Non-market valuation, and the economic value of birdsong

When economists want to know the value of something, we typically look at the price it is traded for in a market. That works well when you want to know the value of an apple, a haircut, or a rare Pokémon card. It doesn't work at all for goods and services that are not traded in markets (like clean air) or those that are not traded because they don't yet exist (like vaccines for HIV). To value those goods and services, we need to apply non-market valuation.

I've previously written several posts about the contingent valuation method and its problems (most recently here), but it is one of several methods that are used for non-market valuation. A similar method is to run a discrete choice experiment, where survey participants are presented with a series of choices between different alternatives that have different characteristics, and are asked to choose the alternative they prefer. By getting participants to make a number of choices between different alternatives, researchers can establish how important each of the characteristics are to the participants. And if one of the characteristics is a price, then researchers can estimate how much participants are, on average, willing to pay for each characteristic.

One of the issues with the contingent valuation method is that it is vulnerable to context. For example, in this 2015 post I describe research that shows that research participants are less rational in their choices if they complete the task late at night. However, researchers may also be able to use context to improve their estimates of what people are willing to pay.

Consider this new article by Anthony Higney (University of Glasgow) and co-authors, published in the journal Ecological Economics (open access). They use a discrete choice experiment, conducted in an online sample of 1094 people from the UK, to measure willingness to pay for ecological restoration. Specifically, they presented each research participant with a series of choices about habitat restoration for birds, where the characteristics of each alternative included the habitat type (woodland, grassland, or arable farmland), the size of bird habitat (compared with baseline), and the quality of the habitat (in terms of the number of bird species), as well as a one-time donation amount. The inclusion of the donation amount means that Higney et al. can use the results to estimate the willingness-to-pay for each of the other characteristics.

Research participants were randomised so that half of the participants had access to a sound recording of birdsong for each of the alternatives (the treatment group), while the other half had no birdsong (the control group). Higney et al. essentially argue that listening to birdsong increases the salience of the habitat quality in the participants' decisions about which alternative to choose. They found that:

...the treatment of playing birdsong causes respondents to be less sensitive to costs in making their choices and rate quality changes from the status quo more highly.

Higney et al. can't tell whether their respondents actually listened to the birdsong or not. So, what their results really say is that respondents who had access to birdsong were willing to pay more for habitat restoration than respondents who did not have access. How much more? The willingness-to-pay for medium-quality habitat (compared with low-quality) was £276.5 higher for those with access to birdsong than those without access, while the willingness-to-pay for high-quality habitat (compared with low-quality) was £274.6 higher. That compares with a baseline willingness-to-pay in the control group of £99.5 for medium quality, and £86.0 for high quality. Interestingly, much of the difference arose because respondents who had access to birdsong were less sensitive to the donation amount, rather than simply placing greater value on improvements in habitat quality.

One reason for caution about the size of these estimates is that the highest donation amount participants were offered in the experiment was £200, which is smaller than the estimated willingness-to-pay for medium-quality and high-quality habitat among the treatment group. So, participants were never actually asked to consider donation amounts as high as their estimated willingness-to-pay. This is not an unusual outcome in this sort of hypothetical exercise, but some follow-up research using a wider range of donation amounts would be useful.

Overall, there was no significant difference in willingness-to-pay between the medium-quality and high-quality habitats (either for the treatment or the control group), and Higney et al. don't have a good explanation for why people weren't sensitive to the quality of habitat restoration beyond medium quality. That would also be a good avenue for future research.

Why would listening to birdsong change what people are willing to pay for habitat restoration? Higney et al. suggest that this could be due to framing:

Listening to the sound clips might have reduced the use of cognitive shortcuts in the treatment group. This could include negativity bias, i.e. giving disproportionate weight to negative information such as the payment vehicle...

Alternatively, respondents in the treatment group may consciously place less weight on other aspects of their choice because the sounds make them feel more connected to nature.

Framing does seem like an obvious explanation, and Higney et al. show in a follow-up regression analysis that participants who had access to birdsong reported significantly greater feelings of connection to nature. That is consistent with the addition of the sound clip meaningfully changing how the alternatives are interpreted. Higney et al. intentionally created sound clips to match the quality of the habitat they were asking about. It would have been interesting to run the same exercise, but with every sound clip being the same (or very similar) generic level of birdsong. Or to have two treatments: (1) generic birdsong; and (2) birdsong that conveys the quality of habitat. That may have helped tease out whether the participants were receiving better information about the alternatives by having access to the sound clip, or whether they were simply feeling more connected to nature after hearing the birdsong.

When valuing the benefits of ecological restoration, we can't rely on market prices and so non-market valuation techniques are really important. The way that we undertake those non-market valuation exercises is important, and understanding how framing affects people's decisions in discrete choice experiments is important for designing research that will give us the most accurate assessment of value. This research can't tell us whether playing birdsong to research participants gives us estimated willingness-to-pay that is closer to the 'true' value. But it does tell us that the information that research participants are presented with, and how that information is presented, matters.

Nobel Prize for Robert Hall and Michael Woodford

New Keynesian economics came into its own in the wake of the Global Financial Crisis, and has provides the playbook for central banks' monetary policy decisions. So, it was fitting to see New Keynesianism recognised in the latest Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (aka the Nobel Prize in Economics), awarded to Robert Hall (Stanford University) and Michael Woodford (Columbia University), "for microeconomic foundations of macroeconomic stabilisation".

The Nobel Prize Committee's citation noted:

Robert E. Hall and Michael Woodford are awarded this year’s prize in economic sciences for demonstrating how economic models could be reconstructed to revitalise stabilisation policy. Rather than basing the models on statistical links between macroeconomic variables, such as inflation, production and consumption, they chose to build them on a microeconomic foundation. This encompasses an understanding for how the total mass of individual households and businesses affect the economy through their actions.

Because I almost exclusively teach microeconomics, my students wouldn't necessarily recognise their work. However, the macroeconomics topics in ECONS101 include consumption functions as well as the role of expectations, both of which are key features of New Keynesian economics.

