Taking advantage of loss aversion in education

Many years ago (I forget exactly when), I introduced extra credit into my ECON110 class (which is what is now ECONS102). The idea was to provide an incentive for students to attend class, since they could earn extra credit for completing various in-class exercises. A couple of years later, I briefly changed the way that I framed the extra credit, from being "extra marks that would be gained from attending", to "extra marks that would be lost by not attending".

If students were purely rational, the change from 'gain framing' to 'loss framing' the extra credit should have had no impact on student attendance. However, I was looking to exploit the fact that most people are quasi-rational, rather than purely rational. Quasi-rational decision-makers are loss averse, meaning that they value losses more than equivalent gains. For a loss averse person, losing $20 makes them unhappy to a greater extent than winning $20 makes them happy.

Does a change from 'gain framing' to 'loss framing' work? That is the question that this new article by Antal Ertl, Éva Holb (both Eötvös Lóránd Science University), and Barna Bakó (Corvinus University of Budapest), published in the Journal of Economic Behavior and Organization (open access), tries to answer. They use data from a field experiment at Corvinus University of Budapest, where students enrolled in a compulsory macroeconomics course for business students were randomised into one of three conditions: (1) Gain group, which earned points in each of four tests and the final examination as usual; (2) Loss group, which started each test and the final exam with full points, but had points deducted for each incorrect answer; and (3) Hybrid group, which was the same as the Gain group for the tests, but switched to the loss framing for the final examination.

Ertl et al. have a sample of 321 students who consented to be part of the research, completed an initial questionnaire at the start of the term, and earned a non-zero grade. Randomisation was conducted at the level of the tutorial group (so all students in a tutorial were in the same treatment), in such a way that each teacher had groups across more than one treatment. One wrinkle in their analysis is that the best three out of the four tests would count towards a student's grade, meaning that students may end up putting differential effort into each test, depending on how they have performed in the other tests already completed. So, in addition to looking at the effect of treatment on each test mark individually, Ertl et al. look at the effect on the 'best three' tests collectively, as well as the exam mark.

If randomisation were perfect and the treatment groups were balanced, the comparison between the Loss group and the Gain group would demonstrate the overall effect of loss framing on student performance. The comparison between the Loss group and the Hybrid group for the final exam, compared with the same comparison for the best three tests, would demonstrate whether students adjust in such a way that the loss framing has less impact over time (because the Hybrid group would be in their first loss-framed assessment, while the Loss group would be in their fifth such assessment). The treatment groups weren't perfectly balanced, with students sorting into tutorial groups in part based on whether they worked part-time. So, Ertl et al. control for working part-time, the tutorial day and time, and the tutorial group teacher, as well as other demographic and background variables.

In their main analysis, they find support for the positive effects of loss framing:

For the Loss treatment, the effect on the average of the Best 3 Tests is 3.2 percentage points, although the difference is not statistically significant. The treatment effect on the Final Test score, however, shows a large difference of 9.6 percentage points when not controlling for Best 3 Tests’ scores, i.e., how well students did throughout the semester before the Final Test.

After controlling for performance in the best three tests, the effect of the loss framing on performance in the final examination is a statistically significant 7.8 percentage points. Turning to the comparison of the Loss and Hybrid groups, Ertl et al. find that:

...the estimated effect sizes for Loss and Hybrid are essentially the same for the Final Test, once we take into account how well students did perform throughout the semester...

These results are consistent with loss framing leading to better student performance, and there being no novelty effect - the effect of loss framing doesn't appear to decline over time. Ertl et al. go on to show that the effects are similar for both male and female students, but larger for students who did not take advanced mathematics in high school than for those that did. They also show that the treatment did not seem to negatively affect students' perceptions of the course, because the teaching evaluations were similar for the different treatment groups.

Finally, Ertl et al. do provide a note of caution in their conclusion:

previous studies have highlighted possible psychological and motivational costs associated with loss framing... These findings suggest that the mechanism by which loss framing improves performance may, at least in part, operate through heightened tension and concern about avoiding mistakes rather than through enhanced intrinsic motivation. Moreover, in extreme cases, loss-framed grading may even produce adverse effects — for example, low-performing students might become discouraged early in the semester after ‘‘losing’’ too many points. Once it becomes apparent that only a passing grade is attainable at best, the loss-framed structure may make this limitation increasingly salient, potentially exacerbating anxiety and disengagement. Over time, this could have broader implications for students’ well-being and their willingness to enroll in courses or programs that employ such systems.

Ertl et al. don't directly test for these effects, but they should be a concern. We may be able to improve student performance through loss-framing assessments, but that might come at a cost to student mental health and wellbeing.

And that brings me back to the example I started with, from my ECON110 class. When I switched extra credit from gain-framed to loss-framed, student attendance in class did improve slightly. However, the bigger impact seemed to be the number of students who would contact me by email, seeking special consideration for missing the extra credit, offering to provide medical certificates or other evidence to explain their absence, and asking for extra chances to complete the in-class exercises. It turned out to be administratively much more costly for me, and so the change was short-lived (to the extent that I cannot even remember which year I tried this in). Those reactions could suggest a negative psychological effect of the switch from gain framing to loss framing.

So, not all interventions that are effective for promoting student performance should be adopted. We need to carefully consider both the benefits and the costs of the intervention first. Taking advantage of student loss aversion might be worth exploring further, but I would want to see a wider evaluation that included student wellbeing outcomes before adopting it.

Changing their minds could be a good thing for economists

People don't like to change their minds. This may partly be an expression of loss aversion - we really want to avoid losses, including the loss of an idea that we previously thought was true. This leads to status quo bias - we prefer not to change things, and keep them the same, because changing things entails a loss. But what if changing our minds could make us better off? Would we be so reluctant to do so?

This 2025 paper by Matt Knepper (University of Georgia) and Brian Wheaton (UCLA) suggests that economists, at least, should not be afraid to change their minds, because doing so increases the number of citations to their research. Knepper and Wheaton investigate authors who undergo an 'ideological reversal' - previously publishing research that could be considered right-wing, before switching and publishing a paper that draws a left-wing-consistent conclusion, or the reverse (switching from left-wing to right-wing). Their main data source is every economics paper ever published in the top 100 economics journals indexed in Web of Science - some 200,000 articles. They also have a narrower dataset of papers referenced in meta-analyses on policy topics, including:

...the minimum wage, the economics of unions, the taxable income elasticity, the fiscal multiplier, intergenerational transfers, trade and productivity, trade and domestic employment, crowd-out, the gender wage gap, unemployment insurance, disability benefits, universal preschool, childcare and employment, immigration and wages, and more.

Knepper and Wheaton use this narrower dataset to train a machine learning model to categorise the rest of the papers in the dataset, as to how left-wing (or right-wing) the conclusions are. For instance, a paper that concludes that the minimum wage reduces employment is more right-wing, whereas one that concludes that there is no disemployment effect of the minimum wage is more left-wing. Knepper and Wheaton define an author as left-wing if they published more left-wing papers than right-wing ones over the previous five years, and the reverse for right-wing authors. They then use the larger dataset to investigate what happens to each economist who undergoes an 'ideological reversal'. They first outline some descriptive facts based on their dataset, including:

• Fact #1: The typical author mostly publishes results on one side of the political spectrum.

• Fact #2: Ideological reversals are not rare; they occur at least once for 40% of authors.

• Fact #3: Ideological reversals become much more common later in an author’s career, with authors essentially never undergoing a reversal in the first decade of their career.

• Fact #4: Most ideological reversals do not represent a permanent defection to the other side of the political spectrum, but rather the beginning of repeatedly publishing results on both sides of the spectrum.

• Fact #5: Ideological reversals occur much more frequently amongst authors who are (initially) classified as right-wing.

That does seem like a surprisingly high proportion of economists who undergo at least one ideological reversal. However, perhaps we should take comfort in that - if the results point in a particular direction, our conclusions should say that, even if that conclusion is inconsistent with our previous conclusions on the same topic.

