So how does this small size influence outcomes for New Zealanders?

The growth of big cities has been one of the main features of the world economy since 1950. Back then, there were only 15 cities with more than 3 million people. Now there are over 110. People flock to big cities for a variety of reasons. Incomes tend to be higher, and the selection of occupations wider. Oftentimes education and health care are better. For young people, the social life tends to be more interesting, and less subject to traditional restrictions. Cities tend to have a lower environmental footprint, although it does always appear this way because any excesses are more concentrated.

In the last three decades, the growth of big cities in OECD countries has been associated with another dynamic – they have increasingly become centres for service sectors that rely on highly educated workers.

Up until 1980, this phenomena was not so noticeable. Big cities and small cities both had large amounts of manufacturing work, and big cities were a great places for people with middle levels of education (a full high school level education) as they offered a wage premium relative to small cities. As manufacturing and clerical jobs vanished, however, the earnings premium for people with medium levels of education disappeared (Autor 2019). Given big cities typically have higher real estate prices than small cities, big cities are now much less attractive destinations for people with moderate education levels.

The decline of manufacturing

The decline in manufacturing has been massive. New Zealand’s experience is typical – in 1976 25% of jobs were in the manufacturing sector, but now it is only 10%. Many of the jobs that have replaced them are in the health and education sectors, or in professional and business services.

Firms in the latter sectors seem to have a strong preference to locate in big cities. For example, when employment in New Zealand’s finance sector expanded from 4.9 to 6.7 percent of total employment between 1976 and 2013, or from 50,000 to 100,000 job, two-thirds of the increase occurred in Auckland. This might be fine if you live in Auckland or want to live in Auckland. However, it creates difficulties for smaller towns that lost their manufacturing jobs but could not easily take advantage of the expansion in employment in other sectors (Coleman, Maré and Zheng 2019).

Globally, the McKinsey Institute has identified 50 “super cities” which have very high incomes, a large fraction of the largest and most dynamic service sector firms, and highly educated workforces. Singapore but not Auckland features; Sydney and Melbourne are in the next tier above Auckland.

These super cities are attractive destinations for the most productive firms and the most skilled workers, creating a virtuous circle of high income, highly productive locations. Labour economists attribute a large fraction of the rise in income inequality to this assortative matching process, as the most skilled people earn increasingly large amounts in the most productive firms.

If other firms were able to copy the productivity performance of these frontier firms, the increased productivity would filter down to everyone, raising wages generally. This is not happening, however.

In the last two decades it has proven increasingly hard for other firms to copy the best firms. These means productivity gaps and wage gaps have widened. Big-time bankers in London earn more than “big”-time bankers in Auckland, who earn more than those in Christchurch. This seems to be an important component of the rise in inequality in OECD countries.

Questions of scale

At one level, the problem for small town New Zealand relative to Auckland is the same as the problem of small-town Auckland relative to major world cities.

If it is difficult to copy the production techniques of the best firms, a city needs to attract the best firms to obtain the benefits of the high productivity and high wages they offer. If a city can’t attract these firms or copy their productivity levels, wage levels will lag behind.

In this case people wanting the better life-styles normally associated with higher incomes will find it easier to move to where the good firms are located rather than wait for the good firms to locate where they live. Lots of New Zealanders have worked this out – both those that have moved from New Zealand to higher productivity locations abroad (or within New Zealand), and New Zealanders who have migrated here from lower productivity locations abroad when they had the chance.

To take just one example, the statistics are very clear that the easiest way Maori have found to make high incomes is to migrate to Australia – Maori earn nearly the same as other Australians and nearly the same of Pakeha migrants, as they take advantage of the high incomes and high productivity levels that are offered by Australian firms but which New Zealand firms do not seem able to match. (Kukutai and Pawar 2013 provide data on Maori incomes in Australia).

Nineteenth century America was characterised by boosterism – competition by cities to promote themselves to attract the best firms and the best people (Chicago was the bigtime winner, as William Cronon documented in his magnificent book “Nature’s Metropolis.” ) 

Does this provide a clue to understanding New Zealand’s low productivity woes?

