exogenous (time-varying)covariates c , parallel trends in unobservables hold for all MSAs in the same population density cohort s.
E[ c;

c;

1 jZc

= 1;

c ] = E[ c;

c;

1 jZc

= 0;

c ];

8 c 2 s; 8 s 2 S:

Table 6 reports the DD-IV estimation results on mean NO2 concentrations, using all
three geography-based instruments interacted with the reported ride-hailing entry dummy.
We use the same set of controls and the standard errors are clustered at the MSA level as in
Table 2. Our DD-IV estimates are robust to varying sets of controls. Hence, we only report31the estimates with full controls. In the table, we also report Cragg-Donald’s F statistics forweak IV as well as Hansen’s J statistics for over-identi…cation (for urban-area regressions
only). The F statistics are well above Stock-Yogo’s critical values, and J statistics are notstatistically signi…cant. There is a sign, however, that the DD-IV estimates are less precise
than the TWFE estimates. This is expected not only because the 2SLS estimation generally
leads to larger standard errors but also because we run the 2SLS estimation on each density
subsample.
The table indicates that the DD-IV estimates have essentially the same signs as theTWFE estimates, but are much larger in magnitude. Ride-hailing entry is estimated to reduce mean NO2 concentrations for cities with high population density (i.e., the 4th quartile),
particularly in urban areas. The signs of the estimates turn positive for medium- and lowestdensity MSAs. The estimated e¤ects on the 2nd quartile MSAs are negative but statisticallyinsigni…cant due to their large standard errors (consistent with the TWFE regression). We
take these as supportive evidence for our main results.
We also run the DD-IV regressions on household-level commuting modes, in the same
way as in Table 6. The results are reported in Table 7. The DD-IV estimates are quite
similar, at least in signs, with the TWFE estimates in Table 3, and are also consistent withthe estimated impacts on air quality in Table 6. For the highest density MSAs, ride-hailingentry is estimated to decrease private car commuters by 6.1 ppt, but much of this decline
is associated with an increase in public transit commuters (by 5.0 ppt) rather than in othermodes. In contrast, for the 3rd quartile MSAs, a decrease in private car commuters (by 1.3
ppt) is accompanied by an increase in the share of other commuting modes (by 0.9 ppt), andmuch of this change comes from an increase in the share of working at home (by 0.6 ppt).
The magnitudes of the estimates seem economically too large, however, particularly forlower population density cohorts. For example, ride-hailing entry is estimated to increasemean urban-area NO2 concentrations by 0.121 log points in the lowest quartile and to decrease by 0.119 log points in the second quartile. There can be two interpretations on thisresult. The …rst is, of course, that our instruments satisfy all identifying assumptions in
A2, and thus, our DD-IV estimates get at the true LATE estimates for the ‘compliers’: i.e.,
the true impacts on MSAs that are a¤ected by the instruments. The second interpretation,
however, is that our instruments fail to satisfy one or more assumptions in A2, and thus, ourestimates are biased. We are lean toward the second interpretation for the following reason.
Recall that our treatment variable is a binary indicator of entry, and hence, our DD-IV
estimand can be written in a manner similar to the familiar Wald formula. Hence, if our
instruments do not predict entry well, then it tends to overly in‡ate the estimated impacts.
Now, recall our discussion on reported entry (or see Figure 3-(a)). Reported entry dates
32are incomplete, and their coverage is particularly poor for small, low-density cities. This isevident in relatively small Cragg-Donald’s F statistics in Table 6. Therefore, our DD-IV
estimates are likely biased away from zero, particularly for lower-density MSAs, although
the signs of the estimates should stay the same as long as the other identifying assumptions hold. Given this, we refrain from drawing too strong conclusions, particularly on lower
density cohorts. Nonetheless, ride-hailing entry seems to a¤ect commuting patterns quitedi¤erently across di¤erent MSAs, and these di¤erences are well associated with di¤erentialchanges in air pollution levels.

