Kirschenbaum et al. recruited students with varying academic majors, half of whom were first-year undergraduates on academic probation or close to being on probation, who agreed to participate in the SIP. Students were first divided into “low-grade” and “high-grade” groups, the low-grade group consisting of students with cumulative grade point averages (GPAs) of 2.1 or less and the high-grade group with GPAs greater than 2.1. Roughly half of the low- and high-grade group members were then randomly assigned to respective control groups. The other halves were randomly assigned to one of six different treatment groups: daily plans-low grades, daily plans-high grades, monthly plans-low grades, monthly plans-high grades, no plans-low grades, and no plans-high grades.[60]
Students assigned to the daily and monthly plan groups used their course syllabi and textbooks to complete their plans (flow charts), which were then reviewed by other participants in the group and group leaders. The plans specified tasks to be accomplished, where and when they were to be worked on, the criterion of accomplishment, the self-administered reward to be earned, and where and when the reward was to be allocated. Daily-plan students completed highly specific plans in which they indicated study behaviors (e.g., activities, criteria, rewards) pertaining to each day (four days were planned on each flow chart). In contrast, monthly-plan students developed less-specific plans indicating larger chunks of activities that spanned one month’s work on each flow chart, including a reward to be self-administered at the end of the month if the criterion level of accomplishment was achieved. Both groups were instructed to continue creating new plans when their flow charts became outdated (i.e., once a month for monthly plans and once every four days for daily plans). Daily-plan students also graphed their study time every day, while monthly-plan students graphed total study time each month. All participants self-monitored their study time on a daily basis.
Over the course of the ensuing semester, Kirschenbaum et al. found that monthly-plan students self-monitored more study hours and more “effective” (i.e., undistracted) study hours, and indicated less of a tendency to procrastinate, than did daily- and no-plan students. Monthly-plan students also maintained a relatively high rate of studying throughout the semester, whereas both daily- and no-plan students decelerated their study hours from the first to the second five-week period of the semester. While the authors found that within their cohorts both low-grade and high-grade students who participated in the SIP improved their GPAs relative to their respective control groups (i.e., low-grade SIP student GPAs improved relative to low-grade control group student GPAs and similarly for the high-grade SIP and control groups). No other statistically significant effects were found for student performance. High-grade students with daily and monthly plans did not perform better than high-grade students without plans, and similarly for low-grade students with and without plans.
Kirschenbaum et al. conclude that daily planning may have inhibited effective self-regulation by overburdening students with the task of planning for each day or by causing negative reactions in students who failed to meet their daily criteria for positive evaluation. In contrast, by writing out a proposed schedule for a longer period of time, monthly-plan students were able to increase their perception of control or choice. Either way, these results suggest that wise though it may be, students should be wary of the sage advice about planning far in advance.
Keep Your Options Open?
Remember the old saying, Jack of all trades, master of none? It has historically (and pejoratively) distinguished someone who has developed relatively low-level competencies in a number of different occupations from one who has mastered the skills required of a single occupation. Each of us likely knows someone who has excelled in (i.e., mastered) their career at the expense of developing their skills in other facets of life, and someone who has dabbled in (i.e., jacked) different trades but never quite built a career in any one of them. Another example is someone who lacks hesitation when it comes to making decisions—they tend to “jump right in”—versus someone who likes to keep their options open, and thus typically takes more time in making up their mind. Homo sapiens can indeed be fickle this way.
In an interesting set of experiments, Shin and Ariely (2004) investigate the extent to which Homo sapiens’ penchant for keeping our options open leads to making inefficient choices. The authors ask whether the threat of future unavailability makes currently less-desirable options seem more appealing and whether this causes Homo sapiens to overinvest in these less-desirable options. In other words, do doors that threaten to close appear more attractive than doors that remain open? And if so, will individuals overinvest just to keep them open? As Shin and Ariely point out, we would expect Homo economicus to value an option (having the ability to make a choice) based solely upon the expected utility of the outcomes the option provides. To the contrary, we would expect Homo sapiens to be swayed by a preference for flexibility and by loss aversion, causing an option’s subjective value to exceed its expected value.
Shin and Ariely’s experiments all followed the same basic procedure. Participants interact with a computer game consisting of three different doors opening to three different rooms. One door is red, another blue, and the third green. By clicking with the mouse on one of the doors (i.e., by door-clicking), a participant opens the door and enters the room. Once in the room, the participant can begin clicking repeatedly in the room (i.e., room-clicking) to obtain points randomly drawn from a given distribution of points, or, after any number of room-clicks within one room, door-click another door (for no points on that click) to begin clicking in the new room for points randomly drawn from a different (albeit mean-preserving) distribution of points. The expected value and other moments of each distribution are unknown to the participants. Note that charging a participant a click for switching rooms creates a switching cost. The total number of clicks is prominently displayed on the computer screen, in terms of how many clicks have been used and how many remain until the end of the game.
Participants were initially given a “click budget” to use on door- and room-clicks at their discretion. Once participants used up all their clicks the game ended and they were paid the sum of their room-click payoffs. The main goal of the experiment was to measure the relationship between the actions of a participant and door availability (i.e., “option availability”), which varies on two levels: “constant availability” or “decreased availability.” Under constant availability, all three rooms remain viable options throughout the experiment, irrespective of a participant’s actions. Under decreased availability, door availability depends upon the actions of the participant. Each time the participant clicks on a door or within a room, the doors of the other two rooms decrease in size by 1/15th of their original width. A single door-click on a shrinking door re-sizes it to its original size and the process continues. If a door’s size reaches zero it is eliminated for the remainder of the game. Poof!
Shin and Ariely’s first experiment was designed to test the hypothesis that as doors shrink in the decreased-availability treatment, participants will invest in keeping their options open (i.e., spending a click to reverse a door’s shrinkage). For this experiment, the expected value of a room-click (in any room) was 3 cents; however, the doors’ point distributions were (1) Normal with a variance 2.25 cents and min/max 0/7 cents, (2) Normal with a variance 0.64 and min/max 1/5, and (3) Chi-Square with a variance 10 and min/max -2/10 for doors 1 – 3, respectively. Each participant was given a budget of 100 clicks. Over 150 students participated.
Shin and Ariely found that door switching was indeed significantly more likely to occur under decreased as opposed to constant availability. The authors also found that, overall, there was a decreased tendency among participants to switch rooms later in the game. However, more switching still occurred in the decreased-availability treatment. The authors point out that as click numbers increase, participants are gaining more experience, can better estimate the point distributions, and thus should have less of a need to explore other rooms (i.e., options). Further, the expected value of exploring other options is reduced with click number because the time horizon during which this constantly improving information can be put to use is being reduced.
It turns out that because of switching costs, picking a room and remaining there for the duration of the experiment is the optimal (rational) strategy in terms of earning the highest expected payout from the experiment (yes, this would be Homo economicus’ strategy). Indeed, Shin and Ariely calculate that across both the constant- and decreased-availability treatments participants surrendered 11% of their payouts as a consequence of switching rooms (the average participant switched rooms 12 times during the course of the experiment).
Shin and Ariely’s second experiment manipulated participants’ knowledge about the point distributions for the various rooms, the hypothesis being that providing more information should substantially reduce the difference in switching between the constant- and decreased-availability treatments. On the other hand, if the information provided does not reduce this difference, then the difference is driven by a preference for flexibility and loss aversion rather than a paucity of information. The authors found evidence for the latter—Homo sapiens exhibit an inherent tendency to keep their options open—borne of a preference for flexibility in decision making or by loss aversion, even when doing so is costly.
Anticipated Versus Unanticipated Income
In a series of laboratory experiments, Arkes et al. (1994) tested whether Homo sapiens tend to spend (as opposed to save) unanticipated income (affectionately known as windfall gains) to a greater extent than anticipated gains (e.g., from owned assets). This is a non-issue for Homo economicus, who would never succumb to the temptation of spending more lavishly from unanticipated income. One experiment (Experiment 1) was designed to test whether students receiving unanticipated income would be more likely to risk that income on a simple lottery. The other experiment (Experiment 2) tested whether students receiving unanticipated income were more likely to spend the income on a consumer good. In each experiment, one randomly chosen group of students (the control group) arrived at the experiment anticipating some payment while the other group (the treatment group) was surprised by being given a payment upon arrival.
Students in both the treatment and control groups were informed between one and five days ahead of the experiment about the experiment’s time and location. Students in the control group were also provided the following information:
Although it wasn’t mentioned on the sign-up sheet for participating in the experiment, we want you to know that you will be paid for being in this experiment. We usually pay all our subjects $3.00 for participating. You will be paid when you get there. I thought you should know that. Also, I’d like to ask you not to mention to anyone that you’re being paid. The reason for this is that not all psychology experiments pay the participants, so it’s better if no one knows one way or the other.
This additional information effectively distinguishes anticipated income (obtained by members of the control group) and unanticipated income (eventually obtained by members of the treatment group). Members of the treatment group unexpectedly received the $3 just prior to the start of the experiment. Unlike members of the control group, treatment-group members were not provided with any information prior to the experiment.
After receipt of the $3 the control- and treatment-group students participating in Experiment 1 were presented with the following gamble:
You can bet as much of your $3 on the roll of a pair of dice, from 25₵ to the entire $3. If you roll a number seven or greater, you win. If you roll a number less than seven, you lose. For example, if you bet $1 and you roll a number seven or greater, I will pay you $1. If you roll a number less than seven, you will pay me $1. You can roll the dice only once.  How much do you want to bet?
Students in the control group wagered an average of $1 on the gamble, while those in the treatment group wagered a statistically different average of $2.16. In other words, those students experiencing a windfall gain of unanticipated income wagered more than double the amount of income on the gamble than students whose gain in income was anticipated.
Control- and treatment-group students participating in Experiment 2 were distinguished in the same manner as in Experiment 1. However, in this case, the students were paid $5 rather than $3, and rather than subsequently facing a gamble, the students were sent to a basketball game. After the game, the amount of the $5 each student spent on concessions at the game was tallied.
Similar to the results from Experiment 1, students in the control group spent an average of just under 40₵ at the game while those in the treatment group spent a statistically different average of 90₵. This is yet another indication that Homo sapiens tend to be more spendthrift with windfall gains!
Optimistic Overconfidence in the Stock Market
As Barber and Odean (2001) point out, theoretical models of investor behavior predict excessive trading in the stock market by overconfident investors. Likewise, psychological research demonstrates that in areas such as personal finance, men are more overconfident than women (c.f. Karabenick and Addy, 1979). Together, these two theories predict that men will trade more excessively than women, a prediction the authors test by partitioning investors according to gender. Using account data for over 35,000 households from a large discount brokerage, they analyze the common stock investments of men and women over the course of six years, from 1991 through 1997. The authors find that men trade 45% more of their portfolios than women and that trading reduces men’s net returns by 2.65 percentage points per year (relative to what their net returns would have been had they not traded) as opposed to a 1.72 percentage-point reduction in net returns for women traders.
Barber and Odean (2001) find that, across all households in the sample, men’s average monthly turnover of stocks in their portfolio (technically defined as where = shares sold, = beginning-of-month price per share, and = total beginning-of-month market value of the owner’s portfolio) was roughly 2% greater than the average woman’s. This average difference in turnover rate between men and women was larger than that for the subsample of married households and lower than that for single-headed households. Across all households, women traders earn net monthly returns (what Barber and Odean call own-benchmark monthly abnormal net returns) that are 0.143% lower than those earned by the portfolio they held at the beginning of the year, while men traders earn net monthly returns that are 0.221% lower than those earned by the portfolio they held at the beginning of the year. Both shortfalls are statistically significant at the 99% confidence level, as is their difference of 0.078%. As with the stock turnover rates, the differences in net monthly returns between men and women traders are lower among married households and higher among single-headed households.
