Book review: Not in Narrow Seas

It's been a while since my last book review, back in August. And that's because I read a couple of novels, and after that I've been working my way through Brian Easton's Not in Narrow Seas. Regular readers of this blog will know that I have a soft spot for economic history, and Easton sets out to provide an in-depth account of the economic history of New Zealand. He justifies his approach as follows:

Many historians have told the Aotearoa New Zealand story before; but an economist uses a particular lens and, I would argue, helps us to see our nation's history in a new way.

Easton won't get any disagreement from me on that point. However, despite the prominence that Easton accords the economic lens, I found that the book fell short on that point in two ways. First, the book is heavily focused on political history, rather than economic history. Now, I will grant that it can be difficult to disentangle the political from the economic. However, a great number of words in this book are devoted to cataloguing national electoral results and the shifting political climate, which makes the economics seem somewhat of secondary importance. I would have preferred more emphasis on explaining the economic mechanisms underlying New Zealand's history and development, rather than recounting the political decisions and events that accompanied it. The core economic history context is actually available in a concise form in the Appendix, which I thought was actually one of the highlights of the book!

Second, when economic theory or technical detail does make an appearance, as opposed to the purely descriptive economic statistics that tend to suffuse books on economic history, that theory or technical detail is sometimes insufficiently explained to the reader. Consider this, on measuring the political position of government after the introduction of MMP:

The first coalition was about 0.8 standard deviations to the right of the New Zealand centre on a left-right scale and the second about the same distance to the left. That means that only 15 percent of the population were on the right of the first coalition or on the left of the second. On the social conservative-progressive scale the first coalition was also about 0.85SDs on the conservative side, while the second was a slightly closer 0.65SDs on the progressive side, with about 25 percent more progressive. The sharp change in the politics of the government did not reflect a sharp change in the politics of the voters.

None of that passage is really explained to the reader, and we are left trying to figure it out for ourselves. The book is not footnoted, but there are references at the end of each chapter. Except for the chapter that quoted passage comes from, where there are no sources provided. Easton is usually much more careful than this, so I was surprised. And it wasn't the only example of sometimes quite technical aspects of economic or political theory that were not really explained. Perhaps in a book that is already over 600 pages, there isn't room for detailed explanations? I was disappointed.

Having said that, there is a lot to learn from the book. I have to admit being under-informed about the political history of New Zealand prior to Muldoon, so despite my gripe above about the political content, it actually turned out that I appreciated that aspect of the book. There is also good coverage of early New Zealand, including pre-European times and the early encounters between Pākehā and Māori. I also learned some surprising facts, such as that New Zealand's first liquor 'laws' date to an 1833 agreement between grog sellers, brokered by British Resident James Busby. Or that New Zealand's first Labour Day celebrations, in 1890, marked the first anniversary of the formation of the Maritime Council, a union that would go on to suffer a crushing defeat in the Great Maritime Strike later that year. And this:

Wellington, which in 1843 was so much of the doldrums that one visitor described it as 'rotten to the core', was saved by sheep.

I won't forget that sentence in a hurry. So. Many. Jokes.

It takes a lot of commitment to write a 600-plus page book on economic history, and to do so in such a way that you keep the reader's attention to the end. Easton is to be commended on that, and I found Easton's writing considerably more engaging than Gary Hawke's The Making of New Zealand (which I have never reviewed, as I read it before I started blogging). I'm not sure that this book is "The" economic history of New Zealand, as the subtitle claims. But it is certainly "A" history of New Zealand, with an economic flavour, and worth reading if you have an interest in that area.

Job fatality and injury risk, and compensating differentials

Last week, my ECONS102 class covered health economics. Part of that topic involves discussion of approaches to measuring the 'value of a preventable fatality' (sometimes also called the 'value of a statistical life', or VSL). One of those approaches is the willingness-to-pay (WTP) approach, which involves estimating what people would be willing to pay in order to avoid small increases in risk. One way of applying the WTP approach in practice is to look at revealed preferences, based on people's actual risk avoidance behaviour. And one example that I use in class to illustrate the revealed preference method is to measure compensating differentials which, rather than measuring willingness-to-pay to avoid risk, measure workers' willingness-to-accept additional risk.