8. Conclusion

Air pollution and congestion are major concerns for cities around the world. There is an
ongoing debate as to whether ride-hailing services such as Uber and Lyft decrease or increase
air pollution and congestion. We investigate this question empirically, exploiting staggeredrollout of ride-hailing entry into U.S. cities. Though ours is not the …rst to investigate thequestion, we answer it with three new approaches: (1) we construct MSA-level de facto
entry dates from the Google Trends Index for both Uber and Lyft; (2) we use satellite-based
data, which allow us to compare spatially delineated subareas of MSAs consistently overtime; and (3) we take three complementary identi…cation strategies, namely di¤erence-indi¤erences with strati…cation, the …ngerprint rule in Austin as a quasi-experiment, and the
instrumental variable method (along with the di¤erence-in-di¤erences with strati…cation).
The paper provides robust evidence that ride-hailing entry tends to reduce air pollution inlarge, dense cities, but has no signi…cant e¤ect in lower dense cities. We further support theseresults by another set of evidence, showing that private car use declines, with an increase inuse of public transit, for large cities.
Our results are also consistent with recent studies that …nd mixed and heterogeneous
impacts of public transportation infrastructures [Chen-Whalley (2012); Li et al. (2019);
Gendron-Carrier et al. (2022)]. In a study covering 58 subway openings worldwide, for example, Gendron-Carrier et al. (2022) …nd highly heterogeneous impacts of subway openings:
in 12 cities, subway openings had no e¤ect; in 20 cities, air quality got worse; and air quality
improved in 23 cities with initially high levels of air pollution. Gendron-Carrier et al. argue
that ridership is a key driver explaining these heterogeneous impacts — meaningful air pollution reduction occurred in cities where ridership is largest. The same goes for ride-hailing— the pollution-reducing e¤ect of ride-hailing is largest in cities where its complementarity
with public transit is highest.

33

Our results also have important policy implications. City and urban planning practitioners around the world are increasingly concerned with the e¤ect of ride-hailing on air pollution
and congestion in urbanized areas. In response to this rising concern, Uber launched a new
Uber Green service and made a $800 million fund available for Uber drivers to transit toEVs by 2025 in Canada, Europe, and the U.S. While we agree that such an e¤ort would
likely reduce air pollution per hired ride, its e¤ect on carbon emissions and congestion may
be ambiguous as ride-hailing may still increase hours of driving. In this context, our results
suggest, in line with Agrawal and Zhao (2023), that policies that incentivise complementarity
between hired rides and mass transit commuting, say, via monetary incentives for commutersor better public transportation planning, may have the double e¤ects of increasing ridershipwhile reducing air pollution and carbon emissions from road transportation.

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39

Figure 1. Complementarity Effect of Ride-hailing Entry on Daily Transport Choices

𝑠
𝑟ℎ

𝑠
𝑟

𝑚

40

Figure 2. Google Trends as Measure of Uber/Lyft Market Presence41

Figure 3. De Facto versus Reported Entry

Note: The top panel report the cumulative distribution of ride-hailing entry using three alternative measures of entry: ourmeasure based on Google Trends indices, UberX entry dates from Hall et al. (2018), and official Uber entry dates. The
bottom panel report the event study estimates regressing entry timings on the composite Google Trends index using
alternative entry measures. Whiskers represent the 95% confidence intervals using robust standard errors clustered at the
MSA level.

42

Figure 4. Satellite-based versus Monitoring Data on NO2 Concentration
Monthly Average in January 2014
A. Chicago, IL

B. Austin and San Antonio, TX

C. San Francisco, CA

43

Figure 5. Event Study Estimates of
the Effect of Entry on NO2 Concentrations

Note: The two graphs on the top panel report the event study estimates from the dCDH estimator on the pooled sample.
The other four graphs plot the event study estimates on each population density quartile using monthly maximum NO2
concentrations. Whiskers represent the 95% confidence intervals using robust standard errors clustered at the MSA level.

