Two new studies on who works from home, and its mental health impacts

The pandemic caused a massive rise in working from home and now, even though lockdowns are long since over and many workers have returned to the workplace, we are beginning to understand working from home (WFH) a lot better. Two new studies have recently added to our understanding.

The first is this article by Cevat Giray Aksoy (European Bank for Reconstruction and Development) and co-authors, published in the AEA Papers and Proceedings (ungated earlier version here). They use data from the monthly US Survey of Working Arrangements and Attitudes, limiting their data to the period from January 2024 to December 2025, and document three facts about WFH. First, employees are more likely to work from home if they work for a younger firm, and peaks among those working for employers that were founded in the height of the pandemic, in 2020.

Second, employees are more likely to work from home if they work at a firm with a younger CEO. Specifically:

Firms led by CEOs under 30 have an average of 1.4 WFH days per week, compared with 1.1 days at firms led by CEOs who are 60 or older.

That doesn't seem like a lot, but an additional 0.3 days per week is a little more than three working weeks per year of WFH for those working for the youngest CEOs compared with those working for the oldest. However, this relationship between CEO age and WFH appears to be partly explained by the fact that younger CEOs are more likely to be leading younger firms. When Aksoy et al. put both CEO age and firm age in the same regression model, only firm age remains statistically significant. It is a similar story for CEO gender, which is initially statistically significant, but since female CEOs tend to be younger and to be CEOs of younger firms, CEO gender isn't statistically significant once those other variables are controlled for.

Third, the self-employed are much more likely to work from home. Specifically:

Self-employed workers report two to three times as many WFH days per week as wage and salary employees, depending on employer size. Compared to wage and salary employees, the self-employed are more than three times as likely to work in a fully remote capacity.

This last result is not entirely surprising, given that the self-employed typically have a lot more flexibility over scheduling. And, the self-employed may be the type of people who most value flexibility as well.

The second new article is this one by Natalia Emanuel (Federal Reserve Bank of New York), Emma Harrington (University of Virginia), and Amanda Pallais (Harvard University), published in the prestigious journal Science (open access). They look at the mental health impacts of WFH, using US data from a variety of sources, and a difference-in-differences approach. This involves comparing occupations that are more or less amenable to WFH, between the time before the pandemic and the time after the pandemic. They refer to the occupations that are more amenable to WFH as 'remotable'.

Emanuel et al. first document the dramatic rise of WFH:

The pandemic led to a large increase in remote work for those in remotable jobs, such that by 2024, workers in remotable jobs spent 31.1% of workdays fully remote, whereas people in nonremotable jobs spent only 8.9% fully remote... Those in remotable jobs experienced a 17.9 percentage point (pp) differential increase in fully remote work...

They then show that this rise is associated with more time spent alone:

Along with spending less time in the office, workers in remotable jobs spent more time working alone after the pandemic, logging 1.2 more work hours alone per day relative to nonremotable workers (58.0% increase; P < 0.0001).

Even for those of us who are introverts, more alone time may not necessarily be a good thing. Emanuel et al. are concerned about how WFH and working alone affects mental health. Their main outcome variable is the Kessler (K-6) Psychological Distress Scale, which is:

...based on how often in the past 30 days the respondent felt worthless, hopeless, restless, nervous, that everything is an effort, or so sad that nothing could cheer them up...

Their main source of data is the Panel Study of Income Dynamics covering the period from 2011 to 2023 (from which they exclude the pandemic years 2020 and 2021). Analysing that data, they find that:

Between the pre-and postpandemic periods, mental distress increased for everyone, but it increased significantly more for those in remotable jobs...

Among those in remotable jobs, there was a 0.3 unit increase in the K-6 distress score relative to an average score of 3.0 before the pandemic (standard deviation change = 0.08; P = 0.063) in the Panel Study of Income Dynamics (PSID). In the National Health Interview Study (NHIS), we found the same 0.3 unit deterioration (P = 0.007). We saw deterioration in each of the six subcomponents of the K-6 distress scale: feeling worthless, hopeless, restless, nervous, that everything is an effort, and so sad that nothing can cheer them up...

Importantly, the deterioration in mental health is concentrated among people living alone, which is consistent with the idea that WFH affects mental health through increasing social isolation. Emanuel et al. also find that people in remotable jobs are more likely to seek help from a mental health practitioner, and take relatively more prescription medications for mental health conditions such as anxiety or depression. These changes aren't simply the result of greater flexibility allowing more time to be devoted to health care generally, as there was no change in visits to the doctor and no change for other prescription medications such as statins.

Finally, Emanuel et al. looked at whether the rise of generative AI, rather than the increase in WFH, might explain the results (an important check, given the paper I will blog about tomorrow). They find that results from the same analysis, but substituting an AI occupational exposure index in place of the 'remotability' index, are not statistically significant.

Now, many workers are very keen on WFH - as noted in this post, about half of Australian workers would be willing to give up some salary in order to work from home. Why would people choose more WFH if it may worsen their mental health? Of course, a rational worker would weigh up the benefits and costs of WFH, and may decide that the mental health costs are more than offset by other benefits. However, Emanuel et al. point to another related possibility, which is:

...that the benefits of remote work (e.g., skipping a daily commute) are immediate and salient, whereas the costs of remote work (e.g., frayed connections with co-workers) take time to materialize.

So, a rational worker may be essentially weighing up benefits that occur today, against uncertain costs that may occur sometime in the future and therefore should be discounted (in the same way that we should discount future cashflows in a financial analysis). In that sort of exercise, where the mental health costs are discounted, it is more likely that workers would choose to work from home. They would be even more likely to do so if they are quasi-rational and heavily discount the future, as I note in the first week of my ECONS102 class. In that case, the mental health costs would be heavily discounted. Finally, maybe workers are simply unaware of the mental health costs of WFH. If that is the case, then an information intervention might be helpful in improving mental health among workers who would otherwise be WFH. In the meantime, this research suggests that the post-pandemic rise in WFH may have contributed to some part of the growing mental health crisis, especially through increased time spent alone.

[HT: Marginal Revolution for the Emanuel et al. article]

Read more:

• Remote work, wages, and compensating differentials
• Willingness-to-pay for working from home

The impact of British austerity on mortality and life expectancy

In 2010, the British government adopted a contractionary fiscal policy (austerity) to try and reduce government debt, which had built up during the Global Financial Crisis. Education and social security (social welfare) bore the brunt of the reductions in government spending, but other areas of spending, such as health, were not immune to the cuts (although health spending did not reduce, the increase in spending from year to year reduced substantially). However, austerity is not a free lunch. What were some of the consequences of the reduction in spending?

