metadata: authors: 0: author: Bosker,Maarten
1: author: Roberts,Mark
2: author: Tiwari,Sailesh
3: author: Wibisana,Putu Sanjiwacika
4: author: Wihardja,Maria Monica
5: author: Yanurzha,Ramda
count: Indonesia
docna: 0: docna: Are Ride-Hailing Services and Public
            Transport Complements or Substitutes ? Evidence from the
            Opening of Jakarta’s MRT System
docty: Policy Research Working Paper
theme: Private Sector Development,ICT Policies,ICT,ICT Solutions
abstracts: cdata!: Motorbike-based ride-hailing services
            are widespread in many of the most congested cities in the
            developing world. These services often predate the
            construction of modern public mass rapid transit systems.
            Ride-hailing services may complement such investments by
            providing important first and last mile connectivity.
            However, it has also been argued that they undermine the
            viability of mass rapid transit systems as people prefer to
            use ride-hailing services given their convenience and low
            prices. This paper applies an event study research design to
            proprietary, high-frequency data from one of
            Indonesia&#x2019;s largest ride-hailing services, Gojek.
            The findings show that the opening of stations on
            Jakarta&#x2019;s first mass rapid transit line led to
            large increases in ride-hailing activity in the immediate
            vicinities of the stations. This was accompanied by a
            significant decline in the average distance of ride-hailing
            trips to and from the station locations. These findings are
            consistent with ride-hailing services complementing public
            transport by providing first and last mile connectivity to
            the newly opened mass rapid transit system. Interestingly,
            this holds for both commuting and non-commuting trips and is
            strongest for mass rapid transit station locations that were
            not already served by Jakarta&#x2019;s bus rapid transit system.
display_title: Are Ride-Hailing Services and Public
            Transport Complements or Substitutes ? Evidence from the
            Opening of Jakarta’s MRT System
listing_relative_url: /research/2023/12/34213264/ride-hailing-services-public-transport-complements-or-substitutes-evidence-opening-jakarta’s-mrt-system
topicv3: Urban Transport m1327133 788,Transport and Urban Development m1327134 1727,Urban Transport Infrastructure m1582416 4241,Urban Accessibility m1582676 4246
publishtoextweb_dt: 2023-12-12T00:00:00Z
dois: 10.1596/1813-9450-10640
no_of_pages: 34
author_keys: 0: author_key: 997692
1: author_key: 931768
2: author_key: 687885
3: author_key: 1875147
4: author_key: 1245101
5: author_key: 1270422
model_extractions: Policy Research Working Paper 10640 Are Ride-Hailing Services and Public Transport Complements or Substitutes? Evidence from the Opening of Jakarta’s MRT System _Maarten Bosker Mark Roberts Sailesh Tiwari Putu Sanjiwacika Wibisana Maria Monica Wihardja Ramda Yanurzha_ Urban, Disaster Risk Management, Resilience and Land Global Practice December 2023
Policy Research Working Paper 10640

# **Abstract**

Motorbike-based ride-hailing services are widespread in many of the most congested cities in the developing world. These services often predate the construction of modern public mass rapid transit systems. Ride-hailing services may complement such investments by providing important first and last mile connectivity. However, it has also been argued that they undermine the viability of mass rapid transit systems as people prefer to use ride-hailing services given their convenience and low prices. This paper applies an event study research design to proprietary, high-frequency data from one of Indonesia’s largest ride-hailing services, Gojek. The findings show that the opening of stations on Jakarta’s first mass rapid transit line led to large increases in ride-hailing activity in the immediate vicinities of the stations. This was accompanied by a significant decline in the average distance of ride-hailing trips to and from the station locations. These findings are consistent with ride-hailing services complementing public transport by providing first and last mile connectivity to the newly opened mass rapid transit system. Interestingly, this holds for both commuting and non-commuting trips and is strongest for mass rapid transit station locations that were not already served by Jakarta’s bus rapid transit system.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mroberts1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Are Ride-Hailing Services and Public Transport Complements or Substitutes? Evidence from the Opening of Jakarta’s MRT System**

Maarten Bosker, Mark Roberts, Sailesh Tiwari, Putu Sanjiwacika Wibisana, Maria Monica Wihardja, and Ramda Yanurzha<sup>*</sup>

**Keywords:** ride-hailing, mass rapid transit, urban transport, commuting **JEL codes:** R15, R41, R42

**Acknowledgments:** The authors thank Jonathan Hall, Marco Gonzalez-Navarro, Sacha Kapoor, Harrison Wheeler, Muneeza Alam and Winnie Wang, as well as participants at the 2022 North American Meeting of the Urban Economics Association, the 2022 SMU-Jinan Conference on Urban and Regional Economics, and a World Bank Transport Global Practice webinar for their helpful comments and suggestions. The sharing of data by Gojek and PT Mass Rapid Transit Jakarta is also very gratefully acknowledged. Th <mark>e findings, interpretations, and conclusions expressed in this paper</mark> are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> * Maarten Bosker is based at the Erasmus School of Economics, Erasmus University, Rotterdam, The Netherlands. Mark Roberts, Sailesh Tiwari and Putu Sanjiwacika Wibisana are all based at the World Bank, while Maria Monica Wihardja is affiliated with the ISEAS Yusof Ishak Institute, Singapore, and Ramda Yanurzha with _Gojek_ , Jakarta, Indonesia. **Corresponding author:** Mark Roberts (mroberts1@worldbank.org).
# **1. Introduction**

Motorbike-based ride-hailing services have grown exponentially in many of the heavily congested cities in the developing world. While this growth has expanded the mobility options of urban residents, it has also prompted fears that the convenience and low prices of these services are undermining the viability of public transport investments, with potentially negative repercussions for traffic congestion, greenhouse gas emissions, local air pollution levels, and, through these, the health and productivity of a city’s residents. Against this, however, it has also been argued that, far from being substitutes for public transport, ridehailing services may, in fact, be complements due to the first- and last-mile connectivity that they provide. A trip from, say, the workplace to home that involves public transport for the main portion of the trip plus use of a ride-hailing service for the first and/or last mile of the trip may offer a more convenient, less costly, and/or less polluting transportation option than was hitherto available.<sup>1</sup> In this paper, we empirically investigate whether ride-hailing services and public transport are substitutes or complements in such a developing country city context. In particular, we examine the impacts of the staggered opening of Jakarta’s mass rapid transit (MRT) system on the use of ride-hailing services in the immediate neighborhoods of newly opened MRT stations. In doing so, we take advantage of (anonymized) spatially very fine-grained daily ride-hailing activity data from _Gojek_ , which, together with _Grab_ , dominates the Indonesian ride-hailing market, as well as daily tap-in and tap-out data from Jakarta MRT.

