Airports and regional development

Most large regional cities have their own airports. Is that because growing regions are more likely to open an airport, or because having an airport leads to faster population growth for small regions? Probably, it is a combination of both, but empirically they are difficult to disentangle. However, this 2025 article by Jørn Rattsø (Norwegian University of Science and Technology) and Nicholas Sheard (Deakin University), published in the Journal of Economic Geography (open access) attempts to answer the question of how much regional airports contribute to growth.

Rattsø and Sheard focus on the example of Norway, where the number of regional airports grew rapidly from the 1950s, with fifty new airports opening between 1950 and 2019. They apply an event study difference-in-differences approach with synthetic controls. That means that they compare regions where an airport opened with synthetic controls made up of a weighted average of other regions, between the time before and the time after the opening of the airport. The outcome variable they concentrate on is the regional population, but they also look at employment (in total and by broad industry category).

Rattsø and Sheard find that:

...regions where airports were opened subsequently experienced growth in both population and employment, relative to otherwise similar regions that had been on similar growth paths before the airports were opened...

The size of the effect is relatively modest, with population growth about 0.4 percent higher after 1-5 years, 0.9 percent highers after 6-10 years, 0.5 percent higher after 11-15 years, and no difference after 16-20 years of the airport opening. Rattsø and Sheard also report a number of heterogeneity analyses, which are interesting too:

The population growth effects of new airports are largest and most significant for airports established in the first decade studied (the 1950s) and for new airports opened where there were no other airports within 100km... the growth effects are relatively large and more often statistically significant for airports that are physically larger (measured by length of runway) and that have a connection to at least one of the four largest cities in the country.

The first of those heterogeneity results points to a potential problem with the analysis. Airports are not opened randomly. Governments are more likely to open airports in regions where those airports are likely to have the largest effects first. And so, the effects being largest for the airports that were opened in the 1950s may be because those regions were going to grow rapidly regardless of whether an airport was located there or not. The synthetic control method attempts to deal with this by comparing regions with an airport with a weighted average of other regions without an airport, where the weighted average control 'looks like' the region that received an airport. However, this approach can only ever provide an imperfect control, because the reality is that the regions that are part of the control did not receive an airport, and if airports are allocated first to regions that are likely to grow faster, then the comparison with the synthetic control may simply pick up that fact.

The other heterogeneity results are consistent with what we would expect if regional airports do lead to faster population growth. If airports increase growth, then larger airports should increase growth by more. And connectivity matters, particularly to larger regions (although it is worth noting that when you have an airport, the flights go in both directions, and so it is by no means a given that increasing connectivity leads to net in-migration). The results for employment are also consistent with expectations, with increases in employment in the 'transport and communications' sector, as well as services.

Rattsø and Sheard rightly conclude that:

...the effects were concentrated in the early era of expansion when the air network was much less developed and similar benefits are not likely to be available today. In addition, the effects of having a small airport are limited: having an airport with little air traffic and few connections is not helpful for regional development. For peripheral regions, it may be better to improve road and other infrastructure to reduce travel times to larger airports with better connections, rather than building their own airports.

None of those conclusions should be surprising. However, the results from this study should caution against small regions in modern times arguing strongly for the opening of a new airport. Taking the results from this study at face value, where the air network is already extensive, adding an additional small airport will have little effect on population growth. There may be other good reasons to open a small regional airport, but expecting an increase in population growth should not be among them.

The changing system of regional economic development in New Zealand

I just finished reading the edited volume Economic Development in New Zealand, edited by James Rowe and published in 2005. Edited volumes are difficult to review, particularly when the collection of chapters have only a loose connection and lack a common thread, and that was the case with this book. Instead, I want to share one overall takeaway from reading the book, and that is how the policy environment for regional economic development has changed immensely since the 2000s. This matters because the way that we organise regional development determines who sets priorities, where capability accumulates, and whether regional growth is sustainable or merely a sequence of centrally funded projects.

