Signalling theory suggests that we may need two categories of university degree

In The Conversation last week, Ananish Chaudhuri (University of Auckland) argued the case for having two categories of university degree:

And while cognitive abilities such as reading, writing and maths matter, so too do social skills such as empathy, resilience and an ability to work in diverse groups and with diverse views...

Universities play a crucial role in developing these skills. But the emerging two groups of students – on campus and off – are not getting the same education. The increasing emphasis on online instruction and exams is devaluing degrees...

This suggests we may need to distinguish between online and on-campus students in each of our courses. The course content will be the same, but the assessment methods will be different.

Online students can take tests, quizzes and exams remotely. Some of this may also be available to on-campus students. But on-campus students will be expected to come to lectures regularly, ask questions, write, speak and engage in interactive tasks, including group work.

Would students sign up for on-campus courses but simply not attend? This could be prevented by making sure each student completes tasks that earn participation marks that count toward on-campus credits. If they fail to do so, they will automatically become online students.

Is this unfair to online students? Not necessarily. Many with jobs may prefer it. In any event, they will have to consider whether the benefits of coming to campus are worth it in terms of job prospects or earning potential.

It is worth reviewing the purposes of education from the perspective of the student. On the one hand, education provides useful cognitive and non-cognitive skills that have value in the workforce. In theory, skills development need not necessarily be different between in-person students and online students. However, some 'transferable skills' like teamwork and interpersonal skills are more difficult to develop in an online environment (as Chaudhuri notes).

On the other hand, education provides a signal to employers about the quality of the job applicant. Signalling is necessary because there is an adverse selection problem in the labour market. Job applicants know whether they are high quality or not, but employers do not know. The 'quality' of a job applicant is private information. High-quality (intelligent, hard-working, etc.) job applicants want to reveal to employers that they are hard-working. To do this, they need a signal - a way of credibly revealing their quality to prospective employers.

In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality job applicants, would provide the signal), and it must be costly in a way that makes it unattractive for the lower quality job applicants to attempt (such as being more costly for them to engage in).

Qualifications (degrees, diplomas, etc.) provide an effective signal (they are costly, and more costly for lower quality applicants who may have to attempt papers multiple times in order to pass, or work much harder in order to pass). So by engaging in university-level study, students are providing a signal of their quality to future employers. The qualification signals to the employer that the student is high quality, since a low-quality applicant wouldn't have put in the hard work required to get the qualification. Qualifications confer what we call a sheepskin effect - they have value to the graduate over and above the explicit learning and the skills that the student has developed during their study.

Now we can see where there is a key difference between in-person and online education. In-person education is more costly than online education, even if the tuition fees are the same, because it requires effort for a student to get themselves onto campus and into class each day. Higher-quality students (who will be higher-quality job applicants) are more likely to be conscientious and make this effort than lower-quality students. So, having an in-person education should provide an additional signal of quality to employers, over and above the signal provided by the degree itself.

However, if there is no way for the in-person students to distinguish themselves from the online students, then the value of the signal provided by in-person education is lost. Chaudhuri argues that the solution is to create a two-tiered qualification system. One (in-person) qualification would convey a signal of high quality to employers, and the other (online) qualification would convey a signal of lower quality to employers.

I'd argue that this already happens to some extent. It's the reason why Massive Open Online Courses (MOOCs) haven't displaced traditional education, despite being free or low cost. Employers can tell the signalling value of a degree when compared with studying online using a MOOC. The problem that Chaudhuri notes is that students' university transcripts probably don't clearly identify students who have done their study in-person from those who have done their study online.

Of course, savvy employers don't only rely on job applicants providing signals. Employers engage in screening, in order to reveal the private information for themselves. They do job interviews and subject job applicants to various pre-employment tests, which helps the employer to tell the high-quality and low-quality job applicants apart. It would not surprise me at all to learn that, as part of the standard job interview script, employers now ask how much of their degree a job applicant completed online. However, job applicants can lie in job interviews, so screening is unlikely to be as effective as signalling in solving the adverse selection problem. Perhaps, two categories of university degree is the best solution after all.

Regardless, the takeaway for students is, as I noted in this 2017 post, that they should be acutely aware of the signals that they are sending to future employers. Education is a signal, but the type of education matters as well.

Read more:

• Why all students need to understand adverse selection and signalling

Some evidence against 'greedflation' in New Zealand

Last month, Sense Partners wrote a report for BusinessNZ on 'greedflation' in New Zealand. As the National Business Review reported earlier this month (paywalled), when the report was released publicly:

Businesses don’t appear to be using Covid-19 and rampant inflation to cover for ‘super profits’, according to a new report out today.

The research by Sense Partners, commissioned by BusinessNZ, assessed whether ‘greedflation’ was happening in New Zealand.

Greedflation in New Zealand? An imported narrative found while costs have gone up across the board, ‘inflated’ profits were not the catalyst.

You can read the Sense Partners report here. Unlike the flaky PriceSpy analysis that I discussed yesterday, Sense Partners actually looked at how profits and input costs changed over time. They describe the methods very briefly in the report:

We can get a better understanding profit margins and components of price increases from detailed quarterly financial data published by Statistics New Zealand... This data is available for non-financial private sector firms.

We can use this data to analyse profit margins from 2017 to 2022, allowing us to compare pre- and post-Covid periods.

We can also use this data combined with real production GDP data... (a good proxy for quantity unit of output) to calculate sale price per unit, which adds up to the increase in cost of inputs, spend on labour and gross profits.