Do these ideological reversals matter though? Knepper and Wheaton employ a difference-in-differences analysis, comparing the difference in citations (and other metrics) between authors who did, and did not, undergo an ideological reversal, between the time before, and after, the reversal occurred. In other words, they look at whether citation counts rise more for economists who have an ideological reversal than for otherwise similar economists who do not. The results are striking, with:

...a sharp clear increase in citation count following an ideological reversal with essentially no evidence of pre-trends... The citation boost accumulates to approximately 9 over a one-decade period and 30 over a two-decade period.

The results remain consistent when Knepper and Wheaton limit the analysis to papers published before the ideological reversal, and when they limit the analysis to papers in the meta-analysis only (showing that the machine learning approach doesn't drive the results). Knepper and Wheaton also find evidence consistent with no change in the quality of papers before and after the ideological reversal, and that:

Both left-to-right and right-to-left reversals are rewarded by increased citations of roughly the same magnitude. The boost in citations received subsequent to a left-to-right reversal is mostly driven by citations from right-wing authors, and the boost in citations received subsequent to a right-to-left reversal is mostly driven by citations from left-wing authors. Encouragingly, however, the new right-wing (left-wing) audience garnered by a left-to-right (right-to-left) reversal... also engages with and cites the author's previous left-wing (right-wing) papers. This dynamic suggests that ideological reversals help prevent the formation of echo chambers in economics academia and expose authors to opposite ideological findings.

This last result is particularly important, and I believe it allows us to conclude that economists need not fear ideological reversals. In doing so, they can attract a new audience from the other side of the ideological spectrum, bringing the two sides closer together. Hopefully through that, we end up with higher-quality research overall.

[HT: Marginal Revolution, last year]

Americans' beliefs about trade, and why compensation matters

Do people understand trade policy? Or rather, do they understand trade policy the way that economists understand it? Given current debates in the US and elsewhere, it would be fair to question people's (or politicians') understanding of trade policy, and to consider what it is about trade that generates negative reactions. After all, the aggregate benefits of free trade are one of the things about which economists most agree.

Last year, Stefanie Stantcheva won the John Bates Clark Medal (which is awarded annually to the American economist under age 40 who has made the most significant contributions to the field). Stantcheva's medal-winning work included three main strands, one of which was the use of "innovative surveys and experiments to measure what people know". One of the papers from that strand of research is this 2022 NBER Working Paper (revised in 2023), which describes Americans' understanding of trade and trade policy and importantly, it answers the question of why people support trade (or not).

The paper reports results from three large-scale surveys in the US run between 2019 and 2023, with a total sample size of nearly 4000. The surveys also included experiments that primed respondents to think about trade from particular angles. Overall, Stantcheva is interested in teasing out the factors that affect Americans' support for trade policies. Essentially, she tests the mechanisms that are described in Boxes I-V in Figure 2 from the paper:

Box I picks up views on whether trade lowers prices and increases variety for consumers. Box II picks up the threats from increasing trade to workers in import-competing sectors. Those two boxes together constitute self-interest as an effect on people's views on trade policy. Their views might also be affected by broader social and economic concerns, such as trade's efficiency effects (Box III), its distribution impacts (Box IV), and patriotism, partisanship, or geopolitical concerns (Box V).

Before we turn to the specific results on the mechanisms, it is worth considering Americans' overall views on trade first. Stantcheva reports that:

Most respondents (63%) are supportive of more free trade and decreasing trade restrictions in general... Only 36% believe that import restrictions are the best way to help U.S. workers.

Nevertheless, there is support for more targeted trade restrictions. 40% of respondents believe the US should restrict food imports to ensure food security. 54% think the US should protect their “infant” industries. 78% support protection of key consumer products, namely food items and cars. 50% believe the US should restrict trade in key sectors, such as oil and machinery...

And general knowledge about trade policy is not too bad, as:

...almost 80% of respondents know what an import tariff is, but just around half know what an import quota is. Two-thirds of respondents appear to understand the basic price effects of tariffs and export taxes, i.e., that an import tariff on imported goods will likely raise the price of that good and that an export tax will increase the price of the taxed good abroad. The final question... considers a scenario in which the US can produce a good (“cars”) at a lower cost than the foreign country. Respondents are asked whether, under some circumstances, it would still make sense to import cars from abroad. 68% of respondents agree that it could make sense. This suggests that respondents either understand the concept of comparative advantage or have in mind some model of love-for-variety or quality differential.

So far, so good. How do Americans perceive the impacts of trade? Figure 9 Panel A reports perceptions related to the self-interest motivation (Boxes I and II from the figure above):

From the bottom of that figure, it is clear that a majority of Americans believe that they are better off from trade, but a substantial minority (39%) believe that they are worse off. Still focusing on the self-interest motivations (Boxes I and II), Stantcheva finds that:

In general, a respondent’s (objective) negative exposure to trade through their sector, occupation, or local labor market is significantly positively correlated with a feeling that trade has made them worse off and that it has negatively affected their job. People exposed to trade through their job also feel worse off as consumers and are less likely to believe that trade has reduced the prices of goods they buy, perhaps because they feel that their purchasing power is lower than it would otherwise be. Furthermore, college-educated respondents are significantly less likely to feel negatively impacted in their role as consumers and workers.

Notice those results are mostly consistent with the figure above. What about consumer gains through reduced prices on imported products? Stantcheva reports that:

...the belief that prices decrease from trade is not significantly related to either support for trade or redistribution. Consistent with this lack of correlation, the experiment priming people to think of their benefits as consumers (precisely, the prices and variety of goods they purchase) does not move their support for trade either.

So, in terms of self-interest, Americans' support for trade is more negative when they are negatively affected as workers, but is not more positive when they are positively affected as consumers. In my ECONS102 class, we talk about the tension between the gains from trade and loss aversion. Every trade involves gaining something, in exchange for giving something up. However, quasi-rational decision-makers are affected much more by losses than equivalent gains (what we call loss aversion). So, loss aversion might mean that many profitable trades are not undertaken, because the decision-makers prefer to keep what they have, rather than giving it up for something that may be objectively worth more. In the case of Stantcheva's survey respondents, the workers who are negatively impacted experience a loss, which would be weighed much more heavily than the gain that a consumer receives.

An alternative explanation is salience. Job losses are very visible and impactful on the people who lose their jobs and those around them. Consumers' gains in terms of lower prices and increased variety, on the other hand, are not really as visible - many people wouldn't even notice them, unless they were pointed out to them. So even if people weren’t loss averse, attention would still be drawn disproportionately to the negative impacts of trade, rather than the positive. Taken altogether, Stantcheva's results here are not surprising.

What about the broader social and economic concerns, and their impact on views about trade? In terms of efficiency effects (Box III), Stantcheva reports that:

Respondents are generally optimistic about these effects. For instance, 61% of respondents think that international trade increases competition among firms in the US, 69% that it fosters innovation, and 62% that it generates more GDP growth.

Moreover:

...efficiency gains from trade are significantly associated with more support for free trade... This relation can be seen in the correlations and the experimental effects: the Efficiency treatment significantly improves support for free trade.

And interestingly:

Respondents who believe that trade can improve innovation, competitiveness, and GDP are more supportive of redistribution policy to help those who do not benefit from these efficiency gains.

Turning to distributional impacts (Box IV), Stantcheva reports that:

Overall, respondents know that trade can have adverse distributional consequences through the labor market. Just around half of all respondents believe that trade has, on balance, helped US workers. 79% of people think that trade is the reason for “unemployment in some sectors and the decline of some industries in the U.S..” More respondents (63%) believe that high-skilled workers could easily change their work sector if their jobs were destroyed by trade than that low-skilled workers could switch sectors (37%)...

Consequently, around two-thirds of respondents think that trade is a major reason for the “rise in inequality” in the US. Notably, despite being aware of the potential adverse distributional consequences of trade, a majority (62%) of respondents believe that, in principle, trade could make everyone better off because it is possible to “compensate those who lose from it through appropriate policies.”

It is interesting that so many people believe in the compensation principle (although I bet that few of them would know that term for it). And it turns out that belief in the compensation principle is really important, as:

...the strongest predictor of support for free trade is the belief that, in principle, losers can be compensated... free trade. As long as respondents believe that adverse consequences from trade on some groups can be dampened by redistributive policy, they are likely to support more free trade, even if they believe that there are adverse distributional consequences. The perceived distributional impacts of trade also substantially matter for support for compensatory redistribution. Respondents who believe that trade hurts low-income and low-skilled workers and that it fosters inequality support redistribution much more.

