The Hamilton vs. Wellington population showdown

Some of my research was profiled on the front page of the Waikato Times today (paywalled):

Forget Wellington — Hamilton is on track to overtake the capital within 14 years.

New University of Waikato projections show the city’s population could climb to 242,716 by 2040, cementing its status as New Zealand’s fastest-growing city.

Hamilton’s population projection is under the “high variant” forecasts — the growth estimates council staff are recommending, and which the Government requires councils to use when planning their Long Term Plans.

If that is compared to Stats NZ's and the Wellington Regional Growth Framework estimates for Wellington for the same year, Hamilton's population will be larger by 2716 people.

Now, this Hamilton versus Wellington head-to-head population battle seems to be attractive to the media (see this post from 2019, talking about this 2019 Waikato Times article). However, they've got things wrong this time, for a couple of reasons.

First, they are comparing Hamilton City with Wellington City, which is a valid comparison of city council areas, but may not be the comparison many people have in mind. I'll come back to that point at the end of the post.

Second, and more importantly, you shouldn't compare a projection from one source, based on one set of assumptions, with a projection from a totally different source, based on a different set of assumptions. Especially when projections from the same source are available, using consistent assumptions. Otherwise, you are not comparing apples with apples.

So, let's make some consistent comparisons. Stats NZ's projections are available on Aotearoa Data Explorer, Stats NZ’s online data tool. Search for "subnational population projections", and then scroll down to "Subnational population projections, by age and sex, 2023(base)-2053". Stats NZ offers three variants (low, medium, and high) of 2023-base population projections. The difference between the variants is that low variant projections assume low fertility, high mortality, and low international migration, while high variant projections assume high fertility, low mortality, and high international migration (and the medium variant projection is, obviously, in-between the low and high). Here are the three variant Stats NZ projections for Wellington City and Hamilton City:

The bold lines are for Hamilton City. The dotted lines are for Wellington City. The low, medium, and high variants are coloured blue, green, and brown respectively. The key thing to notice is that the lines cross over. Where the lines of the same colour cross, that is the point in time when Hamilton catches up with Wellington under that projection variant. So, with Stats NZ's projections, Hamilton is projected to be larger than Wellington by 2038 under all three projections. If we do a linear interpolation (because Stats NZ only reports their projections for five-year intervals), then Hamilton is projected to be larger than Wellington by 2034 in the low and medium variant projections, and by 2035 in the high variant projections.

Turning to the University of Waikato (UoW) projections (which I produced), there are also three variants (low, medium, and high) that can be interpreted similarly to Stats NZ's projections. The methods and assumptions differ from those used by Stats NZ. These are the projections that Hamilton City Council uses in its planning (as do several other local councils). Here are the three variant UoW projections for Wellington City and Hamilton City:

In my projections, Hamilton is projected to be larger than Wellington by 2040 in the low variant projection, by 2048 in the medium variant projection, and by 2066 in the high variant projection (which is beyond the projection horizon for Stats NZ projections as they only project for 30 years).

Why the difference? The difference between the timing using Stats NZ projections and the timing using my projections is due to differences in assumptions and the underlying models. It would take a long post to unpack all the differences in detail. The differences between the low, medium, and high variants are easier to explain. Wellington has a head start - it was much larger in 2023 than Hamilton. However, Hamilton has both higher fertility and greater net migration than Wellington. That head start makes a bigger difference in the high variant projections than in the low variant projections, because the higher fertility and international migration in the high variant projections allow Wellington to maintain that lead for longer. In the low variant projections, Hamilton's higher fertility and net migration allow it to catch up much faster. In other words, because Wellington starts from a larger population base, assumptions that lift population growth across the whole country add more people to Wellington in absolute terms, delaying Hamilton's catchup, even though Hamilton’s underlying growth rate is higher.

What is interesting is that the differential effect between low-variant and high-variant projections doesn't seem to be anywhere near as prominent in the Stats NZ projections as it is in my (UoW) projections. In part that is because the uncertainty expressed in my projections (proxied by the difference between the low and high variant projections) is much higher than the projections by Stats NZ. I'm comfortable with that, given that international migration in particular is highly uncertain. So, we should expect a fairly high degree of uncertainty when we project future population.

One final thing to note is a point I made in my 2019 post on this topic. Wellington City is only one part of a larger urban area ('Greater Wellington') that also includes Porirua City, Upper Hutt City, and Lower Hutt City. There is no projection that has the Hamilton urban zone catching up in population to the broader Wellington urban zone any time soon. I suspect that many people would be flabbergasted by the suggestion that Hamilton might become larger than Wellington. Many of those people would be thinking about Greater Wellington, and they would be right.

So, Hamilton will eventually be New Zealand's number three city council area in terms of population. However, the celebrations could easily be put on hold by a council amalgamation process that the government has started, which could conceivably merge some Wellington councils together, putting their combined population out of reach of Hamilton for the foreseeable future.

[HT: The incomparable Emeritus Professor Jacques Poot]

Read more:

• Hamilton won't be our second largest city any time soon

The challenges of projecting the population of Waikato District

Some of my work was referenced on the front page of the Waikato Times earlier this week:

Towns between Hamilton and Auckland are crying out for more land and houses for the decades ahead, as population is set to rapidly climb.

Waikato District, between Hamilton and Auckland, will need nearly 9,000 new houses by 2031, as up to 19,000 more people are projected to move there.

Pōkeno has already transformed from a sleepy settlement to a sprawling town, while Ngāruawāhia, north of Hamilton, is in demand for its new housing developments...

Waikato University associate professor of economics Michael Cameron told Stuff there had been “rapid” growth in Waikato District in the last 10 years, which he expects to continue.

In the last decade, the population grew by 27 per cent, or 18,647 people.

“In relative terms, Waikato has been one of the districts experiencing the fastest population growth in New Zealand.

“We’ve projected growth faster than Statistics NZ, and the growth has even overtaken our own projections,” Cameron said.

Most of that growth has been from Aucklanders spilling over the Bombay Hills into Pōkeno, and Cameron tipped that town to keep growing faster.

“There’s a lot of emphasis on central Auckland, but a lot of growth and industry is happening in the South.

“You can see it when you drive along the motorway. Drury is growing, Pukekohe is growing and Pōkeno is growing.”