Jakarta officially opened its first MRT line, which consists of 13 stations, on March 24, 2019. This opening, however, was staggered in the sense that it was immediately preceded by a 12-day limited public trial during which no charge was made for use of the metro, but ridership was capped. This was followed by a further one-week period during which users could continue to ride the MRT for free. On April 1, 2019 fares were introduced, but still at a 50 percent discount. On May 13, 2019 full fares started to be charged. Using an event study research design, we examine how this staggered opening of Jakarta’s first MRT line impacted _Gojek_ activity – defined as the sum of the number of trips originating (pick-ups) and terminating (dropoffs), as well as the average distance of those trips, within 50 and 100-meter radii of the newly opened MRT stations – relative to that in a set of control locations. These control locations consist of both planned, but not yet opened, station locations on the next phase of Jakarta’s MRT system, as well as locations on the newly opened MRT line in-between the actual stations.

Overall, we find strong evidence in favor of the complementarity hypothesis. Relative to the control locations, the opening of Jakarta’s MRT system had a large, positive impact on the number of ride-hailing

> 1 See Gendron-Carrier _et al._ (2022) for evidence of beneficial effects of the opening of mass rapid transit systems on both local levels of air pollution and health for initially highly polluted cities. Heger _et al_ . (2019) provide similar evidence for the extension of Cairo’s metro line in the Arab Republic of Egypt.

2
trips in the neighborhoods of the newly opened stations that, moreover, steadily increased over time. Moreover, the number of daily _Gojek_ pick-ups and drop-offs at the newly opened MRT stations and the total number of tap-outs (i.e., exits from) and tap-ins (i.e., entries into) the MRT system are positively associated. To be more specific, the number of ride-hailing trips starting or ending within 50 meters of a newly opened MRT station increased by 86 percent (0.62 log points) between the start of the MRT system’s public trial and 20 days later, when fares came into effect. After about two months, this effect had leveledoff to around a 215 percent (1.15 log points) increase. At the same time, the average distance of ride-hailing trips to and from station locations significantly decreased: by 16 percent (0.18 log points) in the first 20 days of the MRT being operational, leveling off at a 23 percent (0.26 log points) decrease after about two months following the MRT system’s opening.

Taken together, these results provide strong evidence that ride-hailing services provide important first- and last-mile connectivity to the newly opened MRT system. In further analyses, we find this result to hold for trips on regular weekdays, weekends and public holidays, as well as rush- and non-rush hour trips alike. This suggests that the complementarity hypothesis holds for both commuting and non-commuting trips. Last but not least, we see a more pronounced impact of the opening of the MRT system on ride-hailing activity in the vicinity of MRT station locations that were not already served by Jakarta’s bus rapid transit (BRT) system. For these locations, the opening of the MRT increased mass public transportation options at the extensive margin. By contrast, for locations that were already served by the BRT, the opening of the MRT “only” increased transportation options at the intensive margin, explaining the weaker response of ride-hailing activity to the opening of the MRT near those stations.

Our paper contributes to a rapidly expanding literature investigating whether ride-hailing services and public transportation are substitutes or complements. In one of the first papers to tackle this question, Hall, Palsson and Price (2018) examine the effect of the arrival of _Uber-X_ into US metropolitan areas on public ridership volumes using a diff-in-diff research design. They find _Uber-X_ to be a complement for the average transit agency, but with considerable heterogeneity across metro areas with _Uber_ most strongly complementing small transit agencies in large cities. By contrast, also using US data, Graehler _et al._ (2019), Erhardt _et al._ (2021), and Diao _et al._ (2021) find ride-hailing to be a substitute for public transport. Meanwhile, Nelson and Sadowsky (2018), Babar and Burtch (2020), and Cairncross _et al._ (2021) find mixed or statistically insignificant results.

Our paper differs from these papers in two important respects. First, rather than focusing on a developed country in which the existence of mass transit systems pre-dates ride-hailing, we focus on a large, heavily congested, developing country city, Jakarta, for which the existence, and widespread use of, ride-hailing pre-dates the opening of its MRT system. Second, our paper differs in its event-study research design, 3
identifying the impacts of the opening of Jakarta’s MRT system on ride-hailing activity using plausibly exogenous variation, exploiting the very fine spatial and temporal resolution of our data on daily ridehailing activity and MRT travel, provided by _Gojek_ and Jakarta MRT, respectively.

In its research design and the nature of its data, our paper more closely resembles Rao (2016) and GonzalezNavarro _et al_ . (2021). Rao (2016) examines the impact of the opening of London’s “night tube” service in August 2016 on _Uber_ journeys starting within 200 meters of a “night tube” station.<sup>2</sup> He finds a 22 percent increase in _Uber_ journeys on nights when the night tube is operational, with a decrease in pick-ups around central London stations accompanied by a large increase in station pick-ups outside central London. Gonzalez-Navarro _et al._ (2021) consider a similar question to ours. Instead of focusing on a single public transport project, they use data on the opening of new metro stations from 35 different cities<sup>3</sup> and use a “ring-based” empirical design that compares monthly _Uber_ ridership within 100-meter distance bands before and after a transit station opens relative to the next further out distance band up to a maximum distance of 1,100 – 1,200 meters. They estimate that a new metro station opening increases _Uber_ ridership up to 300 meters of the station by 53 percent in the 6 months after its opening. This is accompanied by a 1.26 km decrease in average _Uber_ trip distance to/from the stations.

