Working and academic performance at university

Does working negative impact on students' academic performance at university? It's an interesting and important question, especially as students are increasingly finding themselves in financial positions where working is a necessity in order to meet their living costs, rather than a source of 'beer money' as it was in earlier decades.

However, the theoretical relationship between working and academic performance is not straightforward. As Moris Triventi (European University Institute) outlines in this 2014 article published in the journal Economics of Education Review (ungated earlier version here), there are four possibilities for this relationship:

1. Zero-sum: Every hour spent working is one less hour that could be spent studying or attending classes, and since studying and attendance are important for academic performance, working has a negative causal impact on academic performance;
2. Reconciliation: Students are reasonably flexible in their allocation of time between working, studying, and leisure, so an additional hour spent studying does not necessarily reduce studying or attendance, and students can be strategic in choosing classes that are less demanding, so there is likely no effect of working on academic performance;
3. Negative selection: Students who choose to work during university are different in meaningful ways from students who do not work, perhaps because they did not achieve as well at high school, or they don't qualify for scholarships or other assistance, or because their aren't as motivated towards their studies, so working is negatively correlated with academic performance, but the relationship is not causal;
4. Positive selection: Students who choose to work during university are high achievers and highly motivated, and are able to work because they are confident that it will not negatively impact on their studies, so working is positively correlated with academic performance or there may be no effect, but if there is a positive relationship then it is not causal.

Triventi goes on to test these theories using representative survey data collected from 1834 Italian freshman students in 2004. First, he separates students into three groups (non-working students; low-intensity workers; and high-intensity workers), and notes that:

Among Italian freshmen in 2002–2003, 17% worked up to 20 h during their first academic year, while 10.5% worked on average more the 20 h per week... Low-intensity workers worked on average 11.3 h per week, while high-intensity workers worked 35.4 h.

Then, looking at the relationship between working and academic performance (measured by the number of credits earned in the first year of university), he finds that:

Looking at high-intensity work there are few doubts: in all models it has a large detrimental effect on academic progression, even when controlling for both observed and unobserved variables. This means that the zero-sum approach fully applies to the condition of high-intensity workers, who devoted on average 35 h per week to their job. It is likely that such a degree of involvement makes it difficult to devote a sufficient amount of hours to study, and thus to maintain a regular academic progression...

The second noteworthy finding refers to the low-intensity employment status. In this case, the results change in different models. Bivariate and traditional multivariate analyses show no major gap in academic progression between low-intensity workers and non-working students... once accounting for unobserved variables – which are likely to capture motivation and multitasking skills in our work – the picture changes, since the low-intensity employment status also negatively affects the number of credits acquired (albeit to a less extent compared to the high-intensity employment status). This means that, in line with the positive-selection hypothesis, the standard analyses in Italy mask the fact that low-intensity workers are positively selected (conditional on observed covariates) and thus are able to compensate the difficulties of being employed while studying by their higher commitment to pursue the two activities at the same time.

In other words, working does have a negative effect on academic performance. For students trying to study while working essentially full-time, the effect is negative and large (these students do much worse than non-working students). Students working part-time are able to compensate for the negative effects of working, but that is because of the types of students who decide to work part-time while studying.

The results of this study are interesting, and probably should worry us. As we increasingly move university teaching to more online and flexible modes, this opens up the possibility for more full-time workers to engage in studying, or for students who would otherwise be working a little or not at all, to work more. Both of those are likely to lead to a reduction in academic performance.

A university system that encourages students to take on paid work due to financial reasons is setting those students up to fail. To avoid this negative outcome, we really need a student allowances and loans system that is able to provide adequately for students' living costs (unlike what we have now).

How has applied microeconomics changed over time?

In a 2020 NBER Working Paper (alternative ungated version here), Janet Currie, Henrik Kleven, and Esmée Zwiers (all Princeton University) looked at how the methods employed in applied microeconomics research have changed over time. They mined text-based data from a sample of 10,324 NBER Working Papers over the period from 1980 to 2018, and 2,830 journal articles published in the top five economics journals from 2004 to 2019. They find a number of interesting trends, including:

...a virtually linear rise in the fraction of papers, in both the NBER and top-five series, which make explicit reference to identification. This fraction has risen from around 4 percent to 50 percent of papers...

