One of the best things done recently by governments on both sides of the Tasman has been the establishment of Productivity Commissions tasked with investigating the economic efficiency (or lack thereof) of various policy areas. The NZ Productivity Commission, which only started up in 2011, has been diligently building an evidence base on issues as diverse as international freight, service sector productivity, and land use policies.
Transportblog reviewed their recent inquiry on using land for housing, which had some excellent insights and recommendations. Not all of the recommendations made by the Commission have been picked up (yet), but they form a useful basis for future policymaking.
We’ve been paying attention to the NZ Productivity Commission, but it’s also worth keeping an eye on the Australian version, which is grappling with many of the same issues. They recently published a nice summary of their 2014 inquiry into public infrastructure, which highlighted some key steps for improving the efficiency of public investment.
The Commission highlighted the scale of capital investment. Australia’s expecting to spend megabucks on new capital investment, most of which will go into the built environment – i.e. houses, roads, railways, water infrastructure, etc:
In 2013-14, there was an estimated $5.1 trillion or more of installed capital that was available for use in the Australian economy — over three times the value of production in that year (figure 3.1, top panel). Over the next 50 year period, the Commission has estimated that new capital investment will be more than five times the cumulative investment made over the last half century to around $38 trillion in today’s prices (PC 2013).
The largest component of today’s installed capital is in the form of non-dwelling constructions — which consists of non-residential buildings (i.e. buildings other than dwellings, including fixtures, facilities and equipment integral to the structure) and other structures (including streets, sewers, railways and runways) — which amount to over $2 trillion (or 43 per cent of the total). The next single most important component relates to dwellings, amounting to around 35 per cent of the total installed capital (figure 3.1, bottom panel).
Given this, it’s important to get new public investments right:
Additions to the stock of capital will usually increase output and add to labour productivity. However, for productivity to improve, the growth in output must exceed the growth in inputs. Poorly selected projects can detract from productivity as the resources they use would have delivered a higher output elsewhere in the economy.
The Commission had some stern words about the quality of decision-making about public investments. Their analysis suggests that “there is considerable scope to improve the quality and efficiency of government investment in public infrastructure investment in Australia”.
This could probably be translated as: “You guys are doing a bunch of stupid stuff. For the love of god, please stop it!”
So how could infrastructure investment improve? The Commission discusses public-private partnerships (PPPs) as a potentially useful option, but observes that they are not a magic panacea for bad project selection. In some cases, the PPP process can drive governments towards costly, oversized solutions:
Most relevant to enhancing the efficiency of the provision of public infrastructure is improving project selection processes. Australia’s cities and towns generally function adequately and assets undergo usual maintenance, although problems have emerged in some major cities. Nevertheless, the Commission found numerous examples of poor value for money arising from inadequate project selection and prioritisation. In particular, there was a bias toward large investments despite the returns to public investment often being higher for smaller, more incremental investments. In part, this was because the private sector is more interested in financing large investments (due to the costs involved), and governments have increasingly seen public private partnerships (PPPs) as a way of harnessing not just finance, but expertise in project delivery and operation.
Furthermore, the fact that PPPs typically involve complex negotiations and ongoing relationships between governments and private investors can make it difficult to properly assess costs, benefits, and risks. In essence, this creates a situation in which private sector involvement does not result in a better outcome, due to muddled incentives.
The Commission concluded by encouraging Australian public infrastructure providers to conduct rigorous, consistent cost-benefit analysis for all major projects and – equally important – to publish the results prior to committing to a project:
A key recommendation of the report was that governments should undertake a comprehensive and rigorous social cost—benefit analysis to all public infrastructure investment projects above $50 million. Such analyses should be publicly released during the commitment phase and be made available for due diligence. In general, cost-benefit analyses should be done prior to any in-principle commitment to a project or as soon as practicable thereafter.
Doing this would have avoided the farcical situation facing Melbourne’s East-West Link, an A$8.6 billion project to build a massive tunnel to link up several motorways:
As far as I can tell, the project was announced and confirmed before a business case was completed or published. As a result, nobody really knew whether it was a good idea. The project was subsequently cancelled, albeit at a significant cost for cancelling the contract.
Do the figures stack up?
The truth is that we don’t know. Those against all new major road projects may not care about the figures one way or the other, but those who follow these things closely say the project is unprecedented for its lack of transparency. It’s been a nagging political problem for the government, and a key reason the East West Link is so contentious.
“Normally we would see more detail, and historically it’s been much clearer on what basis we are proceeding with projects like this,” [infrastructure consultant William] McDougall says.