While similar in spirit, our paper differs from Gonzalez-Navarro _et al._ (2021) in three important ways. _First_ , we use a different research design that identifies the effect of the opening of the MRT system by comparing _Gojek_ activity before and after the opening of the MRT in the immediate neighborhoods of the newly opened stations to that in (different sets of) control locations. As we discuss in section 5.4, our results are however robust to using a “ring-based” empirical design instead.<sup>4</sup> _Second_ , we focus on one public transport project in a specific developing country city. Gonzalez-Navarro _et al._ ’s sample consists mostly of cities in developed economies, often with well-developed (rapid) public transit options before the entry of ride-hailing services. Our focus instead is on a heavily congested city in a developing country context, where fewer people own their own transport, ride-hailing services are primarily motorbike-based, and (much) fewer rapid public transit options are available. Although our limited scope may raise questions about the external validity of our findings, they are arguably more relevant for other cities in the developing world, where the use of motor-bike based ride hailing services is widespread and mass rapid public transport options are still few.

> 2 Rao (2016) is not a formally published paper, but a blog account of internal research performed by _Uber_ on the impacts of the opening of London’s “night tube.”

> 3 Gonzalez-Navarro _et al._ ’s sample does not include Jakarta as _Uber_ is not present in the Indonesian market.

> 4 See Pollmann (2023) for a discussion of the different assumptions underlying ours and the “ring-based” method.

4
_Third_ , the extremely rich _Gojek_ data allows us to provide much more fine-grained, and, therefore, in-depth, evidence on the complementarity versus substitution hypothesis. Notably, **1)** we show the effect(s) of the opening of the MRT on a _daily_ , as opposed to a monthly, basis, allowing us to show how they evolve throughout each phase of Jakarta MRT’s staggered opening; **2)** we document how the MRT opening affects both rush- and non-rush hour ride-hailing activity, as well as regular weekday, and weekend and public holiday ride-hailing activity; **3)** we show that results differ for station locations at which there was no previous alternative BRT stop; and **4)** the data on daily tap-ins and tap-outs into each of the newly opened MRT stations allow us to show that daily _Gojek_ ride volume and the actual use of the MRT system are positively associated, thereby alleviating concerns that the increased ride-hailing volume might be associated with, for example, the opening of new urban amenities around the MRT stations rather than the use of the MRT system itself.

Last, but certainly not least, our study complements several more qualitative studies that provide surveybased evidence on either ride-hailing customers’ (Irawan _et al._ , 2020), or public transport users’ (Saffan and Rizki, 2018; <mark>Sunitiyoso</mark> _<mark>et al</mark>_ <mark>, 2022) commuting behavior to better understand the role of ride-hailing services in public transport trips in Jakarta. Also, we</mark> contribute to a literature that is specifically focused on evaluating the impacts of various transportation policies for Jakarta. Gaduh, Gračner, and Rothenberg (2022) evaluate the impacts of Jakarta’s BRT system, the _TransJakarta_ , on vehicle ownership, travel times and commuting flows. Kreindler _et al._ (2023) add to this by showing how the recent expansions of the BRT network affected bus ridership and overall commuting flows. Meanwhile, Hanna, Kreindler, and Olken (2017) identify the effect of Jakarta’s so-called “3-in-1” high occupancy vehicle policy, aimed to reduce congestion, on travel speeds in the city.

The remainder of the paper is structured as follows. Section 2 provides an overview of Jakarta’s MRT system. Section 3 describes our event study design, which leverages the highly spatially and temporally granular data that we describe in Section 4. Section 5 presents our results. Finally, Section 6 concludes.

# **2. Overview of Jakarta’s MRT System**

Jakarta is Indonesia’s largest city. In 2016 it had a population of 10 million in the “city proper”, and of 31 million in the wider metropolitan area, also known as Jabodetabek.<sup>5</sup> It is also consistently rated one of the 10 most congested cities in the world (Roberts, Gil Sander, and Tiwari, 2019). Around 3.2 million (or 11 percent) of Jabodetabek’s inhabitants are commuters (BPS, 2020). Most of them (63.3 percent) use their own motorbike for travel, followed by “public” transport (26.9 percent) (BPS, 2020). The latter

> 5 Jakarta’s “city proper” corresponds to the district of DKI ( _Daerah Khusus Ibukota_ ) Jakarta.

5
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Beyond the Phase 1 line, the Phase 2A line of Jakarta’s MRT is currently under construction, extending the current line for around 6.3 km further north (the solid red line connecting the stations with yellow markers in Figure 1). On top of this, other plans such as Phase 2B (a further line branch of Phase 2A) and Phases 3 and 4 (both East-West lines) also exist, although construction of these lines has yet to begin.

Prior to the official opening of the 13 stations on the Phase 1 line on 24 March 2019, the system underwent a limited 12-day public trial, which commenced on 12 March 2019. During this period, the MRT system operated on all 13 stations between 8 am and 4 pm, with a total of 98 trips per day and a 10-minute headway interval between the arrival of each train. The number of passengers was limited to 4,000 on the first day, and then gradually increased each day by 2,000, reaching 28,800 on the final trial day. Throughout the trial period, no fares were charged to use the MRT.<sup>7</sup> Following the MRT’s official inauguration, on 24 March 2019, the system commenced with its normal operating hours from 5 am to midnight, with 285 trips per day, and it switched to different headway intervals for on peak and off-peak hours. During peak hours (5 – 9 am and 5 – 7 pm), trains arrive every five minutes, while, in off-peak hours, they run every 10 minutes. Weekends and public holidays are classified as entirely off-peak.<sup>8</sup> In the first week following the MRT’s official inauguration, passengers continued to travel free without any cap on passenger numbers. Passenger fares were introduced on 1 April 2019, but still at a 50 percent discount. It was only on 13 May 2019 that full fares started to be charged. They range from IDR 3,000 to IDR 14,000 depending on distance traveled (roughly between US$ 0.21 and US$ 1), with IDR 1,000 fare increases for each station passed. Six months after fares were introduced, an average 90,000 daily trips were made using the MRT. For comparison, Jakarta’s much more extensive BRT system (13 lines and 228 stops) attracts around 900,000 passengers per day ( <mark>Sunitiyoso</mark> _<mark>et al.,</mark>_ <mark>2022)</mark> .