...a somewhat slower rise in the use of experimental and quasi-experimental methods... Currently, over 40 percent of NBER papers and about 35 percent of top-five papers make reference to randomized controlled trials (RCTs), lab experiments, difference-in-differences, regression discontinuity, event studies, or bunching...

...a very similar pattern in references to administrative data. The NBER series starts increasing in the mid-1990s, rising to about 30 percent today. The top-five series shows a similar increase, but with a lag of about three years... The term Big Data suddenly skyrockets after 2012, with a more recent uptick in the top five...

The importance of figures relative to tables has increased substantially over time and in two phases. The first phase happened in the 1990s and likely reflects the diffusion of new software such as STATA that made it easier to create impactful figures. The second phase has happened in the last 10-12 years and is still ongoing. This is likely due to the increasing use of administrative datasets, which lend themselves to compelling graphical representation using raw data and non-parametric approaches...

...a sharp rise in the fraction of NBER working papers discussing randomized controlled trials since 2005, and especially since 2010...

Laboratory experiments have grown steadily in popularity since the late 1990s, which is connected to the rise of Behavioral Economics during this time period...

...authors have become increasingly concerned with whether their estimates are precisely estimated, and not merely with whether they are significantly different from zero in a statistical sense...

...a sharp rise in references to confidence intervals since the mid-1990s...

...after year 2000, there has been a massive increase in attention paid to clustering of standard errors...

Currie have clearly identified the most important changes in the types of research methods used in applied microeconomics over the last 30 or so years. One thing they haven't noted is the rise in the use of textual analysis, including sentiment analysis, as a trend within the use of big data. This is somewhat ironic, since that's what their paper uses!

It would be interesting to see a similar exercise conducted for applied macroeconomics.

[HT: Marginal Revolution, last year]

Scalpers and the irrationality of Ontario Parks

CBC reported earlier this week:

Ontario Parks is cracking down on people who book camping sites and resell their reservations for profit.

The province doesn't condone reselling reservations. because it's been particularly difficult to book a site, said a spokesperson for Jeff Yurek, minister of environment, conservation and parks.

This year has seen a particular surge in campsite bookings and competition for coveted spots...

"We know that there are instances where individuals are attempting to sell reservations with the intention to make a profit," Chelsea Dolan said in an email to CBC Kitchener-Waterloo on Thursday evening.

As of Saturday, anyone with reservations won't be allowed to resell them.

A common rationale for preventing the scalping (or resale) of tickets is that the scalpers make consumers worse off. But should that in itself be a reason to discourage scalping? Consider the market for camping sites, where there is a fixed number of sites made available, as shown in the diagram below. The supply of camping sites S0 is fixed at Q0 - if the price rises, more sites will not suddenly be made available (note that the diagram assumes that the marginal cost of providing sites up to Q0 is zero).

Demand for camping sites is high (D0), leading to a relatively high equilibrium price (P0). However, camping sites are priced at P1, below the equilibrium (and market-clearing) price. At this lower price, there is excess demand for camping sites (a shortage) - the quantity of sites demanded is Qd, while the quantity of sites that are available is fixed at Q0.

With the low camping site price P1, the consumer surplus (the difference between the price the consumers are willing to pay, and the price they actually pay) is the area ABCP1. Producer surplus (essentially the profits for Ontario Parks) is the area P1CDO. Total welfare (the sum of producer and consumer surplus) is the area ABCDO. At the higher price P0 due to the actions of scalpers (buying at P1 and selling at P0), the consumer surplus decreases to ABP0, while producer surplus remains unchanged. The scalpers gain a surplus (or profit) of the area P0BCP1, and total welfare (the sum of producer and consumer surplus, and scalper surplus) remains ABCDO. So the camping site scalpers don't change total welfare at all, just the distribution of that welfare between the parties. However, they do clearly make consumers worse off as a group, because consumer surplus is lower.

However, let's go back to considering the price at P1. At that price, the excess demand means that there are many campers who are willing and able to pay a price that is higher than P1, who miss out on a camping site because of the excess demand. If the price was a little higher than P1, then that would reduce the amount of excess demand, and fewer willing buyers (at the higher price) would be missing out on a camping site. In fact, you would need to raise the price all the way to P0 before you get to a situation where no willing buyers are missing out. And, that's exactly what the scalpers do. By banning scalpers, Ontario Parks are essentially protecting the excess demand. They are ensuring that some people, who are willing and able to pay the market price, will miss out on camping sites.

