This week in research #20

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

• Ciancio et al. (open access) study the impact of a randomised information intervention on risky sex in Malawi, and find that treated individuals are less likely to engage in risky sexual practices one year after the intervention (not dissimilar to this research)
• Litina and Fernández show that societies more exposed to solar eclipses grew more, and that solar eclipses are associated with deeper and more intricate thinking among peoples in societies more exposed, and that total solar eclipses increase curiosity both at the social and individual levels

Finally, in very exciting news, the first issue of Australasian Journal of Regional Studies (AJRS) with myself as Managing Editor has just been published (although it is backdated to December 2023, due to some unexpected delays in getting the articles online). This issue has four papers, as well as my editorial:

• Cochrane, Poot, and Roskruge (open access) look at the uptake of social security benefits in New Zealand territorial authorities after the Global Financial Crisis and after the COVID-19 pandemic, and show that the most resilient territorial authorities had a low unemployment rate and a large public sector prior to the shock (this paper won the John Dickinson Memorial Award for the best paper published in AJRS in 2023)
• Ho (open access) looks at migration and rural-urban wage differentials in Australia, and finds that high wage growth in the year following rural-urban migration is most likely explained by the migrant taking jobs that below their ability upon arrival
• Vidyattama, Yudhistira, and Husna (open access) explore descriptively the impact of COVID-19 on the economies of provinces in Eastern Indonesia, and how the provincial governments responded
• Parkin and Hardcastle (open access) look at population trends and policy in South Australia's Limestone Coast region, especially in the wake of the COVID-19 pandemic

Jonathan Haidt and Candice Odgers debate the relationship between social media and mental health

Does social media worsen mental health for young people, especially young women? It has become an article of faith for many that it does. And there is bountiful anecdotal and research evidence that supports the view. Take, for example, the furore that erupted back in 2021 around Frances Haugen's leaking of internal Facebook research showing the negative impacts of Instagram on young women.

I've written on this topic several times before (most recently here, but see the list of links at the bottom of this post as well). My take is that much of the research on social media and mental health, or social media and subjective wellbeing, shows correlation, but not causation. The challenge here is that perhaps people with mental health issues (or people with lower wellbeing) are more likely to use online social networks, in which case there is reverse causality (the causality runs from mental health to social media, not from social media to mental health).

So, I was interested to read this recent article in Nature by Candice Odgers, reviewing the new Jonathan Haidt book The Anxious Generation (which I have yet to read, but it is currently on my Amazon Wish List). Odgers really takes Haidt to task, claiming that all that Haidt is demonstrating is correlation, not causation:

The plots presented throughout this book will be useful in teaching my students the fundamentals of causal inference, and how to avoid making up stories by simply looking at trend lines.

Hundreds of researchers, myself included, have searched for the kind of large effects suggested by Haidt. Our efforts have produced a mix of no, small and mixed associations. Most data are correlative. When associations over time are found, they suggest not that social-media use predicts or causes depression, but that young people who already have mental-health problems use such platforms more often or in different ways from their healthy peers...

Odgers then suggests some alternative explanations:

There are, unfortunately, no simple answers. The onset and development of mental disorders, such as anxiety and depression, are driven by a complex set of genetic and environmental factors. Suicide rates among people in most age groups have been increasing steadily for the past 20 years in the United States. Researchers cite access to guns, exposure to violence, structural discrimination and racism, sexism and sexual abuse, the opioid epidemic, economic hardship and social isolation as leading contributors...

The current generation of adolescents was raised in the aftermath of the great recession of 2008. Haidt suggests that the resulting deprivation cannot be a factor, because unemployment has gone down. But analyses of the differential impacts of economic shocks have shown that families in the bottom 20% of the income distribution continue to experience harm... 

Haidt responded, initially on X, but then in thorough detail in this post on After Babel. He starts by pointing to the range of published evidence:

Zach Rausch, Jean Twenge, and I began to collect all the studies we could find in 2019, and we organized them by type: correlational, longitudinal, and experimental. We put all of our work online in Google Docs that are open to other researchers for comment and critique. You can find all of our “collaborative review” documents at AnxiousGeneration.com/reviews

The main document that collects studies on social media is here:
Social Media and Mental Health: A Collaborative Review

Then notes that:

In that document, we list dozens of correlational and longitudinal studies...

In that document, we also list 22 experimental studies, 16 of which found significant evidence of harm (or of benefits from getting off of social media for long enough to get past withdrawal symptoms)...

In that document, we also list nine quasi-experiments or natural experiments (as when high-speed internet arrives in different parts of a country at different times), eight of which found evidence of harm to mental health, especially for girls and women...

I am not saying that academic debates are settled by counting up the number of studies on each side, but bringing so many studies together in one place gives us an overview of the available evidence, and that overview supports three points about problems with the skeptics’ arguments.

First, if the skeptics were right and the null hypothesis were true (i.e., social media does not cause harm to teen mental health), then the published studies would just reflect random noise... and Type I errors (believing something that is false). In that case, we’d see experimental studies producing a wide range of findings, including many that showed benefits to mental health from using social media (or that showed harm to those who go off of social media for a few weeks). Yet there are hardly any such experimental findings. Most experiments find evidence of negative effects; some find no evidence of such effects, and very few show benefits. Also, if the null hypothesis were true, then we’d find some studies where the effects were larger for boys and some that found larger effects for girls. Yet that’s not what we find. When a sex difference is reported, it almost always shows more harm to girls and women. There is a clear and consistent signal running through the experimental studies (as well as the correlational studies), a signal that is not consistent with the null hypothesis.

Haidt supports this with a further footnote:

Yes, there could be a “file drawer problem” if researchers on one side are systematically discouraged from publishing, so the missing “positive” studies are all sitting in file drawers in researchers’ offices. But because findings of benefits would be unusual and newsworthy, I don’t believe that there is a strong or consistent bias against the skeptics. 

However, simply asserting that there is no file drawer problem is not the same as showing that there isn't. That's where meta-analysis comes in. Haidt could easily conduct a meta-analysis with these studies to demonstrate what the overall effect is, and whether there is evidence of publication bias. In fact, he even cites some meta-analyses that have already been conducted (such as this one, which found "mildly significant" publication bias in one of two tests of bias, with the other being statistically insignificant).

Haidt then goes on to address Odgers' suggested alternative explanations, focusing on her assertion that the Global Financial Crisis explains the sudden change in adolescent mental health. Haidt concludes that:

Odgers has pointed to an alternative causal explanation that A) does not fit the timing in the U.S., B) does not fit the social class data in the U.S., and C) does not fit the international scope of the crisis.

Having satisfied himself that he has rebutted Odgers' critique, Haidt then reiterates some solutions from the book:

In contrast, if leaders and change makers were to embrace my account of the “great rewiring of childhood,” in which the phone-based childhood replaced the play-based childhood, what policy implications follow? That we should roll back the phone-based childhood, especially in elementary school and middle school because of the vital importance of protecting kids during early puberty. More specifically, we’d try to implement these four norms as widely as possible: 

1. No smartphones before high school (as a norm, not a law; parents can just give younger kids flip phones, basic phones, or phone watches).
2. No social media before 16 (as a norm, but one that would be much more effective if supported by laws such as the proposed update to COPPA, the Kids Online Safety Act, state-level age-appropriate design codes, and new social media bills like the bipartisan Protecting Kids on Social Media Act, or like the state level bills passed in Utah last year and in Florida last month).
3. Phone-free schools (use phone lockers or Yondr pouches for the whole school day, so that students can pay attention to their teachers and to each other)
4. More independence, free play, and responsibility in the real world.

