CHAPTER
293
18BEHAVIORAL ECONOMICS
Learning Objectives
After reading this chapter, students will be able to
• explain why rational decision making has its limits,• describe some ways that bounded rationality affects decision making,
and• identify several ways to use behavioral economics to improve decision
making.
Key Concepts
• Brainpower and time are scarce resources, so decision shortcuts make sense.
• Some shortcuts result in poor decisions. • Some decisions appear to reveal inconsistent preferences.• Status quo bias means that some people tend to avoid even beneficial
changes.• Overconfidence often leads to poor decisions.• Problematic shortcuts include availability, anchoring, confirmation, and
hindsight bias.• Awareness of framing bias is especially important in management.• Changes in how choices are set up can improve decision making.
18.1 Introduction
Standard economic models start with assumptions that are not really true. These assumptions include the notions that decision makers are always ratio-nal, have unlimited willpower, and are concerned only about themselves. These assumptions were previously viewed as harmless simplifications, but researchers have demonstrated that being more realistic could be important in management and policy. For example, cash bonuses may reduce work
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effort (especially if the work is intrinsically interesting or important), but symbolic payments (e.g., praise) tend to increase work effort (Bareket-Bojmel, Hochman, and Ariely 2014). For a purely rational worker, that find-ing would not make sense. Surely praise coupled with cash would be a more powerful motivator than praise alone. Economics that drops the assumptions of complete rationality, complete willpower, and complete selfishness is called behavioral economics.
Behavioral economics addresses the choices that individuals make when they use shortcuts and rules of thumb in decision making. Our brain-power and our time are scarce resources, so it makes sense to use rules of thumb in making decisions. Unfortunately, these shortcuts sometimes result in poor decisions.
18.2 Inconsistent Preferences
A standard assumption in economics is that consumers make reasonable forecasts about what they will do in the future and make plans on that basis. Behavioral economics notes, to the contrary, that many people appear to have inconsistent preferences. A classic example is the tendency to procrastinate. For example, we may conclude that the cost of exercising is more than offset by its benefits, especially if we commit to starting exercising next week. But when next week arrives, we do not want to work out; we want to put it off for another week. Last week the costs were in the future; this week they will be realized right now. Decisions that I make today may conflict with decisions that I make next week, even though nothing has changed.
This inconsistency appears to involve rather odd patterns of discount-ing future benefits and costs (Rice 2013). For example, if you regard being paid $988 today as being just as good as being paid $1,000 in three months, your personal discount rate is less than 5 percent per year.1 Would you prefer getting $790 now to getting $1,000 in three months? If so, you are acting as though your discount rate is more than 150 percent per year. A discount rate of more than 150 percent per year seems pretty high, but the real anomaly is that people sometimes use 5 percent and sometimes use 150 percent or more for seemingly similar transactions.
A standard assumption is that people will use the same discount rate for short-term financial gains, long-term financial gains, short-term financial costs, and long-term financial costs because someone could make money by exploiting discount rate variations. But many people discount the future heavily and treat short delays much differently from longer delays. For example, would you be willing to pay an annual rate of more than 300 per-cent for a $200 three-week loan? More than 18 million taxpayers thought
behavioral economics A field of study that integrates psychology and economics.
discounting Adjusting the value of future costs and benefits to reflect the willingness of consumers to trade current consumption for future consumption. (Usually future values are discounted by 1/(1 + r)
n, with r
being the discount rate and n being the number of periods in the future when the cost or benefit will be realized.)
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this proposition was a good deal in 2011, when they signed up for refund anticipation checks that allowed them to pay their tax preparation fees out of their tax refunds (Wu, Fox, and Feltner 2013). The fee for this privilege was typically $30 or more. Most people who agreed to refund anticipation checks had very low incomes (so coming up with $200 to pay a tax prepara-tion fee would be a problem) and probably were not financially sophisticated (given that a number of ways to have a simple return filled out cost much less than $200).
Encouraging Employees and Patients to Be Active
Many struggle to change health-related behaviors. One reason is that people seeking to lose weight, increase exercise, or stop smoking act in a time-inconsistent manner. For example, someone joins a gym but does not go. These inconsistencies not only affect the individual’s health but increase health insurance costs because of poor health. As a result, firms, insurers, policymakers, and health professionals are exploring using financial incentives to change health behaviors (Royer, Stehr, and Sydnor 2015). Using financial incentives to change behav-iors has two potential problems. First, participants may just be paid for doing what they planned to do anyway (i.e., people who go to the gym three times per week would have done so without the incentive). Second, participants may revert to their old behaviors when the incen-tive ends.