You might argue that raising the price will squeeze consumers out of the market, starting with those who are willing and able to pay the least for a camping site, and that such an outcome is unfair. [*] But is it really fairer that there are many people willing to pay the market price who are missing out on camping sites?

Finally, Ontario Parks is clearly missing a trick here. Why are they pricing so low, and allowing scalpers to earn a surplus at all. If Ontario Parks raised the price of camping sites to P0, there would be no market for scalpers, or at least there would be no profits for them to make, and all of the combined producer surplus and scalper surplus would belong to Ontario Parks. Think of how much of an improved service Ontario Parks could offer with all that additional revenue. What are they thinking?

[HT: Marginal Revolution]

*****

[*] Market pricing is incredibly unfair. Personally, I strongly believe that the pricing of Lamborghinis is unfair. They should be priced in such a way that I can afford to drive a different coloured one to work each day of the week. I demand action!

Read more:

• The Super Rugby final and ticket scalping
• Harry Potter and the chamber of scalpers
• The economics of ticket scalping

Meaningful jobs and compensating differentials

In The Conversation last week, Andrew Bryce (University of Sheffield) wrote about the meaningfulness of jobs. These bits in particular caught my eye:

Work provides many things over and above the monthly pay cheque: status and identity, community and social connection, doing tasks that we find stimulating, and the opportunity to make a positive contribution to society. All of these things make work feel meaningful.

My research explores how paid work is experienced as meaningful compared to the other activities people do in their everyday lives. I also identify the types of job in which people experience the most meaningfulness and explore how these results can be explained by the particular qualities of different occupations...

People in community and social service occupations (which includes social workers, counsellors and clergy) experience the most meaningfulness in their work.

The other top-ranking occupations are: healthcare practitioner and technical occupations; education, training and library occupations; and, perhaps surprisingly to some, legal occupations. More broadly, people working in the non-profit sector and self-employed people report significantly more meaningfulness in their work than those employed in private sector for-profit firms...

When work is meaningful, then that becomes a reward in itself and generous pay offers are not prioritised to motivate people and retain staff. In contrast, less meaningful work has no such intrinsic value, so a monetary reward is needed to get people to do these jobs.

This of course leads to the perverse situation where the most socially useful jobs are those that are paid the least. It may seem unfair but it’s the reality of how the labour market works.

It's the reality of how the labour market works because of compensating differentials. Jobs have both monetary and non-monetary characteristics. Monetary characteristics include the pay and other monetary benefits. Non-monetary characteristics include the whole range of other things associated with the job. Perhaps it is dirty, dangerous, or boring. Or perhaps it is clean, safe, or fun. When a job has attractive non-monetary characteristics, 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, when a job has negative non-monetary characteristics, 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. It is the difference in wages between jobs with attractive non-monetary characteristics and jobs with negative non-monetary characteristics that we refer to as a compensating differential (essentially, workers are being compensated for taking on jobs with negative non-monetary characteristics, through higher wages).

The meaningfulness of a job is a non-monetary characteristic. If a job is meaningful, then more people will want to do the job (holding other job characteristics constant), and wages will consequently be lower. Another way of thinking about it is that employers of workers who offer meaningful jobs don't have to compete hard to attract workers, and so they don't have to offer as high a wage to fill the job. Either way, meaningful jobs pay less because of compensating differentials.

Book review: The Courage to Teach

Many years ago, after I won my first staff teaching award at the University of Waikato, the excellent Dorothy Spiller at our Teaching Development Unit (as it was known at the time) strongly recommended that I read the book The Courage to Teach, by Parker J. Palmer. Finally, more than ten years and several additional teaching awards later, I have gotten around to reading it.

I can now see why Dorothy recommended the book to me. However, I am glad that I didn't read it sooner, as it is unclear whether I would have gotten much out of it then, and I'm probably still not ready or open enough to get a lot out of it now. Or maybe the book just isn't speaking to me in the way that it seems to do to other readers. Anyway, let me go back to the beginning.

This is not simply a book about teaching. It doesn't contain a magic formula to turn average teaching into excellent teaching. This is a book about thinking about teaching. As Palmer puts it early on:

...good teaching cannot be reduced to technique; good teaching comes from the identity and integrity of the teacher.