Note that these four reforms, taken together, cost almost nothing, have strong bipartisan support, and can be implemented all right now, this year, if we agree to act collectively.

Even if Haidt is wrong about the causal relationship here, I agree that these reforms are relatively low-cost, and the precautionary principle suggests that they might be appropriate. However, I have argued previously that we should be cautious about regulation that allows parents discretion over their children's social media use. Odgers even partially agrees at the end of her review:

Many of Haidt’s solutions for parents, adolescents, educators and big technology firms are reasonable, including stricter content-moderation policies and requiring companies to take user age into account when designing platforms and algorithms. Others, such as age-based restrictions and bans on mobile devices, are unlikely to be effective in practice — or worse, could backfire given what we know about adolescent behaviour.

It will be interesting to see how this debate progresses. Odgers clearly needs to step things up, because Haidt was very well-prepared for her critique, and had clearly anticipated the points that she (and other skeptics) would raise. I look forward to reading the book after I place my next book order.

[HT: Marginal Revolution for the Odgers article, and Haidt's initial response on X]

Read more:

• Quitting Facebook might make you happier
• The more you use Facebook, the worse you feel
• Does the Internet make people happier?
• What happens when you disconnect from Facebook?
• The latest NZ research on social media and mental health is nothing special
• Online social networks, social capital, and wellbeing

The impacts of home care vs. day care of young children

The second half of the 20th Century involved some massive social change in Western countries. One of those changes was the rapid increase in female labour force participation, including an increase in labour force participation among mothers of young children. As mothers have increasingly gone to work, fathers have in the main not compensating by decreasing their work time. So, childcare has become increasingly important over time. On top of that, there is a lot of tension (and judgment) associated with the decision by mothers to return to work or not.

A useful question to consider, then, is what is the impact of mothers working on their child's outcomes, compared to the mother staying out of work to care for the child. That is essentially the question addressed in this 2023 NBER Working Paper (ungated version here) by Jonathan Gruber (MIT), Tuomas Kosonen (VATT Institute for Economic Research), and Kristiina Huttunen (Aalto University School of Economics). They look at the case of Finland, where they look at the effects of:

...the Finnish Home Care Allowance program (HCA). This program provides substantial payments to mothers who stay home with their children from age ten months through 3 years old, rather than placing the children in formal child care, which is almost exclusively publicly-financed and of relatively high-quality in international comparison. The HCA program has a long tradition in Finland. It was introduced in 1985 and more than 80% of mothers in Finland utilize the HCA. As a result, the share of children in formal child care is much lower in Finland than in other Nordic countries...

Gruber et al. exploit variations in the value of HCA across municipalities, because each municipality could provide a supplement to the HCA, and many have done. The value of these supplements changes over time, and that is the variation that is key to assessing the impact on mothers and children. Essentially, they look at how differences in the amount of HCA assistance affect mothers' work, and children's outcomes, using linked data from the Finnish population register, birth registry, tax and benefits records, early childhood data from clinical assessments of children's readiness for school, and education and youth crime records for when the children were older. The early childhood data are interesting:

The individual tests we consider for four years olds (from 2010 onwards) are Cross (needing to draw a cross, where the two lines intersect), Ask (the child is able to ask following types of questions: when and where?), Details (the child is able to explain details from a specific picture), and Colors (the child is able to identify three out of four main colors from a color card). The tests for five years old (prior to 2010) are Circle (the child can cut a circle from a paper with scissors), Square (the child is able to draw a square on paper), Human (the child can draw human that has at least head, body and limbs come out of body, not from head), and Instruct (the child is able to follow three-part instructions).

Gruber et al. apply a difference-in-differences approach with a continuous treatment variable (the amount of HCA assistance received), which essentially estimates how a 100-Euro change in the HCA supplement amount affects outcomes between the years before and after the supplement changes. The results in terms of mothers' employment are summarised in Figure 1 in the paper:

Notice the big drop in employment that occurs at Time 0, which is when there is a 100-Euro increase in the HCA supplement. However, eyeballing the figure, while the point estimates are negative, it looks like they are not statistically significant (at the 5 percent level). When they move to a more standard difference-in-differences (rather than a dynamic DID approach), the results are statistically significant. Nevertheless, for this analysis Gruber et al. note that:

Maternal labor supply then falls by about 1.5% for each 100 euro increase in the homecare allowance and remains at that level in the municipalities that increased their supplement amount. So supplements are clearly reducing maternal work in favor of at home care, and the effect corresponds to about 5 percent reduction when compared with mean share of employment of mothers of one-year-old children.

Gruber et al. also show that the HCA supplement leads to a decrease in maternal labor income, but an increase in total income." Interestingly, they go on to show that their:

...estimates are large enough to explain the entire difference between the Finnish and (for example) Danish levels of short run child penalties of 20%.

Turning to children's outcomes, they find that:

...children become more likely to fail the cognition test at age four or five when their parents were eligible for higher HCA supplements at child’s age 1...

After an increase in supplement when one year old, enrolling to academic high school declines and committing a youth crime increases.

Needless to say, these are all bad outcomes for children. Gruber et al. then move onto a standard DID approach in order to better quantify the effects, and find that:

...the impact of receiving a 100 euros per month supplement when the eligible child is one year old is to reduce the employment of mothers by -1.27 percentage points, which is a roughly 5% decline in the odds of working... the impact on annual labor earnings is –194 Euros. Given the increase in HCA of 273 Euros... this suggests an almost three-quarters “crowdout” of the income benefits of HCA; that is, for every dollar of HCA received, mothers offset 72 cents through lower labor earnings... the effect of supplement on all income including earnings and taxable income transfers (including HCA and supplements). The effect on this outcome is 237 Euros.

So, mothers work less, earn less labour income, but receive higher total income as a result of the HCA supplement. In terms of child outcomes though:

...a 100 euro per month increase in the supplement leads to a statistically significant 1.78 percentage point increase in the odds of failing a test; the effect size represents about 7% increase from the baseline failing rate...

...higher HCA in form of supplements when the child is one year old leads to -.6 percentage point decline in the odds of enrollment in an academic high school, which is about 1 percent of the sample mean...

...each 100 euros per month of supplement leads to a rise in youth criminal sentencing of .22 percentage points, off a mean of 4 percentage points, a roughly 6% effect...

All of those effects are statistically significant. And they all point to the homecare supplement having negative impacts on children's short-term and long-term outcomes. Perhaps that is uniquely due to the homecare supplement? Gruber et al. go on to investigate a daycare reform in 1997 that unified daycare fees across the country. As a result, some families ended up paying higher daycare fees, while others paid lower fees. A 100-Euro increase in daycare fees should have a similar (but opposite direction) effect to a 100-Euro increase in the HCA supplement. Indeed, Gruber et al. find an effect of the daycare fee change that is of a similar magnitude, but in the opposite direction, to the HCA supplement results.

That they find similar effects based on changes in HCA supplement and daycare fees should provide some confidence in the results. However, we should treat them with a little bit of caution for at least a couple of reasons. First, as I noted in this post, the 'two-way fixed effects' approach that they have adopted has recently attracted a lot of criticism (which is nicely outlined in two posts on the Development Impact blog, here and here, as well as this post). The short version is that the two-way fixed effects approach is likely to lead to biased estimates of the treatment effect. Gruber et al. do try a few ways of dealing with this, and the results are robust to the approaches they adopt, but given that their dynamic DID results are statistically insignificant, this still leaves me concerned. Second, Finland is somewhat unique in the pervasiveness of homecare. As they note in the paper, 80 percent of mothers make use of the homecare allowance, which is a high take-up rate. It's not clear that similar effects would be observed in other countries.