This year's award appears to have been very positively received, although as I noted in this post from last week, the prediction markets had different favourites in mind (as did I). Marginal Revolution has a series of posts (see here and here from Tyler Cowen, and here and here from Alex Tabarrok), and there is a good writeup in the Financial Times, especially noting the disconnect between Hall and Woodford's work and the practice of the current Fed Chair, Kevin Warsh.

As always, the Nobel Committee provided some excellent commentary in the form of a popular science summary of Hall and Woodford's work. That is well worth a read if you are not familiar with them.

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

Modelling the Nobel Prize in Economics

The Nobel Prize in Economics (technically, the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel) will be announced next Monday. Who will win? It is a closely guarded secret, but of course there are prediction markets - at the time of writing, Kalshi has Ariel Pakes as the favourite, with Susan Athey a close second, and Richard Blundell a distant third.

In a discipline where modelling is ubiquitous, it is worth asking whether it is possible to model who will win the Nobel Prize. This recent working paper by Peter Dolton and Richard Tol (both University of Sussex) makes a good attempt. They build a dataset of past winners and candidates (based on research performance as well as winners of other awards), and then estimate a model that shows the factors correlated with winning the Nobel Prize.

Dolton and Tol start with a simple assumption, which is that the Nobel Prize Committee first chooses a field to award the prize to, and then they choose the best candidate within that field. They justify this assumption by showing that there is some regularity in the way that the award goes from field to field over time (based on their categorisation of 14 fields of economics). They use this to construct a 'transition matrix', that records how often the Nobel Prize goes from one field in one year to another the next year. 

Which fields get Nobel Prizes? Dolton and Tol show that larger fields (those with more candidates), those that have waited longer since their last prize win, and the transition matrix, are all statistically significantly correlated with which field wins the prize.

Who wins the Nobel Prize within the winning field? Dolton and Tol find that winning candidates are older (although the relationship with age is non-linear, and peaks at age 70-71 before declining), and those who have had a student already win a Nobel Prize are more likely to win.

Some important things come out of this paper. First, who will win the Nobel Prize in 2026? The 2025 winners were Joel Mokyr, Philippe Aghion, and Peter Howitt. Their research field, according to Dolton and Tol's categorisation, is 'Growth'. According to the transition matrix in Table E.8 of the paper, the most likely field to follow 'Growth', is 'Equilibrium and Welfare' (although 'Development and Economic History' and 'Macro' are also possible). 

I got ChatGPT to comb through Dolton and Tol's candidate list and identify the top candidates in 'Equilibrium and Welfare'. ChatGPT suggested Andreu Mas-Colell or Partha Dasgupta. I have heard Dasgupta's name in people's shortlists before, but not Mas-Colell. Of course, if the Committee considered they had already ticked the 'Economic History' box with Joel Mokyr last year, then next according to the transition matrix would be 'Games and Market Structure', 'Information', or 'Macroeconomics'. Across those fields, ChatGPT suggested many names, but noted that David Kreps is the strongest overall, to which I would add Ariel Pakes (note the consistency with the Kalshi market prediction). ChatGPT rated Oliver Blanchard the top candidate in Macroeconomics. Anyway, we will see next week!

Second, I love the list in Table 4 of the paper, which shows the economists who, according to Dolton and Tol's model, have had the greatest chance of winning the Nobel Prize but have not done so. Top of this list is Michal Kalecki (died in 1970), followed by Lionel Robbins (1984), Jacob Marschak (1977), Arthur Burns (1987), and Frank Hahn (2013). Other notable names in that list (at least, according to me) are Bill Phillips (died in 1975), Harold Hotelling (1983), Bill Baumol (2017), and Henri Theil (2000). There are a few on the list who are still alive, including Tim Besley, Robert Barro, Guido Tabellini, George Loewenstein, and Torsten Persson.

One reason why so many outstanding economists died without receiving the prize is that it was only first awarded in 1969, by which time there was a long backlog of worthy candidates. The third important thing to come out of this paper is a counterfactual exercise, predicting who would have won the Nobel Prize each year if the prize had been first awarded in 1901 (along with the other Nobel Prizes). Table F.15 in the paper has the results (up to 1976, after which it is assumed that the awards would continue as they have been). A number of important names appear in this list, including the first three winners being Leon Walras, Carl Menger, and Francis Edgeworth. My students would no doubt recognise Alfred Marshall (1910), Corrado Gini (1924), Joseph Schumpeter (1930), Arthur Pigou (1935), John Maynard Keynes (1938), and Joan Robinson (1958), among others. Interestingly, in this counterfactual exercise, the first woman to win the prize would have been Beatrice Webb in 1924 (jointly with Sidney Webb and Corrado Gini), some 85 years before Elinor Ostrom.

The Nobel Prize announcement is one of the highlights of my year. Now that I've considered the results from the model, with some assistance from my special adviser ChatGPT, I'm prepared to make my prediction: Kreps and Pakes. We'll find out next week!

[HT: Marginal Revolution]

The beauty premium doesn't seem to extend to billionaires

There is a broad literature supporting the existence of a 'beauty premium' in labour markets - more attractive workers tend to earn more. There are a number of channels proposed to account for this, including confidence, social or communication skills being higher among more attractive workers, attractive workers sorting into occupations that reward attractiveness, and discrimination by employers (or by customers). How does the beauty premium extend to wealth, and in particular to extreme wealth?

In this 2022 article published in the journal Labour Economics (ungated earlier version here), Daniel Hamermesh (University of Texas at Austin) and Andrew Leigh (Australian Member of Parliament) investigate the relationship between attractiveness and wealth among billionaires on the 2008 Forbes billionaires list. The attractiveness ratings for each billionaire are derived from 0-10 scores given by 16 students at Australian National University, based on photos published with the list. Hamermesh and Leigh control for age (and age-squared), education (whether the billionaire was a college graduate), and whether they were male, from a Western country, and inherited their wealth.

