Vice-chancellor narcissism and university performance

Over the last two decades (or more), universities have increasingly come to be managed like businesses. In that case, the role of the university vice-chancellor (or president) has come to resemble that of a business CEO. As a consequence, the types of skills a successful vice-chancellor must possess have changed. And, the types of academics attracted to becoming a vice-chancellor have also changed. If I claimed that current vice-chancellors are, on average, more narcissistic than vice-chancellors from ten years ago, I think many academics would agree.

In fact, that is one of the findings of this new article by Shee-Yee Khoo (Bangor University), Pietro Perotti (University of Bath), Thanos Verousis (University of Essex), and Richard Watermeyer (University of Bristol), published in the journal Research Policy (sorry, I don't see an ungated version online). Khoo et al. aren't interested in the level of narcissism of vice-chancellors per se, but whether vice-chancellor narcissism is related to university performance. They use data from British universities from 2009/10 to 2019/20 to investigate this question. Their sample includes 133 universities, and 261 vice-chancellors.

Interestingly, they:

...measure narcissism based on the size of the signature of the VC...

First, we obtain the signature of each VC from the university annual report, or the university strategic plan or letter when the signature is unavailable in the annual report. Second, we draw a rectangle around each VC's signature, where the signature touches its furthermost endpoint, ignoring any dot at the end of the signature or/and underline below the signature... Third, we measure the area covered by the signature by multiplying the length and width (in centimetres) of the rectangle. Fourth, we divide the area by the number of letters in the VC's name to control for the length of the VC's name.

Apparently, this is a widely used measure of narcissism <quickly checking the size of my signature>, and Khoo et al. note that it has advantages over survey-based measures because it is "unobtrusive". It is also less subject to manipulation than survey-based measures, although I was worried that the size of the signature on an electronic document (like an annual report) would create a lot of measurement error. However, in the section on robustness checks, Khoo et al. report that:

...we compare the handwritten and electronic signature sizes of the same VC, for the sample where we have both types. The size of the signature remains the same irrespective of the signature type.

Ok then. For their measures of university performance:

Firstly, the Research Excellence Framework (REF) and its predecessor until 2014, the Research Assessment Exercise (RAE), is a system for assessing the research quality of UK universities and other HE institutions... We use the overall quality of research based on the REF (formerly known as the RAE) as our research quality indicator...

Secondly, we employ the National Student Survey (NSS) which assesses teaching quality in UK universities... In particular, we employ the Student Satisfaction Score, which is the average score from across the organisation and management, learning resources, learning community and student voice sections of the NSS...

Thirdly, we use the overall university ranking, based on the Guardian newspaper.

Based on the way that they describe their results though, it seems like they use the ranking of each university, rather than the scores themselves for research quality and teaching quality. For the analysis, they perform the analysis separately for 'old universities' (those that were created before 1992) and 'new universities' (those created after 1992). The main difference between those groups of universities is that the old universities tend to be more research-focused, while the new universities tend to be more teaching-focused. The analysis for each type of university compares the university ranking two years before and two years after a change in vice-chancellor:

In particular, we rely on the universities that face a VC transition, i.e., a change from a low narcissist VC to a high narcissist VC, within the sample period. We define a high narcissist VC as one in the top quartile of the distribution. In our analysis, our treatment group consists of universities that appointed a high narcissist VC during the sample period (i.e., low-to-high transition universities). The baseline group consists of universities that appointed a low narcissist VC during the sample period (i.e., low-to-low transition universities).

Collapsing vice-chancellor narcissism to a dichotomous variable (equal to one only when a university transitions from a low-narcissism vice-chancellor to a high-narcissism vice-chancellor) deals with some of the measurement error issues I noted above. However, it does open up their analysis to some criticism, since there are many alternative arbitrary cut-offs that they could have used (and they don't report the robustness of their results to this particular choice). With that limitation in mind, Khoo et al. find that:

...a change from a low narcissist VC to a high narcissist VC is associated with a deterioration in research performance for both New and Old universities. VC Change×Narcissism Change is negative and significant, confirming that VC narcissism has a negative effect on research performance. Controlling for university as well as VC characteristics, and year and university fixed effects, a change from a low narcissist VC to a high narcissist VC is associated with a drop of approximately 16 places (VC Change×Narcissism Change = –16.07) in research performance for the sample of New universities and nine places for the sample of Old universities (VC Change×Narcissism Change = –9.57). This finding also demonstrates that New universities are more susceptible to VC transitions.

Then for teaching quality:

The coefficient of VC Change×Narcissism Change is negative and significant, indicating a drop of approximately 12 places for the group of New universities and 19 places for the group of Old universities (VC Change×Narcissism Change = –12.06 for New universities and –19.52 for Old universities).

