ISSN (Print) 0473-453X

Discussion Paper No. 1218

ISSN (Online) 2435-0982

LOSS AVERSION

Taisuke Imai
Klaus M. Schmidt

November 2023

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Loss Aversion†
Taisuke Imai∗
Osaka University and CESifo
Klaus M. Schmidt∗∗
University of Munich, CESifo, and CEPR

This version: November 21, 2023

Prepared for the
Edward Elgar Encyclopedia
in Behavioral and Experimental Economics

Abstract: Loss aversion postulates that people prefer avoiding losses over acquiring gains of equal size. It is a central part of prospect theory and, according toDaniel Kahneman, “the most significant contribution of psychology to behavioral
economics” (Kahneman, 2011, p. 300). It has powerful implications for decision
theory and has been fruitfully applied in many subfields of economics. However,
because the reference point is often not well defined and loss aversion interacts
with other behavioral biases, there is some controversy about the concept.
Keywords: Loss aversion, reference point, prospect theory, endowment effect,
decision theory, risk.

†
Financial support by Deutsche Forschungsgemeinschaft through CRC-TRR 190 (project number
280092119) is gratefully acknowledged.
∗
Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka 567-0057,
Japan, email: taisuke.imai@iser.osaka-u.ac.jp∗∗
Department of Economics, University of Munich, Ludwigstr. 28 (Rgb.), D-80539 München, Germany,
email: klaus.schmidt@econ.lmu.de1

A brief history of loss aversion

The concept of loss aversion was introduced by Kahneman and Tversky (1979) as a central
part of prospect theory and later refined in Tversky and Kahneman (1991). Prospect theory
assumes that people evaluate outcomes in comparison to a reference point. Because losses
loom larger than gains, there is a kink in the utility function at the reference point. Lossaversion elegantly explains many anomalies on risk-taking behavior in experimental settings,
in particular, why many people reject small favorable lotteries involving gains and losses
which cannot be explained by expected utility theory (Rabin, 2000). It can also be applied
to decisions under certainty, e.g. to explain the status-quo bias (Samuelson and Zeckhauser,
1988) and the endowment effect (Knetsch, 1989). Despite many criticisms, it is still widely
seen as the best available model of how people make actual decisions.1
An important question for the application of loss aversion to economic models is how todefine the reference point. The early literature equated the reference point with the statusquo. In an influential paper, Kőszegi and Rabin (2006) argue that the reference point should
be modeled as the decision maker’s rational expectations held in the recent past about outcomes. Thus, the reference point is determined endogenously by the economic environment.
It must be consistent with optimal behavior given rational expectations, i.e. behavior and expectations must form a personal equilibrium. If the outcome is stochastic, the decision maker
will compare the actual outcome to the full distribution of equilibrium outcomes, weighted
with their respective probabilities. While this model is internally consistent and gives rise tosome interesting predictions (Kőszegi and Rabin, 2007), it also imposes an extreme rationality
assumption on expectation formation.

2

Empirical evidence

There is a large body of empirical evidence supporting the existence of loss aversion in bothrisky and riskless contexts, but there are also some studies challenging these results.
A classic example is the endowment effect, i.e. the observation that the maximum price1

See Wakker (2010) and Barberis (2013) for excellent surveys.

1individuals are willing to pay to obtain a good is typically lower than the minimum pricethey are willing to accept to give up the same good (Knetsch, 1989; Kahneman, Knetsch, andThaler, 1990). While the endowment effect has been generally interpreted as evidence of loss
aversion, several more recent papers have challenged the robustness of these findings (Plottand Zeiler, 2005, 2007), their validity in the field (List, 2003, 2004), and their relationshipwith loss aversion for risky gambles (Chapman et al., 2023). See Ericson and Fuster (2014)
for further discussion.
Additional evidence of reference dependence and loss aversion come from a variety offield settings that combine rich observational data with a careful specification of reference
points. For exaxmple, Genesove and Mayer (2001) and Andersen et al. (2022) find that
homeowners facing nominal losses relative to the purchase price of their house tend to set
list prices significantly higher than those facing nominal gains. Rees-Jones (2018) finds that
taxpayers with a positive “balance due” (i.e. facing a payment on tax day) are more likely
to pursue tax-reducing activities. In a high-stakes context, Pope and Schweitzer (2011) findthat even highly-experienced professional golfers are significantly more likely to make a putt
for par than a putt for other scores, a pattern consistent with loss aversion relative to thereference point of par.
Another big strand of the empirical literature is concerned with the estimation of structural parameters in prospect theory. Since Tversky and Kahneman (1992), these experimental
studies estimate individuals’ degree of loss aversion (typically represented by the λ parameter)from a series of lottery choice questions involving mixed gambles (Von Gaudecker, Van Soest,
and Wengström, 2011; l’Haridon and Vieider, 2019; Gächter, Johnson, and Herrmann, 2022).
See Brown et al. (2023) for a meta-analysis of empirically estimated loss aversion parameters.
There are also several studies shedding light on the neural and psychological underpinningsof loss aversion. Tom et al. (2007) find evidence for loss aversion in neural circuitry. De
Martino, Camerer, and Adolphs (2010) report that two patients with damage to the amygdalaarea of the brain were not averse to losses. Sokol-Hessner et al. (2009) show that “thinking
like a trader,” a form of intentional cognitive regulation, significantly reduces behavioral loss
aversion.