Bottom line for Homo sapiens? Not only are men overconfident in their investing acumen relative to women, but they also suffer larger losses in their investment portfolios than they otherwise would had they not been so confident in their investing abilities.[61]
The Equity Premium Puzzle
As pointed out by Benartzi and Thaler (1995), historically there has been an enormous discrepancy between the returns on equity (e.g., stocks) and fixed-income securities (e.g., treasury bills and bonds). Since 1926, the annual real return on stocks has been roughly 7% while, for treasury bills, the return has been less than 1%. In an early attempt to explain the extent of this “equity premium,” Mehra and Prescott (1985) found that plausible levels of investor risk aversion were an unlikely culprit, which, in turn, signaled the need for an explanation grounded in a framework other than that offered by the rational choice model of Homo economicus. What makes it so puzzling is not so much that the premium exists. Rather, given the premium exists, why would investors ever choose to hold securities?
Benartzi and Thaler offer an explanation for this puzzle that is firmly rooted in Prospect Theory. Investors are by nature loss averse, and long-term investors evaluate their portfolios frequently. Together, these two traits of Homo sapiens investors lead to what the authors call myopic loss aversion. In conjunction with being loss averse, the more often he evaluates his portfolio (i.e., the shorter his evaluative horizon), the less attractive an investor finds a high mean, high-risk investment such as stocks. By being loss averse, the investor effectively overreacts to the downside risk (i.e., when his stocks fall in value) and therefore over-invests in treasury bills or bonds.
The authors demonstrate how myopic loss aversion among investors can explain the equity premium puzzle by answering the question, “If investors exhibit myopic loss aversion, how often would they have to evaluate their investment portfolios in order to explain the equity puzzle” (page 81)? To answer this question, Benartzi and Thaler draw samples from the historical (1926-1990) monthly returns on stocks, bonds, and treasury bills provided by the Center for Research in Security Prices (CRSP). Using this data, they can simulate prospective utility levels associated with portfolios holding purely stocks and purely bonds for evaluation periods starting at one month and increasing one month at a time. The authors find that the evaluation period at which stocks and bonds are equally attractive, i.e., where the common portfolio consisting of 50% stocks and 50% bonds is optimally held, occurs at roughly 13 months. In other words, the common portfolio is most likely to occur when the representative investor evaluates stock and bond returns roughly once per year.
The moral of this story is striking. Because stock returns have historically outperformed bond returns, there is no reason to believe that a pure stock portfolio (or at least a portfolio heavily weighted in stocks) should not continue to outperform a portfolio more heavily weighted in bonds. Therefore, if you are a Homo sapiens prone to suffer from myopic loss aversion, it is best to invest in a stock portfolio at the outset, and then avoid evaluating your returns for as many years as you can. Yes, this is a case where procrastination (in reviewing your stock portfolio) actually pays off!
If constraining yourself to this extent places too heavy a burden on your curiosity, then at the very least consider practicing what Galai and Sade (2006) and Karlsson et al. (2009) have coined the Ostrich Effect. This effect occurs when an investor evaluates his returns more often after a rise in the stock market (i.e., after receiving good news) than after receiving bad news about a fall in the market. By practicing the Ostrich Effect an investor helps to offset the inherent, negative impacts of myopic loss aversion.
Endowment Effects Among Experienced Versus Inexperienced Traders
In Chapter 6, a simple laboratory experiment was proposed to test for an Endowment Effect in a constructed market setting. We surmised that in a market characterized by a relatively strong endowment effect exhibited by the sellers, one would expect most sellers’ WTA values to exceed buyers’ WTP values, resulting in few sales ultimately being consummated. In other words, to the extent that Homo sapiens sellers betray an Endowment Effect, we would not expect them to behave like their dispassionate Homo economicus counterparts, who, by virtue of their immunity to such an effect, would likely consummate more sales in the market and thereby generate larger gains from trade.
Experimenting in a constructed market setting has its advantages, foremost among them the ability of the experimenter to mitigate potential confounding factors correlated with any given real-world context. However, as List (2004) demonstrates, when it comes to testing for an Endowment Effect among sellers of an everyday consumable good, a well-functioning, real-world marketplace provides an ideal setting for a field experiment. In such a marketplace (which in List’s case is a sports card trader’s show), List can distinguish experienced from inexperienced sellers and thus measure the divergence in the strength of the Endowment Effect among these two seller types. In his experiment, List’s “everyday consumable goods” are candy bars and coffee mugs. He finds that inexperienced sellers exhibit a relatively strong Endowment Effect. Experienced sellers behave more like Homo economicus; they are capable of eschewing the endowment urge.
List designed two versions of his experiment—one in which the market mimics a typical private market where buyers and sellers interact in an uncoordinated setting (Experiment 1), the other in which a collective-choice mechanism guides the sellers’ individual decisions toward a coordinated outcome (Experiment 2).
In Experiment 1, each subject was randomly assigned to one of four treatments. The treatments differ by type of endowment. Subjects in treatment are initially endowed with one coffee mug, subjects in treatment one candy bar, subjects in treatment one mug and one candy bar, and subjects in treatment neither a mug nor a candy bar. The coffee mug retailed for just under $6 at the University of Arizona bookstore. The candy bar was an extra-large, fine Swiss chocolate bar that also sold for roughly $6.00. Fundamental insights gained from the treatments come from subjects’ choices when asked if they would like to trade their initial endowment (with the experimenter). A subject can either keep her initial endowment or trade for the other good (e.g., mug for candy bar if in treatment , candy bar for mug if in , both the mug and candy bar for one or the other if in (weird I know, but this helps serve as a control on the treatments and ), and if in treatment , the subject chooses either the mug or the candy bar from the experimenter (this treatment also serves as a control vis-a-vis and ).
 The four collective-choice treatments use identical mugs and candy bars. In public good treatment , for example, subjects are initially endowed with a candy bar and must vote on a proposition to fund Mr. Twister, a small metal box placed at the front of the room that dispenses mugs. If the group chooses to fund Mr. Twister via simple majority rule, all subjects in the room are required to give their candy bars to the experimenter. Mr. Twister’s handle is then cranked times, which delivers mugs. The other three treatments are the public-good analogues of treatments , , and .
The author conducted some of the treatments with professional dealers and others with ordinary consumers, which allowed him to exploit the distinction between Homo sapiens who have more trading experience (“dealers”) and those who have less trading experience (“non-dealers”). As List reminds us, under both individual and group choice, the rational model of Homo economicus and Prospect Theory (as applied to Homo sapiens) have disparate predictions about choice behavior across the various endowment points. For preferences overall to be consistent with the rational model, the proportion of subjects in treatment who trade their mugs for candy bars should be equal to one minus the proportion of subjects in treatment who trade their candy bars for mugs. For example, if 70% of the subjects in treatment decide to trade their mugs for candy bars, 30% of subjects in treatment should trade their candy bars for mugs. As a result, 70% of subjects in each treatment end up owning a candy bar.
List found that 81% of non-dealers in Experiment 1’s treatment chose to keep their candy bars rather than trade for mugs. Similarly, 77% of non-dealers in Experiment 1’s treatment chose to keep their mugs rather than trade for candy bars. Relative to the control treatments and , the Prospect Theory prediction of an Endowment Effect among non-dealers holds. Similar results hold among non-dealers in Experiment 2 for the public good.[62]
The outcome was noticeably different for dealers in Experiment 1. In this case, the percentages of subjects holding onto their endowments in treatments and of 47% and 56% indicate a (statistically significant) absence of an Endowment Effect among dealers. Hence, List concludes that Prospect Theory demonstrates strong predictive power for inexperienced Homo sapiens. To the contrary, the traditional rational model of Homo economicus better predicts the behavior of Homo sapiens who already have considerable previous experience selling in a marketplace. Hence, when it comes to measuring the Endowment Effect, it seems that prior market experience is a key determining factor in predicting the choice behavior of Homo sapiens.[63], [64]
Reluctance to Sell in the Stock Market
As Odean (1998) explains, the tendency among stock market investors to hold on to “loser stocks” for too long and sell “winner stocks” too soon is known as the Disposition Effect. This effect is a type of loss aversion whereby a Homo sapiens investor is averse to realizing a loss on the sale of stock whose market price has fallen below the investor’s cost basis (i.e., the price at which the investor originally purchased the stock).
To test for the existence of a Disposition Effect among stock market investors, Odean obtained trading records for 10,000 accounts at a large discount brokerage house from 1987 through 1993. In analyzing these records, Odean finds that, overall, investors realized their gains from winner stocks more frequently than their losses from loser stocks. His analysis also indicates that a large number of investors engage in the tax-motivated selling of loser stocks, especially in December, in order to declare losses on their tax returns and reduce their income tax burden (i.e., he finds evidence of what one might call a Tax-Loss Declaration Effect).
Because there are competing explanations for why investors might sell their winners while retaining their losers, Odean’s analysis simultaneously tests for these as well. For example, investors may simply believe that their current losers will in the future outperform their current winners. Thus, they may sell their winners to rebalance their investment portfolios (a Rebalancing Effect).[65] It could also be the case that investors refrain from selling losers due to higher transactions costs associated with trading at lower prices (Transaction Cost Effect). When the author controls for these two potential effects in the data he still finds evidence of a Disposition Effect, but not for Transaction Cost or Rebalancing Effects. Regarding the latter effect, Odean finds that the winners investors choose to sell continue in subsequent months to outperform the losers they keep. This result indicates that while investors may have an intent to rebalance their portfolios for improved performance, in general, they do not achieve this goal.
For insight into how Odean uses his data to test for the Disposition Effect, he provides the following example:
“Suppose an investor has five stocks in his portfolio, A, B, C, D, and E. A and B are worth more than he paid for them; C, D, and E are worth less. Another investor has three stocks F, G, and H in her portfolio. F and G are worth more than she paid for them; H is worth less. On a particular day, the first investor sells shares of A and C. The next day the other investor sells shares of F. The sales of A and F are counted as realized gains. The sale of C is a realized loss. Since B and G could have been sold for a profit but weren’t, they are counted as paper gains. D, E, and G are similarly counted as paper losses. So, [across these two investors over these two days, there were a total] of two realized gains, one realized a loss, two paper gains, and three paper losses counted. Realized gains, paper gains, realized losses, and paper losses are then summed for each account and across accounts” (p. 1782).
Odean then calculates the “proportion of gains realized” () and “proportion of losses realized” () according to the following two formulae:
, and
.
From the example above, = 0.5 and = 0.25. As Odean points out, a Disposition Effect has unconditionally occurred if measured over the time horizon of analysis (1987-1993). Further, a tax-loss declaration effect has occurred if ( in December) > ( in January through November).
Odean finds that over the course of an entire year, and that the difference between the two is statistically significant. Thus, evidence supports the Disposition Effect among his sample of investors. Indeed, as Odean points out, the ratio of to is a little over 1.5, indicating that, all else equal, a stock that has increased in value is more than 50% more likely to be sold from day to day than a stock whose value has decreased. Further, the author finds that ( in December) > ( in January through November), again with correspondingly high statistical significance. This result is evidence supporting the Tax-Loss Declaration Effect.