The irony, perhaps, is that the implication is that good teaching itself cannot be taught. So, rather than attempting to tell people how to teach, this book invites readers to ask the question of who they are as a teacher, and how they can bring that authenticity to their teaching, in a way that connects students to the subject matter. The assertion that technique matters less than enthusiasm for the subject made me realise some things about my own teaching and the roots of its success.

However, what I feel was all the good material, the parts that I felt connected to and could recognise myself in, came within the first couple of chapters of the book. Having noted early on that the book is not about technique, Palmer then veers off course and argues strongly against what he refers to as 'objectivism' in teaching. I found that a little confusing, because Palmer is not referring to the philosophical objectivism of the Ayn Rand variety, but rather an ontological objectivism that assumes that there are objective 'truths' that teachers can teach and students can learn. I suspect that most academics would refer to that ontology as positivism, not objectivism. Given that a belief in positivism (to use the more commonly adopted terminology) lends itself to the adoption of particular teaching techniques, and Palmer clearly has no time for positivism , then the book really does privilege some teaching techniques over others. Unfortunately, I am clearly sited on the other side of the ontological divide from Palmer. And so I found the rest of the book interesting, but neither as stimulating nor rewarding to read as others might.

The last section of the book is devoted to a consideration of movements for social change in education. The book was originally written in 1997, and I was reading the 10th anniversary edition (written with additional foreword and afterword from 2007). Palmer could see learning institutions had lost their focus on teaching and learning, and increasingly focused on standardised testing (at K-12 level) or research outputs (at tertiary level). This last section is a call to action, a rallying cry for teachers to stand up and push back against the forces that seek to reduce recognition of the value of excellent teaching. In the afterword, Palmer notes some modest successes in the ten years since the release of the original book. However, I wonder whether he would feel that those successes have been sustained. My experience is that research has even more primacy now than it did when I started as an academic, and the focus on international rankings has doubled down on this due to the weighting that rankings place on excellent research.

Overall, this book is probably worth reading if you agree with Palmer that positivism is contrary to your teaching philosophy. For others like myself, there's got to be something better.

A hidden cost to watch out for in the proposed health system reform

The New Zealand government has proposed a massive reform of the health system. Part of the goal is to simplify the health system and make it easier to manage (see the government's white paper on health reform). One consequence will be job losses in back office functions. The New Zealand Herald reported earlier in the week:

Health Minister Andrew Little today confirmed jobs would go in the proposed district health board shake-up...

He highlighted two non-medical roles as an example of jobs likely to disappear when the health boards are abolished by June 30 next year.

"When you're chunking down 20 DHBs into one single national organisation you don't need 20 chief IT managers, you don't need 20 chief HR managers, so there will be some roles and positions that will go," Little told The AM Show...

The biggest change as a result of the reforms is the scrapping of the 20 District Health Boards (DHBs), to be replaced by a single organisation, Health NZ. So, along with job losses there is likely to be some cost savings in terms of IT provision, human resources management, governance, and so on. And that should mean more of the health budget could be spent on core health services and increasing equity (which is another goal of the reforms).

But not so fast. We currently have 20 DHBs, with 20 IT systems, 20 HR systems, 20 patient management and hospital services scheduling systems. Merging those different systems together is not trivial, as we learned when the eight councils (seven district or city councils, and the Auckland Regional Council) merged to form a single Auckland Council. The cost of replacing the IT systems alone more than doubled to $140 million compared with the original estimates. And that was only merging eight entities together, not 20.

This is a case of 'watch this space'. We haven't heard anything as yet about the expected costs of the health reforms, and in particular the costs of merging systems. There may be some cost savings to be had in back office functions, but it is likely they will be overwhelmed by the substantial transitional costs. And the government has a long history of underestimating how substantial transitional costs can be.

To be clear, the existence of transitional costs, even substantial costs, is not a case against reform. If it were, then we would constantly be stuck with expensive mistakes of past governments. However, ignoring or underestimating those costs does us no good, in terms of understanding the overall costs and benefits of the reforms.

This is how the market responds to shortages of agricultural labour

The New Zealand Herald reported on Monday:

An Australian recruiter hopes the transtasman travel bubble will help fill huge shortages of labour on Australian farms.

In November the Australian Government began offering $2000 for New Zealanders to relocate to help with the shortage of horticulture and agriculture workers...