“This is new for Australia,” says [transport policy lecturer John] Stone. “The fact that through all these court cases and all this political focus the government has never released its business plan – it released a back of envelope estimate – means probably there’s nothing to back it up. If they had a better number they would have put it out there.“
Fortunately, New Zealand seems to do a better job when it comes to consistent cost-benefit analysis of transport projects (although we don’t always take the findings seriously). However, there is always room for improvement. The Commission highlights three key factors that can undermine the effectiveness of cost-benefit analysis:
- Optimism bias. There is a systematic tendency for project appraisers conducting cost-benefit analysis to be overly optimistic — the bias is toward overstating benefits, and understating timings and costs, both with respect to initial capital commitment and operation costs. Over estimates of traffic forecasts on toll roads and tunnels are a particular problem…
- Treatment of risk and uncertainty. Costs and benefits are expected values based on the probability of different outcomes. Cost-benefit ratios may be sensitive to certain assumptions which have to be made without sufficient evidential support. For example, inappropriate assumptions about allowance for project risk in the discount rate (that is, the risk premium) may alter the ranking of projects and lead to suboptimal project selection…
- Treatment of ‘wider economic benefits’. Infrastructure projects create direct benefits for users of the resulting service provided by public infrastructure. Where cost-benefit analysis is done, such benefits are routinely estimated and included. However, projects can also create wider economic benefits and costs. For example, investment in transportation infrastructure brings consumers closer to more businesses, potentially facilitating greater competition and leading to a more innovative and a dynamic economy. However, such wider economic benefits are hard to quantify and their inclusion in a cost-benefit analysis has the potential to show one project to be superior to another purely because of differences in the way such benefits are defined and estimated. Cautious and consistent treatment across options of wider economic benefits is warranted.
Transportblog’s highlighted a few of these issues in the past. I’ve discussed optimism bias on the benefits/usage side here and optimism bias on costs here and here. Stu’s covered off treatment of wider economic benefits here. And it’s probably high time we took another look at discount rates / risk premia…
I’m not a financial economist, but I occasionally take a look at the Reserve Bank’s fantastic collection of credit statistics. Their short-term and long-term data series often say a lot about where the New Zealand economy’s currently going and where it’s been.
One interesting indicator is the 10-year New Zealand government bond yield, which measures the interest rate that the government must pay on its debt. Here’s a chart of the last 20 years of government bond yields (unadjusted for inflation):
As you can see, bond yields were pretty consistent throughout the mid-2000s – sitting at around 6%. However, they started a major downward slide in 2011, rose in 2013, and then fell back again in 2014. At the time of writing (12 August 2015; these posts are written and scheduled in advance), bond yields were sitting at 3.29%.
Essentially, the government’s borrowing costs are sitting at historic lows. This is important for two reasons.
First, it is a symptom of persistent economic weakness in New Zealand and the rest of the world. Pessimism about growth prospects has led people to prefer to lend money to governments, where they can get a lower but more certain return, rather than invest in businesses. The more people try to lend to governments, the more bond rates are driven down.
Second, low government bond yields enable governments to borrow more to make long-term investments in infrastructure and state housing. This doesn’t mean that we should simply borrow money and spend it at random. We still need to exercise some rigour in project selection and economic evaluation, and ensure that we’re not overheating the construction industry by trying to do too much at once.
However, it does mean that we can afford to go a bit further down the list of worthy projects. Many of the issues currently bedevilling New Zealand – Auckland’s growth pressures and Christchurch’s halting rebuild – could be addressed if central government simply borrowed a bit more money to build new streets, public transport facilities, cycleways, and housing developments. It’s literally never been cheaper.
House prices in Auckland are high and rising, which is causing concern in many quarters. But do we know what sort of effects high house prices may have on New Zealand society, now and in the future?
Politicians and commentators from all quarters have argued that they are undermining the equity of New Zealand society by making it harder for young people to buy houses and squeezing household budgets. Here, for example, are some recent comments from Finance Minister Bill English:
The Finance Minister agrees rising house prices in Auckland are making inequality worse by shutting low and middle-income earners out of the property market.
Opposition parties say rising inequality is not only hurting those who cannot afford to buy a home, but is also bad for the economy.
Bill English said house prices were making life tougher for low and middle income earners in Auckland and said inequality was a problem.
“We’ve been concerned about that for some time, that there’s parts of Auckland where there’s been really no new supply of lower value houses that low and middle-income families can afford.”
I’ve seen Minister English making similar comments elsewhere – saying that limited housing supply is worsening poverty and inequality. How true is this?