This external validity problem is the biggest issue for me. Mothers are essentially choosing between homecare, where they look after the children and teach them the basics required to prepare them for school (with whatever resources and support they have available to them), or they send the children to a daycare service, which employs professionally-trained early childhood educators to perform the task. There are pros and cons either way, but in terms of school readiness, the daycare may have the edge. On top of that, Finland has a high-quality, publicly funded daycare system, which further tilts the balance in favour of daycare. In countries where the daycare system is of lower quality, the negative impacts on child outcomes are likely to be smaller, or perhaps absent entirely.

Gruber et al. conclude that:

...there may be limits to general international lessons from such policy analyses, and that conclusions are best drawn on a country-by-country basis.

Given the issues of external validity I've noted above, I'd say that further research in other countries is imperative, before we definitively conclude that homecare of children is bad for them.

[HT: Marginal Revolution, last year]

The gender of a doctor matters for medical evaulations

There is lots of evidence that there is gender bias in healthcare. This Medical News Today article summarises some examples and consequences. It seems plausible that at least some of the gender bias in healthcare arises when male doctors examine or treat female patients. A useful question to ask, then, is what would happen to bias if patients were examined by same-gender doctors?

That is essentially the research question underlying this recent article by Marika Cabral (University of Texas at Austin) and Marcus Dillender (Vanderbilt University), published in the journal American Economic Review (ungated earlier version here). Cabral and Dillender first outline the problem, being that:

...female patients, relative to male patients, receive less health care for similar medical conditions and are more likely to be told by providers that their symptoms are emotionally driven rather than arising from a physical impairment... Differences in doctors’ evaluations of medical issues for male and female patients may be a key factor contributing to observed differences in treatment. Beyond influencing the treatments patients receive, medical evaluations also impact benefit eligibility in social insurance programs. Recent evidence suggests there are large gender disparities in social insurance programs that rely on medical evaluations...

Cabral and Dillender make use of:

...comprehensive administrative data and random assignment of doctors to patients within the Texas workers’ compensation insurance system. Random assignment of doctors to patients occurs in this setting through the dispute resolution process. Insurers and injured workers may request independent medical evaluations to settle disputes over an injured worker’s impairment level... The random assignment of doctors to patients means that differences in assessments between male and female doctors stem from the doctors themselves rather than from differences in the types of patients assigned to doctors.

That last point is important. It is the random assignment of patients to doctors that means that the results from this study can be interpreted as causal evidence of the effect of doctor gender on patients' outcomes, and evaluate the difference in those outcomes between male and female patients. Essentially, this is a form of difference-in-differences analysis, looking at the difference in outcomes between male and female patients with a male doctor, and comparing that with the difference in outcomes between male and female patients with a female doctor.

The outcomes that Cabral and Dillender look at are whether the patient is evaluated as having a disability, and the amount of cash disability benefits they receive after the evaluation. Having controlled for patient characteristics such as the type of injury and the industry that the patient worked in, there should be no differences between male and female patients in either disability assessment or disability benefits, depending on whether they have a male or female doctor. Instead, Cabral and Dillender find that:

...patient-doctor gender match increases evaluated disability and subsequent cash disability benefits for female patients but has little impact on outcomes of male patients... Compared to differences among their male patient counterparts, female patients randomly assigned a female doctor rather than a male doctor are 3.1 percentage points more likely to be evaluated as having an ongoing disability and receive 8.6 percent more cash benefits on average, or $483 evaluated at the mean of $5,622. There is no analogous gender-match effect for male patients. We note the magnitude of these effects is sizable. The estimated 3.1 percentage point increase in the likelihood of being evaluated as disabled is nearly large enough to offset the entire observed gender gap in this outcome when male doctors evaluate claimants.

Cabral and Dillender then turn to explaining why this gender bias exists, and find that:

Controlling for available baseline patient information, the estimates indicate that female doctors evaluate female and male patients as similarly disabled while male doctors evaluate female patients as less disabled than male patients. While only suggestive, this evidence is consistent with male doctors evaluating female patients against a stricter standard than male patients and female doctors applying similar standards to male and female patients.

On that last point though, as Cabral and Dillender note in one of the footnotes in the paper, these results alone can't distinguish between whether it is male doctors who evaluate female patients to a higher standard, or female doctors who evaluate male patients to a lower standard. However, Cabral and Dillender report a range of survey evidence from a sample of over 1500 people that is consistent with the former, including:

...that women—relative to men—more often report having a negative experience where a doctor didn’t understand their concerns, had assumed something without asking, talked down to them, made them feel uncomfortable, or didn’t believe them. When asked about how a doctor’s gender influences the likelihood of having a positive interaction, women were much more likely than men to report an own-gender doctor would be more likely to treat them with respect, understand their concerns, believe them, provide needed testing and treatments, make them feel comfortable, and ask appropriate questions instead of making assumptions.

Cabral and Dillender also report on the intensity of preferences over doctor gender, showing that:

...48.5 percent of women are willing to pay an additional $5 copay to see an own-gender provider compared to only 29.3 percent of men—a 19.2 percentage point difference.

It would have been interesting if they had extended that analysis to an estimate of the female patients' average willingness-to-pay for having a female (rather than a male) doctor, but they didn't. Finally, Cabral and Dillender looked at the policy implications, noting that based on their results:

...increasing the share of independent medical evaluations performed by female doctors from 17 percent to 50 percent would cause a 0.88 percentage point increase in the share of female patients evaluated as disabled, closing approximately 41 percent of the gender gap conditional on observables among disputed claims.

Given that still less than half of medical school graduates in the US are female, there is a long way to go before we get to that point. For comparison, in New Zealand in 2019, over 58 percent of medical school graduates were female. I guess that is good news for New Zealand, in terms of reducing the gender bias in medical evaluations here.

This week in research #19

Here's what caught my eye in research over the past week (a really quiet week):

• Strozza et al. (open access) find that COVID-19 mortality disproportionately affected those of lower socioeconomic status and exacerbated existing social inequalities in Denmark, using Danish population register data
• Nguyen finds a positive association between temperature and women's exposure to intimate partner violence across 34 developing countries, and that women from rural areas, those from poor households, those having low education, and those living with low-educated partners are particularly vulnerable to intimate partner violence as temperatures increase

The recorded video of my Professorial Lecture, titled Beyond the Buzz: The Sobering Economics of Alcohol, is now available on YouTube. Watch it here if you weren't able to attend in person. Like and subscribe, or whatever.

When analogies fail... Marine Protected Areas edition

In The Conversation last week, Mark John Costello (Nord University) wrote an article explaining the economic benefits of Marine Protected Areas (MPAs). MPAs are areas where fishing is prohibited. The article is interesting, and in short it makes the case that MPAs have unrecognised (or under-recognised) economic benefits, in terms of positive spillover effects on fishing and tourism.

However, this bit struck me as really odd:

Although it may seem counterintuitive that a full restriction of fishing in an area will result in more fish elsewhere, this happens because MPAs act like a reservoir to replenish adjacent fisheries.

In financial terms, the capital is invested and people benefit from the interest on the investment. To count the establishment of an MPA as a cost to fisheries is like claiming that interest earned on money is a cost.

I had to read that second paragraph a couple of times, because it didn't make sense to me. And it didn't make sense to me because it doesn't work as an analogy. Let me labour that point by unpacking the analogy.

First, think about a financial investment. The return on the financial capital that the investor employs is the interest that they receive. The less financial capital they invest, the less interest they will receive. Now think about a fishery. The return on the natural capital is the value of the fish that the fishermen take from the fishery. The less natural capital in the fishery, the less fish the fishermen can take.