Finally, in terms of patriotism, partisanship, or geopolitical concerns (Box V), Stantcheva reports that:

...those who worry about geopolitical ramifications from trade restrictions, i.e., retaliatory responses, are more likely to support policies to compensate losers from trade rather than support outright trade restrictions. Patriotism is significantly correlated with support for trade restrictions in many industries and to protect U.S. workers, as well as with lower support for compensatory transfers...

Stantcheva draws a number of conclusions from her results, including:

First, respondents perceive gains from trade as consumers to be vague and unclear but perceive potential losses as workers to be concentrated and salient. Actual and perceived exposure to trade through the labor market is significantly associated with policy views...

Second, people’s policy views on trade do not only reflect self-interest. Respondents also care about trade’s distributional and efficiency impacts on others and the US economy...

Third, respondents’ experience, as measured by their exposure to trade through their sector, occupation, and local labor market, shapes their policy views directly (through self-interest) and indirectly by influencing their understanding and reasoning about the broader efficiency and distributional impacts of trade.

Overall, I take away from this paper that Americans have more correct views about trade than I suspected. Their support for trade is not determined simply by self-interest, but is more nuanced. However, negative impacts weigh far more heavily for those who are negatively impacted than the weight attached to positive impacts for those who are positively impacted. That may relate to loss aversion, and to the more concentrated nature of negative impacts compared with more diffuse positive impacts. That asymmetry also explains why a majority have positive views of trade (since fewer people will have been negatively impacted on the whole). The most surprising aspect to me, though, was the views on the compensation principle. Those results provide a clear policy prescription. To get more people on board with trade, making compensatory policy more explicit and salient may help to ensure that there is greater support for trade. On the other hand, politicians who want to exploit the negative views on trade might benefit from obscuring any such compensatory policies. Unfortunately, there are too many who are willing to do just that.

[HT: Marginal Revolution, last year]

The emerging debate on Oprea's paper on complexity and Prospect Theory

Late last year, an article in the American Economic Review by Ryan Oprea caught my attention (and I blogged about it here). It purported to show that the key experimental results underlying Prospect Theory may in part be driven by the complexity of the experiments that are used to test them. These were extraordinary results. And when you publish a paper with extraordinary results, that could potentially overturn a large literature on a particular theory, then those results are going to attract substantial scrutiny. And indeed, that is what has happened with Oprea's paper.

The team at DataColada, most well-known for exposing the data fakery of Dan Ariely and Francesca Gino (and the resulting lawsuit, which was dismissed), have a new working paper, authored by Daniel Banki (ESADE Business School) and co-authors, looking at Oprea's results (see also the blog post on DataColada by Uri Simonsohn, one of the co-authors). To be clear before I discuss Banki et al.'s critique, they don't accuse Oprea of any misconduct. They mostly present an alternative view of the data and results that appears to contradict key conclusions that Oprea finds in his paper. Oprea has also provided a response to some of their critique.

I'm not going to summarise Oprea's original paper in detail, as you can read my comments on it here. However, the key result in the paper is that when presented with risky choices, research participants' behaviour was consistent with Prospect Theory, and when presented with choices that involved no risk at all but were complex in a similar way to the risky choices ('deterministic mirrors'), research participants' behaviour was also consistent with Prospect Theory. This suggests that a large part of the observed results that underlie Prospect Theory may arise because of the complexity of the choice tasks that research participants are presented with.

Banki et al. look at a number of 'comprehension questions' that Oprea presented research participants with, and note that:

...75% of participants made an error on at least one of the comprehension questions, such as erroneously indicating that the riskless mirror had risk.

Once the data from those research participants is excluded, Banki et al. show that research participant behaviour differs between lotteries and mirrors for the research participants who 'passed' the comprehension checks (by getting all four of the comprehension questions correct on their first try). This is captured in Figure 2 from Banki et al.'s paper:

The two panels on the left of Figure 2 show the results for the full sample, and notice that both lotteries (top panel) and mirrors (bottom panel) look similar in terms of results. In contrast, when the sample is restricted to those that 'passed' the comprehension checks, the results for lotteries and mirrors look very different. Which is what we would expect, if research participants are not 'fooled' by the complexity of the task.

Banki et al. provide a compelling reason why the results for the research participants who failed the comprehension checks looks the same for lotteries and mirrors: regression to the mean. As Simonsohn explains in the DataColada blog post, this arises because of the way that a multiple-price list works:

When the dependent variable is how much people value prospects, regression to the mean creates spurious evidence in line with prospect theory. When people answer randomly for 10% chance of $25, they overvalue it, because the “right” valuation is $2.50, and the scale mostly contains values that are higher than that. When people answer randomly for 90% chance of $25, they undervalue it, because the “right” valuation is $22.50 and the scale mostly contains values that are lower than that. Thus, random or careless responding will produce the same pattern predicted by prospect theory.

Oprea responds to both of these points, noting that:

...a range of imperfectly rational behaviors including noisy valuations, anchoring-and-adjustment heuristics, compromise heuristics and pull-to-the-center heuristics will all tend to produce prospect-theoretic patterns of behavior simply because of the nature of valuation. BSWW offer this possibility as an alternative to the Oprea (2024)’s account of his data, but in fact these are examples of exactly the types of cognitive shortcuts Oprea (2024) was designed to study.

In other words, Banki et al.'s results don't refute Oprea's results, but are very much in line with Oprea's. One thing that Oprea does take issue with is Banki et al.'s use of medians as the preferred measure of central tendency. Oprea uses the mean, and when reanalysing the data with the same exclusions as Banki et al., Oprea shows that the mean results look similar to the original paper. So, Banki et al.'s results are not simply driven by excluding the research participants who failed the comprehension checks, but also by switching from using the mean to using the median.

On that point, I'm inclined to agree with Banki et al. The median is often used in experimental economics, because it is less influenced by outliers. And if you look at Oprea's data, there are a lot of large outliers, which become quite influential observations when the mean is used as the summary statistic. However, the outliers are likely to be the observations you want to have the smallest effect on your results, not the largest effect.

Oprea also critiques Banki et al.'s interpretation of the comprehension questions. Oprea rightly notes that:

...it is important to emphasize that these training questions weren’t designed to measure beliefs (e.g., payoff confusion), and because of this they are poorly suited to the task BSWW repurpose it for, ex post. Indeed, evidence from the patterns of mistakes made in these questions suggests that overall training errors largely serve as a measure of the cognitive effort (an important ingredient in Oprea (2024)’s account) subjects apply to answering these questions, and that BSWW therefore substantially overestimate the level of payoff confusion with which subjects entered the experiment.

In other words, the 'comprehension questions' are not comprehension questions at all, but they are really 'training questions' that were used to train the research participants to understand the choice tasks that they would be presented with. And so, using those training questions overall as a measure of understanding misses the point, and seriously underestimates the amount of understanding of the task that research participants had by the time they had completed the training questions.

Oprea's response is good on this point. However, if the training questions had really done a good job of training the research participants, then all participants should have had a similar level of understanding by the end of the training questions, and there should be no detectable differences in behaviour between those with more, and those with fewer, 'failed' training questions. That wasn't the case - the behaviour of the research participants who made errors in training was much more likely to be the same for lotteries and mirrors than was the behaviour of research participants who made no errors. To clear this up, it would have been interesting to have research participants also complete 'comprehension questions' at the end of the experimental session, to see if they still understood the tasks they were being asked to complete. At that point, those failing the comprehension questions could be dropped from the dataset.

One point of Banki et al.'s critique that Oprea hasn't engaged with (yet, although he promises to do so in a future, more complete response), is their finding that a larger than 'usual' proportion of the research participants fail 'first order stochastic dominance' (FOSD). A failure of FOSD in this context means that a research participant valued a lottery (or mirror) lower than a similar lottery that was strictly better. For example, valuing a 90% chance of receiving $25 less than a 10% chance of receiving $25 is a failure of FOSD. Banki et al. show that:

We begin by examining G10 and G90. Violating FOSD here involves valuing the 10% prospect strictly more than the 90% one. Across all participants (N = 583), 14.8% violated FOSD for mirrors, and 13.9% for lotteries. These rates are quite high given that the prospects differ in expected value by a factor of nine.