While gross profits will generate taxes and there will be other expenses such as interest payments or money to cover maintenance for example, this gives us a comparable approach to understanding the drivers of inflation.

The key data come from Statistics New Zealand's Infoshare service here. [*] It provides data by industry on sales (operating income), purchases and operating expenditure, salaries and wages, and operating profit. By comparing the changes in these categories over time, we can get a sense of how much changes in prices (which Sense Partners derive from sales deflated by GDP by industry) are detemined by changes in labour costs (salaries and wages), input costs (purchases and operating expenditure), and profits. The results are summarised for New Zealand overall in the following figure:

Notice that most of the change in prices (the black line on the left, or the brown bar on the right) is explained by changes in input costs (the grey and blue bars on the left, or the top two blue bars on the right). Very little of the change in prices is associated with changes in profits. As the report notes:

We found that, over the three years to December 2022, prices rose by 14% (or an average of 4.6% per year) – 71% of that price increase can be attributed to the increase in input costs, 15% to an increase in labour costs and 14% to an increase in gross profits.

And when looking at different industry sectors:

In the sectors with the highest inflation, input costs was the biggest contributor, not wages or profits.

This leads Sense Partners to conclude that:

We found no evidence of widespread increases in profit margins driving up inflation in New Zealand. It is an imported narrative not supported by the evidence.

One of my pet hates is the tendency for media and other commentators to transplant narratives from other countries to New Zealand. Inequality is one example (there is little evidence that it has been increasing in New Zealand in recent years), and greedflation is clearly another. Of course, I am also sceptical about the case for greedflation in other countries as well, as my previous posts on this topic make clear.

Of course, much could be made about the funder of this research (BusinessNZ) having a vested interest in the results not showing greedflation. Also, the omission of the financial sector, which is often held up as an exemplar of greedflation, is notable. The way that Sense Partners determined price changes (deflating sales revenues by GDP by sector) could also be criticised, as it isn't a direct measurement of price changes. It's difficult to say how much bias that introduces into the analysis. Nevertheless, this report demonstrates that the evidence for greedflation in general is very weak at best. 

*****

[*] At least, I hope the link works. If not, you can find the data by going to Infoshare, then sequentially choosing: Industry sectors; Business Data Collection; and Industry by financial variable.

Read more:

• The case against 'greedflation'
• Noah Smith on the case against 'greedflation'
• How not to identify 'greedflation'

How not to identify 'greedflation'

The blog has been a bit quiet of late, while I've been travelling and working in the UK. However, the real world doesn't stop while you're travelling, and I note that the media have continued their crusade against 'greedflation' while I'm away. In the latest instalment, the New Zealand Herald's Front Page podcast reported last week:

Numbers crunched by the independent cost comparison site PriceSpy show that the impact of inflation is not uniform across all companies.

The data shows that some companies are definitely increasing prices much faster than their competitors, often at a far higher rate than overall inflation figures indicate.

Figures released by Stats NZ showed inflation sitting at 6.7 per cent for the year to March, down from 7.2 per cent in December.

Despite this, the pricing data released by PriceSpy across a number of popular categories showed that some products had increased by as much as 29 per cent when comparing January to May in 2022 and 2023 respectively.

By definition, inflation is an increase in the general price level. That doesn't mean that every price goes up, only that prices are going up in general terms. It should be self-evident that price rises are not exactly the same for all goods and services at all times, and that price rises are not the same for all substitute goods within a particular category at all times. In fact, if all firms increased their prices exactly in concert with each other, we should be deeply concerned about collusion in the market, and no doubt the Commerce Commission would take a dim view of that behaviour.

So, given that firms don't all raise prices at the same time or by the same amount, it should be easy to identify some goods or brands that have risen in price more than others. That is all that PriceSpy has done. They could do this any year, whether inflation is higher or lower, and show something similar. That some firms raised prices more than others isn't evidence of 'greedflation'. It is an observation of the normal way that firms change the price of their products (as I have noted before). If you really believe that there is greedflation, then you need to show that price rises aren't resulting primarily from increases in input costs. PriceSpy hasn't done that (but more on that point in my next post).

And to make matters worse, there is this misunderstanding:

“Our research suggests that inflation may not be the sole factor driving price points up, as we are increasingly seeing competing manufacturers up their prices at differing rates, not only to each other but in comparison to the rate of inflation,” [PriceSpy head of public relations] Lindholm says.

This is proof that you shouldn't get your head of public relations to talk about economics. Inflation doesn't cause prices to go up. Inflation is a measure of how much prices have gone up, in general (on average, if you like). It's like saying that your car's speed is causing your car to go faster.

Anyway, coming back to the original point of this post, I'm still not seeing a lot of convincing evidence for greedflation in New Zealand. The analysis by PriceSpy certainly isn't it.

Read more:

• The case against 'greedflation'
• Noah Smith on the case against 'greedflation'

Book review: Why Nations Fail

There are a number of books that have been sitting on my shelves for years, unread. Often, it's no fault of the books, which have good reputations, but because the prospect of picking up a 500-page tome when I could read through a much shorter book, is a bit daunting. That's been the fate thus far of Daron Acemoglu and James Robinson's 2012 book Why Nations Fail. Until this month, when I finally read it. The arguments that the book makes were familiar to me from other sources (including articles written by Acemoglu, Robinson, and others): that countries that are prosperous today tend to have inclusive economic and political institutions, while countries that are poor tend to have extractive economic and political institutions. Acemoglu and Robinson define inclusive economic institutions as those that:

...feature secure private property, an unbiased system of law, and a provision of public services that provides a level playing field in which people can exchange and contract; it also must permit the entry of new businesses and allow people to choose their careers.