Now think about a marine protected area. The MPA sets aside some of the fishery (some of the natural capital). Lower natural capital means that the fishermen will be able to take less fish. The fishermen will receive a lower return from their fishing. Now go back to the financial investment. The equivalent of an MPA for the financial investment is setting aside some of the financial capital. Lower financial capital means that the investor will receive less interest. The investor will receive a lower return from their investing.

The establishment of a MPA really is a cost to fisheries. Fishermen can take less fish. Unlike Costello's claim, this is nothing at all like "claiming that interest earned on money is a cost". It is more like claiming that 'losing interest that you would have otherwise earned if the financial capital hadn't been set aside and paying no interest is a cost'. Which it is. It is what economists refer to as an opportunity cost. Costello's analogy fails.

The rest of the article is interesting and does raise some important and valid points. There may be spillover benefits to fishermen outside of MPAs because the MPA can act as a nursery for young fish. Focusing solely on the fish that were not taken in the MPA area would overstate the cost to fishermen, if they are able to take more fish from outside of the MPA. Tourist operators also benefit from the MPA, because tourists like to look at wildlife, including fish. The case that MPAs have significant benefits is strong. It's a pity that the analogy that was employed to make part of the case was much weaker.

Loss leading with free puncture repairs

Driving to work this morning, I saw an advertisement on the back of a bus for free puncture repairs from Top Town Wheel and Tyre in Te Rapa. Why would a tyre retailer offer to fix punctures for free? As I note in my ECONS101 class, when we see an interesting pricing strategy in the real world, it is likely that it is a strategy that is working for the firm.

In this case, the free puncture repair offer is an example of loss leading, which I discussed with my ECONS101 class a couple of weeks ago. Loss leading happens when a firm sells some of their goods or services intentionally at a loss, in order to encourage more customers to visit them, with the goal of getting those customers to buy other goods and services that the firm can profit from. Offering free puncture repairs, which costs the retailer some staff time and some consumables, will make a loss.

What is the tyre retailer hoping to profit from? Once a customer arrives at Top Town with their punctured tyre looking for a repair, Top Town can easily up-sell the customer to a replacement tyre (which is not free) if the puncture cannot be repaired. That is probably the case fairly often (in my experience, more than half the time when I go to get a puncture repaired, the tyre has been damaged beyond repair). Top Town then profits from the replacement tyre, which they wouldn't have sold if the customer hadn't been encouraged (by the free puncture repair offer) to go to Top Town in the first place.

There is also a soft form of customer lock-in at work here too. Having discovered that their puncture cannot be repaired, the customer could go to a different tyre retailer to get a replacement tyre. However, that involves some additional hassle, time, and effort. Why go somewhere else, when they are already at a tyre retailer? In other words, there is a switching cost here - the additional time and effort required to find and travel to a different tyre retailer represents the cost of switching to an alternative seller. That switching cost, however minor, may lock many customers into buying their replacement tyre from Top Town, rather than going somewhere else. By doing so, they avoid the switching cost.

So, offering free puncture repairs is a smart pricing strategy for Top Town, which likely increases their profits. The surprising thing may be that every tyre retailer doesn't do the same.

Book review: Economists at War

Economists have a key role in advising the government about fiscal and monetary policy. At no time is this more important than during times of crisis. And no crisis is quite like a war. So, I was really interested to read Alan Bollard's 2020 book Economists at War, which the subtitle promises to explain: "how a handful of economists helped win and lose the world wars".

The book essentially covers seven economists: Takahashi Korekiyo, H.H. Kung, Hjalmar Schacht, John Maynard Keynes, Leonid Kantorovich, Wassily Leontief, and John von Neumann, with one chapter devoted primarily to each of them. Bollard is clear that the book is not a biographical account of their lives, and neither is it an economic history of the world wars. Instead, the book focuses on:

...a description of the complex and sometimes terrible positions these economists found themselves in, and how they used their economics and their personalities to address this.

I enjoyed this book, mostly because of the gaps in my own knowledge of the history of economics that it helped to fill. I had never heard of Korekiyo, Kung, or Schacht, and had read little about Kantorovich or Leontief, although I am of course quite familiar with their key contributions to economics, for which Kantorovich and Leontief each won the Nobel Prize in the 1970s. The book appears to be well-researched and thorough in its treatment of each of its subjects, and Bollard shares a lot about their lives outside of economics and on either side of the world wars in which they were key figures. So, in that sense, the book is perhaps more biographical than Bollard may have intended.

As you may expect from a collection of the key figures in economics at the time, there were a large number of connections between them. Many of them met in person, or taught or studied at the same institutions (albeit at different times). However, that leads to the main detraction from this book for me. Because each chapter is devoted to one of these economists, there is a fair amount of repetition across the chapters. There is even, in a few places, repetition within chapters. I felt like those repetitive parts could have been edited out without great loss, and would have made the book flow a little better.

I learned a great deal from reading the book. It gave me a new appreciation of the Japanese economy in the inter-war period, and the rampant corruption in the Chinese government of Chiang Kai-shek, Also, I hadn't appreciated the challenges that Russian economists faced during the premiership of Josef Stalin:

Among his many paranoias, Stalin distrusted economists and particularly mathematical ones. He had said the planned economy of the USSR was 'dizzy with success', and therefore any criticism would be seen as being anti-Soviet. Instead he called for what he labelled 'political economy', which should provide ideological support for party policies. Mathematical economics could be seen as removing the need for ideology and supporting self-regulating market mechanisms, which was a very dangerous direction.

It strikes me that there may be an interesting parallel with modern times, with governments having a preference for ideology over economics. Anyway, that is a story for another time. The key point is that Kantorovich in particular faced some real difficulties in Soviet Russia, given that he was a mathematical economist.

Overall, I rate this book highly, but in avoiding being biographical and simultaneously avoiding being an economic history of the world wars, it sits somewhat awkwardly in the middle. I've read better books on how economics (and operations research) was applied during World War II (Blackett's War, which I reviewed here). And there are better biographies of Keynes and von Neumann - Keynes is well covered in The Worldly Philosophers (which I reviewed here), while Prisoner's Dilemma (which I reviewed here) is a good book on von Neumann. This book does collect good stories of the life and economics of some of the more obscure (in my view) but still important economists of the time, so is worthwhile in that respect, for people interested in the world's economic heritage.

This week in research #18

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

• Carro and Gallardo (open access) use a reduction in school class size in Spain driven by the pandemic to evaluate the effect of class sizes on student performance, and find a positive and significant effect of the class size reduction of 0.11 standard deviations on student performance
• Tsaneva and LaPlante (open access) examine the impact of district-level crime rates in South Africa on individual depression symptoms using panel data, and find an increase of one standard deviation in property (violent) crime is associated with a 7.2 (8.7) percentage point increase in the probability of depression symptoms
• Lamonaca, Bozzola, and Santeramo show, using a gravity model, that climate differences are associated with bilateral trade flows (add climate distance to the types of distance that matter for trade, beyond geographical distance and cultural distance)

Public service targets and Goodhart's Law

A couple of people have asked me this week what I think about the government's new public sector targets (see New Zealand Herald stories here and here, or the DPMC page (with associated fact sheers) here. It's fair to say that I have mixed views about these targets.

On the one hand, having a target gives something meaningful for the public sector to aim for. That's better than an aimless exercise in reproducing the status quo. That is essentially the government's argument, and it is somewhat persuasive.

On the other hand, I am mindful of Goodhart's Law, which essentially says that once you change the rules, players will change the way they play the game [*]. I prefer the version of the Law expressed by the anthropologist Marilyn Strathern (in this 1997 article (gated)), which is: "when a measure becomes a target, it ceases to be a good measure".