# **3. Event-Study Design**

The phased opening of the Jakarta MRT system provides an ideal setting to study whether ride-hailing is a complement or substitute for public transport, not least because the timing of the opening is plausibly exogenous to underlying ride-hailing trends. The Indonesian Ministry of Transport approved the Phase 1

> 7 https://megapolitan.kompas.com/read/2019/03/12/10084401/uji-coba-operasi-mrt-jakarta-untuk-publik-dimulai-hari-ini.

> 8 https://finance.detik.com/infrastruktur/d-4489109/mulai-mei-2019-headway-kereta-mrt-jadi-5-menit-saat-jam-sibuk.

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centroid of any current station location so that none of the locations overlap.<sup>10, 11</sup> One advantage of this set of control locations is that they are arguably more likely to share similar neighborhood characteristics to the treatment locations than the planned Phase 2A locations owing to their greater physical proximity to the actually opened stations. But even though the boundary of these “in-between” locations is at least 300 m (200 m) away from that of the nearest treated location when using 50 m (100 m) radii to define locations, this does come at the possible risk of contamination bias. The “in-between” locations might also be “treated” by the opening of the MRT. People living in these control locations might now e.g., start taking a shorter _Gojek_ ride to/from the newly opened nearby MRT station, possibly instead of the longer ride that they took before to their final destination. Alternatively, they might no longer take a _Gojek_ ride to get to their final destination, and instead now walk to the newly opened MRT station and use the MRT to get to their final destination.

Our main results for the volume of ride-hailing trips always consider _both sets of control location_ s. In contrast, to avoid contamination bias, we show our main results on average trip distance using _only the planned locations_ as control locations.<sup>12</sup> Of course, our results hinge on the validity of our choice of control locations. It is therefore very important to note at this point, that our findings do not depend on using only either one of our two sets of control locations, or both of them together. Neither the volume nor the average distance of _Gojek_ trips to/from both our sets of control locations do not show much change following the opening of the MRT (see e.g., Figure 4 below), so that our findings our first and foremost driven by the changes in _Gojek_ activity near the newly opened stations.

On top of this, in robustness checks (see Section 5.4), we show that our results are also robust to using an entirely different set of control locations inspired by a “ring-based” empirical design such as also adopted in Gonzalez-Navarro _et al._ (2021). And, we additionally perform two “placebo tests” that each lends strong support to the validity of our two different sets of control locations. First, we show no impact of the opening

> 10 For example, if two stations are 1,000 m apart along the alignment of the MRT line, there will be one possible control location between these stations, but two possible control locations if the stations are 1,200 m apart. We make sure that the centroids of the control locations are spaced evenly between the two stations that they fall in-between. Thus, for stations that are 1,300 meters apart, there will be two possible control locations, the centroids of each of which are spaced 433 m apart, as well as 433 m from their nearest respective MRT stations.

> 11 We use 400 m as 200 m is the maximum radius that we use to define locations (see results in Annex B).

> 12 We do this in our baseline average distance results only, given that, in contrast to the total number of _Gojek_ pickups and drop-offs, the average distance traveled on _Gojek_ trips to/from our control locations might also be impacted by the opening of the MRT if people who were using _Gojek_ before, to get to their final destination or another type of public transport station located further away, now switch to taking a _Gojek_ to/from a newly opened MRT station and get to/from their final destination by MRT. This would leave the number of _Gojek_ trips to/from these control locations unchanged, whereas it would still affect the average distance traveled.

10
of the Jakarta MRT on ride-hailing activity in the vicinity of our, still-to-be-opened, Phase 2A station control locations relative to the intermediate Phase 1 control locations. Second, we apply a “ring-based” method to our control locations only and find no differential impact of the opening of the MRT on locations closest to these control locations.

In our event study design, we take 12 March 2019, i.e., the date on which the limited public trial period of the Jakarta MRT commenced (see Section 2 above), as our baseline event date for which we set _k = 0_ . Next, we estimate the effect of the opening of the MRT for each daily time window, _k = {-90, -89 ,..., +89,_ + _90}_ up to 90 days before and after 12 March 2019: up to 90 days before and after 12 March 2019: 90 ∙ [1] i = ∑ i kk kk=−90 kk ii ii ii ii where i is the log number of ride-hailing trips originating and terminating at location yy ββ DD ∙TT + αα + γγ + εε _i_ (i.e., the sum of drop-offs and pick-ups at location _i_ ) on date _t_ . is a dummy variable that equals one if date _t_ lies _k_ days yy kk from 12 March 2019, and zero otherwise, and ii is a dummy variable that equals one if location _i_ is a DD treatment location (i.e. a station location on the Phase 1 line) and zero otherwise. ii and are location and TT date-specific fixed effects respectively. The location fixed effects control for time-invariant location ii ii αα γγ characteristics, such as fixed neighborhood amenities, which may be correlated with both ride-hailing activity and treatment status. The date-specific fixed effects control for contemporaneous shocks that are common across all locations. This includes, for example, whether the date falls on a weekend or is a public holiday, as well as weather-related shocks that may affect ride-hailing activity. i is the error term. Our main parameters of interest are the ’s. They tell us whether, and if so how, for each day in our sample εε period separately, ride hailing activity near the newly opened MRT stations relative to that in the control kk ββ locations differs from that on 12 March 2019, the day on which the limited public trial of the Jakarta MRT = commenced. Prior to 12 March 2019, we therefore expect 0. In fact, our ability to estimate treatment effects prior to the opening of the MRT allows us to assess the crucial assumption underlying our estimates kk ββ that ride-hailing activity did not show differential trends in treated and control locations before the MRT was opened. In the period after the opening of the MRT, we expect > 0 if the MRT and ride-hailing are complements and < 0 if they are substitutes. Furthermore, our daily estimates also allow us to look for kk ββ patterns in the estimated kk coefficients that would, for example, be consistent with people gradually ββ adjusting their behavior following the opening of the MRT, or to look for differences in the effect between kk ββ the limited public trial period, the fully operational, but continued fare free, period between 24 March 2019 and 1 April 2019, the period between 1 April 2019 and 13 May 2019 during which fares where charged at a 50 percent discount, and following the introduction of full fares on 13 May 2019. We estimate equation [1] clustering standard errors by location.