On the other hand, extractive economic institutions are those that:

...are designed to extract incomes and wealth from one subset of society to benefit a different subset.

It will come as no surprise that the subset of society that benefits from extractive institutions are the political and economic elite. As for political institutions, and the interactions between political and economic institutions, Acemoglu and Robinson note that:

Extractive political institutions concentrate power in the hands of a narrow elite and place few constraints on the exercise of this power. Economic institutions are then often structured by this elite to extract resources from the rest of the society. Extractive economic institutions thus naturally accompany extractive political institutions. In fact, they must inherently depend on extractive political institutions for their survival. Inclusive political institutions, vesting power broadly, would tend to uproot economic institutions that expropriate the resources of the many, erect entry barriers, and suppress the functioning of markets so that only a few benefit.

It's an attractive hypothesis, and that Acemoglu and Robinson demonstrate with many detailed examples, both contemporary and historical, that differences in institutions are strongly associated with prosperity, and poverty. However, Acemoglu and Robinson are careful not to claim that institutions explain everything. Indeed, they show that states with extractive institutions can maintain long periods of high economic growth; however, that growth ultimately reaches a limit. The obvious example of this process is the Soviet Union.

The book will not be convincing to everyone. In particular, Australia is held in high regard for its inclusive institutions, along with New Zealand and Canada. Indigenous peoples in those three countries would no doubt be able to provide many counterpoints to the inclusiveness of institutions in those countries. This is all the more surprising given that Acemoglu and Robinson use the distinction between institutions in the US South from the rest of the country as one of their key examples. They also hold up Botswana as an exemplar for other countries, although:

Today Botswana looks like a homogenous country, without the ethnic and linguistic fragmentation associated with many other African nations. But this was an outcome of the policy to have only English and a single national language, Setswana, taught in schools to minimize conflicts between different tribes and groups within society.

I'm not sure that the exclusion of minority languages and cultures, in favour of a national majority, is necessarily an example to aspire to. It made me wonder about the durability of inclusive institutions in Botswana - how long before the minority groups begin to agitate for their own cultures to no longer be minimised.

The one thing that the book is light on is a policy prescription for developing inclusive institutions. That is for good reason - it's not an easy task. Acemoglu and Robinson note that:

A confluence of factors, in particular a critical juncture coupled with a broad coalition of those pushing for reform or other propitious existing institutions, is often necessary for a nation to make strides toward more inclusive institutions. In addition some luck is key, because history always unfolds in a contingent way.

So, anyone who is looking for policy advice is going to come away disappointed. Nevertheless, this is a good book, and I think it should be read alongside books that look at long arcs of history and development, such as Jared Diamond's Guns, Germs and Steel. Acemoglu and Robinson skewer geographical determinism in their book, but that doesn't mean that there aren't geographical factors that have contributed to the development of modern institutions.

Overall, I really enjoyed reading this book, and I've now moved onto their 2019 follow-up, The Narrow Corridor. You can expect a review of that book in due course.

Taxing carbon emissions in agricultural markets with exports

In my previous post, I discussed the effect of an agricultural emissions tax. The effect of the tax would be to reduce New Zealand production of agricultural goods, and less production of agricultural goods in New Zealand means less agricultural emissions originating in New Zealand. However, as I noted in the footnotes to the post, the analysis left out the market effects of international trade. I argued that:

...the omission of trade doesn't affect the conclusions we draw from the model, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded.

Let's look at the market again in this post, but with international trade included. Before we get that far, it is worth revisiting the effect of international trade in an exporting country. This is shown in the diagram below. This is the market for an exporting country, which means that this country has a comparative advantage producing the agricultural good. That means that this country can produce the agricultural good at a lower opportunity cost than other countries, which is represented on the diagram by the domestic market equilibrium price of (PD) being below the price on the world market (PW). Because the domestic price is lower than the world price, if the country is open to trade there are opportunities for traders to buy the agricultural good in the domestic market (at the price PD), and sell it on the world market (at the price PW) and make a profit (or maybe the suppliers themselves sell directly to the world market for the price PW). In other words, there are incentives to export the agricultural good. The domestic consumers would end up having to pay the price PW for the agricultural good as well, since they would be competing with the world price (and who would sell at the lower price PD when they could sell on the world market for PW instead?). At this higher price, the domestic consumers choose to purchase Qd0 of the agricultural good, while the domestic suppliers sell Qs0 of the agricultural good (assuming that the world market could absorb any quantity of agricultural goods that was produced). The difference (Qs0 - Qd0) is the quantity of agricultural goods that is exported. Essentially the demand curve with exports follows the red line in the diagram.

Now consider what happens if the government decides to tax production of the agricultural good. For the moment, let's assume that the tax doesn't affect the world price (we'll come back to that later). Sellers who sell to the world market receive the world price PW, but then must pay a tax (T) to the government, which leaves them with (PW - T). There is less incentive to sell the agricultural good, so less will be produced. The quantity of the agricultural good supplied decreases to Qs1. The domestic consumers must still compete with the world market, so they still face the price of PW, so the quantity of the agricultural good demanded remains Qd0. So, as we saw in my previous post, the agricultural emissions tax would reduce domestic production of the agricultural good.

But what happens if the reduction in domestic production is large enough that it reduces global supply of the good? Then, we are back to the analysis in the previous post, where the price increases in the domestic market (because the world price increases and domestic consumers must also pay the world price), and production of the agricultural good decreases.