And for overall ranking:

The transition from a low to a high narcissist VC is associated with a drop of approximately 27 places in the Guardian ranking for the group of New universities but has no effect on the Guardian ranking of Old universities.

So, narcissistic vice-chancellors lower the research and teaching performance of universities, but have more negative impact on new universities (except for teaching quality, where the impact is greater for old universities). Why do vice-chancellors negatively impact performance? It turns out that the answer may be different for old universities and new universities. Khoo et al. go on to show several additional results, including that:

...for Old universities, financial risk substantially increases with VC narcissism. Specifically, the appointment of a highly narcissistic VC deteriorates the financial sustainability of Old universities by approximately five to six [Financial Security Index] points...

Hence, the appointment of a highly narcissistic VC is associated with higher financial risk (i.e., lower financial sustainability) and lower effectiveness of the use of the resources. These results are consistent with excessive risk-taking behaviour. For Old universities, the findings suggest that highly narcissistic VCs take unnecessary risk, which might lead to a decrease in university performance...

The results for Capital Expenditures are insignificant. However, when using Expenses to Revenue as the dependent variable, the coefficient on VC Change×Narcissism Change is positive and significant at the 5 % level for the group of New universities. This evidence, although based on only one of the two measures, is consistent with highly narcissistic VCs engaging in empire-building strategies in New universities, which might be detrimental to the performance of the organisation.

I don't find the ratio of expenses to revenue very convincing as a variable, but Khoo et al. use it to suggest that narcissistic vice-chancellors in new universities are engaging in empire building. In contrast, Khoo et al argue that narcissistic vice-chancellors in old universities are engaging in excessive risk-taking behaviour. Both of these behaviours have been noted of narcissistic business CEOs as well.

So, what should universities do in order to mitigate the negative impacts of vice-chancellor narcissism? Khoo et al. recommend that:

...university councils and relevant committees should take into account and, if possible, measure the narcissism of the candidates for the role of VC... Given, however, that narcissists tend to appeal to recruiters, we also recommend that VC selection committees should undertake rigorous training that will allow them to control for this implicit bias in favour of narcissistic applicants.

I don't find those recommendations to be very convincing (and they aren't really supported by Khoo et al.'s results). What is supported is higher-quality governance, since in their final set of results:

The core finding that VC narcissism has a negative effect on research performance still holds after controlling for the effect of university governance; however, we note a decrease in the magnitude of the effect...

The results here are not consistent across all measures of university performance, but in all cases the measure of university governance does appear to moderate the effect of a shift from a low-narcissism vice-chancellor to a high-narcissism vice-chancellor.

So, the takeaway message from this research is the narcissistic vice-chancellors harm university performance, but having strong university governance can limit the damage. The only question remaining is, how did the vice-chancellors of these researchers' universities respond when they learned of this research?

New results on the bat-and-ball problem

A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?

If you guessed ten cents, you would be in the majority. You would also be quite wrong. The correct answer is five cents. This 'bat-and-ball' problem is quite famous (and you may have seen it, or a question like it, before - a variant was in a pub quiz that I competed in a few weeks ago, for example). The problem is one of three questions included in the Cognitive Reflection Test, which purports to measure whether people engage in cognitive reflection, or are more prone give into 'intuitive thinking'. It also relates to what Daniel Kahneman referred to in his book Thinking Fast and Slow as System 1 and System 2 thinking. System 1 is intuitive and automatic (and gives a ready answer of ten cents to the bat-and-ball problem), while System 2 is slower and reflective (and is more likely to lead to the correct answer of five cents).

However, a new article by Andrew Meyer (Chinese University of Hong Kong) and Shane Frederick (Yale University), published in the journal Cognition (open access), may give us reason to question the theory of System 1 and System 2 thinking (or reason to question the validity of the bat-and-ball question). Frederick is the author who introduced the Cognitive Reflection Test, so the results reported in this paper should be considered especially notable.

Meyer and Frederick conducted a number of studies of the bat-and-ball problem, showing a number of increasingly disquieting results. First:

...verifying the intuitive response requires nothing more than adding $1.00 and $0.10 to ensure that they sum to $1.10 (they do) and subtracting $0.10 from $1.00 to ensure that they differ by $1.00 (they don't). Since essentially everyone can perform these verification tests, the high error rate means that they aren't being performed or that respondents are drawing the wrong conclusion despite performing them.

If respondents aren't attempting to verify their answer, encouraging them to do so may help. We tested this in five studies involving a total of 3219 participants who were randomly assigned to either a control condition or to one of four warning conditions shown below. Two studies were administered to students who used paper and pencil. The rest were web-based surveys of a broader population...

