The Cost of Traffic: Evidence from the London Congestion Charge☆
Introduction
Traffic congestion is an urban disamenity generated from the agglomeration of economic activities. Attracted by productivity gains and amenities in cities, firms and individuals congregate in urban areas and compete for space, attributing to outward expansion of cities. With the proliferation of automobiles, the surge in traffic on roads inevitably leads to congestion, an ubiquitous problem many cities around the world faces. These traffic delays affect London as well. Average on-road commuting speed in the 1990s was slower than that at the beginning of twentieth century before car travel became prevalent (Newbery, 1990). By 2002, travel speed for motor vehicles during morning peak hours fell by almost 30% compared to that in 1974, from 14.2 to 10.0 miles per hour, and drivers spent, on average, 27.6% of their on-road time stationary (Department of Environment and the Regions, 1998).
Traffic is also a major source of air pollution. According to figures from Environmental Protection Agency, automobiles contribute to more than 50% of the nitrogen oxide, 30% of the volatile organic compounds and 20% of the PM10 in US.1 These emissions have detrimental effects on health outcomes, increasing infant mortality, reducing birth weight and inducing premature births (Currie, Walker, 2011, Knittel, Miller, Sanders, 2016). Heavier traffic can also increase the risk of traffic accidents and fatalities (Li, Graham, Majumdar, 2012, Green, Heywood, Navarro, 2016). It is evident that traffic externalities are undesirable and can affect the attractiveness of neighbourhoods, influencing household location decisions.
This paper measures the average marginal willingness to pay (WTP) to avoid negative traffic externalities (e.g noise pollution, traffic exhaust, elevated traffic accident risk and congestion delays) using the housing market. Because an explicit market for traffic does not exist, the hedonic approach is broadly adopted in the literature to value this non-market amenity.2 The idea is that traffic varies across space and, holding all other factors constant, differences in home values should reflect the willingness to pay (WTP) to avoid traffic. While the concept is simple, attempts to estimate the casual effect of traffic on home prices are fraught with difficulties. First, traffic is not randomly distributed across space and the heaviest traffic is usually around the city center where economic activities are congregated. Unobserved neighbourhood differences between properties across space are likely to confound the estimates. Second, more affluent households who incur costlier time delays could sort themselves from congested neighbourhoods to reduce commuting time. The concern is whether the WTP to avoid traffic could be confounded with the WTP for better neighbourhoods.
Bearing these challenges in mind, this paper exploits the substantial but localised changes in traffic conditions induced by the London Congestion Charge (LCC) to recover the cost of traffic. The charge boundary is drawn around the city centre to alleviate congestion from the most gridlocked roads in London. A flat fee of £5 is imposed for driving into the charge zone during weekdays from 7:00am to 6:30pm. This Pigouvian tax forces drivers to internalize the congestion externalities they impose on others. Closing the gap between the marginal private cost and the marginal social cost of driving, the LCC reduces equilibrium traffic volume and pushes it closer to socially optimum level (Pigou, 1924, Vickrey, 1963). The effects were immediate. Six months into implementation, the volume of cars into Central London fell by 27% and average travel speed was 20% higher than before (TfL, 2003a).
Estimation is based on a difference-in-difference instrumental variable (IV) approach for sales around the charge boundary. I rely on the implementation of the LCC as an IV for changes in local traffic conditions and examine its effect on house prices. Put differently, I am exploiting the sharp variation in traffic conditions in and around the zone induced by the charge. I then compare the changes in traffic flow with the changes in house prices before and after the LCC is implemented to recover WTP estimates to avoid traffic. To obtain consistent estimates for these parameters, the charge must not only significantly affect traffic flow, but it must also influence house prices only via changes in traffic conditions (also known as exclusion restriction).
I identify several possibilities that this assumption could be violated and adopt different strategies to mitigate them. First, unobserved shocks to neighbourhoods could be correlated with the enforcement of the LCC. Hence, I restrict the analysis to sales close to the charge boundary (up to 500 m) such that I am comparing properties in similar neighbourhoods but at different sides of the charge boundary. Second, home purchasers could pay more to relocate into the charge zone because residents living inside enjoy a 90% waiver to the charge. Exploiting sales that are entitled to the discount but are outside the charge zone, I demonstrate that these subsidies have a negligible effect on home prices. Furthermore, affluent households, who incur higher cost for being caught in the traffic, could sort themselves into the charge zone. These households could purchase better quality units, affecting the composition of houses sold after the charge is enforced. Conducting a battery of balancing tests on a rich set of housing and neighbourhood characteristics, I do not find households sorting across the boundary, and any changes in the composition of houses sold after the charge is enforced.
