BEHAVIORAL ECONOMICS 18
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 rational, 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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294 Economics for Healthcare Managers
effort (especially if the work is intrinsically interesting or important), but
symbolic payments (e.g., praise) tend to increase work effort (BareketBojmel, Hochman, and Ariely 2014). For a purely rational worker, that finding 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 brainpower 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 discounting 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 percent 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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Chapter 18: Behavioral Economics 295
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 preparation 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 behaviors 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 incentive ends.
Royer, Stehr, and Sydnor (2015) tried two approaches with employees 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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296 Economics for Healthcare Managers
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. Combining 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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Chapter 18: Behavioral Economics 297
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 typically 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 discount the future less than someone who is risk seeking, so these two tendencies 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 significantly 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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298 Economics for Healthcare Managers
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 remember. 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 relevant 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 candidate 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 admitted 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 opponent’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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Chapter 18: Behavioral Economics 299
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 comments 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 situations 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, researchers have found that investment bankers who earned the least had the largest
hindsight bias (Merkle 2017). Hindsight bias also makes effective investigations 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 pattern. For example, you might find that merged practices led by accountants
were more likely to fail. Nonetheless, you may be tempted to think that having an accountant as the CEO is important.
Several factors are at work here. First, humans are prone to see patterns 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 forget 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.
representativeness bias
The tendency to
overstate how
typical a small
sample is.
law of small
numbers bias
Generalizations
based on small
samples.
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300 Economics for Healthcare Managers
The problem is greatest when our own experience suggests a conclusion. We easily dismiss colleagues’ stories as being mere anecdotes. Our stories 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 differ. 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 mortality 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 avoiding 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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Chapter 18: Behavioral Economics 301
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 mathematics 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 competition 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, LaskeAldershof, 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 aversion 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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302 Economics for Healthcare Managers
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 wellknown 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 seriously 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 Insurance Program. These reforms allowed states to permit continuous
enrollment, to eliminate face-to-face interviews, to simplify verification 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 possible, 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 determinations 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 Medicaid and the Children’s Health Insurance Program. For example, Alabama 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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Chapter 18: Behavioral Economics 303
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 strategic 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 limited 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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304 Economics for Healthcare Managers
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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Chapter 18: Behavioral Economics 305
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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306 Economics for Healthcare Managers
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.