That is the question that this discussion paper by Yonatan Berman and Tora Hovland (both King’s College London) takes up, focusing on the impacts on mortality and life expectancy. Berman and Hovland note that the reductions in welfare and health spending did not affect all parts of the country equally. They use the differential impacts between different local authorities (or regions, in some analyses) to evaluate the impact of the austerity measures, in a difference-in-differences research design. That essentially involves comparing areas that were more impacted by austerity to those that were less impacted, between the time before and the time after austerity was introduced in 2010. Berman and Hovland measure exposure to austerity by the reduction in welfare (or health) spending per capita at the local authority level (or region). Their data covers the period from 2002 to 2019 in annual time steps. In addition to a pooled difference-in-differences analysis (which estimates one overall impact of austerity), they also conduct an event study, which estimates the impact of austerity over time. The event study analysis is the more interesting, so that's what I will focus on. The key results for reductions in welfare spending are summarised in Figure 5 in the paper:

The y-axis on the figure shows the coefficient (how much life expectancy changes for a  £100 per capita per year reduction in spending, relative to the pre-austerity baseline). The red vertical line shows the point in time where austerity began (in mid-2010). Notice that there is a clear reduction in life expectancy for both males and females, starting from about 2013, and increasing over time. Berman and Hovland note that:

...after the onset of austerity measures, we observe a clear reduction in life expectancy, with a more pronounced effect among females. The results indicate that every £100 per capita per year of lost benefits led to a decrease in life expectancy of approximately 0.5–2.5 months.

The results are qualitatively similar for health spending, as shown in Figure 6 of the paper:

Again, the negative impact on life expectancy is noticeable from 2013, and increases over time. Also, notice that the effect from health spending is much larger in magnitude for each £100 per capita per year reduction in spending. This is not surprising, given that health spending has a more direct impact on health, mortality, and longevity. However, the overall impact of austerity also depends on the amount of spending that was cut, which was much larger for welfare than for health. 

Now, it would have been good for Berman and Hovland to explore a little further why the impact of austerity on life expectancy was delayed by two or three years. The delay might raise concerns about whether there were other things that changed between 2010 and 2013 that affected mortality and life expectancy differentially by exposure to austerity. Having said that, we might expect cuts to spending to take some time to filter through into worse health outcomes, and that is also consistent with the increasing magnitude of the impact over time shown in Figures 5 and 6.

Combining the two effects (of welfare spending and health spending), and conducting some back-of-the-envelope calculations, Berman and Hovland find that:

Between 2010 and 2019, austerity measures caused a three-year setback in life expectancy progress, equivalent to about 190,000 excess deaths, or 3 percent of all deaths.

The costs of austerity were quite substantial! However, were there offsetting benefits? Berman and Hovland conduct a Marginal Value of Public Funds (MVPF) analysis, which essentially weighs up the costs and benefits of austerity (in this context, it is basically a cost-benefit analysis for austerity). In this analysis, they find that (when combining both welfare and health effects), the total costs (in terms of the value of life years lost) was £89.6 billion, while the savings on government spending were £38.75 billion. So, every pound of government spending saved had a cost to society of £2.31. On a cost-benefit basis, austerity was not a good deal for society. Moreover, the distributional impacts were important, because:

...poorer local authorities saw smaller increases in life expectancy between 2010 and 2019, or even decreases, compared to richer local authorities (defined by average pay in 2010). These results indicate that austerity measures were not only regressive in their impact on post-tax and transfer income, but they also led to more unequal health outcomes.

If governments are looking to implement policy, ideally those policies shouldn't make society worse off. That should go without saying. Based on this paper, British austerity appears to have made British people significantly worse off, trading lower government spending for higher mortality and lower life expectancy. Berman and Hovland stop short of saying that this was bad policy, instead concluding that:

Paradoxically, this fiscal strategy appears to have contributed to an increase in mortality, potentially offsetting its financial gains. However, it is possible that without austerity, the economic recession in the early 2010s might have been more severe.

It may be the case that the recession would have been worse without austerity, but that is not a certainty. However, given the choice up front, would people living in Britain have preferred a longer recession with fewer deaths, or a shorter recession with more deaths? If austerity really did reduce the length of the recession, the implied tradeoff here is quite stark, and Berman and Hovland's analysis suggests that a longer recession may have been the preferable option.

[HT: Les Oxley]

The mental health of economics PhD students and staff in Europe

Back in 2021, I wrote a post about the mental health of PhD students in economics. It was based on two studies and this Substack post by Scott Cunningham. The conclusion was that economics PhD students were suffering, but perhaps no more so than PhD students in other disciplines. However, patting ourselves on the back for being no worse than any other discipline seems like a failure to me, especially when many students are genuinely in mental health crises.

The study in Cunningham's post that was focused exclusively on economics PhD students was US-based, so it is worth wondering if the results apply elsewhere. This new article by Elisa Macchi (Brown University) and co-authors, forthcoming in the American Journal of Health Economics (ungated earlier version here), provides an answer to that, being based on data from 14 top European economics departments. The study uses a similar methodology to the US study that Cunningham discussed, and two of the authors (Valentin Bolotnyy and Paul Barreira) are the same. So, these studies are about as comparable as they can get. However, this new study also looks beyond PhD students, also considering the mental health of staff in economics departments.

Specifically, Macchi et al. got survey responses from 556 students and 255 staff, from 14 universities across Europe:

...Bocconi University, Bonn Graduate School of Economics, Central European University, European University Institute, London School of Economics, Mannheim Graduate School of Economics, Paris School of Economics, Sciences Po, Stockholm School of Economics and Social Sciences, University College London, Universitat Pompeu Fabra, University of Warwick, University of Zurich, and Uppsala Universitet.

It's worth noting that most of the top-ranked European economics departments are included in that list. Notable exceptions are Toulouse School of Economics, Oxford University, and Barcelona School of Economics (all ranked in the top ten in Europe, according to RePEc). Macchi et al. explain that the "restricted our interest to Economics departments that offer a cohort-style PhD program, where graduate students are admitted in cohorts to a graduate school, rather than following a chair-style model. That might explain the exclusion of other top universities from the sample.

The surveys were quite detailed, and in terms of mental health they included commonly used measures of depression, anxiety, suicidality, loneliness, and 'imposter phenomenon'. The last of these deserves a bit more explanation, and Macchi et al. note that imposter phenomenon:

...is a condition in which one feels like a fraud and worries about being found out. Individuals experiencing imposter phenomenon do not believe that their success is due to their competence, but rather ascribe success to external factors such as luck. Those experiencing imposter phenomenon often experience fear, stress, self-doubt, and discomfort with their achievements. Imposter fears interfere with a persons ability to accept and enjoy their abilities and achievements, and have a negative impact on emotional well-being...

Many PhD students (and indeed, many academic staff) can probably relate to that. Given the range of measures employed, the two samples (students and staff), and the comparisons with the US sample (where enabled by the use of the same questions), the paper has a huge amount of detail, and so it's difficult to excerpt from. The relevance of the comparisons with the US are somewhat limited because Macchi et al. conducted their survey starting in November 2021, when many people were still feeling the mental health impacts of the COVID-19 pandemic. However, Macchi et al. note attempt to establish how much of the difference in results (for depression and anxiety) relate to the pandemic.

The headline results are that there are:

...high rates of depression and anxiety symptoms, as well as suicidal or self-harm ideation, loneliness, and imposter phenomenon among graduate students in European Economics departments. 34.7% of graduate students experience moderate to severe symptoms of depression or anxiety and 17.3% report suicidal or self-harm ideation in a two-week period. 59% of students experience frequent or intense imposter phenomenon.

And in comparison with the US sample:

The prevalence of severe and moderate depression and anxiety symptoms in our sample of European Economics graduate students is notably higher than in the 2017-2018 sample of graduate students from top Economics departments in the U.S. (Bolotnyy, Basilico, and Barreira 2022) and higher than in a meta-analysis of depression, anxiety, and suicidal ideation among PhD students prior to the COVID-19 pandemic (Satinsky et al. 2021).