I am not sure, but understanding why productive firms choose to locate in one place and avoid other locations is a key part of understanding productivity performance when it is difficult to copy what the best firms do.  For years I have joked that New Zealand’s approach to attracting the best international companies is to give them the chance to pay some of the highest business taxes in the world. Unfortunately, the productivity statistics suggest this strategy is not enough. If New Zealand’s cities are not a desirable location for the world’s most productive companies, additional effort might be needed to work out how to attract them in the future.

Furthermore, the authors linked these entry and exit rates to “dynamism” and therefore productivity growth – a link I wanted to think about a bit more carefully.

Hopenhayn et al (2018) found that declining business dynamism in the US over time has been due to the declining labour force growth. Marginal Revolution has a nice run down of it here.

The intuition behind this result is that, when labour force growth accelerates current firms cannot absorb the new workers. As a result, new firms (young and small) enter. Such a dynamic leads to additional competition, a reduction in concentration and the average size of firms, and the destruction of inefficient firms – helping to drive dynamism.

How does this logic apply to NZ economy?

This finding fits our business dynamism and labour force growth story very well. NZ firms have high entry/exit rates as shown by the Productivity Commission, earlier MED/MBIE work, Treasury, and the OECD. Furthermore, the labour force has grown considerably due to immigration and rising labour force participation rates.

As a result, there is a link between the high labour force participation level and high entry and exit rates – which seems consistent with the Hopenhayn et al (2018) study but with the opposite sign! [Note: We would want more information on how entry and exit rates had changed to say it was the same result]

However, if the NZ experience can be viewed this way it provides somewhat of a puzzle – if “business dynamism” is happening here why is our multi-factor productivity so low?

Often the assumption is that high rates of firm creation and destruction facilitate innovation and new technology adoption. This creative destruction then helps to boost productivity growth – and explains the positive relationship observed between entry and exit rates and productivity in the OECD.

However, it is important to dig deeper. The OECD’s “One size does not fit all” paper shows that two countries have very high entry rates but low average post-entry growth: New Zealand and Turkey.

Not all destruction and creation are creative

Imagine a world where it is very cheap and easy to set up a business, and moreso you are given support when you do that. But after a year or two the support dries up and everything becomes compliance heavy.

In that sort of situation firms will be destroyed and new firms created that are doing the same thing with the same technology. Another term for this is that there is excessive churn in the number of firms that is not due to changing economic fundamentals – such churn limits the incentives to invest, and reduces institutional capital that comes from operating for a protracted period of time.

As a result, understanding why firms are being destroyed and created – rather than simply viewing all creation and destruction as a product of “creative destruction” matters.

Economic growth is a much bemoaned topic, but what this tweet nicely illustrates is that the type of growth economists often talk about differs from the type of growth that concerns a number of people. Here we’ll briefly consider how we can understand this tweet.

For an economist there is some set of resources, and they can be combined to create output. This suggests that output = function of inputs.

Technological growth, or growth in productivity is when this function changes. Specifically imagine that we can write the equation the following way:

Where Y is the output produced, f is the “production function”, L, K, and N are labour, capital, and natural resources while A is some technology scaling factor.

Economists will tend to think about growth in terms of an increase in A – namely we can make more output with the same inputs. This mirrors the Productivity Commissions discussion of productivity, and what Arthur Grimes was expressing here.

Many “non-economists” will instead see growth as the product of using more inputs – often with a concern towards non-renewable natural resources.

Both frames are valid, and we do need a conception of the physical limits of our ability to use natural resources. As the blog has argued in the past an economist will often state that there will be innovation as some forms of scarce resources (that are non-renewable) are used up.

Ultimately, this suggests that understanding technology and innovation matter for both growth, and the way we vary the utilisation of resources. When economists state that “growth, innovation, and incentives are what is necessary to ensure sustainability” this is what they mean.

There are areas of market failure – common pool resources, hyperbolic discounting and/or a failure to value future generations. However, an economist would argue that these are the areas that require policy intervention – not an intermediate heuristic like growth. [Update: The original tweet published a post that shows they were singing from the same song-sheet – give it a read over here.]

In the same way that we shouldn’t “target growth for growths sake” we shouldn’t “limit growth for the sake of it” – as many of the sources of growth and increases in the technological frontier may be associated with finding new ways to do things which help to promote sustainability!

The one warning I would give economists when they say this is the following. If there is a better sustainable way of doing things with different inputs, that we can discover through innovation, which would then increase GDP – why aren’t we already doing it? Yes a relative price change incentivises change, but I’m not sure it follows that it generates growth.