So, what has changed? We can think about how leadership and decision-making has changed, how funding and strategy-setting has changed, and how the roles of business, educational institutions, and the research sector have changed.

In the mid-2000s, regional economic development had a lot of prominence, and it has seen a bit of a revival in recent years. However, there are some substantial differences in how that prominence manifests between the two eras. In the mid-2000s, regional economic development was led by the regions. The central government had an important role in setting the policy environment and steering the direction through funding, but regional development initiatives typically came from the regions. This is exemplified by the Regional Partnerships Programme (RPP), which involved central government funding regions to develop their own plans, build capability, and then back major initiatives coming out of those plans. Business had a strong role in partnership with government, not just as part of the RPP, but more generally. Region-wide strategy and plan development tended to rely on input from local business and industry leaders. There was also an important role for training , research and development, and innovation, and so universities, polytechnics, and Crown research institutes were all closely involved in regional development.

Fast forward to today, and regional development has been embodied in the Provincial Growth Fund, which has a lot of different aims, one of which is to "create jobs, leading to sustainable economic growth", and more recently the Regional Strategic Partnership Fund, which had a much more narrow aim to "make regional economies stronger and more resilient to improve the economic prospects, wellbeing and living standards of all New Zealanders". In both cases, it is central government that is largely the decision-maker, in addition to funding the initiatives, rather than the regions themselves. Business input is now largely channeled through consultation and deal-making, rather than input into the strategic direction of regional development. The rhetoric for business has changed to more of an emphasis on innovation and increasing productivity. That applies to the education and research sectors as well, where the role has shifted to more of a focus on core skills development and innovation, rather than being part of regional strategic plan development.

In between the mid-2000s and today, regional development did go through a bit of a quiet patch. It is clearly back in vogue now, although the policy environment and systems have changed tremendously. What that means is that there is not much from Rowe's edited volume that translates directly to today's situation, sadly. The initiatives that the authors were writing about are long gone, even the AUT Masters degree in Economic Development that one chapter describes has long since closed down. However, the value in reading Rowe's book is that it provides a useful reminder that regional development has long been a goal of central government, and that there is more than one way to approach that goal.

What's new in regional and urban economics?

The journal that I edit, the Australasian Journal of Regional Studies, is about to release its latest issue (more on that in a future post). That makes it timely to think about what's new in regional and urban economics. Actually, it's probably always a good time to think about what's new, but this is a particularly useful time because we can rely on this new NBER Working Paper (ungated here) by Ran Abramitzky (Stanford University), Leah Boustan (Princeton University), and Adam Storeygard (Tufts University).

The paper mainly covers new data sources that have come into more regular use in recent years, and provides a good survey of the literature that has developed using each source. Abramitzky et al. also identify some new use cases for some of the data sources, which points to new research directions or extensions of existing work. For new (or experienced) researchers looking for inspiration for their next research project in regional and urban economics, this paper is a good one to read.

To save you a little bit of time though, here are some of the key data sources that Abramitzky et al. discuss (some of which I have grouped together differently than they do). The first is historical (US) Census records:

The US Census is far from a “new” data source, having provided the backbone of empirical research in urban economics and other applied fields for decades. Yet advances in record linkage have allowed researchers to convert (historical) census data into large panel datasets that follow individuals over time. This longitudinal data opens up a set of new research questions on spatial topics, including the determinants of geographic mobility, the long-run effect of childhood exposure to environmental conditions or economic shocks, and the causes and consequences of neighborhood change within cities.

Complete census records, including an individual’s name and detailed location information, becomes available to the public 72 years after the Census is taken; the 1950 Census was just released in 2022.

Sadly, this is not a data source that is available for many countries (including New Zealand, where Census unit records prior to 1966 were destroyed). However, the ability to link people over long periods of time (including between generations) has opened up a wealth of new research questions. Second, Abramitzky et al. discuss digitised historical maps and directories:

Digital spatial data in Geographic Information Systems (GIS) is indispensable for a variety of modern urban applications but, until recently, historical maps were not compatible with this tool. In recent years, economic historians and other social scientists have digitized a wide range of historical maps, including census geography, and environmental and land management maps. These efforts have opened up study of historical neighborhoods and the effects of proximity to relevant geographic features like administrative boundaries, industrial sites, religious and cultural institutions and the epicenters of natural disasters.