Those failure rates are much higher than for other similar research studies. Banki et al. note an overall rate of 20.8 percent in the Oprea results, compared with an average of 3.4 percent across eight other highly cited studies. It will be interesting to see how Oprea responds to that point in the future.

This is an interesting debate so far. Oprea does a good job of summing up where this debate should probably go next:

Ultimately, however, these questions and ambiguities can only be fully resolved by further research. While BSWW’s critique has not convinced me that the interpretation offered in Oprea (2024) is mistaken, I am eager to see new experiments that deepen, alter, or even overturn this interpretation. First, concerns that the Oprea (2024)’s results are a consequence of the design being too confusing to yield insight can only really be resolved one way or another by followup experiments that vary his procedures, instructions and other design choices in such a way as to satisfy us that the Oprea (2024) results are (or are not) overfit to that design.

Indeed, more follow-up research is needed. Prospect Theory hasn't been overturned, yet (and as I noted in my earlier post, it is consistent with a lot of real-world behaviour). However, now we know that it may be vulnerable, and Oprea's paper provides a starting point for testing more thoroughly how much of the experimental results arise from complexity.

[HT: Riccardo Scarpa]

Read more:

• Are experimental measures of loss aversion and behaviour under risk just an artefact of complexity?

Are experimental measures of loss aversion and behaviour under risk just an artefact of complexity?

Loss aversion has been under fire in the economics literature recently (see here and here). As one of the foundations of behavioural economics, this is a big deal. So, I was interested to read this recent paper by Ryan Oprea (University of California, Santa Barbara), published in the journal American Economic Review (ungated earlier version here). Oprea essentially tests the key tenets of Prospect Theory, that when faced with a risky choice such as a lottery, people are risk averse when it comes to gains, but risk seeking when it comes to losses. Oprea's argument is that we observe that behaviour in lottery experiments, not because it is real, but because it is an artefact of the complexity of the lotteries that the research participants are faced with.

Here's what Oprea did:

In each task in our experiment, we elicit subjects’ dollar valuations for a set of 100 “boxes,” each of which contains some dollar amount. For example, in one of our tasks (called G90), we ask subjects to value a set consisting of 90 boxes that each contain $25 and 10 boxes that each contain $0. Acquiring a set of boxes influences the subject’s earnings in the experiment according to a payoff rule, and we compare how subjects value these sets under two contrasting payoff rules.

By opening one of the boxes from the set at random and paying the subject the amount inside, we turn the set into a lottery (i.e., G90 becomes a risky prospect of earning $25 with probability 0.9), and the dollar value the subject attaches to it becomes a certainty equivalent: the certain dollar amount the subject judges to be equivalently valuable to the risky lottery.

Using those results, Oprea replicates the key results from Prospect Theory, which he refers to as the 'fourfold pattern' of risk (a term that actually comes from Kahneman and Tversky), as well as loss aversion. Then:

Our contribution is to compare these valuations to the valuations of what we call “deterministic mirrors” of the same lotteries. A deterministic mirror of a lottery consists of the same set of 100 boxes used to describe the lottery but is characterized by a different payoff rule: instead of paying the dollar amount in one of the 100 boxes selected at random as a lottery does, a mirror pays the sum of the rewards in all of the boxes, weighted by the total number of boxes. Thus, instead of paying $25 with probability 0.9 (as a lottery does), the mirror of G90 pays 0.9 × $25 = $22.50 with certainty.

In other words, the 'deterministic mirror' of a lottery retains all of the complexity associated with the choice, but eliminates all of the risk (because the amount received is certain, rather than risky). So, if the 'fourfold pattern' is real and arises from the riskiness of the lottery, it should disappear in these experiments. Instead, using data from 673 research participants (and with similar results in a second sample of 489 research participants):

...we find that

(i) The fourfold pattern arises in the valuations of deterministic mirrors just as it does in lotteries, and with roughly the same strength. Importantly, this means that we find strong evidence of what is usually called “probability weighting” in settings without probabilities.

(ii) Loss aversion arises in deterministic mirrors even though at the relevant margins they cannot actually produce losses. Thus, we find strong evidence of what is usually called “loss aversion” in settings without risk of loss.

(iii) Across subjects, the severity of each of these anomalies in lotteries is strongly predicted by their severity in deterministic mirrors, suggesting that the behaviors in the two settings are strongly linked, deriving from a common behavioral mechanism (which, clearly, cannot be grounded in risk or risk preferences).

In other words, Oprea finds strong evidence that it is complexity that drives the 'fourfold pattern' of risk in lottery experiments, because when risk is removed (but complexity remains), the 'fourfold pattern' is still there. On top of that, loss aversion remains even when there is no risk of loss. So, loss aversion may also be an artefact of complexity of lottery experiments. Oprea concludes that:

First, theories of risk preferences designed to explain these anomalies (e.g., prospect theory) are unlikely to contain much normative content and therefore should not be accommodated in the inference of welfare or the design of policy. Second, our finding of systematic departures from neoclassical benchmarks in perfectly deterministic settings suggests that many of our descriptive theories of preferences for risk are really descriptive theories of the way people evaluate complex things.

That's a really nice way of saying that behavioural economists may need to reconsider some of their key theories, because the lab experiments they have been using to verify them do not stand up to this scrutiny. And Oprea's results may also help to explain some of the recent anomalies in the loss aversion literature (see here and here).

Oprea's results are important, and even though the working paper version of this article has already been cited over 50 times, I still don't think this research has received the attention that it deserves (and see Eric Crampton's take here). However, it may not be time to throw away behavioural economics or loss aversion entirely. Oprea notes that:

We do not claim, for instance, on the basis of these data that risk preferences or even loss preferences do not exist but only that they are unlikely to be reliably revealed in lottery valuations.

That is an important caveat. Behavioural economists may simply need to find a new way of demonstrating the 'fourfold pattern' of risk, and loss aversion, without resorting to complex lotteries. These effects may still be real. After all, there is a lot of real-world behaviour that is very consistent with loss aversion (see my various posts on that topic here).

Read more:

• Meta loss aversion
• Only a minority of real people may actually be loss averse

Taylor Swift tickets and the endowment effect

The Wall Street Journal reported last month (paywalled, but see here for an alternative):

Taylor Swift ended the European leg of her Eras tour on Tuesday at London’s Wembley Stadium, delighting nearly 100,000 cheering “Swifties”—but leaving many who couldn’t snag a ticket disappointed. One reason: the failure of the secondary market in tickets. Swifties have the same mental biases as the rest of us, making them reluctant to sell even at eye-watering prices.

Markets work on the basis of supply and demand setting a price. If there is more demand than supply, the price rises until fewer people are willing to buy and more are willing to sell. The basic problem is that Swifties mostly aren’t willing to sell, so the price soars until demand is destroyed—hitting well over $1,000 for many tickets...

I have firsthand experience: My eldest offspring snagged tickets months ago to take my besequinned wife (but not me) to the latest Eras concert. By this week the tickets were changing hands at more than eight times face value, and both agreed they wouldn’t buy them at such a high price.

Given they wouldn’t buy at this price they ought to be, on traditional economic assumptions, willing sellers. But both dismissed the idea out of hand—and not merely because trading tickets is trickier than trading shares. There probably would be some ludicrous price at which they would have parted with the tickets, but even a quick profit of eight times their outlay in a matter of months wouldn’t do it.

The WSJ rightly offers up loss aversion and the endowment effect as explanations for this behaviour. Loss aversion is the idea that decision-makers value losses much more than otherwise-equivalent gains. The pain of giving something up is worth much more than the pleasure of gaining that same thing. One consequence of loss aversion is the endowment effect. Since giving something up makes people very unhappy (because they are loss averse), people prefer to hold onto the things that they already have. That means that, when a person owns something, like a Taylor Swift ticket, they have to be given much more to compensate them for giving it up than what they would have been willing to pay to get it in the first place.