Looking at the public sector targets that have been proposed, there is a fair amount of scope for the players to change how the game is played, to ensure their success in meeting the targets (or, at least, looking like they are making meaningful progress towards the targets). Take the example of shorter stays in emergency departments. The target is: "95% of patients to be admitted, discharged, or transferred from an emergency department within six hours". Hospitals could make meaningful progress on this target by actually admitting, discharging, or transferring patients more quickly. However, that is hard work, and probably requires more resources, which they may not have. It is much easier to simply not record patients at the time they arrive at the emergency department (by having some sort of informal queue or waiting list, before their visit is recorded and the six-hour clock starts), or having the patients sit in ambulances (which is so common that there is even a term for it - 'ramping').

Or what about the aim of having fewer people on the Jobseeker Support Benefit. The target is: "50,000 fewer people on Jobseeker Support Benefit". That could be achieved by finding jobs for a net 50,000 people on the benefit. Again, that is hard work for the Ministry of Social Development, and probably requires more resources, which they may not have. It is much easier to simply reduce the number of beneficiaries in other ways, such as by making them more onerous to apply for in the first place, and imposing additional requirements on beneficiaries so that more of them are penalised by losing their benefits (and yes, that has already been announced).

You could tell similar stories about many of the other targets that have been announced. On the one hand, these are areas where society should genuinely seek improvement. Who would argue against having shorter waiting times at emergency departments, or having fewer people out of work? However, the unintended consequence of these targets may be that we get improved on what is actually measured (the number of patients being dealt with within six hours, or the number of people on the Jobseeker Support Benefit), but no meaningful change on what we actually want (hospital waiting times, or the number of people out of work). 

None of this is to say that there shouldn't be targets at all, only that we should be cautious about interpreting success based on a single target that can be subject to manipulation. There is a case to be made that we need some secondary measures that allow us to ensure that the public sector is not gaming the primary measures. So, perhaps we measure the number of patients turned away from hospitals, ambulance utilisation, or patient satisfaction with hospitals, and benefit cancellations, 'discouraged workers', and the number of people outside the labour market (or the employment rate). There is no need to attach targets to those secondary measures, but monitoring them might help people to interpret how the public sector is achieving gains in the primary measures. [**]

Without the government keeping a closer eye on a range of secondary measures, in addition to the primary measures that are being targeted, you can see why I have mixed views about these targets.

*****

[*] What Charles Goodhart actually wrote, in this 1975 book chapter (gated) is: "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes".

[**] As an aside, what we definitely don't need is multiple primary measures, such as the nine measures of child poverty that are used, thereby allowing the government to cherry-pick the measure that has the most positive interpretation on a given day.

When you offer free rubbish disposal, you'll get more rubbish

The Rotorua Daily Post reported today:

Kāinga Ora has been forced to pick up the clean-up tab after “mountains of rubbish” were dumped on Rotorua’s Wrigley Rd following what was supposed to be a friendly day to bring the street’s community together.

More than 40 tyres, whiteware, lawnmowers, old mattresses, drawers, old bikes, bags of household rubbish and dirty nappies were dumped on Fordlands’ Wrigley Rd, mainly over the weekend, after people who don’t live on the street “abused” a clean-up day organised by Kāinga Ora that offered free skip bins...

One resident, who didn’t want her name published for fear of repercussions, told the Rotorua Daily Post it was initially an awesome day as residents were pitching in to help each other carry heavy items, including old fridges and mattresses...

She said word then quickly spread, including on social media, that Wrigley Rd was the place to go to dump rubbish free of charge.

She said on Friday she was concerned when more people arrived in cars with trailers and started to dump rubbish.

None of this should be particularly surprising. When the cost of doing something (in this case, rubbish disposal) reduces, people will tend to more of that thing. When the cost is lowered to zero, people may do a lot more of that thing. When the residents of Wrigley Road were offered free rubbish disposal, we shouldn't be surprised that the Wrigley Road residents disposed of lots of extra rubbish.

However, this came with an unintended consequence. Because there was no way of easily policing who was disposing of rubbish, other enterprising individuals took the opportunity to dispose of their rubbish in Wrigley Road as well. As I note in my ECONS102 class, this might have come as a shock to the Kāinga Ora staff, because they didn't think through the incentives that free rubbish disposal creates. That's because it's easy to envision how people who think like you do will react to an incentive plan, but not everyone thinks like you do. In fact, people are often far smarter at taking advantage of incentives than we give them credit for.

So, it should not surprise us at all that offering free rubbish disposal resulted in a lot of rubbish being dumped.

Shortages in national telehealth services arise from low pay

It was a bit disheartening to read this article in the New Zealand Herald this morning:

The national telehealth service is struggling to recruit enough qualified clinical staff to operate 24/7 phone lines for triaging people with mental health problems, according to employees and union representatives.

They say the understaffing at Whakarongorau Aotearoa’s specialist mental health team, which provides triage lines for many of Health NZ’s public mental health services, as well as supporting police and ambulance services by handling some 111 calls, is causing distressed callers to wait longer and putting enormous strain on its workforce...

According to an internal document, Whakarongorau’s EMHR unit has a budget for 29 full-time clinicians but has “significant gaps” in its rosters because of staff turnover, sick leave, and recruitment challenges. In a recent four-week period, more than half the shifts were understaffed.

“We are expecting it to become even more difficult in the coming weeks and months until we can recruit more clinicians,” the document said. Hiring more qualified staff was challenging because of national workforce shortages and because Whakarongorau pays less than Health NZ.

That last sentence is really the driver of this situation. There is a shortage of workers for the mental health triage lines because they simply don't pay enough. Consider the market for mental health clinicians working in call centres (or similar), as shown in the diagram below. The market wage for these workers is W0, which is below the equilibrium wage of W1. At the market wage, the quantity of clinician hours demanded is QD, but the quantity of clinician hours supplies is only QS. The difference between QS and QD is the shortage of clinician hours.

If wages were more competitive with those offered by Health NZ, then more clinicians would agree to work in these services. In other words, if the wage were allowed to rise, that would increase the number of clinician hours supplied. The market would move up the supply curve. If wages increased to the equilibrium wage W1, then the quantity of clinician hours supplied would increase to Q1, which would then match the quantity of clinician hours demanded (which would decrease to Q1, moving up along the demand curve [*]). Since the quantity of clinician hours demanded would be equal to the quantity of clinician hours supplied, there would no longer be a shortage.

So, in order to resolve the shortage of clinicians here, the workers need to be paid more.

*****

[*] I've opted to show a downward-sloping demand curve here, which suggests that, as clinician wages increase, the quantity of hours demanded would decrease. Arguably, the number of clinician hours demanded doesn't depend on wages, in which case the demand curve should be vertical (perfectly inelastic). That wouldn't materially change any of the remaining points though, so I've gone with a conventional downward-sloping demand curve.

Daniel Kahneman, 1934-2024

I was saddened to hear that Daniel Kahneman passed away last week. Kahneman was a psychologist, but had a profound effect on economics, winning the Nobel Prize in 2002 "for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty".

Kahneman's early work on decision-making, much of it with Amos Tversky, laid many of the foundations for what we now know as behavioural economics. In fact, such was the importance of his work that I replicate many of the key experiments early in my ECONS102 class each year. More recently, Kahneman made a number of contributions to our understanding of subjective wellbeing, or life satisfaction (see here, for example). He was clearly research active, and exploring new areas, right up until the end.

There are a number of good obituaries that summarise Kahneman's work, including at the New York Times, Washington Post, Bloomberg, and the Financial Times. Daniel Read (University of Warwick) also wrote a great article on The Conversation, reflecting on interactions with Kahneman since Read was a student. I also highly recommend Michael Lewis' book The Undoing Project (which I reviewed here), which tries to capture the unique story of Kahneman and Tversky's friendship and work together.