The warnings improved performance, but not by much... This suggests that they failed to engage a checking process, or that the checking process was insufficient to remedy the error...

Specifically, only 13 percent of research participants in the pure control group got the bat-and-ball problem correct. In the treatment group that received the simplest warning (which simply warned: "Be careful! Many people miss this problem"), this increased to 23 percent. There were modest increases in performance across other studies that Meyer and Frederick report (with various different wordings of the warning), ranging from -9 percentage points to +17 percentage points. They don't report a measure of statistical significance, but the magnitude of the change is not large, and warnings to check the answer don't eliminate the intuitive response. Evidently, research participants aren't great at checking their answer. Or maybe, they simply don't perform any check at all. What about being more directive that research participants should check their answer if their original answer was ten cents:

Since these warnings were ineffective, we next tried an even stronger manipulation by telling respondents that 10 cents is not the answer. We conducted eight such experiments, with a total of 7766 participants. In five studies (three online and two paper and pencil), participants were randomly assigned to either the control condition or to a Hint condition in which the words “HINT: 10 cents is not the answer” appeared next to the response blank...

In three other studies (two online and one in-lab), we used a within-participant design in which the Hint was provided after the participant's initial response. In those studies, respondents could revise their initial (unhinted) response, and we recorded both their initial and final responses...

The hint that the answer wasn't 10 cents helped substantially, but, more notably, many – and sometimes most – still failed to solve the problem...

Receiving the hint increased performance in the bat-and-ball problem by between +17 percentage points and +23 percentage points in a between-subjects comparison (comparing research participants who received the hint with those that didn't receive the hint), and between +16 and +22 percentage points in a within-subjects comparison (where research participants could change their answer after they received the hint). The latter results lead Meyer and Frederick to note that:

Though the bat and ball problem is often used to categorize people as reflective (those who say 5) or intuitive (those who say 10), these results suggest that the “intuitive” group can – and should – be further divided into the “careless” (who answer 10, but revise to 5 when told they are wrong) and the “hopeless” (who are unable or unwilling to compute the correct response, even when told that 10 is not the answer).

Why would so many research participants still maintain that the answer is ten cents, even when they are explicitly told that ten cents is not the correct answer? Meyer and Frederick suggest that:

This result has hallmarks of simultaneous contradictory belief (Sloman, 1996), because respondents who report that $1.00 and $0.10 differ by $1.00 obviously do not actually believe this. It is also akin to research on Wason's four card task showing that participants will rationalize their faulty selections, rather than change them (Beattie & Baron, 1988; Wason & Evans, 1974). It could also be considered as an Einstellung effect (Luchins, 1942), in which prior operations blind respondents to an important feature of the current task or as an illustration of confirmation bias, in which initial erroneous interpretations interfere with the processes needed to arrive at a correct interpretation (Bruner & Potter, 1964; Nickerson, 1998).

I would put a lot of this down to motivated reasoning. However, it gets even worse:

...we ran two studies on GCS in which we asked respondents to either consider the correct answer (N = 2002) or to simply enter it (N = 1001)...

Asking respondents to consider the correct answer more than doubled solution rates, but only to 31%. Asking them to simply enter the correct answer worked better, as 77% did so, though, notably, the intuitive response emerged even here.

So, when research participants are asked to consider if the answer could be five cents, more than half still get it wrong. And even when research participants were told that the answer is five cents, and directed to write down five cents as the answer, nearly a quarter of research participants still get the answer wrong. That leads Meyer and Frederick to conclude that:

...the very existence of such manipulations (and their lack of complete efficacy) undermines a conclusion many draw from dual process theories of reasoning: that judgmental errors can be avoided merely by getting respondents to slow down and think harder...

Meyer and Frederick use all of these results (and others) to suggest that people engage in an 'approximate checker' process, wherein if the intuitive result provided by System 1 is approximately correct, then the more deliberative System 2 doesn't go through a complete process of checking. They demonstrate this with some further results that show that:

As the price difference between the bat and ball decreases, participants slow down... and solution rates rise markedly – from 14% to 57%...

So, perhaps these results are not fatal for the idea of System 1 and System 2 thinking, but psychologists and behavioural scientists need to re-think the conditions under which System 2 operates, and whether it always operates optimally. The results also suggest that the bat-and-ball problem may not actually show quite what it purports to - at least, it doesn't necessarily show cognitive reflection, as even when such reflection is explicitly invoked (through asking research participants to check their answer, or telling them to consider if the answer might be five cents), many do not exhibit such reflection (or else, they reflect and still get the answer wrong. Meyer and Frederick finish by noting that:

...the remarkable durability of that error paints a more pessimistic picture of human reasoning than we were initially inclined to accept; those whose thoughts most require additional deliberation benefit little from whatever additional deliberation can be induced.