Reluctance to Sell in the Housing Market
In the housing market, sellers incur a loss when they sell their house for less than they paid for it. Because housing markets typically consist of some sellers incurring losses and others incurring gains, the housing market, like the stock market, provides an opportune setting within which to test for the presence of a Disposition Effect among sellers. Genesove and Mayer (2001) performed this test using sales data from downtown Boston, MA between 1990 and 1997. Their data consisted of the price originally paid for an apartment (i.e., the purchase price) by an owner who later listed the apartment for sale, the price subsequently listed by the owner cum seller (i.e., the asking price), an estimate of the apartment’s market value, and any outstanding loan at its sale (which enabled the authors to remove from their sample any sellers who would be reluctant to sell at a price below what they originally paid for the apartment because they had to repay the loan used for their original purchase).
The authors found that sellers do indeed exhibit a Disposition Effect. Sellers who are expected to make a loss on the sale of their apartment set a higher asking price, all else equal. Genesove and Mayer’s (2001) main results are presented in Columns (1) and (2) in the table below. The two variables of most interest to us are LOSS and LTV. Variable LOSS is the difference between a seller’s purchase price, on the one hand, and the apartment’s estimated value in the quarter it is listed, or zero, whichever is larger. In other words, if a seller is facing a projected loss on the sale of her apartment, LOSS records the estimated extent of that loss for that seller. Otherwise, if a seller is not facing a projected loss on the sale of her apartment (i.e., the seller is facing a projected gain), then LOSS records a zero for that seller. Variable LTV is defined as the difference between a seller’s loan-to-value ratio and 0.8, or zero, whichever is larger. In other words, if a seller’s loan-to-value ratio is greater than 0.8, then LTV records the difference between that ratio and 0.8 for the seller. Otherwise, if a seller’s loan-to-value ratio is less than 0.8, then LTV records a zero for that seller.
In Column (1) of the table, the statistically significant coefficient estimate for LOSS of 0.35 indicates that a 10% increase in a prospective loss leads a seller to set the asking price 3.5% higher, all else equal. The corresponding coefficient estimate for LOSS in Column (2) of 0.25 indicates that a 10% increase in a prospective loss leads a seller to set the asking price only 2.5% higher. Genesove and Mayer (2001) interpret the estimate from Column (1) as an upper-bound and that from Column (2) as a lower-bound on the true relationship between a prospective loss and the seller’s asking price. Similarly, the statistically significant coefficient estimate for LTV of 0.06 in Column (1) indicates that a 10% increase in the loan-to-value ratio for those sellers with ratios already above 0.8 leads these more highly indebted sellers to set an (upper-bound) asking price just over 6% higher, all else equal. Column (2) indicates that the lower-bound on the relationship between LTV and a seller’s asking price is 0.5%. Together, these results corresponding to LOSS and LTV indicate that sellers facing a loss on the sale of their apartment—both concerning their original purchase price and their loan-to-value ratio—exhibit a Disposition Effect by setting their asking prices above those set by sellers who do not face a loss.
So as not to shield buyers from their share of “Homo sapiensism” in the housing market—in particular Homo sapiens’ affinity for reference dependence—Simonsohn and Loewenstein (2006) investigated the US housing market between 1983 and 1993 to discern whether the average monthly rent or house price in the location where households moved from (i.e., their old locations) affected the monthly rent or house price they paid in the location they moved to (i.e., their new locations). In other words, do households that paid more for their housing in their old locations on average pay more for housing in their new locations. The authors find that the higher the rent or price paid in the old location, the higher the rent or price paid in the new location. Further, when households move for a second time within their new location, this positive relationship between prices paid in the old and new locations disappears. Simonsohn and Loewenstein conclude that households readjust their reference points after having lived in an area for some time. Therefore, the moral of Simonsohn and Loewenstein’s story is that even though Homo sapiens exhibit reference dependence in the housing market, at least their reference points are flexible.
Deal or No Deal?
When faced with an uncertain situation, do Homo sapiens set reference points based upon prior expectations, similar to how we set reference points in certain situations based upon prior experience? Post et al. (2008) set out to answer this question by assessing risky decisions made by 150 contestants from the Netherlands, Germany, and the US in the high-stakes game show Deal or No Deal.[66] In the game, contestants choose among 26 briefcases, each containing some uncertain amount of money, ranging from €0.01 to €5 million (in the Dutch edition of the game). Each contestant selects one of the briefcases and thereafter owns its unknown contents. Next, he picks six of the remaining 25 briefcases to open. Each of the opened briefcases reveals a prize that is not in the contestant’s initially chosen briefcase. The contestant is then presented with a “bank offer” from the game’s host, which is the opportunity to walk away with a sure amount of money based loosely upon the average amount contained in the remaining unopened briefcases (Deal?), or to choose another five briefcases to open, followed by another bank offer (No Deal?). The game continues in this fashion until the contestant either accepts a bank offer or rejects them all and walks away with whatever amount of money is in the initially chosen briefcase.
Post et al. (2008) find that the typical contestant’s choices can be explained in large part by previous outcomes experienced during the game (e.g., the amounts of money in the opened briefcases and associated bank offers). Aversion to risk diminishes as prior expectations are either shattered by unfavorable outcomes (i.e., the opening of high-value briefcases) or surpassed by favorable outcomes (the opening of low-value briefcases)—known as Break-Even and House-Money Effects, respectively. This process of reference-point adjustment made by the contestant represents what the authors call path dependence, a form of dependence to which Homo economicus would never succumb.
Post et al.’s (2008) basic results for the Deal or No Deal contestants are presented in the table below. A contestant is labeled a Loser if his average remaining prize in the unopened briefcases (after having eliminated the lowest remaining prize) is among the worst one-third across all contestants in the same round of the game. A contestant is a Winner if his average remaining prize is among the highest one-third, and Neutral if neither a Loser nor a Winner. The column titled %BO lists the bank offer as a percentage of the money amounts in the remaining unopened briefcases per round, No. indicates the number of contestants who take the bank offer (i.e., take the deal) per round, and %D indicates the percentage of contestants who take the bank offer per round.
Focusing on the US sample of contestants (the results for the Netherlands and Germany samples are similar), we see that (1) %BO generally increases for each type of contestant (Loser, Winner, or Neutral) as the game progresses (i.e., the number of rounds increases), and (2) generally lower percentages of both Losers and Winners take the deal as compared to Neutrals as the game progresses. Overall, across rounds 2–9, 18% of all Deal or No Deal choices in the Neutral group are Deal, while only 8% and 14% of choices were Deal in the Loser and Winner groups, respectively. Post et al. (2008) interpret these results as evidence that risk aversion diminishes for both Losers and Winners, particularly for Losers, who have been unlucky in selecting which briefcases to open. Thus, prior outcomes are indeed an important reference points for risky choices.
Health Club Membership
What do we do when Homo sapiens are naive about their time-inconsistent preferences? Using attendance data for close to 8,000 health club members in New England from 1997–2000, DellaVigna and Malmendier (2006) were able to test whether members prone to making time-inconsistent choices choose a membership plan that helps them overcome this tendency most efficiently.[67] In their sample, gym-goers have four different membership plans to choose from: (1) pay a $12 fee per visit, (2) pay $100 for a 10-visit pass, (3) sign an (automatically renewed, cancelable) monthly contract for unlimited visits at a standard fee of $85 per month, or (4) sign an annual contract (requiring in-person or in-writing renewal at the end of the contract) for unlimited visits at $850 per year. The authors find that, on average, members forego $600 in savings over the course of their memberships, indicating that they choose suboptimal membership plans given their attendance frequencies. DellaVigna and Malmendier attribute this suboptimality to optimistic overconfidence on the part of club members in terms of their future self-control or efficiency in attending the club. Sound familiar?
In particular, the authors find that members who choose a monthly membership pay on average 70% more than they would under the pay-as-you-go, fee-per-visit contract for the same number of visits. Eighty percent of these monthly members would have been better off had they paid per visit for the same number of visits. In addition, members who choose a monthly contract are 17% more likely to stay enrolled beyond one year than users committing upfront to an annual membership. Monthly members, therefore, end up paying higher fees for the option to cancel each month. Further, low-attendance members delay canceling their monthly contracts despite the small transaction costs of doing so.
Because of its automatic-renewal provision, the monthly contract’s default position is “opt-out,” meaning if a member decides to terminate the contract, she must opt out of it. By contrast, because of its non-automatic renewal provision, the annual contract’s default position is “opt-in,” whereby a member must opt into the contract on a yearly basis. In this way, the monthly contract is well suited for members who suffer from time inconsistency associated with procrastinating in joining the club, and then remembering to renew thereafter, while the annual membership better suits those members who have difficulty in motivating themselves to regularly attend the club for their workouts. For those members who end up attending less than they originally imagined they would, paying the per-visit fee is the best option. For those who follow through with attending often, the annual-fee membership seems to make the most sense. And for those who at the outset are unsure of how often they will attend, the monthly-fee membership seems best. DellaVigna and Malmendier (2006) investigate whether members choose the best membership plan for themselves at the outset and if not, then whether they learn and adjust to overcome their time-inconsistency problem.
The authors find that for monthly members there is not a month where their average price was beneath the standard $12 fee-per-visit or the $10 cost-per-visit associated with the 10-visit pass. On average, the price paid by the monthly members was above $17 per visit. Likewise, the average price paid by the annual members was above $15 per visit. Thus, on average, monthly and annual members are overconfident about their attendance at the club. They are not choosing their membership plans optimally.
Regarding the question of whether annual and monthly members learn and adjust to overcome their time-inconsistency problem, DellaVigna and Malmendier find that after the first year on an annual contract, the average annual member increases his monthly attendance to a point where the corresponding average price per visit falls from over $15 to approximately $11.30—lower than the $12 fee-per-visit, but still higher than the $10 cost-per-visit associated with the 10-visit pass. After the first six months on a monthly contract, the average monthly member decreases (yes, decreases) his monthly attendance to a point where the corresponding average price per visit rises to roughly $20 per visit. It appears that members with an annual membership adjust their attendance to an extent that mitigates the inefficiency of their choice but does not eliminate it. Members with monthly memberships, on the other hand, exacerbate the inefficiency of their choice. Alternatively stated, annual members learn to mitigate their time-inconsistency problem, while monthly members exacerbate theirs.[68]
Lessons From an ‘All-You-Can-Eat’ Experiment
In Chapter 2, we were introduced to the notion of flat-rate pricing. Later, in Chapter 6, we encountered the Sunk Cost Fallacy. Conventional wisdom suggests that because All-You-Can-Eat (AYCE) restaurants charge a fixed price (i.e., flat rate) for a meal, the Sunk Cost Fallacy should be relatively easy to detect in Homo sapiens consumption behavior when they belly up to the buffet.
Because an AYCE customer faces zero marginal cost associated with additional amounts of food consumed, the rational model of Homo economicus suggests that he should continue to eat until the marginal utility of consumption reaches zero. This is because the per-unit cost of consumption continually decreases with the amount consumed. Once the AYCE customer has paid the fixed price for the meal, his budget constraint on added consumption is effectively obviated. The only thing stopping him now is his epidemiological and neurological impulses. As Just and Wansink (2011) point out, in an AYCE setting price can influence whether one chooses to eat at the restaurant, but it should not affect the amount of food one consumes once he has chosen to eat there.
As we know, some Homo sapiens are driven to “get their money’s worth” in various situations (recall the experiments in Chapter 6 involving the choice of whether to drive through snowstorms and rainstorms to get to a sporting event). In other words, Homo sapiens are susceptible to the Sunk Cost Fallacy. To the extent that the flat-rate pricing of AYCE restaurants triggers the Sunk Cost Fallacy in their customers, increasing the price of an AYCE buffet should increase the amount of food a customer ultimately eats. Just and Wansink test this hypothesis by designing an innovative field experiment that assigned customers to one of two prices at an AYCE pizza buffet restaurant.[69] The authors find that those assigned to the higher-price treatment consumed just under 40% more pizza than those assigned to the lower-price treatment. In other words, a higher flat rate did indeed increase the amount of food consumed. But is it a Sunk Cost Fallacy that drove these results, or perhaps an alternative effect?