Waikato District is in a bit of a sweet spot, strategically located between two fast-growing cities (Auckland and Hamilton), and not far from one of the fastest growing areas in the country (the western Bay of Plenty). It isn't much of a stretch to project high future growth for Waikato District.

The main challenges occur when trying to project where in the district that growth will occur. Most territorial authorities in New Zealand are either centred on a single main settlement (e.g. Rotorua, or Taupo), or have a couple of large towns (e.g. Cambridge and Te Awamutu, in Waipa District). Waikato District has several mid-sized towns (Ngaruawahia, Huntly, Pokeno, Raglan), and a bunch of smaller settlements that are also likely to attract population growth (e.g. Tuakau, Taupiri, and Te Kauwhata). So, there are lots of options as to where a growing future population might be located.

That makes small-area population projections that are used by planners (such as those that I have produced for Waikato District Council) somewhat endogenous. If the projections say that Pokeno is going to grow, the council zones additional land for residential growth in Pokeno, and developers develop that land into housing, and voila!, Pokeno grows. The same would be true of any of the other settlements, and this is a point that Bill Cochrane and I made in this 2017 article published in the Australasian Journal of Regional Studies (ungated earlier version here).

The way we solved the challenge is to outsource some of the endogeneity, by using a land use change model to statistically downscale the district-level population to small areas (that's what that 2017 article describes). However, that doesn't completely solve the problem of endogeneity, because the land use model includes assumptions about the timing of zoning changes and the availability of land for future residential growth - if those assumptions put that land use change in other locations, or changed the order of the opening up of zoned land, the population growth would follow.

It is possible to develop more complicated models that incorporate both supply and demand of housing in order to project the location of future growth. However, from what I have seen of these approaches, the added complexity does not improve the quality of the projection (however, it might improve the believability of those projections). For now, models based on land use change are about as good as we can get.

The projected demographic impact of COVID-19 in Hamilton

Yesterday, I posted about a new working paper on the projected demographic impact of COVID-19 in Australia. They estimated an impact on the Australian population that would see the population smaller by 1.4 million people (about 4.2 percent) by 2040. I mentioned that I had been doing some work for local councils here in the Waikato region, so I want to talk a little bit about that now (ahead of presenting on this work to Hamilton City Council next week).

As I mentioned yesterday, projecting the population using the cohort-component method involves projecting three components: (1) fertility; (2) migration; and (3) mortality. Let's talk a little bit about the impact of COVID-19 on each of those components.

Although the scariest aspect of COVID-19 is undoubtedly its impact on mortality rates, New Zealand has thankfully been spared. And provided we are sensible about managing public health at the borders, it is possible we could stay that way. The impact so far on mortality has therefore been minimal, and so my projections assumed no change in mortality (note: this was the same assumption that the Australian paper mentioned yesterday adopted, even though deaths in Australia have been rising rapidly over the last couple of weeks, in Victoria especially).

In terms of fertility, as I noted yesterday:

Despite media articles suggesting that there could be a 'COVID baby boom', that seems unlikely. In fact, fertility tends to decrease in times of economic recession.

My projections assumed no impact on fertility. Given that we are undoubtedly in a recession, that might tend to overstate future births, and therefore over-estimate the population. However, in the absence of good data on changing fertility intentions, it is difficult to pin down the impact on fertility. The Australian study assumed a small reduction in fertility, which would persist for some time. So far, unemployment rates have not spiked as much as expected in New Zealand (probably thanks to the wage subsidy), so it is possible that there will be no change in fertility. We won't know for another six months or more.

Finally, the current model that I have been using combines net international migration and net internal migration into a single net migration rate for each territorial authority. However, based on past inter-Censal population movements, we know what proportion of migration flows arise from international rather than internal migration (and I am also using that data in updating my projections model to separately account for internal and international migration - more on that in a future post). I assumed that net international migration would fall to zero for one year (from March 2020 to March 2021), before bouncing back to normal. At the time I made the projections for Hamilton City (in early May this year), that seemed like a sensible assumption, based on what we knew at the time. Compared with the Australian study, my assumptions are a mixture of their severe scenario (zero net international migration) and light scenario (bouncing back to normal after one year). Probably now, we might expect lower net international migration to persist for longer than one year, and possibly to take some time to return to normal. My projections might therefore overstate the future population as a result.

What do the results look like? The diagram below shows two projections for Hamilton, based on different assumptions for fertility, mortality, and net migration: (1) a medium (baseline) scenario; and (2) a low scenario, that assumes lower fertility and net migration, and higher mortality. Both scenarios start in 2013, and are calibrated to replicate the estimated Hamilton population in June 2019, before diverging. Can you see any impact of COVID-19? Barely. If you tilt your head just right, you might see that the projection gets slightly flatter from 2019 to 2020. However, the takeaway message is that, even though net international migration is projected to be zero for one year, that impact is swamped by the long-run upward trend in the total population. 

The impact is easier to see if we break down projected annual population change in Hamilton into two components: (1) natural increase (births minus deaths); and (2) net migration. This is shown in the diagram below. Here, you can clearly see that there is a sizeable impact on migration for the 2020 and 2021 years, after which things get back to normal fairly quickly. However, that impact is almost imperceptible when we look at population change in the longer term, as in the previous diagram.

You might doubt that the impact of COVID-19 would be so small. After all, it is the most salient thing to happen to population change in New Zealand since the baby boom. However, consider the diagram below, which tracks the total population of New Zealand from 1900 to 1935. In the middle of that time series is the combined impact of World War I and the Spanish Flu. Both events resulted in large increases in mortality and decreases in net international migration, relative to 'normal'. And, because mortality was concentrated in prime age adults, there was also a negative impact on fertility. And despite all of those compounding negative impacts on the population, there is only a flattening out of the population before it returned to its previous trajectory. Given that COVID-19 is a much smaller event in terms of population impact that the combined World War I-Spanish Flu, it should be no surprise that it is difficult to see any change in the projected total population.

Compared with the Australian research I discussed yesterday, my work is showing a much smaller (not zero, but small) impact on the future population of Hamilton City. I've done similar work for Waipā District, which shows an even smaller impact (because population change is drive more by net international migration in Hamilton than in Waipā). I expect to also see something similar in some forthcoming work for Waikato District. However, as I noted above, I may be overestimating future births by not adjusting fertility downwards in response to the recession, and I may be overestimating net international migration if border closures persist for some time. As in the case of the Australian research, that could be addressed by projecting alternative scenarios.