I have a project in progress (which is, unfortunately, somewhat stalled due to non-map-related data issues) that has made use of digitised boundary maps for the electoral boundaries from past New Zealand elections (more on that in a future post, if that project ever gets re-started). However, the key point is that there is a wealth of information stored in historical maps and archives that are currently underutilised. On a related note, Abramitzky et al. note that:

Beyond mapping the location of households or firms, GIS is also useful for reconstructing historical transportation infrastructure via waterways, roads or railroads.

Given that past infrastructure patterns, including transportation and other networks, affect the patterns observed today, these seem like important sources. Third, Abramitzky et al. talk about a range of remote sensing data, including night lights (from satellite imagery), and physical attributes like air quality, weather variables, and building footprints and heights. These data are often available at small spatial resolutions, allowing analysis at very fine-grained spatial levels. However, it is worth reading the paper (and the references) carefully, as they also identify issues to be aware of with remote sensing data.

Fourth, Abramitzky et al. very briefly discussed picture and video data, including Google Streetview, and CCTV camera data. There are definitely some interesting use cases for these atypical data sources, and you can expect to see a growing use of them in future research. Fifth, Abramitzky et al. discuss mobile phone or smartphone data, including data derived from particular smartphone apps:

Mobile phones provide information about the location of the people who use them, and sometimes the vehicles they drive. Broadly, there are two kinds of cell phone data. Call data records (CDRs), provided by network operators, report the location of the phone at the time a call was made or received, as triangulated from the network of cell towers. In some cases, the counterparty to the call can also be identified...

For research purposes, CDRs have been mostly replaced by data from smartphones, whose apps collect more accurate GPS-based locations at all times (regardless of whether a call is placed)...

Researchers have used location data from individual apps with which they have developed relationships. Most prominently, Uber has provided data on its trips to several groups of researchers...

Similarly, they discuss transportation data derived from vehicle location trackers, transit cards, or electronic tolling stations, or electronic payment systems for transit riders. All of these sources are useful for identifying transportation and commuter flows, which have high policy relevance.

Finally, Abramitzky et al. give a rapid-fire selection of other data sources that are only beginning to be used, including e-commerce and payment card transactions data, posted prices and listings (often scraped from websites), routinely collected administrative data (which in my experience will generally require a lot more data cleaning), and text as data.

Clearly there are lots of new and emerging data sources in use in regional and urban economics. However, Abramitzky et al. are clear that developing skills with these data sources and the appropriate methods for dealing with them is not feasible for everyone. They do, however, suggest a solution:

We encourage urban economists, both young and old, to familiarize themselves with these data sources and to become conversant in some of the methods needed to build new data from textual corpora, digital traces, and images and video of the world around us, including large language models and deep learning more broadly. We emphasize the word “conversant” because we do not think that all of us need to become experts in these techniques. Rather, we anticipate and encourage interdisciplinary collaboration with scholars around the university in data science, computational linguistics, computer science, geography and the natural sciences who know these methods well and can thus complement the research focus and conceptual framework specific to urban economics.

So, we should definitely expect the current trend for larger, more interdisciplinary, research teams to continue into the future.

[HT: Marginal Revolution]

Regional resilience to the Global Financial Crisis and Covid-19 shocks in New Zealand

This week the Waikato Economics Discussion Group discussed this article by William Cochrane, Jacques Poot, and Matthew Roskruge, published in the Australasian Journal of Regional Studies (open access). This paper won the John Dickinson Memorial Award for the best paper published in AJRS last year. In the paper, Cochrane et al. look at the take-up of social security benefits in New Zealand territorial authorities (the lowest administrative level of government in New Zealand) as a result of two shocks: (1) the Global Financial Crisis (GFC) in 2008-09, and the Covid-19 pandemic in 2019-20. Interestingly, comparing those two shocks they note that:

...the initial impact of the GFC on social security benefit uptake was of a similar magnitude to that of the COVID-19 pandemic: a mean increase across TAs of 1.86 per cent versus 2.23 per cent respectively.