First, here’s a graph showing indexed house prices, rents, and consumer price levels over the last two decades. While housing prices have been rising faster than the CPI throughout this period, house prices have really only taken off dramatically since 2002. Meanwhile, rents have risen at a more consistent pace:
Keep that timing in mind. Most of the increase in Auckland’s house prices has occurred between 2002 and 2008, and again since 2012.
So now, let’s take a look at what’s been happening to inequality over the same time period. Here’s a useful chart from a Stuff article on the topic. It looks at the 80-20 ratio – i.e. the ratio of incomes for a household near the top of the earning distribution (80th percentile) to one near the bottom (20th percentile). While it’s not a perfect measure, as it misses outcomes at the upper and lower tails, it does seem to track with the Gini coefficient, another common measure.
Essentially, what this measure is telling us is that income inequality rose during the late 1980s and early 1990s, during NZ’s painful economic restructuring, flattened, and then trended down slightly since the mid-2000s. Importantly, the same basic trends hold true even after taking housing costs into account, which suggests that rising Auckland house prices haven’t altered the underlying dynamics of income inequality:
In other words, recent increases in Auckland house prices have not coincided with rising income inequality. Minister English is well aware of this. For example, in May 2014 he answered a few questions on inequality in Parliament:
Hon BILL ENGLISH: Well, the facts as laid out in the annual report issued by the Government agency initiated by the previous Labour Government, and now backed up by the OECD, show that income inequality in New Zealand has been flat to falling in recent years, and, on average, it has remained unchanged for the last 15 years.
This does not necessarily mean that we can be sanguine about the impact of housing costs on New Zealand society. For one thing, statistics published in the Ministry of Social Development’s most recent (2014) Household Incomes in New Zealand report suggest that the share of households paying more than 30% of their income for housing has risen since the early 2000s. These changes seem to have affected households across all five quintiles of income, albeit to varying degrees. However, from an equity perspective they’re dwarfed by the impact of early-1990s changes to social welfare and housing policy:
Another issue is that rising house prices may be causing inequality of wealth to increase. The distribution of household wealth has received more attention in recent years, e.g. in Thomas Piketty’s Capital in the 21st Century, which analysed trends in wealth and income distributions over the past two centuries.
There are reasons to believe that higher house prices may increase wealth inequality. For example, 2013 Census data shows that upper-income households are much more likely to own, partly own, or hold in a family trust their primary residence. 80% of households earning over $100,000 per annum own their houses, compared with only 52% of households earning less than $30,000.
However, we simply don’t have enough recent data to form robust conclusions about the impact of rising house prices on wealth inequality. During the 2000s, the Survey of Family, Income, and Employment (SoFIE) did collect some data on household wealth. An analysis of the 2003/2004 data showed that wealth was much more unevenly distributed than income – 51.8% of all net wealth was held by the richest 10% of New Zealanders.
As SoFIE was discontinued in 2010, we have no way of knowing whether or not wealth inequality has increased during the most recent run-up in Auckland house prices. Consequently, I’d say that a hypothesised link between house prices and wealth inequality is potentially concerning, but unproven. If the Finance Minister is concerned about that issue, I’d recommend that he re-start SoFIE so we can get a better idea of whether rising house prices have coincided with rising wealth inequality.
Finally, commentators should note that this post isn’t arguing for or against any particular policy directed at inequality or the housing market. It’s just taking a look at the data (or lack of data) on the topic. With that in mind, what’s your perspective on the link between housing and inequality?
Last month, Local Government New Zealand called for charging rates (i.e. local government property taxes) on Crown-owned land. The idea’s also been supported by the Productivity Commission and ACT’s sole MP, David Seymour:
Local Government NZ, which represents the country’s 78 local and regional authorities, is holding its annual conference in Rotorua.
Its members – including Auckland Council – have voted to investigate the possibility, practicality and principle of local authorities extending rates charges to land owned by the Crown.
The same topic has also been covered by a review by Local Government NZ (LGNZ) of local government funding. A manifesto on this will be presented today.
Act Party leader David Seymour is a strong supporter of charging rates on Crown land. He said that although conservation and recreational land could be excluded, as a matter of fairness schools and hospitals should pay rates because they compete with private organisations.
Mr Seymour said that although there would likely be a slightly lower rates burden on households, better and more efficient use of Crown land would be a bigger benefit.
“We have got this 430ha of Crown land [in Auckland] that allegedly could be turned into houses. Well, why have they waited so long? I suspect one of the reasons is that the Crown can keep land rates-free.”
In June, a draft report by the Productivity Commission, titled Using Land for Housing, recommended the Government start paying rates on Crown land.