It is easy to see the world falling apart around us, and jump to doomsday scenarios for population change. However, it would take something much larger and longer-lasting than what we are observing in New Zealand right now, before we would see a substantial change in the trajectory of future population.

Read more:

• The projected demographic impact of COVID-19 in Australia

The projected demographic impact of COVID-19 in Australia

I've been meaning to post about the demographic impact of COVID-19 for some time, based on some work I've been doing for local councils in New Zealand. However, before I get to my work (perhaps in my next post), I thought I should post about this new working paper by Elin Charles-Edwards (University of Queensland), Tom Wilson (University of Melbourne), Aude Bernard and Pia Wohland (both University of Queensland), that presents some scenarios on the impact of COVID-19 on the Australian population (see also this article in The Conversation about the research).

Projecting the future population in a sensible way requires understanding that there are essentially only three things that happen to the population: (1) people are born; (2) people move from place to place; and (3) people die. So, if you can project fertility, migration (international and internal), and mortality, then you can project the population - this is the basis of the cohort-component method, which involves projecting each component of population change.

The biggest problem with estimating the demographic impact of COVID-19 is uncertainty - we don't know what the impact will be on any of the components of population change. New Zealand has been lucky - it appears the impact on mortality is minimal if anything. Australia has been similar (they have more COVID-19 deaths, but also have a larger population, and deaths in Australia are still not high compared to other countries). Despite media articles suggesting that there could be a 'COVID baby boom', that seems unlikely. In fact, fertility tends to decrease in times of economic recession. However, the biggest uncertainty comes from migration. How long international borders will remain closed is a big unknown, reduced international migration as a result of COVID-19 will have the largest impact on the future population.

Charles-Edwards et al. tackle this uncertainty by presenting three scenarios for the future population of Australia:

1. A light scenario, which assumes a 28% decrease in net international migration for 2020/21, with a quick return to normal after one year;
2. A medium scenario, which assumes a nearly two-thirds decrease in net international migration for 2020/21, with a gradual return to normal after four years; and 
3. A severe scenario, which assumes zero net international migration for 2020/21, and a gradual return to normal only after eight years.

Their scenarios also include some differences in fertility and net internal (inter-State) migration (but no differences in mortality). They also run a business-as-usual No pandemic scenario. They find that:

The reference No pandemic scenario has Australia’s population reaching 27.6 million by 2025, 29.5 million by 2030 and 33.2 million by 2040... Based on the modelled scenarios, COVID-19 is expected to have a measurable and persistent impact on Australia’s population. Under the Severe scenario, Australia’s population will reach 26.6 million by 2025, 29 million by 2030 and 31.8 million by 2040; 1.4 million or four per cent fewer than the No pandemic scenario. The impact is less under the Light and Moderate scenarios, with Australia’s population reaching 0.18 million fewer and 0.50 million fewer by 2040 respectively... The impacts of COVID-19 are felt most strongly in the short term with annual population growth dropping [from] 1.40 per cent in 2020-21 in the no pandemic scenario to 1.14 per cent in the Light scenario, 0.78 per cent in the Moderate scenario and just 0.41 per cent in the Severe scenario. For historical context, Australia’s annual population growth last dropped below 0.78 per cent in 1935, and last dropped below 0.41 per cent in 1916...

Those impacts are quite large (and much larger in relative terms than the impacts I have estimated for some districts in New Zealand, as I will discuss in my next post). At the State level, they find that:

In relative terms, the largest impact out to 2040, based on the severe scenario, will be in Victoria followed by Western Australia, both of which will have a population 5 per cent smaller than in the no pandemic scenario.

However, as I noted above, there is a huge amount of uncertainty here. The international migration scenarios in the Charles-Edwards et al. research are based on a survey of just six demographic experts. It's hard to see how you could do much better than this though. As they point out in the conclusion to their working paper, identifying leading indicators of demographic change (or even contemporary indicators of change) is incredibly difficult. Yet, that is what you would need in order to have demographic projections that adjust dynamically to real-time events. However, as I will demonstrate in my next post, it would be easy to overstate the impact of these events, especially when you look at them alongside the impacts of large events in the past. I think that is a real risk with this Australian research.

Hamilton won't be our second largest city any time soon

I was interviewed for the Waikato Times last week, and the story appeared on the Stuff website yesterday:

Hamilton city might be growing, but not as explosively as some people might dream. 

Commentators have recently said Waikato is 'ready to go pop' with development, citing the hundreds of millions of dollars spent on community facilities and transport infrastructure. 

Labour list MP Jamie Strange said he believes Hamilton's growth is so significant it could become New Zealand's second biggest city in the next 30 years. 

But a Waikato University professor said it would take drastic change for Hamilton to surpass Wellington and Christchurch in three decades...

"If you are talking about is Hamilton going to be a city of 200,000 - sure it's going to be that.

"But Wellington is certainly going to be big and bigger and Christchurch is also going to be big and bigger."

Read the full story for more from me. The overall point is that the population of Hamilton is growing. But the populations of Wellington and Christchurch are growing as well, and they have a large head start. How long will it take Hamilton to catch up? I gave the reporter (Ellen O'Dwyer) some quick back-of-the envelope calculations.

Based on Census Usually Resident Population counts (available here), Hamilton City grew from 129,588 in 2006 to 141,612 in 2013 and 160,911 in 2018. Wellington City grew from 179,466 in 2006 to 190,956 in 2013 and 202,737 in 2018. Christchurch City declined from 348,456 in 2006 to 341,469 in 2013, then grew to 369,006 in 2013. Hamilton grew faster than Wellington and Christchurch (in both absolute and relative terms) between each of the last two Censuses.

If the absolute rates of growth of both Hamilton and Wellington (from the previous paragraph) between 2013 and 2018 continued, Hamilton would catch up to Wellington in the early 2040s. Based on the absolute rates of growth between 2006 and 2018, this wouldn't happen until the 2080s. As for Christchurch, forget it - the comparable catch-up time is measured in centuries.

None of those calculations take into account the fact that Wellington City is only one part of a larger urban conglomeration that includes Porirua City, Lower Hutt City, and Upper Hutt City. Once you factor those areas in as well, the Wellington urban area is far larger than Hamilton and it would take something spectacular for the Hamilton urban area (even if you include fast-growing Te Awamutu, Cambridge, and Ngaruawahia) to catch up.