Permission to conduct the experiment was granted by the Pizza Garden, an AYCE restaurant located one mile south of Champaign, Illinois. The experiment was conducted during the restaurant’s exclusive lunch buffet hours on a Tuesday, Wednesday, and Thursday in early April 2005. Members of the experiment’s control group paid for the pizza buffet at the regular price of $5.98, while members of the treatment group were given coupons for 50% off this regular price. A total of 66 subjects participated in the experiment.
As Just and Wansink point out, customers choosing to eat at this restaurant would have already decided to eat the buffet at the regular price. In fact, no individuals included in either the treatment or control group failed to purchase the pizza buffet. While in the restaurant, pizza consumption was measured by three assistants who served as hostesses. The assistants were blind to the purpose of the study and had no knowledge of which patrons had been randomly assigned to the control or treatment groups. The assistants noted how many pieces of pizza each customer brought back from the buffet table, and how much was left uneaten after each customer completed their meal. Because the assistants were also responsible for busing tables, collecting the uneaten food was possible to do without raising suspicion. Uneaten pizza was weighed in a back room to more accurately assess what percentage of the pizza taken from the buffet table was actually eaten. After paying for their meals, the experiment’s participants completed a short questionnaire concerning their demographics, how much they believed they ate, and their quality assessments of the pizza. The size of the group each participant was a part of while eating their meal was also noted, as group size can be a determinant of how much an individual eats at a restaurant.
The authors find that, on average, participants paying the full price ate roughly one slice more than participants paying half price, which nevertheless resulted in the full-price participants paying roughly $0.58 more per slice than the half-price participants. Full-price participants also left more uneaten pizza on their plates as food waste. Therefore, Just and Wansink find evidence that higher prices do indeed lead to greater pizza consumption. The Sunk Cost Fallacy seems to be in play at AYCE restaurants.
The Persistence of Political Misperceptions
Political misperceptions have probably existed for as long as Homo sapiens have practiced politics. Although their frequency, intensity, and the extent to which they are disseminated among the general public via social media outlets are worthy of concern, misinformation campaigns (and the unsubstantiated conspiracy theories they spawn) have a long history in worldwide politics.
In a series of field experiments with self-identified ideological subgroups of adults, Nyhan and Reifler (2010) investigated the extent to which corrective information embedded in realistic news reports can succeed in reducing prominent misperceptions about contemporary political issues (according to the authors, misperceptions occur when people’s beliefs about factual matters are not supported by clear evidence and expert opinion—a definition that includes beliefs about the world that are both false and unsubstantiated). In each of the experiments, the subgroups failed to update their beliefs when presented with factually corrective information that runs counter to their ideological predispositions. In several instances, the authors find that the corrections actually strengthen (yes, strengthen) rather than weaken misperceptions among those most strongly tied to their predispositions.
Nyhan and Reifler premise their experiments on previous research showing that many citizens base their policy preferences on false, misleading, or unsubstantiated information related to their political ideologies and what they believe to be true. For instance, after the US invasion of Iraq in 2003, the belief that Iraq had stockpiled weapons of mass destruction prior to the invasion was closely aligned with one’s level of support for President Bush. As the authors point out, people are typically exposed to corrective information within objective news reporting, pitting two sides of an argument against each other. Nevertheless, we Homo sapiens are likely to resist or reject arguments and evidence contradicting our opinions.
Specifically, Nyhan and Reifler test three hypotheses about the extent to which corrective information overrides, or at least tempers, the effect of a subject’s political ideology:
- Hypothesis 1. The effect of corrective information on misperceptions will be moderated by political ideologies.
- Hypothesis 2. Corrective information will fail to reduce misperceptions among the ideological subgroup that is likely to hold the misperception.
- Hypothesis 3. In some cases, the interaction between corrective information and political ideology will be so strong that misperceptions will actually increase for the ideological subgroup in question. Ouch. This is known as a Backfire Effect.
In the experiments, subjects read mock newspaper articles containing a statement from a political figure who reinforced a widespread misperception concerning the war in Iraq, tax cuts, or stem cell research (three popular issues at the time in American politics). Subjects were randomly assigned to read articles that either included or did not include corrective information immediately after a false or misleading statement.
The first experiment tested the effectiveness of corrective information embedded in a news report on beliefs that Iraq had stockpiled weapons of mass destruction (WMD) immediately before the US invasion. As Nyhan and Reifler point out, one possible explanation for why this misperception persisted was that journalists had failed to adequately fact-check Bush administration assertions that the US had found WMDs in Iraq. Another was people’s fear of death in the wake of the September 11, 2001, terrorist attacks, known as “salience of mortality.”
Subjects were instructed to read a mock news article attributed to the Associated Press that reported on a Bush campaign speech in Pennsylvania in October 2004. As Nyhan and Reifler describe it, the article describes Bush’s remarks as a rousing, no-retreat defense of the Iraq War. The article included a quote from Bush: “There was a risk, a real risk, that Saddam Hussein would pass weapons or materials or information to terrorist networks after September 11th, that was a risk we could not afford to take (page 312).” A control group received only this information, while one treatment group also received corrective information based upon the Duelfer Report, which documented the lack of both Iraqi stockpiles of WMDs and an active production program immediately prior to the US invasion. Another treatment group received a mortality-salience question: “Please briefly describe the emotions that the thought of your own death arouses in you.
All subjects were then asked to state whether they agreed with the summary statement: “Immediately before the US invasion, Iraq had an active weapons of mass destruction program, the ability to produce these weapons, and large stockpiles of these weapons, but Saddam Hussein was able to hide or destroy these weapons right before US forces arrived.” Responses were measured on a five-point Likert scale ranging from 1 = “strongly disagree” to 5 = “strongly agree.” To gauge a subject’s ideological disposition, subjects self-identified according to a centered seven-point Likert scale, ranging from -3 = “strongly liberal” to 3 = “strongly conservative.” An additive five-question scale measuring political knowledge used conventional factual questions.
As the results in the table below demonstrate, Nyhan and Reifler find that, as expected, more knowledgeable subjects were less likely to agree with the summary statement (the coefficient estimates for Political Knowledge of -1.133 and -1.081 are statistically significant), conservatives were more likely to agree with the statement (the coefficient estimates for Ideology of 0.347 and 0.199 are also statistically significant), but neither the corrective information (Correction) nor mortality salience question (Mortality Salience) had statistically significant effects on subjects’ responses.[70]
In Model 2, the introduction of the interaction term Correction*ideology tests whether the effect of the corrective information is moderated by subjects’ political ideologies (Hypothesis 1), in particular whether the corrective information will be increasingly ineffective as subjects’ political ideologies increase their susceptibility to misperception—in the case of justifying the Iraq War among politically more conservative subjects (Hypothesis 2)—or whether the corrective statement will backfire and that misperceptions will actually increase among politically conservative subjects (Hypothesis 3). Because the coefficient estimate on this interaction term is positive and statistically significant, it supports each of the three hypotheses. Looking more closely at their data, the authors find that the corrective information worked as expected. When exposed to the corrective information, very liberal subjects became more likely to disagree with the summary statement. No statistical effect was found for subjects describing themselves as liberal, somewhat left of center, or centrist. And conservatives became more likely to agree with the summary statement…kaboom, corrective information backfired!
The authors go on to test whether the Backfire Effect occurred because conservative participants distrusted the news source, Associated Press. They find that news source (New York Times vs. Fox News) has no impact on the result. Interestingly, when the context of the mock news article is changed from the 2004 Bush campaign speech to a 2005 Bush statement about Iraq, the Backfire Effect not only disappears in general, but transforms into a Forward-Fire Effect—conservatives receiving the corrective statement are less likely to agree with the summary statement. This is evidence of a framing effect. Nevertheless, among those subjects who rated Iraq as the nation’s “most important problem,” the Backfire Effect persisted with the 2005 Bush statement about Iraq.
Nyhan and Reifler put forth two possible justifications for the Forward-Fire Effect. They point out that conservatives may have shifted their rationale for supporting the war in tandem with the Bush administration, which over time sought to distance itself from the WMD rationale for the war. By early 2006, national polls suggested a decline in Republican beliefs that Iraq had stockpiled WMDs before the US invasion. Another possible explanation is that conservatives generally placed less importance on the war by early 2006, and thus were less likely to counterargue the corrective information.
Lastly, the authors found a similar Backfire Effect when the issue at hand was misperceptions about the Bush tax cuts of 2001 and 2003. Regarding the issue of misperceptions regarding stem cell research in the early 2000s—a misperception held among liberals—Nyhan and Reifler find that corrective information worked (i.e., had a statistically significant negative effect on misperceptions) among subjects self-identifying as centrists and right-of-center, but failed to affect subjects identifying as liberal (i.e., left-of-center and beyond). Thus, in this experiment evidence is found in favor of Hypotheses 1 and 2, but not Hypothesis 3 concerning stem cell research. Thankfully, stem cell research is not an issue inspiring a Backfire Effect among liberals.
Temptation and Self-Control – The Case of Potato Chips
As Wertenbroch (1998) observes, we Homo sapiens often cave in to temptation (e.g., about the consumption of “vice goods” such as cigarettes, as opposed to “virtue goods” such as reduced-fat yogurt) against our own better judgment and self-interest. In dealing with temptation, realizing immediate utility from consumption conflicts with the longer-term utility associated with self-control. Self-rationing is a form of self-control that limits a consumer’s stock of vice goods and thus the possibility of consuming them. Self-rationing imposes transactions costs on additional consumption and is perhaps an expression of attendant feelings of guilt. One way to test the extent of a consumer’s self-control is to answer the question, Are consumers less likely to purchase larger quantities of a vice good than a virtue good in response to equal unit-price reductions? If the answer to this question is “yes,” then consumers exhibit self-control in the face of temptation.
Using an experimental market approach, Wertenbroch tests whether vice consumers are less price sensitive than virtue consumers by examining consumers’ demands for potato chips at two different quantity-discount price depths offered for a large-purchase quantity. The potato chips are framed as either 25% fat (relative vice good) or 75% fat-free (relative virtue good). Approximately 300 MBA students at Yale University participated in the experiment. The subjects were first shown a 6-oz. bag of an existing brand of potato chips as a reference package size. A questionnaire then offered them the opportunity to buy zero, one, or three 6-oz. bags of a new brand of potato chips at different prices per bag—$1 for a single bag and $2.80 for three bags (if the subject had been randomly assigned to the “shallow discount” treatment group), or $1.80 for three bags (if randomly assigned to the “deep discount” treatment group). A single bag represents the small size and three bags represent the large size. The new brand was described as having an innovative mix of ingredients and as currently being test marketed.
Subjects were informed that approximately one in 10 of those who completed their questionnaires would be randomly selected in a lottery to receive $10 in compensation for agreeing to participate in the experiment. To ensure that subjects would accurately reveal their demand for the chips, they were informed that the lottery winners would have to purchase (out of their $10 compensation payment) the respective amounts of potato chips they had chosen in the questionnaire at the given prices.