In their main analysis, Hamermesh and Leigh find that wealth is not statistically significantly correlated with attractiveness in their billionaires sample. They find the same for education - it is not statistically significantly correlated with wealth in their sample either. They try various alternative specifications, none of which show a significant correlation. Hamermesh and Leigh conclude that their results:

...show that a sample that is taken from the extreme tail of the dependent variable no longer exhibits the systematic patterns seen across the distribution.

They illustrate this further using data from the American Community Survey. While they don't have attractiveness in that data source, they do have education and labour market experience variables. They find that, for the whole sample, both college education and labour market experience are positively and statistically significantly associated with income. However, when they restrict the sample to the 0.1 percent highest earners in the sample, there is no significant correlation between education and income (or between labour market experience and income).

Hamermesh and Leigh use their results to caution us against drawing conclusions from analyses conducted on extreme tails of a distribution, noting that:

Empirical regularities that are common in the general population may not hold up among atypical subsets of the population, whether drawn from the elite (e.g., Olympic athletes, Fortune 500 CEOs) or the most disadvantaged (e.g., prisoners, the homeless). Within these groups, outcomes are more likely to be due to unobservables and pure chance than observable traits.

Coming back to the billionaires, why is there no correlation between attractiveness and wealth? Think about the main mechanisms that are proposed to underlie the beauty premium. More attractive billionaires likely aren't benefiting from employer discrimination, and customer discrimination seems less obviously important. Sorting into different occupations that reward attractiveness is unlikely to explain much of the differences in wealth at the extreme (and if we treat 'billionaire' as an occupation, then arguably they all have the same occupation). That leaves differences in confidence, social or communication skills as more plausible mechanisms. There might be differences between billionaires in those traits, but would they really explain the difference in wealth between Warren Buffett (US$62 billion in 2008) and Larry Ellison (US$25 billion)? Perhaps some further research might uncover the relationship between confidence and wealth among billionaires, but I think it more likely that a lot of it is luck. Buffett even famously said as much.

This research is a useful reminder to pay attention to how a sample has been selected. Relationships that are strong across the population may disappear when we look only at people drawn from the extreme tail of the outcome distribution. That doesn't necessarily mean that attractiveness doesn't matter at all for billionaires, only that we cannot expect the same relationship to hold in such an unusual sample. As F. Scott Fitzgerald wrote about the very rich: "They are different from you and me".

Read more:

• The beauty premium in undergraduate study is small, and more attractive women major in economics
• New results questioning the beauty premium should be treated with caution
• The beauty premium in the LPGA
• The beauty and height premiums in the labour market
• Blonds have more fun (or rather, they get paid more)
• The beauty premium for economists
• More evidence on the blond wage premium
• The beauty premium at the intersection of race and gender
• Narrowing down on the source of the beauty premium
• The beauty premium and student grades for in-person and online education
• Is there a premium for very unattractive workers?

How Alaska is like South Waikato

I've written a couple of times about the challenges that South Waikato faces in attracting workers (see here and here). This problem is not unique to South Waikato - a lot of rural and remote areas face the same challenges. In just the latest example I've seen, Jordan McGillis reports in City Journal on the situation in Alaska:

My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures...

That’s no surprise to [Ray Weber, Dean of technical and vocational education at the University of Alaska Anchorage]. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”

Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.

Workers with the same skills can often accept lower wages in Texas or Louisiana in exchange for a more attractive lifestyle.

Wages differ for the same job in different firms or locations. Consider the same job in two different locations. If the job in the first location has positive non-monetary characteristics (e.g. it is in an area that has high amenity value, where people like to live), 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 job in the second location (Alaska, for example) has negative non-monetary characteristics (e.g. it is in an area with lower amenity value, where fewer people like to live, where it is cold, dark, lonely, and more dangerous), 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. As McGillis notes, 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.

And so, jobs that pay less in the rest of the US must pay much more in Alaska in order to attract workers.

Read more:

• Reason to be wary if a job in Taumarunui offers an Auckland salary
• The compensating differential for rural GPs must be enormous
• Compensating differentials are alive and well in Tokoroa
• Rural Australia sets a new high bar for compensating differentials
• The $300,000 job that no one wants
• The South Waikato compensating differential strikes again

This week in research #146

Here's what caught my eye in research over the past week:

• James and Kjorstad find that spelling and grammar errors significantly reduced perceived research quality within a sample of US economists, while manuscripts prepared in LaTeX were generally evaluated more favourably than those prepared in Microsoft Word within an international sample of economists
• Fornwagner (with ungated earlier version here) finds no effect of menopause on women’s risk preferences, using an online sample of over 1700 UK women
• Wang, Wang, and Zhou (with ungated earlier version here) use global air traffic data as instrumental variable to estimate the effect of in-person interactions on bilateral trade, finding that online interactions are an imperfect substitute for in-person contact
• Garthwaite et al. (with ungated earlier version here) find that the rent-sharing between college basketball and football programmes and other sports transfers spending away from students who are more likely to be Black and come from poor neighbourhoods toward students more likely to be White and come from higher-income neighbourhoods

The declining returns to higher education in Australia

In yesterday's post, I discussed a recent meta-analysis on the returns to compulsory schooling. That research was necessarily focused on secondary education. What about the returns to higher education? Two recent articles tell an interesting story about the change over time in returns to higher education in Australia.