Sorry Jamie. Hamilton isn't going to catch Wellington (and definitely not Christchurch) any time soon.

Japan will be able to teach New Zealand about dealing with rural population decline

I've written a number of times about population decline in New Zealand's rural and peripheral areas (see here and here and here and here). New Zealand has seen a period of significant population growth driven by historically high net international migration. That might have been enough to turn around the fortunes of some previously-declining areas (although we won't know for sure until the delayed Census results are released), mainly through internal migration out of the larger cities (or, at least, that has been the main source of positive population change when it has occurred for those areas). However, the reprieve is likely to be short-lived as we return to more 'normal' net international migration. So, what is a declining region to do?

The Maxim Institute released a report in 2017 that made some suggestions (which I blogged about here). However, New Zealand is not the first country to face population decline in rural and peripheral areas. Japan is the poster child for population decline, and as one indicator of its ageing and declining population, consider this: Japan has been closing hundreds of schools each year over the past decade or more. The scary thing is that New Zealand's outlying regions are ageing rapidly, as Natalie Jackson and I pointed out in a 2017 article in the Journal of Population Ageing (ungated earlier version here).

What can Japan teach us about how to deal with declining regional populations? Last year, Brendan Barrett (RMIT University) wrote an interesting article in The Conversation on Japan's decline:

Everyone in Japan is aware of the challenges posed by a rapidly ageing, declining population with low birth rates. The media cover these concerns extensively.

Local governments have been trying to encourage people to move back to rural areas by providing work opportunities and sharing details of vacant houses...

There are no simple answers to these challenges. The Japanese government has been very active but past policies have tended to focus on infrastructure development and construction of public facilities (roads, dams, town halls, libraries, museums, sport facilities), rather than on the economic needs and welfare of local people...

While lots of ongoing initiatives aim to attract young people back to rural areas, the biggest concern is one of livelihoods as long-term job prospects are limited. Yuusuke Kakei covers this topic in his 2015 book Population Decline x Design, presenting proposals for new local economic activity that puts women, creativity and community at the centre. To this we should add what Joseph Coughlin describes as “The Longevity Economy” to respond to the economic and technology needs of an ageing population.

Interest in the notion of the universal basic income has also surged recently in Japan. Some commentators argue that it could play a significant role in revitalising Japan and in making rural life more attractive to young Japanese by providing them with long-term financial security.

One major challenge for local economies is access to finance, especially to support new businesses. While there are several innovative crowdfunding initiatives, Japanese municipalities should also look at the Transition Town movement for inspiration with its focus on “reclaiming the economy, sparking entrepreneurship, reimagining work”.

Specifically, it is worth exploring the potential of local entrepreneur forums. These bring together local investors from within the towns or villages with local entrepreneurs to support new, small business ventures.

The result is that communities pool their resources to support young people who have business ideas but lack financial resources. This is in line with both Masuda’s and Kakei’s recommendations to focus on local needs, rather than physical buildings and infrastructure.

Most of the initiatives highlighted by Barrett seem a little too promissory and results are lacking. A universal basic income might even make the problem of rural population decline even worse, as it allows those who lack the necessary skills for an urban job the opportunity to afford to live in an urban area (taking a broad definition of urban). Anyway, if policymakers are concerned about population decline and want to develop policy to mitigate it, they we should be keeping a close eye on initiatives in Japan, noting what works and what doesn't work. Although there will be important cultural differences to take note of, Japan is leading the way here and we will not want to make mistakes that they have already uncovered.

Read more:

• The relative (un)certainty of subnational population decline
• The Maxim Institute on dealing with population decline
• Newsflash! Population growth will be highest on the fringes of fast-growing urban areas
• Winners and losers in population growth
• Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

Book Review: The Flaw of Averages

I just finished reading The Flaw of Averages, by Sam Savage. Reading the blurb, I would have thought this was a book that presented arguments that I would have a lot of sympathy for. The core argument that underlies the book is that so often decision-makers are looking for a single number that they can use for decision-making (often this is the average), but using that single number results in flawed and costly mistakes, because it ignores the fact that the single number is drawn from a distribution of possible numbers. Savage essentially argues for using simulation modelling, a particular form of which he has developed, called probability management.

In my own work, preparing population projections for local councils and other decision-makers, I often struggle with the decision-makers' needs for a single magic number that they can use for decision-making. Along with Jacques Poot, we pioneered the use of stochastic models for sub-national population projections in New Zealand (see this paper, for one example, or the longer ungated version here). Stochastic models explicitly display the uncertainty in future projections of the population, and there are a few regularities that Jacques and I noticed, such as projections being more uncertain for areas with smaller populations, and surprisingly more uncertainty for slower-growing or stable populations (compared with faster growing populations).

Towards the end of the book, there is a good quote that illustrates why decision-makers prefer not to have to deal with uncertainty, and prefer to focus on a single magic number:

Unfortunately, most organizations don't know how to deal with distributions. They generally ignore that part of the forecast, relying instead on the single number, and, presto, they're back to square one with the Flaw of Averages...

So, as has been my experience, you can provide decision-makers with the extra information on the uncertainty of a projection (or forecast), but you can't make them use it!

Savage's book can essentially be broken down into three parts. In the first part of the book, he essentially tries to make us forget all of the complicated terminology used in what he refers to as 'steam era' statistics, and instead replace the complicated 'red words' with 'green words' that have the Savage stamp of approval. However, in my opinion the green words are more ambiguous and sometimes plain wrong. For instance, Savage would have us replace "utility theory" (a red word) with "risk attitude" (a green word). Now, risk attitudes and utility theory are related, but not so much that you can replace both terms with one of them! Savage is also highly uneven in his disdain for complicated 'red words' - academic terms from finance such as the Capital Asset Pricing Model seem to get a free pass. Given that a lot of the book uses examples drawn from finance, this seems a little biased.

The second section of the book is the highlight. In these chapters, Savage uses personal stories of decision-makers and firms such as the oil company Shell and the pharmaceutical company Merck, to illustrate how simulation modelling can substantially improve the quality of decision-making. This is the really interesting stuff, and if the book had stuck to this, I feel it would have been much better.

The third section is essentially an extended infomercial for Savage's particular implementation of simulation modelling, probability management. While the examples extend those from earlier in the book, they're really just trying to sell the reader on the tools that Savage has developed.