A small gripe is that those are actually percentage point increases, not percent increases. In other words, social security benefit uptake was 1.86 percentage points higher after then GFC than the year before, and was 2.23 percentage points higher during the Covid-19 pandemic than before. Importantly, not only is the increase in social security uptake similar for the two shocks, but the spatial distribution of the uptake of social security benefits is similar for the two shocks, as shown in Figure 2 in the paper:

The areas that are shown in darker blue had larger increases in social security uptake as a result of the shock (the GFC is the map on the left, and Covid-19 is the map on the right). It is clear from the figure that the shocks were more keenly felt in the North Island. In particular, Northland is heavily affected by both shocks, as well as the eastern Bay of Plenty and East Cape.

Cochrane et al. then turn their attention to looking at the factors associated with the increase in social security uptake, asking the question, what factors are associated with greater resilience (that is, what factors are associated with a lower increase in social security uptake). To do this, they rely on Census variables taken from the 2006 Census (three years before the GFC), and the 2018 Census (two years before the Covid-19 pandemic).

Since Cochrane et al. only have 66 observations of change for each period, and over 140 Census variables, this poses a bit of a problem. Cochrane et al. solve this issue in a few ways. First, they categorise their variables into 15 categories, and use just one variable in each category in separate cross-sectional regression models for each shock, and in spatial panel regression models that combine the data across both shock periods. Then, in a separate analysis they use a machine learning algorithm to select the most important variables for inclusion in the model.

The variables that are statistically significantly associated with social security benefit uptake vary somewhat between the models, but there are two variables that are consistently significant. First, territorial authorities that had a lower unemployment rate two years prior to the shock had lower benefit uptake. Second, territorial authorities that had a higher proportion of public sector employment had a lower benefit uptake. From the post-estimation regression model after machine learning, a one percentage point higher unemployment rate in the previous Census was associated with a 0.268 percentage point higher social security benefit uptake. A one percentage point higher public sector employment rate was associated with a 0.076 percentage point lower social security benefit uptake.

The implications of this (if we can interpret these effects as causal), is that if central (or local) government wants regions to be resilient to shocks, then finding ways of reducing unemployment (difficult) or increasing public sector employment (perhaps less difficult) are important things to consider. [*] However, as Cochrane et al. note in their conclusion, the current New Zealand government may actually be doing harm to resilience, because:

...if austerity measures were to be introduced in future years that lead to less public sector employment across all regions, either to reduce public debt or to fund tax cuts, our results do point to a likely decline in regional resilience.

*****

[*] An important consideration here is the definition of public sector employment. This isn't clarified in the paper (and I guess I could ask the authors, given that I know them all quite well, so I will). Table 1 in the paper tells us that public sector employment is, on average, about 14 percent. That is clearly more than just those included in the 'Public Administration and Safety' industry in the ANZSIC classification, which was about 5.4 percent of employment in the 2018 Census. But it is similar to the total of that category plus 'Health Care and Social Assistance', which was 14.9 percent of employment in the 2018 Census. However, if you were to include the health sector in public sector employment, why would you not also include 'Education and Training' as well (bringing the proportion to 23.0 percent in the 2018 Census)?

Jetstar regional services cause loss of $40 million to main centres' economies

Last week, Jetstar announced a report by Infometrics that suggested their introduction of regional flights to Nelson, Palmerston North, Napier, and New Plymouth boosted the economy of those regions by around $40 million. Here's what the New Zealand Herald reported:

Jetstar's regional operations could boost the economy of four centres it serves by about $40 million a year, according to Infometrics research.

The regional GDP growth could support up to 600 new jobs according to the research which notes domestic air travel prices have fallen by close to 10 per cent during the past 12 months.