Royer, Stehr, and Sydnor (2015) tried two approaches with employ-ees at a large company. Randomly selected employees were paid $10 per visit to their company’s on-site exercise facility (for up to three visits per week). After a month, half the group was offered the chance to fund a commitment contract. This contract allowed participants to make a pledge that they would continue to use the gym for the next two months. Employees who kept their pledges got the money back. For employees who did not keep their pledges, the firm donated the money to charity. Visits to the gym fell after the incentives ended, but they fell by less for employees who made pledges.
An alternative strategy is to give a “nudge.” Martin and colleagues (2015) gave randomly chosen patients wearable activity trackers that used Bluetooth to connect with their smartphones. The activity
Case 18.1
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18.3 Risk Preferences
Why do people smoke or drive without seat belts? That these behaviors are risky is not exactly news. One could argue that many smokers are addicted, but that argument just pushes the question back a step. Why do people start smoking if they know that cigarettes are addictive and that smoking is dangerous? One possibility is that people who make risky choices like risk. Another is that they misunderstand the risks they are taking. For example, many people appear to underestimate health risks, and this underestimation is a factor in their decision not to buy insurance. Another way to describe underestimation of risk is to say that people are overconfident (as we discuss further in section 18.4). Whether we should treat this choice as the result of overconfidence, bad information about risk, or difficulty in understanding the meaning of risks does not matter too much. Any of these will lead to poor decisions.
Some evidence links risk preferences to risky behavior. (Recall from chapter 4 that risk seekers seek more variable outcomes and risk-averse people
trackers connected to a smart texting system. Physicians wrote the text-message content, which mentioned the patient’s physician by name. Com-
bining smart texts with activity tracking increased physical activity the most. Compared with patients that did not receive texts, nearly twice as many patients that received texts met the goal of 10,000 steps per day.
Discussion Questions• Why do people act in a time-inconsistent manner?
• Have you ever acted in a time-inconsistent manner? Why?
• Can you find examples of firms incentivizing workers?
• Can you find examples of insurers incentivizing beneficiaries?
• Can you find examples of providers incentivizing patients?
• How could you avoid paying people to do what they were going to do anyway?
• How could you reduce backsliding?
• Why did making a pledge increase gym use?
• Why would getting a “nudge” increase exercise?
• Can you find other examples of nudges?
Case 18.1(continued)
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seek less variable outcomes. Risk seekers seldom buy insurance. Risk-averse people will buy insurance if the premium is not too much larger than the expected loss.) For example, Shults and colleagues (2016) found that teens who did not frequently use seat belts were more likely to be smokers and drinkers.
Misunderstanding the dangers of risky behavior and the likelihood of those dangers is a major problem for younger people. Aversion to risk typi-cally increases with age. Few children are risk averse, a slightly larger share of adolescents are risk averse, and most adults are risk averse (Romer, Reyna, and Satterthwaite 2017). Typically, someone who is risk averse tends to dis-count the future less than someone who is risk seeking, so these two tenden-cies reinforce each other (Jusot and Khlat 2013).
Not surprisingly, most people who are addicted to cigarettes began smoking as adolescents (Barlow et al. 2017). Their willingness to accept risk was high, their concern about the future was low, and their ability to imagine the consequences of becoming addicted was limited.
18.4 Incorrect Beliefs
Drivers of all ages claim to be more skillful than average (Horswill et al. 2017). But does this overconfidence matter? It does because overconfident drivers are more likely to use their cell phones while driving, which signifi-cantly increases the risk of an accident (Engelberg et al. 2015).
More broadly, overconfident decision makers are likely to make bad choices. They are likely to overestimate their chances of success and apt to attribute failures to bad luck (hence not learning from them). For example, the fact that companies often lose money when they buy other companies is common knowledge. Acquiring a company requires a bid above its current market valuation, and its current market valuation is as likely to be too high as it is to be too low. So, it takes a confident management team—one convinced of their skill and of unrecognized synergies—to buy another company. In many cases this confidence amounts to overconfidence and the acquisition is unprofitable (Malmendier and Tate 2015). Overconfident CEOs often make money-losing acquisitions.