It is definitely worth investigating applying rates to Crown-owned properties – and potentially also to Council-owned properties. As Transportblog has highlighted in the past, a surprisingly large amount of land in our cities is owned by governments – for transport reserves, social facilities, golf courses, etc. It’s important to use that land efficiently, and applying property taxes can help encourage that.
By way of illustration, Kent Lundberg recently took a look at a few parcels of publicly-owned land in Auckland, and alternative ways that they could be used. The short story is that there are opportunities that could be grasped if the incentives were right:
From an economic perspective, not charging rates for publicly-owned land will distort public and private investment decisions. That is, it can encourage government departments to use too much land, relative to other inputs. All else being equal, this will in turn reduce the amount of land available for businesses and households.
Let’s consider a few examples. First: schools, which are funded by central government. Schools have a fixed budget based (roughly) on enrollment and the decile rating of the community they serve. In order to educate students, they buy land, construct school buildings, and hire staff.
However, the various inputs to education are not taxed in a consistent fashion. Teachers are taxed – they must pay income taxes regardless of the fact that they work for the government – but land and properties are not taxed. As a result, there is an incentive for schools to use more land, and fewer teachers, as land is untaxed and thus relatively cheaper. This seems troubling in light of evidence that investing in better teaching pays higher dividends than larger sports fields.
A second example: transport infrastructure. William Vickrey, Nobel laureate and originator of congestion pricing, argued that we should be extremely cautious when allocating land to roads, as land used for roads cannot be used for housing or business:
“a cost benefit analysis can justify devoting land to transportation only when the savings in transportation costs yield a return considerably greater than the gross rentals, including taxes, that private businesses would be willing to pay for the space. This in turn means that an even greater preference should be given to space economizing modes of transport than would be indicated by rent and tax levels. And our rubber-shod sacred cow is a ravenously space-hungry, shall I say, monster?”
At present, local and central government do not pay rates for the land under their roads. While they have to buy land to build roads in the first place, they are under no obligation to account for the ongoing and potentially increasing cost of devoting space to roads rather than alternative uses.
To understand why this might be a problem, consider a case in which NZTA is considering building a road through a growing part of the city. At present, land is cheap, but as the area develops it’s expected to get (relatively) more expensive.
If NZTA had to pay rates on the land it used, it would face different incentives that may lead it to pursue a different road design. It may be more willing to consider putting the road underground or configuring intersections in a more space-efficient way, in the expectation that doing so would allow it to avoid rising rates bills in the future. While this could have a higher up-front cost, in the long run society would benefit as it would leave more land for households and businesses.
Lastly, given local governments’ interest in this topic, I’d encourage them to ensure that they are consistently charging rates on council-owned properties, and valuing council-owned land on an equal basis to nearby land in private ownership. At first glance, this seems a bit pointless – councils would just be paying money to themselves! However, if you take arguments about incentives seriously, it’s important for council-owned properties to start to recognise and account for the cost of the land that they occupy.
What do you think about applying rates to publicly-owned land?
Back in June, Stuff published a report on regional airfares, focusing on the way that prices are affected by major events such as concerts and sports competitions. Now, I’m no airline economist, but I’ve got a general interest in transport pricing so I figured that it would be worth taking a look at the topic.
The point of the article seems to be that airplane tickets are higher during periods of high demand. That doesn’t seem too weird, but this guy in Nelson is absolutely ropeable at the thought:
Nelson man Steffan Eden is furious about Air NZ’s fares from Nelson to Auckland and return for the weekend of March 5 and 6 when Madonna will give her first New Zealand concert at the Vector Arena.
Fares that the previous weekend cost $79 are twice that at $159 on the weekend of the concert, an $89 fare rises to $169; and a $129 fare becomes $209…
“Look at the fares the weekends before and after the concert, they’re normal fares. Then on the concert weekend they’re virtually double. It’s quite blatant.”
Eden said the same thing happened when he wanted to go to the Cricket World Cup match between New Zealand and England on February 20. “I wanted to take my kids but didn’t in the end because of the cost,” he said.
The man quoted in the article seems to argue that these jumps up in fares are due to uncompetitive or discriminatory practices by Air NZ. By contrast, the airline says that the price increases are just due to cheaper tickets selling out faster:
An Air New Zealand statement said it has been experiencing high demand for flights into and out of Nelson that weekend due to both the New Zealand Masters Hockey Tournament which is being held in Nelson from February 28 to March 5 and the Madonna concert in Auckland.
“As you will appreciate, where there are major events on flights tend to sell out well in advance, with the cheaper fares selling out the fastest, so booking as early as possible is recommended.”