So, in markets where New Zealand is a large producer and has an appreciable effect on the world price, an agricultural emissions tax would reduce New Zealand production. And, in markets where New Zealand is a small producer and has no effect on the world price, an agricultural emissions tax would reduce New Zealand production and New Zealand emissions. Whether the tax would reduce global emissions, though, is a much more complicated question. It depends on all of the factors I highlighted in the previous post, and we don't have a complete answer to it yet (however, as I noted, it is likely that global emissions will be somewhat lower). I'll reiterate that there is more research needed in this area.

Read more:

• The question of carbon emissions 'leakage' is not as simple as some people would have you believe

The question of carbon emissions 'leakage' is not as simple as some people would have you believe

Agricultural emissions policy has been in the news recently (see here and here), with the National Party in favour of pricing agricultural emissions in such a way that there is "no leakage". What do they mean by leakage? As this RNZ article explains:

...Aotearoa is already one of the most efficient producers of meat and dairy products globally. If we reduce emissions here, will that not simply lead to other, less efficient countries picking up the lost production, while our farmers pay the price?

This idea is known as "carbon leakage" and is often used as an argument against any domestic policy that could result in reduced agricultural production. The issue is important as New Zealand depends heavily on agricultural exports. In 2022, of all merchandise trade, 65 percent were agricultural commodities.

How plausible is carbon emissions leakage as a result of reducing agricultural production in New Zealand? Let's focus on New Zealand production of dairy products [*]. We'll use a basic supply and demand model to explain what is happening. The diagram below shows the domestic market for dairy products [**]. If the market were left alone with no emissions price, it would operate at equilibrium with a price of P0, and Q0 dairy products would be traded. When the emissions price (effectively a type of excise tax on dairy products) is imposed, we represent that with the new curve S+tax. The price the consumer pays for dairy products increases to PC, but the effective price for the seller decreases to PP (which is the consumer's price PC, minus the amount of the emissions tax paid to the government). The quantity of dairy products traded decreases to QT. And, because less dairy products are traded, fewer cows are needed, and agriculture-related emissions are decreased.

Notice that there's no emissions leakage in this model. Nothing else is going on. That's because we're only looking at a model of partial equilibrium. We are looking at the domestic market for dairy products, in isolation. New Zealand is producing less dairy products, but the world still wants lot of dairy products. So, the argument goes, other producers will increase production to fill that demand.

However, that ignores the fact that New Zealand is a huge producer of dairy products for export. Higher prices of New Zealand dairy products (because of the agricultural emissions tax) will increase the global price of dairy products. When the global price goes up, dairy consumers will demand less dairy products. So, we can expect fewer dairy products to be produced globally.

Even that conclusion is incomplete though, because it ignores the dynamics. When the price of dairy products is high, profits are higher, and that might encourage more producers to enter the market, increasing supply. That would lower the global price of dairy products, and increase the quantity of dairy products demanded.

We're still not done though. Those new dairy products manufacturers will be higher-cost than New Zealand producers (if they weren't, then they would be producing dairy products already). So, the increase in global supply won't fully offset the reduction in New Zealand supply. And, an increase in global supply of dairy products must mean a decrease in the global supply of some other agricultural commodity that those new suppliers were previously producing. To understand emissions leakage, we need to get a handle on these general equilibrium and dynamic effects.

And finally, we get to the question of whether the new global dairy product suppliers have higher carbon emissions than New Zealand producers, as well as whether producing dairy products has higher emissions than whatever it was that they were previously producing.

So, you can see, the question of whether pricing agricultural emissions in New Zealand leads to emissions leakage doesn't have a straightforward answer. It requires a substantial understanding of global and local agricultural markets, agricultural supplier response to price changes, and the substitutability of agricultural land between different uses, and the carbon emissions of different land uses, both in New Zealand and overseas.

Some smart economists are working on understanding this though. As the same RNZ article notes:

It's difficult to know exactly what might happen in agriculture, as emissions pricing on agricultural products has not yet been used elsewhere. There is no historical evidence to draw on.

International modelling studies present a mixed picture of the likelihood of leakage: an OECD study estimated 34 percent of agricultural emissions would be leaked, mostly to developing countries.

Recent modelling for New Zealand examines a series of scenarios of domestic pricing on its own as well as international pricing. The results show that for the current proposal where only 5 percent of emissions are priced to begin with, with a 1 percent increase each year, New Zealand's production of meat and dairy products could decline by 2050...

This shows leakage may occur, with reductions in production of New Zealand dairy products. But global meat and dairy production by 2050 would be considerably lower than without the policy, which would have a positive overall impact on the climate.

Perhaps we can take away from the evidence so far that there is some emissions leakage, but it's not complete. Not all emissions reductions are leaked overseas. So, pricing agricultural emissions in New Zealand would likely lead to lower global emissions in total. Clearly, there is more research needed in this area, but based on what we know so far, I think it's ridiculous that we give one of the largest emitting sectors a free pass on contributing to climate change. If anyone tells you that all of our emissions would simply move overseas, you should probably examine their motivations for doing so.

*****

[*] The case is much simpler for the production of other agricultural products, where New Zealand production doesn't affect the global price. If a reduction in New Zealand production is so small that it doesn't affect the world price, then it doesn't create any incentive for other countries to produce more. So, there is unlikely to be any emissions leakage of note.