Overall, I found that the personal stories of models in the real world are great. However, the book seems to have too many purposes and as a result, it doesn't execute as well on any of them as it might. In particular, it's a pity the first part of the book was essentially just a rant against terminology that Savage finds offensive. Moreover, Savage hasn't been as careful as he might with his examples. Fairly early in the book, he presents decision-making based on decision trees. However, despite his strong encouragement for us not to reduce decision-making to single numbers, in that chapter he uses expected value calculations - which reduces the decision to being based on a single number!

Overall, I wouldn't recommend this book for the general reader. If you want to understand why simulation modelling is important (or why it is important not to reduce analyses to a single number), it is useful for that, but I would skip through and start reading from about Chapter 16, and stop when your tolerance for the infomercial at the end is exhausted.

The challenges for producing ethnic population projections

I spent the last two days at the Pathways Conference at the Albany campus of Massey University. The Pathways conference is run by the research team of the CADDANZ (Capturing the Diversity Dividend of Aotearoa New Zealand) project, of which I am one of the team members. Usually, the Pathways conference focuses on migrants and immigration, but this time there was a more direct focus on diversity (you can find links to videos of the keynote speakers' presentations here).

My presentation at the conference was on ethnic population projections, especially for small ethnic groups. I won't post today on the full details of that presentation (which will be a forthcoming working paper that I will talk about then), but I did want to discuss three challenges for producing ethnic population projections.

Statistics New Zealand produces ethnic population projections only for the main 'Level 1' ethnic groups in New Zealand (European or Other, Maori, Pacific, Asian, and the omnibus groups Middle Eastern/Latin American/African), as well as for the three largest 'Level 2' ethnic groups (Samoan, Chinese, and Indian). There are good reasons why they don't produce projections for smaller groups such as Dutch, Fijian, or Vietnamese (being the three groups that I presented projections for at the Pathways conference).

The first challenge is lack of data. If you want to produce population projections using the traditional 'cohort component model', you need to be able to project births, deaths, and migration for the population groups you want to project. To project future births, deaths, and migration, you create a model based on observed numbers and rates of births, deaths, and migration in the past. This is very difficult for small population groups, because the numbers of observed births, deaths, and migration events is smaller and noisier. In some (or many) years, there might be no events of that type. For example, there might be no births to Vietnamese mothers aged 15-19 in some years, which makes it difficult to project.

The second challenge is that, in addition to projecting births, deaths, and migration, you also need to project inter-ethnic mobility. Inter-ethnic mobility occurs when a person's ethnicity changes. You might think that ethnicity is static, but that isn't true at all, as this article by Carolyn Liebler and others explains:

Add something else to the list of things that seem simple but are actually complicated – the way someone reports their race or ethnicity... With over 160 million cases [from the U.S. Census] covering all U.S. race and ethnicity groups we found that 6.1% of people in the (not-nationally-representative) data had a different race or ethnic response in 2010 than they did in 2000.

Rates of inter-ethnic mobility in New Zealand are similar (see the report from Statistics New Zealand here). This challenge arises because people can self-identify with any ethnicity, and their self-identification can change over time. These changes in self-identity are not common, but they provide yet another rare event that needs to be projected as part of an ethnic population projections model.

The third challenge is that people can hold more than one ethnicity. That might not sound like much of a challenge, but traditional models assume that each population group (by age, sex, location, etc.) is mutually exclusive. That is, a person cannot simultaneously belong to more than one group. But, if people can hold more than one ethnicity then they will belong to more than one group, which means that ethnic population projections models must run in a different way to traditional models.

Those challenges are the main reasons why Statistics New Zealand provides ethnic population projections for only a limited number of ethnic groups. In a future post, I'll discuss a method that Jacques Poot and I have been applying that allows us to go a little further and produce projections that are complementary to Statistics New Zealand's projections, and cover a wider number of much-smaller ethnic groups at both the national and regional levels.

The relative (un)certainty of subnational population decline

Over the three years up to March of this year, I was involved in a Marsden Fund project led by Natalie Jackson, looking at subnational depopulation in New Zealand. That is, among other things we were trying to explain why some areas of New Zealand have been declining in population over time, and continue to do so. The outputs of that project have been summarised in a recent issue of the journal Policy Quarterly.

My contribution to that issue of Policy Quarterly (from pages 55-60) is entitled "The relative (un)certainty of subnational population decline", and looks at how certain (or uncertain) population decline is for different territorial authorities in New Zealand. However, the article has a broader purpose, and is worth reading because it outlines some of the key points that decision-makers need to understand about population projections, especially in terms of their uncertainty. If you know nothing about population projections, other than that they are forecasts of the future that can be useful for decision-making, then you should read the article.

The main results categories New Zealand's territorial authorities (TAs) by the probability that they will experience a decline in population over the decades 2023-2033 and 2043-2053. I won't spoil the results by naming particular TAs, but here's a summary:

...the number of TAs appearing in each category increases between the two periods. More TAs are facing population decline in the 2043–2053 decade than in the 2023–2033 decade. This corroborates recent work that has shown similar results... In the 2023–2033 decade 20 TAs face a 90 percent or greater probability of population decline, compared with 26 TAs in the 2043–2053 decade. Granted, these TAs have relatively small population, representing 12.2 percent of the national population in 2023 (for the 2023–2033 group based on median population size) and 17.2 percent of the national population in 2043 (for the 2043–2053 group).

Unsurprisingly, rural and peripheral areas face the highest probability of future population decline. Other papers in that issue of Policy Quarterly posit some reasons why we observe population decline in particular areas. My paper is descriptive and future-focused, and doesn't explore the institutional or other non-demographic factors that might explain why some rural areas, rather than others, are projected to experience population decline. That's something for future research.

Read more:

• The Maxim Institute on dealing with population decline
• Newsflash! Population growth will be highest on the fringes of fast-growing urban areas
• Winners and losers in population growth
• Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

The Maxim Institute on dealing with population decline

Last week the Maxim Institute released a new report on regional development in New Zealand. Radio New Zealand reported on it here, but note that they say the report:

predicts populations in many regions will drop or stagnate within three decades.

Actually, that's based on work that Natalie Jackson and I have done, which is available in this working paper (forthcoming in the Journal of Population Ageing, and with an update based on stochastic projections methodology due in the journal Policy Quarterly later this year - I'll talk about that in a later post).