Several cognitive traps feed into overconfidence:
• Availability bias• Anchoring bias• Confirmation bias• Hindsight bias
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We will discuss each of these in turn.Availability bias can occur because certain outcomes are overly easy
to imagine or overly hard to imagine. For example, if you run a public health agency, which threat to life should be your top priority, tornadoes or asthma? If you were asked this question right after reading about a deadly tornado, you might have said tornadoes. The news reports made them easy to remem-ber. In fact, the two threats are not even close. Between 2015 and 2017, tornadoes killed an average of only 30 people each year (National Weather Service 2018). More than 3,500 people die from asthma each year, and quite a few of these deaths are preventable (National Center for Health Statistics 2017). If you have never known anyone who died as a result of asthma, you might have a difficult time imagining asthma as a cause of death and may pay too little attention to it.
Anchoring bias occurs when some initial estimate, even if it is not based on evidence or is simply wrong, affects future discussions. In a strategy discussion about whether to add a long-term care facility to a system, one of the board members says, “I hope the return on equity is better than the 5 percent that home health care firms earn.” That comment is not really rel-evant because long-term care and home health care are fairly distinct markets. True or not, the comment is likely to influence the subsequent discussion.
Irrelevant information can influence decision making. If a job candi-date starts by mentioning a desired salary of $150,000, the candidate will probably get a higher offer than if the candidate started by mentioning a current salary of $85,000. Neither of these numbers may fall within the pay range for the job in question, but mentioning the $150,000 tends to anchor the discussion.
Even experienced professionals can be affected by anchoring. For example, a young girl with a three-week history of weight loss, diffuse abdominal pain, and fever came to the emergency department (Festa, Park, and Schwenk 2016). Although the evidence was ambiguous, she was admit-ted with a preliminary diagnosis of cat scratch fever. Further tests found no signs of infection, and the patient underwent an MRI (magnetic resonance imaging) scan and a liver biopsy. Finally, on day 13 of her hospitalization, hints of intestinal inflammation were found and a colonoscopy confirmed a diagnosis of Crohn’s disease. In short, the girl received a great deal of low-value care, largely because the medical team stuck with the diagnosis of cat scratch fever despite the lack of evidence supporting it (Festa, Park, and Schwenk 2016).
Confirmation bias occurs when we filter evidence to prove that our conclusions are right. How did you react when a political candidate that you support said something stupid? Most of us will offer an example of the oppo-nent’s failings rather than switch candidates.
availability bias A cognitive trap that occurs when some facts are overly easy or overly hard to recall.
anchoring bias A cognitive trap that occurs when an irrelevant fact influences a decision.
confirmation bias The tendency to focus on information that supports one’s beliefs.
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A management example of confirmation bias can be found in the hiring process. Suppose you interview several people, and Ms. Jones seems to stand out. You are confident that she is the best choice. You call several references, they say mostly good things about her, and those are the com-ments that you include in your notes. Ms. Jones turns out to be a disaster. You used the interview to support your positive impression, not to look for warning signs. You did not follow up when a reference said, “Well, she wasn’t here that long,” and another said, “She was only in my unit for about three months.”
Hindsight bias occurs when you feel that you “knew it all along,” that is, when you believe that you made a prediction that you did not. This bias creates two decision traps. First, your overconfidence may grow. Second, you have no incentive to explore why your predictions were faulty. Neither of these bodes well for future decisions.
Hindsight bias is widespread, having been documented in diverse situ-ations including labor disputes, medical diagnoses, managerial decisions, and public policy (Chelley-Steeley, Kluger, and Steeley 2015). Hindsight bias has serious consequences because it impairs performance. For example, research-ers have found that investment bankers who earned the least had the largest hindsight bias (Merkle 2017). Hindsight bias also makes effective investiga-tions of accidents and near misses difficult, leaving future patients at risk because no fundamental changes are made (Zwaan et al. 2017).
18.5 Representativeness and the Law of Small Numbers
To assess a possible merger with another practice, you interview six CEOs from practices that went through mergers. After you complete the interviews, you notice that the three CEOs from the successful mergers were accountants and the three CEOs from the failed mergers were physicians. What should you infer from that?