[HT: Marginal Revolution. back in September]

AI and the research production function

I've been a bit quiet on the blog lately, due to travelling, and attending the North American Regional Science Conference in San Diego. Regional science is a multidisciplinary field that overlaps economics, geography, planning, and many other disciplines. There were many interesting sessions at the conference, although apparently not the session that I was presenting in, which had an audience of two (one of which was another speaker in the session, with the other three speakers in my session no-showing).

Anyway, one of the most interesting sessions was the day before the conference proper started, on "The Potential of AI for Regional Science", hosted by The Regional Science Academy. Speakers included many of the contemporary great minds in regional science such as Rick Church (University of California, Santa Barbara), Tomaz Dentinho (University of Azores), Peter Nijkamp (Open University), and John Östh (Oslo Metropolitan University), among others. They noted (with examples) the great opportunities that artificial intelligence offers, especially as an aid for research. For example, Östh demonstrated a really interesting application of AI to generating data on neighbourhoods, while Patricio Aroca (Universidad Andres Bello) showed how AI could help researchers whose first language is not English to improve their chances of publication in top-ranked English language journals.

I came away from this session in equal parts excited and concerned (which might be an appropriate mix of reactions to any sufficiently disruptive technology). There is great potential in using AI as a research tool, and I've already seen many examples (and blogged about some ideas here and here). However, my concern comes from how AI will change the research production function.

Before we get that far, let's go back to an earlier technological revolution that greatly changed how research in economics was conducted. This story is not mine, but I forget where I got it from. Consider a simple research production function in economics, with two inputs: (1) econometric modelling; and (2) economic explanations. As computers increased in computational power, the relative price of econometric modelling decreased. So, researchers reallocated their scarce research resources from economic explanations (which had become relatively more expensive as an input) towards econometric modelling (which had become relatively less expensive as an input). The result was a rapid increase in econometric modelling, and a corresponding decline in the quality of economic explanations to go along with the econometric modelling. It is not clear that this was a positive change for the overall quality of economics research (but there certainly was an increase in the quantity of research).

Now consider a similar research production function in economics, where the inputs are: (1) human intuition and explanations; and (2) artificial intelligence. New AI models have made AI radically less expensive to use. We can probably expect research in economics (and in other fields) to adjust to much more use of AI, and much less use of human intuition and explanations. It remains to be seen whether that leads to an increase or a decrease in the quality of research overall. My money is on an overall increase in the quantity of research, but a reduction in the average quality and an increase in the variance (with some very high quality research resulting from AI, as well as a lot of very low quality research).

Unlike the science fiction and fantasy magazine Clarkesworld, which shut off submissions earlier this year due to a flood of AI-generated stories were submitted, I'm not aware of any academic publishers that are feeling the strain, yet. It will be coming though, and may intersect with the increasing volume of predatory publishers (see here and here). As the Managing Editor of a journal myself (the Australasian Journal of Regional Studies), I'm really not looking forward to policing the submission of AI-generated articles of low quality.

Taken altogether, it is clear that there is a trade-off inherent in the impact of AI on research (in regional science, in economics, and in other fields). We will need to accept (and act to mitigate) the negative impacts, while endeavouring to maximise the good.

Jibbitz trading bans offer a missed opportunity to introduce some basic economics to children

I read this article from the New Zealand Herald from earlier this week with some interest:

Jibbitz - accessories that clip on to Crocs - are being banned in schools in Northland due to escalating arguments between youngsters over the sought-after items.

Kamo Primary School principal Sally Wilson was forced to take action after students became upset over Jibbitz trades and some resorted to stealing...

Wilson said attempts to create a safe environment for trades were a learning curve for tamariki and sometimes “ended in tears”.

Often, tamariki trade an item in the hopes of getting it back, and when they realise that isn’t going to happen, they “emotionally can’t cope”, she said.

Eventually, Wilson banned Jibbitz from the school because they had become “disruptive”.

“They were getting stashes and holding on to them, and there was an uneven trade for a certain one that they were after.”

While some kids have their “eye on the prize” and trade cheap Jibbitz for more expensive ones, Wilson said there have also been cases of stealing.

“It’s a learning curve about possessions.”

My children are well beyond the age of adding accessories to Crocs (or going to school, for that matter), but I can remember past crazes for trading Pokemon or Yu-Gi-Oh cards, where trading for sought-after cards got a bit out of hand. So, I can understand the attractiveness of a ban, to protect vulnerable, younger traders from being taken advantage of by older, more savvy traders, who understand that trades are 'for keeps' and better recognise the real value of what is being traded.