Like New Zealand, Australia is experiencing a huge shortfall in staff in the agriculture and horticulture sectors.

Incentives to attract workers such as free accommodation, food, increased pay rates and even cash bonuses are being offered.

The Queensland Strawberry Growers Association has offered cash prizes of up to $100,000 to entice workers to get involved in its winter harvest.

Think about the labour market for agricultural workers in Australia. If there is a shortage of workers, that means that the market wage must be below the equilibrium wage, as shown in the diagram below. The quantity of labour demanded (QD) exceeds the quantity of labour supplied (QS) at the market wage W1. There are more jobs available than there are workers to fill them. So, what do employers do? If they are willing and able to pay a higher wage, they could find themselves a willing employee, and offer to pay slightly more than W1, to ensure that they get that worker to work for them. So, employers will bid the wage up, until eventually the market reaches equilibrium at W0, where the quantity of labour demanded and quantity of labour supplied are both equal to Q0.

However, sometimes it isn't the wage that adjusts. Sometimes, the employer instead offers some other inducement to ensure that they aren't the employer who is short of workers. That's where the other incentives (free accommodation, food, cash bonuses, etc.) come in. If enough employers offer these additional incentives, that may encourage additional workers to enter the market, increasing the supply of workers. This is shown in the diagram below. The supply of labour has increased because of an influx of Kiwi agricultural workers, shifting the supply curve to the right at S1. Now the market is operating at equilibrium, with the wage at W1, and the quantity of labour supplied (and demanded) is equal to QD.

Of course, that would then have flow-on effects on the labour market for agricultural workers in New Zealand, where the supply will reduce and the shortage of agricultural workers will increase. That's going to make business very difficult for New Zealand farmers. So, it will be interesting to see if farmers in New Zealand are willing to match the incentives being offered in Australia, to prevent New Zealand-based workers from departing.

How should health benefits be aggregated?

In my ECONS102 class, in the health economics topic, we cover the value of a statistical life (increasingly now referred to as the value of a preventable fatality). The value of a statistical life (VSL) is a statistical construct that can be used in cost-benefit evaluations as a measure of the benefits of saving lives. So, for example, if government is considering building road safety improvements on a stretch of highway, the VSL can be used as a measure of the benefits of the improvements, which can then be weighed up against the costs of those improvements.

The VSL comes in for criticism though, mainly on moral or ideological grounds (often along the lines of 'you cannot place value on a human life'). However, the alternatives of ignoring the benefits of lives saved, or assuming those lives saved have infinite value, clearly lead to implausible implications for decision-making. For example, if government truly believed that lives had infinite value, they should immediately ban all vehicular travel, because regardless of the costs that such a ban would impose on society, those costs would be outweighed by the infinite benefits from lives saved due to fewer fatal motor vehicle accidents.

A similar critique was raised in this article from The Conversation yesterday by Ilan Noy (Victoria University of Wellington), in the context of measuring the burden of disasters:

But “value of life” prices can vary a lot between and even within countries. There is also an understandable public distaste for putting a price tag on human life. Governments typically don’t openly discuss these calculations, making it difficult to assess their legitimacy.

Noy they suggests: 

An alternative is a “life years lost index”. It is based on the World Health Organization (WHO) measure of “disability-adjusted life years” (DALY), calculated for a long list of diseases and published in a yearly account of the associated human costs.

In conventional measurements of the impact of disaster risk, the unit used is dollars. For this alternative index, the unit of measurement is “lost life years” — the loss of the equivalent of one year of full health.

Both the VSL and the life years lost index can be used to measure the health burden of disasters. They are both ways that can be used to aggregate the health benefits of disaster prevention or mitigation, or the health costs of disasters.

You might believe that avoiding placing a value on lives lost, as the life years lost index does, is an improvement over the VSL. However, there is an implicit assumption that you are making when doing so, that you probably don't even realise that you are making, and which you would probably find just as distasteful as placing a value on human lives. The assumption is that every lost life year has the same value. That's a necessary assumption in order to add up the lost life years from different people.

That assumption doesn't seem so bad, right? But let's think about the implications of making that assumption. If a young person dies aged five years, when they had a life expectancy of 85 years, then that represents 80 lost life years. So, that young person's death would add 80 to the 'life years lost index'. If an older person dies aged 85 years, when they had a life expectancy of 90 years, then that represents 5 lost life years. So, that older person's death would add 5 to the 'life years lost index'. In other words, the young person's life is worth 16 times the older person's life, when you calculate the 'life years lost index'.