You should infer nothing. Your sample is too small and may be biased. If you looked at a larger, more representative sample, you might find any pat-tern. For example, you might find that merged practices led by accountants were more likely to fail. Nonetheless, you may be tempted to think that hav-ing an accountant as the CEO is important.
Several factors are at work here. First, humans are prone to see pat-terns even if no pattern exists. We are apt to think that our experience with a small number of people will be typical of the whole group. This tendency is called representativeness bias (Saposnik et al. 2016). We are also apt to for-get that statistics based on small numbers can be misleading. This tendency is the law of small numbers bias.
hindsight bias The tendency to overstate how predictable an outcome was beforehand.
representative-ness biasThe tendency to overstate how typical a small sample is.
law of small numbers bias Generalizations based on small samples.
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The problem is greatest when our own experience suggests a conclu-sion. We easily dismiss colleagues’ stories as being mere anecdotes. Our sto-ries seem different. They feel meaningful to us. We have no trouble saying, “The plural of anecdote is not evidence,” unless the anecdote is ours. Our stories seem compelling.
18.6 Inconsistent Decision Making: Framing
Real-life choices appear to be affected by how they are presented. In fact, framing appears to be one of the strongest decision-making biases. Framing is especially relevant to health decisions because the stakes are high and because older adults (who are more likely to have to make health decisions) appear more likely to use shortcuts that cause framing bias (Saposnik et al. 2016).
A standard way of illustrating framing bias is via a treatment choice problem. Treatment 1 is guaranteed to save 200 of 1,000 people with a fatal disease. Treatment 2 offers a 20 percent chance of saving 1,000 lives and an 80 percent chance of saving no one. Which do you prefer? Most people prefer treatment 1 because it seems less risky.
Now consider another scenario. If you choose treatment 3, 800 of 1,000 people with a fatal disease will die. With treatment 4, you have an 80 percent chance that everyone will die and a 20 percent chance that no one will die. Which do you prefer? Most people choose treatment 4.
The only difference between these two scenarios is that the first is framed in terms of how many people live and the second is framed in terms of how many die. They are otherwise identical, yet choices typically dif-fer. By changing the emotional context of a decision, framing can change choices.
Framing can take several forms. It can describe the attributes of choices in different ways, describe the outcomes of choices in different ways, and describe the risks of choices in different ways. For example, people tend to prefer a choice when its attributes are presented in positive terms (Nanay 2016). Thus, consumers are more apt to choose a hospital that stresses its high patient satisfaction (a positive attribute frame) rather than its low mor-tality rates (a negative attribute frame). An example of goal framing would be to describe the effect of a new strategy as a gain in market share (a positive goal frame) or as avoiding stagnation (a negative goal frame). Most people are influenced by loss aversion, meaning that they worry more about avoid-ing losses than they do about realizing gains. As a result, people may respond more to negative goal frames. The treatment choice example given earlier in this section illustrates risk framing. The same problem can be presented in terms of lives saved (a positive risk frame) or in terms of deaths prevented (a
framing bias The effect of presenting the same data in different ways.
loss aversion A focus on avoiding losses rather than maximizing gains.
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negative risk frame). People tend to be more willing to accept risk to avoid negative outcomes than they are to gain positive outcomes.
The importance of framing appears to vary. Some decision makers appear to be immune to framing, with decision makers with the best math-ematics skills the least likely to be affected. In addition, goal framing appears to have smaller effects than attribute or risk framing (Harrington and Kerr 2017). The challenge for managers is determining when framing will matter and when it will not.
A number of countries rely on consumer demand to limit medical costs. An important mechanism is the willingness of consumers to switch to less expensive health insurance plans, which pressures insurers to offer low-cost plans and pressures providers to reduce what they charge for care. However, consumers often find insurance choices daunting, which may result in reluctance to switch plans. This reluctance dilutes the effects of competi-tion on costs. For example, in Switzerland (which has an insurance system similar to the Affordable Care Act) rates of switching between insurers were low, even though prices varied among comparable plans (Boonen, Laske-Aldershof, and Schut 2016).
