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.

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