Anyway, the Maxim report (written by Julian Wood) doesn't contribute anything new research-wise, but does do a good job of collating important research on regional development with a particular focus on New Zealand. There are some parts of the report that should be required reading for local council planners, particularly those in rural and peripheral areas where populations are declining. As one example:

When looking at the age composition of population growth this broad-based regional decline is accelerated by the fact that “only 16 TAs will not see all their growth to 2043 at the 65+ years [age group].” In short, in 10 national election cycles (thirty years), the majority of local governments will not only be experiencing population stagnation, but the vast majority will be experiencing far older populations with far fewer people in their prime working age (aged 15-64). This reality means that the vast majority of rural New Zealand shouldn’t be planning for, or counting on population growth as a driver of economic growth.

Rather, as a rural community’s population ages and or declines it will likely come under increasing economic, financial, and social pressure. Fewer people of working age can mean less employment income in a community and less consumer spending and hence less business income. Local government income can also decline as there are fewer people and businesses paying rates.

And this quote from the report pretty much sums it up:

There is a need for a “growth everywhere” reality check.

The reality that population growth is not a given for most of the country has not dawned on many councils (based on many discussions I have had over the years). Many councils still refer to population projections as 'growth projections', which makes no sense whatsoever if your population has been declining for two decades or more!

"But won't Big Project XXXX lead to expanded population growth?" I've heard that one before, and in fact in one of the early population projection projects I was involved in we quantified and accounted for 'big projects', but it turned out later that if we took the projected populations excluding the big project effects we weren't too far off. 'Big projects' are simply business-as-usual for a growing city or district like Hamilton City or Waikato District, but for declining peripheral areas the money would probably be better spent elsewhere. The Maxim report notes:

The overall picture remains, however, that building physical infrastructure alone will be insufficient to economically “restart” a rural economy in long-term population stagnation and decline.

The Maxim report recommends three 're-thinks', which are definitely worth considering:

• Rethink #1: All regional development goals must be explicitly and clearly stated to enable clarity, transparency, scrutiny and co-ordination. As part of this “regional wellbeing indicators” should be explicitly developed and included in these regional development goals.

• Rethink #2: Regional development goals need to be ranked and prioritised with tensions, trade-offs, or the subservient relationships between the goals explicitly outlined and prioritised so as to enable evaluation.

• Rethink #3: New Zealand needs to rethink its sole focus on economic growth, shifting to a framework that also empowers communities to meet both the economic and social needs of their populations in the midst of “no growth or even decline.”

The first two should be obvious for any decision-making, not just in terms of regional development. The third needs policy makers to face up to the reality that population growth is not the destiny of every part of the country. Which is a point I have made before.

Read more:

• Winners and losers in population growth
• Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

[HT: Natalie Jackson]

Future life expectancy is good for NZ men, but be cautious

What are the limits of human life expectancy? I don't think any of us really know. Last month, I posted about an article in the journal Nature that claimed we were already at the limit by the mid-1990s. Now, almost at the other extreme, a new article by Vasilis Kontis (Imperial College London) and others, and published in the The Lancet (and appears to be open access), projects larger-than-expected future increases in life expectancy.

Kontis et al. basically ran every major model type that is used to model age-specific death rates (21 models in all) across 35 industrialised (high-income) countries, and then took a weighted average of those models [*]. The modelling approach also allowed them to make probabilistic forecasts (which is something that Jacques Poot and I have been working on, in terms of population projections, for many years). They looked at life expectancy at birth, and life expectancy (remaining life years) at age 65. I'm just going to focus on the first of those. Here's what they found:

Taking model uncertainty into account, we project that life expectancy will increase in all of these 35 countries with a probability of at least 65% for women and 85% for men, although the increase will vary across countries. There is nonetheless a 35% probability that life expectancy will stagnate or decrease in Japanese women by 2030, followed by a 14% probability in Bulgarian men and 11% in Finnish women...

There is 90% probability that life expectancy at birth among South Korean women in 2030 will be higher than 86·7 years, the same as the highest life expectancy in the world in 2012, and a 57% probability that it will be higher than 90 years... a level that was considered virtually unattainable at the turn of the 21st century by some researchers.

So, they are offering better than even odds that life expectancy for South Korean women will exceed 90 years by 2030, a point that has been picked up in the media (see for example here and here). However, something that wasn't picked up (even by the NZ media) was the somewhat surprising projection for male life expectancy in New Zealand. Here's the relevant part of their Figure 3:

Male life expectancy in New Zealand for 2010 is already one of the highest in those 35 countries (ranking us sixth out of 35), but look at the left panel of the graph, which is their projection for 2030. Notice that, based on the median projection (the red dot), New Zealand pretty much retains its same ranking. However, notice also that the green smudge (the distribution of projected life expectancies) for New Zealand is much wider than for other countries (I guess they are much less certain about their projection for New Zealand). That leads to, on the right panel of the graph, New Zealand having a relatively high probability of holding the top ranking for male life expectancy in 2030. I must say I was a little surprised by this. Maybe good reason for men to stay in New Zealand?

There's a lot to commend in this research, and the BMJ article is not too mathy (but I wouldn't recommend reading the online appendix on that score). However, it isn't without its problems. The authors have done a good job of using multiple models and bringing them together using a weighted average.

However, one of the key problems with models is that they are less good at extrapolating beyond the range of data that are inputs into the model. And essentially, that is unavoidable when it comes to projecting life expectancy. No industrialised country has ever had life expectancy before as high as it is in those countries today, let alone projecting future further gains in life expectancy. One of the big remaining questions in human biology is the limits to human lifespan, and this sort of trend extrapolation doesn't really help us to understand that. There may be biological limits to lifespan that we haven't approached yet and maybe we won't even know we have approached them until we hit them. At which point, extrapolations of past trends will not be a good predictor of future gains in life expectancy.

The authors themselves note:

Early life expectancy gains in South Korea, which has the highest projected life expectancy, and previous to that in Japan, were driven by declines in deaths from infections in children and adults; more recent gains have been largely due to postponement of death from chronic diseases.