However, I can't help but feel that there is a missed opportunity here. The gains from trade is a cornerstone of economic principles, and can be taught very easily. And Jibbitz offer the opportunity to teach the gains from trade in a way that young children can readily understand. Jibbitz trading also offers an opportunity for young children to understand that there are two sides to every trade, and that trading is a voluntary activity. So, thinking about Jibbitz trading, whenever there is a trade of Jibbitz from one child to another (there are two traders), the trade will only happen if both children agree to the trade (either trader can say "no" to a trade), and each child will only agree to the trade if they think that what they are receiving is better than what they are giving away (there are gains from trade for both traders).

Instead of a ban, putting some simple rules for trading Jibbitz in place could really help. Here's a few. First. all trades are voluntary. Both children have to agree. Forced trades can be cancelled by a teacher. Second, all trades are 'for keeps'. There are no take-backs. As an extra rule perhaps a 'current price list' could be maintained, where the number of common Jibbitz expected to be traded for particularly rare or valuable Jibbitz are recorded. This may help avoid problems of a thin market for rare Jibbitz.

There are also opportunities for young children to better understand demand (some Jibbitz are more sought after than others), relative prices (the most sought-after Jibbitz may be traded for several less-sought-after Jibbitz), and scarcity (the rarest Jibbitz are likely to be the most valuable). All of these economic principles can be taught simply, and without recourse to economic jargon, and would help children to better understand some simple economics.

Of course, then there is this objection to Jibbitz trading:

Dargaville mother Taiāwhio Wati-Kaipo was first annoyed when Jibbitz were recently banned at Dargaville Primary School, worrying for her children’s ability to express their “individuality”.

But Wati-Kaipo soon considered the issue and realised the ban had a “deeper meaning”.

She believed the ownership of Jibbitz is a “social indication” of where someone is “sitting on the financial bracket”.

Wati-Kaipo said the price hike in Crocs themselves has created a “has and has not” situation among students.

“Without the Jibbitz, the Crocs were already speaking volumes about someone’s identity,” she said.

Wilson said the craze created a social comparison, as it was about who had the coolest ones and who had the most.

I know that at least some people really believe that inequality can be addressed by banning markets, but it's not correct. In this case, banning Jibbitz will simply shift the outward expression and indicators of social status to some other margin. There are many ways that social status is conveyed. Why stop at banning Jibbitz? Why not ban Crocs altogether? Or premium school bags? Or premium stationery? Or phone games or apps where players can buy special skins or other in-game virtual merchandise? Or phones entirely? Anyway, I'm getting off topic. The Jibbitz ban is a missed opportunity to help young children to better understand some key economic concepts that will be helpful in their development as economic citizens. A ban isn't necessary, and there are better responses to protect children from exploiting each other in the Jibbitz market.

Who benefits the most from free speech?

Earlier this year, I read a most interesting article by Diana Voerman-Tam, Arthur Grimes (both Victoria University of Wellington), and Nicholas Watson (Motu), published in the Journal of Economic Behavior and Organization (open access, with less technical summaries here and here). We also discussed it in the Waikato Economics Discussion Group earlier this year. The article looks at the economics of free speech. Specifically:

Can we measure the impact of free speech on people’s wellbeing (rather than its impact on economic growth per se), and can we determine which groups value free speech the most? This paper takes an empirical approach to answer these questions. We test whether free speech is valued differently by different groups in society, according to their level of re- sources as proxied by income or education. These relative valuations are analysed both using surveyed stated preferences and using estimates of the realized relationships between individuals’ subjective wellbeing (SWB), their country’s degree of freedom of speech, and individuals’ income or education levels, controlling also for other influences.

Voerman-Tam et al. used individual subjective wellbeing data from the World Values Survey and the Latino Barometer, and looked at the correlation with data on free speech and other human rights drawn from the CIRIGHTS database and the Varieties of Democracy database. They test two hypotheses:

The first reflects a view that free speech is a ‘luxury good’... The second reflects a view that free speech has an ‘empowerment effect’ for people with lower socio-economic status and who are therefore more likely to be marginalized in society...

If free speech is a luxury good, then people with higher income (or education) would have a more positive relationship between free speech and subjective wellbeing. That is, higher income people would have the greatest wellbeing gains from more free speech. On the other hand, if free speech has an empowerment effect, then people with lower income (or education) would have a more positive relationship between free speech and subjective wellbeing. That is, lower income people would have the greatest wellbeing gains from more free speech.

Interestingly, the paper starts with an analysis of stated preferences over the importance of free speech, based on responses to a World Values Survey question that reads:

If you had to choose, which one of the things on this card would you say is most important?: 1. Maintaining order in the nation. 2. Giving people more say in important government decisions. 3. Fighting rising prices. 4. Protecting freedom of speech.