The first article is this one by Elisa Birch and Alison Preston (both University of Western Australia), published in the journal Economic Record (open access). Using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, they document a striking decrease in the returns to higher education over the period from 2001 to 2023. They apply a fairly standard Mincer wage equation, comparing hourly earnings between workers aged 28 to 38 years with no post-secondary qualification to those with one of seven categories of education: PhD; Master’s; Graduate Diploma; Graduate Certificate; Honours; Bachelor’s; and Diploma/Certificate. Strictly speaking, these estimates are wage premiums associated with different qualifications, rather than necessarily causal estimates of the returns to obtaining those qualifications. Nevertheless, they do show some interesting trends, and their headline results are summarised in Figure 2 from the paper:

Notice that, in all cases (except, arguably, Graduate Certificates), the general trend in the returns to higher education has been downward. The wage premium between higher education and no post-secondary education is lower in 2019-2023 than it was in 2001-2005. Birch and Preston also look in more detail at the difference in returns between the early period in their data (from 2001-2011) and the later period (from 2011-2023), and the differences by gender, and find that:

...over the study period, the wage premium for a Master’s degree declined by 13.2 per cent for males. For females, the decline in the Master’s wage premium was even more pronounced, falling by 16.6 per cent... The reduction in the return to a Bachelor’s degree was similar for both males and females, with a decrease of approximately 13 per cent in both groups. In Period 2, the wage premium on a Master’s degree was equal to 35 per cent among men and 26 per cent among women (a gender gap of 9 percentage points). Among Bachelor’s degree holders the wage premium in 2012–23 was equal to 27 per cent among men and 22 per cent among women (a gender gap of 5 percentage points).

So, there isn't even anything positive to say about the changes in relation to the gender wage gap. The higher wage premium for male graduates remained the same, or increased, compared to the wage premium for female graduates. Even worse, when looking across the whole wage distribution, Birch and Preston find that:

While the male wage structure appears to have shifted downward and flattened, the female wage structure shows both a downward shift and a notable decline in returns at higher wage levels.

So, the returns to higher education have been declining in Australia (a result that other studies have also found), particularly at the upper end of the wage distribution for women. Birch and Preston largely leave open the question of why. That is where this new article by Michael Coelli and Jeff Borland (both University of Melbourne), published in the journal Australian Economic Review (open access), comes in.

Coelli and Borland first note that the decline in the returns to higher education is concentrated in the period after 2001, using five-yearly Australian Census data from 1981 to 2021, and using income as a measure (rather than hourly earnings, which isn't available in the Census). Then, they explore different explanations for why the returns to higher education changed, focusing on explanations that might explain a decrease starting around 2001. They find little support for a slowdown in skill-biased technical change relative to increases in the supply of skilled workers. Instead, they find that the decrease is associated with a relative increase in the wages of workers with no post-secondary education since 2001, and link that to two main probable causes.

First, the Federal Minimum Wage began increasing in real terms from 1996. On top of that:

The method of setting wages for employees covered by awards during 1993 to 2010 may also have raised the relative wages of low‐skill employees. During that time, the annual increases to rates of pay for employees covered by awards were made in flat dollar amounts, resulting in higher percentage growth in wages for lower wage earners. This may have underpinned stronger wage growth in low‐skill occupations... Award rate increases were above inflation at the lower end of the wage distribution (especially at the minimum wage), but below inflation further up the distribution. Since 2011, a common percentage increase has been set for employees covered by all awards.

So, the process of wages being determined by awards, alongside an increasing real minimum wage, may have compressed the wage distribution, especially before 2011. This would have the effect of raising wages for workers at the bottom of the wage distribution, including many workers without post-secondary education, relative to workers at the top of the wage distribution, including many workers with higher education.

Second, Coelli and Borland note that:

Starting around 2001, Australia experienced a large and extended boom in mining, coinciding with the rise of China in global trade after its accession to the WTO. This affected employment in mining and in construction. Its impact was felt more strongly among low‐skilled workers than among the higher‐skilled.

Since workers with no postsecondary education are more likely to be working in mining and construction than workers with higher education, the former workers likely benefited disproportionately from the mining boom.

Alongside those two explanations, Coelli and Borland also note a phenomenon that they call 'occupational downgrading':

Growth in the supply of workers with a bachelor's degree generally exceeded demand growth throughout the 1981 to 2021 period, leading to them moving down the jobs ladder.

These three explanations (minimum wages and wage awards; the mining boom; and occupational downgrading) are Coelli and Borland's explanations for the decrease in the returns to higher education. They do leave some questions from Birch and Preston unanswered though - why has the decrease in the returns to higher education been concentrated among the top of the wage distribution for female workers? The top of the wage distribution is least likely to be affected by minimum wages and wage awards. However, that leaves the other explanations for the decrease. Is it because female workers are more likely to 'occupationally downgrade'? Or, did female workers miss out on the mining boom? Clearly, there is more research to be done here.

Read more:

• Internal rates of return for college majors in the US
• The returns to each additional year of compulsory schooling

The returns to each additional year of compulsory schooling

This week, my ECONS102 class is covering the economics of education. Part of that topic considers the private education decision - the decision each individual makes about the amount of education they receive. This depends on the private costs and benefits of education. Arguably the primary benefit of education is incremental income - the additional lifetime earnings that education brings.

How big are those gains? Previous studies I have seen (such as a study reported in Paul Oyer's book Everything I Ever Needed to Know about Economics I Learned from Online Dating (which I reviewed here)) have estimated effects in the order of 10 percent per additional year of education. However, that was a single study, which could easily provide a biased view if considered in isolation.

What are the effects more generally? This recent article by Gregory Clark and Christian Nielsen (both University of Southern Denmark), published in the journal Kyklos (open access), provides an answer based on a meta-analysis of 79 causal estimates of the effect of an additional year or years of compulsory schooling on earnings, drawn from 53 other papers that apply methods consistent with causal inference. Meta-analysis provides a method of combining the estimates across many studies into one overall estimate. Importantly, Clark and Nielsen apply various methods to correct for the effects of publication bias (which is the tendency for statistically significant results to be more likely to be published, while statistically insignificant results tend to be missing from the research record).

Clark and Nielsen report baseline results that are somewhat lower than I expected:

With these procedures the average percentage gain in earnings from an additional year of schooling was 8.2% for 79 independent estimates, from 53 papers... If these estimates are weighed by their precision in a random effects estimation, the gains are reduced to 6.0%, and similarly 6.2% for a fixed effects weighting.