Behavioural economics is still not embedded within the mainstream of economic theory, teaching, and research. It is clearly still a work in progress. When (or perhaps if) economic theory develops in such a way that the behavioural insights are captured within a core model of decision-making and behaviour, we will have Kahneman to thank for pointing the way. He will be missed. 

Customer lock-in for frozen embryos

This week, my ECONS101 class covered pricing and business strategy, and one aspect of that is customer lock-in. Customer lock-in occurs when customers find it difficult (costly) to change once they have started purchasing a particular good or service. The seller can then profit by increasing the price for their locked-in customers, or by selling them complementary goods or services.

There are lots of examples of customer lock-in (once you know what to look for). Any time a customer finds it difficult (or costly) to switch, then they are to some extent locked in. Think of mobile phone contracts, where there is a termination fee that constitutes a switching cost. However, the switching cost need not be monetary. Most people don't switch often between Apple and Windows computers, or between Apple and Android phones, or between GMail and Outlook, because of the cost of learning the new system, transferring files and photos and contacts, etc.

As another example, consider this article (possibly paywalled) from the Washington Post last month:

Allison Puca, 41, a project manager in Bethesda, Md., started her journey to become a single mother by choice in 2019 after dreaming of being a mother her whole life.

She underwent four intrauterine insemination attempts using sperm from two donors.

After doing one in vitro fertilization cycle during the coronavirus pandemic, Puca was able to get four embryos. She now has a 16-month-old daughter.

She spent about $10,000 on donor sperm from a total of four donors.

After her fertility treatments, Puca says she was spending $50 per month to keep one vial of donor sperm frozen and another $60 per month to keep her three remaining embryos frozen. “My rates were just going up and up,” she said.

Puca is debating whether to give her daughter a sibling, and is not considering destroying or donating her embryos. But in March 2023, she decided to discard a vial of sperm she spent $1,200 on.

There was a $15 online notary fee to discard the sperm, Puca said. “It was just like salt to a wound, in a sense. I had paid so much already. It felt like nonsense,” she said.

She lamented opaque and inconsistent pricing around storage costs. “You just feel chained,” she said. “They have your genetics, and they can just throw them away if you don’t pay. It’s like you don’t have control.”

Once a prospective parent has frozen embryos (or eggs, or sperm) at a facility, they must keep paying the monthly storage costs in order to keep them viable. They can't simply pick them up and store them at home. They are locked in, for as long as they hope to become a future parent. There are several other examples in the article.

This is likely to be a highly profitable situation for the storage facility to be in. Their customers could (in theory) transfer their frozen embryos to a competing facility, but that is both costly and risky. So, once stored in one location, they will tend to be kept there. The facility then has a locked-in customer paying storage fees.

It would be interesting to know if storage facilities offer some form of inducement to attract new customers. For example, they could offer the first three months of storage for free. That would be an example of multi-period pricing - setting the price initially low, then profiting later from the locked-in customer by having a higher regular price. That wouldn't surprise me at all, but it isn't mentioned in the article (and a casual Google search didn't turn up anything like that). However, most storage facilities are attached to IVF clinics, and that industry is quite profitable so perhaps there isn't a need to try and extract additional profits by attracting storage customers from other facilities. At the margin though, it might be something we would expect to see.

In any case, locked-in customers provide a key source of additional profits, and this is another example.

[HT: Marginal Revolution]

This week in research #17

Here's what caught my eye in research over the past week (another quiet week):

• Ghorbani and Meltzer test whether local retail services are considered a nuisance or an amenity in New York City, and find that retail services that are more frequently consumed and experiential, and that are located in relatively more mixed-use neighbourhoods are positively capitalised into property values
• Alkusari, Datta Gupta, and Etcoff (open access) find that more attractive female junior researchers are significantly more likely to hold an academic job five years post-PhD
• Adda and Ottaviani (with ungated earlier version here) show that grading research proposals on a curve inefficiently discourages even the very best candidates from applying when evaluation is perfect, so there are gains to ensuring that evaluation is in some way imperfect (seems quite counterintuitive)

Why the price elasticity of demand is not constant along a straight-line demand curve

This week my ECONS101 class covered elasticities. The most important elasticity that we cover in that topic is the price elasticity of demand. The price elasticity of demand can be calculated as [Percentage change in quantity demanded]/[Percentage change in price], or in shorthand, [%ΔQd]/[%ΔP]. Because price and quantity demanded always move in opposite directions (because of the Law of Demand - when price goes up, people buy less, and when price goes down, people buy more), the price elasticity of demand is always a negative number.

One important aspect of the price elasticity of demand is the determination of whether demand is elastic or inelastic. Demand is elastic if the percentage change in quantity demanded is greater than the percentage change in price. In other words, the price elasticity of demand is greater than one (in absolute terms). Demand is inelastic if the percentage change in quantity demanded is less than the percentage change in price. In other words, the price elasticity of demand is less than one (in absolute terms).

Now, here's where things get a little tricky. A straight-line demand curve that is relatively more elastic will be flatter than a demand curve that is relatively less elastic. That seems pretty straightforward, but the word 'relatively' is important. That's because the price elasticity of demand is not constant for a demand curve that is a straight line. Although the slope of the curve does not change as we move along the demand curve, the price elasticity of demand does.

To see why, consider the straight-line demand curve shown in the diagram below. At the top of the demand curve, such as the point A, the quantity demanded is low and the price is high. As we move along the demand curve a little bit towards the point B, the percentage change in quantity demanded is going to be large (because it's a percentage of a small number), but the percentage change in price will be small (because it's a percentage of a large number). That means that the price elasticity of demand, [%ΔQd]/[%ΔP], will be a large number (because we're dividing a large number by a small number). So, at the top of the demand curve, demand is elastic.

Now, at the bottom of the demand curve, such as the point C, the quantity demanded is high and the price is low. As we move along the demand curve a little bit towards the point D, the percentage change in quantity demanded is going to be small (because it's a percentage of a large number), but the percentage change in price will be large (because it's a percentage of a small number). That means that the price elasticity of demand, [%ΔQd]/[%ΔP], will be a small number (because we're dividing a large number by a small number). So, at the top of the demand curve, demand is inelastic.

Now consider the same example, but with some numbers, as shown in the diagram below. As we move from point A to point B (at the top of the demand curve), the percentage change in quantity demanded is 100%, as we move from a quantity demanded of 1 to a quantity demanded of 2 (we can calculate this percentage change as [[2-1]/1]-1). [*] The percentage change in price is -10%, as we move from a price of $10 to a price of $9 (we can calculate this percentage change as [[9-10]/10]-1). So, the price elasticity of demand when we move from point A to point B is [100]/[-10] = -10. This is larger than one (in absolute terms), so demand when we move from point A to point B is elastic.

As we move from point C to point D (at the bottom of the demand curve), the percentage change in quantity demanded is 11.1%, as we move from a quantity demanded of 9 to a quantity demanded of 10 (we can calculate this percentage change as [[10-9]/9]-1). The percentage change in price is -50%, as we move from a price of $2 to a price of $1 (we can calculate this percentage change as [[1-2]/2]-1). So, the price elasticity of demand when we move from point C to point D is [11.1]/[-50] = -0.22. This is smaller than one (in absolute terms), so demand when we move from point C to point D is inelastic.

So, there you have it. Although the demand curve may be a straight line, and so the slope doesn't change, the price elasticity of demand does change. At the top of the demand curve, demand is elastic, while at the bottom of the demand curve, demand is inelastic. As we move down the demand curve, demand becomes progressively less elastic. And as we move up the demand curve, demand becomes progressively more elastic. That means that there is a point where the demand curve transitions from being elastic to inelastic. That point occurs exactly halfway along the straight-line demand curve. At that point, the price elasticity of demand will be exactly equal to -1, which we refer to as unitary elastic.

