A recent paper in the Journal of Consumer Psychology (JCP) has started a debate on the accuracy of "loss aversion," the idea that people are driven by fear of losses more than they are by the potential for gain. Core to behavioral economics, this idea has been rather universally accepted and been part of the ...
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A recent paper in the Journal of Consumer Psychology (JCP) has started a debate on the accuracy of "loss aversion," the idea that people are driven by fear of losses more than they are by the potential for gain. Core to behavioral economics, this idea has been rather universally accepted and been part of the awarding of two economics Nobel Prizes, in 2002 to Daniel Kahneman and in 2017 to Richard Thaler.
One of the authors of the JCP article, Professor David Gal at the University of Illinois at Chicago, summarizes their findings in the Scientific American and concludes, “Our critical review of loss aversion highlights that, even in contemporary times, wrong ideas can persist for a long time despite contrary evidence.”
The inserted “even in contemporary times” is symptomatic of the ignorant hubris of modern-day social scientists. They believe (and, to be fair, have been taught) that the chance for errors in studies should be lower the more advanced they (and we) are. Because we have access to more data, more measurements, and greater computing power, we shouldn’t make errors.
This is, of course, nonsense, since the social world cannot be properly measured. Consequently, blind data mining without theoretical guidance means the conclusions can depend on a single data point, which tips the scales or suggests a different trend line (not to mention the researcher’s opportunities to fabricate whatever results, intentionally or not, by excluding data points, enforcing boundaries to data sets, or by choice of statistical method).
The "loss aversion" dogma is a good example of this. It is a seemingly reasonable finding that has lasted because researchers want to find "biases in human behavior." Or, as Gal puts it, “In the case of loss aversion, contradictory evidence has tended to be dismissed, ignored or explained away, while ambiguous evidence has tended to be interpreted in line with loss aversion.” Indeed, and it is easily done if there is no proper theory to limit one’s use and abuse of data.
So has loss aversion been disproven? Not really, and it was — as so much of the "pop psychology" of behavioral economists — never really an issue to discuss. It was not worthy of any note, and absolutely not a Nobel Prize (much less two!).
Gal’s review in the JCP suggests that there is no such thing as a loss aversion:
People do not rate the pain of losing $10 to be more intense than the pleasure of gaining $10. People do not report their favorite sports team losing a game will be more impactful than their favorite sports team winning a game. And people are not particularly likely to sell a stock they believe has even odds of going up or down in price.
This does not mean people necessarily value losses and gains equally, however, Gal points out:
To be sure it is true that big financial losses can be more impactful than big financial gains, but this is not a cognitive bias that requires a loss aversion explanation, but perfectly rational behavior. If losing $10,000 means giving up the roof over your head whereas gaining $10,000 means going on an extra vacation, it is perfectly rational to be more concerned with the loss than the gain. Likewise, there are other situations where losses are more consequential than gains, but these require specific explanations not blanket statements about a loss aversion bias.
But do they really “require specific explanations”? Hardly. The behavioral economics crowd is ignorant of (or simply wants to do away with) economic theory, which means that they are unable to see the obvious explanations. Economic theory, as Austrians know, has no problem explaining this "phenomenon," but offers a much better and more coherent explanation than any data mining can muster.
The truth is neither that people suffer from a "loss aversion" bias nor that there is no such thing as one. As value is subjective, actors always choose their highest-valued use for equally serviceable goods. In other words, goods have diminishing marginal utility. Consequently, if I have five equally serviceable goods, I will use them to satisfy (to me) the five most highly valued wants. If I gain one, it will satisfy a want of lesser value than the first five do; if I lose one, I will lose the value of a want that is higher on my value scale (the fifth rather than the sixth).
So of course losses hurt more than gains provide! This must be true for equally serviceable goods, since they are always used to satisfy the subjectively most highly valued wants first. But it does not mean that we can simply put dollar amounts on all goods and compare them in terms of prices.
Gal’s example is illustrative: let’s say you live in the house worth ten thousand dollars and have already paid that same amount for a trip. Now, what if you must lose one and it is your choice? Since both are "worth" ten thousand dollars, the expected result would be approximately 50:50 in any population. Yet it is likely that losing the trip will probably be a lesser loss than losing the house despite their equal dollar value.
Does this mean people suffer from a bias in their choices — a "vacation aversion" bias? No, and to most people it is not strange — and certainly not a paradox — that people almost without exception would choose to be vacation-less over being homeless. The reason is simple: your home and a vacation trip are not equally serviceable. Most people value having a roof over their heads more than taking a trip. The estimated dollar value, and possibly the market price, is not people’s actual valuation.
Yet the "data" don’t show this: they show only two data points "worth" ten thousand dollars each. So the inductive researcher can point to the data and label it a new type of previously unobserved bias, get published in highly regarded journals (and perhaps win a prize or two), and make a great career out of it. All it takes is a disregard of theory and to let the data "speak" for itself.
Existing theory, and the explanations already at hand, creates no careers. It offers only truth.