Given that the true effect is likely to vary across populations and study settings, I would consider the random-effects estimate of 6.0 percent for each additional year of schooling as the more plausible of the three. However, Clark and Nielsen also employ meta-regression, using PET (Precision Effect Test) and PEESE (Precision Effect Estimate with Standard Error) regression models, which are designed to address imprecision and publication bias in the overall sample of results. After removing some outliers, these approaches suggest:

...a true effect of 6.1%–6.4% to an extra year of education and an insignificant effect of publication selection...

So again, it seems like the effect may be in the order of six percent per additional year of schooling. Interestingly, Clark and Nielsen themselves note that the PET-PEESE evidence for publication bias is highly model-dependent, and that these results could instead reflect genuine heterogeneity in the true effect. However, Clark and Nielsen go a little further, looking in more detail at the distribution of effects across the 79 estimates. They note that if the estimates of the earnings return were normally distributed, then there are a lot of 'missing' studies with negative returns. They also consider whether the underlying returns might be log-normally distributed. Once you consider sampling error, the log-normal distribution can still generate negative estimates, and Clark and Nielsen argue that there are fewer negative estimates in the published literature than the model would predict. Consider Figure 10 from the paper:

In the figure, the yellow bars show a histogram of the 79 estimates in the sample. The blue line shows a log-normal distribution based on the estimates. Notice that, compared with the log-normal distribution, there are more studies than expected with estimates in the 0-4 percent range, and too few in the negative range. Clark and Nielsen argue that, if the 'missing' studies were included, a better estimate of the returns to each additional year of schooling may be in the range of 0-3 percent.

However, I’m not convinced that Clark and Nielsen’s distributional assumptions are justified here. Figure 10 (like Figure 8 in the paper) shows that the observed distribution of estimates doesn’t match their chosen distribution. But they haven’t established that their chosen distribution is what we should expect in the absence of publication bias. That distribution would depend on differences in estimation methods, the precision of the estimates, and genuine differences in returns across the populations studied. A mismatch between their chosen distribution and the observed study estimates therefore does not, by itself, demonstrate that studies with negative estimates are missing. So, while publication bias remains a possible explanation, I would treat Clark and Nielsen’s proposed 0-3 percent range for the average return very cautiously.

Nevertheless, overall it does seem clear from their other results that the returns to an additional year of schooling may be somewhat lower than the 10 percent found in other studies. What would be interesting to know next is whether the estimated returns have been changing over time (which my next blog post will look at, for Australia), and what study features lead to different estimates. Clark and Nielsen do note that instrumental variable regression leads to higher estimates than difference-in-differences or regression discontinuity estimates. However, there are other contextual variables that may matter as well. Hopefully, someone else can pick up on this work and take it to the next step. That might help to distinguish publication bias from the more mundane possibility that there simply isn't a single 'true' return to an additional year of schooling.

Read more:

• Internal rates of return for college majors in the US

The costs of traffic noise

Traffic noise is an example of a negative externality - the impact of an action on a third party (a 'bystander') who has not consented to or played any role in the carrying out of that action. In this case, traffic noise caused by drivers on the road negatively affects people who live nearby, and those people haven't played any role in creating the noise.

To the extent that traffic noise negatively affects people living nearby, it should be reflected in property prices. Under hedonic demand theory, when someone buys a property they are really buying a bundle of characteristics of the property, one of which is the presence of traffic noise. Since traffic noise is a negative characteristic, we would expect properties that are exposed to more traffic noise to have lower prices, ceteris paribus (holding all else constant).

That means that the cost of traffic noise can be evaluated by carefully looking at the relationship between property values and traffic noise. That is what this 2025 NBER Working Paper (ungated here) by Enrico Moretti (University of California, Berkeley) and Harrison Wheeler (University of Toronto), sets out to do. They start by looking at the effect of roadside noise barriers on property prices, noting that these barriers provide an exogenous source of variation in traffic noise. They compare houses that are close to the barrier (within 500 metres directly away from the barrier) with those that are further away (between 500 and 1500 metres), using a difference-in-differences (DiD) strategy. That involves comparing the change in property prices between before and after the barrier was erected, between properties close to and those further away.

Moretti and Wheeler primarily use data from Florida, which provides details about the noise barriers that were completed, but also about barriers that were proposed but not completed. That also allows them to use a 'triple-differences' strategy, by matching areas that had a barrier erected, with those that didn't (but where one was proposed). Now, it turns out that the results from the triple-differences model are similar to the more standard DiD, but that should provide some further comfort with the robustness of the DiD results. The data on house prices and other characteristics comes from CoreLogic and covers the period from 1990 to 2022 (house prices) or 2006 to 2022 (property characteristics). Their final dataset includes nearly 600,000 home sales within 1500m of a noise barrier (and on the same side of the road as the barrier).

Focusing on their results that include parcel fixed-effects (so that time-invariant property characteristics are controlled for), Moretti and Wheeler find that:

For houses within 100 m of the barrier, the estimated effect increases to 8.59%. For houses 100–200 and 200–300 m from the barrier, the estimated effects increase to 5.79% and 4.41%, respectively. The effect on properties 300–400 m from the barrier is marginally statistically significant.

So, reducing traffic noise increases property values, and the effect is largest for properties closest to the road generating the noise. Beyond about 300 metres, the effect is statistically indistinguishable from zero, but within 300 metres, the construction of a noise barrier increases property values by between 4.41 and 8.59 percent. Moretti and Wheeler then note that:

Since our data report the construction cost of each barrier, we can compute the marginal value of public funds (MVPF), defined as the property value appreciation over costs... The average MVPF for barriers that were built amounts to 1.7, while the MVPF for barriers proposed but not built is 1.4. This is to be considered as a back-of-the-envelope calculation that ignores property taxes. Property taxes would reduce both the social benefits (since some of the home value increase gets taxed), and the social costs (since property taxes end up in local government coffers).

So, on this measure at least, noise barriers appear to be a good use of public funds, since the increases in property values exceed the costs of erecting the barriers (although noting that there may be other uses of public funds with even higher MVPF values).