*****

[*] For simplicity, I'm not using the midpoint method for calculating the price elasticity of demand here. We'd get qualitatively very similar results if we did so.

Disney adopts a combination of menu pricing and block pricing for Disney+

My ECONS101 class covered price discrimination this past week. Menu pricing (or second-degree price discrimination) occurs when consumers are offered different options (that the firm knows appeal to consumers with different price elasticities of demand), and consumers select their preferred option. Specifically, the firm will offer a lower price to consumers who are more price-sensitive (those with a higher price elasticity of demand), and a higher price to consumers who are less price-sensitive (those with a lower price elasticity of demand).

So, it was interesting to read this story in the New Zealand Herald last week:

Disney+ has become the latest in a procession of streaming services to hike its rate - though those willing to live with fewer features can stick with the old pricing.

Disney+ currently costs $14.99 per month.

From members’ next billing period, the price will increase by 27 per cent to $18.99 as the service is renamed Disney+ Premium - while the pricing for those who choose to pay annually also increases by 27 per cent from $149.99 to $189.99.

But there will also be a new Disney+ Standard option, which will stay at $14.99 (or $149.99 annually) - but support for two screens at once (compared to the Premium plan’s four) and standard high definition (the Premium plan offers 4K or ultra high definition).

This is an example of Disney using menu pricing. The Disney+ Standard option has a lower price, and will appeal to more price-sensitive consumers, while Disney+ Premium will appeal to consumers who are less price-sensitive.

Interestingly, the annual subscription price also offers an example of block pricing, which I will be covering in class this week. Block pricing occurs when the firm charges a declining price on subsequent blocks of product. In this case, the monthly price for Disney+ Premium is $18.99. However, those who pay for the full year pay just $189.99. In effect, after the first ten months of the year, the last two months are free (for those paying the annual fee). In other words, the first block of ten months cost $18.99, and the second block of two months costs nothing. It is a similar story for Disney+ Standard ($14.99 for the first ten months, and then free for the last two months).

Block pricing tends to work best when demand is homogeneous (as I noted in this post). One way that firms like Disney can get homogeneous demand is to first use price discrimination to separate consumers into relatively homogeneous groups. So, the shift to menu pricing (offering Disney+ Standard and Disney+ Premium) will likely make the block pricing strategy even more effective (and more profitable) for Disney.

This week in research #16

Here's what caught my eye in research over the past week (a quiet week, as preparing for my Professorial lecture took up far too much of my attention):

• Wright and Nguyen (open access) have a Treasury Analytical Note on the effect of taxes and benefits on household incomes in New Zealand in 2018/19 which, among many other results, shows how much the benefit and transfer system reduces inequality
• Kirchmaier, Langella, and Manning (open access) find that crime tends to happen close to the offender’s residence because criminals face a high cost of commuting
• Granato et al. (open access) look at the impact of the Erasmus study abroad programme in Europe, and find a positive and significant impact on the final graduation marks of undergraduate students, although somewhat weirdly this effect is larger if the student studies abroad at a university that is lower quality than their home university (more evidence on the benefits of study abroad, to sit alongside this)

Higher inflation is modestly associated with higher income inequality

There is a fairly large literature looking at the relationship between inflation and income inequality. Some studies find that there is a positive correlation (more inflation is associated with more income inequality). Some studies find the opposite, a negative correlation (more inflation is associated with less income inequality). Still other studies find no relationship at all between (or, at least, no statistically significant relationship). So, what are we to make of this literature?

To the rescue comes this recent article by Andreas Sintos (University of Luxembourg), published in the journal Economic Systems (sorry, I don't see an ungated version online). Sintos presents a meta-analysis of 124 journal articles, containing 1767 estimates of the relationship between inflation and income inequality. Sintos distinguishes between two strands of the literature: (1) looks at how the level of inflation affects the level of income inequality (in other words, the variables are measured in levels); and (2) looks at how changes in inflation affect changes in income inequality (in other words, the variables are measured in differences). The difference is important. In my view, measuring the relationship in levels doesn't make a lot of sense. If you find that the relationship is positive, then that implies that, since inflation is generally positive, income inequality should be ever-increasing. That seems somewhat inconsistent with reality. In contrast, it seems to me that when the level of inflation changes, that might change inequality.

Anyway, Sintos finds that:

...once the correction for publication bias is made, we find that, on average, inflation has a (small-to-moderate) inequality increasing effect for both level and difference estimates...

In other words, inflation increases inequality (to the extent that we can attribute causality to these results - more on that later in this post). The bias-corrected average effect size ranges between 0.051 and 0.120 (which are interpreted as small and moderate effect sizes respectively). Sintos then goes on to investigate the study-level factors that are associated with the estimated relationship. For the result in differences (which I find more theoretically plausible):

...we find that ten regressors matter significantly for the underlying effect of inflation on income inequality in the primary studies... the BMA [Bayesian Model Averaging] results for difference estimates reveal a decisive effect for eight regressors: GDP deflator, Panel data, Time span, Log transformation, GDP growth, Financial development, Publication year, and Citations. Moreover, we find a strong effect for Trade openness and a weak effect for Education.

Specifically, studies that cover a longer time span, use log-transformed variables, and those that control for GDP growth, financial development, and trade openness find a more positive effect of inflation on inequality, as well as those studies that have attracted more citations. Studies that use the GDP deflator (rather than the change in the Consumer Price Index) as a measure of inflation, use panel data, and those that were published most recently, find a more negative effect (or a smaller positive effect) of inflation on inequality. A couple of things jump out from that. First, the fact that more recent studies, which we would expect to use more sophisticated methods and better-quality data, find smaller effects, should lead us to believe that the 'true' effect is somewhat smaller (less positive) than what Sintos finds on average. However, when Sintos goes on to simulate the effect that would be obtained from the theoretical 'best study', they find that:

The associated prediction, which represents the model average across the models estimated using BMA, is 0.275, with a standard error of 0.115 (95% CI 0.051–0.500), for level estimates, and 0.540, with a standard error of 0.236 (95% CI 0.077–1.002), for difference estimates.

This is somewhat larger than the bias-corrected average effects reported in the paper. That makes me wonder whether the assumption that studies are improving in quality over time actually holds. Could it be that more lower-quality studies, or perhaps studies with lower-quality data, are increasingly being published? We don't have a direct answer to that question, but the correlation matrix reported in Figure 2 in the paper suggests that more recent publications are less likely to use OLS regression, and more likely to control for the variables that have important effects (as noted above). So, I remain somewhat at a loss to explain why the 'best study' estimates are larger than the bias-corrected average effects.

Second, the fact that studies that report more positive results have attracted more citations should be a bit of a concern. The literature had a diversity of results, and while the bias-corrected average effect is positive, that in itself shouldn't lead researchers to cite papers with positive effects more than those with the opposite, or with null effects. There is clearly a bit of cherry picking going on in terms of what results are cited in the literature.

Finally, the results don't establish causality definitively. Many of the studies deal with endogeneity problems, but not all of the studies do. So, while we can tell a plausible causal story here, we can't be sure about it. Nevertheless, this paper is another model of reporting meta-analytic results, the second such paper that I've read this year (see here for my post about the other paper). Given the importance of meta-analysis for estimating the average effect across a literature as a whole, the trend towards clearer exposition and interpretation of the results of meta-analyses is very welcome.