11
# **4. Data**

The ride-hailing data that we use to implement our event study design comes from _Gojek_ , which, along with _Grab_ , is Indonesia’s biggest ride-hailing company. These two companies each control about half of the ride-hailing market in Indonesia that for the largest part consists of two-wheeled ride-hailing services on motorbike. _Gojek_ is an Indonesian-owned firm that started in 2010 with a fleet of 20 drivers connected to a call-center, but it was only after the launch of its mobile phone application in January 2015 that the company experienced exponential growth.<sup>13</sup> It now has a fleet of over 1 million drivers and, as of November 2019, _Gojek_ had an estimated 29.2 million monthly active users in Indonesia.<sup>14</sup> The ride-hailing data from _Gojek_ has a daily frequency and covers the period from 1 September 2018 to 1 November 2019, which encompasses the staggered opening of the Phase 1 MRT line that took place between 12 March and 13 May 2019. For each day, we have information on the number of, as well as the average distance-traveled on, _Gojek_ trips originating from (pick-ups) and terminating at (drop-offs) both our treatment and (both sets of) control locations as defined using circles of 50 m and 100 m radii.<sup>15</sup> The data further provide a breakdown between rush and non-rush hour trips, where rush hour trips are defined by _Gojek_ as pick-ups and drop-offs that take place between either 5 – 8 am (morning rush hours) or 4 – 8 pm (evening rush hours).<sup>16</sup> In total, our data contains information on more than 17 million ride-hailing journeys between September 1, 2018 and November 1, 2019.

Meanwhile, our data on daily tap-ins and tap-outs into the MRT system at each individual newly opened MRT station comes from <mark>PT Mass Rapid Transit Jakarta. It covers the period from 1 April 2019 onwards, which is the date on which discounted fares started to be charged and therefore tracking of tap-ins and tapouts started.</mark> A preliminary glance of our data is provided in Figure 4, which plots the 7-day moving average of the sum of _Gojek_ pick-ups and drop-offs within 50 m of all actual Phase 1 MRT stations, and similarly for our control locations. In addition, the figure also plots the 7-day moving average of the combined total number

> 13 The app is a “lifestyle app” which offers a wide-range of other products alongside _Gojek_ ’s ride-hailing service. The ride-hailing service is now referred to as _GoRide_ , but, for ease of exposition and to avoid confusion, we refer to the ride-hailing service as _Gojek_ throughout this paper.

> 14 After our sample period, _Gojek_ completed a merger with _Tokopedia_ in May 2021, with both becoming subsidiaries of a new company, _GoTo_ .

> 15 We also have data on the volume of _Gojek_ trips (drop-offs plus pick-ups) for treatment and control locations as defined using circles of 200 m radius. We unfortunately lack data on average trip distance for these 200m radius circles. Moreover, we are unable to distinguish between rush and non-rush hour trips for these circles.

> 16 These hours differ slightly from the regular weekday peak hours of the MRT system, which are 5 – 9 am and 5 – 7 pm.

12
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<!-- Start of picture text --> Difference of Log in Total Activities - 100m radius<br>= i oto |<br>I It |<br>_J 11 IoIo {,| Tr<br>Eo 1 Pot oe ee aor oe aie]<br>2°Eo€ - . ma Tt te To Te III Cllalij| hon} I |Wie| }<br>:© | iatlla (a FL lalai | CV SLUT eeHAL ll<br>Fs3 AM (etreie i\ IE<br>2)& : TA! CIPLyUe|| ! eeAl eeWeerPie ot4ot ||I<br>6 a 4: 11 iotot ||<br>. I 1 | I<br>£8 oo ¥<br>Hy<br>a<br>Day before/after opening<br><!-- End of picture text -->
the cap on MRT ridership and the switch to a full operating schedule. For the 50 m radius locations, by the time (discounted) fares are introduced on 1 April 2019, the estimated increase in ride-hailing trip volume because of the opening of the MRT system has reached 0.62 log points (86 percent), which is statistically significant at the 5 percent level (figure 5(a)). Following this, the estimated impact of the opening of the MRT system on ride-hailing trip volume gradually increases further and is always statistically significant at the 5 percent level. It levels-off at around a 1.15 log point increase (216 percent) about two months after the start of the public trial, with no discernable effect of the introduction of full fares on 13 May 2019.<sup>19</sup> This pattern is consistent with a process of travelers experiencing positive learning of using ride-hailing services in combination with the newly opened MRT system and the dissemination of news of these positive learning experiences to other travelers. This effect levels off after about two months, when the volume of _Gojek_ trips near the newly opened MRT station appears to have reached a new steady state.