In the end I think there is one thing we can all agree on – we want a world that our children and grandchildren can love, one that is no worse than the world we inherited. Lets ask where there may be failures that are preventing us from doing that, rather than fixating on either increasing or decreasing some arbitrary “production” or “growth” measure

The focus of this post is on digitization. In an era of digitization, economists have become more and more concerned about whether the conventional way of calculating GDP is appropriate for asking questions about changes in consumer welfare (surplus) through time.

GDP associates a dollar value with a product in terms of its market price and aggregates up all the products – we can think of this as a proxy for the way society could trade-off between the products, and also the relative value consumers of the final good have over the products they are buying. In this way, an increase in GDP is associated with an increase in consumer surplus in traditional goods markets .

With the rise of free services provided by digital goods such as free apps on smartphones, Facebook and etc, Erik Brynjolfsson notes that we can end up in a situation where the GDP can stay constant (or even falls), but the technological change increases consumer surplus. Brynjolfsson proposes a new GDP measure called “GDP-B” where he adds contributions from digital platforms into the GDP growth. 

Why should we make adjustments to the conventional GDP measure?

Imagine a world where we all had a product that gave us a whole bunch of service value through time. The value of the (durable) product would be measured in GDP as a purchase, but the flow of services would not – so when describing the “flow” of consumption there would be a pretty severe problem.  Hence why we put things like “imputed rents” into the GDP data.

Furthermore, we may expect this durable good price to represent the value of the services it provides – but in the face of competition, the price of the durable good may be lower than the price of the services it is replacing.

Now imagine a world where a paid product suddenly becomes “free”.  We know there is a “price” somewhere as the price of the cellphone will now in some way reflect this additional value, but how well does that capture the increase in “consumer surplus” that is occurring.  If we are trying to use GDP as a reflection of welfare this is an issue.

Both Sharon Pells from MBIE and the Productivity Commission have recognised the importance of these measurement issues, and how they may give us a misleading read on consumer surplus.

Tying these points together, the digital economy leads to the provision of a huge number of previously paid services for *free, on the basis of purchasing a cellphone and a cellphone plan. Bundling together all these final services (the things consumer actually value) there has been a massive decrease in the cost of purchasing these services. However, looking at GDP measures we would see a decline in GDP in the industries that previously provided them (eg Kodak cameras) and an increase in purchases of ICT equipment.

If we measured the final consumer service all that has happened is that prices have fallen for market provided services, and so consumer surplus would have risen. But our way of measuring doesn’t capture this reality as it is based on these intermediate product types rather than the final service to the consumer.

Erik Brynjolfsson suggests an adjusted measure, GDP-B, to augment traditional GDP for new and free goods – with the goal of more closely associating changes in the new measure with consumer surplus.

The idea behind these measures is to associate a reservation price with goods that are provided, that is estimated to be the lowest price which would have had zero quantity demanded before the product existed.

1. For “new goods” this gives an indication of the reduction in the price associated with the introduction of the good, and a measure of surplus.
2. For a “free good” it is necessary to calculate their compensating variation, or how much expenditure would they need in order to be indifferent to losing the good.

To estimate these, Brynjolfsson used a series of large scale, cool, experiments.

For instance, he ran an experiment with cellphone camera usage to identify the importance of quality adjustments for goods with rapid quality change. People would state an amount of money they would be willing to receive in order to avoid using the camera, and then would have their camera taped up – if they took off the tape they would forfeit the money, providing a revealed preference of their valuation!

The experiment gave a strong evidence that consumers obtain a significant amount of surplus from using their smartphone cameras.

So this replaces GDP?

Brynjolfsson, Collis and Eggers suggest that we shouldn’t be replacing GDP, but rather use a different metric to capture consumer wellbeing through consumer surplus, which in sense a new GDP-B metric does.  

“We propose a way of directly measuring consumer well-being using massive online choice experiments. We find that digital goods generate a large amount of consumer welfare that is currently not captured in GDP.”

As they note “GDP is a measure of production not wellbeing”. If the question at hand is one regarding domestic production, or the use of factors of production, GDP still has a role. Additional measures are just more appropriate when we have different questions.