Why were switching rates so low? Behavioral economics offers several reasons. First, a significant status quo bias is at work. Consumers are often reluctant to make changes if they do not have to. Second, too many choices can stop consumers from making any choice. This problem is called decision overload. Because the average Swiss consumer had 56 plans to choose from, decision overload appears to have been relevant. Third, consumers tend to worry about making what turns out to be a bad choice. One way to avoid regret about a choice is to avoid making a choice. Fourth, consumers appear to give more weight to avoiding losses than to realizing gains. This loss aver-sion tends to inhibit making changes.
status quo bias The tendency not to change, even when it would be advantageous to do so.
decision overload Poorer decision making that occurs as choices become more complex.
Children’s Health Insurance
More than two-thirds of the millions of children without health insurance appear to be eligible for
Medicaid or the Children’s Health Insurance Program (Kenney et al. 2015). For many of these children, health insurance would be free. Not accepting free health insurance makes sense in standard economics only if you believe that the hassles of signing up for these programs outweigh their considerable benefits, but behavioral economics notes
Case 18.2
(continued)
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several reasons for this pattern. First, parents may focus on the up-front hassles and give much less emphasis to the future benefits. That is, the
parents may heavily discount the future benefits. Second, the well-known problem of procrastination means that tomorrow or next week is always a better time than today to go to the trouble of enrolling a child. Third, we know that many decision makers have trouble with probabilities, meaning that the parents of these uninsured children make poor assessments of the chance that their child will become seri-ously ill or that better access to medical care will be important.
Between 1984 and 2009, a series of reforms sought to streamline and simplify enrollment in Medicaid and the Children’s Health Insur-ance Program. These reforms allowed states to permit continuous enrollment, to eliminate face-to-face interviews, to simplify verifica-tion procedures, to grant temporary eligibility, and to use eligibility for other programs (e.g., the Supplemental Nutrition Assistance Program) to determine eligibility.
Advances in information technology made these reforms pos-sible, and the Affordable Care Act financially supported upgrades to outdated Medicaid eligibility systems, which are integrated with or connected to health insurance marketplaces in every state. As of January 2017, 39 states could make Medicaid eligibility determina-tions within 24 hours, and in 28 states, applicants could apply using mobile devices (Brooks et al. 2017). Not surprisingly, the increased convenience of these new systems has boosted enrollment in Med-icaid and the Children’s Health Insurance Program. For example, Ala-bama removed asset tests for children, stopped requirements for an in-person interview, made eligibility last for a full year, and simplified the application process in other ways. As a result, the share of eligible children with coverage rose from 91 percent in 2008 to 95 percent in 2015 (Georgetown University Center for Children 2017). Much of this growth occurred after implementation of the Affordable Care Act, but not because many children got coverage via marketplace plans. Less than 1 percent of the eligible children got their coverage this way.
Alabama’s enhancements incorporate ideas from behavioral economics. They make enrollment easier, rather than emphasizing traditional outreach strategies or price reductions. Unfortunately, many children who are eligible for health insurance subsidies remain
Case 18.2(continued)
(continued)
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18.7 Conclusion
People use shortcuts when they make hard or emotionally charged decisions. In other words, patients, clinicians, and managers use shortcuts when they buy insurance, when they make medical decisions, when they make strate-gic decisions at work, and when they hire and fire employees. Shortcuts are common.
Sometimes, unfortunately, shortcuts lead to poor choices. Patients may choose insurance plans that expose them to significant financial risks. Clinicians may recommend problematic treatment plans. And managers may
uninsured. Rice (2013) suggests that parents’ failure to understand the risks that their children face, excessive discounting of the future, or lim-
ited grasp of how insurance works might explain this. An experiment (Flores et al. 2016) suggests that knowledge may be a major issue. The experiment funded parent mentors (experienced parents with a child covered by Medicaid or the Children’s Health Insurance Program), who received two days of training and then helped families apply for insurance, find providers, and access social services. The result was that more children got coverage, access to medical and dental care improved, out-of-pocket costs fell, parental satisfaction increased, and quality of care improved.
Discussion Questions• Why are children who are eligible for free coverage uninsured?
• What behavioral economics approaches would further increase coverage?
• Why are adults who are eligible for low-cost coverage uninsured?
• What behavioral economics approaches would further increase coverage?
• How does status quo bias affect health insurance decisions?
• How does loss aversion affect health insurance decisions?
• How does decision overload affect health insurance decisions?
• How might insurance decisions be reframed to increase enrollment?