Next, Moretti and Wheeler change their model in order to allow the change in price to vary based on the expected decibel reduction. They use a model where the effect is non-linear in noise reduction, and find that:

The effect plateaus at 10 dB of reduction, which represents the 96th percentile in our sample. The effect is estimated to be zero when noise reductions are 4.9 dB...

The average barrier in our sample reduces noise by 7.15 decibels. At this level of noise reduction, our estimates imply that the average price of a decibel is 0.94%...

That is the result that Moretti and Wheeler use later in their paper to estimate the economic cost of the externality. However, first they need to rule out some competing explanations for their effects. They show that air quality is somewhat better near to the barriers, but the effect is small and not statistically significant. They show that the results do not change when accounting for tree canopy or the presence of other buildings, meaning that noise barriers blocking views of the road is unlikely to explain the results. And, they show that the construction of new homes (with higher unobserved quality) does not explain the results.

Moretti and Wheeler next use their results to estimate the economic cost of the externality for each census tract in the US. From that, they find that there is:

...a negative correlation between the cost of the externality and median family incomes. The slope is -0.10 (0.01), indicating that a 10% lower income is associated with a 1% higher per capita cost. The correlations with the share of residents who are Black and the poverty rate are positive. The slopes are 0.08 (0.01) and 0.63 (0.05), respectively, indicating that a 10 percentage point higher share of Blacks or a 1 percentage point higher poverty rate are associated with 0.8% and 0.6% higher per-capita costs...

That means that the externality cost is regressive. That is, the cost of the externality is a larger proportion of income for low-income families than for higher-income families (taking into account the location of low-income and higher-income families and the property values where they live and the traffic noise they face).

In total, Moretti and Wheeler estimate the cost of traffic noise to be US$7.0 billion in Florida, and $109.75 billion for the US as a whole. Looking across cities, they find that per-capita traffic noise costs increase with urban share of the population and population density, which they suggest is because cities with greater urban share or those that are denser have both higher noise exposure, and higher property values.

Finally, Moretti and Wheeler estimate that a one-off Pigovian tax equivalent to US$974 per car would be equal to the marginal external cost of the traffic noise externality (noting that the optimal Pigovian tax is one that is equal to the marginal external cost). They also estimate the potential benefit of a move to 100 percent electric vehicles (which have lower engine noise) at US$5.39 billion for Florida, and $77.28 billion for the US as a whole.

This research tells us that traffic noise is a costly negative externality. Those costs are capitalised into property values and are borne disproportionately by lower-income households. Fortunately, the research also suggests that there are worthwhile ways of reducing those costs, including erecting noise barriers and rolling out more electric vehicles, and the benefits of those solutions may be substantial.

[HT: Marginal Revolution, last year]

Climate change and the tragedy of the commons

My ECONS102 class covered externalities and common resources this past week. In the final slide of content in my lectures, I talked about the challenges of getting global agreement on climate change, because the atmosphere's limited capacity to absorb emissions without causing harmful climate change provides a special case of the tragedy of the commons.

Why is climate change a common resource problem? Common resources are rival and non-excludable. The atmosphere's capacity to absorb emissions is both rival (one country's emissions reduce the capacity available for other countries) and non-excludable (if the capacity is reduced for one country, it is reduced for all countries). The social incentive is for all countries to reduce emissions to the point where the marginal social cost of emissions is equal to the marginal social benefit. The private incentive for each country is to reduce emissions only to where marginal private cost of emissions is equal to marginal private benefit for that country. Each country’s emissions impose a cost on other countries, meaning that each country doesn’t face the full cost of their emissions (so the marginal social cost of emissions is greater than the marginal private cost for each country), so they will emit too much. And since all countries have the same incentive, there are too many emissions relative to the socially optimal quantity.

Within a country, we might be able to solve a common resource problem like this by relying on the government to assign some form of property rights. However, there is no supra-national government to perform this role, so that means we need to arrive at a 'private solution' (albeit one where the private actors in the negotiation are countries).

The 2009 Nobel Prize winner Elinor Ostrom noted that users of a common resource may be able to solve the problem by working together (a common governance approach). A number of things would likely be necessary for such common governance to work. Ostrom noted a number of principles for common governance, one of which was that the boundary of the common resource and the group of users must be well-defined. In the case of climate change, the boundary is the environment, and the group of users is all countries. So, that principle would be no problem, provided all countries agreed to be involved (and that may be a challenge).

For common governance to be successful, the user community must also be relatively homogeneous, so that they can trust each other and develop common goals (and norms or customs) for protecting and allocating the resource. Here is where the challenge lies. The user community (countries) are not homogeneous at all. Countries at different levels of development have different goals and aspirations, and see the role of emissions in contributing to those goals and aspirations differently. And it seems unlikely that countries will really trust each other to do the right thing in relation to any climate agreement.

Ostrom also noted that protecting the resource would be best achieved through persuasion rather than coercion, since this would maintain trust within the user community. Persuading other sovereign countries to do something that makes them individually worse off is obviously a challenge. And so climate change remains one of humanity's greatest challenges. This isn't to say that it isn't an important challenge to solve, only that there are good reasons why, nearly 50 years on from the First World Climate Conference in Geneva in 1979, we are still looking for an effective agreement to protect the climate.

