Causal inference methods in transport research
Transport investments are used to improve the quality of the system (e.g. through reducing travel times for passengers and freight) in turn to encourage growth and/or development of an area or a country. However, transport investments also generate spillovers: improvements in transport can induce positive benefits through agglomeration economies, yet drawbacks can also be observed. Consequences of transport initiatives are not as clear-cut as they may seem, which is why leading ex-post evaluations is crucial.
Identifying causal effects is central to us as we try to isolate the sole impact of the transport intervention net of other effects. Leading transport-related policy evaluation and longitudinal and causal analyses, we also account for potential limitations (e.g. reverse causality). Researchers at the TSC make the best use of state-of-the-art econometric techniques to identify causality in transportation dynamics.
Some of our recent work focuses on the causal economic effects of the Jubilee line on London (1), aviation in China (2) and High-Speed Rail in Spain (3). We are also working on the causal impacts of metro disruptions in London using smart card data (4), and in the returns to scale and density of urban rail operations around the world (5). More recently, we started working on assessing differences in the perception of public transportation by women (6).
- Li, H., Graham, D. J., & Liu, P. (2017). Safety effects of the London cycle superhighways on cycle collisions. Accident Analysis & Prevention, 99, 90-101.
- Mohammad, S. I., Graham, D. J., & Melo, P. C. (2017). The effect of the Dubai Metro on the value of residential and commercial properties. Journal of Transport and Land Use, 10(1), 263-290.
- Li, H., & Graham, D. J. (2016). Quantifying the causal effects of 20 mph zones on road casualties in London via doubly robust estimation. Accident Analysis & Prevention, 93, 65-74.
Do changes in air transportation capacity affect productivity? Evidence from the deregulation of aviation in China
Air transport capacity expansions are often justified on the grounds that they will improve economic performance and induce growth. Such causal impacts are hard to identify empirically due to the fundamentally endogenous nature of the relationship between air transport and the economy. This paper contributes to the empirical literature on aviation-economy effects by conducting a case study of the impacts of air transportation activity on productivity in Chinese provinces. For exogenous variation we exploit a policy scenario created by the 2003 deregulation of the Chinese aviation sector, which was applied in all provinces of China except Beijing and Tibet.
We find that this policy intervention resulted in substantial growth in air transport passengers and cargo. We estimate the causal effect of air transport on productivity by comparing GDP per employee in Tibet relative to a synthetic control region affected by the deregulation policy. We find a significant positive productivity effect from aviation expansion following the 2003 deregulation. Use of a differences-in-differences specification confirms this result.
Authors: José M. Carbo, Daniel J. Graham
Current status: Under review
Evaluating the causal economic impacts of transport investments: Evidence from the Madrid-Barcelona high speed rail corridor
This paper evaluates economic impacts arising from the introduction of high-speed rail (HSR) between Madrid and Barcelona. Using difference-in-differences estimation we estimate an average treatment effect for provinces with stops on the HSR line of 2.4% for economic output, 3.3% for numbers of firms, and 1.1% for labour productivity. We complement our DID results with a synthetic control analysis for Lleida and Tarragona, two provinces that we argue were assigned HSR stations largely due to their incidental location. We find that both the number of firms and labour productivity are substantially higher in these provinces than in their synthetic counterparts.
Authors: José M. Carbo, Daniel J. Graham, Anupriya, Daniel Casas
Current status: Published
Carbo, J. M., Graham, D. J., Anupriya, Casas, D., & Melo, P. C. (2019). Evaluating the causal economic impacts of transport investments: evidence from the Madrid–Barcelona high speed rail corridor. Journal of Applied Statistics, 46(9), 1714-1723.
A causal analysis of the impact of differential pricing on commuter trip scheduling in Hong Kong
To address overcrowding problems in public transit services, transit operators often adopt demand management measures aimed at spreading peak hour demand, including
'soft policies' in the form of monetary incentives to in uence passenger behaviour. Identifying the causal impact of such policies using a traditional travel survey is challenging due to the resource-intensive nature of such surveys. Smart card data offers the possibility of observing highly disaggregate patterns of passenger volumes continuously over time. It provides a level of granularity that allows us to estimate causal behavioural responses at a highly disaggregate level and then link these to observed contextual characteristics. Obtaining causal estimates from smart card data, however, requires application of appropriate methods, which can allow the research to disentangle the direct impact of monetary incentives on regular commuter's trip scheduling behaviour from other potential in uences on demand. To this end, this paper quantifies the causal impact of dierential pricing on trip-scheduling by regular commuters on the Mass Transit Railway (MTR) in Hong Kong. We use the difference-in-difference method to estimate the causal effect of the Early Bird Discount (EBD) on commuter trip-scheduling. We find significant but small effect from introduction of the EBD in terms of earlier departure times. In a second-stage regression we identify travel costs, crowding and work-schedule exibility as the key determinants of commuter responsiveness to the policy. Our analysis suggests that higher income commuters are more responsive, so the policy might be regressive.
Authors: Anupriya, Daniel J. Graham, Daniel Hörcher, Richard J. Anderson
Current status: Under review