The Satinsky et al. paper is the other research that Cunningham referred to in his Substack post that I mentioned earlier. So, European PhD students have worse mental health that US PhD students. However, how much of that is due to the pandemic? Macchi et al. use data on the trends in mental health among Harvard University students, and note that:

...we can attribute approximately 74% of the difference in the prevalence of moderate-severe depression and 30% of the difference in the prevalence of moderate-severe anxiety between our European sample and the 2017-2018 U.S. sample to the impact of the COVID-19 pandemic.

So, the differences in mental health were not entirely driven by the pandemic. European PhD students do indeed appear to suffer more from depression and anxiety than US PhD students. What about staff though? Macchi et al. find that:

In our faculty sample, the prevalence of severe and moderate anxiety is on average lower than graduate students as well as than comparable statistics for the post COVID-19 European population. This average, however, hides a substantial heterogeneity by seniority level. Untenured tenure-track faculty in Europe are as likely to experience depression and anxiety symptoms as graduate students in our sample, and non-tenure track faculty show even higher prevalence of depression or anxiety symptoms. In contrast, the prevalence of depression and anxiety symptoms among European tenured faculty in our sample is about 70% lower than among their graduate students and is well below the comparable rates in the post-pandemic European population.

That makes a lot of sense. Un-tenured junior academics face many of the same workload and other pressures that PhD students do. Senior and tenured academics do not. So, it shouldn't be a surprise that there is a demonstrable difference in mental health measures between junior and senior academic staff.

Macchi et al. then turn to other results from their survey, showing that:

...25.9% of students in the European sample report having experienced at least one form of sexual harassment. Excluding a form of harassment not included in the U.S. study, the sexual harassment prevalence rate in our European graduate student sample (19.5%) is comparable to the U.S. sample (19.4%).

Again, that is not good. And worryingly:

...European Economics PhD students with moderate-severe symptoms of depression or anxiety are less likely to be in treatment (19.2%) than Economics PhD students in U.S. top departments (25.2%).

That difference in access to treatment may explain some of the differences in mental health between European and US PhD students. That also leads to the first of several recommendations that Macchi et al. make (which I think should be read alongside the recommendations that Bolotnyy et al. made for the US study, which I outlined in this post). Macchi et al. recommend that: (1) the usage of mental health services by students and staff be normalised and enabled; (2) that sexual harassment be addressed; (3) that relationships between students and their advisors be improved; and (4) more structure be offered in PhD programmes to avoid students getting into ruts. I think we can and should support all of those recommendations, and they're certainly something that would help PhD students, not just in Europe and not just in economics, but more generally.

[HT: Marginal Revolution, back in 2023]

Read more:

• Scott Cunningham on the mental health of PhD students in economics

Minimum wages and health

Picking up again on the theme of last week's posts about recent research on the minimum wage, this 2024 article by David Neumark (University of California-Irvine), published in the journal Labour (open access), reviews the literature on the impacts of minimum wages on health and health behaviours. It's somewhat of a systematic review, although it doesn't closely follow the PRISMA reporting guidelines. Nevertheless, it is a helpful summary of the literature relating minimum wages to health, which is important in light of statements such as this one from the American Public Health Association, claiming unambiguously that higher minimum wages would improve health.

As you might expect the reality is somewhat more nuanced. Neumark starts by pointing out why the effect of higher minimum wages on health is theoretically ambiguous:

The potential for higher minimum wages to improve health is clear, as a higher minimum wage unambiguously raises incomes for some workers (and their families). On the other hand, job loss can reduce income among other workers and their families... it is entirely possible that health benefits from income gains for some workers outweigh adverse health effects for others who lose their jobs, perhaps because there are almost certainly more income gainers than job losers. This net gain might be more likely if there was clear evidence that minimum wages raise incomes in lower income families (rather than for low-wage workers). However, the evidence on family income is ambiguous, in part because many minimum wage workers are not in poor or low-income families, and many low-income families have no workers...

That latter point relates to my most recent post on the effect of minimum wages on poverty, covering research by Burkhauser et al. that demonstrated (as has been shown before) that only a minority of minimum wage workers live in poor families. Neumark's review covers 63 published and peer-reviewed articles, mostly using US data, and mostly published in the last decade. He separated his review into sections on adult and teen health, infant and child health, diet and obesity, mental health, suicide, family structure and children, risky behaviour, crime (which seems a little out of place, but many studies that consider risky behaviour also consider crime), and mechanisms that can affect health (like access to health insurance). Neumark briefly summarises each paper, notes some of the positives and negatives of the methods employed, and draws a conclusion about how convincing (or otherwise) each study is (generally on the basis of the methods employed).

There is a lot to unpack in the review, and I'm not going to try to summarise it all here. Instead, here's what Neumark says in the concluding section:

...the evidence, even focusing on the more-compelling studies (which I do), is decidedly mixed. The evidence on overall physical health points in conflicting directions, and may lean toward adverse effects—possibly a reflection, in part, of the conflicting influences of minimum wages on factors that can affect health (related to how higher income is spent). In particular, research on the effects of minimum wages on diet and obesity sometimes points to beneficial effects, whereas other evidence indicates that higher minimum wages increase smoking and drinking and reduce exercise (and possibly hygiene). In contrast, there is rather strong evidence that higher minimum wages reduce suicides, perhaps partly consistent with the evidence on effects on other measures of mental health/depression being either positive or mixed.

Going a little farther afield, research on minimum wage effects on family structure and children indicates that mothers spend more time with children, provides no clear indication of changes in treatment of children, but point to declines in children's test scores—clearly a mixed picture. There are many good studies of the effects of minimum wages on crime, but the conclusions are mixed. Turning to channels of influence on health (most notably, health insurance), the stronger evidence points to declines in employer-provided health insurance, and other adverse effects on potential influences on health, but there is no clear evidence of effects on unmet medical needs.

When Neumark narrows his focus only to those studies where the evidence is most convincing, he concludes that:

...the mixed conclusions on how minimum wages affect health and related behaviors undermine the evidence base for concluding that the minimum wage is an effective means of improving health.

However, one thing that this review highlights is the comparative lack of research on the effect of minimum wages on health, particularly in comparison to, say, studies on the effect of minimum wages on labour market outcomes (of which there are many). It also highlights that few studies, even relatively recent studies, perform even basic supplementary analysis such as placebo checks on the effects of minimum wages on groups unlikely to be affected by higher minimum wages (such as those with high education), and many studies over-control by including unemployment, income, or poverty in their analyses. Clearly, there is substantial scope for additional research in this space. Indeed, in a footnote to the paper, Neumark notes that:

Effects of minimum wages on drug use, perhaps particularly opioids, could impact health and suicides (as well as other outcomes). This would be a natural question to consider. However, I have not found any such evidence.

So, not only is there scope to improve on the extant studies, there is also scope for studies on areas of health that have not been considered to date. Clearly, there will be more research to come on this theme.