Respondents were then asked which of the four choices was the next most important. Voerman-Tam et al. create two measures of the priority attached to free speech. The first is equal to one only if the survey respondent ranked freedom of speech as the most important (and zero otherwise). The second is equal to one if the survey respondent ranked freedom of speech either first or second in importance (and zero otherwise). Then, looking at the relationship between these variables and personal characteristics (including income and education), they find that:

A positive gradient is observed across both income and education for each of the free speech prioritization variables.

In other words:

...people with higher incomes or education place higher priority on free speech (relative to other alternatives that they are asked to rank).

I don't think too many people would find it surprising that people with higher income (or education) are more likely to state that free speech is more important to them than people with lower income (or education. This bit was also interesting, and mostly unsurprising:

Several other associations stand out: free speech is prioritized more by people who are young, students, and/or have no children, and by people to the left of the political spectrum. These characteristics are more in keeping with the hypothesis that people who are more marginalized favour free speech.

So, people with higher income (and education) say that free speech is more important. That is suggestive that free speech is a luxury good. However, it is based on stated preferences for free speech. People with low income (or education) likely have much more important things to worry about in their daily lives than whether they are able to exercise free speech.

A better question to ask, then, is who gains the most (in terms of wellbeing) from free speech? In the second part of their analysis, Voerman-Tam et al. find that overall, there is no correlation between free speech and subjective wellbeing (after controlling for other variables). However, that isn't what they were really interested in. When they interact free speech with income (or education), they find that:

...people with lower income (relative to others within their own country) benefit more with free speech than those with higher incomes, especially in countries with full free speech.

The results are similar for education. Overall, these results support the second hypothesis. So, despite the stated preference results suggesting that free speech is a luxury good, it turns out that people with lower income (or education) benefit the most from free speech (in terms of its contribution to subjective wellbeing).

In my view, the combination of these two results is important. People with higher incomes (or education) make up the elites in most societies, often holding positions of political (or if not political, then at least bureaucratic) power. If those groups believe that free speech is important (which according to these results, they do), then they are more likely to argue for more free speech. That, in turn, creates the greatest benefits (in terms of wellbeing) for people with lower incomes (or education), who are likely to be more disenfranchised. These results suggest to me that we might be optimistic for increases in free speech and somewhat of an equalising of wellbeing between the richer and poorer segments of society as a result.

However, before we get carried away, there are some important caveats. This research is based on correlations. It doesn't demonstrate that greater free speech causes increases in subjective wellbeing. We'd want to establish that more definitively. And, as with all research that involves subjective wellbeing data, we must be aware of its limitations and the criticisms it faces (for example, see here and here, but also see this post by Arthur Grimes as well).

Nevertheless, in the meantime high-income people should feel good about continuing to believe that free speech is of high importance.

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]

Can information about risk alter risky behaviour?

Why do people engage in risky behaviour, like drink-driving, or risky sexual behaviour? In theory, if people are rational, they weigh up the costs and benefits of each action, and undertake actions only when the benefits of the action outweigh the costs. People who engage in risky behaviour look (to a rational observer) like they are engaging in behaviour where the costs (including an assessment of the risk) likely outweigh the benefits. Why do they do it?

I see two potential explanations here. First, perhaps this is a case of behaviour that is boundedly rational. The people engaging in the risky behaviour may not have accurate information about the costs of the behaviour, and underestimate those costs. Making their decision based on the benefits and (underestimated) costs makes them more likely to engage in the risky behaviour. Second, perhaps these people are quasi-rational, and one of the characteristics of quasi-rational decision-making (as I discuss in my ECONS102 class) is a tendency to heavily discount the future. In cases where the benefits of a risky activity occur now, but the costs are faced at some point in the future (and so, heavily discounted), a quasi-rational decision-maker may be more likely to engage in the risky behaviour.

Either of those explanations may account for risky behaviour like drink-driving, or risky sexual behaviour. If people are unaware of, or discount the value of, the full costs of drink-driving or risky sexual behaviour, they may be more likely to engage in those activities. Also, the costs of drink-driving (such as harm to themselves, or others, or property, or the risk of penalties if they are caught) occur in the future, as do the costs of risky sexual behaviour (such as the risk of a sexually-transmitted disease, or unwanted pregnancy), while the benefits occur immediately.

Which explanation accounts for more of the activity? This is unclear. However, if most risky activity is explained by a lack of accurate information, then there is an obvious policy solution: provide accurate information about the risks and costs of the activity. If most risky activity is explained by discounting the future, then it would be more difficult to address the behaviour easily with a policy solution.