Results are qualitatively very similar for the 100 m radius locations in figure 5(b). However, the estimated positive impacts of the opening of the MRT on ride-hailing trip volume are much smaller – an estimated 0.37 log point increase (45 percent) by the time fares are introduced on 1 April 2019, leveling-off at an estimated 0.51 log point increase (66.5 percent) two months after the opening of the MRT. Moreover, they tend to be less precisely estimated. These smaller, less precisely estimated effects for larger defined locations are as we might expect – the larger are the areas of the neighborhoods that we define around MRT stations, the smaller is the proportion of ride-hailing trips to and from those neighborhoods that are likely to be associated with MRT use, i.e., the noisier the data becomes. Further increasing the radius to 200 m, our estimated effects become smaller still (see Annex B).<sup>20</sup> _The average distance traveled on ride-hailing trips_ The impacts of the opening of the Jakarta MRT on the average distance of _Gojek_ trips originating and terminating at the Phase 1 station locations is shown in Figure 6, again using either 50 m or 100 m radius circles to define locations. To minimize potential contamination bias, we exclude the “in-between” locations along the Phase 1 line from the control group (see our discussion in Section 3, and footnote 12). The estimated increase in the volume of ride-hailing trips in Figure 5 is mirrored by a (statistically) significant decline in the estimated average distance of those trips. The estimated size of this decline is increasing throughout the MRT system’s free trial period and levels-off about 1.5 weeks after the

> 19 Annex C presents results for the estimated daily impacts of the opening of the MRT on ride-hailing activity for the full-period for which we have data – i.e., 12 March – 1 April 2019. They show some further, much more modest, increases in the impact on the volume of ride-hailing trips beyond 90 days following the start of the limited public trial.

> 20 With the caveat that, due to lack of data availability on average trip distance for our locations defined as 200 m circles, we can only claim this for total _Gojek_ trip volume.

15
<!-- Start of picture text --> Difference of Log in Average Trip Distance - 50m radius<br>TI Io I<br>z8 7 I] i ] ] zl Tharjl Lani il bet]WiEMi14Wl |I .<br>| hr Il 7 |<br>2 | WT al Hote Tard a<br>3s$ - | || VA i | TIM| yr<br>Oe S| a ee : mE \<br>Fs ] lea |<br>7 - re ot |<br>go | t ~ HL in |<br>ge | | tr I<br>= | iol | 1<br>] io I<br>© \ Io |<br>8 8 8 +B ges g 8<br>2 ad<br>a $8 2<br>uw 58<br>a<br>Day before/after opening<br><!-- End of picture text -->

<!-- Start of picture text --> Difference of Log in Average Trip Distance - 100m radius<br>“ | it |<br>+ ila |<br>g5 L T | HeaI ahLot \I<br>8 | | 7\I\\(! Hl \ |<br>A i ier ai-. mt<br>w2 ! I| |Wl rl 7ite + WhiWin ;<br>5 ; I {5 vA Hh |<br>3 p<br>3° ! : a AMAA A A GTA AN<br>5 1 I Tha Wy iF<br>5 I eine I L<br>5 | trae I! lel 1<br>Li} J<br>< I ot I<br>28 2® 28 =B 2:52898 8 28<br>2 69 =<br>2 32 z<br>a 2g<br>a<br>Day before/after opening<br><!-- End of picture text -->
<!-- Start of picture text --> Estimated Coefficient on Pickup/Dropoff - 50m radius<br><4<br>I] +<br>i I . 1<br>F ASL th I \ NG sit) evi " HT ALL<br>| |Tv ihKi cg a is ie<br>noe He adi ele<br>wo |!<br>g8§88&83<br>:<br>fa<br>Day after opening<br><!-- End of picture text -->
increase/decrease in the daily number of _Gojek_ drop-offs/pickups.<sup>23</sup> These results provide additional confidence that the increase in ride-hailing activity near the newly opened MRT stations is the result of people actually using ride-hailing services to connect to the MRT system, rather than, for example, taking ride-hailing trips to/from the possibly newly opened shops, restaurants, and other urban amenities in the immediate neighborhoods of MRT stations.<sup>24</sup> Summing up the results presented in Figures 5 to 7, we find a strong (local) complementary relationship between the use of ride-hailing services and Jakarta’s newly opened, first, MRT system. Ride-hailing services provide important first- and last-mile connectivity.<sup>25</sup> In doing so, they also help to extend the potential spatial reach of the newly opened MRT system to attract customers, especially for those that cannot afford their own vehicle, live in places without reliable public transport alternatives, and/or for whom walking to the newly opened station is not an option (either because of safety or distance considerations).<sup>26</sup>

# **_5.2. Work versus non-work related commuting_**

About 80 percent of all commutes in Jakarta are work-related (BPS, 2020). To probe the extent to which our baseline results are driven by work-related commuting versus other trip types for leisure or study purposes, we extend our baseline analysis to distinguish between trips that are more and less likely to be work-related. Specifically, we only consider regular weekdays (Monday – Friday), excluding weekdays that are public holidays, and compare results when focusing on rush hour trips _versus_ non-rush hour trips. Rush hour trips are defined as pick-ups and drop-offs between the hours of 5 – 8 am (morning rush hours)

> 23 A small note on the insignificance of the daily association between daily station-level _Gojek_ drop-offs and MRT tap-ins is in order here. Estimating day-specific associations is quite demanding, easily resulting in the wide daily confidence intervals that we see in the figure. The estimated daily association between _Gojek_ drop-offs and MRT tapins is very stable after about a month following the introduction of fares. Allowing it to differ only every 3 days or every week, giving us more degrees of freedom, yields exactly the same estimated associations, _but turns them statistically significant_ .

> 24 Sunitiyoso et al. (2022) report that 25 percent (32 percent) of their surveyed public transit users in Jakarta (BRT, Commuter Line (KRL), or MRT) use ride-hailing services for their first-mile (last-mile) connectivity. Given that _Gojek_ shares the ride-hailing market with _Grab_ , our estimated associations between MRT tap-ins/outs and _Gojek_ dropoffs/pickup, once the initial dust of the opening of the new MRT stations has settled (and commuting patterns have reached their new steady state), are in line with these survey findings.

> 25 In robustness tests reported in Annex A, we also estimate the impacts of the opening of the MRT on pick-up and drop-off activity separately. Impacts on pick-up and drop-off activity are quantitatively similar, suggesting that ridehailing services are being used by passengers to provide both first- and last-mile connectivity to the MRT.

> 26 Sunitiyoso _et al._ (2022) report the average walking distance of first-/last-mile trips to be 820 m for Jakarta’s public transport users, against an average 3.6 km for two-wheeled ride-hailing services. Trahjono, Kusuma and Septiawan (2020) add to this by reporting that 46 percent, 60 percent and 82 percent of their surveyed commuters are not willing to walk more than 300 m, 500 m and 1km, respectively, to/from a public transport stop.