That's not just true in South Korea and Japan, but all industrialised countries. For most of these countries, the future gains in life expectancy at birth that could arise from further reductions in infant and child mortality are limited. The low-hanging fruit of life expectancy gains have already been picked. Further increases in life expectancy now are most likely to arise through reductions in the 'stupid-young-male effect' (e.g. reductions in injury deaths). Remember that life expectancy at birth is measured as the age by which half of that birth cohort will have died (half would still be alive). So, life extending medical technologies that work for the very-old (likely to be those already above the median age for those born in their cohort) will have no effect on measured life expectancy.

Overall, it might be wise to be more cautious in interpreting these projected gains in life expectancy. For New Zealand men, it may be premature to be popping champagne in anticipation of our long-livedness.

*****

[*] What they actually did is called Bayesian model averaging, which essentially means that they weighted the models by how good they are at predicting actual data, with models that are better predictors receiving higher weights.

Climate change won't much affect internal migration in NZ

Climate change is likely to be one of the key challenges facing humankind over the coming century (or more). We are likely facing increases in mean temperature, desertification, rising sea levels, and increasing frequency and intensity of extreme weather. But how big is the impact likely to be on a country like New Zealand, anyway?

In a new working paper, I evaluate the impact of climate change on internal migration in New Zealand, and what that means for the future spatial distribution of population. That is, which regions are likely to gain population from climate change, and which will lose population? I make use of a gravity modelling framework (which I have written about before). Essentially, a gravity model suggests that the migration flow between two regions is positively related to the population of the origin and the population of the destination, and negatively related to the distance between the two places. I tried out a bunch of climate variables from NIWA to find those that appeared to have the biggest impact on internal migration, using data on inter-regional migration from the last four Censuses (1991-2013).

Three climate variables are found to have statistically significant associations with internal migration: (1) mean sea level pressure in the destination; (2) surface radiation in the origin; and (3) wind speed at ten metres at the destination. The sign of the effects suggest that migrants attracted to areas with more settled weather (higher mean sea level pressure); migrants are less likely to move away from areas with more sunlight hours (but interestingly, don't move towards those areas); and migrants prefer to avoid moving to areas that are windier.

I then embedded the gravity model within a cohort-component population projection model, which is something that Jacques Poot and I have been working on for a number of years. I used the projections model to evaluate the effect of different climate change scenarios on regional populations out to a horizon of 2100.

Including the three climate variables in the population projection model makes a small difference to the regional population distribution. The inclusion of climate variables increases the projected populations of Northland, Bay of Plenty, Gisborne, Hawke’s Bay, Taranaki, and Nelson. The overall impact is quite small, as you can see from the diagram below for Northland. The orange line tracks the projected population of Northland excluding any impact of climate, while the grey line includes the impact of climate. Bear in mind that Northland shows the biggest effects in relative terms - the effects on other regions are smaller.

I also looked at the effect of different climate change scenarios, and the difference between different climate scenarios is negligible. The diagram below shows the projections under different climate scenarios for the Southland region. As you can see, there is little difference between them (and that result is similar for other regions as well).

Overall, the results suggest that, while statistically significant, climate change will have a negligible effect on the population distribution of New Zealand at the regional level. This is not to say that climate change will not have important and substantial effects at very localised levels, as a result of sea level rise, for instance. However, most if not all of the displacement of people will be within regions. For example, maybe those displaced by sea level rise simply move a little further inland, or we build walls to keep the sea at bay.

Read the full working paper here.

Newsflash! Population growth will be highest on the fringes of fast-growing urban areas

I'm not sure how this is news:

Infometrics has this morning released its Regional Hotspots 2016 report, showing the country's top future population growth areas between 2013 and 2023, revealing some obvious and less obvious areas...

The hotspots were concentrated around the country's main metropolitan centres, "reflecting the highly urbanised nature of New Zealand's population and the greater density of potential new markets offered by these growth areas".

Well, duh. The Infometrics report is here, but it doesn't really say much that isn't obvious to anyone with local knowledge who hasn't been living under a rock. For instance, North Hamilton is one of the 'hotspots' and this is part of what they have to say about it:

The choice of this hotspot reflects the ongoing trend of the growth in Hamilton’s metropolitan area towards the north. Although there are also longer-term plans for expansion of the city southwards towards the airport, growth in the shorter-term will be focused on the fringes around Flagstaff, Rototuna North, and Huntington.

The whole report is full of re-packaged Statistics NZ data on area unit population estimates (to 2016) and projections (to 2043), which anyone can view here, so it doesn't even include anything new. Last Thursday must have been a slow news day.

For more on small-area population projections though, you can read my report with Bill Cochrane for the Waikato Region here (there is a more recent update to that report, but it isn't available online - if you would like a copy, drop me an email). We use a model of statistically downscaling higher-level population projections using a land use projections model (a more detailed paper is currently in peer review for journal publication). This is a significant advance over the method employed by Statistics New Zealand, because it takes into account the planning decisions of councils at the local level. The results (in some cases) are strikingly different from Statistics New Zealand's projections, and suggest that more can be done to improve the quality of 'official' small-area population projections.

Winners and losers in population growth

I was quoted at length in a story by Michael Daly published in Stuff last week. My comments were based on my ongoing research programme (with many collaborators) on subnational population projections and migration:

While New Zealand's population was continuing to grow it was becoming much more concentrated in the main centres, Cameron said. "For a lot of regions it really is about managing the decline." 

Declining areas could have a reverse momentum. "You can get young people moving out of the area. You're going to get less natural increase, that's going to reinforce population decline," Cameron said.

Declining rural areas tended to have more older people, while the larger centres had tertiary education opportunities that drew in the young.

"Areas that have more job growth, better income availability, lower unemployment, those tend to be places that are attractive for people to live," he said.

Good amenities were also important. "There's quite a difference between the sorts of things you can do in Auckland from Taumarunui, for instance. People like to be able to be able to do things, and urban centres tend to have more of those opportunities."

Migration was one factor contributing to fast growth in some areas but so was natural increase - the difference between births and deaths.

Although some migrants were retirees, most tended to be younger than average. "Younger people have more babies so that reinforces itself."

Cameron did not expect there would be a tipping point where Auckland's high house prices and traffic congestion would lead to an avalanche of people moving out. "It's a trickle rather than a torrent," he said.

But Auckland's high property prices were benefiting Hamilton and the Waikato District. While some people were commuting north into Auckland, jobs were also spilling over from Auckland into Waikato, where land was much cheaper.

Waikato also had good road and rail links to the ports in Tauranga and Auckland, Cameron said. The dairy boom, although ending 18 months or so ago, had also brought considerable income into Waikato, as well as into Taranaki.