In a ray of light, this morning’s FT (£) reported a study of over 15,000 German employees that examined the relationship between ageing and productivity. One of the authors is quoted saying:

As workforces age, employers are concerned that productivity will decrease. That is not so. What matters is not chronological age but subjective age.

The research suggests that older people are systematically excluded from training activities, and are relegated to less creative and meaningful work, which renders them less productive. As the workforce ages, that may begin to change. As it changes, the relationship between growth and age structures is likely to weaken.

PRP schemes can be effective in improving outcomes across the three public services for which evidence is available (health, education and the civil service), although the central conclusion is that the outcomes from PRP are mixed, which much dependent upon organisational and occupational context and scheme design and implementation. Where positive effects have been found, effect sizes are sometimes small and may also be short-lived. As well as evidence gaps across much of the public services, the weight of evidence also varies, with the more robust evidence coming from education and health rather than the civil service. Cost-effectiveness data to assess the value for money of PRP interventions is also rare.

The implication is that performance-related pay isn’t a quick fix: it requires careful development to fit it to the context, and organisations might take a while to adapt to it and see benefits. Without more examples in the public sector it isn’t possible to say whether it will prove cost-effective.

New Zealand only just misses the cut of counties whose performance improved. The average annual increase in GDP per hour worked in these later years was 1.1% compared to 1.3% from 2000 to 2007. Further, while New Zealand had the 26^th highest average annual growth in productivity in the years to 2007, since 2010 New Zealand has had the 10th highest average rate of growth in productivity in the OECD. When this performance is measured for 2008 to 2013 then we are only one of 5 countries whose average annual growth rate in productivity increased (along with Australia, Luxembourg, Poland and Spain).

But the story is not so good when levels, not just rates of productivity growth, are considered. OECD data on GDP per hour worked in constant 2005 prices show that of the 34 OECD countries New Zealand had the 24^th highest level of productivity in 2013. Indeed, New Zealand’s GDP per hour worked would need to increase by 34% to reach the OECD average. This is a worse than in 2000 (where the OECD average was 31% higher in terms of New Zealand’s figure) and shows that while the last few years have been promising, New Zealand’s productivity problems are far from over.

GDP per hour worked

Source: OECD.Stat

As we are a NZ audience, I’ll quote this bit:

While New Zealand faces different challenges, its experience can throw light on the UK’s situation. OECD research recently published by the New Zealand Productivity Commission has shown that the country has good resources – investment in physical capital and average years of schooling are broadly consistent with other countries – and policy settings. It is one of the easiest countries in the world in which to set up a business and its tax and regulation regimes are often seen as world class.

Indeed, the OECD estimates that New Zealand should have GDP per capita 20% above the OECD average. But its productivity performance means it is 20% below. In short, New Zealand poses a real challenge for standard prescriptions for what countries should do to lift their productivity performance.

– See more at: http://www.publicfinanceinternational.org/features/2014/06/solving-the-productivity-puzzle/#sthash.qZST25PS.dpuf

With this in mind, the Productivity Commission has been thinking about New Zealand’s productivity performance.  And given that along many characteristics New Zealand and Australia are similar they have decided that looking into the productivity gap between these countries helps us to understand this issue.  This led them to release a working paper titled “Investigating New Zealand-Australia productivity differences:  New comparisons at industry level” on their main site (links can be found here).

By comparing what has happened to productivity in Australia to what has happened in New Zealand, this work adds additional understanding to their previous discussion of New Zealand’s productivity performance in September (their blog, paper, my reading).  To quote straight from the Commission”

The majority of New Zealand industries under-perform from a productivity perspective compared to the same industries in Australia, including mining, agriculture, most branches of manufacturing, construction,
retail and wholesale trade and financial and insurance services. However, New Zealand has areas of relatively strong performance in food and drink manufacturing, utilities (electricity, gas and water supply) and arts and recreation services.

In 2009, capital invested per hour worked across total market industries in New Zealand was almost 40% below the Australian level. Australia is also 3% ahead in terms of skills – measured here using data on workforce qualifications and relative pay levels – but this gap is much narrower than that found for capital per hour worked and multi-factor productivity.

Describing the industries where New Zealand firms are not as productive (note this is not meant as an insult) relative to Australia helps us to think about “why” the productivity difference exists – and whether it is due to specific characteristics of New Zealand (eg population density, distance to market), policy (eg relative tax treatment), or just bad luck and path dependence.