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3

Applications

The concept of loss aversion has been fruitfully applied to many fields of economics. In thefollowing, we briefly describe some of the most notable applications.
In finance, loss aversion has been used to explain the famous equity premium puzzle,
i.e. the observation that the average return of the US stock market has exceeded the average
return of treasury bills by a margin that cannot be explained by traditional consumption-basedmodels of asset pricing. Benartzi and Thaler (1995) argue that investors look at the historical
distribution of annual stock market returns. Because investors are loss averse, they require ahigh average rate of return to accept the high volatility of the stock market. This argument
implicitly relies on an additional assumption (narrow framing) and has been theoreticallyrefined by Barberis, Huang, and Santos (2001) and Pagel (2016). Loss aversion can also
explain the non-participation puzzle, i.e. the fact that most households do not participate
in the stock market despite the historically high returns. Dimmock and Kouwenberg (2010)
find that survey-based measures of loss aversion can predict stock market participation. Lossaversion has also been used to explain the disposition effect, i.e. the empirical finding thatinvestors are more likely to sell stocks that have risen than stocks that have fallen in value
since purchase (Odean, 1998). If investors compare the price at which they sold to the priceat which they bought, loss aversion implies that they will be reluctant to buy a stock that has
fallen in value.
In insurance, loss aversion can explain why consumers insure small losses at a large
cost in expected payments. For example, Sydnor (2010) shows that customers of a large
home insurance company frequently choose no deductible, even though the expected cost ofa deductible is much smaller than the difference in insurance premiums. Since the insurance
premium has to be paid with certainty, while the deductible will be paid only in the unlikely
event of a claim, this is consistent with expectation-based loss aversion (Kőszegi and Rabin,2007).
Kőszegi and Rabin (2009) apply expectation-based loss aversion to consumption andsaving behavior. A consumer facing possible income shocks in the future will engage in precautionary savings to reduce the expected pain that she experiences when finding out that she

3cannot consume as much as previously planned. Pagel (2017) shows that this model can beused to explain a realistic hump-shaped pattern of consumption over the life cycle.
Some puzzling observations on labor supply can be understood through loss aversion.
Camerer et al. (1997) document that the number of hours that a New York City cab driverchooses to work on a given day is higher the lower the average hourly wage on this day – theexact opposite of what traditional theory predicts. However, if cab drivers are loss averse andcompare their income on a given day to their average daily income, then they want to work
longer on a day with a low average wage in order to avoid the loss of earning less than average.
Goette, Huffman, and Fehr (2004) provide a survey and critical discussion of this and several
related studies.
In industrial organization, loss aversion has been used to explain the pricing decisionsof a monopolist. For example, Heidhues and Kőszegi (2014) show that if consumers sufferfrom expectation-based loss aversion, it is optimal to have a high “regular” price and a lower“sale” price. Because the consumer will buy at the lower sale price, buying becomes part ofher reference point, which induces her to buy at the higher regular price. See Heidhues andKőszegi (2018) for a survey of this literature.
Loss aversion has also been used in contract Theory to explain some frequently observed
contractual features that are difficult to understand in a neo-classical framework. For example,
Herweg, Müller, and Weinschenk (2010) show that loss aversion of the agent implies that the
principal will use a binary payment scheme (a “bonus contract”) as an incentive scheme.
Herweg and Schmidt (2015) show that renegotiation outcomes will be inefficient if parties are
loss-averse and if the initial contract serves as a reference point. This can explain why parties
sometimes do not write long-term contracts even though this would be feasible. Fryer et al.
(2022) argue that incentive contracts framed in terms of losses should give stronger incentives
than contracts framed in terms of gains. However, this prediction could not be confirmed byde Quidt (2018). Kőszegi (2014) offers a survey of this literature.
In summary, loss aversion offers a powerful explanation for many puzzles in behavioral
economics. However, the reference point is not always well defined, and loss aversion often
interacts with other behavioral biases such as mental accounting, framing, imperfect memory,and attention. Thus, additional, more careful studies are needed to disentangle these effects.
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