Jetstar Group CEO, Jayne Hrdlicka, said the report highlighted how important cheap fares were to growing local economies.

That sounds like a good news story, but as with most (if not all) economic impact studies, it only provides half the picture. That's because flying to the regions doesn't suddenly create new money. So, every dollar that is spent by travellers to the regions is one less dollar that would have been spent somewhere else. In the case of domestic travellers who would not have otherwise travelled to those regions if Jetstar hadn't been flying there (which is the assumption made in the report), every dollar they spend on their trip to Napier is one less dollar they would have spent at home in Auckland. One could make a similar case for international travellers, although perhaps cheaper flights encourage them to spend more on other things than they otherwise would (although this is drawing a pretty long bow).

So, if it's reasonable to believe that Jetstar flights add $40 million to the economies of those regions, it is also reasonable to believe that Jetstar flights cost around $40 million in lost economic activity elsewhere in the country (depending on differences in multiplier effects between different regions), and much of this will likely be from the main centres.

To be fair, the Infometrics report (which I obtained a copy of, thanks to the Jetstar media team) does make a similar point that:

...the economic effects of this visitor spending should only be interpreted on a region-by-region basis, rather than as an aggregate figure for New Zealand as a whole. It is likely that some of the increase in visitor spending in regions with additional flights represented spending that was diverted from other parts of New Zealand.

The Infometrics report has some other issues, such as assuming a fixed proportion of business travellers to all four airports, which seems fairly implausible but probably doesn't have a huge impact on the estimates. A bigger issue might be the underlying model for calculating the multiplier effects, since multi-region input-output models (I'm assuming this is what they use) are known to suffer from aggregation bias that overstates the size of multiplier effects. I had a Masters student working on multi-region input-output models some years ago, and that was one of the main things I took away from that work. However, that's a topic that really deserves its own post sometime in the future.

Of course, these problems aren't important to Jetstar, which only wants to show its regional economic impact in the best light possible. The next step for them might be to say: "Ooh, look. We've done such a great job enhancing the economy of these regions. The government should subsidise us to fly to other regions as well so we can boost their economies too". Thankfully, they haven't taken it that far. Yet.

You might argue that boosting the economies of the regions, even if it is at the expense of the main centres, is a good thing. That might be true (it is arguable), but it isn't clear to me that increased air services is the most cost effective mechanism for developing the regional economies. I'd be more convinced by an argument that improved air services are a consequence of economic development, not a source of it.

For now, just take away from this that we should be sceptical whenever firms trumpet their regional economic impact based on these sorts of studies.

Try this: Regional activity report

Last week I wrote a post about tourism, that looked at data from the Ministry of Business, Innovation and Employment (MBIE)'s Regional Activity Report. This online data is a treasure-trove of summary statistics for all of the regions and territorial authorities.

If you scroll down you can also see how the regions and territorial authorities compare across eight sets of indicators:

1. Social and Income - including household income, household income distribution, earnings by industry, deprivation index, and internet;
2. Housing - including mean weekly rent, median house price, mean house value, and new dwellings;
3. Workforce - including employment rate, labour force participation rate, NEET rate, unemployment rate, quarterly turnover rate, employment by industry, and employment by occupation;
4. Education - including national standards achievement, and NCEA Level 2;
5. Population - including population estimates, population projections, international migration, population by ethnicity, population by age group, and rural-urban proportions;
6. Economic - including GDP per capita, GDP by industry, businesses by employees, new building consents, and new car registrations;
7. Agriculture - including agricultural share of regional GDP, and area in farms; and
8. Tourism - including guest nights per capita, accommodation occupancy rate, tourism spend, international guest nights, and international visits.

In most cases the data tracks changes over time as well, some of it back to 2001. 

While most of this data was already freely available (from Statistics New Zealand, mostly), having it all collected in a single place and in a very user friendly interface, makes it an excellent resource. Even better, you can easily download any of the data into CSV files to play with yourself.

I won't provide an example of what you can do with it. I'm sure you're all more than capable of playing with the data yourselves. Enjoy!