For those that don’t read Transportblog on a daily basis, this is the third part of a series I’m writing on the economics of public transport fare policies. Part 1 discussed a key rationale for public transport subsidies – lower fares keep people from clogging up already-congested roads. Part 2 considered the case for distance- or zone-based fares to ensure that people taking longer (and hence more expensive) trips pay more.
In the comments on those posts, several sharp readers asked about the relationship between fare levels and ridership, and whether there are any opportunities to improve outcomes by targeting lower fares to highly price-sensitive groups. These are excellent questions to ask!
In this post, I’ll take a look at the first question: In the aggregate, how does ridership respond to changes in fares? Hopefully, this will give us the theoretical tools to take a look at the second question in the next installment of the series.
In economic terms, we are asking about the “price elasticity of demand” for public transport. Fare elasticities measure how responsive people are to higher (or lower) prices. They’re usually estimated empirically by analysing data on changes in fares, patronage, and other control variables (e.g. per capita income or GDP) over time.
There are many studies on fare elasticities from around the world, some of which are summarised in the Australia BITRE elasticities database and this useful summary paper by Todd Litman. NZTA has also commissioned research into the structure of demand for public transport – see e.g. Wang (2011) and Allison, Lupton and Wallis (2013).
These studies don’t always arrive at precisely the same result, but they agree on one key thing: Demand for public transport is relatively “inelastic”. All else being equal, a 10% reduction in fares will increase ridership by less than 10% in the short and long run.
The implication of this is that if a public transport agency reduces fares, it will tend to collect a smaller amount of money from users and hence require a larger subsidy. And, conversely, raising fares can increase overall revenue, albeit at the cost of unintended consequences for increased traffic congestion.
Here’s Litman’s best-guess estimates of elasticities for public transport. The key figures are in the first row – “transit ridership with respect to transit fares” for the overall market. Litman’s estimates a long-run fare elasticity between -0.6 and -0.9. This means that a 10% increase in fares would be expected to reduce ridership by 6-9% in the long run.
Notice that short-run elasticities tend to be smaller, indicating that people take a while to fully respond to changes in prices. For example, if someone’s fares for their bus to work went up significantly, they may tolerate it for a little while but choose to buy a car (or rent a parking space) six months down the line.
Personally, I wonder if Litman’s estimates are a bit on the high side. Figures from Wang (2011) suggest that long-run fare elasticities (in the second row of the following table) are -0.46 in Wellington and -0.34 in Christchurch. This would indicate that a 10% increase in fares would reduce ridership by 3.4-4.6%.
Both of these tables also contain information on how people’s demand for public transport changes in response to other price changes and service changes, which is another interesting topic. Without going into a great deal of depth, I’d note two things:
- First, increasing petrol prices do tend to increase public transport demand, but this effect may be relatively modest. Car ownership, on the other hand, can have a big impact, as people who have already paid the fixed costs to own a car have strong incentives to get as much use out of it as possible.
- Second, improved service quality – meaning better frequency and reliability of buses and trains – has a stronger impact on ridership than lower fares. This has important implications for transport agencies, who are often better off putting their marginal dollar towards upping frequencies.
Lastly, it’s worth considering how this might play out in practice. Let’s assume, for a moment, that fare elasticities of demand are at the low end of Litman’s range, i.e.:
- Short-run fare elasticity = -0.2
- Long-run fare elasticity = -0.6.
Now, let’s consider a hypothetical scenario in which public transport fares are $2 and there are 1,000 daily riders on a given bus route. The public transport agency collects $2,000 in fares every day ($2*1,000 riders).
Now let’s consider what would happen if the agency chose to reduce fares by 10%, from $2 to $1.80. This is obviously great for people who are already on the bus, as they can pay less to get the same service. Daily revenue collected from them drops to $1,800 ($1.80*1,000 riders).
However, the lower fares also attract new riders. In the short run (0-2 years), we predict that a 10% reduction in fares will lead to a 2% increase in ridership (-10%*-0.2). This means that an additional 20 people (1,000 riders*2%) will take the bus and pay a total of $36 in fares every day ($1.80*20).
So far, this is not looking great from a financial perspective. The transport agency has lost $200 in fare revenue from existing riders and gained only $36 from new riders.
Things aren’t much better in the long run, where a 10% reduction in fares is expected to lead to a 6% increase in ridership (-10%*-0.6). This means an added 60 riders who pay $108 in fares every day. Again, this is not enough to cover the loss in revenue from existing riders.
Does this mean that fare reductions are never worth it? Not necessarily – if the reductions in congestion from fewer people driving are sufficiently large, then we should be willing to pay a bit more in subsidies.
A second factor is that different people and different types of journeys respond to higher prices in different ways. In principle, we may be able to increase patronage at a relatively low cost by targeting fare discounts to price-sensitive people. But that is a topic for next time!
What do you make of the data on fare elasticities of demand?