Read more:

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

Airlines have to pay more compensation for death or injury, but it probably still isn't enough

The value of a preventable fatality (a more palatable term than the value of a statistical life) for New Zealand was increased last year to $12.5m (see here). That is the value that Waka Kotahi New Zealand Transport Agency uses in evaluating the benefits of road safety improvements, for example. The new value was a substantial increase from the previous value of $4.88 million.

So, I was interested to read this week that the International Civil Aviation Organisation (ICAO) has revised the amount that airlines must pay in compensation in the event of a death or injury, to just $335,000. As the New Zealand Herald reported:

Travellers will be eligible for higher compensation for international flights, with the International Civil Aviation Organisation (ICAO) setting new liability limits for death, injury, delays, baggage and cargo issues.

This means airlines must pay out at least $335,000 for death or “bodily injury” on flights as a result of the review of payment levels that come into force late this year.

While liability limits are set by the international Montreal Convention agreement, there are no financial limits to the liability for passenger injury or death if a court rules against an airline.

Why is the ICAO value so much lower? After some fruitless searching, I haven't been able to find anything to say how the ICAO sets its value. It dates back to 1999, where the value was set as 100,000 SDRs (Special Drawing Rights - an international reserve asset created by the International Monetary Fund, based on a basket of five currencies).

One reason that might account for this difference is the way that the two estimates are measured. The value of statistical life for New Zealand noted above is measured using the willingness-to-pay approach. Essentially, that method involves working out how much people are willing to pay for a small reduction in the risk of death, then scaling that value up to work out how much they would be willing to pay for a 100 percent reduction in the risk of death, which becomes the estimated value of a statistical life.

An alternative is to use the human capital approach, which involves estimating the value of life as the total amount of economic production remaining in the average person's life. The value of that production is estimated as their wages. Essentially then, this approach involves working out the total amount of wages that the average person will earn in their remaining lifetime. Typically, the human capital approach will lead to a much smaller estimate than the willingness-to-pay (WTP) approach (and for an unsurprising reason - people are worth more than just the value they generate in the labour market!).

So, this difference in approach might account for the different estimates. Why might the ICAO use the human capital approach? One reason may be that the human capital approach leads to lower liability for compensation (in cases where the airline is not found to be at fault - if the airline is found by courts to be at fault, then the compensation is uncapped). Given that many airlines that belong to ICAO are national carriers, each country has an incentive to try and limit the liability of their own airline to paying compensation. A second reason is explained in Kip Viscusi's book Pricing Lives (which I reviewed here). In the book, Viscusi argues that the WTP approach is more appropriate when considering what society is willing to pay to prevent deaths (e.g. in road safety improvements), and that the human capital approach is more appropriate approach when considering a particular life (e.g. in calculating a legal penalty for wrongful death). If we believe Viscusi's argument, then the human capital approach should be used by ICAO.

However, even if we believe that the human capital approach is the right approach (and I'm not convinced that it is), it probably still underestimates the compensation that should be paid, at least for New Zealanders. Consider the following details. The median age in New Zealand is 38.1 years (at the 2023 Census). Life expectancy (at birth) is 80 years for males, and 83.5 years for females. The median weekly earnings (from wages and salaries) was $1343 in June 2024, or $69,836 per year. Using those numbers, and assuming that the median-aged person works only until age 65, and using a social discount rate of 3 percent per year, the discounted value of future wages for the average New Zealander is $1.35 million. That is more than four times higher than ICAO's figure, and is estimated using the human capital approach. Even if we used a discount rate of 10 percent, rather than 3 percent, the value is still about $715,000, more than double the ICAO value.

The ICAO is seriously understating the value of compensation that should be paid in the case of a death on a flight (and where the airline is not at fault). It's just as well that these are rare events!

Adrian Katz on public sector discounting

This week, my ECONS102 class has been covering the economics of education. One of the key aspects of the topic is the introduction of discounting. This is important in the private education decision (about how much education to invest in), because the benefits of education come in the future, while the costs (including opportunity costs) happen now. In order to make an effective decision, we need to discount the future benefits so that they can be compared with the present costs.

Discounting is also important in public sector decision-making, such as decisions on funding healthcare (and my ECONS102 class will turn its attention to health economics next week), transport and other infrastructure, environmental policy, and many other decisions where there are long-term benefits (and/or long-term costs).

So, I was interested to read this post on the Asymmetric Information substack yesterday by Adrian Katz (senior economist at NZIER), who gives a quick run-down on public sector discounting:

The public-sector discount rate plays a central role in determining which government interventions get the green light, and which stay on red...

The Treasury currently recommends a discount rate of 5.0% for most projects. The Treasury’s CBAx guidance also suggests an alternative rate of 2.0% but does not explain how this should be used.

Some government agencies use different discount rates. For example, NZTA uses a rate of 4.0%, and Pharmac uses 3.5%. Having different discount rates for different government organisations is at odds with the Treasury’s current approach and makes it harder to compare different types of government spending.

Katz then briefly outlines the debate over how the discount rate should be set. It is worth reading that debate if you want to understand more about how the discount rate is set currently. Katz then concludes that:

Arguments about the discount rate are often motivated by political views about the role of government in society or ethical views about what we owe to future generations. Policy advisors face difficulties in making these judgements on behalf of society without clear evidence of New Zealanders’ views on these complex issues.

The choice of discount rate can make a big difference to evaluating the present value of future costs and benefits. The Treasury recommendation of a 5 percent discount rate would discount an amount in 20 years' time by 62.3 percent (=1-[1/(1+0.05)^20]), and an amount in 50 years' time by 91.3 percent (=1-[1/(1+0.05)^50]). In contrast, using the Pharmac rate of 3.5 percent would discount those amounts by 49.7 percent (=1-[1/(1+0.035)^20]) and 82.1 percent (=1-[1/(1+0.035)^50]) respectively. So, $1000 in 20 years' time would have a present value of $377 using the Treasury rate, but $503 using the Pharmac rate. And $1000 in 50 years' time would have a present value of $87 using the Treasury rate, but $179 using the Pharmac rate.

With a lower discount rate (like Pharmac) more alternatives with long-term benefits and near-term costs would have benefits that are greater than costs (that is, a benefit-cost ratio greater than one). As a result, the government would have evidence in favour of investing in more infrastructure, more climate change mitigation, more education and more healthcare. Discount rates matter.

Student strikes and teenage pregnancy in Chile

In 2011, university and high school students in Chile undertook a massive protest against the government (known as the 'Chilean winter'). Thousands of students participated in these strikes, refusing to attend classes or even taking over school buildings, and demanded a new framework for education. Given the widespread nature of the strikes, and the fact that they would affect non-participating students as well (who wouldn't be able to attend a school that had been taken over by protestors), it is worth asking what the consequences of the strike were.

That is the question that this new article by Pablo Celhay, Emilio Depetris-Chauvin (both Pontificia Universidad Católica de Chile), and Cristina Riquelme (University of Maryland, College Park), published in the Journal of Development Economics (ungated earlier version here), seeks to answer. Specifically, they focus on teenage pregnancy as an outcome, defined as a birth to a 15-17-year-old woman, backdated based on gestational age of the baby on the day of birth.

Celhay et al. don't have individual-level data though, so they look at the relationship between the number of births, at the municipality level, and a measure of 'strike intensity', which measures how likely a female student living in a given municipality was exposed to the student strike. It is based on the proportion of schools in a municipality that were on strike, and the length of the strikes overall. In Chile at that time, births were a good measure of conceptions, because abortion was illegal.