Don't just take my word for it though. This 2012 article by Niggol Seo (University of Sydney), published in the journal Economic Affairs (sorry, I don't see an ungated version online), outlines the case, supported by some estimates of the globally optimal policy (as it would have been at the time). Seo uses the model results to outline the incentives that each of thirteen world regions face in global negotiations over climate change. Seo compares a business-as-usual (BAU) scenario with a scenario based on the globally optimal policy (GOP). Focusing on the GOP scenario, the net costs of addressing climate change in that scenario for seven of the largest world regions are shown by Figure 2 from the paper:

Notice the large costs that China (green) and the US (blue) would face over the entire period up to the end of the century. It should be little wonder, then, that those two countries in particular would have less incentive to agree to emissions restrictions to address climate change. In contrast, the EU, India, Africa, Latin America, and Russia face more modest costs initially, and by 2075 (or 2065 in the case of Africa), the GOP scenario actually shows net benefits for those regions. Again, it should be little wonder that they have greater incentive to support of climate change agreements. Seo concludes that:

...some countries have a strong incentive to push for global regulation due to the expected reduction in climate related damages. The optimal regulation saves these countries hundreds of billions dollars annually by the century’s end. However, it would cause additional costs to China, Russia, Canada and the USA.

To that, I would observe that it particularly impacts China and the US. Climate change is a challenging problem. For an efficient global agreement that would adequately address this challenge, we need all countries to participate. However, the incentives do not necessarily help us to achieve cooperation.  Countries that face relatively low net costs from addressing climate change may need to offer transfers, concessions, technology, or some other form of compensation to countries that face high net costs, in order to change their incentives and get them on board. We may not think that outcome is fair. However, achieving an efficient and effective agreement may require some compromise on fairness, if that is what is needed to ensure that the incentives encourage all countries to participate.

Read more:

• Reason to be surprised, or not, about the Paris Agreement on climate change coming into force

This week in research #145

Here's what caught my eye in research over the past week:

• Guzman, McGuinness, and Turner investigate 16 'mega-universities' in the US, and report that students at these mega-universities have lower average completion rates and leave with higher average student loan debt compared to students at other schools
• Melo et al. (open access) find little evidence that higher minimum wages increase job search for low-skilled jobs, and more evidence that higher minimum wages decrease the number of workers seeking employment in these jobs (file this under surprising results on the supply side of the labour market)
• Malesky et al. (with ungated earlier version here) find that cohorts exposed to the university expansion in Vietnam are 87% more likely to have a university education, but that education increases the incidence of bribe requests and perceptions of corruption

Minimum wages and the adoption of robots

Manufacturing firms typically have a choice of various production technologies. Some production technologies involve more labour. Others involve more automation (robots, as in this post). If labour becomes relatively more expensive compared with robots, firms have a greater incentive to adopt robots. That suggests that higher minimum wages, which make labour relatively more expensive for firms, may not only decrease employment (see the links at the end of this post for more on that point), but may increase the adoption of robots.

The extent to which firms adopt robots in the face of increasing minimum wages is the subject of this recent NBER Working Paper by Erik Brynjolfsson (Stanford University) and co-authors (ungated version here). They look at this question in two ways. First, Brynjolfsson et al. create a state-level measure of exposure to robots, which captures the extent to which robots are over- or under-adopted in each state, given the state's mix of industries and employment. They then correlate changes in that measure with changes in the state-level minimum wage over the period from 2003 to 2015. That correlation is illustrated in Figure 1(c) from the paper:

The regression line in the figure implies that a 10 percent increase in the minimum wage is associated with an increase in robot exposure equivalent to about 8 percent of the sample mean level of robot exposure.

Second, Brynjolfsson et al. use microdata from the US Census Bureau, including the Longitudinal Business Database and Longitudinal Firm Trade Transactions Database (LFTTD), to construct a panel dataset of robot adoption among US manufacturing firms from 1992 to 2021. They use the LFTTD data to identify which firms imported industrial robots. They then compare robot adoption between firms in adjacent counties on opposite sides of state borders, which face different state-level minimum wages but are likely to share many local economic conditions. Their measure of robot adoption in this analysis is simply whether a given firm adopted a robot in a given year, or not. In this second analysis, they find that:

...a 10% increase in minimum wage leads to an 8.4% rise in robot adoption relative to the sample average...

Notice how similar in magnitude the effects from their two analyses are, despite being quite different in nature, as well as covering different time periods. Both analyses suggest that higher minimum wages are associated with greater levels of robot adoption. The state-level relationship in the first analysis is clearly correlational, rather than causal. However, the comparison between firms in adjacent counties provides some plausibly causal effects (at least, there are plenty of other research papers that use a similar approach to estimate the causal effects of minimum wages). More generally, this research provides another example of how firms may respond to minimum wages on margins other than employment (see the links at the end of this post for more). When the relative price of labour increases, firms may change not only how many workers they employ, but also the production technology they use.

[HT: Marginal Revolution, back in February] 

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance
• Higher minimum wages and poverty revisited
• Minimum wages and health
• Effects of the minimum wage on the nonprofit sector
• 15 years of US research on the minimum wage elasticity of employment
• Local minimum wages and low-quality housing rents in Japan
• Minimum wages and student summer employment

Evidence that tradeable pollution permits can work

This week, my ECONS102 class covered externalities. As part of the topic, we spent a bit of time considering the economics of pollution control, where the government has three main options: (1) regulation (command-and-control); (2) Pigovian taxes; or (3) tradeable pollution permits. I've posted before about the latter two options (see here and here).

Tradeable pollution permits often attract attention from my students, as it seems surprising that granting polluters 'the right to pollute' might be an effective way of reducing pollution. However, economic theory suggests that this can be both an effective and a cost-effective way of reducing pollution. And the research reported in this 2025 article by Michael Greenstone (University of Chicago) and co-authors, published in the Quarterly Journal of Economics (ungated earlier version here), provides some compelling evidence that tradeable pollution permits are effective.

Greenstone et al. collaborated with the Gujarat Pollution Control Board (GPCB) in India to design and experimentally evaluate a particulate-matter emissions market, the first market of its type anywhere. They describe the experiment as follows:

GPCB launched the market for industrial plants in and around Surat, Gujarat, a rapidly growing city of 7 million people, in 2019. Under the command-and-control status quo, plants are mandated to install abatement equipment and are sporadically inspected in person by government regulators and auditors to check that they meet limits on the concentration of pollution emissions... For the present experiment, GPCB mandated a sample of 318 large, coal-burning plants to install continuous emissions monitoring systems (CEMS) to measure the total mass of particulate matter (PM) emitted, compared with the measurement under the status quo of pollution concentrations during spot visits... The emissions market experiment then randomly assigned 162 out of 318 plants to the market and 156 control plants stayed under the command- and-control regime.