This applies to lots of things, not just Taylor Swift tickets (although, honestly, Taylor Swift tickets was the exact example that I used in my ECONS102 class earlier this trimester). The original research example that described endowment effects, by Daniel Kahneman, Jack Knetsch, and Richard Thaler, used free coffee mugs to demonstrate the effect. People given a free coffee mug were generally unwilling to exchange it for a pen, and people given a free pen were generally unwilling to exchange it for a coffee mug.

Returning to concert tickets, tickets to the Oasis reunion tour sold out fairly quickly this week - I bet those who have those tickets also wouldn't be willing to give them up cheaply. A further interesting implication of the endowment effect arises in the case of Oasis tickets, since according to this tweet from the band's official X account:

Tickets can ONLY be resold, at face value, via @TicketmasterUK and @Twickets.

If the endowment effect applies, few ticket-holders will be willing to part with their tickets at face value. These secondary markets are unlikely to help many people who originally missed out to secure tickets. I guess we will see.

[HT: Cyril Morong at The Dangerous Economist, for the WSJ article]

How much is your job worth to you?

A rational decision-maker weighs up the cost and benefits of the alternatives available to them before they decide which alternative is the best option for them. When faced with a 'yes or no' decision, 'yes' is the best alternative when the benefits outweigh the costs (and 'no' is the best alternative when the costs outweigh the benefits). When choosing between mutually exclusive alternatives, the best alternative is the one that provides the greatest net benefit (the difference between benefits and costs).

The costs and benefits might be monetary, but not necessarily. And even if the costs and benefits are not directly monetary, they may still be measurable in dollars. For example, how much is your job worth to you? It seems like an odd question to ask. You didn't 'buy' your job, after all (I hope!). But, as we will come to a bit later, this question has some important policy implications.

How can we work out how much a job is worth to the worker? Since the worker has their job already, we can't use how much they are willing to pay to get a job. However, we can try to find out how much the worker would be willing to accept in order to quit their job. So, how much would you have to be paid to quit your job?

That is the question that Soumaya Keynes asks in this recent article in the Financial Times (paywalled):

A new working paper by researchers at the Centre for Economic Policy Research and Stanford University, deploys this approach, asking Europeans what they would do if they received sums ranging from €5,000 to €100,000.

Below around €25,000, people say they would plough on with work. But for sums between that threshold and €100,000, their likelihood of working falls by 3 percentage points on average. Women, as well as people who are older, who have less debt or who are close to retirement are more likely to drop out.

What does that imply about the value of a job? If paying someone €100,000 reduces their likelihood of working by three percentage points on average, then reducing their likelihood of working by 100 percentage points would cost €3.33 million (about NZ$5.85 million). [*]

Why does this matter? Keynes notes that:

The question of how one might respond to a financial windfall of this sort is a fun thought experiment. But for policymakers it carries more weight. They have to consider whether a stimulus cheque or a tax break could encourage people to quit their job, or make them deaf to pleas from desperate employers. They have to ask how much money it takes to turn someone idle.

It seems like it would take a substantial windfall to cause most people to quit their jobs, beyond the scope of what a stimulus cheque, or even a universal basic income, would provide. That doesn't mean that no one will quit after receiving even a modest windfall, but policymakers can probably rest easy about the labour market disincentive effects of windfalls.

*****

[*] Now, my ECONS102 students should recognise that this amount is probably an overestimate of the 'true value' of a job to a worker. Like all decision-makers, on average workers are loss averse - they value losses much more than otherwise-equivalent gains. One consequence of loss aversion is the endowment effect - decision-makers require more in compensation to give something up than what they would have been willing to pay to obtain it in the first place. This applies to jobs, as it does to other things. So, we might expect people to need to be paid more to give up a job, than what they would have been willing to pay to get the job in the first place. So, the estimate of €3.33 million is probably an overestimate of the 'value' of a job to a worker.

Drip pricing and quasi-rational behaviour

In an interesting article in The Conversation last month, Ralf Steinhauser (Australian National University) explains the idea of drip pricing:

You see a fantastic offer, like a hotel room. You decide to book. Then it turns out there is a service fee. Then a cleaning fee. Then a few other extra costs. By the time you pay the final price, it is no longer the fantastic offer you thought.

Welcome to the world of drip pricing – the practice of advertising something at an attractive headline price and then, once you’ve committed to the purchase process, hitting you with unavoidable extra fees that are incrementally disclosed, or “dripped”.

Drip pricing – a type of “junk fee” – is notorious in event and travel ticketing, and is creeping into other areas, such as movie tickets. My daughter, for example, was surprised to find her ticket to the Barbie movie had a “booking fee”, increasing the cost of her ticket by 13%.

Steinhauser then goes on to explain why consumers are susceptible to drip pricing, blaming present bias and loss aversion:

In the case of booking that hotel room, you could abandon the transaction and look for something cheaper once the extra charges become apparent. But there’s a good chance you won’t, due to the effort and time involved.

This is where the trap lies.

Resistance to the idea of starting the search all over again is not simply a matter of laziness or indecision. There’s a profound psychological mechanism at play here, called a present-bias preference – that we value things immediately in front of us more than things more distant in the future...

Beyond the challenge of starting over, there’s another subtle force at work when it comes to our spending decisions. Drip pricing doesn’t just capitalise on our desire for immediate rewards; it also plays on our innate fear of losing out.

This second psychological phenomenon that drip pricing exploits is known as loss aversion – that we feel more pain from losing something than pleasure from gaining the same thing...

Imagine you’re booking tickets for a show. Initially attracted by the observed headline price, you are now presented with different seating categories. Seeing the “VIP” are within your budget, you decide to splurge.

But then, during the checkout process, the drip of extra costs begins. You realise you could have opted for lower-category seats and stayed within your budget. But by this stage you’ve already changed your expectation and imagined yourself enjoying the show from those nice seats.

Going back and booking cheaper seats will feel like a loss.

In my view, Steinhauser is absolutely correct that drip pricing exploits consumers' quasi-rationality (that is, that consumers are subject to biases in their decision-making). However, he is not fully correct about the sources of the quasi-rational behaviour.

First, present bias would tend to work against drip pricing, because (using Steinhauser's example) consumers are weighing up the cost of the tickets (which they face now) against the benefit of the concert they will attend (which is in the future). If consumers weigh the present more heavily than the future, then the costs weigh more heavily than the benefits, which would work against the consumers paying the junk fees.

Second, Steinhauser is correct about loss aversion, but for the wrong reason. Nobel Prize winner Richard Thaler noted that people engage in mental accounting related to particular decisions. People like to keep their mental accounts in positive balances, and are reluctant to give up on something if the mental account has a negative balance, because that would result in 'booking a loss'. Since people are loss averse, they will only want to close mental accounts that have a positive balance.

What does that mean for a consumer buying a concert ticket? They have spent some time and effort selecting their seats and completing most of the booking process. That puts their mental account for the concert into a negative balance. So, facing a small additional fee seems like a good deal, when compared to closing the mental account with a loss. The consumer pays the fee. They don't necessarily feel happy about it, but it is better than the alternative. The only way to get their mental account for the concert into a positive balance is to attend the concert.

A related way of thinking about the process of buying concert tickets with junk fees is the concept of switching costs. Switching costs are the costs of switching from one seller to another, or from one good or service to another. In this case, for a quasi-rational consumer who is running a mental account for the concert, giving up on buying the ticket when they are faced with the junk fees creates a switching cost - the loss in their mental account. When consumers face high switching costs, they can become locked in to buying a product. The seller can then take advantage of their locked in consumers by increasing the price (which is what the junk fees effectively do).

If you are a strong believer in the tenets of neoclassical economics, then the consumer response to drip pricing seems somewhat at odds with rational behaviour. For a purely rational consumer, the time and effort spent on the booking process up to the time that they face the additional of the junk fees is a sunk cost. It shouldn't affect the decision about whether to proceed with buying the ticket or not, because that decision should depend only on the costs and benefits of attending the concert. If the junk fees increase the costs of attending the concert to such an extent that they are higher than the benefits of attending the concert, a purely rational consumer would stop the ticket-buying process at that point. However, a quasi-rational consumer, who is running a mental account for the concert, would be more likely to proceed with the purchase even when presented with the junk fees.