44

Figure 6. Synthetic vs. Observed NO2 Trends in Austin, Texas

Note: Circles (navy) are the predicted residuals from the regression of monthly NO2 concentrations on all covariates andfixed effects using only pre-treatment data. Squares (maroon) are the counterfactual NO2 concentration residuals basedon the synthetic control method using the 12-month lags as covariates. The solid lines plot the local polynomial
smoothing of the actual residuals. The dashed lines plot the local polynomial smoothing of the counterfactual residuals.

45

46
0.27

(3.155)(0.487)

12.34
35.46

S.D.

6.54
(5.094)(0.498)
35.17
0.08

M ean
30
(0.09)
385(538.30)
60,592(13,991)
39.8(3.842)0.80(0.132)0.20(0.062)0.11(0.045)0.89(0.045)0.02(0.026)

S.D.

Entry in 2013

62
(0.18)
117(79.68)51,635(7,592)41.3(3.906)0.86(0.109)0.16(0.044)0.10(0.057)0.91(0.036)0.01(0.007)

Note: All statistics are as of year 2010.

N. of M SAs(Share of full sample)
Pop. density
M edian household income
M edian age
Share of white
Share of college degree
M anf. employment share
Share of private car commuters
Share of pub. transit com muters
NO 2 concentrations (mean)
EPA (ppb)
Satellite (log of molec./ cm 2)
Share of EPA sample

M ean

No Entry

Table 1. Pre-treatment Condi tions by Treatment Status

S.D.

9.52
35.54
0.22

(3.978)(0.438)217(0.62)
335(339.83)
57,361(10,759)
41.1(4.018)0.82(0.112)0.19(0.059)0.12(0.070)0.90(0.043)0.02(0.026)

M ean

Entry in 2014
S.D.

7.38
35.47
0.05

(0.850)(0.362)39(0.11)
228(131.77)
55,307(8,200)40.7(3.900)0.85(0.110)0.17(0.042)0.12(0.061)
0.91(0.031)0.01(0.010)

M ean

After 2015

S.D.

9.57
35.46
0.18

(4.087)(0.465)348(1.00)
288(327.03)56,389(10,592)
41.0(3.973)0.83(0.114)0.19(0.056)0.11(0.065)0.90(0.041)0.01(0.022)

M ean

All

Table 2. TWFE Estimates of Effect of Ride-hailing Entryon Ambient Air Quality
A. Monthly Mean NO2 Concentrations

Note: In parenthesis are robust standard errors clustered at the MSA level.

B. Monthly Maximum NO2 Concentrations

Note: In parenthesis are robust standard errors clustered at the MSA level.

47

Table 3. TWFE Estimates of Effect of Ride-hailing Entryon Commuting Mode Choice
A. Primary Commuting Mode

Note: In parenthesis are robust standard errors clustered at the MSA level. Repeated cross-section, household-levelcommuting mode data from American Community Survey (ACS). Demographic controls include household-levelcovariates.

B. Other Commuting Mode: Subcategories

Note: In parenthesis are robust standard errors clustered at the MSA level. Repeated cross-section, household-levelcommuting mode data from American Community Survey (ACS). Demographic controls include household-levelcovariates.

48

Table 4. TWFE Estimates of Effect of Ride-hailing Entry on Ambient Air Quality
Texas MSAs Only
A. Monthly Mean NO2 Concentrations

Note: In parenthesis are robust standard errors clustered at the MSA level.

B. Monthly Maximum NO2 Concentrations

Note: In parenthesis are robust standard errors clustered at the MSA level.

49

Table 5. TWFE Estimates of Effect of Ride-hailing Entry on Commuting Mode Choice
Texas MSAs Only
A. Primary Commuting Mode

Note: In parenthesis are robust standard errors clustered at the MSA level. Climate and other time-varying controls thatvary only at the state level are excluded from these regressions.

B. Other Commuting Mode: Subcategories

Note: In parenthesis are robust standard errors clustered at the MSA level. Climate and other time-varying controls thatvary only at the state level are excluded from these regressions.

50

Table 6. DD-IV Estimates of Effect of Ride-hailing Entry on Ambient Air Quality

Note: In parenthesis are robust standard errors clustered at the MSA level. We use the full set of controls for
all regressions. Weak identification/overidentification statistics are reported for urban area regressions only.
Regressions are separately estimated for each population density strata.