Wertenbroch finds that subjects who bought potato chips were more likely to prefer the large size when the chips were framed as 25% fat (again, the vice good) than as 75% fat-free (the virtue good). However, the probability of buying the large size under the virtue frame increased from 20% under the shallow discount to 65% under the deep discount (i.e., an increase of 225%). Under the vice frame, the corresponding increase was from 41% to 53%, an increase of merely 29%. Therefore, as the author points out, increasing the depth of the quantity discount was less effective in enticing vice consumers to increase their purchase quantities, suggesting that they self-imposed a rationing constraint as external price constraints were relaxed.[71]
Dishonesty’s Temptation
According to the National Retail Federation (NRF), customer and employee theft, fraud, and losses from other “retail shrink” in the US totaled just under $62 billion (or approximately 1.6% of total sales) in 2019, representing a 22% increase over the previous year (NRF, 2020). You read that correctly, $62 billion, with a “b.” Interpreting retail shrink as the aggregation of consumers being dishonest with the businesses that supply our retail goods and employees being dishonest with the businesses that employ them, this $62 billion can be thought of as representing the monetary cost of dishonesty in the retail sector of the economy.[72]
Let’s face it. Dishonesty is an inexorable part of the human experience, so inexorable that even Homo economicus can be expected to be dishonest in any given situation when the coldly calculated expected benefit of dishonesty outweighs its expected cost. To demonstrate dishonesty’s pervasiveness among Homo sapiens (or, as Mazar et al. (2008) describe it, to measure the extent to which a little bit of dishonesty yields profit without spoiling one’s positive self-view) Mazar et al. conducted a series of experiments enabling a comparison between the performance of participants in control conditions (in which the participants had no opportunity to be dishonest) with “cheating conditions” (in which participants had the latitude to cheat).
In the first experiment, the authors tested whether reminding participants of their standards for honesty would induce greater levels of honesty among them than among participants who were not preempted with such reminders. Over two hundred MIT and Yale students participated in the experiment, which consisted of multiple paper-and-pencil tasks appearing together in a booklet. To begin, participants were asked to either write down the names of 10 books they had read in high school (no moral reminder) or the Ten Commandments (moral reminder) within a two-minute time limit.[73] Next, the participants were provided with a test sheet and an answer sheet. The test sheet consisted of 20 matrices, each based upon a set of 12 three-digit numbers. Participants had four minutes in which to find two numbers per matrix that added up to 10. An example matrix is depicted below.
The answer sheet was used by a participant to report her total number of correctly solved matrices. At the end of the session, two randomly selected participants earned $10 for each correctly solved matrix.
At the end of the four-minute matrix task, the experimenter verified each participant’s answers. Participants in the experiment’s two treatment (or recycle) groups indicated the total number of correctly solved matrices on their answer sheets, and then tore out the original test sheets from the booklet and placed them in their belongings (to recycle on their own later), thus providing these groups of participants with an opportunity to cheat.
The results from this experiment were as anticipated. The type of reminder (10-books vs. Ten Commandments) did not affect the average participant’s performance in the two control conditions—each group averaged just over three correctly solved matrices—suggesting that type of reminder influenced neither ability nor motivation. However, in the two treatment conditions, reminder type mattered. Following the 10-book recall task, participants self-reported an average of slightly more than four correctly solved matrices (which was significantly higher statistically than the control groups’ three-matrix average), thus pointing to the likely presence of cheating among this group of participants. To the contrary, participants in the Ten Commandment recall task self-reported an average of slightly less than three correctly solved matrices (which was not significantly different statistically than the control groups’ averages). Mazar et al. conclude that reminding participants of standards for morality eliminates cheating.
In the second experiment conducted with over 200 MIT and Yale students, the Ten Commandments recall task was replaced with an Honor Code treatment, the 10-book recall task was eliminated, and payments for correctly solved matrices (for randomly chosen participants) were either 50 cents or $2 per matrix (to test for possible payment-level effects). In the two control groups, participants again handed both the test and answer sheets to the experimenter at the end of the matrix-solution task. The experimenter verified their answers and wrote down the number of correctly solved matrices on the answer sheet. In the two recycle treatments (one without any recall task, henceforth, “recycle” treatment, the other with the Honor Code recall task, henceforth, “recycle+HC”) participants indicated the total number of correctly solved matrices on the answer sheet, folded the original test sheet, and then placed it in their belongings, similar to the first experiment. In the recycle+HC treatment, there was a statement located at the top of the matrices test sheet that read: “I understand that this short survey falls under MIT’s [Yale’s] honor system (page 637).” Participants printed and signed their names below the statement.
Results from this experiment are depicted in the figure below. Similar to the results for the first experiment, we see that while the recycle treatment resulted in a statistically significant increase in self-reported correctly solved matrices relative to the control groups and recycle+HC treatments (the whiskers do not overlap), the control groups and recycle+HC treatments did not result in statistically different scores. Interestingly, the different payment amounts (50 cents and $2) did not result in different scores within each respective group type—control, recycle, or recycle+HC.
Lastly, in a third experiment, Mazar et al. tested whether the opportunity for dishonest behavior occurred in terms of money or in terms of an intermediary medium (tokens). The authors posited that introducing tokens would offer participants more latitude in interpreting their actions, hence making it easier for participants to justify cheating. Participants (450 MIT and Yale students) had five minutes each to complete the matrix task and were promised 50 cents for each correctly solved matrix. The same control and recycle treatment as in the second experiment were used, along with a “recycle+token” treatment where participants knew that each (self-reported) correctly solved matrix earned one token, which could be exchanged for 50 cents a few seconds later.
Similar to their previous findings, Mazar et al. found that the average participant in the recycle treatment reported having solved significantly more matrices than the average participant in the control group, suggesting the presence of dishonesty among the former group. Interestingly, introducing tokens as the medium of immediate exchange further increased the magnitude of dishonesty in the recycle+token treatment, such that it was significantly larger than that exhibited in the recycle treatment. This leads the authors to conclude that a medium, such as tokens, facilitates dishonesty, which helps explain the high levels of employee theft and fraud (e.g., stealing office supplies and merchandise, and putting inappropriate expenses on expense accounts) found in the US retail industry. As Ariely (2008) puts it, “what a difference there is in cheating for money versus cheating for something that is a step away from cash!” (page 299)[74]
Bigger Universities, Smaller Silos
In Chapter 1, the concept of homophily and the Silo Effect were briefly explored. Left unanswered was the question of how the size and diversity of social choices (i.e., social ecology) affect the similarities between relationship partners (i.e., the extent of homophily existing between the two). As Bahns et al. (2010) point out, the initiation of an interpersonal or inter-organizational relationship is not only a dyadic process. The process is also influenced by the broader group of social contacts present in the local environment. It is the social ecology that shapes the kinds of communication and interactions that occur between the two partners, potentially hardening or softening the pretext for a silo effect.
Bahns et al. aver that, in general, when Homo sapiens have a choice, they tend to initiate and build relationships with partners who are similar to them. In their study, they compare the degree of similarity within dyads in a particular social ecology—a college campus—that varies in the size of the available pool of relationship choices. The authors compare dyads formed among students in public settings at a large state university to dyads formed in the same way in smaller colleges in the same state. Because students located at the larger universities can choose among a greater variety of fellow students, Bahns et al. hypothesize that these students will also be able to match their interests and activities more closely with partners than students located at smaller universities, which leads to a straightforward, albeit ironic, hypothesis. Greater human diversity within an environment leads to less personal diversity within dyads.
To test this hypothesis 110 students (55 dyads) were recruited from a large campus (the University of Kansas) and 158 students (79 dyads) from four small universities located in small eastern and central Kansas towns. To collect their data, experimenters visited each campus on a midweek day and located a public space where students were interacting with each other (e.g., the student union and a cafeteria). Naturally occurring dyads, which were randomly identified, were defined as any group of exactly two people who appear to be interacting in some way. The experimenter then administered a five-section questionnaire.
The first section gathered information about the students’ socio-demographics and the nature of their relationship (e.g., how long they had known their dyad partner, how close they were, and how many hours per week they spent with the partner). The second section of the questionnaire asked about different social attitudes concerning abortion, religious observance, birth control, the importance of maintaining traditional husband-wife roles in a marriage, and capital punishment. The third section measured what the authors call “feeling thermometers” of attitudes toward/prejudices against five different social groups—Arabs, Black Americans, overweight people, gay men, and Jews. The fourth section measured health-related behaviors (e.g., tobacco use, alcohol use, and exercise). The fifth section measured the extent of agreement with what the authors call relational mobility statements, i.e., (1) “At this school, it is easy to meet new people,” (2) “People at this school have few chances to get to know new people,” (3) “It is common for me to see people on campus who are unfamiliar” (page 123), and psychological independence statements, i.e., (1) “If a person hurts someone close to me, I feel personally hurt as well,” (2) “My close relationships are unimportant to my sense of what kind of person I am,” and (3) “Even when I strongly disagree with group members, I avoid an argument” (page 124).
Bahns et al. find that dyads on the smaller campuses reported less relational mobility, implying greater perceived relationship opportunities on the large campus. However, no evidence is found that distinguishes the degree of psychological independence across the large and small universities. Similarly, there was no statistically significant difference across universities regarding length of relationships and amount of time spent together. However, dyads from the smaller universities rated their relationships as being closer than those from the large university.
Participants from small universities reported somewhat more conservative political beliefs, more prejudice toward Black people, more negative attitudes toward abortion, and more positive attitudes toward religion compared to participants from the large university. Participants from the large university exercised less, drank more alcohol, and smoked more tobacco than participants from the small universities.
The authors conclude that attitudes and behaviors are meaningful and important dimensions of social relationships in both social ecologies—students sort into dyads along these lines. Most importantly for this study, Bahns et al. find significantly greater degrees of similarity within dyads formed at the large university than at the small universities in terms of socio-demographics and social attitudes. In other words, greater diversity within the university environment leads to less personal diversity within dyads. As the authors state,
“It cannot be surprising that size of opportunity leads to the ability to fine-tune the outcome. When opportunity abounds, people are free to pursue more narrow selection criteria, but when fewer choices are available, they must find satisfaction using broader criteria” (p. 127).
Tipping Points
To the extent that a given population of Homo economicus is comprised of risk-averse vs. risk-neutral vs. risk-loving individuals (and those whose (time-consistent) discount rates are relatively small vs. relatively large), tipping points like those explored in Gladwell (2002) are possible in a variety of social settings, such as the spread of disease (e.g., HIV/AIDs and syphilis), crime (e.g., use of crack cocaine and Methamphetamines), fashion trends (e.g., wearing of Hush Puppy and Airwalk shoes), popular children’s shows (e,g., Sesame Street and Blues Clues), and new technologies (e.g., fax machines and cellular phones) in epidemic (or geometric) proportions. Throw in Homo sapiens’ predispositions for reference dependence, loss aversion, and the many effects and biases encountered in Chapters 1 and 2, and the proverbial stage is set for tripping over the myriad of tipping points lurking out there in the real world.
As Gladwell points out, three interconnected characteristics underpin the spread of epidemics: (1) contagiousness of the micro-organism, fad, idea, or behavior in question, (2) the dependence of big effects on relatively small causes, and (3) the suddenness of change (i.e., the presence of tipping points). Particularly when it comes to epidemics depending upon word-of-mouth, these characteristics can also be thought of, roughly and respectively, as (1) the “stickiness factor” of an initiating message, (2) the “law of the few” individuals with rare social gifts, and (3) the “power of context” (i.e., the recognition that Homo sapiens are quite sensitive to their environments).
Gladwell specifies the Law of the Few as consisting of a confluence of three types of individuals: connectors (gregarious and intensely social individuals who know lots of other people from different walks of life, more as acquaintances than friends); mavens (information specialists who not only gather information but also revel in the opportunity to spread the information to others); and salesmen (those with the ability to persuade others who are unconvinced about what they are hearing from mavens and connectors).