Now, as an economist I’m always wary of the potential for companies with few immediate competitors to exercise market power over their customers. But in this particular case, I don’t think that’s happening. What we are seeing is the normal, and in fact beneficial, working of supply and demand.
Let’s start with the supply side. Air NZ doesn’t have an infinite budget for airplanes and staff. It faces constraints. If it wanted to run more services between Nelson and Auckland on particular weekends of high demand, it would have to either:
- Pull airplanes off other regional routes, which would potentially satisfy Nelson’s demand but would in turn lead to similar stories about how unfair Air NZ was being to Napier or Timaru or what-have-you, or
- Buy extra airplanes and hire extra staff that would sit idle most of the time and fly only during a few periods of exceptionally high demand. This is superficially appealing, but it would mean an across-the-board increase in fares to pay for a bunch of empty planes.
This isn’t really related, but it’s an interesting picture (Source)
So that’s the supply side. What about demand?
Air NZ has observed, correctly, that demand for flights is not constant over time. Simply put, more people want to fly at some time periods than during others. Airlines can respond to this in a few different ways. The first would be to keep prices constant, regardless of demand. This would turn air travel into a first-come-first served game, which is great if you always buy tickets months in advance but terrible if you have to take a last-minute trip for work or a medical emergency.
The second approach, which Air NZ may be using, is to charge higher prices during periods of higher demand. This may seem less fair, but it’s actually better for (almost) everyone. It means that airlines aren’t constantly booking out flights well in advance or misallocating resources in a futile attempt to give everyone a cheap flight. Travellers also benefit – they get a choice between paying more to travel at their preferred time or finding a cheaper fare at an off-peak time.
I fly for work on a semi-regular basis so I’ve noticed some of the patterns over time. Between 4-6pm, departure gates fill up with suit-wearing men and women headed home from their meetings in time for dinner. Not surprisingly, prices are highest at this time. Later on, prices drop, planes get a bit emptier, and the suits get replaced with casual clothes. By the end of the night, most of the people who want to get home have gotten there, and for a price that they’re willing to pay.
Occasionally, this means that somebody decides not to go to a Madonna concert. But that’s not a flaw with supply and demand – that’s how it’s supposed to work! If the man quoted in the Stuff article didn’t go, it’s only because someone who valued being there more bought the ticket instead.
Finally, I have to ask: Why are people outraged when the principles of supply and demand are applied to airfares? Perhaps it’s because we routinely ignore those principles everywhere else in our transport system.
As numerous economists have observed, we manage our roads like a Soviet supermarket. The price to use roads is set at a single, low value – i.e. NZ’s comparatively low petrol taxes – and thus people queue up for ages to drive on them every morning and evening. The same thing happens with parking, where we have regulated to make it abundant and free and ended up in a situation where people can never get enough parking.
In economic terms, there is no difference between this:
They are both situations in which scarce resources, including people’s time, are misallocated due to poorly-functioning price signals. So rather than asking “why don’t we price air travel as inefficiently as roads?”, we should ask “why don’t we price roads as efficiently as we price airfares?”
A failure to price roads efficiently badly distorts our supply decisions. We are forever pouring more asphalt and concrete that accommodates a few more slowly-moving cars at peak times and sits idle much of the rest of the time. By contrast, congestion pricing would allow us to avoid many of these expenditures by giving people an incentive to travel differently.
What do you think about airfares – and transport pricing in general?
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?
A few months back, I took a critical look at some dodgy arguments about the need for expensive security measures for cities to be “resilient” against terrorism. The whole thing got me thinking: how should we value protection against low-probability events?
This is quite relevant to transport policy at the moment. I’ve seen a number of references to resilience in discussions of major transport projects:
Today’s official construction start on the Transmission Gully Motorway marks a major step towards a safer and more resilient transport link in and out of Wellington.
In 2013 the Government announced its support for a tunnel in preference to a bridge. “With increasing demands on Auckland’s transport network, the Government will continue to work closely with its local government partners to provide a resilient network and wider transport choices,” Mr Bridges says.
But while resilience seems like a good thing, it seems to be more of a slogan than a careful piece of analysis. The whole discussion sometimes reminds me of this commercial featuring my favourite B-movie star, Bruce Campbell:
“If you have it, you don’t need it. If you need it, you don’t have it. If you have it, you need more of it. If you have more of it, you don’t need less of it.”
But is more resilience always good? Do we need more of it? Or do we already have too much of it? And how would we know?
First, it’s worth defining “resilience”. According to Mirriam-Webster dictionary, resilience means
the ability to become strong, healthy, or successful again after something bad happens
In transport, resilience seems to be defined as the ability to respond flexibly to unlikely or low-probability events. For example, here’s a picture of Tamaki Drive during some floods back in April 2014.