Celhay et al. find that:

A municipality with an additional exposure of 10 percentage points, signifying a ten percentage point increase in the number of resident high school female students attending schools on strike, witnessed a monthly rise in conceptions during the strike period ranging from 10% to 11%. For a more straightforward interpretation, consider that a municipality with an average proportion of students on strike (26% according to the combined measure) experienced a 2.7% increase in teenage pregnancies during the strike period.

I guess that tells us what some students were spending at least some of their spare time doing during the strike period? Importantly, this effect was concentrated among women aged 15-17 years, with no statistically significant impact on other age groups. That provides some confidence that the results are specific to school-aged women, and not a general increase in pregnancy and births across the whole population.

However, perhaps more importantly, Celhay et al. then find a more enduring effect on human capital development. Specifically:

Before the strike, schools that eventually experienced strikes were similar to non-striking schools regarding dropout rates and college admission test take-up. However, a significant increase in dropout rates and a decrease in college admission test take-up is observed in the year when the strike occurred. The effects are similar if we disaggregate outcomes by gender. In particular, the schools that took up strikes experienced an increase of 0.7 percentage points in their dropout rate. This represents a 20% increase in dropout rates in comparison to the average level of dropouts in the year 2010 (3.4%)... 

The results show a drop of approximately 20% in the number of students taking the test to be admitted to college during the strike year.. Furthermore, our study reveals that it takes approximately two to three years for dropout rates and college admission test take-up to return to pre-strike levels. This indicates a gradual recovery process after the disruption caused by the strike, as the educational system and student engagement stabilize over time.

I guess one way to interpret those results is that, when students don't attend school, they tend to drop out and don't sit the college admission test. That seems somewhat obvious. What is less obvious is that this impact remains for more than two years after the strikes subsided, and that should be a worry. It is also somewhat consistent with the long recovery of the education system, and student engagement, following the pandemic.

Celhay et al. interpret their results as showing:

...the potential benefits of policy interventions such as sexual education and counseling within schools, as well as initiatives that promote access to contraception among teenagers.

I think that interpretation oversteps, as they didn't actually look at the impact of sex education or counselling, or initiatives that promote contraception. It would be interesting if there was variation in those activities between schools, because then they could have looked at the impacts, but didn't. Nevertheless, I do agree with Celhay et al. that:

...we can interpret the observed effects as primarily related to reduced time spent under adult supervision. 

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.

It's not a surprise that medical practices might try to avoid sick patients

The New Zealand Herald reported yesterday:

Some GP clinics which are nearly at capacity say they are selecting which patients they enrol, raising concerns they could be discriminating against some groups or excluding difficult patients.

A survey of 220 general practice staff in New Zealand found four out of five had stopped or limited their enrolments over the previous three years.

Some staff reported they had selectively enrolled patients by refusing those with high health needs - a practice known as “cream-skimming”.

Associate Professor Mona Jeffreys, an epidemiologist at Victoria University, said previous studies had focused narrowly on how many practices were open or closed, without considering how many had limited their enrolment and how...

“Some are only taking family members, some are taking people who are new to the area. But some are making decisions based on health, which means that people who have … poorer health are less likely to be enrolled because practices know there is a greater burden.”

The research that this article was based on, published in the New Zealand Medical Journal, is here (gated). Now, medical practices making a decision to exclude patients with poorer health might seem a bit surprising. However, it is quite rational behaviour on the part of those practices. That's because, as this Bay of Plenty Times story from yesterday notes:

[Pāpāmoa Pines Medical Centre’s co-owner and partner Pamela] Sheahan said government funding for GPs through the capitation model was “not fit for purpose” and needed to be “significantly overhauled”.

Capitation-based payments are based on the numbers of people enrolled with individual general practices who belong to a primary health organisation population, the Te Whatu Ora website says.

“We’re paid for four visits per year per patient. If you get children and older people, they come to the GP far more than four times … sometimes up to 20 times a year,” Sheahan said.

“We just don’t get paid for any of those visits so we have to claw that back by charging patients over the counter the additional fees.”

Sheahan said the only way the business made money – apart from government funding – was charging patients.

A rational (and profit-maximising, or at least loss-minimising) medical practice will take on a patient as long as the benefits (to the practice) of that patient exceed the costs. The benefit the practice receives is the government capitation funding plus any patient fees. The capitation funding covers the cost of the first four visits for any patient. The practice will break even if every patient visited exactly four times per year (and the practice charged no fees). The practice will make a profit from patients that visit fewer than four times per year (typically the most healthy patients), and from patient fees charged to those that visit four or fewer times per year.

However, patients that visit more than four times per year pose a problem. Patient fees might be enough to ensure that the practice breaks even on patients visiting maybe six times per year (as an example). Patients that visit more times than those (typically the patients in the poorest health) will be a net loss to the practice. For those patients, the cost of providing care exceeds the benefits that the practice receives (in terms of capitation funding plus patient fees).

A rational and profit-maximising medical practice would therefore make an assessment of each potential patient, and take on only those patients that are likely to visit four or fewer times per year (or maybe six or fewer times). They would reject any patients that would be likely to visit more often than that. This is the 'cream-skimming' that the first article mentions.

Fortunately, medical practices are not quite that cold-hearted. There will certainly be some cross-subsidisation, with the profits that the practice receives from some patients covering the shortfall on the care provided to other patients. However, there are limits to the amount of cross-subsidisation that can occur. Eventually, the profits from the healthy patients are overwhelmed and at that point the medical practice has few options left. They can raise the patient fees, they can limit their exposure to patients in poor health (as noted above), or they can shut down.

It would be easy to blame the medical practices here, but it really isn't their fault. The health system, and in particular the funding model and funding level for general practice, are the real problem (see here and here, for example). If the government continues the chronic underfunding of general practice, then we will simply continue to see more of this rational behaviour from medical practices.

[Update: More evidence of cream skimming from the New Zealand Herald]

Reason to be sceptical about trends in adult height in India

A couple of years ago, I read this 2021 article by Krishna Kumar Choudhary, Sayan Das, and Prachinkumar Ghodajkar (Jawaharlal Nehru University), published in the journal PLoS ONE (open access). I've been holding off blogging about it, in the hopes that I could get one of my past PhD students interested in exploring this data and testing the claims further, but no one seems too interested (or, at least, they're too busy doing other exciting things). So, here we go.

Choudhary et al. use data across multiple waves of the Indian National Family Health Survey, and track trends in adult height in Indian provinces over the period from 1998-99 (NFHS-II) to 2015-16 (NFHS-IV). They found that:

Between NFHS-III and NFHS-IV, the average height of women in the age group of 15–25 showed a decline by 0.12 cm [95% CI, -0.24 to 0.00, p-0.051] while in the 26–50 years age strata it demonstrated significant improvement in the mean height by 0.13 cm [95% CI, 0.02 to 0.023, p-0.015]. However, Between NFHS III and IV, the average height of women in the poorest wealth index category registered a significant decline [-0.57cm, 95% CI, -0.76 to -0.37, p-0.000]. Between NFHS III and IV, the average height of Scheduled Tribe (ST) women in the age group of 15–25 years also exhibited a significant decline by 0.42 cm, [95% CI, -0.73 to -0.12, p-0.007]. Among men, between the two surveys, both the age groups of 15–25 years and 26–50 years showed significant decline in average height: 1.10 cm [95% CI, -1.31 to -.099 cm, p-0.00] and 0.86 cm [95% CI, -1.03 to -0.69, p-0.000], respectively.