[**] For simplicity, I'm ignoring two things in this market diagram. First, the diagram doesn't show a negative externality, for example the impact of dairy emissions on the climate. So, the supply curve shouldn't be equal to marginal social cost (MSC), as MSC should also include the social cost of the emissions. However, correcting for that wouldn't change the general conclusion, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded. It would simply move the market closer to the socially-optimal quantity. Second, the diagram doesn't include international trade, which has important effects on the New Zealand domestic market for dairy products. However, including trade in a diagram of the dairy products market for New Zealand is not as simple as including the world price (as in this post, for example), because the world price of dairy products is directly affected by New Zealand production. One way of solving that problem is to assume that demand in the New Zealand market reflects global (rather than just domestic) demand, and then the diagram looks much as we have drawn it. Again, the omission of trade doesn't affect the conclusions we draw from the model, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded.

Noah Smith on the case against 'greedflation'

A couple of weeks ago, I outlined my case against 'greedflation' (broadly, the idea that firms exploit inflation by raising prices to create excessive profits). On the Noahpinion blog this week, Noah Smith has made a related case, closely examining some of the key claims that have recently been made in favour of greedflation and price controls:

Zachary Carter masterfully harnesses the Iconoclast’s Journey in a recent New Yorker profile of economist Isabella Weber. Weber, a professor at UMass-Amherst, is a prominent proponent of the theory many call “greedflation” — the notion that inflation is caused by companies’ monopolistic behavior rather than by macroeconomic factors, and that the proper solution to this is price controls...

In addition to the policy ideas around price controls, Weber is a proponent of the idea of “greedflation”. Her paper with Wasner has a theory of what that means, and how it’s supposed to work. The theory is basically game-theoretic — it relies on ideas about strategic interactions between companies that end up causing inflation at the macro level. But because the game structure is not explicitly specified, it’s difficult to tell exactly how Weber thinks the strategic interaction is working. For example, on p. 189, Weber writes:

Firms do not lower prices, as doing so may spark a price war. Firms compete over market shares, but if they lower prices to gain territory from other firms, they must expect their competitors to respond by lowering their prices in turn. This can result in a race to the bottom which destroys profitability in the industry. Price wars are very risky for firms that are already in the market and are therefore typically launched by new entrants.

This leaves me with many questions. Do Weber and Wasner think incumbent firms never lower prices? (That’s obviously wrong.) Or do they just not lower prices in specific situations? What’s different about the situations where firms refuse to lower prices? What does it mean to “gain territory” from other firms, and how does that work? Where does the “race to the bottom” end? And so on. Mainstream economists specify the answers to these questions by writing out strategic interactions explicitly in the form of game theory; this approach certainly has its drawbacks, but at least it allows the reader to start to think about how to falsify or confirm the theory being presented.

And how is the above assertion supported? Weber and Wasner cite A) a number of economists from the early 20th century, whose observations of markets she claims to have distilled, and B) recent earnings calls by a few public corporations. I would not label this support “crap”, but neither do I believe that it’s sufficient to support the assertions about how strategic pricing interactions work. Sraffa, Kalecki, Galbraith, Robinson, etc. were all very smart people, and I am sure their close observations of firm behavior were useful and carefully made. But they were working decades ago, without the benefit of modern empirical data collection methods and analysis techniques; a lot of important work on strategic interaction between companies has been done since the 1950s, and I think Weber and Wasner should take it into account. The strategic landscape businesses face may have changed as well. As for corporate earnings calls…public-facing corporate statements can offer clues as to how companies behave and interact, but that’s more of a jumping-off point for the development of a theory rather than evidence confirming a theory.

The whole paper is like this, more or less. It feels like a proto-theory — a collection of ideas that might offer a good description of how pricing works in a modern economy, but one that needs to be made more concrete before we can properly evaluate how accurately it describes reality. And we should definitely test any theory as best we can, before using it to make policy.

For broader context, you should read Noah's whole post. As I noted when I was interviewed for RNZ's The Detail podcast last month, the evidence that proponents cite for 'greedflation' seems to be a case of 'I know it when I see it'. As Smith strongly implies in his blog, that isn't a strong and falsifiable theoretical basis for an idea that leads people to prefer strong policy, and potentially catastrophic, responses like price controls. Do we really want to have to resort to the black market to buy milk and toilet paper, as Venezuelans had to in 2015 as a result of price controls? We need to be very careful before adopting a policy that prioritises price controls to deal with inflation. As far as I can see, the proponents of the greedflation idea are not showing that level of care. And with glowing (and unwarranted) attention from some sections of the media, why would they?

[HT: Marginal Revolution]

Read more:

• The case against 'greedflation'

The effect of banning indoor mass gatherings on the spread of COVID-19

One of the first responses that many governments enacted during the coronavirus pandemic was limiting or banning mass gatherings like sporting events, concerts, conferences, and weddings. But how effective were those measures in reducing the number of coronavirus infections and subsequent mortality? In a recent article by Alexander Ahammer, Martin Halla, and Mario Lackner (all Johannes Kepler University), published in the journal Contemporary Economic Policy (open access), we get an answer. Ahammer et al. make use of a cool natural experiment:

We quantify how NBA and NHL games have contributed to the early spread of COVID‐19 in the United States... We analyze how much games held between March 1 and March 11 have contributed to the community spread of COVID‐19 in counties surrounding NBA and NHL venues. Since the game schedules were determined long before the first COVID‐19 case became public, their spatial and temporal distribution should be unrelated to the initial spread of COVID‐19 in the US...