According to Gladwell, the power of context relates to the subtle, hidden, and often unspoken messages or cues that are transmitted in the run-up to a tipping point. This implies that an individual’s behavior is, to varying degrees, a function of social context. In the case of crime, for example, subtle messages sent by broken windows in a community or graffiti and broken turnstiles in a subway station help create a social context suggesting that it is ok to commit crime here.
Messages, both spoken and unspoken, are sticky when they are memorable and ultimately compel the recipient of the message to take a targeted action. Stickiness in this sense relates to a message’s effectiveness, similar to the previously encountered messages designed to reduce littering, environmental theft, drunk driving, and to promote energy conservation and better health care.
Gladwell provides several examples of epidemics that have adhered to the patterns identified above (e.g., contagiousness, dependence of big effects on relatively small causes, and the presence of tipping points). These examples include the massive and rapid-fire success of the late 1960s children’s educational TV show Sesame Street (and the show it later spawned in the mid-1990s, Blue’s Clues), the direct-marketing campaign of the Columbia Record Club in the 1970s, the surge of crime and its subsequent reversal on the NY City subway system in the mid-1980s, adoption by US farmers of new hybrid seeds in the late 1930s, teenage suicide in Micronesia in the mid-1960s through the 1980s, and even Paul Revere’s midnight ride at the outset of the American Revolutionary War in the late 1700s.
Consider the epidemic of teenage smoking in the US. Gladwell ascribes primary billing in this epidemic’s cause to salespeople (recall the law of the few), in particular extroverts, individuals who tend to be more rebellious and defiant and who make snap judgments and take more risks. These are people who are not perceived as being cool because they smoke, rather they smoke because they are cool. In effect, this epidemic’s salespeople are also its tipping points, or “tipping people.” Adolescents are naturally drawn to them.
According to Gladwell, the epidemic’s stickiness factor occurs naturally. Because the smoking experience is so memorable and powerful for certain people, they cannot stop smoking—the habit sticks. Whether a teenager picks up the habit depends upon whether he comes in contact with a salesperson who effectively gives the teenager permission to engage in deviant acts. Of course, whether a teenager likes smoking cigarettes enough to keep using them depends upon a very different set of criteria. As Gladwell points out, nicotine is highly addictive but only in some people some of the time.  For these individuals, smoking is contagious but not sticky.
What to do? Gladwell suggests we might attack the epidemic from different, albeit self-enforcing, angles. One angle would be to prevent the salespeople from smoking in the first place. Another would be to convince all those who look to salespeople for permission to smoke that they should look elsewhere, to get their social cues from non-smoking adults. Further, as with other neurologically triggered addictions, zeroing in on combatting depression among teenagers would enable the exploitation of a critical vulnerability in the addiction process.
Regardless of which angle is emphasized in the effort to countervail the profuse tipping points in teenage smoking, it is never too late to consider new approaches in the campaign to control the epidemic. According to the American Lung Association (2020), every day, almost 2,500 children under 18 years of age try their first cigarette, and more than 400 of them will become new, regular, daily smokers. Of adolescents who have smoked at least 100 cigarettes in their lifetime, most of them report that they would like to quit but are unable to do so. If current tobacco use patterns persist, an estimated 5.6 million of today’s youth under 18 will die prematurely from a smoking-related disease.
Magical Thinking
It is (hopefully) safe to say that the majority of Homo sapiens distinguish both themselves and Homo economicus from superheroes (Homo vir fortis)—those with magical powers. However, the extent to which we Homo sapiens engage in magical thinking from time-to-time is perhaps less distinguishable. As Pronin et al. (2006) puts it,
“Every so often, we may learn that someone we have wished ill actually has become ill, or that the sports team for which we are cheering has in fact gone and won the game. When such things happen, although we are far from causal, we may nonetheless experience a sense of authorship—a feeling that we caused the events we had imagined” (p. 218).
To investigate the prevalence of this type of magical thinking, the authors designed experiments to examine whether and when such experiences of everyday magic might arise.[75] They propose the formal hypothesis that belief in one’s own magical powers can arise when we infer that we have personally caused events based upon perceiving a relation between our thoughts and subsequent events. One experiment tests whether college students might come to believe that they have caused another person pain through a voodoo curse when they have thoughts about the person consistent with such harm.
In this experiment, participants assumed the role of “witch doctor” in an ostensible voodoo enactment involving a confederate (a role-playing experimenter) as their “victim.” The authors arranged for participants to encounter either a victim who was offensive (henceforth evil) or one who was neutral. Following this encounter, participants were instructed to stick pins in a voodoo doll representing the victim, in the victim’s presence. The victim subsequently responded by reporting a slight headache, and participants were queried about their reactions to this reported symptom. The experiment made possible the investigation of whether participants who harbor evil thoughts toward a victim are more likely than neutral-thinking participants to perceive that they caused the victim harm.
Slightly fewer than 40 residents of Cambridge, MA were randomly assigned to either a neutral-thoughts condition or an evil-thoughts condition. Each participant and confederate (a 22-year-old man) was greeted in a waiting area by the experimenter and escorted to the laboratory. The participant and confederate were seated at a table with a handmade twig-and-cloth voodoo doll lying on it. The experimenter explained that the experiment was designed to assess psychosomatic symptoms and physical health symptoms resulting from psychological factors, and that the study was investigating this question in the context of Haitian Voodoo. For background, the experimenter furnished both individuals with an abridged version of Cannon’s (1942) Voodoo Death. This scientific account of how voodoo curses might impact physical health was included to bolster the plausibility of curse effects.
In the condition designed to induce evil thoughts, the confederate arrived at the experiment 10 minutes late, thus keeping the participant and experimenter waiting. When the experimenter politely commented that she was really glad he made it, he muttered with condescension: What’s the big deal?  When the experimenter informed the participant and confederate that they had been given an extra copy of their consent form to keep, the confederate crumpled up his copy and tossed it toward the garbage can. He missed, shrugged, and left it on the floor.  Post-experiment interviews indicated that participants in the evil-thoughts condition were cognizant of many of these annoyances and found themselves disliking the confederate. Although the confederate was, by design, aware of these adjustments in his behavior, he was otherwise uninformed about the study’s hypotheses.
After reading Voodoo Death, the participant and confederate were each asked to pick slips from a hat to determine who would be the witch doctor and who would be the victim. Both slips were labeled witch doctor, but the confederate pretended that his said victim. The confederate victim was then asked to write his name on a slip of paper to be affixed to the doll. Both victim and witch doctor then completed a page entitled Baseline Symptom Questionnaire that asked them to indicate whether they currently had any of 26 physical symptoms (e.g., runny nose, sore muscles, and/or headache). The confederate circled “No” for each symptom. To ensure that the participant knew the victim’s purported health status, the experimenter verbally confirmed that the victim currently had no symptoms.
The experimenter then informed both individuals that reported cases of voodoo suggest that the witch doctor should have some time alone to direct attention toward the victim, and away from external distractions (before invoking the curse by pricking the voodoo doll), and she escorted the victim from the room. The participant was then asked to generate vivid and concrete thoughts about the victim but not to say them aloud. Afterward, the experimenter returned with the victim, who was again seated across from the participant. The participant was instructed to stick the five available pins into the doll in the locations of the five major weaknesses of the body: the head, heart, stomach, left side, and right side. Once the participant completed this task of piercing the doll, the victim was asked to complete a second symptom questionnaire (identical to the first). However, this time the victim invariably circled one symptom: a headache. He elaborated at the bottom of the page: I have a bit of a headache now. When asked to confirm this symptom, he averred with a slightly uncomfortable facial expression and the response “Yeah.” The experimenter then stated that she would like to take some time with the victim to question him in detail about his symptoms but that she would first quickly ask the witch doctor some questions about his or her experiences in the experiment.
With the victim escorted from the room, the witch doctor was asked the following six questions:
- “Did you feel like you caused the symptoms that the ‘victim’ reported, either directly or indirectly?”
- “Do you feel that your practice of voodoo affected the victim’s symptoms?”
- “How much do you feel like you tried to harm the victim?”
- “Do you feel that sticking the pins in the doll was a bad thing to do?”
- “Did any negative thoughts about the victim pop into your head during the minute you had to yourself before the voodoo exercise?”
- “Did you have any negative thoughts toward the victim before (or while) you did the pin pricks?” (p. 221)
Pronin et al.’s results were as expected. Witch doctors in the evil-thoughts condition were successfully induced to think ill of their victim; they reported significantly more negative thoughts about the victim than those in the neutral-thoughts condition. Most importantly, witch doctors in the evil-thoughts condition were more likely than those in the neutral-thinking condition to believe that they had caused the victim’s headache. Witch doctors prompted to think evil thoughts reported feeling no more guilt than those prompted to think more neutrally about their victim. The authors conjecture that witch doctors saw the victim’s headache as a just reward for his unpleasant behavior, and so they were not upset at having caused him pain. Ouch!
Concluding Remarks
As mentioned in this section’s Introduction, the empirical studies and field experiments discussed here exemplify how behavioral economists have tested for the existence of the biases, effects, and fallacies underpinning Homo sapiens’ choice behaviors, as well as the extent to which different implications of Prospect Theory (e.g., loss aversion, reference dependence, and the endowment effect), hyperbolic discounting, and mental discounting help explain these behaviors. Several of the case studies examined in this section also broach a host of contexts in which Homo sapiens exhibit socially degenerative behaviors (such as racial discrimination, criminal behavior, time-inconsistency, deadweight gift-giving, and procrastination), and empirically measure the extent of these behaviors in real-world situations.
Thankfully, the proverbial story does not stop there. Several of this section’s case studies examine what Thaler and Sunstein (2009) call “nudges” to correct degenerative behaviors. For example, we explored ways in which the design of default options can be used to save lives, the extent to which basic-income and microfinance programs can help alleviate poverty, the extent to which simply raising awareness can help reduce racial discrimination, and how monetary reward/punishment schemes and information campaigns can (at least to some extent) mitigate social ills such as homelessness, food waste, and drunk driving, and promote improvements in such areas as energy conservation, public health, income tax compliance, and voter turnout. These types of nudges are the bridges between what behavioral economics teaches about Homo sapiens’ quirks and consequent choice behaviors, on the one hand, and public policies that, with varying degrees of success, reorient these choice behaviors for the social good.
The number of organizations that have formed during the past decade to promote public policies incorporating insights from behavioral economics (i.e., to nudge) is impressive. For example, The Behavioral Insights (BI) Team began as a small agency of the United Kingdom’s (UK’s) government whose mission was to design innovative nudges to improve the workings of British society. Today the BI team is a global social purpose company whose projects span over 30 different countries. The BI Team’s policy areas include finance, crime and justice, education, energy, the environment and sustainability, health and well-being, international development, taxation, and work and the economy.
GreeNudge is a Norwegian non-profit organization focusing on Norway’s health-care system, specifically how to nudge consumers to choose healthier and more environmentally friendly foods in grocery stores, and, through an effort called Behaviourlab, apply behavioral science toward the realization of the United Nation’s 17 Development Goals. In Peru, the nation’s Ministry of Education has established MineduLAB, a laboratory designed to leverage lessons from behavioral economics to improve the country’s educational policies. And the World Bank’s Mind, Behavior, and Development Unit (eMBeD) is the spearhead of a worldwide network of scientists and practitioners working closely with governments and other partners to diagnose, design, and evaluate behaviorally informed interventions to eliminate poverty and increase social equity.
These organizations work to operationalize nudges similar to those we have studied in this section. Hopefully, the list of these types of organizations will grow over time reflecting the impact insights from behavioral economics can have on the collective will of the very species whose quirks and irrationalities serve as the basis of the economists’ discoveries.
Study Questions
Note: Questions marked with a “†” are adopted from Just (2013), and those marked with a “‡” are adopted from Cartwright (2014).