You read that right. According to Choudhary et al., people in India are shorter in 2015-16 than they were in 2005-06 (NFHS-III). The distribution of mean height by age for those two surveys is given in Figure 4 in the paper:

Notice that, within every age group, the mean height is lower in 2015-16 than in 2005-06. However, here is where I have severe doubts about this analysis. The sample of Indian men in 2015-16 is (for the most part) the same as the sample of men ten years younger in 2005-06. So, if you compare a given age group's mean height in 2015-16, it shouldn't be too much different from the mean height of the age-group ten years younger in 2005-06. And yet, that doesn't appear to be true for almost any comparison in Figure 4. Look at the mean height for any age on the bold line in the figure, move to the right by ten years, and you will never intersect with the dashed line.

So, one of three things is going on here. Either, Indian men are shrinking, there are measurement errors that are changing over time, or there are compositional changes in the sample that explain the differences. It seems unlikely that people are genuinely shrinking. So, that leaves the other two explanations.

Although the NFHS is a 'nationally representative survey', there are serious issues with the survey (as documented by Sylvia Karpagam here). That suggests that measurement error might be at play. However, it would have to be measurement error that occurs in a way that heights were either systematically under-reported in NFHS-IV, systematically over-reported in NFHS-III, or both. That does seem a little unlikely.

What about compositional changes? There may be differences in survey coverage (see here), especially between women in NFHS-II (which only included ever-married women) and NFHS-III (which included both ever-married women and never-married women). However, it is less clear that the changes affected men in the sample. On the other hand, this bit from the Choudhary et al. caught my attention:

The samples drawn for analysis of women’s height were 83876 out of 90303 from NFHS-II, 121728 out of 138592 from NFHS-III, and 700602 out of 749344 from NFHS-IV. For men’s height, sample of 66468 out of 74396 from NFHS- III and 105783 out of 126543 from NFHS-IV were drawn.

Notice that the sample for women increases nearly six-fold between NFHS-III and NFHS-IV, but the sample for men increases only by about 60 percent. That might be accurate, but it strikes me as odd, unless men are only surveyed in a subset of households, and the proportional subset that were selected was different (and much smaller) in NFHS-IV than in NFHS-III. That could cause a change in the composition of the survey sample, and might explain the results for men (less so for women). Anyway, there is reason to doubt these results, and it might be an interesting project for a suitably motivated Honours or Masters student to follow up on.

Christian missions and HIV in Africa

Spreading Christianity was seen by the colonial powers as a way of civilising the native populations in Africa. Indeed, in 1857 David Livingstone wrote that "neither civilization nor Christianity can be promoted alone. In fact, they are inseparable" (see here). Among the many effects of colonisation, the spread of Christianity is seen as one of the few positive aspects (or, at least, one of the least negative aspects). Christian missions were associated with increased availability of education and (Western) health care. However, this may not have meant that health improved along all dimensions. This 2020 article by Julia Cagé (Sciences Po) and Valeria Rueda (University of Nottingham), published in the Journal of Demographic Economics (ungated earlier version here) presents evidence that historical Christian missions were associated with higher prevalence of HIV.

Cagé and Rueda use data on the locations and characteristics of Protestant missions from 1903 (from the Geography and Atlas of Christian Missions) and data on the locations and characteristics of Catholic missions from 1929 (from the Atlas Hierarchicus), in each case distinguishing between missions with and missions without health facilities. That allows them to compare outcomes for people living closer or further away from historical Christian mission locations with and without health facilities. The key outcome variable is HIV infection status, as recorded in the Demographic and Health Surveys from 2003 to 2013 (which includes over 344,000 individuals across 17 African countries).

In their main analysis, they find that:

...a 10% increase in distance to any mission is associated with a 0.003 unit lower probability of an HIV-positive result... Ceteris paribus, at the median distance to a mission, a 15 km increase in distance decreases the average probability of HIV positivity by approximately 5%.

So, people living closer to historical Christian missions are more likely to be infected with HIV. However, the story doesn't end there, as:

...a 10% increase in the distance to a mission with a health investment is associated with a 0.0005-unit increase in the probability of HIV positivity... This result suggests that, ceteris paribus, at the median distance to a mission that invested in health, a 15 km increase in distance to the health investment increases the average probability of HIV by approximately 0.7% to 1.2%.

So, Christian missions are associated with higher HIV prevalence, but this is offset if the mission had a health facility. The results appear to be somewhat greater for Protestant missions than for Catholic missions. Cagé and Rueda show that their results are robust to various alternatives, including limiting the sample to people living in more urban areas, and the results are similar when using a matching approach (although the sample size is much smaller when relying on the matched sample).

Here's where things get interesting though. The results are not generalisable across all health conditions, as when Cagé and Rueda look at anaemia or stunting, they find that:

...unlike for HIV, proximity to a mission does not statistically significantly correlate with these health outcomes. If anything, we observe improved outcomes (less positive results of anemia or stunted growth), but the relationship is not significant.

So, what is it about HIV that sets the results apart? Cagé and Rueda argue that there are:

...two possible countervailing effects of missions on HIV prevalence. On the one hand, their early investments in health facilities have a positive long-term impact on HIV prevalence, through the persistence of infrastructure and safer sexual behaviors. On the other hand, missionaries left a profound cultural imprint: conversion to Christianity increased the risk of contagion by changing family structures and increased exposure to religious institutions that have struggled to effectively address the epidemic.

Then, they find that sexual behaviours differ markedly for Christians and non-Christians in the DHS sample:

We observe that despite being more educated on average than non-Christians, Christians have riskier sexual behaviors. They have more sexual partners over their lifetime and are more likely to use the services of sex workers. Furthermore, they are also less likely to be abstinent before marriage. Despite being more likely to know that condoms lower the chances of transmitting HIV, they are less likely to know where to find them.

And then, comparing Catholics and Protestants (while noting that the categories are not perfectly separable in the survey), they find that:

Catholics exhibit certain riskier behaviors, like a larger age gap inside the household, or a larger number of sex partners. Protestants are nonetheless more likely to use the services of sex workers over their lifetime, which is a very strong determinant of HIV transmission, and less likely to know that condoms lower the chances of HIV contamination. Although it is statistically significant, the difference is quantitatively very small.

This, combined with the greater success of Protestant conversion in African than Catholic conversion, may explain the larger impact of Protestant missions than Catholic missions in their initial results.

So, it appears that Christian missions have had a long-term impact on health in Africa, and not entirely in a positive way. What can we learn from this? Cagé and Rueda point out that:

...our results may help us reflect on contemporary HIV prevention policies. In the United States, religious conservatives strongly support abstinence-until-marriage (AUM) as the central element of HIV prevention efforts, and this policy periodically receives a large share of the Federal funding... Our long-term perspective suggests that a focus only on “Christianizing” marriage patterns and sexual behaviors is unlikely to be successful.

Indeed. Add this to the evidence base against an abstinence-only approach to the HIV pandemic.