Compensating differentials recognise that jobs that differ in non-monetary characteristics may offer different wages. For example, consider two jobs that are similar in terms of skill requirements. If one of the jobs has positive non-monetary characteristics (e.g. it is safe, clean, and engaging), then more people will be willing to do that job. This leads to a higher supply of labour for that job, which leads to lower equilibrium wages. In contrast, if the other job has negative non-monetary characteristics (e.g. it is dangerous, dirty, and boring), then fewer people will be willing to do that job. This leads to a lower supply of labour for that job, which leads to higher equilibrium wages. The difference in wages between the attractive job that lots of people want to do and the unattractive job that fewer people want to do is called a compensating differential. The compensating differential essentially compensates workers for working in jobs with negative non-monetary characteristics compared with working in jobs with positive non-monetary characteristics.

The risk of being injured on the job is a substantial negative non-monetary characteristic for some jobs. Based on the idea of compensating differentials, workers should only be willing to take on a job with a higher risk of death or injury if that job comes with a higher wage. Measuring that additional wage would provide a measure of the compensating differential, which could then be used to estimate the value of a preventable fatality.

As an example, consider this 2022 article by Zac Reynolds, Daehoon Nahm and Craig MacMillan (all Macquarie University), published in the journal Economic Record (open access). They estimate compensating differentials for job fatality and injury risk in Australia, using income data from the Household, Income and Labour Dynamics in Australia (HILDA) survey from 2010-2018, and risk data from Safe Work Australia. Their regression model estimates the compensating wage differential (CWD) controlling for industry and occupation, as well as for a range of demographic and labour force characteristics of the workers in the sample. In their preferred set of results:

...the CWD and corresponding VSL for fatal risk are 0.13% and $9.7 million respectively.

Interestingly, union members receive significantly higher compensation for fatality risk. In their preferred model, the CWD for union members is 0.44 percent for an increase in fatality risk of one in 100,000 workers, significantly higher than for non-members. If you need a good reason to join a union, this might provide it.

Surprisingly, Reynolds et al. find no evidence of a CWD for non-fatal risk overall. In fact, their preferred results suggest that higher non-fatal injury risk is associated with lower wages, rather than higher wages, for non-union workers. However, alongside that there is a significant CWD for union members, where:

...union members receive a CWD of 0.09% for a unit increase in injury risk from the mean level which corresponds to a VSI of $65,300.

VSI is the 'value of a statistical injury', which is the non-fatal equivalent of the VSL. One limitation of Reynolds et al.'s analysis is the potential for omitted variable bias. Their models include either fatal risk or non-fatal risk, but never both together. Since these risks are positively correlated, omitting one may bias upwards the estimated effect of the other. However, Reynolds et al. explain that including both risks would create multicollinearity problems, meaning that the results would potentially be less biased, but also less precise.

Finally, Reynolds et al. compare their results with the 'official' VSL applied by the Australian government, noting that:

For Australian workers who belong to a union, this estimate implies a VSL of $34.9 million. Ignoring union coverage results in a VSL of $9.7 million. These estimates are both well above the Australian government VSL estimate of $5.1 million...

For comparison, the New Zealand government currently applies a VSL of $12.5 million (in 2021 dollars, which is comparable with the 2021 VSL that Reynolds et al. report for Australia) for measuring the benefits of road safety improvements. This is equivalent to about AU$10 million at the current exchange rate.

The 'value of a preventable fatality' (or value of a statistical life, if you prefer) isn't a price tag on anyone's life, but an estimate based on how people trade off money against small changes in the risk of death. As Reynolds et al. demonstrate, those estimates can vary substantially, which is worth remembering when governments use a single number to represent the benefits of increased safety and in other applications.

Read more:

• The likely consequences of New Zealand's new value of a statistical life

This week in research #147

Here's what caught my eye in research over the past week (a very quiet week, it seems):

• Silva, Sá, and Biscaia (open access) find that a one-standard-deviation increase in high school GPA adds 5.9 first-year ECTS (European Credit Transfer System) at university, versus 1.8 ECTS for a one-standard-deviation increase in standardised national exam score, with the difference larger in the most selective university programs

The economic impact of the 2010 FIFA World Cup

Economic impact studies undertaken in advance of large sporting events typically anticipate large positive economic effects. However, studies undertaken after the event almost universally fail to demonstrate the anticipated effects. To that list (and see the links at the end of this post for specific examples), you can now add the 2010 FIFA World Cup, hosted by South Africa.