- Take a good look at the Airbnb website. If you have never visited this site, it is a marketplace for short-term rentals of apartments, homes, and even guest rooms in owner-occupied homes. To see the type of information displayed on the website, first, click on the “Anywhere” tab at the top of the screen. Then, in the “Where” box, type in the name of your hometown. Now type in hypothetical Check-In and Check-Out dates, and click on the “Search” button. You can now click on a few of the featured rentals and browse through the information provided about the rentals and hosts. Based upon what you can learn about these rentals, do you think there is enough information provided to empirically test for racial discrimination among people who book reservations through this site (recall the discussion in this chapter on peer-to-peer lending)? If “yes,” explain how you might use the information to conduct your empirical test. If “no,” then what additional information would you need to obtain from Airbnb in order to conduct your test of potential racial discrimination?
- Recall the field experiments discussed in this chapter that were designed to test the effectiveness of monetary rewards in changing an individual’s behavior (e.g., to improve student and teacher performances and reduce substance abuse through “contingency management” programs). (a) Do you see anything that may be ethically wrong or socially degenerative with the use of monetary reward schemes like these? (b) Design a field experiment of your own to test the efficacy of using a monetary reward to either boost positive behaviors or reduce negative behaviors among a target population of people. In your design, be sure to clearly identify the target population, whether there are control and treatment groups (and what distinguishes these groups), and what outcome will support your hypothesis concerning whether the monetary reward was effective or not.
- During the 2020 Democratic Party Primary season, Presidential candidate Andrew Yang proposed a basic-income program called the Freedom Dividend. His candidate website provides detailed information about the program. Read through the information provided on this website. What do you see as the pros and cons of a program like this? Explain your reasoning.
- † You are considering buying gifts for two of your friends. Both friends enjoy playing video games. However, both have reduced their budgets for these types of purchases because of the temptation they cause. David is tempted to buy games when they are first released rather than waiting to purchase the games later once prices have fallen. To thwart this temptation, David has committed himself to spend no greater than $35 for any given game. Alternatively, Avita is tempted to play video games for long periods of time, causing her to neglect other important responsibilities in her life. To combat this temptation, Avita has committed herself to play video games only when she is visiting other people’s homes. Would David be better off receiving a new game that costs $70 or a gift of $70 cash? How about Avita?
- ‡ In this section, we were introduced to a study of how Minnesota worked to increase income tax compliance among its citizens. How do reference dependence and the overweighting of improbable events (recall the experiment discussed in Chapter 6) contribute to compliance?
- Recently, while accessing the Wikipedia website, I was confronted with the following appeal that popped up and covered the bulk of the page: “To all our readers in the US, it might be awkward, but please don’t scroll past this. This Saturday, for the 1st time recently, we humbly ask you to defend Wikipedia’s independence. 98% of our readers don’t give; they simply look the other way. If you are an exceptional reader who has already donated, we sincerely thank you. If you donate just $2.75, Wikipedia could keep thriving for years. Most people donate because Wikipedia is useful. If Wikipedia has given you $2.75 worth of knowledge this year, take a minute to secure its future with a gift to the Wikimedia Endowment. Show the volunteers who bring you reliable, neutral information that their work matters. Thank you.” Suggested payment amounts that I could then choose were: $2.75, $5, $10, $20, $30, $50, $100, and “Other” amount.(a) Given what you have already learned about public goods (see Section 3, Chapter 8) and messaging/information campaigns in this chapter (e.g., reducing environmental theft, littering, drunk driving, and tax evasion, and promoting energy conservation in this chapter), comment on Wikipedia’s fundraising strategy.(b) Can you recommend any ways Wikipedia might improve upon its strategy? Explain why your recommendations could improve Wikipedia’s fundraising performance.
- ‡Suppose Donald never pays his taxes and resents having to transfer money to the government. His current wealth level is $1.1 million. Donald interprets paying any amount of tax as a loss. His worst outcome would be that he chooses not to report any income (thus paying no tax) and ends up getting audited. His best outcome would be reporting no income and not getting audited. Suppose Donald (unwittingly or not) calculates his decision weight on being audited () as,, and his decision weight on not being audited as . Suppose further that = 0.02 and = 0.69, resulting in = 0.06 and = 0.94. Note that because Donald is indeed overweighting the improbable event of being audited. Now suppose Donald’s value function is given by,where represents Donald’s current wealth and his reference point. At what reference point will Donald choose to not report any income?
- To what extent should the results of Haney et al.’s (1973) simulated prison study serve to inform the current debate about prison reform? Explain.
- What are some differences between New York City taxi drivers, on the one hand, and Uber and Lyft drivers on the other, that make the latter drivers less likely to exhibit a negative wage elasticity?
- † Governments often require people to obtain insurance. For example, all drivers in the US are required to carry auto insurance to cover damages to others in the event of an accident. Homeowners are often required by banks to carry insurance on their homes. Why do these requirements exist? One characteristic of an overconfident person is that she is continually surprised when what she thought was unlikely or impossible comes to pass. What would happen in these cases if people were not required to insure? What problems might arise if governments also prepared for emergencies in a way that displays overconfidence? What mechanisms could prevent overconfidence in government action?
- In this section, we learned about Banerjee et al.’s (2013) study of microfinancing in Hyderabad, India, in particular the extent to which this approach can potentially enhance the profitability of small businesses. Search the internet for a microfinance program implemented in another part of the world, and report on its approach to financing small businesses in its market area.
- As we learned in this section, appropriately assigned default options can save lives and help employees save more money for retirement. Can you think of how airlines might harness this “Default-Option Effect” to help their customers reduce their environmental footprints when it comes to traveling by air?
- † Some businesses thrive on Homo sapiens’ proclivity for hyperbolic time discounting. For example, payday loan companies offer short-term loans with ultrahigh interest rates designed to be paid off the next time the person is paid. (a) Suppose you were considering opening a payday loan company. Given that hyperbolic discounters often fail to follow through on plans (e.g., they procrastinate and exhibit time inconsistency), how might you structure your loans to ensure earlier repayments from your customers? (b) Lotteries typically offer winners the option of receiving either an annual payment of a relatively small amount that adds up to the full prize amount over several years or a one-time payment at a steep discount. Describe how time inconsistency might affect a lottery winner’s decision. How might a lottery winner view his decision over time?
- Why might an owner of a health club choose not to offer a pay-per-visit option to her customers?
- † Researchers have found that hungry people tend to have greater cravings for more indulgent foods (i.e., foods that are high in sugar, fat, and salt). Suppose you are creating a line of convenience foods— either snack foods or frozen foods.  What state of mind are they likely to be in? Given this, what types of convenience foods are most likely to be eaten? (b) Describe your strategy for creating a line of convenience foods. Is there any way to create a successful line of healthy convenience foods?
- Discuss the pros and cons of employers choosing to penalize poorly performing employees versus rewarding well-performing employees with bonuses.
- What are some ways in which Homo sapiens can avoid contributing to the maligned Deadweight Loss of Gift Giving?
- Consider monetary rewards and punishments, on the one hand, versus information campaigns on the other. Which “hand” do you think is more effective in nudging Homo sapiens toward better behavior/performance? Why?
- In this section, we learned about a strategy to control food waste and a strategy to control procrastination. What do these two strategies have in common?
- In this section, we learned about the emergence of Projection Bias in two different contexts—grocery shopping on an empty stomach and ordering winter clothing on a relatively cold day. Describe another context where the potential for Projection Bias is likely to emerge.
- Recall Caplan and Gilbert’s (2008) results for back-loading procrastinators. Compare the results with those Dr. Adam Grant attributes to “Originals” in this Ted talk.
- One conclusion drawn from Benartzi and Thaler’s (1995) study of the Equity Premium Puzzle was that the Ostrich Effect can actually serve as an antidote for Homo sapiens investors’ myopic loss aversion. Can you think of a situation where the Ostrich Effect could instead hinder Homo sapiens?
- Recall the Sunk Cost Fallacy depicted in Chapter 6 in the context of whether or not to brave a rainstorm to see a basketball game and described in this section in the context of how much to consume at an All-You-Can-Eat (AYCE) restaurant. In both of these instances, Homo sapiens who succumb to this fallacy are implicitly assumed to suffer its consequences—take an undue risk to attend a basketball game and overeat at the AYCE restaurant. Can you think of an instance where succumbing to the fallacy could instead result in favorable consequences? Explain.
- Referring to Nyhan and Reifler’s (2010) study of the persistence of political misperceptions, if you were to define a typical individual’s value function over “accurate perception” (i.e., gains) and “misperception” (i.e., losses), what would it look like? What do you consider to be the traits of a “typical individual” in terms of how they value accurate perceptions and misperceptions?
- Do you think the results from Wertenbroch’s (1998) potato chip study would be replicated if a field experiment was conducted with casino patrons, where the vice good is higher-stakes gambling at the craps, blackjack, or poker tables, and the virtue good is low-stakes gambling at the slot machines? How would you even design a field experiment to be conducted in a casino to answer Wertenbroch’s main research question: Are consumers less likely to purchase larger quantities of a vice good or a virtue good in response to equal unit-price reductions?
- This section presented several examples of how well-intentioned incentives can sometimes lead to perverse outcomes—recall the perverse long-term impact of monetary incentives on student performance in the Chicago public school system, the unexpected response of tardy parents at an Israeli pre-school, and the perverse outcomes associated with health care report cards in the US and targets for emergency ambulance services in the UK. Can you think of another example of an incentive system gone bad? Describe how it went bad.
- In the section on Tipping Points, it was pointed out that even Homo economicus can trip over tipping points. Throw in Homo sapiens’ predispositions for reference dependence, loss aversion, and the many effects and biases encountered in Chapters 1 and 2, it was stated, and the proverbial stage is set for tripping over the myriad of tipping points lurking out there in the real world. Discuss how reference dependence and loss aversion can expedite the process of tripping over a tipping point. Discuss how the Endowment Effect and Status Quo Bias might work to delay the tripping process.
- Suppose you own a small coffee shop in a busy metropolitan area. You decide to initiate a punch-card reward program to help build a loyal customer base. How should you train your baristas to exploit the Small-Area Effect?
- Do you see any connection between Magical Thinking and Confirmation Bias (that you learned about in Chapter 2)? Why or why not?
- Do you see any connection between conceptual information’s effect on one’s consumption experience and Confirmation Bias (that you learned about in Chapter 2)? Why or why not?
- Do you see any connection between the arguments given for not “keeping your options open” and the Burning Bridges game of Chapter 7? Explain.
- Is Willingness to Accept Pain necessarily a better measure of pain tolerance/threshold than Willingness to Avoid Pain? Explain.
- What is it about the use of tokens as direct payment, rather than money, that induces more dishonesty among Homo sapiens? Based upon what you have learned from Mazar et al.’s dishonesty experiments, how might a store owner go about reducing theft among his or her employees?