Specifically, Ahammer et al. look at how the number of NBA and NHL games (combined) between 1-11 March 2020 relate to the cumulative number of coronavirus cases and deaths as of 30 April 2020 (6-8 weeks later) in the county that hosted the games, or neighbouring counties. They find that:

...that each additional mass gathering between March 1 and 11 increased cases by 269 per million and deaths by approximately 15 per million population. These are substantial effects. Compared to the average case and death rates across the counties in the data, our estimates correspond to increases of 9.2% and 10.3% per game, respectively. Both estimates are statistically significant at the 1% level.

When they run separate models for cumulative cases (and deaths) as at each day from 13 March to the end of April, where:

...we expect effects to be strongest around 3 weeks after the shutdown. This is precisely what we find. The effect of games starts to pick up around March 19 and increases at a decreasing rate since then. This is true for both cases and deaths. Furthermore, we see that cases respond sooner than deaths, which makes sense given the natural lag between diagnosis and death. In terms of magnitudes, estimates for COVID‐19 deaths (cases) range between 0.002 (0.367) on March 13 and 15.195 (269.131) on April 30.

And, in case you're wondering:

If we split our treatment variable and count NBA and NHL games separately, we find that games in both leagues positively affect COVID‐19 spread...

Finally, when they stratify their analysis, they find that:

These effects are larger in densely populated areas and in colder regions.

No surprises there. The obvious conclusion overall is that limiting or banning mass gatherings was an effective strategy in arresting the spread of coronavirus. Ahammer et al. conclude that:

...banning indoor mass gatherings has an enormous potential to save lives. This is especially important given that such measures are relatively easy and cheap to implement.

Their results don't necessarily extend to outdoor gatherings, but at least we have some surety now of the effectiveness of one of the early tools that governments employed during the pandemic.

Jetstar profits from selling flights, but doesn't only profit from selling flights

Jetstar has been in the news this week for its birthday celebrations. As the New Zealand Herald reported:

Jetstar is marking its 14th year of flying New Zealand domestic routes with fares as low as $29.

The airline is also offering fares from Wellington to the Gold Coast starting at $155.

The low-cost airline launched services within New Zealand on June 10, 2009...

Jetstar’s head of New Zealand, Shelley Musk, said the airline remains committed to New Zealand.

“For the past 14 years we’ve been offering Kiwi customers great-value fares and choice so they can decide how they want to fly."

Aren't Jetstar great? Out of an abundance of kindness towards New Zealand travellers, they're offering really low fares. Or maybe not. As I noted in this post from 2017, airlines are known for taking advantage of having locked-in consumers in order to increase profits. Here's what I said then:

In the usual discussion of customer lock-in, customers become locked into buying from a particular seller if they find it difficult (costly) to change to an alternative seller once they have started purchasing a particular good or service. Switching costs (like contract termination fees) typically generate customer lock-in, because a high cost of switching can prevent customers from changing to substitute products.

In this case, once the airline customer has purchased a ticket from an airline, they are locked into travelling with that airline (and often, they are locked into a particular flight, if they have selected a ticket type that is non-transferable). The airline knows that the customer won't switch to another airline (or flight) if they charged additional fees for complementary services... such as for checked bags, in-flight meals, selecting their own seat, and so on.

This is a highly profitable proposition for the airlines... and this is because customer demand for those extra services is relatively inelastic. Once you have purchased a plane ticket for a given flight, there are few (if any) substitutes that allow you to get your checked baggage to the same destination as you are going. So your demand for checking a bag onto your own flight (if you have a bag that needs checking in) is probably very inelastic. Similarly, if you are not prepared for your flight and buy some snacks to take onto the plane with you (and/or you don't have a meal before boarding and are unwilling to wait until you land to eat), there are no substitutes to buying a meal while in the air. When there are few substitutes for a good or service, demand will be relatively more inelastic, and the optimal mark-up over marginal cost is high. As many of you will have observed, the mark-up on in-flight snacks and meals is very high. It is these high mark-ups that leads these extra charges to be highly profitable for the airlines.

While the extra charges have been increasing, ticket prices have been declining. 

Is that what Jetstar is doing? You be the judge. From the New Zealand Herald article:

“By keeping our starter fares low, customers can choose to add a meal, select a seat or bundle their bags — it’s all about choice.’'...

The airline’s business model is to operate with low costs and maximum ancillary revenue. Before the pandemic, revenue from add-ons such as seat selection, paid baggage and food and drinks grew 38 per cent.

The airline aims for similar growth rates over the next five years as it adds more optional extras and technology to enable buying them easier. It is also getting bigger and more efficient A321 aircraft into its fleet.

Seems to me like it's working well for them. Especially when they can get good press for their 'low fares', while maintaining their profitability through the 'ancillary revenue'. As I note in my ECONS101 class, there are many ways for firms to increase profits, and they need not sell every good and service at its individual profit-maximising price.

Read more:

• How airlines use extra charges to boost their profits

Using ChatGPT for economics research

As I posted last month, ChatGPT convincingly passed the Test of Understanding of College Economics. Maybe ChatGPT is coming for the jobs of economists. In the meantime though, we can use it as a tool to improve our productivity in teaching and research. Two new papers show the way.