So, overall, drip pricing leads to more sales if consumers are quasi-rational than if consumers are purely rational. It's lucky (and very profitable) for the ticket sellers that so many of us are not purely rational consumers.

The endowment effect in the trading of professional sports draft picks

If we believe that decision-makers are loss averse (and until recently, that seemed reasonably clear), then one consequence of loss aversion is the endowment effect. The explanation is fairly simple. When people are loss averse, they value losses much greater than otherwise equivalent gains. Giving something up therefore makes people very unhappy, and so people prefer to hold onto the things that they have. That means that, when a person owns something, they have to be given much more to compensate them for giving it up than what they would have been willing to pay to get it in the first place.

With the NFL regular season starting later this week, I was interested to read this new article by Jeff Hobbs (Appalachian State University) and Vivek Singh (University of Michigan), published in the journal Economic Inquiry (open access), because it looked at the endowment effect in professional sports. Specifically, Hobbs and Singh investigate whether draft picks in the NBA, NFL, and NHL over the period from 1988 to 2017 demonstrate an endowment effect. Their data set includes nearly 17,000 draft picks. For a little more context for those unfamiliar with professional sports drafts, Hobbs and Singh explain that:

Every year, each of the major professional sports leagues in the United States holds what is known as its “entry draft.” During the entry draft the teams select, in inverse order of success from the previous season such that the worst teams get the first picks, amateur players with a view toward signing them to professional contracts. In most of these leagues, teams can trade draft picks (before they are used to select players) at least as freely as they can trade players who are already under contract.

So, teams are initially endowed with a certain number of draft picks. They can choose to keep those picks (which they can use to select young players who are eligible to be drafted), or they can trade picks to other teams (and those teams can use the picks instead). Teams trade picks for a variety of reasons, often trading picks for players. Teams can also trade picks that they themselves acquired in some other trade. However, the nature of the trade doesn't matter for Hobbs and Singh's analysis. They are only interested in whether teams are more or less likely to trade draft picks that they originally endowed with, than other draft picks.

To do this, they look at what happens after a pick is first traded. If there is an endowment effect, then the team that originally had the pick should be less willing to trade than a team that acquired the pick in a trade. They do this by comparing the proportion of times that a traded pick is 're-traded', compared with the pick just before or just after that pick in the draft order. They find that:

After we control for the frequency of selling, we find that non‐endowed picks for all three leagues combined were 12%-15% more likely to trade again than were their adjacent, endowed counterparts from the same point in time afterward. These results are statistically significant, but we notice some differences when we look at each league individually. Regardless of whether we attempt first to match the once‐traded pick with the pick directly below it or above it, the results for the NFL become insignificant. However, the results for the other two leagues remain significant in both a statistical and economic sense. In the NBA, the average once‐traded and non‐endowed pick is between 24.5% and 29.2% more likely to trade afterward than is its match. In the NHL, the once‐traded, non‐endowed pick is between 14.8% and 23.6% more likely to trade.

In other words, there is a substantial endowment effect for draft picks in the NBA and NHL, but it appears not for the NFL. However, Hobbs and Singh aren't willing to let the NFL off completely, noting in their conclusion that:

The relative rationality of the NFL documented here pertains only to the endowment effect with respect to the trading of draft picks; other studies have found examples of other irrationalities in professional football.

Fair enough, but it seems like a bit of a cheap shot. I'm sure there's a lot of other irrationalities in basketball and hockey as well. As one example, the endowment effect probably doesn't just play out in the draft. It is likely to be present when considering free agent players as well (as I noted in this 2017 post). The sabermetrics revolution may have increased the use of analytics in sports, but it doesn't appear to have eliminated quasi-rationality entirely.

Read more:

• Free agents, the draft, and the winner's curse

Only a minority of real people may actually be loss averse

I've written a couple of posts this week about loss aversion (see here and here). However, loss aversion is not uncontested in the research literature. In fact, the research by Gal and Rucker that I discussed in this 2018 post argued that there was "little evidence to support loss aversion as a general principle". One way of thinking about this is that Gal and Rucker are arguing that not everyone is loss averse. And that is likely true, in the same way that not everyone is risk averse, and not everyone is averse to pineapple on pizza.

A new working paper by Jonathan Chapman (University of Bologna), Erik Snowberg (University of Utah), Stephanie Wang (University of Pittsburgh), and Colin Camerer (Caltech) provides some more evidence for this. In fact, they don't just show that some people are not loss averse. They show that about half of people may in fact be 'loss tolerant'.

Chapman et al.'s main results are based on a sample of 1000 people who completed a survey with the survey panel provider YouGov in 2020. The specific method that they used is quite detailed, but essentially involved 20 different 'gambles', with each gamble using information from the earlier gambles to provide a nuanced understanding of each research participant's attitudes towards risk and towards loss. This Dynamically Optimized Sequential Experimentation (DOSE) method provides estimates for both risk aversion and loss aversion for each research participant.

Importantly, the sample of research participants in the YouGov survey is representative of the underlying US population. Chapman et al. contrast the results from the representative sample with those from a smaller sample of 437 students from the University of Pittsburgh. This comparison is important, because most experimental economics samples are based on student populations (and it has been shown before that student samples are meaningfully different to representative population samples in economics experiments - for example, see here).

For the Chapman et al. paper, the key results are demonstrated in their Figure 3:

Looking at the blue distribution, there are some people in the general population sample who are loss averse (λ>1), but also a lot of people who are loss tolerant (λ<1), as well as some people in the middle. In terms of raw numbers, 57% of the general population sample is loss tolerant. For the student sample, again there is a distribution where some are loss tolerant, but a far higher proportion are loss averse. In the student sample, just 32% are loss tolerant.

So, what is it that makes the student population so much more loss averse than the general population? Chapman et al. show that:

...more educated and more cognitively-able individuals - both characteristics of student samples... - tend to be more loss averse and also less risk averse.

So, university students may be more loss averse because they have higher cognitive ability than the general population and are more educated. That may be good reason to think carefully about whether student samples are necessarily always the best choice to economics experiments.

However, Chapman et al. don't stop there. They then look at why it is that less cognitively-able and less educated people are more loss tolerant, hypothesising that:

...the groups that tend to be more loss tolerant - the less educated, lower income, and less cognitively able - are also those that we might expect to have encountered more losses in life. This raises the intriguing possibility that loss tolerance is shaped by everyday experiences.

And that is what they find:

...loss-tolerant individuals appear more likely to gamble, commit a greater portion of their assets to equities, experience financial shocks, and have lower overall wealth...

So, what should we take away from this research? First, not everyone is loss averse. In fact, a majority of people may be loss tolerant. That doesn't mean that loss aversion is irrelevant for understanding individual decision-making. It just means that we should not assume that everyone is loss averse. Second, we need to take care in extrapolating from student samples in economics experiments to the general population. This is not a new finding (as I noted above), but it is important that we don't lose sight of it. Third, and probably most important, when people routinely experience losses as part of their everyday life, they become more loss tolerant (and less loss averse). That may or may not be a good thing. After all, we talk about loss aversion as a deviation from purely rational decision-making. Being less loss averse may not be a bad thing. On the other hand, if loss tolerance leads to greater losses in the future, that may require a policy response. On this last point, we really need more research.

[HT: Ranil Dissanayake]

Loss aversion and the endowment effect in health-seeking behaviour

When I teach loss aversion in my ECONS102 class, I raise one of the consequences of loss aversion as the endowment effect. The explanation is fairly simple. When people are loss averse, they value losses much greater than otherwise equivalent gains. Giving something up therefore makes people very unhappy, and so people prefer to hold onto the things that they have. That means that, when a person owns something, they have to be given much more to compensate them for giving it up than what they would have been willing to pay to get it in the first place.