18
<!-- Start of picture text --> Difference of Log in Total Activities - 50m radius, Rush and Non-rush Hours<br>“<br>: at<br>2 itt 4 ]<br>¢ : i | |<br>Be ALA A a a ae onlaysa TY I Y aa |<br>F WIG) |<br>lA Aad ee<br>BLM iif ST NRINYA Nd it |<br>€ rt Hil i | I | | | tol |<br>aanr Lr Eun a Pa ~Rat tyt et1 | I|<br>8 g § 2 23 8 & 8<br>a fs<br>i<br>a<br>Day before/after opening<br><!-- End of picture text -->

<!-- Start of picture text --> Difference of Log in Average Trip Distance - 50m radius, Rush and Non-rush Hour<br>-< ; 1 1 |<br>g - I ot \<br>« lal ir Hit i Wan 4 \<br>&s Ae | } ial es em NHNey IH;tizmtin {fd<br>] |<br>al EON<br>2 EE EU Re<br>a I iol . t\7<br>a I i ot an<br>© | | i) i}<br>g g 3 B23 & : 5<br>re<br>a<br>Day beforo/after opening<br><!-- End of picture text -->
<!-- Start of picture text --> Difference of Log in Total Activities - 50m radius, BRT vs Non-BRT Stations<br>° 1 rod 1<br>3<br>pot! J tt oth<br>So HielMldL dnd Asa | |<br>AAaril He Mh!MAANil a i<br>fa] ES SRA line ta I<br>TI : PEs apt Alt tot I<br>8; 98 8 @ 828 g 8<br>i ; i 5<br>3<br>Day before/after pening<br><!-- End of picture text -->

<!-- Start of picture text --> Difference of Log in Average Trip Distance - 50m radius, BRT vs Non-BRT Stations<br>ry i 4 i<br>_ | it |<br>2, = a 1 Ul Mit ii 4 rh My<br>e3 +STeEY TC<br>2 I Io 1<br>a ! ! |<br>~ +<br>g 3 8 : E : 8 : 8<br>2 fig = ire<br>5<br>Day beforelattr opening<br><!-- End of picture text -->
Figure 9 presents results when considering these two different types of station locations separately.<sup>28</sup> While there is some overlap in the confidence intervals, they indeed suggest bigger impacts of the opening of the MRT on both the volume of ride-hailing trips and on average ride-hailing trip distance for station locations that are not also served by the BRT. This is consistent with our reasoning above that new ride-hailing trip creation for the purposes of first- and last-mile connectivity is likely to be more extensive for locations that were not already being served by the BRT. These results also raise a cautionary note for (future) studies examining the impact of public transit options on ride-hailing activity using a similar empirical design as ours: considering the presence of pre-existing rapid mass public transit options is important. One runs the risk of underestimating the degree of complementarity between the newly opened public transit option and ride-hailing activity when most of the newly opened stations in the sample were already well-served by other forms of mass-rapid transit.

# **_5.4. Robustness – the validity of our choice of control locations_**

The validity of our choice of control locations is a crucial assumption underlying our event-study design. As noted in Section 3, one could raise concerns regarding both sets of control locations used in our baseline estimations. For the planned Phase 2a station locations there is an active intent to treat. Meanwhile, the intermediate locations along the opened Phase 1 line could, due to their closer proximity to the newly opened stations, experience spillovers from the opening of the MRT, running the risk of contamination bias. In fact, it was for this reason that, in all baseline results that consider average trip distance as the outcome variable, we restricted the control locations to planned Phase 2a locations only (see also footnote 12). In this section we show results that instill further confidence in our choice of control locations.

_First_ , we conduct the following thought experiment: we re-run our baseline analysis, only now using as the treatment group the yet to open Phase 2A station locations, and the intermediate locations on the already opened Phase 1 line as control locations.<sup>29</sup> Figure 10 shows that the opening of the MRT system had no noticeable significantly different impact on either the total volume of ride-hailing trips or average trip distance for the yet to open Phase 2A station locations compared to the intermediate locations along the already opened Phase 1.<sup>30</sup> The fact that these two different sets of control locations, each chosen for completely different and unrelated reasons, do not experience significantly different trends in ride-hailing

> 28 Nine out of 13 MRT stations have intermodal integration to BRT stops: ASEAN, Bendungan Hilir, Blok M, Bundaran HI, Dukuh Atas, Istora, Lebak Bulus, Senayan and Setiabudi (https://jakartamrt.co.id/id/stasiun/). 29 Note that it is immaterial for our results which of the two sets of control locations we take as “treated”.

> 30 Again, these results also hold if we define locations using 100 m radii circles rather than 50 m radii circles. Results available on request.

21
<!-- Start of picture text --> Difference of Log in Total Activities - 5om| radius,io Phase 2A (T) vs In-between] (Cc)<br>= I io ]<br>62 |I io14 ]|<br>a7 4 arta ; |1 11 4| I].lie Gt al ilta |<br>Fa;a lig ay ae mete HNWl aT)Ger MN AU] lelNVR7 .<br>Leo it Ay. NI" MV\s Na A. MN WlWOIY VN eyitaMAN WV<br>g | | | l/ Wy y H ¥) | \| | W vir<br>3 nell I in | SARNIA| \ HL Ly i 4slit \l}<br>7 1 ! TOI |<br>| to |<br>§ 8 3 @ g2s8 2 8<br>a" .<br>Day before/after opening<br><!-- End of picture text -->