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- Roughly speaking, an effect is statistically significant when its corresponding SE is half of the size the magnitude of the effect or less. ↵
- The fact that the effect of being Asian is negative and statistically significant suggests “reverse discrimination” is potentially being practiced by contestants. ↵
- Pope and Sydnor also find less-statistically significant discrimination against older and overweight borrowers, as well as borrowers who either did not provide a photograph of themselves or look unhappy in the photo provided. To the contrary, the authors find discrimination in favor of women and military veterans. ↵
- Chiang and Wainwright (2004) provide nice examples of necessary and sufficient conditions in a variety of contexts. My favorite is the fact that being male is a necessary condition for being a father (not vice-versa).  Bellemare et al. (2023) investigate similar patterns of discrimination among people with physical disabilities in a large-scale field experiment. They find that roughly 50% of private firms discriminate against people with physical disabilities. However, on average callback rates for disabled individuals is double that of the non-disabled. And in their study of PhD job candidates, Ge and Wu (2024) find that name fluency is negatively related to both the probability of landing an academic or tenure-track position and research productivity of the initial institutional placement. ↵
- These Appearance Effects uncovered by Rudder (2014) on OkCupid hound academic economists in their profession as well. Hale et al. (2021) find robust evidence that physical appearance has predictive power for the job outcomes and research productivity of PhD graduates from ten of the top economics departments in the US. Attractive individuals are more likely to study at higher-ranked PhD institutions and are more likely to be placed at higher-ranking academic institutions not only for their first job, but also for jobs as many as 15 years after their graduation. More attractive economics PhD graduates also have their published research cited more often by other researchers. On the flip side of this phenomenon, New York Times opinion columnist David Brooks says it best: “We live in a society that abhors discrimination on the basis of many traits. And yet one of the major forms of discrimination is lookism, prejudice against the unattractive. And this gets almost no attention and sparks little outrage” (Brooks, 2021; italics added). ↵
- In an innovative field experiment conducted in Columbia, Barrera-Osorio et al. (2008) studied the effects of conditional cash rewards on student attendance and graduation rates (as well as contingent intra-family and peer-network dynamics). The authors’ experiment consisted of three treatments: a basic conditional cash transfer treatment based upon school attendance, a savings treatment that postponed the bulk of the conditional cash transfer to just before the student was scheduled to re-enroll in school, and a tertiary treatment where a portion of the cash transfer was made conditional upon a student’s graduation and tertiary enrollment. Barrera-Osorio et al. found that, on average, the combined cash incentives increased school attendance, pass rates, enrollment, graduation rates, and matriculation to tertiary institutions. Changing the timing of the payments (e.g., in the savings and tertiary treatments) did not affect attendance rates relative to the basic cash transfer treatment, but did significantly increase enrollment rates at both the secondary and tertiary levels. The tertiary treatment was particularly effective, increasing attendance and enrollment at secondary and tertiary levels more than the basic treatment. The authors also found some evidence that the cash transfers caused a reallocation of responsibilities within a student’s household. Siblings (particularly sisters) of participating students worked more and attend school less than siblings of students who did not participate in the experiment. In addition, peer influences were relatively strong in influencing a student’s attendance decisions. ↵
- Unrelated to loss-aversion treatments, but nevertheless of interest when it comes to the question of student performance, Gershenson et al. (2022) use data from a field experiment with K-3 public school students in Tennessee to test whether the teacher's race has an impact on performance. They find that Black students randomly assigned to at least one Black teacher are roughly 13 percent more likely to graduate from high school and 19 percent more likely to enroll in college compared to their Black schoolmates who did not study under at least one Black teacher. Black teachers have no statistically significant effect on White students' high school graduation rates or likelihood of enrolling in college. ↵
- Regarding the “diminishing sensitivity” result, recall the portion of the value function presented in Chapter 4 defined over disutility (i.e., losses). Although more steeply sloped than the portion defined over utility (i.e., gains), the portion defined over losses is nevertheless concave shaped. ↵
- A negative effect associated with putting for double bogey suggests that the typical golfer suppresses his inclination for loss aversion when putting for a score worse than bogey. ↵
- Conlin et al. (2007) point out that Projection Bias also manifests itself in the consumption of addictive goods. For example, people may often become addicted to cigarettes, illicit drugs, and alcohol because (1) they underappreciate the negative consequences of being an addict, and (2) they underappreciate how hard it will be to quit once addicted. ↵
- Current smokers are classified as those who reported smoking at least 20 cigarettes per day for at least the past five years and had a Fagerström Test for Nicotine Dependence (FTND) score of at least six. Never-smokers were those who reported never smoking. Ex-smokers were those who reported abstinence from cigarettes for at least one year, and who had smoked at least 20 cigarettes per day for at least five years prior to quitting. ↵
- Twenty-six different monetary payment amounts were used to measure the participants’ time discounting behavior—$1000, $990, $960, $920, $850, $800, $750, $700, $650, $600, $550, $500, $450, $400, $350, $300, $250, $200, $150, $100, $80, $60, $40, $20, $10, $5, and $1—in concert with seven payment delay periods—one week, two weeks, one month, six months, one year, five years, and 25 years. To ensure that the magnitudes of the monetary and cigarette payment schemes were equal for current smokers, the current smokers were each asked how many cartons of cigarettes they could purchase with $1000. Participants then chose between amounts of money (or cigarettes for the cigarette payment scheme) delivered immediately and corresponding amounts delivered after a given payment delay period. ↵
- As Haney et al. report, “the most dramatic evidence of the impact of this situation upon the participants was seen in the gross reactions of five prisoners who had to be released because of extreme emotional depression, crying, rage, and acute anxiety” (Page 81). Today, the need for what is known as institutional review board (IRB) pre-approval of human subjects research makes research of this nature impermissible. ↵
- In what may be the most well-known field experiment designed to induce obedience to authority, Milgram’s (1963) participants were led to believe that they were assisting in an unrelated experiment in which they were instructed to administer electric shocks to an unseen “learner.” The participants gradually increased the levels of the electric shocks (which, unbeknownst to them, were fake) to levels that would have been fatal had they instead been real. ↵
- With respect to the media’s effect on corruption in bubble matches, recall Pope et al.’s (2018) similar result for racial discrimination in the NBA. ↵
- Because the criteria for loan eligibility were that a potential borrower be a woman, aged 18-59, and having resided in the area for at least one year, the study’s sample consisted solely of individuals who met these criteria. ↵
- For an interesting laboratory experiment addressing the propensity of Homo sapiens to underreport their earnings from their micro-financed business in order to reduce the level at which they would otherwise be considered capable of repaying the loan, see Abbink et al. (2006). ↵
- Below we look at studies that have explored the role of descriptive and injunctive norms and their saliency in reducing the ill effects of such social predicaments as littering, environmental theft, and drunk driving, as well as encouraging social enhancements such as energy conservation. ↵
- To help convince you, note that demand response programs are voluntary programs in which people allow their utility company to remotely restrict their electricity consumption during peak hours and thus reduce the risk of a blackout across the service area. To do so, the utility usually installs a remote switch in-line with the circuitry of an appliance such as a water heater or air conditioner. Excessive electricity usage during peak hours reduces grid reliability, drives up energy costs, increases the risk of blackouts, and harms the environment. ↵
- According to Yoeli et al., the effect of the observability treatment (measured in dollars) was over seven times that of a $25 incentive payment, which is what the utility had been offering before the experiment. The authors estimate that the utility would have had to offer an incentive of $174 to match the participation rate achieved via their observability treatment. ↵
- To the extent that they are averse to opting-out of the default savings plan, and enrollment into the plan is automated, Homo sapiens succumb to what’s known as Automation Bias. ↵
- As you might imagine, nudging Homo sapiens via opt-out programs such as Sweden’s public retirement savings program has received much attention (and been put into practice quite extensively) during the past few decades. Indeed, you will be learning about how default options have been used to save lives in the section below entitled Can Default Options Save Lives? As an example of how opt-out programs have been tested in randomized clinical trials, Montoy et al. (2016) conducted a natural experiment with patients in the emergency wing of an urban hospital. The authors found a statistically significant difference between patients agreeing to be tested for HIV under an opt-out agreement as opposed to an opt-in agreement (66% vs. 38% of patients agreeing to participate in the testing, respectively), a difference they call the Opt-Out Effect. Interestingly, the Opt-Out Effect was significantly smaller among those patients reporting high HIV risk behaviors. ↵
- Three years later, and using a different experimental design, Solnick and Hemenway (1996) reported a welfare gain (as opposed to deadweight loss) associated with gift-giving. The average subject in their experiment valued her gifts at 214% of the price paid by her gift-givers! ↵
- The descrambling task consisted of 30 sets of five jumbled words. Participants created sensible phrases using four of the five words. In the control and play-money treatment, the phrases primed neutral concepts (e.g., “cold it desk outside is” became “it is cold outside”). In the real-money treatment, 15 of the phrases primed the concept of money (e.g., “high a salary desk paying” became “a high-paying salary”), whereas the remaining 15 were neutral phrases. Participants in the play-money treatment were primed with money by a stack of Monopoly money in their visual periphery while completing the neutral descrambling task. ↵
- See Hart (2013) for a discussion of innovative field experiments conducted at Columbia University with heroin and crack cocaine users that also test the efficacy of using monetary rewards to dissuade users from abusing these substances. ↵
- The size of the ice-water bath enabled a fully open hand to be immersed in water to a depth of approximately 120 mm. ↵
- Recall the discussion of Less is More in Chapter 2. There we learned that an individual who conforms to the Peak-Ed Rule would prefer, say, three minutes of pain over two minutes of intense pain plus one minute of moderate pain over two minutes of intense pain (the same sequence minus the moderate pain) because the pain at the end of the longer sequence is lower than that at the end of the shorter sequence. ↵
- In a nod to Denver’s innovative spirit, the city of Steamboat Springs, CO recently installed re-purposed parking meters at local trailheads to encourage hikers to donate to trail maintenance on the spot (Associated Press, 2019). ↵
- We specify “fully” here because in an all-you-can-eat cafeteria, the students themselves choose how much food to bring to their table. In contrast, at sit-down restaurants, the students would only partially choose how much food is brought to the table. The owners of the restaurant determine the quantity of food on the plate that the waiter delivers, and then the student decides how much food to leave as waste at the end of the meal. ↵
- Had the Alfred University researchers wanted (and been able) to generalize their results, they would have needed to run the experiment over a longer period of time in order to account for seasonal and academic-scheduling effects (e.g., during exam weeks some students cope with the added stress of exams by adding plates of food to their trays as a way of comforting themselves). The researchers would also have needed to randomly assign some cafeterias to a treatment group (where the trays are removed) and a control group (where they are not) and periodically reassign the cafeterias from one group to the other throughout the semester. Further, they would need to periodically and randomly survey cafeteria patrons in both groups in order to identify those students who choose which cafeteria to dine at based at least partially upon whether that cafeteria is tray-less or not. This would allow the researchers to control for students who knowingly and purposefully avoid dining at the tray-less cafeterias to begin with, thus biasing the treatment effect downward. ↵
- Gladwell (2002) describes a similar theory, known as the Broken Window Theory, which states that a building’s broken window can play a role in the proliferation of neighborhood crime. ↵
- As Akbulut-Yuksel and Boulatoff point out, Halifax households are required to sort waste in four ways: (1) recyclable containers (plastics, glass, and aluminum) are put in a transparent blue bag, (2) paper and cardboard are put in a separate bag, (3) organic food waste goes in a green bin provided by the city, and (4) the remaining waste (refuse) goes into garbage bags. Recyclable materials are collected each week, while garbage and organic waste are each collected every other week on opposite weeks (except in the summer months when, thank goodness, organic waste is collected on a weekly basis). ↵
- Households were categorized as higher-than-average or lower-than-average based upon their energy usage during a two-week period prior to the commencement of the experiment. ↵
- Allcott (2011) studied the outcomes associated with sending descriptive-norm (D) messages to over half a million energy customers across the US. The author finds that households receiving a D message on average reduced their energy consumption by roughly 2 percent over a year. In a recently conducted natural experiment with 4,500 households in Southern California, Jessoe et al. (2020) find that semi-monthly D messages promoting water conservation (in the form of “Home Water Reports”) spilled over into promoting short-lived reductions in electricity use during the summer months, when wholesale electricity prices and emissions are typically highest in the region. ↵