Moreover, if government was making decisions on the basis of the 'life years lost index', they would be well justified to devote excess resources to saving young people and protecting their health. After all, saving a young person leads to a significantly greater improvement in the index than does saving an older person. If you extend the index to considering disability, then any treatment that reduces disability or improves health among young people is similarly going to be preferred over the same treatment being offered to older people. Are you still feeling good about not valuing lives saved?

To be fair, the value of statistical life has exactly the opposite problem. It values all lives equally, so a life saved near the end of life is valued the same as a life saved near its beginning. That doesn't seem so bad on the surface, until you think about what that means for the value of a life year. Each year of additional life added for the older person I used in the example above would be valued at 16 times each year of additional life added for the young person. So, a government would be well justified to divert resources to treatments for older people that extend their lives, rather than the same treatments for younger people.

Aggregating health benefits is a difficult problem. These issues (and several related issues) are well covered in Kip Viscusi's excellent book Pricing Lives (which I reviewed here). There isn't a perfect solution to this dilemma, and economics cannot answer the question of which option - pricing lives saved (as the value of a statistical life does), or life years (as the life years lost index does) - is better. It is quite reasonable to hold an opinion either way, and quite unreasonable to impose your opinion on others. Critiques of VSL like the one that Noy uses do little to help people understand the issues, or to make an informed judgement for themselves.

Regressive regional fuel taxes

A few weeks ago, my ECONS102 class covered taxes. One aspect of that topic is consideration of whether income taxes are progressive or regressive. A progressive tax is one where higher income people pay a higher proportion of their income in tax than lower income people, while a regressive income tax is one where higher income people pay a lower proportion of their income in tax than lower income people. Note that whether income tax is progressive or regressive isn't determined by whether higher income people pay more tax or not. That happens under all but the most regressive tax systems. This is about the proportion of their income that higher income and lower income people pay.

Anyway, the same descriptors (progressive or regressive) not only apply to income taxes, but can also be applied to other taxes (as well as to subsidies, property rates, surcharges, and basically any sort of government fee). If lower income people will end up paying a higher proportion of their income in the tax (or other fee) than higher income people do, then the tax (or fee) is regressive.

To illustrate this point in class, I usually talk about cigarette taxes. However, they are far from the only example. Take this example from the New Zealand Herald this morning:

Efeso Collins raised eyebrows in 2018 when he opposed Auckland's regional fuel tax, designed to not only fund low and zero-carbon public transport but slash emissions.

After all, the Manukau ward councillor is a strong believer in taking action to address climate change.

But his opposition was rooted in arguments that have long divided climate action debates, about equity.

For people living in central Auckland, on high incomes, in walking or biking distance of their jobs, close to fast and frequent public transport, the fuel tax impact was minimal, and for many had the desired impact of a reduction in driving.

But for many in Collins' ward, on lower incomes with large families and poor public and active transport choices, many Māori and Pasifika, there was little choice but to continue driving.

The tax made up a much larger share of their income, essentially subsidising projects miles away like the City Rail Link in the central city.

The regional fuel tax in Auckland is an excise tax on fuel, set at ten cents per litre of fuel. People who spend a higher proportion of their income on fuel, will also spend a higher proportion of their income on the regional fuel tax. If lower income people spend a higher proportion of their income on fuel than higher income people do, then the regional fuel tax is regressive.

Collins essentially argues that this is the case because lower income people live further from central Auckland, therefore travel further and spend more on transport. It's fair to make the assertion that the fuel tax is regressive, but I'd want to see that backed up by some evidence. Maybe lower income people drive less (they are lower income after all, and driving is already expensive), and make more use of public transport. If we want to really know whether the regional fuel tax is regressive or not, it pays to look at some data.

Statistics New Zealand collects data on household expenditures, and it is reported in two places: NZ.Stat and Shinyapps. Unfortunately, neither of these sources quite do what we want, which is to disaggregate spending on fuel by income group. However, the detailed data on Shinyapps suggests that a little more than 70 percent of spending in the category "Private transport supplies and services" is petrol and other fuels and lubricants. That is one of the categories that NZ.Stat uses in disaggregating spending by income group, so we'll use it as a proxy for spending on fuel. [*]

In 2019 (the latest Household Expenditure Survey data), the top income group (Decile 10; incomes above $199,400 per year) spent $116.70 per week on "Private transport supplies and services". Using the bottom of the income bin ($199,400), that spending works out to about 3.0 percent of their annual income.