Tamaki Drive isn’t built with a massive barrier against the sea, so when a tropical cyclone hit the North Island, things got wet. Drivers were able to get home using other roads further up the hill, but it took much longer than usual. But, as this gent on a paddleboard shows, individuals came up with a range of innovative solutions to the short-term outage:
So with that in mind, how should we value our ability to be resilient to low-probability, potentially high-impact events?
One approach would be to calculate the value of resilience using actuarial techniques. Now, I’m not an actuarial scientist, but I’ve worked with people whose job it is to assess financial risks and picked up a few concepts in the process. An actuarial assessment of risk is conceptually pretty simple. It involves:
- Calculating the impact (i.e. net cost) of a given adverse event,
- Calculating the likelihood (i.e. probability over a given time period) of that event, and
- Multiplying the two together to obtain the expected value of protecting yourself against that risk.
So how does this work in practice. Suppose we’re dealing with a hypothetical case – the Auckland Harbour Bridge example mentioned above. Let’s say that we’re interested in making Auckland “resilient” against volcanic activity knocking out the existing bridge. So let’s make up some figures, and assume that:
- If the bridge was destroyed, it would take 1 year to get another one in place
- There are around 140,000 working people who live north of the bridge. Let’s assume that those people earn an average of $60,000 annually, and, furthermore, that their income would be reduced by 50% if the bridge was out. (Either due to reduced employment or increased cost and inconvenience of longer commutes around the Western Ring Route.)
- The Auckland volcano field has erupted at least 53 times, and the first eruption occurred around 248,000 years ago. This implies that we can expect one eruption every ~4,000 years, on average. So there’s perhaps a 1% chance that an eruption happens within the next 40 years.
Let’s be generous and assume that the next volcano will definitely destroy the existing bridge while leaving an adjacent crossing untouched. Multiplying these figures together, we find that the actuarial value of a second, volcano-proof harbour crossing is: (1 year outage)*(140,000 workers)*($60,000/year)*(50% loss in income)*(1% probability of volcano) = $42 million.
Frankly, that’s not a lot of benefit compared with the cost of a new harbour crossing. This is obviously a rather crude hypothetical example, but so far it doesn’t look like we should place that much weight on resilience to low-probability events. Even if we made a higher estimate of the cost of a bridge-destroying natural disaster, it wouldn’t change the outcome very much as a disaster probably won’t happen within our evaluation period.
However, there are some other factors at work. The first is that people are risk-averse and as a result may be willing to pay “over the odds” to avoid low-probability events. (The existence of profits in the insurance industry is good evidence for this hypothesis – people usually pay more for insurance than insurance companies pay out in claims.)
Returning to the Auckland Harbour Bridge hypothetical, it might be the case that Aucklanders, especially those living on the North Shore, are happy to pay more than $42 million to avoid the unlikely outcome of a volcano cutting one link to the shore. But how much more?
It’s hard to say without data, but I suspect that while people might be willing to pay (say) 20% or 50% above the odds, they wouldn’t be willing to pay 1,000% more. This is probably a productive angle for research, possibly with surveys or psychological studies.
A second factor at work is that we might not be able to accurately predict the likelihood or impact of some events. Nassim Taleb popularised the concept of “black swan events“, which should really be called “white swan events” in Australia and New Zealand. He points out that we often lack a good understanding of the statistical distribution of risks. This can be due to the fact that some events are outside the range that we have previously observed, or due to the fact that there can be a bunch of hard-to-predict indirect impacts in complex systems.
I’m not going to deal with the probability distribution issue that Taleb raises – I’m not a risk expert! – but I’d like to come back to the point about indirect impacts in a later post. Essentially, as the paddle-boarder on Tamaki Drive shows, people have a lot of different ways to react to a transport outage, so the net cost may actually be lower than we might initially assume.
How do you think we should value resilience in our transport system?
The announcement of Auckland Transport’s new fare policy made me curious about the economics of fare policies, so I’m taking a quick look at them. In part 1 of this series, I argued that 100% cost-recovery isn’t a realistic goal for public transport. While charging public transport users for the full costs of their journey may seem appealing, it will result in the perverse outcome of increased congestion on the roads. In the absence of congestion pricing, subsidising public transport can be a useful “second best policy” to improve the efficiency of roads.
In other words, if you like driving, you should also like public transport subsidies, as they make your life a little bit easier.