51

Table 7. DD-IV Estimates of Effect of Ride-hailing Entry on Commuting Mode ChoiceNote: In parenthesis are robust standard errors clustered at the MSA level. We use the full set of controls for
all regressions. Weak identification/overidentification statistics are reported for urban area regressions only.
Regressions are separately estimated for each population density strata.

52

Appendix A
The City of Chicago provides detailed ride-hailing trip-level data since November 2018 atdata.cityofchicago.org. The data record all trips served by transportation network companies
that took place within the community area boundaries. The trip data exclude taxi trips. We
use the data to calculate the travel distance of each trip as well as the distance between eachtrip’s pickup location to the centroid of the central business district (CBD) in Chicago. Figure
A1 plots (A) the frequency distribution of ride-hailing trips during the weekday rush hours in2019 (before the Covid-19 pandemic) and (B) the distribution of travel distance against the
distance to the CBD. The rush hours are defined as 6-9 am in the morning and 16-19 pm in
the evening. These rush hours are determined from the observed peaks in the number of ridehailing rides during the weekdays, and thus, may not necessarily match the rush hours in othertransportation modes such as freeway traffic and public transit. From panel (A), we see a large
fraction of ride-hailing rides occur outside the CBD, although the frequency of rides tends todecrease with the distance to CBD. Panel (B) shows the centiles of trip distance originatingfrom each community area. The straight line in red is the 45-degree line. The idea here is that
if a resident takes the ride to commute from the pickup location all the way to CBD, then thetrip distance (on the vertical axis) should be greater than the distance to CBD (on thehorizontal axis) so that it should lie above the 45-degree line. For example, the figure indicatesthat more than 75% of ride-hailing trips originating from O’Hare Airport travel lie above the
45-degree line, implying that customers on these trips are likely to be traveling to CBD. The
figure demonstrates that roughly 75% of weekday rush-hour rides that originate from 5 miles
away or more from CBD do not directly travel to CBD.
Figure A1. Geographic Distribution of Ride-hailing Trips in Chicago
Morning and Evening Rush Hours, Weekdays, 201953

Appendix B
We run the following OLS and Tobit regressions to examine the correlation between
entry timing/location and MSA-level socioeconomic variables as of year 2010. For the Tobit
regression, we assume the entry dates are truncated at December 2020 for no-entry MSAs.
𝐸𝑛𝑡𝑟𝑦𝐷𝑎𝑡𝑒 = 𝛼 + 𝑋 ,𝛽+𝜖Some socioeconomic variables are missing for three of the 348 MSAs used in our study, andhence are omitted from the regressions.
Table A1. Association between entry timing and Pre-treatment Covariates.

Entry timing
OLS

Tobit

Pop2010-3.246(2.06)-3.661(1.98) *Pop. density2010-9.850(2.51) ***-11.893(3.27) ***Median age2010-0.258(0.45)-0.294(0.58)Median income2010-28.261 (18.04)-32.445 (19.63) *College2010-5.056 (46.48)-7.377 (51.65)

Non-white2010-20.169 (15.83)-23.570 (21.79)

Manuf. share2010-61.602 (27.70) **-73.591 (31.09) **Poverty2010-14.184 (100.44)-18.643 (120.08)

Car commute2010

47.177 (68.17)

49.750 (81.96)

Pub. transit2010

200.934 (120.13) *234.579 (111.73) *345
0.1945

345

N
R2

54

Appendix C
The following map illustrates the entry timing and location using our de facto entry
measure. The map also shows the urban areas used to calculate urban-area NO2
concentrations in our main analysis.
Figure A2. Geographic Distribution of Ride-haling Entry and Urban Area Boundaries

55Appendix D
The following figure examines the relationship between EPA monitoring data versus
NASA’s satellite-based data. Although there is a clear (statistically significant) relationshipbetween the two, there is also substantial variation in the satellite-based NO2 concentrations
for each level of EPA NO2 concentrations. There is also an indication that EPA data might
be missing for high concentration episodes, as in Zou (2021) and Grainger (2021).
Figure A3. Satellite-based versus EPA Monitoring Data