The tradeable pollution permits market worked much as we describe in class:

GPCB set a cap on the total mass of particulates that could be collectively emitted by all treatment plants over a compliance period. They allocated permits to treatment plants, with permits summing to 80% of the cap distributed for free, in proportion to plant emissions potential, and 20% sold off in weekly auctions. Thereafter, treatment plants could trade permits with each other. At the conclusion of each compliance period, any treatment plant that did not hold enough permits to cover their emissions was subject to fines based on the size of the shortfall.

As I note in my ECONS102 class, the advantage of allowing pollution permits to be traded is that plants with relatively low abatement costs (low costs of reducing pollution) have an incentive to sell their surplus permits and abate pollution instead, while plants with relatively high abatement costs have an incentive to buy permits rather than undertake costly abatement. Transferability is one of the important features of efficient property rights, and having permits that are tradeable helps to ensure that.

Greenstone et al. look at compliance (did they have enough permits to cover their emissions), and then compare treatment and control plants in order to estimate the effect of the permit scheme on particulate emissions and variable abatement costs. They find that:

Treatment plants complied—held enough permits to cover their emissions—in 99% of plant-periods. By contrast, the compliance rate with concentration standards at baseline was 66%...

Second, the treatment reduced particulate emissions by 20%–30%, relative to control-plant emissions in the command- and-control regime...

Our third main finding is that the market reduced variable abatement costs by 11% at a constant level of emissions.

Taken altogether, those results provide strong field experimental evidence that the permits scheme worked as intended, that treatment plants were compliant, and that emissions decreased while also decreasing abatement costs. Greenstone et al. then conduct a cost-benefit analysis of an expansion of the market. Based on a range of assumptions on the mortality effects of particulate pollution, they estimate that the benefits of the market are at least 25 times larger than the costs. Needless to say, that suggests that tradeable pollution permits have been strongly worthwhile in this setting, and that they would be worth exploring in other settings as well. And in a postscript in the conclusion to the paper, Greenstone et al. note that the GPCB has launched another particulate market in the largest city in the state, Ahmedabad. So clearly, the GPCB was sufficiently convinced that they decided to extend the approach elsewhere.

[HT: Marginal Revolution, back in 2024]

Read more:

• The inefficiency of New Zealand's emissions trading scheme
• Pigovian taxes vs. tradeable pollution permits when clean technology becomes available
• The Greens vs. Labour on carbon emissions, taxes and permits

Taking window shopping to the next level online

When a consumer consumes a good or service, they receive utility (satisfaction, or happiness) from that consumption. That is part of the standard theory underlying consumer behaviour in neoclassical economics. Behavioural economists like the Nobel Prize winner Richard Thaler, in contrast, may distinguish between two types of utility that a consumer receives when they buy a good or service. First, there is acquisition utility, which is the net benefit from acquiring the good or service relative to what the consumer gives up to get it. This is essentially the neoclassical utility from the good or service, after allowing for the cost of purchasing it. Second, there is transaction utility, which is the utility received from how good or bad the deal itself feels to the consumer, compared to what they expected to pay for the good or service.

Transaction utility arises when consumers feel like they are 'getting a good deal'. The better the deal, the greater the transaction utility. One problem is that transaction utility is largely temporary. While acquisition utility essentially lasts as long as the good or service that is purchased, transaction utility may last only as long as the transaction. For that reason, transaction utility may explain the phenomenon of 'buyer's remorse'. The purchase seems like a great idea at the time of purchase, but later, once the transaction utility has dissipated, the purchase doesn't seem like such a great idea at all.

Ordinarily, acquisition utility and transaction utility come as a bundle. When you buy a good or service, you get both. That is, until now. An article in Rest of World reported last month:

This week, I placed orders for a $44,860 Patek Philippe hand-engraved watch, a $12,500 Hermès handbag, a $9,800 Tiffany diamond ring, and a $7,350 Cartier Love bracelet in yellow gold.

I don’t need any of them. I certainly can’t afford them. Thankfully, they’ll never arrive.

Instead of Amazon, I spent the past week browsing a new breed of websites known as “dopamine sites,” a trend that emerged in South Korea. These websites — like Dopamine Shop and FoodNeverComes — recreate the entire ritual of online shopping: You search for products, compare reviews, add items to your cart, enter a shipping address, place an order, and even track your delivery. 

Then … nothing happens. No money changes hands. No package arrives...

Purchasing and shopping are not necessarily the same act. The former is about acquiring and the latter is more of a ritual. The pleasure from dopamine websites comes from the ritual.

In some ways, this is just a high-tech version of window shopping. People have always been able to get some enjoyment from browsing goods that they have no intention of buying. But these websites go a step further by recreating the transaction itself, right through to placing the order and tracking its delivery, while removing the actual receipt of the good or service that was 'purchased'.

The article argues that the pleasure comes from ritual. However, at least some of the pleasure may be transaction utility. There is no acquisition utility here. The consumer knows from the outset that they will never receive the watch or handbag. But the 'consumer' still receives some value from the 'transaction'. These websites may come about as close as possible to offering transaction utility on its own. And since consumers are never charged for their purchase, this is a low-cost way for consumers to receive that transaction utility. All it costs is the opportunity cost of their time spent browsing the website.

It is interesting to consider what the business model of Dopamine Shop or FoodNeverComes might be. The Rest of World article is silent on this point. One possibility is advertising or affiliate links to real products and retailers. After all, retailers might value the attention of people who are shopping without buying, in the hope that some of that activity eventually spills over into 'real' purchasing behaviour.

Whatever the business model, these websites provide an interesting example that demonstrates just how much utility consumers may get from the process of buying, even if they don't actually buy anything.

[HT: Marginal Revolution]