That brings me to this 2018 article by Pascaline Dupas (Stanford University), Elise Huillery (University of Paris-Dauphine), and Juliette Seban (Sciences Po), published in the Journal of Economic Behavior and Organization (ungated earlier version here). They report on a randomised experiment conducted with teenage schoolgirls in Cameroon, where they provided information about the risks of unprotected sex, and measured the effect on health outcomes and teen pregnancy rates. Specifically:

We use a field experiment conducted with teenage girls in 318 junior high schools in Cameroon to study, within one context, how the type of risk information being provided and the delivery method (teacher, outside professional or questionnaire) affect adolescents knowledge, perceived risks and behavior... We randomized HIV and sexual education interventions that differed in their delivery mechanism and intensity, as well as content, across schools. In each school, one eighth grade class was targeted for the study.

We consider four interventions. The first (In-Class Quiz) was completely “hands-off”, and not labeled as an educational intervention: students were simply asked to fill in an anonymous questionnaire with questions on HIV as well as on their own sexual behavior and that of their peers. The questionnaire took about one hour to go through, including the time to introduce it. The In-Class Quiz was a group-administered questionnaire and did not provide students with direct information, but required that students think actively about risk levels...

The other three interventions were clearly labeled as HIV education programs. Two of them consisted of general information on HIV prevention methods (abstinence, faithfulness and condom use) and the average HIV prevalence at the national level (the “basic message”). A third one mimicked the “sugar daddy risk information” first proposed in Dupas (2011) and included, on top of the basic message, detailed information on HIV prevalence disaggregated by gender and age group and a special module on cross-generational relationships, locally known as relationships with “sponsors”, and their contribution to the spread of HIV. The difference between the two “basic message” interventions is that one was delivered through regular school staff which received special training (Teacher Training), while the second one was delivered by an outside consultant who did a special visit to the school to deliver the message (Consultant). The intervention that included the sugar daddy module was also delivered by an outside consultant (Consultant +). Both interventions by consultants lasted approximately one hour.

Since classes were randomised to receive one of the four treatments, comparisons across the treatments (at the class level) provide an assessment of the impact of the intervention. The primary outcome was self-reported pregnancy measured 9-12 months after the treatment. Dupas et al. found that:

...all interventions were successful at reducing the incidence of teenage pregnancy during our follow-up period. The magnitude of the effects are relatively large, with an average drop of 2.9% points in the likelihood of having started childbearing at the time of the endline, off a mean in the control group of 9.5%, thus a 30% reduction.

So, score one for information as an intervention to reduce risky behaviour! Except that:

The most surprising results is that the most hands-off intervention, the In-Class Quiz, was successful, by itself, at reducing the incidence of unprotected sex and hence pregnancy in the following 12 months.

The in-class quiz provided no information, and only asked the participants to reflect on risk. So, what happened? Dupas et al. look into the mechanisms, finding that:

...interventions increased the likelihood that girls report adopting a clear, one-pronged strategy against HIV: abstinence...

Importantly, the mechanism through which the interventions helped girls adopt a clear and simple strategy against HIV differs between the In-Class Quiz and the education interventions. The In-Class Quiz led participants to revise upward their subjective beliefs about risk, while the other interventions improved knowledge without changing risk perceptions... In contrast, the education interventions did not change perceived risks (it did not make them even more pessimistic as the Quiz, but it did not bring them much closer to reality either), but it did affect girls’ knowledge about HIV transmission and prevention. In our context, these two mechanisms (change in subjective beliefs about risk and change in knowledge) turnout to be equally effective at changing girls’ plans and behaviors.

So, it wasn't just information about risk that mattered, but making the risks more salient as well. When people are provided with information about risk, it has to make the risks seem more important, if we want them to change their behaviour. However, there is reason to be sceptical about this single study. As anyone who has tried any information intervention can tell you, simply providing people with information will almost always have little to no effect on behaviour (although David Evans enumerates a number of counter-examples here). So, we should be cautious before we proclaim that we have the ultimate solution to risky behaviour (and, to be fair, the authors don't claim this), until this study has been replicated in other settings.

Book review: An Economist Goes to the Game

Some years ago, I read Everything I Ever Needed to Know about Economics I Learned from Online Dating" by Paul Oyer (which I reviewed here), and thought that it was really good. Evidently, Oyer has since learned some things about economics other than from online dating, because I just finished reading his 2022 book An Economist Goes to the Game. As the title suggests, this is a book about sports economics, which is an area of interest for me, so I was looking forward to reading it.

Like Oyer's earlier book, this one is well researched. All chapters make multiple references to the research literature on a particular topic. These topics range broadly across the economics of sports, including game theory applied to tennis serves and soccer penalties, ticket scalping, and the economics of hosting the Olympics or building a sports stadium. Those are all topics that I have written about on this blog, so for me, there was little that was new or exciting in those chapters. Even the chapters where I have less familiarity, such as discrimination in sport, and the economics of gambling, did not include many surprising insights.