Antimicrobial resistance vs. climate change

Careful readers of yesterday's post on antimicrobial resistance might wonder whether I also prefer regulation as a solution for climate change. After all, the problems are superficially similar. Both antimicrobial resistance and climate change are listed among WHO's top ten global public health threats, and both involve negative externalities (where one person's actions make others worse off). Both problems will require concerted international action to properly address.

However, there is an important distinction between the two problems, which means that taxes or tradeable permits (such as the Emissions Trading Scheme) are likely to be effective solutions to climate change, but are less effective for antimicrobial resistance. That distinction may have gotten a little bit lost in yesterday's post.

In the case of climate change, all reductions in carbon emissions are good, in terms of reducing future climate change. So, any policy instrument that reduces carbons emissions is moving us towards the 'optimal' level of carbon emissions (which is not zero gross emissions - all of us emit carbon dioxide when we breathe, for example). The question then becomes, how do we reduce those emissions at the lowest cost to society? Taxes and tradeable permits are credible options for reducing emissions at the lowest cost, while regulation is not (a point that Eric Crampton has made many times, such as here).

Unlike carbon emissions, reducing all antibiotic use is not a good thing. Antibiotics are still needed to deal with infections. So, taxes and tradeable permits are not good instruments for reducing the problem of antimicrobial resistance, because we shouldn't want to reduce all antibiotic use, only inappropriate antibiotic use. So, as I noted in yesterday's post, antibiotic use in agriculture is an appropriate target for a tax to reduce use, but taxing all antibiotics used in medical care would likely make us all worse off.

Sometimes, regulation may actually be the best available option. But that sure doesn't mean that regulation is always the best available option.

Read more:

• Antimicrobial resistance, and health care as a negative externality

Antimicrobial resistance, and health care as a negative externality

In 2019, the World Health Organization declared antimicrobial resistance one of the top ten global public health threats facing humanity. The idea that antibiotics may soon be ineffective, making relatively minor infections life-threatening again (as they were before antibiotics became widely available after World War II) is frankly scary. This is definitely a public health issue to watch.

The Conversation has a series of articles on antimicrobial resistance, published less frequently than is probably warranted. However, they have had a couple of articles in the last week, and this article in particular caught my attention, by Allen Cheng (Monash University):

The concept of antibiotics as a valuable resource has led to the concept of “antimicrobial stewardship”, with programs to promote the responsible use of antibiotics. It’s a similar concept to environmental stewardship to prevent climate change and environmental degradation.

Antibiotics are a rare class of medication where treatment of one patient can potentially affect the outcome of other patients, through the transmission of antibiotic resistant bacteria. Therefore, like efforts to combat climate change, antibiotic stewardship relies on changing individual actions to benefit the broader community.

An externality is the uncompensated impact of the actions of one or more people on a third party (a bystander). Externalities can be positive (they make the third party better off), or they can be negative (they make the third party worse off). Usually, economists think of health care as exhibiting positive externalities. Think about a vaccination for an infectious disease. It makes the person getting vaccinated better off, because they are less likely to get sick. It also makes other people better off, because they are also less likely to get sick (because there is one more vaccinated person who cannot pass on the infectious disease).

However, what Cheng is suggesting is that, in some cases, antibiotic use may create a negative externality, because one person using antibiotics in the wrong way increases the chances that an antibiotic-resistant bacteria emerges, which would make other people sick (and potentially, unable to be easily treated). So, while some aspects of health care have positive externalities, this seems like an example where the externality is negative.

What is to be done? Cheng suggests:

There is a lot we can do to prevent antibiotic resistance. We can:

• raise awareness that many infections will get better by themselves, and don’t necessarily need antibiotics

• use the antibiotics we have more appropriately and for as short a time as possible, supported by co-ordinated clinical and public policy, and national oversight

• monitor for infections due to resistant bacterial to inform control policies

• reduce the inappropriate use of antibiotics in animals, such as growth promotion

• reduce cross-transmission of resistant organisms in hospitals and in the community

• prevent infections by other means, such as clean water, sanitation, hygiene and vaccines

• continue developing new antibiotics and alternatives to antibiotics and ensure the right incentives are in place to encourage a continuous pipeline of new drugs.

Some of these suggestions may be more effective than others. However, I want to take a step back and see what is in the economists' toolkit for dealing with negative externalities. We need to recognise, though, that unlike canonical negative externalities like air pollution, the goal here is not to reduce all antibiotic use, but only to reduce inappropriate antibiotic use.

We can start by setting aside bargaining solutions to the externality. There are simply too many parties involved (all patients prescribed an antibiotic, all doctors, and all farmers who may want to use antibiotics) for a general agreement on antibiotic use to be negotiated. That leaves public solutions, which really comes down to command-and-control policies (that is, regulation), or market-based policies (for example, taxes).

Let's start with taxes. Taxes increase the price to consumers, and decrease the effective price received by producers, and therefore create incentives for less to be produced and consumed. That would be a good solution if we were interested in reducing antibiotic use in general, but that isn't the goal here. Except in one case, which is farm use of antibiotics. Taxing antibiotic use in agriculture, would reduce the use of antibiotics, and would probably be effective. The higher costs of production (arising from the greater direct cost of raising animals, as well as the greater indirect cost as less antibiotic use slows animal growth rates) would likely be passed onto the consumers of animal products, as well as reducing farm profits.

In the health sector though, regulation is the only remaining policy alternative. The first two of Cheng's suggested solutions fit in here - raising awareness and using antibiotics more appropriately. It does appear that governments are attempting these solutions already (for example, see here for the advice provided by New Zealand's Ministry of Health, or here for the advice provided by the Australian Government). Providing advice and recommendations is about as weak as policy can get. It is unlikely to drive substantial change. For one of the top ten global public health threats, governments should be doing more to reduce the inappropriate use of antibiotics.

I'm not usually in favour of adding layers of bureaucracy, all of which come with attendant costs. However, in this case the national oversight part of Cheng's recommendations is important. This could be implemented through initially tracking antibiotic prescriptions, then a program of random audits of patient records to ensure prescriptions are warranted, and the most appropriate antibiotic (based on what was known at the time) was prescribed. The tracking component need not be too onerous, because this information is already captured. Audits would require some funding (presumably through Te Whatu Ora Health New Zealand), but as cumulatively more audits are conducted, the audits could become better targeted over time towards unusual patterns of antibiotic prescription.

Antibiotic resistance is a serious public health concern, and is a negative externality arising from inappropriate antibiotic use. This is something that can be addressed, and should be.

[Update: I wrote a brief follow-up to this post]

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

This past week, my ECONS102 class covered health economics. As part of that topic, we cover the value of a statistical life (VSL) [*]. When I was looking up the current VSL for New Zealand, I realised that a pretty important change earlier this year had completely passed me by. As reported in this Newsroom article:

Over the past 30 years, there's a strong economic argument to be made that government has not valued human life highly enough; it failed to acknowledge that New Zealanders placed greater value on saving their friends, family and neighbours from injury or death than they did on shaving a few seconds off their morning commute.

Last month, that changed. With no fanfare, no press release, no ministerial statement, the transport agency Waka Kotahi published a document entitled Monetised benefits and costs manual v1.6 April 2023. The 429-page manual is a dense compendium of tables, formulae and, to the layperson, impenetrable economic justifications for obscure policies.