However, this principle doesn’t tell us much about how we should price different types of public transport trips. For example, should people pay more to take longer journeys on public transport? Some public transport agencies, like the New York Subway or the Los Angeles PT system, don’t think so – they allow you to ride as far as you want for a flat fare. Others, like the San Francisco BART system and most transport agencies in New Zealand, charge higher prices for longer trips.
To give another example, should people pay more to travel at certain times of the day? Most transport agencies in New Zealand don’t think so – Auckland Transport charges users the same price during peak times and the middle of the day. But other agencies, such as the Wellington’s rail system and the Brisbane public transport agency, do raise their prices during peak times.
So we have some choices available to us. What principles should we use to choose relative fares for different routes, once we’ve decided on an overall level of public transport subsidy?
In my view, it’s appropriate to charge a fare that accounts for the marginal cost of using the network at different times and in different locations. For example, if it costs twice as much to get people between points A and B as it does to get them between points A and C, then the trip between A and B should cost twice as much as the trip between A and C.
If we didn’t do that – i.e. if we set fares at the same level for those two trips – we’d expect people to demand more trips between A and B, which are expensive to provide, and fewer cheap trips between A and C. This can in turn make the whole system less efficient.
Similarly, there may be a case to vary prices by time of day. It tends to be more costly to provide public transport capacity to meet peak demands. This is because it’s necessary to buy buses (or trains) and hire drivers that run for two hours in the morning and evening and sit idle the rest of the time. But it might not be possible to go too far in this direction – after all, putting up peak fares too high means pushing more people back onto congested roads.
So if we set aside time-of-use pricing for the moment, we’re left looking at varying charges for different types of trips. In most cases, this means charging more for longer journeys than for shorter journeys. How can we do this?
One option is to use zone-based fares. This is what Auckland Transport has traditionally done, and what it’s proposing in its Simplified Fares policy. The advantage of zones is their simplicity and transparency. You can pinpoint your origin and destination on a map, and know exactly how much you will pay:
However, zone-based fares can result in some odd outcomes near boundaries. For example, under the zones above, if I travelled from Henderson to New Lynn – a four station journey – I’d pay for a single stage. But if I travelled from Fruitvale Road to Avondale – only two stations – I’d have to pay for two stages. Does it really make sense to pay more for a shorter journey just because it crosses a line on a map?
Perhaps it doesn’t. So one alternative would be to move to a fully distance-based fare structure. In effect, you’d pay based on the number of kilometres travelled, regardless of where you were going or how many transfers you made in the process. This has advantages – it eliminates boundary effects, for one – but it’s administratively complex and potentially confusing for users. For example: what happens to paper tickets, which are important for visitors and casual users?
How do you think that we should set prices for different types of public transport trips?
A few months back, Auckland Transport put out its new fare policy for consultation. The draft policy, which they call Simplified Fares, has two main elements:
- Standardised fare zones that ensure that journeys within or between zones cost the same regardless of whether you’re travelling by bus or rail [ferries are excluded]
- No transfer penalties between services, which is a key element in enabling a frequent connective network.
Those are indeed simple principles, but developing and implementing a fare policy is seldom simple. So the whole thing got me thinking: Why do public transport fares work the way they do? And could we do things differently?
As I’m curious, I figured that I should take a quick look at the economics of fare policies. Part one of the series looks at the biggest-picture question: Why do we subsidise public transport?
First, some background. In most developed-world cities, public transport systems are subsidised by taxpayers. Users pay some of the operating costs – ranging from as low as 10% to as high as 80% – but seldom all. In New Zealand, the national farebox recovery policy requires all regional transport agencies to cover 50% of their public transport costs from fares. However, data from the Ministry of Transport suggests that some agencies are closer than others to this target:
Is 50% the right number for all regions? I don’t know – and the answer depends in part on what other goals we’re trying to accomplish with public transport pricing. But it’s clear that some level of subsidy must be provided in order for the entire transport system to work efficiently.
To see why, we need to take a look at what economists call “second-best pricing”. According to Wikipedia, it can be desirable to impose a subsidy to “offset” for an uncorrected market failure elsewhere:
In an economy with some uncorrectable market failure in one sector, actions to correct market failures in another related sector with the intent of increasing economic efficiency may actually decrease overall economic efficiency. In theory, at least, it may be better to let two market imperfections cancel each other out rather than making an effort to fix either one.
In transport, we have a situation where people have multiple options for getting around. They can drive, take the bus (or train), cycle, etc. In this situation, a price change in one market – say, a fare increase for public transport – can encourage people to switch to another mode instead of paying more.