However, it turns out that loss aversion may not be the best (or only) explanation for the endowment effect. In a new NBER Working Paper (ungated version here, with a non-technical summary here), Emily Beam (University of Vermont), Yusufcan Masatlioglu (University of Maryland), Tara Watson (Williams College), and Dean Yang (University of Michigan) look at how people respond to a $50 incentive to attend a health service provider, when it is framed as a loss versus when it is framed as a gain. More specifically:

In this study, we implement a randomized field experiment that compares loss versus gain framing to promote preventive health care utilization. We offer individuals in and near Dearborn, Michigan, an incentive to visit a health clinic run by our partner organization, the Arab Community Center for Economic and Social Services (ACCESS). In the “Visa gift card” (loss framing) treatment, participants are given a Visa gift card of either $50 or $10 that can be activated by visiting the clinic; they will effectively lose the value of the card if they choose not to visit a clinic. In the “reminder card” (gain framing) treatment, participants are given a physically similar generic reminder card with the promise that it will be exchanged for a gift card if they visit the health clinic, but they are not given the gift card up front. In both cases, any individual who went to the health clinic would receive an active Visa gift card, and any funds remaining after the visit could be spent elsewhere. Because of random assignment to the treatments, differences in responsiveness to the incentives are attributable to the differences in the frames.

Research participants who were given a Visa gift card essentially face a loss if they choose not to activate it. That's because, once they finish the initial survey, they have the card in hand and choosing not to activate it is like losing $50. The other participants only receive a reminder card, which can be converted into a gift card. So, if they don't go to the clinic, they aren't losing in the same way. So, if there is an endowment effect, we'd expect those who received the gift card to be more likely to visit the clinic.

However, that isn't really what Beam et al. are looking at. They are investigating why there is an endowment effect. So, in the initial survey, they ask questions that are designed to provide an estimate of how loss averse people are. If the endowment effect is related to loss aversion, then the effect should be larger for people who are more loss averse. However, the endowment effect might also arise because of trust. As Beam et al. explain:

A second possible explanation for the effectiveness of loss framing is that giving an incentive up front induces an individual to have more confidence that the incentive will be provided as promised. The perceived probability of receiving a reward is likely higher for someone who has a tangible reward in hand relative to someone hearing about a promised reward. This trust‐related response is likely more relevant in field contexts outside the lab, and it is expected to be most relevant when individuals do not initially trust the person or institution offering the incentive.

If the endowment effect arises because of trust issues, then it should be larger for people who report less trust in the health care organisation, ACCESS. So, armed with measures of trust and loss aversion for around 1500 people (whose gift card was worth $50, and ignoring a smaller group whose gift card offer was only $10), and knowing which of the research participants visited the clinic (to receive or activate their Visa gift card), Beam et al. then find that:

The overall average difference in take‐up between those who receive the $50 Visa gift card (loss frame) and $50 reminder card (gain frame) is about 2.2 percent... The differences between Visa gift card and reminder card redemption rates are 4 to 5 percentage points for more loss‐loving participants and 1 to 2 percentage points for more loss‐averse participants. These results are not statistically significant... There is no clear pattern linking loss aversion to take‐up rates, nor to the gap in take‐up rates between gift card and reminder treatments.

So, there is a small endowment effect, but it isn't related to loss aversion. What about trust? Beam et al. find that:

Participants without trust of the organization at baseline are much more responsive to the gift card treatment (loss frame); the impact of the gift card is 7.2 percentage points for this group... The statistically significant interaction term... suggests that there is no comparable effect for those who do trust ACCESS at baseline.

In other words, there is an endowment effect for research participants who do not trust ACCESS, but no endowment effect for research participants who do trust ACCESS. When I first read those results, I was a little concerned that they arose only because Beam et al. combined people who reported low trust with people who had no opinion because they hadn't heard of ACCESS before. However, when those categories are separated, the results remain similar.

So, it really does seem that it is trust, and not loss aversion, that likely explains the endowment effect in this context. That doesn't necessarily mean that loss aversion is never a source of the endowment effect though, so I think I am safe (for the moment) in continuing to teach it as closely related to loss aversion.

The Beam et al. paper is also interesting in noting some of the real-world difficulties in research, especially this bit:

During our first survey wave, we encountered several safety issues: some interviewers were harassed by residents; on another day, interviewers witnessed gunfire a few blocks away. After these experiences, we excluded tracts that reported relatively high recent crime levels, and we contacted the Dearborn police department to exclude any additional tracts that they considered to be unsafe...

 Yikes! And slightly more mundane, this bit on how they had to adapt the measurement of loss aversion:

Although these questions are typically worded as a gamble, we adjusted the wording to be an “opportunity” after pilot testing revealed many subjects would reject all gambles because of religious objections to gambling.

Real-world field research is often not as straightforward as we hope it would be.

[HT: Ranil Dissanayake]

Loss aversion may affect how students answer multiple choice questions

In my ECONS102 class this week, among other things we covered a bunch of concepts in behavioural economics. One such concept was loss aversion - the idea that people value losses much more than equivalent gains (in other words, they like to avoid losses much more than they like to capture otherwise equivalent gains). Loss aversion seems to explain a lot of quasi-rational behaviour (however, as a concept, loss aversion is contested - more on that in another post later this week). People do seem to adjust their behaviour to try and avoid losses.

One example is the answering of multiple choice questions in tests and exams, but only where a wrong answer results in a penalty (subtracting marks from the overall test or exam score). I never grade multiple choice in that way, but many academics do. The argument they put forward is that, when incorrect answers are penalised, it creates a disincentive to students guessing. However, because the penalty creates a loss, it may be that loss averse students avoid answering when they are a little bit unsure, even if their not-quite-sure guess would have been the correct answer.

So, how much does loss aversion affect students' multiple choice answering behaviour? That is the question that this recent article by Heiko Karle (Frankfurt School of Finance and Management), Dirk Engelmann (Humboldt-Universität zu Berlin) and Martin Peitz (University of Mannheim), published in the Scandinavian Journal of Economics (ungated earlier version here), sets out to answer. Karle et al. use data from 646 students, combining an experimental-based measure of loss aversion (that the students completed early in the semester) with the results of a 30-question multiple-choice examination (held some three months later), where:

There are four possible answers to each question: a correct answer gives three points, no answer one point, and an incorrect answer gives zero points, as in the exam in our data set.

Notice that, on the face of it, there is no penalty for an incorrect answer, since an incorrect answer receives zero points. However, choosing not to answer yields one point, so considering not answering as the status quo reference point, choosing an incorrect answer makes a student worse off by one point (a loss, which they would try to avoid). Karle et al. hypothesise that students who are more loss averse will answer fewer questions, and will get more of the questions that they do answer correct. Both hypotheses are obvious from loss aversion - students try to avoid the loss, so are more likely not to answer questions when they are unsure, so more loss averse students will answer fewer questions. That means that the questions that more loss averse students do answer are those that they are surer about, and so they are more likely to get those answers correct. Karle also test a related hypothesis, that students who are more loss averse get fewer questions correct overall, which depends on how many the students answer (fewer, when the students are more loss averse) and how many of the ones they do answer are answered correctly (more, when the students are more loss averse). What to expect for this overall hypothesis is unclear.

In addition to loss aversion, Karle et al. measure students' self-confidence, which is based on the difference between students' estimates of the percentage of their own correct answers to a set of general interest questions and the average percentage of other students’ correct answers. Students who are more self-confident can be expected to be more likely to answer questions in the multiple choice exam.

Now, using their data, Karle find that:

...loss aversion and confidence have a negative and positive effect, respectively, on the number of answered questions... This effect is statistically significant at the 1 percent level... Our estimates suggest that loss neutral students answer approximately two questions more than otherwise identical students in the highest category of loss aversion (and 5/3 more than those in the middle category).

So, more loss averse students answer fewer questions than less loss averse students, as expected. Next:

We do not find a statistically significant effect (at the 5 percent level) of loss aversion or confidence on the ratio of correct answers per questions answered. However, the coefficient of loss aversion (but not strong loss aversion) turns positively significant at the 10 percent level when considering loss aversion and confidence together...