The first paper is this one by Kevin Bryan (University of Toronto), which is essentially a bunch of notes from a talk that Bryan gave at Princeton last month. Bryan offers a 'user's guide' to ChatGPT for economics research. Bryan summarises the main takeaways of his user's guide as:

1. Controlling the output of LLMs is difficult

2. The “Raw” ChatGPT online is far from state of the art

3. Hallucinations are mostly fixable (this guide includes some tips to avoid them)

4. The technology’s rate of improvements is fast

5. You should get access to the API. Most use cases for economists require using the API and accompanying with code. This will give you much more control on the output, and it is cheap to do so

Bryan then offers some examples of four uses of ChatGPT for economists:

1. Cleaning data

2. Programming and making graphs

3. Spelling and grammar checks

4. Summarizing literature

The examples are interesting and a useful starting point, but a little bit obvious to anyone who has used ChatGPT. Also, Bryan doesn't provide the associated ChatGPT prompts, which would have made them even more useful. An even better, and more comprehensive coverage, of uses of ChatGPT for economics research is provided in this new NBER Working Paper (alternative ungated version here), by Anton Korinek (University of Virginia). Korinek lays out:

...six different areas in which LLMs can be useful. In the process of ideation, LLMs can help to brainstorm, evaluate ideas, and provide counterarguments. In writing, they can synthesize text, provide examples, edit and evaluate text, and generate catchy tweets or titles for a paper. In background research, they can be useful for searching and summarizing the literature, translating text, explaining concepts, and formatting references. LLMs are also very capable in coding, writing code based on instructions in natural language, explaining code, translating code between programming languages, and even debugging code. For data analysis, LLMs can extract data from text, reformat data, classify text, extract sentiment, and even simulate humans to generate data. Finally, LLMs are starting to display emergent capabilities in mathematical derivations, starting from setting up models and working through derivations to explaining models.

Korinek provides examples that include both the prompts and ChatGPT's response, which helps to better illustrate its capabilities. Like Bryan, some of Korinek's examples are straightforward and obvious. However, some of them will be huge timesavers, especially:

Once references are found and it is verified that they are not hallucinated, LLMs are very capable of formatting them in the desired manner...

No more adjusting from one referencing standard to another for me (although it has to be said that EndNote and similar tools automate this already, but entering the references into EndNote can be time consuming in itself). Generating catchy titles and headlines is also going to prove really helpful (and no, I didn't use it for the title of this post). However, ChatGPT is not equally useful for every task. Korinek provides a helpful rating of ChatGPT for each task, in terms of its usefulness (1 = experimental; 2 = useful; 3 = highly useful). Here is Table 1 from the paper:

Those usefulness ratings are as of February 2023. ChatGPT and other large language tools are improving rapidly in their usefulness. By the time you read this post, it is likely that ChatGPT will have improved in its ability to perform one or more of those tasks beyond the point it was at when Korinek was testing it (based on text-davinci-003, which is slightly better than ChatGPT, but not as advanced as the more-recently-released GPT-4).

Korinek's paper also provides some optimism for economists who may worry about their future job security:

Ultimately, I believe that the most useful attitude towards the current generation of LLMs is to heed the lessons of comparative advantage that Ricardo taught us two centuries ago: LLMs increasingly have comparative advantage in generating content; humans currently have comparative advantage in evaluating and discriminating content. LLMs also have super-human capabilities in processing large amounts of text. All this creates ample space for productive collaboration...

If anyone should not forget the lessons of comparative advantage, it is economists.

[HT: Marginal Revolution for the Korinek paper, and David McKenzie at the Development Impact blog for the Bryan paper]

The latest Australian research on the four-day workweek

John Hopkins (Swinburne University of Technology) outlined the results of research on the four-day workweek in Australia in The Conversation today:

Four of the ten organisations in our research have adopted the change permanently after trials. The other six have extended their trials, though are still to formally make the move permanent...

In each case, the initiatives were management-led, as a strategy to tackle employee burnout, increase productivity, and keep and attract talent in a tight labour market...

Three of the ten managers reported no loss of productivity despite a 20% reduction in hours – so effectively staff were about 20% more productive.

The other seven reported productivity being even higher than before.

Six said improvements in recruitment and retention had been the biggest success of the initiative so far. Five underlined important reductions in absenteeism.

Three companies needed to maintain their previous hours of availability for customers and clients, despite their staff now working 20% less time. This illustrates it is possible for “client-facing” organisations to implement four-day work weeks.

Hopkins' research is available here. None of the results so far strike me as surprising. The positive effects that they observe are similar to the effects of efficiency wages. An efficiency wage is a wage that is voluntarily offered by an employer and is above the equilibrium wage in the labour market. Employers offer these efficiency wages because they know they have positive effects - they attract and retain higher quality employees who work harder for the firm, higher productivity, lower absenteeism, and lower staff turnover. Why do all these positive effects happen? In the simplest sense, having lots of job applicants and being the first-choice employer for most available workers means you get to choose the best (most productive) workers. Nobel Prize winner George Akerlof also noted that workers will volunteer greater effort in exchange for being better paid (perhaps because of good feelings towards their employer), as a form of 'gift exchange'.

Notice that offering better working conditions, like a four-day workweek, is similar to offering higher wages (in fact, when worked out on a pay-per-hour-worked basis, it is exactly the same as offering higher wages). A gain in productivity that offsets (or more than offsets) a reduction in weekly work hours should not be surprising.

However, as I pointed out in this post about the living wage, these efficiency wage effects only accrue to employers when a few employers offer the efficiency wage. The gains from paying an efficiency wage arise in part because the alternative jobs for employees pay much less. If every other employer also offers an efficiency wage in the form of a four-day workweek, then the employees don't need to work so hard because if they lose their job they can go somewhere else that is also offering the same conditions. Same goes for absenteeism, staff turnover, etc. The benefits of the efficiency wage may evaporate if lots of employers pay efficiency wages.