As I argued in a recent post on congestion pricing, road space is usually not priced “efficiently”. All road users pay fuel taxes or road user charges based on the total number of kilometres driven or litres of petrol used. But they don’t pay more to drive on busy roads, where they impose delays on other drivers. As this diagram from a 2012 UK study on the external costs of driving shows, the last 10-20% of car trips impose significant costs on society.
Public transport can play a useful role in smoothing off the big spike at the right hand side of that chart, by providing a more space-efficient option for travelling on popular, congested routes. Another way of saying that is that in the absence of congestion pricing (and in the presence of other subsidies for driving, such as minimum parking requirements), higher public transport fares can result in a perverse outcome – additional congestion and delays for existing road drivers. This is shown in the following diagram:
Effectively, a failure to price roads efficiently means that we have to provide subsidies for public transport to prevent car commutes from being even more painful than they currently are. Public transport subsidies are, in that sense, subsidies for drivers. By making your neighbor’s bus fare cheaper, they in turn make your drive to work a bit easier.
Finally, it’s worth considering how we got into this situation. 80 or 100 years ago, public transport systems tended to cover their operating costs with fares. For example, Auckland’s tram system was profitable, if in need of maintenance and refurbishment, up until its removal in the mid-1950s. (Mees ref?) This changed, in large part, due to the introduction of subsidised motorways.
This article by Joseph Stomberg at Vox describes how the US interstate highway system was developed in the 1950s as an explicitly subsidised – i.e. not tolled – transport mode:
The first step was changing how roads were funded. In the 1930s, there were already privately owned toll roads in the East, and some public toll highways, like the Pennsylvania Turnpike, were under construction. But auto groups recognized that funding public roads through taxes on gasoline would allow highways to expand much more quickly.
They also decided to call these roads “free roads,” a term that was later replaced by “freeways.” Norton argues that this naming shift was essential in persuading the federal government — and the public — to shift away from tolls. “It started with calling the roads drivers pay for ‘toll roads,’ and calling the ones that taxpayers pay for ‘free roads,'” he says. “Of course, there’s no such thing as a free road.”
In other words, the “original sin” of transport subsidies was the construction of non-tolled highways paid for out of general tax revenues. This choice led in turn to a situation in which we must adopt “second best pricing” in public transport, and offer an offsetting subsidy. I’m not necessarily opposed to this… but it does mean that I am skeptical to complaints that buses and trains are subsidised.
What do you think we should do about public transport pricing?
Last week, I took a high-level look at the opportunity cost associated with Auckland’s car-centric transport system. Simply put, cars use up lots of land, and public transport, walking, and cycling don’t. At a time when we’re struggling to find space to accommodate the city’s residential and economic growth, this is likely to be increasingly inefficient.
For example, here’s a graph that shows, roughly speaking, the last 50 years of trends in traffic volumes (using the Auckland Harbour Bridge as a proxy) and land values (using national house prices as a proxy). In the last decade or two, demand for intensive land use has far outstripped demand for driving:
However, this isn’t always acknowledged in the activities of transport agencies. As Matt highlighted two weeks ago, Auckland Transport is currently proceeding with a plan to knock down a house (on the bottom left corner of the intersection, shaded in magenta) for an intersection widening project:
A bit earlier, Stu also pointed out an intersection design down in Hamilton that seems quite hazardous to people on foot. Now, there are certainly reasons to redesign – and even widen – intersections. But what worries me is that intersection layout sometimes seem to be on auto-pilot, without any deep consideration of the conflicting values at play or the opportunity costs associated with particular designs.
Take, for example, this intersection at the junction of St Johns Road and College Road in Remuera. It’s large. Very large. Although there’s only a single lane in each direction on the roads in and out of the intersection, it widens to implausible dimensions in the intersection itself. I can only imagine what it’s like to try to cross the intersection on foot.
I asked my friend Lennart, who originally spotted this intersection, to show me how things could be done differently. He quickly sketched up a simplified design – shown in the green and magenta lines – that eliminated the big islands and the split lanes but still left enough room for buses to turn smoothly.
(Caveats: This is not necessarily a better design from a traffic engineering perspective – just a more space-efficient one. As we haven’t looked at traffic volumes, it’s difficult to say whether a signalised intersection or other safety treatments would be required if the slip-lanes were taken out.)
Overall, we found that there would be up to 2,000 square metres of space left over if the intersection was downsized. That’s enough space for three or four reasonably-sized houses on reasonably-sized lots.
Is this expenditure of space worth it? Would it be better to narrow the intersection and sell off the residual land for housing? Possibly. Possibly not. But no matter what the answer is, I hope that those questions are being asked of Auckland’s road designs.
What do you think of the space occupied by our intersections? Good, bad, indifferent?