<!-- Start of picture text --> Difference. of Log in Average Trip Distance - 50m' radiusrot - Phase 2A (T) vs In-between'<br>| ||<br>oe3 , 1 1<br>82 7 i \ i i<br>Hi Na Sh Weedcal 7)eal WeWi ea 7 Tz!Ge rl<br>SANA5 Wil Ha intMUAY aaN | AUW HtLAANvy V {NtalAN yi WT<br>® A | |<br>3ay + ime J i WTA AaE tt idH “EI!\ sf :<br>| ot |<br>‘ | iol \ L<br>8 8 3 = gg 8 g 8<br>a4<br>Day before/after opening<br><!-- End of picture text -->
<!-- Start of picture text --> oid Difference of Log in Total Activities,' U0-50m4 vs 50-100mCf band<br>_ \ ty pala alll<br>3°: ae : 1 |<br>5 |<br>Bo NVA id MT Md AN Mi Me a .<br>oe ay a 10 |<br>z 32 z<br>Day before/after openingj<br>Difference of Log in Total Activities, 0-50m vs 50-100m band, BRT vs Non-BRT Stati:<br>ol |1 totIo real1 5 TI i<br>i lH iain ial ihe ins<br>el AMP OFAMAMLAL I jahal i<br>Oe Ee<br>a4 di hoa 1<br>8 8 8 2 8s 8 5 &<br>Day before/after opening<br><!-- End of picture text -->

<!-- Start of picture text --> Difference of Log in Average Trip Distance, 0-50m vs 50-100m band<br>N ' ' ' '<br>Micra alta Mallard Mla :<br>get4 Avy AIHfy scat aM Ate| . tL to oF<br>1 allyl) l<br>3° Pp 1 uN Ht | il<br>I tot I 7<br>z & ; =<br>bay betorelater opening<br>‘erence of Log in Average Trip Distance, 0-50m vs 50-100m band, BRT vs Non-BRT S<br>=< T |I todIo |1<br>; si Hi ages<br>5 i Hlia eetod a Nt CCA vant<br>i ae i Ni it<br>© I Io |<br>8 8 3 2 g¢ 8 g &<br>Day before/after opening<br><!-- End of picture text -->
<!-- Start of picture text --> ~<br>-<br>H it ‘<br>\ It ]<br>g It<br>I fir t - \<br>BOESE5 | LI mFby ai Fal hf1 wit)til)Wt al| ip | ni|| | I<br>Ce) a | |<br>fe A Nt<br>= ~ Lt | ut 1 > J u<br>6 + 1 I | J i4 |<br>-|. 4 I| t| 4I | I1<br>8 8 8 z22 gé &2 8 8se 8<br>:<br>5<br>Day before/after opening<br><!-- End of picture text -->

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There is no evidence whatsoever that ride-hailing activity changed differentially in locations nearer to our control locations, providing (even more) confidence in the choice of control locations underlying our baseline estimates.

# **6. Conclusion**

In this paper, we have empirically investigated whether ride-hailing services are a substitute or complement for mass rapid transit (MRT) in the specific case of Jakarta, one of the largest and most heavily traffic congested cities in the world. To do so, we exploited the phased and staggered opening of Jakarta’s MRT system, employing an event study design to estimate the impacts of the system’s Phase 1 opening on ridehailing activity within the immediate neighborhoods of newly opened MRT stations. We find that the system’s opening led to large increases in the volume of ride-hailing trips in the immediate neighborhoods of newly opened MRT stations, while at the same time reducing the average distance of ride-hailing journeys to and from these station locations. These results hold for journeys made on regular weekdays, weekends, during rush hours and during non-rush hours alike. And, they are more prominent in the vicinity of newly opened MRT stations that were not already served by Jakarta’s BRT system. For these locations, the opening of the MRT increased their rapid public transit options at the extensive margin, whereas it only did so at the intensive margin for MRT station locations that were already served by the BRT.

Taken together, our results strongly suggest that ride-hailing and public transport are complements, with the new ride-hailing trips associated with the MRT system’s opening providing important first- and lastmile connectivity, both for trips made for commuting and non-commuting purposes. These effects are very localized. They are primarily driven by the changes in _Gojek_ -activity within 50 m of the newly opened stations. The motorbike-based ride-hailing in Jakarta, much more flexible in terms of being able to pick up and drop off customers as close as possible to their desired pick-up or drop-off location compared to the mostly car-based ride-hailing activity analyzed in earlier studies (notably Gonzalez-Navarro _et al._ , 2021), is an important explanation of this more localized effect. It is very likely to carry over to other developing country city contexts, where ride-hailing services are also primarily motorbike-based.

Our results carry several important potential policy implications. The strong complementarity between the MRT system and ride-hailing services suggests that, far from being a threat, ride-hailing services help boost demand for mass rapid public transport in Jakarta with positive implications for its long-run financial viability, as well as for the overall mobility of both workers and households within the Jakarta metro area. Especially for those that cannot afford their own vehicle, live in places without reliable public transport 25
alternatives, and/or for whom walking to the newly opened station is not an option (either because of safety or distance considerations).

It is also worth noting, however, that our study has several limitations. Most importantly, perhaps, while our analysis focuses on the impacts of the opening of the MRT system on demand for ride-hailing services in the immediate neighborhoods of newly opened MRT stations, it is silent on impacts elsewhere in the city. In other words, our findings should strictly speaking be taken as strong evidence for _local complementarity_ . The opening of the MRT may, for example, have decreased ride-hailing activity to/from locations near the newly opened MRT stations to locations further away, as people now take an MRT ride, (possibly using a _Gojek_ ride for the first/last mile only) to get to that location, instead of, as before, using ride-hailing for the entire journey.

In this respect it would be particularly interesting for future research to be able to trace back how/where people who are now using ride-hailing services for their first and/or last-mile connectivity to the MRT were using ride-hailing services, possibly in combination with other forms of public transport, before the MRT system opened. Another fruitful direction of future research would be to estimate the impacts of the MRT system on local levels of pollution, greenhouse gas emissions, traffic congestion, and the use of other forms of public transport, as well as to replicate our analysis for other cities in Indonesia, and elsewhere in the developing world where the rise of ride-hailing services predates the construction of rapid mass public transit options, fewer people own their own transport, and where ride-sharing is predominantly motorbikebased.

# **REFERENCES**

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- BPS, 2020. Jabodetabek Commuter Statistics (Statistik Komuter Jabodetabek: Hasil Survei Komuter Jabodetabek) 2019. Badan Pusat Statistik (BPS).

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