Each weekend we dig into the archives. This post by Stu was first published in September 2017.
When we talk about transport, it’s natural for our minds to focus on moving people from A to B. That is, we tend to think about how to meet the demand for travel between locations, assuming that the demand for being in those locations is unaffected by the transport policies that we implement. In this post I present some evidence that questions this assumption, and argue there is a need to re-frame how we think about the economic effects of transport policy.
In some situations the effects of transport infrastructure on location choice is readily apparent. The Auckland Harbour Bridge, for example, opened up vast swathes of extremely amenable land close to Auckland’s City Centre. Surprise, surprise: After the Bridge opened many people and firms chose to locate on the North Shore, increasing the demand for travel between there and other locations. If we had better understood the effects of transport on location choice at that time (no doubt some people did), then perhaps we would have retained the ability to add rapid transit to the AHB?
A growing number of economic studies analyse the locational effects of transport investment in a systematic way, and find that they are indeed significant. Let’s consider a couple of recent studies.
Duranton and Turner (2011) analyse the relationship between the supply of road capacity (measured in lane-kilometres) and the demand for vehicle travel (measured in vehicle kilometres travelled) in the U.S. The authors find a nearly perfectly elastic relationship between supply and demand, which they refer to as the “fundamental law of traffic congestion”. That is, a 10% increase in supply is met by a ~10% increase in demand. Duranton and Turner present evidence that a large chunk of this demand response is caused by changes in location, with the balance associated with changes in travel behaviour (such as mode and route choice).
Teulings et al (2014) analyse the effects of rail services on location choice in the Netherlands. After motivating and estimating their economic framework, the authors then simulate how people relocate in response to the withdrawal of rail services to a region north of Amsterdam. They find relatively large changes in the location of people and firms when rail services are withdrawn. The economic value of the land use effects of rail services are approximately 25 percent of the transport (modal split) benefits. And in this context, the main effects are felt by the highly-educated, who are more likely to live and work in the locations where rail is available.
While not published in an academic journal, Auckland Council’s recent (excellent) research into the effects of walking times on effective density and productivity sit on the same tentacle of the economic octopus.
In a similar vein, my recently completed masters thesis uses commuting data to analyse the effects of walking and cycling travel-time on where people choose to live and work in three Australian cities. The intuition underlying my analysis is that commuting data reveals information on people’s preferences for home and work locations, and the perceived costs of travelling between the two. For those of you who are short on time, here’s my results in a nut-shell:
I find that walking and cycling travel-time has an economically and statistically significant effect on where people choose to live and/or work. A one minute saving on a 15 minute journey between two locations is predicted to cause a 1-8 percent increase in commuting flows between those locations. The magnitude of the effect decreases with commute length, and varies by city and model specification. I find some evidence that low-income commuting flows are more sensitive to walking and cycling travel-time.
In my thesis I focus on Australia in general and Brisbane in particular. Australia provides a useful setting for such analysis due to its comprehensive census data and high levels of residential mobility. People change where they live and work relatively frequently, which in turn enables them to take advantage of differences between locations, e.g. due to changes in walking and cycling travel-time. Brisbane is particularly interesting because of the Brisbane River, which carves a torturous path through inner-city suburbs and introduces considerable variation in travel-times between transport modes and locations. I exploit the quasi-experimental setting created by the Brisbane River to identify the effects of walking and cycling separately from other transport modes.
The effect of the River on my data can be seen in the following two figures. The left-hand figure plots car travel-times (horizontal axis) versus cycle travel-times (vertical axis) between origins and destinations in my data set (NB: Locations are defined as the centroids of census areas). The 45-degree line demarcates journeys for which cycle and car travel-times are equal; points that lie below the line relate to journeys for which cycling is faster and vice versa for points that lie above the line. The right-hand figure shows the ratio of cycle travel-times to car travel-times. While car is, on average, approximately twice as fast as cycling, I find 155 origin-destination pairs for which cycling is faster than car. Many of these locations relate to suburbs that are close to the Brisbane River.
As noted above I find that reducing the walking and cycling travel-time between two locations causes an increase in commuting flows between those locations. The magnitude of the effect is relatively large for short journeys: A one minute saving on a 15 minute journey between two locations increases commuting flows by 1-8 percent. Somewhat intuitively, the size of this effect decreases (linearly) with distance. I find some evidence that low-income (possibly part-time?) commuters are more sensitive to walking and cycling travel-time, although the difference is not always significant. The effect in Brisbane > Perth > Adelaide, which I hypothesise may reflect differences in congestion levels in each city, with larger locational responses in larger cities.
So what does all this mean for other places? While one must always be careful when extrapolating analytical results beyond the context in which they are derived, there are three fairly general implications that seem likely to transfer to other settings, such as Auckland.
First, my results align with the growing body of economic evidence which finds that transport investment has significant effects on location choice. When thinking about transport policies, we should consider not just how they relate to the demand for travel, but also the demand for locations. So my pitch to Mayor Goff and other councillors is that — when looking to prioritise the transport budget — it would seem wise to cull transport investment in areas that will not accommodate much growth. I realise this is a bitter pill for some, but the reality is that in the context of limited transport budgets, we should seek to reduce the costs of travel between locations that are able to accomodate development.
I note this finding is general insofar as it extends beyond walking and cycling. Public transport fare policies that reduce the costs of long distance journeys, for example, will increase the demand for those journeys by virtue of their effects on location choice. And if we are to extend rail services to regions south of Auckland, then this investment should be accommmodated by supportive land use policies. I’d go as far to say that such policies should be a pre-requisite for rail service. And if towns aren’t able or willing to support land use development, then perhaps they should be dropped from the network?
Second, my results suggest that improvements in walking and cycling infrastructure have significant effects on location choice, especially where journeys are short. The implication is that I am more excited about the effects of pedestrian access to tunnels under Albert Park than, for example, the SkyPath walking and cycling connection over the Harbour Bridge. Now don’t get me wrong, I think the latter is desperately needed, and is likely to be well-used. However, I don’t expect many people will re-locate to the North Shore because of SkyPath, mainly because the journey is, for many people, a relatively long one. On the other hand, the Albert Park tunnels will shorten walking and cycling journeys between the city centre and Parnell, which are already well within walking and cycling distance.
Third, in congested urban areas even small-scale changes in walking and cycling travel-time, can – at the margin – have relatively large effects on location choice. A one minute saving could arise from changes in the phasing of traffic signals, for example. This effect works in both directions: By reducing walking and cycling connectivity, the Central Motorway Junction may have increased walking and cycling travel-times and reduced the relative attractiveness of Newton. If we now want these areas to intensify, perhaps we should look to mitigate the effect of the CMJ as best we can?
What do you think? Comments, criticisms, and witty lyrical waxing are — as always — welcome.