This is very weak support (or, rather, no support) for the hypothesis that more loss averse students get more of the questions that they do answer correct. Combining the two hypotheses, I'm sure you can guess for the overall hypothesis, that Karle et al. find that:

...loss aversion and confidence have a negative and positive effect, respectively, on the dependent variable [the number of correct questions overall]... Our estimate suggests that, ceteris paribus, students in the highest category of loss aversion give approximately 1.5 fewer correct answers than otherwise identical students who are loss neutral.

Karle et al. then spend a bit of effort trying to determine whether the effect of loss aversion on question answering behaviour is causal or not, and their results suggest that it is causal for some, but not all, students. I don't find those results as convincing as the overall takeaway that students' loss aversion is related to how they answer questions.

Nevertheless, these results are interesting even putting aside the question of causality. Loss aversion isn't something that is easy to change, so even a correlation between loss aversion and question answering behaviour is potentially important. And it may be even more important given that Karle et al. show that female students in their sample are more loss averse than male students. So, the effect of this style of grading multiple choice questions is a bias against female students, decreasing female grades relative to male grades. That's the last thing we need in economics. That may have contributed to this:

In our setting, according to a university directive, the differential treatment of wrong responses and no responses was no longer allowed after the academic year 2013/2014, which is the exam year we used in this paper.

Coming back to my earlier point, many academics like the style of grading that doesn't award 'free' marks to student guessing. However, there is a trade-off. If students are penalised for guessing, they are likely also penalised based on how loss averse they are. So, if multiple choice questions come with a penalty, the exam score will be more precise for each question (in terms of telling the grader whether students actually were confident in the answer) but also more biased overall (because more loss averse students will get fewer questions correct than less loss averse students). The trade-off seems untenable to me. I'll continue to use multiple choice marking that implicitly includes a reward for guessing.

Climate change denial, narratives, and loss aversion

The Conversation had an interesting Climate Explained article a few weeks ago by Peter Ellerton (University of Queensland), which answered the question:

Why do humans instinctively reject evidence contrary to their beliefs?

The question was, of course, asked in the context of climate change denial. Ellerton's response included this:

We understand the world and our role in it by creating narratives that have explanatory power, make sense of the complexity of our lives and give us a sense of purpose and place.

These narratives can be political, social, religious, scientific or cultural and help define our sense of identity and belonging. Ultimately, they connect our experiences together and help us find coherence and meaning.

Narratives are not trivial things to mess with. They help us form stable cognitive and emotional patterns that are resistant to change and potentially antagonistic to agents of change (such as people trying to make us change our mind about something we believe).

If new information threatens the coherence of our belief set, if we cannot assimilate it into our existing beliefs without creating cognitive or emotional turbulence, then we might look for reasons to minimise or dismiss it.

I like the framing about understanding the world through the narratives we tell ourselves. However, Ellerton could easily have gone a bit further in his explanation, linking the unwillingness to accept new information that threatens our narrative to the behavioural economics concepts of endowment effects and loss aversion, as I have previously done in this 2018 post:

People are loss averse. We value losses much more than equivalent gains (in other words, we like to avoid losses much more than we like to capture equivalent gains). Loss aversion makes people subject to the endowment effect - we are unwilling to give up something that we already have, because then we would face a loss (and we are loss averse). Or at least, there would have to be a big offsetting gain in order to convince us to give something up that we already have. The endowment effect applies to objects (the original Richard Thaler experiment that demonstrated endowment effects gave people coffee mugs), but it also applies to ideas.

I've thought for a long time that ideology was simply an extreme example of the endowment effect and loss aversion in practice. Haven't you ever wondered why it's so difficult to convince some people of the rightness of your way of thinking? It's because, in order for them to agree with you, that other person would have to give up their own way of thinking, and that would be a loss (and they are loss averse). It seems unlikely that the benefits of agreeing with you are enough to offset the loss they feel from giving up their prior beliefs, at least for some people. Once you consider loss aversion, it's easy to see how ideologies can become entrenched. An ideology is simply lots of people suffering from loss aversion and the endowment effect.

Climate change denial is a good example of an ideological viewpoint. People are endowed with a particular view about climate change. They are unwilling to give up that view, because that would involve a loss to them (a loss of one of their beliefs), and people are loss averse (they want to avoid losses). So, people are reluctant to adjust their internal narratives about climate change, even in the face of overwhelming evidence, because they are loss averse.

Read more:

• Confirmation bias and climate change maths

Framing, loss aversion, transaction utility, and reusable coffee cups

This article in The Conversation yesterday, by Sukhbir Sandhu, Robert Crocker, and Sumit Lodhia (all University of South Australia) caught my attention, because it nicely illustrates some of the concepts from behavioural economics that I discussed with my ECONS102 class last week:

Many cafe owners offer discounts ranging from 10c - A$1 to customers who bring in their own reusable cups.

But our findings reveal these discounts are ineffective in changing consumer behaviour.

A cafe owner we interviewed described how, despite providing a 20c discount for reusable cups, she didn’t think saving money motivated her customers:

The regulars were people who’d happily drop in a dollar tip into the jar kept on the counter. They were therefore not that concerned about 20c discount.

We know from previous behavioural psychology literature consumers are more likely to be what’s called “loss averse” as opposed to “gain seekers”. In other words, people hate paying extra for takeaway coffee cups more than they like getting a discount for bringing their reusable cups.

So, if you own a cafe, focus on making consumers pay extra for choosing takeaway coffee cups rather than offering discounts for reusable cup use. It’s more likely to motivate customers.

Let's say that a cafe owner wants to encourage customers to use reusable cups. They might do this because of concern for the environmental effects of disposable coffee cups, or the cafe owner might simply recognise that disposable cups cost them money, and so offering to fill a customer's own cup must be slightly more profitable for the cafe owner, because then they don't incur the cost of providing a cup.

Putting aside any cost differences, cafe owners could discourage their customers from disposable cups by making coffee sold in disposable cups more expensive. We know that when something is more costly, rational consumers will buy less of it. This also makes coffee in reusable cups relatively cheaper, and so would encourage some consumers to switch. Let's consider two different framings of the price difference: (1) consumers who use a reusable cup receive a 20 cent discount; or (2) consumers who use a disposable cup have to pay an extra 20 cents. 

How many consumers would switch? If consumers were purely rational, it wouldn't matter how the price difference was framed. A 20-cent discount for using a reusable cup and paying 20 cents extra for a disposable cup are exactly the same (provided the prices are the same in each case). Both options would lead to the same number of customers switching to a reusable cup.

Now here's where some behavioural economics comes in. Consumers (like every decision-maker) are not purely rational, they are quasi-rational - they are affected by cognitive biases and use heuristics when making decisions. Framing makes a difference to quasi-rational consumers, but it's not clear which framing should make consumers use fewer disposable cups.

One of the cognitive biases that quasi-rational decision-makers are affected by is loss aversion - decision-makers dislike losses much more than they like equivalent gains. In this case, the loss in utility (or satisfaction, or happiness) for the consumer from paying 20 cents extra for a disposable cup, is 'worth' much more than the gain in utility (or satisfaction, or happiness) for the consumer who receives a 20-cent discount for using a reusable cup. So, we would expect the 'loss framing' (20 cents extra) to have a much bigger effect on consumer behaviour than the 'gain framing' (20-cent discount). 

However, another cognitive bias that affects consumers is transaction utility, which I have blogged about before. Transaction utility recognises that consumers not only receive utility from the good or service that they purchase, but also from the act of purchasing. If a consumer feels that they are 'getting a good deal', this makes them happier (higher utility), and makes them more likely to purchase. So, based on transaction utility, we would expect the 'gain framing' (20-cent discount) to have a bigger effect on consumer behaviour than the 'loss framing' (20 cents extra).

Given that Sandhu et al. found that the negative framing had a bigger effect overall, it appears that the loss aversion effect is larger than the transaction utility effect. It would be good to see more research on this though, that disentangles those two effects more.

Overall, the takeaway message from this research is that if you, as a seller, want to steer consumers away from something using a price difference, present it as involving a loss to them (they have to pay extra). On the other hand, if you want to steer consumers towards something using a price difference, present the alternative as involving a loss to them. At least until this has been investigated a bit more, it appears that paying extra is a more powerful motivator for changing consumer behaviour than a discount.