What we can take away from this paper is that higher inflation is modestly associated with higher income inequality. Given the sheer number of things that appear to be correlated with inequality, it would be expecting too much for inflation to have a large effect. But nevertheless, when we consider income inequality, inflation (or change in the inflation rate) appears to be an important consideration.

In The Three Body Problem trilogy, Wallfacer Rey Diaz needed to better understand game theory

Regular readers of this blog may have noticed that I haven't posted a book review in a while. That's because I've been reading Cixin Liu's Three Body Problem trilogy (technically, the Remembrance of Earth's Past trilogy, an adaptation of which has just been released on Netflix as The Three Body Problem). I'm currently reading the second book, The Dark Forest.

Warning: Spoiler alert!

To give you some context, in the first book of the trilogy, Earth made contact with an alien civilisation, the Trisolarans. The Trisolaran fleet is currently on its way to Earth, in order to conquer us. Their homeworld is about to be destroyed, and their only hope of survival is to take over another planet. The fleet will take some 400 years to arrive, so Earth has some time to prepare. However, the Trisolarans have advanced technology, including deploying sophons, which are able to prevent Earth from conducting basic research in physics and other areas. So, Earth is stuck in a low-technology state, awaiting the arrival of the Trisolaran fleet. Even worse, the sophons can watch anything that happens on Earth and relay the information back to the Trisolarans, so Earth's preparations will be known to the Trisolaran fleet. To combat this, in the second book, Earth appoints four 'Wallfacers', who are given access to almost unlimited resources to execute plans that are known only to themselves, hidden from the rest of the Earth's population (and to the sophons, because the sophons can't read minds).

The second book of the trilogy is devoted to the Wallfacers and their plans (admittedly, I haven't finished reading it yet). I want to focus on the plans of Wallfacer Rey Diaz, whose plan involved planting large solar hydrogen bombs on Mercury, which when detonated would set off a chain reaction, destroying most of the solar system, including Earth. Diaz's plan was to negotiate with the Trisolaran fleet, warning them that if they didn't divert, Earth would be destroyed, sealing the fates of both the human and Trisolaran populations.

However, Wallfacer Rey Diaz's strategy is flawed. He needs to understand some basic game theory. To see why, consider the game shown in the payoff table below. The two players are Earth and the Trisolaran fleet (we'll assume that Diaz would choose strategy on behalf of Earth). Earth's two strategies are to blow up Mercury (detonate) or not. The Trisolaran fleet's two strategies are to continue to Earth, or divert. If Earth blows up Mercury, then Earth becomes extinct, regardless of what the Trisolaran fleet does. If Earth doesn't detonate Mercury, then Earth loses if the Trisolaran fleet continues, and wins if the Trisolaran fleet diverts. If the Trisolaran fleet diverts, they become extinct. If they continue to Earth, they become extinct if Earth blows up Mercury, but win if Earth does not.

To find the Nash equilibrium in this game, we use the 'best response method'. To do this, we track: for each player, for each strategy, what is the best response of the other player. Where both players are selecting a best response, they are doing the best they can, given the choice of the other player (this is the definition of Nash equilibrium). In this game, the best responses are:

1. If the Trisolaran fleet continues to Earth, Earth's best response is to not detonate (since losing is a better payoff than extinction [*]) [we track the best responses with ticks, and not-best-responses with crosses; Note: I'm also tracking which payoffs I am comparing with numbers corresponding to the numbers in this list];
2. If the Trisolaran fleet diverts, Earth's best response is to not detonate (since winning is a better payoff than extinction);
3. If Earth chooses to detonate, the Trisolaran fleet's best response is either option (since both payoffs are the same - extinction - both are best responses); and
4. If Earth chooses not to detonate, the Trisolaran fleet's best response is to continue to Earth (since winning is a better payoff than extinction).

Note that Earth's best response is always to choose not to detonate. This is their dominant strategy. A player would always choose to play their dominant strategy, because choosing the other strategy makes them unambiguously worse off. And the Trisolarans would know this. This is what Wallfacer Rey Diaz gets wrong in his strategy. Earth won't blow Mercury up, and the Trisolarans know this, so there is no leverage for Earth in the negotiations.

The Trisolaran fleet has a weakly dominant strategy. Notice that continuing to Earth is always the Trisolaran fleet's best response. However, diverting is a best response if Earth chooses to detonate. So, continuing to Earth is not always better for the Trisolaran fleet, but it is never worse than the other strategy.

The single Nash equilibrium occurs where both players are playing a best response (where there are two ticks), which is where all Earth chooses not to detonate, and the Trisolaran fleet continues to Earth. It is little wonder then, that when Rey Diaz's strategy was revealed, the Earth governments were not happy. Not only was his strategy imperilling the Earth to the same extent as the Trisolarans, it was a strategy that simple game theory shows would not have succeeded.

*****

[*] You may wonder what the difference between losing and extinction is. Earth could lose, but some humans remain alive as slaves, or otherwise escape the planet before the Trisolarans arrive. It's not a great outcome, but better than extinction.

This week in research #15

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

• May, McGarvey, and Toshmatova (open access) identify gender differences in US graduate students' views on the professional climate in economics, focusing on stress and work/life balance, disciplinary climate in the profession, departmental climate, and the prevalence of sexual harassment
• Blake, Thomas, and Hess (with ungated earlier version here) find that making recreational marijuana legal at the state level increases applications to the three largest state public schools, which received on average a 54% increase in applications (but this should be considered alongside a negative impact on student performance)
• Gershenson, Holt, and Tyner (with ungated earlier version here) find that high teacher grading standards increase both contemporaneous student achievement in first-year algebra and performance in subsequent math classes (so grade inflation is likely actively harming students)
• Carpenter et al. (open access) find that payday lenders don't protect regional economies, and in fact reduce the dynamism of the economy be reducing entry and exit of firms
• Preston and Wright (open access) find that the gender gap in financial literacy begins well before adulthood in Australia (although I'm a little disappointed they didn't cite my work here, with ungated earlier version here)
• Gulek (with ungated earlier version here) finds that driving while fasting (during Ramadan) at rush hour is associated with a 25% increase in the probability of having a traffic accident (timely, with Ramadan happening right now)
• Ragni, Ippolito, and Masci find, for engineering students in Milan, a subtle rise in earned credits and a slight decrease in grade point averages for those exposed to hybrid teaching (not too dissimilar from earlier results - see this post and all the links at the end of it)
• Bertacchini, Revelli, and Zotti (open access) find that UNESCO world heritage listing has a significant impact on income and property prices in urban areas of Italy
• Houseworth and Fisher find a raw interracial marriage wage penalty for White male spouses and a raw interracial marriage wage premium for Black male spouses, with a larger penalty for White males and a smaller premium for Black males in states that were forced to allow interracial marriage by the US Supreme Court
• Fischer (open access) finds that when gasoline sales were prohibited at gas stations in the German state of Baden-Wuerttemberg, gasoline margins dropped by 5%
• Lin, Liu, and Zhou find using a gravity model of trade that COVID-19 led countries to trade more with countries they were geographically closer to
• Tandon uses data from Yemen to show that large policy changes or shocks cause individuals to change how they answer subjective wellbeing (life satisfaction, or happiness) questions in ways that have little to do with changes in objective wellbeing measures (not surprising, but another challenge to happiness data)
• Anaya and Zamarro (with ungated earlier version here) find that the gender gap in PISA test scores may be underestimated, because boys put less effort into the test than girls (which is related to this as well)

A final reminder that I am giving a Professorial Lecture at the University of Waikato next Tuesday (26 March), titled Beyond the Buzz: The Sobering Economics of Alcohol. There are still tickets available, and you can register here. There's no livestream, but the event will be recorded and available sometime afterwards.