This 2025 article by Busani Moyo (University of South Africa) and Tendai Gwatidzo (University of the Witwatersrand), published in the Journal of Sports Economics (open access), provides the details. They apply the synthetic control method, which involves comparing the time series of outcome variables for South Africa with a counterfactual made up of a weighted average of other countries, chosen because that weighted average best replicates the trajectory of the outcome variable from before the treatment date.

In their main analysis, they use 2010 as the treatment date, in order to demonstrate any effect of the staging of the 2010 FIFA World Cup itself. The synthetic control they employ is made up of 31.7 percent Brazil, 21.3 percent Morocco, 18.1 percent Algeria, 15.5 percent Botswana, and small weightings of Kazakhstan, Malaysia, Mauritania, and Argentina. The results are shown in Figure 1 from the paper:

The solid blue line is the actual GDP per capita for South Africa, while the dashed red line is the counterfactual 'synthetic South Africa', which is supposed to show what would have happened to GDP per capita if South Africa hadn't hosted the FIFA World Cup. The results clearly demonstrate that actual South Africa performed worse than synthetic South Africa, suggesting that hosting the FIFA World Cup may have made South Africa worse off (at least, using GDP per capita as a measure).

But wait, not so fast. A lot of economic activity occurs in the lead-up to the hosting of the event. Moyo and Gwatidzo therefore change the treatment date to 2004, when South Africa was announced as the host, and check whether there was any impact then. The synthetic control changes (since they are using a different treatment date), now being made up of 31.3 percent Morocco, 24.7 percent Brazil, 17.9 percent Algeria, 14.3 percent Malaysia, and small weightings of Kazakhstan, Costa Rica, and Mexico. The results from this analysis are shown in Figure 3 from the paper:

Moyo and Gwatidzo interpret this as showing a positive effect on GDP per capita in the lead-up to the event, but I struggle to see evidence of any effect in Figure 3 (at least, before 2010 where, as in Figure 1, actual South Africa performs worse than synthetic South Africa). Actual GDP per capita tracks synthetic GDP per capita remarkably closely between 2004 and 2010, with little indication of any sustained positive effect. Moyo and Gwatidzo don't provide a clear quantitative estimate of the effect, which makes it difficult to assess whether the apparent positive effect is economically meaningful or statistically significant. Both seem unlikely to me.

Moyo and Gwatidzo then turn to looking at tourism arrivals, where the results are mixed. Using 2010 as the treatment date, actual tourism arrivals exceed those for synthetic South Africa for several years after the World Cup. However, using 2004 as the treatment date, synthetic South Africa clearly outperforms actual South Africa.

Moyo and Gwatidzo then turn to looking at tourism arrivals. In this case, the effect is modestly positive, as shown in Figure 10 from the paper:

Notice that in this case, there is a small positive difference in international tourism arrivals between actual South Africa and synthetic South Africa, at least up to 2013 or 2014. So, at least there is some good news for South Africa. Nevertheless, given the apparent negative effects on GDP per capita after the event, I am not convinced by their conclusion that:

These results, at least for South Africa, show that it is not the actual hosting of the World Cup that has a positive effect on GDP but the period of preparation for the tournament. The actual hosting of the tournament is only beneficial in the form of international tourism inflows.

Like many other studies, I think the results suggest that South Africa had a good party, which attracted extra visitors from outside the country, but failed to deliver the large positive economic benefits that were anticipated. I suggest that you file this study along with the multitude of others that cast doubt on the large positive economic benefits that are routinely predicted for major sporting events.

Read more:

• Tim Hazeldine on economic impact studies
• Beware economists bearing impact studies of the America's Cup
• Even less reason to believe in the economic impact of the America's Cup
• The overstated impact of the Super Bowl
• The economic impact of the 2010 World Cup in South Africa
• The economic impact of the 2000 Sydney Olympics
• Maybe hosting the Olympics just shuffles income around a country, rather than increasing it