Hamilton did have a similarity with Dunedin that counted against the cities. "They have the university there (Dunedin), which brings in a lot of young people but once they finish they are all heading out of Dunedin. We have the same thing here in Hamilton."

Dunedin's slow growth was a long term trend. It had been New Zealand's largest city in the 19th century and it was hard to pull out the causal factors that had led to its decline in importance.

Queenstown had a booming tourist industry, which was labour intensive, Cameron said. "There's a lot of jobs available. Those jobs pull in people. The more people you have the more hairdressers and things you need. It gets a little bit of momentum going."

Nelson had the same sort of sunbelt migration that Tauranga did, including the arrival of many retired people. Gisborne was "so far away from everywhere. It's very isolated out there."

The notoriety of Wellington's weather didn't seem to be a massive disadvantage, Cameron said. He had looked into the effects of climate on migration, and while it had an effect it wasn't very large.

"People do tend to move to sunny, warmer, less wet places, but the actual size of that effect is pretty small."

Some work had been done on whether regions could arrest population decline by attracting migrants, he said. "But the amount of migration you would need to offset both the ageing population and the fact young people want to move out - it's unrealistic."

Overseas, where areas with declining populations had managed a resurgence, it was usually because of some sort of black swan event. For example, the only thing that turned the population change in North and South Dakota around had been the fracking boom. "It was really a one-off," Cameron said.

One small district that had done a good job of turning around declining fortunes was Otorohanga, which had been losing people for a long time before growing between the last two censuses.

"They managed to retain a lot of their young people," he said. Dale Williams, who was mayor from 2004-2013, had a compact with local employers to make jobs available for young people.

"Because young people could stay in Otorohanga and have a good job, many chose to stay. Then you have more natural increase in the population, as well."

Daly did a good job of collecting and summarising my comments. The key point is that the areas that are already growing fast (especially Auckland, Tauranga, and Hamilton - the so-called 'Golden Triangle' of the upper North Island) are doing so not solely because of migration. Migrants tend to be younger than non-migrants (even for Tauranga a lot of in-migration is young people), and younger people generate additional population growth because they have children. At the other extreme, rural and peripheral areas of the country are experiencing sustained out-migration of the younger population, which is a double-blow (again because there will be fewer children as a result). It could be (and may yet become) worse though - consider the situation in Japan.

The idea that there will be 'winners' and 'losers' in future population growth is nothing new. Consider the discussion of 'zombie towns' in New Zealand (which I discussed here). The Marsden funded project Tai Timu Tangata (led by Natalie Jackson, and including me) will begin producing some final outputs over the coming months. I look forward to outlining some of those outputs here.

Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

In the NZ Herald last Sunday, Bernard Hickey looks at the possibility of depopulation in New Zealand. He quotes this Royal Society of New Zealand report:

Some territorial local authorities will have increasing difficulty in maintaining service levels for an ageing and possibly dwindling population, not to mention burgeoning numbers of visitors and tourists.

Hickey says:

Councils will have to make difficult decisions to return tarseal roads to gravel, turn off town water and let parks return to bush... Anyone buying property in places such as Wanganui, Gisborne, Whangarei and Greymouth should look at their area's population projections before putting deposits on houses or office buildings.

But forget future 'zombie towns', there's entire districts that are already depopulating, and that trend is only going to increase. Consider this Treasury Guest Lecture (PDF) given by my NIDEA colleague Natalie Jackson. Between 2006 and 2013, the population of the Gisborne Region declined (all other regions increased in population), and the population of 20 of the 66 territorial authorities declined.

And this isn't a new phenomenon either. Consider these examples [*]: between 1964 and 1984, Patea (in Taranaki) declined from a population of 2,040 to 1,928; Raetihi (in the central North Island) declined from 1,390 to 1,247; Taihape (the gumboot capital of the world!) declined from 2,800 to 2,586; and Runanga (on the West Coast) declined from 1,720 to 1,264. By the 2013 Census, the populations were 1,098 for Patea, 1,002 for Raetihi, 1,512 for Taihape, and 1,023 for Runanga (excluding neighbouring Rapahoe). So, those rural towns have been in decline for a long time.

And there's more to come. Bill Cochrane and I have been working on population projections for the Waikato Region. Of the ten component territorial authorities in the region (excluding Rotorua District, of which a little bit is in the Waikato), all but Waikato District and Hamilton City are projected to peak in population and begin to decline sometime between now and 2063. Three of them are projected to experience immediate and sustained decline in population (Otorohanga, South Waikato and Waitomo Districts). Moreover, South Waikato District is projected to decline in population in even the most optimistic high scenario (the other two increase slightly in population in the highest scenario).

We saw something similar when we did projections for the Bay of Plenty region earlier in the year (see here, PDF). Of the six territorial authorities in the Bay of Plenty, only Western Bay of Plenty District and Tauranga City are projected to avoid any population decline, and three of the other four (Kawerau, Whakatane, and Opotiki Districts) are projected to experience sustained decline. See for example Kawerau:

Maybe it's not all bad news though. Population decline could just be a slow-burn version of the collapses that lead to a redistribution of towns and cities to more preferable locations. And there is lots to learn from population decline, which hasn't been investigated nearly as much as population growth. Natalie Jackson is leading a multi-disciplinary team in a Marsden-funded research project to further explore these issues, by identifying, classifying and modelling the mechanisms and thresholds of subnational decline. Bill Cochrane and I are both contributing to this project, through which we hope to be able to better project population decline and when and where it might begin.

Will that help local councils that are trying to arrest population decline and encourage more people to live in their jurisdictions? Unfortunately it's unlikely to help much - these councils are largely engaged in a zero-sum battle for future population. If one council comes up with a new 'sure-fire' attractor of new migrants, then other councils will quickly copy it. It's a Tiebout competitive race-to-the-bottom at the subnational level, that none of them can 'win'. Absent any sudden shift in economic fortunes (like the shale oil boom that has led to massive increases in population in North Dakota), future subnational population growth in New Zealand will most likely continue to be concentrated in the major cities, particularly in the golden triangle of Auckland, Hamilton and Tauranga.

*****

[*] These figures are taken from the New Zealand Official Yearbooks for 1965 and 1985. It's pretty cool that these historical treasure troves are all freely available online.