That's not the only problem with the four-day workweek, of course. As I've noted before, it doesn't work in occupations where there are tournament effects, and Hopkins points out that:

And while the “client-facing” companies we surveyed managed to maintain their operations, it remains to be seen if that’s the case for all workplaces, such as shops, hospitals and nursing homes where any reduction in hours worked by current employees would probably need to be covered by additional staff.

Don't expect your favourite local barista to be on a four-day workweek any time soon. Or if they are, there will be one day each week where you will have to forego your coffee.

Read more:

• Perpetual Guardian, four-day workweeks and the Hawthorne effect
• Iceland and the four-day workweek
• The benefits of the four-day workweek, in the presence or absence of tournament effects

Should economists have licensing, or a Code of Ethics?

Many occupations require a license before a practitioner (or service provider) can operate. The full list of licensed occupations differs by jurisdiction (here is New Zealand's list), but in most places doctors, dentists, and nurses are included in the list. However, economists are not. Olivia Wills recently wrote a great post on the Asymmetric Information Substack today questioning that outcome. She started by outlining four reasons why the government might put a licensing regime in place:

Safety. Some roles, like those covered by the Health Practitioners Competence Assurance Act 2003, directly impact public health and safety. That Act’s purpose is to protect the health and safety of members of the public by providing mechanisms to ensure the life long competence of health practitioners. By setting minimum standards for education, training, and experience, regulatory bodies ensure that practitioners have the necessary knowledge and skills to do their work.

Reputation. Regulation helps uphold the reputation of a profession by setting standards. For example, an experience with one rogue chiropractor could put you off the whole profession, but by limiting who can call themselves chiropractors to those with suitable experience and qualification, there’s a reduced risk of reputational damage to the non-rogues.

Frameworks for when it goes wrong. No matter what restrictions are put on entering a profession, there will always be some rogue players who cause harm. Registration also means an established process for de-registration (e.g. doctors can be struck off the register, and barristers can be disbarred). These steps prevent further harm, allow justice and voice for victims, and again uphold the reputation of the profession.

Reducing information asymmetry. Regulated professions tend to involve specialised knowledge that can be difficult for the rest of us to assess. If I’m injured and looking for a physiotherapist, the idea of choosing based on who has the best search engine optimisation might not get me the best results. A list of accredited practitioners gives a level of assurance about qualifications and competence.

When I discuss licensing in my ECONS101 class, I focus only on the first (safety). Government doesn't want consumers to come to great harm (physically, financially, or psychologically) by dealing with unlicensed service providers. In my ECONS102 class, we also consider asymmetric information. Consumers don't know which service providers are high quality, and a licensing system can overcome that if it ensures that only high-quality service providers with appropriate qualifications are licensed. Where there is both a risk of substantial harm and asymmetric information, that provides a good justification for licensing. This is the reason for only allowing licensed doctors, dentists, or nurses to operate (pun intended).

However, in her post Wills only considers licensing as a possibility, when there is another option: certification. Where licensing prohibits unlicensed service providers from operating, certification allows both certified and uncertified providers to operate. A certification system has lower transaction costs than a licensing system, and doesn't provide the certified service providers with as much market power as licensed service providers have (since it doesn't keep uncertified service providers out of the market, which keeps competition higher than is the case for an occupation governed by a licensing system). Certification makes more sense where there is asymmetric information (about the quality of service providers), but the degree of harm is unlikely to be great. Certification provides consumers with information about the likely quality of the service providers, but doesn't fully exclude the lower-quality service providers from the market. In New Zealand, accountants have a certification regime (they can be Chartered Accountants, or not), but not a licensing regime. This often comes as a surprise to my students.

Anyway, Wills' post is about whether or not economists should be licensed. On that point, she notes that:

In practice, economics is best when exploring new ideas and methodologies, and signing up to an overarching way of working is likely to inhibit this freedom. The reality of enforcing regulation would present practical challenges, particularly in defining the boundaries of the profession. Determining who should be subject to regulation and who should be exempt could be a complex, unfruitful task.

Yet the capacity for economists to cause harm is inescapable. This is why I conclude that, while formal accreditation is neither feasible nor desirable, an ethical declaration would be a useful reminder of our role and responsibility. A Code of Ethics might go some way to encourage humility in our estimations, models and predictions, and appreciate the role our own world views take when informing our work.

When it comes to the strength of the regime governing an occupation, a voluntary Code of Ethics is an even weaker option than certification. It does little to prevent harm, since economists would be free to adhere to the Code or not. It also doesn't solve the asymmetric information problem, if anyone can sign up to the Code at little to no cost.

It seems to me that, in terms of interactions with 'consumers' (broadly defined), there is little for economists to gain from a Code of Ethics. It comes with real practical problems. Economists have legitimate disagreements on models, data analysis, and policy implications, and even what constitutes best practice in those areas. It's not clear that a Code of Ethics would solve those issues, and if not, then it would do little to improve the reputation of economists in the eyes of the general public.

On the other hand, there are positive aspects to a Code of Ethics, if it was focused on some of the problems that are internal to the discipline (rather than those affecting 'consumers'). For example, there is a persistent gender gap in economics (see this post, and the links at the end of it). A Code of Ethics, albeit voluntary, would at least give the impression (and associated warm glow for those who signed up to it) that economics is addressing its internal problems. Maybe it would be one more step in that direction.