But buried in this manual are two big changes to v1.5, published two years earlier. It raises the value we place on saving time stuck behind a wheel driving to and from work, from $7.80/hr to $19.53 an hour – an increase by a factor of 2½. That figure increases to as much as $36.18/hr if that's what it costs to avoid being stuck in congestion.

And at the same time, it increased an esoteric number called the VoSL – the Value of a Statistical Life – from $4.88m to a somewhat breathtaking $12.5m. That's an even bigger increase.

That's not an inconsequential change (for reasons I will come to a bit later), but it is very surprising. And that's because:

In 1991 government researchers completed a survey of 700 New Zealanders to find out what value they placed on safety. 

They wanted to measure the amount society would pay for the avoidance of one premature statistical death – and they did it by asking individuals the amount they would pay for safety improvements. They came up with a Value of a Statistical Life of $2m.

A new more thorough survey in 1998 doubled the figure to $4 million – but the government of the day refused to adopt it, seemingly dismayed at the cost implications for road, rail and aviation infrastructure.

So it is that the flawed 1991 survey result has been updated in line with wage inflation, every subsequent year. Extraordinarily, that outdated and discredited survey was still used to decide whether or not to build transport and other infrastructure – until now.

That's right. Until earlier this year, the VSL that was used in government decision-making was based on a survey of 700 people conducted in 1991. Among other uses, the VSL is a number that is used to measure the benefits of road safety improvements. If straightening a road, or reducing the speed limit, or installing median barriers, would save on average one life per year, then the value of those benefits was equal to $4.88 million per year. And now, that value has jumped to $12.5 million.

The Newsroom article argues that this means that more road safety projects would be funded. It isn't quite that simple. The government (or, rather, Waka Kotahi NZ Transport Agency) calculates benefit-cost ratios for each potential project, then ranks those projects from those with a high benefit-cost ratio, to those with a low benefit-cost ratio. Not all projects with a benefit-cost ratio greater than one (that is, those with benefits that outweigh costs) will be funded, as the roading budget doesn't stretch that far. Some projects with benefit-cost ratios less than one (that is, those with benefits that are smaller than costs) may be funded, depending on the political priorities of government. [**]

If all potential roading projects have similar road safety improvements and travel time savings, then the ranking of those projects wouldn't change. So, even though the benefit-cost ratios would be more favourable, there would be no additional roading projects funded, and the projects that were funded would be no different. However, to the extent that not all projects result in the same road safety improvements, the change in the VSL will tend to shift benefit-cost ratios in favour of projects with greater statistical lives saved.

Moreover, because the change in the VSL is greater than the change in the value of travel time savings, the benefit-cost ratios will tend to shift more in favour of projects that result in road safety improvements, and less in favour of projects that result in travel time savings. And that means that we can expect more roads with median barriers and slower speed limits in the future, and fewer road changes that result in travel time savings.

*****

[*] This is now often termed the 'value of a preventable fatality'. The difference is mostly semantic, but I buy into the argument that the old terminology feels somewhat uncomfortable when discussing with non-economists.

[**] Many times, politics trumps good economics.

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

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

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

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

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

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

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

And, in case you're wondering:

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

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

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

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

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

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

The health effects of Swedish prisons

Does time in prison make people healthier, or less healthy? On the one hand, prisons are a challenging environment. They are stressful, and the risks of harm through violence are high. As we've discovered recently, they are also an excellent super-spreading environment for infectious diseases. And prisoners may suffer from reduced nutrition, or reduced access to health care. On the other hand, perhaps prisoners' nutrition and/or access to health care may be improved by being incarcerated. Many prisoners have untreated (or undiagnosed) mental health problems, or substance abuse problems, that can be more effectively treated in an institutional setting.

So, which is it? No doubt, it depends on the particular prison context, and the health of the prison population at the time they go into prison. Let's take a particular prison context: Sweden, which has an excellent reputation for rehabilitation (see here and here), and a relatively low prison population (74 per 100,000 population, compared with 155 in New Zealand, and 505 in the US). This recent article by Randi Hjalmarsson (University of Gothenburg) and Matthew Lindquist (Stockholm University), published in the American Economic Journal: Applied Economics (ungated earlier version here), looks at the impact of time in Swedish prisons on health outcomes.

Hjalmarsson and Lindquist make use of a neat natural experiment, being:

...Sweden’s 1993 and 1999 early release reforms, which held sentences constant but increased the share of time inmates were required to serve from 50 percent to 67 percent. Exposure to the two- thirds reform depended on the date of conviction and sentence length. Shorter sentences (4–12 months) were fully treated by the first reform and longer sentences (≥ 24 months) by the latter; intermediate sentences were partially treated by both.

Because time in prison changed, but prison sentences did not, Hjalmarsson and Lindquist look at how the increase in the number of days in prison affects health, while holding sentence length (and therefore, the severity of crime the prisoner is being sentenced for) constant. Their final sample consists of nearly 47,000 sentences of between 4 and 48 months, which commenced between 1992 and 2001. Because Swedes have an effective population register, Hjalmarsson and Lindquist are able to link prisoner records with hospital and mortality data. Looking at the impacts up to ten years after release from prison, they find a variety of impacts, including that:

...exposure to the two-thirds reform does not harm post-release health and actually improves it. Though the reduction in mortality risk is not quite significant when looking at the entire sample, these aggregate results mask important heterogeneity in two dimensions. First, significant reductions in the overall chance of death (especially in the first two years post release) are seen for positively selected subsamples, including those with no past prison exposure, property offenders, relatively young offenders, and those with some past employment. Second, significant effects are seen for the whole sample when zooming in on causes of death particularly relevant for this population. There is a large, significant, and immediate reduction in the chance of suicide; the chance of suicide is still reduced by 38 percent ten years after release. These suicide results are driven by individuals with previously identified mental health issues and by violent offenders.

Taken all together, these are positive results for the Swedish prison system. Why does it do so well? Hjalmarsson and Lindquist can't definitively tell, but note that:

First, health care in Swedish prisons is of high quality. Second, more time in prison is positively related to visits with medical professionals (doctors, nurses, and psychologists), medication, and starting and completing treatment programs. High-quality health care and treatment that increases with time served is consistent with our findings of the health-improving effects of the reform.

So, if we want to improve the health of prisoners, should we be keeping them in prison longer? That would probably be extending these results too far. Remember that context matters. As Hjalmarsson and Lindquist note, the health care available in the Swedish prisons is high quality, and prisoners access it readily. That is not the case in all prison settings. So, we shouldn't use these results to conclude that prisons improve health, but rather that Swedish prisons improve health, and that moving towards the Swedish model may have positive impacts. Of course, then we run into other problems, because Hjalmarsson and Lindquist also note that:

...Sweden spends more money per inmate than any other country...

If we want better health incomes for prisoners, this comes with an increase in cost. We'd need to weigh up those costs and benefits to make a sensible decision about what is best to do. As the saying goes, there is no such thing as a free lunch (as my ECONS101 students will learn when teaching starts next week! [*]).

[HT: Marginal Revolution, last year]

*****

[*] Not literally though. I'm not offering them free food. Instead, we will cover the concept of opportunity cost in the second lecture.