Moving down the income distribution to the middle, the fifth income group (Decile 5; incomes between $60,800 and $77,199) spent $72.50 per week on "Private transport supplies and services". Using the middle of that income bin ($69,000), that spending works out to about 5.5 percent of their annual income.

Moving further down to the bottom of the income distribution, the bottom income group (Decile 1; incomes below $23,700 per year) spent $38.60 per week. Using the top of that income bin ($23,700), that spending works out to about 8.5 percent of their annual income.

It is pretty clear that lower income households spend a higher proportion of their income on "Private transport supplies and services". It therefore seems highly likely that they spend a higher proportion of their income on fuel, and so they also spend a higher proportion of their income on the regional fuel tax. The regional fuel tax is regressive.

*****

[*] Spending on "Private transport supplies and services" is not a perfect proxy for spending on fuel. As a category, in addition to petrol, and fuels and other lubricants, it includes vehicle parts and accessories, vehicle servicing and repairs, and "other private transport services". Those other spending categories make up around 30 percent of the spending on "Private transport supplies and services". However, provided the share of this category that is spent on fuel doesn't differ too much by income, then we should be ok. So, we're essentially assuming that, for all income groups, about 70 percent of their spending in the "Private transport supplies and services" category is on petrol, fuel and other lubricants.

Reduced exports due to border restrictions and the domestic market for strawberries

Last week, my ECONS102 class covered international trade, including the effects of trade restrictions on economic welfare. Usually, the examples I use involve the government interfering in the market, through the use of quotas or tariffs, and those trade policies invariably lead to a loss of economic welfare (a deadweight loss). However, sometimes other things get in the way of international trade, such as this recent example from HortNews:

Strawberry prices fell 43% in November 2020 as Covid-19 border restrictions reduced exports, Stats NZ said.

Consumer prices manager Katrina Dewbery says that fewer exports have meant there is more supply available for domestic consumption.

Prices averaged $3.45/250g punnet in November, down from $6.04 in October.

“Prices are lower than we typically see for a November month with December generally being when they are cheapest. Some people may be seeing even cheaper prices during the first half of December,” Dewbery said.

There was no government intervention here, but a lack of capacity to export strawberries due to the COVID-19 border restrictions reduced the quantity that could be exported. We could interpret that as being similar to an export quota on strawberries (where the quantity of exports was restricted to less than it would have been with open borders), so let's look at the effect on the market for strawberries.

First, consider the case without any border restrictions. This is shown in the diagram below. New Zealand is an exporting country, which means that New Zealand has a comparative advantage producing strawberries. That means that New Zealand can produce strawberries at a lower opportunity cost than other countries. On a supply-and-demand diagram like the one below, it means that the domestic market equilibrium price of strawberries (PD) would be below the price of strawberries on the world market (PW). Because the domestic price is lower than the world price, if New Zealand is open to trade there are opportunities for traders to buy strawberries 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 strawberries. The domestic consumers would end up having to pay the price PW for strawberries 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 strawberries, while the domestic suppliers sell Qs0 strawberries (assuming that the world market could absorb any quantity of strawberries that was produced). The difference (Qs0 - Qd0) is the quantity of strawberries that is exported. Essentially the demand curve with exports follows the red line in the diagram.

In terms of economic welfare, if there was no international trade in strawberries, the market would operate at the domestic equilibrium, with price PD and quantity Q0. Consumer surplus (the gains to domestic strawberry consumers) would be the area AEPD, the producer surplus (the gains to domestic strawberry producers) would be the area PDEF, and total welfare (the sum of consumer surplus and producer surplus, or the gains to society overall) would be the area AEF. With trade, the consumer surplus decreases to ABPW, the producer surplus increases to PWCF, and total welfare increases to ABCF. Since total welfare is larger (by the area BCE), this represents the gains from trade.