However, professional and academic economists are not the audience for this book. Oyer writes in a nice style, and includes just enough personal anecdotes to keep the reader engaged in each topic. I really enjoyed the writing, and I'm sure that readers who are less familiar with the topic area will get a lot out of reading this book. That is especially true of sports fans (particularly US sports fans), who may be familiar with many of the specific sports examples, but unfamiliar with how those examples link to economic theory, or just how much economic research has been conducted on sports.

Overall, I recommend this book as a good way to get sports fans to appreciate a bit more economics, as well as for economics fans to appreciate a bit more sports!

The returns to pre-primary education in developing countries

Despite critiques of the 'Heckman curve' (see here), it is reasonable to believe that the returns to education are not only positive, but somewhat higher for the first years of education than for later years. That would be a simple application of what economists refer to as diminishing marginal returns (to education). So, I was interested to read this post on the Development Impact blog by Lelys Dinarte-Diaz and Alaka Holla, which summarises the results of a meta-analysis of the benefit-cost ratio for pre-primary education in developing countries. They write that:

Using robust-variance meta-analysis to aggregate study-level estimated effects, we find significant average effect sizes of preschool interventions on outcomes measured during preschool – around 0.15 standard deviations for cognitive skills and around 0.12 standard deviations for social-emotional skills and behavior.  

Next, to see whether these estimated benefits are worth their cost, we then take our sample of studies from low- and middle-income countries and restrict our attention to those that provided sufficient information for us to back out the cost per child of the pre-primary intervention. To convert these average effects into a monetary value, we looked to a few longitudinal studies that could estimate how much earnings in adulthood increase when cognitive skills increase during the early childhood period. With this conversion factor, along with other empirically informed assumptions, our most conservative calculations suggest benefit-to-cost ratios ranging from 1.7 to 14.2. That is, in all cases where we had data, benefits outweighed the costs. Returns were certainly positive.

You can find the working paper by Holla et al. here. The results are based on 50 studies conducted across 19 developing countries. The benefit-cost ratios certainly suggest that there is a lot of value in pre-primary education. This in itself doesn't tell us anything about the Heckman curve, because we would also need to know corresponding benefit-cost ratios for higher levels of education.

In their blog post, Dinarte-Diaz and Holla go on to discuss why there is under-investment in pre-primary education in developing countries:

Despite these high returns, it isn’t too difficult to understand why there is under-investment in a service like preschool. Though a government would have to front the costs today of expanding preschool or improving its quality, it is only in the future that the government would realize most of its monetized return once children enter the labor market as adults. That is, the returns could lag the costs by about 15-20 years. With political cycles and attention spans much shorter than this, getting preschool on any agenda would be a challenge.

As I discuss in my ECONS102 class, credit constraints are a serious impediment to investment in education for poorer households. The issue is that poorer households find it more difficult than richer households to save in order to pay for education costs, and more difficult (or impossible) to borrow to pay for education costs. And, if a poorer household can borrow, they tend to pay higher interest costs - because they are higher risk to lend to, lenders will require a higher interest rate in order to compensate for the risk.

These points about credit constraints extend to poorer countries in a very similar way. Dinarte-Diaz and Holla offer bonds as a solution, noting that:

The World Bank’s Treasury also raises funds to support the Bank’s work through Sustainable Development Bonds - $43 billion worth in 20 different currencies last year – which have had a AAA rating since 1959. They also have cause-specific bonds such as green bonds and bonds focused on women’s empowerment and on health and well-being.  

Why shouldn’t we add a preschool bond to the list?

This makes sense. In many developed countries, we deal with household credit constraints in education either by the government paying the costs of education (as is the case for primary and secondary education), or through student loans (as is often the case for tertiary education). The proposal by Dinarte-Diaz and Holla is a good suggestion, but with a potentially serious flaw. If it is the World Bank that is issuing and paying back the bond, then the suggestion is good. The World Bank is seen as being relatively low risk to lend to. However, the question becomes whether investment in pre-primary education is the best use of funds offered to the World Bank.

However, if Dinarte-Diaz and Holla are suggesting that developing countries themselves raise the money for pre-primary education through issuing bonds, then I think that runs into the same problems that poorer households do - bonds issued by developing countries tend to have high interest rates, to compensate for the risk of default. And issuing bonds for pre-primary education would compete with all of the other things that a country may wish to issue bonds for.

The takeaway message for me is that pre-primary education is very much worth the investment, but the real challenge is how that investment would be funded.