The headline finding is that homeowners moving into the charge zone pay more to enjoy better traffic conditions. After the LCC is implemented, traffic volume is 8.77% lower (1562 fewer vehicles every day) relative to neighbourhoods outside the charge zone, illustrating the efficacy of the charge in reducing traffic. Corresponding to this improvement in traffic conditions, home prices are approximately 2.84% (£18,555) higher in the zone. Putting these results together, the instrumental variable (IV) estimates suggest that the elasticity of housing values with respect to traffic volume is around 0.30. These estimates remain stable and significant across a battery of robustness tests. Multiplying these capitalization gains with the number of dwellings in the charge zone, I document that the LCC has generated substantial windfall of more than £3.8 billion for homeowners in the zone. This gain measures the present value of the local benefits associated with the LCC. Further analyses reveal that these house price gains could stem from safer roads and better air quality after the charge is enforced.
The remainder of this paper is structured as follows. Section 2 provides an overview on the Congestion Charge in London. Section 3 describes the existing literature on this subject. Section 4 outlines the data and Section 5 illustrates the identification strategy. Findings are then discussed in Sections 6 and 7 concludes.
Section snippets
Road pricing in London
The initial Congestion Charge Zone (CCZ) covered a total of 21 square kilometres (slightly more than 1% of the Greater London Area) and encompassed the financial centre (Bank), parliament and government offices (Palace of Westminster), major shopping belts (Oxford Circus) and tourist attractions (Trafalgar Square, Westminster Abbey, Big Ben, St Paul Cathedral etc).3
Literature review
To estimate the marginal willingness to pay (WTP) to avoid traffic, the hedonic property value approach is widely adopted in the existing literature. An association between traffic externalities, measured by traffic volume (Hughes and Sirmans, 1992) or noise (Palmquist, 1992, Andersson, Jonsson, Ögren, 2010), and housing prices are established using regression adjusted for differences in observable housing and neighbourhood characteristics. A review of the previous literature indicates that the
Data
Average annual daily traffic flow (AADF) collected at each count point (CP) from 2000 to 2010 is retrieved from the Department of Transport (DfT).11
Identification strategy
Traditionally, hedonic regressions estimating the effects of traffic on house prices adopt the following specification:where is the logarithm of price for property in neighbourhood sold at time . is the logarithm of local traffic conditions measured by local traffic volume near property at time . The key variable of interest, measures the percentage change in home prices from a 1% change in local traffic flow. This exercise
Empirical results
In this section, I examine the effects of the London Congestion Charge on traffic and house prices. First, I describe the dataset with summary statistics. I then examine the impact of the LCC on both traffic and home prices before combining the estimates to recover the WTP to avoid traffic. Next, I assess the sensitivity of the estimates to a battery of robustness tests. I then examine the impact of the LCC on traffic safety and air quality to understand why homeowners are paying more to reside
Conclusion
This paper exploits the sharp but localised changes in traffic conditions induced by the London Congestion Charge (LCC) in the Congestion Charge Zone (CCZ) and the Western Extension Zone (WEZ) to estimate the marginal willingness to pay (WTP) to avoid traffic using the housing market. Using the LCC as an instrumental variable for traffic conditions, this study is an improvement from typical cross-sectional approaches that are blighted by omitted variable bias and sorting.
Results suggest that
CRediT authorship contribution statement
Cheng Keat Tang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization.
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I would like to thank the editor and the two anonymous referees for the useful comments and suggestions. I would also like to thank Antonio Bento, Chris Cunningham, Christian Hilber, Diego Puga, Daniel Sturm, Gilles Duranton, Jos Van Ommeren, Killian Heilmann, Matthew Turner, Henry Overman, Hans Koster, Steve Gibbons, Stefano Carattini, Nic Sanders, Olmo Silva, Felipe Carozzi, Sefi Roth, Vernon Henderson and the other participants from the UEA Summer School in Barcelona, London School of Economics Work-In-Progress seminar, University of Southern California, Eureka Seminar in VU Amsterdam, National University of Singapore Brown Bag Seminar, UEA European Conference in Copenhagen, AEA-ASSA conference in Philadelphia for their comments. This paper is part of my PhD thesis in the London School of Economics. The earlier version of this paper is titled ”Traffic Externalities and Housing Prices: Evidence from the London Congestion Charge”.