Now consider what would happen if the quantity of strawberry exports was restricted below (Qs0 - Qd0). This is shown in the diagram below as an export quota. Let's say that the quantity of exports is reduced to the amount between B and G on the diagram (about half the amount of unrestricted exports). Now consider what happens to the demand curve (including exports). The upper part represents the domestic consumers with high willingness-to-pay for strawberries. Then there is a limited quantity of exports that can get through the border restrictions, at the world price PW. After that, there are still profit opportunities for domestic suppliers (that is, there are still some domestic consumers who are willing to pay more than what it costs the suppliers to produce strawberries). So, the demand curve (including the export quota) pivots at the point G, and follows a parallel path to the original demand curve (i.e. the demand curve including exports follows the red line in the diagram). The domestic price is the price where supply is equal to demand (P1). The domestic consumers choose to purchase Qd1 strawberries at the price P1, while the domestic suppliers sell Qs1 strawberries at that price. The difference (Qs1 - Qd1) is the quantity of exports. Notice that the price of strawberries that consumers pay has fallen, just as the article linked above noted.

Now consider the areas of economic welfare. The consumer surplus is larger than it was without the restricted exports (it is now the area AJP1), the producer surplus is smaller than it was without the restricted exports (it is now the area P1HF plus the area KLHJ. The first area (P1HF) is producer surplus as if the farmers sold all of their products to the domestic market, while the second area (KLHJ) is the extra profits the farmers get from selling the limited amount of exports that are able to get through the border restrictions. Total welfare is smaller than without the restricted exports (it is now the area AJHF+KLHJ). There is a deadweight loss (a loss of total welfare arising from the restricted exports) equal to the area [BKJ + LCH] - these areas were part of total welfare with trade and no restricted exports, but have now been lost.

The lost exports make strawberry farmers worse off, as well as society overall (in terms of economic welfare in total). However, strawberry consumers are the unwitting recipients of a gain. The interesting thing here is that the government is not responsible for the deadweight loss - this is a deadweight loss caused by a more general disruption in international trade. And it was not just strawberries that were affected - domestic consumers will have been made better off in all exported commodities that cannot be stored for long periods of time.

Robert Mundell, 1932-2021

Robert Mundell, the 1999 Nobel Prize winner, passed away early this week. His main contributions to macroeconomics came from work he did while at the International Monetary Fund in the 1950s and 1960s, but they continued to have an influence long after. All undergraduate and graduate macroeconomics students in New Zealand will have come across the Mundell-Fleming model, which is important for understanding the relationship between exchange rates, interest rates, and output in a small open economy (like New Zealand). He is also credited as being a father of the Euro, and a key contributor to the 1980s economics movement known as 'supply-side economics' as his New York Times obituary notes:

He is known as the “father of the euro,” for his work that encouraged many European nations to give up their currencies to join a larger monetary union. And he provided intellectual grounding for lowering the top tax rates on the rich, whose advocates rallied under the banner of supply-side economics and won over many right-leaning politicians and policymakers in the United States, Britain and elsewhere while drawing the scorn of more progressive economists, who disputed the notion that cutting taxes for the wealthy was the best way to spur economic growth.

I hadn't realised his link to supply-side economics. As Mundell was a macroeconomist, my teaching and research doesn't really draw too much on his work. However, last week in my ECONS102 class we covered economic integration as part of the topic on international trade and globalisation. I guess there's a chance we wouldn't have the example of the Eurozone to use, if he and his students hadn't advocated so strongly in favour of it (or if politicians hadn't listened).

The Economist has a good obituary, as does the Washington Post. The story of him singing the Frank Sinatra song "My Way" after collecting his Nobel Prize is excellent, and as the obituaries note, is a good summary of his life and work. He will be missed.

[HT: Marginal Revolution]

Which famous (American) economist are you most similar to?

Marginal Revolution pointed me to this new tool by Chris Said: Which Famous Economist Are You Most Similar To? The tool asks you for your opinions on a number of different policies and the state of the art in economic theory and evidence. The questions are the same that have recently been asked of the Initiative on Global Markets panel of experts. Then based on your responses, it tells you which economist (from the panel) you are most similar to. You get an output that looks like this:

Yes, that's mine. I'm most similar in opinions to Markus Brunnermeier (Princeton University) according to the tool.. But eyeballing the graph, my dot looks far closer to the dot just up and to the right of mine, belonging to Richard Schmalensee (MIT).

The only downside of the tool is that you need to be reasonably familiar with US policy debates to get the most out of it. Aside from that caveat, try it out for youself!

[HT: Marginal Revolution]