• How could enrollment in health insurance for children be further simplified?
• How could health insurance be simplified overall?
Case 18.2(continued)
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make business decisions that harm patients or their organizations. The stakes can be high.
What can managers do to limit bad decision making due to shortcuts? Fortunately, a number of strategies are available:
• Look hard for evidence that you are wrong.• Appoint someone to tear apart your analyses.• Reward those who express honest disagreement.• Seek out the opinions of people who disagree with you, and listen
carefully.• Try to reframe problems to view them from different perspectives.• Talk about your feelings to see if they are leading you astray.• Postpone committing to strategies as long as you can. • Make sure that a review process and an exit strategy are part of
decision making.• Be aware that your decision making can lead to mistakes.
These steps will not shield you from making errors. They may help you make fewer mistakes, though.
Exercises
18.1 You will receive a $10,000 insurance payment in two months. If you are willing to pay for expedited handling, you can be paid in one month. Would you be willing to pay $50? $100? $200? More?
18.2 You will receive a $20,000 insurance payment in 12 months. If you are willing to accept a reduced payment, you can be paid in 11 months. Would you be willing to accept $19,800? $19,500? $19,000? Less?
18.3 What annual interest rate is implied by your answer to exercise 18.1? You calculate this rate by dividing $10,000 by the difference of $10,000 and the amount you are willing to pay for expedited handling, then taking the result to the twelfth power and subtracting 1. For example, if you were willing to pay $100, the result would be ($10,000/$9,900)12 − 1 = 0.1281781, or 12 percent.
18.4 What annual interest rate is implied by your answer to exercise 18.2? Is it the same as the rate in exercise 18.3? Why would this comparison matter?
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18.5 How are exercises 18.1 and 18.2 different? How are your answers to them different?
18.6 How could you use behavioral economics to increase the number of insured employees in your firm?
18.7 How likely is someone aged 25 to 44 to have an emergency department visit? What is the probability of having two visits? What is a typical charge for an emergency department visit? On the basis of your answers, would someone aged 25 to 44 be willing to buy coverage for emergency department care (with a $50 copayment) if it cost $250 per year?
18.8 What was your forecast of emergency department spending in exercise 18.7? Your forecast should equal the probability of one emergency department visit times the typical charge plus the probability of two visits times the typical charge.
18.9 A town has two hospitals. One averages 30 births per day; the other averages 15. Overall, half of the babies are boys, but some days more than 60 percent of the babies are boys. Is either hospital likely to have a greater number of days with a high proportion of boys?
18.10 Eighty percent of the participants at a meeting are physicians. The rest are nurse practitioners. Your neighbor Amy is there. She is 40, married, and highly motivated. Colleagues have told you that Amy is extremely capable and promises to be very successful. What is the probability that Amy is a physician?
18.11 Will you be in the top half of your class or the bottom? What proportion of your classmates will forecast that they will be in the top half? What implications does this scenario have for decision making?
18.12 You have finished interviewing candidates for an assistant director position. One of them stands out as the best candidate to you. You know that this view sets you up for confirmation bias as you check references. What steps can you take to prevent this bias?
18.13 Your vice president is an accountant and believes that accountants make the best practice managers. One of the three finalists for a practice management role has an accounting background. Everyone on the search team has ranked this candidate lowest of the finalists. You fear that your vice president will tend to selectively read the team’s recommendations and lean toward hiring this person. What can you do to offset this potential confirmation bias?
18.14 Thirty-four percent of the employees in your health system are obese, and 16 percent of their children are obese as well. Obese employees are less productive, have higher medical costs, and miss
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more work. Employees with obese children also miss more work, so persuading employees and their families to lose weight looks like a good investment for the system. In fact, effective, clearly cost-effective interventions are available to reduce obesity, and you offer them to your employees and their families. You have recently begun to make weight-loss interventions available for free, but only 1 percent of your employees have signed up for them. What behavioral economics tools can you use to help your employees lose weight?
Note
1. If getting $988 is as good as getting $1,000 in three months, your discount rate is 4.95 percent per year. Dividing $1,000 by $988 gives a three-month discount factor of 1.012145749. Taking this result to the fourth power (to convert it to an annual rate) gives 1.04948. It is customary to subtract 1.00 from this discount factor and express the results in percentage terms. Doing so gives 4.95 percent.
References
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