56

Appendix E
The following map illustrates the entry timing and location for Texas, in a manner
similar to Figure A1.
Figure A4. Geographic Distribution of Ride-haling Entry in Texas

57

Appendix F. Geography-based Instruments
This appendix explains how we construct our geography-based instruments.
For the highway construction plan as of 1947, we use a digital image of the 1947 highway
plan from Duranton and Turner (2011; 2012) and covert it into a GIS map so that the two
maps' coordinates match each other. Figure A5 is a map showing which MSAs were planned
to connect on the 1947 highway plan. As Duranton and Turner note, many interstate
highways were built subsequently based on this highway plan.
For the railroad network as of 1870, we use GIS shapefiles from Katherine Walter's GIS
Railroads and the Making of Modern America Project at the University of Nebraska--Lincoln.
Figure A6 displays historical railroad maps between 1840 and 1970 using this data. As
Duranton and Turner (2011; 2012) argue, many of the railroads from this period areabandoned and turned into roads, and hence, are good predictors of MSA-level road networks
and economic activities today.
To construct the Euclidean spanning tree network, we start with the cities that existed asof 1860 and had a population of 10,000 or more. We then follow the Kruskal's algorithm tocompute the minimum number of edges that connect these large cities. The resulting route is
depicted in Figure A7. As shown in the figure, the Euclidean spanning tree network is lessprecise in predicting today's road networks than the first two IVs, yet covers several important
MSAs that are not on either IV's routes.
Figure A5. 1974 highway plan

58

Figure A6. Railroad as of 1870

Figure A7. Euclidean network connecting large cities as of 1860

59

Appendix G. IV Falsification Test
We run the following regression to examine if our IVs are likely to satisfy the exclusion
restriction.
𝑌

=𝛼 +𝜆

+

𝛽 𝑍

+𝑋

𝛾 + 𝜃 (𝑡) + 𝜖

In this regression, our main outcome variables, the monthly means and maximums ofambient NO₂ concentration levels, are regressed on our three IVs on a subsample of MSAs
with no ride-hailing entry, with the same set of full controls used in Table 2 in the main text.
If the exclusion restriction holds, our IVs should not significantly affect the outcomes forthese MSAs.
Note, however, that by definition, there are no reported entry dates for these MSAs.
Hence, we create fictitious entry dates by using year dummies. Because most entry occurs in
2014, we use year 2013, 2014, and 2015 dummies and run each regression separately.
𝑍= 𝐺 × 𝐼 (𝑡 = 𝑦𝑒𝑎𝑟)

The following table displays the results from this falsification test. We see that the Fstatistics on excluded instruments are statistically insignificant for all regressions. Hence, we
conclude that our IVs pass the falsification test and, thus, are unlikely to be correlated withother unobserved confounds. There is a sign, however, that one of our IVs, the railroadbased instrument, is weakly correlated with the monthly maximums and in the non-urban
areas. We, thus, interpret our IV results cautiously, particularly when using the monthly
maximums as the main outcome variable.
Table A2. Falsification Test (Reduced-form Regressions on Non-entry MSAs)
Mean NO 2 Concentration

Max NO 2 Concentration

Urban

SuburbanNon-urban

Urban

SuburbanNon-urban

0.33(0.804)0.65(0.603)0.56(0.655)0.51(0.687)1.08(0.410)0.84(0.511)0.22(0.883)2.14(0.174)0.69(0.581)2.42(0.141)2.38(0.146)1.70(0.244)1.74(0.236)1.73(0.238)1.99(0.194)2.73(0.114)2.61(0.123)1.01(0.439)

5,791
0.742

5,788
0.749

5,776
0.778

5,791
0.597

5,788
0.618

5,776
0.69

F Statistics on
Excluded Instruments
Year 2013
Year 2014
Year 2015

Obs.
Adj. R 2
Note : P-values in parenthesis.

60